SCEMENT: scalable and memory efficient integration of large-scale single-cell RNA-sequencing data.

Cell type prediction is one of the most challenging goals in single-cell RNA sequencing (scRNA-seq) data. Existing methods use unsupervised learning to identify signature genes in each cluster, followed by a literature survey to look up those genes for assigning cell types. However, finding potential marker genes in each cluster is cumbersome, which impedes the systematic analysis of single-cell RNA sequencing data. To address this challenge, we proposed a framework based on regularized multi-task learning (RMTL) that enables us to simultaneously learn the subpopulation associated with a particular cell type. Learning the structure of subpopulations is treated as a separate task in the multi-task learner. Regularization is used to modulate the multi-task model (e.g., W1, W2, … Wt) jointly, according to the specific prior. For validating our model, we trained it with reference data constructed from a single-cell RNA sequencing experiment and applied it to a query dataset. We also predicted completely independent data (the query dataset) from the reference data which are used for training. We have checked the efficacy of the proposed method by comparing it with other state-of-the-art techniques well known for cell type detection. Results revealed that the proposed method performed accurately in detecting the cell type in scRNA-seq data and thus can be utilized as a useful tool in the scRNA-seq pipeline.

Multiple myeloma (MM) is a common hematologic malignancy characterized by high tumor cell heterogeneity, which significantly impacts the clinical prognosis of patients. Angiogenesis and the molecular features of tumor cells play a critical role in tumor progression and drug resistance. This study aims to explore the impact of tumor cell heterogeneity and angiogenesis-related genes on the prognosis of MM. We collected transcriptomic data and single-cell RNA sequencing data from MM patients through the Xena and GEO databases. The data were processed and analyzed using bioinformatics methods, including differential gene expression analysis, single-cell clustering, CNV analysis, transcription factor analysis, screening of angiogenesis-related genes, cell communication analysis, and immune infiltration analysis. Through integrative analysis of transcriptomic data and single-cell RNA sequencing data, we identified significant genomic copy number variations in the tumor cells of MM patients. Additionally, different tumor subgroups exhibited differences in angiogenic activity, gene expression, and tumor progression. Notably, high expression of the transcription factor cAMP-responsive element-binding protein 3-like 2 (CREB3L2) in the C1 subgroup was associated with the inhibition of angiogenesis and tumor cell proliferation and migration. Furthermore, the prognostic model based on angiogenesis and transcription factors demonstrated high accuracy in predicting the prognosis of MM patients. This study highlights the critical roles of tumor cell heterogeneity and angiogenesis-related genes in MM. By constructing a prognostic model, it provides new theoretical insights for the precise diagnosis and personalized treatment of MM.

Cancer-associated fibroblasts (CAFs) have long been known as one of the most important players in tumor initiation and progression. Even so, there is an incomplete understanding of the identification of CAFs among tumor microenvironment cells as the list of CAF marker genes varies greatly in the literature, therefore it is imperative to find a better way to identify reliable markers of CAFs. To this end, we summarized a large number of single-cell RNA-sequencing data of multiple tumor types and corresponding normal tissues. As a result, for 9 different types of cancer, we identified CAF-specific gene expression signatures and found 10 protein markers that showed strongly positive staining of tumor stroma according to the analysis of IHC images from the Human Protein Atlas database. Our results give an insight into selecting the most appropriate combination of cancer-associated fibroblast markers. Furthermore, comparison of different approaches for studying differences between cancer-associated and normal fibroblasts (NFs) illustrates the superiority of transcriptome analysis of fibroblasts obtained from fresh tissue samples. Using single-cell RNA sequencing data, we identified common differences in gene expression patterns between normal and cancer-associated fibroblasts, which do not depend on the type of tumor.

Whole exome and genome sequencing, coupled with refined bioinformatic pipelines, have enabled improved diagnostic yields for individuals with Mendelian conditions and have led to the rapid identification of novel syndromes. For many Mendelian neurodevelopmental disorders (NDDs), there is a lack of pre-existing model systems for mechanistic work. Thus, it is critical for translational researchers to have an accessible phenotype- and genotype-informed approach for model system selection. Single-cell RNA sequencing data can be informative in such an approach, as it can indicate which cell types express a gene of interest at the highest levels across time. For Mendelian NDDs, such data for the developing human brain is especially useful. A valuable single-cell RNA sequencing dataset of the second trimester developing human brain was produced by Bhaduri et al in 2021, but access to these data can be limited by computing power and the learning curve of single-cell data analysis. To reduce these barriers for translational research on Mendelian NDDs, we have built the web-based tool, Neurodevelopment in Trimester 2 - VIsualization of Single cell Data Online Tool (NeuroTri2-VISDOT), for exploring this single-cell dataset, and we have employed it in several different settings to demonstrate its utility for the translational research community.

Background Sepsis, a life-threatening organ dysfunction caused by a dysregulated host response to infection, remains a significant medical challenge with high mortality rates, necessitating a deeper understanding of its underlying mechanisms. This study aims to elucidate the differential expression of necroptosis-related genes in sepsis and their impact on immune characteristics. Methods We obtained gene expression profiles and single-cell RNA sequencing data from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the limma package, followed by functional enrichment analysis through the clusterProfiler package for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) were conducted to explore pathway enrichments. Immune cell infiltration differences between sepsis (SE) and healthy control (HC) groups were quantified using the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. Differential marker genes between SE and HC groups were identified by single-cell data analysis using Seurat and SingleR packages. Results Our results revealed 849 necroptosis-related DEGs, with 843 upregulated and 16 downregulated in SE group. LASSO regression identified 22 key DEGs including CTSS, MAPK8, and MPRIP. 157 necroptosis-related DEGs were identified between SE and HC groups. GO analysis indicated significant enrichment in biological processes such as regulation of apoptotic signaling pathways and I-kappaB kinase/NF-kappaB signaling. KEGG pathway analysis revealed involvement in necroptosis, apoptosis and NOD-like receptor signaling pathways. While GSVA showed WNT signaling was upregulated in SE group. There were significant differences between sepsis and healthy control groups for activated B cells and CD4 T cells. Single-cell RNA sequencing identified 33,287 cells categorized into 26 clusters, with neutrophils being predominant. Key necroptosis genes such as CTSS, TXN, MYH9, FPR1, FMR1, and MPRIP exhibited significant expression differences across various immune cell types. Conclusions Our integrated bioinformatics approach provides new insights into the role of necroptosis-related genes in sepsis pathogenesis and their influence on immune responses. These findings enhance our understanding of sepsis pathogenesis and might guide future therapeutic strategies targeting necroptosis pathways.

Auditory hair cells transduce sound to the brain, and in mammals, these cells reside together with supporting cells in the sensory epithelium of the cochlea, called the organ of Corti. To establish the organ's delicate function during development and differentiation, spatiotemporal gene expression is strictly controlled by chromatin accessibility and cell type–specific transcription factors, jointly representing the regulatory landscape. Bulk sequencing technology and cellular heterogeneity obscured investigations on the interplay between transcription factors and chromatin accessibility in inner ear development. To study the formation of the regulatory landscape in hair cells, we collected single-cell chromatin accessibility profiles accompanied by single-cell RNA data from genetically labeled murine hair cells and supporting cells after birth. Using an integrative approach, we predicted cell type–specific activating and repressing functions of developmental transcription factors. Furthermore, by integrating gene expression and chromatin accessibility data sets, we reconstructed gene regulatory networks. Then, using a comparative approach, 20 hair cell–specific activators and repressors, including putative downstream target genes, were identified. Clustering of target genes resolved groups of related transcription factors and was used to infer their developmental functions. Finally, the heterogeneity in the single-cell data allowed us to spatially reconstruct transcriptional as well as chromatin accessibility trajectories, indicating that gradual changes in the chromatin accessibility landscape are lagging behind the transcriptional identity of hair cells along the organ's longitudinal axis. Overall, this study provides a strategy to spatially reconstruct the formation of a lineage-specific regulatory landscape using a single-cell multi-omics approach.

<div>Abstract<p>Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by an immunosuppressive tumor microenvironment enriched with cancer-associated fibroblasts (CAF). This study used a convergence approach to identify tumor cell and CAF interactions through the integration of single-cell data from human tumors with human organoid coculture experiments. Analysis of a comprehensive atlas of PDAC single-cell RNA sequencing data indicated that CAF density is associated with increased inflammation and epithelial–mesenchymal transition (EMT) in epithelial cells. Transfer learning using transcriptional data from patient-derived organoid and CAF cocultures provided <i>in silico</i> validation of CAF induction of inflammatory and EMT epithelial cell states. Further experimental validation in cocultures demonstrated integrin beta 1 (ITGB1) and vascular endothelial factor A (VEGFA) interactions with neuropilin-1 mediating CAF-epithelial cell cross-talk. Together, this study introduces transfer learning from human single-cell data to organoid coculture analyses for experimental validation of discoveries of cell–cell cross-talk and identifies fibroblast-mediated regulation of EMT and inflammation.</p>Significance:<p>Adaptation of transfer learning to relate human single-cell RNA sequencing data to organoid-CAF cocultures facilitates discovery of human pancreatic cancer intercellular interactions and uncovers cross-talk between CAFs and tumor cells through VEGFA and ITGB1.</p></div>

BackgroundIt was previously reported that the production of exerkines is positively associated with the beneficial effects of exercise in lung adenocarcinoma (LUAD) patients. This study proposes a novel scoring system based on muscle failure-related genes, to assist in clinical decision making.MethodsA comprehensive analysis of bulk and single cell RNA sequencing (scRNA-seq) of early, advanced and brain metastatic LUAD tissues and normal lung tissues was performed to identify muscle failure-related genes in LUAD and to determine the distribution of muscle failure-related genes in different cell populations. A novel scoring system, named MFI (Muscle failure index), was developed and validated. The differences in biological functions, immune infiltration, genomic alterations, and clinical significance of different subtypes were also investigated.ResultsFirst, we conducted single cell analysis on the dataset GSE131907 and identified eight cell subpopulations. We found that four muscle failure-related genes (BDNF, FNDC5, IL15, MSTN) were significantly increased in tumor cells. In addition, IL15 was widely distributed in the immune cell population. And we have validated it in our own clinical cohort. Then we created the MFI model based on 10 muscle failure-related genes using the LASSO algorithm, and MFI remained an independent prognostic factor of OS in both the training and validation cohorts. Moreover, we generated MFI in the single-cell dataset, in which cells with high MFI received and sent more signals compared to those with low MFI. Biological function analysis of both subtypes revealed stronger anti-tumor immune activity in the low MFI group, while tumor cells with high MFI had stronger metabolic and proliferative activity. Finally, we systematically assessed the immune cell activity and immunotherapy responses in LUAD patients, finding that the low MFI group was more sensitive to immunotherapy.ConclusionOverall, our study can improve the understanding of the role of muscle failure-related genes in tumorigenesis and we constructed a reliable MFI model for predicting prognosis and guiding future clinical decision making.

The tumor microenvironment (TME) is crucial in cancer development and therapeutic response. Immunotherapy is increasingly recognized as a critical component of cancer treatment. While immunotherapies have shown efficacy in various cancers, including breast cancer, patient responses vary widely. Some patients receive significant benefits, while others experience minimal or no improvement. This disparity underscores the complexity and diversity of the immune system. In this study, we investigated the immune landscape and cell-cell communication within the TME of breast cancer through integrated analysis of bulk and single-cell RNA sequencing data. We established profiles of tumor immune infiltration that span across a broad spectrum of adaptive and innate immune cells. Our clustering analysis of immune infiltration identified three distinct patient groups: high T cell abundance, moderate infiltration, and low infiltration. Patients with low immune infiltration exhibited the poorest survival rates, while those in the moderate infiltration group showed better outcomes than those with high T cell abundance. Moreover, the high cell abundance group was associated with a greater tumor burden and higher rates of TP53 mutations, whereas the moderate infiltration group was characterized by a lower tumor burden and elevated PIK3CA mutations. Analysis of an independent single-cell RNA-seq breast cancer dataset confirmed the presence of similar infiltration patterns. Further investigation into ligand-receptor interactions within the TME unveiled significant variations in cell-cell communication patterns among these groups. Notably, we found that the signaling pathways SPP1 and EGF were exclusively active in the low immune infiltration group, suggesting their involvement in immune suppression. This work comprehensively characterizes the composition and dynamic interplay in the breast cancer TME. Our findings reveal associations between the extent of immune infiltration and clinical outcomes, providing valuable prognostic information for patient stratification. The unique mutations and signaling pathways associated with different patient groups offer insights into the mechanisms underlying diverse tumor immune infiltration and the formation of an immunosuppressive tumor microenvironment.

Joint reconstruction of multiple gene regulatory networks (GRNs) using gene expression data from multiple tissues/conditions is very important for understanding common and tissue/condition-specific regulation. However, there are currently no computational models and methods available for directly constructing such multiple GRNs that not only share some common hub genes but also possess tissue/condition-specific regulatory edges. In this paper, we proposed a new graphic Gaussian model for joint reconstruction of multiple gene regulatory networks (JRmGRN), which highlighted hub genes, using gene expression data from several tissues/conditions. Under the framework of Gaussian graphical model, JRmGRN method constructs the GRNs through maximizing a penalized log likelihood function. We formulated it as a convex optimization problem, and then solved it with an alternating direction method of multipliers (ADMM) algorithm. The performance of JRmGRN was first evaluated with synthetic data and the results showed that JRmGRN outperformed several other methods for reconstruction of GRNs. We also applied our method to real Arabidopsis thaliana RNA-seq data from two light regime conditions in comparison with other methods, and both common hub genes and some conditions-specific hub genes were identified with higher accuracy and precision. JRmGRN is available as a R program from: https://github.com/wenpingd. Supplementary data are available at Bioinformatics online.

Single-cell RNA-sequencing is an invaluable research tool that allows for the investigation of gene expression in heterogeneous cancer cell populations in ways that bulk RNA-seq cannot. However, normal (i.e., non tumor) cells in cancer samples have the potential to confound the downstream analysis of single-cell RNA-seq data. Existing methods for identifying cancer and normal cells include copy number variation inference, marker-gene expression analysis, and expression-based clustering. This work aims to extend the existing approaches for identifying cancer cells in single-cell RNA-seq samples by incorporating variant calling and the identification of putative driver alterations. We found that putative driver alterations can be detected in single-cell RNA-seq data obtained with full-length transcript technologies and noticed that a subset of cells in tumor samples are enriched for putative driver alterations as compared to normal cells. Furthermore, we show that the number of putative driver alterations and inferred copy number variation are not correlated in all samples. Taken together, our findings suggest that augmenting existing cancer-cell filtering methods with variant calling and analysis can increase the number of tumor cells that can be confidently included in downstream analyses of single-cell full-length transcript RNA-seq datasets.

ABSTRACTFoundation Models (FMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of FMs for single‐cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single‐cell data. Overall, the top FMs include scGPT, Geneformer, and CellFM by considering model performances and user accessibility among ten single‐cell FMs. However, by comparing these FMs with task‐specific methods, we found that single‐cell FMs may not consistently excel than task‐specific methods in all tasks, which challenges the necessity of developing foundation models for single‐cell analysis. In addition, we evaluated the effects of hyperparameters, initial settings, and stability for training single‐cell FMs based on a proposed scEval framework, and provide guidelines for pre‐training and fine‐tuning to enhance the performances of single‐cell FMs. Our work summarizes the current state of single‐cell FMs, points to their constraints and avenues for future development, and offers a freely available evaluation pipeline to benchmark new models and improve method development.

Study question Can we map the key maturation stages of human spermatogonial stem cells (SSCs) within their niche during infancy and puberty? Summary answer Using highplex spatial proteomics, we describe the key cellular events taking place during human testis maturation. What is known already The human testis is a complex “ecosystem” that matures in response to endocrine and exocrine signals and interactions between its diverse cellular components. Unlike animal models, in humans, testicular maturation is a slow process that takes place over more than a decade. In contrast to ovaries, the human testis retains its immature state until the onset of puberty. Single-cell RNA sequencing data has revealed significant developmental changes for germ cell and the somatic niche during infancy and puberty. Study design, size, duration Using the hyperplex imaging technique (CellDIVE) and a panel of 30 protein markers - selected to label key components of the developing human testis - we have systematically analyzed a time series of ∼75 testicular biopsies from boys aged 8 months to adulthood. Following sequential staining and capture of high-resolution images, our analysis pipeline involves the conversion of image into single-cell data, which allows a systematic description of the spatial SSC/niche microenvironment interactions across development. Participants/materials, setting, methods The panel of antibody markers was defined using integrated scRNAseq data and represents all key cell types, including germ cell markers (e.g., MAGEA4), Sertoli cell markers (e.g., SOX9), Leydig cell markers (e.g., INSL3), myoid cell markers (e.g., ACTA2) ... Main results and the role of chance To describe the events occurring during human testicular maturation, we build a Tissue Microarray (TMA) containing ∼75 biopsies of boys of different ages (0-16yrs old) and using an in-house panel of 30 markers for high-dimensional multiplex CELL DIVE images. Our analytical strategy uses Uniform Manifold Approximation and Projection (UMAP) maps to quantify and statistically compare single-cell data. Our marker panel allows us to phenotype testicular cells at different stages of development, including SSCs (state 0 to state 4), Sertoli cells, Leydig cells, and so on. Moreover, employing spatial-latent Dirichlet allocation (LDA), we identified recurrent cellular neighbourhoods (RCNs), revealing distinct cell clusters and their corresponding RCNs. These RCNs reflect recurring spatial arrangements of cells within testicular tissues, allowing us to infer the spatial organization and interplay among different cell types. Additionally, we explored spatial distances and neighbourhood frequencies across different tissues and conducted spatial co-occurrence analysis to assess the likelihood of finding adjacent cell types. The identification of RCNs offers valuable insights into the microenvironmental dynamics and cellular relationships taking place within the developing testis. Looking ahead, we aim to delve into the functional significance of RCNs in shaping the developmental trajectories and physiological processes within human testicular tissues. Limitations, reasons for caution Due to limitations in proteomics techniques, we could only include ∼30 protein markers on our panel. Although the biopsy samples used in our study are all from chemo-naïve patients, it possible that the conditions affecting these paediatric patients may impact the architecture or development of their testicular tissues. Wider implications of the findings Our study provides a systematic approach to study the development and organization of human testicular structures, which will allow to enumerate the cellular content of individual testicular biopsies and infer their stage of maturation, therefore providing a personalised baseline assessment for each patient at the time of cryopreservation. Trial registration number not applicable

Single-cell RNA sequencing has substantially advanced our understanding of gene expression dynamics and cellular heterogeneity. In recent years, deep learning (DL) has emerged as a promising approach to infer genetic regulation. However, these methods still face challenges in representing complex regulatory mechanisms. Thus, it remains imperative to develop new algorithms to enhance both effectiveness and reliability. We propose DeepCE, a DL framework for correlation-enhanced gene regulatory network (GRN) inference. DeepCE strengthens the extraction of dynamic regulation by integrating bidirectional gated recurrent units with convolutional neural networks (CNNs). Specifically, bidirectional gated recurrent units captures dynamic temporal dependencies, while CNNs focuses on local spatial patterns within single-cell data, enabling the model to uncover complex gene-gene interactions and generate high-quality GRNs. This framework improves the accuracy and robustness of GRN inference by smoothing noisy gene expression data, extracting time-lagged regulatory signals, and filtering out spurious correlations. Experiments conducted on mouse and human datasets demonstrate the strong performance of DeepCE. Performance evaluations show that DeepCE outperforms existing methods, achieving the highest AUROC and AUPR scores. Codes for DeepCE are free available in the GitHub https://github.com/sxiaodai/DeepCE.

Childhood cancer survivors (CCSs) face lifelong side effects related to their treatment with chemotherapy. Anthracycline agents, such as doxorubicin (DOX), are important in the treatment of childhood cancers but are associated with cardiotoxicity. Cardiac toxicities represent a significant source of chronic disability that cancer survivors face; despite this, the chronic cardiotoxicity phenotype and how it relates to acute toxicity remains poorly defined. To address this critical knowledge gap, we studied the acute effect of DOX on murine cardiac nonmyocytes in vivo. Determination of the acute cellular effects of DOX on nonmyocytes, a cell pool with finite replicative capacity, provides a basis for understanding the pathogenesis of the chronic heart disease that CCSs face. To investigate the acute cellular effects of DOX, we present single-cell RNA sequencing (scRNAseq) data from homeostatic cardiac nonmyocytes and compare it with preexisting datasets, as well as a novel CyTOF datasets. SCANPY, a python-based single-cell analysis, was used to assess the heterogeneity of cells detected in scRNAseq and CyTOF. To further assist in CyTOF data annotation, joint analyses of scRNAseq and CyTOF data using an artificial neural network known as sparse autoencoder for clustering, imputation, and embedding (SAUCIE) are performed. Lastly, the panel is tested on a mouse model of acute DOX exposure at two time points (24 and 72 h) after the last dose of doxorubicin and examined with joint clustering. In sum, we report the first ever CyTOF study of cardiac nonmyocytes and characterize the effect of acute DOX exposure with scRNAseq and CyTOF.NEW & NOTEWORTHY We describe the first mass cytometry studies of murine cardiac nonmyocytes. The mass cytometry panel is compared with single-cell RNA sequencing data. Homeostatic cardiac nonmyocytes are characterized by mass cytometry to identify and quantify four major cell populations: endothelial cells, fibroblasts, leukocytes, and pericytes. The single-cell acute nonmyocyte response to doxorubicin is studied at 24 and 72 h after doxorubicin exposure given daily for 5 days at a dose of 4 mg/kg/day.