Single cell multi-omics is a potentially powerful

method to explore more complete information at

single-cell level. As it’s relatively new, we sought to

provide data to demonstrate its power via BD flow

cytometry and BD scM platform. After confirming

the reliability of scM, we found CD27 as a potential

biomarker of CD4+ naïve T cells, which could be an

illustration of scM’s function in exploring new

biomarkers. We also enumerated three examples to

show the power of scM to find new cell populations.

Besides, scM also brought better cell clustering in

our lymphocyte model. The more clearly of the

cluster, the more likely we could find new cell

subsets with specific phenotype and/or function.

Thus, BD flow cytometry allying with BD scM

platform offers us an opportunity to characterize and

explore more in cell biology and immunology.

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Poster#

P041

BD FACSseq Cell Sorting1AMore and more researchers realize that it is far from enough to find the mechanism through traditional“bulk” methods. Only the average profiling of a group of cells, for example, can get through “bulk”

methods, leaving the heterogeneity at the single-cell level being covered up. Analysis is greatly

improved, however, when single-cell multi-omics (scM) appears. ScM adapts next generation

sequencing (NGS) to single cell analysis to simultaneously provide information both at mRNA and

protein levels. Here, we employ BD flow cytometers and scM platform (Rhapsody™ Single-Cell

Analysis System) to see how it can help research.

After being sorted via BD FACSMelody TM flow cytometer, human T and B cells were labelled with 42

Abseqs and 2 sample tags, then loaded onto and lysed by BD Rhapsody TM instrument. mRNA as well

as oligonucleotides associated with cell-bound antibodies (Abseq and sample tag) were captured by

beads via poly A-oligo dT interactions. Single cell sequencing then revealed the corresponding

information of 42 proteins and 399 targeted immune cell transcripts (BD Biosciences) with the help of

BD SeqGeq TM and FlowJo TM softwares.

Comparing with the data required via BD LSRFortessa TM flow cytometer, scM showed similar

specificity and resolution of CD8+T cells, CD4+T cells and B cells via CD45RA vs. CCR7, CD25 vs.

CD127 and IgD vs. CD27, respectively, indicating the high credibility of scM-Abseq technology. We

then compared antigen expression pattern between CD4+CD45RA+ naïve T cells and

CD4+CD45RO+memory T cells. Apparent disparity was observed, for example, CD27, with nearly all

cells expressing high level CD27 in CD4+CD45RA+T cells but co-existing high-level-CD27 and low-

level-CD27 cells in CD4+CD45RO+T cells. Flow cytometry verification showed similar results. We

then gained enlightenment to hypothesize that CD27 might help better distinguish naïve from memory

CD4+T cells. It’s well-known now that CD27 can indeed do so, but just because of that, it’s exactly the

evidence telling us the power of scM to find new reliable cell subsets or biomarkers. Furthermore,

when combing mRNA and Abseq/protein, cells can be clustered clearer. For instance, we can catch

three dominant populations when using Abseq as the only parameter to cluster, but when mRNA was

added, at least eight populations could be resolved from B cells. Similar results can get when it comes

to naïve B cells and class-switched memory B cells.

Single cell multi-omics is a powerful technology to explore more information at single-cell level.

Combining flow cytometry with Single-cell multi-omics technologies enables deep diving into immune

cell research. Results from our case indicate: 1) similar specificity and resolution between Abseq and

flow cytometry; 2) scM platform could be supportive in new biomarker discovery and screening ; 3)

better cell clustering can be achieved through scM platform, which may help to redefine traditional cell

subsets.

1. PBMC isolation

2. Flow cytometry

3. Co-labelling single cell with Abseq and Sample Tag

4. Single cell capture and cDNA synthesis

5. Library preparation

6. Sequencing and data diving

(1B) Each cell type is clearly defined by relative expression of CD44 and Her2/Neu. Jurkat cells are shown in orange, T47D cells in red, HeLa cells in blue, and SKBR3 cells in green.(1C) Jurkat (top left), HeLa (top right), T47D (bottom left), and SKBR3 (bottom right) were sorted based on Her2/Neu expression. Three populations within each cell type were collected: Her2/Neu low, Her2/Neu intermediate, and Her2/Neu high. High, low and intermediate designations were relative to the expression within a given cell type. Single cells were sorted directly into each well of a BD Precise breast cancer 96-well assay plate. Each quadrant shows the gating strategy used to ensure that sorted cells were live single cells with the desired Her2/Neu expression profile.

Relative Protein Expression of CD44 and Her2/Neu

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Figure 1

Workflow of scM.

Figure 2

Consistency between flow cytometry and scM-Abseq.

Introduction/Abstract

Methods

1. Results

2. Results Conclusions

Figure 1. Human PBMC were prepared to sort T and B cells through

BD FACSMelodyTM, single cells were captured and lysed using BD

RhapsodyTM system after being labeled with Sample Tags and Abseq.

The captured mRNA was then reverse transcribed into cDNA and used

to prepare corresponding libraries. After sequencing, FASTQ files were

transferred into csv files and used for data diving through SeqGeq

software. A 14-color panel was designed for data verification by BD

LSRFortessaTM.

Flow Cytometry ally with Single-cell multi-omics: new technology drive deep diving into immune cell researchJie Dong1, Yuping Wang2, Biqing Li3, Xingyu Zhong4, Xi Yang1, Xu Wu1, Liang Fang5

1COE, BD Biosciences, Beijing, China, 2COE, BD Biosciences, Shanghai, China, 3Technical consulting, BD Biosciences, Shanghai, China, 4Technical Consulting, BD Biosciences, Shanghai, China, 5Technical Service, BD

Biosciences, Beijing, China

Figure 2. Representative comparison between flow cytometry

and scM-Abseq, the first line and second line demonstrate plots

of FCM and scM respectively: (A) Naïve/memory CD8+T cells

and CD4+ regulatory T cells shown via contour plots. (B) CD25

and CD45RA expression of T cells shown in histogram. (C)

Naïve/memory B cells in contour plots. (D) CD20 and IgD

expression of B cells displayed via histogram.

Figure 3

New biomarker exploring example: CD27 might help better

define traditional naïve CD4+T cells.

Figure 3. (A) Differential expression analysis of CD4+CD45RA+

naïve T cells and CD4+CD45RO+ memory T cells. (B) Different

CD27 expression pattern among three different CD4+T cell

subsets: CD4+CD45RA+ naïve T cells, transitional CD4+T cells

and CD4+CD45RO+ memory T cells.

Figure 4

New population exploring: CD7-/low population exists

exclusively in CD45RO+T cells, not in CD45RA+T cells.

Figure 4. Different CD7 protein expression pattern between

CD45RO+ memory T cells (left) and CD45RA+ naïve T cells

(right). (A) CD4+T cells; (B) CD8+T cells. (C) CD45RO vs. CD7

expression in CD4+T cells and CD8+T cells in contour plot.

Figure 5

New population exploring: cluster “T-pop 12” might

contain resting and memory/activated CD4+Treg cells.

Figure 5. CD25 expression pattern in different T cell

clusters via heatmap plot. (B) 15 corresponding clusters

named T-pop0 to T-pop14. (C) down-expressed proteins and

mRNA (left) and up-expressed ones in T-pop12 cluster

comparing with other T cell clusters. (D) different protein

expression pattern between the two subpopulations of T-

pop12 cluster.

Figure 6

New population exploring: cluster “T-pop 3” might be a

subpopulation of CD4+memory T cells with CD279 up-

expression..

Figure 6. CD279 expression pattern in different T cell clusters via

heatmap plot. (B) 15 corresponding clusters named T-pop0 to T-

pop14. (C) down-expressed proteins and mRNA (left) and up-

expressed ones in T-pop3 cluster comparing with other T cell clusters.

(D) different protein expression (left) and mRNA expression (right)

pattern between the two subpopulations of T-pop3 cluster.

Figure 7

Lymphocytes are clustered clearer when combining mRNA

and Abseq.

Figure 7. T cells were clustered via highly dispersed mRNA only

(left) or Abseq and mRNA simultaneously. (B-D) B cells were

clustered via Abseq only (left) or Abseq and mRNA simultaneously

(right). (B) whole B cells, (C) naïve B cells and (D) class-switched

memory B cells.