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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
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.
References
Liu, Y., Beyer, A. & Aebersold, R. On the dependency of cellular protein levels on mRNA abundance. Cell 165, 535–550 (2016).
Jovanovic, M. et al. Dynamic profiling of the protein life cycle in response to pathogens. Science 347, 1259038 (2015).
Lackner, D. H., Schmidt, M. W., Wu, S., Wolf, D. A. & Bähler, J. Regulation of transcriptome, translation, and proteome in response to environmental stress in fission yeast. Genome Biol. 13, R25 (2012).
Lee, M. V. et al. A dynamic model of proteome changes reveals new roles for transcript alteration in yeast. Mol. Syst. Biol. 7, 514 (2011).
Hoogendijk, A. J. et al. Dynamic transcriptome-proteome correlation networks reveal human myeloid differentiation and neutrophil-specific programming. Cell Rep. 29, 2505–2519 (2019).
Gautier, E.-F. et al. Comprehensive proteomic analysis of murine terminal erythroid differentiation. Blood Adv. 4, 1464–1477 (2020).
van der Flier, L. G. & Clevers, H. Stem cells, self-renewal, and differentiation in the intestinal epithelium. Annu. Rev. Physiol. 71, 241–260 (2009).
Crompton, M. J., Dexter, T. M., Wright, N. A. & Potten, C. S. Stem cells in gastrointestinal epithelium: numbers, characteristics and death. Philos. Trans. R. Soc. Lond. B Biol. Sci. 353, 821–830 (1998).
Zheng, L., Kelly, C. J. & Colgan, S. P. Physiologic hypoxia and oxygen homeostasis in the healthy intestine. A review in the theme: Cellular responses to hypoxia. Am. J. Physiol. Cell Physiol. 309, C350–C360 (2015).
Tropini, C., Earle, K. A., Huang, K. C. & Sonnenburg, J. L. The gut microbiome: connecting spatial organization to function. Cell Host Microbe 21, 433–442 (2017).
Moor, A. E. et al. Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis. Cell 175, 1156–1167 (2018).
Bahar Halpern, K. et al. Lgr5+ telocytes are a signaling source at the intestinal villus tip. Nat. Commun. 11, 1936 (2020).
Shoshkes-Carmel, M. et al. Subepithelial telocytes are an important source of Wnts that supports intestinal crypts. Nature 557, 242–246 (2018).
McCarthy, N. et al. Distinct mesenchymal cell populations generate the essential intestinal BMP signaling gradient. Cell Stem Cell 26, 391–402 (2020).
Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).
Budnik, B., Levy, E., Harmange, G. & Slavov, N. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol. 19, 161 (2018).
Brunner, A. D. et al. Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation. Preprint at bioRxiv https://doi.org/10.1101/2020.12.22.423933 (2020).
Lundberg, E. & Borner, G. H. H. Spatial proteomics: a powerful discovery tool for cell biology. Nat. Rev. Mol. Cell Biol. 20, 285–302 (2019).
Specht, H. et al. Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2. Genome Biol. 22, 50 (2021).
Virant-Klun, I., Leicht, S., Hughes, C. & Krijgsveld, J. Identification of maturation-specific proteins by single-cell proteomics of human oocytes. Mol. Cell. Proteom. 15, 2616–2627 (2016).
Ben-Moshe, S. et al. Spatial sorting enables comprehensive characterization of liver zonation. Nat. Metab. 1, 899–911 (2019).
von Furstenberg, R. J. et al. Sorting mouse jejunal epithelial cells with CD24 yields a population with characteristics of intestinal stem cells. Am. J. Physiol. Gastrointest. Liver Physiol. 300, G409–G417 (2011).
Haber, A. L. et al. A single-cell survey of the small intestinal epithelium. Nature 551, 333–339 (2017).
Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).
Ammar, C., Gruber, M., Csaba, G. & Zimmer, R. MS-EmpiRe utilizes peptide-level noise distributions for ultra-sensitive detection of differentially expressed proteins. Mol. Cell. Proteom. 18, 1880–1892 (2019).
Epifano, C. & Perez-Moreno, M. Crossroads of integrins and cadherins in epithelia and stroma remodeling. Cell Adhes. Migr. 6, 261–273 (2012).
Goodman, J. & Weare, J. Ensemble samplers with affine invariance. Commun. Appl. Math. Comput. Sci. 5, 65–80 (2010).
Foreman-Mackey, D., Hogg, D. W., Lang, D. & Goodman, J. emcee: the MCMC hammer. Publ. Astron. Soc. Pac. 125, 306 (2013).
Gelman, A., Meng, X.-L. & Stern, H. Posterior predictive assessment of model fitness via realized discrepancies. Stat. Sin. 6, 733–760 (1996).
Arike, L. et al. Protein turnover in epithelial cells and mucus along the gastrointestinal tract is coordinated by the spatial location and microbiota. Cell Rep. 30, 1077–1087 (2020).
Moor, A. E. et al. Global mRNA polarization regulates translation efficiency in the intestinal epithelium. Science 357, 1299–1303 (2017).
Geva-Zatorsky, N. et al. In vivo imaging and tracking of host–microbiota interactions via metabolic labeling of gut anaerobic bacteria. Nat. Med. 21, 1091–1100 (2015).
Rolfe, D. F. & Brown, G. C. Cellular energy utilization and molecular origin of standard metabolic rate in mammals. Physiol. Rev. 77, 731–758 (1997).
Cablé, S., Kedinger, M. & Dauça, M. Peroxisomes and peroxisomal enzymes along the crypt–villus axis of the rat intestine. Differentiation 54, 99–108 (1993).
Lodhi, I. J. & Semenkovich, C. F. Peroxisomes: a nexus for lipid metabolism and cellular signaling. Cell Metab. 19, 380–392 (2014).
TAKAHARA, E. et al. Ultrastructural demonstration of the absorption and transportation of minute chylomicrons by subepithelial blood capillaries in rat jejunal villi. J. Vet. Med. Sci. 75, 1563–1569 (2013).
Flamholz, A., Phillips, R. & Milo, R. The quantified cell. Mol. Biol. Cell 25, 3497–3500 (2014).
Thwaites, D. T. & Anderson, C. M. H. H+-coupled nutrient, micronutrient and drug transporters in the mammalian small intestine. Exp. Physiol. 92, 603–619 (2007).
Ben-Moshe, S. & Itzkovitz, S. Spatial heterogeneity in the mammalian liver. Nat. Rev. Gastroenterol. Hepatol. 16, 395–410 (2019).
Tóth, B., Ben‐Moshe, S., Gavish, A., Barkai, N. & Itzkovitz, S. Early commitment and robust differentiation in colonic crypts. Mol. Syst. Biol. https://doi.org/10.15252/msb.20167283 (2017).
VanInsberghe, M., van den Berg, J., Andersson-Rolf, A., Clevers, H. & van Oudenaarden, A. Single-cell ribo-seq reveals cell cycle-dependent translational pausing. Nature 597, 561–565 (2021).
Blanpain, C., Lowry, W. E., Geoghegan, A., Polak, L. & Fuchs, E. Self-renewal, multipotency, and the existence of two cell populations within an epithelial stem cell niche. Cell 118, 635–648 (2004).
Klein, A. M. & Simons, B. D. Universal patterns of stem cell fate in cycling adult tissues. Development 138, 3103–3111 (2011).
Hoggatt, J., Kfoury, Y. & Scadden, D. T. Hematopoietic stem cell niche in health and disease. Annu. Rev. Pathol. Mech. Dis. 11, 555–581 (2016).
de Wit, N. J. et al. The role of the small intestine in the development of dietary fat-induced obesity and insulin resistance in C57BL/6J mice. BMC Med. Genomics 1, 14 (2008).
Yilmaz, Ö. H. et al. mTORC1 in the Paneth cell niche couples intestinal stem cell function to calorie intake. Nature 486, 490–495 (2012).
Ferguson, A., Sutherland, A., MacDonald, T. T. & Allan, F. Technique for microdissection and measurement in biopsies of human small intestine. J. Clin. Pathol. 30, 1068–1073 (1977).
Manco, R. et al. Reactive cholangiocytes differentiate into proliferative hepatocytes with efficient DNA repair in mice with chronic liver injury. J. Hepatol. 70, 1180–1191 (2019).
Preibisch, S., Saalfeld, S. & Tomancak, P. Globally optimal stitching of tiled 3D microscopic image acquisitions. Bioinformatics 25, 1463–1465 (2009).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Bagnoli, J. W. et al. Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq. Nat. Commun. 9, 2937 (2018).
Parekh, S., Ziegenhain, C., Vieth, B., Enard, W. & Hellmann, I. zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs. GigaScience 7, giy059 (2018).
Elinger, D., Gabashvili, A. & Levin, Y. Suspension trapping (S-Trap) is compatible with typical protein extraction buffers and detergents for bottom-up proteomics. J. Proteome Res. 18, 1441–1445 (2019).
Bednarczyk, D., Aviv-Sharon, E., Savidor, A., Levin, Y. & Charuvi, D. Influence of short-term exposure to high light on photosynthesis and proteins involved in photo-protective processes in tomato leaves. Environ. Exp. Bot. 179, 104198 (2020).
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized ppb-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
Manco, R. et al. Clump sequencing exposes the spatial expression programs of intestinal secretory cells. Nat. Commun. 12, 3074 (2021).
Kafri, R. et al. Cellular dynamics extracted from populations of fixed cells reveals a feedback between growth and progression through the cell cycle. Nature 494, 480–483 (2013).
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. ScyPy 2010 https://doi.org/10.25080/Majora-92bf1922-011 (2010).
Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).
Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
Waskom, M. et al. mwaskom/seaborn: v0.11.1 (December 2020). Zenodo https://doi.org/10.5281/zenodo.4379347 (2020).
McKinney, W. Data structures for statistical computing in python. Proc. 9th Python Sci. Conf. https://doi.org/10.25080/Majora-92bf1922-00a (2010).
Foreman-Mackey, D. corner.py: scatterplot matrices in Python. J. Open Source Softw. 1, 24 (2016).
Biton, M. et al. T helper cell cytokines modulate intestinal stem cell renewal and differentiation. Cell 175, 1307–1320 (2018).
Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014.e22 (2018).
Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).
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
Authors and Affiliations
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.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
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.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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
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
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s42255-021-00504-6
This article is cited by
-
Intestinal plasticity and metabolism as regulators of organismal energy homeostasis
Nature Metabolism (2022)