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High-Resolution

Transcriptome Analysis:

One Cell at a Time

AMATA 2013

Queensland, Australia

October 16, 2013

Jian-Bing Fan

Senior Director, Scientific Research

2

All junctions are covered uniformly in RNA-Seq

The Intuitive Beauty of RNA-Seq Data

3

RNA-Seq has evolved in 5 years

New methods: Stranded vs. Non-stranded

– New Stranded RNA Prep kits

New methods: Poly-A vs. Total RNA

– RiboZero kits method of choice for rRNA reduction

– Total RNA methods reveal ncRNAs and allow “RIN independent” preps

Lower Input Levels

– Standard input levels into all TruSeq RNA kits today is only 100 ng total RNA

Methods for studying highly degraded RNA

– Can sequence RNA from FFPE samples

Single Cell RNA Sequencing Methods

4

Cellular heterogeneity

– What is a cell type?

– How many cell types are there?

Non-symptomatic somatic mutations

– Cells at terminal differentiation contain “substantial” variations

Development and cellular differentiation

– Cell lineage

– Reprogramming

Metagenomes

Circulating cells (liquid biopsy)

– CTC

– Stem cells

– Fetal cells

Why single cells

5

Single cell transcriptional landscapes

6

Unbiased cell-type discovery

Sten Linnarsson, MBB, Mol Neuro

7

STRT (single-cell tagged reverse transcription)

Based on template-switching at 5’ of mRNA

Barcoding already at RT step, pooling before amplification

Sequence ~50 bp from 5’ end of mRNA (= TSS)

Highly multiplexed: 96 cells at a time

Sten Linnarsson, MBB, Mol Neuro

8 Sten Linnarsson, MBB, Mol Neuro

Reverse transcription, with TdT activity adding Cs

Template switching, PCR

Fragmentation, retaining 5’ end

P2 adapter

P1 adapter (library PCR)

Finished library

STRT (single-cell tagged reverse transcription)

9 Sten Linnarsson, MBB, Mol Neuro

1

10

100

1000

10000

1 10 100 1000 10000

Nu

mb

er

of

mo

lecu

les

(sin

gle

we

ll)

Number of molecules (single well)

R2 = 0.98

Synthetic mRNA ES cells

Reproducibility

mRNA molecules (ES cell #1)

mR

NA

mo

lec

ule

s (

ES

ce

ll #

2)

R2 = 0.97

10

Distinguish cell types by clustering

1. 96 individual cells, representing 3

different cell types were profiled.

2. Transcripts from each cell was

tagged by a short 5-base code

(during RT) and pooled from 96 cells

for amplification and made into

sequencing library for mRNA-Seq.

3. Cell neighborhood was calculated

based on individual cell expression

profiles.

4. The results is a set of clusters of

mutually similar cells, which

reflected the true identity of cells

Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Islam S,

Kjällquist U, Moliner A, Zajac P, Fan JB, Lönnerberg P, Linnarsson S. Genome Research. 2011.

Sten Linnarsson, Karolinska Inst

Embryonic stem cells

Embryonic fibroblasts (MEF)

Neuroblastoma

(Neuro2A)

11

Gene expression mapped on the

cellular landscape. The number of hits

to each gene, normalized to

transcripts per million (t.p.m.)

sequencing reads is shown on a

logarithmic color scale (inset, upper

left). The left column shows

housekeeping genes selected from a

range of average t.p.m. levels. The

middle column shows genes known as

ES cell markers. The right column

shows genes that were determined in

this study to be preferentially

expressed in Neuro2A.

Cell type specific expression pattern

12

Single-cell transcriptional profiling

13

Clontech SMARTer ultra low RNA kit

for Illumina sequencing

14

Sequencing the transcriptome of a single cell

Sort Cells Smart-Seq

Amplification

Illumina

Library Prep

NGS

Sequencing

cDNA

Good

Bad

Cells RNA

1 0.01 ng

10 0.1ng

100 1 ng

1000 10 ng

10000 100 ng

15

SMARTer™ technology overview

Key aspects of SMARTer™ protocol:

switching mechanism at 5’ end of

RNA template

Single tube, single enzyme cDNA

synthesis

SMARTer oligo provides increased

template switching efficiency of RT

Minimal handling of starting material

lowers the probability of RNA

degradation

Enrichment for full-lengths cDNA

transcripts

16

DAY TWO

Workflow overview

Total RNA

SMART cDNA Synthesis

Full-length ds cDNA Amplification

Covaris

End Repair

A tailing

Adp ligation

PCR Amplification

1 day

Total RNA

SMART cDNA Synthesis

Full-length ds cDNA Amplification

Nextera Tagmentation

PCR Amplification

• ~ 5 hour

• Automatable Spri

purification

• < 2 hour

• Automatable Spri

purification

17

Primary sequencing metrics

0.00

20.00

40.00

60.00

80.00

100.00

120.00

10ng

rep1

10ng

rep2

1ng rep1 1ng rep2 0.1ng

rep1

0.1ng

rep2

0.05ng

rep1

0.05ng

rep2

0.01ng

rep1

0.01ng

rep2

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

%unique reads

%mapped reads

% rRNA

gene

18

Reproducibility with various amounts of input RNA

Scatter plots comparing gene counts (i.e., log2 RPKM values) for replicate

samples prepared using 10 ng, 1 ng, and 0.1 ng of mouse brain total RNA

Input levels represent the amount of RNA obtained from ~500, 50, and 5

cells, respectively

With decreased amount of input reproducibility is typically decreased

1 ng 0.1 ng 10 ng

19

Sequencing coverage of SMARTer ultra low library

724 genes analyzed for average coverage across the entire length of

the transcripts

The graphs show consistent results between the 1 ng, 0.1 ng, 0.5 ng

and 0.01 ng input amount of mouse brain total RNA

% distance from 5’

Ba

se

Cove

rage

20

Number of genes retained: 705

Correlation (R): 0.942

Slope: 0.913

Number of genes retained: 581

Correlation (R): 0.856

Slope: 0.754

-10 -5 0 5 10

-10

-50

51

0

Log2 sequencing count ratio (brain vs UHR)

Lo

g2

qP

CR

ra

tio

(b

rain

vs U

HR

)

-10 -5 0 5 10

-10

-50

51

0

Log2 sequencing count ratio (brain vs UHR)

Lo

g2

qP

CR

ra

tio

(b

rain

vs U

HR

)

MAQC UHR/Brain

1ng Total RNA

Accuracy of SMARTer ultra low compared to Taqman

MAQC UHR/Brain

0.1ng Total RNA

21

Performance summary

Sensitive cDNA synthesis technology combined with Illumina next-

generation sequencing

Single-tube protocol, robust library generation starting from picogram

quantities of total RNA

High mapping rate, wide dynamic range, accurate gene

quantification, and uniform transcript coverage

The SMARTer kit has been used and validated by more than 100

labs around the world

Fluidigm C1 Single-Cell Autoprep system has been customized for

SMARTer assay

22

Example 1:

Gene-expression “landscape” of

hematopoietic stem cells (HSCs)

23

Transcriptional ‘architecture’ of the first steps of the

human hematopoietic hierarchy

The transcriptional architecture of early human hematopoiesis identifies multilevel control of

lymphoid commitment. Elisa Laurenti, Sergei Doulatov, Sasan Zandi, Ian Plumb, Jing Chen, Craig April,

Jian-Bing Fan & John E Dick. Nature Immunology. 2013.

John Dick, University of Toronto

‘Distances’ between

hematopoietic populations,

as measured by difference in

expression in the

downstream population

relative to that in its

progenitor (over twofold

difference; FDR, <0.05),

overlaid on the present

hierarchical model of human

hematopoietic differentiation.

24

Example 2:

Single-cell transcriptome analysis of

mammalian cell cycle

25

Single-cell transcriptomes of different cell cycle stages

E xpres s ion of C dt1 and G eminin

-5000

0

5000

10000

15000

20000

25000

30000

35000

40000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

C yc le Number

Flu

ore

sc

en

ce

(d

R)

G2

G1

Li et al, Biotechnol. Adv. 2013

John Zhong, USC

26

Molecular map of cell cycle

John Zhong, USC

Single-cell transcriptomes can be organized by similarity into a molecular

map to re-constructs stepwise cell cycle events at the molecular level

27

Example 3:

NIH single cell analysis program (SCAP)

28

29

The UCSD (PI)/Harvard/Scripps/Illumina team

George

Church Jerold Chun

Kun Zhang

Wei Wang Mostafa Ronaghi

Jian-Bing Fan

TSRI

UCSD

Illumina

Harvard

Samples

Data

Methods

30

NIH Single Cell Analysis Program

Three centers funded from the National Institutes of

Health's Common Fund, through its Single Cell

Analysis Program (SCAP).

– UCSD, USC and UPenn

Single-cell sequencing and in-situ mapping of

mRNA transcripts in human brains:

– Generating total-RNAseq data on 10,000

microdissected single cells or flow-sorted single

nuclei from Human Cortex and to create a 3D

transcriptional map of the human brain.

– Development and optimization of an in-situ RNA

sequencing technology.

– In-situ mapping of ~500 transcripts in 36 cortex

sections, and integration with 10,000 sets of total-

RNAseq data.

– Includes UCSD (Kun Zhang (PI), Wei Wang), Scripps

(Jerold Chun), Harvard (George Church), Illumina

(Jian-Bing Fan, Mostafa Ronaghi)

.

31

Approach

Sample preparation (TSRI).

– Microdissection of neurons and glia.

– Flow sorting of neuronal and non-neuronal nuclei.

Single-cell total-RNAseq (Illumina & UCSD).

– RNA transcripts +/- A-tails.

– Long and short transcripts.

– Strand-specificity.

– Batch processing in 96-well plates.

RNA in situ sequencing (UCSD, Harvard & Illumina).

– In-situ conversion of single RNA molecules into DNA nanoballs (rolonies).

– In-situ decoding and counting by hybridization or sequencing on automated confocal microscope with customized fluidic devices.

32

Single-cell transcriptome sequencing methods

Surani/LifeTech: Full length mRNA (Tang et al. 2009)

STRT: mRNA 5’-end sequencing (Islam et al. 2011)

CEL-seq: mRNA 3’-end sequencing (Hashimshony et al. 2012)

Smart-seq: Full-length mRNA (Ramskold et al. 2012)

Smart-seq2: Full-length mRNA (Picelli et al. 2013)

Toto-RNAseq (UCSD/Illumina, being developed)

– Full length

– Strand specific

– mRNAs and ncRNAs

– High throughput

33

Context is important

Murray et al. Nat. Method, 2008

34

RNA FISH

RNA FISH +

epifluorescent imaging

Barcoded RNA FISH +

STORM

Raj et al. Nat. Methods, 2008

Lubeck et al. Nat. Methods, 2012

35

In situ sequencing for RNA analysis in preserved tissue

and cells

Ke and Nilsson et al. Nat. Method, 2013

36

Fluorescent in situ sequencing (FISSEQ)

Jay Lee and George Church, Harvard

37

Two sequencing chemistries

Jay Lee and George Church, Harvard

38

Characterization of the 3D RNA-Seq library

The system was able to

sequence the whole

transcriptome in situ in 3D,

mapping over 100,000

reads and 6000 clusters,

detecting mRNA, ncRNA,

and antisenseRNA which

can then strongly indicate

the cell type.

Jay Lee and George Church, Harvard

39

Cancer

– Early diagnosis of cancer

Circulating tumor cells may be present before …

Limited clinical samples and early stage cancers

Heterogeneity in tumors

– Change in clonal population post-treatment

Brain transcriptome

– 3-D transcriptome map of a brain at high resolution

Human cell lineage tree in health and disease (European Commission)

Embryo to Adult

– Accumulation of somatic mutations with cell division

– Stem cell differentiation

– Cellular origin mapping

Fetal cells

Single cell microbes (metagenomes)

Single cell sequencing applications

40

Summary

Single cell transcriptomes provided comprehensive molecular characterization of

individual cells and revealed unique cell types/stages; discovered cell types

correspond to marker-based cell types

Systematic whole-organism cell mapping is feasible

– Millions of single-cell transcriptomes needed

Future technology development and integration

– Isolation, identification & characterization of cells from all organs and systems in

health, disease, & post-mortem

– Molecular characterization of individual cells (e.g. single cell RNA-Seq)

– Platforms: Next-gen sequencing, microfluidics, DNA arrays, & other analyses of

individual cells

– Three-dimensional subcellular transcriptome sequencing in situ

– Real-time measurement

– Computer Science & Systems: Extremely large-scale data capture, analysis,

coalescence & management tools, methods & algorithms, cell lineage analysis &

reconstruction algorithms, interactive data analyses & presentation.

– Mathematics & Statistics

41

Acknowledgements

STRT technology development

Sten Linnarsson (Karolinska Inst)

Saiful Islam (Karolinska Inst)

SMART kit development

Shujun Luo (Illumina)

Gary Schroth (Illumina)

Richard Sandberg (Ludwig Institute for Cancer Research)

Daniel Ramskold (Ludwig Institute for Cancer Research)

Andrew Farmer (Clontech)

HSC and cell cycle projects

John Dick (Ontario Cancer Institute, University of Toronto)

Elisa Laurenti (Ontario Cancer Institute)

John Zhong (University of Southern California)

NIH SCAP

Kun Zhang (PI; UCSD)

Wei Wang (UCSD)

Jerold Chun (Scripps)

Jian-Bing Fan (Illumina)

Mostafa Ronaghi (Illumina)

Jay Lee (Harvard)

George Church (Harvard)

42

Thank You

43

Fluorescent in situ sequencing (FISSEQ)

Jay Lee and George Church, Harvard