Combined single-cell gene and isoform expression analysis in haematopoietic stem and progenitor cells
Mincarelli, L.; Uzun, V.; Rushworth, S. A.; Haerty, W.; Macaulay, I. C.
Show abstract
Single-cell RNA sequencing (scRNA-seq) enables gene expression profiling and characterization of novel cell types within heterogeneous cell populations. However, most approaches cannot detect alternatively spliced transcripts, which can profoundly shape cell phenotype by generating functionally distinct proteins from the same gene. Here, we integrate short- and long-read scRNA-seq of hematopoietic stem and progenitor cells to characterize changes in cell type abundance, gene and isoform expression during differentiation and ageing.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Blood-based Epigenetic Instability Linked to Human Aging and Disease 97%
- Identification of leukemic and pre-leukemic stem cells by clonal tracking from single-cell transcriptomics 96%
- Expression of terminal deoxynucleotidyl transferase (TdT) identifies lymphoid-primed progenitors in human bone marrow 95%
Similar papers in this journal
- A single cell framework identifies functionally and molecularly distinct multipotent progenitors in adult human hematopoiesis 96%
- Distinct causes of three phenotypic hallmarks of hematopoietic aging 96%
- Integrating Natural and Engineered Genetic Variation to Decode Regulatory Influence on Blood Traits 95%
Similar papers in this journal
- Comprehensive single-cell genome analysis at nucleotide resolution using the PTA Analysis Toolbox 96%
- Epigenomic mapping in B-cell acute lymphoblastic leukemia identifies transcriptional regulators and noncoding variants promoting distinct chromatin architectures 94%
- Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.