A Targeted Multi-Omic Analysis Approach Measures Protein Expression and Low Abundance Transcripts on the Single Cell Level

Mair, Florian; Erickson, Jami R.; Voillet, Valentin; Simoni, Yannick; Bi, Timothy; Tyznik, Aaron J.; Martin, Jody; Gottardo, Raphaël; Newell, Evan W.; Prlic, Martin

doi:10.2139/ssrn.3426658

Cited by 24 publications

(35 citation statements)

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“…6000 reads/cell) was insufficient to reveal the complete spectrum of CD127 and CD25 expression, resulting in reduced power to fully resolve CD25 low/int T cells, when compared to flow cytometry. Although increased sequencing coverages in our experiment would provide only minimal gain for the functional characterisation of single cells and clustering, they are necessary to describe the complete dynamic range of protein expression, especially for highly expressed proteins such as CD127 and CD25, thereby leading to similar sensitivity between molecular flow cytometry, as previously demonstrated for CITE-seq [9], REAP-seq [10] and BD AbSeq [19].…”

Section: Resultsmentioning

confidence: 90%

Discovery of CD80 and CD86 as recent activation markers on regulatory T cells by protein-RNA single-cell analysis

et al. 2020

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Background: Traditionally, the transcriptomic and proteomic characterisation of CD4 + T cells at the single-cell level has been performed by two largely exclusive types of technologies: single-cell RNA sequencing (scRNA-seq) and antibody-based cytometry. Here, we present a multi-omics approach allowing the simultaneous targeted quantification of mRNA and protein expression in single cells and investigate its performance to dissect the heterogeneity of human immune cell populations. Methods: We have quantified the single-cell expression of 397 genes at the mRNA level and up to 68 proteins using oligo-conjugated antibodies (AbSeq) in 43,656 primary CD4 + T cells isolated from the blood and 31,907 CD45 + cells isolated from the blood and matched duodenal biopsies. We explored the sensitivity of this targeted scRNA-seq approach to dissect the heterogeneity of human immune cell populations and identify trajectories of functional T cell differentiation. Results: We provide a high-resolution map of human primary CD4 + T cells and identify precise trajectories of Th1, Th17 and regulatory T cell (Treg) differentiation in the blood and tissue. The sensitivity provided by this multi-omics approach identified the expression of the B7 molecules CD80 and CD86 on the surface of CD4 + Tregs, and we further demonstrated that B7 expression has the potential to identify recently activated T cells in circulation. Moreover, we identified a rare subset of CCR9 + T cells in the blood with tissue-homing properties and expression of several immune checkpoint molecules, suggestive of a regulatory function. Conclusions: The transcriptomic and proteomic hybrid technology described in this study provides a cost-effective solution to dissect the heterogeneity of immune cell populations at extremely high resolution. Unexpectedly, CD80 and CD86, normally expressed on antigen-presenting cells, were detected on a subset of activated Tregs, indicating a role for these co-stimulatory molecules in regulating the dynamics of CD4 + T cell responses.

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Section: Resultsmentioning

confidence: 90%

Discovery of CD80 and CD86 as recent activation markers on regulatory T cells by protein-RNA single-cell analysis

et al. 2020

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“…We flow-sorted naïve CD4 + T-cells from a healthy adult donor and polarized these cells using standard cytokine combinations (Cano-Gamez et al, 2020) in combination with fecal waters (1:100 dilution) collected from newborns fed B. infantis EVC001 or control children lacking B. infantis ( Figure 6A). To evaluate the polarization and cellular states of the T-cells in the cultures we applied a targeted multiomics approach (Rhapsody, BD Biosciences) in which we combined the analysis of 259 mRNA by targeted sc-mRNA-sequencing and 10 proteins using oligo-coupled antibodies (BD Abseq , BD Biosciences) (Mair et al, 2020). In this way we were able to assess transcription factor and cytokine gene expression often more difficult to detect in untargeted sc-mRNA-sequencing applications, and although the overall UMAP distribution of cells is similar across conditions, subtle differences in cell states were observed ( Figure 6B).…”

Section: B Infantis Evc001 Metabolites Influence T-cell Polarizationmentioning

confidence: 99%

Bifidobacteria-mediated immune system imprinting early in life

Henrick

Rodriguez

Lakshmikanth

et al. 2020

Preprint

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Immune-microbe interactions early in life influence an individual's risk of developing allergies, asthma and some autoimmune disorders. Breastfeeding helps guide the development of healthy immune-microbe relationships, in part by providing nutrients to specialized microbes that in turn benefit the host and its developing immune system. Such bacteria having co-evolved with humans are associated with reduced risks of immune mediated diseases but are increasingly rare in modern societies. Here we map an immunological sequence of events, triggered by microbial colonization that distinguish children with different gut bacterial composition. Lack of bifidobacterial species is associated with elevated markers of intestinal inflammation and immune dysregulation and in a randomized trial of breastfed infants, the infant-adapted Bifidobacterium infantis EVC001 silenced intestinal Th2 and Th17 immune responses, while inducing IFN􀁅, and its metabolites skew T-cell polarization in vitro, from Th2 towards Th1, suggesting a healthier immune imprinting during the first critical months of life.

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“…1; Experimental Methods). Donors were selected based on the diversity of blood types (e.g., A, B, O, and Rhesus factor +/-) and PBMC samples were tagged with donor-specific MULTI-seq 2 and Cell Hashing DNA barcodes (BD Biosciences) 14 . PBMCs were mixed for 30 minutes at 4°C prior to emulsion across four droplet microfluidics lanes (10x Genomics) at room temperature.…”

Section: Methodsmentioning

confidence: 99%

“…By avoiding the usual requirement for processing distinct samples individually, these technologies increase scRNA-seq cell and sample throughput while minimizing technical confounders (e.g., doublets and batch effects). Two main types of sample multiplexing approaches have been described: (i) in silico genotyping using single nucleotide polymorphisms (SNPs) and (ii) tagging cell membranes with sample-specific DNA barcodes using lipid-modified oligonucleotides (LMOs; e.g., MULTI-seq) 2 or DNAconjugated antibodies 3,4 (e.g., BD single-cell multiplexing kit (SCMK) 9 ). Despite the increasing popularity of sample multiplexing, direct measures of transcriptional changes induced by mixing human samples from different individuals during scRNA-seq sample preparation have not been performed.…”

Section: Introductionmentioning

confidence: 99%

No detectable alloreactive transcriptional responses during donor-multiplexed single-cell RNA sequencing of peripheral blood mononuclear cells

McGinnis

Siegel

Xie³

et al. 2020

Preprint

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Single-cell RNA sequencing (scRNA-seq) provides high-dimensional measurement of transcript counts in individual cells. However, high assay costs limit the study of large numbers of samples. Sample multiplexing technologies such as antibody hashing and MULTI-seq use sample-specific sequence tags to enable individual samples (e.g., different patients) to be sequenced in a pooled format before downstream computational demultiplexing. Critically, no study to date has evaluated whether the mixing of samples from different donors in this manner results in significant changes in gene expression resulting from alloreactivity (i.e., response to non-self immune antigens). The ability to demonstrate minimal to no alloreactivity is crucial to avoid confounded data analyses, particularly for cross-sectional studies evaluating changes in immunologic gene signatures,. Here, we compared the expression profiles of peripheral blood mononuclear cells (PBMCs) from a single donor with and without pooling with PBMCs isolated from other donors with different blood types. We find that there was no evidence of alloreactivity in the multiplexed samples following three distinct multiplexing workflows (antibody hashing, MULTI-seq, and in silico genotyping using souporcell). Moreover, we identified biases amongst antibody hashing sample classification results in this particular experimental system, as well as gene expression signatures linked to PBMC preparation method (e.g., Ficoll-Paque density gradient centrifugation with or without apheresis using Trima filtration).

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A Targeted Multi-Omic Analysis Approach Measures Protein Expression and Low Abundance Transcripts on the Single Cell Level

Cited by 24 publications

References 0 publications

Discovery of CD80 and CD86 as recent activation markers on regulatory T cells by protein-RNA single-cell analysis

Discovery of CD80 and CD86 as recent activation markers on regulatory T cells by protein-RNA single-cell analysis

Bifidobacteria-mediated immune system imprinting early in life

No detectable alloreactive transcriptional responses during donor-multiplexed single-cell RNA sequencing of peripheral blood mononuclear cells

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