Introduction into single-cell RNA-seq

Kersti Jääger

19/02/2014

Cell is the smallest functional unit of life

A Cell

Nucleus

….ATGC…

….UACG….

…KLTSH….

How many cell types? Ca 200 cell types

How many cells? Ca 1014 cells

How much DNA in the cell? Ca 3x 109 base pairs

How many genes? Ca 24 000 genes

How many mRNA molecules in the cell? Ca 250 000 transcripts

Regulation:

DNA modifications

Protein-DNA interactions

Protein modifications

Protein-protein interactions

In disease, something goes

wrong but: what?

The complexity of biology

Biological processes Large-scale Measurements

Next-generation sequencing (NGS)

High-throughput DNA sequencing of a large number of DNA

molecules in parallel.

Whole-genome amplification (WGA)

Refers to methods that are used to amplify the genomic DNA of

single cells to increase the number of copies of DNA for

downstream processing.

DNA sequencing-based analysis methods and their anticipated integration

RNA-sequencing

• Genome-wide transcriptome analysis -

transcriptomics

• Analyzes the ‘message’ or expression of genes

• Characterizes cell type or function in normal and

diseased states

• Technically (to date) it is DNA sequencing; RNA is

converted to cDNA

RNA-seq: Differential gene expression visualized on PCA plot

Jääger et al 2012

(A) Stromal cells originating from different tissues are initially distinct

(B) and stay subtly distinct in the differentiated state

AdMSC – adipose-derived stromal cells

FB – skin-derived stromal cells

Single-cell RNA-seq: the molecular state of cell populations (cell-to-cell variation;

co-expression)

Applications of single-cell RNA-seq

Analysis of rare cell types – circulating tumor cells, CTCs; cells from human

embryo; transient adult stem cells

Understanding evolution and diversity - individual cells vary in

morphology, size, developmental origin, functional properties

Characterise transcriptional fluctuations – dynamics of cellular processes;

covariant expression

Single-cell RNA-seq workflow

1. Single-cell isolation

2. Cell lysis (breakdown), reverse

transcription (RNA>cDNA),

barcoding (indexing)

3. WGA

4. Library construction (target

enrichment)

5. NGS

6. Computational analysis (mapping

of the reads, single-cell readout,

normalization, differential gene

expression, visualization)

7. Biological insight

Library preparation

Errors in single-cell RNA-seq analysis

arise from biological features of transcriptional process:# of different transcripts (RNA molecules) ranges over several orders of

magnitude

# of transcripts is not fixed in an individual cell

Kinetics of the generation of transcripts (a process of transcription) adds

heterogeneity

arise from sample preparation techniques:Reverse transcription: RNA>cDNA; efficiency 5-25%

Amplification: PCR is non-linear; distortion of relative abundance of

transcripts

Bioinformatics – quantification of RNA molecules

• Readout of the abundance of a transcript within a cell

• Calculated as # of reads mapping to a particular transcript

• Normalised to the overall # of reads (and for transcript length if full-

length RNA sequenced)

• Gene variability within a population identifies heterogeneous

expression

• Clustering variable genes identifies co-expression

Solutions and future perspectives

Detection:

Direct sequencing of RNA; Linear amplification of

transcriptome (eg CEL-seq)

Automated sample preparation; microfluidics,

nanofluidics

Quantification:

RNA spike-ins; relative efficiency, detection limits,

technical noise of amplification method

UMIs; unique molecular identifiers; absolute

molecule counting

Questions:

What is RNA-seq used for?

Why we need single-cell RNA-seq?

What is the most basic output of RNA-seq analysis?

References:Macaulay IC, Voet T. PLoS Genet. (2014) Jan 30;10(1):e1004126.Shapiro E, Biezuner T, Linnarsson S. Nat Rev Genet. (2013) Sep;14(9):618-30.