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Overcoming challenges in cellular analysisMultiparameter analysis of rare cells
January 28, 2015
Webinar Series
Participating ExpertsBrought to you by the Science/AAAS Custom Publishing Office
Overcoming challenges in cellular analysisMultiparameter analysis of rare cells
January 28, 2015
Andrea Cossarizza, M.D., Ph.D.University in Modena and Reggio
Emilia School of MedicineModena, Italy
David Cousins, Ph.D.University of LeicesterLeicester, UK
Webinar Series
Sponsored by:
ANDREA COSSARIZZA
Multiparameteranalysis of rare cells
• Rare cell analysis: background and keypoints
OUTLINE OF THE TALK
• Rare cell analysis: background and keypoints
• Main problems in the detection of such cells
OUTLINE OF THE TALK
• Rare cell analysis: background and keypoints
• Main problems in the detection of such cells
• Possible solutions: from hardware to software
OUTLINE OF THE TALK
• Rare cell analysis: background and keypoints
• Main problems in the detection of such cells
• Possible solutions: from hardware to software
• Rare cells in the immune system: the case of iNKT
OUTLINE OF THE TALK
• >30 years ago: enumeration of fetal red blood cells in the maternal circulation at a frequency of 1/10,000 to 1/100,000 by Cupp.
BACKGROUND
• >30 years ago: enumeration of fetal red blood cells in the maternal circulation at a frequency of 1/10,000 to 1/100,000 by Cupp.
• Now: detection and quantitation of several rare cell populations in blood or bone marrow.
BACKGROUND
• >30 years ago: enumeration of fetal red blood cells in the maternal circulation at a frequency of 1/10,000 to 1/100,000 by Cupp.
• Now: detection and quantitation of several rare cell populations in blood or bone marrow.
• Essential tool in the diagnosis and monitoring of hematological cancers and immunological disorders, as well as in the identification of Ag‐specific cells.
BACKGROUND
• Rare‐event analysis is the art of finding a needle in a haystack
WARNING
• Rare‐event analysis is the art of finding a needle in a haystack
• The frequency of the event of interest, and the signal‐to‐noise ratio of the method used to detect the event are the two most important factors.
WARNING
• ‘‘Rare‐event analysis’’: detection of events that occur at a frequency of 1 in 1,000 (0.1%) or less, although the record claimed in the literature has long stood at 1 cell in 10,000,000 (0.00001%) for tumor cells spiked into peripheral blood.
KEY POINTS
• ‘‘rare‐event analysis,’’: detection of events that occur at a frequency of 1 in 1,000 (0.1%) or less, although the record claimed in the literature has long stood at 1 cell in 10,000,000 (0.00001%) for tumor cells spiked into peripheral blood.
• Detecting an event at low frequency requires a high signal‐to‐noise ratio and the acquisition of a large number of events.
KEY POINTS
• Ag‐specific T cells• NKT and iNKT cells• Circulating endothelial cells and precursors• Stem cells (CD34+)• Particular lymphocytes subpopulations• Circulating tumor cells• Polyfunctional assays• ..........
IMMUNOLOGIST'S INTERESTS
Open pre‐analytical questions
• How much blood from patients?
• How much blood from patients• Lack of available standardized method
Open pre‐analytical questions
• How much blood from patients• Lack of available standardized method• Enriched or non enriched populations
Open pre‐analytical questions
• How much blood from patients• Lack of available standardized method• Enriched or non enriched populations• How many markers/colors
Open pre‐analytical questions
• How much blood from patients• Lack of available standardized method• Enriched or non enriched populations• How many markers/colours• How many cells
Open pre‐analytical questions
• How much blood from patients• Lack of available standardized method• Enriched or non enriched populations• How many markers/colours• How many cells • Exclusion of doublets, dead cells and debris:
use of a DUMP CHANNEL
Open pre‐analytical questions
Number of events to acquire
CV (%) 1 2.5 5 10 20Positive cells required
10,000 1,600 400 100 25
Frequency
EVENT NUMBER TO ACQUIRE %
l/n 10 10 100,000 16,000 4,000 1,000 250 1 100 1,000,000 160,000 40,000 10,000 2,500 0.1 1,000 10,000,000 1,600,000 400,000 100,000 25,0000.01 10,000 100,000,000 16,000,000 4,000,000 1,000,000 250,000 0.001 100,000 1,000,000,000 160,000,000 40,000,000 10,000,000 2,500,000
• Which instrument, and which performances
Open analytical questions
• Which instrument, and which performances• Flow cytometer rates of acquisition
Open analytical questions
• Which instrument, and which performances• Flow cytometer rates of acquisition• Maximize the signal‐to‐noise ratio of the
cells of interest from the background
Open analytical questions
• Which instrument, and which performances• Flow cytometer rates of acquisition• Maximize the signal‐to‐noise ratio of the
cells of interest from the background• Data acquisition: instrument clean and the
background level of noise below the threshold
Open analytical questions
• Which instrument, and which performances• Flow cytometer rates of acquisition• Maximize the signal‐to‐noise ratio of the
cells of interest from the background• Data acquisition: instrument clean and the
background level of noise below the threshold
• Spill over and carry over
Open analytical questions
• Which instrument, and which performances• Flow cytometer rates of acquisition• Maximize the signal‐to‐noise ratio of the
cells of interest from the background• Data acquisition: instrument clean and the
background level of noise is below the threshold
• Spill over and carry over• Adequate software
Open analytical questions
Our previous experiencePolyfunctional analysis of Ag‐specific cells
2012
THE INTERBETWEENERS: INNATE‐LIKE LYMPHOCYTES
Types of lymphocyte that blur the traditional boundaries betweeninnate and adaptive immunity
Types of lymphocyte that blur the traditional boundaries betweeninnate and adaptive immunity
Invariant Natural Killer T cells(iNKT)
Poised to robustly produce cytokines more rapidly than conventional naïve
T cells
THE INTERBETWEENERS: INNATE‐LIKE LYMPHOCYTES
Types of lymphocyte that blur the traditional boundaries betweeninnate and adaptive immunity
Invariant Natural Killer T cells(iNKT)
Mucosal associated invariant T cells(MAIT)
Poised to robustly produce cytokines more rapidly than conventional naïve
T cells
Preferentiallylocalized in the mucosal tissues
THE INTERBETWEENERS: INNATE‐LIKE LYMPHOCYTES
Types of lymphocyte that blur the traditional boundaries betweeninnate and adaptive immunity
Invariant Natural Killer T cells(iNKT)
Mucosal associated invariant T cells(MAIT)
T cells
Poised to robustly produce cytokines more rapidly than conventional naïve
T cells
Pre‐programmed to acquire their
effector functionsbefore egress from
thymus
Preferentiallylocalized in the mucosal tissues
THE INTERBETWEENERS: INNATE‐LIKE LYMPHOCYTES
iNKT cells MAIT cellsFrequency
(% among human PBMCs)
0.01‐1% 1‐10%
Receptors semi‐invariantVα24‐Jα18 TCR,NK receptors
semi‐invariantVα7.2‐Jα33 TCR, high
levels of CD161,IL‐18Rα.
Antigen recognized glycolipid antigenspresented by CD1d
microbial antigenspresented by MR1
Subsets CD4+, CD8+, and CD4‐CD8‐
CD4+, CD8+, and CD4‐CD8‐
Function Regulatory Effector‐memoryphenotype
DIFFERENT CHARACTERISTICS OF INKT AND MAIT CELLS
Gating strategy for iNKT cellsand their main subsets
SSC
CD3 DUMP CHANNEL(CD14,CD19)
Vα24Jα18Vβ11 TCR
SSC
SSC
CD8
CD4
CD161
CD161
SSC
SSC
Gating strategy for iNKT cellsand their main subsets
SSC
CD3 DUMP CHANNEL(CD14,CD19)
Vα24Jα18Vβ11 TCR
SSC
SSC
NKT cells and Multiple Sclerosis (MS)
Berzins SP., Nat Rev Immunol. 2011
Studies on a subset of NKT cells
Polyfunctionality of iNKT cells in patients affected by different forms of Multiple Sclerosis (Relapsing‐Remitting RR, Primary Progressive PR, Secondary Progressive SP), in the framework of a project sponsored by the Italian Foundation for Multiple Sclerosis ‐ FISM
• 3 RR patients (treated with Natalizumab)
• 2 PR patiens• 5 SP patients• 5 CTR (healthy subjects)
• PBMCs isolation from >30 mL of blood• Stimulation with PMA (100 ng/ml) plus ionomycin (1 g/ml) for 4 hrs• ICS with following markers:
Live Dead (Aqua)CD3 PE‐CY5 CD4 AF700CD8 APC‐CY7iTCR (V24‐J18) PEIFN‐gamma FITCIL‐4 APCIL‐17 BV421TNF‐alpha BV605
Methods
• PBMCs isolation from >30 mL of blood• Stimulation with PMA (100 ng/ml) plus ionomycin (1 g/ml) for 4 hrs• ICS with following markers:
Live Dead (Aqua)CD3 PE‐CY5 CD4 AF700CD8 APC‐CY7iTCR (V24‐J18) PEIFN‐gamma FITCIL‐4 APCIL‐17 BV421TNF‐alpha BV605
• 10‐20 millions events acquired on Attune NxT (Life Technologies/Thermo Fisher), at a speed up to 35,000 cells/second
• mAbs titrated on 20 millions PBMCs• Compensation matrix set using single stained samples and FMO (Fluorescence
Minus One) approach
Methods
Gating strategy
FSC‐H
FSC‐A
Live Dead
CD3
CD4
CD8
CD3
iTCR
CD4
CD8
Live Dead
CD3
iTCR
TNF‐
TNF‐
IL‐17A
23,053 iNKT cells
6,720,084 T cells
Junk removal
72.40.08
39.6
56.32.4
29.0 1.5
52.217.3
1.7
IL‐17A
IFN‐ IFN‐
TNF‐
IL‐4
TNF‐
TNF‐
IL‐17A
GATED ON CD8+ T CELLS
GATED ON CD4+ T CELLS
GATED ON CD4‐CD8‐ T CELLS
Analysis of CD3+ T cells
0
GATED ON CD8+ iNKT CELLS
GATED ON CD4+ iNKT CELLS
GATED ON CD4‐CD8‐ iNKT CELLS
IFN‐
IL‐17A
IFN‐
TNF‐
IL‐4
TNF‐
TNF‐
IL‐17A
Analysis of iNKT cells
Software: Attune NxTFlowJo using Boolean Gate function Pestle to transfer filesSPICE to plot data
First analysis of the polyfunctionality of: • CD4+ T cells• CD8+ T cells• CD4‐CD8‐ T cells
Then, evaluation of the polyfunctionality of the rare cells of interest, i.e.:
• CD4+ iNKT cells• CD8+ iNKT cells• CD4‐CD8‐ iNKT cells
Considering 4 intracellular cytokines, there are 24 (=16) different cell populations, well represented by using SPICE.
Data analysis
CTRRR(Nat)
PP
CD4+ T CELLSresponse
SP
CD8+ T CELLSresponse
CTRRR(Nat)
PP SP
CD4‐CD8‐ T CELLSresponse
CTRRR(Nat)
PP SP
CD4+ iNKT CELLSresponse
RR(Nat)
PP SP CTR
CD8+ iNKT CELLSresponse
RR(Nat)
PP SP CTR
CD4‐CD8‐ iNKT CELLSresponse
RR(Nat)
PP SP CTR
• CECs and EPCs are extremely rare events (0.1 – 0.0001% in buffy coat)
• Absence of standardized protocol
• Lack of unique markers
• The needle and the damage done (by the venipuncture)...
Circulating Endothelial Cells (CEC)Circulating Endothelial Cell Precursors (EPC)
FSC‐A
FSC‐H
CD45‐BV 510 LIVE DEAD AQUA
SSC
Syto16
SSC
CD34‐BV 605
SSC
TimeSSC
CD31‐APC‐CY7
CD13
3‐AP
C
CD276‐PE
SSC
CD309‐PECY7
GATED ON CD133‐,CD31+
GATED ON CD133+,CD31+
Gated on Syto16
Gating strategy for their identification
EPC
CEC
CONCLUSIONS
• Studying rare cells requires careful attention, optimal methodologies in all phases, including collection of biological samples, adequate software and hardware.
CONCLUSIONS
• Studying rare cells requires careful attention, optimal methodologies in all phases, including collection of biological samples, adequate software and hardware.
• I have shown you some examples (besides Ag‐specific cells) that could be of interest for immunologists.
CONCLUSIONS
• Studying rare cells requires careful attention, optimal methodologies in all phases, including collection of biological samples, adequate software and hardware.
• I have shown you some examples (besides Ag‐specific cells) that could be of interest for immunologists.
• "Next generation" instruments that work at a very high speed and sensitivity are now allowing an easy detection and analysis of such cells.
ACKNOWLEDGEMENTS
SARA DE BIASI, PhD
More acknowledgments
Neurology Clinic, NOCSAE, ModenaPatrizia Sola, MD PhD
Diana Ferraro, MD PhD
Francesca Vitetta, MD
Anna Maria Simone, MD
Chair of Pathology and Immunology, ModenaProf. Marcello Pinti, PhDMilena Nasi, PhDLara Gibellini, PhDDr. Regina BartolomeoDr. Elena Bianchini
Elisa Nemes, PhD (now at Univ. of Cape Town, South Africa)Enrico Lugli, PhD (now at Humanitas Institute, Milan, Italy)
Participating ExpertsBrought to you by the Science/AAAS Custom Publishing Office
Overcoming challenges in cellular analysisMultiparameter analysis of rare cells
January 28, 2015
Andrea Cossarizza, M.D., Ph.D.University in Modena and Reggio
Emilia School of MedicineModena, Italy
David Cousins, Ph.D.University of LeicesterLeicester, UK
Webinar Series
Sponsored by:
Studying human innate lymphoid cells using multi‐parameter flow cytometry
Prof David CousinsDepartment of Infection, Immunity
and InflammationNIHR Leicester Respiratory BRU
University of Leicester
Allergic Mechanisms ‐ Th2 diseases
Akdis CA. Therapies for Allergic Inflammation. Nature Medicine 2012.
Innate Lymphoid Cells (ILCs)
• Lymphocyte‐like innate cells, lacking known lineage specific markers.
• Derived from an ID2+ common ILC precursor.
• Currently subdivided into 3 classes based on effector function (i.e. cytokine output).
• ILC1: IFN
• ILC2: IL‐5/IL‐13.
• ILC3: IL‐17/IL‐22.
• Early cytokine producers during immune response. Spits, H. et al. Nat Rev Immunol 13, 145–149
(2013)
Type 2 responses – 2013
Walker, Barlow & McKenzie. Innate lymphoid cells — how did we miss them?Nature Reviews Immunology 2013; 13, 75‐87.
Type 2 Innate Lymphoid Cells (ILC2s) ‐mice
• ILC2s (nuocytes) first identified in the small intestine using an IL‐13 eGFPreporter mouse (Neill et al. Nature 2010).
Type 2 Innate Lymphoid Cells (ILC2s) ‐mice
• ILC2 numbers have been found to increase in multiple mouse models of airways disease (Stockinger lab/Umetsu lab/Hendriks lab).
• ILC2s are responsible for early source of Type 2 cytokines (Halim et al. 2014).
(■)
(■)
(□)
Wild type + Papain
Rag1‐/‐ + Papain
Wild type untreated
Influenza infectionChang et al. Nat Immunol, 2011 vol. 12 (7) 631
Human ILC2s – Challenges
• Lack of Lineage specific markers
• Cannot use fluorescent reporters
• Cells are rare!
(Neill et al. 2010)
Type 2 Innate Lymphoid Cells (ILC2s) ‐ humans
• lung parenchyma and BAL (Monticelli et al. 2011).
a
c
TCR
,C
D11
c
CD11b
CD
56
CD19
CD
56
CD127
CD
3
CD3
Lung ILC
BAL
TCR
,C
D11
c
CD3 CD11b
CD
56
CD19
CD
56
CD127
CD
3
ILC
105
103
102
0
104
105
103
102
0
104
105
103
102
0
104
105
103
102
0
104
0102 103 104 105 0102 103 104 105 0 102 103 104 105 0 102 103 104 105
105
103
102
0
104
105
103
102
0
104
105
103
102
0
104
105
103
102
0
104
0102 103 104 105 0102 103 104 105 0 102 103 104 105 0 102 103 104 105
b HC
71105
104
103
102
102103104
CRTH2HC
*
CRS
4
1050
0
CD
117
CR
TH2+ c
ells
(% o
f CD
45+
popu
latio
n)2
225
CRS
33 11
3224
3210
• Fetal and adult lung and nasal polyps, CRTh2positive (Mjosberg et al. 2011).
Human ILC2s – Aims
Multi parameter flow cytometry:
• Determine whether an ILC2 population could be identified in human peripheral blood.
• Optimise a protocol for ILC2 isolation and culture.
• Phenotype human ILC2s based on surface marker expression and secreted inflammatory mediators.
• Examine a role in experimental rhinovirus infection.
Instrumentation
• Life Technologies Attune acoustic focussing flow cytometero High flow rate – 1ml/minute with low variationo Good for rare eventso New machine NxT capable of 14 colours
• BD FACS Aria II flow sortero 5 laser instrument – very flexibleo Can sort cells – enrichment needed prior to sorting
ILC2s were Identified in Human Peripheral Blood
PBMC magnetic depletion strategy developed using mouse anti‐human CD3/14/16/19 and pan mouse IgG Dynabeads for ILC2 enrichment.
CRTh2
Hematopoietic Lineage cocktail – FITC (ebio):CD2/3/14/16/19/56/235a + CD123.
ILC2s Have a Distinct Phenotype
• ILC2s: Lin-, : CD34-, CRTH2+ CD127+,CD45+CXCR3- CCR4+IL17BR+ with populations of CD117 and CCR6 positive cells.
ILC2s express IL‐13 ex vivo and after in vitro proliferation
ILC2s after 7 days of culturewith rIL2/7/25/33
PBMCs stimulated withPMA/Ionomycin 4hrs
ILC2s proliferate in response to IL25/IL33 (Day 7)
ILC2 activation ‐ time‐course
Rhinovirus infected BEC cultures Nasal mucosal fluid
IL‐25 is required for RV‐induced asthma exacerbation
• RV infection induces ILC2s in mouse asthma model• RV induced inflammation inhibited by anti IL17RB
IL‐25 is required for RV‐induced asthma exacerbation
David Jackson et al.Am J Respir Crit Care Med 190, 1373‐1382DOI: 10. 1164/rccm.201406
IL-33–Dependent Type 2 Inflammation during Rhinovirus-inducedAsthma Exacerbations In Vivo
IL‐33 is required for RV‐induced asthma exacerbation
• RV infection of asthmatics increases type‐2 cytokine release• IL‐33 correlates with cytokine release and symptoms
David Jackson et al.Am J Respir Crit Care Med 190, 1373‐1382DOI: 10. 1164/rccm.201406
IL-33–Dependent Type 2 Inflammation during Rhinovirus-inducedAsthma Exacerbations In Vivo
IL‐33 is required for RV‐induced asthma exacerbation
A, RV infection of Bronchial epithelial cells
B, Cytokine release from ILC2s stimulated with supernatant from BEC cultures
Conclusions
• Multi‐parameter flow cytometry is a useful tool in identifying ILCs
• Human ILC2s can be identified in peripheral blood (Lin‐CD45+CRTh2+CD127+CD25+)
• ILC2s can be cultured in vitro in an isolated system• ILC2s expand in response to both IL‐25 and IL‐33
• Experimental rhinovirus challenge induces both IL‐25 and IL‐33.• Both may contribute to ILC2 activation in asthma exacerbations.
Therapeutic opportunities?
Andreakos et al. IL‐25: The missing link between allergy, viral infection, and asthma? Sci. Transl. Med. 6, 256fs38 (2014).
IL‐33
AcknowledgementsBatika Rana (PhD Student)Celine Parmentier (PhD Student)Joanne McDonald (PhD Student)Cate Weston (post‐doc)Paul LavenderTak LeeHolly BowenGreg WoszczekRebecca BeavilMatthew Arno (KCL Genomics Centre)BRC Flow Cytometry CoreBRC Genomics Core Melissa Lennartz‐WalkerHolly FosterElisabeth Fuerst
Funded by:
Collaborators:Andrew McKenzie (MRC LMB)Sebastian Johnston (IC)Mike Edwards (IC)James Pease (IC)Ross Walton (IC)Nathan Bartlett (IC)Roberto Solari (IC/GSK)
Participating ExpertsBrought to you by the Science/AAAS Custom Publishing Office To submit your
questions, click theAsk a Question
button
Overcoming challenges in cellular analysisMultiparameter analysis of rare cells
January 28, 2015
Andrea Cossarizza, M.D., Ph.D.University in Modena and Reggio
Emilia School of MedicineModena, Italy
David Cousins, Ph.D.University of LeicesterLeicester, UK
Webinar Series
Sponsored by:
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Brought to you by the Science/AAAS Custom Publishing Office
Overcoming challenges in cellular analysisMultiparameter analysis of rare cells
January 28, 2015
Webinar Series