Mouse studies have highlighted the requirement of the extracellular matrix Fras and Frem proteins for embryonic epidermal adhesion. Mutations of the genes encoding some of these proteins underlie the blebs mouse mutants, whereas mutations in human FRAS1 and FREM2 cause Fraser syndrome, a congenital disorder characterized by embryonic blistering and renal defects. We have cloned the zebrafish homologues of these genes and characterized their evolutionary diversification and expression during development. The fish gene complement includes fras1, frem1a, frem1b, frem2a, frem2b, and frem3, which display complex overlapping and complementary expression patterns in developing tissues including the pharyngeal arches, hypochord, musculature, and otic vesicle. Expression during fin development delineates distinct populations of epidermal cells which have previously only been described at a morphological level. We detect relatively little gene expression in epidermis or pronephros, suggesting that the essential role of these proteins in mediating their development in humans and mice is recently evolved.
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http://dx.doi.org/10.1002/dvdy.21729 | DOI Listing |
Arch Pathol Lab Med
January 2025
the Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles (Petersen, Stuart, He, Ju, Ghezavati, Siddiqi, Wang).
Context.—: The co-occurrence of plasma cell neoplasm (PCN) and lymphoplasmacytic lymphoma (LPL) is rare, and their clonal relationship remains unclear.
Objective.
Brief Bioinform
November 2024
Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, Brisbane, QLD 4072, Australia.
Regulatory genes are critical determinants of cellular responses in development and disease, but standard RNA sequencing (RNA-seq) analysis workflows, such as differential expression analysis, have significant limitations in revealing the regulatory basis of cell identity and function. To address this challenge, we present the TRIAGE R package, a toolkit specifically designed to analyze regulatory elements in both bulk and single-cell RNA-seq datasets. The package is built upon TRIAGE methods, which leverage consortium-level H3K27me3 data to enrich for cell-type-specific regulatory regions.
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November 2024
State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, 2 Sipailou, Xuanwu District, Nanjing 210096, China.
Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the spatial context of tissues. Paired with spatial transcriptomics data, platforms often provide histology and (or) chromatin images, which capture cellular morphology and chromatin organization. Additionally, single-cell RNA sequencing (scRNA-seq) data from matching tissues often accompany spatial data, offering a transcriptome-wide gene expression profile of individual cells.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju 61005, Republic of Korea.
Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex interactions among drugs and their wide-ranging effects. To address this issue, we introduce DD-PRiSM (Decomposition of Drug-Pair Response into Synergy and Monotherapy effect), a deep-learning pipeline that predicts the effects of combination therapy.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Department of Electronic Engineering, Tsinghua University, 100084 Beijing, China.
Single-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities such as RNA gene expression and Assay for Transposase-Accessible Chromatin (ATAC) within individual cells, have become a powerful tool for deciphering the intricate complexity of cellular systems. Most current methods rely on motif databases to establish cross-modality relationships between genes from RNA-seq data and peaks from ATAC-seq data. However, these approaches are constrained by incomplete database coverage, particularly for novel or poorly characterized relationships.
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