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Spatial discordances between mRNAs and proteins in the intestinal epithelium

Nature Metabolism volume 3, pages 1680–1693 (2021)Cite this article

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Abstract

The use of transcriptomes as reliable proxies for cellular proteomes is controversial. In the small intestine, enterocytes operate for 4 days as they migrate along villi, which are highly graded microenvironments. Spatial transcriptomics have demonstrated profound zonation in enterocyte gene expression, but how this variability translates to protein content is unclear. Here we show that enterocyte proteins and messenger RNAs along the villus axis are zonated, yet often spatially discordant. Using spatial sorting with zonated surface markers, together with a Bayesian approach to infer protein translation and degradation rates from the combined spatial profiles, we find that, while many genes exhibit proteins zonated toward the villus tip, mRNA is zonated toward the villus bottom. Finally, we demonstrate that space-independent protein synthesis delays can explain many of the mRNA–protein discordances. Our work provides a proteomic spatial blueprint of the intestinal epithelium, highlighting the importance of protein measurements for inferring cell states in tissues that operate outside of steady state.

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Fig. 1: Proteins and mRNAs can become discordant outside of steady state.
Fig. 2: Spatial sorting approach for measuring proteomics of zonated enterocyte populations.
Fig. 3: Spatial sorting yields zonated enterocyte populations.
Fig. 4: A spatial proteomic map of the intestinal villus.
Fig. 5: Discordance of mRNA and protein zonation profiles.
Fig. 6: Bayesian inference of protein translation and degradation rates based on the combined mRNA–protein zonation profiles.
Fig. 7: Division of labor along the intestinal villus.

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Data availability

Bulk RNA-seq data have been deposited in the GenBank GEO database under accession code GSE164746. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE67 partner repository with the dataset identifier PXD024906. Source data are provided with this paper.

Code availability

All code is available at https://github.com/LiBuchauer/spatial_MP_discordances and at https://github.com/yotamharnik/Spatial_sorting_SI_epithelium. MCMC results and figures showing the model’s fit to the data for each gene can be accessed at https://zenodo.org/record/5136420.

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Acknowledgements

We thank E. Zalckvar and A. Amir for valuable discussions. S.I. is supported by the Wolfson Family Charitable Trust and Wolfson Foundation, the Edmond de Rothschild Foundations, the Fannie Sherr Fund, the Helen and Martin Kimmel Institute for Stem Cell Research, the Minerva Stiftung grant, the Israel Science Foundation grant no. 1486/16, the Broad Institute‐Israel Science Foundation grant no. 2615/18, the European Research Council under the European Union’s Horizon 2020 research and innovation program grant no. 768956, the Chan Zuckerberg Initiative grant no. CZF2019‐002434, the Bert L. and N. Kuggie Vallee Foundation and the Howard Hughes Medical Institute international research scholar award. L.B. is supported by the European Molecular Biology Organization under EMBO Long-Term Fellowship ALTF 724-2019.

Author information

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  1. These authors contributed equally: Yotam Harnik, Lisa Buchauer.

Authors and Affiliations

  1. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

    Yotam Harnik, Lisa Buchauer, Shani Ben-Moshe, Inna Averbukh & Shalev Itzkovitz

  2. The De Botton Institute for Protein Profiling, The Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot, Israel

    Yishai Levin & Alon Savidor

  3. Department of Veterinary Resources, Weizmann Institute of Science, Rehovot, Israel

    Raya Eilam

  4. Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland

    Andreas E. Moor

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Contributions

S.I. and Y.H. conceived the study. Y.H. collected all biological samples and performed RNA-seq experiments. L.B. performed the computational modeling and parameter estimation analysis. Y.L. and A.S. performed mass spectrometry proteomics. S.I., Y.H. and L.B. performed data analysis. Y.H. performed smFISH, immunofluorescence and image analysis. R.E. was involved in supervising immunofluorescence experiments. I.A., S.B.M. and A.E.M. contributed computational and conceptual tools. All authors approved the manuscript.

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Correspondence to Shalev Itzkovitz.

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The authors declare no competing interests.

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Peer review information Nature Metabolism thanks Nikolai Slavov, Arjun Raj and the other, anonymous, reviewer for their contribution to the peer review of this work. Primary handling editors: Ashley Castellanos-Jankiewicz and George Caputa.

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Extended data

Extended Data Fig. 1 Zonated surface markers enable FACS isolation of cellular populations that are stratified for tissue zone.

a, heatmap of surface markers (GO:0009986) peaking at different villi zones based on single-cell RNA-seq data11. CD24a (bottom, green), Dpp4 (middle, orange), Nt5e (tip, red) are highlighted. b-e, FACS gating strategy. FSC-A and SSC-A were used for size selection of all events (b), FSC-A and FSC-W were used to exclude cells doublets or larger clumps (c), Non-viable cells were filtered out using the DAPI staining (d) and finally staining with antibodies to CD31, CD45 and CD24 enabled the gating out of non-villus epithelial cells. Numbers indicate gated population percent from the parent gate. f, CIBERSORTx24 was used to asses the fraction of secretory epithelial cells in each gate. g, CIBERSORTx was run against pure single-cell RNA-seq data11 for comparison, showing underestimation of enterocyte signature in bottom villus zones. h-i, CIBERSORTx of bulk RNA-seq was used to impute villus zone fraction for each sorted population in this work (h) and Moor et al 201811. (i). Numbers are estimated cellular fractions. Colors are normalized to the maximal fraction detected by the deconvolution for each column. j, Venn diagram showing proportions of significantly zonated proteins in iBAQ (Cyan, n = 1671) and corresponding peptides (Blue, n = 1109). k, Center-of-mass correlation of 902 proteins from j, indicating zonation profiles similarities by each of the approaches.

Extended Data Fig. 2 Protein translation and degradation rates along the villus axis.

a-d, Average of max-normalized protein zonation profiles for gene sets: Ribosomal proteins (a, Rps and Rpl genes), Lysosomal proteins (b, KEGG), Proteasome complex proteins (c, GO:0000502) and Ubiquitin mediated proteolysis proteins (d, KEGG). Patches represent standard errors of the means.

Extended Data Fig. 3 Key enterocyte functions exhibit mRNA–Protein discordances.

Shown are zonation profiles of representative genes from KEGG pathways. mRNA (blue) values from single-cell RNAseq data11, protein (red) values from iBAQ of spatially sorted populations. All values are normalized to the mean; patches are standard errors of the means.

Extended Data Fig. 4 Genes for which proteins peak at lower villi zones than the corresponding mRNAs.

a, Centers of mass of proteins and matched mRNAs, colored by residuals. Dashed line is the x = y line. Red dots mark outlier genes with proteins peaking at villus zones lower than the corresponding mRNAs, defined as genes with residuals below −0.2. Shown are representative genes from distinct classes that are highlighted in b. b, mRNA and Protein profiles of the representative genes from a. Patches are standard errors of the means. Gene names colors in a and b distinguish from different groups as described in the results.

Extended Data Fig. 5 Total mRNA and protein amounts per cell along the villus axis.

Violin plots of the unique molecular identifier (UMI) count sum of single enterocytes from villus zones V1 (bottom) to V6 (tip). Each gray dot indicates one cell. Horizontal lines show the median. Single-cell RNAseq data from11. b, Violin plots of enterocyte length (L) and radius (R) at different positions along the villus axis measured in 180 enterocytes in microscopy images of the mouse jejunum (3 mice, 4 villi per mouse, 5/5/5 cells from bottom/middle/tip were quantified per villus). Enterocyte volume was approximated from these measures using a cylindrical model for cell shape, V=πR2L. The last panel shows the resulting volume interpolation used for rescaling protein profiles (Methods).

Extended Data Fig. 6 Evaluation of model performance on simulated data with non-constant post-transcriptional rates.

a, Two simulated mRNA profiles representing typical profiles found in the data, one peaking at the villus bottom (black curve) and one peaking in the middle (green curve). b, Example of non-constant decay rate functions employed for the simulation of protein data. Generally, the decay rate switches from the villus bottom value to the villus top value over a period of 24 hours centered at the middle of the villus (48 hours). c, Distribution of mean relative standard errors of the protein measurements of 2,866 genes (for each gene, the standard error in protein expression was divided by the mean protein expression per zone and then the mean over all 6 zones was computed). d, Outline of the simulation procedure to obtain 16,000 synthetic mRNA–protein profiles. e, Results of model fitness evaluation (heatmaps) and parameter estimation (boxplots) on simulated data with non-constant decay rates. The color scale of the heatmaps indicates what fraction of simulated profiles for a given set of {mRNA type, lower villus half-life, upper villus half-life, noise (simulated relative standard error, denoted sim_RSE)} could be rejected by the constant-rate model (N=100 per tile). The box plots show the estimated half-lives for these estimation procedures when assuming the constant-rate model. Horizontal lines are medians, boxes delineate the 25–75 percentiles, whiskers extend to the most extreme data point within 1.5× the interquartile range (IQR) from the box. Green dashed line: mean of upper and lower villus half-lives; black dashed line: upper villus half-life. f, Violin plots showing bulk translation efficiency of enterocyte-specific genes stratified by the villus zone in which they peak. g, Translation efficiency curves used in the constant-rate (black curve) and declining-rate (green curve) models respectively. The declining-rate curve is derived from the medians of the data in f to which an exponentially decaying curve has been fit.

Extended Data Fig. 7 Validation and meta-analysis of protein translation and decay rate estimates.

Scatter plot showing correlation between protein half-life estimates from this work and published values from15. Dashed lines shows linear regression of log-transformed values. b, Scatter plot showing correlation between translation-rate estimates from this work and published values from15. c, Scatter plot showing correlation between protein half-life estimates from this work and published values from30. d, Scatter plot showing correlation between translation-rate estimates from this work and translation efficiency values from31. P values in a-d are two-sided, representing the probability of a zero correlation. e, Results of gene set enrichment analysis (GSEA) comparing gene sets with estimated long protein half-lives to genes with estimated short protein half-lives. Only pathways with false discovery rate (FDR) below 0.25 are included in the plot. Dot size indicates number of genes involved in each pathway.

Extended Data Fig. 8 Zonation profiles of genes for which the constant-rate model is rejected.

Shown are the 3% of the genes that were rejected by the uniform model (FDR 0.25, methods). mRNA (blue) and protein (red) values were transformed from scRNA-seq and iBAQ units to unit/cell as described in the methods and are normalized to the mean. Model MAP (black, dashed) represent the best fit of the uniform translation-rate model (Supplementary Table 4). Red patches are standard errors of the means for protein abundances. Gray cornered values are mean relative standard errors.

Supplementary information

Reporting Summary

Supplementary Table

Six supplementary tables in one file, separated to sheets in a consecutive order. The first sheet of each table contain a title and a legend with a more detailed description of the data. Supplementary Tables 1–3 contain spatial measurement of mRNAs, peptides and proteins along the small intestine epithelium villus. Tables 3 and 4 contain all the data needed to generate the parameter estimation model and the estimated protein degradation and translation rates. Table 6 contains mean expression of 44 cell types in the small intestine.

Source data

Source Data Fig. 3

RAW scale immunofluorescence images in TIFF format zipped

Source Data Fig. 4

RAW scale immunofluorescence images in TIFF format zipped

Source Data Fig. 5

RAW scale immunofluorescence images in TIFF format zipped

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Harnik, Y., Buchauer, L., Ben-Moshe, S. et al. Spatial discordances between mRNAs and proteins in the intestinal epithelium. Nat Metab 3, 1680–1693 (2021). https://doi.org/10.1038/s42255-021-00504-6

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