Holden T. Maecker, PhD Stanford University Mass Cytometry Assays for Antigen-specific T cells using...

Holden T. Maecker, PhDStanford University

Mass Cytometry Assays for Antigen-specific T cells using CyTOF

Immunity to chronic infections: HIV, CMV

Both require cellular immunity for protectionVirus-infected cells are targets for CTL killing

Both result in chronic antigen persistenceBut CMV is usually at undetectable levels

CMV does not cause pathology in immunocompetent hosts

What is unique about the CMV signature?

Early & Late Functions of Cellular Immunity

IL-4IL-2

TNFαIFNγ

TCR engagement

Cytokineexpression

Proliferation/Death

Cyto-toxicity

Why multicolor flow cytometry

The concept of a T cell response signature:CD4 + CD8 + Multiple cytokines + memory/effector

markers

Based on the idea that quality as well as quantity of responding T cells is important:

Polyfunctional T cells associated with LTNP (Betts et al.)Central memory T cells assocated with protection from SIV

challenge (Letvin et al.)

Principle of ICS Assays

Antigenic stimulus + brefeldin A

and/or monensin

Incubate 6-10 h

• Fix cells• Permeabilize• Stain

PBMC orwhole blood

Gate on cellsof interest

Magnitude of CMV & HIV responses

CD4+ IFNγ+

CMV HIV

105

104

103

102

101

0

Abso

lute

cells

/ml

CD8+ IFNγ+

CMV HIV

105

104

103

102

101

0

Nomura et al., BMC Immunol 2006

Function of CMV & HIV responses

0.0005

CMV HIV

105

104

103

102

101

0

0.0005

0.0

0.1

0.2

0.3

0.4

CMV HIV

Absolute counts of IL-2+ cells: Ratio IL-2+/IFNγ+ cells:

Nomura et al., BMC Immunol 2006

Phenotype of CMV & HIV responses

CMV

HIV

CD27: + + - - + + - - + + - - + + - -

CD28: + + + + - - - - + + + + - - - -

CD45RA: + - - + - + - + + - - + - + - +

Cyto

kine+

cells

/ml blo

od

0

20002000

9000

0

2000

4000

CD4+ CD8+

HIV-responsive CD8+ T cells lack IL-2 production regardless of phenotype

+ - - + + - -

+ + + - - - -

- - + - + - +

HIVresponse

0.0

0.1

0.2

0.3

Rati

o o

f IL

-2+

to IFN

γ+ c

ells

CD27:+ - - + + - -

CD28:+ + + - - - -

CD45RA: - - + - + - +

0.0

0.1

0.2

0.3 CMVresponse

Hypothesis

IL-2 producing CD8+ T cells may be required to drive terminal effector differentiation

The signature of HIV responses

Magnitude: similar to CMV

Functions: Lack of IFNγ+IL-2+ CD8+ T cells

Phenotype: High fraction of intermediately differentiated CD8+ T cells (CD27+CD28-CD45RA-)

But what about other functions and phenotypes?

T cell markers of interest

Lineage markers: CCR7 CD3 CD4 CD8 CD14 CD16 CD19 CD20 CD25 CD27 CD28 CD33 CD38 CD45RA CD49d CD56 CD57 CD85j CD94 CD127 HLA-DR IgD TCR /g d

Activation markers: CD154 IL-2 IL-4 IL-10 IL-17 IFNg GM-CSF TNFa Granzyme B Perforin CD38 CD69 CD107a PD-1

Mass Cytometry Rationale

Fluorescence cytometry Mass cytometryY Y

• Many more labels (antibodies)• Little or no spillover

CyTOF Principle

Element-LabeledAntibodies

tim

e

atomic mass

Initial gating of CyTOF data

Intact cell gate:

Singlet gate:

Live/dead discrimination:

Lymph vs mono gate:

CyTOF vs. fluorescence

IgD CD4 CD56C

D16

CD

8

CD

27

B cells: T cells: NK cells:

CyT

OF

LSR

II

So what’s wrong with mass cytometry?

Collection speed: ~500 events per second maximum to avoid too many doublets

Cell efficiency: only ~1/3 of injected cells are collected

Sensitivity: no channel as bright as PE in fluorescence cytometryBut multiple markers for each cell subset can partially

overcome this

Destructive: can’t sort cells of interest for downstream applicationsProbably not that important

Overcoming CyTOF speed limitations

Enrichment of activated cells (Axel Schultz, HIMC):

% IFNg+

Pre-enrichment Negative fraction Positive fraction

0.40 % 0.08 % 35 %

Enrichment - - 86x

Loss - ≈ 20 % -

IFNg

CD

40L

Cell-surface barcoding(Henrik Mei, HIMC)

• Small sample loading time savings

• Large savings of staining Abs

• Improved staining consistency

CD

45

Pd

10

5

CD

45

Pd

10

4

CD

45

Pd

11

0

CD

45

Pd

10

6

CD

45

Pr1

41

CD

45

Pd

10

8

Composite sample

CD45 Pr141 CD45 Pd110 CD45 Pd108 CD45 Pd106 CD45 Pd105 CD45 Pd104

CyTOF ICS: CMV pp65 stimulation(Sheena Gupta, HIMC)

Dead CD14 CD3 CD4

CCR7 IFNg TNF GM-CSF

CCR7 IFNg TNF GM-CSF

CD

33

CD

8

CD

45

RO

IL-2

MIP

-1b

IL-1

7

CD

45

RO

IL-2

MIP

-1b

IL-1

7

Basicgates

CD4+

CD8+

Alternative visualization/analysis approaches

SPADE (Qiu et al., 2011):Clustering of events in N dimensionsDisplay of clusters by relatedness in a 2-dimensional “tree”:

CD4 naïve/CMCD4 EM CD8 naïve/CM

CD8 EM/effector

CD45RA

SPADE – CMV-specific cytokine expression

IFNγ TNFα

CD4 CM

CD4 EM

CD8 EM

CD8 CM

CD4 CM

CD4 EM

CD8 EM

CD8 CM

SPADE – CMV-specific cytokine expression

IL-2 GM-CSF

CD4 CM

CD4 EM

CD8 EM

CD8 CM

CD4 CM

CD4 EM

CD8 EM

CD8 CM


Principal components analysis (PCA):No clusteringAll events are shown in a 2- or 3-dimentional space, with

axes that are composite vectors of the actual markers usedIdea is to maximize separation of events

PCA analysis of CMV responseto pp65 vs. IE-1


Sparse clustering (Tyson Holmes):K-means type clustering in N

dimensions, after filtering for relevant dimensions

All events are shown in a 2-dimensional projection, with vectors that show the individual dimensions that were useful in cluster definition

Can also test for significant differences between groups for each cluster

EBV – Solid organ transplant study(Olivia Martinez, Dongxia Lin)

Organ transplant recipients are at risk for EBV disease, including post-transplant lymphoproliferative disorder (PTLD)

Anti-viral prophylaxis is expensive, has side effects, and is not always effective for EBV

Following viral load and adjusting immunosuppression is not idealToo little, too lateEarlier prognosis of risk is needed: T cell response?

unstimulated Lytic proteins mix Latent proteins mix

Example of CD8+ T cell response to EBV peptide mixes

Sparse clustering of EBV-responsive CD8+ T cells

Latent Lytic

Hea

lthy

Tra

nspl

ant

Unstimulated

Sparse clustering of EBV-responsive CD8+ T cells

Conclusions (1)

CMV- and EBV-specific T cell responses can be measured by mass cytometry, and analyzed by:SPADEPrincipal Components Analysis (PCA)Sparse clustering analysis

EBV-specific T cell responses in organ transplant patients may lack polyfunctional cells responding to lytic antigens

The argument for measuring immunocompetence in cancer patients

Tumors are treated with surgery, radiation, and chemotherapy, all of which are immunosuppressive

There is growing interest and success in combining these with immunotherapyBlockade of inhibitory pathways (CTLA-4, PD-1)Augmentation of costimulatory pathways (CD137)Direct vaccination for inducing tumor-specific immunity

Yet we don’t usually check the immunocompetence of patients to respond to immunotherapyCould be prognostic for response generally, or to specific

agents

IL-2 expression (PMA+ionomycin)

PatientsControls

monocytesCD4+ T cells

CD8+ T cells NK cells

B cells



B cells



B cells



B cells



B cells

TNFa expression (PMA+ionomycin)

PatientsControls



B cells



B cells



B cells



B cells



B cells

IFNg expression (PMA+ionomycin)

PatientsControls



B cells



B cells



B cells



B cells



B cells

IL-17 expression (PMA+ionomycin)

PatientsControls



B cells



B cells



B cells



B cells



B cells

PD-1 expression (unstimulated)

PatientsControls



B cells



B cells



B cells



B cells



B cells



B cells

CD27+CD27-

CD45RO+CD45RO-CD49d+CD49d-CD57+CD57-

CD85j+CD85j-CD94+CD94-

CD137+CD137-

CD137L+CD137L-

GranzymeB+GranzymeB-

IFNg+IFNg-IL-2+IL-2-

IL-17+IL-17-IL10+IL10-

MIP1b+MIP1b-

Perforin+Perforin-

Q1: CD69–, CD107a+Q2: CD69+, CD107a+Q3: CD69+, CD107a–Q4: CD69–, CD107a–

Q5: IL-2–, GMCSF+Q6: IL-2+, GMCSF+Q7: IL-2+, GMCSF–Q8: IL-2–, GMCSF–

Q9: GranzymeB–, Perforin+Q10: GranzymeB+,

Perforin+Q11: GranzymeB+, Perforin–Q12: GranzymeB–, Perforin–

Q13: IFNg–, IL10+Q14: IFNg+, IL10+Q15: IFNg+, IL10–Q16: IFNg–, IL10–

Q17: TNF–, IL-17+Q18: TNF+, IL-17+Q19: TNF+, IL-17–Q20: TNF–, IL-17–

Q21: MIP1b–, PD1+Q22: MIP1b+, PD1+Q23: MIP1b+, PD1–Q24: MIP1b–, PD1–

Q25: CD25–, CD127+Q26: CD25+, CD127+Q27: CD25+, CD127–Q28: CD25–, CD127–

Q29: CD45RA–, CCR7+Q30: CD45RA+, CCR7+Q31: CD45RA+, CCR7–Q32: CD45RA–, CCR7–

Q33: CD45RO–, CD45RA+Q34: CD45RO+, CD45RA+Q35: CD45RO+, CD45RA–Q36: CD45RO–, CD45RA–

Q37: CD127–, CCR7+Q38: CD127+, CCR7+Q39: CD127+, CCR7–Q40: CD127–, CCR7–Q41: CD27–, CCR7+Q42: CD27+, CCR7+Q43: CD27+, CCR7–Q44: CD27–, CCR7–

TNF+TNF-

P1 P2 P3 P4 P5 P6 P7 C1 C2 C3

Summary of patients and controls:CD4+ T cells, PMA+ionomycin

patients ctrls

P1 P2 P3 P4 P5 P6 P7 C1 C2 C3

CD8+ T cells, PMA+ionomycin

CD27+CD27-

CD45RO+CD45RO-CD49d+CD49d-CD57+CD57-

CD85j+CD85j-CD94+CD94-

CD137+CD137-

CD137L+CD137L-GMCSF+GMCSF-

GranzymeB+GranzymeB-

IFNg+IFNg-IL-2+IL-2-

IL-17+IL-17-IL10+IL10-

MIP1b+MIP1b-PD1+PD1-

Perforin+Perforin-

Q1: CD69–, CD107a+Q2: CD69+, CD107a+Q3: CD69+, CD107a–Q4: CD69–, CD107a–

Q5: IL-2–, GMCSF+Q6: IL-2+, GMCSF+Q7: IL-2+, GMCSF–Q8: IL-2–, GMCSF–

Q9: GranzymeB–, Perforin+Q10: GranzymeB+, Perforin+Q11: GranzymeB+, Perforin–Q12: GranzymeB–, Perforin–

Q13: IFNg–, IL10+Q14: IFNg+, IL10+Q15: IFNg+, IL10–Q16: IFNg–, IL10–

Q17: TNF–, IL-17+Q18: TNF+, IL-17+Q19: TNF+, IL-17–Q20: TNF–, IL-17–

Q21: MIP1b–, PD1+Q22: MIP1b+, PD1+Q23: MIP1b+, PD1–Q24: MIP1b–, PD1–

Q25: CD25–, CD127+Q26: CD25+, CD127+Q27: CD25+, CD127–Q28: CD25–, CD127–

Q29: CD45RA–, CCR7+Q30: CD45RA+, CCR7+Q31: CD45RA+, CCR7–Q32: CD45RA–, CCR7–

Q33: CD45RO–, CD45RA+Q34: CD45RO+, CD45RA+Q35: CD45RO+, CD45RA–Q36: CD45RO–, CD45RA–

Q37: CD127–, CCR7+Q38: CD127+, CCR7+Q39: CD127+, CCR7–Q40: CD127–, CCR7–Q41: CD27–, CCR7+

Q42: CD27+, CCR7+Q43: CD27+, CCR7–Q44: CD27–, CCR7–

TNF+TNF-

patients ctrls

Conclusions (2)

Mass cytometry (CyTOF) is a powerful platform for both broad immune profiling and detailed tracking of rare (antigen-specific) populations

Cancer patients appear to have more heterogenous immune profiles than healthy controls

Specific immune profiles may be prognostic for responses to immunotherapy

HIMC:• Mike Leipold

• Serena Chang

• Sheena Gupta

• Meena Malipatlolla

• Rosemary Fernandez

• Dongxia Lin

• Xuahai Ji

• Igor Goncharov

• Iris Herschmann

• Ajay Fernandez

• Sanchita Bhattacharya

• Nicole Dalal

• Henrik Mei

• Axel Schultz

• Rohit Gupta

• Janine Sung

• Alaina Puleo

• Blanca Calvillo

• Glenn Dawes

• Yael Rosenberg-Hasson

• Other Stanford:

• Holbrook Kohrt

• Olivia Martinez

• Evan Newell

• Mark Davis

• Sean Bendall

• Garry Nolan

• Tyson Holmes

• BD Biosciences:

• Laurel Nomura

• Maria Jaimes

• Skip Maino

• UCSF:

• Mike McCune

• Doug Nixon

• Steve Deeks

AcknowledgementsThe Human Immune Monitoring Centerat Stanford

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