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Ulcerative colitis (UC) is a chronic inflammatory disorder of the colon, and its pathogenesis remains unclear. Polyamine metabolic enzymes play a crucial role in UC. In this study, we aimed to identify pivotal polyamine-related genes (PRGs) and explore the underlying mechanism between PRGs and the disease status and therapeutic response of UC. We analyzed mRNA-sequencing data and clinical information of UC patients from the GEO database and identified NNMT, PTGS2, TRIM22, TGM2, and PPARG as key PRGs associated with active UC using differential expression analysis and weighted gene co-expression network analysis (WCGNA). Receiver operator characteristic curve (ROC) analysis confirmed the accuracy of these key genes in UC and colitis-associated colon cancer (CAC) diagnosis, and we validated their relationship with therapeutic response in external verification sets. Additionally, single-cell analysis revealed that the key PRGs were specific to certain immune cell types, emphasizing the vital role of intestinal tissue stem cells in active UC. The results were validated in vitro and in vivo experiments, including the colitis mice model and CAC mice model. In conclusion, these key PRGs effectively predict the progression of UC patients and could serve as new pharmacological biomarkers for the therapeutic response of UC. [ABSTRACT FROM AUTHOR]

A novel optically induced dielectrophoresis (ODEP) system that can operate under flow conditions is designed for automatic trapping of cells and subsequent induction of 2D multi‐frequency cell trajectories. Like in a "ping‐pong" match, two virtual electrode barriers operate in an alternate mode with varying frequencies of the input voltage. The so‐derived cell motions are characterized via time‐lapse microscopy, cell tracking, and state‐of‐the‐art machine learning algorithms, like the wavelet scattering transform (WST). As a cell‐electrokinetic fingerprint, the dynamic of variation of the cell displacements happening, over time, is quantified in response to different frequency values of the induced electric field. When tested on two biological scenarios in the cancer domain, the proposed approach discriminates cellular dielectric phenotypes obtained, respectively, at different early phases of drug‐induced apoptosis in prostate cancer (PC3) cells and for differential expression of the lectine‐like oxidized low‐density lipoprotein receptor‐1 (LOX‐1) transcript levels in human colorectal adenocarcinoma (DLD‐1) cells. The results demonstrate increased discrimination of the proposed system and pose an additional basis for making ODEP‐based assays addressing cancer heterogeneity for precision medicine and pharmacological research. [ABSTRACT FROM AUTHOR]

The encapsulation of cells within droplets is a crucial aspect of various cell analysis applications. Current research has focused on accurately detecting and identifying cell types or cell counts within droplets using object detection in bright-field images. However, there are only a few in-depth investigations into the impact of the image data quality acquired from optical systems on computer vision models. This study examines several popular machine learning object detection models to analyze scenarios complicating the identification of bead locations within a droplet, posing challenges for computer vision models. A microfluidic droplet generation system was developed and implemented, coupled with optical devices to capture images of encapsulated beads within the droplet. To identify the most efficient model, a specific dataset was meticulously selected from the overall data, encompassing images depicting overlapping beads and edge-drifting scenarios. The proposed method achieved up to 98.2% accuracy on the testing dataset and 95% in real-time testing with the YOLOv8 model, enhancing bead count precision within droplets and clarifying the correlation between accuracy and frame recognition thresholds. This work holds particular importance in single-cell sorting, where precision is critical in ensuring meaningful outcomes, particularly concerning rare cell types such as cancer cells. [ABSTRACT FROM AUTHOR]

Analysis of single cancer cells is critical to obtain accurate patient diagnosis and prognosis. In this work, we report the selective dielectrophoretic capture and electrochemical analysis of single melanoma cells at an array of interlocked spiral bipolar electrodes (iBPEs). Following dielectrophoretic capture, individual melanoma cells are hydrodynamically transferred into picoliter‐scale chambers for subsequent analysis. The interlocked spiral end of the iBPE (the sensing pole) is utilized to read out an electrochemical enzyme‐linked immunosorbent assay (eELISA), which quantifies the expression of a cell surface antigen, melanoma cell adhesion marker (MCAM). The opposite pole of each BPE is located in a fluidically isolated compartment containing reagents for electrogenerated chemiluminescence (ECL), such that luminescence reports iBPE current. In a preliminary device design, the ECL intensity was insufficient to detect MCAM expression on single cells. To achieve single‐cell analysis, we decreased the gap size between the interlocked spirals tenfold (5.0 μm to 0.5 μm), thereby creating a more sensitive biosensor by enhanced redox cycling of the product of eELISA. This work is significant because it allows for the selective isolation and sensitive analysis of individual melanoma cells in a device amenable to point‐of‐care (POC) application by combining dielectrophoresis (DEP) with interdigitated bipolar electrodes (IDBPEs). [ABSTRACT FROM AUTHOR]

Autofluorescence lifetime imaging microscopy (FLIM) is sensitive to metabolic changes in single cells based on changes in the protein‐binding activities of the metabolic co‐enzymes NAD(P)H. However, FLIM typically relies on time‐correlated single‐photon counting (TCSPC) detection electronics on laser‐scanning microscopes, which are expensive, low‐throughput, and require substantial post‐processing time for cell segmentation and analysis. Here, we present a fluorescence lifetime‐sensitive flow cytometer that offers the same TCSPC temporal resolution in a flow geometry, with low‐cost single‐photon excitation sources, a throughput of tens of cells per second, and real‐time single‐cell analysis. The system uses a 375 nm picosecond‐pulsed diode laser operating at 50 MHz, alkali photomultiplier tubes, an FPGA‐based time tagger, and can provide real‐time phasor‐based classification (i.e., gating) of flowing cells. A CMOS camera produces simultaneous brightfield images using far‐red illumination. A second PMT provides two‐color analysis. Cells are injected into the microfluidic channel using a syringe pump at 2–5 mm/s with nearly 5 ms integration time per cell, resulting in a light dose of 2.65 J/cm2 that is well below damage thresholds (25 J/cm2 at 375 nm). Our results show that cells remain viable after measurement, and the system is sensitive to autofluorescence lifetime changes in Jurkat T cells with metabolic perturbation (sodium cyanide), quiescent versus activated (CD3/CD28/CD2) primary human T cells, and quiescent versus activated primary adult mouse neural stem cells, consistent with prior studies using multiphoton FLIM. This TCSPC‐based autofluorescence lifetime flow cytometer provides a valuable label‐free method for real‐time analysis of single‐cell function and metabolism with higher throughput than laser‐scanning microscopy systems. [ABSTRACT FROM AUTHOR]

Genetic gains made by plant breeders are limited by generational cycling rates and flowering time. Several efforts have been made to reduce the time to switch from vegetative to reproductive stages in plants, but these solutions are usually species-specific and require flowering. The concept of in vitro nurseries is that somatic plant cells can be induced to form haploid cells that have undergone recombination (creating artificial gametes), which can then be used for cell fusion to enable breeding in a Petri dish. The induction of in vitro meiosis, however, is the largest current bottleneck to in vitro nurseries. To help overcome this, we previously described a high-throughput, bi-fluorescent, single cell system in Arabidopsis thaliana, which can be used to test the meiosis-like induction capabilities of candidate factors. In this present work, we validated the system using robust datasets (>4M datapoints) from extensive simulated meiosis induction tests. Additionally, we determined false-detection rates of the fluorescent cells used in this system as well as the ideal tissue source for factor testing. [ABSTRACT FROM AUTHOR]

Melanoma, a malignant skin cancer arising from melanocytes, exhibits rapid metastasis and a high mortality rate, especially in advanced stages. Current treatment modalities, including surgery, radiation, and immunotherapy, offer limited success, with immunotherapy using immune checkpoint inhibitors (ICIs) being the most promising. However, the high mortality rate underscores the urgent need for robust, non-invasive biomarkers to predict patient response to adjuvant therapies. The immune microenvironment of melanoma comprises various immune cells, which influence tumor growth and immune response. Melanoma cells employ multiple mechanisms for immune escape, including defects in immune recognition and epithelial-mesenchymal transition (EMT), which collectively impact treatment efficacy. Single-cell analysis technologies, such as single-cell RNA sequencing (scRNA-seq), have revolutionized the understanding of tumor heterogeneity and immune microenvironment dynamics. These technologies facilitate the identification of rare cell populations, co-expression patterns, and regulatory networks, offering deep insights into tumor progression, immune response, and therapy resistance. In the realm of biomarker discovery for melanoma, single-cell analysis has demonstrated significant potential. It aids in uncovering cellular composition, gene profiles, and novel markers, thus advancing diagnosis, treatment, and prognosis. Additionally, tumor-associated antibodies and specific genetic and cellular markers identified through single-cell analysis hold promise as predictive biomarkers. Despite these advancements, challenges such as RNA-protein expression discrepancies and tumor heterogeneity persist, necessitating further research. Nonetheless, single-cell analysis remains a powerful tool in elucidating the mechanisms underlying therapy response and resistance, ultimately contributing to the development of personalized melanoma therapies and improved patient outcomes. [ABSTRACT FROM AUTHOR]

The study aims to explore the central genes that Kawasaki disease (KD) and Obesity (OB) may jointly contribute to coronary artery disease. Investigating single-cell datasets (GSE168732 and GSE163830) from a comprehensive gene expression database, we identified characteristic immune cell subpopulations in KD and OB. B cells emerged as the common immune cell characteristic subgroup in both conditions. Subsequently, we analyzed RNA sequencing datasets (GSE18606 and GSE87493) to identify genes associated with B-cell subpopulations in KD and OB. Lastly, a genome-wide association study and Mendelian randomization were conducted to substantiate the causal impact of these core genes on myocardial infarction. Quantitative real-time PCR (qRT-PCR) to validate the expression levels of hub genes in KD and OB. The overlapping characteristic genes of B cell clusters in both KD and OB yielded 70 shared characteristic genes. PPI analysis led to the discovery of eleven key genes that significantly contribute to the crosstalk. Employing receiver operating characteristic analysis, we evaluated the specificity and sensitivity of these core genes and scored them using Cytoscape software. The inverse variance weighting analysis suggested an association between TNFRSF17 and myocardial infarction risk, with an odds ratio of 0.9995 (95% CI = 0.9990–1.0000, p = 0.049). By employing a single-cell combined transcriptome data analysis, we successfully pinpointed central genes associated with both KD and OB. The implications of these findings extend to shedding light on the increased risk of coronary artery disease resulting from the co-occurrence of OB and KD. [ABSTRACT FROM AUTHOR]

Conventional magnetophoresis techniques for manipulating biocarriers and cells predominantly rely on large‐scale electromagnetic systems, which is a major obstacle to the development of portable and miniaturized cell‐on‐chip platforms. Herein, a novel magnetic engineering approach by tailoring a nanoscale notch on a disk micromagnet using two‐step optical and thermal lithography is developed. Versatile manipulations are demonstrated, such as separation and trapping, of carriers and cells by mediating changes in the magnetic domain structure and discontinuous movement of magnetic energy wells around the circumferential edge of the micromagnet caused by a locally fabricated nano‐notch in a low magnetic field system. The motion of the magnetic energy well is regulated by the configuration of the nanoscale notch and the strength and frequency of the magnetic field, accompanying the jump motion of the carriers. The proposed concepts demonstrate that multiple carriers and cells can be manipulated and sorted using optimized nanoscale multi‐notch gates for a portable magnetophoretic system. This highlights the potential for developing cost‐effective point‐of‐care testing and lab‐on‐chip systems for various single‐cell‐level diagnoses and analyses. [ABSTRACT FROM AUTHOR]

Multiplexed in situ RNA imaging offers new opportunities for gene expression profiling by providing high-throughput spatial information. In this work, we present a cyclic combinatorial fluorescent in situ hybridization (combFISH) assay to achieve multiplexed detection of RNA in cell cultures and tissues. Specifically, multiplexing is achieved through cyclic interrogation of barcode sequences on the rolling circle amplicons generated from the padlock probe assay by using sets of combinatorial detection probes. Theoretically, combFISH can detect 64 genes in three hybridization cycles by combinatorial barcoding using 12 fluorescently labeled detection probes. Our method eliminates sequencing-by-ligation (SBL) chemistry in the in situ sequencing protocol and directly uses RNA as targets for ligation, making it more straightforward. We showed that our method works in fresh-frozen and formalin-fixed paraffin-embedded tissue sections. With its straightforward protocols, we expect our method to be adopted by the scientific community and extended to clinical settings. Multiplexed RNA in situ imaging by combinatorial FISH decoding [ABSTRACT FROM AUTHOR]

Objective The process of spermatogenesis is complex involving multiple regulatory mechanisms and requiring complex interactions between germ cells and somatic cells. The mechanisms of Sertoli cells involving in spermatogenesis remain to be explored. Methods We collected single-cell RNA sequencing data from a high-throughput gene expression database (GEO: GSE113293) from wild-type C57BIL6J male mice with normal spermatogenesis and four different mutant mouse strains (knockout Mlh3, Hormadl, and Cul4a; Cnp transgene). Then bioinformatics analyses such as gene function enrichment analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enriched pathway analysis, protein- protein interaction (PPI) network analysis, transcription factor regulation analysis (SCENIC), and gene set variation analysis (GSVA) on the Sertoli cells were further indentified. Results We identified nine subtypes of spermatogenic cell clusters (undiferentiated spermatogonia, diferentiating sperrmatogonia, leptotene/zygotene, pachytene, diplotene, round sperrmatids, elongating spermatids, and spermatozoa), and two types of somatic cell clusters (Leydig cells, and Sertoli cells), and one unknown type of cell cluster. Compared to the normal spermatogenesis model, 276 genes were upregulated and 234 genes were downregulated in the Sertoli cells of the mouse spermatogenesis defect model. GO analysis showed that differential genes in Sertoli cells were significantly enriched in spermatogenesis and energy metabolism. KEGG enrichment analyses showed that the homogeneous ribosomal pathway, glycolytic pathway, and oxidative phosphorylation pathway were significantly enriched in Sertoli cells. Protein-protein interaction (PPI) network analysis of diferentially expressed genes identified 20 hub genes, all of which belonged to ribosomal genes, and GSVA analysis revealed that the upregulated genes in Sertoli cells were closely related to the Notch signaling pathway. Conclusion The hub genes and the Notch signaling pathway in Sertoli cells are involved in the process of spermatogenesis. This study provides new perspectives on infertility caused by spermatogenesis dis orders. [ABSTRACT FROM AUTHOR]

Gastric cancer, the fifth most prevalent cancer worldwide, is often diagnosed in advanced stages with limited treatment options. Examining the tumor microenvironment (TME) and its metabolic reprogramming can provide insights for better diagnosis and treatment. This study investigates the link between TME factors and metabolic activity in gastric cancer using bulk and single-cell RNA-sequencing data. We identified two molecular subtypes in gastric cancer by analyzing the distinct expression patterns of 81 prognostic genes related to the TME and metabolism, which exhibited significant protein-level interactions. The high-risk subtype had increased stromal content, fibroblast and M2 macrophage infiltration, elevated glycosaminoglycans/glycosphingolipids biosynthesis, and fat metabolism, along with advanced clinicopathological features. It also exhibited low mutation rates and microsatellite instability, associating it with the mesenchymal phenotype. In contrast, the low-risk group showed higher tumor content and upregulated protein and sugar metabolism. We identified a 15-gene prognostic signature representing these characteristics, including CPVL, KYNU, CD36, and GPX3, strongly correlated with M2 macrophages, validated through single-cell analysis and an internal cohort. Despite resistance to immunotherapy, the high-risk group showed sensitivity to molecular targeted agents directed at IGF-1R (BMS-754807) and the PI3K-mTOR pathways (AZD8186, AZD8055). We experimentally validated these promising drugs for their inhibitory effects on MKN45 and MKN28 gastric cells. This study unveils the intricate interplay between TME and metabolic pathways in gastric cancer, offering potential for enhanced diagnosis, patient stratification, and personalized treatment. Understanding molecular features in each subtype enriches our comprehension of gastric cancer heterogeneity and potential therapeutic targets. [ABSTRACT FROM AUTHOR]

The link between ferroptosis, a form of cell death mediated by iron and acute kidney injury (AKI) is recently gaining widespread attention. However, the mechanism of the crosstalk between cells in the pathogenesis and progression of acute kidney injury remains unexplored. In our research, we performed a non-negative matrix decomposition (NMF) algorithm on acute kidney injury single-cell RNA sequencing data based specifically focusing in ferroptosis-associated genes. Through a combination with pseudo-time analysis, cell–cell interaction analysis and SCENIC analysis, we discovered that proximal tubular cells, macrophages, and fibroblasts all showed associations with ferroptosis in different pathways and at various time. This involvement influenced cellular functions, enhancing cellular communication and activating multiple transcription factors. In addition, analyzing bulk expression profiles and marker genes of newly defined ferroptosis subtypes of cells, we have identified crucial cell subtypes, including Egr1 + PTC-C1, Jun + PTC-C3, Cxcl2 + Mac-C1 and Egr1 + Fib-C1. All these subtypes which were found in AKI mice kidneys and played significantly distinct roles from those of normal mice. Moreover, we verified the differential expression of Egr1, Jun, and Cxcl2 in the IRI mouse model and acute kidney injury human samples. Finally, our research presented a novel analysis of the crosstalk of proximal tubular cells, macrophages and fibroblasts in acute kidney injury targeting ferroptosis, therefore, contributing to better understanding the acute kidney injury pathogenesis, self-repairment and acute kidney injury-chronic kidney disease (AKI-CKD) progression. [ABSTRACT FROM AUTHOR]

Background: The involvement of cytotoxic CD4+ T cells (CD4+ CTLs) and their potential role in dictating the response to immune checkpoint inhibitors (ICIs) in patients with metastatic renal cell carcinoma (mRCC) remains an unexplored area of research. Methods: Utilizing single-cell RNA sequencing, we analyzed the immunophenotype and expression patterns of CD4+ T lymphocyte subtypes in mRCC patients, followed by preliminary validation via multi-immunofluorescent staining. In addition, we obtained a comprehensive immunotherapy dataset encompassing single-cell RNA sequencing datasets and bulk RNA-seq cohorts from the European Genome-Phenome Archive and ArrayExpress database. Utilizing the CIBERSORTx deconvolution algorithms, we derived a signature score for CD4+ CTLs from the bulk-RNA-seq datasets of the CheckMate 009/025 clinical trials. Results: Single-cell analysis of CD4+ T lymphocytes in mRCC reveals several cancer-specific states, including diverse phenotypes of regulatory T cells. Remarkably, we observe that CD4+ CTLs cells constitute a substantial proportion of all CD4+ T lymphocyte sub-clusters in mRCC patients, highlighting their potential significance in the disease. Furthermore, within mRCC patients, we identify two distinct cytotoxic states of CD4+ T cells: CD4+GZMK+ T cells, which exhibit a weaker cytotoxic potential, and CD4+GZMB+ T cells, which demonstrate robust cytotoxic activity. Both regulatory T cells and CD4+ CTLs originate from proliferating CD4+ T cells within mRCC tissues. Intriguingly, our trajectory analysis indicates that the weakly cytotoxic CD4+GZMK+ T cells differentiate from their more cytotoxic CD4+GZMB+ counterparts. In comparing patients with lower CD4+ CTLs levels to those with higher CD4+ CTLs abundance in the CheckMate 009 and 25 immunotherapy cohorts, the latter group exhibited significantly improved OS and PFS probability. Conclusion: Our study underscores the pivotal role that intratumoral CD4+ CTLs may play in bolstering anti-tumor immunity, suggesting their potential as a promising biomarker for predicting response to ICIs in patients with mRCC. [ABSTRACT FROM AUTHOR]

The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (IAC) involves a complex evolution of tumour cell clusters, the mechanisms of which remain largely unknown. By integrating single‐cell datasets and using inferCNV, we identified and analysed tumour cell clusters to explore their heterogeneity and changes in abundance throughout LUAD progression. We applied gene set variation analysis (GSVA), pseudotime analysis, scMetabolism, and Cytotrace scores to study biological functions, metabolic profiles and stemness traits. A predictive model for prognosis, based on key cluster marker genes, was developed using CoxBoost and plsRcox (CPM), and validated across multiple cohorts for its prognostic prediction capabilities, tumour microenvironment characterization, mutation landscape and immunotherapy response. We identified nine distinct tumour cell clusters, with Cluster 6 indicating an early developmental stage, high stemness and proliferative potential. The abundance of Clusters 0 and 6 increased from AAH to IAC, correlating with prognosis. The CPM model effectively distinguished prognosis in immunotherapy cohorts and predicted genomic alterations, chemotherapy drug sensitivity, and immunotherapy responsiveness. Key gene S100A16 in the CPM model was validated as an oncogene, enhancing LUAD cell proliferation, invasion and migration. The CPM model emerges as a novel biomarker for predicting prognosis and immunotherapy response in LUAD patients, with S100A16 identified as a potential therapeutic target. [ABSTRACT FROM AUTHOR]

Flow cytometry and fluorescence‐activated cell sorting are widely used to study endothelial cells, for which the generation of viable single‐cell suspensions is an essential first step. Two enzymatic approaches, collagenase A and dispase, are widely employed for endothelial cell isolation. In this study, the utility of both enzymatic approaches, alone and in combination, for endothelial cell isolation from juvenile and adult mouse lungs was assessed, considering the number, viability, and subtype composition of recovered endothelial cell pools. Collagenase A yielded an 8‐12‐fold superior recovery of viable endothelial cells from lung tissue from developing mouse pups, compared to dispase, although dispase proved superior in efficiency for epithelial cell recovery. Single‐cell RNA‐Seq revealed that the collagenase A approach yielded a diverse endothelial cell subtype composition of recovered endothelial cell pools, with broad representation of arterial, capillary, venous, and lymphatic lung endothelial cells; while the dispase approach yielded a recovered endothelial cell pool highly enriched for one subset of general capillary endothelial cells, but poor representation of other endothelial cells subtypes. These data indicate that tissue dissociation markedly influences the recovery of endothelial cells, and the endothelial subtype composition of recovered endothelial cell pools, as assessed by single‐cell RNA‐Seq. [ABSTRACT FROM AUTHOR]

An intriguing field of research has recently emerged in which biological cells can be demonstrated to behave as photonics devices, such as optical microlenses. This research highlights yeast cells as promising candidates for engineering biolenses with customizable focal properties. Typically serving as positive biolenses due to their quasi‐spherical shape and positive refractive index (RI) contrast, yeast cells can achieve a negative lensing effect by manipulating their intracellular content, without altering their overall morphology. In fact, it shows that exposure to hypotonic conditions induces the formation of a large, roundish vacuole with lower RI values with respect to the surrounding cytoplasm, thus altering the cell's optical properties. This behavior is investigated by using 3D RI tomograms obtained through a holo‐tomographic phase imaging flow cytometry system, demonstrating that the anisotropic nature of yeast cells results in focal properties dependent on their specific orientations in respect to the incident light. Furthermore, Zemax OpticStudio is utilized to perform comprehensive assessments for optical design analysis of the obtained cell biolenses. The methods described here and the related results represent a pioneering investigation of biological cells' metamorphoses via intracellular content manipulation for biolens applications. [ABSTRACT FROM AUTHOR]

Progress in the discovery of potent antibodies for therapeutic and research purposes has been accelerated by mining the antibody repertoire of plasma cells and plasmablasts. Magnetic activated cell sorting (MACS) is a well recognized method for cell sorting, however, secreted IgGs rapidly diffuse away from cells, therefore requiring specialized technique to compartmentalize individual cells and capture their secretions for MACS isolation. To this end, a scalable method to prepare hydrogel‐based microcontainers for single cell culture and subsequent sorting based on their secreted IgG products at high‐throughput level are described. In situ crosslinkable microgels featuring tunable viscosity are fabricated to act as soft compartments that accommodate delicate primary cells and capture secreted proteins, while providing solid supports after solgel transition to enable immune recognition with chemically functionalized magnetic nanorods in aqueous medium for MACS separation. Splenocytes are sorted based on antigen‐specific IgG production from mice immunized with an antibiotic target ceftriaxone sodium, enriching a population of 220 000 000 to 800 000 prevalence following sorting. This enrichment ratio is substantially higher than conventional fluorescence activated cell sorting (FACS) methods, likely due to the multivalency of the antigen‐conjugated magnetic nanorods. [ABSTRACT FROM AUTHOR]

Objective: To investigate the association between liver enzymes and ovarian cancer (OC), and to validate their potential as biomarkers and their mechanisms in OC. Methods Genome-wide association studies for OC and levels of enzymes such as Alkaline phosphatase (ALP), Aspartate aminotransferase (AST), Alanine aminotransferase, and gamma-glutamyltransferase were analyzed. Univariate and multivariate Mendelian randomization (MR), complemented by the Steiger test, identified enzymes with a potential causal relationship to OC. Single-cell transcriptomics from the GSE130000 dataset pinpointed pivotal cellular clusters, enabling further examination of enzyme-encoding gene expression. Transcription factors (TFs) governing these genes were predicted to construct TF-mRNA networks. Additionally, liver enzyme levels were retrospectively analyzed in healthy individuals and OC patients, alongside the evaluation of correlations with cancer antigen 125 (CA125) and Human Epididymis Protein 4 (HE4). Results: A total of 283 single nucleotide polymorphisms (SNPs) and 209 SNPs related to ALP and AST, respectively. Using the inverse-variance weighted method, univariate MR (UVMR) analysis revealed that ALP (P = 0.050, OR = 0.938) and AST (P = 0.017, OR = 0.906) were inversely associated with OC risk, suggesting their roles as protective factors. Multivariate MR (MVMR) confirmed the causal effect of ALP (P = 0.005, OR = 0.938) on OC without reverse causality. Key cellular clusters including T cells, ovarian cells, endothelial cells, macrophages, cancer-associated fibroblasts (CAFs), and epithelial cells were identified, with epithelial cells showing high expression of genes encoding AST and ALP. Notably, TFs such as TCE4 were implicated in the regulation of GOT2 and ALPL genes. OC patient samples exhibited decreased ALP levels in both blood and tumor tissues, with a negative correlation between ALP and CA125 levels observed. Conclusion: This study has established a causal link between AST and ALP with OC, identifying them as protective factors. The increased expression of the genes encoding these enzymes in epithelial cells provides a theoretical basis for developing novel disease markers and targeted therapies for OC. [ABSTRACT FROM AUTHOR]

Kidney renal clear cell carcinoma (KIRC) pathogenesis intricately involves immune system dynamics, particularly the role of T cells within the tumour microenvironment. Through a multifaceted approach encompassing single‐cell RNA sequencing, spatial transcriptome analysis and bulk transcriptome profiling, we systematically explored the contribution of infiltrating T cells to KIRC heterogeneity. Employing high‐density weighted gene co‐expression network analysis (hdWGCNA), module scoring and machine learning, we identified a distinct signature of infiltrating T cell‐associated genes (ITSGs). Spatial transcriptomic data were analysed using robust cell type decomposition (RCTD) to uncover spatial interactions. Further analyses included enrichment assessments, immune infiltration evaluations and drug susceptibility predictions. Experimental validation involved PCR experiments, CCK‐8 assays, plate cloning assays, wound‐healing assays and Transwell assays. Six subpopulations of infiltrating and proliferating T cells were identified in KIRC, with notable dynamics observed in mid‐ to late‐stage disease progression. Spatial analysis revealed significant correlations between T cells and epithelial cells across varying distances within the tumour microenvironment. The ITSG‐based prognostic model demonstrated robust predictive capabilities, implicating these genes in immune modulation and metabolic pathways and offering prognostic insights into drug sensitivity for 12 KIRC treatment agents. Experimental validation underscored the functional relevance of PPIB in KIRC cell proliferation, invasion and migration. Our study comprehensively characterizes infiltrating T‐cell heterogeneity in KIRC using single‐cell RNA sequencing and spatial transcriptome data. The stable prognostic model based on ITSGs unveils infiltrating T cells' prognostic potential, shedding light on the immune microenvironment and offering avenues for personalized treatment and immunotherapy. [ABSTRACT FROM AUTHOR]

Mitochondrial RNA modification (MRM) plays a crucial role in regulating the expression of key mitochondrial genes and promoting tumor metastasis. Despite its significance, comprehensive studies on MRM in lower grade gliomas (LGGs) remain unknown. Single-cell RNA-seq data (GSE89567) was used to evaluate the distribution functional status, and correlation of MRM-related genes in different cell types of LGG microenvironment. We developed an MRM scoring system by selecting potential MRM-related genes using LASSO regression analysis and the Random Survival Forest algorithm, based on multiple bulk RNA-seq datasets from TCGA, CGGA, GSE16011, and E-MTAB-3892. Analysis was performed on prognostic and immunological features, signaling pathways, metabolism, somatic mutations and copy number variations (CNVs), treatment responses, and forecasting of potential small-molecule agents. A total of 35 MRM-related genes were selected from the literature. Differential expression analysis of 1120 normal brain tissues and 529 LGGs revealed that 22 and 10 genes were upregulated and downregulated, respectively. Most genes were associated with prognosis of LGG. METLL8, METLL2A, TRMT112, and METTL2B were extensively expressed in all cell types and different cell cycle of each cell type. Almost all cell types had clusters related to mitochondrial RNA processing, ribosome biogenesis, or oxidative phosphorylation. Cell–cell communication and Pearson correlation analyses indicated that MRM may promoting the development of microenvironment beneficial to malignant progression via modulating NCMA signaling pathway and ICP expression. A total of 11 and 9 MRM-related genes were observed by LASSO and the RSF algorithm, respectively, and finally 6 MRM-related genes were used to establish MRM scoring system (TRMT2B, TRMT11, METTL6, METTL8, TRMT6, and TRUB2). The six MRM-related genes were then validated by qPCR in glioma and normal tissues. MRM score can predict the malignant clinical characteristics, abundance of immune infiltration, gene variation, clinical outcome, the enrichment of signaling pathways and metabolism. In vitro experiments demonstrated that silencing METTL8 significantly curbs glioma cell proliferation and enhances apoptosis. Patients with a high MRM score showed a better response to immunotherapies and small-molecule agents such as arachidonyl trifluoromethyl ketone, MS.275, AH.6809, tacrolimus, and TTNPB. These novel insights into the biological impacts of MRM within the glioma microenvironment underscore its potential as a target for developing precise therapies, including immunotherapeutic approaches. [ABSTRACT FROM AUTHOR]

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high dimensional, sparse, and heterogeneous and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations. [ABSTRACT FROM AUTHOR]

Background: Sporadic parathyroid adenoma (PA) is the most common cause of hyperparathyroidism, yet the mechanisms involved in its pathogenesis remain incompletely understood. Methods: Surgically removed PA samples, along with normal parathyroid gland (PG) tissues that were incidentally dissected during total thyroidectomy, were analysed using single‐cell RNA‐sequencing with the 10× Genomics Chromium Droplet platform and Cell Ranger software. Gene set variation analysis was conducted to characterise hallmark pathway gene signatures, and single‐cell regulatory network inference and clustering were utilised to analyse transcription factor regulons. Immunohistochemistry and immunofluorescence were performed to validate cellular components of PA tissues. siRNA knockdown and gene overexpression, alongside quantitative polymerase chain reaction, Western blotting and cell proliferation assays, were conducted for functional investigations. Results: There was a pervasive increase in gene transcription in PA cells (PACs) compared with PG cells. This is associated with high expression of histone‐lysine N‐methyltransferase 2A (KMT2A). High KMT2A levels potentially contribute to promoting PAC proliferation through upregulation of the proto‐oncogene CCND2, which is mediated by the transcription factors signal transducer and activator of transcription 3 (STAT3) and GATA binding protein 3 (GATA3). PA tissues are heavily infiltrated with myeloid cells, while fibroblasts, endothelial cells and macrophages in PA tissues are commonly enriched with proinflammatory gene signatures relative to their counterparts in PG tissues. Conclusions: We revealed the previously underappreciated involvement of the KMT2A‒STAT3/GATA3‒CCND2 axis and chronic inflammation in the pathogenesis of PA. These findings underscore the therapeutic promise of KMT2A inhibition and anti‐inflammatory strategies, highlighting the need for future investigations to translate these molecular insights into practical applications. Highlights: Single‐cell RNA‐sequencing reveals a transcriptome catalogue comparing sporadic parathyroid adenomas (PAs) with normal parathyroid glands.PA cells show a pervasive increase in gene expression linked to KMT2A upregulation.KMT2A‐mediated STAT3 and GATA3 upregulation is key to promoting PA cell proliferation via cyclin D2.PAs exhibit a proinflammatory microenvironment, suggesting a potential role of chronic inflammation in PA pathogenesis. [ABSTRACT FROM AUTHOR]

Pollution from toxic spores has caused us a lot of problems because spores are extremely resistant and can survive most disinfectants. Therefore, the detection of spore response to disinfectant is of great significance for the development of effective decontamination strategies. In this work, we investigated the effect of 0.5% sodium hypochlorite on the molecular and morphological properties of single spores of Bacillus subtilis using single‐cell techniques. Laser tweezers Raman spectroscopy showed that sodium hypochlorite resulted in Ca2+‐dipicolinic acid release and nucleic acid denaturation. Atomic force microscopy showed that the surface of treated spores changed from rough to smooth, protein shells were degraded at 10 min, and the permeability barrier was destroyed at 15 min. The spore volume decreased gradually over time. Live‐cell imaging showed that the germination and growth rates decreased with increasing treatment time. These results provide new insight into the response of spores to sodium hypochlorite. [ABSTRACT FROM AUTHOR]

Background: Globally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates. A key focus is glutamine metabolism, governed by select genes. This study explored genes associated with T cells and linked them to glutamine metabolism to construct a prognostic staging index for breast cancer patients for more precise medical treatment. Methods: Two frameworks, T‐cell related genes (TRG) and glutamine metabolism (GM), stratified breast cancer patients. TRG analysis identified key genes via hdWGCNA and machine learning. T‐cell communication and spatial transcriptomics emphasized TRG's clinical value. GM was defined using Cox analyses and the Lasso algorithm. Scores categorized patients as TRG_high+GM_high (HH), TRG_high+GM_low (HL), TRG_low+GM_high (LH), or TRG_low+GM_low (LL). Similarities between HL and LH birthed a "Mixed" class and the TRG_GM classifier. This classifier illuminated gene variations, immune profiles, mutations, and drug responses. Results: Utilizing a composite of two distinct criteria, we devised a typification index termed TRG_GM classifier, which exhibited robust prognostic potential for breast cancer patients. Our analysis elucidated distinct immunological attributes across the classifiers. Moreover, by scrutinizing the genetic variations across groups, we illuminated their unique genetic profiles. Insights into drug sensitivity further underscored avenues for tailored therapeutic interventions. Conclusion: Utilizing TRG and GM, a robust TRG_GM classifier was developed, integrating clinical indicators to create an accurate predictive diagnostic map. Analysis of enrichment disparities, immune responses, and mutation patterns across different subtypes yields crucial subtype‐specific characteristics essential for prognostic assessment, clinical decision‐making, and personalized therapies. Further exploration is warranted into multiple fusions between metrics to uncover prognostic presentations across various dimensions. [ABSTRACT FROM AUTHOR]

Single-cell joint analysis of methylome and transcriptome reveals how the methylation regulates the transcriptional activity. However, traditional bench-top protocols for single-cell DNA methylation and RNA transcription co-detection are labor-intensive, cost-ineffective and contaminant-prone. Herein, we establish the DMF-scMT-seq, a highly-efficient and cost-effective method to simultaneously analyze single-cell DNA methylation and transcriptional activity based on digital microfluidics. DMF-scMT-seq automates the workflow of single-cell isolation, cellular hypotonic lysis, nucleic acid separation and methylome/transcriptome library construction in a contactless and addressable way. The system ensures high accuracy (R>0.85), high gene detection ability (14,697 genes per cell at 4 million sequencing depth), and high CpG coverage (677,198 CpG sites per cell at 1 million sequencing depth). By using DMF-scMT-seq, the relationship of DNA methylation and RNA transcription under different genomic contexts is resolved. We further apply DMF-scMT-seq to study the dynamics of transcription regulation with methylation-inhibiting anti-tumor Decitabine, and identify the methylated promoter/gene body driven genes in response to Decitabine treatment. DMF-scMT-seq facilitates the construction of the correlation of DNA methylation and transcriptional activity at the single-cell level in a flexible, sensitive and accurate way, which is anticipated to be a powerful tool in studying single-cell biological systems. [ABSTRACT FROM AUTHOR]

Human bone marrow (BM) plasma cells are heterogeneous, ranging from newly arrived antibody-secreting cells (ASCs) to long-lived plasma cells (LLPCs). We provide single-cell transcriptional resolution of 17,347 BM ASCs from five healthy adults. Fifteen clusters are identified ranging from newly minted ASCs (cluster 1) expressing MKI67 and high major histocompatibility complex (MHC) class II that progress to late clusters 5-8 through intermediate clusters 2-4. Additional ASC clusters include the following: immunoglobulin (Ig) M predominant (likely of extra-follicular origin), interferon responsive, and high mitochondrial activity. Late ASCs are distinguished by G2M checkpoints, mammalian target of rapamycin (mTOR) signaling, distinct metabolic pathways, CD38 expression, utilization of tumor necrosis factor (TNF)-receptor superfamily members, and two distinct maturation pathways involving TNF signaling through nuclear factor κB (NF-κB). This study provides a single-cell atlas and molecular roadmap of LLPC maturation trajectories essential in the BM microniche. Altogether, understanding BM ASC heterogeneity in health and disease enables development of new strategies to enhance protective ASCs and to deplete pathogenic ones.

Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.

Cells make decisions through their communication with other cells and receiving signals from their environment. Using single-cell transcriptomics, computational tools have been developed to infer cell-cell communication through ligands and receptors. However, the existing methods only deal with signals sent by the measured cells in the data, the received signals from the external system are missing in the inference. Here, we present exFINDER, a method that identifies such external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. In particular, exFINDER can uncover external signals that activate the given target genes, infer the external signal-target signaling network (exSigNet), and perform quantitative analysis on exSigNets. The applications of exFINDER to scRNA-seq datasets from different species demonstrate the accuracy and robustness of identifying external signals, revealing critical transition-related signaling activities, inferring critical external signals and targets, clustering signal-target paths, and evaluating relevant biological events. Overall, exFINDER can be applied to scRNA-seq data to reveal the external signal-associated activities and maybe novel cells that send such signals.

MOTIVATION: Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in individual cells. While the increasing availability of multiple data modalities is expected to provide a more accurate clustering and characterization of cells, the development of computational methods that are capable of extracting information embedded across data modalities is still in its infancy. RESULTS: We propose SnapCCESS for clustering cells by integrating data modalities in multimodal single-cell omics data using an unsupervised ensemble deep learning framework. By creating snapshots of embeddings of multimodality using variational autoencoders, SnapCCESS can be coupled with various clustering algorithms for generating consensus clustering of cells. We applied SnapCCESS with several clustering algorithms to various datasets generated from popular multimodal single-cell omics technologies. Our results demonstrate that SnapCCESS is effective and more efficient than conventional ensemble deep learning-based clustering methods and outperforms other state-of-the-art multimodal embedding generation methods in integrating data modalities for clustering cells. The improved clustering of cells from SnapCCESS will pave the way for more accurate characterization of cell identity and types, an essential step for various downstream analyses of multimodal single-cell omics data. AVAILABILITY AND IMPLEMENTATION: SnapCCESS is implemented as a Python package and is freely available from https://github.com/PYangLab/SnapCCESS under the open-source license of GPL-3. The data used in this study are publicly available (see section Data availability).

BackgroundPeriodontitis is a common complication of type II diabetes (T2D). However, the existing research cannot fully elucidate the association between them, let alone identify therapeutic targets for precise treatment of diabetic periodontitis. Therefore, we employed integrated genetic approaches such as single-cell analysis, Mendelian randomization (MR) analysis and colocalization analysis to uncover novel therapeutic targets for T2D and periodontitis.MethodsThis study integrated single-cell analysis, MR analysis, colocalization analysis, phenotype scanning, cell-cell communication analysis and metabolic pathway activity analysis to unveil novel therapeutic targets for periodontitis and T2D. We firstly identified core cell clusters of T2D and periodontitis, and important marker genes were selected. The causal associations between these genes and the two diseases were evaluated through MR analysis. Reverse MR analysis, colocalization analysis, additional validation and phenotype scanning further supported our findings. Finally, cell-cell communication analysis and metabolic pathway activity analysis were employed to preliminarily investigate the mechanisms of the observed causal associations.ResultsThrough analysis of scRNA-seq data, we identified classical monocytes and intermediate monocytes as core cell subclusters. Differential analysis identified 221 differentially expressed genes (DEGs). MR analysis identified 13 genes exhibiting causal associations with T2D, and 11 causal genes with periodontitis. Colocalization analysis, reverse MR analysis, additional validation and phenotype scanning further enhanced the robustness of our results. Finally, we identified NCF1 as the core therapeutic target for T2D (OR = 1.09, 95% CI: 1.03-1.14, p = 1.85 ×10−3) and LRRC25 for T2D (OR = 0.96, 95% CI: 0.93-0.99, p = 3.44 ×10−2) and periodontitis (OR = 0.92, 95% CI: 0.84-0.99, p = 4.45 ×10−2). At last, cell-cell communication analysis indicated significant differences in functions and metabolic pathway activity between monocytes expressing or not expressing the core causal genes, which preliminarily interpreted the observed causal associations.ConclusionThis study integrated single-cell analysis, MR analysis and colocalization analysis to identified novel therapeutic targets for T2D and periodontitis. 13 causal genes were identified for T2D, and 11 for periodontitis. Among them, NCF1 and LRRC25 were regarded as core therapeutic targets. Our findings bridge the gap in the understanding of the association between T2D and periodontitis, and pave the way for targeted therapy of the two diseases.

BackgroundProstate cancer is one of the leading causes of cancer-related deaths in men. Its molecular pathogenesis is closely linked to various genetic and epigenetic alterations, including posttranslational modifications like SUMOylation. Identifying biomarkers that predict outcomes and specific therapeutic targets depends on a comprehensive understanding of these processes. With growing interest in SUMOylation as a mechanism affecting prostate cancer-related genes, this study aimed to investigate the central role of SUMOylation in prostate cancer prognostics, focusing on the significance of NOP58.MethodsWe conducted a comprehensive bioinformatics analysis, integrating differential expression analysis, survival analysis, gene set enrichment analysis (GSEA), and single-cell transcriptomic analyses using data from The Cancer Genome Atlas (TCGA). Key genes were identified through intersections of Venn diagrams, Boralta algorithm signatures, and machine learning models. These signaling mechanisms were validated through experimental studies, including immunohistochemical staining and gene ontology analyses.ResultsThe dual-gene molecular subtype analysis with SUMO1, SUMO2, and XPO1 genes revealed significant differences in survival outcomes across molecular subtypes, further emphasizing the potential impact of NOP58 on SUMOylation, a key post-translational modification, in prostate cancer. NOP58 overexpression was strongly associated with shorter overall survival (OS), progression-free interval (PFI), and disease-specific death in prostate cancer patients. Immunohistochemical analysis confirmed that NOP58 was significantly overexpressed in prostate cancer tissues compared to normal tissues. ROC curve analysis demonstrated that NOP58 could distinguish prostate cancer from control samples with high diagnostic accuracy. Gene Ontology analysis, along with GSVA and GSEA, suggested that NOP58 may be involved in cell cycle regulation and DNA repair pathways. Moreover, NOP58 knockdown led to increased BCL2 expression and decreased Ki67 levels, promoting apoptosis and inhibiting cell proliferation. Colony formation assays further showed that NOP58 knockdown inhibited, while its overexpression promoted, colony formation, highlighting the critical role of NOP58 in prostate cancer cell growth and survival. Additionally, NOP58 was linked to drug responses, including Methotrexate, Rapamycin, Sorafenib, and Vorinostat.ConclusionNOP58 is a key regulator of prostate cancer progression through its mediation of the SUMOylation pathway. Its expression level serves as a reliable prognostic biomarker and an actionable therapeutic target, advancing precision medicine for prostate cancer. Targeting NOP58 may enhance therapeutic efficacy and improve outcomes in oncology.

IntroductionHuman peripheral blood mononuclear cells (hPBMCs) are widely used in fundamental research and clinical applications as studying their responses to in vitro activation is an effective way to uncover functional alterations and disease associated phenotypes. However, the availability of samples in large numbers at a specific time and location remains challenging, hence they often might preferably be collected and cryopreserved for later analysis. While the effect of cryopreservation on viability and cell surface expression is well established, changes in activity and cytokine secretion still lead to conflicting results as it is often measured in bulk or within the cells.MethodsHere, we used our platform for dynamic single-cell multiplexed cytokine secretion measurement and compared it to a traditional intracellular cytokine staining to quantify the effect of cryopreservation on cytokine secretion and expression of individual hPBMCs.ResultsFollowing stimulation with LPS or anti-CD3/CD28 antibodies for up to 36 or 72 h incubation, we observed distinct alterations in cytokine responses due to cryopreservation when comparing to fresh samples, but also remarkable consistencies for some cytokines and parameters. In short, the frequencies of cytokine-secreting cells in cryopreserved samples were lower for IL-6 (LPS), IL1-β (CD3/CD28) and IFN-γ (CD3/CD28), while the frequency and dynamics of IL-8 secretion were strongly impacted in all cases. We observed a large disconnect between cytokine expression and secretion for TNF-α, where the expression dramatically increased after cryopreservation, but actual secretion was, in comparison, remarkably stable. The polyfunctionality of single cells was altered by cryopreservation in specific co-secreting populations led by the effects on IL-6 or IL-8 secretion. Among immune cells, cryopreservation seemed to affect lymphocytes and monocytes differently as effects appeared early on in lymphocytes while generally observed in later time points in monocytes.ConclusionTogether, this study offers an in-depth quantitative insight into the biological behavior of immune cells in response to cryopreservation and stimulation, further providing some insights into conflicting results in the literature as well as guidelines for researchers planning to assess cytokine-secreting from frozen hPBMCs in immunological research or clinical applications.

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.

Multimodal single-cell omics technologies enable multiple molecular programs to be simultaneously profiled at a global scale in individual cells, creating opportunities to study biological systems at a resolution that was previously inaccessible. However, the analysis of multimodal single-cell omics data is challenging due to the lack of methods that can integrate across multiple data modalities generated from such technologies. Here, we present Matilda, a multi-task learning method for integrative analysis of multimodal single-cell omics data. By leveraging the interrelationship among tasks, Matilda learns to perform data simulation, dimension reduction, cell type classification, and feature selection in a single unified framework. We compare Matilda with other state-of-the-art methods on datasets generated from some of the most popular multimodal single-cell omics technologies. Our results demonstrate the utility of Matilda for addressing multiple key tasks on integrative multimodal single-cell omics data analysis. Matilda is implemented in Pytorch and is freely available from https://github.com/PYangLab/Matilda.

Emerging spatial technologies, including spatial transcriptomics and spatial epigenomics, are becoming powerful tools for profiling of cellular states in the tissue context1-5. However, current methods capture only one layer of omics information at a time, precluding the possibility of examining the mechanistic relationship across the central dogma of molecular biology. Here, we present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications (H3K27me3, H3K27ac or H3K4me3) and gene expression on the same tissue section at near-single-cell resolution. These were applied to embryonic and juvenile mouse brain, as well as adult human brain, to map how epigenetic mechanisms control transcriptional phenotype and cell dynamics in tissue. Although highly concordant tissue features were identified by either spatial epigenome or spatial transcriptome we also observed distinct patterns, suggesting their differential roles in defining cell states. Linking epigenome to transcriptome pixel by pixel allows the uncovering of new insights in spatial epigenetic priming, differentiation and gene regulation within the tissue architecture. These technologies are of great interest in life science and biomedical research.

The enteric nervous system (ENS) consists of glial cells (EGCs) and neurons derived from neural crest precursors. EGCs retain capacity for large-scale neurogenesis in culture, and in vivo lineage tracing has identified neurons derived from glial cells in response to inflammation. We thus hypothesize that EGCs possess a chromatin structure poised for neurogenesis. We use single-cell multiome sequencing to simultaneously assess transcription and chromatin accessibility in EGCs undergoing spontaneous neurogenesis in culture, as well as small intestine myenteric plexus EGCs. Cultured EGCs maintain open chromatin at genomic loci accessible in neurons, and neurogenesis from EGCs involves dynamic chromatin rearrangements with a net decrease in accessible chromatin. A subset of in vivo EGCs, highly enriched within the myenteric ganglia and that persist into adulthood, have a gene expression program and chromatin state consistent with neurogenic potential. These results clarify the mechanisms underlying EGC potential for neuronal fate transition.

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.

Abstract The co-existence of inflammatory bowel disease (IBD) and non-alcoholic steatohepatitis (NASH) has raised interest in identifying shared molecular mechanisms and potential therapeutic targets. However, the relationship between these two diseases remains unclear and effective medical treatments are still lacking. Through the bioinformatics analysis in this study, 116 shared differentially expressed genes (SDEGs) were identified between IBD and NASH datasets. GO and KEGG pathway analyses revealed significant involvement of SDEGs in apoptotic processes, cell death, defense response, cytokine and chemokine activity, and signaling pathways. Furthermore, weighted gene co-expression network analysis (WGCNA) identified five shared signature genes associated specifically with IBD and NASH, they were CXCL9, GIMAP2, ADAMTS5, GRAP, and PRF1. These five genes represented potential diagnostic biomarkers for distinguishing patients with diseases from healthy individuals by using two classifier algorithms and were positively related to autophagy, ferroptosis, angiogenesis, and immune checkpoint factors in the two diseases. Additionally, single-cell analysis of IBD and NASH samples highlighted the expression of regulatory genes in various immune cell subtypes, emphasizing their significance in disease pathogenesis. Our work elucidated the shared signature genes and regulatory mechanisms of IBD and NASH, which could provide new potential therapies for patients with IBD and NASH.

Abstract Background Single-cell analysis, a rapidly evolving field, encounters significant challenges in detecting individual cells within complex plant tissues, particularly oil cells (OCs). The intricate process of single-cell isolation, coupled with the inherent chemical volatility of oil cells, necessitates a comprehensive methodology. Results This study presents a method for obtaining intact OC from Asari Radix et Rhizoma (ARR), a traditional herbal medicine. The developed approach facilitates both qualitative and quantitative analysis of diverse OCs. To determine the most reliable approach, four practical methods—laser capture microdissection, micromanipulation capturing, micromanipulation piping, and cell picking—were systematically compared and evaluated, unequivocally establishing cell picking as the most effective method for OC isolation and chemical analysis. Microscopic observations showed that OCs predominantly distribute in the cortex of adventitious and fibrous roots, as well as the pith and cortex of the rhizome, with distinct morphologies—oblong in roots and circular in rhizomes. Sixty-three volatile constituents were identified in OCs, with eighteen compounds exhibiting significant differences. Safrole, methyleugenol, and asaricin emerged as the most abundant constituents in OCs. Notably, cis-4-thujanol and tetramethylpyrazine were exclusive to rhizome OCs, while isoeugenol methyl ether was specific to fibrous root OCs based on the detections. ARR roots and rhizomes displayed marked disparities in OC distribution, morphology, and constituents. Conclusion The study highlights the efficacy of cell picking coupled with HS–SPME–GC–MS as a flexible, reliable, and sensitive method for OC isolation and chemical analysis, providing a robust methodology for future endeavors in single-cell analyses.

Abstract Uveal cancer (UM) offers a complex molecular landscape characterized by substantial heterogeneity, both on the genetic and epigenetic levels. This heterogeneity plays a critical position in shaping the behavior and response to therapy for this uncommon ocular malignancy. Targeted treatments with gene-specific therapeutic molecules may prove useful in overcoming radiation resistance, however, the diverse molecular makeups of UM call for a patient-specific approach in therapy procedures. We need to understand the intricate molecular landscape of UM to develop targeted treatments customized to each patient's specific genetic mutations. One of the promising approaches is using liquid biopsies, such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), for detecting and monitoring the disease at the early stages. These non-invasive methods can help us identify the most effective treatment strategies for each patient. Single-cellular is a brand-new analysis platform that gives treasured insights into diagnosis, prognosis, and remedy. The incorporation of this data with known clinical and genomics information will give a better understanding of the complicated molecular mechanisms that UM diseases exploit. In this review, we focused on the heterogeneity and molecular panorama of UM, and to achieve this goal, the authors conducted an exhaustive literature evaluation spanning 1998 to 2023, using keywords like "uveal melanoma, “heterogeneity”. “Targeted therapies”," "CTCs," and "single-cellular analysis".

Abstract Increased use of therapeutic monoclonal antibodies and the relatively high manufacturing costs fuel the need for more efficient production methods. Here we introduce a novel, fast, robust, and safe isolation platform for screening and isolating antibody-producing cell lines using a nanowell chip and an innovative single-cell isolation method. An anti-Her2 antibody producing CHO cell pool was used as a model. The platform; (1) Assures the single-cell origin of the production clone, (2) Detects the antibody production of individual cells and (3) Isolates and expands the individual cells based on their antibody production. Using the nanowell platform we demonstrated an 1.8–4.5 increase in anti-Her2 production by CHO cells that were screened and isolated with the nanowell platform compared to CHO cells that were not screened. This increase was also shown in Fed-Batch cultures where selected high production clones showed titers of 19–100 mg/L on harvest day, while the low producer cells did not show any detectable anti-Her2 IgG production. The screening of thousands of single cells is performed under sterile conditions and the individual cells were cultured in buffers and reagents without animal components. The time required from seeding a single cell and measuring the antibody production to fully expanded clones with increased Her-2 production was 4–6 weeks.

Abstract Osteosarcoma (OS) is the most common malignant bone tumor with high pathological heterogeneity. Our study aimed to investigate disulfidptosis-related modification patterns in OS and their relationship with survival outcomes in patients with OS. We analyzed the single-cell-level expression profiles of disulfidptosis-related genes (DSRGs) in both OS microenvironment and OS subclusters, and HMGB1 was found to be crucial for intercellular regulation of OS disulfidptosis. Next, we explored the molecular clusters of OS based on DSRGs and related immune cell infiltration using transcriptome data. Subsequently, the hub genes of disulfidptosis in OS were screened by applying multiple machine models. In vitro and patient experiments validated our results. Three main disulfidptosis-related molecular clusters were defined in OS, and immune infiltration analysis suggested high immune heterogeneity between distinct clusters. The in vitro experiment confirmed decreased cell viability of OS after ACTB silencing and higher expression of ACTB in patients with lower immune scores. Our study systematically revealed the underlying relationship between disulfidptosis and OS at the single-cell level, identified disulfidptosis-related subtypes, and revealed the potential role of ACTB expression in OS disulfidptosis.

Xiang Xu,1,2 Fuling Qiu,2 Man Yang,3 Xiaoyong Liu,2 Siming Tao,4,* Bingrong Zheng1,* 1School of Medicine, Yunnan University, Kunming City, Yunnan Province, People’s Republic of China; 2Department of Cardiology, The Second Affiliated Hospital of Kunming Medical University, Kunming City, Yunnan Province, People’s Republic of China; 3School of Medicine, Dali University, Dali City, Yunnan Province, People’s Republic of China; 4Department of Cardiology, The Affiliated Hospital of Yunnan University, Kunming City, Yunnan Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Bingrong Zheng; Siming Tao, Email zhengbr@ynu.edu.cn; taosm6450@ynu.edu.cnBackground: Dysregulated macrophages are important causes of Atherosclerosis (AS) formation and increased plaque instability, but the heterogeneity of these plaques and the role of macrophage subtypes in plaque instability have yet to be clarified.Methods: This study integrates single-cell and bulk-seq data to analyze atherosclerotic plaques. Unsupervised clustering was used to reveal distinct plaque subtypes, while survival analysis and gene set variation analysis (GSVA) methods helped in understanding their clinical outcomes. Enrichment of differential expression of macrophage genes (DEMGs) score and pseudo-trajectory analysis were utilized to explore the biological functions and differentiation stages of macrophage subtypes in AS progression. Additionally, CellChat and the BayesPrism deconvolution method were used to elucidate macrophage subtype interaction and their prognostic significance at single-cell resolution. Finally, the expression of biomarkers was validated in mouse experiments.Results: Three distinct AS plaque subtypes were identified, with cluster 3 plaque subtype being particularly associated with higher immune infiltration and poorer prognosis. The DEMGs score exhibited a significant elevation in three macrophage subtypes (SPP1+/VCAN+ macrophages, IL1B+ macrophages, and FLT3LG+ macrophages), associated with cluster 3 plaque subtype and highlighted the prognostic significance of these subtypes. Activation trajectory of the macrophage subtypes is divided into three states (Pre-branch, Cell fate 1, and Cell fate 2), and Cell fate 2 (SPP1+/VCAN+ macrophages, IL1B+ macrophages, and FLT3LG+ macrophages dominant) exhibiting the highest DEMGs score, distinct interactions with other cell components, and relating to poorer prognosis of ischemic events. This study also uncovered a unique SPP1+/VCAN+ macrophage subtype, rare in quantity but significant in influencing AS progression. Machine learning algorithms identified 10 biomarkers crucial for AS diagnosis. The validation of these biomarkers was performed using Mendelian Randomization analysis and in vitro methods, supporting their relevance in AS pathology.Conclusion: Our study provides a comprehensive view of AS plaque heterogeneity and the prognostic significance of macrophage subtypes in plaque instability. Keywords: atherosclerosis, macrophage subtypes, single-cell analysis, plaque heterogeneity, biomarkers, pseudo-trajectory analysis