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BD™ Abseq on the BD Rhapsody™ System: Exploration of Single-Cell Gene Regulation by Simultaneous Digital Mrna and Protein Quantification

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BD™ Abseq on the BD Rhapsody™ System: Exploration of Single-Cell Gene Regulation by Simultaneous Digital Mrna and Protein Quantification BD™ AbSeq on the BD Rhapsody™ system: Exploration of single-cell gene regulation by simultaneous digital mRNA and protein quantification Overview of BD AbSeq antibody-oligonucleotide conjugates. High-throughput sequencing has allowed researchers to examine sequencing can be used to estimate the abundance of the hundreds to thousands of RNA targets and greatly advanced our protein. Using BD AbSeq with 3’-based single-cell analysis understanding of complex biological systems. However, systems—such as BD Rhapsody—allows simultaneous understanding gene regulation and single-cell heterogeneity interrogation of mRNA and protein in the same sample. often requires information about both RNA and protein expression. There are few technologies that allow concurrent BD AbSeq can be incorporated into examination of both types of molecules in a single experiment with a single readout. BD AbSeq allows simultaneous the BD Rhapsody system for a measurement of protein and RNA expression at the single-cell complete workflow solution. level, in combination with the BD Rhapsody high-throughput single-cell capture system. BD AbSeq uses oligonucleotide- Cells are incubated with BD AbSeq Ab-oligos in the same way conjugated antibodies (Ab-oligos) to examine protein expression that cells are labelled with antibodies before typical cytometry from high-throughput sequencing data. Each antibody clone in workflows. After incubation of labelled cells with BD AbSeq the BD AbSeq portfolio is conjugated to a unique oligonucleotide Ab-oligos, cells can be analyzed as bulk populations or taken containing an antibody-specific barcode (ABC). Adjacent to the through the BD Rhapsody workflow to capture transcriptome ABC is a poly(A) sequence on the 3’ end (for capture by oligo-dT- and BD AbSeq expression at the single-cell level (Figure 1). The based RNA-seq systems) and a 5’ universal PCR amplification BD Rhapsody platform uses a micro-well technology to partition 1 sequence that can be efficiently amplified during library individual cells with BD Rhapsody cell capture beads. Capture of preparation. Decoding of the ABCs using next-generation mRNA and BD AbSeq Ab-oligos within the micro-wells allows cell-specific barcoding of each mRNA and Cells BD Rhapsody RNA-derived library Ab-oligo. After recovery of the oligonucleotide-coated beads, parallel RNA and BD AbSeq sequencing libraries are then generated with BD Rhapsody Oligo-conjugated Ab BD AbSeq library library amplification components (included in each BD Rhapsody kit). The oligonucleotide portion of the Ab-oligo is carried through to sequencing to determine the identity of the protein detected. After determining the Bead/cell pairing RNA/AbOligo capture On-bead cDNA synthesis (in microwell) proportion of reads desired from each Barcoded cDNA library, they can be pooled and bead Microwell mRNA/AbOligos sequenced using Illumina sequencers. Barcoded bead Barcoded bead Cell Lysed cell Figure 1. The BD AbSeq workflow is integrated into the BD Rhapsody single-cell analysis system. A B mRNA-driven t-SNE Multi-omic-driven t-SNE Multi-omic analysis provides more 50 40 40 30 defined clustering. 30 20 Single-cell analysis allows researchers to uncover new cell 20 10 B Cells signatures;B Cells however, this relies on the ability to effectively 10 0 CD4+ T Cells CD4+ T Cells CD8+ T Cells CD8+ T Cells G/D T Cells G/D T Cells Monocytes Monocytes Coord 2 identify different cell subsets within a complex sample. To 0 -10 NK Cells NK Cells Dendritic Cells Dendritic Cells -10 -20 determine whether the protein data provided by BD AbSeq -20 -30 enables more specific cell-type identification, peripheral blood -30 -40 mononuclear cells (PBMCs) were analyzed using only mRNA data -40 -50 -40 -30 -20 -10 0 10 20 30 40 -40 -30 -20 -10 0 10 20 30 40 or mRNA and protein data together. PBMCs were isolated from a Coord 1 B cells (10.9%) CD4+ T cells (35.5%) healthy individual and incubated with a 30-antibody panel CD8+ T cells (8.4%) γδ T cells (1.9%) Monocytes (23.6%) (Appendix 1) before preparing mRNA and BD AbSeq libraries NK cells (18.3%) Dendritic Cells (1.4%) using the BD Rhapsody workflow. Cell subsets within the ~5,000 cells identified were first defined based on canonical protein and C D mRNA markers retrieved from the sequencing data. After 30 40 defining these cell types, they were visualized using t-distributed 35 20 stochastic neighbor embedding (t-SNE). These t-SNE projections 30 10 collapse multi-dimensional data into two dimensions to show, on 25 arbitrary axes, how different cells are based on gene expression, CD4+ T Cells (77.6%) CD4+ T Cells (77.6%) 0 20 CD8+ T Cells (18.2%) CD8+ T Cells (18.2%) G/D T Cells (4.1%) G/D T Cells (4.1%) Coord 2 Coord 2 protein expression, or a combination of the two. t-SNE 15 -10 projections were calculated using only mRNA data (Figure 2A) or 10 -20 mRNA and protein data (Figure 2B). While mRNA-based analysis 5 revealed distinct groups of monocytes and lymphocytes (B cells, -30 0 -35 -30 -25 -20 -15 -10 -5 0 5 10 -40 -30 -20 -10 0 10 20 30 40 Coord 1 Coord 1 T cells, and NK cells), subsets within the CD3+ population were CD4+ T cells CD8+ T cells γδ T cells especially difficult to distinguish (Figure 2C). Multi-omic-driven Figure 2. t-SNE clustering of PBMCs using mRNA or multi-omic data. projections revealed cleaner cell separation both between all cell A. t-SNE coordinates are calculated for all PBMCs based on mRNA data only. Cells types (Figure 2B) and within the CD3+ population (Figure 2D). are colored based on cell type. B. t-SNE coordinates are calculated for all PBMCs These analyses show that multi-omic data enables better based on multi-omic data (mRNA and protein). Cells are colored based on cell type. C. Same coordinates as in A, but only T-cell subsets are displayed. D. Same identification of different cell subsets within a complex sample. coordinates as in B, but only T-cell subsets are displayed. Markers used for cell-type This increased ability to discern different cell types will be crucial definition (protein data used for all markers except FcγRIIIa, for which mRNA data for experiments that seek to define novel cell subsets in healthy was used): CD4- CD3+CD4+; CD8- CD3+CD8+; γδT Cells- CD3+TCRγδ+; B Cells- CD19+; Monocytes-CD14+; NK Cells- CD3-CD45RA+ FcγRIIIA-high or diseased models. Direct detection of protein targets with low-expressed corresponding mRNAs such as PD1. The ability to examine protein directly, rather than inferring protein information based on mRNA information, could be especially useful in situations where mRNA level is not correlated to protein expression, or when mRNA is difficult to detect due to low expression. To determine whether BD AbSeq data can provide clearer information in cases where mRNA and protein expression are not correlated, we compared mRNA and BD AbSeq data for CD4 and the γδTCR receptor, which are both targets used at the protein level to define different cell types. Examination of CD4 mRNA showed that it is expressed in both monocytes and CD4+ T cells (Figure 3A, top). In contrast, when BD AbSeq data was used to examine CD4 protein levels, only the CD4+ T cells showed high expression (Figure 3A, bottom). Similarly, examination of the TCR delta constant domain at the mRNA level revealed expression in both NK cells and γδ T cells (Figure 3B, top). When examining γδTCR expression at the protein level, only the smaller γδ T-cell population emerges as high-γδTCR- expressing (Figure 3B, bottom). These results show that protein data can be used to define more precise cell subsets, particularly in cases where mRNA expression and protein expression are not correlated. A B C CD4 δTCR PD1 40 40 40 2254 cells (45.7%) 980 cells (19.9%) 1.4 17 cells (0.3%) 3885 mols (0.1%) 4312 mols (0.1%) 14 mols (0.0%) 0.5 30 30 30 1 1.2 0.45 20 20 20 0.4 1 0.8 10 10 10 0.35 0 0 0.8 0 0.3 0.6 0.25 mRNA -10 -10 -10 0.6 0.2 -20 0.4 -20 -20 0.4 0.15 -30 log10(Number of molecules per cell),normalized -30 log10(Number of molecules per cell),normalized -30 log10(Number of molecules per cell),normalized 0.1 0.2 0.2 -40 -40 -40 0.05 -50 0 -50 0 -50 0 -40 -30 -20 -10 0 10 20 30 40 -40 -30 -20 -10 0 10 20 30 40 -40 -30 -20 -10 0 10 20 30 40 40 40 2 40 4432 cells (89.9%) 4115 cells (83.4%) 4452 cells (90.3%) 701792 mols (12.7%) 13297 mols (0.2%) 20074 mols (0.4%) 2 30 30 1.8 30 2.5 1.8 1.6 20 20 20 1.6 2 1.4 10 10 10 1.4 1.2 0 0 0 1.2 1.5 1 1 -10 -10 -10 Protein 0.8 0.8 -20 1 -20 -20 0.6 0.6 -30 log10(Number of molecules per cell),normalized -30 log10(Number of molecules per cell),normalized -30 log10(Number of molecules per cell),normalized 0.4 0.4 0.5 -40 -40 -40 0.2 0.2 -50 0 -50 0 -50 0 -40 -30 -20 -10 0 10 20 30 40 -40 -30 -20 -10 0 10 20 30 40 -40 -30 -20 -10 0 10 20 30 40 Figure 3.
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