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Increasingly, scRNA-Seq studies explore cell populations across different samples and the effect of sample heterogeneity on organisms phenotype. However, relatively few bioinformatic methods have been developed which adequately address the variation between samples for such population-level analyses. We propose a framework for representing the entire single-cell profile of a sample, which we call a GloScope representation. We implement GloScope on scRNA-Seq datasets from study designs ranging from 12 to over 300 samples and demonstrate how GloScope allows researchers to perform essential bioinformatic tasks at the sample-level, in particular visualization and quality control assessment.

Type I interferon (IFN-I) and IFN-γ foster antitumor immunity by facilitating T cell responses. Paradoxically, IFNs may promote T cell exhaustion by activating immune checkpoints. The downstream regulators of these disparate responses are incompletely understood. Here, we describe how interferon regulatory factor 1 (IRF1) orchestrates these opposing effects of IFNs. IRF1 expression in tumor cells blocks Toll-like receptor- and IFN-I-dependent host antitumor immunity by preventing interferon-stimulated gene (ISG) and effector programs in immune cells. In contrast, expression of IRF1 in the host is required for antitumor immunity. Mechanistically, IRF1 binds distinctly or together with STAT1 at promoters of immunosuppressive but not immunostimulatory ISGs in tumor cells. Overexpression of programmed cell death ligand 1 (PD-L1) in Irf1-/- tumors only partially restores tumor growth, suggesting multifactorial effects of IRF1 on antitumor immunity. Thus, we identify that IRF1 expression in tumor cells opposes host IFN-I- and IRF1-dependent antitumor immunity to facilitate immune escape and tumor growth.

BACKGROUND: Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable. RESULTS: Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html . CONCLUSIONS: Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

Sample multiplexing enables pooled analysis during single-cell RNA sequencing workflows, thereby increasing throughput and reducing batch effects. A challenge for all multiplexing techniques is to link sample-specific barcodes with cell-specific barcodes, then demultiplex sample identity post-sequencing. However, existing demultiplexing tools fail under many real-world conditions where barcode cross-contamination is an issue. We therefore developed deMULTIplex2, an algorithm inspired by a mechanistic model of barcode cross-contamination. deMULTIplex2 employs generalized linear models and expectation-maximization to probabilistically determine the sample identity of each cell. Benchmarking reveals superior performance across various experimental conditions, particularly on large or noisy datasets with unbalanced sample compositions.

Fanconi anemia (FA) is the predominant hereditary syndrome of bone marrow failure (BMF), distinguished by impairments in DNA repair mechanisms. The deficiency in the FANC pathway, which governs DNA repair and replication rescue, results in aberrant responses to DNA damage in individuals with FA. The objective of this study is to examine the involvement of the FANC core complex in BMF and ascertain nucleolar homeostasis-related genes by conducting transcriptome analysis on primary hematopoietic stem cells obtained from FA patients with FANCA and FANCC variants. In the present study, we analyzed scRNA-seq data obtained from both healthy donors and individuals diagnosed with FA in order to investigate the phenomenon of cell–cell communication. Through the implementation of trajectory analysis, the differentiation pathways of several progenitor cell types, such as HSC cells transitioning into LMPP, N, M, B-prog, and E cells, were elucidated. Moreover, by scrutinizing the inferred interactions, notable disparities in cell–cell communication were observed between FA patients and their healthy counterparts. Our analysis has unveiled heightened interactions involving TNFSF13B, MIF, IL16, and COL4A2 in patients with FA. Furthermore, we have developed a prognostic model for predicting the outcome of acute myeloid leukemia (AML) which has exhibited remarkable predictive precision, with an AUC exceeding 0.83 at the 3- and 5-year intervals following the baseline assessment. In summary, the prognostic model that has been developed provides a valuable instrument for forecasting outcomes of AML by utilizing the genes identified through both single-cell and bulk transcriptome analyses. [ABSTRACT FROM AUTHOR]

Free-range and confined feeding are typical farming strategies that produce vastly different outcomes in pig immune development. In this study, we extensively evaluated peripheral blood mononuclear cells (PBMCs) obtained from individually raised Meishan pigs under two distinct conditions: normal captivity (NC) and arch soil free-range (ASF), using single-cell sequencing technology. The purpose of this study was to elucidate the influence of various farming approaches on immune cell development. Our analysis identified 11 immune cell subtypes. Using differential gene analysis, we demonstrated that ASF produced the largest impact on the B cells and gamma delta T cell (γδ T cells) transcriptional profile, with subsequent impact on naïve CD4+ T cells and naïve CD8+ T cells. Based on our IL-16 network central analysis, relative to NC cells, signals obtained by B cells, effector and memory CD4+ T cells, and naïve CD4+ T cells were drastically diminished in the ASF cells. Additionally, we conducted an extensive network analysis using CellChat, and demonstrated a marked augmentation of axes related to cellular signal transduction and immunologic activity, including IL-16, RESISTIN, and MIF, among ASF cells. This indicated that various farming approaches differently impacted pig immune activity, and this process was primarily mediated by cell-cell communication. In conclusion, these findings not only enhance the available knowledge of the association between farming approaches and immune modulation, but also provide essential information for the sustainable establishment of animal husbandry and animal health management. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion: Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment. [ABSTRACT FROM AUTHOR]

The pathophysiology of Crohn's-like disease of the pouch (CDP) in patients with a history of ulcerative colitis (UC) is unknown. We examined mucosal cells from patients with and without CDP using single-cell analyses. Endoscopic samples were collected from pouch body and prepouch ileum (pouch/ileum) of 50 patients with an ileal pouch-anal anastomosis. Single-cell RNA sequencing was performed on pouch/ileal tissues of patients with normal pouch/ileum and CDP. Mass cytometry was performed on mucosal immune cells from patients with UC with normal pouch/ileum, CDP, pouchitis, and those with familial adenomatous polyposis after pouch formation. Findings were independently validated using immunohistochemistry. The cell populations/states in the pouch body differed from those in the prepouch ileum, likely secondary to increased microbial burden. Compared with the familial adenomatous polyposis pouch, the UC pouch was enriched in colitogenic immune cells even without inflammation. CDP was characterized by increases in T helper 17 cells, inflammatory fibroblasts, inflammatory monocytes, TREM1+ monocytes, clonal expansion of effector T cells, and overexpression of T helper 17 cells–inducing cytokine genes such as IL23 , IL1B , and IL6 by mononuclear phagocytes. Ligand-receptor analysis further revealed a stromal-mononuclear phagocytes-lymphocyte circuit in CDP. Integrated analysis showed that up-regulated immune mediators in CDP were similar to those in CD and pouchitis, but not UC. Additionally, CDP pouch/ileum exhibited heightened endoplasmic reticulum stress across all major cell compartments. CDP likely represents a distinct entity of inflammatory bowel disease with heightened endoplasmic reticulum stress in both immune and nonimmune cells, which may become a novel diagnostic biomarker and therapeutic target for CDP. [Display omitted] The pathophysiology of Crohn's-like disease of the pouch in patients with a prior history of ulcerative colitis is revealed and new targets to better treat this challenging condition are identified. [ABSTRACT FROM AUTHOR]

Cancer-intrinsic immune evasion mechanisms and pleiotropy are a barrier to cancer immunotherapy. This is apparent in certain highly fatal cancers, including high-grade gliomas and glioblastomas (GBM). In this study, we evaluated two murine syngeneic glioma models (GL261 and CT2A) as preclinical models for human GBM using functional genetic screens, single-cell transcriptomics and machine learning approaches. Through CRISPR genome-wide co-culture killing screens with various immune cells (cytotoxic T cells, natural killer cells, and macrophages), we identified three key cancer-intrinsic evasion mechanisms: NFκB signaling, autophagy/endosome machinery, and chromatin remodeling. Additional fitness screens identified dependencies in murine gliomas that partially recapitulated those seen in human GBM (e.g., UFMylation). Our single-cell analyses showed that different glioma models exhibited distinct immune infiltration patterns and recapitulated key immune gene programs observed in human GBM, including hypoxia, interferon, and TNF signaling. Moreover, in vivo orthotopic tumor engraftment was associated with phenotypic shifts and changes in proliferative capacity, with murine tumors recapitulating the intratumoral heterogeneity observed in human GBM, exhibiting propensities for developmental- and mesenchymal-like phenotypes. Notably, we observed common transcription factors and cofactors shared with human GBM, including developmental (Nfia and Tcf4), mesenchymal (Prrx1 and Wwtr1), as well as cycling-associated genes (Bub3, Cenpa, Bard1, Brca1, and Mis18bp1). Perturbation of these genes led to reciprocal phenotypic shifts suggesting intrinsic feedback mechanisms that balance in vivo cellular states. Finally, we used a machine-learning approach to identify two distinct immune evasion gene programs, one of which represents a clinically-relevant phenotype and delineates a subpopulation of stem-like glioma cells that predict response to immune checkpoint inhibition in human patients. This comprehensive characterization helps bridge the gap between murine glioma models and human GBM, providing valuable insights for future therapeutic development. [ABSTRACT FROM AUTHOR]

Introduction: Tumor microenvironments (TMEs) encompass complex ecosystems of cancer cells, infiltrating immune cells, and diverse cell types. Intercellular and intracellular signals within the TME significantly influence cancer progression and therapeutic outcomes. Although computational tools are available to study TME interactions, explicitly modeling tumor progression across different cancer types remains a challenge. Methods: This study introduces a comprehensive framework utilizing single-cell RNA sequencing (scRNA-seq) data within a multilayer network model, designed to investigate molecular changes across glioma progression stages. The heterogeneous, multilayered network model replicates the hierarchical structure of biological systems, from genetic building blocks to cellular functions and phenotypic manifestations. Results: Applying this framework to glioma scRNA-seq data allowed complex network analysis of different cancer stages, revealing significant ligand‒receptor interactions and key ligand‒receptor-transcription factor (TF) axes, along with their associated biological pathways. Differential network analysis between grade III and grade IV glioma highlighted the most critical nodes and edges involved in interaction rewiring. Pathway enrichment analysis identified four essential genes— PDGFA (ligand), PDGFRA (receptor), CREB1 (TF), and PLAT (target gene)—involved in the Receptor Tyrosine Kinases (RTK) signaling pathway, which plays a pivotal role in glioma progression from grade III to grade IV. Discussion: These genes emerged as significant features for machine learning in predicting glioma progression stages, achieving 87% accuracy and 93% AUC in a 3-year survival prediction through Kaplan-Meier analysis. This framework provides deeper insights into the cellular machinery of glioma, revealing key molecular relationships that may inform prognosis and therapeutic strategies. [ABSTRACT FROM AUTHOR]

The proteome serves as the primary basis for identifying targets for treatment. This study conducted proteomic range two-sample Mendelian randomization (MR) analysis to pinpoint potential protein markers and treatment targets for ankylosing spondylitis (AS). A total of 4907 data points on circulating protein expression were collected from a large-scale protein quantitative trait locus investigation involving 35,559 individuals. Using data from a Finnish study on AS as the outcome, the dataset comprised 166,144 individuals of European ancestry (1462 cases and 164,682 controls), and causal relationships were determined through bidirectional Mendelian randomization of two samples. Proteins were further validated and identified through single-cell expression analysis, certain cells showing enriched expression levels were detected, and possible treatment targets were optimized. Increased HERC5 expression predicted by genes was related to increased AS risk, whereas the expression of the remaining five circulating proteins, AIF1, CREB3L4, MLN, MRPL55, and SPAG11B, was negatively correlated with AS risk. For each increase in gene-predicted protein levels, the ORs of AS were 2.11 (95% CI 1.44–3.09) for HERC5, 0.14 (95% CI 0.05–0.41) for AIF1, 0.48 (95% CI 0.34–0.68) for CREB3L4, 0.54 (95% CI 0.42–0.68) for MLN, 0.23 (95% CI 0.13–0.38) for MRPL55, and 0.26 (95% CI 0.17–0.39) for SPAG11B. The hypothesis of a reverse causal relationship between these six circulating proteins and AS is not supported. Three of the six protein-coding genes were expressed in both the AS and healthy control groups, while CREB3L4, MLN, and SPAG11B were not detected. Increased levels of HERC5 predicted by genes are related to increased AS risk, whereas the levels of the remaining five circulating proteins, AIF1, CREB3L4, MLN, MRPL55, and SPAG11B, negatively correlate with AS risk. HERC5, AIF1, and MRPL55 are potential therapeutic targets for AS. This study advanced the field by employing a novel combination of proteomic range two-sample MR analysis and single-cell expression analysis to identify potential protein markers and therapeutic targets for AS. This approach enabled a comprehensive understanding of the causal relationships between circulating proteins and AS, which has not been extensively explored in previous studies. [ABSTRACT FROM AUTHOR]

Background: Liver cancer has a high global incidence, particularly in East Asia. Early detection difficulties lead to poor prognosis. Single-cell sequencing precisely identifies gene expression differences in specific cell types, making it valuable in tumor microenvironment research and immune drug development. However, the characteristics of tumor cells themselves are equally important for patient prognosis and treatment. Methods: We downloaded single-cell sequencing data from GSE189903, grouped cells by cluster markers, and classified epithelial cells into adjacent non-tumor, normal, and tumor cells. Differential gene and survival analyses identified significant differential genes. Using TCGA-LIHC data, we divided 370 patients into test and training sets. We constructed and validated a LASSO model based on these genes in both sets and two external datasets. Functional, immune infiltration, and mutation analyses were performed on high and low-risk groups. We also used RNA-seq and IC50 data of 15 liver cancer cell lines from GDSC, scoring them with our prognostic model to identify potential drugs for high-risk patients. Results: Dimensionality reduction and clustering of 34 single-cell samples identified five subgroups, with epithelial cells further classified. Differential gene analysis identified 124 significant genes. An 11-gene prognostic model was constructed, effectively stratifying patient prognosis (p < 0.05) and achieving an AUC above 0.6 for 5 year survival prediction in multiple cohorts. Functional analysis revealed that upregulated genes in high-risk groups were enriched in cell adhesion pathways, while downregulated genes were enriched in metabolic pathways. Mutation analysis showed more TP53 mutations in the high-risk group and more CTNNB1 mutations in the low-risk group. Immune infiltration analysis indicated higher immune scores and less CD8 + naive T cell infiltration in the high-risk group. Drug sensitivity analysis identified 14 drugs with lower IC50 in the high-risk group, including clinically approved Sorafenib and Axitinib for treating unresectable HCC. Conclusion: We established an 11-gene prognostic model that effectively stratifies liver cancer patients based on differentially expressed genes between tumor and adjacent non-tumor cells clustered by scRNA-seq data. The two risk groups had significantly different molecular characteristics. We identified 14 drugs that might be effective for high-risk HCC patients. Our study provides novel insights into tumor cell characteristics, aiding in research on tumor development and treatment. [ABSTRACT FROM AUTHOR]

Single cell RNA sequencing (scRNA-seq) is a relatively new technology that provides an unprecedented, detailed view of cellular heterogeneity and function by delineating the transcriptomic difference among individual cells. This will allow for mapping of cell-type-specific signaling during physiological and pathological processes, to build highly specific models of cellular signaling networks between the many discrete clusters that are present. This technology therefore provides a powerful approach to dissecting the cellular and molecular mechanisms that contribute to autoimmune diseases, including rheumatoid arthritis (RA). scRNA-seq can offer valuable insights into RA unique cellular states and transitions, potentially enabling development of novel drug targets. However, some challenges that still limit its mainstream utilization and include higher costs, a lower sensitivity for low-abundance transcripts, and a relatively complex data analysis workflow relative to bulk or traditional RNA-seq. This minireview explores the emerging application of scRNA-seq in RA research, highlighting its role in producing important insights that can help pave the way for innovative and more effective therapeutic strategies. [ABSTRACT FROM AUTHOR]

The liver is the predominant metastatic site for diverse cancers, including pancreatic and colorectal cancers (CRC), etc. The high incidence of hepatic metastasis of pancreatic cancer is an important reason for its refractory and high mortality. Therefore, it is important to understand how metastatic pancreatic cancer affects the hepatic tumor immune microenvironment (TME) in patients. Here, we characterized the TME of liver metastases unique to pancreatic cancer by comparing them with CRC liver metastases. We integrated two single-cell RNA-seq (scRNA-seq) datasets including tumor samples of pancreatic cancer liver metastasis (P-LM), colorectal cancer liver metastasis (C-LM), primary pancreatic cancer (PP), primary colorectal cancer (PC), as well as samples of peripheral blood mono-nuclear cells (PBMC), adjacent normal pancreatic tissues (NPT), to better characterize the heterogeneities of the microenvironment of two kinds of liver metastases. We next performed comparative analysis on cellular compositions between P-LM and C-LM, found that Mph_SPP1, a subset of macrophages associated with angiogenesis and tumor invasion, was more enriched in the P-LM group, indicating this kind of macrophages provide a TME niche more vulnerable for pancreatic cancers. Analysis of the developmental trajectory implied that Mph_SPP1 may progressively be furnished with increased expression of genes regulating endothelium. Cell–cell communications analysis revealed that Mph_SPP1 potentially interacts with endothelial cells in P-LM via FN1/SPP1-ITGAV/ITGB1, implying this macrophage subset may construct an immunosuppressive TME for pancreatic cancer by regulating endothelial cells. We also found that Mph_SPP1 has a prognostic value in pancreatic adenocarcinoma that is not present in colon adenocarcinoma or rectum adenocarcinoma. This study provides a new perspective for understanding the characteristics of the hepatic TME in patients with liver metastatic cancer. And it provides a subset of macrophages specifically associated with the liver metastasis of pancreatic cancer, and its detection and intervention have potential value for preventing the metastasis of pancreatic cancer to the liver. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Diabetic nephropathy (DN) is one of the main causes of end‐stage renal disease (ESRD), but its mechanism has not been clearly studied. We utilized integrative transcriptome analysis to explore the pathogenesis of DN.We conducted an analysis by combining bulk dataset and single‐cell transcriptome dataset. Through this approach, we identified that Serpine2 may regulate the ‘collagen‐containing extracellular matrix’ pathway involved in DN. Subsequently, we established DN animal and cell models using db/db mice and mesangial cells (MCs) to validate the role of Serpine2 in DN. In the animal model, we detected the expression level of Serpine2 in DN using western blotting (WB) and immunofluorescence (IF) assays. To further clarify the molecular mechanism of Serpine2 in DN, we knocked down Serpine2 and observed its effects on MCs proliferation and extracellular matrix (ECM) accumulation.Our single‐cell analysis of DN models highlighted a pivotal role for MCs in the disease's initiation. Next, through Cytoscape analysis of differentially expressed genes (DEGs) in MCs, we identified the following 10 hub genes: Acta2, Angpt2, Ccn1, Col4a1, Col4a2, Col8a1, Kdr, Thbs1, Tpm4 and Serpine2. Subsequently, we identified that Serpine2 and Kdr were also significantly DEGs in the bulk analysis of glomeruli. Additionally, our integrated gene set enrichment analysis of bulk dataset and single‐cell RNA dataset revealed that the ‘collagen‐containing extracellular matrix’ was a key pathway in DN progression. Serpine2 was one of the crucial genes involved in regulating this pathway. Therefore, we speculated that the regulation of the ‘collagen‐containing extracellular matrix’ pathway by Serpine2 was an important mechanism. Importantly, WB and IF staining confirmed that Serpine2 expression was upregulated in the MCs of diabetic mice. Knockdown of Serpine2 in cultured MCs alleviated high‐glucose‐induced excessive MCs proliferation and ECM accumulation. Finally, we found that ERK agonist Ro 67‐7476 eliminated the effect of Serpine2 siRNA.In summary, Serpine2 regulates MCs proliferation and ECM synthesis through activation of the ERK1/2 pathway, which is an important pathogenesis mechanism of DN. These findings offer fresh perspectives on the mechanisms of glomerulosclerosis in DN pathogenesis and may provide new targets for treating DN. [ABSTRACT FROM AUTHOR]

Summary Long noncoding RNAs, including intergenic lncRNAs (lincRNAs), play a key role in various biological processes throughout the plant life cycle, and the advent of single‐cell RNA sequencing (scRNA‐seq) technology has opened up a valuable avenue for scrutinizing the intricate roles of lincRNAs in cellular processes. Here, we identified a new batch of lincRNAs using scRNA‐seq data from diverse tissues of plants (rice, Arabidopsis, tomato, and maize). Based on well‐annotated single‐cell transcriptome atlases, plant lincRNAs were found to possess the same level of cell‐type specificity as mRNAs and to be involved in the differentiation of certain cell types based on pseudo‐time analysis. Many lincRNAs were predicted to play a hub role in the cell‐type‐specific co‐expression networks of lincRNAs and mRNAs, suggesting their trans‐acting abilities. Besides, plant lincRNAs were revealed to have potential cis‐acting properties based on their genomic distances and expression correlations with the neighboring mRNAs. Furthermore, an online platform, PscLncRNA (http://ibi.zju.edu.cn/psclncrna/), was constructed for searching and visualizing all identified plant lincRNAs with annotated potential functions. Our work provides new insights into plant lincRNAs at single‐cell resolution and an important resource for understanding and further investigation of plant lincRNAs. [ABSTRACT FROM AUTHOR]

Non-melanoma skin cancer (NMSC) mainly includes basal cell carcinoma, cutaneous squamous cell carcinoma, and Merkel cell carcinoma, showing a low mortality rate but the highest incidence worldwide. In recent decades, research has focused on understanding the pathogenesis and clinical treatments of NMSC, leading to significant advances in our knowledge of these diseases and the development of novel therapies, including immunotherapy. Nevertheless, the low to moderate objective response rate, high recurrence, and therapeutic resistance remain persistent challenges, which are partly attributable to the intratumoral heterogeneity. This heterogeneity indicates that tumor cells, immune cells, and stromal cells in the tumor microenvironment can be reshaped to a series of phenotypic and transcriptional cell states that vary in invasiveness and treatment responsiveness. The advent of single-cell RNA sequencing (scRNA-seq) has enabled the comprehensive profiling of gene expression heterogeneity at the single-cell level, which has been applied to NMSC to quantify cell compositions, define states, understand tumor evolution, and discern drug resistance. In this review, we highlight the key findings, with a focus on intratumoral heterogeneity and the mechanism of drug resistance in NMSC, as revealed by scRNA-seq. Furthermore, we propose potential avenues for future research in NMSC using scRNA-seq. [ABSTRACT FROM AUTHOR]

Aim: The present work set out to classify cell subpopulations related to liver metastasis from gastric cancer (GC) and the mechanisms of their interactions with other immune cell subpopulations. Background: GC is characterized by a high degree of heterogeneity and liver metastasis. Exploring the mechanism of liver metastasis of GC from the perspective of heterogeneity of the tumor microenvironment (TME) might help improve the efficacy of GC treatment. Objective: Based on the cellular subpopulation characteristics of GC with liver metastasis, the regulatory mechanisms contributing to GC progression were analyzed, with special focuses on the roles of signaling pathways, transcription factors (TFs) and ligand–receptor pairs. Methods: The GSE163558 dataset was downloaded from the Gene Expression Omnibus (GEO) database to collect single-cell transcriptomic data of GC patients and their metastasis groups for cell clustering and relevant analyses. Differentially expressed genes (DEGs) in the GC and GC liver metastasis groups were screened and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. SCENIC analysis was used to mine TFs that affected cellular subpopulations during liver metastasis from GC. The relative expression levels of TFs in GC were determined using qRT-PCR. Transwell and wound healing assays were utilized to verify the regulation of the TFs on the migration and invasion of GC cells. Interaction network between the cellular subpopulations was developed applying CellChat. Results: Single-cell clustering was performed to group six major cell subpopulations, namely, Myeloid cells, B cells, Mast cells, Epithelial cells, Fibroblasts, and TNK cells, among which the number of TNK cells was significantly increased in the GC liver metastasis group. Differentially enriched pathways of TNK cells between GC and GC liver metastasis groups mainly included IL-17 and Pi3k–Akt signaling pathways. TNK cell subsets could be further categorized into CD8 T cells, Exhausted T cells, NK cells, NKT cells, and Treg cells, with the GC liver metastasis group showing significantly more CD8 T cells and NKT cells. FOS and JUNB were the TFs of TNK cell marker genes that contributed to liver metastasis from GC and the invasion and migration of GC cell lines. Significant differences in immune cell communication ligand–receptor pairs existed between the GC and GC liver metastasis groups. Conclusion: This study revealed the critical role of TNK cell subsets in GC with liver metastasis applying single-cell transcriptomics analysis. The findings provided an important theoretical basis for developing novel therapies to inhibit liver metastasis from GC. [ABSTRACT FROM AUTHOR]

Elucidating organismal developmental processes requires a comprehensive understanding of cellular lineages in the spatial, temporal, and molecular domains. In this study, we introduce Zebrahub, a dynamic atlas of zebrafish embryonic development that integrates single-cell sequencing time course data with lineage reconstructions facilitated by light-sheet microscopy. This atlas offers high-resolution and in-depth molecular insights into zebrafish development, achieved through the sequencing of individual embryos across ten developmental stages, complemented by reconstructions of cellular trajectories. Zebrahub also incorporates an interactive tool to navigate the complex cellular flows and lineages derived from light-sheet microscopy data, enabling in silico fate-mapping experiments. To demonstrate the versatility of our multimodal resource, we utilize Zebrahub to provide fresh insights into the pluripotency of neuro-mesodermal progenitors (NMPs) and the origins of a joint kidney-hemangioblast progenitor population. [Display omitted] • Zebrahub is a transcriptional and spatiotemporal resource of zebrafish development • Single-embryo scRNA-seq shows robust transcriptional regulation across specimens • scRNA-seq-derived cell state transition indicates late multipotent progenitors • Imaging-based lineage mapping of progenitors validates fate dynamics Zebrahub is a comprehensive web-based atlas integrating spatiotemporal and transcriptional data at single-cell resolution to elucidate developmental state transitions in zebrafish. [ABSTRACT FROM AUTHOR]

Duchenne muscular dystrophy (DMD) is a progressive muscle-wasting disease caused by mutations in the DMD gene. Muscle fibers rely on the coordination of multiple cell types for repair and regenerative capacity. To elucidate the cellular and molecular changes in these cell types under pathologic conditions, we generated a rhesus monkey model for DMD that displays progressive muscle deterioration and impaired motor function, mirroring human conditions. By leveraging these DMD monkeys, we analyzed freshly isolated muscle tissues using single-cell RNA sequencing (scRNA-seq). Our analysis revealed changes in immune cell landscape, a reversion of lineage progressing directions in fibrotic fibro-adipogenic progenitors (FAPs), and TGF-β resistance in FAPs and muscle stem cells (MuSCs). Furthermore, MuSCs displayed cell-intrinsic defects, leading to differentiation deficiencies. Our study provides important insights into the pathogenesis of DMD, offering a valuable model and dataset for further exploration of the underlying mechanisms, and serves as a suitable platform for developing and evaluating therapeutic interventions. [Display omitted] • Generation of DMD monkey model recapitulating the pathogenesis of human patients • The immune cell landscape undergoes highly dynamic changes in DMD monkeys • Abnormal differentiation of FAPs causes TGF-β-independent fibrosis • Profound muscle stem cell defects contribute to the muscle dysfunction of DMD A DMD rhesus monkey model exhibits profound changes in immune cells, fibro-adipogenic progenitors (FAPs), and muscle stem cells at the early stage of the disease, constituting a suitable model for investigating disease mechanisms. [ABSTRACT FROM AUTHOR]

Background: The efficiency of mesenchymal stem cells (MSCs) in treating myocardial infarction (MI) remains inconsistent, which limits their therapeutic applications. Therefore, exploring the mechanism for the inconsistent efficacy of MSCs and identification the criteria for screening MSCs are important for improving the efficiency of MSCs. Methods: Mouse model after MI was utilized to test the role of MSCs from different donors and the functional subpopulation in improving cardiac function. Heterogeneity of MSCs was identified using single-cell RNA sequencing (scRNA-seq) of MSC-GY. GSEA and Scissor analyses were used to find the functional subpopulations of MSCs that promote angiogenesis. The role of functional subpopulations in promoting angiogenesis was verified by detecting the secretory proteins, the ratio of N-CADHERIN+/CD168− subpopulations in MSCs, and the tube formation, migration, and proliferation of HUVECs after treatment with conditional medium (CM) derived from different MSCs. Results: We found that umbilical cord-derived MSCs (UC-MSCs) from different donors have varied therapeutic efficacy in MI mice and UC-MSCs with higher therapeutic effectiveness exhibited the most potent pro-angiogenic effects by secreting elevated levels of angiogenesis-related proteins, such as MYDGF, VEGFA, and FGF2. ScRNA-seq of 10,463 UC-MSCs revealed that the N-CADHERIN+/CD168− subpopulation was closely associated with pro-angiogenic effects, and the ratio of this cell subpopulation was positively correlated with the angiogenic potential of MSCs. We also found that the N-CADHERIN+/CD168− subpopulation was the functional subpopulation of MSCs in improving cardiac function of MI mice. Conclusions: Our study identified that the N-CADHERIN+/CD168− subpopulation was the functional subpopulation of MSCs in treating MI, which was essential for the development and utilization of MSCs in MI treatment. [ABSTRACT FROM AUTHOR]

Background: Single-cell RNA sequencing experiments commonly use 10x Genomics (10x) kits due to their high-throughput capacity and standardized protocols. Recently, Parse Biosciences (Parse) introduced an alternative technology that uses multiple in-situ barcoding rounds within standard 96-well plates. Parse enables the analysis of more cells from multiple samples in a single run without the need for additional reagents or specialized microfluidics equipment. To evaluate the performance of both platforms, we conducted a benchmark study using biological and technical replicates of mouse thymus as a complex immune tissue. Results: We found that Parse detected nearly twice the number of genes compared to 10x, with each platform detecting a distinct set of genes. The comparison of multiplexed samples generated from 10x and Parse techniques showed 10x data to have lower technical variability and more precise annotation of biological states in the thymus compared to Parse. Conclusion: Our results provide a comprehensive comparison of the suitability of both single-cell platforms for immunological studies. [ABSTRACT FROM AUTHOR]

Background: Bladder cancer (BC) is a prevalent malignancy characterized by significant cellular heterogeneity. While single-cell multi-omics studies have provided valuable insights, much of the existing data remains underexplored, limiting our understanding of BC's molecular mechanisms. Uncovering the pathogenesis of BC and finding new treatment methods are urgent problems to be solved. This study aims to address this gap by re-analyzing available single-cell datasets to uncover novel insights into BC. Methods: In this study, we retrieved three single-cell transcriptome datasets by searching the Gene Expression Omnibus (GEO) database, focusing on single-cell sequencing of normal mouse bladder within the past 5 years. Through quality control and batch effect elimination, we obtained a total of 24,930 cells including epithelial, stromal, and immune cells. Subgroup analysis, pseudotemporal analysis, cell–cell communication, and transcription factor analysis were conducted specifically on epithelial cells to identify a transitional state during basal cell differentiation. We further compared the expression profiles of key transcription factors in cancer and normal tissues. In addition, we also performed immunohistochemical staining and survival analysis for key transcription factors. Results: Subgroup analysis revealed multiple subtypes of epithelial cells, including basal, umbrella, and intermediate cells. Through pseudotemporal analysis, we discovered the developmental trajectory from basal cells to umbrella cells and further found that Basal_I is a transitional state for basal cell differentiation. Cell-to-cell communication analyses highlighted the pivotal role of Basal_I in cell–cell interactions, and key ligand-receptor pairs associated with cancer progression were also identified. Furthermore, elevated expression levels of key transcription factors in Basal_I were found to be closely associated with the stage and prognosis of BC. Immunohistochemical staining results further confirmed the upregulated expression of these transcription factors in BC. Conclusions: Collectively, we found a transitional state of basal cells in normal bladder epithelial cells in mice, which may be related to the occurrence and development of BC, providing important clues for further understanding of the pathogenesis of BC. Our study provided possible molecular mechanisms or target for the research and treatment of BC. [ABSTRACT FROM AUTHOR]

Background: Sepsis survivors exhibit immune dysregulation that contributes to poor long-term outcomes. Phenotypic and functional alterations within the myeloid compartment are believed to be a contributing factor. Here we dissect the cellular and transcriptional heterogeneity of splenic CD11b+Ly6Chigh myeloid cells that are expanded in mice that survive the cecal ligation and puncture (CLP) murine model of polymicrobial sepsis to better understand the basis of immune dysregulation in sepsis survivors. Methods: Sham or CLP surgeries were performed on C57BL/6J and BALB/c mice. Four weeks later splenic CD11b+Ly6Chigh cells from both groups were isolated for phenotypic (flow cytometry) and functional (phagocytosis and glycolysis) characterization and RNA was obtained for single-cell RNA-seq (scRNA-seq) and subsequent analysis. Results: CD11b+Ly6Chigh cells from sham and CLP surviving mice exhibit phenotypic and functional differences that relate to immune function, some of which are observed in both C57BL/6J and BALB/c strains and others that are not. To dissect disease-specific and strain-specific distinctions within the myeloid compartment, scRNA-seq analysis was performed on CD11b+Ly6Chigh cells from C57BL/6J and BALB/c sham and CLP mice. Uniform Manifold Approximation and Projection from both strains identified 13 distinct clusters of sorted CD11b+Ly6Chigh cells demonstrating significant transcriptional heterogeneity and expressing gene signatures corresponding to classical-monocytes, non-classical monocytes, M1- or M2-like macrophages, dendritic-like cells, monocyte-derived dendritic-like cells, and proliferating monocytic myeloid-derived suppressor cells (M-MDSCs). Frequency plots showed that the percentages of proliferating M-MDSCs (clusters 8, 11 and 12) were increased in CLP mice compared to sham mice in both strains. Pathway and UCell score analysis in CLP mice revealed that cell cycle and glycolytic pathways were upregulated in proliferating M-MDSCs in both strains. Notably, granule protease genes were upregulated in M-MDSCs from CLP mice. ScRNA-seq analyses also showed that phagocytic pathways were upregulated in multiple clusters including the classical monocyte cluster, confirming the increased phagocytic capacity in CD11b+Ly6Chigh cells from CLP mice observed in ex vivo functional assays in C57BL/6J mice. Conclusion: The splenic CD11b+Ly6Chigh myeloid populations expanded in survivors of CLP sepsis correspond to proliferating cells that have an increased metabolic demand and gene signatures consistent with M-MDSCs, a population known to have immunosuppressive capacity. [ABSTRACT FROM AUTHOR]

Background: Acute myeloid leukemia (AML) is a rapidly progressing blood cancer. The prognosis of AML can be challenging, emphasizing the need for ongoing research and innovative approaches to improve outcomes in individuals affected by this formidable hematologic malignancy. Methods: In this study, we used single-cell RNA sequencing (scRNA-seq) from AML patients to investigate the impact of L-glutamine metabolism-related genes on disease progression. Results: Our analysis revealed increased glutamine-related activity in CD34 + pre-B cells, suggesting a potential regulatory role in tumorigenesis and AML progression. Furthermore, intercellular communication analysis revealed a significant signaling pathway involving macrophage migration inhibitory factor signaling through CD74 + CD44 within CD34 + pre-B cells, which transmit signals to pre-dendritic cells and monocytes. Ligands for this pathway were predominantly expressed in stromal cells, naïve T cells, and CD34 + pre-B cells. CD74, the pertinent receptor, was predominantly detected in a variety of cellular components, including stromal cells, pre-dendritic cells, plasmacytoid dendritic cells, and hematopoietic progenitors. The study's results provide insights into the possible interplay among these cell types and their collective contribution to the pathogenesis of AML. Moreover, we identified 10 genes associated with AML prognosis, including CCL5, CD52, CFD, FABP5, LGALS1, NUCB2, PSAP, S100A4, SPINK2, and VCAN. Among these, CCL5 and CD52 have been implicated in AML progression and are potential therapeutic targets. Conclusions: This thorough examination of AML biology significantly deepens our grasp of the disease and presents pivotal information that could guide the creation of innovative treatment strategies for AML patients. [ABSTRACT FROM AUTHOR]

Non-syndromic cleft lip with/without cleft palate (nsCL/P) is one of the most common birth defects and has a multifactorial etiology. To date, over 45 loci harboring common risk variants have been identified. However, the effector genes at these loci, and the cell types that are affected by risk alleles, remain largely unknown. To address this, we combined genetic data from an nsCL/P genome-wide association study (GWAS) with single-cell RNA sequencing data obtained from the heads of unaffected human embryos. Using the recently developed single-cell disease relevance score (scDRS) approach, we identified two major cell types involved in nsCL/P development, namely the epithelium and the HAND2+ pharyngeal arches (PA). Combining scDRS with co-expression networks and differential gene expression analysis, we prioritized nsCL/P candidate genes, some of which were additionally supported by GWAS data (e.g., CTNND1, PRTG, RPL35A, RAB11FIP1, KRT19). Our results suggest that specific epithelial and PA sub-cell types are involved in nsCL/P development, and harbor a substantial fraction of the genetic risk for nsCL/P. [ABSTRACT FROM AUTHOR]

Since previous studies have suggested that the RNAs of human endogenous retrovirus (HERV) might be involved in regulating innate immunity, it is important to investigate the HERV transcriptome patterns in innate immune cell types such as CD14 + monocytes. Using single cell RNA-seq datasets from resting or stimulated PBMCs mapped to 3,220 known discrete autonomous proviral HERV loci, we found individual-specific variation in HERV transcriptomes between HERV loci in CD14 + monocytes. Analysis of paired datasets from the same individual that were cultured in vitro with LPS or without (i.e. control) revealed 36 HERV loci in CD14 + monocytes that were detected only after activation. To extend our analysis to in vivo activated CD14 + monocytes, we used two scRNA-seq datasets from studies that had demonstrated activation of circulating CD14 + monocytes in patients with physical trauma or patients hospitalized with COVID-19 infections. For direct comparison between the trauma and COVID-19 datasets, we first analyzed 1.625 billion sequence reads from a composite pangenome control of 21 normal individuals. Comparison of the sequence read depth of HERV loci in the trauma or COVID-19 samples to the pangenome control revealed that 39 loci in the COVID-19 and 11 HERV loci in the trauma samples were significantly different (Mann-Whitney U test), with 9 HERV loci shared between the COVID-19 and trauma datasets. The capacity to compare HERV loci transcriptome patterns in innate immune cells, like CD14 + monocytes, across different pathological conditions will lead to greater understanding of the physiological role of HERV expression in health and disease. [ABSTRACT FROM AUTHOR]

Background: Natural killer (NK) cells play crucial roles in mediating anti-cancer activity in breast cancer (BRCA). However, the potential of NK cell-related molecules in predicting BRCA outcomes and guiding personalized therapy remains largely unexplored. This study focused on developing a prognostic and therapeutic prediction model for BRCA by incorporating NK cell-related genes. Methods: The data analyzed primarily originated from the TCGA and GEO databases. The prognostic role of NK cells was evaluated, and marker genes of NK cells were identified via single-cell analysis. Module genes closely associated with immunotherapy resistance were identified by bulk transcriptome-based weighted correlation network analysis (WGCNA). Following taking intersection and LASSO regression, NK-related genes (NKRGs) relevant to BRCA prognosis were screened, and the NK-related prognostic signature was subsequently constructed. Analyses were further expanded to clinicopathological relevance, GSEA, tumor microenvironment (TME) analysis, immune function, immunotherapy responsiveness, and chemotherapeutics. Key NKRGs were screened by machine learning and validated by spatial transcriptomics (ST) and immunohistochemistry (IHC). Results: Tumor-infiltrating NK cells are a favorable prognostic factor in BRCA. By combining scRNA-seq and bulk transcriptomic analyses, we identified 7 NK-related prognostic NKRGs (CCL5, EFHD2, KLRB1, C1S, SOCS3, IRF1, and CCND2) and developed an NK-related risk scoring (NKRS) system. The prognostic reliability of NKRS was verified through survival and clinical relevance analyses across multiple cohorts. NKRS also demonstrated robust predictive power in various aspects, including TME landscape, immune functions, immunotherapy responses, and chemotherapeutic sensitivity. Additionally, KLRB1 and CCND2 emerged as key prognostic NKRGs identified through machine learning and external validation, with their expression correlation with NK cells confirmed in BRCA specimens by ST and IHC. Conclusions: We developed a novel NK-related gene signature that has proven valuable for evaluating prognosis and treatment response in BRCA, expecting to advance precision medicine of BRCA. [ABSTRACT FROM AUTHOR]

The integration of single-cell RNA sequencing (scRNA-seq) data from multiple experimental batches enables more comprehensive characterizations of cell states. Given that existing methods disregard the structural information between cells and genes, we proposed a structure-preserved scRNA-seq data integration approach using heterogeneous graph neural network (scHetG). By establishing a heterogeneous graph that represents the interactions between multiple batches of cells and genes, and combining a heterogeneous graph neural network with contrastive learning, scHetG concurrently obtained cell and gene embeddings with structural information. A comprehensive assessment covering different species, tissues and scales indicated that scHetG is an efficacious method for eliminating batch effects while preserving the structural information of cells and genes, including batch-specific cell types and cell-type specific gene co-expression patterns. [ABSTRACT FROM AUTHOR]

Methods that predict fate potential or degree of differentiation from transcriptomic data have identified rare progenitor populations and uncovered developmental regulatory mechanisms. However, some state-of-the-art methods are too computationally burdensome for emerging large-scale data and all methods make inaccurate predictions in certain biological systems. We developed a method in R (stemFinder) that predicts single cell differentiation time based on heterogeneity in cell cycle gene expression. Our method is computationally tractable and is as good as or superior to competitors. As part of our benchmarking, we implemented four different performance metrics to assist potential users in selecting the tool that is most apt for their application. Finally, we explore the relationship between differentiation time and cell fate potential by analyzing a lineage tracing dataset with clonally labelled hematopoietic cells, revealing that metrics of differentiation time are correlated with the number of downstream lineages. [ABSTRACT FROM AUTHOR]

The gene regulatory network (GRN) plays a vital role in understanding the structure and dynamics of cellular systems, revealing complex regulatory relationships, and exploring disease mechanisms. Recently, deep learning (DL)–based methods have been proposed to infer GRNs from single-cell transcriptomic data and achieved impressive performance. However, these methods do not fully utilize graph topological information and high-order neighbor information from multiple receptive fields. To overcome those limitations, we propose a novel model based on multiview graph attention network, namely, scMGATGRN, to infer GRNs. scMGATGRN mainly consists of GAT, multiview, and view-level attention mechanism. GAT can extract essential features of the gene regulatory network. The multiview model can simultaneously utilize local feature information and high-order neighbor feature information of nodes in the gene regulatory network. The view-level attention mechanism dynamically adjusts the relative importance of node embedding representations and efficiently aggregates node embedding representations from two views. To verify the effectiveness of scMGATGRN, we compared its performance with 10 methods (five shallow learning algorithms and five state-of-the-art DL-based methods) on seven benchmark single-cell RNA sequencing (scRNA-seq) datasets from five cell lines (two in human and three in mouse) with four different kinds of ground-truth networks. The experimental results not only show that scMGATGRN outperforms competing methods but also demonstrate the potential of this model in inferring GRNs. The code and data of scMGATGRN are made freely available on GitHub (https://github.com/nathanyl/scMGATGRN). [ABSTRACT FROM AUTHOR]

Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolution. Several multi-omics single-cell sequencing techniques have been developed to explore various aspects of cellular behavior. Using NEAT-seq as an example, this method simultaneously obtains three kinds of omics data for each cell: gene expression, chromatin accessibility, and protein expression of transcription factors (TFs). Consequently, NEAT-seq offers a more comprehensive understanding of cellular activities in multiple modalities. However, there is a lack of tools available for effectively integrating the three types of omics data. To address this gap, we propose a novel pipeline called MultiSC for the analysis of MULTIomic Single-Cell data. Our pipeline leverages a multimodal constraint autoencoder (single-cell hierarchical constraint autoencoder) to integrate the multi-omics data during the clustering process and a matrix factorization–based model (scMF) to predict target genes regulated by a TF. Moreover, we utilize multivariate linear regression models to predict gene regulatory networks from the multi-omics data. Additional functionalities, including differential expression, mediation analysis, and causal inference, are also incorporated into the MultiSC pipeline. Extensive experiments were conducted to evaluate the performance of MultiSC. The results demonstrate that our pipeline enables researchers to gain a comprehensive view of cell activities and gene regulatory networks by fully leveraging the potential of multiomics single-cell data. By employing MultiSC, researchers can effectively integrate and analyze diverse omics data types, enhancing their understanding of cellular processes. [ABSTRACT FROM AUTHOR]

This study develops a prognostic model to predict metastasis and prognosis in colorectal cancer liver metastases by identifying distinct macrophage subsets. Using scRNA-seq data from primary colorectal cancer and liver metastases, we dissected the cellular landscape to find unique macrophage subpopulations, particularly EEF1G + macrophages, which were prevalent in liver metastases. The study leveraged data from GSE231559, TCGA, and GEO databases to construct an 8-gene risk model named EMGS, based on the EEF1G + macrophage gene signature. Patients were divided into high-risk and low-risk groups using the median EMGS score, with the high-risk group showing significantly worse survival. This group also demonstrated upregulated pathways associated with tumor progression, such as epithelial-mesenchymal transition and angiogenesis, and downregulated metabolic pathways. Moreover, the high-risk group presented an immunosuppressive microenvironment, with a higher TIDE score indicating lower effectiveness of immunotherapy. The study identifies potential drugs targeting the high-risk group, suggesting therapeutic avenues to improve survival. Conclusively, the EMGS score identifies colorectal cancer patients at high risk of liver metastases, highlighting the role of specific macrophage subsets in tumor progression and providing a basis for personalized treatment strategies. [ABSTRACT FROM AUTHOR]

• A machine learning model, AtML, was constructed to predict the stages of Arabidopsis root cells and identify biomarkers. • Performance testing revealed that AtML achieved very high accuracy and recall. • The AtML model identified 160 important marker genes for annotating cell type. Arabidopsis thaliana synthesizes various medicinal compounds, and serves as a model plant for medicinal plant research. Single-cell transcriptomics technologies are essential for understanding the developmental trajectory of plant roots, facilitating the analysis of synthesis and accumulation patterns of medicinal compounds in different cell subpopulations. Although methods for interpreting single-cell transcriptomics data are rapidly advancing in Arabidopsis, challenges remain in precisely annotating cell identity due to the lack of marker genes for certain cell types. In this work, we trained a machine learning system, AtML, using sequencing datasets from six cell subpopulations, comprising a total of 6000 cells, to predict Arabidopsis root cell stages and identify biomarkers through complete model interpretability. Performance testing using an external dataset revealed that AtML achieved 96.50% accuracy and 96.51% recall. Through the interpretability provided by AtML, our model identified 160 important marker genes, contributing to the understanding of cell type annotations. In conclusion, we trained AtML to efficiently identify Arabidopsis root cell stages, providing a new tool for elucidating the mechanisms of medicinal compound accumulation in Arabidopsis roots. [ABSTRACT FROM AUTHOR]

Existing therapeutic strategies are challenged by long times to achieve effect and often require frequent administration. Peanut-allergic individuals would benefit from a therapeutic that provides rapid protection against accidental exposure within days of administration while carrying little risk of adverse reactions. Guided by the repertoire of human IgE mAbs from allergic individuals, we sought to develop a treatment approach leveraging the known protective effects of allergen-specific IgG4 antibodies. We applied our single-cell RNA-sequencing SEQ SIFTER platform (IgGenix, Inc, South San Francisco, Calif) to whole blood samples from peanut-allergic individuals to discover IgE mAbs. These were then class-switched by replacing the IgE constant region with IgG4 while retaining the allergen-specific variable regions. In vitro mast cell activation tests, basophil activation tests, ELISAs, and an in vivo peanut allergy mouse model were used to evaluate the specificity, affinity, and activity of these recombinant IgG4 mAbs. We determined that human peanut-specific IgE mAbs predominantly target immunodominant epitopes on Ara h 2 and Ara h 6 and that recombinant IgG4 mAbs effectively block these epitopes. IGNX001, a mixture of 2 such high-affinity IgG4 mAbs, provided robust protection against peanut-mediated mast cell activation in vitro as well as against anaphylaxis upon intragastric peanut challenge in a peanut allergy mouse model. We developed a peanut-specific IgG4 antibody therapeutic with convincing preclinical efficacy starting from a large repertoire of human IgE mAbs from demographically and geographically diverse individuals. These results warrant further clinical investigation of IGNX001 and underscore the opportunity for the application of this therapeutic development strategy in other food and environmental allergies. [ABSTRACT FROM AUTHOR]

Neutrophils and eosinophils share common hematopoietic precursors and usually diverge into distinct lineages with unique markers before being released from their hematopoietic site, which is the bone marrow (BM). However, previous studies identified an immature Ly6g(+) Il-5Rα(+) neutrophil population in mouse BM, expressing both neutrophil and eosinophil markers suggesting hematopoietic flexibility. Moreover, others have reported neutrophil populations expressing eosinophil-specific cell surface markers in tissues and altered disease states, confusing the field regarding eosinophil origins, function, and classification. Despite these reports, it is still unclear whether hematopoietic flexibility exists in human granulocytes. To answer this, we utilized single-cell RNA sequencing and cellular indexing of transcriptomes and epitopes by sequencing to profile human BM and circulating neutrophils and eosinophils at different stages of differentiation and determine whether neutrophil plasticity plays role in asthmatic inflammation. We show that immature metamyelocyte neutrophils in humans expand during severe asthmatic inflammation and express both neutrophil and eosinophil markers. We also show an increase in trilobed eosinophils with mixed neutrophil and eosinophil markers in allergic asthma and that interleukin-5 promotes differentiation of immature blood neutrophils into trilobed eosinophilic phenotypes, suggesting a mechanism of emergency granulopoiesis to promote myeloid inflammatory or remodeling response in patients with chronic asthma. By providing insights into unexpectedly flexible granulocyte biology and demonstrating emergency hematopoiesis in asthma, our results highlight the importance of granulocyte plasticity in eosinophil development and allergic diseases. [ABSTRACT FROM AUTHOR]

Mammalian hair cells (HCs) are arranged spirally along the cochlear axis and correspond to different frequency ranges. Serving as primary sound detectors, HCs spatially segregate component frequencies into a topographical map. HCs display significant diversity in anatomical and physiological characteristics, yet little is known about the organization of the cochleotopic map of HCs or the molecules involved in this process. Using single-cell RNA sequencing, we determined the distinct molecular profiles of inner hair cells and outer hair cells, and we identified numerous position-dependent genes that were expressed as gradients. Newly identified genes such as Ptn, Rxra, and Nfe2l2 were found to be associated with tonotopy. We employed the SCENIC algorithm to predict the transcription factors that potentially shape these tonotopic gradients. Furthermore, we confirmed that Nfe2l2, a tonotopy-related transcription factor, is critical in mice for sensing low-to-medium sound frequencies in vivo. the analysis of cell-cell communication revealed potential receptor-ligand networks linking inner hair cells to spiral ganglion neurons, including pathways such as BDNF-Ntrk and PTN-Scd4, which likely play essential roles in tonotopic maintenance. Overall, these findings suggest that molecular gradients serve as the organizing principle for maintaining the selection of sound frequencies by HCs. [ABSTRACT FROM AUTHOR]

Background: Glioma is the predominant malignant brain tumor that lacks effective treatment options due to its shielding by the blood-brain barrier (BBB). Astrocytes play a role in the development of glioma, yet the diverse cellular composition of astrocytoma has not been thoroughly researched. Methods: We examined the internal diversity of seven distinct astrocytoma subgroups through single-cell RNA sequencing (scRNA-seq), pinpointed crucial subgroups using CytoTRACE, monocle2 pseudotime analysis, and slingshot pseudotime analysis, employed various techniques to identify critical subgroups, and delved into cellular communication analysis. Then, we combined the clinical information of GBM patients and used bulk RNA sequencing (bulk RNA-seq) to analyze the prognostic impact of the relevant molecules on GBM patients, and we performed in vitro experiments for validation. Results: The analysis of the current study revealed that C0 IGFBP7+ Glioma cells were a noteworthy subpopulation of astrocytoma, influencing the differentiation and progression of astrocytoma. A predictive model was developed to categorize patients into high- and low-scoring groups based on the IGFBP7 Risk Score (IGRS), with survival analysis revealing a poorer prognosis for the high-IGRS group. Analysis of immune cell infiltration, identification of genes with differential expression, various enrichment analyses, assessment of copy number variations, and evaluation of drug susceptibility were conducted, all of which highlighted their significant influence on the prognosis of astrocytoma. Conclusion: This research enhances comprehension of the diverse cell composition of astrocytoma, delves into the various factors impacting the prognosis of astrocytoma, and offers fresh perspectives on treating glioma. [ABSTRACT FROM AUTHOR]

Background: Cancer cells evolve under unique functional adaptations that unlock transcriptional programs embedded in adult stem and progenitor-like cells for progression, metastasis, and therapeutic resistance. However, it remains challenging to quantify the stemness-aware cell state of a tumor based on its gene expression profile. Methods: We develop a developmental-status-aware transcriptional decomposition strategy using single-cell RNA-sequencing-derived tissue-specific fetal and adult cell signatures as anchors. We apply our method to various biological contexts, including developing human organs, adult human tissues, experimentally induced differentiation cultures, and bulk human tumors, to benchmark its performance and to reveal novel biology of entangled developmental signaling in oncogenic processes. Results: Our strategy successfully captures complex dynamics in developmental tissue bulks, reveals remarkable cellular heterogeneity in adult tissues, and resolves the ambiguity of cell identities in in vitro transformations. Applying it to large patient cohorts of bulk RNA-seq, we identify clinically relevant cell-of-origin patterns and observe that decomposed fetal cell signals significantly increase in tumors versus normal tissues and metastases versus primary tumors. Across cancer types, the inferred fetal-state strength outperforms published stemness indices in predicting patient survival and confers substantially improved predictive power for therapeutic responses. Conclusions: Our study not only provides a general approach to quantifying developmental-status-aware cell states of bulk samples but also constructs an information-rich, biologically interpretable, cell-state panorama of human cancers, enabling diverse translational applications. [ABSTRACT FROM AUTHOR]

The nasal mucosa forms a critical barrier against the invasion of respiratory pathogens. Composed of a heterogeneous assortment of cell types, the nasal mucosa relies on the unique characteristics and complex intercellular dynamics of these cells to maintain their structural integrity and functional efficacy. In this study, single-cell RNA sequencing (scRNA-seq) of porcine nasal mucosa was performed, and nineteen distinct nasal cell types, including nine epithelial cell types, five stromal cell types, and five immune cell types, were identified. The distribution patterns of three representative types of epithelial cells (basal cells, goblet cells, and ciliated cells) were subsequently detected by immunofluorescence. We conducted a comparative analysis of these data with published human single-cell data, revealing consistent differentiation trajectories among porcine and human nasal epithelial cells. Specifically, basal cells serve as the initial stage in the differentiation process of nasal epithelial cells, which then epithelial cells. This research not only enhances our understanding of the composition and transcriptional signature of porcine nasal mucosal cells but also offers a theoretical foundation for developing alternative models for human respiratory diseases. [ABSTRACT FROM AUTHOR]

Introduction: Lung adenocarcinoma, a prevalent and lethal malignancy globally, is characterized by significant tumor heterogeneity and a complex tumor immune microenvironment during its histologic pattern progression. Understanding the intricate interplay between tumor and immune cells is of paramount importance as it could potentially pave the way for the development of effective therapeutic strategies for lung adenocarcinoma. Methods: In this study, we run comparative analysis of the single-cell transcriptomic data derived from tumor tissues exhibiting four distinct histologic patterns, lepidic, papillary, acinar and solid, in lung adenocarcinoma. Furthermore, we conducted immunofluorescence assay and spatial transcriptomic sequencing to validated the spatial co-localization of typical co-inhibitory factors. Results and Discussion: Our analysis unveiled several co-inhibitory receptorligand interactions, including PD1-PDL1, PVR-TIGIT and TIGIT-NECTIN2, that potentially exert a pivotal role in recruiting immunosuppressive cells such as M2 macrophages and Tregs into LUAD tumor, thereby establishing immunosuppressive microenvironment and inducing T cells to exhaustion state. Furthermore, The expression level of these co-inhibitory factors, such as NECTIN2 and PVR, were strongly correlated with low immune infiltration, unfavorable patient clinical outcomes and limited efficacy of immunotherapy. We believe this study provides valuable insights into the heterogeneity of molecular, cellular interactions leading to immunosuppressive microenvironment during the histological progression of lung adenocarcinoma. The findings could facilitate the development of novel immunotherapy for lung cancer. [ABSTRACT FROM AUTHOR]

Accumulating evidence suggests that berberine (BBR) exhibits anti‐cancer effects in hepatocellular carcinoma (HCC). However, the mechanisms by which BBR regulates the immunological microenvironment in HCC has not been fully elucidated. In this study, a mouse model of orthotopic HCC is established and treated with varying doses of BBR. BBR showed effectiveness in reducing tumor burden in mice with HCC. Cytometry by time‐of‐flight depicted the alterations in the tumor immune landscape following BBR treatment, revealing the enhancement in the T lymphocytes effector function. In particular, BBR decreased the proportion of TCRbhiPD‐1hiCD69+CD27+ effector CD8+ T lymphocytes and increased the proportion of Ly6ChiTCRb+CD69+CD27+CD62L+ central memory CD8+ T lymphocytes. Single‐cell RNA sequencing further elucidates the effects of BBR on transcriptional profiles of liver immune cells and confirms the phenotypical heterogeneity of T lymphocytes in HCC immune microenvironment. Additionally, it is found that BBR potentially regulated the antitumor immunity in HCC by modulating the receptor‐ligand interaction among immune cells mediated by cytokines. In summary, the findings improve the understanding of BBR's impact on protecting against HCC, emphasizing BBR's role in regulating intrahepatic T cell heterogeneity. BBR has the potential to be a promising therapeutic strategy to hinder the advancement of HCC. [ABSTRACT FROM AUTHOR]

Despite substantial breakthroughs in the treatment of hepatocellular carcinoma (HCC) in recent years, many patients are diagnosed in the middle or late stages, denying them the option for surgical excision. Therefore, it is of great importance to find effective therapeutic targets of HCC. In this study, it is found that Gap junction protein beta‐2 (GJB2) is highly enriched in malignant cells based on single‐cell RNA sequencing and higher expression of GJB2 indicates a worse prognosis. The localization of GJB2 in HCC cancer cells is changed compared with normal liver tissue. In cancer cells, GJB2 tends to be located in the cytoplasm and nucleus, while in normal tissues, GJB2 is mainly located on the cell membrane. GJB2 is related to glycolysis, promoting NF‐κB pathway via inducing the ubiquitination degradation of IκBa, and activating HIF‐1α/GLUT‐1/PD‐L1 pathway. In addition, GJB2 knockdown reshapes tumor immune microenvironment and Salvianolic acid B inhibits the activity of GJB2. In conclusion, GJB2 promotes HCC progression by activating glycolysis through cytoplasmic translocation and generating a suppressive tumor microenvironment. Salvianolic acid B inhibits the expression of GJB2 and enhances the sensitivity of anti‐PD1 therapy, which may provide insights into the development of novel combination therapeutic strategies for HCC. [ABSTRACT FROM AUTHOR]

Introduction: Adoptive cell therapy using TCR-engineered T-cells is one of the most effective strategies against tumor cells. The TCR T-cell approach has been well tested against a variety of blood neoplasms but is yet to be deeply tested against solid tumors. Among solid tumors, cancer-testis antigens are the most prominent targets for tumor-specific therapy, as they are usually found on cells that lie behind blood-tissue barriers. Methods: We have employed a novel efficient protocol for MAGE-A3-specific Tcell clonal expansion, performed single-cell multi-omic analysis of the expanded T-cells via BD Rhapsody, engineered a selected T-cell receptor into a lentiviral construct, and tested it in an in vitro LDH-cytotoxicity test. Results and discussion: We have observed a 191-fold increase in the MAGE-A3-specific T-cell abundance, obtained a dominant T-cell receptor via single-cell multi-omic BD Rhapsody data analysis in the TCRscape bioinformatics tool, and observed potent cytotoxicity of the dominant-clonotype transduced TCR T-cells against a MAGE-A3-positive tumor. We have demonstrated the efficiency of our Tcell enrichment protocol in obtaining potent anti-tumor T-cells and their T-cell receptors, especially when paired with the modern single-cell analysis methods. [ABSTRACT FROM AUTHOR]

Calcium is involved in vision processes in the retina and implicated in various pathologies, including glaucoma. Rod cells rely on store-operated calcium entry (SOCE) to safeguard against the prolonged lowering of intracellular calcium ion concentrations. Zebrafish that lacked the endoplasmic reticulum Ca2+ sensor Stim2 (stim2 knockout [KO]) exhibited impaired vision and lower light perception-related gene expression. We sought to understand mechanisms that are responsible for vision impairment in stim2 KO zebrafish. The single-cell RNA (scRNA) sequencing of neuronal cells from brains of 5 days postfertilization larvae distinguished 27 cell clusters, 10 of which exhibited distinct gene expression patterns, including amacrine and γ-aminobutyric acid (GABA)ergic retinal interneurons and GABAergic optic tectum cells. Five clusters exhibited significant changes in cell proportions between stim2 KO and controls, including GABAergic diencephalon and optic tectum cells. Transmission electron microscopy of stim2 KO zebrafish revealed decreases in width of the inner plexiform layer, ganglion cells, and their dendrites numbers (a hallmark of glaucoma). GABAergic neuron densities in the inner nuclear layer, including amacrine cells, as well as photoreceptors significantly decreased in stim2 KO zebrafish. Our study suggests a novel role for Stim2 in the regulation of neuronal insulin expression and GABAergic-dependent vision causing glaucoma-like retinal pathology. [ABSTRACT FROM AUTHOR]

Background: Chronic Kidney Disease (CKD) impacts over 10% of the global population, and recent advancements in high-throughput analytical technologies are uncovering the complex physiology underlying this condition. By integrating Genome-Wide Association Studies (GWAS), RNA sequencing (RNA-seq/RNA array), and single-cell RNA sequencing (scRNA-seq) data, our study aimed to explore the genes and cell types relevant to CKD traits. Methods: GWAS summary data for end-stage renal failure (ESRD) and decreased eGFR (CKD) with or without diabetes and (micro)proteinuria were obtained from the GWAS Catalog and the UK Biobank (UKB) database. Two gene Expression Omnibus (GEO) transcriptome datasets were used to establish glomerular and tubular gene expression differences between CKD patients and healthy individuals. Two scRNA-seq datasets were utilized to obtain the expression of key genes at the single-cell level. The expression profile, differentially expressed genes (DEGs), gene-gene interaction, and pathway enrichment were analysed for these CKD risk genes. Results: A total of 779 distinct SNPs were identified from GWAS across different CKD traits, involving 681 genes. While many of these risk genes are specific to the CKD traits of renal failure, decreased eGFR, and (micro)proteinuria, they share common pathways, including extracellular matrix (ECM). ECM modeling was enriched in upregulated glomerular and tubular DEGs from CKD kidneys compared to healthy controls, with the expression of relevant collagen genes, such as COL1A2, prevalent in fibroblasts/myofibroblasts. Additionally, immune responses, including T cell differentiation, were dysregulated in CKD kidneys. The late podocyte signature gene THSD7A was enriched in podocytes but downregulated in CKD. We also highlighted that the regulated risk genes of CKD are mainly expressed in tubular cells and immune cells in the kidney. Conclusions: Our integrated analysis highlight the genes, pathways, and relevant cell types associational with the pathogenesis of kidney traits, as a basis for further mechanistic studies to understand the pathogenesis of CKD. [ABSTRACT FROM AUTHOR]

Serrated lesions are common precancerous pathways in colorectal cancer (CRC), but the process by which they progress to malignancy remains unclear. We aimed to elucidate this progression through a single-cell RNA landscape. We conducted single-cell RNA sequencing on three normal colonic tissues and fifteen SLs (including HPs, SSLs, SSLD, and TSAs) and integrated these data with datasets containing tumor samples. We identified three invasive malignant epithelial cell subtypes related to CRC progression: SLC1, SLC2, and tumor cell. SLC1, specific to SSLs, is involved in cell proliferation and shows a continuum of malignancy in gene expression. TSA-specific SLC2 exhibited FOXQ1 upregulation and active EMT, indicating invasiveness. The trajectory analysis showed that HPs do not progress to cancer, and different SL types are linked to the MSI status of advanced CRCs. We validated molecular drivers in premalignant lesions and later carcinogenesis. In the tumor microenvironment, CAF and pre-CAF fibroblast subtypes associated with progression were identified. During the premalignant stage, SLC1 triggered CD8+ T cell responses, while at the advanced stage, CAFs promoted tumor invasion and metastasis via FN1-CD44, influencing tumor progression and the treatment response. Our findings highlight transcriptional changes across serrated pathway stages, aiding in early CRC diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Prior studies have described the complex interplay that exists between glioma cells and neurons; however, the electrophysiological properties endogenous to glioma cells remain obscure. To address this, we employed Patch-sequencing (Patch-seq) on human glioma specimens and found that one-third of patched cells in IDH mutant (IDHmut) tumors demonstrate properties of both neurons and glia. To define these hybrid cells (HCs), which fire single, short action potentials, and discern if they are of tumoral origin, we developed the single cell rule association mining (SCRAM) computational tool to annotate each cell individually. SCRAM revealed that HCs possess select features of GABAergic neurons and oligodendrocyte precursor cells, and include both tumor and non-tumor cells. These studies characterize the combined electrophysiological and molecular properties of human glioma cells and describe a cell type in human glioma with unique electrophysiological and transcriptomic properties that may also exist in the non-tumor brain. [Display omitted] • Patch-seq identifies spiking tumor cells in human glioma • Spiking glioma cells exhibit GABAergic and glial properties in IDH mutant gliomas • SCRAM algorithm accurately annotates tumor and non-tumor cells from Patch-seq data • Spiking glioma cells confer increased survival in IDH mutant glioma Curry et al. employ simultaneous electrophysiological and genomic profiling of single cells from human glioma to identify glioma cells that fire action potentials (APs). This study introduces single cell rule association mining (SCRAM), a computational tool that provides integrated genomic and transcriptomic profiles for each single cell. [ABSTRACT FROM AUTHOR]

Sepsis is a life-threatening organ malfunction induced by an imbalanced immunological reaction to infection in the host. Many studies have utilized traditional RNA sequencing (RNA-seq) data to identify important biological targets to predict sepsis prognosis. However, alterations in core cells and functional status cannot be effectively detected in sepsis patients. The goal of this study was to identify key cells through single-cell RNA-seq (scRNA-seq), and combine bulk RNA-seq data and multiple algorithm analysis to construct a stable prognostic model for sepsis. The scRNA-seq and bulk RNA-seq data from sepsis patients were collected from the Gene Expression Omnibus (GEO) database. The R package "Seurat" was used to process the scRNA-seq data. Cell communication was investigated using the R package "CellChat". The pseudo-time of the cells was calculated using the R package "monocle". The R package "limma" was used to identify differentially expressed genes (DEGs) between the sepsis group and the control group. Weighted gene correlation network analysis (WGCNA) was used to identify critical modules. Eight kinds of machine learning and 90 algorithm combinations were used to construct the prognostic model for sepsis. Quantitative real-time PCR (qRT‒PCR) was performed to determine the expression of key genes in the cecal ligation and puncture (CLP)-induced sepsis mouse model. The immunological status and related properties of DEGs were then investigated in the high- and low-risk groups delineated by the model. By combining the scRNA-seq data from nine samples, 13 clusters and 9 cell types were identified. CellChat analysis revealed that the number and strength of interactions between platelets and a variety of cells increased. We identified key platelet genes from the scRNA-seq data and combined these genes and the results of differential analysis and WGCNA of the bulk RNA-seq data. After univariate Cox regression analysis, we calculated the Cindex of the model constructed by the combination of 90 algorithms, and we finally determined the "CoxBoost + Lasso" combination. Multivariate Cox regression was used to construct the final prognostic model. The qRT-PCR results revealed significant differences in five key prognostic genes between the CLP and sham groups. The data was classified into high- and low-risk groups based on the model score. The high-risk group had a poorer survival rate and less immune infiltration. We identified the importance of platelets in sepsis patients through scRNA-seq, and established prognostic models with key genes that were identified via scRNA-seq combined with bulk RNA-seq analysis. The results of this model were closely associated with patient survival rates and immunological status and this model is useful for the prognostic management of sepsis. [ABSTRACT FROM AUTHOR]