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Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate rare cell types and achieve accurate identification. We benchmark scCAD on 25 real-world scRNA-seq datasets, demonstrating its superior performance compared to 10 state-of-the-art methods. In-depth case studies across diverse datasets, including mouse airway, brain, intestine, human pancreas, immunology data, and clear cell renal cell carcinoma, showcase scCAD's efficiency in identifying rare cell types in complex biological scenarios. Furthermore, scCAD can correct the annotation of rare cell types and identify immune cell subtypes associated with disease, thereby offering valuable insights into disease progression. Identifying rare cells is essential for advancing our understanding of complex biological systems and disease mechanisms. Here, authors propose scCAD, a method that combines cluster decomposition and anomaly detection to effectively identify rare cell types across diverse biological scenarios. [ABSTRACT FROM AUTHOR]

The structure and function of the circulatory system, including the heart, have undergone substantial changes with the vertebrate evolution. Although the basic function of the heart is to pump blood through the body, its size, shape, speed, regeneration capacity, etc. vary considerably across species. Here, we address the differences among vertebrate hearts using a single-cell transcriptomics approach. Published datasets of macaque (Macaca fascicularis), mouse, and zebrafish hearts were integrated and compared to the human heart as a reference. While the three mammalian hearts integrated well, the zebrafish heart showed very little overlap with the other species. Our analysis revealed a mouse-specific cell subpopulation of ventricular cardiomyocytes (CM), represented by strikingly different expression patterns of specific genes related to high-energy metabolism. Interestingly, the observed differences between mouse and human CM coincided with actual biological differences between the two species. Smooth muscle and endothelial cells (EC) exhibited species-specific differences in clustering and gene expression, respectively, which we attribute to the tissues selected for sequencing, given different focuses of the original studies. Finally, we compared human and zebrafish heart-specific fibroblasts (FB) and identified a distinctively high expression of genes associated with heart regeneration following injury in zebrafish. Together, our results show that integration of numerous datasets of different species and different sequencing technologies is feasible and that this approach can identify species-specific differences and similarities in the heart. [ABSTRACT FROM AUTHOR]

Current biotechnologies can simultaneously measure multiple high-dimensional modalities (e.g., RNA, DNA accessibility, and protein) from the same cells. A combination of different analytical tasks (e.g., multi-modal integration and cross-modal analysis) is required to comprehensively understand such data, inferring how gene regulation drives biological diversity and functions. However, current analytical methods are designed to perform a single task, only providing a partial picture of the multi-modal data. Here, we present UnitedNet, an explainable multi-task deep neural network capable of integrating different tasks to analyze single-cell multi-modality data. Applied to various multi-modality datasets (e.g., Patch-seq, multiome ATAC + gene expression, and spatial transcriptomics), UnitedNet demonstrates similar or better accuracy in multi-modal integration and cross-modal prediction compared with state-of-the-art methods. Moreover, by dissecting the trained UnitedNet with the explainable machine learning algorithm, we can directly quantify the relationship between gene expression and other modalities with cell-type specificity. UnitedNet is a comprehensive end-to-end framework that could be broadly applicable to single-cell multi-modality biology. This framework has the potential to facilitate the discovery of cell-type-specific regulation kinetics across transcriptomics and other modalities. Multimodal biological data is challenging to analyze. Here, the authors develop UnitedNet, an explainable deep neural network for analyzing single-cell multimodal biological data and estimating relationships between gene expression and other modalities with cell-type specificity. [ABSTRACT FROM AUTHOR]

Large compendia of gene expression data have proven valuable for the discovery of novel biological relationships. Historically, most available RNA assays were run on microarray, while RNA-seq is now the platform of choice for many new experiments. The data structure and distributions between the platforms differ, making it challenging to combine them directly. Here we perform supervised and unsupervised machine learning evaluations to assess which existing normalization methods are best suited for combining microarray and RNA-seq data. We find that quantile and Training Distribution Matching normalization allow for supervised and unsupervised model training on microarray and RNA-seq data simultaneously. Nonparanormal normalization and z-scores are also appropriate for some applications, including pathway analysis with Pathway-Level Information Extractor (PLIER). We demonstrate that it is possible to perform effective cross-platform normalization using existing methods to combine microarray and RNA-seq data for machine learning applications. An evaluation of normalization methods shows that it is possible to combine microarray and RNA-seq data for machine learning applications. [ABSTRACT FROM AUTHOR]

The repressor element 1 silencing transcription factor (REST) has been proposed to function as a transcription factor to silence gene transcription by binding to repressor element 1 (RE1), a highly conserved DNA motif. The functions of REST in various tumors have been studied, but its role and correlation with immune cell infiltration remains uncertain in gliomas. REST expression was analyzed in datasets of The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) and validated by the Gene Expression Omnibus and Human Protein Atlas databases. The clinical prognosis of REST was evaluated by clinical survival data of TCGA cohort and validated by Chinese Glioma Genome Atlas cohort. MicroRNAs (miRNAs) contributing to REST overexpression in glioma were identified by a combination of a series of in silico analyses, including expression analysis, correlation analysis, and survival analysis. The correlations between immune cell infiltration level and REST expression were analyzed by TIMER2 and GEPIA2 tools. Enrichment analysis of REST was performed using STRING and Metascape tools. The expression and function of predicted upstream miRNAs at REST and their association with glioma malignancy and migration were also confirmed in glioma cell lines. REST was highly expressed and associated with poorer overall survival and disease-specific survival in glioma and some other tumors. MiR-105-5p and miR-9-5p were identified as the most potential upstream miRNAs of REST in glioma patient cohort and experiments in vitro. REST expression was positively correlated with infiltration of immune cells and the expression of immune checkpoints such as PD1/PD-L1 and CTLA-4 in glioma. Furthermore, histone deacetylase 1 (HDAC1) was a potential REST-related gene in glioma. Enrichment analysis of REST found chromatin organization and histone modification were the most significant enriched terms, and Hedgehog-Gli pathway might be involved in the effect of REST on the pathogenesis of glioma. Our study suggests REST to be an oncogenic gene and the biomarker of poor prognosis in glioma. High REST expression might affect the tumor microenvironment of glioma. More basic experiments and large clinical trials aimed at the carcinogenetic study of REST in glioma will be needed in the future. [ABSTRACT FROM AUTHOR]

Global alignment of complex pseudotime trajectories between different single-cell RNA-seq datasets is challenging, as existing tools mainly focus on linear alignment of single-cell trajectories. Here we present CAPITAL (comparative analysis of pseudotime trajectory inference with tree alignment), a method for comparing single-cell trajectories with tree alignment whereby branching trajectories can be automatically compared. Computational tests on synthetic datasets and authentic bone marrow cells datasets indicate that CAPITAL has achieved accurate and robust alignments of trajectory trees, revealing various gene expression dynamics including gene–gene correlation conservation between different species. Global alignment of complex cell state trajectories between single-cell datasets remains challenging. Here, the authors present a computational method called CAPITAL to compare branching trajectories, and demonstrate that this method achieves accurate and robust alignments. [ABSTRACT FROM AUTHOR]

The boom in single-cell technologies has brought a surge of high dimensional data that come from different sources and represent cellular systems from different views. With advances in these single-cell technologies, integrating single-cell data across modalities arises as a new computational challenge. Here, we present an adversarial approach, sciCAN, to integrate single-cell chromatin accessibility and gene expression data in an unsupervised manner. We benchmarked sciCAN with 5 existing methods in 5 scATAC-seq/scRNA-seq datasets, and we demonstrated that our method dealt with data integration with consistent performance across datasets and better balance of mutual transferring between modalities than the other 5 existing methods. We further applied sciCAN to 10X Multiome data and confirmed that the integrated representation preserves biological relationships within the hematopoietic hierarchy. Finally, we investigated CRISPR-perturbed single-cell K562 ATAC-seq and RNA-seq data to identify cells with related responses to different perturbations in these different modalities. [ABSTRACT FROM AUTHOR]

The concept of personalized medicine has led to a paradigm shift in recent years. It integrates multiple clinical and biological levels of investigation aimed at offering the best possible and patient-tailored healthcare. This holds great potential in a rare and heterogeneous disease such as systemic sclerosis (SSc). The development of validated clinical screening algorithms and the identification of predictors for disease outcomes can help in stratifying patients according to their individual risk of progression. The ongoing search for biomarkers and key pathogenic molecules has brought valuable insights into molecular networks operative in SSc. In parallel, genetic and genomic studies have revealed new SSc susceptibility loci and validated gene expression profiles that might identify patients benefiting from specific therapies. In this review, we focus on recent findings relevant for the concept of personalized medicine in patients with SSc. [ABSTRACT FROM AUTHOR]