biostudies-literatureWu RNatural Science Foundation of Guangxi ProvinceNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of China122https://www.ebi.ac.uk/biostudies/studies/S-EPMC10242993biostudies-literatureUnknown16(1)<h4>Background</h4>Some bladder-related diseases, such as bladder urinary tract infection (UTI) and bladder cancer (BCa), have significant six differences in incidence and prognosis. However, the molecular mechanisms underlying these sex differences are still not fully understood. Understanding the sex-biased differences in gene expression in normal bladder cells can help resolve these problems.<h4>Methods</h4>We first collected published single-cell RNA sequencing (scRNA-seq) data of normal human bladders from females and males to map the bladder transcriptomic landscape. Then, Gene Ontology (GO) analysis and gene set enrichment analysis (GSEA) were used to determine the significant pathways that changed in the specific cell populations. The Monocle2 package was performed to reconstruct tBMC medical genomicsSingle-cell RNA sequencing reveals sexual diversity in the human bladder and its prospective impacts on bladder cancer and urinary tract infection.PMC10242993ZR2019ZD39819711912014GXNSFDA118030Liao JSong QWu RTeng XZou CChen SWang LfalseSingle-cell RNA sequencing reveals sexual diversity in the human bladder and its prospective impacts on bladder cancer and urinary tract infection.<h4>Background</h4>Some bladder-related diseases, such as bladder urinary tract infection (UTI) and bladder cancer (BCa), have significant six differences in incidence and prognosis. However, the molecular mechanisms underlying these sex differences are still not fully understood. Understanding the sex-biased differences in gene expression in normal bladder cells can help resolve these problems.<h4>Methods</h4>We first collected published single-cell RNA sequencing (scRNA-seq) data of normal human bladders from females and males to map the bladder transcriptomic landscape. Then, Gene Ontology (GO) analysis and gene set enrichment analysis (GSEA) were used to determine the significant pathways that changed in the specific cell populations. The Monocle2 package was performed to reconstruct t2023-01-01T00:00:00Z2023 Jun2025-05-29T20:24:22.065Z2025-05-29T20:24:22.065ZS-EPMC102429933727778410.1186/s12920-023-01535-6