Digital transcriptome profiling from attomole-level RNA samples
Accurate 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 quan...
| Main Authors: | , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Cold Spring Harbor Laboratory Press
2012
|
| Online Access: | http://hdl.handle.net/1721.1/74518 https://orcid.org/0000-0001-8567-2049 https://orcid.org/0000-0002-6086-3903 |
| _version_ | 1826206332489826304 |
|---|---|
| author | Ozsolak, Fatih Goren, Alon Gymrek, Melissa A. Guttman, Mitchell Regev, Aviv Bernstein, Bradley E. Milos, Patrice M. |
| author2 | move to dc.description.sponsorship |
| author_facet | move to dc.description.sponsorship Ozsolak, Fatih Goren, Alon Gymrek, Melissa A. Guttman, Mitchell Regev, Aviv Bernstein, Bradley E. Milos, Patrice M. |
| author_sort | Ozsolak, Fatih |
| collection | MIT |
| description | Accurate 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. |
| first_indexed | 2024-09-23T13:27:50Z |
| format | Article |
| id | mit-1721.1/74518 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:27:50Z |
| publishDate | 2012 |
| publisher | Cold Spring Harbor Laboratory Press |
| record_format | dspace |
| spelling | mit-1721.1/745182022-09-28T14:24:28Z Digital transcriptome profiling from attomole-level RNA samples Ozsolak, Fatih Goren, Alon Gymrek, Melissa A. Guttman, Mitchell Regev, Aviv Bernstein, Bradley E. Milos, Patrice M. move to dc.description.sponsorship Massachusetts Institute of Technology. Department of Biology Goren, Alon Gymrek, Melissa A. Regev, Aviv Bernstein, Bradley E. Accurate 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. Burroughs Wellcome Fund 2012-10-30T17:47:02Z 2012-10-30T17:47:02Z 2010-02 2009-10 Article http://purl.org/eprint/type/JournalArticle 1088-9051 http://hdl.handle.net/1721.1/74518 Ozsolak, F. et al. “Digital Transcriptome Profiling from Attomole-level RNA Samples.” Genome Research 20.4 (2010): 519–525. © 2010 by Cold Spring Harbor Laboratory Press https://orcid.org/0000-0001-8567-2049 https://orcid.org/0000-0002-6086-3903 en_US http://dx.doi.org/10.1101/gr.102129.109 Genome Research Creative Commons Attribution Non-Commercial http://creativecommons.org/licenses/by-nc/3.0/ application/pdf Cold Spring Harbor Laboratory Press Cold Spring Harbor Laboratory Press |
| spellingShingle | Ozsolak, Fatih Goren, Alon Gymrek, Melissa A. Guttman, Mitchell Regev, Aviv Bernstein, Bradley E. Milos, Patrice M. Digital transcriptome profiling from attomole-level RNA samples |
| title | Digital transcriptome profiling from attomole-level RNA samples |
| title_full | Digital transcriptome profiling from attomole-level RNA samples |
| title_fullStr | Digital transcriptome profiling from attomole-level RNA samples |
| title_full_unstemmed | Digital transcriptome profiling from attomole-level RNA samples |
| title_short | Digital transcriptome profiling from attomole-level RNA samples |
| title_sort | digital transcriptome profiling from attomole level rna samples |
| url | http://hdl.handle.net/1721.1/74518 https://orcid.org/0000-0001-8567-2049 https://orcid.org/0000-0002-6086-3903 |
| work_keys_str_mv | AT ozsolakfatih digitaltranscriptomeprofilingfromattomolelevelrnasamples AT gorenalon digitaltranscriptomeprofilingfromattomolelevelrnasamples AT gymrekmelissaa digitaltranscriptomeprofilingfromattomolelevelrnasamples AT guttmanmitchell digitaltranscriptomeprofilingfromattomolelevelrnasamples AT regevaviv digitaltranscriptomeprofilingfromattomolelevelrnasamples AT bernsteinbradleye digitaltranscriptomeprofilingfromattomolelevelrnasamples AT milospatricem digitaltranscriptomeprofilingfromattomolelevelrnasamples |