Adipose tissue consists of multiple types of cells with different proliferative capacities, different directions of differentiation, and different executive functions, so parsing the subcellular types of adipose tissue is of great significance in elucidating the molecular mechanisms of the formation of important traits in the organism. In recent years, with the substantial improvement of the sensitivity of scientific instruments, the continuous improvement of the automation level and precision from sample preparation to data analysis, and the need to improve the resolution of scientific research, scientists have come up with a variety of studies on single cells, and the single-cell multi-omics technology has developed rapidly. Single-cell multi-omics technology mainly includes single-cell genomics, single-cell transcriptomics, single-cell proteomics and single-cell metabolomics and the joint application of each of them. This technology realizes the ability to reveal the heterogeneity of adipocytes, helps discovering new adipocyte isoforms and exploring the trajectory of adipocyte differentiation at the level of single-cell resolution at different levels of biological processes, and has achieved great breakthrough progress in the field of biology and medicine. Single-cell multi-omics technology has been serving as an entry point for research on the occurrence and development of many diseases and accelerating the development of key emerging technologies such as gene editing and stem cell breeding. In this paper, we summarize the types of single-cell multi-omics technology and its characteristics, review the progress of the application of this technology in the deposition of intramuscular, intermuscular, subcutaneous and visceral adipose tissues, and discuss its prospects and challenges. This review aims to demonstrate the important role of this technology in the study of adipose deposition, and provide theoretical basis for the analysis of the trait formation mechanism and the treatment of metabolic disorders, such as obesity, in human beings.

Single-cell and spatial omics technologies are spearheading a profound paradigm shift in the life sciences, moving beyond ‘population averages’ to ‘single-cell resolution’ and reintegrating ‘cellular constitution’ with ‘tissue spatial architecture’, thereby dramatically advancing our understanding of biological complexity. This review provides a brief comprehensive overview of recent advancements in the field. Technologically, the evolution has progressed from single-cell transcriptomics to integrated approaches capturing multiple molecular layers simultaneously, while the emergence of transcriptome-badsed spatial omics has successfully preserved the spatial positioning of cells or microscosystem within native tissues, and further enabling spatial epigenomics and spatial-multiomics. Computationally, artificial intelligence and machine learning have become central engines, powering tools for data integration, spatial deconvolution, cellular communication and other novel foundation models, which not only tackle the challenges of massive datasets but also serve as instruments for novel biological discovery. These technological leaps have fostered significant theoretical innovations. In clinical translation, these technologies, particularly in precision oncology, demonstrate transformative potential by dissecting tumor heterogeneity, mapping the spatial architecture of the tumor immune microenvironment, and enabling disease modeling through single-cell-guided deconvolution of bulk data, offering new avenues for diagnosis, prognosis, and personalized therapy. Despite ongoing challenges in technological throughput, computational scalability, and clinical integration, the continued convergence of single-cell and spatial omics with AI promises to propel basic research towards a more mechanistic and predictive era, ultimately reshaping the future of precision medicine.

Hepatocellular carcinoma (HCC), which is essentially primary liver cancer, is closely related to CD8^＋ T cell immune infiltration and immune suppression. We constructed a CD8⁺ T cells related risk score model to predict the prognosis of HCC patients and provided therapeutic guidance based on the risk score. Using integrated bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) datasets, we identified stable CD8⁺ T cell signatures. Based on these signatures, a 3-gene risk score model, comprised of KLRB1, RGS2, and TNFRSF1B was constructed. The risk score model was well validated through an independent external validation cohort. We divided patients into high-risk and low-risk groups according to the risk score and compared the differences in immune microenvironment between these two groups. Compared with low-risk patients, high-risk patients have higher M2-type macrophage content (P<0.0001) and lower CD8⁺ T cells infiltration (P<0.0001). High-risk patients predict worse response to immunotherapy treatment than low-risk patients (P<0.01). Drug sensitivity analysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients, while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients. Moreover, expression of these 3-gene model was verified by immunohistochemistry. In summary, the establishment and validation of a CD8⁺ T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.

As an important tissue of the body (accounting for approximately 40% of body weight), skeletal muscle is composed of various cell types such as muscle fibers, muscle stem cells, and endothelial cells. It participates in physiological processes including movement, energy metabolism, and internal environment homeostasis through temporal and spatial specific regulation. Its development is divided into two critical stages: embryonic and postnatal periods. Abnormal development can lead to diseases such as muscular dystrophy and directly affect the yield and quality of livestock meat. In recent years, the combination of single-cell transcriptomics (scRNA-seq) and spatial omics (single-cell spatial omics technology) has provided a high-resolution research tool for analyzing the spatiotemporal dynamic regulatory network and intercellular interactions in skeletal muscle development. This article reviews the molecular mechanisms of skeletal muscle development and its application value in animal husbandry breeding, and systematically combs the research progress, analysis processes, data resources, and future directions of single-cell omics, spatial omics, and single-cell spatial omics technology in skeletal muscle development. Among them, single-cell omics can reveal the heterogeneity of skeletal muscle cells, myofiber differentiation trajectories in different livestock and poultry (such as cattle, pigs, and Tibetan chickens) through methods like pseudotime analysis and RNA velocity analysis. Furthermore, single-cell omics can identify key transcription factors (e.g., MYF5, MYOD1) and cell communication pathways (e.g., FGF7-FGFR2), and simultaneously clarify the molecular differences in myoblast differentiation timing and cell composition ratio among different breeds. Relying on technologies such as Visium and Seq-Scope, spatial omics realizes the spatial localization of gene expression in pathological models of mice, Atlantic salmon, and broiler chickens. Spatial omics also clarifies the spatial distribution laws of neuromuscular junction region-specific genes and inflammation-fibrosis cascade reactions, and makes up for the defect of losing spatial context in single-cell technology. Although there are limited direct application cases of single-cell spatial omics technology, it has already analyzed the abnormal fate of myoblasts in facioscapulohumeral muscular dystrophy through MERFISH technology. In terms of technology selection, it is necessary to consider research objectives, molecular modalities, and resolution requirements. At the same time, data analysis needs to address challenges such as data sparsity through methods like DCA denoising and RCTD cell type mapping. In addition, this article summarizes 16 muscle development-related databases including HCA and PanglaoDB. This review further discusses the potential applications of these three types of technologies in the directional regulation of myoblast fate, precise intervention in the growth cycle, improvement of microenvironment interactions, and the development of multi-omics genetic breeding models. This paper is providing a more comprehensive and detailed theoretical reference and technical support for basic research on skeletal muscle development and practical applications in the animal husbandry industry.

Inflammatory response, immunosuppression, and drug sensitivity have been reported to have a significant correlation with the disulfidptosis levels in cancer patients. However, the value of disulfidptosis in colorectal cancer therapy remains unclear. Therefore, we classified the CRC cells into different cell types using single-cell sequencing data and cell-specific markers and analyzed their relationship with the cell disulfidptosis level. We found that the high disulfidptosis regions were concentrated in epithelial-like CRC cells. Further exploration using the disulfidptosis and programmed cell death 1 inhibitor therapy treated differential expression genes indicated that CRC patients with high disulfidptosis levels exhibited a lower risk profile and increased sensitivity to immunotherapy. By using the spatial transcriptomic analysis, we found that ubiquinol-cytochrome c reductase core protein 1 (UQCRC1), a disulfidptosis-related gene, is highly expressed in epithelial-like CRC cells and co-localized with immune-infiltrated tumor regions. Additional bioinformatic analyses and experimental validation further confirmed that UQCRC1 was downregulated in CRC tissues. Overexpression of UQCRC1 suppressed CRC cell proliferation and migration.These findings indicate that UQCRC1 is a potential target for CRC diagnosis and treatment.

Mesenchymal stem cells (MSCs) hold great promise in regenerative medicine due to their multi-lineage differentiation potential and immunomodulatory properties. However, their functional heterogeneity and strong dependency on the microenvironment remain major challenges for clinical application. In recent years, the combination of single-cell transcriptome sequencing (scRNA-seq) and spatial transcriptomics sequencing (ST-seq) has provided revolutionary tools for systematically deciphering the heterogeneity, functional diversity, and microenvironmental interactions of MSCs. Using scRNA-seq, researchers have successfully resolved the functional heterogeneity of MSCs and identified key functional subpopulations, such as pro-angiogenic, immunoregulatory, and matrix-remodeling subsets. Meanwhile, ST-seq has revealed the distinct spatial distribution of MSCs within tissues and their dynamic interaction networks with the microenvironment. The integration of these two technologies has not only enabled the construction of a three-dimensional “identity-location-function” atlas of MSCs, but also uncovered spatiotemporal dynamic regulatory mechanisms of specific subpopulations during tissue repair. Looking forward, the combination of ultra-high-resolution ST-seq platforms such as Xenium, multi-omics integration, and artificial intelligence-driven analysis will shift MSCs research from descriptive studies toward precise intervention, offering new strategies for functional subpopulation screening and microenvironment reprogramming therapy. This review systematically summarizes the latest advances in scRNA-seq and ST-seq technologies in MSC research, discusses their applications in elucidating cellular heterogeneity, spatial microenvironment, and clinical translation, and provides a theoretical basis and technical guidance for precision treatment in regenerative medicine.

Ischemic stroke (IS) research has faced bottlenecks due to the limitations of conventional technologies in resolving cellular heterogeneity and spatiotemporal dynamics. The development of single-cell and spatial omics technologies provides revolutionary tools to break through these constraints. Single-cell omics technologies, by performing high-throughput sequencing on thousands of cells, reveal the high heterogeneity and dynamic state transitions of neurons, glial cells, immune cells, and others post-IS. For instance, microglia contain pro-inflammatory and anti-inflammatory functional subsets, while astrocytes exhibit distinct activation state spectra. Pseudotime analysis further reconstructs the fate trajectories of cells during the damage and repair processes. Spatial omics technologies, conversely, reconstruct spatial maps of gene expression through in situ capture, elucidating molecular gradients between the ischemic core, penumbra, and healthy brain regions, and enabling the analysis of critical cell-cell interaction networks. Integrating the deep phenotyping capability of single-cell sequencing with the in situ localization information from spatial omics constitutes the current core strategy. This multimodal analysis allows for precise anchoring of cell subtypes to their spatial microenvironments, revealing their distribution patterns and functions, and constructing a more accurate atlas of cell-cell communication. This significantly advances the refined dissection of IS mechanisms. This strategy has already accelerated the discovery of potential biomarkers and spatiotemporally specific therapeutic targets. Although challenges remain in sample preparation, data integration, and technical noise, future interdisciplinary collaboration, multi-omics integration, and in-depth mining with artificial intelligence promise to comprehensively transform our understanding of IS. Ultimately, it holds the potential to promote advances in its early diagnosis, precise subtyping, and the development of individualized treatment strategies.

Single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) is a powerful technique for studying cellular heterogeneity and gene regulatory networks, widely applied in epigenetic research. However, the complexity of data analysis workflows and high programming requirements have limited its broader adoption among non-programmer researchers. To address this issue, we developed Signac.UIO, a modular and visual scATAC-seq analysis platform based on the R Shiny framework, integrating mainstream tools such as Signac and Seurat. The platform includes ten key modules covering quality control, cell filtering, dimensionality reduction, clustering, differential analysis, cell annotation, pathway enrichment, motif analysis, and transcription factor footprinting. Through a graphical user interface, users can perform full analyses and obtain interactive visualization results. The platform’s stability and utility have been validated using a public PBMC dataset and it is currently deployed online (https://xulabgdpu.org.cn/Signac.UIO), providing an efficient and user-friendly tool for single-cell epigenomics research.

This review aims to summarize the progress of multi-omics technologies in the study of antibiotic resistance in Cronobacter, with the goal of gaining a deep understanding of its resistance mechanisms and providing a scientific basis for the development of new treatment methods and prevention strategies. By integrating genomics, transcriptomics, proteomics, and metabolomics, the study analyzes gene variations, expression patterns, protein function changes, and metabolic pathway adjustments in Cronobacter. This includes the use of whole-genome sequencing to reveal gene variations related to antibiotic resistance, RNA-seq technology to monitor changes in gene expression patterns, proteomics to study protein expression and function, and metabolomics to analyze dynamic changes in metabolites. The research has found that factors such as biofilm formation and outer membrane proteins significantly affect the antibiotic resistance of Cronobacter. In addition, new potential influencing factors have been identified, including the expression changes of multidrug efflux pump genes, which may play a key role in enhancing antibiotic efflux and reducing intracellular antibiotic concentrations. Multi-omics technologies provide a comprehensive and in-depth perspective for the study of antibiotic resistance in Cronobacter, revealing multiple factors and potential mechanisms that affect resistance. Although some new influencing factors have been identified, their specific molecular mechanisms still require further investigation. The application prospects of multi-omics technologies are broad, and they are expected to provide important support for the development of new treatment methods and prevention strategies.