1Hye-Won Song, 1Gisele Baracho, 2Nidhanjali Bansal, 3Ian Taylor, 2Eleen shum, 1Stephanie Widman, 2Stefanie Mortimer

1BD Biosciences, San Diego, CA; 2BD Biosciences, San Jose, CA; 3FlowJo LLC, Ashland, OR

Simultaneous Analysis of mRNA and Proteins in Mouse Immune Cells Using the BDTM Mouse Single-

Cell Multiplexing Kit and the BDTM AbSeq Assay on the BD RhapsodyTM System

Abstract• Obesity can weaken the body’s immune system and reduce its ability to fight off infections.• To understand the role immune cells play in obesity-related disorders, thousands of immune cells isolated from lymphoid organs and

adipose tissue from a diet-induced obesity mouse model were examined using the BD RhapsodyTM, a high-throughput single cell analysissystem.

• DNA-barcoded universal antibodies from the BDTM Mouse Single-Cell Multiplexing Kit (SMK) allowed us to combine a total of 8samples into a single pool, significantly reducing experimental scale and cost while eliminating any potential batch effect.

• The targeted immune-focused mRNA (~400 genes) and BDTM AbSeq panel (30 DNA-barcoded antibodies) provided robust clusteringof immune cell types and showed genes such as Pdcd1 and Tigit that are critical to functional immune responses.

• Using this multiomics approach, we propose a model to explain certain obesity-related disease phenotypes.

BDTM AbSeqBDTM Single-Cell Multiplexing Kit1A 1B

Technology

BD RhapsodyTM mRNA, SMK and AbSeq captured on bead

1C

cDNA synthesis on bead High multiplex-targeted amplification1DFigure 1. A) BDTM SMK can be used to multiplex up to 12 different samples into one cartridge and bioinformatically demultiplex samples after sequencing. B) BDTM AbSeq uses DNA-barcoded antibodies to simultaneously analyze mRNA and protein with high-throughput sequencing. C) mRNA and oligos from BDTM SMK and BDTM AbSeq are captured on BD Rhapsody™ Cell Capture Beads. D) Captured mRNA and oligos are now associated with a cell label and unique molecular index (UMI) and are amplified by targeted multiplexed PCR.

Experimental Design

1. Broad BDTM AbSeq panel identifies immune cell populations across mouse tissues

CD45+ cells sortedBD SMK, BDTM

AbSeq and CD45-FITC

BD RhapsodyTM

Tissues isolated:Fat

ThymusSpleen

Bone Marrow

Amplification:•Sample Tag•BDTM AbSeq•BD Rhapsody™

Immune Response Panel Mm

Sequencing SeqGeqTM analysis

Figure 2. At 6 weeks of age 2 mice were placed on a control diet (Control, 10% DIO) and 1mouse was placed on a high fat diet (HFD, 60% DIO) for 17 weeks before the experiment.Epididymal fat, thymus, spleen, and bone marrow cells were dissociated and stained witha 30-plex BD AbSeq panel, SMK, and CD45-FITC. CD45+ immune cells from fat tissue werethen sorted. All 8 different samples were pooled prior to loading onto two BD Rhapsody™Cartridges. The two replicates showed consistent data and so were combined for analysis.

CD19

TCR beta

All Tissues(HFD and Control)

CD11b

CD49b

3CFigure 3. A) Sample Tag metrics table shows the8 different samples that were successfullydemultiplxed from both replicates.B) SeqGeq™-generated t-SNE plot of all cells,combined using mRNA and protein profile. Thesimilarity and difference between tissues andobesity condition are shown. Use of SampleTags helps to minimize the technical noise frombatch effect for easier sample-to-samplecomparisons. C) t-SNE analysis with heat mapshowing expression levels of BDTM AbSeq lineage markers shows cell type specificclustering. D) Major immune cell types wereidentified using the BDTM AbSeq lineage markers shown in 3C. t-SNE, t-distributedstochastic neighbor embedding.

2. Unbiased clustering reveals disease-associated cell phenotypes

4B

Figure 4. A) PhenoGraph identified 12 cell clusters that showed tissue-specific enrichment. This unbiased clustering uncovered 4 clusters (PG-1,PG-5, PG-7, PG-12) from fat and thymus showing differences in t-SNElocalization between control and obese mice. B) Cell composition in 12clusters of control and obese mice fat tissue consistently showsdifferences in 4 identified clusters. C) t-SNE plot of fat tissue withPhenoGraph clustering. Using marker-expression analysis, cell subtypesfor each cluster were identified; (ATM: Adipose tissue macrophages). PG-7 B cells were increased in obese mouse fat and monocle and markeranalysis showed that these B cells resemble B-2 regulatory cells.

Results

Sample name Sample tag ID Rep1 Rep2 TotalControl Fat ST1 972 928 1900Control Bone marrow ST2 1501 1527 3028Control Spleen ST3 1566 1337 2903Control Thymus ST4 1074 1006 2080HFD Fat ST5 1196 1029 2225HFD Bone marrow ST6 1471 1308 2779HFD Spleen ST7 1389 1229 2618HFD Thymus ST8 1129 1039 2168

Multiplet 1048 818 1866Undetermined 72 42 114Total 11418 10263 21681

3A

3D

• BD SMKTM allows high confidence of relationship between samples without batch effects.• BDTM AbSeq allows effective cell clustering, identification of obesity related phenotypes, and investigation of the

underlying mechanism behind these phenotypes.• SeqGeqTM provides various tools to allow deeper analysis of single cell multiomics data.

3. Adipose tissue macrophages (ATM) and CD11c+ myeloid cells in obese mice exhibit inflammatory and adipogenic gene signatures

Figure 5. A) Heat map showing differentially expressed genes (DEG) in HFDATM. Genes involved in inflammation such as Cd9, Cxcl1, and Il1b wereupregulated in HFD ATM. B) Trem2 + ATM increased while Cd163+ anti-inflammatory ATM decreased in the HFD mouse. C) Heat map showing DEGin HFD CD11c+ myeloid cells. Genes inducing adipogenesis (i.e. Lgals1) andrecruiting inflammatory cells (i.e. Clec10a, Clec4e) were upregulated in HFDCD11c+ myeloid cells.

BD, the BD Logo, BD AbSeq, BD Rhapsody, SeqGeq and BD Single Cell Multiplexing Kit are trademarks of Becton, Dickinson and Company or its affiliates. All other trademarks are the property of theirrespective owners. © 2019 BD. All rights reserved 23-21496-00

4. Adipose tissue infiltrating cytotoxic cells from obese mouse showed signs of exhaustion

Figure 6. A) SeqGeqTM-generated Monocle trajectory from CD8+ cells from all tissues. The most differentiated CD8+ cells were located at the end of the trajectory and corresponded to cells in the fat. B) Monocle trajectory of fat CD8+ cells. (left) Monocle defined 5 states. Direction of differentiation states were marked by an arrow. The most differentiated cells marked by an oval. (right) HFD CD8+ cells accumulated at the end of the trajectory. C) Exhaustion markers, PD-1, Pdcd1 (PD-1 mRNA), and Tigit were co-expressed in states 3 through 5 while CD49a and CD69 were co-expressed in state 2 cells. This suggests that HFD cytotoxic cells were transitioning from an activated to an exhausted state.

Working Model

BD Rhapsody

Cell-Capture

Bead

NK CellsB CellsT CellsMyeloid

Thymus – HFDThymus – ControlSpleen – HFDSpleen – ControlFat – HFDFat – ControlBone marrow – HFDBone marrow - Control

4A

Fat t-SNE

Control HFD

Fat

Control HFD

4C

ATMControl

ATMHFD

Lgals3Trem2Cd9Cxcl1Il1bMmp12Cd72Spp1Il1mQpctI-A_I-E (BD AbSeq)CD64 (BD AbSeq)CD1d (BD AbSeq)CD49b (BD AbSeq)FcnaCd163F13a1TfrcCd38Mmp9

Control HFD

Trem2

Cd163

(promotes diet-induced obesity)

(control inflammation)

Up inHFD

Down in

HFD

5A 5B

CD11c+

ControlCD11c+

HFDCD44 (BD AbSeq)

Lgals1

Mmp12

Cd63

Trem2

Clec10a

Cd9

Clec4e

Clec4d

Chil3

CD5 (BD AbSeq)

CD49b (BD AbSeq)

Il6

Up inHFD

Down in

HFD

5C

Monocle of fat CD8+ cells

FatBone MarrowThymusSpleen

Control

HFD

Monocle of all tissue CD8+ cells6A 6B

6C

1

2

3

4

5

CD103 (AbSeq) PD-1 (AbSeq) Pdcd1 Lag3Tigit CD49a (AbSeq) CD69 (AbSeq)

CD11c

Myeloid cells

F4/80

Adipocytes

Cytotoxic cells

B cells

Adipocytes

CD11c

Myeloid cells

F4/80

CD163Trem2

Clec10a

Healthy fat Obese fat Cell exhaustion

CD8

CD49a

PD-1

CD11b CD11b

Anti-inflammatory signals

CD1dCD1d

CD11b CD11b

Inflammatory signals

Galectin-1(Lgals1)

Cytotoxic cells

CD8

CD49a

PD-1

CD8PD-1 PD-1

TigitTigitLag3

Adipogenesis Cell activation

Cell migration

Conclusions

Introduction

3B

Class 1 Laser Product.For Research Use Only. Not for use in diagnostic or therapeutic procedures.