In this deck from DOE CSGF 2019, Sarah Middleton from GlaxoSmithKline presents: Single-Cell Sequencing for Drug Discovery: Applications and Challenges. "Most gene expression studies have been performed on “bulk” tissue samples, consisting of millions of cells of various types and functions mixed together. Although such research is extremely useful for comparing general characteristics of tissues – for example, before and after therapeutic drug treatment – they are limited in their ability to inform on the characteristics and responses of different cell types within the tissue. More recently, advances in techniques for single-cell RNA sequencing have made it possible to profile gene expression in individual cells on a large scale, opening up the possibility to explore the heterogeneity of expression within and across cell types. This exciting technology is now being applied to almost every tissue in the human body, with some experiments generating expression profiles for more than 100,000 cells at a time. However, several roadblocks still stand in the way of making full use of this information, including issues related to missing data and scaling up existing bioinformatics analytical tools. Here, I will discuss some of the ways single-cell RNA-sequencing is being used to improve drug discovery and development, as well as some of the challenges we currently face. I’ll also highlight a new approach I am developing to help overcome some of these challenges." Watch the video: https://wp.me/p3RLHQ-lkw Learn more: https://www.krellinst.org/csgf/conf/2019/agenda Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

1. Single Cell Sequencing
for Drug Discovery:
Applications and
Challenges
Sarah Middleton
Computational Biologist, GSK
Image: Quanta Magazine

3. Single Cell Sequencing
for Drug Discovery:
Applications and
Challenges
Sarah Middleton
Computational Biologist, GSK
Image: Quanta Magazine

4. Target validation
• Perturbation ‘omics analysis (e.g. CRISPR)
• In vitro / in vivo expression analysis
Target identification
• Genetic disease association (GWAS)
• Disease vs healthy gene expression
• Pathway/network analysis
Pre-clinical development
• Biomarker prediction
• Animal model data analysis
• Patient stratification
Post-market
research
• Drug repurposing
• Combination therapy
Clinical trials
• Patient stratification
• Medical image analysis
Lead optimization &
Candidate selection
• Mechanism of action
• Pharmacodynamics
Compound screening
• Tractability prediction
• Assay development/analysis
• Rational design
Images adapted from: www.researchamerica.org; Owens, B. (2012) Nature
Computational biology in pharma R&D

5. Target validation
• Perturbation ‘omics analysis (e.g. CRISPR)
• In vitro / in vivo expression analysis
Target identification
• Genetic disease association (GWAS)
• Disease vs healthy gene expression
• Pathway/network analysis
Pre-clinical development
• Biomarker prediction
• Animal model data analysis
• Patient stratification
Post-market
research
• Drug repurposing
• Combination therapy
Clinical trials
• Patient stratification
• Medical image analysis
Lead optimization &
Candidate selection
• Mechanism of action
• Pharmacodynamics
Compound screening
• Tractability prediction
• Assay development/analysis
• Rational design
Images adapted from: www.researchamerica.org; Owens, B. (2012) Nature
Computational biology in pharma R&D
‘Omics data:
• Genome (DNA-seq)
• Transcriptome (RNA-seq;
aka “gene expression”)
• Proteome (mass spec)
• Metabolome, interactome, ...

6. Gene expression
analysis
(RNA-seq)
Image adapted from: Grun et al. (2015) Nature
“Single cell”

7. Gene expression
analysis
(RNA-seq)
Image adapted from: Grun et al. (2015) Nature
Millions
of cells
“Bulk”
“Single cell”

8. Gene expression
analysis
(RNA-seq)
Image adapted from: Grun et al. (2015) Nature
Millions
of cells
“Bulk”
“Single cell”
Cell 1 Cell 2 Cell 3

9. Image adapted from: Owens, B. (2012) Nature
RNA
RNA
RNA

10. Image adapted from: Owens, B. (2012) Nature
Reverse Transcription:
~50% capture efficiency
Several other steps:
80-90% efficiency each
Overall: 10-20%
RNA molecules
(~100-300k/cell)
RNA
RNA
RNA

11. Single cell application: mechanisms of therapy-resistant cancer
Image: Valdes-More et al. (2018) Frontiers in Immunology

12. Single cell application: mechanisms of therapy-resistant cancer
Image: Valdes-More et al. (2018) Frontiers in Immunology
Cancer patients
Responders Non-responders
Immunotherapy
(activates immune response
to fight the cancer)
Collect tumor
single cells
before and after
treatment

13. Single cell application: mechanisms of therapy-resistant cancer
Image: Valdes-More et al. (2018) Frontiers in Immunology
Cancer patients
Responders Non-responders
Immunotherapy
(activates immune response
to fight the cancer)
Collect tumor
single cells
before and after
treatment
Pre-treatment cells
Identify predictive
markers of response
Post-treatment cells
Identify mechanisms of
immunotherapy
resistance

14. Single cell application: mechanisms of therapy-resistant cancer
Image: Valdes-More et al. (2018) Frontiers in Immunology
CDK4/6 inhibitors as potential combination therapy to improve response
Jerby-Arnon et al. (2018) Cell

15. Single cell application: Disease-specific cell types in Alzheimer’s
Keren-Shaul et al. (2017) Cell

16. Single cell application: Disease-specific cell types in Alzheimer’s
Keren-Shaul et al. (2017) Cell

17. Single cell application: Disease-specific cell types in Alzheimer’s
Keren-Shaul et al. (2017) Cell

18. Single cell application: Disease-specific cell types in Alzheimer’s
Keren-Shaul et al. (2017) Cell
Regular
microglia
Disease-specific microglia
Activated; digest
aggregates, plaques
Negative
feedback
(checkpoints)

19. Scaling up single cell analysis
Images: Angerer et al. (2017) Curr Op in Sys Biol; Broad Inst; darwinian-medicine.com
1.3 million neurons
from mouse brain

20. Scaling up single cell analysis
Goal: create a map of all cells in the human body
Images: Angerer et al. (2017) Curr Op in Sys Biol; Broad Inst; darwinian-medicine.com
1.3 million neurons
from mouse brain
Several trillion cells; hundreds of cell types

21. Scaling up single cell analysis
Goal: create a map of all cells in the human body
Images: Angerer et al. (2017) Curr Op in Sys Biol; Broad Inst; darwinian-medicine.com
1.3 million neurons
from mouse brain
Human microbiome
100 trillion microbes/person
Several trillion cells; hundreds of cell types

22. Scaling up single cell analysis
Wolf et al. (2018) Genome Biology
HDF5 files for large ‘omics data

23. n > p
n = observations (cells, 100k-1mil+)
p = features (genes, 20k)

24. https://towardsdatascience.com/deep-learning-for-single-cell-biology-935d45064438; Lin et al. (2017) Nucleic Acids Research; Shaham et al. (2017) Bioinformatics
Autoencoder (AE) for non-linear
dimensionality reduction
PCA AE
Big data has advantages! Neural networks for cell type identification
Residual neural networks for batch effect correction

25. The missing data problem
Genes
Cells
Truth
Observed
Undercounting & “dropout”
Color = # molecules
van Dijk et al. (2018) Cell
Reverse Transcription:
~50% capture efficiency
Several other steps:
80-90% efficiency each
Overall: 10-20%
RNA molecules
(~100-300k/cell) Dropout

26. The missing data problem
1. Model missing information
2. Imputation
van Dijk et al. (2018) Cell; Arisdakessian et al. (2018) bioRxiv

27. Missing data makes cell subtype identification difficult
Lambrechts et al. (2018) Nat Medicine; Russ et al. (2013) Frontiers in Genetics
Coarse-grained types separate well...
tsne1
tsne2

28. Missing data makes cell subtype identification difficult
Lambrechts et al. (2018) Nat Medicine; Russ et al. (2013) Frontiers in Genetics
Coarse-grained types separate well...
tsne1
tsne2

29. Missing data makes cell subtype identification difficult
Lambrechts et al. (2018) Nat Medicine; Russ et al. (2013) Frontiers in Genetics
Coarse-grained types separate well...
...But finer subdivisions can be murky
tsne1
tsne2
tsne1
tsne2
T cells

30. Hirahara (2016) Int Immunol; Lloyd and Hessel (2010) Nat Rev Immunol

31. Hirahara (2016) Int Immunol; Lloyd and Hessel (2010) Nat Rev Immunol
T cell subtypes in Asthma

32. Example: CD4+ T cells from lung
Russ et al. (2013) Frontiers in Genetics
Lung biopsy Blood
CD4+
T cells
CD8+
T cells
CD45+
T cells
Healthy Asthma
T regulatory cells (“Tregs”)
Immune-suppressive functions
 Difference in Treg abundance between healthy and asthma?
 Difference in Treg gene expression between healthy and asthma?

33. Example: CD4+ T cells from lung
Cells belonging to each donor
tsne1
tsne2
Result: can’t confidently identify sub-types of CD4 T cells
Two problems:
1. Marker only detected in a few cells (likely due to dropouts)
2. Clustering is driven by donor (likely due to batch effects)
Treg marker expression
tsne1
tsne2

34. Simple approach to improve cell type identification
1. Aggregated info gives a clearer picture
• Individual gene expression is noisy & prone
to dropout
• Combining signal from multiple genes can be
more reliable
2. Biologically relevant genes are better for
clustering
• Variation less dominated by technical/batch
effects
Two concepts that help:
Summed expr of 21
Treg-related genes
Treg
markers

35. Simple approach to improve cell type identification
A better feature space for cell type
identification
1. Define a set of axes to measure similarity of
each cell to various cell types or functional
states
2. Place cells into the space according to their
scores for each axis & identify clusters
Axis 1: “Treg-ness” = FOXP3 + CTLA4 + IL10 + ...
Axis 4: “Exhaustion” = PDCD1 + CD244 + CD160 + ...
Axis 3: “G2/M Phase” = CDK1 + CCNA2 + CCB1 + ...
Axis 2: “Th1-ness” = TBX21 + CXCR3 + CCR5 + ...
...
Th1-ness
Treg-ness
Exhaustion
“Marker space”

36. Improved cell type identification
Clusters not driven by patient!
Before
After
tSNE of marker space
tsne1
tsne2

37. Improved cell type identification
Th1-nessTreg-nessTh17 / Th22-ness
Terminal differentiationAnti-inflammatoryType II Interferon Response
Now we can compare treatment groups to identify:
• Changes in cell type composition
• Changes gene expression within a cell type
Before
After
tSNE of marker space
tsne1
tsne2

38. Future directions
Cell type,
functional state
Image: Broad Inst