Endometriosis negatively impacts the health-related quality of life of 190 million women worldwide. Novel advances in nonhormonal treatments for this debilitating condition are desperately needed. Macrophages play a vital role in the pathophysiology of endometriosis and represent a promising therapeutic target. In the current study, we revealed the full transcriptomic complexity of endometriosis-associated macrophage subpopulations using single-cell analyses in a preclinical mouse model of experimental endometriosis. We have identified two key lesion-resident populations that resemble i) tumor-associated macrophages (characterized by expression of Folr2, Mrc1, Gas6, and Ccl8+) that promoted expression of Col1a1 and Tgfb1 in human endometrial stromal cells and increased angiogenic meshes in human umbilical vein endothelial cells, and ii) scar-associated macrophages (Mmp12, Cd9, Spp1, Trem2+) that exhibited a phenotype associated with fibrosis and matrix remodeling. We also described a population of proresolving large peritoneal macrophages that align with a lipid-associated macrophage phenotype (Apoe, Saa3, Pid1) concomitant with altered lipid metabolism and cholesterol efflux. Gain of function experiments using an Apoe mimetic resulted in decreased lesion size and fibrosis, and modification of peritoneal macrophage populations in the preclinical model. Using cross-species analysis of mouse and human single-cell datasets, we determined the concordance of peritoneal and lesion-resident macrophage subpopulations, identifying key similarities and differences in transcriptomic phenotypes. Ultimately, we envisage that these findings will inform the design and use of specific macrophage-targeted therapies and open broad avenues for the treatment of endometriosis.

UNASSIGNED: Pancreatic cancer (PC), characterized by its aggressive nature and low patient survival rate, remains a challenging malignancy. Anoikis, a process inhibiting the spread of metastatic cancer cells, is closely linked to cancer progression and metastasis through anoikis-related genes. Nonetheless, the precise mechanism of action of these genes in PC remains unclear.
UNASSIGNED: Study data were acquired from the Cancer Genome Atlas (TCGA) database, with validation data accessed at the Gene Expression Omnibus (GEO) database. Differential expression analysis and univariate Cox analysis were performed to determine prognostically relevant differentially expressed genes (DEGs) associated with anoikis. Unsupervised cluster analysis was then employed to categorize cancer samples. Subsequently, a least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted on the identified DEGs to establish a clinical prognostic gene signature. Using risk scores derived from this signature, patients with cancer were stratified into high-risk and low-risk groups, with further assessment conducted via survival analysis, immune infiltration analysis, and mutation analysis. External validation data were employed to confirm the findings, and Western blot and immunohistochemistry were utilized to validate risk genes for the clinical prognostic gene signature.
UNASSIGNED: A total of 20 prognostic-related DEGs associated with anoikis were obtained. The TCGA dataset revealed two distinct subgroups: cluster 1 and cluster 2. Utilizing the 20 DEGs, a clinical prognostic gene signature comprising two risk genes (CDKN3 and LAMA3) was constructed. Patients with pancreatic adenocarcinoma (PAAD) were classified into high-risk and low-risk groups per their risk scores, with the latter exhibiting a superior survival rate. Statistically significant variation was noted across immune infiltration and mutation levels between the two groups. Validation cohort results were consistent with the initial findings. Additionally, experimental verification confirmed the high expression of CDKN3 and LAMA3 in tumor samples.
UNASSIGNED: Our study addresses the gap in understanding the involvement of genes linked to anoikis in PAAD. The clinical prognostic gene signature developed herein accurately stratifies patients with PAAD, contributing to the advancement of precision medicine for these patients.

Advances in single-cell transcriptomics provide an unprecedented opportunity to explore complex biological processes. However, computational methods for analyzing single-cell transcriptomics still have room for improvement especially in dimension reduction, cell clustering, and cell-cell communication inference. Herein, we propose a versatile method, named DcjComm, for comprehensive analysis of single-cell transcriptomics. DcjComm detects functional modules to explore expression patterns and performs dimension reduction and clustering to discover cellular identities by the non-negative matrix factorization-based joint learning model. DcjComm then infers cell-cell communication by integrating ligand-receptor pairs, transcription factors, and target genes. DcjComm demonstrates superior performance compared to state-of-the-art methods.

OBJECTIVE: Gastric cancer (GC) ranks among the prevalent types of cancer, and its progression is influenced by the tumor microenvironment (TME). A comprehensive comprehension of the TME associated with GC has the potential to unveil therapeutic targets of significance.
METHODS: The complexity and heterogeneity of TME interactions were revealed through our investigation using an integrated analysis of single-cell and bulk-tissue sequencing data.
RESULTS: We constructed a single-cell transcriptomic atlas of 150,913 cells isolated from GC patients. Our analysis revealed the intricate nature and heterogeneity of the GC TME and the metabolic properties of major cell types. Furthermore, two cell subtypes, LOX+ Fibroblasts and M2 Macrophages, were enriched in tumor tissue and related to the outcome of GC patients. In addition, LOX+ Fibroblasts were significantly associated with M2 macrophages. immunofluorescence double labeling indicated LOX+ Fibroblasts and M2 Macrophages were tightly localized in GC tissue. The two cell subpopulations strongly interacted in a hypoxic microenvironment, yielding an immunosuppressive phenotype. Our findings further suggest that LOX+ Fibroblasts may act as a trigger for inducing the differentiation of monocytes into M2 Macrophages via the IL6-IL6R signaling pathway.
CONCLUSIONS: Our study revealed the intricate and interdependent communication network between the fibroblast and macrophage subpopulations, which could offer valuable insights for targeted manipulation of the tumor microenvironment.

OBJECTIVE: To ascertain the connection between cuproptosis-related genes (CRGs) and the prognosis of hepatocellular carcinoma (HCC) via single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data, relevant data were downloaded from the GEO and TCGA databases. The differentially expressed CRGs (DE-CRGs) were filtered by the overlaps in differentially expressed genes (DEGs) between HCC patients and normal controls (NCs) in the scRNA-seq database, DE-CRGs between high- and low-CRG-activity cells, and DEGs between HCC patients and NCs in the TCGA database.
RESULTS: Thirty-three DE-CRGs in HCC were identified. A prognostic model (PM) was created employing six survival-related genes (SRGs) (NDRG2, CYB5A, SOX4, MYC, TM4SF1, and IFI27) via univariate Cox regression analysis and LASSO. The predictive ability of the model was validated via a nomogram and receiver operating characteristic curves. Research has employed tumor immune dysfunction and exclusion as a means to examine the influence of PM on immunological heterogeneity. Macrophage M0 levels were significantly different between the high-risk group (HRG) and the low-risk group (LRG), and a greater macrophage level was linked to a more unfavorable prognosis. The drug sensitivity data indicated a substantial difference in the half-maximal drug-suppressive concentrations of idarubicin and rapamycin between the HRG and the LRG. The model was verified by employing public datasets and our cohort at both the protein and mRNA levels.
CONCLUSIONS: A PM using 6 SRGs (NDRG2, CYB5A, SOX4, MYC, TM4SF1, and IFI27) was developed via bioinformatics research. This model might provide a fresh perspective for assessing and managing HCC.

The central nervous system (CNS) comprises a diverse range of brain cell types with distinct functions and gene expression profiles. Although single-cell RNA sequencing (scRNA-seq) provides new insights into the brain cell atlases, integrating large-scale CNS scRNA-seq data still encounters challenges due to the complexity and heterogeneity among CNS cell types/subtypes. In this study, we introduce a self-supervised contrastive learning method, called scCM, for integrating large-scale CNS scRNA-seq data. scCM brings functionally related cells close together while simultaneously pushing apart dissimilar cells by comparing the variations of gene expression, effectively revealing the heterogeneous relationships within the CNS cell types/subtypes. The effectiveness of scCM is evaluated on 20 CNS datasets covering 4 species and 10 CNS diseases. Leveraging these strengths, we successfully integrate the collected human CNS datasets into a large-scale reference to annotate cell types and subtypes in neural tissues. Results demonstrate that scCM provides an accurate annotation, along with rich spatial information of cell state. In summary, scCM is a robust and promising method for integrating large-scale CNS scRNA-seq data, enabling researchers to gain insights into the cellular and molecular mechanisms underlying CNS functions and diseases.

Alzheimer\'s disease (AD) is the most common cause of dementia, characterized by memory loss, cognitive decline, personality changes, and various neurological symptoms. The role of blood-brain barrier (BBB) injury, extracellular matrix (ECM) abnormalities, and oligodendrocytes (ODCs) dysfunction in AD has gained increasing attention, yet the detailed pathogenesis remains elusive. This study integrates single-cell sequencing of AD patients\' cerebrovascular system with a genome-wide association analysis. It aims to elucidate the associations and potential mechanisms behind pericytes injury, ECM disorder, and ODCs dysfunction in AD pathogenesis. Finally, we identified that abnormalities in the pericyte PI3K-AKT-FOXO signaling pathway may be involved in the pathogenic process of AD. This comprehensive approach sheds new light on the complex etiology of AD and opens avenues for advanced research into its pathogenesis and therapeutic strategies.

Heteroplasmic mitochondrial DNA (mtDNA) variants accumulate as humans age, particularly in the stem-cell compartments, and are an important contributor to age-related disease. Mitochondrial dysfunction has been observed in osteoporosis and somatic mtDNA pathogenic variants have been observed in animal models of osteoporosis. However, this has never been assessed in the relevant human tissue. Mesenchymal stem cells (MSCs) are the progenitors to many cells of the musculoskeletal system and are critical to skeletal tissues and bone vitality. Investigating mtDNA in MSCs could provide novel insights into the role of mitochondrial dysfunction in osteoporosis. To determine if this is possible, we investigated the landscape of somatic mtDNA variation in MSCs through a combination of fluorescence-activated cell sorting and single-cell next-generation sequencing. Our data show that somatic heteroplasmic variants are present in individual patient-derived MSCs, can reach high heteroplasmic fractions and have the potential to be pathogenic. The identification of somatic heteroplasmic variants in MSCs of patients highlights the potential for mitochondrial dysfunction to contribute to the pathogenesis of osteoporosis.

In our cells, a limited number of RNA binding proteins (RBPs) are responsible for all aspects of RNA metabolism across the entire transcriptome. To accomplish this, RBPs form regulatory units that act on specific target regulons. However, the landscape of RBP combinatorial interactions remains poorly explored. Here, we perform a systematic annotation of RBP combinatorial interactions via multimodal data integration. We build a large-scale map of RBP protein neighborhoods by generating in vivo proximity-dependent biotinylation datasets of 50 human RBPs. In parallel, we use CRISPR interference with single-cell readout to capture transcriptomic changes upon RBP knockdowns. By combining these physical and functional interaction readouts, along with the atlas of RBP mRNA targets from eCLIP assays, we generate an integrated map of functional RBP interactions. We then use this map to match RBPs to their context-specific functions and validate the predicted functions biochemically for four RBPs. This study provides a detailed map of RBP interactions and deconvolves them into distinct regulatory modules with annotated functions and target regulons. This multimodal and integrative framework provides a principled approach for studying post-transcriptional regulatory processes and enriches our understanding of their underlying mechanisms.

Pluripotent mouse embryonic stem cells (ESCs) can differentiate to all germ layers and serve as an in vitro model of embryonic development. To better understand the differentiation paths traversed by ESCs committing to different lineages, we track individual differentiating ESCs by timelapse imaging followed by multiplexed high-dimensional Imaging Mass Cytometry (IMC) protein quantification. This links continuous live single-cell molecular NANOG and cellular dynamics quantification over 5-6 generations to protein expression of 37 different molecular regulators in the same single cells at the observation endpoints. Using this unique data set including kinship history and live lineage marker detection, we show that NANOG downregulation occurs generations prior to, but is not sufficient for neuroectoderm marker Sox1 upregulation. We identify a developmental cell type co-expressing both the canonical Sox1 neuroectoderm and FoxA2 endoderm markers in vitro and confirm the presence of such a population in the post-implantation embryo. RNASeq reveals cells co-expressing SOX1 and FOXA2 to have a unique cell state characterized by expression of both endoderm as well as neuroectoderm genes suggesting lineage potential towards both germ layers.