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OBJECTIVE: Osteoporosis is a severe bone disease with a complex pathogenesis involving various immune processes. With the in-depth understanding of bone immune mechanisms, discovering new therapeutic targets is crucial for the prevention and treatment of osteoporosis. This study aims to explore novel bone immune markers related to osteoporosis based on single-cell and transcriptome data, utilizing bioinformatics and machine learning methods, in order to provide novel strategies for the diagnosis and treatment of the disease. METHODS: Single cell and transcriptome data sets were acquired from Gene Expression Omnibus (GEO). The data was then subjected to cell communication analysis, pseudotime analysis, and high dimensional WGCNA (hdWGCNA) analysis to identify key immune cell subpopulations and module genes. Subsequently, ConsensusClusterPlus analysis was performed on the key module genes to identify different diseased subgroups in the osteoporosis (OP) training set samples. The immune characteristics between subgroups were evaluated using Cibersort, EPIC, and MCP counter algorithms. OP's hub genes were screened using 10 machine learning algorithms and 113 algorithm combinations. The relationship between hub genes and immunity and pathways was established by evaluating the immune and pathway scores of the training set samples through the ESTIMATE, MCP-counter, and ssGSEA algorithms. Real-time fluorescence quantitative PCR (RT-qPCR) testing was conducted on serum samples collected from osteoporosis patients and healthy adults. RESULTS: In OP samples, the proportions of bone marrow-derived mesenchymal stem cells (BM-MSCs) and neutrophils increased significantly by 6.73% (from 24.01% to 30.74%) and 6.36% (from 26.82% to 33.18%), respectively. We found 16 intersection genes and four hub genes (DND1, HIRA, SH3GLB2, and F7). RT-qPCR results showed reduced expression levels of DND1, HIRA, and SH3GLB2 in clinical blood samples of OP patients. Moreover, the four hub genes showed positive correlations with neutrophils (0.65-0.90), immature B cells (0.76-0.92), and endothelial cells (0.79-0.87), while showing negative correlations with myeloid-derived suppressor cells (negative 0.54-0.73), T follicular helper cells (negative 0.71-0.86), and natural killer T cells (negative 0.75-0.85). CONCLUSION: Neutrophils play a crucial role in the occurrence and development of osteoporosis. The four hub genes potentially inhibit metabolic activities and trigger inflammation by interacting with other immune cells, thereby significantly contributing to the onset and diagnosis of OP.

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Based on the joint analysis of multi-omic data and the biological experiments, we demonstrate that FOXF1 inhibits invasion and metastasis of lung adenocarcinoma cells and enhances anti-tumor immunity via regulating MFAP4/FAK signal axis in this study. The levels of FOXF1 and MFAP4 are significantly down-regulated in LUAD, and the increased levels of two genes can improve the clinical prognosis of LUAD patients. Fluorescein reporter gene determination, chromatin immunoprecipitation and gene co-expression analysis indicate that MFAP4 level is positively regulated by transcription factor FOXF1. The function enrichment analysis shows that the levels of FOXF1 and MFAP4 are closely associated with an enrichment of tumor metastasis signatures. FOXF1 can inhibit the migration and invasion of LAUD cells by transcriptionally activating MFAP4 expression. And the overexpression of FOXF1/MFAP4 can reduce focal adhesion kinase (FAK) phosphorylation, while their knockdown result in the opposite effects. The increased levels of FOXF1/MFAP4 enhance the antitumor immunity by increasing the infiltration of dendritic cells and CD4+ T cells, and the interactions between LUAD cells and immune cells, and activating multiple anti-tumor immunity-related pathways. In conclusion, our study reveals the potential function of FOXF1/MFAP4/FAK signal axis in inhibiting metastasis of LUAD cells and modulating anti-tumor immunity of LUAD patients.

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Chromosomal aneuploidy, that is, numerical chromosome aberrations, is one of the molecular hallmarks of cancer. However, when neoplasms are studied with sequencing- and array-based approaches, chromosome numbers and ploidy states are typically inferred from bulk DNA data. Furthermore, published molecular estimates of neoplasia-associated aneuploidy often also include genomic imbalances resulting from various types of structural rearrangement, which likely result from other mechanisms than numerical chromosome aberrations. We thus analyzed chromosome numbers using single-cell cytogenetic data from 83,862 tumors, and show that both benign and malignant tumors are highly heterogeneous with regard to deviations from the normal, diploid state. Focusing on the chromosome numbers in 112 specific tumor types, defined by both exact morphologic diagnosis and organ location and from which data from ≥50 cases were available, we found two major clusters: one predominated by near-diploid neoplasms and one by neoplasms with extensive aneuploidy and one or more whole genome doublings. The former cluster included most benign solid tumors, myeloid neoplasms, and malignant gene fusion-associated solid tumors, whereas the latter was predominated by malignant solid tumors and lymphomas. For 16 malignant tumor types, the distribution of chromosome numbers could be compared to TCGA ploidy level data. Cytogenetic and molecular data correlated well, but the former indicates a higher level of clonal heterogeneity. The results presented here suggest shared pathogenetic mechanisms in certain tumor types and provide a reference for molecular analyses.

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This study investigates the oxygen (O2) consumption of single cells during changes in their migration direction. This is the first integration of nanotopographies with an O2 biosensor in a platform, allowing the real-time monitoring of O2 consumption in cells and the ability to distinguish cells migrating in the same direction from those migrating in the opposite direction. Advanced nanofabrication technologies were used to pattern nanoholes or nanopillars on grating ridges, and their effects were evaluated using fluorescence microscopy, cell migration assays, and O2 consumption analysis. The results revealed that cells on the nanopillars over grating ridges exhibited an enhanced migration motility and more frequent directional changes. Additionally, these cells showed an increased number of protrusions and filopodia with denser F-actin areas and an increased number of dotted F-actin structures around the nanopillars. Dynamic metabolic responses were also evident, as indicated by the fluorescence intensity peaks of platinum octaethylporphyrin ketone dye, reflecting an increased O2 consumption and higher mitochondria activities, due to the higher energy required in response to directional changes. The study emphasizes the complex interplay between O2 consumption and cell migration directional changes, providing insights into biomaterial science and regenerative medicine. It suggests innovative designs for biomaterials that guide cell migration and metabolism, advocating nanoengineered platforms to harness the intricate relationships between cells and their microenvironments for therapeutic applications.

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Imaging flow cytometry, which combines the advantages of flow cytometry and microscopy, has emerged as a powerful tool for cell analysis in various biomedical fields such as cancer detection. In this study, we develop multiplex imaging flow cytometry (mIFC) by employing a spatial wavelength division multiplexing technique. Our mIFC can simultaneously obtain brightfield and multi-color fluorescence images of individual cells in flow, which are excited by a metal halide lamp and measured by a single detector. Statistical analysis results of multiplex imaging experiments with resolution test lens, magnification test lens, and fluorescent microspheres validate the operation of the mIFC with good imaging channel consistency and micron-scale differentiation capabilities. A deep learning method is designed for multiplex image processing that consists of three deep learning networks (U-net, very deep super resolution, and visual geometry group 19). It is demonstrated that the cluster of differentiation 24 (CD24) imaging channel is more sensitive than the brightfield, nucleus, or cancer antigen 125 (CA125) imaging channel in classifying the three types of ovarian cell lines (IOSE80 normal cell, A2780, and OVCAR3 cancer cells). An average accuracy rate of 97.1% is achieved for the classification of these three types of cells by deep learning analysis when all four imaging channels are considered. Our single-detector mIFC is promising for the development of future imaging flow cytometers and for the automatic single-cell analysis with deep learning in various biomedical fields.

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In collective systems, influence of individuals can permeate an entire group through indirect interactionscom-plicating any scheme to understand individual roles from observations. A typical approach to understand an individuals influence on another involves consideration of confounding factors, for example, by conditioning on other individuals outside of the pair. This becomes unfeasible in many cases as the number of individuals increases. In this article, we review some of the unforeseen problems that arise in understanding individual influence in a collective such as single cells, as well as some of the recent works which address these issues using tools from information theory.

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Background: The incidence of glioma, a prevalent brain malignancy, is increasing, particularly among the elderly population. This study aimed to elucidate the clinical importance of epithelial-mesenchymal transition (EMT) in gliomas and its association with malignancy and prognosis. Background: The incidence of glioma, particularly among elderly individuals, is on the rise. The malignancy of glioma is determined not only by the oncogenic properties of tumor cells but also by the composition of the tumor microenvironment, which includes immune system macrophages. The prevalence of M2-type macrophages typically fosters tumor progression, yet the underlying mechanism remains elusive. Our study explored the clinical importance of epithelial-mesenchymal transition (EMT) in gliomas and its association with malignancy and prognosis. Methods: Our study used the gene set variation analysis (GSVA) algorithm to classify different levels of EMT activation based on the transcriptomic and multi-omics data. Machine learning (ML) and single-cell analysis were integrated into our model for comprehensive analysis. A predictive model was constructed and in vitro experiments were performed to validate our findings. Results: Our study classified 1,641 samples into two clusters based on EMT activation: the EMT-hot group and the EMT-cold group. The EMT-hot group had elevated copy number loss, tumor mutational burden (TMB), and a poorer survival rate. Conversely, the EMT-cold group showed a better survival rate, likely attributed to lower stromal and immune cell scores, as well as decreased expression of human leukocyte antigen-related genes. Driving genes were identified through weighted gene coexpression network analysis (WGCNA) and dimensionality reduction techniques. These genes were then utilized in the construction of a prognostic model using ML and protein-protein interaction (PPI) network analysis. Furthermore, the impact of the core genes identified through single-cell analysis on glioma prognosis was examined. Conclusion: Our research underscores the efficacy of our model in predicting glioma prognosis and elucidates the connection between the M2 macrophages and EMT. Additionally, core genes such as LY96, C1QB, LGALS1, CSPG5, S100A8, and CHGB were identified as pivotal for mediating the occurrence of EMT induced by M2 macrophages.

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Extracellular vesicles (EVs) are secreted by cells with a membrane structure and complex components such as DNA, RNA and proteins. These biomolecules play an important role in cell communication, cell proliferation, cell migration, vascularization, immune response and other physiological and pathological processes. Most current research on EVs focused on populations of EVs. Heterogeneity of EVs is neglected. Considering the heterogeneity of single EVs may offer critical molecular insights into cell-cell interactions, it is necessary to enhance our understanding about molecular characteristics from EVs derived from cell population to a single EV of derived from a single cell. This transformation is expected to provide a new insight into the understanding of cellular biology and the accurate description of the law of disease progress. In this article, we review the current research progress of single EV analysis technology for single EVs derived from cell population (SECP) and discuss its main applications in biological and clinical medicine research. After that, we propose the development direction, main difficulties and application prospect of single EV analysis technology for single EVs derived from single cells (SESC) according to our own research work, to provide new perspectives for the field of EV research.

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Background: Autism spectrum disorder (ASD) is a disease characterized by social disorder. Recently, the population affected by ASD has gradually increased around the world. There are great difficulties in diagnosis and treatment at present. Methods: The ASD datasets were obtained from the Gene Expression Omnibus database and the immune-relevant genes were downloaded from a previously published compilation. Subsequently, we used WGCNA to screen the modules related to the ASD and immune. We also choose the best combination and screen out the core genes from Consensus Machine Learning Driven Signatures (CMLS). Subsequently, we evaluated the genetic correlation between immune cells and ASD used GNOVA. And pleiotropic regions identified by PLACO and CPASSOC between ASD and immune cells. FUMA was used to identify pleiotropic regions, and expression trait loci (EQTL) analysis was used to determine their expression in different tissues and cells. Finally, we use qPCR to detect the gene expression level of the core gene. Results: We found a close relationship between neutrophils and ASD, and subsequently, CMLS identified a total of 47 potential candidate genes. Secondly, GNOVA showed a significant genetic correlation between neutrophils and ASD, and PLACO and CPASSOC identified a total of 14 pleiotropic regions. We annotated the 14 regions mentioned above and identified a total of 6 potential candidate genes. Through EQTL, we found that the CFLAR gene has a specific expression pattern in neutrophils, suggesting that it may serve as a potential biomarker for ASD and is closely related to its pathogenesis. Conclusions: In conclusion, our study yields unprecedented insights into the molecular and genetic heterogeneity of ASD through a comprehensive bioinformatics analysis. These valuable findings hold significant implications for tailoring personalized ASD therapies.

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Single-cell multiomic and exosome analyses are potent tools in various fields, such as cancer research, immunology, neuroscience, microbiology, and drug development. They facilitate the in-depth exploration of biological systems, providing insights into disease mechanisms and aiding in treatment. Single-cell isolation, which is crucial for single-cell analysis, ensures reliable cell isolation and quality control for further downstream analyses. Microfluidic chips are small lightweight systems that facilitate efficient and high-throughput single-cell isolation and real-time single-cell analysis on- or off-chip. Therefore, most current single-cell isolation and analysis technologies are based on the single-cell microfluidic technology. This review offers comprehensive guidance to researchers across different fields on the selection of appropriate microfluidic chip technologies for single-cell isolation and analysis. This review describes the design principles, separation mechanisms, chip characteristics, and cellular effects of various microfluidic chips available for single-cell isolation. Moreover, this review highlights the implications of using this technology for subsequent analyses, including single-cell multiomic and exosome analyses. Finally, the current challenges and future prospects of microfluidic chip technology are outlined for multiplex single-cell isolation and multiomic and exosome analyses.

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BACKGROUND: Fabry disease is a progressive, X chromosome-linked lysosomal storage disorder with multiple organ dysfunction. Due to the absence or reduced activity of alpha-galactosidase A (AGAL), glycosphingolipids, primarily globotriaosyl-ceramide (Gb3), concentrate in cells. In heterozygous women, symptomatology is heterogenous and currently routinely used fluorometry-based assays measuring mean activity mostly fail to uncover AGAL dysfunction. The aim was the development of a flow cytometry assay to measure AGAL activity in individual cells. METHODS: Conventional and multispectral imaging flow cytometry was used to detect AGAL activity. Specificity was validated using the GLA knockout (KO) Jurkat cell line and AGAL inhibitor 1-deoxygalactonojirimycin. The GLA KO cell line was generated via CRISPR-Cas9-based transfection, validated with exome sequencing, gene expression and substrate accumulation. RESULTS: Flow cytometric detection of specific AGAL activity is feasible with fluorescently labelled Gb3. In the case of Jurkat cells, a substrate concentration of 2.83 nmol/mL and 6 h of incubation are required. Quenching of the aspecific exofacial binding of Gb3 with 20% trypan blue solution is necessary for the specific detection of lysosomal substrate accumulation. CONCLUSION: A flow cytometry-based assay was developed for the quantitative detection of AGAL activity at the single-cell level, which may contribute to the diagnosis of Fabry patients.

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RNA-seq or transcriptome analysis of individual cells and small cell populations is essential for virtually any biomedical field. Here, we examine and discuss the different methods of RNA isolation specific to ctenophores. We present a convenient, inexpensive, and reproducible protocol for RNA-seq libraries that are designed for low quantities of samples. We demonstrated these methods on early (one, two, four, eight cells) embryonic and developmental stages, tissues, and even a single aboral organ from the ctenophore Pleurobrachia bachei and other ctenophore species (e.g., Mnemiopsis, Bolinopsis, and Beroe).

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Butterfly wings display a diversity of cell types, including large polyploid scale cells, yet the molecular basis of such diversity is poorly understood. To explore scale cell diversity at a transcriptomic level, we employ single-cell RNA sequencing of ∼5,200 large cells (>6 µm) from 22.5- to 25-h male pupal forewings of the butterfly Bicyclus anynana. Using unsupervised clustering, followed by in situ hybridization, immunofluorescence, and CRISPR-Cas9 editing of candidate genes, we annotate various cell types on the wing. We identify genes marking non-innervated scale cells, pheromone-producing glandular cells, and innervated sensory cell types. We show that senseless, a zinc-finger transcription factor, and HR38, a hormone receptor, determine the identity, size, and color of different scale cell types and are important regulators of scale cell differentiation. This dataset and the identification of various wing cell-type markers provide a foundation to compare and explore scale cell-type diversification across arthropod species.

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High-throughput drug screening (HTDS) has significantly reduced the time and cost of new drug development. Nonetheless, contact-dependent cell-cell communication (CDCCC) may impact the chemosensitivity of tumour cells. There is a pressing need for low-cost single-cell HTDS platforms, alongside a deep comprehension of the mechanisms by which CDCCC affects drug efficacy, to fully unveil the efficacy of anticancer drugs. In this study, we develop a microfluidic chip for single-cell HTDS and evaluate the molecular mechanisms impacted by CDCCC using quantitative mass spectrometry-based proteomics. The chip achieves high-quality drug mixing and single-cell capture, with single-cell drug screening results on the chip showing consistency with those on the 96-well plates under varying concentration gradients. Through quantitative proteomic analysis, we deduce that the absence of CDCCC in single tumour cells can enhance their chemoresistance potential, but simultaneously subject them to stronger proliferation inhibition. Additionally, pathway enrichment analysis suggests that CDCCC could impact several signalling pathways in tumour single cells that regulate vital biological processes such as tumour proliferation, adhesion, and invasion. These results offer valuable insights into the potential connection between CDCCC and the chemosensitivity of tumour cells. This research paves the way for the development of single-cell HTDC platforms and holds the promise of advancing tumour personalized treatment strategies.

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Enhancers bind transcription factors, chromatin regulators, and non-coding transcripts to modulate the expression of target genes. Here, we report 3D genome structures of single mouse ES cells as they are induced to exit pluripotency and transition through a formative stage prior to undergoing neuroectodermal differentiation. We find that there is a remarkable reorganization of 3D genome structure where inter-chromosomal intermingling increases dramatically in the formative state. This intermingling is associated with the formation of a large number of multiway hubs that bring together enhancers and promoters with similar chromatin states from typically 5-8 distant chromosomal sites that are often separated by many Mb from each other. In the formative state, genes important for pluripotency exit establish contacts with emerging enhancers within these multiway hubs, suggesting that the structural changes we have observed may play an important role in modulating transcription and establishing new cell identities.

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At present, GPX4's role in the occurrence and development of diffuse large B lymphoma (DLBCL) is rarely reported. This study's purpose is to explore GPX4's significance in the diagnosis, treatment, and pathological mechanisms of DLBCL. The TIMER 2.0, GEPIA, and GEO databases were used to analyze GPX4's expression levels in DLBCL tissue, peripheral blood, and single cells, and evaluate its potential performance as a therapeutic and diagnostic marker. Cell experiments validate GPX4's role in DLBCL cells. And revealed the potential mechanism of GPX4's action from three aspects: immunity, pathogenic gene expression, and protein interaction. The results indicate that GPX4 can be used as a biomarker for treatment and diagnosis (FC > 1.5, P < 0.05, AUC>0.8, KM-P value < 0.05). In single cell data, GPX4 also showed high expression in immune cells. Besides, cell experiments have confirmed that GPX4's high expression can inhibit DLBCL cells' proliferation. Meanwhile, we found a negative correlation between GPX4 and the 16 core DLBCL's pathogenic genes, and a significant negative correlation with immune B cell infiltration. In summary, GPX4 can serve as a potential therapeutic and diagnostic marker for DLBCL. GPX4's high expression can lead to a good prognosis in DLBCL patients, which may be related to its inhibition of cancer cell proliferation, high expression of key pathogenic genes, and infiltration of immune B cells.

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Stress urinary incontinence (SUI) greatly affects the daily life of numerous women and is closely related to a history of vaginal delivery and aging. We used vaginal balloon dilation to simulate vaginal birth injury in young and middle-aged rats to produce a SUI animal model, and found that young rats restored urethral structure and function well, but not the middle-aged rats. To identify the characteristics of cellular and molecular changes in the urethral microenvironment during the repair process of SUI. We profiled 51,690 individual female rat urethra cells from 24 and 48 weeks old, with or without simulated vaginal birth injury. Cell interaction analysis showed that signal networks during repair process changed from resting to active, and aging altered the distribution but not the overall level of cell interaction in the repair process. Similarity analysis showed that muscle, fibroblasts, and immune cells underwent large transcriptional changes during aging and repair. In middle-aged rats, cell senescence occurs mainly in the superficial and middle urothelium due to cellular death and shedding, and the basal urothelium expressed many Senescence-Associated Secretory Phenotype (SASP) genes. In conclusion, we established the aging and vaginal balloon dilation (VBD) model of female urethral cell anatomy and the signal network landscape, which provides an insight into the normal or disordered urethra repair process and the scientific basis for developing novel SUI therapies.

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Comprehensive genome-wide analyses of single cells represent an important tool for clinical applications, such as pre-implantation diagnostic and prenatal diagnosis, as well as for cancer research purpose. For the latter, studies of tumor heterogeneity, circulating tumor cells (CTCs), and disseminated cancer cells (DCCs) require the analysis of single-cell genomes. Here we describe a reliable and robust array-based comparative genomic hybridization (aCGH) protocol based on Ampli 1™ whole genome amplification that allows the detection of copy number alterations (CNAs) in single cancer cells as small as 100 kb.

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One of the main components of articular cartilage is the chondrocyte's pericellular matrix (PCM), which is critical for regulating mechanotransduction, biochemical cues, and healthy cartilage development. Here, individual primary human chondrocytes (PHC) are encapsulated and cultured in 50 µm diameter alginate microgels using drop-based microfluidics. This unique culturing method enables PCM formation and manipulation of individual cells. Over ten days, matrix formation is observed using autofluorescence imaging, and the elastic moduli of isolated cells are measured using AFM. Matrix production and elastic modulus increase are observed for the chondrons cultured in microgels. Furthermore, the elastic modulus of cells grown in microgels increases ≈ten-fold over ten days, nearly reaching the elastic modulus of in vivo PCM. The AFM data is further analyzed using a Gaussian mixture model and shows that the population of PHCs grown in microgels exhibit two distinct populations with elastic moduli averaging 9.0 and 38.0 kPa. Overall, this work shows that microgels provide an excellent culture platform for the growth and isolation of PHCs, enabling PCM formation that is mechanically similar to native PCM. The microgel culture platform presented here has the potential to revolutionize cartilage regeneration procedures through the inclusion of in vitro developed PCM.

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This work proposes the concept of single-cell microRNA (miR) therapy and proof-of-concept by engineering a nanopipette for high-precision miR-21-targeted therapy in a single HeLa cell with sensitive photoelectrochemical (PEC) feedback. Targeting the representative oncogenic miR-21, the as-functionalized nanopipette permits direct intracellular drug administration with precisely controllable dosages, and the corresponding therapeutic effects can be sensitively transduced by a PEC sensing interface that selectively responds to the indicator level of cytosolic caspase-3. The experimental results reveal that injection of ca. 4.4 × 10-20 mol miR-21 inhibitor, i.e., 26488 copies, can cause the obvious therapeutic action in the targeted cell. This work features a solution to obtain the accurate knowledge of how a certain miR-drug with specific dosages treats the cells and thus provides an insight into futuristic high-precision clinical miR therapy using personalized medicine, provided that the prerequisite single-cell experiments are courses of personalized customization.

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