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

12

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

11

• 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

12

• 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

13

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

22

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

23

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

26

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

28

dissociation

myelin removalTumor Sample

CyTOF analysis

Untangling the different cell types in a GBM tumor

29

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

31

Access to CYTOF technology: FLOW core

pierandree.penttila@kuleuven.vib.be

Helios Mass Cytometer, Fluidigm

Olga.karpus@fluidigm.com

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

33

Mapping the tumor microenvironment

34

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

36

Panels for various cell types

Tumor cells Neurons MacrophagesPMN

DCsT and B cellsEndothelial cells

Francesca.Bosisio@uzleuven.be

Mapping the tumor microenvironment

37

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

41

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

Francesca.Bosisio@uzleuven.be

Flow core

Prof. StevenDe Vleeschouwerand colleagues

Neurosurgery Oncology

Prof. Clement

Pathology

Prof. Raf Sciot Pier Andrée PenttilaProf. Keith LigonDFCI, Boston

Dana-Farber

42

Prof. BechterDr. Van Herck

Dr. Bosisio, PhD+ pathology dept.Dr. Marcelis

Pradeep Kumar

Prof. Yvan SaeysDr. Sofie Van GassenVIB-UGent

Data analysis