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Galectin-9 inhibits B cell receptor (BCR) signaling by
regulating BCR organization and mobility
by
Anh Cao
A thesis submitted in conformity with the requirements
for the degree of Master of Science
Department of Immunology, Faculty of Medicine University of Toronto
© Copyright by Anh Cao 2017
ii
Galectin-9 inhibits B cell receptor signaling by regulating
BCR organization and mobility
Anh Cao
Master of Science
Department of Immunology
University of Toronto
2017
Abstract
B cells are part of the adaptive immune system and secrete antibodies targeting foreign
pathogen (referred to as antigen). B cells are activated by binding of antigen to the antigen-
specific B cell receptor (BCR), which initiates downstream signaling cascades necessary for the
production of antibodies. Recent data from our lab identified that the glycan-binding lectin
known as galectin-9 (Gal-9) binds to IgM-BCR and inhibits B cell activation. However, the
molecular mechanism for Gal-9 mediated inhibition B cell activation is still unknown. In this
study, we showed that exogenous Gal-9 reorganizes IgM-BCR into larger clusters within the
Gal-9 lattice, and concomitantly immobilizes IgM. We also demonstrated that Gal-9 increases
the density of inhibitory molecules including CD45 and CD22 together with IgM-BCR in the
Gal-9 lattice, consistent with the inhibitory effect of Gal-9 on B cell activation. These findings
elucidate a novel extracellular mechanism to regulate signal transduction through BCR.
iii
Acknowledgments
It has been a long journey for me from a Vietnamese high school student to a Vietnamese
Master's degree candidate at the University of Toronto. I am thankful to be here, to sit in this
office, to write these lines, to do what I love and especially to meet people, who keep bringing
positive changes into my life. When you read these lines, please accept my deepest thankfulness
for being a part of my life. Thank you!
To my supervisor, Dr. Bebhinn Treanor, thank you for your constant presence, support
and guidance. I still remember the first email I sent to Dr. Treanor to ask for a volunteering
position in my second year. She opened the door and let me in. She trusted me and let me pursuit
whatever came up into my mind. For a person like me, who has too little confidence in myself
and who is always in a constant self-doubt, that trust is indispensable. Even during the darkest
stage of my life, she was still there, supported and guided me through. It is impossible to express
my thankfulness for what she did. At this moment, I have a chronic regret. I wish I have had
done more. I wish I have had been less whiny and more productive. It is impossible for me to
return what I received. Hence, I will pay back by being there, supporting and guiding my future
students like the way Dr. Treanor did to me (under an assumption that I can accomplish my
Ph.D., my postdoctoral fellowship(s) and secure a position in academia).
To my thesis committee Dr. Cindy Guidos and Dr. Jean-Philippe Julien, thank you for
your encouragements and suggestions. Thank you for keep challenging me and for keep pushing
me on the path of finding answers for the role of Galectin-9. If I had a chance to start over, I
would have committee meetings once every four months.
iv
To my Mom, thank you for your non-stop emails for the last six years. It is hard to
imagine how I am here without my Mom. My Mom was there listening to all of my projects
since I was a second-year student. Her answer for all of my questions was: “You will make it.” It
is truly a blessing to know that I am her whole world. For me, she is also the biggest inspiration.
One day, she will not be with me anymore. However, her words will be with me to make this
world a better place (even just a tiny little bit better).
To my Dad, thank you for being there for Mom to support her. Thank you for raising and
loving me so much. I will try my best to be a son who you can be proud of.
To Karen, who adopted me as her grandson, who kept giving me food, thank you for your
generosity. During my six years in Canada, my biggest accomplishment is to find a place to call
home, to find a person treated me like her family member. I am proud of being Grandma's
grandson.
To my boyfriend, to Corgi, thank you so much for being with me. Although Corgi is the
last one I expect to read my thesis, I still want to save space for him. My life is no longer perfect
and straightforward as it used to be. However, thanks to Corgi my life is completed and
connected. I am thankful for his presence.
To my lab members, to Mithunah, to Tina, to Hifza, to Trisha, to Laabiah, to Nouf and
Zaki, thank you for being wonderful parts of my life in the last four years. We were like a cluster
of B cell receptors. There were ups and downs. However, we are still together, still noisy and
still “signaling” earnestly. To Mithunah, thank you for your magic hands, which can fix any
equipment by just touching and for being a role model. To Tina, thank you for your noodle, for
your eagerness and an unexplainable parallel between two of us. To Hifza, thank you for being a
perfect D98 buddy, for being a connection when I needed the most and for being a tough
v
competitor (I will never forget her name). To Trisha, thank you for listening to me, for lending
me your shoulder to cry on and for being such an amazing human being. To Laabiah, thank you
for your intellectually stimulating discussion, and please keep being confident in yourself and
enjoying sciences. To Nouf, thank you for being my sister and for forgiving my stubbornness. To
Zaki, thank you for being my brother and for sharing lunch with me. Thank you, everyone, for
completing my life, for making my life more meaningful and for sharing the best years of my
life.
I also want to acknowledge all of the staffs at the animal facility and the Centre of
Neurobiology of Stress (CNS) for their constant technical supports.
To more than 90 million Vietnamese people, who I will never know, who I will always
be in debt, thank you for trusting me and for being reasons for me to keep moving forward. The
quality I am proud the most about myself is being a Vietnamese. It is not because Vietnam is a
rich and powerful country. In contrast, millions of people are living in poverty, with incurable
diseases, and with inequalities. Even under those circumstances, I received hundred thousand
dollars for my education, which is unimaginable. Each Vietnamese person, who is living in
poverty, suffering an incurable disease or struggling for equality is a reminder why I need to
wake up every morning, to work and to move forward. I apologize for not working hard enough
in the last two years. In the future, I may be an unknown researcher in Vietnam, who barely
publishes in peer-reviewed journals. However, I will help to nurture the next generation of
Vietnamese scientists, who will help me pay back my debt and to make Vietnam and this world a
better place to live.
vi
Declaration:
The IgD colocalization data was acquired by Hifza Mohamad (Figure 10). All other work
presented in this thesis is my own.
This thesis, along with additional data, will be submitted for publication in a peer-reviewed
journal.
vii
One day, I added recombinant galectin-9 to B cells
They clumped and were shredded by the centrifugal force
Desperate, I was. Disappointed, I was
Now looking back, it was truly a blessing
Galectin-9 may regulate actin organization (please help me to prove it).
One day, I thought about dying
Jumping from a balcony, from the 11th floor
Desperate I was. Disappointed, I was
Now looking back, it was truly a blessing
I love the breath going through my nose, my chest right now.
One day, something may look so bad
Everything seems to end, myself seems to crumble
Desperate, I will be. Disappointed, I will be
Just breathe, it is truly a blessing
Every moment is a gift, I am alive.
I am doing what I love – finding answers.
Anh Cao
viii
Table of Contents
Contents
Acknowledgments ..................................................................................................................... iii
List of Figures ............................................................................................................................ x
Abbreviations ............................................................................................................................ xi
INTRODUCTION ................................................................................................................. 1
1.1 B Cells ............................................................................................................................ 1
1.2 B Cell Activation ............................................................................................................ 2
1.3 Regulation of BCR Signaling .......................................................................................... 5
1.3.1 CD19 .................................................................................................................. 6
1.3.2 CD45 .................................................................................................................. 8
1.3.3 CD22 .................................................................................................................11
1.4 The Actin Cortex Regulates BCR Mobility and Signaling ..............................................12
1.5 Surface Proteins Organize into Nanoclusters ..................................................................14
1.6 Galectin Lattice Regulates Protein Organization ............................................................19
1.7 Hypothesis and Aims .....................................................................................................25
MATERIALS AND METHODS ..........................................................................................26
2.1 Mice ..............................................................................................................................26
2.2 B Cell Purification .........................................................................................................26
2.3 Surface Staining and Confocal Microscopy ....................................................................26
2.4 B Cell Activation ...........................................................................................................28
2.5 Western Blot ..................................................................................................................28
2.6 Single Particle Tracking .................................................................................................29
2.6.1 Recombinant Galectin-9 and Fab Fragment Labeling .........................................29
2.6.2 Recombinant Galectin-9 Treatment ....................................................................30
ix
2.6.3 Glass Coverslip Coating .....................................................................................30
2.6.4 Cell Labeling for Single Particle Tracking ..........................................................30
2.6.5 Instrument ..........................................................................................................30
2.7 Co-Immunoprecipitation ................................................................................................31
2.8 Direct Stochastic Optical Reconstruction Microscopy (dSTORM) .................................32
2.8.1 Sample Preparation ............................................................................................32
2.8.2 dSTORM Acquisition and Image Reconstruction ...............................................33
2.8.3 Hopkins Index and Ripley’s H Function Analysis...............................................34
2.8.4 Bayesian Cluster Analysis ..................................................................................34
2.9 Statistical Analysis .........................................................................................................35
RESULTS ............................................................................................................................36
3.1 Recombinant Galectin-9 Reorganizes IgM into Larger Clusters .....................................36
3.2 Galectin-9 Reduces the Mobility of IgM-BCR ...............................................................41
3.3 IgM and CD45 Density is Increased in the Galectin-9 Lattice ........................................44
3.4 CD22 Density is Increased in the Galectin-9 Lattice ......................................................47
3.5 CD19 Phosphorylation is Enhanced in Galectin 9-KO Upon Activation .........................51
DISCUSSION ......................................................................................................................53
References .................................................................................................................................67
x
List of Figures
Figure 1. BCR-antigen microcluster and immunological synapse formation
Figure 2. CD19 amplifies BCR signaling
Figure 3. Schematic diagram of the structure of CD45 and localization during B cell activation
Figure 4. Schematic diagram of the structure of CD22 and inhibitory effect of CD22 on BCR
signaling
Figure 5. BCR and coreceptors are organized into distinct nanoclusters
Figure 6. Galectins and the galectin-glycoprotein lattice
Figure 7. rGal-9 alters IgM nanoclusters.
Figure 8. Gal-9 immobilizes IgM-BCR.
Figure 9. The Gal-9 lattice increases the molecular density of IgM and CD45.
Figure 10. The Gal-9 lattice does not affect the molecular density of IgD.
Figure 11. The Gal-9 lattice increases the molecular density of CD22.
Figure 12. Gal-9 does not increase the interaction between IgM and CD22
Figure 13. Phosphorylation of CD19 is increased in Gal-9-KO B cells.
Figure 14. Proposed model for Gal-9-mediated inhibition of BCR signaling
xi
Abbreviations
µg/ml
Microgram per millimeter
µM
Micromolar
ADCC
Antibody-dependent cell-mediated cytotoxicity
Ag
Antigen
APC
Antigen presenting cell
Asn
Arginine
B220
B cell isoform of 220kDa
BCR B cell Receptor
Blnk
B-cell linker
Btk
Bruton’s Tyrosine Kinase
Ca2+
Calcium ion
CD19
Cluster of Differentiation 19
CD22
Cluster of Differentiation 22
CD45
Cluster of differentiation 45
CD81
Cluster of Differentiation 81
CLP Common lymphoid progenitor
CRD Carbohydrate recognition domain
cSMAC
Central supramolecular activation cluster
DAG
Diacylglycerol
DC
Dendritic cell
EDTA
Ethylenediaminetetraacetic acid
EM Electron-multiplying
ER
Endoplasmic reticulum
ERK
Extracellular signal-regulated kinase
ERM
Ezrin-Radixin-Moesin proteins
FBS
Fetal Bovine Serum
FDC
Follicular dendritic cell
Gal-1
Galectin-1
Gal-3
Galectin-3
Gal-4
Galectin-4
Gal-9
Galectin-9
Gal-9-KO
Galectin-9 Knockout
GEM
Glycolipid-enriched membrane
HIV Human immunodeficiency virus
HRP
Horseradish peroxidase
ICAM-1 Intercellular adhesion molecules 1
IgD
Immunoglobulin D
xii
IgH
Immunoglobulin heavy chain
IgL
Immunoglobulin light chain
IgM
Immunoglobulin M
Igα Immunoglobulin alpha
Igβ
Immunoglobulin beta
IL-7
Interleukin 7
IP3
Inositol 1,4,5-triphosphate
ITAMs
Immunoreceptor tyrosine-based activation motifs
ITIMs Immunoreceptor tyrosine-based inhibitory motifs
LFA-1
lymphocyte function-associated antigen 1
MAPK
mitogen activated protein kinase
Mgat5
β 1,6-N-acetylglucoaminyl transferase V
MHC II Major Histocompatibility Complex Class II
mIg Transmembrane immunoglobulin
NaCl
Sodium chloride
NaN3
Sodium azide
NF-κB
Nuclear factor-κB
PBS
Phosphate buffered saline
pERK
Phosphorylated extracellular signal-regulated kinase
PFA
Paraformaldehyde
PI3K
Phosphatidylinositol-,4,5-bisphosphate 3-kinase
PIP2
Phosphatidylinositol-4, 5-bsiphosphate
PIP3
Phosphatidylinositol-3, 4, 5-trisphosphate
PKC
Protein kinase C
PLC-γ2
Phospholipase C gamma 2
pSMAC
Peripheral supramolecular activation cluster
PTPase
Protein phosphatase
PTPRC
Protein tyrosine phosphatase, receptor type, C
RAG-1
Recombination-activating genes-1
RAG-2
Recombination-activating genes-2
Ras GRP Ras gauanine nucleotide releasing protein
rGal-9 Recombinant galectin-9
RPMI
Roswell Memorial Institute Media
SEM
Standard Error of Means
Ser
Serine
SFK
Src family kinases
SH2 domain Src homolog 2 domain
SHP-1 Src homology domain containing phosphatase-1
SLE Systemic lupus erythematosus
Syk Spleen tyrosine kinase
TCR
T cell receptor
TH1
T helper 1
xiii
TH2
T helper 2
Thr
Threonine
Tim3 T cell Immunoreceptor mucin 3
TIRFM
Total internal Reflection Fluorescence Microscopy
Treg
Regulatory T cells
TSRI
The Scripps Research Institute
Tyr Tyrosine
v/v
Volume/volume
Vav
Protein encoded by VAV gene
VCAM-1 Vascular cell adhesion mediator-1
VLA-4
Very late antigen-4
VpreB
Immunoglobulin iota chain
WT Wild type
Λ5
Lambda 5
1
INTRODUCTION
1.1 B Cells
B cells are the centre of humoral immunity, which provides specific and long-lasting
protection from foreign molecules, referred as antigens. B cell receptors (BCRs) recognize
specific antigens and initiate B cell activation leading to proliferation and differentiation into
plasma cells, which secrete soluble antigen-specific antibodies, or into memory B cells, which
provide long-term protection (Pierce, 2002).
B cells are an important mediator of both the adaptive and the innate immune systems.
When BCR encounters specific antigens, ligand-receptor complexes are internalized through a
clathrin-dependent process (Stoddart et al., 2002). Antigens are degraded into peptides by
enzymes in the lysosome, and these peptides are presented on major histocompatibility complex
class II (MHC II) to CD4+ T-helper (TH) cells (Germain, 1994), which coordinate the adaptive
immune system by releasing various cytokines. Secreted antigen-specific antibodies also mediate
innate immune responses such as antibody-dependent cell-mediated cytotoxicity (ADCC) by
recruiting natural killer (NK) cells to lyse infected cells (Hashimoto et al., 1983). Antibodies can
also opsonize antigens and enhance the efficacy of phagocytosis by macrophages to clear
antibody-antigen complexes (Swanson and Hoppe, 2004).
Due to the wide range of B cell functions, the process of B cell activation and
differentiation needs to be tightly regulated to prevent immune disorders. The hyper-reactivity of
B cells may lead to autoimmune diseases such as systemic lupus erythematosus (SLE), in which
B cells secrete autoantibodies against nuclear complexes, which under normal conditions do not
2
activate B cells (Odendahl et al., 2000). On the other hand, in the context of chronic viral
infection such as human immunodeficiency virus (HIV) infection, B cells may become hypo-
reactive or exhausted, which impairs B cell activation and antibody secretion (Moir and Fauci,
2013). Hence, it is important to study the mechanisms that regulate B cell activation, which may
provide new targets for immunotherapy to treat B cell-related disorders.
1.2 B Cell Activation
B cells are activated by both soluble antigen and membrane-bound antigen presented by
antigen-presenting cells (APCs) such as follicular dendritic cells (FDCs), dendritic cells (DCs)
and macrophages (Defranco et al., 1982; Szakal et al., 1988; Batista et al., 2001, Wykes et al.,
1998; Harvey et al., 2007). B cells recognize specific antigen by BCR, which is a complex
comprised of transmembrane immunoglobulin (mIg) together with immunoglobulin α (Igα) and
immunoglobulin β (Igβ) in a 1:1 stoichiometry (Schamel and Reth, 2000). mIg is composed of
two heavy chains and two light chains connected by disulfide bonds. Each mIg has two antigen-
binding sites consisting of variable regions of both heavy and light chain. Because mIg has no
signaling motif, BCR signaling requires the intracellular domains of both Igα and Igβ containing
immunoreceptor tyrosine-based activation motifs (ITAMs) (Reth, 1989). Phosphorylated ITAMs
recruit other intracellular signaling molecules to transduce and amplify the signal from
extracellular BCR-antigen interaction into the cell to alter cellular activities, gene expression and
proliferation (Kurosaki, 2000). The engagement of BCR with membrane-bound antigen leads to
the formation of BCR microclusters. BCR microclusters, which are a group of 50 to 500 BCR
molecules, are observed in the early stage of B cell engagement with antigen-presenting
membrane (Fleire et al., 2006; Depoil et al., 2008). Although the mechanism for BCR
3
microcluster formation is still unknown, the process is dependent on at least two factors
including the rearrangement of the actin cortex (Fleire et al., 2006) and spontaneous clustering of
IgM Cμ4 domain (Tolar et al., 2009). BCR microclusters mediate the recruitment of multiple
intracellular molecules to the submembrane regions where BCRs engage antigens. The assembly
of signaling molecules at BCR microclusters defines a microsignalosome, which coordinates
intracellular signaling during B cell activation. Upon engagement of mIg with specific antigen,
BCR is brought closer to Lyn, a Src family kinase (SFK) highly expressed in hematopoietic
cells, which phosphorylates the ITAMs of Igα and Igβ (Saouaf et al., 1994). These
phosphorylated motifs provide binding sites and activate cytosolic spleen tyrosine kinase (Syk)
(Rowley et al., 1995). Syk phosphorylates the adaptor protein B-cell linker (Blnk), which
provides a platform to recruit Bruton's tyrosine kinase (Btk) and Phospholipase C Gamma 2
(PLC-γ2) (Oellerich et al., 2011). In parallel, BCR signaling activates phosphatidylinositol-,4,5-
bisphosphate 3-kinase (PI3 kinase), which converts phosphatidylinositol-4,5-bisphosphate
(PI(4,5)P2) into PI(3,4,5)P3. PIP3 further enhances the recruitment of Btk and PLC-γ2 to the
membrane. PLC-γ2 hydrolyzes PIP2 to generate inositol (1,4,5)-trisphosphate (IP3) and
diacylglycerol (DAG) (Hempel et al., 1992). IP3 binds to IP3-receptor to induce an elevation of
Ca2+ in the cytoplasm (Sugawara et al., 1997). DAG activates protein kinase C (PKC) as well as
Ras guanine nucleotide releasing protein (RasGRP) (Matthews et al., 2003). RasGRP activates
the mitogen-activated protein kinase (MAPK) pathway, which phosphorylates extracellular
signal-regulated kinase (ERK). Activated ERK regulates multiple cellular processes including
proliferation, migration, and cell survival (Stone, 2011). PKC also activates the nuclear factor-κB
(NF-κB) pathway (Su et al., 2002), which regulates gene expression to modulate cellular
activities and differentiation.
4
In parallel with BCR microcluster formation, B cells also spread their membranes across
the surface of APCs to maximize the contact area to increase the number of microsignalosomes.
The interface between B cells and the antigen-presenting membrane is defined as an
immunological synapse. The immunological synapse was first described in T cells and is defined
by a central cluster of T cell receptor (TCR) known as the central supramolecular activation
cluster (cSMAC), and an outer ring of adhesion molecules such as LFA-1 and VLA-4 known as
the peripheral SMAC (pSMAC) (Monks et al., 1998). The immunological synapse has also been
described in B cells with similar features including a central BCR cluster and peripheral ring of
adhesion molecules (Batista et al., 2001; Carrasco et al., 2004) (Figure 1). The spreading of the B
cell membrane to form an immunological synapse is coordinated through BCR signaling and
actin cortex rearrangement (Fleire et al., 2006). The formation of the cSMAC in T cells is
explained by a retrograde actin flow, in which actin polymerization at the edge of cells "pushes"
actin filaments inward. Simultaneously, actin depolymerizes at the centre of the immunological
synapse and TCR microclusters associated with the actin cytoskeleton move inward to form the
cSMAC (Kaizuka et al., 2007) (Figure 1). While peripheral microclusters of immunoreceptors
are actively signaling during the spreading phase, the aggregation of immunoreceptors at the
cSMAC is believed to mark the attenuation of signaling and the initiation of immunoreceptor
internalization (Yokosuka et al., 2005; Depoil et al., 2008; Weber et al., 2008, Nguyen et al.,
2008). During the internalization process, BCR-antigen complexes are brought to MHC-
containing endosomal/lysosomal vesicles, which degrade antigens into peptides and load these
peptides into MHC II for antigen presentation, which activates CD4+ T cells (Chesnut and Grey,
1981). Activated CD4+ T cells upregulate CD40 ligand (CD40L), which binds to the B cell
costimulatory receptor CD40, and secrete soluble cytokines such as IL-4, IL-5 and IL-6, which
5
regulate the proliferation and differentiation of B cells (Parker, 1993). Importantly, the degree of
B cell spreading is dependent on both the affinity and density of antigen in the membrane and
determines the amount of antigen which is gathered, internalized, processed and presented to T
cells and this directly impacts on the degree of T cell help the B cell receives (Fleire et al., 2006).
Figure 1. BCR-antigen microcluster and immunological synapse formation
After initial contact between the B cell and the antigen-presenting cell (APC), the B cell quickly
spreads on the surface of APC and reaches maximal spreading followed by the contraction
phase. The engagement between BCR and membrane-bound antigen leads to formation of BCR
microclusters (red circle). During the spreading phase, actin (green line) polymerization leads to
the outward movement of BCR microclusters. At maximal spreading, actin polymerization at the
edge of the cells "pushes" the actin filament inward. Simultaneously, actin depolymerizes at the
centre of the immunological synapse and BCR microclusters associated with the actin
cytoskeleton move inward. During the contraction phase, BCR microclusters gather at the centre
to form the central supramolecular activation cluster (cSMAC). Integrins like LFA-1 and VLA-4
form a ring structure (blue) surrounding the cSMAC, called the peripheral (p)-SMAC.
1.3 Regulation of BCR Signaling
Besides BCR signaling, B cells require further input from other receptors, which provide
additional information about the nature of the antigen and the context in which antigen is
presented. The additional input finely tunes B cell activation to initiate an effective immune
6
response while preventing overreaction, which may lead to autoimmune diseases. These
receptors are classified into activating receptors, which amplify BCR signaling, or inhibitory
receptors, which dampen BCR signaling.
1.3.1 CD19
CD19 is a component of a complex consisting of complement receptor 2 (CR2 or CD21),
the tetraspanin family protein CD81 and the interferon-induced transmembrane protein leu13
(CD225) (Matsumoto et al., 1991; Bradbury et al., 1992). CD19 contains two extracellular C2-
type Ig domains and nine intracellular domains. Upon B cell activation, the intracellular domains
of CD19 are phosphorylated and provide binding sites for other signaling molecules (Zhou et al.,
1991). Following BCR ligation, Lyn phosphorylates tyrosine residues in the intracellular domain
of CD19, which provide docking sites for Src-homology domain 2 (SH2) containing molecules
including Lyn, PI3K, and Vav, which synergistically enhance BCR signaling-mediated
recruitment of these proteins (Fujimoto et al., 2000) (Figure 2). Phosphorylation of CD19 creates
a positive feedback loop or ‘processive amplification’ by providing binding sites for Lyn, which
autophosphorylates and activates more Lyn to further amplify BCR signaling (Fujimoto et al.,
2000). In vitro, CD19 decreases the threshold of B cell activation by lowering the number of
antigens required to initiate BCR signaling; co-ligation of CD19 with IgM lowered the
concentration of anti-IgM required to initiate BCR activation by 100 times (Carter and Fearon,
1992). In addition, CD19 is crucial in promoting B cell spreading and microcluster formation in
response to membrane-bound ligand (Depoil et al., 2008). CD19-deficient B cells have lower
spreading area and consequently antigen accumulation is also decreased in comparison to WT B
cells in response to membrane-bound antigen (Depoil et al., 2008). In the presence of CD19, the
7
B cell has larger spreading area, which accumulates more antigens and leads to a higher degree
of T cell help. Supporting this model, in vivo, CD19-KO mice showed a severe impairment in
germinal centre formation and affinity maturation when immunized with antigens that require T
cell help (Rickert et al., 1995). CD19-KO mice also had IgM and IgG1 titers ten times lower than
WT control 8 days after being immunized with T cell dependent antigen (Engel et al., 1995).
Thus, through recruiting signaling molecules and facilitating B cell spreading, CD19 decreases
the threshold of B cell activation and amplifies BCR signaling.
Figure 2. CD19 amplifies BCR signaling
A) BCR stimulation induces the relocalization of BCR and CD19 into lipid rafts (indicated by a
red line) where Lyn phosphorylates CD19 at Tyr-513. B) Phosphorylated CD19-Y513 provides a
binding site for Lyn, which phosphorylates Tyr-482 and Tyr-391 through a process called
“processive phosphorylation.” C) Phosphorylated sites of CD19 are binding sites for Src-
homology domain 2 (SH2) containing molecules including PI3K and Vav, which synergistically
enhance BCR signaling (Fujimoto et al., 2000).
8
1.3.2 CD45
CD45, protein tyrosine phosphatase, receptor type C (PTPRC), is an abundant cell
surface glycoprotein expressed on all nucleated hematopoietic cells including B and T cells
(Hermiston et al., 2003). CD45 has multiple isoforms, generated through alternative splicing of
exons at the N-terminal, which are specific for cell type, developmental stage, and activation
state (Hermiston et al., 2003; Fujii et al., 1992; Johnson et al., 2002). B cells express the largest
isoform of CD45, B220, which has three alternative exons at the N terminus and a heavily
glycosylated extracellular domain (Okumura et al., 1996). The intracellular domain of CD45
consists of two protein phosphatase (PTPase) domains; however only the membrane-proximal
domain is phosphatase active and regulates immunoreceptor signaling (Desai et al., 1994)
(Figure 3A).
Although CD45 has been extensively studied over the last 30 years, the precise role of
CD45 in B cells is still ambiguous. The best-characterized substrate for the PTPase activity of
CD45 in B cells is Lyn (Katagiri et al., 1999, Shrivastava et al., 2004). Lyn has two tyrosine
phosphorylation sites: Y508, a negative regulatory site; and Y397, an autophosphorylation site.
Dephosphorylation of Y508 converts Lyn from a closed conformation to an active confirmation.
The open form of Lyn phosphorylates Y397 through intermolecular autophosphorylation to lock
the catalytic pocket into a fully active conformation (Ingley, 2012). Based on in vitro data, the
PTPase domains of CD45 dephosphorylates both positive (Y397) and negative (Y508) regulatory
tyrosine residues of Lyn (Katagiri et al., 1999). Currently, available evidence strongly indicates
that CD45 is a positive regulator of BCR signaling. In DT40, a chicken derived B cell line, CD45
deficient cells exhibited a lower level of total phosphorylation and delayed calcium signaling
9
(Yanagi et al., 1996). Supporting this finding, supra-physiological expression of CD45 in
primary murine B cells increases calcium signaling and phosphorylation of ERK (Zikherman et
al., 2012). However, these studies used soluble anti-BCR antibodies to activate cells, neglecting
the importance of the temporal and spatial regulation of CD45 (Shrivastava et al., 2004) in
modulating BCR signaling. In WEHI-231 cells, during the resting state, some CD45 molecules
constitutively associate with glycolipid-enriched microdomains (GEMs or lipid rafts), where
multiple signaling molecules participating in B cell activation localize, including Lyn. CD45
molecules residing inside lipid rafts inhibit Lyn by dephosphorylating both regulatory sites
(Figure 3B). Upon ligation of BCR in the early stage of B cell activation, CD45 is transiently
segregated from lipid rafts, which releases its inhibitory effect, enabling Lyn activation (Figure
3C). 15 minutes post BCR stimulation, CD45 re-associates with GEMs, which may play a role
in attenuating BCR signaling (Shrivastava et al., 2004). This finding elegantly emphasizes the
importance of CD45 membrane organization in regulating PTPase activity and controlling
activation of Lyn. Moreover, the context of antigen presentation is also critical when studying
the effect of CD45 on receptor signaling, which is not addressed when soluble cross-linking
antibodies are used. According to the kinetic-segregation model, the close apposition of the
plasma membrane of the T cell and the antigen-presenting membrane during formation of the
immunological synapse leads to exclusion of CD45 from the synapse due to its bulky
extracellular domain (Davis and van der Merwe, 2006). Importantly, a recent study demonstrated
that the segregation of CD45 out of the immunological synapse correlates with TCR signaling
(Chang et al., 2015). Using chimeric forms of the Src family kinase Lck fused to the
transmembrane and extracellular domain of CD45 of varying length, Chang and colleagues show
that the fraction of cells activated by anti-TCR coated glass is reversely correlated with the
10
length of the extracellular domain of the chimeric structures. The chimeric structure composed of
the extracellular domain of the B220 has the lowest activation efficiency. These findings
emphasize the importance of the temporal and spatial regulation of CD45 as well as the crucial
role of the extracellular domain of CD45 in regulating immunoreceptor signaling.
Figure 3. Schematic diagram of the structure of CD45 and localization during B cell
activation
A) CD45 in B cells is the largest isoform of protein tyrosine phosphatase, receptor type, C
(PRPRC or CD45). The intracellular domain consists of two phosphatase domains including one
membrane-proximal active domain. The putative wedge structure inhibits phosphatase activity of
an adjacent CD45 by inserting into the phosphatase domain active site. The extracellular domain
consists of three fibronectin type III repeats and one cysteine-rich region, which are similar
among CD45 isotypes. CD45 isoform expressed by B cells (B220) consists of all three
alternatively spliced exons at the N terminus. In addition, the extracellular domain is highly
glycosylated (red circles). B) In the resting state, some CD45 molecules localize inside lipid rafts
(indicated by red) and inactivate Lyn by dephosphorylating both phosphorylation sites. C) In the
first 15 minutes of BCR-antigen engagement, CD45 molecules are excluded from lipid rafts,
allowing the activation of Lyn.
11
1.3.3 CD22
CD22 is a well-characterized inhibitory coreceptor expressed on the surface of B lineage
cells. The extracellular domain of CD22 contains seven Ig-like domains including a sialic acid
binding domain, which specifically binds to α2,6-linked sialic acid (Engel et al., 1995) (Figure
4A). The sialic acid-binding domain was reported to be critical in regulating the homotypic
interaction between CD22 molecules as well as heterotypic interaction with other sialic acid-
containing surface proteins (Han et al., 2005; Ramya et al., 2010). The intracellular domain of
CD22 contains three immunoreceptor tyrosine-based inhibitory motifs (ITIMs), which are
rapidly phosphorylated upon BCR ligation (Leprince et al., 1993). These phosphorylated ITIMs
recruit other phosphatases such as Src homology region 2 domain-containing phosphatase-1
(SHP-1), which dephosphorylates Syk, PLC-γ2, and CD19 to dampen BCR signaling (Doody et
al.; 1995, Law et al.; 1996; Pani et al., 1997) (Figure 4B). The importance of CD22 is illustrated
by CD22 deficient primary B cells, which have a lower activation threshold and prolonged
calcium signaling upon IgM-BCR ligation compared to WT B cells. Surface expression of the
costimulatory receptor CD86 is also increased in CD22 deficient B cells and they proliferate
more upon stimulation (O'keefe et al., 1996). Moreover, CD22 deficient mice are more likely to
develop self-reactive antibodies such as anti-dsDNA and anti-cardiolipin after 12 to 18 months
compared to WT control mice (O'keefe et al., 1999). These findings clearly demonstrate the
importance of CD22 inhibitory signaling in regulating B cell activation.
12
Figure 4. Schematic diagram of the structure of CD22 and inhibitory effect of CD22 on
BCR signaling
A) The extracellular domain of CD22 contains seven Ig-like extracellular domains, which are
heavily glycosylated (red circle). The terminal Ig-like domain, called the sialic-binding domain,
binds to α2,6-linked sialic acid. The intracellular domain contains three immunoreceptor
tyrosine-based inhibitory motifs (ITIMs), which are phosphorylated in the early stage of BCR
activation. B) After BCR ligation, Lyn phosphorylates ITIMs of CD22, which recruits and
phosphorylates SHP-1, a phosphatase. SHP-1 dephosphorylates CD19, which attenuates the
amplification of Lyn. SHP-1 also dephosphorylates Syk.
1.4 The Actin Cortex Regulates BCR Mobility and Signaling
The actin cortex is the actin-based cytoskeletal structure immediately underneath the
plasma membrane. The primary function of the actin cortex is to physically support the
membrane and provide driving forces for various biological processes such as cellular mobility,
internalization, and division (Pollard and Cooper, 2009). The actin cortex is a dynamic structure,
in which actin filaments continuously reorganize through the balance between actin
polymerization and depolymerization. Actin reorganization is critical for B cell spreading on the
surface of APCs. Disrupting actin cortex by actin polymerization inhibitors such as Latrunculin
A (LatA) or Cytochalasin D (CytD) inhibits B cell spreading and antigen accumulation (Fleire et
al., 2006). Consistent with this finding, A20 cells expressing a constitutively inactive form of
13
cofilin, which is required for actin reorganization, failed to spread on immobilized anti-BCR
(Freeman et al., 2011). These findings confirm the importance of actin reorganization in
regulating BCR signaling upon engaging antigen. Moreover, the actin cytoskeleton also plays a
critical role in regulating the movement (or mobility) of the BCR on the cell surface and
consequently, BCR signaling (Treanor et al., 2011). Using dual-color total internal reflection
fluorescence microscopy (TIRF) and single particle tracking (SPT), Treanor et al. (2010)
reported that BCR mobility is decreased within actin-rich regions compared to actin-poor
regions. Treating B cells with actin disrupting reagents such as LatA or CytD increases the
mobility of IgM-BCR on the B cell membrane. Interestingly, the increase in IgM-BCR diffusion
rate correlates with an increase in calcium signaling even in the absence of antigen. LatA
treatment not only triggers calcium signaling but also phosphorylation of signaling molecules
such as CD19, Lyn, and ERK, similar to antigen induced B cell activation (Treanor et al., 2011;
Mattila et al., 2013; Gasparrini et al., 2015). The correlation between BCR mobility and BCR
signaling leads to the emergence of the collision coupling model in regulating BCR signaling
(Treanor, 2012). In this model, prior to cell activation the actin cortex confines the mobility of
BCR and separates BCR from activated kinases or co-receptor such as CD19. When the actin
cortex is disrupted, BCR mobility increases, which leads to higher collision with activated
kinases or co-receptors. This model is supported by the finding that when HEL-specific B cells
are activated by HEL-coated cover slips, IgM and CD19 molecules are brought close together
inside Syk clusters, where microsignalosomes are initiated (Mattila et al., 2013). Freeman et al.
(2015) reported that incubating B cells with a low concentration of LPS increases BCR mobility
by enhancing actin severing through cofilin activation. The increase in BCR mobility correlates
with a higher chance of collision between BCRs and higher tonic signaling, as evidenced by
14
increased ERK and Akt phosphorylation in the resting state. These findings confirm the
importance of the actin cortex in regulating BCR mobility, which is important in controlling
BCR signaling. Moreover, this study elucidated a mechanism in which Toll-like receptors
(TLRs) lower the threshold of B activation by regulating the actin cortex to increase BCR lateral
diffusion, and helps explain the efficiency of antigens associated with TLR ligands in triggering
strong immune responses in vivo (Mifsud et al., 2014).
1.5 Surface Proteins Organize into Nanoclusters
According to the fluid mosaic model, plasma membrane proteins were proposed to be
randomly and evenly distributed (Singer and Nicolson, 1972). However, many surface proteins
are non-randomly distributed and highly compartmentalized (Cambi and Lidke, 2011). The non-
random distribution of surface proteins was hypothesized to be based on a hierarchical
organization ranging from dimers, to oligomers, to nanoscale and micrometer-sized clusters. This
hierarchical order may regulate the function and organization of surface proteins at multiple
levels (Garcia-Parjo et al., 2014). Early evidence supporting the existence of constitutive protein
clusters came from flow-cytometry based Förster resonance energy transfer (FRET) experiments,
which can detect an intermolecular distance of less than 10 nm (Chakrabarti et al., 1992; Szöllósi
et al., 1996). Using this technique, MHC I molecules were found to be within the FRET distance,
which would be unlikely if MHC-I molecules were randomly distributed on the cell membrane
(Chakrabarti et al., 1992; Matko et al., 1994). Szöllósi et al. (1996) also described the hetero-
association between MHC II and proteins of the tetraspanin family on the plasma membrane of B
cells using flow-based FRET. The bimolecular fluorescence complementation (BiFC) assay,
which monitors the dimerization of proteins tagged with amino- and carboxy-terminal half
15
domains of a yellow fluorescent protein (YFP) through detection of a fluorescent signal upon
assembly of a complete YFP, has also been used to report on the organization of cell surface
proteins. Using this technique, Yang and Reth (2010) found that IgD-BCR molecules are homo-
associated, which the authors defined as "oligomers".
Although these findings confirmed that proteins on the cell membrane are not randomly
distributed, these studies did not describe how surface proteins are distributed on the cell
membrane, which can only be achieved by visualization of the proteins on the membrane.
However, visualization of single molecules on the cell membrane is restricted by the diffraction
limit of optical microscopy, which defines the minimum lateral distance that two points can be
differentiated and is equivalent to approximately half the illuminating wavelength. To overcome
this limitation, immuno-transmission electron microscopy (TEM), which utilizes short-
wavelength electron beam and metal-conjugated antibodies, was used to visualize the
distribution of proteins on the cell membrane (Damjanovich et al., 1995; Jenei et al., 1997).
Consistent with the FRET experiments, MHC-I was found to form clusters on the cell membrane
with an average of 25 molecules per cluster (Damjanovich et al., 1995; Jenei et al., 1997). To
visualize the distribution of all proteins on the cell membrane, Lillemeier et al. (2006) labelled
surface proteins on T cells with biotin and detected protein localization with gold-conjugated
streptavidin, which was visualized by TEM. The authors found that proteins on the membrane
organized into "protein islands", which ranged in size from 30 to 700 nm. However, these studies
have been criticized because TEM imaging requires extensive sample processing, which may
introduce shearing forces and alter epitopes, thus decreasing the binding efficacy of antibodies,
which is illustrated by a very sparse immunogold detection. These limitations may underestimate
the number of molecules residing in clusters and obscure information about the spatial
16
organization of surface proteins. To address these limitations, many super-resolution imaging
techniques using visible light with less extensive sample processing have been applied to study
the distribution of surface proteins (Hwang et al., 1998; Mattila et al., 2013; Oszmiana et a.,
2016). Direct stochastic optical reconstruction microscopy (dSTORM) is a super-resolution
imaging technique that can achieve a lateral resolution of 10 to 30 nm in fixed cells, a 10-fold
improvement over conventional microscopy (Heilemann et al., 2008). To achieve this
subdiffraction resolution, fluorophores are switched to a metastable dark state by high laser
intensity and subsequently reactivated with continuous low-intensity illumination to randomly
convert a small fraction of fluorophores to a fluorescent state, allowing the detection and
localization of single molecules by Gaussian functions. Using dSTORM, IgM and IgD molecules
were found to be organized in pre-existing nanoscale clusters on the surface of resting primary B
cells (Mattila et al., 2013). Approximately 70% of IgD molecules are present in these clusters
and are more densely packed (30-120 molecules per cluster) compared to IgM molecules, of
which approximately 40% reside in nanoscale clusters containing an estimated 20-50 molecules
per cluster (Mattila et al., 2013). Interestingly, the two isotypes of BCR form distinct
nanoclusters, which do not overlap with each other (Maity et al., 2015). CD19 was also found to
exist in nanoscale clusters with cluster size and density intermediate between IgM and IgD
(Mattila et al., 2013). Gasparrini et al. (2015) reported that CD22 molecules on resting primary B
cells also form nanoscale clusters with an average radius of 100 nm. The distribution of surface
proteins in nanoscale clusters was not only found on B cells but also many other cell types
including T cells, NK cells, and monocytes (Roh et al., 2015; Pageon et al., 2013; van Zanten et
al., 2015). Taken together, the biochemical data, combined with TEM and super-resolution
imaging firmly establish that many proteins on the cell membrane exist in preformed structures
17
ranging from oligomers to nanometer-scale to micrometer-scale clusters, and are a dominant
feature of plasma membrane organization (Garcia-Parajo et al., 2014). Although these studies
established that proteins are non-randomly distributed, there is no consensus nomenclature for
these structures, and they are variably referred to as "oligomer", "protein island", “protein patch”,
or "nanocluster", depending on the experimental perspective; nonetheless, it is likely that these
terms refer to similar structures. In this study, we used "nanoclusters" to describe groups of
molecules organized in clusters up to 200 nm in size, and on average contain fewer than 100
molecules. These structures are different from microclusters, which are structures formed upon
receptor activation, and are composed of hundreds of molecules and are larger in size compared
to nanoclusters (Figure 5).
Multiple mechanisms have been proposed to explain the formation of nanoclusters,
including vesicle trafficking, actin barriers and protein-protein interaction. Lavi et al. (2007)
reported that MHC I molecules are locally concentrated in the plasma membrane where vesicles
originating from the Golgi apparatus are delivered. The density of MHC I molecules inside these
patches decrease exponentially over time, as molecules laterally diffuse. Disruption of the actin
cortex by LatA treatment decreased the lifetime of MHC I patches, due to the increase in the
lateral diffusion of MHC I from patches (Lavi et al., 2012). Consistent with this model,
Lillemeier et al. (2006) reported that treating T cells with actin destabilizing reagents such as
LatA and CytD reduces protein density inside "protein islands", and consequently proteins
formed smaller clusters and were more dispersed from each other. Supporting the role of actin in
organizing surface proteins, the size of regions confined by actin filaments ranges from 50 to 200
nm, which is approximately the size reported for nanoclusters (Morone et al., 2006 and Brown et
al., 2012). Although actin is important in the organization of surface proteins, it is not the only
18
driving force as proteins are still organized into clusters upon actin disruption (Lillemeier et al.,
2006; Mattila et al., 2013), indicating that other factors mediate the organization of these
clusters. Protein-protein interactions are another possible factor organizing nanoclusters.
Evidence for protein-protein interactions in mediating the formation of nanoclusters comes from
the work of Reth and colleagues. Using BiFC and blue native polyacrylamide gel
electrophoresis, Reth and colleagues reported that BCR "oligomers" were dependent on
interactions between the transmembrane domain of mIgD and Igα /Igβ (Schamel and Reth, 2000;
Yang and Reth, 2010). Further support for the importance of protein-protein interaction in
organizing nanoclusters, Gasparrini and colleagues (2015) reported that homotypic interaction
between CD22 molecules is crucial in regulating the size of CD22 nanoclusters. A CD22 mutant
with a point mutation in the sialic binding domain, which mediates the homotypic interaction
between CD22 molecules, forms smaller nanoclusters. These findings suggest that protein-
protein interactions are important in the formation of protein nanoclusters.
Figure 5. BCR and coreceptors are organized into distinct nanoclusters
A) In the resting state, IgM-BCR, IgD-BCR and CD19 form distinct nanoclusters on the cell
membrane. B) Upon activation, BCR nanoclusters are brought closer to CD19 nanoclusters to
form BCR microclusters.
19
1.6 Galectin Lattice Regulates Protein Organization
Another potential mechanism regulating protein organization on the cell surface is the
interaction between glycosylated proteins and cell surface lectins. Glycosylation is a post-
translational modification in which chains of sugars are attached to the polypeptide backbone
catalyzed by different glycosyltransferases. Glycosylation regulates various biological functions
including protein folding, protein compartmentalization, and protein-protein interactions
(Cummings, 2009). The two most common mechanisms to attach glycans to proteins are amide
bonds to asparagine (Asn) side chain (N-glycosylation) and glycosidic bonds to the OH side
chain of serine (Ser), threonine (Thr) and tyrosine (Tyr) (O-glycosylation). N-glycosylation is
catalyzed by enzymes localized in the endoplasmic reticulum (ER) and Golgi complex. N-
glycosylation modifications are common in the extracellular domain of membrane-associated
proteins (Haltiwanger and Lowe, 2004). Glycosylation of extracellular domains is tightly
regulated, and is specific for different cell types as well as different stages of development or
activation (Wolfert and Boons, 2013). In the context of the immune response, the importance of
glycosylation is evidenced by aberrant phenotypes observed in mutant mouse strains deficient in
genes mediating glycan synthesis and glycan-protein interactions (Orr et al., 2012). For example,
Mgat5-/- mice, which lack the enzyme adding β1-6 N-acetylglucosamine to N-glycans, have a
higher risk of autoimmune diseases (Demetriou et al., 2001). Fut-2-/- mice, which lack
fucosyltransferase 2, exhibited abnormal hematopoiesis with lower red blood cell number and
extramedullary hematopoiesis in the spleen (Orr et al., 2012). These findings emphasize the
importance of glycosylation in regulating the development and the function of immune cells
(Demetriou et al., 2001).
20
Given that many surface proteins have glycosylated extracellular domains, these domains
provide potential binding sites for sugar binding proteins, which may regulate the organization of
surface proteins as well as mediate protein-protein interactions. Galectins are a family of β-
galactoside sugar binding proteins with conserved carbohydrate recognition domains (CRDs)
(Rabinovic and Toscano, 2009). Galectins are ubiquitously expressed in various types of tissue
and can be found from lower organisms such as nematodes and sponges to higher mammalian
species such as mouse and human. Galectins are classified into three subgroups including
prototype galectins (galectins-1, 2, 5, 7, 10, 11, 13, 14, 15) containing a single CRD with a short
N-terminal sequence; tandem-repeat-type galectins (galectins-4, 6, 8 ,9 and 12) containing two
non-identical CRDs connected by a short linker peptide; and chimera-type galectin, with
galectin-3 being the only member, containing one CRD with an extended proline-tyrosine-
glycine-rich N terminus, which can form pentamers (Rabinovich and Toscano, 2009) (Figure 6).
The CRDs of galectins have eight strongly conserved amino acids within the sugar-
binding site. These conserved amino acid residues form hydrogen bonds and van der Waals
interaction with the 4-OH, 6-OH of galactose (Gal) and the 3-OH of N-acetylglucose (GluNAc)
(Leffler and Barondes, 1986; Ahmed et al., 1996; Hirabayashi et al., 2002). Consistently, all galectins
have high affinity for N-acetyllactosamine (Galβ1-4GlcNAc) and its linkage isomer lactose-N-
biose (Galβ1-3GlcNAc), which share a similar configuration of the three essential OH groups.
Conversely, substitutions at these OH groups such as α2-6 sialylation greatly abolish affinity to
galectins (Hirabayashi et al., 2002). Within the galectin family, each galectin has a selectivity
toward certain types of saccharides, which depends on three factors including number of
repeated N-acetyllactosamine units, branching pattern and type of substitution (Hirabayashi et
al., 2002). This specificity may determine the ligands that galectins bind to and the cellular
21
pathways triggered by galectin-glycoprotein interactions. For example, in T cells, Galecin-1
(Gal-1) induces apoptosis through a CD7, CD43 and CD45 dependent pathway (Pace et al.,
2000; Hernandez, 2006; Nguyen et al., 2001). Cell lines that lack CD47, CD43 or CD45 are
resistant to apoptosis induced by Gal-1. Although Gal-9 also induces apoptosis in T cells, the
CD7, CD43 and CD45 deficient cell lines are still susceptible to apoptosis induced by Gal-9 (Bi
et al., 2008), indicating that Gal-9 induces apoptosis in T cells by a pathway distinct from Gal-1.
However, there is still a missing link between the specificity of galectins to different saccharides
and the specific saccharides decorated on the surface glycoproteins that bind to galectins. The
question of how proteins from a single cell have different glycosylation that allow specific
interactions with different galectins is still unanswered. Thus, it is critical to further study
glycosylation of surface proteins to understand the specificity of galectins in selectivity binding
to cell surface proteins to regulate cellular activities.
Galectins can form a lattice-like network through glycoprotein-galectin interactions on
the cell surface (Nabi et al., 2015). In T cells, galectins were reported to be crucial in organizing
CD45 and TCR on the cell membrane to regulate TCR signaling (Chen et al., 2007). In T cells,
the galectin lattice retains CD45 inside lipid rafts while preventing TCR partitioning into rafts,
which inhibits TCR signaling at the early immunological synapse (Chen et al., 2007). Gal-1 was
reported to organize surface proteins and induce apoptosis in human thymocytes (Pace et al.,
1999). Treating human thymocytes with recombinant Gal-1 (rGal-1) resulted in a redistribution
of glycoproteins on the cell surface; CD45 and CD3 were found on apoptotic blebs, while CD7
and CD43 were excluded from blebs. Taken together, these findings suggest that galectins play
an important role in organizing the distribution and regulating the function of cell surface
glycoproteins to mediate multiple biological processes.
22
Among the galectin family, Galectin-9 (Gal-9) appears to be an important regulator of the
immune response and is a potential target for immune therapies in human diseases (Wiersma et
al., 2013). Gal-9 is a tandem-repeat-type galectin, which has two non-identical CRDs. Gal-9 was
first isolated from mouse embryonic kidney cells and was found to be ubiquitously expressed in
rat and mouse tissues (Wada and Kanwar, 1997). The most well-known ligand of Gal-9 is T cell
immunoglobulin and mucin-domain-containing molecule 3 (TIM-3), which is expressed in
CD4+ TH1 cells (Zhu et al., 2005; Clayton et al., 2014) and also in a fraction of NK cells
(Gleason et al., 2012) and DCs (Nagahara et al., 2008) in humans. The function of Gal-9 appears
to be dependent on cell type and stage of activation or development. For example, Gal-9 binding
to TIM-3 induces intracellular calcium flux, aggregation, and death of TH1 CD4+ T cells,
selectively reduces the number of interferon-γ-producing cells, and suppresses TH1-mediated
autoimmunity in vivo (Zhu et al., 2005). In TH2 immunity, Gal-9 induces apoptosis of activated
alloreactive CD8+ cytotoxic T cells but not naive CD8+ cells (Wang et al., 2007). The function
of Gal-9 in humoral immunity is still largely unknown. Analysis of Gal-9-KO mice revealed an
enhancement in the humoral immune response with increased B cell proliferation, increased
germinal center size and higher antibody production (Orr et al., 2012). However, the cellular and
molecular mechanisms for these observations were not investigated. Recently, data from our lab
demonstrates that Gal-9 is bound to the surface of primary naïve B cells and is an inhibitory
regulator of B cell activation (Alluqmani, Cao & Treanor, unpublished data). Specifically, Gal-9-
KO primary B cells have higher antigen accumulation compared to WT control when spreading
on artificial lipid bilayers conjugated with model antigen. Consistent with this, BCR signaling
was also enhanced in Gal-9-KO B cells upon stimulation, as evidenced by higher levels of
phosphorylated ERK. Interestingly, treating Gal-9-KO primary B cells with recombinant Gal-9
23
(rGal-9) significantly diminished B cell activation. Although these findings suggest that Gal-9 is
an inhibitory regulator of B cell activation, the molecular mechanism for Gal-9 suppression on B
cell activation has not been investigated. However, we recently identified ligands of Gal-9 in
primary murine B cells (Cao & Treanor, unpublished). Using flag-tagged recombinant Gal-9
(rGal-9) we pulled down ligands of Gal-9 from primary B cell lysate, which were identified by
mass spectrometry. Six proteins were identified including CD45, IgM heavy chain, CD180, Igβ,
CD47, and M6PR. The identification of two main components of BCR including IgM heavy
chain and Igβ as ligands for Gal-9 suggests that BCR may reside within the Gal-9-glycoprotein
lattice, which may alter IgM-BCR organization and mobility on B cell surface, and consequently
B cell signaling and activation.
24
Figure 6. Galectins and the galectin-glycoprotein lattice
A) Schematic representation of the three subgroups of galectins: prototype, tandem-repeat type
and chimera type. Prototype galectins contain one carbohydrate recognition domain (CRD).
Tandem-repeat galectins contain two non-identical CRDs connected by a short peptide linker.
Chimera-type galectin has one CRD with an extended proline-tyrosine-rich N-terminus.
Galectins also form dimers and oligomers. B) Schematic representation of hypothetical galectin-
glycoprotein lattices. The structure and complexity of the lattice are dependent on the structure
and valency of both the galectin and the glycoprotein. (Pereira & Falcão, 2015)
25
1.7 Hypothesis and Aims
Given that Gal-9 is able to form Gal-9 lattices and binds to IgM-BCR as well as CD45, we
hypothesized that Gal-9 reorganizes IgM-BCR into larger clusters and brings inhibitory
glycoproteins closer to IgM-BCR complex within the Gal-9 lattice. We predicted that the
reorganization of IgM-BCR into larger clusters would restrict the lateral diffusion of IgM-BCR,
and thus attenuate BCR signaling. In addition, we also predicted that inhibitory molecules within
the Gal-9 lattice might directly inhibit BCR by regulating the phosphorylation of early signaling
molecules.
This study has three main aims:
Aim 1: Investigate how Gal-9 modulates the cell surface organization of IgM-BCR.
Aim 2: Investigate how Gal-9 modulates IgM-BCR mobility at the cell surface.
Aim 3: Investigate how Gal-9 regulates the localization of inhibitory molecules such as CD45
and CD22 relative to IgM-BCR on the cell surface.
26
MATERIALS AND METHODS
2.1 Mice
Galectin-9-/- (Gal9-KO) mice were provided by Stephen Beverly (Washington
University) on behalf of The Scripps Research Institute (TSRI). Galectin-9+/- (Gal9-Het) mice
were generated by breeding Gal9-KO with C57BL/6 (Wild-type; WT) mice obtained from
Charles River, USA. Littermate controls were generated by breeding Gal9-Het with each other.
Mice were used at 2-3 months of age for all functional and biochemical experiments. Mice were
housed in specific pathogen-free animal facility at the University of Toronto Scarborough,
Toronto, Canada. All experiments were approved by the Local Animal Care Committee (LACC)
at University of Toronto Scarborough.
2.2 B Cell Purification
Single cell suspensions of splenocytes were isolated from WT and Gal9-KO mice using a
70 µm cell strainer. Cells were centrifuged at 300g for 5 min at 4 oC. B cells were purified using
the negative isolation kit (EasySepTM, STEMCELL Technologies) according to the
manufacturer’s protocol.
2.3 Surface Staining and Confocal Microscopy
5 x 106 primary murine B cells from WT mice were treated with 1 µM recombinant
galectin-9 in 1% FBS in RPMI for 30 min at 37 oC (75 µL of reconstituted recombinant galectin-
9 in 125 µL of RPMI 1% FBS). Cells were washed once with 5 mL of PBS. Cells were
27
resuspended in PBS and allowed to spread on anti-MHC II coated Lab-Tek™ chambers for 15
min at 37oC. The supernatant was gently removed to eliminate unbound cells. Cells were fixed in
2% PFA at 37oC for 10 min. Cells were washed three times with PBS. Cells were incubated with
2 µg/ml purified rat anti-mouse CD16/32 (BDPharmingen) in 200 µL blocking buffer (PBS
containing 5 % BSA) for 1 h at 4 oC. Blocking buffer was gently removed and cells were
incubated with 1 µg/mL goat anti-mouse Gal-9 (R&D systems) for 1 h at 4 oC in 200 µL FACS
buffer (PBS, 1% BSA, 0.1% NaN3). Cells were washed three times with 400 µL PBS. Cells were
incubated with 1 µg/mL CyTM3-conjugated bovine anti-goat IgG (H+L) (Jackson
ImmunoResearch) in 200 µL blocking buffer for 1 h at 4oC. Cells were washed three times with
400 µL PBS. Cells were incubated with 1.5 µg/mL Alexa Fluor® 647 or 488 conjugated Fab
fragment goat anti-mouse IgM, µ chain specific (Jackson ImmunoResearch), 5 µg/mL Alexa
Fluor® 647 rat anti-mouse CD22 (clone OX-97, BioLegend) and 5 µg/mL Alexa Fluor® 488 anti-
mouse/human CD45R/B220 (Clone RA3-6B2, BioLegend) in 200 µL blocking buffer for 1 h at
4 oC. Cells were washed three times with 400 µL PBS. Cells were mounted in Fluoro Gel with
DABCO™ (Electron Microscopy).
Confocal images were acquired using a spinning disc confocal microscope (Quorum
Technologies) consisting of an inverted fluorescence microscope (DMI6000B; Leica) equipped
with an EM-CCD camera (Hamamatsu) and a 63x oil immersion objective, NA 1.4. Images were
acquired using the Metamorph software (Molecular Devices).
Images were analysed using Volocity software (Perkin Elmer). The fluorescence signal of
CD45, CD22 or IgD was combined to define a mask delineating the membrane region. Gal-9high
regions were determined by the fluorescence signal of Gal-9. Gal-9low regions were determined
28
by subtracting the galectin-9high regions from the membrane region. The mean flourescence
intensity of CD45, CD22, IgD, and IgM was calculated in Volocity.
2.4 B Cell Activation
5 x 106 primary murine B cells were suspended in 200 µL of RPMI and pre-warmed at 37
oC for 10 minutes. Cells were stimulated with 5 µg/mL F(ab’)2 goat anti-mouse IgM, µ chain
specific (Jackson ImmunoResearch) in a total volume of 400 µL. To stop the reaction, 1 mL of
ice-cold PBS was added at the indicated timepoint and tubes were quickly transferred onto ice.
Cells were pelleted at 15,000g for 30 s. The supernatant was removed before adding lysis buffer
(1 % NP40, 0.15 M NaCl, 20 mM Tris pH 8, 100 mM NaF, 10 mM Na3VO4, and Roche
cOmpleteTM protease inhibitor cocktail) at a concentration of 10 x 107 cells/mL. The cell lysate
was incubated on ice for 30 min with intermittent vortex. The cell lysate was centrifuged at
15,000g for 15 min to remove cell debris and the supernatant was transferred to a clean
microtube. 2X Laemmli buffer containing 0.1 M DTT was added to the cell lysate and boiled at
95oC for 5 min.
2.5 Western Blot
Proteins were resolved by sodium dodecyl sulfate polyacrylamide gel electrophoresis
(SDS-PAGE). For Western blot of phosphorylated proteins, proteins were transferred to PVDF
membranes followed by blocking for 1 h in TBST (20 mM Tris pH 7.5, 150 mM NaCl and 0.1 %
Tween 20) containing 5 % BSA at room temperature. Membranes were incubated with rabbit
anti-mouse phospho-CD19 (Tyr531) (Cell Signaling Technology), rabbit anti-mouse phospho-
SHP-1 (Tyr564) (Cell Signaling Technology) at 1:1000 v/v and mouse anti-phospho-CD22
29
(Tyr822, 12a/CD22) (BD Biosciences) at 1:200 v/v in TBST containing 1% BSA overnight at 4
oC. Membranes were washed three times in 50 mL TBST on a shaking rocker for 10 min.
Membranes were incubated with horseradish peroxidase (HRP)-conjugated Donkey anti-rabbit
IgG or Donkey anti-mouse IgG antibodies (Jackson ImmunoResearch) at 1:5000 v/v in TBST
containing 1% BSA for 1 hour at room temperature. Membranes were washed three times with
TBST as above. Membranes were incubated with Pierce® ECL Western Blotting Substrate and
imaged by ChemiDoc System (Bio-Rad). The intensity of each band was analyzed by ImageJ,
normalized to β-Tubulin and the fold changes were calculated using the signal of WT B cell at
time 0.
2.6 Single Particle Tracking
2.6.1 Recombinant Galectin-9 and Fab Fragment Labeling
Mouse recombinant galectin-9 (rGal-9, R&D Systems) was reconstituted at 0.2 mg/mL
(maximum concentration to reconstitute rGal-9) in 0.1 M NaHCO3 and incubated with 0.2
mg/mL Alexa Fluor® 555 NHS Ester for 1 h at room temperature with gentle mixing. Following
labeling, the mixture was dialyzed against 20 mM MOPS, 500 mM sodium chloride, 0.5 mM
EDTA, and 1 mM DTT using a 10,000 MWCO Slide-A-Lyzer ® Dialysis Cassette (Thermo
Scientific). After changing the buffer two times in 24 h, precipitation was observed in the
dialysis cassette. The membrane of the cassette was cut and protein was collected using a
micropipette. The mixture was mixed by pipetting to dissolve protein precipitation. Labelled
rGal9 was stored at -20oC.
Fab fragment labeling
30
20 µL of 1 M NaHCO3 was added to 200 µL of 1 mg/ml goat anti-mouse IgM, µ chain specific
(Jackson ImmunoResearch) and incubated with 40 µg/mL Attotec® 633 NHS Ester for 1 h at
room temperature with gentle mixing. Following labeling, the mixture was dialyzed against PBS
at 4oC. After changing the buffer two times in 24 h, labelled Fab fragment was collected and
stored at 4oC.
2.6.2 Recombinant Galectin-9 Treatment
5x106 primary murine B cells from Gal-9-KO mice were incubated with 500 µL complete
media containing 0.5 µM labeled rGal9 and 0.5 µM non-labeled rGal9 for 30 mins at 37 oC.
Cells were centrifuged at 300g, washed two times with ice-cold PBS, and resuspended in
chamber buffer (0.1 g/mL glucose, 0.5% heat inactivated FBS, 2 mM MgCl2, 0.5 mM CaCl2).
2.6.3 Glass Coverslip Coating
Glass coverslips were cleaned in chromic acid for 20 min followed by rinsing with water
and acetone. Coverslips were air-dried and then incubated with 1 µg/mL anti-MHC class II
(clone M5/114) for 2 h at room temperature and then washed with PBS.
2.6.4 Cell Labeling for Single Particle Tracking
Primary murine B cells from Gal-9-KO mice were labeled with 4 ng/mL Attotec® 633-
labeled goat anti-mouse IgM Fab fragment (Jackson ImmunoResearch) in 0.5% FBS in PBS for
15 min at 4oC followed by washing with PBS two times and resuspending in chamber buffer.
Labeled cells were stored on ice prior to imaging. Just before imaging, cells were incubated at
37oC for 5 min.
2.6.5 Instrument
Single-molecule fluorescence microscopy was performed with a total internal reflection
fluorescence (TIRF) microscope (Quorum Technologies) based on an inverted microscope
31
(DMI6000B; Leica), HCX PL APO 100x/1.47 oil immersion objective and Evolve Delta
EMCCD camera (Photometrics). Images were acquired continuously at 20 frame/s for 10
seconds with an electron-multiplying (EM) gain of 200 and the exposure time of 50 ms. SPT
analysis was performed as described previously (Treanor et al., 2010).
2.7 Co-Immunoprecipitation
Antibody conjugation was performed using a Pierce Co-Immunoprecipitation Kit
(Thermo Scientific). 30 µg of goat anti-mouse IgM µ-chain specific (Jackson ImmunoResearch)
or 10 µg of goat anti-mouse CD22 (R&D Systems) was conjugated to 50 µL AminoLink Plus
Coupling Resin according to the manufacturer’s protocol.
10 x 106 primary murine B cells from WT and Gal-9-KO were resuspended at 25 x 106
cells/mL in ice-cold PBS, containing 1 mM freshly prepared DTSSP. Cells were incubated for 30
min at 4 oC with gentle shaking. Cells were washed three times with 5 mL ice-cold PBS. The
cell pellet was lysed in 500 µL lysis buffer (1 % Triton X-100, 1 % NP40, 1 mM EDTA, Roche
cOmpleteTM Protease Inhibitor Cocktail in PBS,) for 30 min at 4 oC . Cell lysate was centrifuged
at 15,000g for 15 min at 4 oC. The supernatant was pre-cleared with agarose resin (Pierce®
Control Agarose Resin, Thermo Scientific) for 1 h at 4 oC with gentle shaking. Agarose resins
were removed using Pierce Spin Columns by centrifuging at 1000 g for 1 min.
400 µL of cell lysate was incubated with antibody-conjugated resins for 3 h at 4 oC with
gentle shaking. Resins were washed three times with ice-cold lysis buffer. Resins were incubated
with 70 µL 2X Laemmli buffer at room temperature for 5 mins and at 95 oC for 5 min. Eluent
was collected by centrifugation at 1,000g for 1 min through a Pierce Spin Column. 70 µL of lysis
32
buffer containing 50 mM DTT was added to the eluent. 30 µL of eluent was loaded into each
lane of a 7.5% polyacrylamide gel. Proteins were transferred to a PVDF membrane using transfer
buffer (0.05 % SDS, 10 % Methanol, 25 mM tris(hydroxymethyl)aminomethane (Tris base) and
190 mM glycine at 125 mA (70 V) for 3 h at 4 oC. Membranes were blocked with 5 % milk in
TBST (50 mM Tris, 150 mM NaCl and 0.1% Tween 20, pH 7.6) for 1 h at room temperature.
Membranes were incubated overnight with 1 µg/mL of anti-mouse IgM µ-chain specific
(Jackson ImmunoResearch), 0.1 µg/mL goat anti-mouse CD22 (R&D Systems) and 0.1 µg/mL
goat anti-mouse CD45 (R&D Systems) in TBST containing 1% BSA. Membranes were washed
three times in 50 mL TBST on a shaking rocker for 10 min. Membranes were incubated with
horseradish peroxidase (HRP)-conjugated donkey anti-goat antibodies (Jackson
ImmunoResearch) at 1:5000 v/v in 5% milk in TBST for 1 h at room temperature. Membranes
were washed three times with TBST as above. Membranes were incubated with Pierce® ECL
Western Blotting Substrate and imaged by ChemiDoc System (Bio-Rad). The intensity of each
band was analyzed by ImageJ (Schneider et al., 2012) and background signal subtracted. The
intensity of co-immunoprecipitated proteins were divided by the intensity of the
immunoprecipitated proteins. The ratio was then normalized to the lowest value, which was set
at 1.
2.8 Direct Stochastic Optical Reconstruction Microscopy
(dSTORM)
2.8.1 Sample Preparation
Primary B cells were stained with Alexa Fluor® 647-conjugated Fab fragment goat anti-
mouse IgM, µ-chain specific (Jackson ImmunoResearch) at 1.5 µg/mL in PBS containing 2 %
33
FBS for 15 min at 4 oC. Cells were centrifuged at 300g for 5 min and washed twice with 5 mL
PBS. Cells were resuspended in PBS and allowed to recover at 37 oC for 10 min. Cells were
allowed to spread on anti-MHC-class II coated coverslips (prepared as described above) for 10
min at 37 oC. Coverslips were washed gently with PBS to remove unbound cells. Cells were
fixed with fixation solution (4 % paraformaldehyde, 0.2 % glutaraldehyde in PBS) for 40 min at
room temperature. The coverslips were washed with PBS three times. Before imaging, samples
were incubated in PBS containing 0.1 M mercaptoethylamine (MEA), 0.5 mg/mL glucose
oxidase, 40 ug/mL catalase and 10 % glucose.
2.8.2 dSTORM Acquisition and Image Reconstruction
dSTORM images were acquired on a TIRF microscope as described above. For Alexa
Fluor® 647, photoconversion was achieved with a 633-nm laser (intensity ranged from 80 to 100
mW/cm2) illumination and conversion from the dark state with a 488-nm laser illumination
(intensity range from 5 to 20 mW/cm2). 10,000 images were acquired at a frame rate of 33
frames/s. Reconstructed images were acquired using the ThunderSTORM plugin for ImageJ
(Ovesný et al., 2014) with the camera setup as follows: pixel size (101.5 nm), photoelectron per
A/D count 3.6, base level [A/D count] 414 and an EM gain of 50. Wavelet filter (B-Spline) was
applied with a B-Spline order of 3 and a B-Spline scale of 2.0. Approximate localization of
molecules was detected by local maximum method with a peak intensity threshold of std
(Wave.F1) and a connectivity of 8-neighbourhood. Sub-pixel localization of molecules was
identified using integrated Gaussian with a fitting radius of 3 pixels and the weighted least
squares fitting method with multi-emitter fitting analysis enabled. The reconstructed images were
post-processed with drift correction using the built-in method in the ThunderSTORM plugin. To
reduce processing time, final images were selected from frames ranging from 4000 to 8000. In
34
addition, localizations with an uncertainty greater than 20 nm or intensity lower than 3000
photons were eliminated.
2.8.3 Hopkins Index and Ripley’s H Function Analysis
For each reconstructed image of WT and Gal-9-KO B cell, a 3 x 3 µm region was
randomly selected but excluded from the cell boundary. For each reconstructed image of rGal-9
treated Gal-9-KO B cell, a 3 x 3 µm region that colocalized with Gal-9, was selected for
analysis. The Hopkins index and Ripley’s H function analysis were performed by SuperCluster,
an analysis tool kindly provided by the University of New Mexico’s Spatio Temporal Modeling
Center via their website (http://stmc.unm.edu/).
2.8.4 Bayesian Cluster Analysis
Cluster analysis was performed by a Bayesian, a model-based approach (Griffié et al.,
2016). In brief, uncertainty, x and y coordinates of each localization in post-processed
reconstructed images were exported. For each reconstructed image of WT and Gal-9-KO B cells,
random but boundary-excluded 3 x 3 µm regions were selected for analysis. For each
reconstructed image of rGal-9-KO treated with rGal-9, a 3 x 3 µm region, which colocalized
with Gal-9, was selected for analysis. The selected regions were analyzed by the published
Matlab code of Bayesian cluster analysis (Griffié et al, 2016) with an alpha value of 20,
pbackground of 0.5, rseq of (5, 200, 10) and thseq of (0, 50, 5). The analyzed data were post-
processed to extract data about the percentage of molecules localizing in clusters, cluster radius,
number of clusters, and number of molecules per clusters.
35
2.9 Statistical Analysis
Statistical analysis was performed using GraphPad Prism. The normal distribution of data
was tested using the D’Agostino-Pearson omnibus normality test. Comparisons between two
groups were performed using Student’s t test for data with normal distribution and Mann-
Whitney for data with non-normal distribution. Comparisons between multiple groups were
performed by ordinary one-way ANOVA for data with normal distribution and Kruskal-Wallis
test for data with a non-normal distribution. For post hoc analysis, Tukey’s multiple comparison
was used for normally distributed data and Dunn’s multiple comparisons test was used for non-
normally distributed data.
For Western Blot analysis, the Mann-Whitney test was used to test the statistical
significance of the difference between the phosphorylation level at different time points between
WT and Gal-9-KO B cells upon activation.
36
RESULTS
3.1 Recombinant Galectin-9 Reorganizes IgM into Larger
Clusters
Previously, we discovered that Gal-9 binds to IgM-BCR (Cao & Treanor, unpublished).
Galectins were hypothesized to form a galectin-glycoprotein lattice through the interaction
between CRDs and sugar side chains on cell surface glycoproteins (Nabi et al., 2015). Thus, we
hypothesized that Gal-9 regulates the organization of IgM-BCR on the surface of B cells, and
may be a molecular mechanism that organizes IgM-BCR into nanoclusters. To investigate if Gal-
9 is involved in the organization of endogenous IgM-BCR on the B cell membrane, we utilized
dSTORM, which permits the distinction of two points separated by a lateral distance of
approximately 10 to 30 nm in fixed cells, allowing the study of the organization of IgM-BCR at
the single molecule level (Heilemann et al., 2008; Mattila et al., 2013). Primary murine WT, Gal-
9-KO B cells and Gal-9-KO B cells treated with 1 µM rGal-9 were labelled with Alexa-Fluor
647 conjugated anti-IgM Fab fragment. Cells were allowed to settle on non-stimulatory anti-
MHC II coated coverslips for 10 min prior to fixation. Total internal reflection fluorescence
microscopy (TIRFM) was used to acquire images of IgM-BCR to selectively visualize the
organization of IgM-BCR at the contact surface. Using Thunderstorm software (Ovesný et al.,
2014), 4000 images were combined to generate reconstructed super-resolution images.
37
Consistent with previous findings, IgM molecules on the B cell membrane were
organized into nano-size clusters in WT primary B cells (Figure 7A) (Mattila et al., 2013). To
quantify the clustering tendency of IgM molecules on the surface of primary B cells, we used
two conventional methods including H functions derived from Ripley’s K function and the
Hopkins index (Zhang et al., 2006). First, we used the H function derived from Ripley’s K
function to quantify the extent of BCR clustering according to the number of proteins found
within a distance (r) for each molecule compared with that predicted for a random distribution.
We found that the radius of clusters of IgM molecules on the surface of WT primary B cells
estimated by the peak of Ripley’s H function was approximately 50-100 nm, which is consistent
with previously reported values (Mattila et al., 2013) (Figure 7B). Second, we used Hopkins
index to evaluate the clustering tendency of IgM molecules compared to the hypothetical random
distribution, whose value is 0.5. The Hopkins index of IgM-BCR on the surface of WT primary
B cells is 0.75, confirming that IgM are not randomly distributed on the B cell surface (Figure
7C).
To test the hypothesis that Gal-9 regulates the organization of IgM-BCR nanoclusters, we
studied the organization of IgM molecules in the absence of endogenous Gal-9 in the context of
Gal-9-KO primary B cells. We did not find a significant difference in Hopkins index nor
Ripley’s H function between WT and Gal-9-KO primary B cells (Figure 7B, 7C). This finding
suggests that Gal-9 does not mediate the formation of IgM nanoclusters. We previously found
that Gal-9 localizes in discrete puncta that are sparsely distributed on the B cell surface
(Alluqmani, Cao & Treanor, unpublished). Thus, the comparison between WT and Gal-9-KO B
cells based on randomly selected regions might not account for the effect of Gal-9 on IgM-BCR
organization inside the Gal-9 lattice. To focus on the effect of Gal-9 on IgM organization, we
38
wanted to examine the organization of IgM molecules inside the Gal-9 lattice. The conventional
method using fluorescently labelled anti-Gal-9 antibodies to label Gal-9 was not feasible due to
the low abundance of Gal-9 on the B cell surface (Alluqmani, Cao & Treanor, unpublished). In
addition, using antibodies against Gal-9 may induce crosslinking between Gal-9 molecules,
which possibly causes artificial effects on Gal-9 and IgM organization. To overcome this, we
treated Gal-9-KO primary B cells with fluorescently labelled recombinant Gal-9 (rGal-9) with
the concentration of 1 µM, which was reported to nearly abolish B cell activation (Alluqmani,
Cao & Treanor, unpublished). We then acquired dSTORM images and focused our analysis of
clustering on selected regions with high Gal-9 fluorescence intensity (Gal-9high). Interestingly,
Gal-9-KO primary B cells treated with 1 μM rGal-9 have fewer, but larger IgM clusters inside
Gal-9high regions (Figure 7A). Consistent with our observation from the reconstructed images, we
found that inside Gal9high regions, IgM-BCRs are significantly more clustered compared with
IgM on both WT and Gal-9-KO B cells (Figure 7B). The radius of clusters of IgM molecules
inside Gal-9high regions is approximately 150-250 nm, compared to 50-100 nm in WT cells.
Consistent with the Ripley’s H function analysis, inside Gal-9high regions, Hopkins index of IgM
is significantly higher compared to IgM on WT and Gal-9-KO B cells (0.83±0.02 compared to
0.75±0.02 and 0.75±0.02), indicating that IgM in these regions are significantly more clustered
(Figure 7C).
To further study the effect of rGal-9 on IgM organization on the surface of primary B
cells, we used a Bayesian cluster analysis (Griffié et al., 2016) to assign clusters and to identify
physical properties of these clusters. This method is more effective than the commonly used
Ripley's K function and Hopkins index for two reasons. First, this method accounts for a non-
negligible unclustered background, which can interfere with Ripley’s and Hopkins analysis.
39
Second, this method also takes account of the uncertainty associated with each localization
assigned by the Gaussian function. Using this method, we identified the number of clusters per
region, cluster radii, number of molecules per cluster, and percentage of localizations in clusters
from regions of interest (ROIs) 3000x3000 nm extracted from the reconstructed images. We
found a reduction in the number of clusters per ROI in rGal-9 treated cells (11.12±1.56)
compared to WT (30.92±2.11) and Gal-9-KO (34.12±1.78) cells (Figure 7D). We also observed
a significant increase in cluster radius (35.01 ± 1.48 nm compared to 20.27 ± 0.28 and 19.17 ±
0.23 nm for rGal-9, WT, and Gal-9-KO respectively) (Figure 7E) and number of molecules per
cluster (85.15 ± 9.29 compared to 27.23 ± 0.79 and 25.95 ± 0.62) (Figure 7F). Interestingly, we
did not find a significant difference in the percentage of localizations inside clusters between
WT, Gal-9-KO and Gal-9-KO cells treated with rGal-9 (Figure 7G). These findings indicate that
rGal-9 brings pre-existing IgM nanoclusters together to form larger clusters, which increases the
size of clusters and number of molecules per cluster and consequently reduces the overall
number of clusters, but without affecting the percentage of IgM molecules residing in clusters.
However, it is necessary to acknowledge a limitation of Bayesian cluster analysis is its
assumption that clusters are relatively homogenous in size, which compromises its ability to
detect clusters with large range in size.
40
Figure 7. rGal-9 alters IgM nanoclusters.
A)TIRFM image of surface IgM and fluorescently labelled rGal9 before bleaching for image
acquisition (two left panels respectively). dSTORM images reconstructed from single-molecule
localization processed by Thunderstorm software; the magnified region (3 x 3 µm) from ROI
(white box) is shown as 2D image (middle) and 3D surface plot (right) in the order of WT (top),
Gal-9-KO (middle) and Gal-9-KO + 1 µM rGal-9 (bottom). Scale bar represents 2 µm. B)
Quantification of the distribution of IgM by H function and C) Hopkins index of localizations
inside ROIs. Reconstructed images were analyzed by a model-based Bayesian approach to
identify nanoclusters and their physical properties. D) Number of clusters (one point per ROI).
E) Cluster radii (one point per cluster). F) Number of molecules (one point per cluster). G)
Percentage of localization in clusters (one point per ROI). Each category contains at least 15
ROIs from three independent experiments (at least 4 cells per experiment). Statistical analysis
was performed using Kruskal-Wallis test with Dunn's multiple comparison test (D, E, F) and
one-way ANOVA with Tukey`s multiple comparison test (C,G). Horizontal bars indicate mean
±S.E.M. *P < 0.05, **P < 0.01, *** P < 0.001, ****P < 0.0001
Distance (nm)
41
3.2 Galectin-9 Reduces the Mobility of IgM-BCR
IgM-BCR mobility is critical in regulating BCR signaling. Treanor et al. (2010) reported
that disruption of the actin cortex by LatA treatment increases IgM-BCR mobility and initiates
signaling cascades in the absence of specific antigen. Our dSTORM data demonstrates that Gal-9
drastically changes the organization of IgM molecules on the B cell surface. In addition, it was
reported that a larger cluster size might lead to a lower mobility of proteins on the cell surface
(Gasparrini et al., 2016). To investigate how the Gal-9-mediated change in organization of IgM
may affect IgM mobility on the B cell surface in the steady-state we labelled single particles of
IgM by staining primary B cells from WT and Gal-9-KO mice with a low concentration of Atto-
633 conjugated anti-IgM Fab fragments. Cells were allowed to settle on non-stimulatory anti-
MHC II coverslips and single molecules of IgM were visualized using TIRF microscopy. The
trajectory of each molecule was tracked and the diffusion coefficient of each track was calculated
as previously described (Treanor et al., 2010). Consistent with previous findings (Treanor et al.,
2010), the diffusion coefficient of IgM on the B cell surface is heterogeneous, ranging from
nearly immobilized to 0.25 μm2s-1 (Figure 8A, 8B). The median of diffusion coefficient of IgM-
BCR on primary WT B cells was 0.027 μm2s-1, consistent with previous findings (Treanor et al.,
2010). Interestingly, the median diffusion coefficient of IgM-BCR on primary Gal-9-KO B cells
was approximately 30% higher than primary WT B cells (0.037μm2s-1 compared to 0.027 μm2s-1)
(Figure 8A). Consistent with this, the relative frequency of the diffusion coefficients reveals that
the frequency of slow-moving IgM-BCR is decreased in Gal-9-KO B cells while the proportion
of fast-moving IgM-BCR is increased (Figure 8B). This finding indicates that Gal-9 decreases
the mobility of IgM-BCR on the surface of primary B cells.
42
To study the mobility of IgM-BCR inside the Gal-9 lattice on the surface of primary
naïve B cells, we treated Gal-9-KO B cells with fluorescently-labelled rGal-9 and allowed the
cells to settle onto non-stimulatory anti-MHC II coated coverslips and visualized and tracked
IgM-BCR as described above. To identify regions where rGal-9 bound to the cells, we created a
mask using the fluorescence intensity of Gal-9 to define regions of high and low Gal-9 and then
calculated the diffusion coefficient in these regions (Figure 8C). We observed that inside the
Gal-9 mask the median diffusion coefficient of IgM was greatly reduced compared to molecules
outside the Gal-9 mask (0.012 μm2/s-1compared to 0.028 μm2/s-1) (Figure 8D). The decrease in
the median diffusion coefficient is consistent with an increase in frequency of slow moving IgM
inside the Gal-9 mask compared to outside the mask (Figure 8E). About 65% of tracked IgM
molecules are largely immobilized inside the mask compared to 45% of tracked IgM molecules
outside of the mask. This finding provides direct evidence for the ability of Gal-9 to restrict the
mobility of IgM molecules on the B cell membrane.
43
Figure 8. Gal-9 immobilizes IgM-BCR.
Single-particle tracking of IgM in WT (black circle) or Gal-9-KO (blue triangle) primary B cells.
A) Diffusion coefficients with the median indicated in red. B) Frequency distribution histogram
for the indicated diffusion bins of single molecules of IgM. 500 representative diffusion
coefficients from a total of at least 1500 tracks from three independent experiments. **P < 0.01,
Mann-Whitney test. C) Representative image of fluorescently labelled rGal-9 on primary B cell
(left) and mask created to differentiate tracks inside Gal-9 regions (Gal-9high, black circle) and
tracks outside Gal-9 regions (Gal-9low, blue triangle). Scale bar represents 2 µm. D) Diffusion
coefficients with the median indicated in red and. E) Frequency distribution histogram of single
molecules of IgM in the indicated diffusion bins. 250 representative diffusion coefficients from a
total of at least 900 tracks from three independent experiments. Data ****p < 0.0001, Mann-
Whitney test.
44
3.3 IgM and CD45 Density is Increased in the Galectin-9 Lattice
Our data indicates that rGal-9 induces an increase in the size of IgM-BCR clusters and
immobilization of IgM, features which are consistent with activation of B cells (Tolar et al.,
2009). However, we have found that treatment with rGal-9 significantly diminishes BCR
induced downstream signaling (Alluqmani & Treanor, unpublished). Our data also identified that
Gal-9 binds to CD45, the most abundant phosphatase expressed on the B cell surface (Cao &
Treanor, unpublished). Hence, we predicted that Gal-9 not only alters the mobility and
organization of IgM molecules, but also modifies the interaction between IgM and CD45.
To study the effect of Gal-9 on the organization of CD45 relative to IgM-BCR, we
treated WT primary B cells with 1 μM rGal-9 and allowed the cells to spread on non-stimulatory
anti-MHC II coated coverslips. The cells were then fixed and stained for surface proteins using
anti-CD45, anti-IgM and anti-Gal-9 antibodies and z-section images were acquired by spinning
disk confocal microscopy. Interestingly, rGal-9 forms a cap at one side of the cells, consistent
with the ability of the galectin family of proteins to form a lattice-like structure through galectin-
glycoprotein interactions (Figure 9A). We also observed that within the Gal-9-cap, CD45 and
IgM molecules form clusters with higher fluorescence intensity (Figure 9B). To quantify this
observation, we developed a protocol to create masks defining Gal-9-high regions (Gal9high) and
Gal-9-low regions (Gal9low) (Figure 9C). Consistent with our observation, the mean fluorescence
intensity of both CD45 and IgM are significantly higher in Gal9high regions compared to Gal9low
regions (Figure 9D and 9E).
45
Figure 9. The Gal-9 lattice increases the molecular density of IgM and CD45.
A) Primary wild-type (WT) B cells were treated with 1 µM rGal-9 and settled on non-
stimulatory coverslips then fixed and stained for CD45 (green), IgM (red) and Gal-9 (magenta)..
B) Intensity profile of fluorescent signals of CD45, IgM and Gal-9 along the cell membrane
(indicated by the white segmented line) of a representative cell (shown in A). C) Representative
example of masking output of algorithm to detect Gal-9 high (Gal-9high; blue) and Gal-9 low
(Gal-9low; orange) regions. D) CD45 mean intensity and E) IgM mean intensity in Gal-9low (blue
circle) and Gal-9high (orange circle) regions. Data are representative of at least three independent
experiments. Each point represents one region (cell). Sample size ≥ 68 regions. Scale bar
represents 2 µm. The median is indicated in red. Mann-Whitney test, ***p < 0.001.
To examine if the reorganization of IgM and CD45 induced by Gal-9 was due to a global
change in membrane structure, which may affect all proteins on the surface, we studied the effect
of Gal-9 on the organization of IgD-BCR. IgD-BCR is an isotype of BCR, and is co-expressed
with IgM on the surface of mature naïve B cells. The expression of IgD is 10-fold higher than the
expression of IgM (Mattila et al., 2013), making IgD a perfect candidate to study the specificity
46
of Gal-9 in organizing surface proteins. Interestingly, we did not observe any increase in the
intensity of IgD within the Gal-9 lattice (Figure 10A, 10B). Indeed, quantification of the mean
intensity of IgD in Gal-9high regions was similar to that in Gal-9low regions (Figure 10C). This
finding confirms that the interaction between Gal-9 and CD45 and between Gal-9 and IgM is
specific, and that the increase molecule density in Gal-9high regions is a specific phenomenon that
does not affect all proteins on the B cell membrane.
Figure 10. The Gal-9 lattice does not affect the molecular density of IgD.
A) Primary wild-type (WT) B cells were treated with 1 µM rGal-9 and settled on non-
stimulatory coverslips then fixed and stained for IgD (green), IgM (red) and Gal-9 (magenta).
Scale bar represents 2 µm. B) Intensity profile of fluorescent signals along cell membrane of a
representative cell (shown in A). C) IgD mean intensity and D) IgM mean intensity in Gal-9low
(blue circle) and Gal-9high (orange circle) regions. Data are representative of at least three
independent experiments. Each point represents one region (cell) with at least 58 regions. The
median is indicated in red. Mann-Whitney test, **p < 0.01, ns not significant.
47
3.4 CD22 Density is Increased in the Galectin-9 Lattice
One of the best characterized inhibitory coreceptors expressed on B cells is CD22
(Nitschke et al., 1997). Interestingly, the extracellular domain of CD22 is also glycosylated and
contains a sialic-acid binding domain, which binds to α2,6-linked sialic acids and mediates
homotypic interactions between CD22 molecules and heterotypic interactions with other
glycosylated proteins on B cells including CD45 and IgM (Leprince et al., 1993; Cyster and
Goodnow, 1997; Zhang et al, 2004). Furthermore, the interaction between CD22 and CD45
regulates the interaction between CD22 and IgM in the resting state (Bakker et al., 2012 and
Gasparrini, 2016). Given that Gal-9 induces the reorganization of IgM and CD45 on the B cell
surface, we investigated if rGal-9 also changes CD22 organization on the plasma membrane and
its density inside Gal-9high regions. We observed clusters of CD22 with higher fluorescence
intensity inside Gal-9high regions compared to Gal-9low regions, which colocalized with IgM
clusters (Figure 11A and 11B). Using the same quantification method used for CD45 analysis,
we found that inside Gal-9high regions, the mean intensity of CD22 is also increased (Figure
11C). These findings suggest that rGal-9 increases CD22 density in Gal-9high regions, coincident
with the enrichment of IgM. This may enhance the interaction between IgM and CD22 and
provide a mechanistic basis for the inhibitory effect of Gal-9 on B cell activation.
48
Figure 11. The Gal-9 lattice increases the molecular density of CD22
(A) Primary wild-type (WT) B cells were treated with 1 µM rGal-9 and settled on non-
stimulatory coverslips then fixed and stained for CD22 (green), IgM (red) and Gal-9 (magenta).
Scale bar represents 2 µm. (B) Intensity profile of fluorescent signals along cell membrane of a
representative cell (shown in A). (C) CD22 mean intensity and (D) IgM mean intensity in Gal-
9low (blue circle) and Gal-9high (orange circle) regions. Data are representative of at least three
independent experiments. Each point represents one region (cell). Sample size ≥ 35 regions. The
median is indicated in red. Mann-Whitney test, *p < 0.05.
Zhang et al. (2004) reported that CD22 association with CD45 and IgM are sialic acid
independent. Thus, we hypothesized that Gal-9 may act as a mediator for the association between
CD22 with IgM and CD45. To examine if Gal-9 increases the interaction between CD22 and
IgM, as well as IgM and CD45, we immunoprecipitated IgM or CD22 and immunoblotted for
CD22, IgM and CD45. To preserve the interaction between these molecules, we treated cells
with 1 mM DTSSP, an ionic crosslinker, which crosslinks proteins within a distance of 8-atoms
(Bennett et al., 2000). We isolated primary B cells from WT and Gal-9-KO mice and also treated
WT B cells with 1 μM rGal-9. Cells were lysed and incubated with antibody-conjugated agarose
49
beads specific for CD22 or IgM. After incubation and washing steps, proteins were eluted from
agarose beads using Laemmli buffer. The elution was separated by SDS-PAGE and subjected to
immunoblot to confirm enrichment of immunoprecipitated proteins and to detect binding
partners. We calculated the ratio between CD22 detected and IgM immunoprecipitated and the
ratio between IgM detected and CD22 immunoprecipitated. The ratio was then normalized to the
lowest ratio, set to 1. We did not observe any difference in the normalized ratios between WT,
Gal-9-KO and WT B cells treated with rGal-9 (Figure 12). In addition, we did not detect CD45
co-immunoprecipitation with IgM or CD22, indicating a limitation in our protocol to detect
previously reported interactions between CD45 and both IgM and CD22. Despite this limitation,
our data suggests that Gal-9 does not increase the interaction between IgM and CD22. However,
it is still unknown if the inhibitory effect of CD22 on BCR signaling requires direct interaction
between IgM and CD22. This inhibitory effect may be mediated by CD22 recruitment of SHP-1.
Phosphorylated ITIMs on the intracellular domain of CD22 recruit and phosphorylate SHP-1,
which dephosphorylates activating coreceptors, such as CD19 (Pani et al., 1997). Thus, it is
possible that in Gal-9high regions, the density of IgM and CD22 both increase, which may
increase the number of "effective interactions" between SHP-1 and its substrates. Further
experiments are required to test this hypothesis.
50
Figure 12. Gal-9 does not increase the interaction between IgM and CD22
Wild-type (WT), Gal-9-KO and WT B cells treated 1 µM rGal-9 were crosslinked by DTSSP
and lysed for immunoprecipitation. A) Immunoprecipitation using anti-IgM, followed by
immunoblotting for IgM (top) CD22 (middle) and CD45 (bottom). B) Quantification of the ratio
between CD22 detected and IgM immunoprecipitated (mean ±S.E.M indicated by bar). The
ratios were normalized to the lowest ratio, which was set at 1. Each point represents one
experiment. Mann-Whitney test, not significant. C) Immunoprecipitation using anti-CD22,
followed by immunoblotting for CD22 (top) IgM (middle) and CD45 (bottom). D)
Quantification of the ratio between IgM detected and CD22 immunoprecipitated (mean ±S.E.M
indicated by the bar). The ratios were normalized based to the lowest ratio, which was set at 1.
Each point represents one experiment. Mann-Whitney test, not significant.
51
3.5 CD19 Phosphorylation is Enhanced in Galectin 9-KO Upon
Activation
Based on our finding that rGal-9 increases the molecular density of CD45, CD22 and
IgM-BCR inside the Gal-9 lattice, we predicted that Gal-9-KO B cells would have an abnormal
level of phosphorylation of signaling molecules in the early stage of B cell activation. Gal-9-KO
B cells were activated by soluble anti-IgM F(ab’)2 fragment and stopped at indicated time points
within the first 10 min of activation. Cells were lysed and separated by SDS-PAGE followed by
immunoblotting using antibodies specific for phosphorylated CD22, SHP-1 and CD19. We did
not observe any difference in the phosphorylation of CD22 (pY882) (Figure 13A, 13B) or SHP-1
(pY564) between WT and Gal-9-KO B cells upon IgM-BCR stimulation (Figure 13C, 13D).
Interestingly, however, we observed that Gal-9-KO B cells have significantly higher levels of
phosphorylated CD19 at 1 and 3 min post-stimulation compared to WT B cells (Figure 13E,
13F). Given the important role of CD19 in amplifying BCR signaling, the small, but significant
increase in the phosphorylation of CD19 may lead to more pronounced differences downstream.
This is consistent with previous findings form our lab, in which Gal-9-KO primary B cells have
significantly higher ERK phosphorylation, compared to WT B cells (Alluqmani & Treanor,
unpublished).
52
Figure 13. Phosphorylation of CD19 is increased in Gal-9-KO B cells.
Primary naïve B cells from WT and Gal-9-KO mice were activated with 5 μg/mL anti-IgM
F(ab’)2 fragments for the indicated time. Cells were lysed and separated by SDS-PAGE followed
by immunoblotting. (A, C, E) Immunoblot with A) anti-phospho CD22 (Y822), C) anti-phospho
SHP-1 (Y564), and E) anti-phospho CD19 (Y531) and anti-β-tubulin as loading control. Data
representative of four independent experiments. (B, D, F). Quantification of B) pCD22 (Y822),
D) pSHP-1(Y564), and F) pCD19(Y531) fold change across the time points, with the mean ±
S.E.M indicated by the bar. Mann-Whitney test, * p<0.05.
53
DISCUSSION
Recent findings from our lab identified Gal-9 as a negative regulator of B cell activation
(Alluqmani & Treanor, unpublished data). Specifically, BCR-antigen microclustering and
downstream signaling are increased in Gal-9-KO B cells upon stimulation with anti-IgM. In
addition, treating B cells with rGal-9 nearly abolished BCR signaling. Here, we investigated the
underlying molecular mechanism for the inhibitory effect of Gal-9 on B cell activation. We find
that Gal-9 plays a role in regulating the spatial organization and dynamics of IgM-BCR and its
localization with regulatory surface proteins. We propose that Gal-9 organizes IgM-BCR into
larger clusters to restrict the mobility of IgM-BCR and relocalizes inhibitory molecules including
CD45 and CD22 to directly inhibit BCR signaling (Figure 14).
54
Figure 14. Proposed model for Gal-9-mediated inhibition of BCR signaling Gal-9 lattice brings pre-existing IgM-BCR nanoclusters together to form larger clusters which
restrict the mobility of IgM-BCR. Gal-9 also relocalizes inhibitory molecules including CD45
and CD22 inside Gal-9 lattice together with IgM-BCR. These inhibitory molecules directly
inhibit BCR signaling.
55
The spatial organization of cell surface proteins in pre-existing nanoscale clusters is
emerging as a general principal of cell surface proteins (Garcia-Parajo et al., 2014). The
organization of proteins into nanoclusters was reported to play important roles in regulating
surface protein signaling and protein-protein interactions (Mattila et al., 2013; Gasparrini et al.,
2015; Maity et al., 2015; Pageon et al., 2016). However, the molecular mechanism regulating the
size, composition, and stability of these constitutive assemblies is an open question. In the case
of IgM-BCR, the formation and stability of these nanoscale clusters does not appear to be
dependent on the actin cortex, as treating B cells with the actin depolymerizing agent LatA does
not alter the size or density of IgM-BCR nanoclusters (Mattila et al, 2013). Thus, we
hypothesized that galectins, which specifically bind to β-galactoside side chains on glycoproteins
to form a network, the so-called galectin-glycoprotein lattice, may play a role in the nanoscale
organization of cell surface proteins. Indeed, galectins were reported to organize nanoclusters of
H-Ras and K-Ras on the inner leaflet of the cell membrane (Prior et al., 2003; Belanis et al.,
2008; Shalom-Feuerstein et al., 2009). As we previously identified IgM-BCR as a ligand of Gal-
9 (Cao & Treanor, unpublished), we investigated the effect of Gal-9 on the organization of IgM-
BCR on the surface of primary B cells using the super-resolution technique, dSTORM. This is
the first study to investigate the role of an extracellular component in the organization of IgM
molecules at the nanoscale level. We found no difference in the size or density of IgM-BCR
nanoclusters on the surface of Gal-9-KO B cells compared to WT B cells, suggesting that Gal-9
does not mediate the formation of IgM-BCR nanoclusters. This finding is consistent with our
observation of the sparse distribution of Gal-9 on the surface of primary naïve B cells
(Alluqmani, Cao, & Treanor, unpublished), compared to the relatively dense distribution of IgM-
BCR. Alternatively, it may be that the formation of BCR nanoclusters is largely dependent on
56
the direct interaction between BCR components. According to Reth and colleagues, class-
specific residues within mIg as well as the disulfide bond between Igα and Igβ linker regions are
important for the formation of ‘oligomers’ of BCR (Schamel and Reth, 2000; Yang and Reth
2010). In the case of IgD-BCR, mutation of the class specific hydrophilic and aromatic amino
acids in the transmembrane domain as well as the cysteine residue in the linker region of Igα
resulted in largely monomers of IgD as detected by blue native gel (Schamel and Reth, 2000)
and BiFC assays (Yang and Reth 2010). While these studies provide evidence for a role for the
class-specific amino acids in the transmembrane domain of mIg in the higher order structure of
IgD-BCR, they have not been investigated in the context of IgM-BCR, which may explain the
class-specific differences in the constitutive assemblies of IgM and IgD, with IgD having a much
higher clustering tendency and forming more compacted nanoclusters than IgM nanoclusters
(Mattila et al., 2013).
Although we detected no difference in the organization of IgM-BCR in Gal-9 deficient B
cells compared to WT cells, we found that treatment with rGal-9 significantly altered IgM-BCR
nanoclusters. Inside the Gal-9 lattice, the number of IgM-BCR nanoclusters decreased while the
radius of the clusters, and the number of molecules in each cluster increased. This observation
indicates that the Gal-9 lattice can indeed impact on the organization of IgM-BCR on the surface
of primary B cells. Thus, we propose that Gal-9 provides a second layer of organization to IgM-
BCR by merging pre-existing nanoclusters. This observation, together with our previous finding
that rGal-9 abolishes BCR signaling, are somewhat counter-intuitive given that BCR stimulation
induces the coalescence of BCR nanoclusters to form larger-scale signaling microclusters
(Mattila et al., 2013), and the cross-linking model of B cell activation proposes that clustering of
BCR induced by multivalent or membrane-bound antigens is required to initiate BCR signaling
57
(Bolen, 1995). So, how does the larger cluster of IgM induced by Gal-9 not activate B cells but
instead inhibit BCR signaling? We propose that Gal-9 mediated merging of pre-existing IgM
nanoclusters may regulate the lateral diffusion of IgM-BCR and the interaction between IgM
nanoclusters with other signaling molecules, which together suppress BCR signaling.
Using SPT, we found that Gal-9 immobilizes IgM-BCR. IgM-BCR inside the Gal-9
lattice has significantly lower mobility compared to IgM-BCR outside the Gal-9 lattice. The
decrease in IgM lateral diffusion induced by Gal-9 may be explained by the effect of Gal-9 in
gathering IgM nanoclusters to form bigger IgM clusters. Due to technical limitations of super-
resolution imaging and single particle tracking, it is not impossible to differentiate the mobility
of molecules inside and outside of nanoclusters. Hence, the reported diffusion coefficient is
composed of (at least) two components including the mobility of the whole cluster and the
mobility of individual BCR (or dimers, trimer, etc., which are not detectable by super-resolution
imaging). The mobility of IgM-BCR on the cell membrane may be described as Brownian
motion restricted by the underlying actin cytoskeleton (Freeman et al., 2015; Treanor et al.,
2010). According to the theory of Brownian motion, the larger the particle the slower the
movement of the particle (Bian et al., 2016). Hence, it would be predicted that the larger clusters
of IgM-BCR inside the Gal-9 lattice have a lower diffusion coefficient compared to the smaller
clusters outside the Gal-9 lattice, consistent with our data. Furthermore, the lateral diffusion of
molecules residing in clusters may be restricted by neighbouring IgM molecules, which act as a
fence to confine the diffusion of IgM-BCR inside clusters.
These interpretations are supported by the finding that there is a correlation between
cluster size of CD22 and CD22 lateral diffusion (Gasparrini et al., 2016). In CD45 deficient
58
primary B cells or B cells expressing a CD22 mutant lacking the sialic-acid binding domain, the
homotypic interaction between CD22 is enhanced, leading to the formation of larger CD22
clusters. Correlatively, CD22 molecules on these B cells also have a lower diffusion coefficient.
In addition, 70% of IgD-BCR localize inside nanoclusters, which are more densely packed with
30-120 molecules per cluster, compared to 40% of IgM-BCR (Mattila et al., 2013) localized
inside nanoclusters with around 20-50 molecules per cluster. This correlates with the
phenomenon that the diffusion coefficient of IgD-BCR is ten times lower compared to IgM-BCR
(Treanor et al., 2010). Thus, we propose that Gal-9 restricts IgM-BCR mobility by organizing
IgM-BCR into larger clusters. Importantly, the mobility of BCR on the surface is correlated with
BCR signaling; simply depolymerizing the actin cytoskeleton leads to increased BCR mobility
and spontaneous signaling (Treanor et al., 2011). Consistent with this, treating B cells with LPS
increases actin severing and BCR mobility, which lowers the threshold for B cell activation
(Freeman et al., 2015). The proposed mechanism for these observations is that the increased
diffusion of the BCR (or BCR nanoclusters) increases the probability that the BCR will
encounter the co-receptor CD19 (Treanor, 2012), and is supported by the finding that the
spontaneous BCR signaling induced by depolymerisation of actin requires CD19 (Mattila et al.,
2013). Thus, the decrease in IgM-BCR lateral diffusion induced by Gal-9 may decrease the
interaction between IgM-BCR nanoclusters and the activating coreceptor CD19 and
consequently inhibit BCR signaling. The lower number of interactions between IgM-BCR and
CD19 may suppress BCR signaling by increasing the threshold of B cell activation (Mattila et
al., 2013).
In addition, the decrease in IgM lateral diffusion may also reduce the recruitment of IgM
molecules to the immunological synapse, where antigens are presented. In T cells, it was
59
reported that the lower mobility of ligands presented by lipid bilayers prevents the formation and
maturation of the immunological synapse (Hsu et al., 2012). In the B cell context, during B cell
spreading on APCs, IgM molecules are recruited to the immunological synapse and involved in a
highly dynamic process including microcluster formation, centralization, and internalization. The
decrease in IgM lateral diffusion induced by Gal-9 may interfere with the formation and
maturation of the B cell immunological synapse and thus inhibit B cell activation. Indeed,
treating primary B cells with rGal-9 resulted in decreased microcluster formation and a smaller
spreading area upon activation with lipid bilayers containing anti-BCR as surrogate antigen
(Alluqmani & Treanor, unpublished). Conversely, increased BCR diffusion would be associated
with more microclusters and larger spreading area, consistent with our observation of higher
diffusion coefficient of IgM-BCR in Gal-9-KO B cells compared to WT control, and our
previous findings of enhanced B cell activation (Alluqmani & Treanor, unpublished).
We also propose that Gal-9 induced merging of BCR nanoclusters may regulate the
interaction between IgM and signaling molecules and consequently suppress BCR signaling. It
may be that the merging of nanoclusters and increased density of molecules within the
nanoclusters prior to activation effectively reduces the accessibility of kinases to phosphorylate
the ITAMs on Igα/Igβ. This interpretation is consistent with the model proposed by Reth and
colleagues, in which BCR forms auto-inhibited oligomers in the resting state; the tightly packed
BCR molecules hinders the association of signaling molecules such as Syk with the ITAMs on
Igα/Igβ preventing phosphorylation of these motifs and BCR signaling (Yang and Reth, 2010).
Alternatively, our data supports a model in which treatment with rGal-9 induces the localization
of inhibitory coreceptors, including CD45 and CD22, closer to IgM clusters and we believe this
contributes to the inhibitory effect of Gal-9 on B cell activation.
60
Our finding of CD45 enrichment in the Gal-9 lattice is consistent with our identification
of CD45 as a ligand for Gal-9 using a pull-down assay and mass spectrometry (Cao & Treanor,
unpublished). The N-terminal of CD45 has multiple N-glycosylation sites, which provide
potential binding sites for Gal-9, and therefore make it a likely candidate for interaction with
galectins. Indeed, in T cells, CD45 was reported to bind to Gal-1 and Gal-3 (Pace et al., 1999;
Symons et al., 2000; Stillman et al., 2006; Chen et al., 2007). Moreover, CD45 on diffuse large B
cell lymphoma has been shown to bind to Gal-3. Taken together, these findings suggest that
CD45 may be a promiscuous ligand of the galectin family, although the differential glycosylation
of CD45 in T cells compared to B cells, and perhaps also in B cell lymphomas, may provide
specificity for differential galectin binding. Nonetheless, the enrichment of CD45 inside the Gal-
9 lattice is consistent with the inhibitory effect of Gal-9 on BCR signaling. CD45 is important in
regulating the phosphorylation of Lyn, the earliest kinase involved in BCR signaling (Katagiri et
al., 1999; Shrivastava et al. 2004). The co-enrichment of CD45 and IgM-BCR inside the Gal-9
lattice may lead to the dephosphorylation and inactivation of Lyn, and consequently suppressed
B cell activation. This prediction is supported by previous findings regarding the role of galectins
in regulating the organization and function of CD45 in T cells (Chen et al., 2007). Lactose
treatment, which dissociates Gal-3 from CD45, decreased the localization of CD45 within lipid
rafts and increased the phosphorylation of the positive regulatory tyrosine of Lck (a homolog of
Lyn) in the resting state. Consistent with this finding, Mgat5-deficient (Mgat5-/-) T cells, which
lack the ligands required for galectin binding, has lower CD45 partitioning inside lipid rafts and
higher phosphorylation of the positive regulatory tyrosine of Lck. Furthermore, CD45
localization within the early immunological synapse is also decreased in Mgat5-/- T cells and
this correlates with increased phosphorylation of Lck at the positive regulatory tyrosine upon
61
activation with anti-CD3 conjugated beads. These findings suggest that the galectin lattice is
critical in regulating CD45 localization with respect to lipid rafts to regulate Lck phosphorylation
and thus, TCR signaling under both resting and activating conditions. To determine if our
observation of suppressed BCR signaling upon Gal-9 treatment is due to altered Lyn activity,
future studies should examine Lyn phosphorylation especially at the positive regulatory tyrosine.
Although there is currently no commercially available antibody specific for the positive
regulatory tyrosine in Lyn, antibodies specific for the positive regulatory tyrosine of Src are
available and may cross-react with Lyn.
We also observed the enrichment of the inhibitory molecule, CD22, inside the Gal-9
lattice. Although we did not detect CD22 in our pull-down assay using recombinant Gal-9 (Cao
& Treanor, unpublished), CD22 has been reported to bind to both IgM and CD45 (Zhang et al,
2004; Leprince et al., 1993). Moreover, CD45 is critical in regulating the spatial organization and
activity of CD22 in B cells (Gasparrini et al., 2015; Coughlin et al., 2015). Hence, the
enrichment of CD22 inside the Gal-9 lattice may be due to secondary effects of CD45 and IgM
enrichment. This explanation is consistent with our co-immunoprecipitation results, in which
Gal-9 did not increase the interaction between IgM and CD22. Although the mechanism for the
increase in CD22 density in the Gal-9 lattice is unknown, the higher number of CD22 molecules
colocalizing with IgM molecules inside Gal-9high regions may explain the inhibitory effect of
Gal-9 on BCR signaling. CD22 is a known negative regulator of BCR signaling, which recruits
the phosphatase SHP-1 (Doody et a., 1995). SHP-1 dephosphorylates CD19 and terminates the
amplification of BCR signaling induced by CD19 (Pani et al., 1997). In addition, SHP-1
dephosphorylates PLC-γ2 and attenuates Ca2+ and MAPK activation (Reviewed by Muller and
62
Nitscheke, 2014). Thus, the localization of CD22 together with IgM-BCR upon rGal-9 treatment
may contribute to the inhibitory effect of Gal-9 on B cell activation.
Although we did not detect any significant difference between WT and Gal-9-KO B cells
in the phosphorylation of CD22 and SHP-1, we observed a significant increase in the
phosphorylation of CD19, consistent with higher BCR signaling in Gal-9-KO B cells. CD19
recruits Vav and PI3-K, which are critical in the spreading of B cells on the antigen-presenting
surface (Weber et al., 2008). This is consistent with our previous finding that spreading and
antigen accumulation are enhanced in Gal-9-KO B cells compared to WT control upon activation
with lipid bilayers presenting anti-BCR. Furthermore, treating WT B cells with rGal-9 decreased
the spreading area and number of microclusters (Alluqmani & Treanor, unpublished). Hence,
Gal-9 may regulate the phosphorylation of CD19 by retaining CD45 and CD22 inside IgM
microclusters upon activation, thus decreasing B cell spreading and increasing the threshold for
B cell activation.
Many questions remain to be addressed regarding the mechanism of the inhibitory effect
of Gal-9 on BCR signaling. One important question is whether the Gal-9 lattice localizes with
lipid rafts, where BCR signaling is initiated (Cheng et al., 1999). We found that Gal-9 is
localized in discrete puncta on the surface of primary murine B cells in the steady-state
(Alluqmani, Cao & Treanor, unpublished). Interestingly, when primary B cells are treated with
rGal-9, the size of Gal-9 puncta increases and indeed, often forms a cap on one side of the cell.
Such capping of a protein is reminiscent of the polarization of lipid rafts upon lymphocyte
activation (Viola and Lanzavecchia, 1996; Round et al., 2005). These observations suggest that
Gal-9 may be distributed in lipid rafts in the B cell membrane. Galectin-4 (Gal-4), a member of
63
tandem-repeat type galectins, was reported to be enriched in detergent-resistant lipid rafts in the
microvillar membrane of intestinal brush borders (Braccia et al., 2003). Treating these cells with
lactose released Gal-4 and proteins from the lipid raft, which indicated that the carbohydrate
domains of Gal-4 are critical in retaining proteins inside lipid rafts. These findings are consistent
with the patchy distribution of Gal-9 and the polarization of rGal-9 on the B cell surface,
suggesting that Gal-9 may play an important role in organizing BCR and other coreceptors in
lipid rafts. The spatiotemporal positioning of CD45 with respect to lipid rafts is tightly regulated
during the early stage of B cell activation (Shrivastava et al., 2004). It was proposed that the
partitioning of CD45 inside lipid rafts negatively regulates Lyn to inhibit BCR signaling
(Shrivastava et al., 2004). In T cells, the galectin lattice was proposed to retain CD45 inside
lipid rafts to inhibit TCR signaling in both the resting state and upon activation (Chen et al.,
2007). Given our finding that CD45 is enriched inside the Gal-9 lattice, the localization of Gal-9
with respect to lipid rafts will provide further insight into the molecular mechanism for the
inhibitory effect of Gal-9 on BCR signaling. This could be visualized by treating cells with
cholera toxin B (CTB), which binds to ganglioside GM1, a marker of lipid rafts, following by
fluorescently-labeled CTB-specific antibodies. The localization of Gal-9 in lipid rafts, if
confirmed, will provide direct evidence for Gal-9-mediated localization of CD45 inside lipid
rafts and further contribute to our understanding of the molecular mechanism of the inhibitory
effects of Gal-9 on BCR signaling.
The effect of Gal-9 on the reorganization of IgM-BCR and CD45 raises the question of
whether Gal-9 has an effect on the underlying actin cortex. Since the actin cortex restricts the
lateral diffusion of IgM-BCR (Treanor et al., 2010 and 2011) the merging of IgM-BCR
nanoclusters observed by dSTORM may require the reorganization of actin cortex to allow the
64
movement of BCR nanoclusters. There is evidence to support the idea of coordination between
the galectin lattice and actin cortex in the organization of surface proteins. In T cells, galectins
were proposed to enhance the localization of CD45 inside lipid rafts while the actin cortex has
the opposite effect (Chen et al., 2007). Although the mechanism of actin-mediated exclusion of
CD45 from lipid rafts is still unknown, it was proposed to be dependent on the interaction
between the intracellular domain of CD45 and the ankyrin/spectrin/actin scaffold (Pradhan et al.,
2002). The coordination between the extracellular galectin lattice and intracellular actin cortex
provides counterbalancing mechanisms to regulate lateral movement and membrane partitioning
of CD45 on the cell membrane (Chen et al., 2007). Hence, it will be interesting to investigate the
coordination between Gal-9 and the actin cortex in regulating the organization of surface
proteins during both the resting state and immunological synapse formation.
An important question to address in the future is the in vivo function of Gal-9. Our
finding that Gal-9 regulates BCR signaling raises the question of whether Gal-9 has a role in
regulating pre-BCR signaling during B cell development and selection. Pre-BCR signaling
shares many common signaling molecules with BCR signaling cascades. Pre-BCR aggregation is
induced by an antigen-independent process controlled by charged and glycosylated residues on
pre-BCR molecules (Ohnishi and Melchers, 2003). In addition, galectin-1 has been implicated in
regulating pre-BCR signaling. Gal-1 was reported to bind to the surrogate light chain of pre-BCR
and mediate the formation of a synapse at the contact zone between pre-B cells and stromal cells
(Gauthier et al., 2002). While a role for Gal-9 in B cell development has not been reported,
according to the Immgen database for gene expression in mice (Heng et al., 2008), Lgals9, the
gene encoding Gal-9, is highly expressed in pro/pre-B cell populations in the bone marrow, at a
level four times higher compared to splenic follicular B cells. In addition, Lgals9 expression
65
increases approximately 2.5 times in the transition from pre-pro-B cells (Fraction A) to pre-B
cells (Fraction D), during which IgH locus rearrangement occurs. These findings suggest that
Gal-9 has important biological functions in B cell populations in the bone marrow. Given that
Gal-9 binds to the constant region of IgM heavy chain, and assuming that the glycosylation
pattern of IgM heavy chain is similar between pre-BCR and BCR, Gal-9 could bind to pre-BCR,
which is composed of IgM heavy chain and surrogate light chain. To study the role of Gal-9 in B
cell development, the percentage of different B cell compartments during development should be
compared between WT and Gal-9-KO mice. In addition, to investigate if Gal-9 is involved in B
cell selection, the BCR repertoire in the immature B cell population in the bone marrow could be
examined.
Finally, the interaction between IgM-BCR and Gal-9 needs to be further characterized.
First, how does Gal-9 bind to IgM-BCR? In our pull-down assay using rGal-9, we used lactose to
elute IgM-BCR from Gal-9, indicating that the Gal-9 and IgM-BCR interaction is carbohydrate
dependent. However, it is still unknown which N-glycosylation site is required for the interaction
between Gal-9 and IgM-BCR. To answer this question, site-directed mutagenesis of the N-
glycosylation sites on IgM heavy chain can be performed to find which mutation abrogates the
interaction between Gal-9 and IgM. Second, where does Gal-9 on the B cell surface come from?
Currently, it is not known if B cells secrete Gal-9, which binds to the cell surface or if other cells
secrete it and it binds to the B cell surface. Indeed, Gal-9 is expressed in multiple cell types
including B cells, T cells, NK cells and monocytes (Heng et al., 2008). Given the complexity and
abundance of glycoproteins on the cell membrane, which may restrict the diffusion of Gal-9, we
predict that Gal-9 on the surface of B cells is secreted by B cells themselves. Supporting this
prediction, Gal-9 is expressed on the cell surface of BALL-1, a human B cell line derived from B
66
cell leukemia (Hirashima et al, 2004). To test this prediction in primary B cells, B cells from
Gal-9-KO mice can be injected into B cell deficient mice, such as µMT and surface Gal-9
expression monitored by flow cytometry post-transplant.
In summary, our study sheds light on the molecular mechanism for the inhibitory effect
of Gal-9 on BCR signaling. Gal-9 binds to IgM-BCR, which organizes IgM-BCR into bigger
clusters and restricts the lateral diffusion of IgM-BCR molecules. In addition, Gal-9 also
increases the colocalization between IgM and inhibitory coreceptors including CD45 and CD22,
which modulates the phosphorylation of CD19. These findings elucidate a novel extracellular
mechanism to finely regulate BCR signaling, which may be important in the context of B cell
development, activation, and B cell pathologies. Thus, understanding this mechanism may
provide a potential therapeutic target to treat B cell related diseases.
67
References
Ahmed, H., Pohl, J., Fink, N. E., Strobel, F., & Vasta, G. R. (1996). The primary structure and
carbohydrate specificity of a β-galactosyl-binding lectin from toad (Bufo arenarum Hensel)
ovary reveal closer similarities to the mammalian galectin-1 than to the galectin from the
clawed frog Xenopus laevis. Journal of Biological Chemistry, 271(51), 33083-33094.
Batista, F. D., Iber, D., & Neuberger, M. S. (2001). B cells acquire antigen from target cells after
synapse formation. Nature, 411(6836), 489.
Bennett, K. L., Kussmann, M., BJÖRK, P., Godzwon, M., Mikkelsen, M., SØRENSEN, P., &
Roepstorff, P. (2000). Chemical cross-linking with thiol-cleavable reagents combined with
differential mass spectrometric peptide mapping—a novel approach to assess
intermolecular protein contacts. Protein Science, 9(8), 1503-1518.
Bi, S., Earl, L. A., Jacobs, L., & Baum, L. G. (2008). Structural features of galectin-9 and
galectin-1 that determine distinct T cell death pathways. Journal of Biological
Chemistry, 283(18), 12248-12258.
Bian, X., Kim, C., & Karniadakis, G. E. (2016). 111 years of Brownian motion. Soft
Matter, 12(30), 6331-6346.
Bolen, J. B. (1995). Protein tyrosine kinases in the initiation of antigen receptor
signaling. Current Opinion In Immunology, 7(3), 306-311.
Braccia, A., Villani, M., Immerdal, L., Niels-Christiansen, L. L., Nystrøm, B. T., Hansen, G. H.,
& Danielsen, E. M. (2003). Microvillar membrane microdomains exist at physiological
temperature Role of galectin-4 as lipid raft stabilizer revealed by “superrafts”. Journal of
Biological Chemistry, 278(18), 15679-15684.
Bradbury, L. E., Kansas, G. S., Levy, S. H. O. S. H. A. N. A., Evans, R. L., & Tedder, T. F.
(1992). The CD19/CD21 signal transducing complex of human B lymphocytes includes
68
the target of antiproliferative antibody-1 and Leu-13 molecules. The Journal of
Immunology, 149(9), 2841-2850.
Brown, A. C., Dobbie, I. M., Alakoskela, J. M., Davis, I., & Davis, D. M. (2012). Super-
resolution imaging of remodeled synaptic actin reveals different synergies between NK cell
receptors and integrins. Blood, 120(18), 3729-3740.
Cambi, A., & Lidke, D. S. (2011). Nanoscale membrane organization: where biochemistry meets
advanced microscopy. ACS Chemical Biology, 7(1), 139-149.
Carrasco, Y. R., Fleire, S. J., Cameron, T., Dustin, M. L., & Batista, F. D. (2004). LFA-1/ICAM-
1 interaction lowers the threshold of B cell activation by facilitating B cell adhesion and
synapse formation. Immunity, 20(5), 589-599.
Carter, R. H., & Fearon, D. T. (1992). CD19: lowering the threshold for antigen receptor
stimulation of B lymphocytes. Science, 105-107.
Chakrabarti, A., Matko, J., Rahman, N. A., Barisas, B. G., & Edidin, M. (1992). Self-association
of class I major histocompatibility complex molecules in liposome and cell surface
membranes. Biochemistry, 31(31), 7182-7189.
Chan, V. W., Lowell, C. A., & DeFranco, A. L. (1998). Defective negative regulation of antigen
receptor signaling in Lyn-deficient B lymphocytes. Current Ciology, 8(10), 545-553.
Chang, V. T., Fernandes, R. A., Ganzinger, K. A., Lee, S. F., Siebold, C., McColl, J., ... & Jones,
E. Y. (2016). Initiation of T cell signaling by CD45 segregation at'close contacts'. Nature
Immunology, 17(5), 574-582.
Chen, I. J., Chen, H. L., & Demetriou, M. (2007). Lateral compartmentalization of T cell
receptor versus CD45 by galectin-N-glycan binding and microfilaments coordinate basal
and activation signaling. Journal of Biological Chemistry, 282(48), 35361-35372.
Cheng, P. C., Dykstra, M. L., Mitchell, R. N., & Pierce, S. K. (1999). A role for lipid rafts in B
cell antigen receptor signaling and antigen targeting. Journal of Experimental
Medicine, 190(11), 1549-1560
69
Chesnut, R. W., & Grey, H. M. (1981). Studies on the capacity of B cells to serve as antigen-
presenting cells. The Journal of Immunology, 126(3), 1075-1079.
Clark, M. C., Pang, M., Hsu, D. K., Liu, F. T., De Vos, S., Gascoyne, R. D., ... & Baum, L. G.
(2012). Galectin-3 binds to CD45 on diffuse large B-cell lymphoma cells to regulate
susceptibility to cell death. Blood, 120(23), 4635-4644.
Clayton, K. L., Haaland, M. S., Douglas-Vail, M. B., Mujib, S., Chew, G. M., Ndhlovu, L. C., &
Ostrowski, M. A. (2014). T Cell Ig and Mucin Domain–Containing Protein 3 Is Recruited
to the Immune Synapse, Disrupts Stable Synapse Formation, and Associates with Receptor
Phosphatases. The Journal of Immunology, 192(2), 782-791.
Coughlin, S., Noviski, M., Mueller, J. L., Chuwonpad, A., Raschke, W. C., Weiss, A., &
Zikherman, J. (2015). An extracatalytic function of CD45 in B cells is mediated by
CD22. Proceedings of the National Academy of Sciences, 112(47), E6515-E6524.
Cummings, R. D. (2009). The repertoire of glycan determinants in the human
glycome. Molecular BioSystems, 5(10), 1087-1104.
Damjanovich, S., Vereb, G., Schaper, A., Jenei, A., Matko, J., Starink, J. P., ... & Jovin, T. M.
(1995). Structural hierarchy in the clustering of HLA class I molecules in the plasma
membrane of human lymphoblastoid cells. Proceedings of the National Academy of
Sciences, 92(4), 1122-1126.
Davis, S. J., & van der Merwe, P. A. (2006). The kinetic-segregation model: TCR triggering and
beyond. Nature Immunology, 7(8), 803.
Defranco, A. L., Raveche, E. S., Asofsky, R., & Paul, W. E. (1982). Frequency of B
lymphocytes responsive to anti-immunoglobulin. Journal of Experimental
Medicine, 155(5), 1523-1536.
DeFranco, A. L. (1997). The complexity of signaling pathways activated by the BCR. Current
Opinion In Immunology, 9(3), 296-308.
70
Demetriou, M., Granovsky, M., Quaggin, S., & Dennis, J. W. (2001). Negative regulation of T-
cell activation and autoimmunity by Mgat5 N-glycosylation. Nature, 409(6821), 733-739.
Depoil, D., Fleire, S., Treanor, B. L., Weber, M., Harwood, N. E., Marchbank, K. L., ... &
Batista, F. D. (2008). CD19 is essential for B cell activation by promoting B cell receptor–
antigen microcluster formation in response to membrane-bound ligand. Nature
Immunology, 9(1), 63-72.
Depoil, D., Weber, M., Treanor, B., Fleire, S. J., Carrasco, Y. R., Harwood, N. E., & Batista, F.
D. (2009). Early events of B cell activation by antigen. Sci. Signal., 2(63), pt1-pt1.
Desai, D. M., Sap, J., Silvennoinen, O., Schlessinger, J., & Weiss, A. (1994). The catalytic
activity of the CD45 membrane-proximal phosphatase domain is required for TCR
signaling and regulation. The EMBO Journal, 13(17), 4002.
Doody, G. M., Justement, L. B., Delibrias, C. C., Matthews, R. J., Lin, J., Thomas, M. L., &
Fearon, D. T. (1995). A role in B cell activation for CD22 and the protein tyrosine
phosphatase SHP. SCIENCE-NEW YORK THEN WASHINGTON-, 242-242.
Engel, P., Wagner, N., Miller, A. S., & Tedder, T. F. (1995). Identification of the ligand-binding
domains of CD22, a member of the immunoglobulin superfamily that uniquely binds a
sialic acid-dependent ligand. Journal of Experimental Medicine, 181(4), 1581-1586.
Fleire, S. J., Goldman, J. P., Carrasco, Y. R., Weber, M., Bray, D., & Batista, F. D. (2006). B cell
ligand discrimination through a spreading and contraction response. Science, 312(5774),
738-741.
Freeman, S. A., Jaumouillé, V., Choi, K., Hsu, B. E., Wong, H. S., Abraham, L., ... & Grinstein,
S. (2015). Toll-like receptor ligands sensitize B-cell receptor signalling by reducing actin-
dependent spatial confinement of the receptor. Nature Communications, 6.
Fujii, Y., Okumura, M., Inada, K., Nakahara, K., & Matsuda, H. (1992). CD45 isoform
expression during T cell development in the thymus. European Journal of Immunology, 22(7),
1843-1850.
71
Fujimoto, M., Fujimoto, Y., Poe, J. C., Jansen, P. J., Lowell, C. A., DeFranco, A. L., & Tedder,
T. F. (2000). CD19 regulates Src family protein tyrosine kinase activation in B
lymphocytes through processive amplification. Immunity, 13(1), 47-57.
Garcia-Parajo, M. F., Cambi, A., Torreno-Pina, J. A., Thompson, N., & Jacobson, K. (2014).
Nanoclustering as a dominant feature of plasma membrane organization. J Cell
Sci, 127(23), 4995-5005.
Garner, O. B., & Baum, L. G. (2008). Galectin–glycan lattices regulate cell-surface glycoprotein
organization and signalling.
Gasparrini, F., Feest, C., Bruckbauer, A., Mattila, P. K., Müller, J., Nitschke, L., ... & Batista, F.
D. (2016). Nanoscale organization and dynamics of the siglec CD22 cooperate with the
cytoskeleton in restraining BCR signalling. The EMBO Journal, 35(3), 258-280.
Gauthier, L., Rossi, B., Roux, F., Termine, E., & Schiff, C. (2002). Galectin-1 is a stromal cell
ligand of the pre-B cell receptor (BCR) implicated in synapse formation between pre-B and
stromal cells and in pre-BCR triggering. Proceedings of the National Academy of
Sciences, 99(20), 13014-13019.
Germain, R. N. (1994). MHC-dependent antigen processing and peptide presentation: providing
ligands for T lymphocyte activation. Cell, 76(2), 287-299.
Gleason, M. K., Lenvik, T. R., McCullar, V., Felices, M., O'Brien, M. S., Cooley, S. A., ... &
Niki, T. (2012). Tim-3 is an inducible human natural killer cell receptor that enhances
interferon gamma production in response to galectin-9. Blood, 119(13), 3064-3072.
Griffié, J., Shannon, M., Bromley, C. L., Boelen, L., Burn, G. L., Williamson, D. J., ... & Rubin-
Delanchy, P. (2016). A Bayesian cluster analysis method for single-molecule localization
microscopy data.
Han, S., Collins, B. E., Bengtson, P., & Paulson, J. C. (2005). Homomultimeric complexes of
CD22 in B cells revealed by protein-glycan cross-linking. Nature Chemical Biology, 1(2),
93.
72
Haltiwanger, R. S., & Lowe, J. B. (2004). Role of glycosylation in development. Annual Review
of Biochemistry, 73(1), 491-537.
Harvey, B. P., Gee, R. J., Haberman, A. M., Shlomchik, M. J., & Mamula, M. J. (2007). Antigen
presentation and transfer between B cells and macrophages. European Journal of
Immunology, 37(7), 1739-1751.
Harwood, N. E., & Batista, F. D. (2009). Early events in B cell activation. Annual Review of
Immunology, 28, 185-210.
Hashimoto, G., Wright, P. F., & Karzon, D. T. (1983). Antibody-dependent cell-mediated
cytotoxicity against influenza virus-infected cells. Journal of Infectious Diseases, 148(5),
785-794.
Heilemann, M., Van De Linde, S., Schüttpelz, M., Kasper, R., Seefeldt, B., Mukherjee, A., ... &
Sauer, M. (2008). Subdiffraction‐resolution fluorescence imaging with conventional
fluorescent probes. Angewandte Chemie International Edition, 47(33), 6172-6176.
Hempel, W. M., Schatzman, R. C., & DeFranco, A. L. (1992). Tyrosine phosphorylation of
phospholipase C-gamma 2 upon cross-linking of membrane Ig on murine B
lymphocytes. The Journal of Immunology, 148(10), 3021-3027.
Heng, T. S., Painter, M. W., Elpek, K., Lukacs-Kornek, V., Mauermann, N., Turley, S. J., ... &
Davis, S. (2008). The Immunological Genome Project: networks of gene expression in
immune cells. Nature Immunology, 9(10), 1091-1094.
Hermiston, M. L., Xu, Z., & Weiss, A. (2003). CD45: a critical regulator of signaling thresholds
in immune cells. Annual Review of Immunology, 21(1), 107-137.
Hernandez, J. D., Nguyen, J. T., He, J., Wang, W., Ardman, B., Green, J. M., ... & Baum, L. G.
(2006). Galectin-1 binds different CD43 glycoforms to cluster CD43 and regulate T cell
death. The Journal of Immunology, 177(8), 5328-5336.
73
Hibbs, M. L., Harder, K. W., Armes, J., Kountouri, N., Quilici, C., Casagranda, F., ... &
Tarlinton, D. M. (2002). Sustained activation of Lyn tyrosine kinase in vivo leads to
autoimmunity. Journal of Experimental Medicine, 196(12), 1593-1604.
Hirabayashi, J., Hashidate, T., Arata, Y., Nishi, N., Nakamura, T., Hirashima, M., ... & Yagi, F.
(2002). Oligosaccharide specificity of galectins: a search by frontal affinity
chromatography. Biochimica et Biophysica Acta (BBA)-General Subjects, 1572(2), 232-
254.
Hsu, C. J., Hsieh, W. T., Waldman, A., Clarke, F., Huseby, E. S., Burkhardt, J. K., & Baumgart,
T. (2012). Ligand mobility modulates immunological synapse formation and T cell
activation. PloS one, 7(2), e32398.
Hughes, R. C. (2001). Galectins as modulators of cell adhesion. Biochimie, 83(7), 667-676.
Hwang, J., Gheber, L. A., Margolis, L., & Edidin, M. (1998). Domains in cell plasma
membranes investigated by near-field scanning optical microscopy. Biophysical
Journal, 74(5), 2184-2190.
Ingley, E. (2012). Functions of the Lyn tyrosine kinase in health and disease. Cell
Communication and Signaling, 10(1), 21.
Jenei, A., Varga, S., Bene, L., Mátyus, L., Bodnár, A., Bacsó, Z., ... & Damjanovich, S. (1997).
HLA class I and II antigens are partially co-clustered in the plasma membrane of human
lymphoblastoid cells. Proceedings of the National Academy of Sciences, 94(14), 7269-
7274.
Johnson, S. A., Rozzo, S. J., & Cambier, J. C. (2002). Aging-dependent exclusion of antigen-
inexperienced cells from the peripheral B cell repertoire. The Journal of
Immunology, 168(10), 5014-5023.
Kaizuka, Y., Douglass, A. D., Varma, R., Dustin, M. L., & Vale, R. D. (2007). Mechanisms for
segregating T cell receptor and adhesion molecules during immunological synapse
74
formation in Jurkat T cells. Proceedings of the National Academy of Sciences, 104(51),
20296-20301.
Katagiri, T., Ogimoto, M., Hasegawa, K., Arimura, Y., Mitomo, K., Okada, M., ... & Yakura, H.
(1999). CD45 negatively regulates lyn activity by dephosphorylating both positive and
negative regulatory tyrosine residues in immature B cells. The Journal of
Immunology, 163(3), 1321-1326.
Kurosaki, T. (2000). Functional dissection of BCR signaling pathways. Current Opinion in
Immunology, 12(3), 276-281.
Kuwabara, I., & Liu, F. T. (1996). Galectin-3 promotes adhesion of human neutrophils to
laminin. The Journal of Immunology, 156(10), 3939-3944.
Lavi, Y., Edidin, M. A., & Gheber, L. A. (2007). Dynamic patches of membrane
proteins. Biophysical Journal, 93(6), L35-L37.
Law, C. L., Sidorenko, S. P., Chandran, K. A., Zhao, Z., Shen, S. H., Fischer, E. H., & Clark, E.
A. (1996). CD22 associates with protein tyrosine phosphatase 1C, Syk, and phospholipase
C-gamma (1) upon B cell activation. Journal of Experimental Medicine, 183(2), 547-560.
Leffler, H., & Barondes, S. H. (1986). Specificity of binding of three soluble rat lung lectins to
substituted and unsubstituted mammalian beta-galactosides. Journal of Biological
Chemistry, 261(22), 10119-10126.
Leprince, C., Draves, K. E., Geahlen, R. L., Ledbetter, J. A., & Clark, E. A. (1993). CD22
associates with the human surface IgM-B-cell antigen receptor complex. Proceedings of
the National Academy of Sciences, 90(8), 3236-3240.
Lillemeier, B. F., Pfeiffer, J. R., Surviladze, Z., Wilson, B. S., & Davis, M. M. (2006). Plasma
membrane-associated proteins are clustered into islands attached to the
cytoskeleton. Proceedings of the National Academy of Sciences, 103(50), 18992-18997.
75
Lu, L. H., Nakagawa, R., Kashio, Y., Ito, A., Shoji, H., Nishi, N., ... & Nakamura, T. (2006).
Characterization of galectin-9-induced death of Jurkat T cells. The Journal of
Biochemistry, 141(2), 157-172.
Maity, P. C., Blount, A., Jumaa, H., Ronneberger, O., Lillemeier, B. F., & Reth, M. (2015). B
cell antigen receptors of the IgM and IgD classes are clustered in different protein islands
that are altered during B cell activation. Science signaling, 8(394), ra93-ra93.
Matko, J., Bushkin, Y., Wei, T., & Edidin, M. (1994). Clustering of class I HLA molecules on
the surfaces of activated and transformed human cells. The Journal of Immunology, 152(7),
3353-3360.
Matsumoto, A. K., Kopicky-Burd, J., Carter, R. H., Tuveson, D. A., Tedder, T. F., & Fearon, D.
T. (1991). Intersection of the complement and immune systems: a signal transduction
complex of the B lymphocyte-containing complement receptor type 2 and CD19. Journal
of Experimental Medicine, 173(1), 55-64.
Matsumoto, A. K., Kopicky-Burd, J., Carter, R. H., Tuveson, D. A., Tedder, T. F., & Fearon, D.
T. (1991). Intersection of the complement and immune systems: a signal transduction
complex of the B lymphocyte-containing complement receptor type 2 and CD19. Journal
of Experimental Medicine, 173(1), 55-64.
Matthews, S. A., Dayalu, R., Thompson, L. J., &Scharenberg, A. M. (2003). Regulation of
protein kinase Cν by the B-cell antigen receptor. Journal of Biological Chemistry, 278(11),
9086-9091.
Mattila, P. K., Feest, C., Depoil, D., Treanor, B., Montaner, B., Otipoby, K. L., ... & Batista, F.
D. (2013). The actin and tetraspanin networks organize receptor nanoclusters to regulate B
cell receptor-mediated signaling. Immunity, 38(3), 461-474.
Mifsud, E. J., Tan, A. C., & Jackson, D. C. (2014). TLR agonists as modulators of the innate
immune response and their potential as agents against infectious disease. Frontiers in
Immunology, 5.
76
Moir, S., & Fauci, A. S. (2013). Insights into B cells and HIV‐specific B‐cell responses in HIV‐
infected individuals. Immunological Reviews, 254(1), 207-224.
Monks, C. R., Freiberg, B. A., Kupfer, H., Sciaky, N., & Kupfer, A. (1998). Three-dimensional
segregation of supramolecular activation clusters in T cells. Nature, 395(6697), 82-86.
Morone, N., Fujiwara, T., Murase, K., Kasai, R. S., Ike, H., Yuasa, S., ... & Kusumi, A. (2006).
Three-dimensional reconstruction of the membrane skeleton at the plasma membrane
interface by electron tomography. J Cell Biol, 174(6), 851-862.
Müller, J., & Nitschke, L. (2014). The role of CD22 and Siglec-G in B-cell tolerance and
autoimmune disease. Nature reviews. Rheumatology, 10(7), 422.
Nabi, I. R., Shankar, J., & Dennis, J. W. (2015). The galectin lattice at a glance. J Cell
Sci, 128(13), 2213-2219.
Nagahara, K., Arikawa, T., Oomizu, S., Kontani, K., Nobumoto, A., Tateno, H., ... & Nagahata,
S. I. (2008). Galectin-9 increases Tim-3+ dendritic cells and CD8+ T cells and enhances
antitumor immunity via galectin-9-Tim-3 interactions. The Journal of
Immunology, 181(11), 7660-7669.
Nguyen, J. T., Evans, D. P., Galvan, M., Pace, K. E., Leitenberg, D., Bui, T. N., & Baum, L. G.
(2001). CD45 modulates galectin-1-induced T cell death: regulation by expression of core
2 O-glycans. The Journal of Immunology, 167(10), 5697-5707.
Nguyen, K., Sylvain, N. R., & Bunnell, S. C. (2008). T cell costimulation via the integrin VLA-4
inhibits the actin-dependent centralization of signaling microclusters containing the adaptor
SLP-76. Immunity, 28(6), 810-821.
O’Keefe, T. L., Williams, G. T., Davies, S. L., & Neuberger, M. S. (1996). Hyperresponsive B
cells in CD22-deficient mice. Science, 274(5288), 798.
O'keefe, T. L., Williams, G. T., Batista, F. D., & Neuberger, M. S. (1999). Deficiency in CD22, a
B cell–specific inhibitory receptor, is sufficient to predispose to development of high
affinity autoantibodies. Journal of Experimental Medicine, 189(8), 1307-1313.
77
Odendahl, M., Jacobi, A., Hansen, A., Feist, E., Hiepe, F., Burmester, G. R., ... & Dörner, T.
(2000). Disturbed peripheral B lymphocyte homeostasis in systemic lupus
erythematosus. The Journal of Immunology, 165(10), 5970-5979.
Oellerich, T., Bremes, V., Neumann, K., Bohnenberger, H., Dittmann, K., Hsiao, H. H., ... &
Wienands, J. (2011). The B‐cell antigen receptor signals through a preformed transducer
module of SLP65 and CIN85. The EMBO journal, 30(17), 3620-3634.
Ohnishi, K., & Melchers, F. (2003). The nonimmunoglobulin portion of λ5 mediates cell-
autonomous pre-B cell receptor signaling. Nature Immunology, 4(9), 849-856.
Okumura, M., Matthews, R. J., Robb, B., Litman, G. W., Bork, P., & Thomas, M. L. (1996).
Comparison of CD45 extracellular domain sequences from divergent vertebrate species
suggests the conservation of three fibronectin type III domains. The Journal of
Immunology, 157(4), 1569-1575.
Orr, S. L., Le, D., Long, J. M., Sobieszczuk, P., Ma, B., Tian, H., ... & Varki, N. (2012). A
phenotype survey of 36 mutant mouse strains with gene-targeted defects in
glycosyltransferases or glycan-binding proteins. Glycobiology, 23(3), 363-380.
Oszmiana, A., Williamson, D. J., Cordoba, S. P., Morgan, D. J., Kennedy, P. R., Stacey, K., &
Davis, D. M. (2016). The size of activating and inhibitory killer Ig-like receptor
nanoclusters is controlled by the transmembrane sequence and affects signaling. Cell
Reports, 15(9), 1957-1972.
Ovesný, M., Křížek, P., Borkovec, J., Švindrych, Z., & Hagen, G. M. (2014). ThunderSTORM: a
comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution
imaging. Bioinformatics, 30(16), 2389-2390.
78
Pace, K. E., Lee, C., Stewart, P. L., & Baum, L. G. (1999). Restricted receptor segregation into
membrane microdomains occurs on human T cells during apoptosis induced by galectin-
1. The Journal of Immunology, 163(7), 3801-3811.
Pace, K. E., Hahn, H. P., Pang, M., Nguyen, J. T., & Baum, L. G. (2000). Cutting edge: CD7
delivers a pro-apoptotic signal during galectin-1-induced T cell death. The Journal of
Immunology, 165(5), 2331-2334.
Pani, G., Siminovitch, K. A., & Paige, C. J. (1997). The motheaten mutation rescues B cell
signaling and development in CD45-deficient mice. Journal of Experimental
Medicine, 186(4), 581-588.
Pageon, S. V., Cordoba, S. P., Owen, D. M., Rothery, S. M., Oszmiana, A., & Davis, D. M.
(2013). Superresolution microscopy reveals nanometer-scale reorganization of inhibitory
natural killer cell receptors upon activation of NKG2D. Science Signaling, 6(285), ra62-
ra62.
Parker, D. C. (1993). T cell-dependent B cell activation. Annual review of immunology, 11(1),
331-360.
Pereira, A. R., & Falcão, L. M. (2015). Galectin-3, a prognostic marker–and a therapeutic
target?. Revista Portuguesa de Cardiologia (English Edition), 34(3), 201-208.
Pierce, S. K. (2002). Lipid rafts and B-cell activation. Nature Reviews Immunology, 2(2), 96-
105.
Pollard, T. D., & Cooper, J. A. (2009). Actin, a central player in cell shape and
movement. Science, 326(5957), 1208-1212.
Pradhan, D., & Morrow, J. S. (2002). The spectrin-ankyrin skeleton controls CD45 surface
display and interleukin-2 production. Immunity, 17(3), 303-315.
Rabinovich, G. A., & Toscano, M. A. (2009). Turning'sweet'on immunity: galectin–glycan
interactions in immune tolerance and inflammation. Nature Reviews Immunology, 9(5),
338-352.
79
Ramya, T. N. C., Weerapana, E., Liao, L., Zeng, Y., Tateno, H., Liao, L., ... & Paulson, J. C.
(2010). In situ trans ligands of CD22 identified by glycan-protein photocross-linking-
enabled proteomics. Molecular & Cellular Proteomics, 9(6), 1339-1351.
Reth, M. (1989). Antigen receptor tail clue. Nature, 338, 383-384.
Rickert, R. C., Rajewsky, K., & Roes, J. (1995). Impairment of T-cell-dependent B-cell
responses and Bl cell development in CD19-deficient mice. Nature, 376(6538), 352-355.
Roh, K. H., Lillemeier, B. F., Wang, F., & Davis, M. M. (2015). The coreceptor CD4 is
expressed in distinct nanoclusters and does not colocalize with T-cell receptor and active
protein tyrosine kinase p56lck. Proceedings of the National Academy of Sciences, 112(13),
E1604-E1613.
Round, J. L., Tomassian, T., Zhang, M., Patel, V., Schoenberger, S. P., & Miceli, M. C. (2005).
Dlgh1 coordinates actin polymerization, synaptic T cell receptor and lipid raft aggregation,
and effector function in T cells. Journal of Experimental Medicine, 201(3), 419-430.
Rowley, R. B., Burkhardt, A. L., Chao, H. G., Matsueda, G. R., & Bolen, J. B. (1995). Syk
protein-tyrosine kinase is regulated by tyrosine-phosphorylated Ig alpha/Ig beta
immunoreceptor tyrosine activation motif binding and autophosphorylation. Journal of
Biological Chemistry, 270(19), 11590-11594.
Saouaf, S. J., Mahajan, S., Rowley, R. B., Kut, S. A., Fargnoli, J., Burkhardt, A. L., ... & Bolen,
J. B. (1994). Temporal differences in the activation of three classes of non-transmembrane
protein tyrosine kinases following B-cell antigen receptor surface
engagement. Proceedings of the National Academy of Sciences, 91(20), 9524-9528.
Schamel, W. W., & Reth, M. (2000). Monomeric and oligomeric complexes of the B cell antigen
receptor. Immunity, 13(1), 5-14.
Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of
image analysis. Nature methods, 9(7), 671-675.
80
Singer, S. J., and G. L. Nicholson. 1972. The fluid mosaic model of the structure of cell
membranes. Science. 175:720-731.
Somani, A. K., Yuen, K., Xu, F., Zhang, J., Branch, D. R., & Siminovitch, K. A. (2001). The
SH2 domain containing tyrosine phosphatase-1 down-regulates activation of Lyn and Lyn-
induced tyrosine phosphorylation of the CD19 receptor in B cells. Journal of Biological
Chemistry, 276(3), 1938-1944.
Shrivastava, P., Katagiri, T., Ogimoto, M., Mizuno, K., & Yakura, H. (2004). Dynamic
regulation of Src-family kinases by CD45 in B cells. Blood, 103(4), 1425-1432.
Sieber, J. J., Willig, K. I., Kutzner, C., Gerding-Reimers, C., Harke, B., Donnert, G., ... & Lang,
T. (2007). Anatomy and dynamics of a supramolecular membrane protein
cluster. Science, 317(5841), 1072-1076.
Stoddart, A., Dykstra, M. L., Brown, B. K., Song, W., Pierce, S. K., & Brodsky, F. M. (2002).
Lipid rafts unite signaling cascades with clathrin to regulate BCR
internalization. Immunity, 17(4), 451-462.
Stone, J. C. (2011). Regulation and function of the RasGRP family of Ras activators in blood
cells. Genes & Cancer, 2(3), 320-334.
Su, T. T., Guo, B., Kawakami, Y., Sommer, K., Chae, K., Humphries, L. A., ... & Teitell, M.
(2002). PKC-[beta] controls I [kappa] B kinase lipid raft recruitment and activation in
response to BCR signaling. Nature Immunology, 3(8), 780.
Sugawara, H., Kurosaki, M., Takata, M., & Kurosaki, T. (1997). Genetic evidence for
involvement of type 1, type 2 and type 3 inositol 1, 4, 5‐trisphosphate receptors in signal
transduction through the B‐cell antigen receptor. The EMBO Journal, 16(11), 3078-3088.
Symons, A., N. Cooper, D., & Barclay, A. N. (2000). Characterization of the interaction between
galectin-1 and lymphocyte glycoproteins CD45 and Thy-1. Glycobiology, 10(6), 559-563.
81
Szakal, A. K., Kosco, M. H., &Tew, J. G. (1988). FDC-iccosome mediated antigen delivery to
germinal center B cells, antigen processing and presentation to T cells. In Histophysiology
of the Immune System (pp. 197-202). Springer US.
Szöllósi, J., Horejsí, V., Bene, L., Angelisová, P., & Damjanovich, S. (1996). Supramolecular
complexes of MHC class I, MHC class II, CD20, and tetraspan molecules (CD53, CD81,
and CD82) at the surface of a B cell line JY. The Journal of Immunology, 157(7), 2939-
2946
Swanson, J. A., & Hoppe, A. D. (2004). The coordination of signaling during Fc receptor-
mediated phagocytosis. Journal of Leukocyte Biology, 76(6), 1093-1103.
Tolar, P., Hanna, J., Krueger, P. D., & Pierce, S. K. (2009). The constant region of the membrane
immunoglobulin mediates B cell-receptor clustering and signaling in response to
membrane antigens. Immunity, 30(1), 44-55.
Treanor, B., Depoil, D., Gonzalez-Granja, A., Barral, P., Weber, M., Dushek, O., ... & Batista, F.
D. (2010). The membrane skeleton controls diffusion dynamics and signaling through the
B cell receptor. Immunity, 32(2), 187-199.
Treanor, B., Depoil, D., Bruckbauer, A., & Batista, F. D. (2011). Dynamic cortical actin
remodeling by ERM proteins controls BCR microcluster organization and
integrity. Journal of Experimental Medicine, 208(5), 1055-1068.
Türeci, Ö., Schmitt, H., Fadle, N., Pfreundschuh, M., & Sahin, U. (1997). Molecular definition
of a novel human galectin which is immunogenic in patients with Hodgkin's
disease. Journal of Biological Chemistry, 272(10), 6416-6422.
van Zanten, T. S., Cambi, A., Koopman, M., Joosten, B., Figdor, C. G., & Garcia-Parajo, M. F.
(2009). Hotspots of GPI-anchored proteins and integrin nanoclusters function as nucleation
sites for cell adhesion. Proceedings of the National Academy of Sciences, 106(44), 18557-
18562.
82
Viola, A., & Lanzavecchia, A. (1996). T cell activation determined by T cell receptor number
and tunable thresholds. Science, 273(5271), 104.
Wada, J., & Kanwar, Y. S. (1997). Identification and characterization of galectin-9, a novel β-
galactoside-binding mammalian lectin. Journal of Biological Chemistry, 272(9), 6078-
6086.
Wang, F., He, W., Zhou, H., Yuan, J., Wu, K., Xu, L., & Chen, Z. K. (2007). The Tim-3 ligand
galectin-9 negatively regulates CD8+ alloreactive T cell and prolongs survival of skin
graft. Cellular Immunology, 250(1), 68-74.
Wykes, M., Pombo, A., Jenkins, C., & MacPherson, G. G. (1998). Dendritic cells interact
directly with naive B lymphocytes to transfer antigen and initiate class switching in a
primary T-dependent response. The Journal of Immunology, 161(3), 1313-1319.
Weber, M., Treanor, B., Depoil, D., Shinohara, H., Harwood, N. E., Hikida, M., ... & Batista, F.
D. (2008). Phospholipase C-γ2 and Vav cooperate within signaling microclusters to
propagate B cell spreading in response to membrane-bound antigen. Journal of
Experimental Medicine, 205(4), 853-868.
Wiersma, V. R., Bruyn, M., Helfrich, W., & Bremer, E. (2013). Therapeutic potential of
Galectin‐9 in human disease. Medicinal Research Reviews, 33(S1).
Wolfert, M. A., & Boons, G. J. (2013). Adaptive immune activation: glycosylation does
matter. Nature Chemical Biology, 9(12), 776-784.
Yanagi, S., Sugawara, H., Kurosaki, M., Sabe, H., Yamamura, H., & Kurosaki, T. (1996). CD45
modulates phosphorylation of both autophosphorylation and negative regulatory tyrosines
of Lyn in B cells. Journal of Biological Chemistry, 271(48), 30487-30492.
Yang, J., & Reth, M. (2010). Oligomeric organization of the B-cell antigen receptor on resting
cells. Nature, 467(7314), 465.
Yokosuka, T., Sakata-Sogawa, K., Kobayashi, W., Hiroshima, M., Hashimoto-Tane, A.,
Tokunaga, M., ... & Saito, T. (2005). Newly generated T cell receptor microclusters initiate
83
and sustain T cell activation by recruitment of Zap70 and SLP-76. Nature
Immunology, 6(12), 1253.
Zhang, M., & Varki, A. (2004). Cell surface sialic acids do not affect primary CD22 interactions
with CD45 and surface IgM nor the rate of constitutive CD22
endocytosis. Glycobiology, 14(11), 939-949.
Zhou, L. J., Ord, D. C., Hughes, A. L., & Tedder, T. F. (1991). Structure and domain
organization of the CD19 antigen of human, mouse, and guinea pig B lymphocytes.
Conservation of the extensive cytoplasmic domain. The Journal of Immunology, 147(4),
1424-1432.
Zikherman, J., Doan, K., Parameswaran, R., Raschke, W., & Weiss, A. (2012). Quantitative
differences in CD45 expression unmask functions for CD45 in B-cell development,
tolerance, and survival. Proceedings of the National Academy of Sciences, 109(1), E3-E12.
Zhu, C., Anderson, A. C., Schubart, A., Xiong, H., Imitola, J., Khoury, S. J., ... & Kuchroo, V.
K. (2005). The Tim-3 ligand galectin-9 negatively regulates T helper type 1
immunity. Nature Immunology, 6(12), 1245-1252.
84