Adrian Hayday IJonathan Edgeworth IManu Shankar-Hari KCL … · 2020. 5. 29. · Adrian...

Covid–ImmunoPhenotyping - a preliminary data release, May 22, 2020

Adrian Hayday I Jonathan Edgeworth I Manu Shankar-HariKCL I CIDR I GSTT I ICU I CRICK

Summary

• Our initial high throughput in-depth analysis of over 60 hospitalised Covid-19 patients and over 50 controls has revealed:

1. An immunological signature of Covid-19 disease2. Several discrete components of that signature that track with severity

Potential importance• SARS-Cov-2 virus is not the threat to global stability; that threat stems from the

seriousness of the disease that the virus has the potential to cause

• Understanding the basis for serious disease symptoms may permit new approaches to diagnosis and therapy that may considerably reduce the risk that infection by SARS-CoV-2 would lead to severe, potentially fatal disease.

www.immunophenotype.org

Scene setting

• Despite most exposures being pauci-symptomatic, Covid-19 has been the most socially-transformative infectious disease in living memory - Millions infected; hundreds of thousands dead or only gradually recovering from severe disease -Precipitous burden on health care.

• The virus has not gone away – major rises in global infections continue to be reported, and may “re-surge” in autumn. Effective anti-virals and vaccination are still in development.

• Serious symptoms have been associated with underlying morbidities, age and gender, but are there other factors?

• Serious symptoms are clinically associated with lymphopenia, CRP, and some other metrics, but what mechanisms and processes do these reflect?


Hypothesis for Covid-IP• The immune system lies at the heart of the host-pathogen relationship and may

track with and/or underpin disease course.

Key questions• Can we identify biologically meaningful markers or ‘immune signatures’ that predict

disease course at the front door?

• Can we identify ‘immune signatures’ in patients who do well? This will guide us toward “correlates of protection”, informing vaccine design and helping to predict the likely efficacy of current or proposed medications that can be re-purposed?

• What is the relative contribution to sustained protection of T cells versus antibody production?

• What is the nature of covid-19 lymphopenia and what are its implications - an impact on innateàadaptive switch?


The Covid-IP Pipeline *

Day 1, 3, 9Co-ordination with other studies Serology; IgG/IgM [S/RBP/N]

ELISA- Dr Katie DooresLIPS – Professor Pärt Peterson (Tartu)

• Legendplex assay – plasma cytokines, inc. IL-6, IL-8, IP10, IL10

• Additional analytes, including Tryptophan metabolites.

• Autoantibodies

8 panel flow cytometry on fresh specimens to measure cell types,cell status, and cell cycling

Individual CD4 and CD8 T cellsare sorted and DNA extracted for T cellReceptor repertoire sequencing

Heparin

PAXgene for RNA

Plasma

Leukocytes

Biobank storage for future projects

Research Nurse

Collections

*facilitated by the King’s College Infectious Diseases Biobank

The COVID-IP cohort:hospitalized SARS-CoV-2 infected patients, ~80% of whom recovered without intensive care, and 20% of whom declined, with ~10% declined irreversibly.

Clinical conditions were classified according to a combination of local clinical practice and WHO classifications.


What we have learnedThis is a preliminary data-set, made broadly available in the hope that it can guide and/or inform others internationally. More formal peer-reviewed data releases will follow, respectful of compliance with appropriate scientific standards.


Covid-19: highly selective diminution of T cells, associated with disease but not obviously with severity

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0.0e+00

5.0e+05

1.0e+06

1.5e+06

Healthy Low Moderate SevereWHO_Severity

Time_02.Cells_02.Singlets1_02.Singlets2_02.Live_02.CD45p_02.T_02_Count_back

class_WHO_severityHealthy

Low

Moderate

Severe

class_status_detailed●

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CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

# T

cells

ns****

****

Measures of statistical significance between designated samples

A decrease in blood T cell numbers(T cell lymphopenia) is characteristic of

Covid-19 disease, as is true in several other infections. However, it shows only a weak correlation with disease severity.

This is a T cell-focused phenomenon since B lymphocyte numbers and NK

cells are largely preserved

T cells are one of the major components of the adaptive immune system.

CD4+ Helper T (Th) cells help to shape, activate and regulate the adaptive immune response by activating other immune cells,

releasing cytokines, and helping B cells produce antibodies.

CD8+ Cytotoxic T cells recognise and directly kill virus-infected cells.

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0.0e+00

2.5e+05

5.0e+05

7.5e+05


Time_02.Cells_02.Singlets1_02.Singlets2_02.Live_02.CD45p_02.T_02.CD4_02_Count_back


Low

Moderate

Severe


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CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

# CD

4+T

cells

ns****

****

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1e+05

2e+05

3e+05

4e+05


Time_02.Cells_02.Singlets1_02.Singlets2_02.Live_02.CD45p_02.T_02.CD8_02_Count_back


Low

Moderate

Severe


●

●

●

●

●

●

CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

# CD

8+T

cells

ns****

***

Covid-19: both Helper and Cytotoxic T cells are diminished with disease, with some link to severity

Covid-19: T cell lymphopenia is selective: T helper (Th) cell subsets are differentially affected

The impact on CD4+ Helper T cells is selective Th1, Th17.1 cells are diminished, while Th2 and Th17 are relatively unaffected

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●●0.0e+00

5.0e+04

1.0e+05

1.5e+05


Time_02.Cells_02.Singlets1_02.Singlets2_02.Live_02.CD45p_02.T_02.CD4_02.CD4_CXCR3p_02.CD4_Th17.1_02_Count_back


Low

Moderate

Severe


●

●

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●

CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

# Th17.1 cells

ns

*******

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●●0.0e+00

5.0e+04

1.0e+05

1.5e+05

2.0e+05


Time_02.Cells_02.Singlets1_02.Singlets2_02.Live_02.CD45p_02.T_02.CD4_02.CD4_CXCR3p_02.CD4_Th1_02_Count_back


Low

Moderate

Severe


●

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CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

# Th1 cells

ns

****

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0.00e+00

2.50e+04

5.00e+04

7.50e+04

1.00e+05

1.25e+05


Time_02.Cells_02.Singlets1_02.Singlets2_02.Live_02.CD45p_02.T_02.CD4_02.CD4_CCR4p_02.CD4_Th2_02_Count_back


Low

Moderate

Severe


●

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●

CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

# Th2 cells

ns

nsns

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0.0e+00

5.0e+04

1.0e+05

1.5e+05

2.0e+05


Time_02.Cells_02.Singlets1_02.Singlets2_02.Live_02.CD45p_02.T_02.CD4_02.CD4_CCR4p_02.CD4_Th17_02_Count_back


Low

Moderate

Severe


●

●

●

●

●

●

CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

# Th17 cells

ns

nsns

Covid-19: Other CD4+T cell subsets are also differentially affected

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●●0.0e+00

5.0e+04

1.0e+05

1.5e+05


Time_02.Cells_02.Singlets1_02.Singlets2_02.Live_02.CD45p_02.T_02.CD4_02.CD4_CD45RAn_CCR7n_02_Count_back


Low

Moderate

Severe


●

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●

●

CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

CD4 TEM

ns

****

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0e+00

2e+05

4e+05

6e+05


Time_02.Cells_02.Singlets1_02.Singlets2_02.Live_02.CD45p_02.T_02.CD4_02.CD4_CD45RAn_CCR7p_02_Count_back


Low

Moderate

Severe


●

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●

●

●

CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

CD4 TCM

ns

**

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●●0e+00

2e+04

4e+04

6e+04

8e+04


Time_02.Cells_02.Singlets1_02.Singlets2_02.Live_02.CD45p_02.T_02.CD4_02.CD4_CD45RAp_CCR7n_02_Count_back


Low

Moderate

Severe


●

●

●

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●

CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

CD4 TEMRA

ns

nsns

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0.0e+00

2.5e+05

5.0e+05

7.5e+05


Time_02.Cells_02.Singlets1_02.Singlets2_02.Live_02.CD45p_02.T_02.CD4_02.CD4_CD45RAp_CCR7p_02_Count_back


Low

Moderate

Severe


●

●

●

●

●

●

CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

CD4 TN

ns

*****

Naïve T (TN ) cells have not yet encountered their specific, and

antigen are usually resting

TEMRA - terminally differentiated effector

memory cells - are temporarily exhausted

Central Memory T (TCM) cells act as a reservoir of our response to secondary

infections

Effector Memory T ( TEM ) cells will interact with host cells to

carry out their immune function

Reduced levels of CD4naive and CD4EM in Covid-19, while CD4CM, and CD4EMRA cells are largely protected

Covid-19 : The impact on CD8+ T cells is also selective

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●●0.0e+00

2.5e+04

5.0e+04

7.5e+04

1.0e+05


Time_02.Cells_02.Singlets1_02.Singlets2_02.Live_02.CD45p_02.T_02.CD8_02.CD8_CD45RAn_CCR7n_02_Count_back


Low

Moderate

Severe


●

●

●

●

●

●

CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

CD8 TEM

ns

*****

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0e+00

1e+04

2e+04

3e+04

4e+04

5e+04


Time_02.Cells_02.Singlets1_02.Singlets2_02.Live_02.CD45p_02.T_02.CD8_02.CD8_CD45RAn_CCR7p_02_Count_back


Low

Moderate

Severe


●

●

●

●

●

●

CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

CD8 TCM

ns

***ns

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Time_02.Cells_02.Singlets1_02.Singlets2_02.Live_02.CD45p_02.T_02.CD8_02.CD8_CD45RAp_CCR7n_02_Count_back


Low

Moderate

Severe


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CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

CD8 TEMRA

nsns

**

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5.0e+04

1.0e+05

1.5e+05

2.0e+05

2.5e+05


Time_02.Cells_02.Singlets1_02.Singlets2_02.Live_02.CD45p_02.T_02.CD8_02.CD8_CD45RAp_CCR7p_02_Count_back


Low

Moderate

Severe


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●

CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

CD8 TN

ns****

As for CD4 cells, CD8naive and CD8EM cells are reduced in Covid-19; but no clear link to severity

TEMRA - terminally differentiated effector

memory cells - are temporarily exhausted

Central Memory T (TCM) cells act as a reservoir to our response to secondary

infections

Effector Memory T ( TEM ) cells will interact with host cells to

carry out their immune function

Naïve T (TN ) cells have not yet encountered their specific, and

antigen are usually resting

Covid-19: remaining T cells, although low in numbers, are actively traversing the cell cycle and this is linked to severity

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0

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20

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60

70


G1_5_CD8_CD45RAn_CCR7n_5_freq


Low

Moderate

Severe


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CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

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0

10


S_G2_M_5_CD8_CD45RAn_CCR7n_5_freq


Low

Moderate

Severe


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●

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●

CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

% G

1 CD

8 T E

M

% S

/G2/

M C

D8 T

EM

****

***

ns***

*

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20

30

40


G1_5_CD4_CD45RAn_CCR7n_5_freq


Low

Moderate

Severe


●

●

●

●

●

●

CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

% G

1 CD

4 T E

M

% S

/G2/

M C

D4 T

EM

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5


S_G2_M_5_CD4_CD45RAn_CCR7n_5_freq


Low

Moderate

Severe


●

●

●

●

●

●

CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

*****

****

****

Cell cycle

Covid-19: a collapse of Vδ2 T cells is a disease signature, that is particularly striking in some patients

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40

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60

70

80


gd_Vd1_03.gd_03_freq


Low

Moderate

Severe


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CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

% Vd 1

+T

cells

(of gd )

ns**

**

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30

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70

80

90


gd_Vd2_03.gd_03_freq


Low

Moderate

Severe


●

●

●

●

●

●

CF

control

COVID

COVID_deceased

IMMERSE

Recovered_confirmed

Recovered_suspected

% Vd2

+T

cells

(of gd )

ns****

***

gδ TCR+ T cells express a γδ-TCR rather than the αβ-TCR on the cell surface, and

are a predominant lymphoid population in body surfaces such as the lung which is targeted by SARS-Cov-2.

There is a collapse in numbers of the predominant blood gd cells type (Vd2+). Conversely, Vδ1+ T cells show increased representation and their active cycling

is severity associated (not shown).

Innate immunity markers


Loss of basophils from the blood appears as a biomarker of COVID-19 severity

Basophils assessed via flow cytometry [gated as non-PMN based on ssc and then CD3neg, CD19neg,HLADRneg, CD14neg, CD123+]

Basophils are potentially disease-relevant because of their homing to the lung, wherein Covid-19 disease is manifest, and because of their implication in regulating blood coagulation

Basophils via flow cytometry

****

% B

asop

hils

(of l

ive

cells

)

****

WHO_Severity

• Cell cycling of CD4EM , CD4CM , CD8EM , Vd1 cells• Depletion of specific CD4 and CD8 T cell subsets• Basophil counts crashing• Neutrophil counts increasing• Crash in plasmacytoid dendritic cells• Sustained IL6, IP10, IL10• CRP

Other major changes, e.g. including plasmablastexpansion, major changes in monocyte populations; reductions in CD5+ B cells, change in DC phenotypes,

and production of antibodies compose a disease-associated signature

but are not obviously linked to severity

In sum, several biomarkers of COVID-19 severity are emerging

Basophils via flow cytometry

****

% B

asop

hils

(of l

ive

cells

)

****

WHO_Severity

Take home messages• Phenotyping à define perturbations in immunityà design a ‘panel’ of descriptive

markers tied to severe disease à prospectively evaluated as a predictive score for severity à clinical endpoint e.g. decision aid for early ICU admission

• Phenotypingà insight into disease pathologyà ongoing investigation into what drives T lymphopenia and how this impacts on memory responsesà informs Vaccine design

• Data will be freely available on www.immunophenotype.org.

• Applications alongside GSTT clinical trials e.g. phenotyping before and after therapeutic interventions (e.g. IL7, anti-IL6) à mechanism of action


http://www.immunophenotyping.org/

Acknowledgements

All ward staff and infection departmentAll project PI’s for RECOVERY/IMMERSE/ISARIC/ACT

King’s Infectious Diseases BiobankJohn CasonChristine Mant

Adrian HaydayJonathan EdgeworthManu Shankar-Hari

Irene del Molino del BarrioIsaac Franco QuijornaJosh FreedmanKatie Doores

Eva Bugallo BlancoJulie ChanRozalyn YorkeSarah GeeSarah RyanThomas Hayday

Centre for Clinical Infection and Diagnostics Research (CIDR) - GSTT NHS Foundation Trust

Amy DayKaren BisnauthsingKate BrooksLauren MartinezBlair Merrick

John Black Trust

Manu Shankar-Hari lab at KCLMatthew FishAislinn Jennings

Hayday lab at The CRICK, KCL and CIBCI

Abhishek DasAdam LaingAnna LorencDaniel DaviesDuncan MckenzieEfstathios TheodoridisFernanda Kyle Florencia CanoFurqan Shah

Iva ZlatarevaLeticia Monin AldamaMagdalene JosephMiguel Muñoz-RuizPierre VantouroutRichard DavisShraddha KamdarVassia SofraYin Wu

Kola BabalolaFederico López GómezJeremy MasonVioletta MuñozJonathan Warren

Sara Cipolat

Institute of Molecular Biology and Pathology (CNR-Italy)Francesca Di Rosa

Kai KisandPärt Peterson

UNIVERSITY OF TARTU (Estonia)

GSTT-King’s Biomedical Research CentreMatthew Brown Richard Ellis

Visit here for full list of Team Members & Collaborations


https://www.immunophenotype.org/index.php/project-details/team-and-collaborations/

Adrian Hayday IJonathan Edgeworth IManu Shankar-Hari KCL … · 2020. 5. 29. · Adrian...

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