Protein-based single-cell analysis using flow cytometry ... of Single Cell... · Protein-based...
Transcript of Protein-based single-cell analysis using flow cytometry ... of Single Cell... · Protein-based...
Protein-based single-cell analysis using flow cytometry and
immunohistochemical technologies
Prof. Frederik De Smet
The laboratory for Precision Cancer Medicine (www.lpcm.be)
Translational Cell and Tissue Research Unit,
Department of Imaging and Pathology
KULeuven
1. Technology selection for protein based analysis
2. Flow cytometry principles
3. Optimizing flow cytometry procedures
4. Multiplex panel design
5. Single cell analysis of brain tumor samples
6. Spatial protein-based analysis
Outline
Saadatpour et al. Trends in Genetics
Ritchie et al 2015 Nat Rev GenSingle cell analysis
Protein analysis methods
Mass spectrometry based Antibody based Fluorescence coupling
Unbiased Biased Biased
Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS)
Tandem mass tag (TMT) peptide labeling for MS analysis of lysed cells
100-1000100-1000 (only most abundant proteins)
no no noU
Budnik et al Genome Biol. 2018
Protein detection method
# of analyzed cells/sample
# of measured parameters
Live cells retrieved?
Compensation/autofluorescencecontrol needed?
Subcellular localization possible?
MethodTargeted or unbiased?
SCoPE-MS
Mass cytometry(CyTOF)
FACS
Protein detection method
Staining of fixed cells with mass-tag labeled Abs (RNA/DNA detection coming soon)
Staining of live/fixed cells with fluorescently labeled Abs
# of analyzed cells/sample
104-106
104-106
# of measured parameters
1-135(37 isotopes
available)
1-20 (mostly <10)
Live cells retrieved?
no
yes
Compensation/autofluorescencecontrol needed?
No (minimal)
yes
Subcellular localization possible?
yes
yes
MethodTargeted or unbiased?
T
T
Antibody-based single cell proteomic analysisFl
ow
Sc barcode chips
Sc Western blot
Spatially encoded Ab array for fluorescent immunoassays of lysed cells
Miniaturized automated western blotting on a microchip of lysed cells
Proseek Multiplex Droplet qPCR detection of proximity ligation antibodies (2 Abs/protein needed) of lysed cells
100-1000
100-1000
96
>40
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96
no
no
no
yes
no
no
no
no
no
T
T
T
Ch
ip/m
icro
flu
idic
s b
ased
- Nucleotide labeled antibodies/aptamers + RNAseq- Only surface proteins- Panels of 200 surface markers are available (CITE-seq)
- Limited by number of cells that are sequenced (5-10K)
Integrated single-cell analysis of protein and RNA
CITE-seq/REAP-seq/Apt-seq
Seq
uen
cin
g b
ased
Flo
w b
ased
- Labeled nucleotide probes measured along with Abs- Limited to number of labels (for CyTOF: 37)
- Large amounts of cells can be analysed (104-106)
PLAYR
Levy et al., 2018
Selecting the right technology
Specificity largely defined by quality and optimization of antibody
1. Technology selection for protein based analysis
2. Flow cytometry principles
3. Optimizing flow cytometry procedures
4. Multiplex panel design
5. Single cell analysis of brain tumor samples
6. Spatial protein-based analysis
Outline
Methodology: single cell proteomics by Fluorescence Flow Cytometry
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• Up to 28 markers are measured (long optimization + compensation required for all fluorophores)
• Proteins (by Abs); generally limited to surface proteins
• Cells can be sorted alive and collected for further analysis
• BD Symphony at Flow Core Leuven
Mass spectrometry
• Up to 37 selected markers are measured simultaneously due to no overlap; 5 new isotopes expected later this year (theoretical maximum ~ 130)
• Protein (by Abs) – surface and intracellular
Fluorescence
• Up to 28 markers are measured (long optimization + compensation required for all fluorophores)
• Proteins (by Abs) - surface
Methodology: Focused, single cell proteomics by CyTOF
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• Rare earth-element labeled Abs• Up to 37 proteins with high
sensitivity • >105-106 single cells/condition (rare
populations)• Low cost (~75euro/sample)• Cell typing/signaling events
• See presentation later today by Olga Karpus
Methodology: Focused, single cell proteomics by CyTOF
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1. Technology selection for protein based analysis
2. Flow cytometry principles
3. Optimizing flow cytometry procedures
4. Multiplex panel design
5. Single cell analysis of brain tumor samples
6. Spatial protein-based analysis
Outline
Negative for MDM2
1/1001/2001/5001/10001/1500
1/15001/1001/2001/5001/1000
Positive for MDM2
Optimizing antibody dilutions for Flow Cytometry
Greatly affects costs of experiments!!
Calculation of staining index and Q
MeOH step
Optimizing staining conditions for Flow Cytometry
TritX100 step
neg pos neg pos
Compared to ELISA based assay
MeOH step
Optimizing staining conditions for Flow Cytometry
TritX100 step
neg pos neg pos
Compared to ELISA based assay
Sample preparation for flow cytometry: maintenance of epitopes
Cell dissociation: Trypsin, Accutase (milder)
Tumor tissue samples are dissociated using a tissue specific enzyme mix in an optimized protocol (Miltenyl Biotec) – typically
contain ), Collagenase, dispase, papain, etc
Tissue handling
Cell lines handling
vs blood samples
Staining samples for fluorescence flow cytometry
Dissociate cells/sample
PI (life/death stain)
Run on cytometer
Pre-processing and Antibody staining steps
Surface markers staining
On ice
Staining samples for CyTOF overview
Dissociate cellsIdUIncorporation
(37°C)
Cis (life/death)
Fix in PFA 4% Freeze in 10%DMSO/FBS
Pre-processing steps
Surface markersBarcode
+ Pooling
(20 samples)
Cyto/nuclear markers
Phospho-markers
DNA intercalator
Antibody staining steps
Saponin/TritX MeOH
Run onCyTOF
1. Technology selection for protein based analysis
2. Flow cytometry principles
3. Optimizing flow cytometry procedures
4. Multiplex panel design
5. Single cell analysis of brain tumor samples
6. Spatial protein-based analysis
Outline
Microglia
Oligodendrocytes
T-cells
Tumor cells
Heterogeneity
Cell typing
Brain-specific antibody panel assembly for CyTOF
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Signal transduction Transcriptional activity
DNA damaging therapy
Gene Z
TF YPathway X
Inhibitor X1Inhibitor Y1
Ionizing radiation
CH3
Alkylating agents
Effector molecules
DNA damage
Early cell response markers
Proliferation/Cell cycle
Senescence
Apoptosis
Microglia
Oligodendrocytes
T-cells
Tumor cells
Heterogeneity
Cell typing Molecular drug response
Assembly of a brain-specific panel of marker proteins
Brain-specific antibody panel assembly for CyTOF
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unstained
EGFR-KO
EGFR
QC antibody
- Needs to be without BSA/Gel- Confirmation in regular FACS
Pre-labeled/Antibody labeling
Selecting and labelling antibodies for CyTOF
Pos vs neg cell line Titration in CyTOF
MAXPAR.fluidigm.com
or
1. Technology selection for protein based analysis
2. Flow cytometry principles
3. Optimizing flow cytometry procedures
4. Multiplex panel design
5. Single cell analysis of brain tumor samples
6. Spatial protein-based analysis
Outline
CD44
Sox2 Olig2
TranscriptionFactors
H3 Proliferation
pATM
pH2AX
DNA repair
EGFR PDGFRa
PI3K
PIP2
pAkt
PTEN
Ras/Raf/MEK
pERKERK
P
NCAM1
Myc
pRbRb
Cyclin D p16
P21
P53MDM2
Apoptosis
Cdk6
Cdk4
PARP
Casp-3
NUCLEUS
CCNB1
PLCg
PIP3
IP3
mTOR
pBAD
pS6
pP38P38MEKK
MigrationCa2+
ApoptosisCasp
Intermediate filaments for celltyping
Proliferation
GBM CyTOF panel
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Multiplex, single-cell protein analysis
~30 antibodies labelled with elemental isotopes
CyTOF analysis
Cell 1Cell 2Cell 3…
(Element)
1. Multi-dimensionaldata set
Single cell
3 6 7 2 1
4 5 2 5 3
5 3 1 4 1
A B C D E …
t-SNE X
t-SN
E Y
Dots are single cells
Phenotypicallysimilar
Tumor biopsyTumor cells
?
30 markersSingle-cell resolution
104-106 cellsin a single experiment 27
GBM cell
astrocyte
oligodendrocyte
myelin
endothelial cell
microglial cell
macrophage
leukocyte
T-cell
cancer stem cell
Untangling the different cell types in a GBM tumor
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dissociation
myelin removalTumor Sample
CyTOF analysis
Untangling the different cell types in a GBM tumor
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CyTOF allows identification of tumormicroenvironment in tumor biopsies
tSNE x
LBT120b LBT123b
LBT127b LBT127i
C
CD15 CD45 CD68
CD3 CD4 CD8
Sox2 CD56 CD31
me
an e
xpre
ssio
n
0
8
Sox2 CD56 CD31
CD3 CD4 CD8
CD15 CD45 CD68
30
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Access to CYTOF technology: FLOW core
Helios Mass Cytometer, Fluidigm
https://gbiomed.kuleuven.be/english/corefacilities/facs
1. Technology selection for protein based analysis
2. Flow cytometry principles
3. Optimizing flow cytometry procedures
4. Multiplex panel design
5. Single cell analysis of brain tumor samples
6. Spatial protein-based analysis
Outline
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Mapping the tumor microenvironment
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Multiple Iterative Labeling by Antibody Neodeposition (MILAN)
Mapping the tumor microenvironment
• Optimized on FFPE samples• Up to 100 markers in single slide• Unmodified antibodies• Small needle biopsies
Sample type # markers Flexibility Reagents Ab-tag Ab removal Throughput automated cost/samplecost
instrumentation
storage?
MILAN FFPE Up to 100 high regular Abs none completewhole
slide/TMApartially
automatedmedium low yes
CycIF FFPE 80 high regular Abs none photobleachingwhole
slide/TMApartially
automatedmedium low unkown
MultiOmyx FFPE 60 limited conjugated fluorophorechemical
inactivationwhole
slide/TMAservice medium low unkown
Zellkraftwerk cells/Frozen 70 high regular Abs none photobleachingTMA (2 by 1
cm)service high unknown yes
Hyperion Frozen 37 limited conjugated metal none TMA/slowautomated detection
high high no
IONpath Frozen 37 limited conjugated metal none TMA/slowautomated detection
high high no
Akoya Frozen/FFPE 50 limited conjugated nucleotide nonewhole
slide/TMAautomated detection
high high no
TECHNICAL COMPARISON OF MULTIPLEX METHODS
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Panels for various cell types
Tumor cells Neurons MacrophagesPMN
DCsT and B cellsEndothelial cells
Mapping the tumor microenvironment
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MelanACD8CD4CD68+CD163DAPI
DAPI MASK
PHENOTYPIC IDENTIFICATIONIdentification of solid clusters
Francesca Maria Bosisio, Asier Antoranz, Maddalena Maria Bolognesi, Clizia Chinello, Jasper Wouters, Fluvio Magni, Leonidas Alexopoulos, Giorgio Cattoretti, Joost van den Oord bioRxiv 409011; doi: https://doi.org/10.1101/409011
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Access to MILAN technology: Pathology core
Will become available early 2020
www.kuleuven.be/histology
Lukas MarcelisAsier AntoranzJulie MessiaenYannick Van HerckVeerle HaemelsMatthias Van HaeleJanne Snoeck
Flow core
Prof. StevenDe Vleeschouwerand colleagues
Neurosurgery Oncology
Prof. Clement
Pathology
Prof. Raf Sciot Pier Andrée PenttilaProf. Keith LigonDFCI, Boston
Dana-Farber
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Prof. BechterDr. Van Herck
Dr. Bosisio, PhD+ pathology dept.Dr. Marcelis
Pradeep Kumar
Prof. Yvan SaeysDr. Sofie Van GassenVIB-UGent
Data analysis