Introduction into single-cell RNA-seq. Kersti Jääger 19/02/2014
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1 Introduction into single-cell RNA-seq Kersti Jääger 19/02/2014
2
3 Cell is the smallest functional unit of life Nucleus.ATGC.UACG. A Cell KLTSH.
4 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? Ca 200 cell types Ca cells Ca 3x 10 9 base pairs Ca genes Ca transcripts Regulation: DNA modifications Protein-DNA interactions Protein modifications Protein-protein interactions In disease, something goes wrong but: what?
5 Biological processes Large-scale Measurements
6 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.
7 DNA sequencing-based analysis methods and their anticipated integration
8 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
9 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
10 Single-cell RNA-seq: the molecular state of cell populations (cell-to-cell variation; co-expression)
11 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
12 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
13 Library preparation
14 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
15 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 fulllength RNA sequenced) Gene variability within a population identifies heterogeneous expression Clustering variable genes identifies co-expression
16 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
17 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):e Shapiro E, Biezuner T, Linnarsson S. Nat Rev Genet. (2013) Sep;14(9):
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