single cell transcriptomics – a simple walkthrough...oil oil rt reagents gel bead single cell...

Single Cell Transcriptomics –a simple walkthrough

Adwait Sathe, PhDMcDermott Center Bioinformatics Lab

[email protected]

MCDERMOTT CENTER BIOINFORMATICS LAB

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mailto:[email protected]

RNASeq RNASeq

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MCDERMOTT CENTER BIOINFORMATICS LAB https://www.10xgenomics.com/solutions/single-cell/

Single cell RNA Sequencing Workflow

Dissociation of solid tissue

Isolate single cellsDROP-Seq, Smart-Seq etc.

RNA

cDNA

Amplified RNA

Amplified cDNA

Sequencing library

Sequencing

Single cell expression profile

Clustering and Cell type identification

novel markers for cell types

Constructing Single Cell Trajectories

RT-PCR

IVT PCR

Novel applications!

RNASeq

Single cell RNASeq

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Pea

rso

n R

Mo

lecu

lar

Det

ecti

on

Lim

it

Accuracy

Sensitivity

Single cell RNA Sequencing methods comparisonRNASeq

Single cell RNASeq

ScRNAseqmethods

most genes per cell among methodsSmart-seq2

• Full-length

• No UMI

• FACS

• Throughput (number of cells) – 102–103

microfluidic platformSmart-seq/C1

• Full length

• No UMI

• Fluidigm C1


UMI – unique molecular identifiersChromium,

10X• 3’ counting (only 3’ of transcript sequenced)

• 8-10 bp UMI

• 10X barcoded gel beads


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MCDERMOTT CENTER BIOINFORMATICS LAB Svensson et. Al., Nature Methods (2017)

26bp

10X genomics protocol

https://www.10xgenomics.com/solutions/single-cell/

RNASeq

Single cell RNASeq

ScRNAseqmethods

10X Chromium

Recovery agent

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RNASeq

Single cell RNASeq

ScRNAseqmethods

10X Chromium

10X barcoded Gel Beads

Cells &Enzymes

Oil Oil

RT reagents

Gel bead

Single cell

Cells Lysis & RT

Gel Bead-in-Emulsion (GEM)

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RNASeq

Single cell RNASeq

ScRNAseqmethods

10X Chromium

10X barcoded Gel Beads

Cells &Enzymes

Oil

10X barcoded cDNA

Cells Lysis & RT

Recovery agent

GEMs broken

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(1) Jeanette Baran-Gale et. al., Brief Funct Genomics, 2017. doi:10.1093/bfgp/elx035(2) https://www.10xgenomics.com/solutions/single-cell

Multiplet Rate (%)# of Cells Loaded # of Cells Recovered

~0.4% ~870 ~500

~0.8% ~1700 ~1000

~2.3% ~5300 ~3000

~3.9% ~8700 ~5000

~7.6% ~17400 ~10000

RNASeq

Single cell RNASeq

ScRNAseqmethods

10X Chromium

DESIGN


(1)

(2)

Determine starting cells:http://satijalab.org/howmanycells

QC metrics for your cell typehttp://10xqc.com/

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65%

Reads QC *

Alignment *

Mapping QC *

Cell QC

Normalization *

Confounders

Differential Expression *

Clustering, Marker gene

PseudotimeAnalysis

FastQC

STAR

RSeqC

edgeR Seurat, > 5000 cells MONOCLE2

Feature SelectionM3Drop

Scater

Scran

Combat

Red: Tools used for each step* : Common to Bulk RNASeq

Single cell RNA Sequencing computational pipelineRNASeq

Single cell RNASeq

ScRNAseqmethods

10X Chromium

DESIGN

Analysis pipeline

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Let us carry out a single cell RNASeqexperiment

• Peripheral blood mononuclear cells (PBMCs) from a healthy donor. PBMCs are primary cells with relatively small amounts of RNA (~1pg RNA/cell).

• Sequenced on Illumina Hiseq4000

• 26bp read1 (16bp Chromium barcode and 10bp UMI), 98bp read2 (transcript), and 8bp I7 sample barcode

Dataset: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.0.1/pbmc8k

RNASeq

Single cell RNASeq

ScRNASeq

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10X genomics summaryRNASeq

Single cell RNASeq

ScRNASeq

SUMMARY

Parameter PBMC Observed PBMC Recommended

Estimated Number of Cells/Barcodes 8381 500-10,000

Fraction Reads in Cell 93.10%

Mean Reads per Cell 93,552 50,000

Median Genes per Cell 1297 1000

Total Genes Detected 21,425

Median UMI counts per cell 4084

2000 UMIs/cell

10000 barcodes/cells

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Potential problems

Macaulay and Voet, PLOS Genetics, 2014.Computational Methods for Analysis of Single Cell RNA-Seq Data, Ion Măndoiu, University of Connecticut.

– Low amplification efficiency, typically less than 10%

– Gene dropout rates

– Cell quality: Live/dead, Missing cells, Multiple cells

– Stochastic: cell cycle phases, Transcriptional bursting

– Cell Capture rate ≠ population frequency (multiples/gel beads, empty beads)

– Algorithm development

RNASeq

Single cell RNASeq

ScRNASeq

SUMMARY

PITFALLS

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Exploring the Quality Control requiredRNASeq

Single cell RNASeq

ScRNASeq

SUMMARY

PITFALLS

QC

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Number of Genes Number of UMIs

1000

2000

3000

4000

5000

10000

20000

30000

0

Percentage of Mitochondrial genes

0

5

10

15

20

PBMC sample

Clustering

• Determine a subset of genes to use for clustering; this is because not all genes are informative, such as those that are lowly expressed.

• The approach is to select gene based on their average expression and variability across cells

• We scale the data and remove unwanted sources of variation (technical, cell cycle stage, batches etc.)

RNASeq

Single cell RNASeq

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ScRNASeq

SUMMARY

PITFALLS

QC

Mean vs Variation: Detection of variable genesRNASeq

Single cell RNASeq

ScRNASeq

SUMMARY

PITFALLS

QC

-5

0

5

10

15

0 1 2 43 5 6

Dis

per

sio

n

Average expression (log Normalized to library size)

Use for clustering

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The goal is to identify the ”elbow” in this plot,After this point dividing into more clusters is not informative

5-10

RNASeq

Single cell RNASeq

ScRNASeq

SUMMARY

PITFALLS

QC

CLUSTERING

0Number of clusters (components)

25 50 75 100

Var

ian

ce e

xpla

ined

per

co

mp

on

ent

0

0.2

0.1

How many clusters are enough to divide the data into meaningful

groups?

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Graph Cluster results T-SNE plot visualization

t-Distributed Stochastic Neighbor Embedding

RNASeq

Single cell RNASeq

ScRNASeq

SUMMARY

PITFALLS

QC

CLUSTERING

01234567891011

0

25

-25

0-20-40 4020t-SNE 1

t-SN

E 2

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ScRNASeq

SUMMARY

PITFALLS

QC

CLUSTERING

Differential expression Identification of markers for cell types

Markers Cell Type

IL7R CD4 T cells

CD14, LYZ CD14+ Monocytes

MS4A1 B cells

CD8A CD8 T cells

FCGR3A, MS4A7 FCGR3A+ Monocytes

GNLY, NKG7 NK cells

FCER1A, CST3 Dendritic Cells

PPBP Megakaryocytes

Supervised – PBMC known markers

RNASeq

Single cell RNASeq

HETEROGENIETY

KNOWN MARKER

0

25

-25

0-20-40 4020t-SNE 1

t-SN

E 2

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Differential expressionIdentification of novel markers for cell types

Method used is similar to

edgeR

RNASeq

Single cell RNASeq

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MCDERMOTT CENTER BIOINFORMATICS LAB Cluster No.

Gen

es

Each column is a Cell

ScRNASeq

SUMMARY

PITFALLS

QC

CLUSTERING

HETEROGENIETY

KNOWN MARKER

NOVEL MARKER


Log1

0(U

MIs

)

RNASeq

Single cell RNASeq

Cluster 10 Cluster 11

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ScRNASeq

SUMMARY

PITFALLS

QC

CLUSTERING

HETEROGENIETY

KNOWN MARKER

NOVEL MARKER

RNASeq

Single cell RNASeq


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ScRNASeq

SUMMARY

PITFALLS

QC

CLUSTERING

HETEROGENIETY

KNOWN MARKER

NOVEL MARKER

Cloupe browser further exploration

RNASeq

Single cell RNASeq

MCDERMOTT CENTER BIOINFORMATICS LAB 22

ScRNASeq

SUMMARY

PITFALLS

QC

CLUSTERING

HETEROGENIETY

KNOWN MARKER

NOVEL MARKER

Acknowledgements

McDermott Center Bioinformatics LabChao Xing, PhD - DirectorMohammad KanchwalaAshwani Kumar

McDermott Center NGS core team

Workflow used is based on:

Seurat: Macosko, Basu, Satija et al., Cell, 2015 (Updated approach: Combining dimensional reduction with graph-based clustering)Monocle: Xiaojie Qiu, Andrew Hill, Cole Trapnell et al (2017)

Computational Methods for Analysis of Single Cell RNA-Seq Data, Ion Măndoiu, University of Connecticut

THANK YOU

MCDERMOTT CENTER BIOINFORMATICS LAB 24

single cell transcriptomics – a simple walkthrough...oil oil rt reagents gel bead single cell...

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