BioProbes 74 - Thermo Fisher Scientific webinars, how-to videos, white papers, case studies, tutorials, scientific posters, product selection guides, and much more. To help you browse,

BioProbes 74

thermofisher.com/bioprobes • November 2016

Mohadeseh Mehrabian 1

Gerold Schmitt-Ulms 1

Alexander Tsankov 2

Xinzhu Wang 1

Declan Williams 1

1 University of Toronto 2 Harvard University

IN MEMORIAMAt press time, we learned that Richard P. Haugland—who founded Molecular Probes, Inc., with Rosaria P. Haugland in 1975—had passed away at his home in Chiangmai,

Thailand. He was the original author of the definitive reference on fluorescent dyes, The Molecular Probes Handbook, now in its 11th edition, and the BioProbes Journal.

He was also an inventor on ~80 US patents and an author of ~150 scientific papers in chemistry, biochemistry, and biophysics—from the classic 1967 and 1969

Proc Natl Acad Sci U S A papers on fluorescence resonance energy transfer (FRET) to the 1999 and 2003 J Histochem Cytochem papers on the Alexa Fluor™ dyes.

Dick Haugland had dedicated the last decade-plus of his life to philanthropic activities, supporting schools, hospitals, and orphanages in Asia, as well as the per-

forming arts in Eugene, Oregon. Most recently, he was working on curriculum development for teaching mathematics and the English and Thai languages to preschool

and primary school children. He was a passionate scientist, an influential mentor, and a generous friend, and he will be greatly missed.

EditorsMichelle SpenceGrace Richter

DesignersKim McGovernLynn Soderberg

Production ManagerBeth Browne

ContributorsLaura AllredBrian AlmondJoanna AsprerKris BarnetteDan BeachamRachael BerryJolene BradfordBeth BrowneSuzanne BuckWayne ConsidineWilliam DietrichNick DilianiNick DolmanHelen FleisigEmily HalbraderPeggy JustVictoria LoveKara MachleidtKaren MuellerMonica O’HaraStephen OldfieldCarol OxfordSheetal PatelPriya RangarajAleksey RukavishnikovKari SeversonLaura ShapiroMatt SlaterDeborah TiebergMonica TomaszewskiMarcy Wickett

PROTEIN AND CELL ANALYSIS UPDATES

2 | Online and on the move: Learning centers, coloring books, posters, and more

4 | Just released: Our newest protein and cellular analysis products and technologies

CRISPR-CAS9–BASED RESEARCH

6 | Toward mechanism-based diagnostics and disease interventionsCombining CRISPR-Cas9 with functional proteomics

11 | Apply CRISPR-Cas9 gene editing to high-throughput screeningLentiArray CRISPR libraries

12 | The CRISPR-Cas9 system for genome editingA complete suite of reagents, from Cas9 delivery tools to cell function assays

STEM CELL RESEARCH

15 | Assess the differentiation potential of human pluripotent stem cellsAn improved qPCR-based ScoreCard assay

18 | Light up neural differentiation pathwaysAntibodies for pluripotent stem cells and neural lineage cells

20 | Transcription factor expression during differentiation of hPSC-derived cardiomyocytesA multiparametric approach using the Attune NxT Flow Cytometer

TOOLS FOR IMAGING AND FLOW CYTOMETRY

22 | Seeing red during apoptosisCellEvent Caspase-3/7 Red assays for imaging and flow cytometry

24 | Next-generation detection of potassium ion fluxFluxOR II Green Potassium Ion Channel Assay

26 | Jump-start your experimental design with published antibody and reagent panelsOptimized multicolor immunofluorescence panels (OMIPs)

28 | Quantitative imaging of histological samplesNow possible using the CellInsight CX7 High-Content Analysis (HCA) Platform

30 | Protein misfolding in neurodegenerative diseasesAntibodies specific for misfolded proteins associated with neurodegeneration

JOURNAL CLUB

31 | Current methods and challenges in the characterization of human pluripotent stem cells

Published by Thermo Fisher Scientific Inc. © 2016BioProbes Journal, available in print and online at thermofisher.com/bioprobes, is dedicated to providing researchers with the very latest information about cell biology products and their applications. For a complete list of our products, along with extensive descriptions and literature references, please see our website.

http://thermofisher.com/bioprobes

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ONLINE AND ON THE MOVE BIOPrOBEs 74

We recently launched a virtual educational environment that houses expansive content on protein

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Learning centers: An online source for educational information

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thermofisher.com/bioprobes | 3 © 2016 Thermo Fisher Scientific Inc. All rights reserved. For Research Use Only. Not for use in diagnostic procedures.

BIOPrOBEs 74 ONLINE AND ON THE MOVE

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Pierce protein and peptide assay guide poster

Virtual training labs: Pluripotent stem cell culture

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JUsT rELEAsED BIOPrOBEs 74

By the end of the year, we will offer over 25 different organelle-specific

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Anti–HLA-DR Antibody, Alexa Fluor™ 488 conjugate (clone L243) 100 tests A51009

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Immunofluorescence analysis of ZO-1 in Caco-2 cells. Caco-2 cells were fixed

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Alexa Fluor™ 488 anti–ZO-1/TJP1 antibody (clone ZO1-1A12, Cat. No. 339188)

at a dilution of 5 µg/mL in blocking buffer for 1 hr at room temperature, protected

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Alexa Fluor dye–conjugated organelle-specific antibodies: A cellular paint box

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T cell and B cell antibody conjugates for flow cytometry

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two cell populations based on CD3e detection.

1010

50

100

150

250

102 103 104 106105

CD3e Alexa Fluor 488 �uorescence

Cel

l num

ber

200

* Terms and condit ions apply. For more information, please go to

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BIOPrOBEs 74 JUsT rELEAsED

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SuperSignal™ West Pico PLUS Chemiluminescent Substrate

20 mL200 mL500 mL1 L

34579345773458034578

Thermo Scientific™ SuperSignal™ West Pico PLUS Chemiluminescent

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Sensitive, robust SuperSignal West Pico PLUS Chemiluminescent Substrate

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β-Catenin

4 hr

2 hr

1 hr

Initial

WNT1STAT3

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Reagent Set is shown here.

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CrIsPr-CAs9–BAsED rEsEArCH BIOPrOBEs 74

Toward mechanism-based diagnostics and disease interventions

Combining CRISPR-Cas9 gene editing with functional proteomics.

Declan Williams, Mohadeseh Mehrabian, Xinzhu Wang, Gerold Schmitt-Ulms; University of Toronto.

The development of models and methods for studying proteins that cause neurodegenerative diseases

is the focus of our research at the University of Toronto (Figure 1), with the goal of generating insights

that will lead to novel angles for diagnosis or intervention. Within this general theme, we specialize in the

study of tauopathies [1], which include Alzheimer’s disease and a subset of frontotemporal dementias

(FTDs). We are primarily interested in finding the missing links in aberrant signaling pathways triggered

by the formation of oligomeric amyloid beta peptide (oAβ). Binding of oAβ to the cellular prion protein

(PrPC) contributes to the detachment of the Tau protein from microtubules and causes proteotoxic stress

through a poorly defined chain of events (Figure 2A).

Figure 1 (above). Localization of wild-type and mutant Tau fusion proteins. Co-expression of wild-type and P301L mutant Tau fused to EGFP (green) and ECFP

(pseudocolored red), respectively, in African green monkey CV-1 kidney cells. In addition to the profound overlap of both fusion proteins in their localization to the micro-

tubule network (observed in yellow merged color), note the presence of punctate signals only in the red channel, depicting the cellular distribution of P301L mutant Tau.

See Figure 4 for more information.


BIOPrOBEs 74 CrIsPr-CAs9–BAsED rEsEArCH

Combining CRISPR-Cas9 model building with mass spectrometryThe use of the CRISPR-Cas9 system [2] has been nothing short of

transformative for our work because it allows us to generate relevant

models with reasonable effort. There are two CRISPR-Cas9 applications

we find particularly useful, namely the generation of knockout models of

specific genes of interest, and the introduction of mutations known to

underlie human diseases. Once a suitable model has been generated,

we interrogate the consequences of these genetic changes on cellular

biology through side-by-side comparative analyses with wild-type

control models, using quantitative mass spectrometry (Figure 2B).

For such investigations to be meaningful, sample handling and anal-

ysis should not introduce inadvertent heterogeneity. One approach we

have found useful for minimizing run-to-run variances in protein-directed

research projects involving mass spectrometry is to label peptides with

isobaric tags in order to facilitate sample multiplexing [3,4]. Following

their reversed-phase separation, the mixtures of these isobarically

tagged peptides are directed to the orifice of the Thermo Scientific™

Orbitrap Fusion™ Tribrid™ mass spectrometer by electrospray ionization.

Next, the mass-to-charge ratios of incoming ions are recorded by an

Orbitrap analyzer–based parent scan of exquisite mass resolution

and accuracy. The machine then selects, in an automated

Figure 2. Schematic of central research theme and workflow for combining CRISPR-Cas9 genome-edited models with mass spectrometry. (A) The Schmitt-Ulms

laboratory studies the molecular etiology of tauopathies and prion diseases. Research in the laboratory focuses on signaling downstream of the amyloid beta peptide (Aβ) and the role of the prion protein (PrPC) in these signals, as well as events that lead to cellular toxicity. (B) More recently, the generation of cell models using CRISPR-Cas9

technology has played a major role in our discovery pipeline. A typical analysis compares the effect of a given genome manipulation on the global proteome, as well as on

the molecular interactions or posttranslational modifications of a protein of interest.

BA

Aβ

oligomer Amyloid plaque

Tau

PrP

C

P

Aβ

Microtubule

?

?

?

Tandem mass spectrometry

Af�nity capture of bait protein complexes

5'5'3'3'

WT

Generate mutant or knockout model by CRISPR-Cas9

Comparative analysis

Genome-edited

Effect of genome editing onglobal proteome

Perturbations to proteome

Bait protein interactome

Toxicity

?

P



manner, the most intense ions for mild collisions with an inert gas in

order to obtain fragment ions, which later serve as a fingerprint for

protein identification. Finally, the 10 most intense of these fragment

ions are concomitantly smashed into even smaller pieces to release

their isobaric labels. The relative signal intensities of these mass tags,

which are specific for each sample, allow us to deduce the relative

abundance of a given peptide in each of the multiplexed samples. Here

we describe two projects that illustrate the usefulness of combining

CRISPR-Cas9 model building with downstream mass spectrometry to

address fundamental biomedical research questions.

CRISPR-Cas9–generated knockouts of the gene encoding the cellular prion proteinThe first project combined specific gene knockouts with global

proteome analyses (Figure 3) and was pursued as part of a broader

program aimed at devising mechanism-based strategies to overcome

prion diseases, including Creutzfeldt-Jakob disease (CJD) and bovine

spongiform encephalopathy (BSE). Despite its discovery more than

30 years ago, the normal function of the cellular prion protein (PrPC),

which is known to cause these diseases when it acquires a different

A

wt + TGFB1wt – TGFB1

PrPC –/– + TGFB1

Dataset II

B

DC

126/131 127/131 128/131 129/13 130/131

log 2

Fold

cha

nge

PrPC –/– wt/wt

-2

-1

0

1

5th–25th–50th–75th–95th percentiles

Peptides used for quanti�cationPeptides not used for quanti�cation (duplicates or ambiguous)

xx

x

GGTGGAACACCGGTGGAAGC

gRNA vector

Exon 1 Exon 2 Exon 3

Mouse prion gene (Chr2)

TACCGCCACCTACCCCGGTT

5'3'5'

3'

Cas9 expression vector SpCas9

CMV

U6

Western blot Genomic PCR

CCGGGATACCTGGC

DNA sequencing

Dataset I

TGFB1

250

98

wt (

2)Pr

PCko

PrP

C stab

le s

hRNA

wt (

1)

+ + + +MW

1 3

anti-PrPC

39

28

19

14

180S-S S-S S-S S-S S-S

140

120

SSSSSSSSSSSS SSSSSSSSSSSSSSSSSSSSSSSSSSSS-------------SSSSS---

S-S S-S S-S S-S S-S

SSSSSSSSSSSSSSSS SSSSSSSSSSSSS SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS

SSSSSSSSSSSSSSSSSSSSSSSSSS--------S--S-S-SSSS

S-S S-S S-S S-S S-S

SSSSSSSSSSSSSSSSSSSSSSSSS----SSSS SSSSSSSSSSSSSSSS

FN 3 FN 3

FN 3 FN 3

FN 3 FN 3

Luminal/extracellular

wt/wt

NCAM1 peptide quantitations based on isobaric TMT labels

NC

AM

1 isoforms

2

/wt PrPC –/–/wt PrPC –/–/wt

4

Figure 3. Comparative global proteome analyses of wild-type and PrPC knockout cells identify the role of PrPC in the polysialylation of NCAM1. (A) Generation of

PrPC knockout cells by CRISPR-Cas9 technology. Reprinted with permission from Mehrabian M, Brethour D, MacIsaac S et al. (2014) CRISPR-Cas9–based knockout of

the prion protein and its effect on the proteome. PLoS One 9:e114594. (B) Design of global proteome analysis with the aim to identify proteins whose levels change upon

addition of TGFB1 (a method for inducing epithelial-to-mesenchymal transition) to NMuMG cells (Dataset I) and filter from this list the subset of proteins whose levels are

impacted by the presence or absence of PrPC (Dataset II). (C) Box plot depicting relative levels of NCAM1-derived peptides in wild-type and PrPC knockout cells (extracted

from Dataset II, see (B)). Note the reduction in mean NCAM1 peptide levels in PrPC knockout cells relative to wild-type levels observed in three biological replicates. (D) PrPC

deficiency abrogates NCAM1 polysialylation, identifiable in western blot analyses by the pronounced streaking. In the absence of this posttranslational modification, NCAM1

signals correspond to the relative levels of the three major isoforms of this protein. Panels B–D were reprinted with permission from Mehrabian M, Brethour D, Wang H et

al. (2015) The prion protein controls polysialylation of neural cell adhesion molecule 1 during cellular morphogenesis. PLoS One 10:e0133741.



shape, remains unknown, leaving uncertain to what extent a perturba-

tion of its normal function contributes to cellular death in the disease.

Using CRISPR-Cas9 technology, we generated knockout cells that

no longer can express PrPC [5] (Figure 3A) and compared them with

wild-type parental cells using global proteome analyses (Figure 3B).

These analyses revealed that the absence of PrPC strongly decreased

cellular levels of the neural cell adhesion molecule 1 (NCAM1) [6]

(Figure 3C). Western blot–based analyses then led to the surprising

discovery that, in addition to profoundly affecting NCAM1 protein levels,

the lack of PrPC had abrogated NCAM1 polysialylation (Figure 3D). The

polysialylation of NCAM1 is a critical posttranslational modification in the

brain that controls specific protein interactions, influences chemotactic

guidance, and modulates ion channels, and NCAM1 is the predominant

acceptor of this modification in vertebrates. We then became aware

of a body of literature documenting that impaired polysialylation of

NCAM1 perturbs (i) sleep-wake cycles, (ii) neurogenesis, (iii) neurite

outgrowth of specific mossy fiber axon bundles in the hippocampus,

and (iv) myelination [7]. These phenotypes are highly reminiscent of

independently reported phenotypes observed in mice deficient for the

prion protein [8,9], consistent with the interpretation that the contribution

of PrPC to NCAM1 polysialylation might be its predominant role [10].

CRISPR-Cas9–generated neuroblastoma cells with inducible Tau expressionThe second project highlights a useful application of CRISPR-Cas9

technology for the generation of human cell models that inducibly

express a protein of interest fused to a fluorescent affinity-capture tag

(Figure 4), allowing the production of in-depth interactome datasets

in less than a month. In this project, we were interested in dissecting

molecular events that may cause cellular death in a small subset of

FTDs caused by specific inherited mutations in the gene encoding the

microtubule-associated protein Tau (MAPT). Neural Tau transcripts are

subject to alternative splicing events that generate up to six prominent

Tau isoforms, which can be further classified as having either three

or four repeats (3R or 4R) in the microtubule-binding domains. In the

brain, a balanced amount of 3R and 4R Tau is critical for cellular

Figure 4. Comparative interactome analyses reveal compromised binding of mutant Tau to the proteasome and a subset of chaperones. (A) CRISPR-Cas9–

based genome engineering approach for the generation of human cell models that can be rapidly manipulated to promote the inducible expression of proteins of interest.

(B) Validation of positive clone coding for the inducible expression of Tau-EGFP. (C) Co-expression of wild-type and P301L mutant Tau fused to EGFP (green) and ECFP

(pseudocolored red), respectively, in African green monkey CV-1 kidney cells (a model with favorable characteristics for visualizing the microbutule network). In addition to

the profound overlap of both fusion proteins in their localization to the microtubule network (observed in yellow merged color), note the presence of punctate signals only

in the red channel, depicting the cellular distribution of P301L mutant Tau. Scale bar = 1 µm.

lox

+ iCre

G418R

A

lox

rtTA3 CMV TREProtein of interestEGFP

+ Dox

REtightrtTA3 CMV Protein of interest TREEGFPHomology-directed repair template

AAVS1 locusexon 1

lox lox

exons 2 and 3

Homology arm

+ Cas9 nickase

gRNA2

gRNA1

Step 2: Replacement of reporter with inducible expression cassetteStep 1: Introduction of lox-�anked reporter into safe harbor locus

G418R

C4RWT Tau-EGFP + 4R P301L Tau-ECFP

B

0 3 6 12 18

anti-Tau

+ Dox

Time (hr)

anti-actin

49

38

98

62

MW



References1. Spillantini MG, Goedert M (2013) Lancet Neurol 12:609–622.

2. Mali P, Esvelt KM, Church GM (2013) Nat Methods 10:957–963.

3. Dayon L, Sanchez JC (2012) Methods Mol Biol 893:115–127.

4. Zieske LR (2006) J Exp Bot 57:1501–1508.

5. Mehrabian M, Brethour D, MacIsaac S et al. (2014) PLoS One 9:e114594.

6. Mehrabian M, Brethour D, Wang H et al. (2015) PLoS One 10:e0133741.

7. Rutishauser U (2008) Nat Rev Neurosci 9:26–35.

8. Aguzzi A, Baumann F, Bremer J (2008) Annu Rev Neurosci 31:439–477.

9. Steele AD, Lindquist S, Aguzzi A (2007) Prion 1:83–93.

10. Mehrabian M, Hildebrandt H, Schmitt-Ulms G (2016) ASN Neuro. In press.

11. Myeku N, Clelland CL, Emrani S et al. (2016) Nat Med 22:46–53.

12. Wang X, Williams D, Wang H et al. (2016) Manuscript in preparation.

13. Gunawardana CG, Mehrabian M, Wang X et al. (2015) Mol Cell Proteomics 14:3000–3014.


GeneArt™ CRISPR Nuclease mRNA 15 µg A29378

GeneArt™ CRISPR Nuclease Vector with OFP Reporter Kit

10 reactions A21174

GeneArt™ CRISPR Nuclease Vector with OFP Reporter Kit, with competent cells

10 reactions A21178

GeneArt™ CRISPR Nuclease Vector with CD4 Enrichment Kit

10 reactions A21175

GeneArt™ CRISPR Nuclease Vector with CD4 Enrichment Kit, with competent cells

10 reactions A21177

GeneArt™ Platinum™ Cas9 Nuclease, 1 µg/µLGeneArt™ Platinum™ Cas9 Nuclease, 1 µg/µLGeneArt™ Platinum™ Cas9 Nuclease, 3 µg/µL

10 µg25 µg75 µg

B25642B25640B25641

GeneArt™ Genomic Cleavage Detection KitGeneArt™ Genomic Cleavage Selection Kit

20 reactions10 reactions

A24372A27663

TMTsixplex™ Isobaric Label Reagent Set 6 reactions12 reactions30 reactions

900619006290066

Acknowledgments: This art ic le was contr ibuted by Declan Wi l l iams,

Mohadeseh Mehrabian, Xinzhu Wang, and Gerold Schmitt-Ulms; Tanz

Centre for Research in Neurodegenerative Diseases, University of Toronto.

The lat ter three authors are a lso associated with the Department of

Laboratory Medicine & Pathobiology, University of Toronto. Gerold Schmitt-

Ulms is the corresponding author; please address correspondence to:

[email protected].

health. Available human cell models exhibit unbalanced isoform ratios,

and it has repeatedly been shown that the plasmid-encoded expression

of 4R Tau can cause cellular toxicity by itself [11].

To overcome this confounder to cell-based Tau studies, we

employed a two-step genome engineering approach to generate

human neuroblastoma cell models that express equal levels of 3R and

4R wild-type or mutant Tau [12]. In the first step, we used the double

CRISPR-Cas9 nickase technology to introduce a G418 resistance

marker flanked by lox sites into the AAVS1 genomic safe harbor, a

site known to tolerate insertions without adverse effects on the cell

(Figure 4A).The coding sequences for 3R and 4R Tau, packaged in

an expression cassette flanked by compatible lox sites, were then

switched into the genome via cotransfection of Cre recombinase. To

allow the inducible expression of Tau and facilitate its cellular tracking

and capture, the Tau coding sequence was placed between a tetra-

cycline response element (TRE) promoter and a C-terminal Enhanced

Green Fluorescent Protein (EGFP). Finally, we included in the plasmid

expression cassettes a reverse tetracycline transactivator (rtTa) and a

puromycin selection marker.

As intended, the cells expressed equal levels of 3R and 4R wild-type

or mutant Tau upon induction with doxycycline (Figure 4B). Consistent

with expectations, the presence of the mutation caused Tau not only

to bind microtubules but also to appear in punctate aggresome-like

structures (Figure 4C). The presence of the C-terminal EGFP tag has

been shown to have no adverse effect on Tau biology and facilitated

the capture of Tau-EGFP fusion proteins on GFP-binding protein (GBP)

matrices. Our analyses of these cells are ongoing but have already

revealed several interesting insights, including differential binding of

wild-type and mutant Tau to certain chaperones and the proteasome

[13], consistent with the notion that FTD may be caused by impaired

recycling of the Tau protein. The ability to induce Tau and follow it over

time will allow us to dissect the chronology of events underlying Tau’s

impaired recycling, identify the cellular pathways that are poisoned in

cells when Tau aggregates are forming, and uncover abnormal changes

to molecular interactions and posttranslational modifications of Tau that

facilitate the formation of aggregates.

Future directionsAlthough genome editing is not new, the relative ease with which animal

and cell models can be generated with the CRISPR-Cas9 technology

has already had a profound impact on biomedical research. The system

still suffers from efficacy limitations in regards to achieving a specific

genome edit and in its delivery to cells in complex tissues. On the

proteome analysis side, we are still limited in the proteome coverage

that can be achieved even with the most high-end equipment. The

rapid pace of innovation we have witnessed in CRISPR-Cas9 technol-

ogy and mass spectrometry is not expected to abate any time soon.

The rewards that the combination of both approaches promises are

only beginning to be realized. Good times for biomedical research

are waiting ahead. ■



Apply CRISPR-Cas9 gene editing to high-throughput screeningLentiArray CRISPR libraries.

The CRISPR-Cas9 gene editing system

provides an efficient approach for specific,

complete, and permanent knockout of gene

expression, making it a potent research

tool for determining key players in specific

biological pathways. The new Invitrogen™

LentiArray™ CRISPR libraries extend CRISPR-

Cas9 technology into high-throughput appli-

cations for functional genomic screening.

LentiArray libraries enable the interrogation

of hundreds or thousands of genes in a single

experiment with:

■ Advanced guide RNA (gRNA) designs for

maximum knockout efficiency without

sacrificing specificity

■ Up to 4 high-quality gRNAs per gene

target, for efficient knockout in a wide

variety of cell types

■ Two choices for delivery—high-titer, ready-

to-use lentivirus, or glycerol stocks of

E. coli containing lentiviral plasmids

■ A complete set of controls and lentiviruses

against single-gene targets to support

pre-screen assay development and rapid

post-screen hit validation

■ 19 defined libraries and custom options

available, enabling screens of defined

gene sets or unbiased surveys of the

whole genome (Table 1)

LentiArray library specificsFor example, the LentiArray Human Whole

Genome CRISPR Library targets 18,453

genes with up to 4 gRNAs per gene target

(pooled in a single well), for a total of 73,812

gRNAs. The gene targets within this library

were selected using the most up-to-date

genome databases, including the NCBI

RefSeq database, and cross-referenced to the Gene Ontology Consortium (GO) database

or the HUGO Gene Nomenclature Committee (HGNC). LentiArray CRISPR libraries are con-

structed using our proprietary CRISPR gRNA design algorithm, which incorporates the latest

gRNA design research; gRNAs are selected for maximal editing efficiency and specificity and

are designed to knock out all known isoforms of the target gene. Libraries are delivered as

200 μL of ready-to-use lentiviral particles per gene target at a titer of 1 x 106 TU/mL (functional

titer determined by antibiotic resistance) and are also available as glycerol stocks (Table 1).

Learn more about the LentiArray librariesLentiArray CRISPR libraries are delivered in an arrayed format compatible with existing

high-throughput screening infrastructure and have been designed and constructed to provide

a flexible system that doesn’t impose limitations on your assay design. They utilize a two-

vector design, expressing the Cas9 nuclease and the gRNA from separate lentiviral constructs,

enabling you to dictate when and how the genome editing tools are delivered to your cells.

Explore the LentiArray CRISPR libraries and find out how CRISPR-Cas9 technology can expand

your screening capabilities at thermofisher.com/crisprlibrariesbp74. ■

Table 1. Available defined libraries, supporting focused high-throughput screens as well as unbiased whole genome surveys.

Product No. of genes

Cat. No. (ready to use)

Cat. No. (glycerol stocks)

LentiArray™ Human Whole Genome CRISPR Library 18,453 A31949 A32185

LentiArray™ Human Druggable Genome CRISPR Library 10,128 A31948 A32184

LentiArray™ Human Apoptosis CRISPR Library 904 A31940 A32176

LentiArray™ Human Cancer Biology CRISPR Library 510 A31933 A32169

LentiArray™ Human Cell Cycle CRISPR Library 1,444 A31936 A32172

LentiArray™ Human Cell Surface CRISPR Library 778 A31943 A32179

LentiArray™ Human DNA Damage Response CRISPR Library 561 A31946 A32182

LentiArray™ Human Drug Transport CRISPR Library 98 A31941 A32177

LentiArray™ Human Epigenetics CRISPR Library 396 A31934 A32170

LentiArray™ Human GPCR CRISPR Library 446 A31947 A32183

LentiArray™ Human Ion Channel CRISPR Library 328 A31942 A32178

LentiArray™ Human Kinase CRISPR Library 840 A31931 A32167

LentiArray™ Human Membrane Trafficking CRISPR Library 141 A31937 A32173

LentiArray™ Human Nuclear Hormone Receptor CRISPR Library

47 A31939 A32175

LentiArray™ Human Phosphatase CRISPR Library 288 A31932 A32168

LentiArray™ Human Protease CRISPR Library 475 A31944 A32180

LentiArray™ Human Transcription Factor CRISPR Library 1,817 A31938 A32174

LentiArray™ Human Tumor Suppressor CRISPR Library 716 A31945 A32181

LentiArray™ Human Ubiquitin CRISPR Library 943 A31935 A32171

http://thermofisher.com/crisprlibrariesbp74



The CRISPR-Cas9 system for genome editingA complete suite of reagents, from Cas9 delivery tools to cell function assays.

The transformative CRISPR-Cas9 technology is revolutionizing the

field of genome editing. Derived from components of an adaptive

immune system in bacteria, the CRISPR-Cas9 system enables targeted

gene cleavage and gene editing in a wide variety of eukaryotic cells.

Because the specificity of the endonuclease cleavage is guided by

RNA sequences, editing can be directed to virtually any genomic

locus simply by engineering the guide RNA sequence and delivering

it along with the Cas endonuclease to the target cell. The CRISPR-

Cas9 system has great promise in broad applications such as stem

cell engineering, gene therapy, tissue and animal disease models, and

the development of disease-resistant transgenic plants.

The CRISPR-Cas9 system derives its specificity from a short,

noncoding guide RNA (gRNA) that has two molecular components: a

target-specific CRISPR RNA (crRNA) and an auxiliary trans-activating

CRISPR RNA (tracrRNA). The gRNA guides the Cas9 protein to a specific

genomic locus via base pairing with the target sequence (Figure 1).

Upon binding to the target sequence, the Cas9 protein induces a

specific double-strand break. Following DNA cleavage, the break is

repaired by cellular repair machinery through nonhomologous end

joining (NHEJ) or homology-directed repair (HDR) mechanisms. With

target specificity defined by a very short RNA sequence coupled with

an efficient endonuclease activity, the CRISPR-Cas9 system greatly

simplifies directed genome editing.

Choose the right CRISPR-Cas9 delivery methodSeveral strategies are available for delivering Cas9 protein to target cells,

and this flexibility is one of the key advantages when using CRISPR-

Cas9 genome editing technology in different experimental systems

(Figure 2). Advances in DNA, mRNA, and protein delivery methods

have significantly streamlined the process, making the introduction of

Cas9 more efficient and with minimal off-target effects. Thermo Fisher

Scientific offers four formats for CRISPR-Cas9 delivery: Invitrogen™

GeneArt™ CRISPR Nuclease Vector (DNA), GeneArt™ CRISPR Nuclease

Figure 1. A CRISPR-Cas9 targeted double-strand break. Cas9-mediated cleav-

age occurs on both strands of the DNA, three base pairs upstream of the NGG

proto-spacer adjacent motif (PAM) sequence on the 3’ end of the target sequence.

The specificity is supplied by the guide RNA (gRNA), and changing the target only

requires a change in the design of the sequence encoding the gRNA. After the

gRNA unit has guided the Cas9 nuclease to a specific genomic locus, the Cas9

protein induces a double-strand break at the specific genomic target sequence.

mrNA

Protein

DNA

mRNA/gRNA mix

DNA

mRNAIVT gRNA

CRISPR DNA vector

Translation

Nucleus

Nuclear localization

Cas9

Cas9

Cas9

Cas9

Targetcleavage

Cas9-gRNA complex

RN

P com

plex form

ation

Transcription

Figure 2. Options for efficient CRISPR-Cas9 delivery. In the DNA delivery format,

the CRISPR DNA vector enters the cell and translocates to the nucleus, where the

Cas9 mRNA and gRNA are transcribed. Translated in the cytoplasm, the Cas9 protein

combines with the gRNA to form a ribonucleoprotein (RNP) complex that then enters

the nucleus for targeted gene editing. In the RNA delivery format, the Cas9 mRNA

and gRNA are cotransfected into the cell cytoplasm, where the mRNA is translated

to produce functional Cas9 protein. The Cas9-gRNA (RNP) protein delivery format

streamlines cell engineering by eliminating transcription and translation in the cell

and produces the highest cleavage efficiencies in our labs. With the RNP format

there is no requirement for a specific promoter, nor concern over random integration

into the genome; the Cas9 RNP complex can act immediately after it enters the

cell, since transcription and translation are not required. Moreover, the complex is

rapidly cleared from the cell, minimizing the chance of off-target cleavage events

when compared to vector-based systems.

Cas9

Gene disruption—repair to native sequence results in frameshifts or mutations

Gene correctionDNA insertion—insert promoter, gene tags, and single or multiple genes

NHEJ(nonhomologous end joining) HDR (homology-directed repair)

Cotransfect cells with donor DNA

Target-speci�c crRNA

Target genomic loci PAM

tracrRNA



directly, and when antibiotic selection is used to identify transfected

cells, viability assays can be used to monitor the selection process.

Monitoring the efficiency of genome editing. When using

genome editing tools—such as CRISPR-Cas9, TAL effectors, or zinc

finger nucleases—to obtain targeted mutations, you need to determine

the efficiency with which these nucleases cleave the target sequence,

prior to continuing with labor-intensive and expensive experiments. The

Invitrogen™ GeneArt™ Genomic Cleavage Detection Kit provides a simple

and reliable assay for the cleavage efficiency of genome editing tools

at a given locus. In this assay, a sample of the edited cell population is

used as a direct PCR template for amplification with primers specific to

the targeted region. The PCR product is then denatured and reannealed

to produce heteroduplex mismatches where double-strand breaks

have occurred, resulting in insertion/deletion (indel) introduction. These

mismatches are recognized and cleaved by the detection enzyme,

and the cleavage is easily detectable and quantifiable by gel analysis.

Cell phenotyping. The CRISPR-Cas9 system is routinely used for

knockout, knock-in, or modulation of gene expression, and the primary

on-target effects can be measured using cell analysis techniques; west-

ern blotting, flow cytometry, and fluorescence microscopy are often used

to view changes to protein expression or structure in a cell population.

Flow cytometry provides the throughput for multiparameter analysis on

vast numbers of individual cells. Cell imaging (Figure 4) allows for direct

analysis of changes in protein expression, compartmentalization, and

cell morphology; high-content analysis (HCA) provides automation for

the imaging process coupled with quantitative rigor.

mRNA (mRNA), GeneArt™ Platinum™ Cas9 Nuclease (protein) (Figure 2),

and CRISPR library services (see page 11). Based on the cell type

and application, the most effective delivery format can be chosen

and then paired with optimal cell culture reagents and analysis tools.

Monitor the genome editing process from start to finishWhichever CRISPR-Cas9 delivery strategy you choose, it is important

to carefully monitor the entire genome editing process to validate that

Cas9 protein has been successfully incorporated into cells and that

the target knockout or mutation has been accurately implemented.

This monitoring can be broken down into four categories:

Cell culture. The starting point for genome editing is healthy cells.

Performing cell health assays prior to using the CRISPR-Cas9 system

can serve as an important quality control step and help to avoid wasting

time and reagents. Tests for viability, apoptosis, and stress responses

should be a routine part of cell growth and can provide information to

optimize experimental conditions to produce the most robust cells.

Genome editing. Immunochemical assays such as western blots

can effectively measure the presence of Cas9 in cells. Figure 3 shows

that the accumulation of Cas9 protein varies considerably depending

on the choice of delivery method (plasmid, mRNA, or protein). Together

with immunocytochemistry, antibiotic selection and gene expression are

frequently used to monitor the assembly of CRISPR components for

gene editing in the cell. Fluorescent protein expression can be measured

Figure 3. Western blot detection of Cas9 accumulation over time in cells

transfected with Cas9-expressing plasmid DNA, Cas9 mRNA, or Cas9 protein.

HEK293FT cells were transfected with Cas9 plasmid DNA, mRNA, or protein and

then harvested at indicated times for western blot analysis. Proteins in the cell lysates

were separated on an Invitrogen™ NuPAGE™ Novex™ 4–12% Bis-Tris Protein Gel,

transferred to a PVDF membrane using the Invitrogen™ iBlot™ 2 Gel Transfer Device,

and incubated with an anti-Cas9 mouse monoclonal antibody at 1:3,000 dilution

and an HRP-conjugated rabbit anti–mouse IgG antibody at 1:2,000. The membrane

was developed using Thermo Scientific™ Pierce™ ECL Western Blotting Substrate

(Cat. No. 32106). Reprinted with permission from Liang X, Potter J, Kumar S et

al. (2015) Rapid and highly efficient mammalian cell engineering via Cas9 protein

transfection. J Biotechnol 208:44–53.

DNA

72

Protein

mRNA

4824840(hr)Time

Figure 4. Absence of LC3B in CRISPR-Cas9–edited HAP1 cells after chloro-

quine treatment. HAP1 cells were modified using CRISPR-Cas9 gene editing to

knock out the ATG5 gene. After chloroquine treatment, which normally causes

LC3-containing autophagosomes to accumulate, edited cells (right panel) show the

expected absence of LC3B-positive puncta, whereas wild-type cells (left panel) show

an increase in LC3B accumulation. Cells were labeled with rabbit anti-LC3B antibody

(Cat. No. L10382) followed by Invitrogen™ Alexa Fluor™ 647 goat anti–rabbit IgG

antibody (red, Cat. No. A21245) and counterstained with Hoechst™ 33342 (blue,

Cat. No. H3570). Images were acquired on a Thermo Scientific™ CellInsight™ CX5

High-Content Screening (HCS) Platform.




CRISPR protein

GeneArt™ Platinum Cas9 Nuclease (1 µg/µL)GeneArt™ Platinum Cas9 Nuclease (1 µg/µL)GeneArt™ Platinum Cas9 Nuclease (3 µg/µL)

10 µg25 µg75 µg

B25642B25640B25641

CRISPR mRNA

GeneArt™ CRISPR Nuclease mRNA 15 µg A29378

GeneArt™ Strings U6 DNA >200 ng Contact [email protected]

GeneArt™ Strings T7 DNA >200 ng Contact [email protected]

Custom in vitro–transcribed gRNA 250 nmol Contact [email protected]

CRISPR plasmid

GeneArt™ CRISPR Nuclease Vector with OFP Reporter Kit 10 reactions A21174

GeneArt™ CRISPR Nuclease Vector with OFP Reporter, with competent cells 10 reactions A21178

GeneArt™ CRISPR Nuclease Vector with CD4 Enrichment Kit 10 reactions A21175

GeneArt™ CRISPR Nuclease Vector with CD4 Enrichment Kit, with competent cells 10 reactions A21177

CRISPR-Cas9 gRNA

GeneArt™ Precision gRNA Synthesis Kit A29377

CRISPR libraries: see page 11 and go to thermofisher.com/crisprlibraries. For custom (arrayed or pooled) CRISPR libraries, contact [email protected].

CRISPR engineered cell lines: go to thermofisher.com/engineeredcells. For custom stable cell line generation services, contact [email protected].

Detection and analysis reagents

GeneArt™ Genomic Cleavage Detection KitGeneArt™ Genomic Cleavage Selection Kit

20 reactions10 reactions

A24372A27663

Figure 5. Rapid analysis of various cell health parameters using a high-content analysis (HCA) platform. Wild-type and CRISPR-edited HAP1 cells were analyzed using

the Thermo Scientific™ CellInsight™ CX5 High-Content Screening Platform for (A) apoptosis, using CellEvent™ Caspase-3/7 Green Detection Reagent (Cat. No. R37111),

(B) oxidative stress, using CellROX™ Green Reagent (Cat. No. C10444), (C) protein degradation, with the Click-iT™ HPG Alexa Fluor™ 488 Protein Synthesis Assay Kit (Cat.

No. C10428), and (D) protein synthesis, using the Click-iT™ Plus OPP Alexa Fluor™ 488 Protein Synthesis Assay Kit (Cat. No. C10456).

A B C

Chloroquine concentration (μM)

Mea

n ci

rc a

vg in

tens

ity

1,000

2,000

3,000

01.00.10.01 100100.001

Menadione concentration (μM)

1.00.10.01 100

Mea

n ci

rc a

vg in

tens

ity

400

600

800

1,000

1,200

20010

Staurosporine concentration (μM)

0.00001 0.010.0010.0001 1.0

Mea

n ci

rc a

vg in

tens

ity

400

300

500

600

0.1

WT

KO

WT

KO

WT

KO

D

Cycloheximide concentration (μM)

1.00.10.01 100

Mea

n ci

rc a

vg in

tens

ity

1,000

2,000

3,000

010

WT

KO

0.001

With modulation of any cellular signaling pathway comes the risk of

proximal and distal consequences. It is important to track your targeted

protein and also monitor the impact on other aspects of cell health and

behavior (off-target phenotyping). HCA is particularly suited to this type

of multiparameter investigation (Figure 5).

Resources to help you get startedThermo Fisher Scientific offers a wide range of reagents, kits, and

tools to support your genome editing experiments (Table 1). In addi-

tion to our state-of-the-art online Invitrogen™ CRISPR Search and

Design Tool, we offer several different Cas9 delivery systems as well

as cell culture reagents and cell analysis tools that can be matched

to your experimental system. Our suite of genome editing products is

Table 1. Online CRISPR-Cas9 resources from Thermo Fisher Scientific.

CRISPR-Cas9 resource Where to find it

Genome editing system selection guide

thermofisher.com/genomeeditselect

Delivery format product selection guide

thermofisher.com/genomeedit101

gRNA design tool thermofisher.com/crisprdesign

Products for monitoring genome editing

thermofisher.com/detectcrispr

continually expanding to include the entire cell engineering workflow,

from reagents for cell culture, transfection, and sample preparation to

kits for genome modification and for detection and analysis of known

genetic variants. Go to thermofisher.com/detectcrisprbp74 for an

up-to-date view of our products and technologies. ■

http://thermofisher.com/detectcrisprbp74


BIOPrOBEs 74 sTEM CELL rEsEArCH

Assess the differentiation potential of human pluripotent stem cellsAn improved qPCR-based ScoreCard assay.Alexander M. Tsankov; Broad Institute, Harvard University.

Human pluripotent stem cells (hPSCs) hold great promise for tissue

engineering, regenerative medicine, and disease modeling [1]. The

number of hPSC lines has dramatically increased in the past decade,

which has created a need for hPSC quality standards that can ensure

comparable results across laboratories [2]. We recently introduced a

qPCR-based ScoreCard assay that uses gene expression signatures

to quantify the differentiation potential of hPSC lines [3]. The improved

ScoreCard method enables a rapid, more reproducible assessment of

functional pluripotency than the teratoma assay and allows for a wider

range of applications than previous genomic approaches, including

screening of small molecules, quantifying perturbations of lineage

regulators, and assessing different culture conditions.

Comparison of ScoreCard and teratoma assaysFormation of teratomas in mice is the most frequently used assay for

quantifying the differentiation potential of hPSCs. However, teratoma

generation is a very costly, time-consuming, and variable assay [2,4]

and is not an efficient way to assess the quality of thousands of new

cell lines (Figure 1A). To circumvent these issues, genomic approaches

that instead use gene expression signatures to quantify pluripotency

have emerged. PluriTest uses microarray measurements to assess

with great accuracy the molecular signatures of pluripotency of a new

cell line against a large database of hPSC lines [5]. Also, the original

ScoreCard assay utilized the NanoString™ nCounter™ gene

* **

* **

Teratoma assay

Total time: 8–10 weeksTotal cost: $2,743/sample

Total time: under 2 weeksTotal cost: $265/sample

Teratoma formation Histology Pathology analysis6–8 weeks | $1,323 2–3 days | $220 7 days | $1,200

qPCR ScoreCard assay

EB formation RNA, RT-PCR qPCR analysis12 days | $80 4 hours | $35 4 hours | $150

Pluripotent stem cells2–4 weeks

Pluripotent stem cells2–4 weeks

EC

MEEN

EC PLME EN

1

10

100

1,000

Tera

tom

a

replic

ates

Tera

tom

a

sect

ions

Tera

tom

a

sect

. RNA

EB d12

replic

ates

EB d12

reps.

10+ p

assa

ges

Bet

wee

n ve

rsus

with

in

grou

p v

aria

nce

ratio

EC ME EN *P < 0.0005

A

B

Figure 1. Comparison of teratoma and qPCR-based ScoreCard assays. (A) Schematic of the timelines for teratoma formation (top) and qPCR expression assay (bottom)

for assessing hPSC utility (EC = ectoderm, ME = mesoderm, EN = endoderm, EB = embryoid body, and PL = pluripotency). (B) Ratio of between-group to within-group

variance for germ layer differentiation potential as quantified by teratoma formation (left) and by the qPCR-based ScoreCard assay (right). Germ layer variance ratios are

shown using different colored bars, and the asterisks above bars indicate a significantly lower variance between replicates than between cell lines (P < 0.0005, F-test).

Reprinted by permission from Macmillan Publishers Ltd: Tsankov AM, Akopian V, Pop R et al. (2015) A qPCR ScoreCard quantifies the differentiation potential of human

pluripotent stem cells. Nat Biotechnol 33:1182–1192.


sTEM CELL rEsEArCH BIOPrOBEs 74

expression technology to evaluate both molecular and functional

pluripotency [6], defined as differentiation into the three germ layers.

We have developed a qPCR-based ScoreCard assay (available

commercially as the Applied Biosystems™ TaqMan® hPSC Scorecard™

Panel), which presents several advantages over previous approaches

[3]. The assay is highly accessible to all labs with qPCR machines and

more cost-effective than the previous ScoreCard assay. The gene

panel leverages recent data on genome-wide expression of early germ

layer differentiation [7] to improve the uniqueness of marker genes. We

also improved the statistical analysis using the weighted Z-method,

which combines information across multiple genes in a weighted,

assay-dependent manner while also taking into account dependencies

between genes [8]. The weighted Z-method thus enables a wider array

of applications than the previous ScoreCard assay and provides a

statistical measure of functional pluripotency.

We compared the predictive power of cell line differentiation

potential as quantified by the teratoma assay and by the qPCR-based

ScoreCard assay following embryoid body (EB) differentiation. We

calculated the variance in assay scores between different cell lines

and within replicates from the same cell lines. The between-group and

within-group variances for the teratoma-predicted differentiation potential

were very similar when quantified by an independent pathologist and

when using gene expression signatures of our panel (Figure 1B, left).

In contrast, we found that the EB differentiation potential scores had

a significantly lower within-replicate variance than between–cell line

variance (P < 0.0005, F-tests), even after culturing the replicates for

more than 10 passages (Figure 1B, right). These results show that EB

differentiation potential as quantified by the qPCR-based ScoreCard

assay is a more quantitative and reproducible measure of a cell line’s

germ layer propensity than the teratoma assay.

OTX2PA

X6W

NT1

POU4F1

CDH9

PAPLN

TRPM

8

LMX1A

NOS2

EN1SOX1

EC diff.

poten

tial

control_1control_2

shOTX2_1shOTX2_2shOTX2_3

GATA4

HAND1

BMP10

KLF5FO

XF1

PLVAP

RGS4

COLEC10

CDH5

HAND2

CDX2

ME d

iff.

poten

tial

control_1control_2

shOTX2_1shOTX2_2shOTX2_3

diff. potential, Zw

8 0 -3 10 5 0

A

dE

N H

UE

S64

EOMES

HNF4A

HHEXSOX17

PRDM1

LEFT

Y2

HNF1B

RXRGLE

FTY1

FOXA2

GATA6

WNT,AAR&DWNT,AATFSLiCl,AATFS

LiCl,AAR&D

IDE1LiCl,IDE1

6 0 -5 10 5 0

dEC-PSC

diff. potential, Zw -∆∆C dEN-PSC

EN diff.

poten

tial

y = 9.3x + 24 R = 0.83 P < 0.001

40

60

80

100

2 4 6 8

% C

D56

+ (d

EC

)

EC differentiation potential

PSC line

y = 19x – 80 R = 0.85 P < 0.001

20

40

60

80

6 7 8

% C

D56

+ (d

ME

)

ME differentiation potential

PSC line

y = 31x – 218 R = 0.97 P < 0.001

0

20

40

60

7 8 9

% C

D18

4+ (d

EN

)

EN differentiation potential

PSC line

dEC ef�ciency dME ef�ciency dEN ef�ciency

88665

32646

888823

EC markers:

ME markers:

EN markers:

B

EC ME EN PL

hPSC gene class

−4

–2

0

2

4

EC ME EN PL

hPSC gene class

Mea

n –∆

ΔC

t (w

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w/

feed

ers)

−4

−2

0

2

4

EC ME EN PL

hPSC gene class

Mea

n –Δ

∆C

t(a

dap

ted

− u

nad

apte

d)

Adapted 1+ passages Adapted 6+ passages

* *

*

*

*

*

*P < 0.05 *P < 0.05

D

EC

dE

C H

UE

S64

dE

C H

UE

S64

t,

-∆∆Ct,

Figure 2. New applications of the qPCR-based ScoreCard assay. (A) Linear regression shows a high correlation between directed differentiation potential and traditional

measures of ectoderm (dEC), mesoderm (dME), and endoderm (dEN) efficiency, using FACS quantification for established cell surface markers CD56 and CD184. (B) Box

plot of the distribution of mean expression difference between feeder-free and feeder-cultured hPSC lines for all genes belonging to the four gene classes (EC = ectoderm,

ME = mesoderm, EN = endoderm, and PL = pluripotency). (C) Box plots of the distribution of mean expression difference between adapted and unadapted hPSC lines for

all genes belonging to the four gene classes. EN mean expression decreases after adaptation of lines for 1+ passages (left) and 6+ passages (right) in feeder-free culture.

(D) Heatmaps showing gene expression level (left) and differentiation potential2D (right) of several different protocols for endoderm differentiation. WNT = WNT3A, AA = activin A,

LiCl = lithium chloride, IDE1 = inducer of definitive endoderm-1, R&D = R&D Systems, TFS = Thermo Fisher Scientific. (E) Heatmaps showing gene expression level (left)

and differentiation potential2D (right) for three shRNA knockdowns of OTX2 during ectoderm differentiation in HUES64. We observe decreased EC expression and differ-

entiation potential2D (top) and increased ME expression (bottom) in the knockdowns compared to control experiments. Reprinted by permission from Macmillan Publishers

Ltd: Tsankov AM, Akopian V, Pop R et al. (2015) A qPCR ScoreCard quantifies the differentiation potential of human pluripotent stem cells. Nat Biotechnol 33:1182–1192.



References1. Daley GQ (2012) Cell Stem Cell 10:740–749.

2. Dolgin E (2010) Nat Med 16:1354–1357.

3. Tsankov AM, Akopian V, Pop R et al. (2015) Nat Biotechnol 33:1182–1192.

4. Müller FJ, Goldmann J, Löser P et al. (2010) Cell Stem Cell 6:412–414.

5. Müller FJ, Schuldt BM, Williams R et al. (2011) Nat Methods 8:315–317.

6. Bock C, Kiskinis E, Verstappen G et al. (2011) Cell 144:439–452.

7. Gifford CA, Ziller MJ, Gu H et al. (2013) Cell 153:1149–1163.

8. Lipták T (1958) Magyar Tud Akad Mat Kutato Int Közl 3:171–196.

9. Teo AK, Arnold SJ, Trotter MW et al. (2011) Genes Dev 25:238–250.

10. Borowiak M, Maehr R, Chen S et al. (2009) Cell Stem Cell 4:348–358.

11. Tsankov AM, Gu H, Akopian V et al. (2015) Nature 518: 344–349.

12. Ziller MJ, Edri R, Yaffe Y et al. (2015) Nature 518:355–359.

New applications of the qPCR-based ScoreCard assayThe ScoreCard differentiation potential of

cells lines based on the weighted Z-method

correlates highly (R ≥ 0.83, P < 10–3, Pearson

correlation) with established measures of

directed differentiation efficiency (Figure 2A).

This h igh proport ional i ty between the

ScoreCard assay’s measure of differentiation

potential and germ layer efficiency enables

several new applications of the qPCR-based

ScoreCard assay, including assessing the

effects of culture conditions, small mole-

cules, and knockdown of key transcriptional

regulators.

Using the qPCR-based ScoreCard assay,

we observed a substantial difference in the

gene expression signatures of 11 hPSC lines

grown both on mouse embryonic fibroblast

(MEF) feeder cells and in feeder-free culture

conditions. Figure 2B shows that pluripo-

tency markers were more highly expressed

in hPSC lines grown in feeder-free conditions

(P = 7 × 10–4, weighted Z-method), while

markers of the three germ layers were more

highly expressed in cell lines grown on feed-

ers. This result suggests that a feeder culture

introduces higher background differentiation,

possibly due to differences in signaling [9].

We further observed that cell lines adapted

on feeder-free culture for several passages

had an even greater reduction of endoderm

marker expression (Figure 2C).

The ScoreCard assay also allowed us

to quantify the effect of different small mole-

cules on endoderm differentiation. We found

that replacing recombinant protein WNT3A

with the less costly LiCl molecule did not

affect the differentiation potential of cell line

HUES64 (Figure 2D). However, compound

IDE1 decreased LEFTY1 expression and

endoderm differentiation potential [10].

In addition, we used the ScoreCard assay to quantify the effect of knocking down key

lineage regulators. We knocked down transcription factor OTX2 in undifferentiated hPSCs using

three distinct short hairpin RNAs (shRNAs) and observed lower overall activation of ectoderm

marker genes following directed differentiation towards ectoderm (Figure 2E), supporting the

hypothesis that OTX2 plays a key role in establishing early ectoderm cell fate [11,12]. We also

observed a higher overall expression of mesoderm markers in the OTX2 knockdown ectoderm

cells (Figure 2E, bottom), suggesting that OTX2 may act as a repressor of key mesoderm genes.

Future directionsWe recently developed a qPCR-based ScoreCard assay [3] with an improved gene expression

panel, statistical analysis, and utility for a wider range of applications (latest information on

the TaqMan hPSC Scorecard Assay is available at thermofisher.com/scorecardbp74). The

qPCR-based ScoreCard assay allows for more quantitative and reproducible assessment of

differentiation potential than the teratoma assay and is highly accessible, 5 to 10 times faster,

and more cost-effective (Figure 1A). An area of focus for future development is incorporation of

the improved algorithm described in [3] into the analysis module of the TaqMan hPSC ScoreCard

Assay so that it is available online for all users. Also, single-cell transcriptomics could further

improve the gene selection process and the reduction of gene expression markers needed to

maintain statistical power while further reducing the assay cost. ■


TaqMan® hPSC Scorecard™ Panel, Fast 96-well 2 x 96-well plates A15876

TaqMan® hPSC Scorecard™ Kit, Fast 96-well 2 x 96-well plates A15871

TaqMan® hPSC Scorecard™ Panel, 384-well 1 x 384-well plate A15870

TaqMan® hPSC Scorecard™ Kit, 384-well 1 x 384-well plate A15872

Acknowledgments: This art icle was contr ibuted by Alexander M. Tsankov, who is a member of

the Meissner laboratory at the Broad Institute of MIT and Harvard, the Harvard Stem Cell Institute,

and the Department of Stem Cell and Regenerative Biology, Harvard University. Please address

correspondence to: [email protected].

http://thermofisher.com/scorecardbp74



Light up neural differentiation pathwaysAntibodies for pluripotent stem cells and neural lineage cells.

Stem cells have tremendous potential for use in developmental biology

research, disease modeling, drug screening, and cell therapy for

neurodegenerative disorders, including Alzheimer’s and Parkinson’s

diseases. Stem cells are undifferentiated cells that have the capacity

both to self-renew through mitosis and to differentiate into specialized

cell types such as neuronal, liver, or muscle cells. Embryonic stem cells

(ESCs) and induced pluripotent stem cells (iPSCs) are pluripotent stem

cells (PSCs) that are commonly characterized by their expression of

the transcription factors Nanog, OCT4, and SOX2, and the cell-surface

proteins SSEA3, SSEA4, TRA-1-60, and TRA-1-81 (Figure 1). To verify

the functional pluripotency of PSCs, they must undergo further testing

to confirm their ability to differentiate into the three embryonic germ

layers: ectoderm, mesoderm, and endoderm (also see page 31).

Mammalian neurogenesis begins with the induction of neuro-

ectoderm, which forms the neural plate and then folds to give rise to the

neural tube. These structures are composed of a layer of neuroepithelial

progenitors (NEPs) that can be rapidly turned into primitive neural stem

cells (NSCs). NSCs are self-renewing, multipotent progenitors present

in the developing and adult mammalian central nervous system. During

neural differentiation, NSCs undergo progressive lineage restrictions

leading to glial progenitors (CD44+ A2B5+), which can become astro-

cytes (GFAP+) and oligodendrocytes (Galc+ O4+). The other branch

of lineage restriction is the neuronal path leading to various types of

neurons such as dopaminergic (DA) neurons (Figure 2). Table 1 provides

a list of common markers and the corresponding antibodies used to

characterize PSCs and NSCs (Figure 1) as well as downstream glial

and neuronal cells (Figure 2).

Find your stem cell antibodyThe characterization of stem cells is a critical step in stem cell research.

No matter which detection platform you use—flow cytometry, immuno-

cytochemistry, western blot, ELISA, or another—our collection of over

51,000 Invitrogen™ antibodies provides you with tools compatible with

your experimental design. Select the right antibodies for your stem cell

targets at thermofisher.com/antibodiesbp74. ■

Figure 2. Characterization of astrocytes and dopaminergic (DA) neurons derived

from PSCs. (A) Immunofluorescence staining of glial progenitors and astrocytes

generated from PD-3 iPSC-derived neural stem cells using anti-GFAP (Cat. No.

180063) followed by Alexa Fluor™ 488 goat anti–rabbit IgG (green, Cat. No. A11034)

antibodies and anti-CD44 followed by Alexa Fluor™ 594 goat anti–mouse IgG (red,

Cat. No. A11005) antibodies. (B) Immunofluorescence staining of DA neurons

derived from PSCs using anti–tyrosine hydroxylase (Cat. No. P21962) followed

by Alexa Fluor™ 488 donkey anti–rabbit IgG (green, Cat. No. A21206) antibodies.

Nuclear DNA was counterstained with DAPI (blue, Cat. No. P1306).

Figure 1. Characterization of human induced pluripotent stem cells. Gibco™ Human Episomal iPSCs (Cat. No. A18945) grown with Gibco™ Vitronectin (VTN-N)

Recombinant Human Protein (Cat. No. A14700) in Gibco™ Essential 8™ Flex Medium (Cat. No. A2858501) were stained with the indicated Thermo Scientific™ DyLight™

dye–conjugated primary antibodies and analyzed by imaging or flow cytometry. (A) Immunofluorescence imaging of iPSCs counterstained with DAPI nuclear stain (blue, Cat.

No. D1306). Left panel: DyLight 488 anti-SSEA5 mouse monoclonal antibody (green, Cat. No. MA1-144-D488) and DyLight 650 anti-SSEA4 mouse monoclonal antibody

(red, Cat. No. MA1-021-D650). Middle panel: DyLight 488 anti-LIN28 mouse monoclonal antibody (green, Cat. No. MA1-016-D488). Right panel: DyLight 650 anti-SOX2

mouse monoclonal antibody (red, Cat. No. MA1-014-D650). (B) Histograms of iPSCs analyzed by flow cytometry. Left panel: DyLight 488 mouse IgG1 isotype control

antibody (Cat. No. MA1-191-D488). Right panel: DyLight 488 anti-Nanog mouse monoclonal antibody (Cat. No. MA1-017-D488).

100

20

40

100

160

102 103 104 106105

Mouse IgG1 isotype control DyLight 488 �uorescence

Cou

nts

140

60

80

120

100

20

40

100

160

102 103 104 106105

Nanog DyLight 488 �uorescence

Cou

nts

140

60

80

120

BNanog-positive Nanog-positiveSSEA4 (red)

SSEA5 (green)LIN28 (green) SOX2 (red)

A

http://thermofisher.com/antibodiesbp74



Table 1. Selected antibodies for the characterization of stem cells and neural lineage cells. For a complete list, go to thermofisher.com/antibodies.

Target Antibody Cat. No. (Clone ID)

Characterization of pluripotent stem cells

Pluripotent stem cells

DNMT3B PA1-884, 49-1028

KLF4 710659 (1HCLC), PA1-095

LIN28 MA1-016 (14E6-4E6), MA1-016-D488 (14E6-4E6), MA1-016-D550 (14E6-4E6), MA1-016-D650 (14E6-4E6), PA1-096

NANOG MA1-017 (23D2-3C6), MA1-017-D488 (23D2-3C6), MA1-017-D550 (23D2-3C6), MA1-017-D650 (23D2-3C6), PA1-097

OCT4/POU5F1 A13998 (C30A3), MA1-104 (9B7), MA1-104-D488 (9B7), MA1-104-HRP (9B7), A18525 (EM92)

PRDM14 PA1-114

SALL4 720030

SOX2 48-1400 (20G5), MA1-014 (20G5), MA1-014-D488 (20G5), MA1-014-D550 (20G5), MA1-014-D650 (20G5), PA1094

SSEA1/CD15 MA1-022 (MC-480), MA1-022-D488 (MC-480), MA1-022-D550 (MC-480), MA1-022-D650 (MC-480), MA1-022-PE (MC-480), 18-0122 (MY-1), 41-1200

SSEA3 41-4400 (MC-631), MA1-020 (MC-631), MA1-020-D488 (MC-631), MA1-020-D650 (MC-631), MA1-020-PE (MC-631)

SSEA4 MA1-021 (MC813-70), MA1-021-D488 (MC813-70), MA1-021-D550 (MC813-70), MA1-021-D650 (MC813-70), MA1-021-PE (MC813-70)

SSEA5 MA1-144 (8E11), MA1-144-D488 (8E11), MA1-144-D550, MA1-144-D650 (8E11), MA1-144-D755 (8E11), MA1-144-PE (8E11)

TRA-1-60 411000 (cl.A), MA1-023 (tra-1-60), MA1-023-D488X (tra-1-60), MA1-023-D550 (tra-1-60), MA1-023-D650 (tra-1-60)

TRA-1-81 411100 (cl.26), MA1-024 (tra-1-81), MA1-024-D488 (tra-1-81), MA1-024-D550 (tra-1-81), MA1-024-D650 (tra-1-81)

Germ layer mesendoderm

Brachyury (T) MA5-17185 (1H9A2), PA5-23405

EOMES PA5-12261, MA5-24291 (644730)

GSC MA5-23070 (1C2), PA5-28380

MIXL1 PA5-40323

Germ layer mesoderm

ABCA4 P21933 (3F4)

NKX2.5 701622 (4H5L9), 710634 (4HCLC)

PDGFRα 701142 (7H13L1), 710169 (7HCLC), PA516571, PA516742

Smooth muscle actin MA5-11544 (1A4 (asm-1)), PA5-16697, 701457 (17H19L35), 710487 (17HCLC), MA1-744 (mAbGEa)

Germ layer endoderm

α-Fetoprotein (AFP) 710486 (9HCLC), 18-0003 (ZSA06), MA5-12754 (C3), MA5-14665 (F1-6P2A8-P2B9A9), MA5-14666 (P5B8), PA5-16658

FOXA1 MA1-091 (3A8)

FOXA2 701698 (9H5L7), 710730 (9HCLC), MA5-15542 (7H4B7), 720061, A16568

GATA4 PA1-102

GATA6 PA1-104

KLF5 42-3200

SOX17 PA5-23352, PA5-23382

Germ layer ectoderm

β-III Tubulin MA1-118 (2G10)

PAX6 42-6600, MA1-109 (13B10-1A10)

SOX1 PA5-23351, PA5-23370

Characterization of neural stem cells

Neural stem cells Nestin MA1-110 (10C2)

PAX6 42-6600, MA1-109 (13B10-1A10)

SOX1 PA5-23351, PA5-23370

SOX2 48-1400 (20G5), MA1-014 (20G5), MA1-014-D488, MA1-014-D550, MA1-014-D650, MA1-014-HRP, PA1-094

Neural differentiation and characterization of glial and neuronal cells

Astrocytes GFAP 13-0300 (2.2B10), MA5-12023 (ASTRO6), PA5-16291, A21282 (131-1771), A21294, A21295

Glutamine synthetase 710963 (7HCLC)

S100b 701340 (16H24L21), 710363 (16HCLC)

Cholinergic neurons ChAT PA1-4710, PA1-4738, PA1-9027, PA1-18313, PA5-29653

DA progenitor/ DA neurons

LMX1A 710980 (20HCLC)

Nurr1 MA1-195 (N1404), PA1-4519, PA5-13416

OTX2 701948 (14H14L5), MA5-15854 (1H12C4B5), MA5-15855 (1H12G8B2), PA5-23406, PA5-29914

PITX3 701181 (5H10L5), 710212 (7M5HCLC), 38-2850

Tyrosine hydroxylase P21962, 701949, 710982



Transcription factor expression during differentiation of hPSC-derived cardiomyocytesA multiparametric approach using the Attune NxT Flow Cytometer.

The ability to direct human pluripotent stem

cells (hPSCs) towards differentiated cell

phenotypes offers tremendous potential for

personalized and regenerative medicine [1,2].

The identification of key transcriptional regu-

lators of pluripotency—as well as chemically

defined media and cell culture conditions that

drive PSCs towards distinct cell fates—have

enabled researchers to derive a multitude of

differentiated cell types with a high degree of

control and precision [3]. One of the hallmarks

of the transition from pluripotency towards

terminal differentiation is the orchestrated

nuclear expression of various transcription

factors that act as regulators of cell-fate deter-

mination. In the case of hPSC-derived cardio-

myocytes, the down-regulation and eventual

loss of pluripotency markers is followed by

the sequential expression of other factors that

act to restrict cell-fate potential [4].

Quantifying the dynamic expression

patterns of transcription factors that underlie

cardiomyocyte differentiation often relies on

detection of mRNA transcripts via qRT-PCR

in a heterogeneous cell population. While

this approach is highly sensitive and can

be performed using small amounts of input

material, it does not provide information about

transcription factor expression in individual

cells. An alternative approach is to use specific

antibodies for the detection and quantifi-

cation of transcription factor expression at

the single-cell level (Figure 1) using either

high-content imaging and analysis or multi-

parameter flow cytometry. Here we describe

a flow cytometric method for the simultaneous

quantification of Oct4, a canonical marker

of pluripotency, and Nkx2.5, a marker of cardiac fate, in hPSCs that have been induced to

differentiate towards cardiomyocytes.

Advantages of the Attune NxT Flow Cytometer for stem cell researchStem cells and cardiomyocytes represent traditionally challenging samples for flow cytometric

testing due to their size, fragility, and scarcity. The Invitrogen™ Attune™ NxT Flow Cytometer is

ideally suited for these samples because the acoustic-assisted hydrodynamic focusing tech-

nology and advanced fluidics are designed to minimize clogging and effectively handle a broad

range of cell types with no loss in data quality. With its short acquisition times, the Attune NxT

Flow Cytometer enables the detection of rare events without excess sample manipulation in a

wide range of samples, including those with large cells that tend to clump as well as those with

very low cell concentrations (e.g., due to high dilution or very small sample size).

Flow cytometric analysis of cardiomyocyte differentiationIn this experiment, H9 hPSC differentiation was monitored through the differential expression

levels of the key nuclear differentiation markers, Oct4 and Nkx2.5, via flow cytometry (Figures 1

and 2). Two-parameter plots of the staining profiles of the singlet cells show that initially nearly

all cells were positive for the Oct4 transcription factor and negative for the Nkx2.5 transcription

factor, which is consistent with a pluripotent state (Figure 2B). Over time, cells gradually began to

show reduced levels of Oct4 and increased levels of the cardiac marker Nkx2.5 (Figures 2C–2J).

Prior to differentiation, 97% of all cells were Oct4-positive and Nkx2.5-negative; after day 3,

the frequency of Oct4-positive cells began to decline—consistent with a loss of pluripotency

and transition to a terminally differentiated cardiomyocyte phenotype—and by day 9, more than

half of the cells were expressing Nkx2.5 (Figure 3).

Figure 1. Workflow for cardiomyocyte differentiation. H9 human pluripotent stem cells (hPSCs) were differenti-

ated into cardiomyocytes over a 10-day period with the use of the Gibco™ PSC Cardiomyocyte Differentiation Kit

(Cat. No. A2921201), which is a complete, ready-to-use, xeno-free system. Each day during the differentiation

process, cells were detached from plates using Gibco™ TrypLE™ Express Enzyme solution (Cat. No.12605010) and

combined into a single cell suspension. Cell counts and viability measurements were made using the Invitrogen™

Countess™ II Automated Cell Counter (Cat. No. AMQAX1000). A total of 1 × 106 cells from each time point were

prepared and stained with an Invitrogen™ Alexa Fluor™ 488 anti-Oct4 antibody and an anti-Nkx2.5 antibody that

was detected using Invitrogen™ Alexa Fluor™ 647 donkey anti–rabbit IgG secondary antibody. Cells were analyzed

on the Invitrogen™ Attune™ NxT Flow Cytometer at a flow rate of 200 µL/min with stop criteria set on 10,000 total

events using a forward-scatter threshold.

Culture/expand PSCs Days 0–2Mesodermal commitment

Days 3–4Cardiac mesodermal

induction

Characterize cardiomyocytesfor key markers

PSC Cardiomyocyte Differentiation KitOct4+ Nkx2.5

Essential 8 MediumCytometry

readout

Cardiomyocyte Differentiation

Medium A

Cardiomyocyte Differentiation

Medium B

Cardiomyocyte Maintenance

Medium

+

Days 5–14Cardiomyocyte

maturaton

Nkx2.5 Oct4––



References1. Robinton DA, Daley GQ (2012) Nature 481:295–305.

2. Addis RC, Epstein JA (2013) Nat Med 19:829–836.3. Chen G, Gulbranson DR, Hou Z et al. (2011) Nat

Methods 8:424–429.

4. Zhang J, Wilson GF, Soerens AG et al. (2009) Circ Res 104:e30–e41.

5. Yang L, Soonpaa MH, Adler ED et al. (2008) Nature 453:524–528.


Attune™ NxT Flow Cytometer, blue/red/violet/yellow lasers 1 each A24858

Countess™ II Automated Cell Counter 1 each AMQAX1000

Donkey Anti–Rabbit IgG (H+L) Secondary Antibody, Alexa Fluor™ 647 conjugate

500 µL A31573

Essential 8™ Medium 500 mL A1517001

Nunc™ Cell-Culture Treated Multidishes 1 case of 75 140675

PBS, pH 7.4 10 x 500 mL10 L

1001004910010001

PSC Cardiomyocyte Differentiation Kit 1 kit A2921201

TrypLE™ Express Enzyme (1X), phenol red 100 mL500 mL

1260501012605028

Vitronectin (VTN-N) Recombinant Human Protein, truncated 1 mL10 mL

A14700A31804

Figure 2. Two-parameter plots representing staining profiles for Oct4 and Nkx2.5 in H9 hPSCs during cardiomyocyte differentiation. A dual-parameter plot of forward

scatter height vs. forward scatter width was used to identify singlet cells (A), and a gate was drawn around the singlet-cell population. Using this gate, a dual-parameter

plot of Oct4 Alexa Fluor™ 488 fluorescence vs. Nkx2.5 Alexa Fluor™ 647 fluorescence was created, and a quadrant gate was used to identify the cell population as it

differentiated through time into Oct4-positive events (green), Nkx2.5-positive events (red), and dual-negative events (blue).

Figure 3. Summary of cell populations over the 10-day

cardiomyocyte differentiation. The percentages of Oct4+

(green) and Nkx2.5+ (red) expression over 10 days in culture

were determined by placing quadrant gates on the Oct4

vs. Nkx2.5 dual-parameter plots of singlet cells, shown in

Figure 2. Prior to differentiation, 97% of all cells expressed

the Oct4+ Nkx2.5– phenotype, consistent with a pluripotent

state. With induction, expression of Oct4 declines, consistent

with a loss of pluripotency, and a transition to a terminally

differentiated cardiomyocyte phenotype is seen as expres-

sion of Nkx2.5 increases.

Bring Attune NxT technology to your multiparametric experimentsThe Attune NxT Flow Cytometer enables the single-cell quantification of cells expressing

markers of pluripotency (Oct4) and cardiomyocyte specification (Nkx2.5) in H9 hPSCs

as they differentiate into cardiomyocytes. This flow cytometry assay, which uses specific

antibodies against the two transcription factors, produced results consistent with published

data using qRT-PCR quantification of Oct4 and Nkx2.5 mRNA transcripts [4,5]. With

sample throughput rates over 10 times faster than those of other cytometers, the Attune

NxT Flow Cytometer allows users to process samples more quickly without loss in data

quality, enabling the detection of rare events even in the case of challenging samples.

Learn more about the Attune NxT Flow Cytometer and download the full application note

on cardiomyocyte differentiation at thermofisher.com/attunebp74. ■

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C. Day 3 Singlet

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E. Day 5 Singlet

104

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J. Day 10 Singlet

104

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H. Day 8 Singlet

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-102Oct

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G. Day 7 Singlet

104

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-102Oct

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http://thermofisher.com/attunebp74


TOOLs FOr IMAGING AND FLOW CYTOMETrY BIOPrOBEs 74

Seeing red during apoptosisCellEvent Caspase-3/7 Red assays for imaging and flow cytometry.

Cell death mechanisms such as apoptosis are critical for the survival of

a multicellular organism. Apoptosis not only ensures proper growth and

development by ridding the organism of unneeded cells and tissues, but

also minimizes threats by destroying virus-infected or DNA-damaged

cells. Cells undergoing apoptosis exhibit an array of morphological and

biochemical changes, including decreased mitochondrial membrane

potential, loss of plasma membrane asymmetry, protein degradation,

breakdown of the nucleus, and production of membrane-bound apop-

totic bodies [1]. Caspases, a family of cysteine proteases that cleave

target proteins at aspartic acid residues, are degradative enzymes that

play multiple roles during the initiation and execution of apoptosis. For

example, caspase-3 amplifies the signal from initiator caspases such

as caspase-8, signifying full commitment to cellular disassembly. In

addition to cleaving other caspases in the enzyme cascade, caspase-3

has been shown to cleave poly(ADP-ribose) polymerase (PARP), DNA-

dependent protein kinase, protein kinase Cδ, and actin.

Introducing the CellEvent Caspase-3/7 Red ReagentThe Invitrogen™ CellEvent™ Caspase-3/7 Red Detection Reagent is

a cell-permeant fluorogenic substrate for the detection of activated

caspase-3 and -7. Similar to the CellEvent Caspase-3/7 Green

Detection Reagent, the CellEvent Caspase-3/7 Red Detection Reagent

consists of a 4–amino acid peptide (DEVD) conjugated to a nucleic

acid–binding dye. The DEVD peptide inhibits the ability of the dye to

bind to DNA, and thus the substrate is intrinsically nonfluorescent.

In the presence of activated caspase-3 or -7, however, the DEVD

peptide is cleaved, enabling the dye to bind to DNA and produce

a bright fluorescent signal (Ex/Em = 630/650 nm). The red fluores-

cence of the dye/DNA complex can be observed using a standard

Alexa Fluor™ 647/Cy®5 filter set. Because the cells are alive and

have not been lysed during the CellEvent caspase-3/7 assay, they

can be simultaneously analyzed for other apoptotic changes such as

decreased mitochondrial membrane potential.

The CellEvent Caspase-3/7 Red Reagent is compatible with live-

and fixed-cell imaging, high-content analysis (HCA) and high-throughput

screening (HTS), and flow cytometry, making it useful for both real-time

imaging experiments and endpoint analysis. An important advantage

of the CellEvent caspase-3/7 assay is that no wash steps are required

for analysis, thus preserving fragile apoptotic cells that are typically lost

during these rinses. In addition, the loss of apoptotic cells during wash

steps may lead to an underestimation of the extent of apoptosis in the

sample, resulting in poor assay accuracy.

An easy, flexible method for detecting caspase activityTo measure activated caspase-3 or -7 activity, simply add the CellEvent

Caspase-3/7 Red Detection Reagent to cells (typically, untreated con-

trol cells and cells exposed to an inducer of apoptosis), incubate for

30 minutes, and measure fluorescence. Apoptotic cells with activated

caspase-3 or -7 will exhibit bright red-fluorescent nuclei, whereas cells

without caspase activity will show minimal fluorescence (Figure 1).

Because the cleaved substrate labels nuclei of caspase 3/7–positive

cells, the CellEvent caspase-3/7 substrates can also provide infor-

mation on nuclear morphology, including condensed nuclei typical of

late-stage apoptosis. Additionally, the fluorescent signal produced

with the CellEvent Red Caspase-3/7 Detection Reagent survives

formaldehyde fixation and detergent permeabilization, providing the

flexibility to perform endpoint assays and probe for other proteins of

interest using immunocytochemical analyses.

Figure 1. Detection of caspase-3 and -7 activity using the CellEvent

Caspase-3/7 Red Detection Reagent. HeLa cells were seeded into a poly-D-

lysine–coated, clear-bottom 96-well microplate in complete medium and incubated

overnight to allow for attachment. The next day, cells were treated with (A) DMSO

alone or (B) 0.1 µM staurosporine for 4 hr at 37°C. The medium was removed and

replaced with PBS + 5% FBS containing 1.62 µM Hoechst™ 33342 (blue, Cat. No.

H3570) and 5 µM of Invitrogen™ CellEvent™ Caspase-3/7 Red Detection Reagent

(red, Cat. No. C10730), and cells were incubated for 30 min at 37°C. Images were

captured using the Thermo Scientific™ CellInsight™ CX5 High-Content Screening

Platform equipped with the 10x objective. Four fields per well of a 96-well microplate

were selected for analysis; the calculated Z´ for this assay was 0.45.


BIOPrOBEs 74 TOOLs FOr IMAGING AND FLOW CYTOMETrY

Reference1. Taylor RC, Cullen SP, Martin SJ (2008) Nat Rev Mol Cell Biol 9:231–241.

In addition to traditional fluorescence microscopy, the CellEvent

Caspase-3/7 Red Detection Reagent has been validated for HCA and

HTS. The significant difference in fluorescence between normal and

apoptotic cells within a population provides an excellent assay window,

and the Z´-factor value indicates the reagent is robust enough for use

in HTS assays. High-content imaging platforms provide quantitative

analysis of individual cells, which can be especially informative when

cell responses are not uniform.

Flow cytometry offers an alternative platform for measuring fluores -

cence of individual cells in a population. Figure 2 shows the flow

cytometric analysis of Jurkat cells after treatment with staurosporine to

induce apoptosis, followed by staining with the CellEvent Caspase-3/7

Red Flow Cytometry Assay Reagent. As expected, staurosporine-treated

cells have a higher percentage of apoptotic cells than the basal levels

displayed by the control cells (Figure 2). In addition, using the Invitrogen™

Attune™ NxT Flow Cytometer, we compared the assay performance of

the red- and green-fluorescent versions of the CellEvent Caspase-3/7

reagent with staurosporine-treated Jurkat cells and found very good

correlation between the dose-response curves for the two reagents

(Figure 3).

Caspase detection to match your experimentsThe CellEvent Caspase-3/7 Red Detection Reagent is a robust substrate

for measuring caspase-3 and -7 activity using live- or fixed-cell imaging,

HTS, or flow cytometry. Together with the CellEvent Caspase-3/7

Green reagent, the CellEvent Caspase-3/7 Red Detection Reagent

expands the palette of fluorescence-based caspase assays, allowing

more choice when multiplexing with other fluorescent cell structure

or function probes (Figure 4). Find out more about our series of

caspase activity assays along with other measures of apoptosis at

thermofisher.com/apoptosisbp74. ■


CellEvent™ Caspase-3/7 Red Detection Reagent 25 µL100 µL

C10730C10731

CellEvent™ Caspase-3/7 Red Flow Cytometry Assay Reagent

20 assays100 assays

C10747C10748

CellEvent™ Caspase-3/7 Green Detection Reagent 25 µL100 µL

C10723C10423

CellEvent™ Caspase-3/7 Green Flow Cytometry Assay Kit

20 assays100 assays

C10740C10427

CellEvent™ Caspase-3/7 Green ReadyProbes™ Reagent 1 kit R37111

Figure 2. Flow cytometric detection of caspase activity in Jurkat cells using

the CellEvent Caspase-3/7 Red reagent. Jurkat cells were treated with (A) DMSO

or (B) 0.02 µM staurosporine for 4 hr at 37°C before labeling with the CellEvent™

Caspase-3/7 Red Flow Cytometry Assay Reagent (Cat. No. C10747). Stained

samples were analyzed using the Invitrogen™ Attune™ NxT Flow Cytometer equipped

with a 637 nm laser; fluorescence emission was collected with a 670/14 BP filter.

Figure 3. Flow cytometric analysis of caspase-3/7 activity in Jurkat cells using

CellEvent Caspase-3/7 detection reagents. Log-phase Jurkat cells were resus-

pended in complete medium, adjusted to 2 × 106 cells/mL, aliquoted (100 µL/well)

into a 96-well, V-bottom microplate, and treated in triplicate with 1 of 8 dilutions

of staurosporine for 4 hr at 37°C. Cells were then labeled with 2 µM CellEvent™

Caspase-3/7 Green or Red reagent for 30 min at 37°C and analyzed using the

Invitrogen™ Attune™ NxT Flow Cytometer. The BL1 and RL1 channel were used

for detection of the CellEvent Caspase-3/7 Green and Red signals, respectively.

Figure 4. Multipex detection of apoptosis using the CellEvent Caspase-3/7 Red

Detection Reagent and the R-PE Annexin V conjugate. Jurkat cells were treated

with (A) DMSO or (B) 10 µM camptothecin for 4 hr at 37°C before labeling with the

CellEvent™ Caspase-3/7 Red Flow Cytometry Assay Reagent (Cat. No. C10747)

for 30 min at 37°C. After washing with PBS and Annexin Binding Buffer (Cat. No.

V13246), the cells were stained with the R-phycoerythrin conjugate of annexin V

(R-PE Annexin V, Cat. No. A35111) for 15 min at room temperature, washed, and

analyzed using the Invitrogen™ Attune™ NxT Flow Cytometer. The numbers in each

plot represent the percentage of cell events that fall within each quadrant (labeled Q).

BA

Apoptotic cellsApoptotic cells Live cellsLive cells

CellEvent Caspase-3/7 Red �uorescence

Sid

e sc

atte

r (x

103 )

102 103 104 105 1060

500

1000

CellEvent Caspase-3/7 Red �uorescence

Sid

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r (x

103 )

102 103 104 105 1060

500

1000

Staurosporine concentration (M)

0 10–410–510–6

% P

ositi

ve fo

r in

dic

ated

rea

gent

20

40

60

80

100

0

CellEvent Caspase-3/7 Red Detection Reagent

CellEvent Caspase-3/7 Green Detection Reagent

BA

R-PE Annexin V �uorescence

Cel

lEve

nt C

asp

ase-

3/7

Red

�uo

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ence

0 103 104 105

0

103

104

105

R-PE Annexin V �uorescence

Cel

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3/7

Red

�uo

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ence

0 103 104 105

0

103

104

105

Q53.04%

Q60.95%

Q70.40%

Q895.6%

Q56.80%

Q836.9%

Q654.7%

Q71.60%

http://thermofisher.com/apoptosisbp74



Next-generation detection of potassium ion fluxFluxOR II Green Potassium Ion Channel Assay.

Potassium channels are ion-selective protein pores that span the cell’s

plasma membrane and serve to establish and regulate membrane

potential. In excitable cells such as neurons and myocytes, these

channels function both to shape the action potential and to reset the

cell’s resting membrane potential. The Invitrogen™ FluxOR™ II Green

Potassium Ion Channel Assay is the newest tool for high-throughput

detection of potassium ion channel and transporter activities.

Similar to the first-generation FluxOR assay [1,2], the FluxOR II Green

Potassium Ion Channel Assay is a homogeneous fluorescence-based

microplate assay designed for high-throughput screening (HTS)

measurements of potassium channel activity. The assay takes advan-

tage of both the well-established permeability of potassium channels

to thallium ions and a highly sensitive fluorogenic thallium indicator,

the FluxOR II Green reagent (Figure 1). The fluorescent signal reported

in this assay serves as a surrogate readout of the activity of any ion

channel or transporter that is permeable to thallium, including hERG,

Kv1.3, Kir2.1, KATP, and other pharmacologically important potassium

channels from all branches of this large gene family.

FluxOR II Green in actionThe FluxOR II Green Potassium Ion Channel Assay is easy to use and

is compatible with both stably and transiently expressed potassium

channels and transporters. The cell-permeant, fluorogenic FluxOR II

Green reagent is simply dissolved in DMSO and added to the cells in

a loading buffer (prepared with kit components, including Invitrogen™

PowerLoad™ Concentrate). Once inside the cell, the nonfluorescent

AM ester on the FluxOR II Green dye is cleaved by endogenous ester-

ases to yield the cell-impermeant thallium-sensitive indicator, which

is retained in the cytosol; its extrusion is inhibited by a water-soluble

formulation of probenecid, which is included in the loading buffer to

block organic anion transporters in the cell membrane.

For detection of voltage-gated ion channels, cells preloaded with

the FluxOR II Green reagent are stimulated with an extracellular solution

that contains thallium ions (and optionally potassium ions) to depolarize

the cells. Upon addition of this stimulus buffer, the extracellular thallium

flows down its concentration gradient into the cells and binds to the

indicator dye, which emits a fluorescent signal proportional to the

number of open channels. Potassium channel or transporter activity is

detected by measuring the increase in FluxOR II Green fluorescence

(Ex/Em = 495/525 nm) using standard FITC filters. In this way, the fluor-

escence observed using the FluxOR II Green assay is a quantitative

indicator of any ion channel activity or transport process that allows

thallium into cells.

Advances in the detection of potassium ion flux Through a multi-tiered approach, we have significantly enhanced the

detection of potassium channels with the development of the new-and-

improved FluxOR II Green assay. First, the original thallium-sensitive

FluxOR dye was modified to dramatically lower the resting background

fluorescence before stimulation. The large reduction in background

fluorescence exhibited by the FluxOR II Green dye produces a larger

assay signal window while greatly reducing stray fluorescence from

unincorporated dye. Additionally, an optional background suppressor

has been included in the kit to reduce off-target fluorescence from

cell culture medium and other extracellular sources. To demonstrate

the increased sensitivity achieved with the assay enhancements, we

compared the performance of the FluxOR II Green Potassium Ion

Channel Assay with that of the original FluxOR assay and the Molecular

Devices FLIPR™ Potassium Assay Kit. Using CHO cells stimulated with

a mixture of potassium and thallium ions to activate voltage-gated

hERG potassium channels, we found that the signal-to-noise ratios

(S/N) generated by the FluxOR II Green assay were significantly larger

Ion channel Ion channel

Tl +

Tl+ Tl +

Tl+

Tl+

Tl+

Tl+

Tl+

Tl +

Closed

Open Tl +

Tl+

Tl+

Tl +

Tl+

StimulatedResting

Thallium Dye

Extracellular Intracellular Extracellular Intracellular

Tl +

Tl+

Tl+

Tl+

Figure 1. Mechanism of action for the FluxOR II Green Potassium Ion Channel

Assay. Basal fluorescence from cells loaded with Invitrogen™ FluxOR™ II Green dye

is low when potassium channels remain unstimulated, as shown in the left panel.

When thallium is added to the assay with the stimulus, the thallium flows down its

concentration gradient into the cells, activating the dye as shown in the right panel.



than those of the other two assays throughout the time course of the

experiment (Figure 2).

The FluxOR II Green assay also displays an improved dynamic

range, allowing potassium channels to be detected over a wide range of

concentrations and activities. Figure 3 shows the fluorescence detected

in CHO cells that were preincubated with different concentrations of

E-4031, a hERG-specific blocker, prior to assaying potassium channel

activity. As compared with the FLIPR assay and the original FluxOR

assay, the FluxOR II Green assay produces larger fluorescent signals

throughout the range of inhibitor concentrations tested. The improved

assay sensitivity and signal window makes the FluxOR II Green assay

more effective at measuring potassium flux over a wider array of channel

activities and stimulus and inhibitor concentrations. In these experiments,

the FluxOR II Green, FLIPR, and FluxOR assays produced EC50 values

that correlated well with published values.

Furthermore, we optimized the assay protocol and reagents

through an iterative process that resulted in buffer stabilization and

more flexibility in experimental design. In addition to the typical wash

format, the FluxOR II Green assay can now be performed in a no-wash

format, helping to reduce well-to-well variability by eliminating wash

steps and media manipulations. The no-wash format also provides a

means of assaying pharmacological efficacy using serum-shift assays.

Learn more about the FluxOR II Green assayThe FluxOR II Green Potassium Ion Channel Assay—available in three

different sizes—provides a concentrated thallium solution and all nec-

essary buffers and loading reagents, as well as a detailed protocol for

fluorescence detection of potassium channel activity in a homogeneous

HTS format. The FluxOR II Green assay has been validated in cells

expressing potassium channels either stably or transiently and in 96-,

References1. Titus SA, Beacham D, Shahane SA et al. (2009) Anal Biochem 394:30–38.

2. Beacham DW, Blackmer T, O’Grady M et al. (2010) J Biomol Screen 15:441–446.


FluxOR™ II Green Potassium Ion Channel Assay

2 microplates10 microplates100 microplates

F20015F20016F20017

384-, and 1,536-well plate formats. Learn more about the FluxOR II

Green assay and its compatibility with a range of potassium channels

and transporters at thermofisher.com/fluxorbp74. ■

Figure 2. Increased signal-to-noise ratio for the FluxOR II Green Potassium Ion Channel Assay.

CHO cells were preloaded with the FluxOR II Green reagent, stimulated with a solution containing 2 mM

thallium and 10 mM potassium to stimulate voltage-gated potassium channels, and then analyzed using

a Hamamatsu™ FDSS6000 imaging-based plate reader. The other two assays were carried out similarly,

according to their supplied protocols. The Invitrogen™ FluxOR™ II Green Potassium Ion Channel Assay

(Cat. No. F20016) exhibited a >40% larger signal-to-noise ratio (S/N) as compared with the Molecular

Devices FLIPR™ Potassium Channel Assay Kit, and a >100% larger S/N as compared with the original

FluxOR Potassium Ion Channel Assay.

Time (sec)

1 151 1811219131 61 211

5

4

3

2

1

241 271 301 331

6

Sig

nal-

to-n

oise

rat

io FLIPR

FluxOR II Green

FluxOR

Figure 3. Increased dynamic range of the FluxOR II Green Potassium Ion

Channel Assay. CHO cells were preloaded with the FluxOR II Green reagent

and stimulated with a solution containing 2 mM thallium and 10 mM potassium to

activate voltage-gated potassium channels. Cells were then exposed to different

concentrations of the potassium channel blocker E-4031 and analyzed using a

Hamamatsu™ FDSS6000 imaging-based plate reader, according to the protocol

provided with the Invitrogen™ FluxOR™ II Green Potassium Ion Channel Assay (Cat.

No. F20016); the other two assays were carried out similarly, according to their

supplied protocols. The FluxOR II Green assay displays an improved detection

sensitivity and signal window as compared with the first-generation FluxOR assay

or the Molecular Devices FLIPR™ assay, facilitating the analysis of a wider range

of potassium channels, including low-expressed or weakly conducting channels.

E-4031 concentration (µM) 10–6 10210–210–4

Rel

ativ

e �u

ores

cenc

e (∆

F/F)

1

FLIPRFluxOR II Green

FluxOR

http://thermofisher.com/fluxorbp74



Jump-start your experimental design with published antibody and reagent panelsOptimized multicolor immunofluorescence panels (OMIPs).

Coined by Roederer and Tarnok [1], an optimized multicolor immunofluor-

escence panel (OMIP) refers to a thoroughly tested and validated set

of antibodies and reagents that can be used together for the multicolor

characterization or evaluation of a specific cell state or response. For

example, OMIP-001 is optimized for evaluating the quality and pheno -

type of Ag-responsive human T cells [2]; OMIP-009 is optimized for

characterizing the immunological response of human T cells [3].

Published in the journal Cytometry Part A (Wiley Online Library),

the first group of OMIPs are designed for flow cytometry, but an OMIP

can potentially be defined for image cytometry, fluorescence micros-

copy, and other polychromatic fluorescence-based methods. The

development and publication of these OMIPs not only helps to alleviate

the burden of panel development and optimization by providing the

technical details and experimental conditions used to optimize each

panel (details usually omitted in published research reports), but also

creates an online repository for OMIPs so that all researchers can

easily search and access the information. Moreover, their publication

provides a process for peer review of optimized panel data, as well as

a platform where researchers can get recognition and credit for the

amount of work and effort it takes to develop an OMIP [4].

Components of an OMIPBy definition, an OMIP publication includes all of the necessary informa-

tion required for the execution of the panel of interest. The publication

contains an overview of the purpose of the panel, a listing of antibody

clones and fluorophore combinations, information on sample type,

and similarities to any existing OMIPs; a representative figure shows

experimental results, including the gating scheme. The supplemental

Figure 1. Example of immunophenotyping using OMIP-009. Data shown are from CMV1 donor cells stimulated with pp65 peptide pool (15-mers overlapping by 11).

(A) Singlets are identified through the use of a forward scatter area (FSC-A) vs. forward scatter height (FSC-H) plot. Nonviable and CD3– cells are excluded, allowing for the

selection of the live CD3+ T cells only. A FSC-A vs. side scatter area (SSC-A) plot permits the additional removal of very low-scatter cells. The selected CD3+ T cell popu-

lation is then further delineated into CD4+ and CD8+ T cells. (B) Gated on either CD4+ or CD8+ T cells, the percentage of responding cells for each cytokine is determined.

(C) Using Boolean gating logic in the FlowJo analysis program, the ‘‘or’’ function is used to create a single gate of all cytokine-producing cells from a combination of existing

cytokine gates, i.e., IFN-γ+ or IL-2+ or TNF+. Thus any cell that makes one or more cytokines is included in the gate. The total cytokine response (red) is then overlaid onto

its respective CD4+ (top) or CD8+ (bottom) T cell lineage (gray) to identify the maturation and activation phenotype of the responding cells. Reprinted by permission from

John Wiley & Sons Inc: Lamoreaux L, Koup RA, Roederer M (2012) OMIP-009: Characterization of antigen-specific human T-cells. Cytometry A 81:362–363.

CD45RA PE-Cy7

CD

28 P

E-C

y5

CC

R7

Ale

xa F

luor

680

A

CB

CD4+

T cells

CD8+

T cells

CD

8 P

aci�

c B

lue

CD45RA PE-Cy7

CD3 APC-Cy7Forward scatter height

Forw

ard

sca

tter

are

a

Forw

ard

sca

tter

are

a

Side scatter area

LIV

E/D

EA

D F

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le A

qua

Sta

in

CD8 Paci�c Blue

CD

4 P

E–T

exas

Red

CCR7 Alexa Fluor 680

CD

4 P

E–T

exas

Red

IFN-γ APC

IFN-γ APC

IL-2 PE

IL-2 PE TNF FITC

TNF FITC



Table 1. OMIP-009: Characterization of antigen-specific human T cells [3] and comparable products from Thermo Fisher Scientific.

Reagents used for OMIP-009 [3] Comparable Thermo Fisher Scientific products

Marker Clone Fluorophore Marker Clone Fluorophore Quantity Cat. No.

IFN-γ B27 APC IFN-γ B27 APC 500 µL MHCIFG05

IL-2 MQ1-17H12 PE IL-2 MQ1-17H12 PE 500 µL RHCIL204

TNF Mab11 FITC TNF Mab11 FITC 50 µg A18469

CD3 SP342 APC-Cy®7 CD3 UCHT1 APC-Cy®7 100 µg A15440

CD4 T4 ECD (PE–Texas-Red™) CD4 S3.5 PE–Texas Red™ 0.5 mL MHCD0417

CD8 RPA-T8 Pacific Blue™ CD8 3B5 Pacific Blue™ 500 µL MHCD0828

CD45RA L48 PE-Cy®7 CD45RA HI100 PE-Cy®7 25 tests A16358

CD28 CD28.2 PE-Cy®5 CD28 Not available

CCR7 (CD197) 150503 Alexa Fluor™ 680 CCR7 (CD197) Not available

Dead cells Not applicable LIVE/DEAD™ Fixable Aqua Stain

Dead cells Not applicable

LIVE/DEAD™ Fixable Aqua Stain

80 tests

200 tests

400 tests

L34965

L34957

L34966

material provides the developmental strategy, a detailed staining

protocol, and technical information on antibody conjugate titration

and panel optimization. The nomenclature used to identify each OMIP

began with “OMIP-001” [2], and the numerical designation continues

to increase as new OMIPs are peer-reviewed and then published in

Cytometry Part A. To date, the majority of the OMIPs published have

been developed for flow cytometry, which is likely due to the complexity

of immunophenotyping studies performed using the platform.

An example of an OMIP that was developed for flow cytometry is

OMIP-009, which was designed to study the human T cell immuno-

logical response to patient vaccination [3]. The list of labeling reagents

used for the study and the corresponding products from Thermo Fisher

Scientific can be found in Table 1. The data acquired using the optimized

conditions for this panel are shown in Figure 1 [3].

Getting started with panel designOMIP publications enable researchers to save a significant amount of

time and money in the creation of their own panels. Even if the OMIP is

not a perfect fit for a particular study, reviewing the strategy employed

by other researchers in the development of a similar OMIP could prove

invaluable to the design of the new panel. When developing a new

panel for flow cytometry or other polychromatic fluorescence-based

method, other considerations include:

■ Biology of the system: Information about the cell populations, antigen

density, and marker co-expression will help drive the gating strategy.

■ Instrumentation: The optical configuration of the instrument, including

excitation wavelengths and emission filters available, will dictate

the detection strategy.

■ Antibody characteristics: The specific antibody clones, chosen

after titrating each antibody conjugate for optimal staining index

in the panel, will help to maximize the resolution of different cell

states and cell types.

■ Fluorophore characteristics: Achieving the ideal reagent brightness

will depend on the fluorophore’s extinction coefficient and quantum

yield and the instrument’s excitation source intensity and fluores-

cence collection efficiency, as well as on the spillover spread matrix,

compensation requirements, and autofluorescence in the system.

There are several resources available to help you get started with your

own panel optimization [5–11], including a short article published in

BioProbes 71 called “Flow Cytometry Panel Design: The Basics” [8],

which you can find at thermofisher.com/bp71. For a complete listing

of published OMIPs, as well as information on how to publish an OMIP,

go to thermofisher.com/omipbp74. ■

References1. Mahnke Y, Chattopadhyay P, Roederer M (2010) Cytometry A 77:814–818.

2. Mahnke YD, Roederer M (2010) Cytometry A 77:819–820.

3. Lamoreaux L, Koup RA, Roederer M (2012) Cytometry A 81:362–363.

4. Tárnok A (2016) Cytometry A 89:795–796.

5. FloCyte course options: http://www.flocyte.com

6. ExCyte Expert Cytometry course options: http://expertcytometry.com

7. Verity House Software annual flow cytometry course information: http://www.vsh.com

8. BioProbes 71 Molecular Probes Journal of Cell Biology Applications. (June 2015) Flow cytometry panel design: The basics. http://www.thermofisher.com/bp71

9. Flow cytometry panel design tool from Thermo Fisher Scientific: http://www.thermofisher.com/flowpanel

10. Information on OMIPs from Cytometry A: http://onlinelibrary.wiley.com/ journal/10.1002/%28ISSN%291552-4930/homepage/information_on_omips.htm

11. Data files from many of the OMIPs are free and accessible online at http://flowrepository.org

http://thermofisher.com/bp71

http://thermofisher.com/omipbp74



Quantitative imaging of histological samplesNow possible using the CellInsight CX7 High-Content Analysis (HCA) Platform.

One of the benefits of fluorescence imaging is the ability to obtain quantitative data about

the presence, location, and intensity of fluorescent signals in a fluorescently stained sample.

Unfortunately, it can also be challenging due to the inherent loss of architectural details when

there is no fluorescent marker associated with specific structures in the cells or tissue. Often a

matched, chromogenically stained histological sample is created and viewed with a brightfield

microscope to provide contextual information for use in interpreting the fluorescently stained

sample. However, differences between the fluorescence and brightfield acquisition systems—for

example, differences in spatial resolution between a monochrome and a color camera—can

complicate the comparison of the stained samples.

To address the need for quantitative colorimetric imaging, we have developed a method

for analyzing histological samples on a high-content imaging instrument using a specialized

brightfield unit that allows for acquisition of the target chromophore’s absorption on a mono-

chrome CCD camera. A histological sample is illuminated individually with differently colored

light-emitting diodes (LEDs) in the transmitted light path, and the image from each channel is

captured using a monochrome camera. These individual images can then be used to reconstruct

a composite image of the chromogenically stained tissue by applying Maxwell’s theory of color

composition [1], which states that you can synthesize all colors of light from the three primary

colors (blue, yellow, and red) (Figure 1). Moreover, the contribution of each color component

can be quantified using software that measures optical density, stained pixels, and other spatial

features. Using this methodology, we can automatically, repeatedly, and quantitatively analyze

colorimetric images without user intervention. In addition, because the transmitted light path

remains compatible with the fluorescent light path, we are able to directly compare matched

histological and fluorescently stained samples.

CellInsight CX7 HCA Platform for colorimetric imagingTo demonstrate quantitative colorimetric imag-

ing, we used tissue microarrays (TMA) that

were stained with the blue nuclear counter-

stain hematoxylin, the red cytoplasmic coun-

terstain eosin Y, and a horseradish peroxidase

(HRP) conjugate of human anti–Ki-67 antibody

in conjunction with the HRP substrate diam-

inobenzidine (DAB). All data were acquired

using the Thermo Scientific™ CellInsight™

CX7 High-Content Analysis (HCA) Platform,

which is equipped with a five-color brightfield

LED system that illuminates the samples with

discrete wavelengths of light. Quantitative

analyses were then performed using the

Thermo Scientific™ HCS Studio™ Cell Analysis

Software’s histology algorithm, which was

developed to individually quantify the image

obtained from each LED and also to optimize

the typical parameters for histological analysis,

including optical density staining measures

and user-defined grading systems.

Figure 2 shows a stained TMA core sam-

ple that was acquired using blue, green, and

red LED illumination; these images were then

used to create the composite image either

without or with color coding (the left panel

shows a brightfield image without staining,

the middle panel shows the colored image

with staining, the right panel shows the algo-

rithm overlays for the objects of interest). To

generate an object count for analysis, we

used the LED channel image corresponding

to the blue hematoxylin staining to set object

boundaries on the cell nucleus. To quantify the

Ki-67 protein levels, we measured the brown

DAB staining, which required analysis of both

Figure 1. Application of Maxwell’s theory of color composition to histological staining. Tissue microarrays

(TMA) of normal human tonsil tissue were stained with the blue nuclear counterstain hematoxylin, the red cyto-

plasmic counterstain eosin Y, and human anti–Ki-67 antibody, which targets a nonhistone nuclear protein. The

antibody was detected with a horseradish peroxidase (HRP)–conjugated secondary antibody followed by staining

with the HRP substrate diaminobenzidine (DAB), which forms an insoluble brown product upon oxidation by HRP.

For acquisition, the samples were illuminated with blue, green, and red brightfield light-emitting diodes (LEDs,

chosen to match the absorption spectra of the stains) in the transmitted light path. Samples were automatically

acquired using 10x magnification on the Thermo Scientific™ CellInsight™ CX7 High-Content Analysis Platform

(Cat. No. CX7A1110).



the red and green LED channel images. Once

the cell mask and channel combinations are

defined, the algorithm can detect the assigned

criteria (such as pixels above a staining thresh-

old) in each of the images and composites.

Figure 3 shows the application of this

quantitative analysis to many different tissue

samples and cancer types. Comparisons

to control tissue demonstrated that signifi-

cantly greater Ki-67 protein staining (p < 0.1)

occurred in over half of the cancer samples.

There were some exceptions, however. For

example, sample group 6 (diffuse small non-

cleaved cell lymphoma of colon) exhibited low

levels of Ki-67 protein staining (Figure 3A) but

a highly variable optical density (OD) reading

(Figure 3B). When the images for this group

were manually reviewed, it was determined

that folding of the TMA sample was respon-

sible for this phenomena (data not shown),

indicating that variability of OD within samples

could be a valuable quality control measure.

Learn more about CellInsight CX7The ability to automatically acquire, ana-

lyze, and store colorimetric images using

the CellInsight CX7 HCA Platform enables

the quantitative analysis of histological

samples and provides researchers with a

means of defining and sharing staining cri-

teria. Learn more about histological staining

and the CellInsight CX7 HCA Platform at

thermofisher.com/hcsbp74. ■

Reference1. Maxwell JC (1890) Chapter XXIII: On the theory

of three primary colours. In The Scientific Papers of James Clerk Maxwell. Vol. 1. Edited by WD Niven. Cambridge: Cambridge University Press. pp. 445–450.


CellInsight™ CX7 High-Content Analysis Platform and Store Standard Edition 1 each CX7B1112

HCS Studio™ 2.0 Cell Analysis Software 1 each SX000041A

Figure 2. Histological data acquisition and analysis on the CellInsight CX7 HCA Platform. Tissue microar-

rays of a diffuse large B cell lymphoma of neck were stained with the blue nuclear counterstain hematoxylin,

the red cytoplasmic counterstain eosin Y, and human anti–Ki-67 in conjunction with a horseradish peroxidase

(HRP)–conjugated secondary antibody and diaminobenzidine (DAB), which forms an insoluble brown product.

(A) A stained tissue microarray core sample was acquired as separate fields using blue, green, and red illumi-

nation, a 40x objective, and tiling to create the entire sample. (B) Images were color-encoded to represent the

typical visual acquired with a color camera. (C) Images were then individually analyzed: objects detected (blue

outlines) and Ki-67 staining (red spots) were measured, analyzed, and compared to other groups within the array.

A B C

Bright�eld images Color-encoded images Analysis overlay images

Figure 3. Quantitative analysis of tissue microarray samples. A series of tissue microarray samples were stained

and analyzed using conditions described in Figure 2. All were analyzed using the Thermo Scientific™ HCS Studio™

2.0 Cell Analysis Software’s histology algorithm for (A) total percent staining of Ki-67 (calculated as the number of

DAB-stained pixels/total number of pixels × 100% in the acquired field) and (B) average optical density of each

sample. Samples included: 1) mucosa-associated B cell lymphoma of thyroid, 2) diffuse plasmacytic lymphoma

of small intestines, 3) diffuse large B cell lymphoma of colon, 4) diffuse large B cell lymphoma of groin, 5) diffuse

small noncleaved cell lymphoma of left groin, 6) diffuse small noncleaved cell lymphoma of colon, 7) diffuse large

B cell lymphoma of neck, 8) diffuse lymphocytic plasmacytoid lymphoma of lower jaw, 9) diffuse large B cell

lymphoma of right oxter, 10) diffuse T cell lymphoma of right knee joint, and 11) cancer-adjacent normal tonsil

tissue (control). * Indicates the statistical significance (p < 0.1) of the Ki-67 staining compared to the control (11).

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Protein misfolding in neurodegenerative diseasesAntibodies specific for misfolded proteins associated with neurodegeneration.

Protein misfolding and associated aggregate formation are key patho-

logical features of various neurodegenerative diseases, including

Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s

disease (PD), amyotrophic lateral sclerosis (ALS), and others (Figure 1).

Although both wild-type and mutant proteins may form misfolded protein

aggregates, certain genetic mutations give rise to abnormal amino acid

sequences that increase the propensity for protein misfolding and

aggregate formation [1]. One example is the production of defective

amyloid-β (Aβ) protein, linked to AD. Various amyloid precursor protein

(APP) mutations drive production of mutant Aβ peptides that oligomerize

and induce fibril formation (Figure 1). Overproduction of certain protein

products or increased metabolic, oxidative, and inflammatory stress

responses may also contribute to protein aggregate formation [2].

The ER’s unfolded-protein response pathwayMisfolded proteins initiate a set of signals that induce endoplasmic

reticulum (ER) stress responses, including the unfolded-protein

response (UPR), which protects cells from accumulating aggregated

proteins. This elaborate quality-control mechanism regulates protein

processing, folding, and trafficking in the ER to prevent the buildup of

misfolded proteins. Foldases and molecular chaperones play an essen-

tial role in this process. Misfolded proteins are either retained within

the ER or degraded through autophagy or the proteasome-dependent

ER-associated protein pathway. Dysregulation of the UPR pathway

has been associated with various neurodegenerative, metabolic, and

inflammatory diseases, as well as with cancer [3]. Accordingly, various

UPR proteins are being investigated as potential drug targets for a

range of human diseases [3].

The UPR consists of transmembrane stress sensors and down-

stream transcription factors [4]. Examples of ER membrane proteins

acting as stress sensors include inositol-requiring transmembrane

kinase/endoribonuclease 1 (IRE1), protein kinase–like eukaryotic initiation

factor 2α kinase (PERK), and activating transcription factor 6 (ATF6).

Upon activation, these proteins regulate multiple processes, including

the rate of protein production, expression of proteins that aid in protein

folding, prevention of protein aggregation, and promotion of retrotrans-

location and degradation of proteins produced in the ER [1] (Table 1).

Access our antibody resources todayFind out more about neurodegenerative diseases by accessing our

handbook Antibody-based tools for neurodegenerative disease

research at thermofisher.com/neuroantibody-hb. We also offer

other downloadable handbooks covering topics relevant to cancer

signaling pathways, stem cell research, and tumor-related inflam-

mation, all of which can be accessed by completing the form at

thermofisher.com/abtoolshandbooks. To explore our primary and

secondary antibody search tools and learn more about our range of

immunoassays, go to thermofisher.com/antibodybp74. ■

References1. Rao RV, Bredesen DE (2004) Curr Opin Cell Biol 16:653–662.

2. Chen X, Guo C, Kong J (2012) Neural Regen Res 7:376–385.

3. Wang S, Kaufman RJ (2012) J Cell Biol 197:857–867.

4. Hetz C, Mollereau B (2014) Nat Rev Neurosci 15:233–249.

Figure 1. Protein misfolding and neurodegeneration. Representative proteins

associated with five different proteopathies are shown.

Table 1. Proinflammatory proteins associated with leukocytes of the tumor microenvironment.

Target Function Antibody Cat. No.

IRE1 ER-resident protein that regulates the transcription factor XPB1 and acts as a transducer of unfolded protein responses

IRE1α antibody, rabbit polyclonal (PA1-16928)

PERK Protein kinase that phosphorylates EIF2α to prevent ER influx of pre-modified proteins

PERK antibody, rabbit polyclonal (PA5-15305)

ATF6 Transcription factor that mediates up-regulation of chaperone proteins

ATF6 antibody, rabbit polyclonal (PA5-20215)

XPB1 Transcription factor for ER stress-related proteins that regulate ER retrotranslocation and degradation of misfolded proteins

XBP1 antibody, monoclonal antibody clone 9B7E5 (MA5-15768)

Alzheimer’s disease

Abnormal protein misfolding and

aggregation

Parkinson’s disease

Huntington’s disease(HD SCAs)

Amyotrophic lateralsclerosis

Frontotemporallobar degeneration

Amyloid-β

α-synuclein

Huntingtinataxins

TDP-43SOD1

Tau

Native monomer

Misfolding

β-sheet oligomers

Amyloid �brillaraggregates

Neurodegeneration

http://thermofisher.com/neuroantibody-hb

http://thermofisher.com/abtoolshandbooks

http://thermofisher.com/antibodybp74


BIOPrOBEs 74 JOUrNAL CLUB

Current methods and challenges in the comprehensive characterization of human pluripotent stem cellsAsprer JS, Lakshmipathy U (2015) Stem Cell Rev 11:357–372.

Induced pluripotent stem cells (iPSCs) are valuable tools for disease

modeling, drug discovery, and cell therapy. As new iPSC lines are

generated through somatic reprogramming, a battery of assays are

employed to confirm that they exhibit the hallmark characteristics of

pluripotent stem cells (PSCs), including PSC marker expression and

the ability to generate cells from the three embryonic germ layers.

Asprer and coworkers recently published a review of a broad set of

molecular and cellular methods for the comprehensive characterization

of human PSCs [1]. Here, we distill the most essential steps in the

cellular analyses that are performed on newly reprogrammed iPSCs.

Examine morphologyDuring the process of reprogramming, the emergence of colonies is

initially monitored based on morphological changes and the appear-

ance of embryonic stem cell (ESC)–like colonies. For example,

elongated fibroblasts transform into more compact PSCs that have

high nucleus-to-cytoplasm ratios. In feeder-dependent systems, these

cells form three-dimensional colonies with well-defined edges. By days

21 to 28 after the initiation of reprogramming, the colonies are usually

large enough to be picked and transferred to new culture dishes.

Visualize PSC markers in live cellsThe emerging colonies can consist of partially or fully reprogrammed

cells that sometimes appear indistinguishable, even to the well-trained

eye. The visualization of PSC markers increases the likelihood of

obtaining a fully reprogrammed iPSC line. However, the markers must

be detected without compromising the viability and pluripotency of the

colonies, which will be expanded to establish new PSC lines. This

marker detection can be achieved through live alkaline phosphatase

staining and live-cell immunostaining.

Live alkaline phosphatase (AP) staining. AP is an enzyme that

is up-regulated in PSCs and can be detected using a substrate that

selectively fluoresces as a result of AP activity [2]. This differential

staining method for AP activity is quick and reversible, and it preserves

the viability of the cells. Thus, it can be used to discriminate stem cells

from feeder cells or parental cells during reprogramming (Figure 1).

Live-cell immunostaining. More specific cell staining can be

achieved using antibodies for established markers. Surface proteins

like the positive PSC markers, SSEA4, TRA-1-60, and TRA-1-81, and

the negative PSC markers, CD44 and SSEA1, are particularly useful

because they can be stained quickly while keeping cells in culture [3,4].

Of the positive PSC markers, TRA-1-60 is thought to be most stringent

because it is up-regulated later in reprogramming [5]. In contrast, the

negative PSC marker CD44 is found on many differentiated cell types

as well as partially reprogrammed cells but is absent from PSCs.

Confirming the absence of CD44 expression increases confidence in

picking colonies for expansion, especially when it is combined with a

positive PSC marker [4] (Figure 2).

Visualize PSC markers in fixed cellsWhen emerging iPSC colonies are still being picked, two to three

markers may be analyzed at once. This is sufficient for a quick screen,

but once the cells have been expanded, more markers need

Figure 2. Live-cell immunostaining of human pluripotent stem cells (hPSCs).

Live feeder-dependent hPSCs were stained with Invitrogen™ Alexa Fluor™ 555

anti–TRA-1-60 antibody, supplied in the TRA-1-60 Alexa Fluor™ 555 Conjugate Kit

(Cat. No. A24879, left panel) and Invitrogen™ Alexa Fluor™ 488 anti-CD44 antibody,

supplied in the CD44 Alexa Fluor™ 488 Conjugate Kit (Cat. No. A25528, middle

panel); the right panel shows the merged image. Images were acquired using the

Invitrogen™ EVOS™ FL Imaging System (Cat. No. AMF4300).

Figure 1. Reversible alkaline phosphatase staining of live human pluripotent

stem cells (hPSCs). Invitrogen™ Alkaline Phosphatase (AP) Live Stain (green, Cat.

No. A14353) robustly stains a hPSC colony (left panel). The fluorescent signal is

lost from the cells by 90 min after removal of the dye from the medium (right panel).


JOUrNAL CLUB BIOPrOBEs 74

to be analyzed to increase confidence in the

identity and quality of an iPSC clone. Indeed,

many PSC markers are intracellular proteins

that can only be stained when cultures are

fixed and permeabilized; thus, there must be

enough cells for duplicate cultures before

these markers can be used. OCT4 and SOX2

are two such well-established intracellular

PSC markers (Figure 3); both are transcription

factors known to play key roles in maintaining

pluripotency [3].

Evaluate differentiation potentialAnalyzing iPSCs and confirming the presence

of self-renewal markers or the absence of

original somatic markers is important but

not sufficient for verifying the functional

pluripotency of a newly derived iPSC line.

It is critical to also confirm the iPSCs’ ability

to differentiate into the three germ lineages:

ectoderm, mesoderm, and endoderm.

The most physiological method for testing

this in human iPSCs is to perform teratoma for-

mation, which is labor-intensive, takes around

6 to12 weeks to complete, and is associated

with a high animal-testing burden. The most

common alternative to teratoma formation

is embryoid body (EB) formation, an in vitro

assay involving the spontaneous differentiation

of PSCs into the three germ lineages over 7 to

21 days. Although differentiation occurs under

nonphysiological conditions, EB formation has

the advantage of being shorter, less laborious,

and easier to analyze. Common markers for

analyzing differentiation in EBs include β-III

tubulin (TUJ1) for ectoderm, smooth muscle

actin (SMA) for mesoderm, and α-fetoprotein

(AFP) for endoderm (Figure 4). Even more

markers can be quantitatively and simultane-

ously analyzed using the Applied Biosystems™

TaqMan® hPSC Scorecard™ Assay, which is

discussed in detail on page 15.


3-Germ Layer Immunocytochemistry Kit 20 tests A25538

Alkaline Phosphatase Live Stain 50 µL A14353

CD44 Alexa Fluor™ 488 Conjugate Kit for Live Cell Imaging 50 tests A25528

Pluripotent Stem Cell 4-Marker Immunocytochemistry Kit 40 tests A24881

Pluripotent Stem Cell Immunocytochemistry Kit (OCT4, SSEA4) 40 tests A25526

TaqMan® hPSC Scorecard™ Panel, Fast 96-well 2 x 96-well plates A15876

TaqMan® hPSC Scorecard™ Kit, Fast 96-well 2 x 96-well plates A15871

TaqMan® hPSC Scorecard™ Panel, 384-well 1 x 384-well plate A15870

TaqMan® hPSC Scorecard™ Kit, 384-well 1 x 384-well plate A15872

TRA-1-60 Alexa Fluor™ 555 Conjugate Kit for Live Cell Imaging 50 tests A24879

References1. Asprer JS, Lakshmipathy U (2015) Stem Cell Rev 11:357–372.

2. Singh U, Quintanilla RH, Grecian S et al. (2012) Stem Cell Rev 8:1021–1029.

3. Adewumi O, Aflatoonian B, Ahrlund-Richter L et al. (2007) Nat Biotechnol 25:803–816.

4. Quintanilla RH, Asprer JS, Vaz C et al. (2014) PLoS One 9:e85419.

5. Chan EM, Ratanasirintrawoot S, Park IH et al. (2009) Nat Biotechnol 27:1033–1037.

ConclusionsNewly derived iPSC lines are initially characterized through marker analysis, both in the

undifferentiated and differentiated states. Live AP staining and live-cell immunostaining

facilitates the selection of colonies for expansion, while additional fixed-cell staining allows the

investigation of many more PSC or germ-layer markers. Marker choice, antibody specificity,

and reagent quality are critical considerations for the successful characterization of new iPSC

lines. Thermo Fisher Scientific offers cell analysis tools for each of these characterization steps,

including the Alkaline Phosphatase Live Stain, PSC Immunocytochemistry Kits, and 3-Germ

Layer Immunocytochemistry Kit; learn more at thermofisher.com/stemcellsbp74. ■

Figure 3. Fixed-cell immunostaining of human pluripotent stem cells (hPSCs). Feeder-dependent hPSCs

were fixed and stained with primary and fluorescent secondary antibodies from the Pluripotent Stem Cell 4-Marker

Immunocytochemistry Kit (Cat. No. A24881). The anti-OCT4 (red, left panel) and anti-SOX2 (green, middle panel)

antibodies were used to stain nuclei of hPSCs. DAPI (blue, Cat. No. D1306) served as a nuclear counterstain

and can be seen in the merged image (right panel).

Figure 4. Immunostaining of embryoid bodies (EBs). Day 21 EBs were stained using primary and fluorescent

secondary antibodies provided in the 3-Germ Layer Immunocytochemistry Kit (Cat. No. A25538). The markers

shown are (A) TUJ1 (yellow), (B) SMA (red), and (C) AFP (green), which are merged in (D). DAPI (blue, Cat. No.

D1306) served as a nuclear counterstain.

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