TNF

CD4

IL17

CD57

IL2

CCR7

Ki67

Live/Dead

PD1

CD127

CD8

CD3

2/19/2014

CD107

IFN

CD154

Th2

Integrating Flow Cytometry and Single Cell Transcriptomics: Instrumental Synergy Mario Roederer Vaccine Research Center The National Institutes of Health

1

2/19/2014

T Cell Functions T cells are capable of a large repertoire of cellular functions: Killing Proliferation Secreting Effector Molecules (cytokines) Orchestrate immune responses Induce inflammation Kill target cells Using flow cytometry, we can measure these on a cell-bycell basis, to quantify the different types of effector T cells present.

2

2/19/2014

One Function is Not Enough! De Rosa et al., J. Immunology 173 (2004)

CD4 T cells

IFNγ

10 5

0.008

0.021

10 5

10 4

10 4

10 3

10 3

10 2

10 2

0

0.013

0.05

0

IL2

0.038 0

10

2

Total Response: γIFN+ IL2+

10

3

10

4

0.067 0.029 (43%) 0.059 (88%)

10

5

0.076 0

10

2

10

3

10

4

10 5

0.139 0.063 (45%) 0.126 (91%)

3

Finding the Correlate of Morbidity or Therapeutic Efficacy

% A+B+C+D+…

2/19/2014

X X X X

% A+B+C+D

X

A+B+C+D+…

2 markers

A+B+C+D

3 markers

A+B

4 markers

A B C D

X X X

X

X X

A X X

%A

1 marker

X

% A+B

Total CD8+

X X Disease or Efficacy

4

2/19/2014

The Search for Immune Correlates Antigen-specific lymphocytes display enormous heterogeneity: Differentiation stage (CD62L, CCR7, CD45Rx, CD95, CD28, CD27, CD57, CD11a…) Homing profile (a4b7, CD103, CCR9, CLA…) Regulatory molecules (PD1, TIM3, LAG3, KIRs, CTLA4, ICOS…) Stimulated effector functions (dozens of cytokines, chemokines, degranulation, proliferation…)

Protective responses almost certainly comprise cells expressing a pattern of multiple functions. Even today’s state-of-the-art immunophenotyping panels cannot fully interrogate potential subsets. Single-cell transcriptomics is part of the solution.

5

2/19/2014

Fluidigm BioMark Technology Dispense cDNA into sample vessels

Microfluidics Chip

Primers & probes into reagent vessels

6

2/19/2014

Fluidigm BioMark Technology Dispense cDNA into sample vessels

Sample cDNA

Primers & probes into reagent vessels Primers & Probes

Microfluidics mixes all combinations in nanoliter-sized chambers

7

2/19/2014

Fluidigm BioMark Technology Dispense cDNA into sample vessels

Sample cDNA

Primers & probes into reagent vessels Primers & Probes

Microfluidics mixes all combinations in nanoliter-sized chambers 40 Cycle RT-PCR Monitor fluorescence from each chamber

8

2/19/2014

Fluidigm BioMark Samples (96 cDNAs)

Primers & Probes (96 different genes)

9,216 simultaneous RT PCR reactions

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2/19/2014

Assessing Gene Expression “Nanoarray” Sort 50-5000 desired cells… Quantify gene expression of 96 selected genes (can multiplex plates for 192, 384, … genes)

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2/19/2014

Assessing Gene Expression “Nanoarray” Sort 50-5000 desired cells… Quantify gene expression of 96 selected genes (can multiplex plates for 192, 384, … genes) In principle, similar to microarray analysis… but: Highly directed vs. 40,000 genes Disadvantage: Not interrogating the entire genome; gene selection bias Advantage: Much smaller statistical penalty = more sensitive No pre-amplification; extremely sensitive (1 copy) Large dynamic range (RT PCR: ~104), linear, high degree of precision

11

2/19/2014

Assessing Gene Expression “Nanoarray” Sort 50-5000 desired cells… Quantify gene expression of 96 selected genes (can multiplex plates for 192, 384, … genes) In principle, similar to microarray analysis… but: Highly directed vs. 40,000 genes Disadvantage: Not interrogating the entire genome; gene selection bias Advantage: Much smaller statistical penalty = more sensitive No pre-amplification; extremely sensitive (1 copy) Large dynamic range (RT PCR: ~104), linear, high degree of precision

Single cell Sort 1 cell per well… Quantify gene expression of 96 selected genes No loss in sensitivity or specificity compared to Nanoarray

12

2/19/2014

Single-Cell Expression Profiling RV144: Is there a signature of vaccine-elicited CD4 T cells associated with durable antibody responses (or protection)?

HIV/SIV-Productive Infection: Is there a signature of productively or latently-infected cells?

13

2/19/2014

RV144: HIV Vaccine Trial Analysis of RV144 showed that antibodies were a correlate… but responses waned. Total T cell responses (which were weak) did not correlate. However, a secondary analysis reveals that IL10 and IL13 production by PBMC following T cell stimulation may be a correlate. Can we identify CD4 T cell responses that predict protection and/or durable humoral responses?

14

2/19/2014

Fluidigm & RV144 We applied the nanoarray and single-cell profiling to samples from RV144 to characterize the functions of vaccine-elicited CD4 T cells. RV144 visit 8 samples (n=50: 40 vaccinees, 10 placebo) PBMC were stimulated with HIV env peptides; the CD154 assay (5 hour stimulation) was used to sort live vaccine-specific CD4 T cells.

15

2/19/2014

Fluidigm & RV144 Vaccine-elicited HIV-specific CD4 T cells are very rare! Range: 0.03 – 0.08% (mean 0.05%) >4x above background (~20% “nonspecific” cell contamination) Stimulated

Mock

CD4

105

10

4

10

10

105

0.06

10

4

3

10

3

2

10

2

0

0.01

0

0

2

10

10

3

10

4

CD154

10

5

0

10

2

10

3

10

4

5

10

CD154

16

2/19/2014

Nanoarray Analysis Genes (n=96)

Expression

Samples (n=49)

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2/19/2014

Unsupervised Clustering

Expression

Stim

Unstim

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2/19/2014

Stimulation-regulated Genes Unchanged

Expression

Stim

Unstim

19

2/19/2014

Stimulation-regulated Genes Strongly upregulated

Expression

Stim

Unstim

20

2/19/2014

Stimulation-regulated Genes Mildly upregulated

Expression

Stim

Unstim

21

2/19/2014

Stimulation-Regulated Genes Slightly upregulated

Expression

Stim

Unstim

22

2/19/2014

Stimulation-regulated Genes Down-regulated

Expression

Stim

Unstim

23

2/19/2014

“Classical” Cytokines Are Upregulated

Log2 Expression

30

IFNg

TNF 20

IL2 30

20 20 10

0

10 10

0

0

Unstimulated Stimulated

24

2/19/2014

“Classical” Cytokines Are Upregulated

Log2 Expression

30

IFNg

TNF 30

20 20

1000x

10

0

IL2

85x

20

4800x

10 10

0

0

Unstimulated Stimulated

25

2/19/2014

Other Cytokines (Th2) Are Upregulated

IL3

IL4

IL10

IL13

Log2 Expression

30 30

15

20

10

10

5

0

0

20 20

10

0

10

0

Unstimulated Stimulated

26

2/19/2014

Other Cytokines (Th2) Are Upregulated Correlates of Protection in RV144 IL3

IL4

IL10

IL13

Log2 Expression

30 30

15

20

10

10

5

0

0

20 20

10

0

10

0

Unstimulated Stimulated

27

2/19/2014

Genes Co-Regulated by Stimulation Genes

Sample

Turn on

Unstimulated

Stimulated Ag+CD154+ High Expression

No Expression

CYTOKINE/CHEMOKINE GMCSF (CSF2) MIP1α (CCL3) LIF IL3 IL4 IL10 IL13

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2/19/2014

Genes Co-Regulated by Stimulation Genes

Sample

Unstimulated

Stimulated Ag+CD154+ High Expression

Turn on Strongly Upregulated

No Expression

CYTOKINE/CHEMOKINE GMCSF (CSF2) IL2 MIP1α (CCL3) IFNγ LIF TNFα IL3 IL4 IL10 IL13

ACTIVATION CD109 CD154 (CD40LG) TNFRSF9 (CD137)

IMMUNE REGULATOR LIGHT (TNFSF14)

APOPTOSIS CD95LG (FASLG)

29

2/19/2014

Genes Co-Regulated by Stimulation Genes

Sample

Unstimulated

Stimulated Ag+CD154+ High Expression

Turn on Strongly Upregulated

No Expression

Upregulated

CYTOKINE/CHEMOKINE GMCSF (CSF2) IL2 MIP1α (CCL3) IFNγ LIF TNFα IL3 IL4 IL10 IL13

ACTIVATION CD109 CD154 (CD40LG) TNFRSF9 (CD137) APOPTOSIS

IMMUNE REGULATOR LIGHT (TNFSF14) BAMBI (NMA) PD1 CTLA4

SIGNALLING SLAMF1 CD84 (SLAMF5) SHP2 (PTPN11)

CD95LG (FASLG)

30

2/19/2014

Genes Co-Regulated by Stimulation Genes

Sample

Unstimulated

Stimulated Ag+CD154+ High Expression

Turn on Strongly Upregulated

No Expression

Upregulated Turn off/ Downregulated

CYTOKINE/CHEMOKINE GMCSF (CSF2) IL2 MIP1α (CCL3) IFNγ LIF TNFα IL3 IL4 IL10 IL13 LCF (IL16)

ACTIVATION CD109 CD154 (CD40LG) TNFRSF9 (CD137) APOPTOSIS

IMMUNE REGULATOR LIGHT (TNFSF14) BAMBI (NMA) PD1 CTLA4

CD95LG (FASLG) CD31 (PECAM1) CD38

SIGNALLING SLAMF1 CD84 (SLAMF5) SHP2 (PTPN11) PHENOTYPE DC BAFF CD94 (KLRD1) NK Treg FCRL3

31

2/19/2014

Gene Family Regulation Genes associated with T cell responses showed large changes (≥3 orders of magnitude) with extreme significance. Expression of ~40 (of 96) genes is altered by stimulation of vaccinespecific cells. … Many targets as possible correlates for protection.

32

2/19/2014

Gene Family Regulation Genes associated with T cell responses showed large changes (≥3 orders of magnitude) with extreme significance. Expression of ~40 (of 96) genes is altered by stimulation of vaccinespecific cells. … Many targets as possible correlates for protection.

But this is still a bulk approach! What is the heterogeneity of the T cell response? Can we do Fluidigm on single cells?

33

2/19/2014

Single-Cell Genomics Standard Flow Procedure Short Stimulation Cell Staining

34

2/19/2014

Single-Cell Genomics Standard Flow Procedure Short Stimulation Cell Staining

Indexed Sorting Fluorescence profile of every cell is stored.

35

2/19/2014

Single-Cell Genomics Standard Flow Procedure Short Stimulation Cell Staining

Indexed Sorting Fluorescence profile of every cell is stored.

36

2/19/2014

Single-Cell Genomics Standard Flow Procedure Short Stimulation Cell Staining

Indexed Sorting Fluorescence profile of every cell is stored.

37

2/19/2014

Single Cell Accuracy

Single cell average (Et)

10 stimulated CD4 PBMC samples were sorted for nanoarray (3 x 100 cells) or single cell (150 x 1 cell).

The average 100 cell signal (normalized to 1 cell) is graphed against the average single cell signal for all 10 samples x 96 genes.

20

10

Et = Log2(Expression)

0 0

10

20

100-cell (Et)

38

2/19/2014

Single Cell Accuracy 10 stimulated CD4 PBMC samples were sorted for nanoarray (3 x 100 cells) or single cell (150 x 1 cell).

Single cell average (Et)

~1000 mRNA 20

~1 mRNA 10

0 0

10

20

100-cell (Et)

39

2/19/2014

Single Cell Accuracy

Single cell average (Et)

10 stimulated CD4 PBMC samples were sorted for nanoarray (3 x 100 cells) or single cell (150 x 1 cell).

20

10

Excellent correlation: there is no loss in sensitivity/accuracy at the single cell level

0 0

10

20

100-cell (Et)

40

2/19/2014

Single Cell Genomics
Just like flow cytometry, this technology provides us with two independent pieces of information: – How many cells express a gene? – How much do these cells express?
Standard (bulk) analysis confounds these two measurements to generate an average
Single cell analysis allows us then to answer another question: – What is the co-expression of genes?

41

2/19/2014

BLIMP1

Why is Single Cell Important?

TNFR-1

CD84 (SLAMF5)

TNFR-1

BLIMP1

CD84 (SLAMF5)

42

2/19/2014

BLIMP1

Why is Single Cell Important?

TNFR-1

CD84 (SLAMF5)

TNFR-1

BLIMP1

CD84 (SLAMF5)

Single cell analysis reveals a completely different picture of regulation of these genes!

43

2/19/2014

Standard Subset Identification

105

CD8a

104

54.4

103

102

1

10

14.4 0

10

100

101

102

103

104

105

CD4

44

2/19/2014

Gene Regulation Examples Different by Antigen Specificity CMV-Specific HIV-Specific

100

101

102

103

KLRD1

104

105

100

101

102

103

104

105

HLADR

45

2/19/2014

Gene Regulation Examples Different by Antigen Specificity 95.1% 54.9%

CMV-Specific HIV-Specific

26.7% 47.9%

%Positive

100

101

102

103

KLRD1

104

105

100

101

102

103

104

105

HLADR

46

2/19/2014

Gene Regulation Examples Different by Antigen Specificity 95.1% 54.9%

468 179

CMV-Specific HIV-Specific

26.7% 47.9%

274 395

%Positive MFI (mRNA/Cell)

100

101

102

103

KLRD1

104

105

100

101

102

103

104

105

HLADR

47

2/19/2014

Gene Regulation Examples Different by Antigen Specificity 95.1% 54.9%

468 179

CMV-Specific HIV-Specific

26.7% 47.9%

274 395

%Positive MFI (mRNA/Cell)

100

101

102

103

104

105

100

101

102

KLRD1

103

104

105

HLADR

Regulated by Stimulation 334 809

100

101

102

32.9% 75.9%

103

BIRC3

104

80% Unstimulated 38.5% Stimulated

732 388

105

100

101

102

103

104

105

GZMA

48

2/19/2014

Single-Cell Analysis of RV144 Between 100 and 200 CD154+ CD4 memory T cells were sorted from stimulated cultures from each of 10 vaccinees

CD4

CD45RA

CD127

CD154

CCR7

The phenotype of every cell is known

CD27

A total of 1,289 cells were sorted

CD95

CD45RA

49

2/19/2014

Gene Profiling of Single Cells “Core” set of 6 genes expressed by nearly all responding cells; many are not routinely measured in antigen stimulation assays

Single Cells (n = 989)

BIRC3 CD69 CD154 IL2Rγγ TGFβ β 1 ICOS

Genes (n=39)

50

2/19/2014

Gene Profiling of Single Cells IFNγγ IL2 TNF FasL LIGHT

CCR7 CXCR4 CLEC2B

Single Cells (n = 989)

“Classical” functional genes expressed in a majority of responding cells

Genes (n=39)

51

2/19/2014

Gene Profiling of Single Cells IL3 IL4 IL13 GMCSF

Single Cells (n = 989)

Subsets of classically-responding cells express additional functional genes

Genes (n=39)

52

2/19/2014

Can We Identify a Vaccine-Specific Signature? 20 Individuals 80-150 Single CD154+ CD4 T cells 96 Genes

Fold CD154+

Mock

Env-Stimulated

10 5

CD4

10

105

0.01

4

103

10 2

102

0

0

10

2

10

3

10

4

CD154 CD154

10

5

0.06

4

10 3

0

Net CD154+

10

0

10

2

10

3

10

4

10

5

CD154 CD154

n=10 High Responders (Vaccinated) n=7 Low Responders (Vaccinated) n=3 Placebo

53

2/19/2014

Single-Cell Gene Expression of CD154 Highest Responders (Vaccinated) Copies Per Cell

4096 1024 250 64 16 4 1

Placebo

Low Responders (Vaccinated)

Copies Per Cell

4096 1024 250 64 16 4 1

54

2/19/2014

Single-Cell Gene Expression of CD154 Highest Responders (Vaccinated) Copies Per Cell

4096 1024 250 64 16 4 1

Placebo

Low Responders (Vaccinated)

Copies Per Cell

4096 1024 250 64 16 4 1

55

2/19/2014

Signature of Vaccine-Specific Response Based on the Single-Cell Expression of only 20 Genes

Cluster # 1

2

3

56

2/19/2014

Signature of Vaccine-Specific Response Based on the Single-Cell Expression of only 20 Genes 89% Placebo Stimulated

95% Vaccinated Stimulated

1

2

48% Vaccinated Unstimulated

28% Placebo Stimulated

22% Vaccinated Stimulated

3

57

2/19/2014

Single-Cell Transcriptomics for Vaccine Evaluation We identified ~40 genes whose expression is modulated by stimulation of vaccine specific CD4 T cells Gene expression patterns identify subsets of CD4 T cells within the vaccine-specific response These can be further evaluated as potential correlates of durably humoral responses and/or protection Vaccine-specific signatures can be identified and serve as comparators to other (protective) vaccines

58

2/19/2014

Gene expression analysis of SIV productively infected CD4+ T-cells What is the phenotype and gene expression profile of the rare CD4+ T cells that produce HIV/SIV in vivo?

59

2/19/2014

Gene expression analysis of SIV productively infected CD4+ T-cells What is the phenotype and gene expression profile of the rare CD4+ T cells that produce HIV/SIV in vivo? We take advantage of the kinetics of SIV gene expression to distinguish parts of the viral lifecycle.

gag/LTR RNA: gag/LTR DNA: tat/rev RNA: env RNA:

attachment

entry

-

+

-

RT

integration

+ +

-

+ + + +/-

assembly

+++ + ++ +

60

2/19/2014

Gene expression analysis of SIV productively infected CD4+ T-cells We sorted CD4 memory T cells from an acutelyinfected NHP Fluidigm analysis was performed for gag, LTR, env, and tat-rev gene products, as well as 92 rhesus genes. 6% of cells contain tat-rev mRNA (previously: ~10% of gag+ cells produce virus)

61

2/19/2014

SIV Genes are Co-expressed

tat-rev mRNA

16384 1024

370 cells

64 4 1

1

4

64

1024

16384

env mRNA

62

2/19/2014

SIV Genes are Co-expressed

tat-rev mRNA

16384 1024 64

281 cells 51 cells 38 cells

4 1

1

4

64

1024

16384

env mRNA

63

2/19/2014

SIV Genes are Co-expressed Virus lifecycle

tat-rev mRNA

16384 1024 64

281 cells 51 cells 38 cells

4 1

1

4

64

1024

16384

env mRNA

64

2/19/2014

CD4 Protein

Host Gene Expression During the SIV Lifecycle

CD4 mRNA 0 tat-rev mRNA

65

2/19/2014

CD3 Protein

CD4 Protein

Host Gene Expression During the SIV Lifecycle

CD4 mRNA

CD4 Protein 0 tat-rev mRNA

66

2/19/2014

CD3 Protein

CD4 Protein

Host Gene Expression During the SIV Lifecycle

CD4 mRNA

CD4 Protein 0 tat-rev mRNA

67

2/19/2014

Host Protein Expression in Productively SIV-Infected Cells Surface protein expression differences associated with SIV infection p value

CD3 CD4 ICOS CD69 CD45RA