a 10x Genomic Single Cell Overview

Chromium™ Single Cell 3’
Solution
High-­‐throughput Single Cell Gene Expression
July 2 016, Revision B
The Chromium Single Cell 3’ Solution
Chromium™ Controller
Chromium Single Cell 3’ Consumables
Cell Ranger™ Pipelines
I
• GemCode™ Technology
• Chip for single cell partitioning in GEMs
• Automated
• Reagents for RT, amplification and library construction • High-­‐throughput and scalable
• Informatics solution for single cell expression profiling
• Pre-­‐processing, QC and analytics
2
Gel Bead-­‐in-­‐Emulsion (GEM)
Single T Cell
Functionalized Gel Bead
RT Reagents in Solution
Partitioning Oil
3
High Diversity Barcode Library
Functional Oligo with Barcode
Gel Bead Scaffold
P7
10X BC
R2
High-­‐diversity Library
Poly(dT)VN
• 750,000 Discrete Reagents in One Tube
• Defined 14 base pair barcode sequence
• Highly uniform size and barcode representation
• Built-­‐in sequencing adapter, barcode and primer
4
Partitioning and Barcoding of Single Cells
• Super-­‐Poisson loading of barcoded beads into droplets
• Poisson loading of cells in GEMs
• Beads dissolve for efficient, liquid phase biochemistry
• Cell lysis starts immediately following encapsulation
5
Efficient Scalable Workflow
Single-­‐use microfluidics chip
• Up to 8 channels processed in parallel
• 1,000 to 6,000 cells per channel
• 10 minute run time per chip
Recovery well
• Up to 30 um cell diameter tested
Gel Bead well
Sample well
Oil well
• ~50 % cell processing efficiency
• Temperature Range 18-­‐280C
User controlled trade-­‐off between cell numbers and doublet rate
Number of Cells
Expected Doublet Rate (%)*
1,200
~1.2
3,000
~2.9
6,000
~5.7
6
Assay Scheme for 3’ mRNA Sequencing
In GEM
mRNA
P7
10X R2
RT with oligo(dT)VN
Oligo(dT)VN
First Strand Synthesis
P7
10X R2
Oligo(dT)VN
CCC
GGG TSO
Template Switching RT
P7
10X R2
Oligo(dT)VN
CCC
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Assay Scheme for 3’ mRNA Sequencing
In Bulk
cDNA amplification by PCR
Primer
P7
10X R2
CCC
Oligo(dT)VN
Primer
Shearing, A tailing, ligation and SI PCR
P7
10X R2
Oligo(dT)VN
R1
SI
P5
8
Single Cell 3’ End-­‐to-­‐End Workflow
Reagents and Consumables
in 10X Kit
1
Cell preparation
2
Partition and RT inside each GEM
3
Pool and cDNA amplification
4
Covaris shearing
5
Adapter ligation and sample index PCR
6
Sequencing and analysis
Total Turn-­‐around Time: ~12 Hrs
Total Hands-­‐on Time: ~4 Hrs
9
Cell Ranger – Informatics Workflow
• Complete software package for single cell analysis
• Bundled with STAR for efficient transcriptome alignment
• Outputs are standard formats plus Loupe visualization
BAM
HDF5
BCL
Cell Ranger™
Analysis Pipelines
MEX
LOUPE
Standard Informatics
samtools, Python, R
Loupe™
Cell Browser
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Cell Ranger – Output Files
BAM – Genome-­‐Aligned Reads
• Indexed BAM containing position-­‐sorted, aligned reads
• Barcodes and UMIs attached as standard tags
MEX – Gene/Barcode Matrix
• Market Exchange format
• Suitable for downstream analysis in Python and R
Gene
ATCAGGGACAGA
AGGGAAAATTGA
TTGCCTTACGCG
TGGCGAAGAGAT
TACAATTAAGGC
LOXL4
0
0
0
0
0
PYROXD2
1
0
1
1
0
HPS1
23
12
9
8
3
CNNM1
0
2
1
0
0
GOT1
22
6
7
9
3
HTML, CSV – Run Summary
• Run metrics and basic static visualizations
CSV -­‐ Run Analysis
• 2D projections
• Cell clustering
• Differential expression
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Example 1: Validation of Single Cell Behavior
Mouse Transcript Counts
• 1:1 mixture of ~1,400 human (HEK293T) and mouse (NIH3T3) cells
Human:Mouse
Human only
Mouse only
• 99.4% of cell-­‐occupied GEMs yielded reads mapping to only one species
• 1% inferred doublet rate* 0
0
Human Transcript Counts
60,000
*includes unobserved human:human and mouse:mouse doublets
Number of cells detected: ~1400 cells, Number of r aw r eads per cell: ~130k
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Example 2: Cell Cycle Phases
Combined Expression of Known Phase Markers
• Proliferating HEK293T cells were profiled and scored for expression of markers associated with each major cell cycle phase
G1/S
S
G2
G2/M
Expression
level
M/G1
0
100
200
300
• Cells from all phases were identified
400
HEK293T Cells Ordered by Inferred Cell Cycle Phase Phase-­‐specific genes derived from W hitfield et al., 2002
Number of cells detected: ~400 cells, Number of r aw r eads per cell: ~40k
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Example 3: Breast Cancer Heterogeneity
Unbiased Automatic Clustering of Three Breast Cell Lines
HER2 Expression Matches Expected Cell Line Status
HCC1954
HCC1143
HCC1954
log(x+1) HER2 counts t-­‐SNE Projection
t-­‐SNE Projection
HCC1143
HCC38
Number of cells detected: ~1000 cells, Number of r aw r eads per cell: ~40k
HCC38
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Example 4: Identifying Rare Cell Types
T-Cell (Jurkat)
20
20
10
10
PC2
PC2
B-Cell (Raji)
0
0
-10
-10
-20
-20
-20
-10
0
PC1
10
• Jurkat and Raji cells were combined at 9:1, 99:1 and 199:1 ratios and then profiled
20
• The minority Raji populations were identified in all three mixtures
-20
-10
10
20
1.5%
0.6%
20
20
10
10
10
0
PC2
20
PC2
PC2
12%
0
PC1
0
0
-10
-10
-10
-20
-20
-20
-20
-10
0
PC1
10
20
-20
-10
0
PC1
10
20
Number of cells detected: ~1000 cells, Number of r aw r eads per cell: ~60k
-20
-10
0
PC1
10
20
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Example 5: Primary Cell Populations
Whole Blood
Peripheral Blood Mononuclear Cells (PBMC)
• A complex mixture of different cell types
• Well-­‐studied and readily available primary cells
Platelets and
Plasma Components
Mononuclear Cells
(PBMCs)
Myeloid Cells
Monocytes
Plasmacytoid Dendritic
Cells
Erythrocytes
(RBCs), Eosinophils
and Neutrophils Lymphoid Cells
B Cells
NK Cells
T Cells
Macrophages
Myeloid
Dendritic Cells
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Major populations of PBMCs are detected
CD45 RA+ Naïve T (26.4%)
CD4+ T (28.4%)
TSNE2
Dendritic (1.9%)
CD8+ T (18.7%)
CD19+ B (5.5%)
CD14+ Monocytes (5.3%)
CD34+ Progenitors (0.3%)
CD56+ NK (13.5%)
TSNE1
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Example: 68,000 Human PBMCs
• CD45RA+Naïve T Cells
• CD4+ T Cells
• CD8+ T Cells
• CD14+ Monocytes
• CD19+ B Cells
• CD34+ Myeloid Progenitors
• CD56+ Natural Killer Cells
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