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Results for “pseudotemporal trajectory”

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PubMed2026

Identification of Drug-resistant Cell Subpopulations in Colorectal Cancer Through Single-cell Analysis and Exploration of Potential Therapeutic Strategies.

INTRODUCTION: The therapeutic efficacy of Colorectal Cancer (CRC) is often compromised by resistance to the standard chemotherapy agent oxaliplatin. METHODS: This study obtained single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database. Differentially Expressed Genes (DEGs) between resistant and sensitive epithelial subpopulations were identified, followed by enrichment analysis. Pseudotemporal trajectory and cell-cell communication were analyzed using Monocle2 and CellChat, respectively. The candidate drug was predicted by Connectivity Map (cMAP) analysis. External validation included assessment of the EpC2 signature in an oxaliplatin-resistant cell line dataset (GSE76092), survival analysis using The Cancer Genome Atlas (TCGA) cohorts, and re-analysis of the GSE179784 dataset to assess the reproducibility of EpC2-like subpopulations and their DNA Damage Repair (DDR) scores. RESULTS: Cell subpopulations were divided into 10 clusters. Among them, epithelial cells comprised 5 subpopulations, with EPC2 identified as a potential oxaliplatin-resistant subset. DEGs were enriched in the TNF and IL-17 pathways. External validation confirmed the enrichment of EpC2 in resistant cell lines and its association with poor survival. Pseudotemporal trajectory revealed that epithelial cells underwent state transitions, forming two distinct branches. The resistant group exhibited enrichment in RNA splicing and NF-κB pathways. Cell-cell communication analysis revealed interactions involving MDK- NCL and PPIA-BSG. Dasatinib was predicted as a candidate drug. DISCUSSION: We identified an oxaliplatin-resistant subpopulation of Epithelial Cells (EpC2) in CRC, elucidated its multi-layered resistance mechanisms, and integrated multi- omics and cMAP database analyses to predict a potential intervention drug. CONCLUSION: This study provided potential therapeutic possibilities for oxaliplatin resistance, contributing to CRC treatment.

Humans↗
PubMed2026

MX1+ effector T cells hyperactivation at the maternal-fetal interface in unexplained recurrent pregnancy loss.

BACKGROUND: Immune tolerance breakdown at the maternal-fetal interface is implicated in unexplained recurrent pregnancy loss (URPL), but the interplay between T cell hyperactivation and dendritic cells (DCs)-mediated signaling remains poorly defined. METHODS: First-trimester decidual tissues from 5 healthy controls and 6 URPL patients underwent single-cell RNA sequencing (scRNA-seq, 10× Genomics). Computational analyses included clustering (Seurat), trajectory inference (scTour), intercellular communication (CellChat) and metabolic pathway enrichment (Gene Ontology and scMetabolism). Flow cytometry was performed from 11 patients and 11 healthy controls. Spatial validation was performed via multiplex immunohistochemistry and immunohistochemistry on 12 additional controls and 12 URPL cases. Statistical significance was assessed using Student’s t-test. RESULTS: URPL decidua exhibited marked CD3+ T cells and MX1+effector T (Tem) cells infiltration and activation. Flow cytometry analysis confirmed a significant decidua-specific upregulation of T cell activation markers CD25 and CD69 specifically on the MX1+Tem subset in URPL patients compared to controls. MX1+Tem cell subset demonstrated interferon hyperactivation, proliferative hyperactivity and lipid-biased immunometabolism. Pseudotemporal analysis positioned MX1+ Tem cells between classical Tem and exhausted T cell states, suggesting progressive differentiation. CellChat identified DCs as key regulators of MX1+ Tem expansion via aberrant ICOSL signaling, validated by spatial co-localization of ICOSL+ DCs and MX1+ Tem cells in URPL tissues. CONCLUSION: Our findings demonstrate that the aberrant activation and proliferation of MX1+Tem cells as a key immunological feature associated with URPL patients.

Humans↗