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DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.

Tendinopathy, the most common form of chronic tendon disorder, leads to persistent tendon pain and loss of function. Profiling the heterogeneous cellular composition in the tendon microenvironment helps to elucidate rational molecular mechanisms of tendinopathy. In this study, through a multi-modal analysis, a single-cell RNA- and ATAC-seq integrated tendinopathy landscape was generated for the first time. We found that a specific cell subpopulation with low expression exhibited a higher level of inflammation, lower proliferation and migration ability, which not only promoted tendon injury but also led to microenvironment deterioration. Mechanistically, a motif enrichment analysis of chromatin accessibility showed that was an upstream regulator of PRDX2 transcription, and we confirmed that functional blockade of activity induced silencing. The TNF signaling pathway was significantly activated in the -low group, and TNF inhibition effectively restored diseased cell degradation. We revealed an essential role of diseased cells in tendinopathy and proposed the FOXO1-PRDX2-TNF axis is a potential regulatory mechanism for the treatment of tendinopathy.

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.

The recent advent of genome-scale imaging has enabled single-cell omics analysis in a spatially resolved manner in intact cells and tissues. These advances allow gene expression profiling of individual cells, and hence in situ identification and spatial mapping of cell types, in complex tissues. The high spatial resolution of these approaches further allows determination of the spatial organizations of the genome and transcriptome inside cells, both of which are key regulatory mechanisms for gene expression.

Emerging single-cell technologies profile multiple types of molecules within individual cells. A fundamental step in the analysis of the produced high-dimensional data is their visualization using dimensionality reduction techniques such as t-SNE and UMAP. We introduce j-SNE and j-UMAP as their natural generalizations to the joint visualization of multimodal omics data. Our approach automatically learns the relative contribution of each modality to a concise representation of cellular identity that promotes discriminative features but suppresses noise. On eight datasets, j-SNE and j-UMAP produce unified embeddings that better agree with known cell types and that harmonize RNA and protein velocity landscapes.

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

One of the biggest challenges in microbiome research in environmental and medical samples is to better understand functional properties of microbial community members at a single-cell level. Single-cell isotope probing has become a key tool for this purpose, but the current detection methods for determination of isotope incorporation into single cells do not allow high-throughput analyses. Here, we report on the development of an imaging-based approach termed stimulated Raman scattering–two-photon fluorescence in situ hybridization (SRS-FISH) for high-throughputmetabolism and identity analyses of microbial communities with single-cell resolution. SRS-FISH offers an imaging speed of 10 to 100 ms per cell, which is two to three orders ofmagnitude faster than achievable by state-of-the-art methods. Using this technique, we delineated metabolic responses of 30,000 individual cells to various mucosal sugars in the human gut microbiome via incorporation of deuterium from heavy water as an activity marker. Application of SRS-FISH to investigate the utilization of host-derived nutrients by two major human gut microbiome taxa revealed that response to mucosal sugars tends to be dominated by Bacteroidales, with an unexpected finding that Clostridia can outperform Bacteroidales at foraging fucose.With high sensitivity and speed, SRS-FISH will enable researchers to probe the fine-scale temporal, spatial, and individual activity patterns of microbial cells in complex communities with unprecedented detail.

Background Recent advances in single-cell multimodal omics sequencing have facilitated the simultaneous profiling of transcriptomes and surface proteomes within individual cells, offering insights into cellular functions and heterogeneity. However, the high costs and technical complexity of protocols like CITE-seq and REAP-seq constrain large-scale dataset generation. To overcome this limitation, surface protein data imputation methods have emerged to predict protein abundances from scRNA-seq data. Results We present a comprehensive benchmark of twelve state-of-the-art imputation methods across eleven datasets and six scenarios. Our analysis evaluates the methods’ accuracy, sensitivity to training data size, robustness across experiments, and usability in terms of running time, memory usage, popularity, and user-friendliness. With benchmark experiments in diverse scenarios and a comprehensive evaluation framework of the results, our study offers valuable insights into the performance and applicability of surface protein data imputation methods in single-cell omics research. Conclusions Based on our results, Seurat v4 (PCA) and Seurat v3 (PCA) demonstrate exceptional performance, offering promising avenues for further research in single-cell omics.

Abstract The burgeoning amount of single-cell data has been accompanied by revolutionary changes to computational methods to map, quantify, and analyze the outputs of these cutting-edge technologies. Many are still unable to reap the benefits of these advancements due to the lack of bioinformatics expertise. To address this issue, we present Ursa, an automated single-cell multiomics R package containing 6 automated single-cell omics and spatial transcriptomics workflows. Ursa allows scientists to carry out post-quantification single or multiomics analyses in genomics, transcriptomics, epigenetics, proteomics, and immunomics at the single-cell level. It serves as a 1-stop analytic solution by providing users with outcomes to quality control assessments, multidimensional analyses such as dimension reduction and clustering, and extended analyses such as pseudotime trajectory and gene-set enrichment analyses. Ursa aims bridge the gap between those with bioinformatics expertise and those without by providing an easy-to-use bioinformatics package for scientists in hoping to accelerate their research potential. Ursa is freely available at https://github.com/singlecellomics/ursa.

Current imaging technologies are limited in their capability to simultaneously capture intracellular and extracellular dynamics in a spatially and temporally resolved manner. This study presents a multimodal imaging system that integrates nanoplasmonic sensing with multichannel fluorescence imaging to concomitantly analyze intracellular and extracellular processes in space and time at the single‐cell level. Utilizing a highly sensitive gold nanohole array biosensor, the system provides label‐free and real‐time monitoring of extracellular secretion, while implementing nanoplasmonic‐compatible multichannel fluorescence microscopy enables to visualize the interconnected intracellular activities. Combined with deep‐learning‐assisted image processing, this integrated approach allows multiparametric and simultaneous study of various cellular constituents in hundreds of individual cells with subcellular spatial and minute‐level temporal resolution over extended periods of up to 20 h. The system's utility is demonstrated by characterizing a range of secreted biomolecules and fluorescence toolkits across three distinct applications: visualization of secretory behaviors along with subcellular organelles and metabolic processes, concurrent monitoring of protein expression and secretion, and assessment of cell cycle phases alongside their corresponding secretory profiles. By offering comprehensive insights, the multifunctional approach is expected to enhance holistic readouts of biological systems, facilitating new discoveries in both fundamental and translational sciences. This study leverages the complementary advantages of label‐free nanoplasmonic biosensing and multichannel fluorescence microscopy to develop a multimodal imaging system for real‐time, spatiotemporal monitoring of cellular activities at the single‐cell level. This approach enables simultaneous tracking of intracellular processes and extracellular secretions, providing deeper insights into the dynamic relationships governing cellular behavior.