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| Status |
Public on May 31, 2021 |
| Title |
Sequencing-based quantitative mapping of the cellular small RNA landscape |
| Organism |
Homo sapiens |
| Experiment type |
Non-coding RNA profiling by high throughput sequencing
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| Summary |
Current next-generation RNA sequencing methods do not provide accurate quantification of small RNAs within a sample due to sequence-dependent biases in capture, ligation, and amplification during library preparation. We present a method – AQRNA-seq – that minimizes biases and provides a direct, linear correlation between sequencing read count and copy number for small RNAs in a sample. The library preparation and data processing steps were optimized and validated using a 963-member microRNA reference library, oligonucleotide standards of varying lengths, and northern blots. Application of AQRNA-seq to a panel of human cancer cells revealed >800 detectable miRNAs that varied as a function of cancer progression, while application to bacterial tRNA pools, a traditionally hard-to-sequence class of RNAs, revealed 80-fold variation in tRNA isoacceptor levels, stress-induced site-specific tRNA fragmentation, quantitative modification maps, and evidence for stress-induced tRNA-driven codon-biased translation. AQRNA-seq thus provides a means to quantitatively map the small RNA landscape in cells.
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| Overall design |
15 human mammary epithelial cell (HMEC) samples including 3 cell types and 5 replicates each type were analyzed. The three cell types in the HMEC model represent progressive stages of tumorigenesis conferred by engineering the cells with tumor-promoting genes: reactivation of telomerase by expression of telomerase catalytic subunit (hTERT) immortalizes HMEC 1 cells, additional expression of H-Ras oncoprotein (HRASG12V) further drives aberrant growth in HMEC 2 cells, and further P53 suppression by expression of SV40 large-T and small-t antigens yields HMEC 3 cells that are fully capable of tumor growth in mice.
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| Contributor(s) |
Hu JF, Yim D, Dedon PC, Cao B |
| Citation(s) |
33859402 |
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| Submission date |
Oct 13, 2020 |
| Last update date |
May 19, 2023 |
| Contact name |
Dunaduan Ma |
| E-mail(s) |
maduanduan8@gmail.com
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| Organization name |
MIT
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| Department |
Koch Institute
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| Lab |
Bioinformatics Core
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| Street address |
77 Massachusetts Ave
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| City |
Cambridge |
| State/province |
Ma |
| ZIP/Postal code |
02139 |
| Country |
USA |
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| Platforms (1) |
| GPL18573 |
Illumina NextSeq 500 (Homo sapiens) |
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| Samples (15)
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| Relations |
| BioProject |
PRJNA668926 |
| SRA |
SRP287233 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE159434_gene_counts.xlsx |
43.8 Kb |
(ftp)(http) |
XLSX |
| GSE159434_sequence_counts.xlsx |
1.1 Mb |
(ftp)(http) |
XLSX |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
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