Transcript Flow Cytometry Analysis for HIV/AIDS Immunology Ontologies and Their Applications in Processing Clinical Data June 11-13, Buffalo, NY Director, Biostatistics and Computational Core, Duke CFAR.

Flow Cytometry Analysis
for HIV/AIDS
Immunology Ontologies and Their Applications in
Processing Clinical Data
June 11-13, Buffalo, NY
Director, Biostatistics and Computational Core, Duke CFAR
Overview
•
•
•
•
•
Basics of flow cytometry (optional)
Applications of flow cytometry for HIV/AIDS
Automated analysis
Potential for ontology contributions
Marriage of automated analysis and use of
ontologies (to be continued by Ryan and
Nikesh)
Duke Human Immune Profiling
•
1989-1994; Duke Center for AIDS Research (CFAR) Flow Cytometry Core
•
1991-2000; Central Immunology Laboratory for the AIDS Vaccine Evaluation Group
(AVEG)
•
2000-2005; Central Laboratory for the HIV Vaccine Trials Network (HVTN)
•
2001-2004; Creation of a GCLP-Compliant Immune Monitoring Environment
•
2006-2011; Duke Central Laboratory for the HVTN
•
2005-2015; Duke Center for AIDS Research (CFAR) Flow Cytometry Core
•
2005-2012; Center for HIV/AIDS Vaccine Immunology (CHAVI) Immune Monitoring
Core
•
2006-2011; Duke Translational Research Institute (DTRI) Immune Monitoring Core
•
2011-present; Formation of the CFAR/DTRI Immune Profiling Core
•
2010-2017; External Quality Assurance Program Oversight Laboratory (EQAPOL)
ICS/Polychromatic Flow Cytometry Component
FLOW APPLICATIONS IN HIV/AIDS
Applications in HIV/AIDS
• Clinical
– CD45/CD4 for monitoring HIV+ patients on ART
– CD8/CD38 for monitoring ART compliance
• Cheaper supplement to VL assays
• Research (drugs, vaccines, pathogenesis)
–
–
–
–
–
–
–
–
–
Immunophenotyping (T, B, DC, NK, NKT)
Memory/Maturation
Cellular Function (cytokines, cytotoxicity, Ab production)
Proliferative capacity (CFSE)
Cell death (specific and non-specific assays)
Antigen-specificity (peptide pool stimulation, multimers)
Co-receptor usage
Signaling (PhosFlow)
Subcellular localization and conjugates (imaging cytometers)
• Cell sorting (FACS)
Immunophenotyping – basic T subsets
Basic Gates:
- 3 total
Ungated
Singlets
SSC-A
SSC-A
FSC-H
FSC-H
<Violet H-A>: vAmine CD14PB CD19 PB
Exclusion (Violet H)
88.3
CD3+ Exclusion-
99.3
41.4
FSC-W
FSC-A
<Violet G-A>: CD3 Amcyan
FSC-W
CD3 AmCyan
FSC-A
Scatter
CD4+CD8-
CD4 PerCP-Cy5.5
<G710-A>: CD4 CY55PE
57.8
0.79
CD4+CD8+
36.3
CD8+CD4-
<V705-A>: CD8 Q705
CD8 Alexa700
Duke University Medical Center
Immunophenotyping (B cells)
Naive/Memory
CD38-/IgD+
(CD23+/-/CD27+/-)
<Green E-A>: IgD PE
105
Naïve
CD38+/IgD+
38-D+
17.3
Transitional
CD38++/IgD+
(CD23+/CD27-)
38+D+
79.5
104
38++D+
0.94
103
102
38++D0.064
0
38-D1.11
38+D1.69
2
3
4
0 10
10
10
10
<Red B-A>: CD38 A700
Early Memory
Plasmablasts
Memory
CD38+/IgDCD38++/IgDCD38-/IgD(CD23-/CD27+)
5
Maturation/Memory (CD8)
ANTIGEN
Naive
CM
EM
E
TE
CD45ROCD27+
CD45RO+
CD27+
CD45RO+
CD27-
CD45RO+
CD57+
CD45ROCD57+
Proliferative
Capacity
Effector
Function
Maturation/Memory
Maturational Gates:
- 5 per basic subset
CD45RO ECD
3.98
EM
11.7
TE
E
CD8+CD4-
22.9
28.6
56.4
21.5
51.7
CD45RO ECD
N
6.55
EM
8.46
13.2
5.67
CM
TE
<V545-A>: CD57 Q545
55.9
CM
3.98
CD57 FITC
42.9
N
62.5
CD27 APC-Alexa750
<G660-A>: CD27 CY5PE
2.58
22
<V545-A>: CD57 Q545
EM
E
CD57 FITC
0.33
TE
<G660-A>: CD27 CY5PE
CM
CD4+CD8+
1.07
0.12
CD27 APC-Alexa750
N
<V545-A>: CD57 Q545
54.1
43
CD57 FITC
<G660-A>: CD27 CY5PE
CD27 APC-Alexa750
CD4+CD8-
56.9
E
24.2
CD45RO ECD
Central Effector Terminal
Naive Memory Memory Effector
Effector
Central Effector Terminal
Naive Memory Memory Effector
Effector
Central Effector Terminal
Naive Memory Memory Effector
Effector
Duke University Medical Center
Functional Assays (ICS)
2. Stimulate
3. Surface
Stain
4. Lyse/Fix 5. Permeabilize
6. IC Stain
7. Acquisition 8. Analysis
Brefeldin
Monensin
6 hrs
Rest
6h
CD107
cytokine
lymphocyte
erythrocyte
Wash
Wash
Wash
Wash
TNFα Alexa700
1. Thaw
IFN-γ PE-Cy7
Functional T subsets
7+g+2+T+ Polyfunctional (1: ++++)
Functional & Boolean Gates:
- 4 functional gates per maturational subset
- 16 boolean gates per maturational subset
CM: CD8+CD4-
CD107
2.59
<R710-A>: CD107a AX680
IFN-
4.19
IL-2
TNF-
0.31
Boolean Gates
Key:
7 = CD107
g = IFN-
2 = IL-2
T = TNF-
7+g+2+T7+g+2-T+
7+g-2+T+
7-g+2+T+
Polyfunctional (4: +++)
7+g+2-T7+g-2+T7+g-2-T+
7-g+2+T7-g+2-T+
7-g-2+T+
Bifunctional (6: ++)
7+g-2-T7-g+2-T7-g-2+T7-g-2-T+
Monofunctional (4: +)
7-g-2-T-
Nonfunctional (1: ----)
1.14
Boolean Gating Combinatorics
Proliferation Assays
Tetramers
AUTOMATED ANALYSIS
Automated Analysis
• Several groups pursuing possibility of automated
flow analysis
• Many approaches within large family of
– Statistical learning methods
– Machine learning methods
• Typical pipeline for automated analysis
– Data pre-processing  QA/QC  Event labeling
(“clustering”)  Subset alignment  Subset labeling
 Automated summaries and manual review
QA/QC
• Flag potential errors
– (1) Too few events
– (2) Flow stream
inconsistencies
– (3) Medians outside 95%
bootstrap CI for lab
– (4) Excessive censoring
(% events in min or max
bin for marker channel)
1
2
3
4
New hierarchical DP model improves subset alignment and detection
of rare cell subsets by borrowing strength across samples
Standard
Reference
Pooled
Hierarchical
Correct enumeration and labeling of rare
event cluster in simulation
FSC
CD45
CD8
TET
CD8
SSC
SSC
Hierarchical DP
results in cell subset
alignment, more
consistent
clustering and
eliminates false
positive events that
arise with standard
DP clustering
CD8
Spiked
tetramer data
analysis
HDPGMM
TET
DPGMM
FSC
CD45
CD8
Marker usefulness, equivalence, redundancy
EQAPOL program
• Flow analysis of same sample may give very
different results in different centers
• EQAPOL program sponsored by NIAID to
standardize approach (mandatory for centers
conducting NIH-funded HIV research)
• Automated analysis component to EQAPOL
program introduced for first 4C sendout
• Plan calls for 4C  7C  12C ICS sendouts
• Room to integrate an HIV immune ontology?
Manual/Automated comparison of cytokine+ve
CD4 from EQAPOL sendout #1
POTENTIAL FOR ONTOLOGY
CONTRIBUTIONS TO FLOW
Ambiguities in flow analysis
•
•
•
•
•
•
What markers define a subset?
How many subsets?
Dynamic changes
Context dependence
Higher order relationships
Standardization/Harmonization of assays
– Standardized panels are not the complete answer
– even with lyophilized reagents and prescribed
SOPs, we still get significant site-to-site variability
Types of “markers”
• Physical characteristics
– FSC, SSC
• Non-specific reporters
– Viability stains, CFSE (or PKH26, SNARF)
•
•
•
•
•
Cell surface molecules (e.g. CD3, CD4, CD8)
Intracellular molecules (e.g. TNFa, IL-2, IFNg)
Intranuclear molecules (e.g. FoxP3)
Activation targets (e.g. PhosFlow)
Other features (e.g. imaging cytometers)
Marker equivalence?
CD45RO and CD45RA
10
5
10
4
10
3
10
2
105
150K
100K
50K
<Red B-A>: CD8 A700
FSC-H
76
<Violet H-A>: vAmine
200K
105
<Green D-A>: CD45RO ECD
250K
104
103
4
10
3
10
2
0.38
0
0
0
58
0
0
Ungated
10
50K
100K
150K
200K
250K
FSC-W
CD3+vAmineSinglets
CD4+
<Red A-A>, <Green D-A> s…
0
2
3
4
54.6
5
10
10
10
10
<Violet G-A>: CD3 AmCyan
Singlets
Singlets
CD4+
CD3+vAmine<Red A-A>, <Green D-A> s…
3
4
5
0
10
10
10
<Blue A-A>: CD4 PerCP_Cy5 5
Singlets
Singlets
CD3+vAmineCD3+vAmine<Red A-A>, <Green D-A> s…
CD4+
3
4
5
0
10
10
10
<Red A-A>: CD45RA APC_H7
Singlets
CD3+vAmineCD4+
<Red A-A>, <Green D-A> s…
Definition and number of cell subsets
• More markers = more subsets
• Especially true with unbiased statistical/machine
learning approaches
– Can often find several discrete clusters of events that
have no obvious biological label
• The “same” cell type may have multiple
alternative phenotypic characterizations (e.g.
memory T cells – various combinations of
CD45RA, CD45RO, CD27, CD57, CCR7, CD62L, Bcl2, Ki67 used in human studies; CD28, CD95 used
in NHP studies)
Haddad, and R. P. Sékaly
105
105
CD4
10
CCR7
104
CD3+CD4+
4
103
3
10
Transitional memory cells
Supplementary Figure 1. Gating strategy and quantification of integrated HIV DNA
in sorted CD4+ T-cell subsets.
103
103
CCR7
CD4
104
105
CD27
Naïve (TN )
10 4
CD45RA +CCR7+CD27+
10 3
104
105
CD45RA
b
Integrated HIV DNA
copies per 1X106 cells
CD3
10 5
-CD27+
Subject
CD45RA -CCR7
CD45RA-CCR7-
Count
103
104
104
10,000
1,000
100
10
3
4
6
11
14
17
Sorted on CD45RA and CCR7
2
5
7
8
4
6
11
Supplementary Figure 1. Gating
CD4+ T-cell subsets. (a) CD4+
indicated inTNthe figure by polychro
expression Tof
CM CD45RA, CCR7 a
+
TTM
CCR7-CD27
) and TEM (CD45R
-T
+
(CD45RA CCR7
CD27+). PBMC
EM
individual T
isTDshown. (b) Frequenci
TTM+EM subjects. Results
from 17 aviremic
subset.
100,000
1
3
CD45RA -CCR7-CD27105
CD27
1
1
Sorted
Effector Memory (TEM
) on CD45RA a
10 3
Subject
100
Transitionnal memory
(TTM )
1
CD45RA +CCR7-CD2710 3
10,000
10
Terminally
Differentiated (TTD )
103
100,000
1,000
+
CD45RA -CCR7+CD27
10 5
CD3+CD4+
CD4
Central Memory (TCM )
Count
CD45RA-CCR7+
Integrated HIV DNA
copies per 1X106 cells
b
104
103
105
CD3
a
105
104
9
10
12
13
15
16
Sorted on CD45RA, CCR7 and CD27
Nature Medicine: doi:10.1038/nm.1972
Supplementary Figure 1. Gating strategy and quantification of integrated HIV DNA in sorted
3 cytokines versus 1 or 2 cytokines
Dynamic changes
• Definition of a viable cell
– Depending on what method is used, may get very
different numbers
– Matters because
• Non-viable cells  non-specific binding  FP++
• Cell death is the object of study
– PI, EMA, amine, anti-caspase 3 …
– Detect different stages of cell death
• Down-regulation of receptors
– CD3 following antigenic stimulation
– MHC following HIV infection
Changes with activation
Changes with Proliferation
Non-proliferating lymphocytes
Proliferating lymphocytes
SSH
CFSE
Proliferating lymphocytes
Non-proliferating lymphocytes
CD4
FSC
Context dependence
• Stimulation conditions
– SEB, Costim, specific peptide pools (e.g. IE1 and pp65
CMV pools), mitogens
– Time of assay (minutes in Phosflow, hours to days for
cytokine expression, weeks to months for vaccine
studies)
• Fresh or frozen?
– Some receptors are labile and unstable under
cryopreservation e.g. CCR7
– Cannot use Sekaly gating strategy to identify
transitional memory cells with cryopreserved samples
Fresh vs Cryo
CD4 Subsets
69.3
CD4+
cCBn_FS 1.fcs
28.4
<Green D-A>: CD45RO ECD <Red A-A>: CD45RA APC_H7
98.5
<Red C-A>: CD27 APC
68
<Green E-A>: CD62L PE
57.8
99.8
<Green A-A>: CD197 PE_Cy7 <Green C-A>: CD28 PE_Cy5
0.26
SSC-A
SSC-A
CD4+
fCBn_FS 1.fcs
<Blue B-A>: CD57 FITC
98.6
<Red C-A>: CD27 APC
68.1
<Green E-A>: CD62L PE
58.9
99.9
<Green A-A>: CD197 PE_Cy7 <Green C-A>: CD28 PE_Cy5
0.26
SSC-A
SSC-A
28.3
98.6
<Red C-A>: CD27 APC
68.2
<Green E-A>: CD62L PE
58.9
99.9
<Green A-A>: CD197 PE_Cy7 <Green C-A>: CD28 PE_Cy5
0.27
SSC-A
SSC-A
28.6
<Green D-A>: CD45RO ECD <Red A-A>: CD45RA APC_H7
31
99.5
<Red C-A>: CD27 APC
86.5
<Green E-A>: CD62L PE
64
100
<Green A-A>: CD197 PE_Cy7 <Green C-A>: CD28 PE_Cy5
1.91
SSC-A
SSC-A
29.8
28.4
<Red A-A>: CD45RA APC_H7
67.3
32
<Green D-A>: CD45RO ECD <Red A-A>: CD45RA APC_H7
<Blue B-A>: CD57 FITC
99.7
<Green A-A>: CD197 PE_Cy7 <Green C-A>: CD28 PE_Cy5
0.23
<Blue B-A>: CD57 FITC
98.2
<Red C-A>: CD27 APC
78.3
<Green E-A>: CD62L PE
58.4
99.8
<Green A-A>: CD197 PE_Cy7 <Green C-A>: CD28 PE_Cy5
0.24
<Blue B-A>: CD57 FITC
98
<Red C-A>: CD27 APC
77.7
<Green E-A>: CD62L PE
57.4
99.7
<Green A-A>: CD197 PE_Cy7 <Green C-A>: CD28 PE_Cy5
0.24
<Blue B-A>: CD57 FITC
99.3
<Red C-A>: CD27 APC
89.1
<Green E-A>: CD62L PE
58
99.9
<Green A-A>: CD197 PE_Cy7 <Green C-A>: CD28 PE_Cy5
1.92
<Blue B-A>: CD57 FITC
CD4+
31
<Green D-A>: CD45RO ECD <Red A-A>: CD45RA APC_H7
99.6
<Red C-A>: CD27 APC
86.7
<Green E-A>: CD62L PE
64.4
100
<Green A-A>: CD197 PE_Cy7 <Green C-A>: CD28 PE_Cy5
1.93
SSC-A
SSC-A
68.4
<Green D-A>: CD45RO ECD
<Blue B-A>: CD57 FITC
CD4+
70.1
29.3
<Green D-A>: CD45RO ECD <Red A-A>: CD45RA APC_H7
<Blue B-A>: CD57 FITC
CD4+
99.3
<Red C-A>: CD27 APC
89
<Green E-A>: CD62L PE
57.4
99.9
<Green A-A>: CD197 PE_Cy7 <Green C-A>: CD28 PE_Cy5
2.03
<Blue B-A>: CD57 FITC
CD4+
31.1
<Green D-A>: CD45RO ECD <Red A-A>: CD45RA APC_H7
99.6
<Red C-A>: CD27 APC
86.8
<Green E-A>: CD62L PE
62.6
100
<Green A-A>: CD197 PE_Cy7 <Green C-A>: CD28 PE_Cy5
1.82
<Blue B-A>: CD57 FITC
SSC-A
SSC-A
<Green E-A>: CD62L PE
57.9
CD4+
c701082556h_FS 1.fcs
<Green D-A>: CD45RO ECD <Red A-A>: CD45RA APC_H7
72.4
<Red C-A>: CD27 APC
77.9
CD4+
CD4+
f701082556h_FS 1.fcs
72.9
67
<Green D-A>: CD45RO ECD <Red A-A>: CD45RA APC_H7
<Blue B-A>: CD57 FITC
CD4+
72.9
98.1
CD4+
<Green D-A>: CD45RO ECD <Red A-A>: CD45RA APC_H7
69.4
28.9
<Green D-A>: CD45RO ECD <Red A-A>: CD45RA APC_H7
CD4+
69.7
67.5
67
32
<Green D-A>: CD45RO ECD <Red A-A>: CD45RA APC_H7
99.3
<Red C-A>: CD27 APC
89.2
<Green E-A>: CD62L PE
57.7
99.9
<Green A-A>: CD197 PE_Cy7 <Green C-A>: CD28 PE_Cy5
2.04
<Blue B-A>: CD57 FITC
<Green A-A>: CD197 PE_Cy7
<Green A-A>: CD197 PE_Cy7
<Green A-A>: CD197 PE_Cy7
<Green A-A>: CD197 PE_Cy7
<Green A-A>: CD197 PE_Cy7
<Green A-A>: CD197 PE_Cy7
<Green A-A>: CD197 PE_Cy7
<Green A-A>: CD197 PE_Cy7
<Red A-A>: CD45RA APC_H7
<Green D-A>: CD45RO ECD
<Green E-A>: CD62L PE
<Green E-A>: CD62L PE
<Blue B-A>: CD57 FITC
<Blue B-A>: CD57 FITC
<Red C-A>: CD27 APC
<Red C-A>: CD27 APC
EM
<Green C-A>: CD28 PE_Cy5
<Green D-A>: CD45RO ECD
CM
<Red A-A>: CD45RA APC_H7
<Red C-A>: CD27 APC
0.039
<Green C-A>: CD28 PE_Cy5
<Blue B-A>: CD57 FITC
N
<Green E-A>: CD62L PE
<Green D-A>: CD45RO ECD
<Green D-A>: CD45RO ECD
2.06
<Blue B-A>: CD57 FITC
61.2
<Red A-A>: CD45RA APC_H7
<Red C-A>: CD27 APC
<Red C-A>: CD27 APC
27.1
<Green C-A>: CD28 PE_Cy5
<Blue B-A>: CD57 FITC
<Red C-A>: CD27 APC
TE
<Green E-A>: CD62L PE
<Blue B-A>: CD57 FITC
<Red C-A>: CD27 APC
<Green D-A>: CD45RO ECD
<Green D-A>: CD45RO ECD
<Green E-A>: CD62L PE
<Blue B-A>: CD57 FITC
0.17
<Red A-A>: CD45RA APC_H7
<Green D-A>: CD45RO ECD
<Green E-A>: CD62L PE
<Red C-A>: CD27 APC
<Blue B-A>: CD57 FITC
fCBn_FS 1.fcs
<Green C-A>: CD28 PE_Cy5
<Red A-A>: CD45RA APC_H7
<Green D-A>: CD45RO ECD
<Blue B-A>: CD57 FITC
E
<Green C-A>: CD28 PE_Cy5
<Red A-A>: CD45RA APC_H7
<Green E-A>: CD62L PE
EM
<Green C-A>: CD28 PE_Cy5
<Green D-A>: CD45RO ECD
CM
<Red A-A>: CD45RA APC_H7
<Red C-A>: CD27 APC
3.97e-3
<Green C-A>: CD28 PE_Cy5
<Red C-A>: CD27 APC
<Red C-A>: CD27 APC
<Red C-A>: CD27 APC
N
<Blue B-A>: CD57 FITC
<Blue B-A>: CD57 FITC
<Blue B-A>: CD57 FITC
4.59
<Green E-A>: CD62L PE
<Green E-A>: CD62L PE
<Green E-A>: CD62L PE
<Green D-A>: CD45RO ECD
<Green D-A>: CD45RO ECD
<Green D-A>: CD45RO ECD
<Green D-A>: CD45RO ECD
57.3
<Red A-A>: CD45RA APC_H7
<Red A-A>: CD45RA APC_H7
<Red A-A>: CD45RA APC_H7
30.5
<Green C-A>: CD28 PE_Cy5
<Green C-A>: CD28 PE_Cy5
<Green C-A>: CD28 PE_Cy5
Normal vs HIV+: CD4 maturational subsets
197 vs 27, 57, 62L, 197, RA, 28
f701082556h_FS 1.fcs
1.18
<Green D-A>: CD45RO ECD
E
<Green A-A>: CD197 PE_Cy7
TE
<Green A-A>: CD197 PE_Cy7
CD4+
Fresh
Cryopreserved
c701082556h_FS 1.fcs
31 67.3 68
98.1
99.5
32
77.9
57.8
99.3
86.5
Fresh
99.8 57.9
64
89.1
CD197 (CCR7)
CD4+
Cryopreserved
cCBn_
0.26 99.7
100 58
SSC-A
s
CD62L
67.5
0.23
APC
<Green
E-A>:
CD62L
PEC-A>:
<Green
A-A>:
CD197
PE_Cy7
<Green
C-A>:
CD28
PE_Cy5
<Blue
B-A>:C-A>:
CD57
FITC
<Green
D-A>:
CD45
RA APC_H7
<Red
C-A>:
CD27
APC
<Green
E-A>:
CD62L
PE
<Green
A-A>:
CD197
PE_Cy7
<Green
CD28
PE_Cy5
<Blue
B-A>:<Blue
CD5
A-A>:
CD45RA
APC_H7
<Red
CD27
APCAPC_H7
<Green
E-A>:
CD62L
PEAPC
<Green
A-A>:
CD197
PE_Cy7
C-A>:
CD28
PE_Cy5
<Green
D-A>:
CD45RO
ECD
<Red
A-A>:
CD45RA
<Red
C-A>:
CD27
<Green
E-A>:
CD62L
PE<Green
<Green
A-A>:
CD197
PE_Cy7
<Gr
CD4+
CD4+
99.6
29.3
78.3
58.9
99.3
86.7
99.9 58.4
64.489
0.26 99.8
10057.4
SSC-A
31 70.1
98.2
68.1
0.2467
<Green
D-A>:
CD45
APC
<Green
E-A>:
CD62L
PEC-A>:
<Green
A-A>:
CD197
PE_Cy7
<Green
C-A>:
CD28
PE_Cy5
<Blue
B-A>:C-A>:
CD57
FITC
RA APC_H7
<Red
C-A>:
CD27
APC
<Green
E-A>:
CD62L
PE
<Green
A-A>:
CD197
PE_Cy7
<Green
CD28
PE_Cy5
<Blue
B-A>:<Blue
CD5
A-A>:
CD45RA
APC_H7
<Red
CD27
APCAPC_H7
<Green
E-A>:
CD62L
PEAPC
<Green
A-A>:
CD197
PE_Cy7
C-A>:
CD28
PE_Cy5
<Green
D-A>:
CD45RO
ECD
<Red
A-A>:
CD45RA
<Red
C-A>:
CD27
<Green
E-A>:
CD62L
PE<Green
<Green
A-A>:
CD197
PE_Cy7
<Gr
CD4+
CD4+
68.298
67
99.6
32
58.9
77.7
86.8
99.3
99.9
57.4
62.6
89.2
0.27 99.7
100
57.7
SSC-A
31.1
68.4
0.24
APC
<Green<Red
E-A>:
CD62L
PE APC
<Green A-A>:
CD197
PE_Cy7
<Green
C-A>:A-A>:
CD28 CD197
PE_Cy5PE_Cy7
<Blue
B-A>:C-A>:
CD57 CD28
FITC PE_Cy5 <Green
RA APC_H7
C-A>:
CD27
<Green
E-A>:
CD62L
PE
<Green
<Green
<BlueD-A>:
B-A>:CD45
CD5
A-A>:
CD45RA
APC_H7
APCAPC_H7
<Green
E-A>:
CD62L
PEAPC<Green
A-A>: CD197
PE_Cy7
C-A>:
CD28
PE_Cy5
<Green
D-A>: CD45RO
ECD<Red
<RedC-A>:
A-A>:CD27
CD45RA
<Red
C-A>:
CD27
<Green
E-A>: CD62L
PE<Green
<Green
A-A>:
CD197
PE_Cy7<Blue
<Gr
Higher order relationships
• Immune cells in context
– Cell networks
– Temporal profiles
– Tissue dependence
– Disease state
MARRIAGE OF AUTOMATED
APPROACHES AND ONTOLOGY?
Potential applications of ontology
• Improve statistical/machine learning algorithms
– Patterns of expression
– Higher order relations to constrain assignment
– Machine inference – e.g. report HIV status via
information in IDO about temporal changes in HIV
infection
• Automated annotation of cell subsets
• Automated report generation (e.g. MiFlowcyte,
MIATA requirements)
Acknowledgements
• Theoretical
– Mike West, Andrew
Cron, Lynn Lin, Fernando
Bonassi, Adam Richards,
Jacob Frelinger, Quanli
Wang, Thomas Kepler,
Lindsay Cowell, Anna
Maria Masci
• Experimental
– Kent Weinhold, Janet
Staats, Jennifor Enzor,
Sarah Sparks, Guido
Ferrari, John Whitesides,
Tony Moody, Thad
Gurley, David Murdoch,
Tom Denny, Scott
Palmer, Doug Tyler, CIP.
• Funding
– NIAID, NHLBI, WCF, BMF,
MRA