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Flow cytometry, single-cell RNA sequencing, and other analyses enable us to capture immune profiles of the tumor microenvironment. Here, we present a protocol to characterize the immune profile of tumor-bearing mice. We describe steps for establishing mouse models and preparing single-cell suspensions from tumor tissue and other immune-related organs, which can be further analyzed by flow cytometry and other omics assays. We then detail procedures for staining, flow cytometry analysis, and phenotyping of the immune cell populations. For complete details on the use and execution of this protocol, please refer to Miyauchi et al.1.

Altered transcriptional and epigenetic regulation of brain cell types may contribute to cognitive changes with advanced age. Using single-nucleus multi-omic DNA methylation and transcriptome sequencing (snmCT-seq) in frontal cortex from young adult and aged donors, we found widespread age- and sex-related variation in specific neuron types. The proportion of inhibitory SST- and VIP-expressing neurons was reduced in aged donors. Excitatory neurons had more profound age-related changes in their gene expression and DNA methylation than inhibitory cells. Hundreds of genes involved in synaptic activity, including EGR1, were less expressed in aged adults. Genes located in subtelomeric regions increased their expression with age and correlated with reduced telomere length. We further mapped cell-type-specific sex differences in gene expression and X-inactivation escape genes. Multi-omic single-nucleus epigenomes and transcriptomes provide new insight into the effects of age and sex on human neurons.

Macrophages induce the expression of hundreds of genes in response to immune threats. However, current technology limits our ability to capture single-cell inducible gene expression dynamics. Here, we generated high-resolution time series single-cell RNA sequencing (scRNA-seq) data from mouse macrophages responding to six stimuli, and imputed ensembles of real-time single-cell gene expression trajectories (scGETs). We found that dynamic information contained in scGETs substantially contributes to macrophage stimulus-response specificity (SRS). Dynamic information also identified correlations between immune response genes, indicating biological coordination. Furthermore, we showed that the microenvironmental context of polarizing cytokines profoundly affects scGETs, such that measuring response dynamics offered clearer discrimination of the polarization state of individual macrophage cells than single time-point measurements. Our findings highlight the important contribution of dynamic information contained in the single-cell expression responses of immune genes in characterizing the SRS and functional states of macrophages. [Display omitted] • Imputation of stimulus-induced single-cell gene expression trajectories (scGETs) • Trajectory features reveal unexpected stimulus specificity in macrophage responses • Expression integrals identify cell-cell correlations and co-regulated gene sets • Stimulus-response expression trajectories readily characterize cell functional states Because RNA measurements are cell destructive, it is unclear how variable stimulus-induced dynamic gene expression trajectories (GETs) are. Sheu et al. developed a method to assess single-cell gene expression trajectories (scGETs) in macrophages responding to stimuli and found scGETs to be much more informative than any single time-point measurement. [ABSTRACT FROM AUTHOR]

The mammalian kidney maintains fluid homeostasis through diverse epithelial cell types generated from nephron and ureteric progenitor cells. To extend a developmental understanding of the kidney's epithelial networks, we compared chromatin organization (single-nuclear assay for transposase-accessible chromatin sequencing [ATAC-seq]; 112,864 nuclei) and gene expression (single-cell/nuclear RNA sequencing [RNA-seq]; 109,477 cells/nuclei) in the developing human (10.6–17.6 weeks; n = 10) and mouse (post-natal day [P]0; n = 10) kidney, supplementing analysis with published mouse datasets from earlier stages. Single-cell/nuclear datasets were analyzed at a species level, and then nephron and ureteric cellular lineages were extracted and integrated into a common, cross-species, multimodal dataset. Comparative computational analyses identified conserved and divergent features of chromatin organization and linked gene activity, identifying species-specific and cell-type-specific regulatory programs. In situ validation of human-enriched gene activity points to human-specific signaling interactions in kidney development. Further, human-specific enhancer regions were linked to kidney diseases through genome-wide association studies (GWASs), highlighting the potential for clinical insight from developmental modeling. [Display omitted] • Transcriptomic and epigenomic atlases of human and mouse kidney organogenesis • Cross-species integration identified conserved and divergent molecular features • Human-enriched genes suggest evolutionary divergence between human and mouse kidneys • Human cis -regulatory elements link to kidney disease Kim et al. employed in-depth single-cell/-nuclear transcriptomics and open chromatin accessibility analyses to developing human and mouse kidneys. Cross-species, multiomic integration identified conserved and human-enriched features of developmental, functional, and evolutionary kidney programs. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a chronic and progressive neurodegenerative disease leading to motor dysfunction and, in some cases, dementia. Transcriptome analysis is one promising approach for characterizing PD and other neurodegenerative disorders by informing how specific disease events influence gene expression and contribute to pathogenesis. With the emergence of single-cell and single-nucleus RNA sequencing (scnRNA-seq) technologies, the transcriptional landscape of neurodegenerative diseases can now be described at the cellular level. As the application of scnRNA-seq is becoming routine, it calls to question how results at a single-cell resolution compare to those obtained from RNA sequencing of whole tissues (bulk RNA-seq), whether the findings are compatible, and how the assays are complimentary for unraveling the elusive transcriptional changes that drive neurodegenerative disease. Herein, we review the studies that have leveraged RNA-seq technologies to investigate PD. Through the integration of bulk and scnRNA-seq findings from human, post-mortem brain tissue, we use the PD literature as a case study to evaluate the compatibility of the results generated from each assay and demonstrate the complementarity of the sequencing technologies. Finally, through the lens of the PD transcriptomic literature, we evaluate the current feasibility of bulk and scnRNA-seq technologies to illustrate the necessity of both technologies for achieving a comprehensive insight into the mechanism by which gene expression promotes neurodegenerative disease. We conclude that the continued application of both assays will provide the greatest insight into neurodegenerative disease pathology, providing both cell-specific and whole-tissue level information. [ABSTRACT FROM AUTHOR]

Lupus nephritis (LN), a complex complication of systemic lupus erythematosus, requires in-depth cellular and molecular analysis for advanced treatment strategies, including mRNA vaccine development. In this study, we analyzed single-cell RNA sequencing data from 24 LN patients and 10 healthy controls, supplemented by bulk RNA-seq data from additional LN patients and controls. By applying non-negative matrix factorization (NMF), we identified four distinct leukocyte meta-programs in LN, highlighting diverse immune functions and potential mRNA vaccine targets. Utilizing 12 machine learning algorithms, we developed 417 predictive models incorporating gene sets linked to key biological pathways, such as MTOR signaling, autophagy, Toll-like receptor, and adaptive immunity pathways. These models were instrumental in identifying potential targets for mRNA vaccine development. Our functional network analysis further revealed intricate gene interactions, providing novel insights into the molecular basis of LN. Additionally, we validated the mRNA expression levels of potential vaccine targets across multiple cohorts and correlated them with clinical parameters such as the glomerular filtration rate (GFR) and pathological stage. This study represents a significant advance in LN research by merging single-cell genomics with the precision of NMF and machine learning, broadening our understanding of LN at the cellular and molecular levels. More importantly, our findings shed light on the development of targeted mRNA vaccines, offering new possibilities for diagnostics and therapeutics for this complex autoimmune disease. [ABSTRACT FROM AUTHOR]

Bipolar plates (BPPs) and membrane electrode assemblies (MEAs) are the key components for proton exchange membrane fuel cells (PEMFCs), they are alternately arranged to construct a compact stack with high power generation. This stacking method may introduce several issues, such as the difficulties in maintenance, imprecise assembly and sealing. This paper presents a novel integrated packaged single fuel cell that employs titanium metal bipolar plates to construct a short stack with superior sealing performance and working reliability. To predict the performance of integrated packaged single cell, numerical model was developed to analyze the mass transfer of O 2 , H 2 and H 2 O within the cell, revealing a uniform distribution of reactants and current. A short stack was manufactured with 10 layers of single cells, it retains excellent sealing properties and consistent output across a range of pressure conditions, with a leakage rate deviation lower than 0.03 mL/(min·cell) and membrane current density variation less than 5%. Finally, the electrochemical performance of the integrated packaged short stack was tested, its maximum power is 886.67 mW/cm2. It can achieve a rated output power of 169.14 W and an energy density of 1684.97 mW/cm3. Thus, our developed short stack has high energy utilization efficiency for power generation, and is anticipated to provide a durable power source for drones, robots and new energy vehicles. • An integrated packaged single fuel cell short stack based on titanium metal polar plates was proposed. • Numerical model of single cell was established, simulation on gas molar concentration and current density distribution was conducted. • A 10-layer integrated short stack was fabricated, assembly stress and polarization tests verified its good sealing performance. [ABSTRACT FROM AUTHOR]

Cloning is a key molecular biology procedure for obtaining a genetically homogenous population of organisms or cell lines. It requires the expansion of new cell populations starting from single genetically modified cells. Despite the technical progress, cloning of many cell lines remains difficult. Cloning often fails either due to the strenuous conditions associated with manipulating cells or because many cells don't tolerate a single-cell state. Here we describe a new cloning method utilizing low adhesion microcavity plates. This new technique, named microcavity-assisted cloning (MAC) was developed to clone difficult-to-clone HepG2 cells. The clones were produced following CRISPR/Cas9 knockout of the GSTA1 gene by a random distribution of 200, 400, and 800 cells into 550 microcavities of a 24-well low adhesion plate originally designed for the culture of spheroids. The knockout of GSTA1 was verified at the protein level using Western blotting. The advantages of the MAC method are its low cost, ease of the procedure, and the possibility of scaling up the throughput and automatization. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is a highly aggressive and treatment‐resistant malignancy that poses a significant challenge in modern medicine. Despite advances in surgical resection, radiotherapy and chemotherapy, complete eradication of GBM remains elusive due to its diffuse invasion into the brain parenchyma and propensity for recurrence. The tumour microenvironment (TME), particularly macrophages, has emerged as a critical player in GBM progression, invasion and metastasis. In the immune microenvironment of glioma, MS4A6A exhibits unique expression characteristics in macrophages. This study aimed to investigate the potential role of MS4A6A, a gene associated with aging and neurodegenerative diseases, in GBM and its potential as a prognostic biomarker and therapeutic target. [ABSTRACT FROM AUTHOR]

Background: Latency remains a major obstacle to finding a cure for HIV despite the availability of antiretroviral therapy. Due to virus dormancy, limited biomarkers are available to identify latent HIV-infected cells. Profiling of individual HIV-infected cells is needed to explore potential latency biomarkers and to study the mechanisms of persistence that maintain the HIV reservoir. Methods: Single cell spatial transcriptomic characterization using the CosMx Spatial Molecular Imager platform was conducted to analyze HIV-infected cells in formalin-fixed paraffin-embedded sections of splenic tissue surgically obtained from an HIV-infected humanized mouse model. Regulation of over a thousand human genes was quantified in both viremic and aviremic specimens. In addition, in situ hybridization and immunohistochemistry were performed in parallel to identify HIV viral RNA- and p24-containing cells, respectively. Finally, initial findings from CosMx gene profiling were confirmed by isolating RNA from CD4 + T cells obtained from a person living with HIV on antiretroviral therapy following either PMA/Ionomycin or DMSO treatment. RNA was quantified using qPCR for a panel of targeted human host genes. Results: Supervised cell typing revealed that most of the HIV-infected cells in the mouse spleen sections were differentiated CD4 + T cells. A significantly higher number of infected cells, 2781 (1.61%) in comparison to 112 (0.06%), and total HIV transcripts per infected cell were observed in viremic samples compared to aviremic samples, respectively, which was consistent with the data obtained from ISH and IHC. Notably, the expression of 55 genes was different in infected cells within tissue from aviremic animals compared to viremic. In particular, both spleen tyrosine kinase (SYK) and CXCL17, were expressed approximately 100-fold higher. This data was further evaluated against bulk RNA isolated from HIV-infected human primary CD4 + T cells. A nearly 6-fold higher expression of SYK mRNA was observed in DMSO-treated CD4 + T cells compared to those stimulated with PMA/Ionomycin. Conclusion: This study found that the CosMx SMI platform is valuable for assessing HIV infection and providing insights into host biomarkers associated with HIV reservoirs. Higher relative expression of the SYK gene in aviremic-infected cells from the humanized mouse HIV model was consistent with levels found in CD4 + T cells of aviremic donors. [ABSTRACT FROM AUTHOR]

CRISPR genome engineering and single-cell RNA sequencing have accelerated biological discovery. Single-cell CRISPR screens unite these two technologies, linking genetic perturbations in individual cells to changes in gene expression and illuminating regulatory networks underlying diseases. Despite their promise, single-cell CRISPR screens present considerable statistical challenges. We demonstrate through theoretical and real data analyses that a standard method for estimation and inference in single-cell CRISPR screens—"thresholded regression"—exhibits attenuation bias and a bias-variance tradeoff as a function of an intrinsic, challenging-to-select tuning parameter. To overcome these difficulties, we introduce GLM-EIV ("GLM-based errors-in-variables"), a new method for single-cell CRISPR screen analysis. GLM-EIV extends the classical errors-in-variables model to responses and noisy predictors that are exponential family-distributed and potentially impacted by the same set of confounding variables. We develop a computational infrastructure to deploy GLM-EIV across hundreds of processors on clouds (e.g. Microsoft Azure) and high-performance clusters. Leveraging this infrastructure, we apply GLM-EIV to analyze two recent, large-scale, single-cell CRISPR screen datasets, yielding several new insights. [ABSTRACT FROM AUTHOR]

Despite advancements in assisted reproductive technology, recurrent implantation failure (RIF) remains a challenge. Endometrial factors, including ferroptosis and immunity, may contribute to this issue. This study integrated bioinformatics analysis and Mendelian randomization (MR) to investigate the expression and significance of DEFRGs in RIF. We intersected 484 ferroptosis‐associated genes with 515 differentially expressed genes (DEGs) to identify key DEFRGs. Subsequent analyses included enrichment analysis, molecular subtype identification, machine learning model development for biomarker discovery, immune cell infiltration assessment, single‐cell RNA sequencing, and MR to explore the causal relationships of selected genes with RIF. In this study, we identified 11 differentially expressed ferroptosis‐related genes (DEFRGs) between RIF and healthy individuals. Cluster analysis revealed two distinct molecular subtypes with different immune profiles and DEFRG expressions. Machine learning models highlighted MUC1, GJA1 and FANCD2 as potential diagnostic biomarkers, with high accuracy in RIF prediction. Single‐cell analysis further revealed the cellular localization and interactions of DEFRGs. MR suggested a protective effect of FANCD2 against RIF. Validation in RIF patients confirmed the differential expression of key DEFRGs, consistent with bioinformatics findings. This comprehensive study emphasize the significant role of DEFRGs in the pathogenesis of RIF, suggesting that modulating these genes could offer new avenues for treatment. The FANCD2 is a potential gene contributing to RIF pathogenesis through a non‐classical ferroptosis‐dependent pathway, providing a foundation for personalized therapeutic strategies in RIF management. [ABSTRACT FROM AUTHOR]

Friction has been considered to mediate physiological activities of cells, however, the biological friction between a single cell and its ligand-bound surface has not been thoroughly explored. Herein, we established a friction model for single cells based on an atomic force microscopy (AFM) combined with an inverted fluorescence microscopy (IFM) to study the friction between a highly sensitive platelet and fibrinogen-coated surface. The study revealed that the friction between the platelet and fibrinogen-coated tip is mainly influenced by specific ligand–receptor interaction. Further, we modeled the biological friction, which consists of specific interaction, non-specific interaction, and mechanical effect. Besides, the results suggested that the velocity can also affect specific ligand–receptor interactions, resulting in the friction change and platelet adhesion to fibrinogen surfaces. The study built a friction model between a single cell and its ligand-bound surface and provided a potential method to study the biological friction by the combination of AFM and IFM. [ABSTRACT FROM AUTHOR]

Mechanotransduction is the essential process that cells convert mechanical force into biochemical responses, and electrochemical sensor stands out from existing techniques by providing quantitative and real-time information about the biochemical signals during cellular mechanotransduction. However, the intracellular biochemical response evoked by mechanical force has been poorly monitored. In this paper, we report a method to apply local stretch on single cell and simultaneously monitor the ensuing intracellular biochemical signals. Specifically, a ferromagnetic micropipette was fabricated to locally stretch a single cell labeled with Fe3O4 nanoparticles under the external magnetic field, and the SiC@Pt nanowire electrode (SiC@Pt NWE) was inserted into the cell to monitor the intracellular hydrogen peroxide (H2O2) production induced by the local stretch. As a proof of concept, this work quantitatively investigated the elevated amount of H2O2 levels in single endothelial cell under different stretching amplitudes. This work puts forward a new research modality to manipulate and monitor the mechanotransduction at the single-cell level. [ABSTRACT FROM AUTHOR]

Metabolic reprogramming drives the development of an immunosuppressive tumor microenvironment (TME) through various pathways, contributing to cancer progression and reducing the effectiveness of anticancer immunotherapy. However, our understanding of the metabolic landscape within the tumor-immune context has been limited by conventional metabolic measurements, which have not provided comprehensive insights into the spatiotemporal heterogeneity of metabolism within TME. The emergence of single-cell, spatial, and in vivo metabolomic technologies has now enabled detailed and unbiased analysis, revealing unprecedented spatiotemporal heterogeneity that is particularly valuable in the field of cancer immunology. This review summarizes the methodologies of metabolomics and metabolic regulomics that can be applied to the study of cancer-immunity across single-cell, spatial, and in vivo dimensions, and systematically assesses their benefits and limitations. [ABSTRACT FROM AUTHOR]

The mitogen-activated protein kinase (MAPK) pathway is an important intracellular signaling cascade that plays a key role in various cellular processes. Understanding the regulatory mechanisms of this pathway is essential for developing effective interventions and targeted therapies for related diseases. Recent advances in single-cell proteomic technologies have provided unprecedented opportunities to investigate the heterogeneity and noise within complex, multi-signaling networks across diverse cells and cell types. Mathematical modeling has become a powerful interdisciplinary tool that bridges mathematics and experimental biology, providing valuable insights into these intricate cellular processes. In addition, statistical methods have been developed to infer pathway topologies and estimate unknown parameters within dynamic models. This review presents a comprehensive analysis of how mathematical modeling of the MAPK pathway deepens our understanding of its regulatory mechanisms, enhances the prediction of system behavior, and informs experimental research, with a particular focus on recent advances in modeling and inference using single-cell proteomic data. [ABSTRACT FROM AUTHOR]

Thus far, multiple techniques for single cell analysis have been developed, yet we lack a relatively simple tool to assess DNA and RNA from the same cell at whole-transcriptome and whole-genome depths. Here we present an updated method for physical separation of cytoplasmic RNA from the nuclei, which allows for simultaneous studies of DNA and RNA from the same single cell. The method consists of three steps—(1) immobilization of a single cell on solid substrate, (2) hypotonic lysis of immobilized single cell, and (3) separation of cytosol containing aqueous phase and immobilized nucleus. We found that DNA and RNA extracted from single cell using our approach is suitable for downstream sequencing-based applications. We demonstrated that the coverage of transcriptome and genome sequencing data obtained after DNA/RNA separation is similar to that observed without separation. We also showed that the separation procedure does not create any noticeable bias in observed mutational load or mutation spectra. Thus, our method can serve as a tool for simultaneous complex analysis of the genome and transcriptome, providing necessary information on the relationship between somatic mutations and the regulation of gene expression. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) exhibits considerable heterogeneity on tumour location. However, there is still a lack of comprehensive annotation regarding the characteristics and differences between the left‐sided (L‐CRC) and right‐sided (R‐CRC) CRC. Here, we performed single‐cell RNA sequencing (scRNA‐seq) on immune and stromal cells from 12 L‐CRC and 10 R‐CRC patients. We found that L‐CRC exhibited stronger tumour invasion and poor prognosis compared with R‐CRC. In addition, functional enrichment analysis of a normal cohort showed that fibroblasts of left colon are associated with tumour‐related pathways. This suggested that the heterogeneity observed in both L‐CRC and R‐CRC may be influenced by the specific location within the colon itself. Further, we identified a potentially novel MYH11+ cancer‐associated fibroblast (CAF) subset predominantly enriched in L‐CRC. Moreover, we found that MYH11+ CAFs may promote tumour migration via interacting with macrophages, and was associated with poor prognosis in CRC. In summary, our study revealed the crucial role of MYH11+ CAFs in predicting a poor prognosis, thereby contributing valuable insights to the exploration of heterogeneity in L‐CRC and R‐CRC. [ABSTRACT FROM AUTHOR]

Traumatic spinal cord injury (tSCI) is a severe injury to the central nervous system that is categorized into primary and secondary injuries. Among them, the local microenvironmental imbalance in the spinal cord caused by secondary spinal cord injury includes accumulation of cytokines and chemokines, reduced angiogenesis, dysregulation of cellular energy metabolism, and dysfunction of immune cells at the site of injury, which severely impedes neurological recovery from spinal cord injury (SCI). In recent years, single-cell techniques have revealed the heterogeneity of multiple immune cells at the genomic, transcriptomic, proteomic, and metabolomic levels after tSCI, further deepening our understanding of the mechanisms underlying tSCI. However, spatial information about the tSCI microenvironment, such as cell location and cell cell interactions, is lost in these approaches. The application of spatial multiomics technology can solve this problem by combining the data obtained from immunohistochemistry and multiparametric analysis to reveal the changes in the microenvironment at different times of secondary injury after SCI. In this review, we systematically review the progress of spatial multi-omics techniques in the study of the microenvironment after SCI, including changes in the immune microenvironment and discuss potential future therapeutic strategies. [ABSTRACT FROM AUTHOR]

Non-small cell lung cancer (NSCLC) constitutes a significant portion of lung cancers and cytotoxic drugs (e.g. cisplatin) are currently the first-line treatment. However, NSCLC has developed resistance to this drug, which limits the therapeutic effect and thus affects prognosis. NSCLC sc-RNA-seq data were downloaded from the GEO database and Ku Leuven Laboratory for Functional Epigenetics, and bulk RNA-seq data were obtained from the TCGA database. The "Seurat" package was employed for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were applied for downscaling and cluster identification. Use the FindAllMarkers function to find differential genes (DEGs) for tumor stem cells. Then, we performed univariate regression analyses on the DEGs to identify potential prognostic genes. We created a machine learning framework based on potential prognostic genes, which combines 10 machine learning methods and their 101 combinations to get the optimal prognostic risk model. The model was evaluated in the training set and validation set. A nomogram was developed to provide physicians with a quantitative tool for prognosis prediction. Finally, we evaluated the expression and functionality of SLC2A1. We discovered 22 cell clusters containing 218379 cells by examining single-cell RNA sequencing datasets (GSE148071, KU_lom, GSE131907, GSE136246, GSE127465). Tumour cells were isolated for subpopulation analysis and 162 differential genes from SOX2_cancer were obtained. After univariate Cox analysis, we found 23 genes with prognostic potential prognostic value and utilized them to develop 101‑combination machine learning computational framework. We eventually picked the best performing 'StepCox[both] + RSF', which includes 8 genes. The model has a relatively high prediction accuracy in both TCGA and GEO datasets. In in vitro investigations, targeted suppression of the SLC2A1 gene resulted in significant reductions in proliferation, invasion and migration in A549 cells. In addition, a significant reduction in cisplatin resistance was seen in A549/DDP cells. The outcomes demonstrated the precision and credibility of the prognostic model for NSCLC, highlighting its potential significance in the treatment and prognosis of individuals affected by this disease. SLC2A1 may become a promising prognostic marker and a potential therapeutic target, offering valuable insights to inform clinical treatment decisions. [ABSTRACT FROM AUTHOR]

Caenorhabditis elegans hermaphrodite presents a unique model to study the formation of oocytes. However, the size of the model animal and difficulties in retrieval of specific stages of the germline have obviated closer systematic studies of this process throughout the years. Here, we present a transcriptomic level analysis into the oogenesis of C. elegans hermaphrodites. We dissected a hermaphrodite gonad into seven sections corresponding to the mitotic distal region, the pachytene region, the diplotene region, the early diakinesis region and the 3 most proximal oocytes, and deeply sequenced the transcriptome of each of them along with that of the fertilized egg using a single-cell RNA-seq (scRNA-seq) protocol. We identified specific gene expression events as well as gene splicing events in finer detail along the gonad and provided novel insights into underlying mechanisms of the oogenesis process. Furthermore, through careful review of relevant research literature coupled with patterns observed in our analysis, we delineate transcripts that may serve functions in the interactions between the germline and cells of the somatic gonad. These results expand our knowledge of the transcriptomic space of the C. elegans germline and lay a foundation on which future studies of the germline can be based upon. [ABSTRACT FROM AUTHOR]

Diversity is the cornerstone of the adaptive immune system, crucial for its effectiveness against constantly evolving pathogens that pose threats to higher vertebrates. Accurately measuring and interpreting this diversity presents challenges for immunologists, as changes in diversity and clonotype composition can tip the balance between protective immunity and autoimmunity. In this review, we present the current methods commonly used to measure diversity from single‐cell T‐cell receptor and B‐cell receptor sequencing. We also discuss two case studies where single‐cell sequencing and diversity estimations have led to breakthroughs in autoimmune disease discovery and therapeutic innovation, and reflect upon the necessity and importance of accurately defining and measuring lymphocyte diversity in these contexts. [ABSTRACT FROM AUTHOR]

Motivation Single cell RNA sequencing (scRNA-seq) technique enables the transcriptome profiling of hundreds to ten thousands of cells at the unprecedented individual level and provides new insights to study cell heterogeneity. However, its advantages are hampered by dropout events. To address this problem, we propose a Blockwise Accelerated Non-negative Matrix Factorization framework with Structural network constraints (BANMF-S) to impute those technical zeros. Results BANMF-S constructs a gene-gene similarity network to integrate prior information from the external PPI network by the Triadic Closure Principle and a cell-cell similarity network to capture the neighborhood structure and temporal information through a Minimum-Spanning Tree. By collaboratively employing these two networks as regularizations, BANMF-S encourages the coherence of similar gene and cell pairs in the latent space, enhancing the potential to recover the underlying features. Besides, BANMF-S adopts a blocklization strategy to solve the traditional NMF problem through distributed Stochastic Gradient Descent method in a parallel way to accelerate the optimization. Numerical experiments on simulations and real datasets verify that BANMF-S can improve the accuracy of downstream clustering and pseudo-trajectory inference, and its performance is superior to seven state-of-the-art algorithms. Availability All data used in this work are downloaded from publicly available data sources, and their corresponding accession numbers or source URLs are provided in Supplementary File Section 5.1 Dataset Information. The source codes are publicly available in Github repository https://github.com/jiayingzhao/BANMF-S. [ABSTRACT FROM AUTHOR]

Harnessing the power of single-cell genomics technologies, single-cell Hi-C (scHi-C) and its derived technologies provide powerful tools to measure spatial proximity between regulatory elements and their target genes in individual cells. Using a global background model, we propose SnapHiC-G, a computational method, to identify long-range enhancer–promoter interactions from scHi-C data. We applied SnapHiC-G to scHi-C datasets generated from mouse embryonic stem cells and human brain cortical cells. SnapHiC-G achieved high sensitivity in identifying long-range enhancer–promoter interactions. Moreover, SnapHiC-G can identify putative target genes for noncoding genome-wide association study (GWAS) variants, and the genetic heritability of neuropsychiatric diseases is enriched for single-nucleotide polymorphisms (SNPs) within SnapHiC-G-identified interactions in a cell-type-specific manner. In sum, SnapHiC-G is a powerful tool for characterizing cell-type-specific enhancer–promoter interactions from complex tissues and can facilitate the discovery of chromatin interactions important for gene regulation in biologically relevant cell types. [ABSTRACT FROM AUTHOR]

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology. [ABSTRACT FROM AUTHOR]

Systematic investigation of tumor-infiltrating immune (TII) cells is important to the development of immunotherapies, and the clinical response prediction in cancers. There exists complex transcriptional regulation within TII cells, and different immune cell types display specific regulation patterns. To dissect transcriptional regulation in TII cells, we first integrated the gene expression profiles from single-cell datasets, and proposed a computational pipeline to identify TII cell type-specific transcription factor (TF) mediated activity immune modules (TF-AIMs). Our analysis revealed key TFs, such as BACH2 and NFKB1 play important roles in B and NK cells, respectively. We also found some of these TF-AIMs may contribute to tumor pathogenesis. Based on TII cell type-specific TF-AIMs, we identified eight CD8+ T cell subtypes. In particular, we found the PD1 + CD8+ T cell subset and its specific TF-AIMs associated with immunotherapy response. Furthermore, the TII cell type-specific TF-AIMs displayed the potential to be used as predictive markers for immunotherapy response of cancer patients. At the pan-cancer level, we also identified and characterized six molecular subtypes across 9680 samples based on the activation status of TII cell type-specific TF-AIMs. Finally, we constructed a user-friendly web interface CellTF-AIMs (http://bio-bigdata.hrbmu.edu.cn/CellTF-AIMs/) for exploring transcriptional regulatory pattern in various TII cell types. Our study provides valuable implications and a rich resource for understanding the mechanisms involved in cancer microenvironment and immunotherapy. [ABSTRACT FROM AUTHOR]

Caenorhabditis elegans is a valuable model to study organ, tissue, and cell-type responses to external cues. However, the nematode comprises multiple syncytial tissues with spatial coordinates corresponding to distinct nuclear transcriptomes. Here, we present a single-nucleus RNA sequencing (snRNA-seq) protocol that aims to overcome difficulties encountered with single-cell RNA sequencing in C. elegans. We describe steps for isolating C. elegans nuclei for downstream applications including snRNA-seq applied to the context of alcohol exposure. For complete details on the use and execution of this protocol, please refer to Truong et al. (2023).1.

Dynamic responses of polymer electrolyte membrane fuel cells (PEMFC) were analyzed under varying loads to understand the performance of PEMFCs for ship applications. Experiments employing single cells were conducted under a static load with a constant current of 6.64 A and dynamic loads at a ramp rate of 2.5%/s and 5.0%/s, representing a simplified ship operation condition. Under the static load, the voltage drop was 0.054 V at 1.00 A/cm2 and was dominant below 0.40 A/cm2. In contrast, under the dynamic load (5.0%/s ramp rate), the voltage drop was 0.061 V at 1.00 A/cm2 and was dominant above 0.40 A/cm2, with similar behavior observed at the ramp rate of 2.5%/s. To compare performance across load conditions, degradation rates were calculated through linear regression of the voltage drop for each current density. The degradation rate at 1.00 A/cm2 was 0.143 mV/h under static load conditions and 0.174 mV/h under dynamic load conditions (ramp rate: 5.0%/s), indicating approximately 21% greater degradation rates than those observed under static load conditions. These experimental results provide foundational data for PEMFC performance analysis, particularly regarding performance degradation caused by dynamic loads in marine environments.

Abstract Cloning is a key molecular biology procedure for obtaining a genetically homogenous population of organisms or cell lines. It requires the expansion of new cell populations starting from single genetically modified cells. Despite the technical progress, cloning of many cell lines remains difficult. Cloning often fails either due to the strenuous conditions associated with manipulating cells or because many cells don’t tolerate a single-cell state. Here we describe a new cloning method utilizing low adhesion microcavity plates. This new technique, named microcavity-assisted cloning (MAC) was developed to clone difficult-to-clone HepG2 cells. The clones were produced following CRISPR/Cas9 knockout of the GSTA1 gene by a random distribution of 200, 400, and 800 cells into 550 microcavities of a 24-well low adhesion plate originally designed for the culture of spheroids. The knockout of GSTA1 was verified at the protein level using Western blotting. The advantages of the MAC method are its low cost, ease of the procedure, and the possibility of scaling up the throughput and automatization.

Abstract Background Latency remains a major obstacle to finding a cure for HIV despite the availability of antiretroviral therapy. Due to virus dormancy, limited biomarkers are available to identify latent HIV-infected cells. Profiling of individual HIV-infected cells is needed to explore potential latency biomarkers and to study the mechanisms of persistence that maintain the HIV reservoir. Methods Single cell spatial transcriptomic characterization using the CosMx Spatial Molecular Imager platform was conducted to analyze HIV-infected cells in formalin-fixed paraffin-embedded sections of splenic tissue surgically obtained from an HIV-infected humanized mouse model. Regulation of over a thousand human genes was quantified in both viremic and aviremic specimens. In addition, in situ hybridization and immunohistochemistry were performed in parallel to identify HIV viral RNA- and p24-containing cells, respectively. Finally, initial findings from CosMx gene profiling were confirmed by isolating RNA from CD4 + T cells obtained from a person living with HIV on antiretroviral therapy following either PMA/Ionomycin or DMSO treatment. RNA was quantified using qPCR for a panel of targeted human host genes. Results Supervised cell typing revealed that most of the HIV-infected cells in the mouse spleen sections were differentiated CD4 + T cells. A significantly higher number of infected cells, 2781 (1.61%) in comparison to 112 (0.06%), and total HIV transcripts per infected cell were observed in viremic samples compared to aviremic samples, respectively, which was consistent with the data obtained from ISH and IHC. Notably, the expression of 55 genes was different in infected cells within tissue from aviremic animals compared to viremic. In particular, both spleen tyrosine kinase (SYK) and CXCL17, were expressed approximately 100-fold higher. This data was further evaluated against bulk RNA isolated from HIV-infected human primary CD4 + T cells. A nearly 6-fold higher expression of SYK mRNA was observed in DMSO-treated CD4 + T cells compared to those stimulated with PMA/Ionomycin. Conclusion This study found that the CosMx SMI platform is valuable for assessing HIV infection and providing insights into host biomarkers associated with HIV reservoirs. Higher relative expression of the SYK gene in aviremic-infected cells from the humanized mouse HIV model was consistent with levels found in CD4 + T cells of aviremic donors.

Abstract Thus far, multiple techniques for single cell analysis have been developed, yet we lack a relatively simple tool to assess DNA and RNA from the same cell at whole-transcriptome and whole-genome depths. Here we present an updated method for physical separation of cytoplasmic RNA from the nuclei, which allows for simultaneous studies of DNA and RNA from the same single cell. The method consists of three steps—(1) immobilization of a single cell on solid substrate, (2) hypotonic lysis of immobilized single cell, and (3) separation of cytosol containing aqueous phase and immobilized nucleus. We found that DNA and RNA extracted from single cell using our approach is suitable for downstream sequencing-based applications. We demonstrated that the coverage of transcriptome and genome sequencing data obtained after DNA/RNA separation is similar to that observed without separation. We also showed that the separation procedure does not create any noticeable bias in observed mutational load or mutation spectra. Thus, our method can serve as a tool for simultaneous complex analysis of the genome and transcriptome, providing necessary information on the relationship between somatic mutations and the regulation of gene expression.

Abstract Metabolic reprogramming drives the development of an immunosuppressive tumor microenvironment (TME) through various pathways, contributing to cancer progression and reducing the effectiveness of anticancer immunotherapy. However, our understanding of the metabolic landscape within the tumor-immune context has been limited by conventional metabolic measurements, which have not provided comprehensive insights into the spatiotemporal heterogeneity of metabolism within TME. The emergence of single-cell, spatial, and in vivo metabolomic technologies has now enabled detailed and unbiased analysis, revealing unprecedented spatiotemporal heterogeneity that is particularly valuable in the field of cancer immunology. This review summarizes the methodologies of metabolomics and metabolic regulomics that can be applied to the study of cancer-immunity across single-cell, spatial, and in vivo dimensions, and systematically assesses their benefits and limitations.

Abstract Non-small cell lung cancer (NSCLC) constitutes a significant portion of lung cancers and cytotoxic drugs (e.g. cisplatin) are currently the first-line treatment. However, NSCLC has developed resistance to this drug, which limits the therapeutic effect and thus affects prognosis. NSCLC sc-RNA-seq data were downloaded from the GEO database and Ku Leuven Laboratory for Functional Epigenetics, and bulk RNA-seq data were obtained from the TCGA database. The “Seurat” package was employed for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were applied for downscaling and cluster identification. Use the FindAllMarkers function to find differential genes (DEGs) for tumor stem cells. Then, we performed univariate regression analyses on the DEGs to identify potential prognostic genes. We created a machine learning framework based on potential prognostic genes, which combines 10 machine learning methods and their 101 combinations to get the optimal prognostic risk model. The model was evaluated in the training set and validation set. A nomogram was developed to provide physicians with a quantitative tool for prognosis prediction. Finally, we evaluated the expression and functionality of SLC2A1. We discovered 22 cell clusters containing 218379 cells by examining single-cell RNA sequencing datasets (GSE148071, KU_lom, GSE131907, GSE136246, GSE127465). Tumour cells were isolated for subpopulation analysis and 162 differential genes from SOX2_cancer were obtained. After univariate Cox analysis, we found 23 genes with prognostic potential prognostic value and utilized them to develop 101‑combination machine learning computational framework. We eventually picked the best performing ‘StepCox[both] + RSF’, which includes 8 genes. The model has a relatively high prediction accuracy in both TCGA and GEO datasets. In in vitro investigations, targeted suppression of the SLC2A1 gene resulted in significant reductions in proliferation, invasion and migration in A549 cells. In addition, a significant reduction in cisplatin resistance was seen in A549/DDP cells. The outcomes demonstrated the precision and credibility of the prognostic model for NSCLC, highlighting its potential significance in the treatment and prognosis of individuals affected by this disease. SLC2A1 may become a promising prognostic marker and a potential therapeutic target, offering valuable insights to inform clinical treatment decisions.

Nir Drayman works at the intersection of virology and single-cell biology, studying how cellular heterogeneity shapes the outcome of viral infections (and specifically that of HSV-1). In this mSphere of Influence article, he reflects on how two papers, Remote activation of host cell DNA synthesis in uninfected cells signaled by infected cells in advance of virus transmission (N. Schmidt, T. Hennig, R. A. Serwa, M. Marchetti, and P. OHare, J Virol 89:11107-11115, 2015, https://doi.org/10.1128/jvi.01950-15) and Extreme heterogeneity of influenza virus infection in single cells (A. B. Russell, C. Trapnell, and J. D. Bloom, Elife 7:e32303, 2018, https://doi.org/10.7554/eLife.32303), impacted his research by trail blazing the analysis of viral infections in single cells, as well as by illuminating what is yet left to discover.

Plasmodium falciparum, the human malaria parasite, infects two hosts and various cell types, inducing distinct morphological and physiological changes in the parasite in response to different environmental conditions. These variations required the parasite to adapt and develop elaborate molecular mechanisms to ensure its spread and transmission. Recent findings have significantly improved our understanding of the regulation of gene expression in P. falciparum. Here, we provide an up-to-date overview of technologies used to highlight the transcriptomic adjustments occurring in the parasite throughout its life cycle. We also emphasize the complementary and complex epigenetic mechanisms regulating gene expression in malaria parasites. This review concludes with an outlook on the chromatin architecture, the remodeling systems, and how this 3D genome organization is critical in various biological processes.

Chimeric mouse models have recently been developed to study human microglia in vivo. However, widespread engraftment of donor microglia within the adult brain has been challenging. Here, we present a protocol to introduce the G795A point mutation using CRISPR-Cas9 into the CSF1R locus of human pluripotent stem cells. We also describe an optimized microglial differentiation technique for transplantation into newborn or adult recipients. We then detail pharmacological paradigms to achieve widespread and near-complete engraftment of human microglia. For complete details on the use and execution of this protocol, please refer to Chadarevian et al. (2023).1.

The advent of single cell transposase-accessible chromatin sequencing (scATAC-seq) technology enables us to explore the genomic characteristics and chromatin accessibility of blood cells at the single-cell level. To fully make sense of the roles and regulatory complexities of blood cells, it is critical to collect and analyze these rapidly accumulating scATAC-seq datasets at a system level. Here, we present scBlood (https://bio.liclab.net/scBlood/), a comprehensive single-cell accessible chromatin database of blood cells. The current version of scBlood catalogs 770,907 blood cells and 452,247 non-blood cells from ∼400 high-quality scATAC-seq samples covering 30 tissues and 21 disease types. All data hosted on scBlood have undergone preprocessing from raw fastq files and multiple standards of quality control. Furthermore, we conducted comprehensive downstream analyses, including multi-sample integration analysis, cell clustering and annotation, differential chromatin accessibility analysis, functional enrichment analysis, co-accessibility analysis, gene activity score calculation, and transcription factor (TF) enrichment analysis. In summary, scBlood provides a user-friendly interface for searching, browsing, analyzing, visualizing, and downloading scATAC-seq data of interest. This platform facilitates insights into the functions and regulatory mechanisms of blood cells, as well as their involvement in blood-related diseases.

Summary: Single‐cell proteomics (SCP) is an emerging approach to resolve cellular heterogeneity within complex tissues of multi‐cellular organisms.Here, we demonstrate the feasibility of SCP on plant samples using the model plant Arabidopsis thaliana. Specifically, we focused on examining isolated single cells from the cortex and endodermis, which are two adjacent root cell types derived from a common stem cell lineage.From 756 root cells, we identified 3763 proteins and 1118 proteins/cell. Ultimately, we focus on 3217 proteins quantified following stringent filtering. Of these, we identified 596 proteins whose expression is enriched in either the cortex or endodermis and are able to differentiate these closely related plant cell types.Collectivity, this study demonstrates that SCP can resolve neighboring cell types with distinct functions, thereby facilitating the identification of biomarkers and candidate proteins to enable functional genomics. See also the Commentary on this article by van Schie & Weijers, 244: 1678–1680. [ABSTRACT FROM AUTHOR]

It has long been hypothesized that benthic motile pennate diatoms use phototaxis to optimize photosynthesis and minimize photoinhibitory damage by adjusting their position within vertical light gradients in coastal benthic sediments. However, experimental evidence to test this hypothesis remains inconclusive, mainly due to methodological difficulties in studying cell behavior and photosynthesis over realistic spatial microscale gradients of irradiance and cell position. In this study, a novel experimental approach was developed and used to test the hypothesis of photosynthesis optimization through motility, based on the combination of single-cell in vivo chlorophyll fluorometry and microfluidic chips. The approach allows the concurrent study of behavior and photosynthetic activity of individual cells of the epipelic diatom species Craspedostauros britannicus exposed to a light microgradient of realistic dimensions, simulating the irradiance and distance scales of light microgradients in benthic sediments. Following exposure to light, (i) cells explored their light environment before initiating light-directed motility; (ii) cells used motility to lower their light dose, when exposed to the highest light intensities; and (iii) motility was combined with reversible non-photochemical quenching, to allow cells to avoid photoinhibition. The results of this proof-of-concept study not only strongly support the photoprotective nature of photobehavior in the studied species but also revealed considerable variability in how individual cells reacted to a light microgradient. The experimental setup can be readily applied to study motility and photosynthetic light responses of other diatom species or natural assemblages, as well as other photoautotrophic motile microorganisms, broadening the toolset for experimental microbial ecology research. [ABSTRACT FROM AUTHOR]

Abstract Osteosarcomas are intricate cellular ecosystems, where heterotypic interactions significantly influence disease progression and therapeutic outcomes. Despite their importance, a detailed understanding of their cellular composition and organizational structure remains elusive. In this study, we provide a comprehensive single-cell and spatially resolved transcriptomics analysis of human osteosarcomas. We construct a cellular meta-map to dissect spatial transcriptomic data, unveiling a detailed atlas of osteosarcoma compositional subgroups. We meticulously characterize the unique gene signatures and functional states of each subgroup and investigate the impact of chemotherapy on these cellular subpopulations. Additionally, our spatial transcriptomics analysis identifies a distinct spatial niche, located at the forefront of tumor necrotic zones, potentially associated with chemotherapy resistance. We also delve into the crosstalk between different cellular subgroups. This study furnishes a comprehensive transcriptional atlas of osteosarcoma's cellular architecture, enriching our comprehension of its complexity and laying the groundwork for more targeted therapeutic approaches.

Abstract The occurrence and progression of skin cutaneous melanoma (SKCM) is strongly associated with immune cells infiltrating the tumor microenvironment (TME). This study examined the expression, prognosis, and immune relevance of SIGLEC9 in SKCM using multiple online databases. Analysis of the GEPIA2 and Ualcan databases revealed that SIGLEC9 is highly expressed in SKCM, and patients with high SIGLEC9 expression had improved overall survival (OS). Furthermore, the mutation rate of SIGLEC9 in SKCM patients was found to be 5.41%, the highest observed. The expression of SIGLEC9 was positively correlated with macrophages, neutrophils and B cells, CD8 + T cells, CD4 + T cells, and dendritic cells, according to TIMER. Based on TCGA-SKCM data, we verified that high SIGLEC9 expression is closely associated with a good prognosis for SKCM patients, including overall survival, progression-free interval, and disease-specific survival. This positive prognosis could be due to the infiltration of immune cells into the TME. Additionally, our analysis of single-cell transcriptome data revealed that SIGLEC9 not only played a role in the normal skin immune microenvironment, but is also highly expressed in immune cell subpopulations of SKCM patients, regulating the immune response to tumors. Our findings suggest that the close association between SIGLEC9 and SKCM prognosis is primarily mediated by its effect on the tumor immune microenvironment.

Abstract The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it overcomes limitations seen in previous VAE-based integration methods with regard to batch effect correction and restricted applicable assays. It handles multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover all possible assays and preprocessing pipelines. We demonstrate that scMaui achieves superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.

Abstract Single-cell multi-omics data reveal complex cellular states, providing significant insights into cellular dynamics and disease. Yet, integration of multi-omics data presents challenges. Some modalities have not reached the robustness or clarity of established transcriptomics. Coupled with data scarcity for less established modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross, a tool leveraging variational autoencoders, generative adversarial networks, and the mutual nearest neighbors (MNN) technique for modality alignment. By enabling single-cell cross-modal data generation, multi-omics data simulation, and in silico cellular perturbations, scCross enhances the utility of single-cell multi-omics studies.

Chronic obstructive pulmonary disease (COPD) affects close to 400 million people worldwide and is the 3rd leading cause of mortality. It is a heterogeneous disorder with multiple endophenotypes, each driven by specific molecular networks and processes. Therapeutic discovery in COPD has lagged behind other disease areas owing to a lack of understanding of its pathobiology and scarcity of biomarkers to guide therapies. Single cell RNA sequencing (scRNA-seq) is a powerful new tool to identify important cellular and molecular networks that play a crucial role in disease pathogenesis. This paper provides an overview of the scRNA-seq technology and its application in COPD and the lessons learned to date from scRNA-seq experiments in COPD.

The mesolimbic pathway connects ventral tegmental area dopaminergic neurons and striatal medium spiny neurons, playing a critical role in reward and stress behaviors. Exposure to substances of abuse during development and adulthood has been linked to adverse outcomes and molecular changes. The rise of human cell repositories and whole-genome sequences enables human functional genomics ‘in a dish’, offering insights into human-specific responses to substances of abuse. Continued development of new models is needed, and the characterization of in vitro models is also necessary to ensure appropriate experimental designs and the accurate interpretation of results. This study introduces new culture conditions for generating medium spiny neurons and dopaminergic neurons with an early common media, allowing for coculture and assembloid generation. It then provides a comprehensive characterization of these and prior models and their responses to substances of abuse. Single-cell analysis reveals cell-type-specific transcriptomic responses to dopamine, cocaine, and morphine, including compound and cell-type-specific transcriptomic signatures related to neuroinflammation and alterations in signaling pathways. These findings offer a resource for future genomics studies leveraging human stem cell-derived models.

Osteosarcomas are intricate cellular ecosystems, where heterotypic interactions significantly influence disease progression and therapeutic outcomes. Despite their importance, a detailed understanding of their cellular composition and organizational structure remains elusive. In this study, we provide a comprehensive single-cell and spatially resolved transcriptomics analysis of human osteosarcomas. We construct a cellular meta-map to dissect spatial transcriptomic data, unveiling a detailed atlas of osteosarcoma compositional subgroups. We meticulously characterize the unique gene signatures and functional states of each subgroup and investigate the impact of chemotherapy on these cellular subpopulations. Additionally, our spatial transcriptomics analysis identifies a distinct spatial niche, located at the forefront of tumor necrotic zones, potentially associated with chemotherapy resistance. We also delve into the crosstalk between different cellular subgroups. This study furnishes a comprehensive transcriptional atlas of osteosarcoma's cellular architecture, enriching our comprehension of its complexity and laying the groundwork for more targeted therapeutic approaches. [ABSTRACT FROM AUTHOR]

The occurrence and progression of skin cutaneous melanoma (SKCM) is strongly associated with immune cells infiltrating the tumor microenvironment (TME). This study examined the expression, prognosis, and immune relevance of SIGLEC9 in SKCM using multiple online databases. Analysis of the GEPIA2 and Ualcan databases revealed that SIGLEC9 is highly expressed in SKCM, and patients with high SIGLEC9 expression had improved overall survival (OS). Furthermore, the mutation rate of SIGLEC9 in SKCM patients was found to be 5.41%, the highest observed. The expression of SIGLEC9 was positively correlated with macrophages, neutrophils and B cells, CD8 + T cells, CD4 + T cells, and dendritic cells, according to TIMER. Based on TCGA-SKCM data, we verified that high SIGLEC9 expression is closely associated with a good prognosis for SKCM patients, including overall survival, progression-free interval, and disease-specific survival. This positive prognosis could be due to the infiltration of immune cells into the TME. Additionally, our analysis of single-cell transcriptome data revealed that SIGLEC9 not only played a role in the normal skin immune microenvironment, but is also highly expressed in immune cell subpopulations of SKCM patients, regulating the immune response to tumors. Our findings suggest that the close association between SIGLEC9 and SKCM prognosis is primarily mediated by its effect on the tumor immune microenvironment. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is a highly heterogeneous disease with poor clinical outcomes. To comprehensively dissect the molecular landscape of GBM and heterogeneous macrophage clusters in the progression of GBM, this study integrates single-cell and bulk transcriptome data to recognize a distinct pro-tumor macrophage cluster significantly associated with the prognosis of GBM and develop a GBM prognostic signature to facilitate prior subtypes. Leveraging glioma single-cell sequencing data, we identified a novel pro-tumor macrophage subgroup, marked by S100A9, which might interact with endothelial cells to facilitate tumor progression via angiogenesis. To further benefit clinical application, a prognostic signature was established with the genes associated with pro-tumor macrophages. Patients classified within the high-risk group characterized with enrichment in functions related to tumor progression, including epithelial-mesenchymal transition and hypoxia, displays elevated mutations in the TERT promoter region, reduced methylation in the MGMT promoter region, poorer prognoses, and diminished responses to temozolomide therapy, thus effectively discriminating between the prognostic outcomes of GBM patients. Our research sheds light on the intricate microenvironment of gliomas and identifies potential molecular targets for the development of novel therapeutic approaches. [ABSTRACT FROM AUTHOR]

The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it overcomes limitations seen in previous VAE-based integration methods with regard to batch effect correction and restricted applicable assays. It handles multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover all possible assays and preprocessing pipelines. We demonstrate that scMaui achieves superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations. [ABSTRACT FROM AUTHOR]