Single-cell sequencing methods have enabled in-depth analysis of the diversity of cell types and cell states in a wide range of organisms. These tools focus predominantly on sequencing the genomes1, epigenomes2 and transcriptomes3 of single cells. However, despite recent progress in detecting proteins by mass spectrometry with single-cell resolution4, it remains a major challenge to measure translation in individual cells. Here, building on existing protocols5,6,7, we have substantially increased the sensitivity of these assays to enable ribosome profiling in single cells. Integrated with a machine learning approach, this technology achieves single-codon resolution. We validate this method by demonstrating that limitation for a particular amino acid causes ribosome pausing at a subset of the codons encoding the amino acid. Of note, this pausing is only observed in a sub-population of cells correlating to its cell cycle state. We further expand on this phenomenon in non-limiting conditions and detect pronounced GAA pausing during mitosis. Finally, we demonstrate the applicability of this technique to rare primary enteroendocrine cells. This technology provides a first step towards determining the contribution of the translational process to the remarkable diversity between seemingly identical cells.
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Raw sequencing data, metadata and count tables have been made available in the Gene Expression Omnibus under the accession number GSE162060. Raw sequencing data for comparisons to conventional ribosomal profiling methods were downloaded from Gene Expression Omnibus accessions GSE37744, GSE125218, GSE113751 and GSE67902.
All scripts to process raw data and generate figures are available at https://github.com/mvanins/scRiboSeq_manuscript.
Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).
Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).
Tang, F. et al. mRNA-seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).
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).
Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).
Darnell, A. M., Subramaniam, A. R. & O'Shea, E. K. Translational control through differential ribosome pausing during amino acid limitation in mammalian cells. Mol. Cell 71, 229–243.e11 (2018).
Reid, D. W., Shenolikar, S. & Nicchitta, C. V. Simple and inexpensive ribosome profiling analysis of mRNA translation. Methods 91, 69–74 (2015).
Ingolia, N. T., Brar, G. A., Rouskin, S., McGeachy, A. M. & Weissman, J. S. The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments. Nat. Protoc. 7, 1534–1550 (2012).
Martinez, T. F. et al. Accurate annotation of human protein-coding small open reading frames. Nat. Chem. Biol. 16, 458–468 (2020).
Tanenbaum, M. E., Stern-Ginossar, N., Weissman, J. S. & Vale, R. D. Regulation of mRNA translation during mitosis. eLife 4, e07957 (2015).
Gerashchenko, M. V. & Gladyshev, V. N. Ribonuclease selection for ribosome profiling. Nucleic Acids Res. 45, e6 (2017).
Fang, H. et al. Scikit-ribo enables accurate estimation and robust modeling of translation dynamics at codon resolution. Cell Syst. 6, 180–191.e4 (2018).
Subramaniam, A. R., Pan, T. & Cluzel, P. Environmental perturbations lift the degeneracy of the genetic code to regulate protein levels in bacteria. Proc. Natl Acad. Sci. USA 110, 2419–2424 (2013).
Zinshteyn, B. & Gilbert, W. V. Loss of a conserved tRNA anticodon modification perturbs cellular signaling. PLoS Genet. 9, e1003675 (2013).
Nedialkova, D. D. & Leidel, S. A. Optimization of codon translation rates via tRNA modifications maintains proteome integrity. Cell 161, 1606–1618 (2015).
Artieri, C. G. & Fraser, H. B. Accounting for biases in riboprofiling data indicates a major role for proline in stalling translation. Genome Res. 24, 2011–2021 (2014).
Stumpf, C. R., Moreno, M. V., Olshen, A. B., Taylor, B. S. & Ruggero, D. The translational landscape of the mammalian cell cycle. Mol. Cell 52, 574–582 (2013).
Coldwell, M. J. et al. Phosphorylation of eIF4GII and 4E-BP1 in response to nocodazole treatment: a reappraisal of translation initiation during mitosis. Cell Cycle 12, 3615–3628 (2013).
Ly, T., Endo, A. & Lamond, A. I. Proteomic analysis of the response to cell cycle arrests in human myeloid leukemia cells. elife 4, e04534 (2015).
Miettinen, T. P., Kang, J. H., Yang, L. F. & Manalis, S. R. Mammalian cell growth dynamics in mitosis. elife 8, e44700 (2019).
Sakaue-Sawano, A. et al. Visualizing spatiotemporal dynamics of multicellular cell-cycle progression. Cell 132, 487–498 (2008).
Frenkel-Morgenstern, M. et al. Genes adopt non-optimal codon usage to generate cell cycle-dependent oscillations in protein levels. Mol. Syst. Biol. 8, 572 (2012).
Gribble, F. M. & Reimann, F. Enteroendocrine cells: chemosensors in the intestinal epithelium. Annu. Rev. Physiol. 78, 277–299 (2016).
Gehart, H. et al. Identification of enteroendocrine regulators by real-time single-cell differentiation mapping. Cell 176, 1158–1173.e16 (2019).
Haber, A. L. et al. A single-cell survey of the small intestinal epithelium. Nature 551, 333–339 (2017).
Brannan, K. W. et al. Robust single-cell discovery of RNA targets of RNA-binding proteins and ribosomes. Nat. Methods 18, 507–519 (2021).
Shaltiel, I. A. et al. Distinct phosphatases antagonize the p53 response in different phases of the cell cycle. Proc. Natl Acad. Sci. USA 111, 7313–7318 (2014).
Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2021).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
Ahmed, S., Rattray, M. & Boukouvalas, A. GrandPrix: scaling up the Bayesian GPLVM for single-cell data. Bioinformatics 35, 47–54 (2019).
Schuller, A. P. & Green, R. Roadblocks and resolutions in eukaryotic translation. Nat. Rev. Mol. Cell Biol. 19, 526–541 (2018).
This work was supported by a European Research Council Advanced grant (ERC-AdG 742225-IntScOmics) and Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) TOP award (NWO-CW 714.016.001). This work is part of the Oncode Institute, which is partly financed by the Dutch Cancer Society. In addition, we thank the Hubrecht Sorting Facility and the Utrecht Sequencing Facility, subsidized by the University Medical Center Utrecht, the Hubrecht Institute, Utrecht University and The Netherlands X-omics Initiative (NWO project 184.034.019); H. Viñas Gaza for assistance in preparing samples; and V. Bhardwaj for discussion on data analysis.
The technology described here is the subject of a patent application EP20209743 on which M.V. and A.v.O are inventors.
Peer review information Nature thanks Arjun Raj, Petra Van Damme and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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Extended data figures and tables
a, Distributions of the number of unique coding-sequence mapped reads per cell. b, Distributions of the number of protein-coding genes detected per cell. c, Duplicate rate per cell. The mean ± standard error of each distribution is indicated.
a, b, Heat maps of the percentage of protein-coding reads per library aligning along metagene regions around the start codon (left), in the CDS (middle), and around the stop codon (right). The mapping coordinate of the 5ʹ end (a), or the random-forest predicted P-site of each read (b) is reported. Libraries are from this work (scRibo-seq), and representative bulk ribosomal profiling methods: Darnell6, using MNase on HEK 293T; Ingolia8, using RNase I on HEK 293T; Martinez9, using RNase I on HEK 293T; and Tanenbaum10, using RNase I on RPE-1. c, Frame and read-length distributions of the 5ʹ end of RPFs and random-forest predicted P-sites averaged across library sets. d, Distributions of the percentage of trimmed reads aligning to rRNA and tRNA. e, Region-length normalized distributions of RPF mapping frequencies in the 5ʹ UTR, CDS, and 3ʹ UTR regions of protein-coding transcripts. f, Distributions of the percentage of trimmed reads that uniquely align to protein coding, lncRNA, snoRNAs, or other biotypes. In the box plots in d-f the middle line indicates the median, the box limits the first and third quartiles, and the whiskers the range. Each point is from a single-cell or bulk library. g, Comparisons of the RPF counts per CDS in HEK 293T cells between the different studies. Spearman correlation coefficients for each comparison are indicated.
Extended Data Fig. 3 A Random Forest model corrects the MNase sequence bias to position ribosome active sites within RPF reads.
a, Logos of the sequence context around the 5ʹ and 3ʹ cut locations. b, Schematic illustrating how a nuclease sequence bias can result in a sequence-dependent offset (arrowed lines) between the cut position (triangles) and the ribosome exit, peptidyl, and aminoacyl active sites. Ribosome schematic adapted from ref. 31. c, Schematic describing the parameters used to train the random forest model. Reads spanning a stop codon were used for training. The model predicts the offset between the 5ʹ end of each read and the P-site based on the read length and the sequence context around each end of the read. d, Truth table of the model prediction results on validation data. e, Permutation importance of the model features. f, Frame distributions of the 5ʹ end of RPFs and random-forest predicted P-sites in single cells. Both the 5ʹ and predicted P-sites are uniform between cells and cell types. g, Number of footprints per cell along a metagene region within CDS before (top, reads whose 5ʹ ends align at the given region) and after (bottom, number of predicted P-sites at each location) the random forest correction.
a, Heat map of the log2 fold change of amino acid occupancy in the RPF active sites. b, Distribution of cells exhibiting ribosome pausing in clusters. The threshold used to distinguish pausing cells was calculated as the mean plus 4 standard deviations of the signal of the cells from the rich condition. c, Proportions of treatment type per cluster. d, Proportions of treated cells that show a pausing response per cluster. e, Gene set enrichment analysis28 on the Reactome Pathway database showing the top twenty categories based on marker genes for HEK 293T cell clusters. Categories associated with the cell cycle are highlighted in bold.
a, Heat map of RPF abundance per CDS in hTERT RPE-1 FUCCI cells, showing the translation dynamics of 1,853 significantly differentially translated genes during the cell cycle. Common cell cycle markers are highlighted. b, Heat map showing ribosome-site-specific pausing over all codons for hTERT RPE-1 FUCCI cells. Cells are ordered based on cell cycle progression, and codons are clustered based on the average change in the frequency of occurrence across all sites. Codons with significantly different site occupancies between clusters are indicated with an asterisk.
a, Frequency of arginine and leucine codons in histone genes compared to all other genes. Histone genes (light grey) are highly enriched in CGC and CGU codons compared to other genes. Histone genes were defined as those in HGNC gene group 864. In the box plots the middle line indicates the median, the box limits the first and third quartiles, and the whiskers the range. Each point represents a gene. b, Heat map of the fold change in codon occupancy for CGC and CGU codons in the ribosome active sites (top) and the expression of histone genes (bottom) in RPE-1 cells. The site-agnostic increases in CGC and CGU in RPF active sites are synchronous with the increase in translation of histone genes during late S phase (cluster 5, teal). The increases of CGC and CGU codons in all active sites is distinct from the pattern seen in the GAA site occupancies, where the increase is specific to the A site.
Extended Data Fig. 7 Scatter plots showing the fold change in gene-wise A-site frequency of occupancy between each cell cluster and the background for the listed codons.
The increases (GAA, GAG, and AUA) and decreases (CGA) of the A-site abundance affect the majority of the genes detected across clusters.
a, UMAP (n = 350 cells) generated using the RPF counts per CDS. Corresponding cell types and associated marker genes for each cluster are indicated. b, c, UMAPs illustrating the fluorescence of the mNeonGreen (b) and dTomato (c) markers from the bi-fluorescent Neurog3Chrono reporter24. d, UMAP depicting the intestinal region origin of each cell. As expected, there is no enrichment of the cell types within each region. e, Scatter plots of the Neurog3Chrono fluorescence denoting the position of each cell cluster within the FACS space. As expected, progenitor cells show an increased mNeonGreen fluorescence, that changes through a double-positive population to dTomato-positive as EEC cells develop. f, Heat map showing ribosome-site-specific pausing over CAG and GAA codons. To remove any effects of the uneven distribution of RPFs along highly translated hormone genes, any gene that was more than an average of 2.5% of the RPFs per cell was removed from this analysis. g, h, UMAPs showing the CAG (g) and GAA (h) pausing. i, Heat map showing the distribution of RPF A sites along the Chgb CDS. Cells are grouped based on their CAG and GAA pausing status. The position of CAG (orange) and GAA (purple) codons within the CDS are denoted as ticks at the top, with shared prominent pausing sites for each codon indicated with inverted triangles. j, k, Scatter plots showing the fold change in gene-wise A-site frequency of occurrence between the pausing and non-pausing (normal) cells within each cluster.
a, Heat map of 1,517 genes significantly differentially expressed between the cell clusters. Common EEC marker genes are indicated. b, UMAPs (n = 350 cells) showing the expression of common EEC marker and hormone genes. c, Heat map showing ribosome-site-specific pausing for all codons in the EEC cells. Cells are clustered based on the profiles across the codons. To remove any effects of the uneven distribution of RPFs along highly translated hormone genes, any gene that was more than an average of 2.5% of the RPFs per cell was removed from this analysis (removed genes: Chga, Chgb, Clca1, Fcgbp, Gcg, Ghrl, Gip, Nts, Reg4, Sst).
a, HEK 293T cells. b–d, hTERT RPE-1 FUCCI interphase (b), contact-inhibition G0 (c) and mitotic shake-off fractions (d). e, Primary mouse EEC cells. Points are pseudocoloured based on density.
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VanInsberghe, M., van den Berg, J., Andersson-Rolf, A. et al. Single-cell Ribo-seq reveals cell cycle-dependent translational pausing. Nature 597, 561–565 (2021). https://doi.org/10.1038/s41586-021-03887-4
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