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The α-globin super-enhancer acts in an orientation-dependent manner.
Nat Commun
January 2025
Gene Regulation Laboratory, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, OX3 9DS, Oxford, UK.
Individual enhancers are defined as short genomic regulatory elements, bound by transcription factors, and able to activate cell-specific gene expression at a distance, in an orientation-independent manner. Within mammalian genomes, enhancer-like elements may be found individually or within clusters referred to as locus control regions or super-enhancers (SEs). While these behave similarly to individual enhancers with respect to cell specificity, distribution and distance, their orientation-dependence has not been formally tested.
View Article and Find Full Text PDFHemaScope: A Tool for Analyzing Single-cell and Spatial Transcriptomics Data of Hematopoietic Cells.
Genomics Proteomics Bioinformatics
January 2025
Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Research Unit of Hematologic Malignancies Genomics and Translational Research of Chinese Academy of Medical Sciences, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) techniques hold great value in evaluating the heterogeneity and spatial characteristics of hematopoietic cells within tissues. These two techniques are highly complementary, with scRNA-seq offering single-cell resolution and ST retaining spatial information. However, there is an urgent demand for well-organized and user-friendly toolkits capable of handling single-cell and spatial information.
View Article and Find Full Text PDFTranscriptomic Evidence Reveals the Dysfunctional Mechanism of Synaptic Plasticity Control in ASD.
Genes (Basel)
December 2024
Institute for Complex Systems and Mathematical Biology, King's College, University of Aberdeen, Old Aberdeen AB24 3UE, UK.
Background/objectives: A prominent endophenotype in Autism Spectrum Disorder (ASD) is the synaptic plasticity dysfunction, yet the molecular mechanism remains elusive. As a prototype, we investigate the postsynaptic signal transduction network in glutamatergic neurons and integrate single-cell nucleus transcriptomics data from the Prefrontal Cortex (PFC) to unveil the malfunction of translation control.
Methods: We devise an innovative and highly dependable pipeline to transform our acquired signal transduction network into an mRNA Signaling-Regulatory Network (mSiReN) and analyze it at the RNA level.
Chromatin accessibility provides a window into the genetic etiology of human brain disease.
Trends Genet
January 2025
Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY 10468, USA; Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY 10468, USA. Electronic address:
Neuropsychiatric and neurodegenerative diseases have a significant genetic component. Risk variants often affect the noncoding genome, altering cis-regulatory elements (CREs) and chromatin structure, ultimately impacting gene expression. Chromatin accessibility profiling methods, especially assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq), have been used to pinpoint disease-associated SNPs and link them to affected genes and cell types in the brain.
View Article and Find Full Text PDFAutomated cell annotation in multi-cell images using an improved CRF_ID algorithm.
Elife
January 2025
School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, United States.
Cell identification is an important yet difficult process in data analysis of biological images. Previously, we developed an automated cell identification method called CRF_ID and demonstrated its high performance in whole-brain images (Chaudhary et al., 2021).
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