Single cell RNA sequencing; Methods and applications

Single Cell RNA Sequencing

(Applications & Methods)

Presented by:

Bushra Arif (11)

Farah Arooj (4)

Institute of Biochemistry and Biotechnology

University of Punjab

2

Contents

Introduction of scRNA sequencing

Why we use single cell

Methods of isolating single cell

Methods of scRNA seq

Data Analysis

Applications

3

Introduction

• Genetically identical cells show variations

• Transcriptome: an essential piece of cell identity

• Reveal the state of a cell

• Mammalian cells contain 105–106 mRNA molecules

• Population studies averages the cells transcriptome

• Inappropriate for rare population studies

4

Why Single cell study?

Single-cell studies reveal

• Relationship between intrinsic cellular processes

and extrinsic stimuli

• Hidden variation in gene expression

• Unknown species or regulatory processes of

biotechnological or medical relevance

5

History

• Norman N. Iscove done work on exponential amplification

of cDNAs by PCR

• James Eberwine done linear amplification of cDNA by T-

RNA polymerase-based in vitro transcription

6

Method of the year; 2013

7

Single Cell RNA Sequencing Process

By Huang et al., 2014

8

Florescence-activated cell sorting

(FACS) 33%

Micromanipulation17%

Laser capture microdissection

17%

Optical tweezers6%

Single cell isolation methods

9

Florescent Activated Cell Sorting(FACS)

• Technique for purification of cell and subcellular

populations

• High purity of the sorted population

• Sort as many as 300,000 cells per minute

• Machine can be set to ignore droplets containing dead

cells

10

11

Limitations

• Require large starting material

• Not well suited for the isolation of extremely rare

cells

• Needs antibodies to target specific proteins

12

Micromanipulation

• Microscope assisted manual cell picking tool

• Targeted isolation of single cell

• Select a specific cell through microscope observation

• Aspirate the cell by micropipette suction

13

Limitations

• Manual process confines the overall throughput

• Low microliter samples can’t be manipulated

• Not possible to visually control accurate transfer of

the single cell

14

Optical tweezers

• Optical tweezers use focused laser beam to trap,

manipulate and position micron sized objects.

Limitation:

• Optical set-up can damage

the cells

15

Laser capture microdissection (LCM)

• Advanced technique to isolate cells from solid tissue

samples

• Microscopically visualize target cell or compartment

• Mark the section to be cut on the display

• Cut the tissue and isolate cells

16

LCM

c c c

Methods of single cell RNA sequencing

18

Basic steps

Cell lysis

Reverse transcription

Second strand synthesis

Library preparation

Sequencing

Data analysis

19

Cell lysis and mRNA isolation

• Eukaryotic cells are lysed in hypotonic buffer containing a

detergent e.g. guanidine thiocyanate and Nonidet P-40

• Using oligo-dT coated magnetic beads that will remove proteins,

metabolites, and the cell debris, from the mRNAs

• The lysate buffer then washed away isolating mRNAs with poly(A)

tails

20

Reverse transcription

• Total RNA is isolated which contain all types of RNA.

• To Capture only the mRNA, specific oligo dT primers are used

• Reverse transcriptase of MMLV having the low RNase H

activity, increased thermo stability and produces RNA-DNA

hybrid molecules with an average length of 1.5–2 kb.

• Superscript III

21

2nd strand Synthesis and Amplification

PCR based amplification(Homopolymer tailing OR Template switching)

In vitro transcription

Rolling circle amplification

22

Methods

Tang et al. STRT

SMART-Seq CEL-Seq

23

Tang et al.• Published in 2009

• Total RNA is isolated and fragmented.

• Converted to cDNA by using an oligodT primer with a specific anchor

sequence.

• The second strand is synthesized using a poly T primer with another

anchor sequence.

• PCR amplified from primers against the two anchor sequences.

24

Drawback• Premature termination of RT reduces transcript coverage

at the 5’ end

• Introduction of a polyA tail in addition to its own poly A

sequence at the 3’ end of the input RNA causes a loss of

strand information in the resulting double-stranded

cDNA.

26

Single cell tagged reverse transcription (STRT)

•Based on template switching

•Done to pool different cell RNAs

•5’ end of cDNA are tagged with unique barcodes

•Barcode is 4-5 bp random sequence or restriction site

•Biotin is introduced at both the 3’ and 5’ ends via the use of

biotinylated primers.

27

• Binding to streptavidin beads,

enzymatic cleavage leads to

the selection of only the 5’

fragments for library

construction.

•Subsequent sequencing and

analysis shows 5’ read bias

28

Drawback of template switching is:

• Lower sensitivity compared to homopolymer tailing which

may due to an imperfect efficiency of RT M-MuLV to add

3’cytosines

29

Cell expression by linear amplification and sequencing (CEL-seq.)

• Highly multiplexed

• Based on in vitro transcription of mRNA to amplify only the 3’ end RNA

only

• OligodT primer containing the 5’ Illumina adaptor, a cell barcode, and a

T7promoter.

• cDNA samples are amplified by IVT from the T7 promoter

• RNA fragmentation & Illumina adaptor is ligated at 3’ end

• RNA is reverse transcribed, library is prepared then sequencing

• The first read recovers the barcode, whereas the second identifies the

mRNA transcript.

30

31

SMART-Seq

• Switch mechanism at 5’ end of RNA template

• Based on template switching mechanism

• Generate full transcript coverage.

• Anchor a 5’ universal seq. and 3’ Cs along with Locked nucleic

acid by RT

• cDNA is amplified and tagmentation is used to construct

libraries

32

33

Data Analysis

34

Quality check • Software is fastaqc which checks for Presence and abundance of contaminating sequences. Average read length GC content• Bad quality reads (score <20) are trimmed using

trimmomatric.

Good quality Poor quality

35

Data alignment

• Bowtie or TopHat

• Align large sets of short DNA sequence reads to large

genomes

• Softwares are splice aware and considers the genomic intron-

exon structure by splitting unmapped reads and aligning the

read fragments independently

36

Data Assembly

• Can be done by

Reference based assembly (Cufflinks)

De novo assembly (Without any ref. using Trinity

• Generates a large number of reads that are mapped to contigs

which are then clustered to genes using Corset.

• Cuffmerge is used for final assembly

37

De novo assembly

• Assemble the reads into unique transcript sequences

• Clusters related contigs that into coresponding portions of

alternatively spliced transcripts

• Constructs a de Bruijn graph for each cluster of related contigs

showing the overlapping between variants.

• Finally it reconstructs full-length, linear transcripts by integrating the

individual de Bruijn graphs with the original reads and paired ends.

38

39

Differential gene expression

• Statitsical difference between the set of genes of two populations

or conditions.

• Cuffdiff2 count the number of sequence reads in a window as

FPKM (fragments per kilobase of transcript per million mapped read)

• Moving window along the seq. generates an expression profile in

the form of scores.

• Score normalization help in determining DGE

• Long and highly expressed genes have more reads

40

When samples of yeast species were grown into two different medias. Some genes are over expressed in one of sample.

41

42

Applications of single cell RNA sequencing

Allele specific expression • Different expression patterns of

cells

• Some genes are expressed high

in some while low in other.

43

• Determine the different cell stages, lineages and their

signaling pathways.

44

Study disease

•Non-invasive way to monitor the progress of

human disease

•Monitor rare or precious biological sample

•Knockout or knockdown gene studies

45

46

Stem cell and embryonic differentiation

47

• SNP discovery(only in exonic region)

• Genomic medicine

• Epigenetics & Cell lineage hierarchy

• Combined approaches

48

Thank you