improving single cell characterization with simultaneous gene … · 2020. 9. 11. · cd15 w6d3...

Application Demonstration

Improving Single Cell Characterization with Simultaneous Gene Expression and Cell Surface Protein Measurements at Scale

10x Genomics | Chromium System | Single Cell Gene Expression

AbstractCells establish their function, identity and state through

the careful orchestration of complex molecular mechanisms

leading to gene expression. While gene expression can be

measured by type and quantity of mRNA transcripts produced,

it is well known that the abundance and isoforms of expressed

proteins cannot always be inferred directly from mRNA readout

alone (1). Thus, to more accurately characterize cellular identity,

states and function, it is important to evaluate gene expression

at both transcript and protein levels.

Here, we introduce the Chromium Single Cell Gene Expression

Solution with Feature Barcoding technology. This solution

labels cell surface proteins with DNA-barcoded antibodies and

integrates measurements of cell surface proteins and transcrip-

tomes into a single readout. In this Application Demonstration,

we successfully annotate cell clusters based on different protein

isoforms; information that is usually masked when analyzing

transcript levels alone. Our antibody profiling results obtained

with the Feature Barcoding technology were comparable to

flow cytometry in sensitivity.

Figure 1. Description of Single Cell Gene Expression Solution with Feature Barcoding technology workflow. Shown are the different steps of the single cell gene expression workflow from cell labeling to sequencing and analysis. The workflow generates 2 libraries: a Gene Expression library and a Cell Surface Protein library, which are independently indexed and then sequenced. The transcriptomic and protein expression data are combined during data analysis.

Highlights• Simultaneously examine gene expression and

protein abundance from the same cell

• Detect protein isoforms and under-represented

transcripts of key protein markers

• Evaluate differences between mRNA and cell

surface protein expression profiles

• Obtain single cell protein expression with perfor-

mance similar to gold-standard flow cytometry

LoupeCell Browser

Gel Beads GEMs

LabeledCells

OilLabeled Cell

Alignment

BarcodeProcessing

Gene-cellMatrix

TranscriptCounting

Expression Analysis

Report

Sample Prep PartitioningLabeling

3’ Gene ExpressionLibrary Construction

(SPRI Eluate)

Cell Surface Protein Library Construction

(SPRI Supernatant)

Cell Ranger Analysis for Gene Expressionand Cell Surface Protein Libraries

Sequencing Libraries(3’ Gene Expression)

Sequencing Libraries(Cell Surface Protein)

P5 10xBarcode

UMI Poly(dT)VN P7TruSeqRead 2

TruSeqRead 1

i7: 8SampleIndex

10xBarcode

UMI FeatureBarcode

CaptureSequence 1

Nextera Read 1(Read 1N)

P7TruSeqRead 2

P5

i7: 8SampleIndex

naïve CD8 T cells

memory CD4 T cells

B cellsnaïve CD4 T cells

memory CD8 T cells

NK cellsCD16

monocytes

CD14 mono-cytes

dendritic cells

plasmacytoiddendritic cells

Improving Single Cell Characterization with Simultaneous Gene Expression and Cell Surface Protein Measurements at Scale Application Demonstration

IntroductionIn recent years, single cell gene expression technologies have

seen significant advancement in cell throughput while also

becoming more affordable and accessible for researchers across

biological disciplines. With its ready-to-use solution workflows

and ability to process up to 80,000 cells in a single experiment,

the Chromium System exemplifies these advancements. Our

first solution for single cell gene expression profiling has led to

new discoveries and insights across many research applications

including developmental biology, immunology, cancer and

neuroscience (for more in depth references see the Single Cell

Gene Expression Getting Started Guide).

The Chromium Single Cell Gene Expression Solution

(version 2) has previously been used by two academic groups

for the simultaneous assessment of gene expression and

cell surface protein abundance (with antibodies conjugated

to antibody-specific barcodes and poly(A) oligos) in single

cells (2, 3). 10x Genomics improved on these two initial

protocols by introducing Feature Barcoding technology,

which streamlines this application and provides optimized,

specific sets of barcoded oligonucleotide sequences to

capture additional cell feature information alongside each

cell’s transcriptome, all with increased gene and protein

sensitivities. Examples of theSingle Cell Feature Barcoding

technology applications to detect distinct cellular features

are cell surface proteins, T-cell receptor specific antigens,

and CRISPR-mediated perturbations. In the context of protein

expression, this ready-to-use solution measures both gene

and cell surface protein expression levels from the same

cell. It includes an easy-to-follow workflow, captures the

proteins in a poly(A)-independent manner, provides open

access data analysis tools (Cell Ranger analysis pipeline and

Loupe Cell Browser visualization tools), and incorporates

commercially available oligo-conjugated antibodies from

compatible partners.

MethodsCell preparation, encapsulation, library preparation and

sequencing Peripheral Blood Mononuclear Cells (PBMCs) from

a healthy donor were obtained from AllCells. Dissociated cells

from a Non Hodgkins Lymphoma, Extranodal Marginal Zone

B-Cell (MALT: Mucosa-Associated Lymphoid Tissue) parotid

gland tumor were obtained from Discovery Life Sciences

(formerly Folio Conversant). Cells were thawed following our

demonstrated protocol for Fresh Frozen Human Peripheral

Blood Mononuclear Cells for Single Cell RNA Sequencing

(Document CG000039). The resulting single cell suspension

was then stained and incubated with a panel of 15 TotalSeq™-B

Antibodies obtained from BioLegend (Table 1) as described

in our demonstrated protocol (Document CG000149). The

upgraded Chromium Single Cell Gene Expression Solution

with its new v3 chemistry detects more unique transcripts

per cell, has a low cell doublet rate (0.8% per 1,000 cells), and

achieves industry-leading high cell capture efficiencies (~65%).

Single Cell Gene Expression and Cell Surface Protein librar-

ies were generated as described in our User Guide (Document

CG000185), with each library aiming to recover either ~5,000

(PBMCs) or ~10,000 cells (MALT) (Figure 1). Sequencing of

both Gene Expression and Feature Barcoding libraries was

performed on an Illumina NovaSeq 6000 with NovaSeq

software v1.2 using single-end sequencing, with a 28 bp (R1),

8 bp (i7) and 91 bp (R2) read configuration. Depending on the

experimental needs, Gene Expression and Cell Surface Protein

libraries are sequenced on short read sequencers at depths

ranging from 20,000 to +50,000 reads per cell (rpc) and +5,000

rpc, respectively. For this Application Demonstration, PBMC

and MALT gene expression libraries were sequenced to

~26,000 rpc (see Table 2 for details).

Table 1. BioLegend TotalSeq™-B antibody panel.

Cell Ranger was used to perform demultiplexing, barcode

processing, transcript and Feature Barcode counting and

clustering analysis for both the Gene Expression and Cell

Surface Protein libraries. Outputs from Cell Ranger (.cloupe

files or filtered gene barcode matrices) were visualized with

either Loupe Cell Browser or via the third-party analysis

tool, Seurat R package (www.satijalab.org/seurat).

For flow cytometry, PBMCs were prepared according to the

previously mentioned protocol for single cell gene expression

analysis. Cells were stained and incubated with individual

antibodies labelled with fluorescein isothiocyanate

fluorochrome (BioLegend) and individually analyzed on a

cell sorter. FLOWJo (v10) was used for data analysis. All flow

cytometry data were initially gated on the entire PBMC

population (FSC-A vs SSC-A), on singlets (FSC-A vs FSC-H)

and on FITC-stained cells.

ResultsDetecting Under-represented Transcripts of Key Protein Markers Using Feature Barcoding Technology

Without previous knowledge of gene expression, it can be

difficult to annotate cell clusters. For example, to characterize

the complex immune cell populations present in PBMCs, we

analyzed single cell gene expression and identified 6 major

subpopulations based on the gene markers that were specific

to each population: T cells (CD3D/E), B cells (MS4A1), NK cells

(GLNY, NKG7), CD14+ and CD16+ monocytes (CD14 and FCGR3A,

MS4A7) and dendritic (FCER1A, CST3) cells. Finer sub-structure

was detected within the large T cell cluster; with CD4+ (IL7R)

and CD8+ (CD8A) T cells and naïve CD4/CD8 (CCR7) and memory

CD4/CD8 (S100A4) T cells (Figure 2A). Most of the transcripts

used for the characterization of the cell clusters were actually

not classically known cell surface markers. Transcripts of well

known cell surface proteins such as CD4, CD8 and CD19 are

often expressed at very low level and not always in most

cells (Figure 2B). In contrast, the CD4, CD8A T cell and B cell

clusters show uniform and high level expression of CD4,

CD8A and CD19 proteins (Figure 2B).

Thus, the layering of the protein information over the

transcriptomic information can help identify and annotate

specific cell subpopulations. Adding the protein information

also allows researchers to benefit from the large knowledge

of cell surface protein markers available in the literature.

Specificity Clone Reactivity

CD3 UCHT1 Human

CD19 HIB19 Human

CD45RA HI100 Human

CD4 RPA-T4 Human

CD8a RPA-T8 Human

CD14 M5E2 Human

CD16 3G8 Human

CD56 QA17A16 Human

CD25 BC96 Human

CD45RO UCHL1 Human

PD-1 EH12.2H7 Human

TIGIT A15153G Human

CD127 A019D5 Human

CD45 HI30 Human

CD15 W6D3 Human

tSNE 1

tSN

E 2

MRNA

CD4

CD8A

CD19

PROTEIN

tSNE 1

tSN

E 2

tSNE 1

tSN

E 2

tSNE 1

tSN

E 2

Figure 2. Detection of protein markers associated with low mRNA expression in PBMCs. A. Visualization of the cell clusters in Loupe Cell browser overlaid with manual annotation of the main sub-populations identified in PBMCs. B. Visualization with Loupe Cell Browser of CD4, CD8A and CD19 transcripts and protein expression.

B.

A.

Application Demonstration

Detecting Protein Isoforms Using Feature Barcoding Technology

PTPRC/CD45 mRNA is expressed as multiple isoforms which

vary the exons encoding for the extracellular domains of the

protein and depend on the cell type, developmental stage and

activation state. Alternative splicing of exon 4, 5 and 6 (also

named A, B, C) can generate at least 8 different isoforms, of

which 6 are consistently expressed in humans. These isoforms

contain either all three exons (ABC isoform), two of the three

exons (AB and BC isoforms), only one exon (A and B isoforms)

or no ABC exons (O isoforms) (6, 7) (Figure 3A).

The different isoforms have broad expression and are expressed

at different levels in T cells, B cells, monocytes, dendritic cells

and granulocytes. Specific to the T cell populations, the CD45RO

isoform marks activated and memory T cells. CD45RA and

CD45RB mark naïve T cells. As T cells become activated

Figure 3. Detection of CD45 isoforms using Feature Barcoding technology. A. Description of the CD45/PTPRC mRNA isoforms and their characteristic expression in lymphocyte populations. B. Detection of protein isoforms CD45RA and CD45RO in CD4 T cells highlighted in the T-SNE plot. PBMCs from a healthy patient (Table 1) were analyzed with single cell gene expression (in red) and Feature Barcode-conjugated antibodies (in blue), anti-CD45RA and anti-CD45RO (Table 2). The different transcript isoforms for PTPRC/CD45 are impossible to distinguish at the transcript level and expression is detected throughout the CD4 T cell cluster (top panel) while the protein isoforms CD45RA and CD45RO are specifically detected in naïve CD4 T cells (bottom middle panel) and memory CD4 T cells (bottom right panel), respectively. The sequencing data were processed with Cell Ranger and visualized with Loupe Cell Browser.


CD45RA TotalBPTPRC/CD45 mRNA CD45RO TotalB

CD4 T Cells

Naïve

Memory

B.

3 4 5 6 71 33

A B C

CD45 ABC

CD45 AB

CD45 AC

CD45 BC

CD45 A

CD45 B

CD45 C

CD45 O

3 4 5 6 7

3 4 5 7

3 4 6 7

3 5 6 7

3 4 7

3 5 7

3 6 7

3 7

B cells

Naïve T cells

Memory T cells

Adapted from Cho et al., Genome Biology 2014

AAAAA

~50 KbA.

Flow

Cyt

omet

ry

(Cou

nts)

Feat

ure

Barc

odin

g

tech

nolo

gy (C

ount

s)

CD4

10 0 10 1 10 2 10 3 10 4 10 5

0

50

100

150

200

CD8a

10 0 10 1 10 2 10 3 10 4 10 5

0

50

100

150

CD19

10 0 10 1 10 2 10 3 10 4 10 5

0

50

100

150

200

CD45RA

10 0 10 1 10 2 10 3 10 4 10 5

0

30

60

90

120

CD45RO

10 0 10 1 10 2 10 3 10 4 10 5

0

30

60

90

120

Figure 4. Quantitative comparison between Feature Barcoding technology and flow cytometry. Flow cytometry (top panel) and Feature Barcoding technology (bottom panel) reveal similar cell populations when fluorescence intensity is compared with UMI counts per cell.

Datasets Number of Cells Reads per Cell Median Genes per Cell

Median UMI's per Cell

Sequence Saturation (%)

PBMC 4,630 26,836 1,732 5,274 46.7

MALT 9,481 25,252 1,262 3,767 50.5

Table 2. Sequencing metrics obtained for the PBMC and MALT Gene Expression libraries.

Feature Barcoding Technology Results Are Similar to Flow Cytometry Analysis

Flow cytometry is the gold standard for identification and

enumeration of cell subsets based on quantitative differences

in surface markers (7, 8). Using the same antibodies (Table 2)

conjugated either with DNA barcodes that are compatible with

the Feature Barcoding technology or labeled with fluorophores,

we analyzed the PBMCs with the Chromium Single Cell Gene

Expression Solution with Feature Barcoding technology and flow

cytometry. We compared the distribution of the fluorescence

intensity and the UMI counts obtained from the flow cytometry

and Feature Barcoding signal, respectively. The cell distribution

profiles are remarkably similar between the two technologies

(Figure 4). More in depth comparison between flow cytometry

and antibody-conjugated oligo methods have been carried out

and have also concluded that the level of protein expression is

consistent with gold-standard flow cytometry techniques and

can enable high-resolution immunophenotyping in concert

with single cell transcriptomics (2, 3).

and progress from naïve to memory cells, CD45RA and

CD45RB expression is progressively downregulated to be

replaced with CD45RO.

In the PBMC sample, while PTPRC/CD45 transcripts are e

asily identifiable on the transcript level (Figure 3B, bottom

left panel), its isoforms (CD45RA/RO) can only be resolved by

examining the protein data (Figure 3B, bottom middle and

right panels) demonstrating the power of combining both

measurements into a single assay. Thus, using DNA-barcoded

antibodies specific to the isoforms CD45RA or CD45RO allowed

us to identify the naïve and memory T cells which would

otherwise be very difficult with PTPRC/CD45 transcript 3’

ends alone.

Application DemonstrationImproving Single Cell Characterization with Simultaneous Gene Expression and Cell Surface Protein Measurements at Scale

B.A.naïve CD8 T cells

naïve CD4 T cells

memory CD4 T cells

memory CD8 T cells

pDCs

naïve B cellsmemory B cells

CD16 monocytes

CD14 mono-cytes

dendritic cells

CD8 T cells

NK cells

CD14 monocytes

megakaryocytes

tSNE 1

tSN

E 2

Figure 5. Combining unbiased gene expression profiling with detection of cell surface proteins results in a more comprehensive characterization of distinct cell sub-populations in a heteroge-neous PBMC sample. A. T-SNE plot showing the different sub-populations in a PBMC sample (naïve and memory CD8 T cells, NK cells, naïve and memory CD4 T cells, naïve and memory B cells, CD14+ monocytes, CD16+ monocytes, Dendritic cells and plasmacytoid dendritic cells (pDCs)). Single cell analysis was carried out with Cell ranger pipeline and Seurat with 14 PCs. B. T-SNE plots showing marker transcripts and proteins to identify the naïve and memory CD4 T cells, CD8 T cells, NK cells, B cells, monocytes and naïve and memory T cells.

T CELLS

MONOCYTES

NK CELLS

NAÏVE/MEMORY CELLS

MRNAMRNA

CD

56

PROTEINPROTEIN

B CELLS

CD

19

CD

45R

A

CD

3C

D8

CD

4C

D14

CD

16

CD

45

CD

45RO

Feature Barcoding Technology Improves Resolution of Cell Populations and States

The Chromium Single Cell Gene Expression Solution

generated high complexity libraries resulting in more than a

thousand median genes per cell detected. These data allowed

for comprehensive cell cluster profiling (Figure 5A, 6A). For

example, single cell gene expression analysis with Seurat R

package (4) revealed the major subpopulation of PBMCs at

expected ratios (in percent of leukocytes): 11.6% naïve CD4

T cells (enrichment of IL7R and CCR7 mRNAs), 19.9% memory

CD4 T cells (enrichment of IL7R and S100A4), 9.4% naïve CD8

T cells (enrichment of CD8A and CCR7 transcripts), 5.4%

memory CD8 T cells (enrichment of CD8A and S100A4 mRNAs),

8.7% naïve B cells (enrichment of MS4A1, IGHM and IGHD

transcripts), 4.7% memory B cells (enrichment of MS4A1,

CD27 and IGHA1 mRNAs), 5.5% NK cells (enrichment of NKG7

mRNAs), 29.1% CD14 monocytes (enrichment of CD14 and

LYZ mRNAs), 2.8% CD16 monocytes (enrichment of FCGR3

and MS4A7 transcripts), 1.4% dendritic cells (enrichment of

CST3 and FCER1A mRNAs), 1.0% plasmacytoid dendritic cells

(enrichment of GZMB, SERPINF1, ITM2C mRNAs) and 0.5%

megakaryocytes (enrichment of PF4 and PPBP mRNAs). Finer

sub-structures were found within the CD14 monocytes,

T and B cells. While the monocyte subpopulations remain

to be characterized, naïve and memory cell populations were

characterized with S100A4 and CCR7 mRNA expression for

T cells, and CD27, IGHA1, IGHM and IGHD transcripts for B

cells. These cluster annotations were confirmed via the use

of protein information that was obtained with the Feature

Barcoding technology: T cell (CD4, CD8 and CD3), B cell

(CD19), NK cell (CD16, CD56), CD16 monocytes (CD16), CD14

monocyte populations (CD14) and naïve/memory cells

(CD45RA/CD45RO) (Figure 5B).

In conclusion, the combination of mRNA and protein infor-

mation from the same cell can result in a more comprehensive

characterization of distinct cell sub-populations in a hetero-

geneous sample. In addition, obtaining information from both

the transcript and protein level can improve annotation of cell

clusters as both transcript and protein biomarkers currently

available in the literature can complement each other.


Feature Barcoding Technology Can Help Characterize Tumor Cell Populations and Identify New Biomarkers

Combined gene expression and protein information is even

more advantageous for complex sample types such as tumors.

For example, single cell gene expression analysis of the MALT

sample identified 10 clusters with Seurat including 2 distinct

immune cell populations: T and B cells. Finer substructures

were detected within the T cell cluster, most notably, CD4

helper T cells (enriched for CD3D/E and IL7R and lack of CD8A

mRNAs), CD8 cytotoxic T cells (enriched in CD8A transcripts),

regulatory (Treg) T cells (enriched for FOXP3, RTKN2, TIGIT

mRNAs) and follicular helper (Tfh) T cells (enriched for

CXCL13, TIGIT and PDCD1 transcripts) (Figure 6A-B). Cluster

annotation was confirmed via the use of cell surface protein

data for CD3, CD4, CD8, CD19 and PD-1 (Tfh) and TIGIT (Treg)

antibodies (Figure 6B).

The MALT sample was derived from a patient with low grade

Non-Hodgkins Lymphoma (characterized by proliferation of

the B cell population). Three distinct B cell populations were

identified; a small mature B cell population (enriched in

MS4A1 mRNAs) and two distinct plasma B cell populations.

Exam-ination of the top cluster-specific genes revealed one

of the plasma B cell populations expressed high levels of IGHD

and FCER2/CD23 transcripts (which contradicts the low grade

staging as CD23 negative) while the other expressed high levels

of IGHA1 and IGHM transcripts (data not shown). In addition,

the elevated expression of IGHM, IGHD and IGHA1 transcripts

suggests the occurrence of plasmacytic differentiation.

An additional B cell population characterized by elevated

expression of miRNA155HG, which has been associated with

tumorigenesis in different cancers (9-11), was also noted.

Using protein information, we confirmed the identification

of the plasma B cell population via CD45RA, CD45RO, TIGIT,

PD-1 and CD19 antibodies. Remarkably, we find that the FCER2

positive (gene expression) plasma B cell population is strongly

correlated with CD45RA while the FCER2 negative (gene

expression) population is positive for PD-1 and TIGIT proteins

(Figure 6B). In this case, additional characterization of each of

the plasma B cell populations via these cell surface biomarkers

would aid downstream characterization, analysis and

functional studies.

A. B.

Tregs

plasma B cells

plasma B cells

Tfh cells

monocytes

CD8 T cells

B cells

doublets

CD4 T cellsmiR155+ B cells

doublets

tSNE 1

tSN

E 2

T CELLS B CELLS

MRNAMRNA

CD

19

PROTEINPROTEIN

NAÏVE/MEMORY CELLS

CD

45R

A

CD

3C

D8

CD

4TI

GIT

CD

45

CD

45RO

Figure 6. Characterizing tumor cell populations and identifying new biomarkers in a MALT lymphoma. A. T-SNE plot showing the different sub-populations in a MALT lymphoma (CD8 T cells, CD4 T cells, Tregs, Tfh cells, B cells, plasma B cells and miR155+ B cells). Single cell analysis was carried out with Cell ranger pipeline and Seurat with 40 PCs. B. Feature plots showing transcripts and protein makers identifying the T and B cell populations and protein markers for specific tumor cell populations (CD45RA, TIGIT and PD-1).


References1. Y. Liu, A. Beyer, and R, Aebersold, On the dependency

of Cellular Protein levels on the mRNA abundance. Cell

165, 535-50 (2016).

2. M. Stoeckius, C. Hafemeister, W. Stephenson, B. Houck-

Loomis, P.K. Chattopadhyay, et al., Simultaneous epitope

and transcriptome measurement in single cells. Nat.

Methods 14, 865-868 (2017).

3. V.M.Peterson, K.X. Zhang, N. Kumar, J. Wong, L. Li, et al.,

Multiplexed quantification of proteins and transcripts in

single cells. Nat Biotechnol. 35, 936-939 (2017).

4. A. Butler, P. Hoffman, P. Smibert, E. Papalexi, and R. Satija,

Integrating single-cell transcriptomic data across different

conditions, technologies, and species. Nat. Biotechnol.

36, 411-420 (2018).

5. M.L. Hermiston, Z. Xu, and A. Weiss, CD45: a critical

regulator of signaling thresholds in immune cells.

Annu Rev Immunol. 21, 107-37 (2003).

6. V. Cho, Y. Mei, A. Sanny, S. Chan, A. Enders, et al., The

RNA-binding protein hnRNPLL induces a T cell alternative

splicing program delineated by differential intron retention

in polyadenylated RNA. Genome Biol. 15, R26 (2014).

ConclusionsThe Chromium Single Cell Gene Expression Solution with

Feature Barcode technology enables the simultaneous

measurement of gene expression and protein abundance

in thousands of single cells. While single cell transcriptome

data results in the identification of cell populations at high

resolution, it is sometimes difficult to annotate clusters based

on transcript levels alone. Feature Barcoding-derived antibody

information can take advantage of the extensive knowledge of

cell surface protein markers to help further resolve transcript

level-defined cell population clusters or further identify new

biomarkers of specific cell populations. We showed successful

identification of distinct cell clusters based on different protein

isoforms, information that is masked by transcript levels alone.

More importantly, antibody profiling mediated by Feature

Barcoding technology showed results comparable to flow

cytometry in sensitivity.

With flow cytometry, the amount of proteins that can

be measured is limited due to inherent spectral overlap,

but Feature Barcoding technology has the advantage to

substantially scale the number of antibodies that can be

multiplexed at the same time. Finally, the Chromium Single

Cell Gene Expression Solution coupled with the Feature

Barcoding technology has the ability to measure transcripts

and proteins levels simultaneously with high sensitivity

at single cell level, which is key to accurately characterizing

cellular identity, states, and function.

ResourcesDatasets go.10xgenomics.com/scRNA-3/datasets

Seminars go.10xgenomics.com/scRNA-3/seminars

Application Notes go.10xgenomics.com/scRNA-3/app-notes

Technical Support go.10xgenomics.com/scRNA-3/support

Publications go.10xgenomics.com/scRNA-3/pubs

[email protected] 10x Genomics

6230 Stoneridge Mall Road

Pleasanton, CA 94588-3260

Legal NoticesFor 10x Genomics legal notices visit:

10xgenomics.com/legal-notices

7. J.P. Robinson and M, Roederer, HISTORY OF SCIENCE.

Flow cytometry strikes gold. Science 350, 739-40, (2015).

8. K. Murphy and C. Weaver, Janeway’s Immunobiology.

Garland Science, Taylor & Francis Group, LLC; New York

and London (2017).

9. X. Wu, Y. Wang, T. Yu, E. Nie, Q. Hu, et al., Blocking

MIR155HG/miR-155 axis inhibits mesenchymal transition

in glioma. Neuro. Oncol. 19,1195-1205 (2017).

10. E. Baytak, Q. Gong, B. Akman, H. Yuan, W.C. Chan, et al.,

Whole transcriptome analysis reveals dysregulated

oncogenic lncRNAs in natural killer/T-cell lymphoma and

establishes MIR155HG as a target of PRDM1. Tumour Biol.

39, 1010428317701648 (2017).

11. K. Vargova, N. Curik, P. Burda, P. Basova, V. Kulvait,

et al., MYB transcriptionally regulates the miR-155 host gene

in chronic lymphocytic leukemia. Blood 117, 3816-25.


© 2019 10X Genomics, Inc. FOR RESEARCH USE ONLY. NOT FOR USE IN DIAGNOSTIC PROCEDURES.LIT000034 Rev C The Chromium Single Cell Gene Expression Solution with Feature Barcode Technology Application Note

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