Progress in single-cell multimodal sequencing and multi-omics data integration

A novel coarsened graph learning method for scalable single-cell data analysis

The emergence of single-cell technologies, including flow and mass cytometry, as well as single-cell RNA sequencing, has revolutionized the study of cellular heterogeneity, generating vast datasets rich in biological insights. Despite the effectiveness of graph-based analyses in deciphering the complexities of these datasets, managing large-scale graph representations of single-cell data remains computationally challenging. Coarsening has been employed to tackle this difficulty. However, current coarsening techniques such as Cytocoarsening, Heavy Edge Matching (HEM), and Locally Variable Edges (LVE) often suffer from slow processing speeds and limited adaptability. To address these challenges, we propose a novel approach utilizing Feature-Aware Graph Coarsening via Hashing (FACH), which integrates locality-sensitive hashing for scalable and efficient single-cell data analysis. This method directly extracts informative, low-dimensional cell representations from raw single-cell RNA sequencing and mass cytometry data, significantly improving processing speed while preserving essential data features. We demonstrate its effectiveness in downstream tasks, such as scalable graph neural network training on coarsened single-cell data, highlighting its ability to retain crucial biological features and enable efficient, accurate analyses. Our method directly extracts informative, low-dimensional cell representations from raw single-cell RNA sequencing and mass cytometry data, significantly improving processing speed and preserving critical biological features, such as transcriptional signatures and network topology. It reduces computational time by at least 50% compared to existing methods and achieves superior classification accuracy, such as 88.1% on the Baron Human dataset, underscoring its efficiency and precision in large-scale single-cell analysis.

Single-cell sequencing in diabetic retinopathy: progress and prospects

Diabetic retinopathy is a major ocular complication of diabetes, characterized by progressive retinal microvascular damage and significant visual impairment in working-age adults. Traditional bulk RNA sequencing offers overall gene expression profiles but does not account for cellular heterogeneity. Single-cell RNA sequencing overcomes this limitation by providing transcriptomic data at the individual cell level and distinguishing novel cell subtypes, developmental trajectories, and intercellular communications. Researchers can use single-cell sequencing to draw retinal cell atlases and identify the transcriptomic features of retinal cells, enhancing our understanding of the pathogenesis and pathological changes in diabetic retinopathy. Additionally, single-cell sequencing is widely employed to analyze retinal organoids and single extracellular vesicles. Single-cell multi-omics sequencing integrates omics information, whereas stereo-sequencing analyzes gene expression and spatiotemporal data simultaneously. This review discusses the protocols of single-cell sequencing for obtaining single cells from retina and accurate sequencing data. It highlights the applications and advancements of single-cell sequencing in the study of normal retinas and the pathological changes associated with diabetic retinopathy. This underscores the potential of these technologies to deepen our understanding of the pathogenesis of diabetic retinopathy that may lead to the introduction of new therapeutic strategies.

Mechanisms and technologies in cancer epigenetics

Cancer's epigenetic landscape, a labyrinthine tapestry of molecular modifications, has long captivated researchers with its profound influence on gene expression and cellular fate. This review discusses the intricate mechanisms underlying cancer epigenetics, unraveling the complex interplay between DNA methylation, histone modifications, chromatin remodeling, and non-coding RNAs. We navigate through the tumultuous seas of epigenetic dysregulation, exploring how these processes conspire to silence tumor suppressors and unleash oncogenic potential. The narrative pivots to cutting-edge technologies, revolutionizing our ability to decode the epigenome. From the granular insights of single-cell epigenomics to the holistic view offered by multi-omics approaches, we examine how these tools are reshaping our understanding of tumor heterogeneity and evolution. The review also highlights emerging techniques, such as spatial epigenomics and long-read sequencing, which promise to unveil the hidden dimensions of epigenetic regulation. Finally, we probed the transformative potential of CRISPR-based epigenome editing and computational analysis to transmute raw data into biological insights. This study seeks to synthesize a comprehensive yet nuanced understanding of the contemporary landscape and future directions of cancer epigenetic research.

scMMO-atlas: a single cell multimodal omics atlas and portal for exploring fine cell heterogeneity and cell dynamics

Single-cell multimodal sequencing parallelly captures multiple modalities of the same cell, providing unparalleled insights into cell heterogeneity and cell dynamics. For example, joint profiling of chromatin accessibility and transcriptome from the same single cell (scATAC + RNA) identified new cell subsets within the well-defined clusters. However, lack of single-cell multimodal omics (scMMO) database has led to data fragmentation, seriously hindering access, utilization and mining of scMMO data. Here, we constructed a scMMO atlas by collecting and integrating various scMMO data, then constructed scMMO database and portal called scMMO-atlas (https://www.biosino.org/scMMO-atlas/). scMMO-atlas includes scATAC + RNA (ISSAAS-seq, SNARE-seq, paired-seq, sci-CAR, scCARE-seq, 10X Multiome and so on), scRNA + protein, scATAC + protein and scTri-modal omics data, with 3 168 824 cells from 27 cell tissues/organs. scMMO-atlas offered an interactive portal for visualization and featured analysis for each modality and the integrated data. Integrated analysis of scATAC + RNA data of mouse cerebral cortex in scMMO-atlas identified more cell subsets compared with unimodal omics data. Among these new cell subsets, there is an early astrocyte subset highly expressed Grm3, called Astro-Grm3. Furthermore, we identified Ex-L6-Tle4-Nrf1, a progenitor of Ex-L6-Tle4, indicating the statistical power provided by the big data in scMMO-atlas. In summary, scMMO-atlas offers cell atlas, database and portal to facilitate data utilization and biological insight.

Unraveling immune cell heterogeneity in autoimmune arthritis: insights from single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of immune-mediated arthritis, which comprises rheumatoid arthritis and spondyloarthritis. This review outlines the key findings and advancements in scRNA-seq studies focused on the pathogenesis of autoimmune arthritis and its clinical application. In rheumatoid arthritis, scRNA-seq has elucidated the heterogeneity among synovial fibroblasts and immune cell subsets in inflammatory sites, offering insights into disease mechanisms and the differences in treatment responses. Various studies have identified distinct synovial fibroblast subpopulations, such as THY1+ inflammatory and THY1- destructive fibroblasts. Furthermore, scRNA-seq has revealed diverse T cell profiles in the synovium, including peripheral helper T cells and clonally expanded CD8+ T cells, shedding light on potential therapeutic targets and predictive markers of treatment response. Similarly, in spondyloarthritis, particularly psoriatic arthritis and ankylosing spondylitis, scRNA-seq studies have identified distinct cellular profiles associated with disease pathology. Challenges such as cost and sample size limitations persist, but collaborative efforts and utilization of public databases hold promise for overcoming these obstacles. Overall, scRNA-seq emerges as a powerful tool for dissecting cellular heterogeneity and driving precision medicine in immune-mediated arthritis.

Droplet-based single-cell sequencing: Strategies and applications

Notable advancements in single-cell omics technologies have not only addressed longstanding challenges but also enabled unprecedented studies of cellular heterogeneity with unprecedented resolution and scale. These strides have led to groundbreaking insights into complex biological systems, paving the way for a more profound comprehension of human biology and diseases. The droplet microfluidic technology has become a crucial component in many single-cell sequencing workflows in terms of throughput, cost-effectiveness, and automation. Utilizing a microfluidic chip to encapsulate and profile individual cells within droplets has significantly improved single-cell research. Therefore, this review aims to comprehensively elaborate the droplet microfluidics-assisted omics methods from a single-cell perspective. The strategies for using droplet microfluidics in the realms of genomics, epigenomics, transcriptomics, and proteomics analyses are first introduced. On this basis, the focus then turns to the latest applications of this technology in different sequencing patterns, including mono- and multi-omics. Finally, the challenges and further perspectives of droplet-based single-cell sequencing in both foundational research and commercial applications are discussed.

Single-cell multimodal chromatin profiles revealing epigenetic regulations of cells in hepatocellular carcinoma

Hepatocellular carcinoma (HCC), with its increasing prevalence globally, is emerging as a major health challenge. Hepatocellular carcinoma cells exhibit both significant homogeneity and heterogeneity, governed by complex epigenomic regulations. Recently, numerous single‐cell unimodal techniques have been used to study HCC at various levelswhich have already revealed a complex interplay at genomic, transcriptomic, spatial and epigenomic levels. However, the use of single‐cell multimodal techniques combining different unimodal layers in HCC remains quite limited, necessitating further studies focusing on these methods to uncover novel markers, and mechanisms at epigenetic levels. In this commentary, we highlight how integrating multimodal approaches with epigenetic modifications can provide new insights into HCC and foster future therapeutic advancements.

Recover then aggregate: unified cross-modal deep clustering with global structural information for single-cell data

Single-cell cross-modal joint clustering has been extensively utilized to investigate the tumor microenvironment. Although numerous approaches have been suggested, accurate clustering remains the main challenge. First, the gene expression matrix frequently contains numerous missing values due to measurement limitations. The majority of existing clustering methods treat it as a typical multi-modal dataset without further processing. Few methods conduct recovery before clustering and do not sufficiently engage with the underlying research, leading to suboptimal outcomes. Additionally, the existing cross-modal information fusion strategy does not ensure consistency of representations across different modes, potentially leading to the integration of conflicting information, which could degrade performance. To address these challenges, we propose the 'Recover then Aggregate' strategy and introduce the Unified Cross-Modal Deep Clustering model. Specifically, we have developed a data augmentation technique based on neighborhood similarity, iteratively imposing rank constraints on the Laplacian matrix, thus updating the similarity matrix and recovering dropout events. Concurrently, we integrate cross-modal features and employ contrastive learning to align modality-specific representations with consistent ones, enhancing the effective integration of diverse modal information. Comprehensive experiments on five real-world multi-modal datasets have demonstrated this method's superior effectiveness in single-cell clustering tasks.

Technology-enabled great leap in deciphering plant genomes

Plant genomes provide essential and vital basic resources for studying many aspects of plant biology and applications (for example, breeding). From 2000 to 2020, 1,144 genomes of 782 plant species were sequenced. In the past three years (2021-2023), 2,373 genomes of 1,031 plant species, including 793 newly sequenced species, have been assembled, representing a great leap. The 2,373 newly assembled genomes, of which 63 are telomere-to-telomere assemblies and 921 have been generated in pan-genome projects, cover the major phylogenetic clades. Substantial advances in read length, throughput, accuracy and cost-effectiveness have notably simplified the achievement of high-quality assemblies. Moreover, the development of multiple software tools using different algorithms offers the opportunity to generate more complete and complex assemblies. A database named N3: plants, genomes, technologies has been developed to accommodate the metadata associated with the 3,517 genomes that have been sequenced from 1,575 plant species since 2000. We also provide an outlook for emerging opportunities in plant genome sequencing.

Single cell RNA-seq: a novel tool to unravel virus-host interplay

Over the last decade, single cell RNA sequencing (scRNA-seq) technology has caught the momentum of being a vital revolutionary tool to unfold cellular heterogeneity by high resolution assessment. It evades the inadequacies of conventional sequencing technology which was able to detect only average expression level among cell populations. In the era of twenty-first century, several epidemic and pandemic viruses have emerged. Being an intracellular entity, viruses totally rely on host. Complex virus-host dynamics result when the virus tend to obtain factors from host cell required for its replication and establishment of infection. As a prevailing tool, scRNA-seq is able to understand virus-host interplay by comprehensive transcriptome profiling. Because of technological and methodological advancement, this technology is capable to recognize viral genome and host cell response heterogeneity. Further development in analytical methods with multiomics approach and increased availability of accessible scRNA-seq datasets will improve the understanding of viral pathogenesis that can be helpful for development of novel antiviral therapeutic strategies.

Special Mini-Issue: Quantitative methods to decipher cellular heterogeneity - from single-cell to spatial omic methods

In this mini-issue, we have a collection of eight reviews that discuss various advanced topics on the investigation of cellular heterogeneity. These reviews highlight the latest developments in technologies that capture and assess biology at single cell resolution, as well as approaches for cellular measurements with spatial information. Challenges and opportunities to develop future innovations and approaches are also presented.

Biophysical Reviews: peering into 2024

After introducing the winner of this year's Michèle Auger Award for Young Scientists' Independent Research, this Editorial for Volume 16 Issue 1 then describes the Issue contents. The Editorial concludes by providing a look into what lies ahead for 2024.

Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases

Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.