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Age-related osteoporosis manifests as a complex pathology that disrupts bone homeostasis and elevates fracture risk, yet the mechanisms facilitating age-related shifts in bone marrow macrophages/osteoclasts (BMMs/OCs) lineage are not fully understood. To decipher these mechanisms, we conducted an investigation into the determinants controlling BMMs/OCs differentiation. We performed single-cell multi-omics profiling on bone marrow samples from mice of different ages (1, 6, and 20 months) to gain a holistic understanding of cellular changes across time. Our analysis revealed that aging significantly instigates OC differentiation. Importantly, we identified Cebpd as a vital gene for osteoclastogenesis and bone resorption during the aging process. Counterbalancing the effects of Cebpd, we found Irf8, Sox4, and Klf4 to play crucial roles. By thoroughly examining the cellular dynamics underpinning bone aging, our study unveils novel insights into the mechanisms of age-related osteoporosis and presents potential therapeutic targets for future exploration.

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Sleep deprivation (SD) has negative effects on brain and body function. Sleep problems are prevalent in a variety of disorders, including neurodevelopmental and psychiatric conditions. Thus, understanding the molecular consequences of SD is of fundamental importance in biology. In this study, we present the first simultaneous bulk and single-nuclear RNA sequencing characterization of the effects of SD in the male mouse frontal cortex. We show that SD predominantly affects glutamatergic neurons, specifically in layers 4 and 5, and produces isoform switching of over 1500 genes, particularly those involved in splicing and RNA binding. At both the global and cell-type specific level, SD has a large repressive effect on transcription, downregulating thousands of genes and transcripts. As a resource we provide extensive characterizations of cell-types, genes, transcripts, and pathways affected by SD. We also provide publicly available tutorials aimed at allowing readers adapt analyses performed in this study to their own datasets.

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Background: Emerging evidence suggests that cell deaths are involved in tumorigenesis and progression, which may be treated as a novel direction of cancers. Recently, a novel type of programmed cell death, disulfidptosis, was discovered. However, the detailed biological and clinical impact of disulfidptosis and related regulators remains largely unknown. Methods: In this work, we first enrolled pancancer datasets and performed multi-omics analysis, including gene expression, DNA methylation, copy number variation and single nucleic variation profiles. Then we deciphered the biological implication of disulfidptosis in clear cell renal cell carcinoma (ccRCC) by machine learning. Finally, a novel agent targeting at disulfidptosis in ccRCC was identified and verified. Results: We found that disulfidptosis regulators were dysregulated among cancers, which could be explained by aberrant DNA methylation and genomic mutation events. Disulfidptosis scores were depressed among cancers and negatively correlated with epithelial mesenchymal transition. Disulfidptosis regulators could satisfactorily stratify risk subgroups in ccRCC, and a novel subtype, DCS3, owning with disulfidptosis depression, insensitivity to immune therapy and aberrant genome instability were identified and verified. Moreover, treating DCS3 with NU1025 could significantly inhibit ccRCC malignancy. Conclusion: This work provided a better understanding of disulfidptosis in cancers and new insights into individual management based on disulfidptosis.

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Mesenchymal stem cells (MSCs) are heterogeneous in morphology and transcriptome, resulting in varying therapeutic outcomes. In this study, we found that 3D spheroid culture of heterogeneous MSCs, which have undergone conventional 2D monolayer culture for 5-6 passages, synchronized the cells into a uniform cell population with dramatically reduced cell size, and considerably increased levels of immunosuppressive genes and growth factors. Single-cell RNA sequencing (scRNA-seq) analysis of the cells revealed that 3D MSCs consisted of 2 major cell subpopulations and both expressed high levels of immunosuppressive factors, compared to 6 subpopulations in 2D MSCs. In addition, 3D MSCs showed a greater suppressive effect on T cells. Moreover, intravenous infusion of a large dose of 3D MSCs prior to imiquimod (IMQ) treatment significantly improved psoriatic lesion. Thus, our results indicate that 3D spheroid culture reprograms heterogeneous MSCs into a uniform immunosuppressive phenotype and promises a novel therapeutic potential for inflammatory diseases.

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Gastric cancer (GC) is a prevalent malignancy with high mortality rates. Immunogenic cell death (ICD) is a unique form of programmed cell death that is closely linked to antitumor immunity and plays a critical role in modulating the tumor microenvironment (TME). Nevertheless, elucidating the precise effect of ICD on GC remains a challenging endeavour. ICD-related genes were identified in single-cell sequencing datasets and bulk transcriptome sequencing datasets via the AddModuleScore function, weighted gene co-expression network (WGCNA), and differential expression analysis. A robust signature associated with ICD was constructed using a machine learning computational framework incorporating 101 algorithms. Furthermore, multiomics analysis, including single-cell sequencing analysis, bulk transcriptomic analysis, and proteomics analysis, was conducted to verify the correlation of these hub genes with the immune microenvironment features of GC and with GC invasion and metastasis. We screened 59 genes associated with ICD and developed a robust ICD-related gene signature (ICDRS) via a machine learning computational framework that integrates 101 different algorithms. Furthermore, we identified five key hub genes (SMAP2, TNFAIP8, LBH, TXNIP, and PIK3IP1) from the ICDRS. Through single-cell analysis of GC tumor s, we confirmed the strong correlations of the hub genes with immune microenvironment features. Among these five genes, LBH exhibited the most significant associations with a poor prognosis and with the invasion and metastasis of GC. Finally, our findings were validated through immunohistochemical staining of a large clinical sample set, and the results further supported that LBH promotes GC cell invasion by activating the epithelial-mesenchymal transition (EMT) pathway.

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As one of the most common tumors in women, the pathogenesis and tumor heterogeneity of breast cancer have long been the focal point of research, with the emergence of tumor metastasis and drug resistance posing persistent clinical challenges. The emergence of single-cell sequencing (SCS) technology has introduced novel approaches for gaining comprehensive insights into the biological behavior of malignant tumors. SCS is a high-throughput technology that has rapidly developed in the past decade, providing high-throughput molecular insights at the individual cell level. Furthermore, the advent of multitemporal point sampling and spatial omics also greatly enhances our understanding of cellular dynamics at both temporal and spatial levels. The paper provides a comprehensive overview of the historical development of SCS, and highlights the most recent advancements in utilizing SCS and spatial omics for breast cancer research. The findings from these studies will serve as valuable references for future advancements in basic research, clinical diagnosis, and treatment of breast cancer.

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Background: Leukopenia can be caused by chemotherapy, which suppresses bone marrow function and can impact the effectiveness of cancer treatment. Qijiao Shengbai Capsule (QJSB) is commonly used to treat leukopenia, but the specific bioactive components and mechanisms of action are not well understood. Objectives and results: This study aimed to analyze the active ingredients of QJSB and its potential targets for treating leukopenia using network pharmacology and molecular docking. Through a combination of serum pharmacochemistry, multi-omics, network pharmacology, and validation experiments in a murine leukopenia model, the researchers sought to understand how QJSB improves leukopenia. The study identified 16 key components of QJSB that act in vivo to increase the number of white blood cells in leukopenic mice. Multi-omics analysis and network pharmacology revealed that the PI3K-Akt and MAPK signaling pathways are important in the treatment of leukopenia with QJSB. Five specific targets (JUN, FOS, BCl-2, CASPAS-3) were identified as key targets. Conclusion: Validation experiments confirmed that QJSB regulates genes related to cell growth and inhibits apoptosis, suggesting that apoptosis may play a crucial role in leukopenia development and that QJSB may improve immune function by regulating apoptotic proteins and increasing CD4+ T cell count in leukopenic mice.

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Eosinophilic esophagitis (EoE) is a chronic, allergic inflammatory disease of the esophagus characterized by eosinophil accumulation and has a growing global prevalence. EoE significantly impairs quality of life and poses a substantial burden on healthcare resources. Currently, only two FDA-approved medications exist for EoE, highlighting the need for broader research into its management and prevention. Recent advancements in omics technologies, such as genomics, epigenetics, transcriptomics, proteomics, and others, offer new insights into the genetic and immunologic mechanisms underlying EoE. Genomic studies have identified genetic loci and mutations associated with EoE, revealing predispositions that vary by ancestry and indicating EoE's complex genetic basis. Epigenetic studies have uncovered changes in DNA methylation and chromatin structure that affect gene expression, influencing EoE pathology. Transcriptomic analyses have revealed a distinct gene expression profile in EoE, dominated by genes involved in activated type 2 immunity and epithelial barrier function. Proteomic approaches have furthered the understanding of EoE mechanisms, identifying potential new biomarkers and therapeutic targets. However, challenges in integrating diverse omics data persist, largely due to their complexity and the need for advanced computational methods. Machine learning is emerging as a valuable tool for analyzing extensive and intricate datasets, potentially revealing new aspects of EoE pathogenesis. The integration of multi-omics data through sophisticated computational approaches promises significant advancements in our understanding of EoE, improving diagnostics, and enhancing treatment effectiveness. This review synthesizes current omics research and explores future directions for comprehensively understanding the disease mechanisms in EoE.

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Traditional Chinese medicine (TCM) is considered to be one of the most comprehensive and influential form of traditional medicine. It plays an important role in clinical treatment and adjuvant therapy for cancer. However, the complex composition of TCM presents challenges to the comprehensive and systematic understanding of its antitumor mechanisms, which hinders further development of TCM with antitumor effects. Omics technologies can immensely help in elucidating the mechanism of action of drugs. They utilize high-throughput sequencing and detection techniques to provide deeper insights into biological systems, revealing the intricate mechanisms through which TCM combats tumors. Multi-omics approaches can be used to elucidate the interrelationships among different omics layers by integrating data from various omics disciplines. By analyzing a large amount of data, these approaches further unravel the complex network of mechanisms underlying the antitumor effects of TCM and explain the mutual regulations across different molecular levels. In this study, we presented a comprehensive overview of the recent progress in single-omics and multi-omics research focused on elucidating the mechanisms underlying the antitumor effects of TCM. We discussed the significance of omics technologies in advancing research on the antitumor properties of TCM and also provided novel research perspectives and methodologies for further advancing this research field.

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Ling Gui Zhu Gan decoction (LGZGD) is a traditional Chinese medicine (TCM) prescription that is widely used in cardiovascular disease clinical prevention and treatment with high efficacy. Recent studies have shown that LGZGD can also be used in hyperlipidemia (HL) intervention, but its pharmacodynamic material basis and its mechanisms remains unclear. This study aimed to reveal the protective effects of LGZGD on HL, elucidate the pharmacodynamic material basis. The hamster HL model was established by high-fat diet. Thereafter, non-targeted metabolomics and quantitative lipidomics were established for screening differential metabolites and pathways. Finally, the mechanisms were elucidated based on network pharmacology to screen for shared targets, which were computational selected by molecular docking. After four weeks of LGZGD administration, the TC, TG, and liver index levels decreased notably and hepatocyte injury was obviously reduced. The Multi-omics identified 62 differential metabolites and 144 differential lipids, respectively. The network pharmacology study predicted 343, 85, and 974 relevant targets from LGZGD components, HL, differential metabolites and lipids, respectively. Eventually, seven core targets were selected by molecular docking. Six key components in LGZGD, including genistein and naringenin, could play a therapeutic role in HL by regulating seven pathways, including HMGCR and PPARA. This comprehensive strategy provides a promising example and approach for further research on TCM for the treatment of lipid metabolic diseases.

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As one of the largest tissues in the animal body, skeletal muscle plays a pivotal role in the production and quality of pork. Consequently, it is of paramount importance to investigate the growth and developmental processes of skeletal muscle. Lijiang pigs, which naturally have two subtypes, fast-growing and slow-growing, provide an ideal model for such studies by eliminating breed-related influences. In this study, we selected three fast-growing and three slow-growing 6-month-old Lijiang pigs as subjects. We utilized assay for transposase-accessible chromatin with sequencing (ATAC-seq) combined with genomics, RNA sequencing, and proteomics to screen for differentially expressed genes and transcription factors linked to increased longissimus dorsi muscle volume in Lijiang pigs. We identified 126 genes through ATAC-seq, including PPARA, TNRC6B, NEDD1, and FKBP5, that exhibited differential expression patterns during muscle growth. Additionally, we identified 59 transcription factors, including Foxh1, JunB, Mef2 family members (Mef2a/b/c/d), NeuroD1, and TEAD4. By examining open chromatin regions (OCRs) with significant genetic differentiation, genes such as SAV1, CACNA1H, PRKCG, and FGFR4 were found. Integrating ATAC-seq with transcriptomics and transcriptomics with proteomics, we identified differences in open chromatin regions, transcription, and protein levels of FKBP5 and SCARB2 genes in fast-growing and slow-growing Lijiang pigs. Utilizing multi-omics analysis with R packages, we jointed ATAC-seq, transcriptome, and proteome datasets, identifying enriched pathways related to glycogen metabolism and skeletal muscle cell differentiation. We pinpointed genes such as MYF6 and HABP2 that exhibit strong correlations across these diverse data types. This study provides a multi-faceted understanding of the molecular mechanisms that lead to differences in pig muscle fiber growth.

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Pancreatic cancer, a highly fatal malignancy, is predicted to rank as the second leading cause of cancer-related death in the next decade. This highlights the urgent need for new insights into personalized diagnosis and treatment. Although molecular subtypes of pancreatic cancer were well established in genomics and transcriptomics, few known molecular classifications are translated to guide clinical strategies and require a paradigm shift. Notably, chronically developing and continuously improving high-throughput technologies and systems serve as an important driving force to further portray the molecular landscape of pancreatic cancer in terms of epigenomics, proteomics, metabonomics, and metagenomics. Therefore, a more comprehensive understanding of molecular classifications at multiple levels using an integrated multi-omics approach holds great promise to exploit more potential therapeutic options. In this review, we recapitulated the molecular spectrum from different omics levels, discussed various subtypes on multi-omics means to move one step forward towards bench-to-beside translation of pancreatic cancer with clinical impact, and proposed some methodological and scientific challenges in store.

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Mutations of KRAS, CDKN2A, TP53, and SMAD4 are the four major driver genes for pancreatic ductal adenocarcinoma (PDAC), of which mutations of KRAS and TP53 are the most frequently recognized. However, molecular-targeted therapies for mutations of KRAS and TP53 have not yet been developed. To identify novel molecular targets, we newly established organoids with the Kras mutation (KrasmuOR) and Trp53 loss of function using Cre transduction and CRISPR/Cas9 (Krasmu/p53muOR) from murine epithelia of the pancreatic duct in KrasLSL-G12D mice, and then analyzed the proteomic and metabolomic profiles in both organoids by mass spectrometry. Hyperfunction of the glycolysis pathway was recognized in Krasmu/p53muOR compared with KrasmuOR. Loss of function of triosephosphate isomerase (TPI1), which is involved in glycolysis, induced a reduction of cell proliferation in human PDAC cell lines with the TP53 mutation, but not in PDAC or in human fibroblasts without TP53 mutation. The TP53 mutation is clinically recognized in 70% of patients with PDAC. In the present study, protein expression of TPI1 and nuclear accumulation of p53 were recognized in the same patients with PDAC. TPI1 is a potential candidate therapeutic target for PDAC with the TP53 mutation.

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This chapter primarily focuses on the progress in depression precision medicine with specific emphasis on the integrative approaches that include artificial intelligence and other data, tools, and technologies. After the description of the concept of precision medicine and a comparative introduction to depression precision medicine with cancer and epilepsy, new avenues of depression precision medicine derived from integrated artificial intelligence and other sources will be presented. Additionally, less advanced areas, such as comorbidity between depression and cancer, will be examined.

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Cell surface proteins serve as primary drug targets and cell identity markers. Techniques such as CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) have enabled the simultaneous quantification of surface protein abundance and transcript expression within individual cells. The published data have been utilized to train machine learning models for predicting surface protein abundance solely from transcript expression. However, the small scale of proteins predicted and the poor generalization ability of these computational approaches across diverse contexts (e.g., different tissues/disease states) impede their widespread adoption. Here, we propose SPIDER (surface protein prediction using deep ensembles from single-cell RNA sequencing), a context-agnostic zero-shot deep ensemble model, which enables large-scale protein abundance prediction and generalizes better to various contexts. Comprehensive benchmarking shows that SPIDER outperforms other state-of-the-art methods. Using the predicted surface abundance of >2,500 proteins from single-cell transcriptomes, we demonstrate the broad applications of SPIDER, including cell type annotation, biomarker/target identification, and cell-cell interaction analysis in hepatocellular carcinoma and colorectal cancer. A record of this paper's transparent peer review process is included in the supplemental information.

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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.