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Understanding the composition and function of cells constituting tissues and organs is vital for unraveling biological processes. Single-cell analysis has allowed us to move beyond traditional methods of categorizing cell types. This innovative technology allows the transcriptional and epigenetic profiling of numerous individual cells, leading to significant insights into the development, homeostasis, and pathology of various organs and tissues in both animal models and human samples. In this review, we delve into the outcomes of major investigations using single-cell transcriptomics to decipher the cellular composition of mammalian teeth and periodontal tissues. The recent single-cell transcriptome-based studies have traced in detail the dental epithelium-ameloblast lineage and dental mesenchyme lineages in the mouse incisors and the tooth germ of both mice and humans; unraveled the microenvironment, the identity of niche cells, and cellular intricacies in the dental pulp; shed light on the molecular mechanisms orchestrating root formation; and characterized cellular dynamics of the periodontal ligament. Additionally, cellular components in dental pulps were compared between healthy and carious teeth at a single-cell level. Each section of this review contributes to a comprehensive understanding of tooth biology, offering valuable insights into developmental processes, niche cell identification, and the molecular secrets of the dental environment.

Huntington's Disease (HD) is an inheritable neurodegenerative condition caused by an expanded CAG trinucleotide repeat in the HTT gene with a direct correlation between CAG repeats expansion and disease severity with earlier onset-of- disease. Previously we have shown that primary skin fibroblasts from HD patients exhibit unique phenotype disease features, including distinct nuclear morphology and perturbed actin cap linked with cell motility, that are correlated with the HD patient disease severity. Here we provide further evidence that mitochondrial fission-fusion morphology balance dynamics, classified using a custom image-based high-content analysis (HCA) machine learning tool, that improved correlation with HD severity status. This mitochondrial phenotype is supported by appropriate changes in fission-fusion biomarkers (Drp1, MFN1, MFN2, VAT1) levels in the HD patients' fibroblasts. These findings collectively point towards a dysregulation in mitochondrial dynamics, where both fission and fusion processes may be disrupted in HD cells compared to healthy controls. This study shows for the first time a methodology that enables identification of HD phenotype before patient's disease onset (Premanifest). Therefore, we believe that this tool holds a potential for improving precision in HD patient's diagnostics bearing the potential to evaluate alterations in mitochondrial dynamics throughout the progression of HD, offering valuable insights into the molecular mechanisms and drug therapy evaluation underlying biological differences in any disease stage.

MiR-105 exerts inhibitory effects on the development and progression of various cancers, including breast cancer, lung cancer, and gastric cancer. Through GEO data analysis, we observed decreased expression of miR-105 in liver cancer tissues compared to adjacent tissues. Furthermore, miR-105 downregulates KIFC1 expression levels by targeting its 3' UTR. KIFC1 (Kinesin Family Member C1), a Protein Coding gene, may play a role in mitotic metaphase plate polymerization and mitotic spindle assembly. However, our findings suggest that this gene could serve as a potential chemotherapeutic target for Liver hepatocellular carcinoma (LIHC). We obtained the LIHC dataset from the TCGA database and genotype Tissue Expression Project (GTEx) normal tissue data for differential analysis. Additionally, we utilized the cBioPortal database, tumor immune single-cell center (TISCH) database, gene set enrichment analysis (GSEA), and R software to investigate the possible functions and mechanisms of KIFC1. These findings were further validated through experiments such as immunohistochemistry and wound healing assays. Our results indicate that KIFC1 might be involved in DNA repair and cell cycle regulation in LIHC cells which subsequently impacts tumor cell proliferation; moreover, miR-105 influences hepatoma cell line proliferation via its interaction with KIFC1. Collectively, these results highlight the potential therapeutic significance of targeting KIFC1 for chemotherapy treatment in LIHC patients.

Background: Ovarian cancer (OV) is regarded as one of the most lethal malignancies affecting the female reproductive system, with individuals diagnosed with OV often facing a dismal prognosis due to resistance to chemotherapy and the presence of an immunosuppressive environment. T cells serve as a crucial mediator for immune surveillance and cancer elimination. This study aims to analyze the mechanism of T cell-associated markers in OV and create a prognostic model for clinical use in enhancing outcomes for OV patients. Methods: Based on the single-cell dataset GSE184880, this study used single-cell data analysis to identify characteristic T cell subsets. Analysis of high dimensional weighted gene co-expression network analysis (hdWGCNA) is utilized to identify crucial gene modules along with their corresponding hub genes. A grand total of 113 predictive models were formed utilizing ten distinct machine learning algorithms along with the combination of the cancer genome atlas (TCGA)-OV dataset and the GSE140082 dataset. The most dependable clinical prognostic model was created utilizing the leave one out cross validation (LOOCV) framework. The validation process for the models was achieved by conducting survival curve analysis and receiver operating characteristic (ROC) analysis. The relationship between risk scores and immune cells was explored through the utilization of the Cibersort algorithm. Additionally, an analysis of drug sensitivity was carried out to anticipate chemotherapy responses across various risk groups. The genes implicated in the model were authenticated utilizing qRT-PCR, cell viability experiments, and EdU assay. Results: This study developed a clinical prognostic model that includes ten risk genes. The results obtained from the training set of the study indicate that patients classified in the low-risk group experience a significant survival advantage compared to those in the high-risk group. The ROC analysis demonstrates that the model holds significant clinical utility. These results were verified using an independent dataset, strengthening the model's precision and dependability. The risk assessment provided by the model also serves as an independent prognostic factor for OV patients. The study also unveiled a noteworthy relationship between the risk scores calculated by the model and various immune cells, suggesting that the model may potentially serve as a valuable tool in forecasting responses to both immune therapy and chemotherapy in ovarian cancer patients. Notably, experimental evidence suggests that PFN1, one of the genes included in the model, is upregulated in human OV cell lines and has the capacity to promote cancer progression in in vitro models. Conclusion: We have created an accurate and dependable clinical prognostic model for OV capable of predicting clinical outcomes and categorizing patients. This model effectively forecasts responses to both immune therapy and chemotherapy. By regulating the immune microenvironment and targeting the key gene PFN1, it may improve the prognosis for high-risk patients.

Background: Mitophagy selectively eliminates potentially cytotoxic and damaged mitochondria and effectively prevents excessive cytotoxicity from damaged mitochondria, thereby attenuating inflammatory and oxidative responses. However, the potential role of mitophagy in intervertebral disc degeneration remains to be elucidated. Methods: The GSVA method, two machine learning methods (SVM-RFE algorithm and random forest), the CIBERSORT and MCPcounter methods, as well as the consensus clustering method and the WGCNA algorithm were used to analyze the involvement of mitophagy in intervertebral disc degeneration, the diagnostic value of mitophagy-associated genes in intervertebral disc degeneration, and the infiltration of immune cells, and identify the gene modules that were closely related to mitophagy. Single-cell analysis was used to detect mitophagy scores and TOMM22 expression, and pseudo-temporal analysis was used to explore the function of TOMM22 in nucleus pulposus cells. In addition, TOMM22 expression was compared between human normal and degenerated intervertebral disc tissue samples by immunohistochemistry and PCR. Results: This study identified that the mitophagy pathway score was elevated in intervertebral disc degeneration compared with the normal condition. A strong link was present between mitophagy genes and immune cells, which may be used to typify intervertebral disc degeneration. The single-cell level showed that mitophagy-associated gene TOMM22 was highly expressed in medullary cells of the disease group. Further investigations indicated the upregulation of TOMM22 expression in late-stage nucleus pulposus cells and its role in cellular communication. In addition, human intervertebral disc tissue samples established that TOMM22 levels were higher in disc degeneration samples than in normal samples. Conclusions: Our findings revealed that mitophagy may be used in the diagnosis of intervertebral disc degeneration and its typing, and TOMM22 is a molecule in this regard and may act as a potential diagnostic marker in intervertebral disc degeneration.

Liver transplantation (LT) is the standard therapy for individuals afflicted with end-stage liver disease. Despite notable advancements in LT technology, the incidence of early allograft dysfunction (EAD) remains a critical concern, exacerbating the current organ shortage and detrimentally affecting the prognosis of recipients. Unfortunately, the perplexing hepatic heterogeneity has impeded characterization of the cellular traits and molecular events that contribute to EAD. Herein, we constructed a pioneering single-cell transcriptomic landscape of human transplanted livers derived from non-EAD and EAD patients, with 12 liver samples collected from 7 donors during the cold perfusion and portal reperfusion stages. Comparison of the 75 231 cells of non-EAD and EAD patients revealed an EAD-associated immune niche comprising mucosal-associated invariant T cells, granzyme B+ (GZMB+) granzyme K+ (GZMK+) natural killer cells, and S100 calcium binding protein A12+ (S100A12+) neutrophils. Moreover, we verified this immune niche and its association with EAD occurrence in two independent cohorts. Our findings elucidate the cellular characteristics of transplanted livers and the EAD-associated pathogenic immune niche at the single-cell level, thus, offering valuable insights into EAD onset.

Background: 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. Methods: 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. Results: 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. Conclusion: 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.

Background: Bladder cancer (BLCA) presents as a heterogeneous epithelial malignancy. Progress in the early detection and effective treatment of BLCA relies heavily on the identification of novel biomarkers. Therefore, the primary goal of this study is to pinpoint potential biomarkers for BLCA through the fusion of single-cell RNA sequencing and RNA sequencing assessments. Furthermore, the aim is to establish practical clinical prognostic models that can facilitate accurate categorization and individualized therapy for patients. Methods: In this research, training sets were acquired from the TCGA database, whereas validation sets (GSE32894) and single-cell datasets (GSE135337) were extracted from the GEO database. Single-cell analysis was utilized to obtain characteristic subpopulations along with their associated marker genes. Subsequently, a novel BLCA subtype was identified within TCGA-BLCA. Furthermore, an artificial neural network prognostic model was constructed within the TCGA-BLCA cohort and subsequently verified utilizing a validation set. Two machine learning algorithms were employed to screen hub genes. QRT-qPCR was performed to detect the gene expression levels utilized in the construction of prognostic models across various cell lines. Additionally, the cMAP database and molecular docking were utilized for searching small molecule drugs. Results: The results of single-cell analysis revealed the presence of epithelial cells in multiple subpopulations, with 1579 marker genes selected for subsequent investigations. Subsequently, four epithelial cell subtypes were identified within the TCGA-BLCA cohort. Notably, cluster A exhibited a significant survival advantage. Concurrently, an artificial neural network prognostic model comprising 17 feature genes was constructed, accurately stratifying patient risk. Patients categorized in the low-risk group demonstrated a considerable survival advantage. The ROC analysis suggested that the model has strong prognostic ability. Furthermore, the findings of the validation group align consistently with those from the training group. Two types of machine learning algorithms screened NFIC as hub genes. Forskolin, a small molecule drug that binds to NFIC, was identified by employing a cMAP database and molecular docking. Conclusion: The analysis results supplement the research on the role of epithelial cells in BLCA. An artificial neural network prognostic model containing 17 characteristic genes demonstrates the capability to accurately stratify patient risk, thereby potentially improving clinical decision-making and optimizing personalized therapeutic approaches.

Background: In the context of prostate cancer (PCa), the occurrence of biochemical recurrence (BCR) stands out as a pivotal factor significantly impacting prognosis, potentially leading to metastasis and mortality. However, the early detection of BCR poses a substantial challenge for PCa patients. There is an urgent need to pinpoint hub genes that can serve as predictive indicators for BCR in PCa patients. Methods: Our primary goal was to identify cell differentiation trajectory-related gene signature in PCa patients by pseudo-time trajectory analysis. We further explored the functional enrichment of overlapped marker genes and probed clinically relevant modules and BCR-related genes using Weighted Gene Co-expression Network Analysis (WGCNA) in PCa patients. Key genes predicting recurrence-free survival were meticulously identified through univariate and multivariate Cox regression analyses. Subsequently, these genes were utilized to construct a prognostic gene signature, the expression, predictive efficacy, putative functions, and immunological landscape of which were thoroughly validated. Additionally, we employed immunohistochemistry (IHC) and a western blotting assay to quantify the expression of PYCR1 in clinical samples. Results: Our single-cell RNA (scRNA) sequencing analysis unveiled three subgroups characterized by distinct differentiation trajectories, and the marker genes associated with these groups were extracted from PCa patients. These marker genes successfully classified the PCa sample into two molecular subtypes, demonstrating a robust correlation with clinical characteristics and recurrence-free survival. Through WGCNA and Lasso analysis, we identified four hub genes (KLK3, CD38, FASN, and PYCR1) to construct a risk profile of prognostic genes linked to BCR. Notably, the high-risk patient group exhibited elevated levels of B cell naive, Macrophage M0, and Macrophage M2 infiltration, while the low-risk group displayed higher levels of T cells CD4 memory activated and monocyte infiltration. Furthermore, IHC and western blotting assays confirmed the heightened expression of PYCR1 in PCa tissues. Conclusion: This study leveraged the differentiation trajectory and genetic variability of the microenvironment to uncover crucial prognostic genes associated with BCR in PCa patients. These findings present novel perspectives for tailoring treatment strategies for PCa patients on an individualized basis.

Background: Colorectal cancer (CRC) is the third most prevalent tumor globally. The liver is the most common site for CRC metastasis, and the involvement of the liver is a common cause of death in patients with late-stage CRC. Consequently, mitigating CRC liver metastasis (CRLM) is key to improving CRC prognosis and increasing survival. Exercise has been shown to be an effective method of improving the prognosis of many tumor types. However, the ability of exercise to inhibit CRLM is yet to be thoroughly investigated. Methods: The GSE157600 and GSE97084 datasets were used for analysis. A pan-cancer dataset which was uniformly normalized was downloaded and analyzed from the UCSC database: TCGA, TARGET, GTEx (PANCAN, n = 19,131, G = 60,499). Several advanced bioinformatics analyses were conducted, including single-cell sequencing analysis, correlation algorithm, and prognostic screen. CRC tumor microarray (TMA) as well as cell/animal experiments are used to further validate the results of the analysis. Results: The greatest variability was found in epithelial cells from the tumor group. RPS4X was generally upregulated in all types of CRC, while exercise downregulated RPS4X expression. A lowered expression of RPS4X may prolong tumor survival and reduce CRC metastasis. RPS4X and tumor stemness marker-CD44 were highly positively correlated and knockdown of RPS4X expression reduced tumor stemness both in vitro and in vivo. Conclusion: RPS4X upregulation may enhance CRC stemness and increase the odds of metastasis. Exercise may reduce CRC metastasis through the regulation of RPS4X.

Background: Osteosarcoma (OS), the commonest primary malignant bone tumor, is mainly seen in children and teenagers. LINC00960, a newly discovered long intergenic non-protein coding RNA, has been shown to be important in certain cancers. The objective of this study was to assess LINC00960's prognostic and therapeutic value and analyze its mechanism of action in osteosarcoma. Methods: With the transcriptome information of 85 osteosarcomas from the TARGET database, the Cox regression analyses, K-M curve, and ROC curve, were conducted for survival and prognostic analysis. The functional analysis was conducted using GO, KEGG, GSEA, and GSVA. The ESTIMATE, ssGSEA, MCP-counter, ImmuCellAI algorithms, and immune checkpoint correlation analysis were performed for immune-related analysis. The single-cell RNA sequencing data of 6 osteosarcoma patients was obtained from the Gene Expression Omnibus database. The Tumor Immune Dysfunction and Exclusion algorithm and the “pRRophetic” R package were performed to predict the response to immunotherapy and chemotherapy. Results: LINC00960 overexpression is associated with osteosarcoma metastasis and poor prognosis. Based on the LINC00960 expression, the nomogram prediction model was created, which showed good accuracy and precision to predict the overall survival of osteosarcoma. Single-cell and immune-related analysis showed that LINC00960 is mainly highly expressed in the tumor-exhausted CD8 T cells in osteosarcoma. In osteosarcoma, the expression of LIC00960 was favorably connected with immune checkpoint-related genes and negatively correlated with immune infiltration. TIDE analysis indicated that low LINC00960 expression patients might have a better response to immunotherapy. Drug sensitivity analysis showed that high LINC00960 expression patients might have better responses to Bleomycin and Doxorubicin. Conclusion: LINC00960 has the potential to be a novel biomarker for predicting overall survival in osteosarcoma patients and to guide more individualized treatment and clinical decision-making.

Although immune checkpoint therapy has significantly improved the prognosis of patients with melanoma, urgent attention still needs to be paid to the low patient response rates and the challenges of precisely identifying patients before treatment. Therefore, it is crucial to investigate novel immunosuppressive mechanisms and targets in the tumor microenvironment in order to reverse tumor immune escape. In this study, we found that the cell cycle checkpoint Aurora kinase B (AURKB) suppressed the anti-tumor immune response, and its inhibitor, Tozasertib, effectively activated T lymphocyte cytokine release in vitro and anti-tumor immunity in vivo. Tozasertib significantly inhibited melanoma xenograft tumor growth by decreasing the number of inhibitory CD4+ Treg cells in the tumors, which, in turn, activated CD8+ T cells. Single-cell analysis revealed that AURKB suppressed anti-tumor immunity by increasing MIF-CD74/CXCR4 signaling between tumor cells and lymphocytes. Our study suggests that AURKB is a newly identified anti-tumor immunity suppressor, whose inhibitors may be developed as novel anti-tumor immunity drugs and may have synergistic anti-melanoma effects with immune checkpoint therapies.

Summary: In direct lineage conversion, transcription factor (TF) overexpression reconfigures gene regulatory networks (GRNs) to reprogram cell identity. We previously developed CellOracle, a computational method to infer GRNs from single-cell transcriptome and epigenome data. Using inferred GRNs, CellOracle simulates gene expression changes in response to TF perturbation, enabling in silico interrogation of network reconfiguration. Here, we combine CellOracle analysis with lineage tracing of fibroblast to induced endoderm progenitor (iEP) conversion, a prototypical direct reprogramming paradigm. By linking early network state to reprogramming outcome, we reveal distinct network configurations underlying successful and failed fate conversion. Via in silico simulation of TF perturbation, we identify new factors to coax cells into successfully converting their identity, uncovering a central role for the AP-1 subunit Fos with the Hippo signaling effector, Yap1. Together, these results demonstrate the efficacy of CellOracle to infer and interpret cell-type-specific GRN configurations, providing new mechanistic insights into lineage reprogramming.

Introduction: Activation of skeletal progenitors upon tissue injury and the subsequent cell fate specification are tightly coordinated in the bone repair process. Although known osteoimmunological signaling networks play important roles in the microenvironment of the bone defect sites, the molecular mechanism underlying the bone repair process has not been fully understood. Methods: To better understand the behavior of the skeletal progenitors and the heterogeneity of the cells during bone repair at the microenvironmental level, we performed a combinatorial analysis consisting of lineage tracing for skeletal progenitors using the Sox9-CreERT2;R26RtdTomato mouse line followed by single-cell RNA sequencing (scRNA-seq) analysis using a mouse model of calvarial bone repair. To identify a therapeutic target for bone regeneration, further computational analysis was performed focusing on the identification of the cell–cell interactions, followed by pharmacological assessments with a critical-size calvarial bone defect mouse model. Results: Lineage tracing analysis showed that skeletal progenitors marked by Sox9 were activated upon bone injury and contributed to bone repair by differentiating into osteoblasts. The scRNA-seq analysis characterized heterogeneous cell populations at the bone defect sites; the computational analysis predicted a bifurcated lineage from skeletal progenitors toward osteogenic and adipogenic lineages. Chemokine C–C motif ligand 9 (Ccl9) was identified as a signaling molecule that regulates bone regeneration in the mouse model, possibly through the regulation of adipogenic differentiation at the bone defect site. Conclusion: Multipotential skeletal progenitors and the direction of the cell differentiation were characterized at single cell resolution in a mouse bone repair model. The Ccl9 signaling pathway may be a key factor directing osteogenesis from the progenitors in the model and may be a therapeutic target for bone regeneration.

Immune checkpoint inhibitors (ICIs) have rejuvenated therapeutic approaches in oncology. Although responses tend to be durable, response rates vary in many cancer types. Thus, the identification and validation of predictive biomarkers is a key clinical priority, the answer to which is likely to lie in the tumour microenvironment (TME). A wealth of data demonstrates the huge impact of the TME on ICI response and resistance. However, these data also reveal the complexity of the TME composition including the spatiotemporal interactions between different cell types and their dynamic changes in response to ICIs. Here, we briefly review some of the modalities that sculpt the TME, in particular the metabolic milieu, hypoxia and the role of cancer-associated fibroblasts. We then discuss recent approaches to dissect the TME with a focus on single-cell RNA sequencing, spatial transcriptomics and spatial proteomics. We also discuss some of the clinically relevant findings these multi-modal analyses have yielded.

Background: Osteosarcoma is one of the most prevalent bone malignancies with a poor prognosis. The N7-methylguanosine (m7G) modification facilitates the modification of RNA structure and function tightly associated with cancer. Nonetheless, there is a lack of joint exploration of the relationship between m7G methylation and immune status in osteosarcoma. Methods: With the support of TARGET and GEO databases, we performed consensus clustering to characterize molecular subtypes based on m7G regulators in all osteosarcoma patients. The least absolute shrinkage and selection operator (LASSO) method, Cox regression, and receiver operating characteristic (ROC) curves were employed to construct and validate m7G-related prognostic features and derived risk scores. In addition, GSVA, ssGSEA, CIBERSORT, ESTIMATE, and gene set enrichment analysis were conducted to characterize biological pathways and immune landscapes. We explored the relationship between risk scores and drug sensitivity, immune checkpoints, and human leukocyte antigens by correlation analysis. Finally, the roles of EIF4E3 in cell function were verified through external experiments. Results: Two molecular isoforms based on regulator genes were identified, which presented significant discrepancies in terms of survival and activated pathways. Moreover, the six m7G regulators most associated with prognosis in osteosarcoma patients were identified as independent predictors for the construction of prognostic signature. The model was well stabilized and outperformed traditional clinicopathological features to reliably predict 3-year (AUC = 0.787) and 5-year (AUC = 0.790) survival in osteosarcoma cohorts. Patients with increased risk scores had a poorer prognosis, higher tumor purity, lower checkpoint gene expression, and were in an immunosuppressive microenvironment. Furthermore, enhanced expression of EIF4E3 indicated a favorable prognosis and affected the biological behavior of osteosarcoma cells. Conclusions: We identified six prognostic relevant m7G modulators that may provide valuable indicators for the estimation of overall survival and the corresponding immune landscape in patients with osteosarcoma.

Ruminants have a unique placenta in comparison to other mammalian species. Initially, they possess a non-invasive epitheliochorial type of placenta during conceptus elongation. As the conceptus trophectoderm begins to attach to the luminal epithelium (LE) of the endometrium, binucleate cells (BNCs) develop within the trophoblast of the chorion. The BNCs migrate and fuse with the uterine LE to form multinucleate syncytial plaques in sheep and hybrid trinucleate cells in cattle. This area of the ruminant placenta is semi-invasive synepitheliochorial. The BNCs form the foundation of the placental cotyledons and express unique placenta-specific genes including pregnancy-associated glycoproteins and chorionic somatomammotropin hormone 2 or placental lactogen. Attachment and interdigitation of cotyledons into endometrial caruncles form placentomes that are subsequently vascularized to provide essential nutrients for growth of the fetus. This chapter review will discuss historical and current aspects of conceptus implantation and placenta development in ruminant ungulates with a focus on cattle and sheep. Single-cell analysis promises to provide a much more detailed understanding of the different cell populations and insights into pathways mediating trophoblast and placenta. This fundamental is required to understand pregnancy loss and develop strategies to improve pregnancy outcomes in ruminants.

Summary: Backgrounds: Detecting free cancer cells from ascites and peritoneal lavages is crucial for diagnosing gastric cancer (GC). However, traditional methods are limited for early-stage diagnosis due to their low sensitivity. Methods: A label-free, rapid, and high-throughput technique was developed for separating cancer cells from ascites and peritoneal lavages using an integrated microfluidic device, taking advantage of dean flow fractionation and deterministic lateral displacement. Afterward, separated cells were analyzed using a microfluidic single-cell trapping array chip (SCTA-chip). In situ immunofluorescence for EpCAM, YAP-1, HER-2, CD45 molecular expressions, and Wright-Giemsa staining were performed for cells in SCTA-chips. At last, YAP1 and HER-2 expression in tissues was analyzed by immunohistochemistry. Findings: Through integrated microfluidic device, cancer cells were successfully separated from simulated peritoneal lavages containing 1/10,000 cancer cells with recovery rate of 84.8% and purity of 72.4%. Afterward, cancer cells were isolated from 12 patients’ ascites samples. Cytological examinations showed cancer cells were efficiently enriched with background cells excluded. Afterwards, separated cells from ascites were analyzed by SCTA-chips, and recognized as cancer cells through EpCAM+/CD45− expression and Wright-Giemsa staining. Interestingly, 8 out of 12 ascites samples showed HER-2+ cancer cells. At last, the results through a serial expression analysis showed that YAP1 and HER-2 have discordant expression during metastasis. Interpretation: Microfluidic Chips developed in our study could not only rapidly detect label-free free GC cells in ascites and peritoneal lavages with high-throughput, they could also analyze ascites cancer cells at the single-cell level, improving peritoneal metastasis diagnosis and investigation of therapeutic targets. Funding: This research was supported by National Natural Science Foundation of China (22134004, U1908207, 91859111); Natural Science Foundation of Shandong Province of China (ZR2019JQ06); Taishan Scholars Program of Shandong Province tsqn (201909077); Local Science and Technology Development Fund Guided by the Central Government (YDZX20203700002568); Applied Basic Research Program of Liaoning Province (2022020284-JH2/1013).

The scale of biological discovery is driven by the vessels in which we can perform assays and analyze results, from multi-well plates to microfluidic compartments. We report on the compatibility of sub-nanoliter single-cell containers or “nanovials” with commercial fluorescence activated cell sorters (FACS). This recent lab on a particle approach utilizes 3D structured microparticles to isolate cells and perform single-cell assays at scale with existing lab equipment. Use of flow cytometry led to detection of fluorescently labeled protein with dynamic ranges spanning 2-3 log and detection limits down to ∼10,000 molecules per nanovial, which was the lowest amount tested. Detection limits were improved compared to fluorescence microscopy measurements using a 20X objective and a cooled CMOS camera. Nanovials with diameters between 35-85 µm could also be sorted with purity from 99-93% on different commercial instruments at throughputs up to 800 events/second. Cell-loaded nanovials were found to have unique forward and side (or back) scatter signatures that enabled gating of cell-containing nanovials using scatter metrics alone. The compatibility of nanovials with widely-available commercial FACS instruments promises to democratize single-cell assays used in discovery of antibodies and cell therapies, by enabling analysis of single cells based on secreted products and leveraging the unmatched analytical capabilities of flow cytometers to sort important clones.

In plants, differentiated somatic cells exhibit an exceptional ability to regenerate new tissues, organs, or whole plants. Recent studies have unveiled core genetic components and pathways underlying cellular reprogramming and de novo tissue regeneration in plants. Although high-throughput analyses have led to key discoveries in plant regeneration, a comprehensive organization of large-scale data is needed to further enhance our understanding of plant regeneration. Here, we collected all currently available transcriptome datasets related to wounding responses, callus formation, de novo organogenesis, somatic embryogenesis, and protoplast regeneration to construct REGENOMICS, a web-based application for plant REGENeration-associated transcriptOMICS analyses. REGENOMICS supports single- and multi-query analyses of plant regeneration-related gene-expression dynamics, co-expression networks, gene-regulatory networks, and single-cell expression profiles. Furthermore, it enables user-friendly transcriptome-level analysis of REGENOMICS-deposited and user-submitted RNA-seq datasets. Overall, we demonstrate that REGENOMICS can serve as a key hub of plant regeneration transcriptome analysis and greatly enhance our understanding on gene-expression networks, new molecular interactions, and the crosstalk between genetic pathways underlying each mode of plant regeneration. The REGENOMICS web-based application is available at http://plantregeneration.snu.ac.kr.

Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies.The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues.This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.

Pseudomonas putida KT2440 is a well-studied bacterium for the conversion of lignin-derived aromatic compounds to bioproducts. The development of advanced genetic tools in P. putida has reduced the turnaround time for hypothesis testing and enabled the construction of strains capable of producing various products of interest. Here, we evaluate an inducible CRISPR-interference (CRISPRi) toolset on fluorescent, essential, and metabolic targets. Nuclease-deficient Cas9 (dCas9) expressed with the arabinose (8K)-inducible promoter was shown to be tightly regulated across various media conditions and when targeting essential genes. In addition to bulk growth data, single cell time lapse microscopy was conducted, which revealed intrinsic heterogeneity in knockdown rate within an isoclonal population. The dynamics of knockdown were studied across genomic targets in exponentially-growing cells, revealing a universal 1.75 ± 0.38 h quiescent phase after induction where 1.5 ± 0.35 doublings occur before a phenotypic response is observed. To demonstrate application of this CRISPRi toolset, β-ketoadipate, a monomer for performance-advantaged nylon, was produced at a 4.39 ± 0.5 g/L and yield of 0.76 ± 0.10 mol/mol from p-coumarate, a hydroxycinnamic acid that can be derived from grasses. These cultivation metrics were achieved by using the higher strength IPTG (1K)-inducible promoter to knockdown the pcaIJ operon in the βKA pathway during early exponential phase. This allowed the majority of the carbon to be shunted into the desired product while eliminating the need for a supplemental carbon and energy source to support growth and maintenance.

Realizing the intelligent analysis of the intracellular reactive oxygen species (ROS) is beneficial to quick diagnosis of diseases. Herein, surface-enhanced Raman spectroscopy (SERS) technology was combined with deep learning to establish a smart detection method of intracellular ROS based on neural network to improve the SERS analysis ability. Taking the simultaneous detection of peroxynitrite (ONOO - ) and hypochlorite (ClO - ) as the templates, 4-mercaptophenylboric acid (4-MPBA) and 2-mercapto-4-methoxyphenol (2-MP) molecules were modified on the AuNPs to prepare AuNP/4-MPBA/2-MP nanoprobes. The SERS spectra of AuNP/4-MPBA/2-MP nanoprobes before and after the specific response of ONOO - and ClO - were collected to construct a database, and the neural network model for extraction (ENN) and one-dimensional convolutional neural network model (1D-CNN) for quantification were built. The cosine similarity values of ENN model for ONOO - and ClO - correlation spectra were 0.997 and 0.995, respectively. In addition, the qualitative and quantitative results of the models were basically consistent with the experimental results. Moreover, the models can accurately extract the SERS response spectral information of ONOO - and ClO - and realize their preliminary prediction of concentration in living cells, which has great potential in the high-throughput smart processing and accurate analysis of large-scale complicated SERS data from biological system., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Single cell mass spectrometry (scMS) has shown great promise for label free proteomics analysis recently. To present single cell samples for proteomics analysis by MS is not a trivial task. Existing methods rely on robotic liquid handlers to scale up sample preparation throughput. The cost associated with specialized equipment hinders the broad adoption of these workflows, and the sequential sample processing nature limits the ultimate throughput., Results: In this work, we report a parallel sample processing workflow that can simultaneously process 10 single cells without the need of robotic liquid handlers for scMS. This method utilized 3D printed microfluidic devices to form reagent arrays on a glass slide, and a magnetic beads-based streamlined sample processing workflow to present peptides for LC-MS detection. We optimized key operational parameters of the method and demonstrated the quantification consistency among 10 parallel processed samples. Finally, the utility of the method in differentiating cell lines and studying the proteome change induced by drug treatment were demonstrated., Significance: The present method allows parallel sample processing for single cells without the need of expensive liquid handlers, which has great potential to further improve throughput and decrease the barrier for single cell proteomics., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Peng Li reports financial support was provided by the National Institutes of Health and the National Science Foundation., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Periodontitis is a chronic and multifactorial inflammatory disease. However, existing medications often lack sufficient therapeutic effects. The aim is to identify potential biomarkers and efficient therapeutic targets using Mendelian randomisation (MR) and single-cell analysis., Methods: MR analysis was conducted based on the cis-expression quantitative trait loci (cis-eQTLs) extracted from the eQTLGen Consortium and genome-wide association study (GWAS) data of periodontitis sourced from the Gene Lifestyle Interactions in Dental Endpoints (GLIDE) consortium (17,353 cases, 28,210 controls). Subsequently, colocalisation analysis was employed to detect whether genes and periodontitis shared the same casual variant. Finally, enrichment analysis, protein-protein interaction (PPI) networks, drug prediction, phenome-wide association study (PheWAS), molecular docking, and single-cell analysis were conducted to validate the significance of target genes., Results: Fourteen drug targets were significant related with periodontitis in MR analysis. Following the colocalisation and summary-data-based MR (SMR) analysis, 3 targets (S100A12, S100A9, and S100A8) were classified into tier 1 with strong evidence, 6 therapeutic targets (ADAM12, ADHFE1, BLK, HEBP1, SERPINE2, and TEK) were classified into tier 2 with moderate evidence, and 5 therapeutic targets (LY86, MMEL1, S100B, SPP1, and TRIB3) were classified into tier 3 with convincing evidence. PheWAS analysis showed that only TEK and SPP1 in tier 2 may induce side effects, including cardiometabolic and oncological issues. Molecular docking demonstrated strong binding between drugs and their respective protein targets. In the single-cell analysis, 5 target genes (HEBP1, LY86, S100A8, S100A9, and S100A12) exhibited enrichment in monocytes, while BLK and LY86 were primarily enriched in B cells., Conclusion: The study identified 14 potential therapeutic targets for periodontitis. Among these, 3 therapeutic targets (S100A12, S100A9, and S100A8) demonstrated robust and well-supported results. Drugs designed to target these genes have a higher possibility of success in clinical trials, which are hopeful for prioritising periodontitis drug development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Microbial communities play crucial roles in shaping natural ecosystems, impacting human well-being, and driving advancements in industrial biotechnology. However, associating specific metabolic functions with bacteria proves challenging due to the vast diversity of microorganisms within these communities. In the past decades stable isotope probing (SIP) approaches, coupled with vibrational spectroscopy, have emerged as a novel method for revealing microbial metabolic roles and interactions in complex communities. In this study, we employed various combinations of heavy stable isotopes (D, 13 C, 15 N, and 18 O), to evaluate all possible isotopic spectral shifts in the mid-IR region using Fourier-Transform Infrared (FT-IR) and Optical Photothermal Infrared (O-PTIR) spectroscopy, at both community and single-cell levels. Additionally, we conducted a time-course study to explore the kinetics of CD vibration in Escherichia coli bacteria, allowing time-based sampling and assessment of isotopic labeling kinetics. The FT-IR and O-PTIR, along with the second derivative spectra of E. coli cells cultured in minimal medium supplemented with various combinations of heavy isotopes exhibited notable similarities. Several spectral shifts in primary vibrational peaks were observed due to the incorporation of heavy isotopes into various biomolecules. Remarkably, the incorporation of deuterium into amide groups, resulting in the formation of nitrogen-deuterium bonds, caused a shift in amide A and B into the silent region, overlapping with CD signature peaks. The incorporation of 18 O into the ester group of lipids and the carbonyl group of proteins resulted in a notable shift to the lower wavenumber region. Additionally, the second derivative of FT-IR spectral data highlighted the integration of 18 O into α-helix and β-sheet structures. Furthermore, the spectra, second derivative, and PC-DFA scores and loadings plot of FT-IR data collectively illustrated the practicality of monitoring 13 C and D incorporation into E. coli bacterial cells within the first 30-min incubation period. The findings of this study suggest that FT-IR and O-PTIR can serve as efficient tools for monitoring the incorporation of heavy isotopes into bacteria at both the population and single-cell levels. Additionally, the SIP approach allowed us to assign two new deuterium-associated vibrational peaks to their corresponding functional groups, which to the best of our knowledge have not been reported previously., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Royston Goodacre reports financial support was provided by NERC Environmental Omics Facility, and C.L. EPSRC-SFI. Howbeer Muhamadali reports financial support was provided by Analytical Chemistry Trust Fund (ACTF) and Community for Analytical Measurement Science (CAMS). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Single-cell analysis is critical for advancing personalized medicine, as it reveals cell population heterogeneity that influences disease outcomes. We present a microwell-assembled aluminum substrate platform that enhances single-cell Raman spectroscopy in liquid suspension by isolating individual cells and preventing stacking and movement, which significantly improves signal stability and the signal-to-noise ratio (SNR). We applied this novel platform to analyze PC-9 lung cancer cells and BEAS-2B normal bronchial epithelial cells, identifying distinct biochemical differences. Notably, cancer cells showed higher levels of adenine, cytochromes, DNA/RNA, and unsaturated lipids, along with an increased unsaturation ratio and protein content. These findings were further validated using machine learning models. An eXtreme Gradient Boosting (XGBoost) model achieved perfect classification accuracy of 100 %, underscoring the robustness of the spectral features identified by our platform. Our platform not only enhances single-cell Raman signal detection but also holds promise for biomedical applications, including early cancer detection, treatment monitoring, and drug development. The high-throughput capacity of this platform featuring over 120,000 wells, along with its compatibility with techniques such as Raman-activated cell sorting (RACS) further extends its potential for clinical diagnostics and personalized medicine., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Paraquat (PQ), a commonly used herbicide, is a potent environmental neurotoxin associated with Parkinson's disease (PD) and major depressive disorder (MDD). While the involvement of various brain cell types in the etiology of each disorder is well recognized, the specific cell subtypes implicated in the comorbidity of PD and MDD, especially under PQ neurotoxicity, remain poorly understood. In this study, we used single-cell RNA sequencing (scRNA-seq) to analyze brain tissues from mice with PQ-induced PD with MDD. By integrating genomic data with scRNA-seq profiles, we identified differences in cellular heterogeneity related to the pathogenesis of PD and MDD under PQ exposure. Our analysis of risk enrichment in genes with cell type-specific expression patterns revealed that astrocytes are predominantly linked to the comorbidity of PQ-induced PD and MDD. Furthermore, we identified a specific astrocyte subtype that plays a major role in the comorbidity-related changes observed in PQ-induced PD and MDD. This subtype appears to interact with and potentially transform into MDD-specific and PD-specific subtypes. Additionally, pathways related to chemical synaptic function and neuro-projection development were involved in all key stages of PD and MDD co-occurrence. We also identified RNF7 and MTCH2 as shared diagnostic hub genes for PD and MDD, which changed significantly in astrocytes following PQ exposure. These genes may serve as potential markers for astrocyte-specific prognostic diagnosis of PQ-induced PD with MDD. In summary, this study provides the first scRNA-seq profile of comorbidity in a PQ-exposed model. It highlights the heterogeneity of astrocytes in comorbidity and elucidates potential mechanisms underlying the co-occurrence of PD and MDD. These findings emphasize the need for further research into the pathogenesis of PD comorbid with MDD and offer novel insights into PQ neurotoxicity., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Siying Wu reports financial support was provided by National Natural Science Foundation of China. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Duck reovirus (DRV) is a universal waterfowl virus that causes significant economic losses in the duck industry. However, the role of the host innate immune response of the Bursa of Fabricius to DRV infection is largely unknown. In the present study, we constructed a single-cell resolution transcriptomic atlas of the Bursa of Fabricius of Cairna moschata after infection with HN10 (a novel DRV). Ten cell-type marker genes were used to annotate the cell type, indicating a high degree of cell heterogeneity in the Bursa of Fabricius. Most of the innate and adaptive immune system-related genes were highly expressed in T cells, B cells, neutrophils, macrophages, and DCs. In the Bursa of Fabricius, the proportions of DCs and macrophages were largely increased by HN10 infection at 14 d, suggesting that DCs and macrophages play important roles in the long-term viral response. Notably, a number of innate and adaptive immune system-related genes were highly expressed at 24 h after HN10 infection, indicating that the Bursa of Fabricius has a very strong immune function even in the early developmental stage. In the immune system, the NOD-like receptor signaling pathway and RIG-I-like receptor signaling pathway were significantly activated at the early stage of HN10 infection, while the Toll-like receptor signaling pathway was significantly activated at the late stage. Enrichment analysis suggested that different immune signaling pathways play roles in specific developmental stages. Our data provide an opportunity to reveal the immune response to DRV infection at the single-cell level., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Cell death, including apoptosis and necrosis, collectively known as widespread apoptosis. The present study aims to investigate the mechanism of action in widespread apoptosis-related modification patterns in bladder cancer., Methods: Using a clinical genomics database, we obtained transcriptomic data and related clinical information of bladder cancer patients. By employing the least absolute shrinkage analysis, we were able to construct a risk model and single-cell sequencing analysis of differential genes in bladder cancer., Results: Five differentially expressed genes (TMPRSS4, TPST1, FOXD1, ELOVL4, EMP1) associated with widespread apoptosis were identified as features for predicting the prognosis of bladder cancer patients. Survival curve analysis revealed significant differences in prognosis (P<0.05). Additionally, multivariate Cox regression analysis determined the independent risk factor for bladder cancer prognosis as the risk score (P<0.001), with high confidence in the scoring model validated internally (P<0.001). Single-cell sequencing reveals high expression of CDKN2A, ERBB2, and TMPRSS4 in B cells, while HRAS is significantly expressed in fibroblasts., Conclusion: PANscore, as a potential prognostic and immunotherapeutic biomarker, will provide more precise and rational basis for personalized treatment strategies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Type 1 Diabetes (T1D) is an autoimmune disease characterized by the attack and destruction of Pancreatic islet beta cells by T cells. Understanding the role of T-cell receptor (TCR) in the development of T1D is of paramount importance. This study employs single-cell RNA sequencing (scRNA-seq) to delve into the mechanistic actions and potential therapeutic applications of autoreactive stem cell-like CD8 TCR in T1D. By retrieving T-cell data from non-obese diabetic (NOD) mice via the GEO database, it was revealed that CD8 + T cells are the predominant T-cell subset in the pancreatic tissue of T1D mice, along with the identification of T-cell marker genes closely associated with T1D. Moreover, the gene TRAJ23 exhibits a preference for T1D, and its knockout alleviates T1D symptoms and adverse reactions in NOD mice. Additionally, engineered TCR-T cells demonstrate significant cytotoxicity towards β cells in T1D., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This study aimed to evaluate the efficacy and therapeutic mechanism of parthenolide (PTL) in breast cancer (BC) through a comprehensive strategy integrating network pharmacology, single-cell RNA sequencing (scRNA-seq) and metabolomics. In network pharmacology, 70 therapeutic targets were identified, of which 16 core targets were filtered out through seven classical algorithms of Cytohubba plugin. Additionally, the hub module of PPI network was extracted using MCODE plugin. Molecular docking and molecular dynamics simulation showed a potent binding affinity between PTL and JNK, subsequently validated by MST and SPR assays. Further, Mendelian randomization analysis indicated that JNK was causally associated with BC. GO and KEGG enrichment analyses revealed that PTL counteracted BC via promoting ROS generation, inducing apoptosis and suppressing proliferation, which potentially involved the coordinated regulation of MAPK and FoxO1 pathways. Moreover, ssGSEA and scRNA-seq analysis suggested that PTL may act on T cell immune microenvironment of BC. Subsequently, these bioinformatics-based predictions were experimentally validated using in-vitro and in-vivo models. Finally, metabolome profiling unveiled that PTL remodeled the glycine, serine and threonine metabolism as well as biosynthesis of unsaturated fatty acids, and thereby contributed to BC inhibition. From molecular, immune and metabolic perspectives, this study not only provided a unique insight into the mechanistic details of PTL against BC, but also proposed a novel promising therapeutic strategy for BC., Competing Interests: Declaration of competing interest The authors have no competing interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Musk secreted by the musk glands in male forest musk deer (FMD; Moschus berezovskii) is highly valued for its pharmaceutical and perfumery applications. However, the regulatory mechanisms underlying musk secretion are not well understood. This study aimed to investigate the genes and transcription factors involved in musk secretion across different periods and ages. We analyzed the musk glands of adult male FMD during the non-secretory and secretory periods, as well as juvenile and adult male FMD during the secretory period, using single-cell multiome ATAC+gene expression technique. Our analysis identified 13 cell types, including acinar cells of Types 1 and 2. Chromatin accessibility analysis and gene expression data confirmed that the genes Map3k2, Hsd17b12, and Jun are critical for musk secretion. Additionally, EHF, NR4A2, and FOXO1 proteins play crucial regulatory roles. Weighted gene co-expression network analysis (WGCNA) highlighted the importance of GnRH signaling pathway in musk secretion. Gene set enrichment analysis (GSEA) showed that the steroid hormone biosynthesis pathway is notably enriched in acinar cells. Furthermore, intercellular communication appears to influence both the initiation and maintenance of musk secretion. These findings provide valuable insights into the molecular pathways of musk secretion in FMD, offering potential avenues for increasing musk production and developing treatment for inflammation and tumors., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Saccharomyces cerevisiae is the most common microbe used for the industrial production of bioethanol, and it encounters various stresses that inhibit cell growth and metabolism during fermentation. However, little is currently known about the physiological changes that occur in individual yeast cells during ethanol fermentation. Therefore, in this work, Raman spectroscopy and chemometric techniques were employed to monitor the metabolic changes of individual yeast cells at distinct stages during high gravity ethanol fermentation. Raman tweezers was used to acquire the Raman spectra of individual yeast cells. Multivariate curve resolution-alternating least squares (MCR-ALS) and principal component analysis were employed to analyze the Raman spectra dataset. MCR-ALS extracted the spectra of proteins, phospholipids, and triacylglycerols and their relative contents in individual cells. Changes in intracellular biomolecules showed that yeast cells undergo three distinct physiological stages during fermentation. In addition, heterogeneity among yeast cells significantly increased in the late fermentation period, and different yeast cells may respond to ethanol stress via different mechanisms. Our findings suggest that the combination of Raman tweezers and chemometrics approaches allows for characterizing the dynamics of molecular components within individual cells. This approach can serve as a valuable tool in investigating the resistance mechanism and metabolic heterogeneity of yeast cells during ethanol fermentation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Mass spectrometry (MS) has been one of the most widely used tools for bioanalytical analysis due to its high sensitivity, capability of quantitative analysis, and compatibility with biomolecules. Among various MS techniques, single cell mass spectrometry (SCMS) is an advanced approach to molecular analysis of cellular contents in individual cells. In tandem with the creation of novel experimental techniques, the development of new SCMS data analysis tools is equally important. As most published software packages are not specifically designed for pretreatment of SCMS data, including peak alignment and background removal, their applicability on processing SCMS data is generally limited. Hereby we introduce a Python platform, MassLite, specifically designed for rapid SCMS metabolomics data pretreatment. This platform is made user-friendly with graphical user interface (GUI) and exports data in the forms of each individual cell for further analysis. A core function of this tool is to use a novel peak alignment method that avoids the intrinsic drawbacks of traditional binning method, allowing for more effective handling of MS data obtained from high resolution mass spectrometers. Other functions, such as void scan filtering, dynamic grouping, and advanced background removal, are also implemented in this tool to improve pretreatment efficiency., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Silicosis represents a form of interstitial lung disease induced by the inhalation of silica particles in production environments. A key pathological characteristic of silica-induced pulmonary fibrosis is its localized tissue heterogeneity, which presents significant challenges in analyzing transcriptomic data due to the loss of important spatial context. To address this, we integrate spatial gene expression data with single-cell analyses and achieve a detailed mapping of cell types within and surrounding fibrotic regions, revealing significant shifts in cell populations in normal and diseased states. Additionally, we explore cell interactions within fibrotic zones using ligand-receptor mapping, deepening our understanding of cellular dynamics in these areas. We identify a subset of fibroblasts, termed Inmt fibroblasts, that play a suppressive role in the fibrotic microenvironment. Validating our findings through a comprehensive suite of bioinformatics, histological, and cell culture studies highlights the role of monocyte-derived macrophages in shifting Inmt fibroblast populations into profibrotic Grem1 fibroblast, potentially disrupting lung homeostasis in response to external challenges. Hence, the spatially detailed deconvolution offered by our research markedly advances the comprehension of cell dynamics and environmental interactions pivotal in the development of pulmonary fibrosis., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Live single-cell metabolomic studies encounter inherent difficulties attributed to the limited sample volume, minimal compound quantity, and insufficient sensitivity in the Mass Spectrometry (MS) method used to obtain single-cell data. However, understanding cellular heterogeneity, functional diversity, and metabolic processes within individual cells is essential. Exploring how individual cells respond to stimuli, including drugs, environmental changes, or signaling molecules, offers insights into biology, oncology, and drug discovery. Efficient release of cell contents (lysis) is vital for accurate metabolite detection at the single-cell level. Despite this, traditional approaches in live single cell metabolomics methods do not emphasize efficient lysis to prevent sample dilution. Instead, current live single cell metabolomics methods use direct infusion to introduce the cell into the mass spectrometry without prior chromatographic separation or a lysis step, which adversely affects sensitivity and metabolic coverage., Results: To address this, we developed an integrated single-cell electrical lysis and nano spray (SCEL-nS) platform coupled to an Orbitrap MS capable of efficiently lysing a single cell after being sampled with specially manufactured micropipettes. Lysis efficiency was validated by comparing live cell stain fluorescent intensities of intact and electrically lysed cells through microscopy imaging. The SCEL-nS platform successfully induced the breakdown of a single cell, significantly reducing the live cell stain's fluorescent intensity indicating cell membrane breakdown. Additionally, SCEL-nS was validated by measuring single cells spiked with the anti-cancer drug tamoxifen by MS. SCEL-nS use resulted in statistically significant increase in the peak measured by the method compared to the traditional non-lysis method., Significance: Overall, our results demonstrate that the newly incorporated SCEL-nS platform achieved higher sensitivities compared to traditional live single cell analysis methods., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Human skin displays extensive spatial heterogeneity and maintains distinct positional identity. However, the impact of disease processes on these site-specific differences remains poorly understood, especially in keloid, a skin disorder characterized by pronounced spatial heterogeneity., Objective: This study aimed to assess whether the spatial heterogeneity and positional identity observed in different anatomic sites persist in keloids., Methods: Transcriptome sequencing was conducted on 139 keloid dermal tissues and 19 keloid fibroblast samples spanning seven distinct anatomic sites to identify the spatial transcriptomic heterogeneity. In addition, single-cell RNA sequencing data were utilized to elucidate the contributions of various cell types to the maintenance of positional identity., Results: Keloid dermal tissues from diverse sites were categorized into three anatomic groupings: trunk and extremity, ear, and mandible regions. Enrichment analysis of differentially expressed genes unveiled that keloids across distinct regions retained unique anatomically-related gene expression profiles, reminiscent of those observed in normal skin. Notably, regional disparities consistently prevailed and surpassed inter-donor variations. Single-cell RNA sequencing further revealed that mesenchymal cells, particularly fibroblasts, made major contributions to positional identity in keloids. Moreover, gene expression profiles in primary keloid fibroblasts demonstrated a remarkable persistence of positional identity, enduring even after prolonged in vitro propagation., Conclusion: Taken together, these findings imply that keloids remain positional identity and developmental imprinting characteristic of normal skin. Fibroblasts predominantly contribute to the spatial heterogeneity observed in keloids., Competing Interests: Declaration of Competing Interest The authors state no conflict of interest., (Copyright © 2024. Published by Elsevier B.V.)

Fine particulate matter (PM2.5) exposure has been extensively linked to reproductive and developmental dysfunctions, yet the underlying mechanisms remain elusive. This study employed single-cell RNA sequencing (scRNA-seq) to investigate PM2.5-induced changes in uterine cell populations and gene expression profiles in mice during estrus and early pregnancy. Methodologically, we intranasally inoculated mice with 20 μL of 4.0 mg/mL PM2.5 suspension during their estrus and early pregnancy periods. Utilizing scRNA-seq analysis, we revealed significant alterations in cell type composition following PM2.5 exposure. Notably, we observed a marked decrease in the proportion of natural killer (NK) cells in PM2.5-exposed mice (2.00 % vs. 8.97 % in controls). Further functional enrichment analysis identified suppression of the IL-17 signaling pathway in NK cells as a key mechanism of PM2.5-induced toxicity. GSEA analysis showed in-depth details of the downregulated genes in this pathway, including Fosb, S100a8, Tnfaip3, IL-17a, and S100a9. PM2.5 exposure also disrupted intercellular communication within the uterine microenvironment, with the number of cell interactions decreasing from 483 to 315 and interaction strength reducing from 12.43 to 6.78 compared to controls. Histological examination revealed that PM2.5 exposure led to thinning of the endometrium and less prominent main branches in uterine tissues, and immunofluorescence assays corroborated the altered expression of IL-17 pathway components, showing enhanced Hsp90ab1 expression and reduced FOSB, S100A8, and S100A9 expression in PM2.5-exposed uterine tissues. These findings provide novel insights into the cellular mechanisms of PM2.5-induced reproductive toxicity, highlighting the IL-17 signaling pathway in uterine NK cells as a potential target for therapeutic interventions. Our results underscore the need for air quality regulations and open new avenues for developing biomarkers and targeted therapies to mitigate the reproductive risks associated with PM2.5 exposure., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background and Aims: Tobacco smoking is a known risk factor for atherosclerotic disease, with more elevated risks in women compared to men. We hypothesized that atherosclerotic plaques from smokers show different gene expression patterns compared to non-smokers, in a sex-specific manner., Methods: Gene expression data of 625 carotid plaques (151 females and 474 males) were analyzed for differential gene expression between current smokers (n = 226) and non-smokers (n = 399). All analyses were stratified by sex and by molecular plaque characteristics. Finally, we projected the activity of gene regulatory networks and utilized single-cell transcriptomics from 38 plaques (26 males and 12 females) to interpret the sex- and plaque-type specific signals., Results: We observed higher expression levels of CRLF1 gene in atherosclerotic plaques from smokers compared to non-smokers (log2FC = 0.48, FDR = 0.012). CRLF1 upregulation was interacting with sex (p = 0.01) and was more pronounced in females (log2FC = 0.93, p = 1.53E-05) compared to males (log2FC = 0.35, p = 0.0018). Through single-cell RNA-seq analysis, we identified the highest CRLF1 expression within the transitioning and synthetic smooth muscle cell populations. CRLF1 expression was increased in fibro-inflammatory and fibro-cellular plaque types. Gene annotations pointed to increased expression of CRLF1 in networks with extracellular matrix related genes., Conclusions: Atherosclerotic plaques from current smokers show sex-dependent upregulation of smooth muscle cell gene CRLF1. This may explain the different contributions of smoking to cardiovascular risk in females., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The size of the initial primordial follicle pool in the ovary depends on primordial follicle formation, which determines the female reproductive lifespan. However, the molecular regulation of primordial follicle formation in chickens remains unclear. In this study, the left ovaries of chickens were collected at 2 d posthatch (dph), 5.5 dph, and 10.5 dph to examine the formation of primordial follicles. Single-cell mRNA sequencing (scRNA-seq) and spatial transcriptomic analysis were performed to explore the ovarian microenvironment and identify regulatory pathways involved in the formation of primordial follicles in chickens. Histomorphological analysis of chicken ovary tissues revealed the presence of germ cell cysts at 1 dph, which began to disintegrate at 2 dph. Primordial follicles appeared at 5.5 dph and continued to develop into larger-diameter follicles. scRNA-seq and spatial transcriptomic analysis revealed 24 cellular clusters involved in chicken primordial follicle formation. The metabolic pathway of steroid hormone synthesis was found in pregranulosa and pretheca cells. Histological analysis showed that chicken ovaries did not form primordial follicles after the inhibition of the steroid hormone synthesis pathway by simvastatin or tamoxifen. In addition, mRNA transcriptomic and bioinformatics analyses revealed that GREB1 was a downstream gene of the steroid hormone synthesis pathway during the formation of chicken primordial follicles. This study provides a valuable foundation for investigating primordial follicle formation in avian species and optimizing their reproductive performance., Competing Interests: DISCLOSURES The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Normothermic ex vivo liver perfusion (NEVLP) is an exciting strategy to preserve livers prior to transplant, however, the effects of NEVLP on the phenotype of tissue-resident immune cells is largely unknown. The presence of tissue-resident memory T cells (TRM) in the liver may protect against acute rejection and decrease allograft dysfunction. Therefore, we investigated the effects of NEVLP on liver TRMs and assessed the ability of anti-inflammatory cytokines to reduce TRM activation during NEVLP., Methods: Rat livers underwent NEVLP with or without the addition of IL-10 and TGF-β. Naïve and cold storage livers served as controls. Following preservation, TRM T cell gene expression profiles were assessed through single cell RNA sequencing (scRNA-seq). Differential gene expression analysis was performed with Wilcoxon rank sum test to identify differentially expressed genes (DEGs) associated with a specific treatment group. Using the online Database for Annotation, Visualization and Integrated Discovery (DAVID), gene set enrichment was then conducted with Fisher's exact test on DEGs to highlight differentially regulated pathways and functional terms associated with treatment groups., Results: Through scRNA-seq analysis, an atlas of liver-resident memory T cell subsets was created for all livers. TRM T cells could be identified in all livers, and through scRNA-seq, DEG was identified with Wilcoxon rank sum test at FDR < 0.05. Based on the gene set enrichment analysis of DEGs using Fisher's exact test, NEVLP is associated with downregulation of multiple gene enrichment pathways associated with surface proteins. Furthermore, NEVLP with anti-inflammatory cytokines was associated with down regulation of 52 genes in TRM T cells when compared to NEVLP alone (FDR <0.05), most of which are pro-inflammatory., Conclusion: This is the first study to create an atlas of liver TRM T cells in the rat liver undergoing NEVLP and demonstrate the effects of NEVLP on liver TRM T cells at the single cell gene expression level., Competing Interests: Declaration of competing interest Nothing to report., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Bullous pemphigoid (BP) is a subepidermal blistering skin disease with a complex pathogenesis involving various immune cells. However, the transcriptional features of these cells remain poorly defined. In this study, we constructed a comprehensive and single-cell resolution atlas of various immune cells within BP skin lesions through integrative single-cell analysis, flow cytometry, and multiplex immunohistochemistry. We observed prominent expansion and transcriptional changes in mast cells, macrophages, basophils, and neutrophils within BP lesions. Mast cells within the lesions adopted an active state and exhibited an elevated capacity for producing proinflammatory mediators. We observed an imbalance of macrophages/dendritic cells within BP lesions. Two macrophage subpopulations (NLRP3 + and C1q + ) with distinct transcriptional profiles were identified and upregulated effector programs. T-peripheral helper-like T helper 2 cells were expanded in skin lesions and peripheral blood of patients with BP and were capable of promoting B-cell responses. In addition, we observed clonally expanded granzyme B-positive CD8 + T cells within BP lesions. Chemokine receptor mapping revealed the potential roles of macrophages and mast cells in recruiting pathogenic immune cells and underlying mechanisms within BP lesions. Thus, this study reveals key immune pathogenic features of BP lesions, thereby providing valuable insights for potential therapeutic interventions in this disease., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: To unravel the heterogeneity and function of microenvironmental neutrophils during intervertebral disc degeneration (IDD)., Methods: Single-cell RNA sequencing (scRNA-seq) was utilized to dissect the cellular landscape of neutrophils in intervertebral disc (IVD) tissues and their crosstalk with nucleus pulposus cells (NPCs). The expression levels of macrophage migration inhibitory factor (MIF) and ACKR3 in IVD tissues were detected. The MIF/ACKR3 axis was identified and its effects on IDD were investigated in vitro and in vivo., Results: We sequenced here 71520 single cells from 5 control and 9 degenerated IVD samples using scRNA-seq. We identified a unique cluster of neutrophils abundant in degenerated IVD tissues that highly expressed MIF and was functionally enriched in extracellular matrix organization (ECMO). Cell-to-cell communication analyses showed that this ECMO-neutrophil subpopulation was closely interacted with an effector NPCs subtype, which displayed high expression of ACKR3. Further analyses revealed that MIF was positively correlated with ACKR3 and functioned via directly binding to ACKR3 on effector NPCs. MIF inhibition attenuated degenerative changes of NPCs and extracellular matrix, which could be partially reversed by ACKR3 overexpression. Clinically, a significant correlation of high MIF/ACKR3 expression with advanced IDD grade was observed. Furthermore, we also found a positive association between MIF + ECMO-neutrophil counts and ACKR3 + effector NPCs density as well as higher expression of the MIF/ACKR3 signaling in areas where these two cell types were neighbors., Conclusions: These data suggest that ECMO-neutrophil promotes IDD progression by their communication with NPCs via the MIF/ACKR3 axis, which may shed light on therapeutic strategies., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: The pathophysiology of ulcerative colitis (UC) is believed to be heavily influenced by immunology, which presents challenges for both diagnosis and treatment. The main aims of this study are to deepen our understanding of the immunological characteristics associated with the disease and to identify valuable biomarkers for diagnosis and treatment., Methods: The UC datasets were sourced from the GEO database and were analyzed using unsupervised clustering to identify different subtypes of UC. Twelve machine learning algorithms and Deep learning model DNN were developed to identify potential UC biomarkers, with the LIME and SHAP methods used to explain the models' findings. PPI network is used to verify the identified key biomarkers, and then a network connecting super enhancers, transcription factors and genes is constructed. Single-cell sequencing technology was utilized to investigate the role of Peroxisome Proliferator Activated Receptor Gamma (PPARG) in UC and its correlation with macrophage infiltration. Furthermore, alterations in PPARG expression were validated through Western blot (WB) and immunohistochemistry (IHC) in both in vitro and in vivo experiments., Result: By utilizing bioinformatics techniques, we were able to pinpoint PPARG as a key biomarker for UC. The expression of PPARG was significantly reduced in cell models, UC animal models, and colitis models induced by dextran sodium sulfate (DSS). Interestingly, overexpression of PPARG was able to restore intestinal barrier function in H 2 O 2 -induced IEC-6 cells. Additionally, immune-related differentially expressed genes (DEGs) allowed for efficient classification of UC samples into neutrophil and mitochondrial metabolic subtypes. A diagnostic model incorporating the three disease-specific genes PPARG, PLA2G2A, and IDO1 demonstrated high accuracy in distinguishing between the UC group and the control group. Furthermore, single-cell analysis revealed that decreased PPARG expression in colon tissue may contribute to the polarization of M1 macrophages through activation of inflammatory pathways., Conclusion: In conclusion, PPARG, a gene related to immunity, has been established as a reliable potential biomarker for the diagnosis and treatment of UC. The immune response it controls plays a key role in the progression and development of UC by enabling interaction between characteristic biomarkers and immune infiltrating cells., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In chordates, the central nervous system arises from precursors that have distinct developmental and transcriptional trajectories. Anterior nervous systems are ontogenically associated with ectodermal lineages while posterior nervous systems are associated with mesoderm. Taking advantage of the well-documented cell lineage of ascidian embryos, we asked to what extent the transcriptional states of the different neural lineages become similar during the course of progressive lineage restriction. We performed single-cell RNA sequencing (scRNA-seq) analyses on hand-dissected neural precursor cells of the two distinct lineages, together with those of their sister cell lineages, with a high temporal resolution covering five successive cell cycles from the 16-cell to neural plate stages. A transcription factor binding site enrichment analysis of neural specific genes at the neural plate stage revealed limited evidence for shared transcriptional control between the two neural lineages, consistent with their different ontogenies. Nevertheless, PCA analysis and hierarchical clustering showed that, by neural plate stages, the two neural lineages cluster together. Consistent with this, we identified a set of genes enriched in both neural lineages at the neural plate stage, including miR-124, Celf3.a, Zic.r-b, and Ets1/2. Altogether, the current study has revealed genome-wide transcriptional dynamics of neural progenitor cells of two distinct developmental origins. Our scRNA-seq dataset is unique and provides a valuable resource for future analyses, enabling a precise temporal resolution of cell types not previously described from dissociated embryos., (Copyright © 2024. Published by Elsevier Inc.)

The heart is the central organ of the circulatory system, and its proper development is vital to maintain human life. As fetal heart development is complex and poorly understood, we use single-cell RNA sequencing to profile the gene expression landscapes of human fetal hearts from the four-time points: 8, 10, 11, 17 gestational weeks (GW8, GW10, GW11, GW17), and identified 11 major types of cells: erythroid cells, fibroblasts, heart endothelial cells, ventricular cardiomyocytes, atrial cardiomyocytes, macrophage, DCs, smooth muscle, pericytes, neural cells, schwann cells. In addition, we identified a series of differentially expressed genes and signaling pathways in each cell type between different gestational weeks. Notably, we found that ANNEXIN, MIF, PTN, GRN signalling pathways were simple and fewer intercellular connections in GW8, however, they were significantly more complex and had more intercellular communication in GW10, GW11, and GW17. Notably, the interaction strength of OSM signalling pathways was gradually decreased during this period of time (from GW8 to GW17). Together, in this study, we presented a comprehensive and clear description of the differentiation processes of all the main cell types in the human fetal hearts, which may provide information and reference data for heart regeneration and heart disease treatment., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2024. Published by Elsevier Inc.)

Cytokine expression is an important biomarker in understanding hypoxia microenvironments in tumor growth and metastasis. In-droplet-based immunoassays performed above the target cell membrane were employed to track the cytokines of single cells with the aid of three types of immuno-nanoprobes (one capture nanoprobe and two reporter nanoprobes). Single cells and nanoprobes were co-packaged in water-in-oil microdroplets (about 100 μm in diameter) using a cross-shaped microfluidic chip. In each droplet, capture nanoprobes would be first fixed to the cell surface by linking to membrane proteins that have been streptavidinized. Then, the capture nanoprobes can collect cell-secreted cytokines (VEGF and IL-8) by the antibodies, followed by two reporter nanoprobes that emit distinguishable fluorescence. Fluorescence imaging was utilized to record the signal outputs of two reporter probes, which reflect cytokine expressions secreted by a single tumor cell. The cytokine levels at different degrees of hypoxia induction were assessed. Multiple chemometric methods were adopted to distinguish differences in the secretion of two cytokines and the results demonstrated a positive correlation. This study developed an in-droplet, dual-target, simultaneous biosensing strategy for a single cell, which is helpful for understanding the impacts of hypoxia microenvironments on cell cytokines that are vital for assessing early cancer diagnosis and prognosis., Competing Interests: Declaration of competing interest The authors declare no competing financial interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Purpose: Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels. This study aimed to analyze the changes underlying heterogeneities and communication properties of CMs in diabetes mellitus (DM)., Methods: GSE213337 dataset was retrieved from NCBI Gene Expression Omnibus, containing the single-cell RNA sequencing data of hearts from the control and streptozotocin-induced diabetic mice. GSEA and GSVA were used to explore the function enrichment of DEGs in CM. Cell communication analysis was carried out to study the altered signals and significant ligand-receptor interactions., Results: Seventeen cell types were identified between DM and the controls. The increasing ratio of CM suggested the occurrence of diabetes induces potential pathological changes of CM proliferation. A total of 1144 DEGs were identified in CM. GSEA and GSVA analysis indicated the enhancing lipid metabolism involving in DM. The results of cell communication analysis suggested that high glucose activated the ability of CM receiving fibroblast and LEC, while inhibited the capacity of receiving ECC and pericyte. Furthermore, GAS and ANGPTL were significantly decreased under DM, which was consistent with the results of GSEA and GSVA. Finally, the ligand-receptor interactions such as vegfc-vegfr2, angptl1 were changes in CM., Conclusions: The CM showed the significant heterogeneities in DM, which played an important role in myocardial fibrosis induce by hyperglycemia., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)