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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.
Improving Single Cell Characterization with Simultaneous Gene Expression and Cell Surface Protein Measurements at Scale
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.
Application DemonstrationImproving Single Cell Characterization with Simultaneous Gene Expression and Cell Surface Protein Measurements at Scale
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).
Application DemonstrationImproving Single Cell Characterization with Simultaneous Gene Expression and Cell Surface Protein Measurements at Scale
References1. Y. Liu, A. Beyer, and R, Aebersold, On the dependency
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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
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10xgenomics.com/legal-notices
7. J.P. Robinson and M, Roederer, HISTORY OF SCIENCE.
Flow cytometry strikes gold. Science 350, 739-40, (2015).
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and London (2017).
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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
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et al., MYB transcriptionally regulates the miR-155 host gene
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Improving Single Cell Characterization with Simultaneous Gene Expression and Cell Surface Protein Measurements at Scale
© 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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