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pj scAS
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A relay velocity model infers cell-dependent RNA velocity https://www.nature.com/articles/s41587-023-01728-5?fbclid=IwAR2UdvHaG0NW0k9q4Kui7SFKcLoaQnEWOKqdNwOW9opGl-PvaIy4AM-t0wc
https://www.nature.com/articles/s41592-023-01829-8?fbclid=IwAR13Oh-E_FffU5Er_NVIT1Xngf7o4N3VoO5tYQPrwvkCWOkR6InXBC3QYAE TEMPOmap enables spatiotemporally resolved transcriptomics
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8306420/
CD44 alternative splicing senses intragenic DNA methylation in tumors via direct and indirect mechanisms Here, we found that HCT116 colon carcinoma cells inactivated for the DNA methylases DNMT1/3b undergo a partial epithelial to mesenchymal transition associated with increased CD44 variant exon skipping. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216461/
CD44: A Multifunctional Cell Surface Adhesion Receptor Is a Regulator of Progression and Metastasis of Cancer Cells Keywords: CD44, cancer, metastasis, hyaluronic acid, migration, angiogenesis, invasion, CD44-ICD The variant isoforms of CD44 (CD44v) comprises of exon 6–15 spliced at various sites between exons 5 and 16 of the standard isoform (Goodison et al., 1999; Zeilstra et al., 2014). Keratinocytes, macrophages, and select epithelial cells express the variant CD44 (CD44v) isoforms and are present on tissues at various stages of development (Sneath and Mangham, 1998). In normal tissues, the importance of CD44 is vital in the regulation of hyaluronic metabolism, activation of lymphocytes, and release of cytokines. However, targeting of CD44 resulting in its loss leads to the disruption of hyaluronic metabolism, wound healing, and keratinocyte proliferation (Yu and Stamenkovic, 1999). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5339222/
Alternative RNA Splicing—The Trojan Horse of Cancer Cells in Chemotherapy Keywords: alternative splicing, splice variants, cancer pathobiology, drug resistance https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8306420/
chimeric antigen receptor, cellular therapy, immunotherapy, acute myeloid leukemia, microenvironment https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7058784/
Acute myeloid leukemia, Microenvironment, Single-cell RNA sequencing, Immune phenotypes, Bone marrow, Immune cells, Myeloid cells, T lymphocytes https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7919996/
https://www.nature.com/articles/s41586-021-03605-0 review: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7772363/
MOCCASIN: a method for correcting for known and unknown confounders in RNA splicing analysis https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184769/
alternative splicing, immune activity, non-coding RNA. Table 1 summary https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8071365/
alternative splicing (AS), nonsense-mediated RNA decay (NMD), AS-NMD, gene expression regulation https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764535/
"Alternative splicing analysis based on scRNA-seq is revolutionizing our understanding of the effect of AS on immune cells. Recently, scRNA-seq revealed the bimodality of AS in immune cells, and bulk RNA-seq might mask differences in AS between single cells12. However, the current computational framework for RNA-seq AS analysis does not effectively detect differential splicing between groups at the single-cell level. DEXSeq13, rMATS14, and MISO15 were developed for bulk RNA-seq data. Therefore, these methods might lead to incorrect results as the underlying algorithms may not be appropriate to process scRNA-seq data due to the low sequencing depth and high dropout rate. Some programs, BRIE16, VALERIE17, Millefy18, Outrigger19, and an NMF-based method20, were recently developed to process scRNA-seq data. However, BRIE requires performing a pairwise comparison between every two cells to detect differential splicing, which is time-consuming and impractical. Outrigger utilizes the distribution mode of percent-spliced-in (Psi) to detect differential splicing between cell groups. However, the distribution modes are limited to five types, and do not accurately reflect reality. Thus, there is an urgent need to develop a convenient and effective computation tool to detect differential splicing between groups." PMC7935992
To explore single-cell splicing heterogeneity in high resolution, we developed a novel computation framework, scAS, to detect differential splicing between groups at the single-cell level. We applied it to a published scRNA-seq dataset from the previous T-cell dataset [PMC7935992]. We compared results and found that our method is more robust against sampling and positional biases occurring at 3'UTRs. We also validated our findings in a single cell dataset from [moumita's singlecell]. Thus, systematic evaluation of differential splicing across T cells and premalignant basal cells and our understanding of the AS characteristics of TILs and will facilitate improvements to cancer diagnosis and treatment.
motivation: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935992
lung cancer alternative splicing: https://pubmed.ncbi.nlm.nih.gov/31152916/
general review : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7902610/ clinical translation: https://pubmed.ncbi.nlm.nih.gov/29279605/
CD44 represents a common biomarker of cancer stem cells, and promotes epithelial-mesenchymal transition. CD44 alternative splicing https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7727191/
Cancer-transcriptomics allows researchers to increase parameters to deal with cancer cell heterogeneity 29279605
"RNA-Seq assays offer potential clinical benefits including the ability to detect expressed structural variants (SVs), alternative isoform usage and splicing variation, and global gene expression, all of which are known to be relevant for understanding the pathogenesis of myeloid malignancies." PMID29279605
"In this work, we show that RNA-Seq based testing exceeds the current clinical standard of care for the assessment of myeloid malignancies, and provides the broadest range of genomic information, when compared to WES- and WGS-based approaches. Further, we develop a novel gene expression signature that allows for the restratification of cases classified by the current protocol as intermediate-risk AMLs into high- or low-risk subgroups, thereby allowing better risk stratification for clinical management. Finally, to demonstrate the utility of transcriptome-based testing in improving therapy selection in AML, we identify a subset of high-risk patients with dysregulated integrin signaling, which is potentially amenable to inhibitors of focal adhesion kinase (FAK). As therapeutic options for myeloid malignancies continue to evolve, a global transcriptome-based approach to diagnostics will allow reconfiguration of mutation- and expression-based predictors to best take advantage of new genomic information as it arises.PMC8087683"
There are some tools for the identification of single-cell level polyadenylation change. Sierra uses splice-aware peaks trusting gene annotations https://pubmed.ncbi.nlm.nih.gov/32641141/ There are emerging needs for alternative splicing tools for single cell data Alternative splicing in single-cell level gives us useful information to describe cell heterogeneity in immune cells PMC7935992 PMC7935992.
four LUAD subtypes according to different APA factor expression patterns displayed distinct clinical results and oncogenic features related to tumor microenvironment including immune, metabolic, and hypoxic status. https://pubmed.ncbi.nlm.nih.gov/33815479/
Aberrant alternative splicing patterns found in lung cancer contribute to important cell functions. These include changes in splicing for the BCL2L1, MDM2, MDM4, NUMB and MET genes during lung tumourigenesis, to affect pathways involved in apoptosis, cell proliferation and cellular cohesion. changes in expression of QKI, RBM4, RBM5, RBM6, RBM10 and SRSF1 proteins regulate many of the most frequently referenced aberrant splicing events in lung cancer.31152916
Nature Koo https://github.com/adamh-broad/single_cell_intestine GSE92332, smart-seq2, https://github.com/GenomicParisCentre/eoulsan/wiki/Smart-Seq2-scRNA-seq-tutorial
review https://github.com/songlab-cal/scquint
Percentage of Distal polyA site Usage Index (PDUI), which indicates lengthening (positive index) or shortening (negative index) of 3′ UTRs (Xia et al., 2014). https://pubmed.ncbi.nlm.nih.gov/33815479/