High Dimensional Flow Cytometry for Comprehensive Immune [PDF]

DNA analysis. (SNP/telomeres). Paxgene tube storage. RNA/mRNA analysis. Ficoll. PhosFlow. Flow cytometry. B cell panel.

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Idea Transcript


HIGH  DIMENSIONAL  FLOW  CYTOMETRY  FOR  COMPREHENSIVE  IMMUNE  MONITORING  IN  CLINICAL  TRIALS    

Dominic  Gagnon,  Yoav  Peretz,  Marylène  For8n,  Claire  Landry,  and  David  Favre  

 ImmuneCarta  Services,  2901  Rachel  Est,  Suite  22,    Montréal,  QC,  Canada,  H1W  4A4  

 

Introduc8on  

Precision,  Stability  and  Reproducibility  

Study  Cases  of  Immune  Monitoring  

ImmuneCarta  Services  is  a  leading  provider  of  services  for  preclinical  and  clinical  studies  related  to  immunology.   Over  the  past  7  years,  we  have  developed  a  broad  bayery  of  innova8ve  assays  to  characterize  cell  popula8ons   and  immune  responses  in  the  sepng  of  infec8ous  diseases,  cancer,  vaccine  trials  and  immune-­‐based  therapies.   Based   in   Montréal,   ImmuneCarta   Services   is   specialized   in   advanced   mul8parametric   flow-­‐based   assays   performed   according   to   GLP   regula8ons,   GCLP   guidelines   governed   by   Quality   Management   Systems   and   standard   opera8ng   procedures.     ImmuneCarta   exper8se   includes   the   assessment   of   phenotypic   and   func8onal   markers,   the   characteriza8on   of   cell   subset   lineages,   ac8va8on   states,   and   signaling   molecules,   as   well   as   the   quan8ta8ve   analysis   of   vaccine-­‐,   pathogen-­‐   or   drug-­‐specific   responses   based   on   an8body   signatures,   cytokine/ chemokine  profiles,  and  signaling  pathways.  We  describe  here  our  experience  as  a  contract  research  organiza8on   providing   services   to   the   biopharmaceu8cal   industry,   in   the   execu8on   of   high   dimensional   flow   cytometry   analysis  of  subjects  enrolled  in  Phase  I/II  clinical  trials.    

Flow   cytometry   is   based   on   fluorescence,   fluidic   and   op8cal   tools   with   the   help   of   signal   and   image   computer   treatment.  ImmuneCarta  Services  uses  3-­‐  and  4-­‐laser  LSR  II  Becton  Dickinson  instruments.  These  instruments  are  high   performance   systems   allowing   simultaneous   analysis   of   up   to   18   colors   using   automated   sampler   in   96-­‐well   plate   format.   High   dimensional   flow   cytometry   requires   sensi8ve   and   precise   methods   with   op8mal   stability   and   reproducibility  of  the  signal.  For  customized  an8body  panels,  qualifica8on  or  valida8on  steps  are  necessary  to  address   specificity,   precision,   accuracy,   lower   and   upper   limits   and   range   of   detec8on,   stability   and   reproducibility   of   the   analysis.  The  precision  and  accuracy  of  flow  cytometry  experiments  also  depends  on  stable  applica8on  sepngs  (CS&T   beads)  as  well  as  internal  and/or  external  quality  control  (QC)  samples.    

Since   2004,   ImmuneCarta   has   applied   a   broad   array   of   innova8ve   assays   for   the   biopharmaceu8cal   industry   and   government   ins8tu8ons   to   characterize   the   immune   profiling   of   adap8ve   and   innate   immunity  and  the  potency  of  immune-­‐related  drugs  or  vaccines  in  exploratory  and  Phase  I  and  Phase  II   clinical  trials.  ImmuneCarta  Services  recently  formed  a  strategic  alliance  with  Caprion  Proteome  Inc.,  the   leading  company  in  proteomics  and  biomarker  discovery  (www.caprion.com)  in  order  to  integrate  single-­‐ cell   mul8parametric   flow   cytometry   analysis   with   soluble   markers,   serological   measurements   and   other   large  datasets  including  genomics  and  proteomics.  

0 Beads

0 Beads

105

102

0

103 PerCP-A

Beads

104

100

200

Over  the  past  7  years,  we  have  implemented  assays  using  high  throughput  analy8cal  methods  with  10  to  18-­‐flow   cytometry   parameters   on   fresh   and   cryopreserved   human   peripheral   blood   samples.   Overall,   immune   monitoring   of   clinical   trials   involve   study   planning,   assay   valida8on,   specimen   handling,   assay   execu8on,   monitoring,   repor8ng,   and   quality   review   performed   as   per   applicable   GLP   regula8ons   and   GCLP   guidelines   governed  by  quality  systems  and  standard  opera8ng  procedures.    

• Innova8on  and  development   program     • Design  of  immune  monitoring   strategy  and  workplan     • Ph.D.-­‐level  Principal  Scien8sts   assigned  to  each  study    

Standard  and   Customized  Immune   Monitoring  Assays  

• Involved  in  study  set-­‐up,  lab   manual  wri8ng,  and  sample   management       • On-­‐site  training  of  clinical   sites  for  maximum  sample   viability  and  recovery  

Study  Progress  and  Final   Report  to  Sponsor   • Conference  calls,  interim  and   final  scien8fic  reports   • Quality  Assurance  Statement   • Ac8ve  par8cipa8on  in   scien8fic  posters  and   publica8ons  

• Assay  development,   qualifica8on,  and  valida8on;  SOP   &  worksheets   • Data  integrity,  high  quality  and   high  throughput  processing  and   analysis   • Documenta8on,  sample  tracking   and  control,  GLP/GCLP  training  

1

1 8-

Ap

r-2

01

1 r-2 Ap

Ap 1-

01

1

01 r-2

-2 ar

-M

6-

1

01

1

01 -2

ar

ar

-M

-M

-M

-2

01 -2

ar

-2 ar M

8-

01

1

1

1

01

1

01 -2

M 4-

ar

M 2-

ar

-2

01

1

1 M 1-

ar

-2

01

01

1 -2

eb

eb

-F

-F

Ap

-2

01

1

1 r-2

01

1 r-2

8-

Ap

Ap

r-2

-2 ar

01

1

01

1

01

1

01

1500

1250

1250

1000

1000

Count/uL

Count/uL

750

6

750

500

500

250

250

Exported  as  “Experiment”   from  Diva  database  to  Raw   Data  Server  

Assay  

Acquisi8on   Sta8on  

•  Phenotyping   •  Func8onal  profiling   •  Enumera8on   •  ICS   •  Prolifera8on   •  PhosFlow   •  Cyotkine  bead  arrays  

Raw  Data   Server  

•  BD  Diva/  LSR  II   •  Daily  and  monthly  QC   •  Acquisi8on  template   applied   •  Applica8on  sepngs  used   •  Compensa8on  and   staining  references  

K

Analysis   Sta8on  

• Users  can  only                                           create  or  read  files   •   Regular  backup  

Batch  layout  PDF,  and   data  table  saved  in   Derived  Data  Server    

.jo,  .csv,  and  .PDF  from   FlowJo  saved  in   Derived  Data  Server    

.fcs  files  read  from  Raw  Data   Server  on  FlowJo  template  

Derived  Data   Server  

QC  Sta8on  

• Delete/modify   restricted   •   Regular  backup  

•   iMac  analysis  sta8on   • Import  FlowJo  assay   template   • Exploratory  analysis   can  also  be  done  with   SPICE/PRISM  

QA  if   needed  

• Another  analyst  on   another  iMac  analysis   sta8on   • Verify  ga8ng  and   documenta8on   • Confirm  data  report   across  all  files  

Final  data  and  report   exported    in  Study  Server    

Client   Report   •   Presenta8ons   •   Data  Table   •   PDF  

0.0154

CD4

DP

Lymph

29.2

70.8

CD3+

: CD4

SSC-A

: CD19

: CD45

: CD3

: CD19

0.623

0.0614

0.157

0.0554

3.07e-3

0.734

0.0294

CD3B Cells

CD8+ T cells

NK

0.672

DN

CD8 : CD8

0.13

IFNg

1.68e-3

0.354

CD107a

TNFa

IL-2

IL-4

IL-17

Performed  at  ImmuneCarta  Services  

Other  soluble   markers  

PhosFlow  

DNA  analysis   (SNP/telomeres)  

RNA/mRNA   analysis    

Figure  3:  Example  of  sample  management  for  assays  performed  at  ImmuneCarta  Services  and  at  collaborator  sites.  In  this  vaccine  clinical  trial  on  200  subjects,  ImmuneCarta  provided  mul8ple   services  including  study  design,  central  lab  ac8vi8es,  assays  and  integrated  analy8cal  report.  In  this  example,  fresh  blood  samples  from  each  subject  and  each  8me  point  were  collected  at  a  CRO   site  nearby  and  immediately  processed  for  analysis  at  ImmuneCarta  or  stored    at  ImmuneCarta  for  analysis  at  collaborator  sites,  e.g.  genomics,  gene8c,  other  soluble  markers  or  PhosFlow.  

wk8  

wk16  

wk20  

wk24  

90  mL  

90  mL  

90  mL  

90  mL  

90  mL  

ImmuneCarta   DC  TARGET   PRODUCTION  

ICS  

CFSE  

POST-­‐THERAPY   IM  SAMPLES  

CFSE  

ICS  and   CFSE  

CFSE  

CFSE  

High Dimensionality Analysis of Hyporesponsiveness to Protein Subunit Vaccines in the Elderly -- Introduction to Study MK0000-131 Carayannopoulos LN1,, Railkar RA1, Favre D2, Landry C2, Schaeffer AK1, Wiener MC1, Chastain M1, Loboda A1, Lukac S1, Duguay D3, Audet D3, St-Maurice F3, Kaslow DC1, Beals CR1, Sekaly RP2,4 1Merck Research Laboratories, USA | 2National Immune Monitoring Laboratory – Genome Quebec, Canada | 3Anapharm-Pharmanet Quebec, Canada | 4VGTI-Florida

Cytometric  Vaccine  Response  Examples   Plasmacyte Counts before (V2) and after (V3) immunization (left) All Plasmacytes (right) IgG+ Plasmacytes

Figure   8:   Reproducibility   and   %CV   from   external   QC   samples:   results   obtained   with  BD  MulN-­‐check  CD4  low  controls  in  14  experiments  with  lot  #  40L  in  April   2010  and  17  experiments  with  lot  #  50L  in  May  2010.  The  first  graph  represents   the  enumera8on  of  lymphocytes  (CD45),  T  cell  popula8ons  (CD3,  CD4,  and  CD8),   B  cells,  and  NK  cells.  The  second  graph  represents  the  enumera8on  of  the  same   popula8ons.  Coefficient  of  varia8on  are  indicated  in  blue.  

Boolean Gating (total CD4+ and CD8+ T cells):

Beads

Performed  at  collaborator  sites  

wk0  

ART:  an8-­‐retroviral  treatment   ARTI:  ART  interrup8on  

Populations

High   throughput   analysis   of   high   dimensional   flow   cytometry   data   requires   advanced   soWware   and   methods.   Data   analysis  performed  at  ImmuneCarta  Services  relies  on  flow  data  acquisi8on  using  DiVa  soWware  (BD  Biosciences),  and   data  analysis  with  FlowJo  (Treestar  Inc.),  Excel  (Windows),  PESTLE/SPICE  (NIH),  Cluster  (Open  source,  Stanford),  Java   TreeView   (Open   source),   Prism   (GraphPad   SoWware)   and/or   other   specialized   soWware   for   sta8s8cal   analysis   and   systems  biology.  All  data  are  acquired  and  analyzed  in  compliance  with  21  CFR  Part11  to  ensure  quality  and  integrity   of   the   raw   data   and   its   analysis.   Pre-­‐defined   FlowJo   templates   are   qualified   or   validated   (GLP   study)   prior   to   being   used  throughout  studies  and  require  minimal  ga8ng  adjustments  that  are  documented  accordingly.  

Paxgene  tube    storage  

Flow  cytometry   B  cell  panel   (11  colors)  

V13  

br 56

K N

NK Populations

CD4+ T cells

Cell  pellet     cryopreservaNon  

V9  

Data  Analysis  

SSC-A

Flow  cytometry   T  cell  panel     (12  colors)     Innate  panel   (12  colors)  

V8  

NK

B

8 D C

4 D

3 C

D

45 D C

NK

B

8 C D

4 C D

45

0

 5.0            4.9              5.6            4.7              5.6            5.2  

Populations

N

B, Mono, and DC Populations

: CD16/CD56

ELISA  assays:   -­‐   Vaccine  Nters   -­‐   Soluble  markers  

PBMC     cryopreservaNon  

ImmuKnow     Assay    

V6  

ig

56

ht

lo

C

w

16

D

lo

16

w

+

16 w lo

3

0

0

56

0

 2.5              3.1            3.7              4.1              3.1            3.2  

Figure   9:   Flow   of   work   for   data   analysis.   Sample   processing,   assays   and   data   acquisi8on   are   based   on   pre-­‐approved   worksheets   related   to   study   requirements.   Samples   are   acquired   on   BD   LSR   II   cytometers  based  on  pre-­‐defined  sepngs  and  templates.  The  raw  FACS  data  are  exported  directly  to  the  Raw  Data  server  and  used  on  FlowJo  analysis  template  on  the  Derived  Data  server.  Once  analysis   has  been  completed  and  documented,  a  full  QC  of  data  analysis  is  performed.  Final  client  data  and  report  are  generated,  while  the  whole  process  is  audited  by  the  Quality  Assurance  (GLP  studies).   Ficoll  

Follow-­‐up  &   Safety  

Figure  14:  Study  protocol  and  immune  monitoring  of  the  AGS-­‐004  pilot  study  to  invesNgate  the  safety  and  immunologic  acNvity  of  an  autologous  HIV  immunotherapeuNc  agent.   (NCT00381212)   ICS   and   CFSE   prolifera8on   assays   were   performed   as   indicated   in   the   study   protocol   (leW).   The   frequencies   of   the   HIV(GNRV)-­‐specific   CD8   and   CD4   T   cell   responses   and   their   func8onal   profiles   (CD107a,   IFNγ   and   IL-­‐2)   (right)   are   compared   between   baseline   and   visit   8   aWer   vaccine   administra8on.   Pie   charts   represent   the   rela8ve   distribu8on   of   the   func8onal   subsets   within   the   total   CD8   and   CD4   T   cell   pools.   Sta8s8cal   significant   differences   (P   <   0.05)   between   pre   (V2)-­‐   and   post   (V8)-­‐vaccine   8me   points   are   indicated   by   Wilcoxon-­‐Rank   and   a   Student’s   t-­‐test   (#   and   +,   respec8vely).   The   90th   and   75th   percen8le   threshold   applied   to   background   subtracted   CD8   and   CD4   T   cell   responses   were   0.01%   and   0.005%,  respec8vely.  

CD4 Low control, lot # 50L

1500

C D

0

Figure  2:  Overview  of  immune  monitoring  services.  ImmuneCarta  Services  include  all  steps  from  strategic  planning  to  final  reports  to  clients.  It  oWen  involves  study  set-­‐up,  lab  manual  wri8ng  and   sample  management  which  are  cri8cal  steps  for  cell-­‐based  analysis  as  flow  cytometry  assays.  Customized  assays  as  per  client  needs  require  assay  development  and  valida8on  prior  to  immune   monitoring  of  clinical  samples.  All  steps  include  an  ac8ve  follow-­‐up  with  the  sponsor,  are  performed  using  proprietary  SOPs  and  worksheets  and  are  documented  as  per  applicable  GLP  regula8ons.  

Serum  aliquoNng/ storage  

Booster   Phase  

Vaccine   ARTI  

Titer  DistribuNons  of  Vaccinated  Subjects  

Figure  7:  Reproducibility  and  %CV  from  internal  QC  samples  (frozen  PBMC,  L747)  on  11-­‐color  T,  B,  and  Innate  cell   phenotype   panels.   Results   from   32   experiments   are   shown   over   one   year   (July   2010   to   September   2011).     The   first   graph   represents   the   enumera8on   of   T   cell   popula8ons   (CD3,   CD4,   and   CD8).     The   second   graph   represents   the   enumera8on   of   B   cells,   the   monocytes,   and   sub-­‐popula8ons   of   dendri8c   cells.     The   third   graph   represents   the   enumera8on  of  natural  killer  (NK)  sub-­‐popula8ons.  Coefficient  of  varia8on  are  indicated  in  blue.  

Immune  Monitoring  

Pro-­‐ac8ve  Rela8onship   with  Sponsors,  CRO,  and   Clinical  Sites  

1200 1600 2000

50

10

100

T Cells Populations

Strategic  Planning  and   Communica8on  

800

100

20

-

   

150

16

 

400

Immune   Monitoring    

30

K

 sjTREC  

wk28  

40

ht

 Cells,  8ssue  

300

300 200

N

T  cell  receptor  excision  circles  (TREC)  

       

50

ig

✓  

   

0

400

2000

br

 Specific  mRNA  quan8fica8on  

0

500

6+

mRNA  expression  (real-­‐Nme  PCR)  

 Cells,  8ssue  

400

500

4000

56

   

wk24  

600

-1

✓  

w20  

6000

K

 Interleukins,  sICAM-­‐1,  sICAM-­‐3,  sVCAM-­‐1,  sPECAM-­‐1,  sE-­‐Selec8n,  sP-­‐   Selec8n,  G-­‐CSF,  IL-­‐8,  MCP-­‐1,  MIG,  MIP-­‐1α,  MIP-­‐1β,  others  

1000

56

 Serum,  plasma,  cell  culture  supernatant  

800

 18              36            23              38                19              23    

N

Cytokines/Chemokines/Adhesion   Molecules/Growth  Factors  

8000

w16  

V3  

BASELINE    IM  SAMPLES  

700

K

   

1200

V2

N

 

60

N K

 Tetanus,  Diphtheria,  Hepa88s  B,  Cholera  toxin  B,  CMV,  others  

50

AL L

 Serum,  plasma  

40

800

 36              66              44                29              24              31  

Count / uL

AnNbody  Titers  

1500

18                      18                    19                      39                  38    

2

✓  

10000

C

 CD107a,  Granzyme,  Perforin,  CD63,  others  

•   •   •   •   •   •   •   •     •   •   •   •   •  

D

 T,  NK,  NKT  cells  and  subsets,  Basophils  

•       •   •   •   •   •     •   •   •  

m

Cytotoxicity/DegranulaNon  

   

•   •     •   •   •   •   •   •   •     •   •   •  

1

✓  

•   •   •   •   •   •   •   •   •   •   •   •   •    

C

 IFNγ  and/or    IL-­‐2,  TNFα, IgG,  IgM  

•       •   •       •     •     •   •    

D

 CD8+/CD4+  T  cells,  B  cells  

•   •     •   •   •     •   •   •     •   •   •  

m

ELISPOT  

      •   •       •       •                     •         •                     •   •       •   •   •                   •               •  

•     •   •   •   •     •   •   •   •     •   •   •   •     •   •   •   •     •   •   •       •   •   •         •           •   •   •   •   •   •   •     •     •     •   •   •   •     •   •   •   •     •   •   •           •   •   •         •   •    

30

CD3 CD4 CD8 DP DN B cells HLA-DR+ B cells HLA-DRMonocytes pDC mDC1 mDC2 All NK NK 56-16+ NK 56bright16NK 56low16NK 56low16+ NK 56bright16low

R2  =  0.9954  

1600

NK L747

PD C

✓  

•   •  

•   •   •   •   •   •   •   •   •   •   •   •   •   •  

20

B, mono, and DC L747

T Cells L747

o

 CFSE,  Ki67,  BrDU  

•   •   •   •   •   •   •   •     •     •   •   •  

10

w12  

Vaccine   ART  

Client    PRODUCT   PRODUCTION  

CD4 Low control, lot # 40L

   

on

 T  cells  and  subsets  

   

•   •   •   •   •   •   •   •     •   •   •   •    

•   •      

M

ProliferaNve  response/cell  cycling  

   

   

           

•   •   •   •   •   •   •   •   •   •   •   •   •    

 

-

 ATP  

   

•  

R

 Total  CD4  cells  

   

 

-D

Lymphocyte  acNvity  (ImmuKnow®)  

                              •  

•  

LA

✓  

•   •   •   •   •   •  

 

0

H

 Akt,  Btk,  Elk,  EGF-­‐R,  Lck,  LAT,    Zap70,  Syk,    MEK1,    NFκB,  PKC,  PLC-­‐γ1,    PLC-­‐ γ2,  p38MAPK,  ERKk1/2  Src,  STAT1  to  STAT-­‐6  

•   •   •   •  

10

lls

 T,  B  and  Innate  Cells,  Tumor  cells  

 

20

ce

PhosphorylaNon  (PhosFlow)  

•  

30

B

✓  

   

H LA -D R +

 Annexin,  caspase  3,  CD95,  PARP,  TUNEL,  Live/Dead,  7-­‐AAD  

   

Count / uL

 T,  B  and  Innate  cells  

   

ce lls

Gene  Expression    &  DNA  Analysis  

Apoptosis/Necrosis  

 

•   •   •   •   •   •   •   •   •  

40

0

V2  

90  mL  

Cell counts (cells/µL)

Figure  6:  Reproducibility  of  high  dimensional  flow  cytometry.    Phenotyping  of  T,  B,  and  innate  cells  using    11-­‐12  color  an8body  panels  was  assessed  independently  on  two  blood  samples  from  20   subjects  at  Visit  2  (V2  and  V2-­‐redo).    Distribu8on  of  popula8ons  and  cell  counts  are  highly  correlated  with  R2  >  0.99.  

B

Serology  &  Soluble   Markers  

✓  

 IL-­‐2,  TNFα,  IFNγ,  IL-­‐4,  IL-­‐17,  IL-­‐22,    IL-­‐10,  TGFβ,  IL-­‐9,  IL-­‐21,  Mip1β,  others  

•  

 

R2  =  0.9955  

50

w8  

Samples    

2000

V2

D N

FuncNonal   Profiling  

 T,  B,  NK,  NKT,  Dendri8c  cells,  Monocytes  

•   •   •   •   •  

100

DP

Intracellular  Cytokine/Chemokine  Staining  

✓  

 FoxP3,  T-­‐bet,  GATA-­‐3,    RORγt,  BCL-­‐6  

   

80

ILD (CD8) LD (CD8) Naive (CD4) CM (CD4) EM (CD4) TEM1 (CD4) TEM2 (CD4) ILD (CD4) LD (CD4) Mem CD45RA- (CD8) Mem CD45RA- (CD4)

V2

8

 Treg,  Th1,  Th2,  Th17,  T~  

   

60

D

TranscripNonal  Factors  

✓  

 CCR4,  CCR5,  CCR6,  CCR7,  CCR9,  CXCR3,  α4β7  integrins,  others  

   

40

C

 T  cells,  B  and  Innate  cells  

 

20

4

Homing  Receptors/Co-­‐receptors  

   

•   •           •   •  

0

D

✓  

   

0

C

 HLA-­‐DR,  CD38,  ICOS,  OX40,  4-­‐1BB,  Ki67,  CD40,  CD95,  PD-­‐1,  CD57,  CD83,     CD80,  CD86,  CD160,  Lag-­‐3,  2B4,  CTLA-­‐4,  Tim-­‐3  

   

V2-Redo

 T,  B  and  Innate  cells  

   

   

3

AcNvaNon/InhibiNon/ExhausNon/  Immune   Senescence  

   

40

D

✓  

   

   

60

20

C

 An8gen-­‐specific,  HLA-­‐restricted  TCR  (using  Tetramer/Pentamer/Dexamer)  

   

   

Chronic  Inflamma8on  

 Tetramer-­‐posi8ve  an8gen-­‐specific  CD8+/CD4+  T  cells  

   

   

Immune  Senescence  

AnNgenic  specificity  

   

  •   •   •                     •         •   •   •   •   •         •   •   •   •   •   •   •       •   •                 •           •   •   •   •   •     •   •   •   •   •   •   •  

   

CD28- (CD8) CD4 (CD3) CD8 (CD3) PD-1 (CD4) PD-1 (CD8) CD57 (CD4) CD57 (CD8) Naive (CD8) CM (CD8) EM (CD8) TEM1 (CD8) TEM2 (CD8)

w4  

Innate  cell  Counts   Switched IgG Switched IgA Unswitched IgM MZ-like Immature B cell Naive IgM-IgD+ Naive IgM+IgD+ Naive IgM+IgDMemory IgA+ Memory IgG+ Memory IgM+IgD+ Memory IgM+IgDMemory IgM-IgD+ Plasma B cells Plasma IgA+ Plasma IgD+ Plasma IgG+ Plasma IgM+

% Parent

60

R2  =  0.9956  

80

Count / uL

Immune   Phenotyping  

Transplanta8on  

•  

Autoimmunity  

✓  

Cardiology  

 CD45RA/RO,  CD27,  CCR7,  CD28,  CD62L/CD27,  IgD/M,  CD20,                           CD38,CD10,IgA/G/E/CD94/NKG2A,  CD7,  KIR2DL/DS/CD11c,/CD123,     BDCA-­‐2/3/4,  /FcγRs,  IgE,  CCR3/CD16,  CD64,  FcγRs  

Oncology  

 T,  B,  NK  and  NKT  cells,  Dendri8c  cells,  Basophils,  Monocytes  

Allergy  

Cell  Subsets  DifferenNaNon  

Infec8ous  diseases  

✓  

Main  therapeuNc  area   Immunotoxicology  

 T,  B,  NK  and  NKT  cells,  Dendri8c  cells,  Basophils,  Monocytes  

Epitope  Mapping  

Cell  Lineage  

 CD3,  CD4,  CD8/CD19,  HLA-­‐DR/CD16,  CD56,  Lin-­‐/Vα24,  αGalCerCD1d     tetramer,  CD3/CD11c,  CD123,  Lin-­‐/CD123,  Lin-­‐/CD14  

Vaccine  

 

Immune  Monitoring  

 DNA,  Cytokera8n,  CD45  

Biologics/Biosimilars  

 Circula8ng  Tumor  Cells  

Biomarkers  

Tumor  Cells  

✓  

 CD34,  CD45,  others  

Drug  development  

✓  

 Circula8ng  CD34+  

Molecular  biology  

 CD3,  CD4,  CD8,  CD16/56,  CD19,  CD14,  CD11c,  CD45,  CD123,  others  

HematopoieNc  stem  cells  

Field  of  applicaNon  

Chemiluminescence  

Cytokine  bead  array  

Cell   EnumeraNon  

Markers  

ELISA/ELISPOT  

Flow  Cytometry  

 T,  B  and  Innate  cells  

       

Assays  

Customizable    

Immune  Cells  

•   •   •   •  

Targeted  Sample     or  Cell  PopulaNon  

% Parent

V2-redo

Table  I:  Overview  of  ImmuneCarta  assays  in  different  fields  of  applicaNon  and  therapeuNc  areas.  

w0  

Pre-­‐treatment   Phase  

Figure  5:  Reproducibility  of  CD34+  absolute  counts  using  reference  QCs  over  Nme.    Three  levels  of  CD-­‐ Chex  CD34  reference  controls  were  used  (3,  35,  and  124  CD34  cells/µL)  and  two  BD  Stem  Cell  Control  Kit   (12.1   and   35.9   CD34   cells/µL).     Coefficient   of   varia8on   for   CD-­‐Chex   level   1,   2,   and   3   are   respec8vely   8.98%,   3.84%,   and   1.98%.     For   BD   Stem   cell   low   and   high   reference   controls,   the   CV   are   respec8vely   6.94%  and  3.48%.  

B  cell  Subsets  

T  cell  Subsets  

Vaccine   Treatment  

Visit/Week    

C

Figure  1:  Overview  of  ImmuneCarta  flow  cytometry  and  analyNcal  processes  (example  of  immune  profiling  of  mucosal  CD4+  T  cells  by  intracellular  cytokine  detecNon)  

Plamorm  

105

Figure  4:  Stability  of  the  signal  using  applicaNon  seangs  coupled  with  CS&T.  Compbeads   were   stained   45   8mes   with   either   FITC,   PE   or   PerCP   (3   month   survey)   and   were   used   to   determine  compensa8on  for  81  experiments  using  cytometer  #  1    and  31  experiments  using   cytometer   #   2.   Voltages   were   determined   by   the   Applica8on   Sepngs   linked   to   the   daily   CS&T   Performance   check.   First   row:   11-­‐color   cytometer,   second   row,   18-­‐color   cytometer   with  yellow-­‐green  laser  (see  increased  posi8ve  PE  signal  without  increasing  the  background).    

Subj-­‐2  

5

31

104

+ 20% Target value - 20%

29

103 PE-A

15 12.1 10

28

102

6-

0

105

Figure  13:  SchemaNc  of  the  drug  discovery  process.  

20

11

104

Phase  IV  /   Market  

- 20%

28

103 FITC-A

Phase  III  

25

25

102

1-

0

250K

Phase  II  

Frequency)(%CD8))

Ungated

200K

30

Phase  I  

Frequency)(%CD4))

100K 150K SSC-A

V2-Redo

50K

-F

-F 0 0

28

0

25

Subj-­‐1  

-2

20

ar

20

-M

20

31

20

1

40

01

40

Target value

35

0

-2

40

+ 20%

40

0

eb

40

+ 30% Target value - 30%

4 3 2

ar

60

-M

60

29

60

1

60

01

80

6

-2

80

+ 20% Target value - 20%

ar

80

50 42 35 28

-M

80

CD34 count (cells/µL)

100

28

100

105

1

Beads

104

01

103 PerCP-A

-2

102

0

ar

105

M

Beads

104

-M

103 PE-A

11

102

1

0

01

105

-2

104

100

% of Max

% of Max

103 FITC-A

Beads

100

Subj-­‐1  

102

0

ar

250K

M

Ungated

200K

1

100K 150K FSC-A

01

50K

- 20%

-2

0

100

8-

0

1

0

01

0

Target value

-2

0

124

ar

20

M

20

4-

20

1

20

01

40

-2

40

ar

40

2-

40

45

+ 20%

Pre-­‐ clinical  

Discovery  

BD Stem Cell Control HIGH BD Stem Cell Control LOW

50

150

01

60

M

60

1-

60

-2

60

CD-CHEX CD34+ Level 3 CD-CHEX CD34+ Level 2 CD-CHEX CD34+ Level 1

eb

80

CD34 count (cells/µL)

80

LSR  II  #  1  

80

% of Max

80

% of Max

100

% of Max

% of Max

StaNsNcal  analysis  and  idenNficaNon  of  group   differences  in  funcNonal  responses  

PerCP  

100

LSR  II  #  2  

Single-­‐cell  analysis  (%  funcNonal  response)  

PE  

100

% of Max

Flow  Cytometry  

FITC  

100

% of Max

Sample  

FSC/SSC  

Figure  10:  Example  of  a  simple  hierarchical  gaNng  for  T,  B  and  NK   cell   enumeraNon.     Lymphocytes   and   BD   TruCount   Beads   are   gated   based   on   SSC/CD45   expression.   CD3-­‐posi8ve   and   CD3-­‐nega8ve   popula8ons   are   defined.   From   CD3-­‐   gate,   natural   killer   (NK)   and   B   cells   are   discriminated   based   on   CD16/CD56+     (NK)   and   CD19+   (B   cells).    From  CD3+  gate,  CD4+  and  CD8+  T  cells  are  defined  as  well  as   double  nega8ve  and  double  posi8ve  T  cells.      

Figure  11:  Example  of  Boolean  gaNng  of  6  funcNonal  markers  expressed  in   CD4+,  CD8+  or  memory  subsets  and  effector  T  cells  by  ICS  aoer  anNgen-­‐ sNmulaNon   of   cryopreserved   PBMCs.   Posi8ve   responses   for   each   of   the   markers  are  defined  from  the  template  analysis  (IFNγ,  CD107a,  TNFα,  IL-­‐2,   IL-­‐4,   and   IL-­‐17).   Boolean   ga8ng   is   generated   by   FlowJo   for   all   possible   combina8ons  (IFNγ+/-­‐  and  CD107a  +/-­‐  and  TNFα+/-­‐,  and  IL-­‐2+/-­‐  and  IL-­‐4+/-­‐   and   IL-­‐17+/-­‐   ),   e.g.   leading   to   2n   different   gates.     In   this   example,   n   =   6,   genera8ng  64  gates  for  8  popula8ons  of  interest  per  sample.  

Figure   12:   Boolean   gaNng   analysis   of   5   markers   in   mucosal   CD4+   T   cells   using   SPICE   analysis.   CorrelaNon   of   specific   funcNonal   signatures   with   immune  acNvaNon  (Ki67+).  Combina8on  of  3  among   5   markers   (IFNγ,   IL-­‐2,   IL-­‐17,   MIP1β,   and   TNFα)   are   displayed   as   dots   and   their   means,   as   grey   histograms.   Sta8s8cally   significant   differences   between  HIV  viral  controllers  and  non-­‐controllers  are   shown  by  #  (Wilcoxon-­‐Rank)  and  +  (Student’s  t-­‐test).  

Figure  15:  High  dimensionality  analysis  of  vaccine  hyporesponse  in  healthy  elderly  subjects  (NCT01119703)   Titer   distribu8on   of   vaccine   responses   to   Tetanus,   Diphtheria   and   Hepa88s   B   vaccines   in   elderly   subjects   (leW)   and   example   of   increase   frequency   of   highly   characterized   B   cell   popula8ons   one   week   (V3)   aWer   vaccine   administra8on   (V2)   (middle).   “Plasma   B   cells”  are  characterized  as  singlet/lymphocyte/CD19+/HLA-­‐DR+/CD3-­‐/CD27+/CD10-­‐/CD20-­‐  cells.  “Plasma  IgG+    B  cells”  are  plasma   B  cells  expressing  IgG  on  the  cell  surface.  An  example  of  heatmap  represen8ng  unsupervised  clustering  of  high  dimensional  flow   cytometric  Boolean  datasets  of  T,  B  and  innate  immune  phenotyping  (y-­‐axis)  is  shown  on  the  same  cohort  (N=120  subjects,  x-­‐axis)   (right).   Such   unsupervised   clustering   of   large   flow   cytometric   datasets     allows   further   analysis   of   genomic   datasets   or   other   large   datasets  by  quan8ta8ve  regression  analysis  related  to  individual  immune  profiling,  as  described  in  Loke,  Favre  et  al.,  Blood  2010.  

Conclusions   Flow  cytometry  is  a  unique  way  to  address  complex  cellular  immunological  profiling  for  drug  development   and  Phase  I-­‐III  clinical  trials  in  infec8ous  diseases,  cancer,  vaccine,  transplanta8on,  autoimmune  disorders   and  related  immunomodula8on-­‐based  therapies.  High  dimensional  mul8parametric  single-­‐cell  analysis  is   not   only   aimed   to   define   mul8ple   markers   of   different   cell   popula8ons   simultaneously   -­‐though   helpful   when   clinical   sample   availability   is   limited-­‐,   it   is   also   one   of   very   few   analy8cal   plazorms   that   can   address   complex  protein-­‐based  signatures  (biomarkers,  disease  stage,  etc.)  and  func8onal  networks  (mechanisms)   from  relevant  and  well-­‐characterized  primary  human  or  animal  cells  at  the  single  cell  level.     The   immune   monitoring   of   Phase   I   to   Phase   III   clinical   trials   aims   to   design,   perform   and   interpret   immunological   data   that   enable   industry   to   move   vaccines,   immunotherapeu8cs   and   drug   candidates   through   the   regulatory   process   (FDA,   EMEA,   others).   High   dimensionality   flow   cytometric   analysis   also   allows  for  the  defini8on  of  immunological  profiles  that  are  disease  and  stage  specific,  enabling  elimina8on   of   many   unsuitable   drug,   vaccine   or   therapy   candidates   prior   or   at   the   8me   of   “in-­‐man”   studies.   This   requires   both   a   scien8fic   exper8se   in   immunology,   physiology   and   pathology   as   well   as   a   clear   understanding  of  technicali8es  related  to  instruments,  reagents  and  high  dimensional  data  mining.  As  a   service   company   for   the   pharmaceu8cal   industry,   ImmuneCarta   regulatory   process   and   standardized   procedures  are  cri8cal  to  ensure  data  integrity  and  quality,  especially  when  interpre8ng  complex  data  sets   to   define   disease   stage,   drug   efficacy   or   toxicity.   Overall,   immune   assays   for   diagnos8c,   research   or   biomarker   discovery   may   impact   on   all   aspects   and   stages   of   immune   system   tes8ng,   vaccine   and   immunotherapeu8c  design  and  development  as  well  as  drug  screening.  They  are  enablers,  permipng  GO/ NO-­‐GO   decision-­‐making,   thus   saving   both   8me   and   money,   enhancing   safety   and   providing   surrogate   markers  of  clinical  efficacy  and/or  mechanis8c  insights.    

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