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Nascent Ribo-Seq measures ribosomal loading time and reveals kinetic impact on ribosome density

Nature Methods volume 18, pages 1068–1074 (2021)Cite this article

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A Publisher Correction to this article was published on 19 January 2023

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Abstract

In general, mRNAs are assumed to be loaded with ribosomes instantly upon entry into the cytoplasm. To measure ribosome density (RD) on nascent mRNA, we developed nascent Ribo-Seq by combining Ribo-Seq with progressive 4-thiouridine labeling. In mouse macrophages, we determined experimentally the lag between the appearance of nascent mRNA and its association with ribosomes, which was calculated to be 20–22 min for bulk mRNA. In mouse embryonic stem cells, nRibo-Seq revealed an even stronger lag of 35–38 min in ribosome loading. After stimulation of macrophages with lipopolysaccharide, the lag between cytoplasmic and translated mRNA leads to uncoupling between input and ribosome-protected fragments, which gives rise to distorted RD measurements under conditions where mRNA amounts are far from steady-state expression. As a result, we demonstrate that transcriptional changes affect RD in a passive way.

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Fig. 1: Illustration of nRibo-Seq.
Fig. 2: nRibo-Seq quantifies the time required for full polysome assembly in macrophages.
Fig. 3: nRibo-Seq quantifies the time required for full polysome assembly in mESCs.
Fig. 4: Cytoplasmic and translated mRNA are uncoupled after LPS stimulation.
Fig. 5: Protein production is uncoupled from mRNA abundance for large ORFs and stable mRNAs.
Fig. 6: Active changes in RD can be identified by separate assessment of FP and IN amounts.

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Data availability

All sequencing data (nRibo-Seq, Ribo-Seq and RNA-Seq) are publicly available under the GEO accession number GSE155236. Proteomics data are publicly available via ProteomeXchange with the identifier PXD026828. Ribo-Seq data of a yeast meiosis timecourse19 was obtained from GEO under the accession number GSE34082. Source data are provided with this paper.

Code availability

A complete workflow for processing nRibo-Seq data is available on OSF (https://osf.io/j2sdb/?view_only=64911179ce8a402590489a3118471451). Scripts used for processing the Ribo-Seq data of the LPS timecourse and the PUNCH-P data as well as scripts for producing figures are available on OSF (https://osf.io/t3h9y/?view_only=8cb449473021416ebcceb0e826cfca11).

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Acknowledgements

We thank A. Teleman (DKFZ, Heidelberg) for his critical reading and thoughts on the manuscript and R. Gao (ECAS, Mannheim) for providing mESCs. We are also grateful for the support of D. Ibberson at the CellNetworks Deep Sequencing Core Facility of Heidelberg University and we thank the NGS Core Facility at the Institute of Clinical Chemistry of the Medical Faculty Mannheim. This work was supported by grants TRR 186 and TRR 319 from the Deutsche Forschungsgemeinschaft (DFG) to G.S.

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Authors and Affiliations

  1. Mannheim Institute for Innate Immunoscience (MI3), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Johanna Schott, Sonja Reitter, Doris Lindner, Jan Grosser, Marius Bruer & Georg Stoecklin

  2. Center for Molecular Biology of Heidelberg University (ZMBH), German Cancer Research Center (DKFZ)-ZMBH Alliance, Heidelberg, Germany

    Johanna Schott, Sonja Reitter, Doris Lindner, Jan Grosser, Marius Bruer & Georg Stoecklin

  3. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Anjana Shenoy & Tamar Geiger

  4. Department of Anatomy and Developmental Biology, European Center for Angioscience (ECAS), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Arthur Mathes & Gergana Dobreva

  5. German Center for Cardiovascular Research (DZHK), Heidelberg, Germany

    Arthur Mathes & Gergana Dobreva

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Contributions

J.S. developed the nRibo-Seq method including the mathematical approach, and analyzed all sequencing data. J.S. and G.S. designed experiments and wrote the manuscript. S.R. performed PUNCH-P experiments. D.L. and J.S. performed Ribo-Seq and nRibo-Seq experiments. A.M. and G.D. provided help with designing and performing nRibo-Seq in mESCs. J.G. performed transcriptome-wide HL measurements in RAW264.7 cells. M.B. performed transcriptome-wide HL measurements in mESCs. A.S. and T.G. provided protocols and help with the PUNCH-P experiment, performed mass spectrometry and analyzed the data.

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Correspondence to Johanna Schott.

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The authors declare no competing interests.

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Peer review information Nature Methods thanks T. Preiss, J. Weissman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Estimation of 4sU/U ratio of nascent mRNA fragments at different time-points.

(a) Following nRibo-Seq in RAW264.7 macrophages, the efficiency of NaIO4-mediated conversion of 4sU to C was determined, for each sample separately, with a spike-in RNA containing a 4sU at position 13. The T-to-C rates along the spike-in RNA are shown for one sample. (b) T-to-C rates at position 13 of the spike-in RNA in all samples as determined from Illumina sequencing. (c) The estimated 4sU/U ratio within nascent reads of experiment 1 as calculated from the conversion efficiency and the T-to-C transition probability, separately for every population of reads based on the number of Ts in the reference. (d) The estimated 4sU/U rate within nascent reads of experiment 2.

Source data

Extended Data Fig. 2 Estimation of T-to-C transition probability within nascent reads.

(a) As an example, the number of reads with one or two T-to-C transitions is shown for IN mRNA of the 40 min 4sU labeling timepoint of experiment 1 from RAW264.7 macrophages. (b) The number of reads with two or three T-to-C transitions is shown for the same sample as in (a). (c) Assuming a binomial distribution, the probability for a T in a read originating from nascent mRNA to transition to C is estimated separately from the ratio of reads containing two to one T-to-C transitions and from the ratio of reads containing three to two T-to-C transitions, for every population of reads based on the number of Ts in the reference. (d) T-to-C transition probability after subtracting a background probability that was estimated by minimizing the discrepancy between the two estimates shown in (c). (e) Estimated number of nascent reads among reads without T-to-C transitions. (f) Estimated number of nascent reads with more than three T-to-C transitions.

Source data

Extended Data Fig. 3 Modeled impact of RLT and mRNA half-life on RD after induction of transcription.

(a) Schematic representation of the loading process, showing two mRNAs at different time-points after onset of transcription (30 min, 60 min, 90 min and 120 min) accumulating ribosomes for 10 min (left side) or 60 min (right side) after export to the cytoplasm. (b) Theoretical course of cytoplasmic and ribosomal mRNA levels (upper right panels) and RD (lower right panels) relative to the new steady state for 120 min after onset of transcription, shown for a combination of three different RLTs and two mRNA half-lives (HLs). (c) Lag between cytoplasmic and ribosomal mRNA levels as a function of RLT for two different HLs. (d) Lag between cytoplasmic and ribosomal mRNA levels as a function of mRNA HL for three different RLTs.

Extended Data Fig. 4 Alternative models with initial delay before ribosome loading.

(a) Proportion of IN and FP mRNA for an initial phase of inactivity (delay, D) of 15 min followed by rapid ribosome loading (RLT) within 2 min. (b) Proportion of IN and FP mRNA for an initial D of 21 min followed by ribosome loading (RLT) within 21 min. (c) Combinations of RLT and D that produce the same lag of 21 min (lower panel) and the corresponding predicted proportion of nascent FP mRNA at 20 and 30 min (dashed lines) compared to the observed proportion (solid lines). (d) Estimation of D and RLT from nRibo-Seq measurements (solid lines) compared to a model with a considerable D that would produce the same lag between IN and FP (dashed lines).

Extended Data Fig. 5 Relationship between ribosome loading and ORF length, tAI or mRNA half-life.

(a) Proportion of nascent mRNA in IN or FP after 40 min 4sU from the nRibo-Seq experiment 2 in RAW264.7 macrophages, depicted separately for reads pooled from groups of mRNAs according to ORF length. (b) Proportion of nascent mRNA as in (a), normalized to IN. (c) As in (a) for mRNAs grouped according to tAI. (d) As in (b) for mRNAs grouped according to tAI. (e) As in (a) for mRNAs grouped according to HL. (f) As in (b) for mRNAs grouped according to HL.

Source data

Extended Data Fig. 6 Coupling factors predicted from models with and without initial delay.

(a) Coupling factors observed after LPS treatment of RAW264.7 macrophages or predicted from a model without initial delay (D = 0) and with initial delay (D = 21). (b) Combinations of RLT and D that all produce the same lag of 21 min (lower panel) and the corresponding predicted (dashed lines) coupling factors at 15 and 30 min of LPS treatment. In addition, observed coupling factors are shown as solid lines.

Extended Data Fig. 7 Relationship between number of reads and coupling factor.

Based on the read numbers in samples of control and LPS-treated RAW264.7 macrophages (15 min, 60 min and 16 h), artificial IN and FP samples were generated with 0.5 to 10 million reads (in steps of 0.5 million). Coupling factors were calculated as in Fig. 4. From 1,000 rounds of simulation, the median, 5th and 95th percentile were determined.

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Schott, J., Reitter, S., Lindner, D. et al. Nascent Ribo-Seq measures ribosomal loading time and reveals kinetic impact on ribosome density. Nat Methods 18, 1068–1074 (2021). https://doi.org/10.1038/s41592-021-01250-z

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