1. Single cell RNAseq
Kathie Mihindukulasuriya, PhD
Senior Scientist, Cruchaga Lab
Department of Psychiatry
Washington University in St. Louis

2. Plan:
Single cell RNA-seq vs bulk RNA-seq
Current single cell protocols and platforms
Processing single cell RNA-seq data
Biology based analysis
Current challenges in single cell RNA-seq processing and analysis

3. Bulk RNAseq vs single cell RNASeq

4. Technique
Advantages
Challenges
Bulk RNAseq
More economical
averaged gene expression across thousands of cells (may lose key signals)
- Deconvolution may restore these signals (CIBERSORT, xCell)
Deeper sequencing
Single cell RNAseq
Can identify rare cell types
Dropout problem
Can identify transcriptional differences in different cell types
preserving the initial relative abundance of mRNA in a cell
technical noise and biological variation
Data noisier and more complex than bulk RNAseq
technical noise and biological variation make analysis more challenging
Hybrid
- Use scRNAseq to estimate cell type proportions in bulkRNAseq (BSEQ-sc, Cell Population Mapping (CPM)

6. What are some types of questions that can be answered by scRNAseq?

7. scRNA-seq protocols
full-length transcript sequencing approaches
3’ end or 5’ end
Smart-seq2, Fluidigm C1 (96 cell)
3’: Drop-seq, Seq-Well, Chromium, DroNC-seq, Fluidigm C1 (800 cell)
5’: STRT-seq,
Pluses
increased number of mappable reads
Suitable for: cell-type discovery, assessing tissue composition, allelic gene expression analysis, isoform discovery
UMIs (multiplexing of samples, improved gene expression quantification and throughput)
Lower cost
Minuses
cannot be multiplexed via sample pooling into a single tube for library generation (increased cost and labor)
No UMIs (no digital quantification of transcripts)
Not suitable for alternative splicing (AS) detection, allelic expression exploration and RNA-editing identification
Less sensitivity than full-length

8. scRNA-seq platforms
Pros
Cons
Fluidigm C1
(microfluidics) / Fluidigm C1 mRNA Seq HT
Allow visual inspection of captured cells (can exclude empty wells and doublets)
Customizable
lower false positives than tube-based technologies
less bias than tube-based technologies
C1 = full-length transcript / HT = 3’
300–7,000 genes per cell
- Only 2 inlets for samples
- Low throughput (up to 96 cells/ 800 cells)
- >10,000/ 1,000 cells required for capture
- Relatively long prep time (2 runs per day)
capture efficiency depends on uniformity of cell size and shape
High cost of cartridges
Cells must be fresh and processed immediately
Droplet-based
(10X Genomics Chromium)
- Very high throughput
- Up to 8 samples per run
- System cost relatively low
500–1,500 genes per cell
Limited customizability (little control over cell input; susceptible to selection biases)
Plate methods
(SMART-seq2)
- Can simultaneously measure gnome DNA and transcriptome
- not restricted by cell size, shape, homogeneity, or total numbers (suitable for very rare cell populations)
- Economical (uses off the shelf reagents)
~4,000–7,000 genes per cell
- No UMIs and barcodes (no gene level quantification or multiplexing of samples)

9. Fluidigm C1

10. Fluidigm C1

11. Fluidigm C1

12. Fluidigm C1

13. Fluidigm C1

14. Fluidigm C1

15. Fluidigm C1

16. Fluidigm C1

17. scRNA-seq platforms
Pros
Cons
Fluidigm C1
(microfluidics) / Fluidigm C1 mRNA Seq HT
Allow visual inspection of captured cells (can exclude empty wells and doublets)
Customizable
lower false positives than tube-based technologies
less bias than tube-based technologies
C1 = full-length transcript / HT = 3’
300–7,000 genes per cell
- Only 2 inlets for samples
- Low throughput (up to 96 cells/ 800 cells)
- >10,000/ 1,000 cells required for capture
- Relatively long prep time (2 runs per day)
capture efficiency depends on uniformity of cell size and shape
High cost of cartridges
Cells must be fresh and processed immediately
Droplet-based
(10X Genomics Chromium)
Very high throughput
Up to 8 samples per run
System cost relatively low
500–1,500 genes per cell
Limited customizability (little control over cell input; susceptible to selection biases)
Plate methods
(SMART-seq2)
Can simultaneously measure gnome DNA and transcriptome
not restricted by cell size, shape, homogeneity, or total numbers (suitable for very rare cell populations)
Economical (uses off the shelf reagents)
~4,000–7,000 genes per cell
- No UMIs and barcodes (no gene level quantification or multiplexing of samples)

18. Methods of single-cell isolation:
Droplet-based
Methods of single-cell isolation:
Limiting dilution:
not very efficient
Micromanipulation: 
Time consuming; low throughput
FACS:
highly purified single cells
IF cells express cell surface marker

19. Methods of single-cell isolation:
Droplet-based
Methods of single-cell isolation:
Laser capture microdissection
isolate cells from solid samples 
Microfluidic technology
low sample consumption
low analysis cost
precise fluid control
Decreased risk of external contamination  
CellSearch
Antibody conjugated to magnetic particles
To isolate desired cells
Good for rare cell types 

20. Droplet-based
cell lysis -> reverse transcription into first-strand cDNA -> second-strand synthesis -> cDNA amplification
UMIs:
- 4–10 random nucleotides that are introduced with the primer used for cDNA generation before amplification
multiple reads with the same UMI sequence map to the same gene = one molecule
Cell barcodes:
labeling of cDNA by a cell-specific DNA sequence that allows multiplexing at an early stage

21. Droplet-based
cell lysis -> reverse transcription into first-strand cDNA -> second-strand synthesis -> cDNA amplification
UMIs:
- 4–10 random nucleotides that are introduced with the primer used for cDNA generation before amplification
multiple reads with the same UMI sequence map to the same gene = one molecule
Cell barcodes:
labeling of cDNA by a cell-specific DNA sequence that allows multiplexing at an early stage

22. scRNA-seq platforms
Pros
Cons
Fluidigm C1
(microfluidics) / Fluidigm C1 mRNA Seq HT
Allow visual inspection of captured cells (can exclude empty wells and doublets)
Customizable
lower false positives than tube-based technologies
less bias than tube-based technologies
C1 = full-length transcript / HT = 3’
300–7,000 genes per cell
- Only 2 inlets for samples
- Low throughput (up to 96 cells/ 800 cells)
- >10,000/ 1,000 cells required for capture
- Relatively long prep time (2 runs per day)
capture efficiency depends on uniformity of cell size and shape
High cost of cartridges
Cells must be fresh and processed immediately
Droplet-based
(10X Genomics Chromium)
Very high throughput
Up to 8 samples per run
System cost relatively low
500–1,500 genes per cell
Limited customizability (little control over cell input; susceptible to selection biases)
Plate methods
(SMART-seq2)
Can simultaneously measure gnome DNA and transcriptome
not restricted by cell size, shape, homogeneity, or total numbers (suitable for very rare cell populations)
Economical (uses off the shelf reagents)
~4,000–7,000 genes per cell
- No UMIs and barcodes (no gene level quantification or multiplexing of samples)

23. Plate-based
Template Switching Oligonucleotide

24. Remove barcodes from cell-free mRNA
Processing scRNA-seq data
Map reads to genome, not transcriptome
Decreases multi-mapping reads
Critical for snRNA-seq
Splice-aware aligners (STAR)
Pseudoaligners (faster)
Associate reads with genes or transcripts
- featureCounts
- HTSeq
remove PCR noise using UMIs
demultiplexing to identify cells
Remove barcodes from cell-free mRNA
(much lower average read count than barcodes derived from intact cells)

25. Processing scRNA-seq data
Remove low-quality ‘cells’ based on mapping statistics:
overrepresentation of mitochondrial RNAs, ribosomal RNAs (>40%), spike-ins, adapters
and/or reads that map outside of exons
Normalization to correct for unwanted variation among cells caused by technical variation
remove batch effects
Biology-based analysis (like differential expression)

26. Some examples of biology-based analysis
Purpose: to directly investigate AD brain changes in cell proportion and gene expression using single cell resolution
Del-Aguila, J.L. et al. A single- nuclei RNA sequencing study of Mendelian and sporadic AD in the human brain. bioRxiv. Mar. 30, doi:

27. To identify different cell types in brain samples by a CGS approach (unsupervised graph-based clustering) and then annotated by cell type using marker genes
 t-distributed Stochastic Neighbor Embedding (tSNE) plot is a dimensionality reduction technique
Differences with PCA:
tSNE always produces a 2D separation
tSNE is non-deterministic (you won't get exactly the same output each time you run it)
tSNE tends to cope better with non-linear signals in your data, (less impact of outliers; visible separation between
relevant groups is improved)
4. After tSNE input features are no longer identifiable, and you cannot make any inference based only on the output of t-SNE
NOTE: very computationally intensive (may need to apply another dimensionality reduction technique like PCA first)

28. To identify different cell types in brain samples:
Classic Gene Set (CGS) from Pooled Subjects: (Seurat FindVariableGenes -> 2,360 genes -> calculate 100 PCs -> identified the optimal number of PCs (65)
6 cell types
25 clusters

29. To identify different cell types in brain samples:
Consensus Gene Set (ConGen) from each subject: (Seurat FindVariableGenes -> 2,447 (S1); 2,354 (S2); 1,972 (S3) -> R function intersection to identify common genes (1,434) -> calculate 100 PCs -> identified the optimal number of PCs (25)
14 cell types; better resolution

30. Cluster annotation
Evaluating the expression of maker genes for neurons, astrocytes, oligodendrocytes, microglia, oligodendrocyte precursor
cells, endothelial cells, excitatory and inhibitory neurons (from literature) -> Seurat DotPlot to visualize the average gene
expression for the marker genes in each cluster

31. Workflow Analysis Plan

32. Single cell analysis: current challenges
- Biggest challenge: missing data (excess zeros) “Dropout”
- technical (not captured)
- biological (really no expression)
sampling (just not deep enough sequencing)
can’t distinguish between these
dropout = largest source of variation
How to deal with missing data?
Increase read depth
Impute the missing data based on clustered cells (DrImpute, CIDR, MAGIC, scimpute)
Impute the missing data based on bulk RNAseq data (SCRABBLE)
Use biological knowledge – gene-gene coexpression (netNMF-sc)

33. Single cell analysis: current challenges
Explosion of methods and software, but not yet clear best practices
Doublet Identification
demuxlet - [shell] - Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
DoubletFinder - [R] - Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. BioRxiv
DoubletDecon - [R] - Cell-State Aware Removal of Single-Cell RNA-Seq Doublets. [BioRxiv](DoubletDecon: Cell-State Aware Removal of Single-Cell RNA-Seq Doublets)
DoubletDetection - [R, Python] - A Python3 package to detect doublets (technical errors) in single-cell RNA-seq count matrices. An R implementation is in development.
Scrublet - [Python] - Computational identification of cell doublets in single-cell transcriptomic data. BioRxiv

34. Single cell analysis: current challenges
Assigning cell types to clusters of cells:
- dimensionality reduction (tSNE, PCA, UMAP) -> unsupervised clustering -> annotation of clusters
Use of marker genes
Known marker genes
Expression high enough to be measured (not always true for known cell surface markers)
Subjective (different researchers choose different markers)
Novel cell types?
Use of annotated training data (e.g. reference atlas)
comparisons with annotated reference data using automatically chosen genes that optimally discriminate
between cell types (scmap, SingleR)
- allow the assignment of cells to an intermediate or unassigned type (CHETAH)
Challenge: human data often clusters by individual, rather than cell type

35. Single cell analysis: current challenges
How to combine datasets for analysis:
scmap: projection of single-cell RNA-seq data across data sets
scMerge: using genes that do not to change across all samples and a robust algorithm to infer pseudoreplicates between datasets. 

36. Single cell analysis: current challenges
Look to see advances in single cell RNA seq cancer research for solutions to problems