Unveiling the role of HIST2H2AC in stroke through single-cell and transcriptome analysis.

Stroke is a leading cause of death and disability, and genetic risk factors play a significant role in its development. Unfortunately, effective therapies for stroke are currently limited. Early detection and diagnosis are critical for improving outcomes and developing new treatment strategies. In this study, we aimed to identify potential biomarkers and effective prevention and treatment strategies for stroke by conducting transcriptome and single-cell analyses. Our analysis included screening for biomarkers, functional enrichment analysis, immune infiltration, cell-cell communication, and single-cell metabolism. Through differential expression analysis, enrichment analysis, and protein-protein interaction (PPI) network construction, we identified HIST2H2AC as a potential biomarker for stroke. Our study also highlighted the diagnostic role of HIST2H2AC in stroke, its relationship with immune cells in the stroke environment, and our improved understanding of metabolic pathways after stroke. Overall, our research provided important insights into the pathogenesis of stroke, including potential biomarkers and treatment strategies that can be explored further to improve outcomes for stroke patients.

Joint Bayesian estimation of cell dependence and gene associations in spatially resolved transcriptomic data.

Recent technologies such as spatial transcriptomics, enable the measurement of gene expressions at the single-cell level along with the spatial locations of these cells in the tissue. Spatial clustering of the cells provides valuable insights into the understanding of the functional organization of the tissue. However, most such clustering methods involve some dimension reduction that leads to a loss of the inherent dependency structure among genes at any spatial location in the tissue. This destroys valuable insights of gene co-expression patterns apart from possibly impacting spatial clustering performance. In spatial transcriptomics, the matrix-variate gene expression data, along with spatial coordinates of the single cells, provides information on both gene expression dependencies and cell spatial dependencies through its row and column covariances. In this work, we propose a joint Bayesian approach to simultaneously estimate these gene and spatial cell correlations. These estimates provide data summaries for downstream analyses. We illustrate our method with simulations and analysis of several real spatial transcriptomic datasets. Our work elucidates gene co-expression networks as well as clear spatial clustering patterns of the cells. Furthermore, our analysis reveals that downstream spatial-differential analysis may aid in the discovery of unknown cell types from known marker genes.

Orthogonal multimodality integration and clustering in single-cell data.

Multimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sources of information while accounting for the dependence among them. We demonstrate the effectiveness of our approach using CITE-seq data sets for cell clustering. Our results show that our approach outperforms existing methods in terms of accuracy, computational efficiency, and interpretability. We conclude that our proposed OMIC method provides a powerful tool for multimodal data analysis that greatly improves the feasibility and reliability of integrated data.

Single-cell insights: pioneering an integrated atlas of chromatin accessibility and transcriptomic landscapes in diabetic cardiomyopathy.

BACKGROUND: Diabetic cardiomyopathy (DCM) poses a growing health threat, elevating heart failure risk in diabetic individuals. Understanding DCM is crucial, with fibroblasts and endothelial cells playing pivotal roles in driving myocardial fibrosis and contributing to cardiac dysfunction. Advances in Multimodal single-cell profiling, such as scRNA-seq and scATAC-seq, provide deeper insights into DCM's unique cell states and molecular landscape for targeted therapeutic interventions. METHODS: Single-cell RNA and ATAC data from 10x Multiome libraries were processed using Cell Ranger ARC v2.0.1. Gene expression and ATAC data underwent Seurat and Signac filtration. Differential gene expression and accessible chromatin regions were identified. Transcription factor activity was estimated with chromVAR, and Cis-coaccessibility networks were calculated using Cicero. Coaccessibility connections were compared to the GeneHancer database. Gene Ontology analysis, biological process scoring, cell-cell communication analysis, and gene-motif correlation was performed to reveal intricate molecular changes. Immunofluorescent staining utilized various antibodies on paraffin-embedded tissues to verify the findings. RESULTS: This study integrated scRNA-seq and scATAC-seq data obtained from hearts of WT and DCM mice, elucidating molecular changes at the single-cell level throughout the diabetic cardiomyopathy progression. Robust and accurate clustering analysis of the integrated data revealed altered cell proportions, showcasing decreased endothelial cells and macrophages, coupled with increased fibroblasts and myocardial cells in the DCM group, indicating enhanced fibrosis and endothelial damage. Chromatin accessibility analysis unveiled unique patterns in cell types, with heightened transcriptional activity in myocardial cells. Subpopulation analysis highlighted distinct changes in cardiomyocytes and fibroblasts, emphasizing pathways related to fatty acid metabolism and cardiac contraction. Fibroblast-centered communication analysis identified interactions with endothelial cells, implicating VEGF receptors. Endothelial cell subpopulations exhibited altered gene expressions, emphasizing contraction and growth-related pathways. Candidate regulators, including Tcf21, Arnt, Stat5a, and Stat5b, were identified, suggesting their pivotal roles in DCM development. Immunofluorescence staining validated marker genes of cell subpopulations, confirming PDK4, PPARγ and Tpm1 as markers for metabolic pattern-altered cardiomyocytes, activated fibroblasts and endothelial cells with compromised proliferation. CONCLUSION: Our integrated scRNA-seq and scATAC-seq analysis unveils intricate cell states and molecular alterations in diabetic cardiomyopathy. Identified cell type-specific changes, transcription factors, and marker genes offer valuable insights. The study sheds light on potential therapeutic targets for DCM.

Demuxafy: improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods.

Recent innovations in single-cell RNA-sequencing (scRNA-seq) provide the technology to investigate biological questions at cellular resolution. Pooling cells from multiple individuals has become a common strategy, and droplets can subsequently be assigned to a specific individual by leveraging their inherent genetic differences. An implicit challenge with scRNA-seq is the occurrence of doublets-droplets containing two or more cells. We develop Demuxafy, a framework to enhance donor assignment and doublet removal through the consensus intersection of multiple demultiplexing and doublet detecting methods. Demuxafy significantly improves droplet assignment by separating singlets from doublets and classifying the correct individual.

Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq.

We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.

Library size confounds biology in spatial transcriptomics data.

Spatial molecular data has transformed the study of disease microenvironments, though, larger datasets pose an analytics challenge prompting the direct adoption of single-cell RNA-sequencing tools including normalization methods. Here, we demonstrate that library size is associated with tissue structure and that normalizing these effects out using commonly applied scRNA-seq normalization methods will negatively affect spatial domain identification. Spatial data should not be specifically corrected for library size prior to analysis, and algorithms designed for scRNA-seq data should be adopted with caution.

Systematic mapping of TF-mediated cell fate changes by a pooled induction coupled with scRNA-seq and multi-omics approaches.

Transcriptional regulation controls cellular functions through interactions between transcription factors (TFs) and their chromosomal targets. However, understanding the fate conversion potential of multiple TFs in an inducible manner remains limited. Here, we introduce iTF-seq as a method for identifying individual TFs that can alter cell fate toward specific lineages at a single-cell level. iTF-seq enables time course monitoring of transcriptome changes, and with biotinylated individual TFs, it provides a multi-omics approach to understanding the mechanisms behind TF-mediated cell fate changes. Our iTF-seq study in mouse embryonic stem cells identified multiple TFs that trigger rapid transcriptome changes indicative of differentiation within a day of induction. Moreover, cells expressing these potent TFs often show a slower cell cycle and increased cell death. Further analysis using bioChIP-seq revealed that GCM1 and OTX2 act as pioneer factors and activators by increasing gene accessibility and activating the expression of lineage specification genes during cell fate conversion. iTF-seq has utility in both mapping cell fate conversion and understanding cell fate conversion mechanisms.

EpiCarousel: memory- and time-efficient identification of metacells for atlas-level single-cell chromatin accessibility data.

SUMMARY: Recent technical advancements in single-cell chromatin accessibility sequencing (scCAS) have brought new insights to the characterization of epigenetic heterogeneity. As single-cell genomics experiments scale up to hundreds of thousands of cells, the demand for computational resources for downstream analysis grows intractably large and exceeds the capabilities of most researchers. Here, we propose EpiCarousel, a tailored Python package based on lazy loading, parallel processing, and community detection for memory- and time-efficient identification of metacells, i.e. the emergence of homogenous cells, in large-scale scCAS data. Through comprehensive experiments on five datasets of various protocols, sample sizes, dimensions, number of cell types, and degrees of cell-type imbalance, EpiCarousel outperformed baseline methods in systematic evaluation of memory usage, computational time, and multiple downstream analyses including cell type identification. Moreover, EpiCarousel executes preprocessing and downstream cell clustering on the atlas-level dataset with 707 043 cells and 1 154 611 peaks within 2 h consuming <75 GB of RAM and provides superior performance for characterizing cell heterogeneity than state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: The EpiCarousel software is well-documented and freely available at https://github.com/biox-nku/epicarousel. It can be seamlessly interoperated with extensive scCAS analysis toolkits.

Single-cell data revealed exhaustion of characteristic NK cell subpopulations and T cell subpopulations in hepatocellular carcinoma.

BACKGROUND: The treatment and prognosis of patients with advanced hepatocellular carcinoma (HCC) have been a major medical challenge. Unraveling the landscape of tumor immune infiltrating cells (TIICs) in the immune microenvironment of HCC is of great significance to probe the molecular mechanisms. METHODS: Based on single-cell data of HCC, the cell landscape was revealed from the perspective of TIICs. Special cell subpopulations were determined by the expression levels of marker genes. Differential expression analysis was conducted. The activity of each subpopulation was determined based on the highly expressed genes. CTLA4+ T-cell subpopulations affecting the prognosis of HCC were determined based on survival analysis. A single-cell regulatory network inference and clustering analysis was also performed to determine the transcription factor regulatory networks in the CTLA4+ T cell subpopulations. RESULTS: 10 cell types were identified and NK cells and T cells showed high abundance in tumor tissues. Two NK cells subpopulations were present, FGFBP2+ NK cells, B3GNT7+ NK cells. Four T cells subpopulations were present, LAG3+ T cells, CTLA4+ T cells, RCAN3+ T cells, and HPGDS+ Th2 cells. FGFBP2+ NK cells, and CTLA4+ T cells were the exhaustive subpopulation. High CTLA4+ T cells contributed to poor prognostic outcomes and promoted tumor progression. Finally, a network of transcription factors regulated by NR3C1, STAT1, and STAT3, which were activated, was present in CTLA4+ T cells. CONCLUSION: CTLA4+ T cell subsets in HCC exhibited functional exhaustion characteristics that probably inhibited T cell function through a transcription factor network dominated by NR3C1, STAT1, and STAT3.

Spatiotemporal Evolution of Developing Palate in Mice.

The intricate formation of the palate involves a series of complex events, yet its mechanistic basis remains uncertain. To explore major cell populations in the palate and their roles during development, we constructed a spatiotemporal transcription landscape of palatal cells. Palate samples from C57BL/6 J mice at embryonic days 12.5 (E12.5), 14.5 (E14.5), and 16.5 (E16.5) underwent single-cell RNA sequencing (scRNA-seq) to identify distinct cell subsets. In addition, spatial enhanced resolution omics-sequencing (stereo-seq) was used to characterize the spatial distribution of these subsets. Integrating scRNA-seq and stereo-seq with CellTrek annotated mesenchymal and epithelial cellular components of the palate during development. Furthermore, cellular communication networks between these cell subpopulations were analyzed to discover intercellular signaling during palate development. From the analysis of the middle palate, both mesenchymal and epithelial populations were spatially segregated into 3 domains. The middle palate mesenchymal subpopulations were associated with tooth formation, ossification, and tissue remodeling, with initial state cell populations located proximal to the dental lamina. The nasal epithelium of the palatal shelf exhibited richer humoral immune responses than the oral side. Specific enrichment of Tgfß3 and Pthlh signals in the midline epithelial seam at E14.5 suggested a role in epithelial-mesenchymal transition. In summary, this study provides high-resolution transcriptomic information, contributing to a deeper mechanistic understanding of palate biology and pathophysiology.

Identification and validation of a novel predictive signature based on hepatocyte-specific genes in hepatocellular carcinoma by integrated analysis of single-cell and bulk RNA sequencing.

BACKGROUND: Hepatocellular carcinoma represents a significant global burden in terms of cancer-related mortality, posing a substantial risk to human health. Despite the availability of various treatment modalities, the overall survival rates for patients with hepatocellular carcinoma remain suboptimal. The objective of this study was to explore the potential of novel biomarkers and to establish a novel predictive signature utilizing multiple transcriptome profiles. METHODS: The GSE115469 and CNP0000650 cohorts were utilized for single cell analysis and gene identification. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets were utilized in the development and evaluation of a predictive signature. The expressions of hepatocyte-specific genes were further validated using the GSE135631 cohort. Furthermore, immune infiltration results, immunotherapy response prediction, somatic mutation frequency, tumor mutation burden, and anticancer drug sensitivity were analyzed based on various risk scores. Subsequently, functional enrichment analysis was performed on the differential genes identified in the risk model. Moreover, we investigated the expression of particular genes in chronic liver diseases utilizing datasets GSE135251 and GSE142530. RESULTS: Our findings revealed hepatocyte-specific genes (ADH4, LCAT) with notable alterations during cell maturation and differentiation, leading to the development of a novel predictive signature. The analysis demonstrated the efficacy of the model in predicting outcomes, as evidenced by higher risk scores and poorer prognoses in the high-risk group. Additionally, a nomogram was devised to forecast the survival rates of patients at 1, 3, and 5 years. Our study demonstrated that the predictive model may play a role in modulating the immune microenvironment and impacting the anti-tumor immune response in hepatocellular carcinoma. The high-risk group exhibited a higher frequency of mutations and was more likely to benefit from immunotherapy as a treatment option. Additionally, we confirmed that the downregulation of hepatocyte-specific genes may indicate the progression of hepatocellular carcinoma and aid in the early diagnosis of the disease. CONCLUSION: Our research findings indicate that ADH4 and LCAT are genes that undergo significant changes during the differentiation of hepatocytes into cancer cells. Additionally, we have created a unique predictive signature based on genes specific to hepatocytes.

High clonal diversity and spatial genetic admixture in early prostate cancer and surrounding normal tissue.

Somatic copy number alterations (SCNAs) are pervasive in advanced human cancers, but their prevalence and spatial distribution in early-stage, localized tumors and their surrounding normal tissues are poorly characterized. Here, we perform multi-region, single-cell DNA sequencing to characterize the SCNA landscape across tumor-rich and normal tissue in two male patients with localized prostate cancer. We identify two distinct karyotypes: 'pseudo-diploid' cells harboring few SCNAs and highly aneuploid cells. Pseudo-diploid cells form numerous small-sized subclones ranging from highly spatially localized to broadly spread subclones. In contrast, aneuploid cells do not form subclones and are detected throughout the prostate, including normal tissue regions. Highly localized pseudo-diploid subclones are confined within tumor-rich regions and carry deletions in multiple tumor-suppressor genes. Our study reveals that SCNAs are widespread in normal and tumor regions across the prostate in localized prostate cancer patients and suggests that a subset of pseudo-diploid cells drive tumorigenesis in the aging prostate.

Single cell tracing of Pomc neurons reveals recruitment of 'Ghost' subtypes with atypical identity in a mouse model of obesity.

The hypothalamus contains a remarkable diversity of neurons that orchestrate behavioural and metabolic outputs in a highly plastic manner. Neuronal diversity is key to enabling hypothalamic functions and, according to the neuroscience dogma, it is predetermined during embryonic life. Here, by combining lineage tracing of hypothalamic pro-opiomelanocortin (Pomc) neurons with single-cell profiling approaches in adult male mice, we uncovered subpopulations of 'Ghost' neurons endowed with atypical molecular and functional identity. Compared to 'classical' Pomc neurons, Ghost neurons exhibit negligible Pomc expression and are 'invisible' to available neuroanatomical approaches and promoter-based reporter mice for studying Pomc biology. Ghost neuron numbers augment in diet-induced obese mice, independent of neurogenesis or cell death, but weight loss can reverse this shift. Our work challenges the notion of fixed, developmentally programmed neuronal identities in the mature hypothalamus and highlight the ability of specialised neurons to reversibly adapt their functional identity to adult-onset obesogenic stimuli.

Single-cell RNA sequencing reveals the cellular and molecular heterogeneity of treatment-naïve primary osteosarcoma in dogs.

Osteosarcoma (OS) is a heterogeneous, aggressive malignancy of the bone that disproportionally affects children and adolescents. Therapeutic interventions for OS are limited, which is in part due to the complex tumor microenvironment (TME). As such, we used single-cell RNA sequencing (scRNA-seq) to describe the cellular and molecular composition of the TME in 6 treatment-naïve dogs with spontaneously occurring primary OS. Through analysis of 35,310 cells, we identified 41 transcriptomically distinct cell types including the characterization of follicular helper T cells, mature regulatory dendritic cells (mregDCs), and 8 tumor-associated macrophage (TAM) populations. Cell-cell interaction analysis predicted that mregDCs and TAMs play key roles in modulating T cell mediated immunity. Furthermore, we completed cross-species cell type gene signature homology analysis and found a high degree of similarity between human and canine OS. The data presented here act as a roadmap of canine OS which can be applied to advance translational immuno-oncology research.

A nanowell platform to identify, sort and expand high antibody-producing cells.

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

Marmosets contain multitudes.

Single-cell RNA sequencing reveals the extent to which marmosets carry genetically distinct cells from their siblings.

Single-cell T-cell receptor repertoire profiling in dogs.

Spontaneous cancers in companion dogs are robust models of human disease. Tracking tumor-specific immune responses in these models requires reagents to perform species-specific single cell T cell receptor sequencing (scTCRseq). scTCRseq and integration with scRNA data have not been demonstrated on companion dogs with cancer. Here, five healthy dogs, two dogs with T cell lymphoma and four dogs with melanoma are selected to demonstrate applicability of scTCRseq in a cancer immunotherapy setting. Single-cell suspensions of PBMCs or lymph node aspirates are profiled using scRNA and dog-specific scTCRseq primers. In total, 77,809 V(D)J-expressing cells are detected, with an average of 3498 (348 - 5,971) unique clonotypes identified per sample. In total, 29/34, 40/40, 22/22 and 9/9 known functional TRAV, TRAJ, TRBV and TRBJ gene segments are observed respectively. Pseudogene or otherwise defective gene segments are also detected supporting re-annotation of several as functional. Healthy dogs exhibit highly diverse repertoires, T cell lymphomas exhibit clonal repertoires, and vaccine-treated melanoma dogs are dominated by a small number of highly abundant clonotypes. scRNA libraries define large clusters of V(D)J-expressing CD8+ and CD4 + T cells. Dominant clonotypes observed in melanoma PBMCs are predominantly CD8 + T cells, with activated phenotypes, suggesting possible anti-tumor T cell populations.

Transcriptome and single-cell analysis reveal disulfidptosis-related modification patterns of tumor microenvironment and prognosis in osteosarcoma.

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

scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data.

BACKGROUND: Accurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH. RESULTS: We introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal . CONCLUSIONS: Joint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.