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TimeLapse-seq: adding a temporal dimension to RNA sequencing through nucleoside recoding
Open accessAbstract
RNA sequencing (RNA-seq) offers a snapshot of cellular RNA populations, but not temporal information about the sequenced RNA. Here we report TimeLapse-seq, which uses oxidative-nucleophilic-aromatic substitution to convert 4-thiouridine into cytidine analogs, yielding apparent U-to-C mutations that mark new transcripts upon sequencing. TimeLapse-seq is a single-molecule approach that is adaptable to many applications and reveals RNA dynamics and induced differential expression concealed in traditional RNA-seq.
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Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
Stan : A Probabilistic Programming Language
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017) · Journal of Statistical Software · doi:10.18637/jss.v076.i01 · Cited by 7,754
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566
Precise Maps of RNA Polymerase Reveal How Promoters Direct Initiation and Pausing
Kwak, H., Fuda, N. J., Core, L. J., & Lis, J. T. (2013) · Science · doi:10.1126/science.1229386 · Cited by 849
Nascent transcript sequencing visualizes transcription at nucleotide resolution
Churchman, L. S., & Weissman, J. S. (2011) · Nature · doi:10.1038/nature09652 · Cited by 823
Thiol-linked alkylation for the metabolic sequencing of RNA
Herzog, V. A., Reichholf, B., Neumann, T., Rescheneder, P., Bhat, P., Burkard, T. R., Wlotzka, W., von Haeseler, A., Zuber, J., & Ameres, S. L. (2017) · doi:10.1101/177642 · Cited by 622
Gaidatzis, D., Burger, L., Florescu, M., & Stadler, M. B. (2015) · Nature Biotechnology · doi:10.1038/nbt.3269 · Cited by 313
High-Resolution Sequencing and Modeling Identifies Distinct Dynamic RNA Regulatory Strategies
Rabani, M., Raychowdhury, R., Jovanovic, M., Rooney, M., Stumpo, D. J., Pauli, A., Hacohen, N., Schier, A. F., Blackshear, P. J., Friedman, N., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.015 · Cited by 226
Research works printed but not linked: 33
- Bhatt, D.M. et al. Transcript dynamics of proinflammatory genes revealed by sequence analysis of subcellular RNA fractions. Cell 150, 279–290 (2012). · DOI · Paper page
- Menet, J.S., Rodriguez, J., Abruzzi, K.C. & Rosbash, M. Nascent-Seq reveals novel features of mouse circadian transcriptional regulation. eLife 1, e00011 (2012). · DOI · Paper page
- Wada, Y. et al. A wave of nascent transcription on activated human genes. Proc. Natl. Acad. Sci. USA 106, 18357–18361 (2009). · DOI
- Schwalb, B. et al. TT-seq maps the human transient transcriptome. Science 352, 1225–1228 (2016). · DOI · Paper page
- Rabani, M. et al. Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells. Nat. Biotechnol. 29, 436–442 (2011). · DOI · Paper page
- Duffy, E.E. et al. Tracking distinct RNA populations using efficient and reversible covalent chemistry. Mol. Cell 59, 858–866 (2015). · DOI · Paper page
- Melvin, W.T., Milne, H.B., Slater, A.A., Allen, H.J. & Keir, H.M. Incorporation of 6-thioguanosine and 4-thiouridine into RNA. Application to isolation of newly synthesised RNA by affinity chromatography. Eur. J. Biochem. 92, 373–379 (1978). · DOI
- Hafner, M. et al. Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141, 129–141 (2010). · DOI · Paper page
- Mishima, Y. & Steitz, J.A. Site-specific crosslinking of 4-thiouridine-modified human tRNA(3Lys) to reverse transcriptase from human immunodeficiency virus type I. EMBO J. 14, 2679–2687 (1995). · DOI
- Yano, M. & Hayatsu, H. Permanganate oxidation of 4-thiouracil derivatives. Isolation and properties of I-substituted 2-pyrimidone 4-sulfonates. Biochim. Biophys. Acta 199, 303–315 (1970). · doi:10.1016/0005-2787(70)90073-0
- Ziff, E.B. & Fresco, J.R. A method for locating 4-thiouridylate in the primary structure of transfer ribonucleic acids. Biochemistry 8, 3242–3248 (1969). · doi:10.1021/bi00836a016
- Dai, Q. et al. Nm-seq maps 2′-O-methylation sites in human mRNA with base precision. Nat. Methods 14, 695–698 (2017). · DOI · Paper page
- Mukherjee, N. et al. Integrative classification of human coding and noncoding genes through RNA metabolism profiles. Nat. Struct. Mol. Biol. 24, 86–96 (2017). · DOI · Paper page
- Trinklein, N.D., Murray, J.I., Hartman, S.J., Botstein, D. & Myers, R.M. The role of heat shock transcription factor 1 in the genome-wide regulation of the mammalian heat shock response. Mol. Biol. Cell 15, 1254–1261 (2004). · DOI · Paper page
- Mahat, D.B., Salamanca, H.H., Duarte, F.M., Danko, C.G. & Lis, J.T. Mammalian heat shock response and mechanisms underlying its genome-wide transcriptional regulation. Mol. Cell 62, 63–78 (2016). · DOI · Paper page
- Shalgi, R., Hurt, J.A., Lindquist, S. & Burge, C.B. Widespread inhibition of posttranscriptional splicing shapes the cellular transcriptome following heat shock. Cell Rep. 7, 1362–1370 (2014). · DOI · Paper page
- Russo, J., Heck, A.M., Wilusz, J. & Wilusz, C.J. Metabolic labeling and recovery of nascent RNA to accurately quantify mRNA stability. Methods 120, 39–48 (2017). · DOI
- Friedel, C.C., Dölken, L., Ruzsics, Z., Koszinowski, U.H. & Zimmer, R. Conserved principles of mammalian transcriptional regulation revealed by RNA half-life. Nucleic Acids Res. 37, e115 (2009). · DOI · Paper page
- Gelsi-Boyer, V. et al. Mutations of polycomb-associated gene ASXL1 in myelodysplastic syndromes and chronic myelomonocytic leukaemia. Br. J. Haematol. 145, 788–800 (2009). · DOI · Paper page
- Scotti, M.M. & Swanson, M.S. RNA mis-splicing in disease. Nat. Rev. Genet. 17, 19–32 (2016). · DOI · Paper page
- Herzog, V.A. et al. Thiol-linked alkylation of RNA to assess expression dynamics. Nat. Methods 14, 1198–1204 (2017). · DOI · Paper page
- Schofield et al. Protocol in Protocol Exchange DOI https://doi.org/10.1038/protex.2018.004 . · doi:10.1038/protex.2018.004
- Langmead, B. & Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012). · DOI · Paper page
- Sexton, A.N., Wang, P.Y., Rutenberg-Schoenberg, M. & Simon, M.D. Interpreting reverse transcriptase termination and mutation events for greater insight into the chemical probing of RNA. Biochemistry 56, 4713–4721 (2017). · DOI
- Siegfried, N.A., Busan, S., Rice, G.M., Nelson, J.A. & Weeks, K.M. RNA motif discovery by SHAPE and mutational profiling (SHAPE-MaP). Nat. Methods 11, 959–965 (2014). · DOI · Paper page
- Xu, H. et al. FastUniq: a fast de novo duplicates removal tool for paired short reads. PLoS One 7, e52249 (2012). · DOI · Paper page
- Kim, D., Langmead, B. & Salzberg, S.L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015). · DOI · Paper page
- Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009). · DOI · Paper page
- Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). · DOI · Paper page
- Robinson, J.T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011). · DOI · Paper page
- Van Herreweghe, E. et al. Dynamic remodelling of human 7SK snRNP controls the nuclear level of active P-TEFb. EMBO J. 26, 3570–3580 (2007). · DOI
- Thomas, P.D. et al. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 13, 2129–2141 (2003). · DOI · Paper page
- Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate - a practical and powerful approach to multiple testing. J. R. Stat. Soc. B Stat. Methodol. 57, 289–300 (1995). · DOI · Paper page
Cited by
RNA sequencing: the teenage years
Stark, R., Grzelak, M., & Hadfield, J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0150-2 · Cited by 1,915
Mechanisms of lncRNA biogenesis as revealed by nascent transcriptomics
Nojima, T., & Proudfoot, N. J. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-021-00447-6 · Cited by 420
Translation affects mRNA stability in a codon-dependent manner in human cells
Wu, Q., Medina, S. G., Kushawah, G., DeVore, M. L., Castellano, L. A., Hand, J. M., Wright, M., & Bazzini, A. A. (2019) · eLife · doi:10.7554/elife.45396 · Cited by 283
Predicting mRNA abundance directly from genomic sequence using deep convolutional neural networks
Agarwal, V., & Shendure, J. (2018) · doi:10.1101/416685 · Cited by 243
Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks
Agarwal, V., & Shendure, J. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107663 · Cited by 243
scSLAM-seq reveals core features of transcription dynamics in single cells
Erhard, F., Baptista, M. A. P., Krammer, T., Hennig, T., Lange, M., Arampatzi, P., Jürges, C. S., Theis, F. J., Saliba, A.-E., & Dölken, L. (2019) · Nature · doi:10.1038/s41586-019-1369-y · Cited by 233
Temporal modelling using single-cell transcriptomics
Ding, J., Sharon, N., & Bar-Joseph, Z. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00444-7 · Cited by 195
Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe
Qiu, X., Rahimzamani, A., Wang, L., Ren, B., Mao, Q., Durham, T., McFaline-Figueroa, J. L., Saunders, L., Trapnell, C., & Kannan, S. (2020) · Cell Systems · doi:10.1016/j.cels.2020.02.003 · Cited by 185
The genetic and biochemical determinants of mRNA degradation rates in mammals
Agarwal, V., & Kelley, D. R. (2022) · Genome Biology · doi:10.1186/s13059-022-02811-x · Cited by 155
Ietswaart, R., Smalec, B. M., Xu, A., Choquet, K., McShane, E., Jowhar, Z. M., Guegler, C. K., Baxter-Koenigs, A. R., West, E. R., Fu, B. X. H., et al. (2024) · Molecular Cell · doi:10.1016/j.molcel.2024.06.008 · Cited by 83
Time-resolved single-cell RNA-seq using metabolic RNA labelling
Erhard, F., Saliba, A.-E., Lusser, A., Toussaint, C., Hennig, T., Prusty, B. K., Kirschenbaum, D., Abadie, K., Miska, E. A., Friedel, C. C., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00157-z · Cited by 73
Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine
Chu, L.-X., Wang, W.-J., Gu, X.-P., Wu, P., Gao, C., Zhang, Q., Wu, J., Jiang, D.-W., Huang, J.-Q., Ying, X.-W., et al. (2024) · Military Medical Research · doi:10.1186/s40779-024-00537-4 · Cited by 29
Rapid UPF1 depletion illuminates the temporal dynamics of the NMD-regulated human transcriptome
Boehm, V., Wallmeroth, D., Wulf, P. O., Popp, O., Teixeira Alves, L. G., Reinecke, L., Riedel, M., Wyler, E., Franitza, M., Becker, K., et al. (2025) · Molecular Cell · doi:10.1016/j.molcel.2025.08.015 · Cited by 23
On the optimal design of metabolic RNA labeling experiments
(2019) · doi:10.1371/journal.pcbi.1007252 · Cited by 22
Subcellular mRNA kinetic modeling reveals nuclear retention as rate-limiting
(2024) · doi:10.1038/s44320-024-00073-2 · Cited by 14
Imai, H., Utsumi, D., Torihara, H., Takahashi, K., Kuroyanagi, H., & Yamashita, A. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad545 · Cited by 9
On the optimal design of metabolic RNA labeling experiments
Uvarovskii, A., Naarmann-de Vries, I. S., & Dieterich, C. (2018) · doi:10.1101/428862 · Cited by 2
Metadata
- Authors
- Schofield, J. A., Duffy, E. E., Kiefer, L., Sullivan, M. C., & Simon, M. D.
- Venue
- Nature Methods
- Published
- 2018-01-22
- Volume
- 15
- Issue
- 3
- Pages
- 221-225
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2784500348
- Cited by
- 260
- References
- 9
- FWCI
- 7.93
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
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