Luyao Jiang^1,#, Longsheng Fu^2,#, Shaofeng Xiong^2,3, Guosheng Cao⁴, Yanqin Mei^2,3, Yaoqi Wu², Jin Chen^1,*, Yanni LV^2,5,6,*

BIOCELL, Vol.49, No.9, pp. 1749-1769, 2025, DOI:10.32604/biocell.2025.068507 - 25 September 2025

Abstract Background: After ischemic stroke, neutrophils hyperactivate, increasing in number and worsening inflammation, causing neural damage. Prior scRNA-seq showed Lrg1 modulates cells subsentence to cerebral ischemia-reperfusion injury, but its mechanism in regulating neutrophil accumulation/differentiation post-injury is unclear. Methods: Lrg1 knockout impact on neutrophil accumulation was assessed via immunofluorescence and western blot. Three-dimensional reconstruction of immunofluorescent staining analyzed cell-cell interactions among neutrophils and microglia. scRNA-seq of WT and Lrg1^-/- mice from GSE245386 and GSE279462 was conducted. Each group conducted oxidative phosphorylation scoring via Gene Set Enrichment Analysis (GSEA), while Metascape was employed to perform GO and KEGG enrichment… More > Graphic Abstract

JUN SHEN^1,#, HONGFANG MA^2,#, YONGXIA CHEN³, JIANGUO SHEN^1,*

Oncology Research, Vol.31, No.6, pp. 955-966, 2023, DOI:10.32604/or.2023.029638 - 15 September 2023

Abstract The process of lymphatic metastasis was proved to be associated with podoplanin-expressing macrophages in breast cancer (BC). This study aimed to investigate the role of the M2 phenotype of tumor-associated macrophages and mine the key M2 macrophages-related genes for lymph node metastasis in BC. We downloaded the GSE158399 dataset from the Gene Expression Omnibus (GEO) database, which includes transcriptomic profiles of individual cells from primary tumors, negative lymph nodes (NLNs), and positive lymph nodes (PLNs) of breast cancer patients. The cell subsets were identified by clustering analysis after quality control of the scRNA-seq using Seurat.… More >

JOE R. DELANEY^*

BIOCELL, Vol.45, No.5, pp. 1167-1170, 2021, DOI:10.32604/biocell.2021.017296 - 12 July 2021

Abstract Single-cell sequencing data has transformed the understanding of biological heterogeneity. While many flavors of single-cell sequencing have been developed, single-cell RNA sequencing (scRNA-seq) is currently the most prolific form in published literature. Bioinformatic analysis of differential biology within the population of cells studied relies on inferences and grouping of cells due to the spotty nature of data within individual cell scRNA-seq gene counts. One biologically relevant variable is readily inferred from scRNA-seq gene count tables regardless of individual gene representation within single cells: aneuploidy. Since hundreds of genes are present on chromosome arms, high-quality inferences More >

Amin Einipour¹, Mohammad Mosleh^{1, *}, Karim Ansari-Asl^{1, 2}

CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.1, pp. 377-397, 2020, DOI:10.32604/cmes.2020.08496 - 01 January 2020

Abstract The emergence of single-cell RNA-sequencing (scRNA-seq) technology has introduced new information about the structure of cells, diseases, and their associated biological factors. One of the main uses of scRNA-seq is identifying cell populations, which sometimes leads to the detection of rare cell populations. However, the new method is still in its infancy and with its advantages comes computational challenges that are just beginning to address. An important tool in the analysis is dimensionality reduction, which transforms high dimensional data into a meaningful reduced subspace. The technique allows noise removal, visualization and compression of high-dimensional data.… More >