Single-cell gene expression analysis – technologies and application Weiwen Zhang
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Transcript of Single-cell gene expression analysis – technologies and application Weiwen Zhang
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Single-cell gene expression analysis
– technologies and application
Weiwen Zhang
Laboratory of Synthetic Microbiology
School of Chemical Engineering & Technology
Tianjin University, Tianjin, P.R. China
July 22, 2014
“2nd International Conference on Oceanography”
July 21-23, Las Vegas, Nevada, USA
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Cancer Stem CellsCancer Stem Cells
Why analyze gene expression in a single cell?
Analysis from the population could be misleading!
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Substantial cell-to-cell heterogeneity even in isogenic populations grown under identical conditions.
Gene expression heterogeneity could cause long-term heterogeneity at the cellular level.
In natural ecosystems, microbial cells with diverse genotypes and phenotypes co-existed.
Only less than 1% of microbial species in natural environments can be cultured and accessed by traditional gene expression analysis methods that typically requires a large number of cells.
Lidstrom, M.E., Meldrum, D.R., 2003. Nat Rev. Microbiol, 1:158-164
heterogeneity cell-to-cell variability
Why analyze gene expression in a single microbial cell?
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Synthetic ecology, new frontier in synthetic biology!
Building more “robust and controllable” eco-systems for biotechnological application
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Single-cell Alternatives to Meta-approaches in Environmental Microbiology
• Meta-approaches average cell-cell difference
• Cells with diverse genotypes and phenotypes were found within any community
• Sub-species (strain) level resolution not available
• Single-cell genomics; Single-cell transcriptomics; Single-cell proteomics (?)
The cellular DNA is amplified >109-fold by multiple displacement amplification (MDA) using random primers
Zhang, et al. Nat. Biotechnol. 24, 681–687 (2006).
Single-cell genomics
A bacterial chromosome = a few femtograms (10-15 g) of DNA
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Single bacterial-cell gene expression
Gene expression analysis at single bacterial cell level, is that possible??
Dilution10 100 1000 10,000 100,000 1,000,000 10,000,000 100,000,000
Cell No. 2.22E+5 2.22E+4 2.22E+3 2.22E+2 22.2 2.22 0.222 0.0222RNA (ng) 4.26 0.426 4.26E-2 4.26E-3 4.26E-4 4.26E-5 4.26E-6 4.26E-7
Cell No. in each reaction (When E. coli OD600 = 1.0, Cell density = 1X109/mL)
2-3: rbcL 4-5: 16S rRNA 6-7: dnaK
M 1 2 3 4 5 6 7
1: groEL2
~ 22 cells
E. coli
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Two-step RT-qPCR to measuring gene expression in single cell
Cell 1: 23.1277 +/- 0.1357
Cell 2: 24.6715 +/- 0.2644
Cell 3: 28.1182 +/- 0.3144
Brief protocol: • RNA extraction: Carried out using ZR RNA MicroPrep Kit (Zymo Research, Orange, CA) with minor modification.
• cDNA synthesis: SuperScript VILO cDNA Synthesis Kit (Invitrogen)
• qPCR analysis: EXPRESS SYBR GreenER qPCR SuperMixs Kit (Invitrogen, San Diego, CA)
• Multiple genes each cell
• Able to separate technical and biological variation
16S rRNA gene is the amplification target Each reaction used 1/20th of the cDNA Three technical replicates for each cell
Amplification of three individual E. coli cells from the exponential growing population
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0 10 20 30 40 Cycle
∆R
n0.
1
1.
0
1
0.0
1
00.0
Negative controls
CC1, CC2, CC3HS1, HS2, HS3
0 10 20 30 40 Cycle
∆R
n0.
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1.
0
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0.0
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00.0
Negative control
CC1, CC2, CC3
HS1, HS2, HS3
0 10 20 30 40 Cycle
∆R
n0.
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CC1, CC2, CC3
HS1, HS2, HS3
Negative control
16S rRNA dnaK groES
ControlCC (Avg_CT ± StDv)
Heat ShockHS (Avg_CT± StDv)
16S rRNACell No. 1Cell No. 2Cell No. 3
20.6777 ± 0.3125 20.7948 ± 0.0689 21.0096 ± 0.1281
Cell No. 1Cell No. 2Cell No. 3
21.7777 ± 0.186423.2901 ± 0.251222.4832 ± 0.0818
dnaKCell No. 1Cell No. 2Cell No. 3
30.2822 ± 0.1763 31.7915 ± 0.3143 31.0435 ± 0.3126
Cell No. 1Cell No. 2Cell No. 3
28.6768 ± 0.100827.7821 ± 0.046828.7926 ± 0.2161
groESCell No. 1Cell No. 2Cell No. 3
31.4224 ± 0.4704 32.1555 ± 0.4673 32.5109 ± 0.7372
Cell No. 1Cell No. 2Cell No. 3
28.7846 ± 0.126828.1949 ± 0.060629.5052 ± 0.0537
Single-cell gene expression analysis of the response to heat shock
• Three cells (biological replicates) for each condition (controls vs. heat-shock) were individually isolated
• Three genes were analyzed in each cell: 16S rRNA, dnaK and groES
• Each reaction used 1/20th of the cDNA
• Three technical replicates for each gene
Average qPCR CT values and standard deviation among all technical and biological replicates
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Dilution Avg_CT StDv
10-1 18.2434 0.0961
10-2 21.6089 0.1713
10-3 25.0732 0.4291
10-4 28.6372 0.5535
10-5 31.9372 0.7767
Gene expression analysis using diluted cDNA from a single bacterial cell
Average qPCR CT values and standard deviation among all technical replicates
E. coli contains 105-106 copies of rRNA molecules!
Gao et al., 2011, J. Microbiol. Method. 85:221-7.
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Very tiny amount of total RNA: 1-10 femtogram per E. coli cell (1 femtogram = 1e-15 gram)!
Scheme of analytical procedureScheme of analytical procedure
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Response heterogeneity of Thalassiosira pseudonana to stress
Selection of internal controlGrowth
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Response heterogeneity of Thalassiosira pseudonana to stress
Analysis of multiple genes in 30 individual cells
Shi et al., 2013, Appl. Environ. Microbiol. 79:1850-8
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Measure mitochondrial gene expression levels in single cells
• Cancer progression is a process associated with a series of complex, step-wise changes at the biomolecular level.
• Esophageal adenocarcinoma (EAC) is a highly lethal cancer type and is believed to develop from esophageal epithelial cells.
• Mitochondria found to play a major role in the transformation.
• Single-cell analysis of the differential hypoxia response in two human Barrett’s esophageal cell lines, CPA and CPC.
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Mt copy number difference
Bulk cells based
Single-cell based
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CP-A CP-C
16S
COXI
COXIII
CYTBI
2
CP-A CP-C
28S
MT3
PTGES
VEGF
1
Simultaneous measurement of multiple genes encoded by chr and mt DNA in single cells
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We proposed that mitochondria may be one of the key factors in the early cancer progression
Wang et al., 2013, PloS One. 8:e75365
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Why transcriptomics for single bacterial cell?
qRT-PCR: 5~20 genes/cell
Fluidigm: 96 or more genes/cell
1,000 ~ 10,000 (and more) genes per
microorganism
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BaSiC-RNAseq: Bacterial Single Cell-RNAseq
RNA isolation
RNA amplification
QC: verification cDNA labeling Illumina Hi-Seq
Single bacterial cells
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BaSiC-RNAseq RNA Amplification
NuGen RNA Amplification KitUnique at:
primers: random/polyT Poly DNA polymerase RNase H SPIA DNA/RNA primer
1 bacterial cell generated 7~19 μg cDNA
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BaSiC-RNAseq: Quality ControlBaSiC-RNAseq: Quality Control
1, 100 bp ladder2, NTC (H2O as input) 3, single bacterial cell
Agarose gel 1%
3 cell cDNA
1 cellNTC
vector
Blunt-end
ligation
Transformation
Clonal sequencing
BlastN against GenBank
1 2 3
Sequencing of clone library: All 30 clones are from cyanobacteria
All Synechocystis sp. PCC 6803 genes!
Cyanobacterial Synechocystis sp. PCC 6803
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End repair, blunt end cloning transformation
Sequencing of random clones
Nitrogen starvation 72 h24 h
Single cell RNA isolation
RNA amplification① First strand cDNA② Double-stranded cDNA③ SPIA amplification④ Post-SPIA modification and purification
Bioanalyzer analysis
RNA-seq library construction and quantification
Single cell RNA-seq analysis
Clone library
Research hypotheses?
1) Heterogeneity could vary upon stress in isogenic bacterial population?
2) The change as a driver for adaption and evolution of the population?
Synechocystis sp. PCC 6803
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RNA-seq coverage of transcripts
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T [1]
T [2
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-100 0 100
60504030
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-10-20-30-40-50
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Bulk-0hBulk-24h
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Bulk-0hBulk-24hBulk-72h
Cell-3 Cell-2 Cell-1
Cell-6
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Cell-6 Cell-3 Cell-2 Cell-1
Cel
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Bul
k-24
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A) B)
C) D)
Clustering analysis PCA analysis
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R2=0.87 R2=0.85 R2=0.77
R2=0.014 R2=0.028 R2=0.088
Expression in 72-h bulk cells Expression in 72-h bulk cells Expression in 72-h bulk cells
Expression in 24-h bulk cells Expression in 24-h bulk cells Expression in 24-h bulk cells
Exp
ress
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in si
ngle
cel
lE
xpre
ssio
n in
sing
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Exp
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in si
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Exp
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A) B) C)
D) E) F)
Heterogeneity increase as part of stress response !
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Heterogeneity variation among functional categories
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30 32 34 36 38 400.0
0.1
0.2
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Rel
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eque
ncy
Ct
24 h 72 h 24 h 72 h
slr1684
32 34 36 38 40 42 440.0
0.1
0.2
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Rel
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eque
ncy
Ct
24 h 72 h 24 h 72 h sll0945
Adj. R-Square sigma
24 h 0.98108 1.1535572 h 0.92073 1.48065
22 24 26 28 30 32 340.0
0.1
0.2
0.3
0.4
0.5
Rel
ativ
e Fr
eque
ncy
Ct
24 h 72 h 24 h 72 h
16S
Adj. R-Square sigma
24 h 0.95977 1.1772372 h 0.95493 1.02529
Adj. R-Square sigma
24 h 0.92063 0.7627672 h 0.97386 1.45617
qRT-PCR verification
Wang et al., 2014, Genome Research., under review
Heterogeneity increase in “Mobile elements” could be a important driver for cell adaption and evolution!
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Summary
• Microbial cell-cell heterogeneity increasingly recognized.
• Two-step qRT-PCR protocol established for analyzing gene expression in single bacterial cells.
• Transcriptomics protocol established for single bacterial cells
• Single-cell transcriptomics reveals increasing heterogeneity upon stress in isogenic cyanobacterial population.
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Acknowledgments
Laboratory of Synthetic MicrobiologyTianjin University
National “973 Program” and “863 program”
National Science Foundation of China
Tianjin University “985” Program”