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Webinar Series Overcoming challenges in cellular analysis Multiparameter analysis of rare cells January 28, 2015 Instructions for Viewers To share webinar via social media: To share webinar via e mail: To see speaker biographies,

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Overcoming challenges in cellular analysis

Multiparameter analysis of rare cells

January 28, 2015

Webinar Series

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SLIDE 2

Participating Experts

Brought to you by the Science/AAAS Custom Publishing Office

Overcoming challenges in cellular analysis

Multiparameter analysis of rare cells

January 28, 2015

Andrea Cossarizza, M.D., Ph.D. University in Modena and Reggio Emilia School of Medicine Modena, Italy David Cousins, Ph.D. University of Leicester Leicester, UK

Webinar Series

Sponsored by:

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ANDREA C ANDREA COSSARIZZA SSARIZZA

Multiparam eter analysis of rare cells

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  • Rare cell analysis: background and keypoints

OUTLINE OF THE TALK

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  • Rare cell analysis: background and keypoints
  • Main problems in the detection of such cells

OUTLINE OF THE TALK

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  • Rare cell analysis: background and keypoints
  • Main problems in the detection of such cells
  • Possible solutions: from hardware to software

OUTLINE OF THE TALK

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  • Rare cell analysis: background and keypoints
  • Main problems in the detection of such cells
  • Possible solutions: from hardware to software
  • Rare cells in the immune system: the case of iNKT

OUTLINE OF THE TALK

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  • >30 years ago: enumeration of fetal red blood cells

in the maternal circulation at a frequency of 1/10,000 to 1/100,000 by Cupp.

BACKGROUND

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  • >30 years ago: enumeration of fetal red blood cells

in the maternal circulation at a frequency of 1/10,000 to 1/100,000 by Cupp.

  • Now: detection and quantitation of several rare cell

populations in blood or bone marrow.

BACKGROUND

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  • >30 years ago: enumeration of fetal red blood cells

in the maternal circulation at a frequency of 1/10,000 to 1/100,000 by Cupp.

  • Now: detection and quantitation of several rare cell

populations in blood or bone marrow.

  • Essential tool in the diagnosis and monitoring of

hematological cancers and immunological disorders, as well as in the identification of Ag‐specific cells.

BACKGROUND

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  • Rare‐event analysis is the art of finding a

needle in a haystack

WARNING

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  • Rare‐event analysis is the art of finding a

needle in a haystack

  • The frequency of the event of interest, and

the signal‐to‐noise ratio of the method used to detect the event are the two most important factors.

WARNING

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  • ‘‘Rare‐event analysis’’: detection of events that occur

at a frequency of 1 in 1,000 (0.1%) or less, although the record claimed in the literature has long stood at 1 cell in 10,000,000 (0.00001%) for tumor cells spiked into peripheral blood.

KEY POINTS

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  • ‘‘rare‐event analysis,’’: detection of events that occur

at a frequency of 1 in 1,000 (0.1%) or less, although the record claimed in the literature has long stood at 1 cell in 10,000,000 (0.00001%) for tumor cells spiked into peripheral blood.

  • Detecting an event at low frequency requires a high

signal‐to‐noise ratio and the acquisition of a large number of events.

KEY POINTS

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  • Ag‐specific T cells
  • NKT and iNKT cells
  • Circulating endothelial cells and precursors
  • Stem cells (CD34+)
  • Particular lymphocytes subpopulations
  • Circulating tumor cells
  • Polyfunctional assays
  • ..........

IMMUNOLOGIST'S INTERESTS

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Open pre‐analytical questions

  • How much blood from patients?
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  • How much blood from patients
  • Lack of available standardized method

Open pre‐analytical questions

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  • How much blood from patients
  • Lack of available standardized method
  • Enriched or non enriched populations

Open pre‐analytical questions

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  • How much blood from patients
  • Lack of available standardized method
  • Enriched or non enriched populations
  • How many markers/colors

Open pre‐analytical questions

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  • How much blood from patients
  • Lack of available standardized method
  • Enriched or non enriched populations
  • How many markers/colours
  • How many cells

Open pre‐analytical questions

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  • How much blood from patients
  • Lack of available standardized method
  • Enriched or non enriched populations
  • How many markers/colours
  • How many cells
  • Exclusion of doublets, dead cells and debris:

use of a DUMP CHANNEL

Open pre‐analytical questions

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Number of events to acquire

CV (%) 1 2.5 5 10 20 Positive cells required 10,000 1,600 400 100 25 Frequency EVENT NUMBER TO ACQUIRE % l/n 10 10 100,000 16,000 4,000 1,000 250 1 100 1,000,000 160,000 40,000 10,000 2,500 0.1 1,000 10,000,000 1,600,000 400,000 100,000 25,000 0.01 10,000 100,000,000 16,000,000 4,000,000 1,000,000 250,000 0.001 100,000 1,000,000,000 160,000,000 40,000,000 10,000,000 2,500,000

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  • Which instrument, and which performances

Open analytical questions

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  • Which instrument, and which performances
  • Flow cytometer rates of acquisition

Open analytical questions

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  • Which instrument, and which performances
  • Flow cytometer rates of acquisition
  • Maximize the signal‐to‐noise ratio of the

cells of interest from the background

Open analytical questions

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  • Which instrument, and which performances
  • Flow cytometer rates of acquisition
  • Maximize the signal‐to‐noise ratio of the

cells of interest from the background

  • Data acquisition: instrument clean and the

background level of noise below the threshold

Open analytical questions

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  • Which instrument, and which performances
  • Flow cytometer rates of acquisition
  • Maximize the signal‐to‐noise ratio of the

cells of interest from the background

  • Data acquisition: instrument clean and the

background level of noise below the threshold

  • Spill over and carry over

Open analytical questions

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  • Which instrument, and which performances
  • Flow cytometer rates of acquisition
  • Maximize the signal‐to‐noise ratio of the

cells of interest from the background

  • Data acquisition: instrument clean and the

background level of noise is below the threshold

  • Spill over and carry over
  • Adequate software

Open analytical questions

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Our previous experience

Polyfunctional analysis of Ag‐specific cells

2012

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THE INTERBETWEENERS: INNATE‐LIKE LYMPHOCYTES

Types of lymphocyte that blur the traditional boundaries between innate and adaptive immunity

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Types of lymphocyte that blur the traditional boundaries between innate and adaptive immunity

Invariant Natural Killer T cells (iNKT) Poised to robustly produce cytokines more rapidly than conventional naïve T cells

THE INTERBETWEENERS: INNATE‐LIKE LYMPHOCYTES

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Types of lymphocyte that blur the traditional boundaries between innate and adaptive immunity

Invariant Natural Killer T cells (iNKT) Mucosal associated invariant T cells (MAIT) Poised to robustly produce cytokines more rapidly than conventional naïve T cells Preferentially localized in the mucosal tissues

THE INTERBETWEENERS: INNATE‐LIKE LYMPHOCYTES

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Types of lymphocyte that blur the traditional boundaries between innate and adaptive immunity

Invariant Natural Killer T cells (iNKT) Mucosal associated invariant T cells (MAIT)  T cells Poised to robustly produce cytokines more rapidly than conventional naïve T cells Pre‐programmed to acquire their effector functions before egress from thymus Preferentially localized in the mucosal tissues

THE INTERBETWEENERS: INNATE‐LIKE LYMPHOCYTES

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iNKT cells MAIT cells

Frequency (% among human PBMCs)

0.01‐1%

1‐10% Receptors semi‐invariant Vα24‐Jα18 TCR, NK receptors semi‐invariant Vα7.2‐Jα33 TCR, high levels of CD161,IL‐18Rα. Antigen recognized glycolipid antigens presented by CD1d microbial antigens presented by MR1 Subsets CD4+, CD8+, and CD4‐CD8‐ CD4+, CD8+, and CD4‐CD8‐ Function Regulatory Effector‐memory phenotype

DIFFERENT CHARACTERISTICS OF INKT AND MAIT CELLS

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Gating strategy for iNKT cells and their main subsets

SSC CD3 DUMP CHANNEL (CD14,CD19) Vα24Jα18Vβ11 TCR SSC SSC

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CD8 CD4 CD161 CD161 SSC SSC

Gating strategy for iNKT cells and their main subsets

SSC CD3 DUMP CHANNEL (CD14,CD19) Vα24Jα18Vβ11 TCR SSC SSC

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NKT cells and Multiple Sclerosis (MS)

Berzins SP., Nat Rev Immunol. 2011

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Studies on a subset of NKT cells

Polyfunctionality of iNKT cells in patients affected by different

forms of Multiple Sclerosis (Relapsing‐Remitting RR, Primary Progressive PR, Secondary Progressive SP), in the framework of a project sponsored by the Italian Foundation for Multiple Sclerosis ‐ FISM

  • 3 RR patients (treated with Natalizumab)
  • 2 PR patiens
  • 5 SP patients
  • 5 CTR (healthy subjects)
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  • PBMCs isolation from >30 mL of blood
  • Stimulation with PMA (100 ng/ml) plus ionomycin (1 g/ml) for 4 hrs
  • ICS with following markers:

Live Dead (Aqua) CD3 PE‐CY5 CD4 AF700 CD8 APC‐CY7 iTCR (V24‐J18) PE IFN‐gamma FITC IL‐4 APC IL‐17 BV421 TNF‐alpha BV605

Methods

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  • PBMCs isolation from >30 mL of blood
  • Stimulation with PMA (100 ng/ml) plus ionomycin (1 g/ml) for 4 hrs
  • ICS with following markers:

Live Dead (Aqua) CD3 PE‐CY5 CD4 AF700 CD8 APC‐CY7 iTCR (V24‐J18) PE IFN‐gamma FITC IL‐4 APC IL‐17 BV421 TNF‐alpha BV605

  • 10‐20 millions events acquired on Attune NxT (Life Technologies/Thermo Fisher),

at a speed up to 35,000 cells/second

  • mAbs titrated on 20 millions PBMCs
  • Compensation matrix set using single stained samples and FMO (Fluorescence

Minus One) approach

Methods

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Gating strategy

FSC‐H FSC‐A

Live Dead CD3 CD4 CD8 CD3 iTCR CD4 CD8

Live Dead

CD3

iTCR

TNF‐

TNF‐

IL‐17A

23,053 iNKT cells 6,720,084 T cells

Junk removal

72.4 0.08 39.6 56.3 2.4 29.0 1.5 52.2 17.3 1.7

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IL‐17A IFN‐ IFN‐ TNF‐ IL‐4 TNF‐ TNF‐ IL‐17A

GATED ON CD8+ T CELLS GATED ON CD4+ T CELLS GATED ON CD4‐CD8‐ T CELLS

Analysis of CD3+ T cells

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GATED ON CD8+ iNKT CELLS GATED ON CD4+ iNKT CELLS GATED ON CD4‐CD8‐ iNKT CELLS

IFN‐ IL‐17A IFN‐ TNF‐ IL‐4 TNF‐ TNF‐ IL‐17A

Analysis of iNKT cells

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Software: Attune NxT FlowJo using Boolean Gate function Pestle to transfer files SPICE to plot data First analysis of the polyfunctionality of:

  • CD4+ T cells
  • CD8+ T cells
  • CD4‐CD8‐ T cells

Then, evaluation of the polyfunctionality of the rare cells of interest, i.e.:

  • CD4+ iNKT cells
  • CD8+ iNKT cells
  • CD4‐CD8‐ iNKT cells

Considering 4 intracellular cytokines, there are 24 (=16) different cell populations, well represented by using SPICE.

Data analysis

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CTR RR (Nat) PP

CD4+ T CELLS response

SP

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CD8+ T CELLS response

CTR RR (Nat) PP SP

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CD4‐CD8‐ T CELLS response

CTR RR (Nat) PP SP

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CD4+ iNKT CELLS response

RR (Nat) PP SP CTR

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CD8+ iNKT CELLS response

RR (Nat) PP SP CTR

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CD4‐CD8‐ iNKT CELLS response

RR (Nat) PP SP CTR

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  • CECs and EPCs are extremely rare events

(0.1 – 0.0001% in buffy coat)

  • Absence of standardized protocol
  • Lack of unique markers
  • The needle and the damage done (by the

venipuncture)...

Circulating Endothelial Cells (CEC) Circulating Endothelial Cell Precursors (EPC)

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FSC‐A FSC‐H CD45‐BV 510 LIVE DEAD AQUA SSC Syto16 SSC CD34‐BV 605 SSC Time SSC CD31‐APC‐CY7 CD133‐APC CD276‐PE SSC CD309‐PECY7 GATED ON CD133‐,CD31+ GATED ON CD133+,CD31+ Gated on Syto16

Gating strategy for their identification

EPC CEC

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CONCLUSIONS

  • Studying rare cells requires careful attention,
  • ptimal methodologies in all phases, including

collection of biological samples, adequate software and hardware.

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CONCLUSIONS

  • Studying rare cells requires careful attention,
  • ptimal methodologies in all phases, including

collection of biological samples, adequate software and hardware.

  • I have shown you some examples (besides Ag‐

specific cells) that could be of interest for immunologists.

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CONCLUSIONS

  • Studying rare cells requires careful attention,
  • ptimal methodologies in all phases, including

collection of biological samples, adequate software and hardware.

  • I have shown you some examples (besides Ag‐

specific cells) that could be of interest for immunologists.

  • "Next generation" instruments that work at a

very high speed and sensitivity are now allowing an easy detection and analysis of such cells.

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ACKNOWLEDGEMENTS

SARA DE BIASI, PhD

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More acknowledgments

Neurology Clinic, NOCSAE, Modena

Patrizia Sola, MD PhD Diana Ferraro, MD PhD Francesca Vitetta, MD Anna Maria Simone, MD

Chair of Pathology and Immunology, Modena

  • Prof. Marcello Pinti, PhD

Milena Nasi, PhD Lara Gibellini, PhD

  • Dr. Regina Bartolomeo
  • Dr. Elena Bianchini

Elisa Nemes, PhD (now at Univ. of Cape Town, South Africa) Enrico Lugli, PhD (now at Humanitas Institute, Milan, Italy)

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Participating Experts

Brought to you by the Science/AAAS Custom Publishing Office

Overcoming challenges in cellular analysis

Multiparameter analysis of rare cells

January 28, 2015

Andrea Cossarizza, M.D., Ph.D. University in Modena and Reggio Emilia School of Medicine Modena, Italy David Cousins, Ph.D. University of Leicester Leicester, UK

Webinar Series

Sponsored by:

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Studying human innate lymphoid cells using multi‐parameter flow cytometry Prof David Cousins Department of Infection, Immunity and Inflammation NIHR Leicester Respiratory BRU University of Leicester

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Allergic Mechanisms ‐ Th2 diseases

Akdis CA. Therapies for Allergic Inflammation. Nature Medicine 2012.

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Innate Lymphoid Cells (ILCs)

  • Lymphocyte‐like innate cells, lacking

known lineage specific markers.

  • Derived from an ID2+ common ILC

precursor.

  • Currently subdivided into 3 classes

based on effector function (i.e. cytokine output).

  • ILC1: IFN
  • ILC2: IL‐5/IL‐13.
  • ILC3: IL‐17/IL‐22.
  • Early cytokine producers during

immune response.

Spits, H. et al. Nat Rev Immunol 13, 145–149 (2013)

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Type 2 responses – 2013

Walker, Barlow & McKenzie. Innate lymphoid cells — how did we miss them? Nature Reviews Immunology 2013; 13, 75‐87.

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Type 2 Innate Lymphoid Cells (ILC2s) ‐ mice

  • ILC2s (nuocytes) first identified in the small intestine using an IL‐13 eGFP

reporter mouse (Neill et al. Nature 2010).

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Type 2 Innate Lymphoid Cells (ILC2s) ‐ mice

  • ILC2 numbers have been found to increase in multiple mouse models of airways

disease (Stockinger lab/Umetsu lab/Hendriks lab).

  • ILC2s are responsible for early source of Type 2 cytokines (Halim et al. 2014).

(■) (■) (□)

Wild type + Papain Rag1‐/‐ + Papain Wild type untreated Influenza infection Chang et al. Nat Immunol, 2011 vol. 12 (7) 631

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Human ILC2s – Challenges

  • Lack of Lineage specific markers
  • Cannot use fluorescent reporters
  • Cells are rare!

(Neill et al. 2010)

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Type 2 Innate Lymphoid Cells (ILC2s) ‐ humans

  • lung parenchyma and BAL (Monticelli et al. 2011).

a c

TCR , CD11c CD11b CD56 CD19 CD56 CD127 CD3 CD3 Lung ILC BAL TCR , CD11c CD3 CD11b CD56 CD19 CD56 CD127 CD3 ILC

105

103 102 104 105 103 102 104 105 103 102 104 105 103 102 104 0102 103 104 105 0102 103 104 105 0 102 103 104 105 0 102 103 104 105 105 103 102 104 105 103 102 104 105 103 102 104 105 103 102 104 0102 103 104 105 0102 103 104 105 0 102 103 104 105 0 102 103 104 105

b

HC

71

105 104 103 102 102103104 CRTH2 HC

*

CRS 4 105 CD117 CRTH2+ cells (% of CD45+ population)

2 2 25

CRS

33 11 32 24

3 2 1

  • Fetal and adult lung and nasal polyps, CRTh2 positive (Mjosberg et al. 2011).
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Human ILC2s – Aims

Multi parameter flow cytometry:

  • Determine whether an ILC2 population could be

identified in human peripheral blood.

  • Optimise a protocol for ILC2 isolation and culture.
  • Phenotype human ILC2s based on surface marker

expression and secreted inflammatory mediators.

  • Examine a role in experimental rhinovirus infection.
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Instrumentation

  • Life Technologies Attune acoustic focussing flow cytometer
  • High flow rate – 1ml/minute with low variation
  • Good for rare events
  • New machine NxT capable of 14 colours
  • BD FACS Aria II flow sorter
  • 5 laser instrument – very flexible
  • Can sort cells – enrichment needed prior to sorting
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ILC2s were Identified in Human Peripheral Blood

PBMC magnetic depletion strategy developed using mouse anti‐human CD3/14/16/19 and pan mouse IgG Dynabeads for ILC2 enrichment. CRTh2 Hematopoietic Lineage cocktail – FITC (ebio): CD2/3/14/16/19/56/235a + CD123.

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ILC2s Have a Distinct Phenotype

  • ILC2s: Lin-, : CD34-, CRTH2+ CD127+,CD45+CXCR3- CCR4+IL17BR+ with

populations of CD117 and CCR6 positive cells.

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ILC2s express IL‐13 ex vivo and after in vitro proliferation

ILC2s after 7 days of culture with rIL2/7/25/33 PBMCs stimulated with PMA/Ionomycin 4hrs ILC2s proliferate in response to IL25/IL33 (Day 7)

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ILC2 activation ‐ time‐course

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Rhinovirus infected BEC cultures Nasal mucosal fluid

IL‐25 is required for RV‐induced asthma exacerbation

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  • RV infection induces ILC2s in mouse asthma model
  • RV induced inflammation inhibited by anti IL17RB

IL‐25 is required for RV‐induced asthma exacerbation

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David Jackson et al. Am J Respir Crit Care Med 190, 1373‐1382 DOI: 10. 1164/rccm.201406

IL-33–Dependent Type 2 Inflammation during Rhinovirus-induced Asthma Exacerbations In Vivo

IL‐33 is required for RV‐induced asthma exacerbation

  • RV infection of asthmatics increases type‐2 cytokine release
  • IL‐33 correlates with cytokine release and symptoms
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David Jackson et al. Am J Respir Crit Care Med 190, 1373‐1382 DOI: 10. 1164/rccm.201406

IL-33–Dependent Type 2 Inflammation during Rhinovirus-induced Asthma Exacerbations In Vivo

IL‐33 is required for RV‐induced asthma exacerbation

A, RV infection of Bronchial epithelial cells B, Cytokine release from ILC2s stimulated with supernatant from BEC cultures

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Conclusions

  • Multi‐parameter flow cytometry is a useful tool in identifying ILCs
  • Human ILC2s can be identified in peripheral blood

(Lin‐CD45+CRTh2+CD127+CD25+)

  • ILC2s can be cultured in vitro in an isolated system
  • ILC2s expand in response to both IL‐25 and IL‐33
  • Experimental rhinovirus challenge induces both IL‐25 and IL‐33.
  • Both may contribute to ILC2 activation in asthma exacerbations.
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Therapeutic opportunities?

Andreakos et al. IL‐25: The missing link between allergy, viral infection, and asthma? Sci. Transl.

  • Med. 6, 256fs38 (2014).

IL‐33

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Acknowledgements

Batika Rana (PhD Student) Celine Parmentier (PhD Student) Joanne McDonald (PhD Student) Cate Weston (post‐doc) Paul Lavender Tak Lee Holly Bowen Greg Woszczek Rebecca Beavil Matthew Arno (KCL Genomics Centre) BRC Flow Cytometry Core BRC Genomics Core Melissa Lennartz‐Walker Holly Foster Elisabeth Fuerst

Funded by:

Collaborators: Andrew McKenzie (MRC LMB) Sebastian Johnston (IC) Mike Edwards (IC) James Pease (IC) Ross Walton (IC) Nathan Bartlett (IC) Roberto Solari (IC/GSK)

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SLIDE 80

Participating Experts

Brought to you by the Science/AAAS Custom Publishing Office To submit your questions, click the Ask a Question button

Overcoming challenges in cellular analysis

Multiparameter analysis of rare cells

January 28, 2015

Andrea Cossarizza, M.D., Ph.D. University in Modena and Reggio Emilia School of Medicine Modena, Italy David Cousins, Ph.D. University of Leicester Leicester, UK

Webinar Series

Sponsored by:

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SLIDE 81

For related information on this webinar topic, go to:

Look out for more webinars in the series at: webinar.sciencemag.org

To provide feedback on this webinar, please e‐mail your comments to webinar@aaas.org

Sponsored by:

lifetechnologies.com/attune Brought to you by the Science/AAAS Custom Publishing Office

Overcoming challenges in cellular analysis

Multiparameter analysis of rare cells

January 28, 2015

Webinar Series