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Cited by 129Single-cell and spatial transcriptomicsRNA Research and SplicingGene Regulatory Network Analysis

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

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Reimegård, Johan, Tarbier, Marcel, Danielsson, Marcus, Schuster, Jens, Baskaran, Sathishkumar, Panagiotou, Styliani, Dahl, Niklas, Friedländer, Marc R., Gallant, Caroline J.·2021·Communications Biology
DOI 10.1038/s42003-021-02142-wPMC PMC8149646
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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.

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Fig. 1 SPARC profiles RNA and intra-cellular protein from the same single cells. a The SPARC procedure. Single cells are isolated and lysed in the presence of oligo-dT conjugated magnetic beads. Following oligo-dT mRNA hybridization, the protein-containing supernatant is removed for subsequent multiplex proximity extension analysis (PEA) and the mRNA is processed using a modified Smart-seq2 approach. (Protein clip art from Servier Medical Art is used under CC BY 3.0 France.) b Overview of the cellular model analyzed using SPARC. Human embryonic stem cells were analyzed in culture (0 h) and following directed neural induction (24 h and 48 h). c Example mRNA (blue) and protein (red) expression in single cells (violin plots) or replicate 100 cell population control (black dots) measured at 0, 24, and 48 h post neural induction.
Fig. 1 SPARC profiles RNA and intra-cellular protein from the same single cells. a The SPARC procedure. Single cells are isolated and lysed in the presence of oligo-dT conjugated magnetic beads. Following oligo-dT mRNA hybridization, the protein-containing supernatant is removed for subsequent multiplex proximity extension analysis (PEA) and the mRNA is processed using a modified Smart-seq2 approach. (Protein clip art from Servier Medical Art is used under CC BY 3.0 France.) b Overview of the cellular model analyzed using SPARC. Human embryonic stem cells were analyzed in culture (0 h) and following directed neural induction (24 h and 48 h). c Example mRNA (blue) and protein (red) expression in single cells (violin plots) or replicate 100 cell population control (black dots) measured at 0, 24, and 48 h post neural induction.CC BY · PubMed Central
Fig. 2 mRNA abundance only partially reflects and protein abundance in single cells. tSNE plot and pseudotime analysis of the a normalized mRNA and b protein expression data. c Comparison of the cell pseudotime-ordering if calculated based on either mRNA or protein expression data. d Example mRNA and protein expression data plotted for SOX2, EPCAM, and POU5F1. The data is shown either as RNA expression vs. protein expression scatter plot with respective density plots included on each axis, or as cells ordered along RNA pseudotime with either RNA and protein expression plotted on the y-axis. Both the RNA and protein data are plotted as log2. Color corresponds to time point in all plots 0 h (green), 24 h (orange), and 48 h (blue).
Fig. 2 mRNA abundance only partially reflects and protein abundance in single cells. tSNE plot and pseudotime analysis of the a normalized mRNA and b protein expression data. c Comparison of the cell pseudotime-ordering if calculated based on either mRNA or protein expression data. d Example mRNA and protein expression data plotted for SOX2, EPCAM, and POU5F1. The data is shown either as RNA expression vs. protein expression scatter plot with respective density plots included on each axis, or as cells ordered along RNA pseudotime with either RNA and protein expression plotted on the y-axis. Both the RNA and protein data are plotted as log2. Color corresponds to time point in all plots 0 h (green), 24 h (orange), and 48 h (blue).CC BY · PubMed Central
Fig. 3 Transcription factor protein but not mRNA correlates with target mRNA. a Scatter plot of POU5F1 expression in 0 h single cells (steady state), both at the level of RNA and protein compared to the RNA expression of POU5F1 regulatory target genes TDGF1 (upper panels) and OTX2 (lower panels). Blue line represents linear model between the two expression patterns. b Correlation between POU5F1 protein and RNA levels with the RNA expression of POU5F1 regulatory targets in steady state (0 h) or dynamic (0, 24, and 48 h) conditions. Darker shading indicates stronger correlation. Bold outlining indicates in which of the analysis groups the correlation was considered to be significant (FDR < 0.05). c Heatmap with expression levels of POU5F1 at the protein and RNA level and the RNA expression of POU5F1 regulatory target candidate genes. The color scale relates to high (red) and low (blue) expression. The columns in the heatmap are ordered based on the protein expression of POU5F1. d Red lines represent POU5F1 (protein)—target (RNA) ROC curves and blue lines represent POU5F1 (RNA)—target (RNA) ROC curves based on regulatory link weights. Solid line represents dynamic conditions, dotted lines indicate steady-state conditions. Filled squares represent the Youden index of the different ROC curves. e Quantification of total area under the curve for ROC (see Fig. 3d) including a permuted control of 200 permutations each AUC represented as a dot.
Fig. 3 Transcription factor protein but not mRNA correlates with target mRNA. a Scatter plot of POU5F1 expression in 0 h single cells (steady state), both at the level of RNA and protein compared to the RNA expression of POU5F1 regulatory target genes TDGF1 (upper panels) and OTX2 (lower panels). Blue line represents linear model between the two expression patterns. b Correlation between POU5F1 protein and RNA levels with the RNA expression of POU5F1 regulatory targets in steady state (0 h) or dynamic (0, 24, and 48 h) conditions. Darker shading indicates stronger correlation. Bold outlining indicates in which of the analysis groups the correlation was considered to be significant (FDR < 0.05). c Heatmap with expression levels of POU5F1 at the protein and RNA level and the RNA expression of POU5F1 regulatory target candidate genes. The color scale relates to high (red) and low (blue) expression. The columns in the heatmap are ordered based on the protein expression of POU5F1. d Red lines represent POU5F1 (protein)—target (RNA) ROC curves and blue lines represent POU5F1 (RNA)—target (RNA) ROC curves based on regulatory link weights. Solid line represents dynamic conditions, dotted lines indicate steady-state conditions. Filled squares represent the Youden index of the different ROC curves. e Quantification of total area under the curve for ROC (see Fig. 3d) including a permuted control of 200 permutations each AUC represented as a dot.CC BY · PubMed Central
Fig. 4 RNA variation is not predictive for protein variation. Patterns of gene expression variation at steady-state in S-phase cells. a Relation between mean RNA expression mean (RPKM) and RNA expression variation (CV2). Colors indicate variably (red) or stably (blue) expressed genes. b Similar to a, but for protein. c Normalized gene expression variation (residuals) at the RNA and protein levels correlate moderately (Spearman’s rho = 0.28). Genes of interest are indicated. d A simple additive model of RNA variation and estimated translation rate effectively predicts protein expression variation (Spearman’s rho = 0.50). A subset of genes from Fig. 3c are included here, as public data were not available to estimate translation rate for all genes of interest.
Fig. 4 RNA variation is not predictive for protein variation. Patterns of gene expression variation at steady-state in S-phase cells. a Relation between mean RNA expression mean (RPKM) and RNA expression variation (CV2). Colors indicate variably (red) or stably (blue) expressed genes. b Similar to a, but for protein. c Normalized gene expression variation (residuals) at the RNA and protein levels correlate moderately (Spearman’s rho = 0.28). Genes of interest are indicated. d A simple additive model of RNA variation and estimated translation rate effectively predicts protein expression variation (Spearman’s rho = 0.50). A subset of genes from Fig. 3c are included here, as public data were not available to estimate translation rate for all genes of interest.CC BY · PubMed Central

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Metadata

Authors
Reimegård, J., Tarbier, M., Danielsson, M., Schuster, J., Baskaran, S., Panagiotou, S., Dahl, N., Friedländer, M. R., & Gallant, C. J.
Affiliations
  • Uppsala University (SE)
  • Stockholm University (SE)
Corresponding author
Gallant, Caroline J.
ORCID
Venue
Communications Biology
Publisher
Springer Science and Business Media LLC
Published
2021-05-25
Volume
4
Issue
1
Pages
624-624
ISSN
2399-3642 (ISSN-L)
DOI
10.1038/s42003-021-02142-w
PMC
PMC8149646
OpenAlex
W3164706810
Cited by
129
References
39
Citations by year
2026: 25 · 2025: 26 · 2024: 22 · 2023: 23 · 2022: 28 · 2021: 5
FWCI
6.12
Type
journal article
Language
en
Open access
Licence
Locations
  • Communications Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Uppsala University Publications (Uppsala University) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Vetenskapsrådet (2017-05229)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
single-cell transcriptomics, single-cell proteomics, proximity extension assay, SPARC, intracellular protein expression, neuronal differentiation, transcription factor activity, mRNA-protein correlation, protein localization
MeSH terms
Humans, Proteins, RNA, Messenger, Transcription, Genetic, Sequence Analysis, RNA, Gene Expression Profiling, Proteomics, Single-Cell Analysis

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