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Introduction into
single-cell RNA-seq
Kersti Jääger
19/02/2014
Cell is the smallest functional unit of life
….ATGC…
Nucleus
….UACG….
A Cell
…KLTSH….
The complexity of biology
How many cell types?
How many cells?
How much DNA in the cell?
How many genes?
How many mRNA molecules in the cell?
Regulation:
DNA modifications
Protein-DNA interactions
Protein modifications
Protein-protein interactions
Ca 200 cell types
Ca 1014 cells
Ca 3x 109 base pairs
Ca 24 000 genes
Ca 250 000 transcripts
In disease, something goes
wrong but: what?
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
AdMSC – adipose-derived stromal cells
FB – skin-derived stromal cells
(A) Stromal cells originating from different tissues are initially distinct
(B) and stay subtly distinct in the differentiated state
Jääger et al 2012
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
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Single-cell isolation
Cell lysis (breakdown), reverse
transcription (RNA>cDNA),
barcoding (indexing)
WGA
Library construction (target
enrichment)
NGS
Computational analysis (mapping
of the reads, single-cell readout,
normalization, differential gene
expression, visualization)
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
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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 fulllength 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.