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Single-cell technologies have revolutionized our ability to study cellular heterogeneity and dynamics at unprecedented resolutions. In this fast-growing field, it becomes increasingly challenging to navigate the vast number of tools and steps for analysis. It is particularly difficult to integrate and analyze large datasets that require extensive collaborations and customized pipelines to obtain robust results. We present CytoAnalyst, a web-based platform that offers a number of important advantages over existing tools for single-cell analysis. First, the platform enables custom pipeline configuration using an efficient study management system and a broad range of analysis modules. Second, the platform supports parallel analysis instances, facilitating the comprehensive comparison of different methods or parameter settings available at each analysis step. Third, the advanced sharing system facilitates real-time synchronization among team members and seamless analysis continuation across different devices. Finally, the grid-layout visualization system supports simultaneous displays of different data aspects, allowing for the comparison of multiple labels and plots side-by-side for comprehensive data insights, with the ability to save and reload visualization settings at any analysis step. The platform incorporates multiple blending modes, allowing users to combine plots in various ways for comprehensive data exploration. CytoAnalyst supports a high level of analytical rigor while providing user-friendly and flexible operations through its carefully designed interface and extensive documentation. The platform supports all major web browsers and is freely available at https://cytoanalyst.tinnguyen-lab.com .

Integrating single‐cell datasets from multiple studies provides a cost‐effective way to build comprehensive cell atlases, granting deeper insights into cellular characteristics across diverse biological systems. However, current data integration methods struggle with interference in partially overlapping datasets and varying annotation granularities. Here, a multiselective adversarial network is introduced for the first time and present UniMap, which functions as a “discerner” to identify and exclude interfering cells from various data sources during dataset integration. Compared to other state‐of‐the‐art methods, UniMap emphasizes type‐level integration and proves to be the best model for preserving biological variability, achieving noticeably higher accuracy in single‐cell automated annotation under various circumstances. Additionally, it enhances interpretability by revealing shared and domain‐specific cell types and providing prediction confidence. The efficacy of UniMap is demonstrated in terms of identifying new cell types, creating high‐resolution cell atlases, annotating cells along developmental trajectories, and performing cross‐species analysis, underscoring its potential as a robust tool for single‐cell research. UniMap introduces a novel multiselective adversarial network that functions as a discerner in single‐cell dataset integration. It enables type‐level integration, ensuring accurate cell type annotation across diverse biological contexts while preserving shared and domain‐specific features. UniMap outperforms by maintaining biological variability and enhancing interpretability, making it a powerful tool for single‐cell research.

Single-cell analysis offers insights into cellular heterogeneity and individual cell function. Cell type annotation is the first and critical step for performing such an analysis. Current methods mostly utilize single-cell RNA sequencing data. Several studies demonstrated improved unsupervised annotation when combining RNA with single-cell ATAC sequencing, but improvements in supervised methods have not been explored. Single-cell 10x genomics multiome datasets containing paired ATAC and RNA from human peripheral blood mononuclear cells (PBMC) and neuronal cells with Alzheimer's Disease were used for supervised annotation. Using linear and nonlinear dimensionality reduction methods and random forest, support vector machine and logistic regression classification models, we demonstrate the improvement in supervised annotation and prediction confidence in PBMC data when using a combination of RNA seq and ATAC-seq data. No such improvement was observed when annotating neuronal cells. Specifically, F1 scores were improved when using scVI embeddings to annotate PBMC sub-types. CD4 T effector memory cells showed the largest improvement in F1 score.

[This corrects the article DOI: 10.3389/fimmu.2022.929922.].

Multiplex imaging technologies are now routinely capable of measuring more than 40 antibody-labeled parameters in single cells. However, lateral spillage of signals in densely packed tissues presents an obstacle to the assignment of high-dimensional spatial features to individual cells for accurate cell-type annotation. We devised a method to correct for lateral spillage of cell surface markers between adjacent cells termed REinforcement Dynamic Spillover EliminAtion (REDSEA). The use of REDSEA decreased contaminating signals from neighboring cells. It improved the recovery of marker signals across both isotopic (i.e., Multiplexed Ion Beam Imaging) and immunofluorescent (i.e., Cyclic Immunofluorescence) multiplexed images resulting in a marked improvement in cell-type classification.

Spatial transcriptomics technologies have been widely applied to decode cellular distribution by resolving gene expression profiles in tissue. However, sequencing techniques still limit the ability to create a fine-resolved spatial cell-type map. To this end, we develop a novel deep-learning-based approach, STASCAN, to predict the spatial cellular distribution of captured or uncharted areas where only histology images are available by cell feature learning integrating gene expression profiles and histology images. STASCAN is successfully applied across diverse datasets from different spatial transcriptomics technologies and displays significant advantages in deciphering higher-resolution cellular distribution and resolving enhanced organizational structures.

Single-cell RNA sequencing (scRNA-seq) has revolutionized understandings of cellular identities and functions due to the ability to study transcriptome-wide gene expression within individual cells. Multi-tissue scRNA-seq atlases have generated holistic understandings of body-wide cell dynamics and serve as key foundational resources for further scientific studies across a variety of species. Pigs are a valuable biomedical model, and pork is an essential global food source, but minimal understanding of immune cell identities and functions across anatomical locations limits agricultural and health advancements in pigs. To address current limitations, we apply scRNA-seq to create an atlas of immune cells recovered from key immune tissues including primary lymphoid organs (bone marrow and thymus) and secondary lymphoid organs (lymph node and spleen). Thymus data was compared to a previously published scRNA-seq dataset of pig thymus and shared a general consensus while also identifying several new thymic cell populations. Comparison of spleen to a human splenic scRNA-seq dataset also revealed conserved features, including two subsets of innate lymphoid cells conserved between pigs and humans. Inference of lymph node cell interactions and proximities from scRNA-seq data resembled some features of follicular organization and conventional germinal center reaction processes. To expand accessibility of the scRNA-seq atlas for biological query, we deploy an interactive application and demonstrate its use for non-computational exploration of diverse cell populations recovered from bone marrow. Overall, results expand current foundational understandings of immune cell identities and functions in pig lymphoid organs and demonstrate pig-to-human immune similarities to consider for future research applications. Materials associated with this work are made readily accessible for others to investigate individual queries requiring foundational knowledge pertaining to pig immunity.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell–cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell–cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.

The transcriptomic profile of a cell population can now be studied at the cellular level using single-cell mRNA sequencing (scRNA-seq). This novel technique provides the unprecedented opportunity to explore the cellular composition of the bronchoalveolar lavage fluid (BALF) of the horse, a species for which cell type markers are poorly described. Here, scRNA-seq technology was applied to cryopreserved equine BALF cells. Analysis of 4,631 cells isolated from three asthmatic horses in remission identified 16 cell clusters belonging to six major cell types: monocytes/macrophages, T cells, B/plasma cells, dendritic cells, neutrophils and mast cells. Higher resolution analysis of the constituents of the major immune cell populations allowed deep annotation of monocytes/macrophages, T cells and B/plasma cells. A significantly higher lymphocyte/macrophage ratio was detected with scRNA-seq compared to conventional cytological differential cell count. For the first time in horses, we detected a transcriptomic signature consistent with monocyte-lymphocyte complexes. Our findings indicate that scRNA-seq technology is applicable to cryopreserved equine BALF cells, allowing the identification of its major (cytologically differentiated) populations as well as previously unexplored T cell and macrophage subpopulations. Single-cell gene expression analysis has the potential to facilitate understanding of the immunological mechanisms at play in respiratory disorders of the horse, such as equine asthma.