ArticleJournal of translational medicine2020
CLEAR: coverage-based limiting-cell experiment analysis for RNA-seq.
Article in Journal of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 22 citations in OpenAlex.
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- Keratin 5 basal cells are temporally regulated developmental and tissue repair progenitors in bladder urothelium.American journal of physiology. Renal physiology · 2024Article
- Estrogen Receptor-β Agonists Modulate T-Lymphocyte Activation and Ameliorate Left Ventricular Remodeling During Chronic Heart Failure.Circulation. Heart failure · 2022Article
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- Robust Transcriptional Profiling and Identification of Differentially Expressed Genes With Low Input RNA Sequencing of Adult Hippocampal Neural Stem and Progenitor Populations.Frontiers in molecular neuroscience · 2022Article
- Rare t(X;14)(q28;q32) translocation reveals link between MTCP1 and chronic lymphocytic leukemia.Nature communications · 2021Article
- Targeting DNA Damage Repair Functions of Two Histone Deacetylases, HDAC8 and SIRT6, Sensitizes Acute Myeloid Leukemia to NAMPT Inhibition.Clinical cancer research : an official journal of the American Association for Cancer Research · 2021Article
- Recurrent XPO1 mutations alter pathogenesis of chronic lymphocytic leukemia.Journal of hematology & oncology · 2021Article
- Eμ-TCL1xMyc: A Novel Mouse Model for Concurrent CLL and B-Cell Lymphoma.Clinical cancer research : an official journal of the American Association for Cancer Research · 2019Article
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundDirect cDNA preamplification protocols developed for single-cell RNA-seq have enabled transcriptome profiling of precious clinical samples and rare cell populations without the need for sample pooling or RNA extraction. We term the use of single-cell chemistries for sequencing low numbers of cells limiting-cell RNA-seq (lcRNA-seq). Currently, there is no customized algorithm to select robust/low-noise transcripts from lcRNA-seq data for between-group comparisons.
methodsHerein, we present CLEAR, a workflow that identifies reliably quantifiable transcripts in lcRNA-seq data for differentially expressed genes (DEG) analysis. Total RNA obtained from primary chronic lymphocytic leukemia (CLL) CD5+ and CD5- cells were used to develop the CLEAR algorithm. Once established, the performance of CLEAR was evaluated with FACS-sorted cells enriched from mouse Dentate Gyrus (DG).
resultsWhen using CLEAR transcripts vs. using all transcripts in CLL samples, downstream analyses revealed a higher proportion of shared transcripts across three input amounts and improved principal component analysis (PCA) separation of the two cell types. In mouse DG samples, CLEAR identifies noisy transcripts and their removal improves PCA separation of the anticipated cell populations. In addition, CLEAR was applied to two publicly-available datasets to demonstrate its utility in lcRNA-seq data from other institutions. If imputation is applied to limit the effect of missing data points, CLEAR can also be used in large clinical trials and in single cell studies.
conclusionslcRNA-seq coupled with CLEAR is widely used in our institution for profiling immune cells (circulating or tissue-infiltrating) for its transcript preservation characteristics. CLEAR fills an important niche in pre-processing lcRNA-seq data to facilitate transcriptome profiling and DEG analysis. We demonstrate the utility of CLEAR in analyzing rare cell populations in clinical samples and in murine neural DG region without sample pooling.
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Registered trials
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