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A combined approach for single-cell mRNA and intracellular protein expression analysis.

ABSTRACT: Combined measurements of mRNA and protein expression in single cells enable in-depth analysis of cellular states. We present SPARC, an approach that combines single-cell RNA-sequencing with proximity extension essays to simultaneously measure global mRNA and 89 intracellular proteins in individual cells. We show that mRNA expression fails to accurately reflect protein abundance at the time of measurement, although the direction of changes is in agreement during neuronal differentiation. Moreover, protein levels of transcription factors better predict their downstream effects than do their corresponding transcripts. Finally, we highlight that protein expression variation is overall lower than mRNA variation, but relative protein variation does not reflect the mRNA level. Our results demonstrate that mRNA and protein measurements in single cells provide different and complementary information regarding cell states. SPARC presents a state-of-the-art co-profiling method that overcomes current limitations in throughput and protein localization, including removing the need for cell fixation.

SUBMITTER: Reimegard J

PROVIDER: S-EPMC8149646 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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A combined approach for single-cell mRNA and intracellular protein expression analysis.

Reimegård Johan J Tarbier Marcel M Danielsson Marcus M Schuster Jens J Baskaran Sathishkumar S Panagiotou Styliani S Dahl Niklas N Friedländer Marc R MR Gallant Caroline J CJ

Communications biology 20210525 1

Combined measurements of mRNA and protein expression in single cells enable in-depth analysis of cellular states. We present SPARC, an approach that combines single-cell RNA-sequencing with proximity extension essays to simultaneously measure global mRNA and 89 intracellular proteins in individual cells. We show that mRNA expression fails to accurately reflect protein abundance at the time of measurement, although the direction of changes is in agreement during neuronal differentiation. Moreover ...[more]

PMID: 34035432

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Project description:BackgroundEndogenous expression of L1 mRNA is the first step in an L1-initiated mutagenesis event. However, the contribution of individual cell types to patterns of organ-specific L1 mRNA expression remains poorly understood, especially at single-locus resolution. We introduce a method to quantify expression of mobile elements at the single-locus resolution in scRNA-Seq datasets called Single Cell Implementation to Find Expressed Retrotransposons (SCIFER). SCIFER aligns scRNA-Seq reads uniquely to the genome and extracts alignments from single cells by cell-specific barcodes. In contrast to the alignment performed using default parameters, this alignment strategy increases accuracy of L1 locus identification by retaining only reads that are uniquely mapped to individual L1 loci. L1 loci expressed in single cells are unambiguously identified using a list of L1 loci manually validated to be expressed in bulk RNA-Seq datasets generated from the same cell line or organ.ResultsValidation of SCIFER using MCF7 cells determined technical parameters needed for optimal detection of L1 expression in single cells. We show that unsupervised analysis of L1 expression in single cells exponentially inflates both the levels of L1 expression and the number of expressed L1 loci. Application of SCIFER to analysis of scRNA-Seq datasets generated from mouse and human testes identified that mouse Round Spermatids and human Spermatogonia, Spermatocytes, and Round Spermatids express the highest levels of L1 mRNA. Our analysis also determined that similar to mice, human testes from unrelated individuals share as much as 80% of expressed L1 loci. Additionally, SCIFER determined that individual mouse cells co-express different L1 sub-families and different families of transposable elements, experimentally validating their co-existence in the same cell.ConclusionsSCIFER detects mRNA expression of individual L1 loci in single cells. It is compatible with scRNA-Seq datasets prepared using traditional sequencing methods. Validated using a human cancer cell line, SCIFER analysis of mouse and human testes identified key cell types supporting L1 expression in these species. This will further our understanding of differences and similarities in endogenous L1 mRNA expression patterns in mice and humans.

Project description:BackgroundHematopoietic stem and progenitor cell (HSPC) subsets in mice have previously been studied using cell surface markers, and more recently single-cell technologies. The recent revolution of single-cell analysis is substantially transforming our understanding of hematopoiesis, confirming the substantial heterogeneity of cells composing the hematopoietic system. While dynamic molecular changes at the DNA/RNA level underlying hematopoiesis have been extensively explored, a broad understanding of single-cell heterogeneity in hematopoietic signaling programs and landscapes, studied at protein level and reflecting post-transcriptional processing, is still lacking. Here, we accurately quantified the intracellular levels of 9 phosphorylated and 2 functional proteins at the single-cell level to systemically capture the activation dynamics of 8 signaling pathways, including EGFR, Jak/Stat, NF-κB, MAPK/ERK1/2, MAPK/p38, PI3K/Akt, Wnt, and mTOR pathways, during mouse hematopoiesis using mass cytometry.ResultsWith fine-grained analyses of 3.2 million of single hematopoietic stem and progenitor cells (HSPCs), and lineage cells in conjunction with multiparameter cellular phenotyping, we mapped trajectories of signaling programs during HSC differentiation and identified specific signaling biosignatures of cycling HSPC and multiple differentiation routes from stem cells to progenitor and lineage cells. We also investigated the recovery pattern of hematopoietic cell populations, as well as signaling regulation in these populations, during hematopoietic reconstruction. Overall, we found substantial heterogeneity of pathway activation within HSPC subsets, characterized by diverse patterns of signaling.ConclusionsThese comprehensive single-cell data provide a powerful insight into the intracellular signaling-regulated hematopoiesis and lay a solid foundation to dissect the nature of HSC fate decision. Future integration of transcriptomics and proteomics data, as well as functional validation, will be required to verify the heterogeneity in HSPC subsets during HSC differentiation and to identify robust markers to phenotype those HSPC subsets.

Dataset Information

A combined approach for single-cell mRNA and intracellular protein expression analysis.

Publications

A combined approach for single-cell mRNA and intracellular protein expression analysis.

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