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Becton, Dickinson and — Technical Presentation (2019)
32 slides from Becton, Dickinson and's technical presentation, 2019
Slide 1: Single cell multiomics analysis for deep characterization of exhausted T cells
Single cell multiomics analysis for deep characterization of exhausted T cells BD, the BD Logo, LSRFortessa, Rhapsody and SeqGeq are trademarks of Becton, Dickinson and Company or its affiliates. All other trademarks are the property of their respective owners. © 2019 BD. All rights reserved. 1
Slide 2: T-cell exhaustion
T-cell exhaustion Chronic viral infections or cancer can lead to persistent stimulation of T cells, which results in T-cell dysfunction. Adapted from Freeman GJ et al. J Exp Med. 2006;203:2223-2227 2 BD, the BD Logo, LSRFortessa, Rhapsody and SeqGeq are trademarks of Becton, Dickinson and Company or
Slide 3: Hallmarks of T-cell exhaustion
Hallmarks of T-cell exhaustion T-cell exhaustion is characterized by the progressive loss of function and sustained expression of inhibitory receptors. Kahan SM et al. Virology. 2015 May;479-480:180-93 3 BD, the BD Logo, LSRFortessa, Rhapsody and SeqGeq are trademarks of Becton, Dickinson and Compan
Slide 4: T-cell exhaustion is reversible
T-cell exhaustion is reversible Adapted from Freeman GJ et al. J Exp Med. 2006;203:2223-2227 4 BD, the BD Logo, LSRFortessa, Rhapsody and SeqGeq are trademarks of Becton, Dickinson and Company or its affiliates. All other trademarks are the property of their respective owners. © 2019 BD. All rights
Slide 5: Immune checkpoint blockade therapy
Immune checkpoint blockade therapy Different inhibitory receptors can be simultaneously expressed on exhausted T cells. Catakovic et al. Cell Commun Signal. 2017 Jan 5;15(1):1 5 BD, the BD Logo, LSRFortessa, Rhapsody and SeqGeq are trademarks of Becton, Dickinson and Company or its affiliates. All o
Slide 6: The importance of studying T-cell exhaustion
The importance of studying T-cell exhaustion • Understand the diversity and complexity of exhausted tumor-infiltrating T cells. • Discover new mechanisms regulating T-cell exhaustion and promoting cancer progression. • Optimize protocols for CAR T-cell generation. • Screen new molecules able to prev
Slide 7: Limitation of current single cell technologies
Limitation of current single cell technologies • High-parameter flow cytometry (conventional flow cytometry, CyTOF®, spectral flow cytometry): Allows simultaneous analysis of 28-40 proteins on a single cell level. • Single cell RNA sequencing: Allows comprehensive assessment of mRNA expression on a
Slide 8: Power of single-cell multiomics
Power of single-cell multiomics • BD® AbSeq reagents, in conjunction with the BD Rhapody™ Targeted Panel, allows for simultaneous proteomic and transcriptomic analysis at the single cell level and enables deeper characterization of cells, understanding of mechanisms governing cell processes, identif
Slide 9: Key findings
Key findings 1. Demonstrated the concordance between flow cytometry and BD AbSeq technology. 2. Assessed kinetics and differential expression of 39 proteins and 400 T- cell specific genes over time. 3. Identified discrete subsets of cells within a single sample and across different samples, allowing
Slide 10: Experimental design and workflow
Experimental design and workflow Magnetically Chronic stimulation IL-2 and CD3/CD28 Dynabeads® enriched T cells from PBMC Transient stimulation IL-2 3 days 7 days 14 days • Magnetically enriched T cells from PBMC were stimulated for 3 days in the presence of IL-2 and CD3/CD28 Dynabeads®. • Chronic s
Slide 11: Experimental design and workflow
Experimental design and workflow BD® Sample BD® AbSeq BD RhapsodyTM SeqGeq™ Software Multiplexing Kit Assays System BD RhapsodyTM T-Cell Targeted Panel + + NGS (5 tags) (39 markers ) Fluorescent Antibodies BD LSRFortessaTM X-20 Cell Analyzer + (12 markers ) • An aliquot of each samples was thawed an
Slide 12: Panel design
Panel design 39-Plex AbSeq Panel 12-color Flow Cytometry CD4 CD278 (ICOS) CD103 CD94 Panel CD8 CD178 (FAS-L) CD30 CD95 Marker Fluorochrome CD279 (PD-1) CD183 (CXCR3) CD38 CD98 CD4 BUV805 CD223 (LAG-3) CD185 (CXCR5) CD39 CD2 CD8 BUV395 CD366 (TIM-3) CD194 (CCR4) CD44 TCRab CD279 (PD-1) PE-Cy7 CD272 (
Slide 13: Validation of the in vitro model system
Validation of the in vitro model system • The optimized panel confirmed that the Fresh Day 3 Day 7 Day 14 Chronic Day 14 Transient in vitro model system for chronic PD-1 stimulation induced immunophenotypic and functional changes associated with T-cell exhaustion: LAG-3 • Progressive expression of i
Slide 14: Sequencing metrics
Sequencing metrics Filtered AbSeq AbSeq Mean AbSeq RNA Putative RNA Mean RNA Mean Raw Reads Mean Raw Seq Saturation Saturation Cell # Reads/Cell Seq Depth (M) Reads/Cell Depth (%) (%) 325 18800 5746 1650 1.15 2.13 24 99 14 BD, the BD Logo, LSRFortessa, Rhapsody and SeqGeq are trademarks of Becton, D
Slide 15: Correlation between flow cytometry and AbSeq technology
Correlation between flow cytometry and AbSeq technology Flow Cytometry Fresh Day 3 Day 7 Day 14 Chronic Day 14 Transient AbSeq Fresh Day 3 Day 7 Day 14 Chronic Day 14 Transient 15 BD, the BD Logo, LSRFortessa, Rhapsody and SeqGeq are trademarks of Becton, Dickinson and Company or its affiliates. All
Slide 16: Concordance between flow cytometry and AbSeq data
Concordance between flow cytometry and AbSeq data PD-1 CD39 GITR TIM-3 CD95 PD-1 P D -1 CD39 C D 39 GITR G IT R TIM-3 T IM -3 CD95 C D 95 M F I o r M M C (F o ld c h a n g e ) M F I o r M M C (F o ld c h a n g e ) 30 10 M F I o r M M C (F o ld c h a n g e ) M F I o r M M C (F o ld c h a n g e ) M F
Slide 17: Clustering of T cells
Clustering of T cells AbSeq Only scRNA-Seq Only AbSeq + scRNA-Seq Key: Fresh Day 3 Day 7 Day 14 Chronic t-SNE_AbSeq_2 t-SNE_CD8QC_2 t-SNE_RNA_2 Day 14 Transient t-SNE_AbSeq_2 t-SNE_RNA_2 t-SNE_CD8QC_2 • Distinct clusters are resolved for each samples and the resolution is equivalent for clustering b
Slide 18: Differential protein expression analysis
Differential protein expression analysis Day 14 Day 14 Fresh Day 3 Day 7 Chronic Transient Cluster 1: Proteins expressed on fresh T cells which are downregulated upon stimulation and then re-expressed after removal of the stimulatory agonist. This pattern is expected for CD45RA and CD27 because cell
Slide 19: Differential gene expression analysis
Differential gene expression analysis Day 14 Day 14 Fresh Day 3 Day 7 Chronic Transient Cluster 1: Genes expressed on fresh T cells and downregulated upon activation. These genes are not significantly upregulated upon stimulus removal. Cluster 2: Genes upregulated upon activation but transiently exp
Slide 20: Benefits of simultaneous analysis of protein and gene expression
Benefits of simultaneous analysis of protein and gene expression • High-parameter flow cytometry and single cell RNA sequencing can independently produce data for differential protein expression and gene expression, respectively. • However, using AbSeq reagents provides protein and gene expression d
Slide 21: Correlating protein and gene expression on a single cell level
Correlating protein and gene expression on a single cell level Day 14 Day 14 Day 14 Day 14 Fresh Day 3 Day 7 Fresh Day 3 Day 7 Chronic Transient Chronic Transient • CD7 protein and mRNA expression show strong correlation throughout the time course. • While CD103 (ITGAE) mRNA is upregulated at day 3
Slide 22: Subpopulation emerges within chronically stimulated CD8+ T cells
Subpopulation emerges within chronically stimulated CD8+ T cells • Two populations (red and green boxes) Expression of 12 proteins and genes emerge based on the expression level of that are significantly upregulated at these markers. day 14 of chronic stimulation • Co-expression patterns are observe
Slide 23: Resolution of subpopulations expressing CD39 and CD94 during chronic stimulation
Resolution of subpopulations expressing CD39 and CD94 during chronic stimulation • Using AbSeq reagents enables you to resolve distinct populations of cells expressing intermediate or high levels of CD39 and CD94. • We can now perform a comprehensive analysis in these subsets of cells by combining t
Slide 24: DPE and DGE between subsets expressing different levels of CD39 and CD94
DPE and DGE between subsets expressing different levels of CD39 and CD94 High Dim Neg High Dim Neg Protein High Dim Neg CD39 24 BD, the BD Logo, LSRFortessa, Rhapsody and SeqGeq are trademarks of Becton, Dickinson and Company or its affiliates. All other trademarks are the property of their respecti
Slide 25: Unsupervised data analysis
Unsupervised data analysis t-SNE_CD8QC_2 t-SNE_Day14ch_2 t-SNE_Day14ch_2 Key: Fresh Day 3 Day 7 Day 14 Chronic Day 14 Transient t-SNE_CD8QC_1 t-SNE_Day14ch_1 t-SNE_Day14ch_1 • Unsupervised analysis of chronically stimulated cells (day 14) resolved the population (blue) into multiple clusters of cell
Slide 26: Unsupervised data analysis for a deeper characterization
Unsupervised data analysis for a deeper characterization t-SNE_Day14ch_2 t-SNE_Day14ch_1 • At least 12 sub-clusters are identified. This allows us to perform differential protein and gene expression analysis to identify these subsets. • A minimal spanning tree is generated to visualize different clu
Slide 27: Differential protein expression analysis on FlowSOM clusters
Differential protein expression analysis on FlowSOM clusters 0 1 2 3 4 5 6 7 8 9 10 1 1 t-SNE_Day14ch_2 t-SNE_Day14ch_1 • DPE analysis revealed the presence of 3 distinct subsets characterized by high expression of activation markers and inhibitory receptors, including CD39, CD38, CD94 and CD49 rese
Slide 28: Differential protein expression analysis
Differential protein expression analysis Cluster 6 Cluster 9 6 Cluster 10 Cluster 11 Cluster 7 • Visual confirmation of the presence of different clusters expressing different levels of CD39 and CD94, spanning from negative to dim and high. • Next, we performed DGE analysis to further dissect and de
Slide 29: Differential protein and gene expression analysis on selected clusters
Differential protein and gene expression analysis on selected clusters 7 11 10 9 6 • Supervised analysis identified CD39, CD38, CD49a and CD94 as markers defining a distinct subset of cells. Unsupervised analysis further dissected these cells based on differential gene expression. • Analysis of prot
Slide 30: Validation of identified genes and proteins using flow cytometry
Validation of identified genes and proteins using flow cytometry Target Target Marker Format Marker Format Genes Genes Live/dead 7-AAD CD18 ITGB2 BUV395 CD4 APC-R700 CD163 SELPLG BUV737 CD8 APC-H7 CD186 CXCR6 BV650 CD39 ENTDP1 BB515 CD258 TNFSF14 AF647 CD11c ITGAX BUV805 CD253 TNFSF10 BV421 CD314 KL
Slide 31: Summary
Summary • A single cell multiomic approach was used to resolve cell heterogeneity and identify potential novel cell populations in two biologically relevant models. • Using AbSeq reagents allows an extensive analysis of a high number of proteins (39 in this study), in a simplified workflow. • AbSeq
Slide 32: Thank you!
Thank you! BD LSRFortessa X-20 cell analyzer is a Class 1 Laser Product. For Research Use Only. Not for use in diagnostic or therapeutic procedures. BD, the BD Logo, LSRFortessa, Rhapsody and SeqGeq are trademarks of Becton, Dickinson and Company or its affiliates. All other 32 trademarks are the pr
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