Stem cell transcriptome profiling via massive-scale mRNA sequencing
Abstract
We developed a massive-scale RNA sequencing protocol, short quantitative random RNA libraries or SQRL, to survey the complexity, dynamics and sequence content of transcriptomes in a near-complete fashion. This method generates directional, random-primed, linear cDNA libraries that are optimized for next-generation short-tag sequencing. We surveyed the poly(A)(+) transcriptomes of undifferentiated mouse embryonic stem cells (ESCs) and embryoid bodies (EBs) at an unprecedented depth (10 Gb), using the Applied Biosystems SOLiD technology. These libraries capture the genomic landscape of expression, state-specific expression, single-nucleotide polymorphisms (SNPs), the transcriptional activity of repeat elements, and both known and new alternative splicing events. We investigated the impact of transcriptional complexity on current models of key signaling pathways controlling ESC pluripotency and differentiation, highlighting how SQRL can be used to characterize transcriptome content and dynamics in a quantitative and reproducible manner, and suggesting that our understanding of transcriptional complexity is far from complete.
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Transcript abundance is directly proportional to the number of sequencing reads that map to a specific transcript, resulting in lower false-negative and false-positive discovery rates and a greater linear range.
“Transcript abundance is directly proportional to the number of sequencing reads that map to a specific transcript, resulting in lower falsenegative and false-positive discovery rates in addition to a greater linear range (Bainbridge et al., 2006;Cloonan et al., 2008;Marioni et al., 2008;Mortazavi et al., 2008;Nagalakshmi et al., 2008;Sultan et al., 2008;Wilhelm et al., 2008;Trapnell et al., 2010;W”
From An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex (2014) · cites this paper as Cloonan et al., 2008
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Self-citationApplications of New Sequencing Technologies for Transcriptome Analysis
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BackgroundUncovering the Complexity of Transcriptomes with RNA-Seq
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BackgroundStatistical Design and Analysis of RNA Sequencing Data
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Unravelling the complexity of metastasis — molecular understanding and targeted therapies
Sethi, N. S., & Kang, Y. (2011) · Nature reviews. Cancer · doi:10.1038/nrc3125 · Cited by 396 · see citing works
Meyer, E., Aglyamova, G. V., & Matz, M. V. (2011) · Molecular Ecology · doi:10.1111/j.1365-294x.2011.05205.x · Cited by 363 · see citing works
“Many of the previously described RNA-Seq procedures require large amounts of input RNA (4–100 lg) (Cloonan et al. 2008; Lister et al. 2008; Mortazavi et al. 2008; Nagalakshmi et al. 2008), with one notable exception (Tang et al.”
MethodInfluentialDetection of splice junctions from paired-end RNA-seq data by SpliceMap
Au, K. F., Jiang, H., Lin, L., Xing, Y., & Wong, W. H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq211 · Cited by 328 · see citing works
Gene Mapping via Bulked Segregant RNA-Seq (BSR-Seq)
Liu, S., Yeh, C., Tang, H. M., Nettleton, D. S., & Schnable, P. S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036406 · Cited by 373 · see citing works
“One of the most widely adopted adaptations of NGS technology is RNA-Seq [21–27], which enables the comparative quantification of gene expression in, for example, various genotypes.”
MethodOuyang, Z., Zhou, Q., & Wong, W. H. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0904863106 · Cited by 329 · see citing works
“T recent development of ultra-high-throughput RNA sequencing (RNA-Seq) technology holds the promise to provide more accurate gene expression measurements (1, 2).”
BackgroundMethodInfluentialThe beginning of the end for microarrays?
Shendure, J. (2008) · Nature Methods · doi:10.1038/nmeth0708-585 · Cited by 331 · see citing works
MicroRNAs and their isomiRs function cooperatively to target common biological pathways
Cloonan, N., Wani, S., Xu, Q., Gu, J., Lea, K., Heater, S., Barbacioru, C., Steptoe, A. L., Martin, H. C., Nourbakhsh, E., et al. (2011) · Genome Biology · doi:10.1186/gb-2011-12-12-r126 · Cited by 345 · see citing works
Self-citationRNA‐Seq: A Method for Comprehensive Transcriptome Analysis
Nagalakshmi, U., Waern, K., & Snyder, M. P. (2010) · Current Protocols in Molecular Biology · doi:10.1002/0471142727.mb0411s89 · Cited by 319 · see citing works
Function annotation of the rice transcriptome at single-nucleotide resolution by RNA-seqPreprint
Lu, T., Lu, G., Fan, D., Zhu, C., Li, W., Zhao, Q., Feng, Q., Zhao, Y., Guo, Y., Li, W., et al. (2010) · Genome Research · doi:10.1101/gr.106120.110 · Cited by 315 · see citing works
“…yeast, mouse and Arabidopsis, opening the entire transcriptional landscape of gene activity and AS in a high-throughput and quantitative manner (Cloonan et al. 2008; Lister et al. 2008; Mortazavi et al. 2008; Nagalakshmi et al. 2008; Pan et al. 2008; Sultan et al. 2008; Wang et al. 2008;…”
BackgroundIntegrative analysis of the melanoma transcriptome
Berger, M. F., Levin, J. Z., Vijayendran, K., Sivachenko, A., Adiconis, X., Maguire, J., Johnson, L. A., Robinson, J., Verhaak, R. G., Sougnez, C., et al. (2010) · Genome Research · doi:10.1101/gr.103697.109 · Cited by 305 · see citing works
Regulating RNA polymerase pausing and transcription elongation in embryonic stem cellsPreprint
Min, I. M., Waterfall, J. J., Core, L. J., Munroe, R. J., Schimenti, J. C., & Lis, J. T. (2011) · Genes & Development · doi:10.1101/gad.2005511 · Cited by 297 · see citing works
“3A), bolstering the view that pausing is associated more with transcribed, rather than strictly nontranscribed, genes (Core et al. 2008).”
BackgroundMethodInfluentialRapid Turnover of Long Noncoding RNAs and the Evolution of Gene Expression
Kutter, C., Watt, S., Stefflova, K., Wilson, M. D., Gonçalves, Â., Ponting, C. P., Odom, D. T., & Marques, A. C. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002841 · Cited by 324 · see citing works
“surprisingly diverse in its extent and encoded functions [1–3],”
BackgroundNookaew, I., Papini, M., Pornputtapong, N., Scalcinati, G., Fagerberg, L., Uhlén, M., & Nielsen, J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks804 · Cited by 309 · see citing works
Alternative expression analysis by RNA sequencing
Griffith, M., Griffith, O. L., Mwenifumbo, J. C., Goya, R., Morrissy, A. S., Morin, R. D., Corbett, R., Tang, M., Hou, Y. C., Pugh, T. J., et al. (2010) · Nature Methods · doi:10.1038/nmeth.1503 · Cited by 306 · see citing works
“, compare gene expression between a pair of contrasting condition”
BackgroundHigh‐throughput sequencing for biology and medicine
Soon, W. W., Hariharan, M., & Snyder, M. P. (2013) · Molecular Systems Biology · doi:10.1038/msb.2012.61 · Cited by 302 · see citing works
“Muchmore accurate measurement of mRNA levels became possible with the introduction of RNAseq, which was invented in both yeast (Nagalakshmi et al, 2008; Wilhelm et al, 2010) and mammalian cells (Cloonan et al, 2008; Mortazavi et al, 2008).”
BackgroundHighly accurate long-read HiFi sequencing data for five complex genomes
Hon, T., Mars, K., Young, G., Tsai, Y., Karalius, J. W., Landolin, J. M., Maurer, N. W., Kudrna, D. A., Hardigan, M. A., Steiner, C., et al. (2020) · Scientific Data · doi:10.1038/s41597-020-00743-4 · Cited by 349 · see citing works
Fort, A., Hashimoto, K., Yamada, D., Salimullah, M., Keya, C. A., Saxena, A., Bonetti, A., Voineagu, I., Bertin, N., Kratz, A., et al. (2014) · Nature Genetics · doi:10.1038/ng.2965 · Cited by 299 · see citing works
Self-citationSingle Cell Isolation and Analysis
Hu, P., Zhang, W., Xin, H., & Deng, G. Y. (2016) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2016.00116 · Cited by 381 · see citing works
Brookes, E., de Santiago, I., Hebenstreit, D., Morris, K. J., Carroll, T., Xie, S. Q., Stock, J. K., Heidemann, M., Eick, D., Nozaki, N., et al. (2012) · Cell stem cell · doi:10.1016/j.stem.2011.12.017 · Cited by 276 · see citing works
“D, mRNA expression levels were determined for the group of 20% highest expressed, 20% lowest expressed and PRC+ genes using published mRNAseq (Cloonan et al., 2008) and microarray (Mikkelsen et al., 2007) data.”
MethodUTX and UTY Demonstrate Histone Demethylase-Independent Function in Mouse Embryonic Development
Shpargel, K. B., Sengoku, T., Yokoyama, S., & Magnuson, T. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002964 · Cited by 310 · see citing works
“Our analysis of publicly available RNA-seq data sets [41,42] revealed similar low-levels of expression for Utx and Uty.”
MethodApplication of 'next-generation' sequencing technologies to microbial genetics
MacLean, D., Jones, J. D. G., & Studholme, D. J. (2009) · Nature Reviews Microbiology · doi:10.1038/nrmicro2088 · Cited by 293 · see citing works
“Several recent papers describe the application of Solexa GA and SOLiD technologies to deeply sequence the transcriptomes of mammal”
RNA sequencing reveals two major classes of gene expression levels in metazoan cells
Hebenstreit, D., Fang, M., Gu, M., Charoensawan, V., van Oudenaarden, A., & Teichmann, S. A. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.28 · Cited by 289 · see citing works
Pradella, D., Naro, C., Sette, C., & Ghigna, C. (2017) · Molecular Cancer · doi:10.1186/s12943-016-0579-2 · Cited by 325 · see citing works
Studying bacterial transcriptomes using RNA-seq
Croucher, N. J., & Thomson, N. R. (2010) · Current Opinion in Microbiology · doi:10.1016/j.mib.2010.09.009 · Cited by 279 · see citing works
“There is also the issue of the PCR amplification stages of sequence library construction for all three second generation sequencing platforms, which result in redundant sequence reads and bias the final dataset.”
MethodInfluentialZhang, N., Zhang, H., Zhao, B., Sun, Q., Cao, Y., Ren, L., Wu, X., Weeda, S. M., Li, L., Ren, S., et al. (2013) · Journal of Pineal Research · doi:10.1111/jpi.12095 · Cited by 302 · see citing works
“This new method dramatically changes the procedures used to identify the differentially expressed genes [29, 30].”
MethodAdvanced Applications of RNA Sequencing and Challenges
Han, Y., Gao, S., Muegge, K., Zhang, W., & Zhou, B. (2015) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s28991 · Cited by 277 · see citing works
“31,53 However, for the short reads that have a very high copy number and repetitive sequences, polymorphism is still a great challenge.”
BackgroundMethodNext is now: new technologies for sequencing of genomes, transcriptomes, and beyond
Lister, R., Gregory, B. D., & Ecker, J. R. (2009) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2008.11.004 · Cited by 274 · see citing works
Trans-Omics: How To Reconstruct Biochemical Networks Across Multiple ‘Omic’ Layers
Yugi, K., Kubota, H., Hatano, A., & Kuroda, S. (2016) · Trends in Biotechnology · doi:10.1016/j.tibtech.2015.12.013 · Cited by 258 · see citing works
“Comprehensive measurement technologies for each omic layer are now becoming available, such as polynucleotide sequencing by next-generation sequencers (NGS) {genome sequencing [3], RNA sequencing [4,5], chromatin immunoprecipitation sequencing (ChIP-seq) [6–8], etc.”
MethodFu, Y., Sun, Y., Li, Y., Li, J., Rao, X., Chen, C., & Xu, A. (2011) · Genome Research · doi:10.1101/gr.115295.110 · Cited by 270 · see citing works
“Total RNA was extracted from cells using QIAGEN RNeasy Mini kits, and ;10 mg total RNA was randomly fragmented by heating (Cloonan et al. 2008).”
MethodAnnotating genomes with massive-scale RNA sequencing
Denœud, F., Aury, J., Silva, C. D., Noël, B., Rogier, O., Delledonne, M., Morgante, M., Valle, G., Wincker, P., Scarpelli, C., et al. (2008) · Genome biology · doi:10.1186/gb-2008-9-12-r175 · Cited by 250 · see citing works
“In recent studies, RNA-Seq has mainly been used to quantify the expression levels of already annotated loci, identify differentially expressed genes, and measure expression outside of those loci (in intronic or intergenic regions) [21-24 ,26].”
- Authors
- Cloonan, N., Forrest, A. R. R., Kolle, G., Gardiner, B. B. A., Faulkner, G. J., Brown, M. K., Taylor, D. F., Steptoe, A. L., Wani, S., Bethel, G., Robertson, A. J., Perkins, A. C., Bruce, S. J., Lee, C. C., Ranade, S. S., Peckham, H. E., Manning, J. M., McKernan, K. J., & Grimmond, S. M.
- Affiliations
- The University of Queensland (AU)
- Griffith University (AU)
- Corresponding author
- Grimmond, Sean M
- ORCID
- Venue
- Nature Methods
- Published
- 2008-05-30
- Volume
- 5
- Issue
- 7
- Pages
- 613-619
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2105776281
- Cited by
- 1,035
- References
- 0
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 20 · 2023: 14 · 2022: 31 · 2021: 32 · 2020: 27 · 2019: 38 · 2018: 47 · 2017: 50 · 2016: 56 · 2015: 67 · 2014: 90 · 2013: 93 · 2012: 110
- FWCI
- 46.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion)
- Griffith Research Online (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Keywords
- mouse embryonic stem cells, embryoid bodies, transcriptome complexity, alternative splicing, single nucleotide polymorphisms, cell differentiation, SOLiD sequencing, RNA-seq
- MeSH terms
- Animals, Cell Differentiation, RNA, Messenger, Sensitivity and Specificity, Signal Transduction, Gene Library, Sequence Analysis, RNA, Expressed Sequence Tags, Polymorphism, Single Nucleotide, Gene Expression Profiling, Pluripotent Stem Cells, Mice, Embryonic Stem Cells
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