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dc.contributor.authorOzsolak, Fatih
dc.contributor.authorGoren, Alon
dc.contributor.authorGymrek, Melissa A.
dc.contributor.authorGuttman, Mitchell
dc.contributor.authorRegev, Aviv
dc.contributor.authorBernstein, Bradley E.
dc.contributor.authorMilos, Patrice M.
dc.date.accessioned2012-10-30T17:47:02Z
dc.date.available2012-10-30T17:47:02Z
dc.date.issued2010-02
dc.date.submitted2009-10
dc.identifier.issn1088-9051
dc.identifier.urihttp://hdl.handle.net/1721.1/74518
dc.description.abstractAccurate profiling of minute quantities of RNA in a global manner can enable key advances in many scientific and clinical disciplines. Here, we present low-quantity RNA sequencing (LQ-RNAseq), a high-throughput sequencing-based technique allowing whole transcriptome surveys from subnanogram RNA quantities in an amplification/ligation-free manner. LQ-RNAseq involves first-strand cDNA synthesis from RNA templates, followed by 3′ polyA tailing of the single-stranded cDNA products and direct single molecule sequencing. We applied LQ-RNAseq to profile S. cerevisiae polyA+ transcripts, demonstrate the reproducibility of the approach across different sample preparations and independent instrument runs, and establish the absolute quantitative power of this method through comparisons with other reported transcript profiling techniques and through utilization of RNA spike-in experiments. We demonstrate the practical application of this approach to define the transcriptional landscape of mouse embryonic and induced pluripotent stem cells, observing transcriptional differences, including over 100 genes exhibiting differential expression between these otherwise very similar stem cell populations. This amplification-independent technology, which utilizes small quantities of nucleic acid and provides quantitative measurements of cellular transcripts, enables global gene expression measurements from minute amounts of materials and offers broad utility in both basic research and translational biology for characterization of rare cells.en_US
dc.description.sponsorshipBurroughs Wellcome Funden_US
dc.language.isoen_US
dc.publisherCold Spring Harbor Laboratory Pressen_US
dc.relation.isversionofhttp://dx.doi.org/10.1101/gr.102129.109en_US
dc.rightsCreative Commons Attribution Non-Commercialen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/en_US
dc.sourceCold Spring Harbor Laboratory Pressen_US
dc.titleDigital transcriptome profiling from attomole-level RNA samplesen_US
dc.typeArticleen_US
dc.identifier.citationOzsolak, F. et al. “Digital Transcriptome Profiling from Attomole-level RNA Samples.” Genome Research 20.4 (2010): 519–525. © 2010 by Cold Spring Harbor Laboratory Pressen_US
dc.contributor.departmentmove to dc.description.sponsorshipen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biologyen_US
dc.contributor.mitauthorGoren, Alon
dc.contributor.mitauthorGymrek, Melissa A.
dc.contributor.mitauthorRegev, Aviv
dc.contributor.mitauthorBernstein, Bradley E.
dc.relation.journalGenome Researchen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsOzsolak, F.; Goren, A.; Gymrek, M.; Guttman, M.; Regev, A.; Bernstein, B. E.; Milos, P. M.en
dc.identifier.orcidhttps://orcid.org/0000-0001-8567-2049
dc.identifier.orcidhttps://orcid.org/0000-0002-6086-3903
dspace.mitauthor.errortrue
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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