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Alzheimers Dement
December 2024
NYU Grossman School of Medicine, New York, NY, USA; NYU, New York City, NY, USA.
Background: Astrocytes, a major glial cell in the central nervous system (CNS), can become reactive in response to inflammation or injury, and release toxic factors that kill specific subtypes of neurons. Over the past several decades, many groups report that reactive astrocytes are present in the brains of patients with Alzheimer's disease, as well as several other neurodegenerative diseases. In addition, reactive astrocyte sub-types most associated with these diseases are now reported to be present during CNS cancers of several types.
View Article and Find Full Text PDFThe role of endothelial cell-related gene COL1A1 in prostate cancer diagnosis and immunotherapy: insights from machine learning and single-cell analysis.
Biol Direct
January 2025
Cancer Research Center Nantong, Affiliated Tumor Hospital of Nantong University & Nantong Tumor Hospital, Nantong, China.
Background: Endothelial cells are integral components of the tumor microenvironment and play a multifaceted role in tumor immunotherapy. Targeting endothelial cells and related signaling pathways can improve the effectiveness of immunotherapy by normalizing tumor blood vessels and promoting immune cell infiltration. However, to date, there have been no comprehensive studies analyzing the role of endothelial cells in the diagnosis and treatment of prostate adenocarcinoma (PRAD).
View Article and Find Full Text PDFTranscript-specific enrichment enables profiling of rare cell states via single-cell RNA sequencing.
Nat Genet
January 2025
Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Single-cell genomics technologies have accelerated our understanding of cell-state heterogeneity in diverse contexts. Although single-cell RNA sequencing identifies rare populations that express specific marker transcript combinations, traditional flow sorting requires cell surface markers with high-fidelity antibodies, limiting our ability to interrogate these populations. In addition, many single-cell studies require the isolation of nuclei from tissue, eliminating the ability to enrich learned rare cell states based on extranuclear protein markers.
View Article and Find Full Text PDFIdentification of a novel disulfidptosis-related gene signature in osteoarthritis using bioinformatics analysis and experimental validation.
Sci Rep
January 2025
Department of Orthopedic Surgery, 920th Hospital of Joint Logistics Support Force of PLA, Kunming, China.
Osteoarthritis (OA) is a degenerative bone disease characterized by the destruction of joint cartilage and synovial inflammation, involving intricate immune regulation processes. Disulfidptosis, a novel form of programmed cell death, has recently been identified; however, the effects and roles of disulfidptosis-related genes (DR-DEGs) in OA remain unclear. We obtained six OA datasets from the GEO database, using four as training sets and two as validation sets.
View Article and Find Full Text PDFInferring metabolic objectives and trade-offs in single cells during embryogenesis.
Cell Syst
December 2024
Center for Bioinformatics and Computational Medicine, Ann Arbor, MI 48109, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA; Program in Chemical Biology, University of Michigan, Ann Arbor, MI, USA; Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA. Electronic address:
While proliferating cells optimize their metabolism to produce biomass, the metabolic objectives of cells that perform non-proliferative tasks are unclear. The opposing requirements for optimizing each objective result in a trade-off that forces single cells to prioritize their metabolic needs and optimally allocate limited resources. Here, we present single-cell optimization objective and trade-off inference (SCOOTI), which infers metabolic objectives and trade-offs in biological systems by integrating bulk and single-cell omics data, using metabolic modeling and machine learning.
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