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Single-cell DNA methylation sequencing technology has seen rapid advancements in recent years, playing a crucial role in uncovering cellular heterogeneity and the mechanisms of epigenetic regulation. As sequencing technologies have progressed, the quality and quantity of single-cell methylation data have also increased, making standardized preprocessing workflows and appropriate analysis methods essential for ensuring data comparability and result reliability. However, a comprehensive data analysis pipeline to guide researchers in mining existing data has yet to be established. This review systematically summarizes the preprocessing steps and analysis methods for single-cell methylation data, introduces relevant algorithms and tools, and explores the application prospects of single-cell methylation technology in neuroscience, hematopoietic differentiation, and cancer research. The aim is to provide guidance for researchers in data analysis and to promote the development and application of single-cell methylation sequencing technology.

The composition of T cell subsets and tumor-specific T cell interactions within the tumor microenvironment (TME) contribute to the heterogeneity observed in breast cancer. Moreover, aberrant tumor metabolism is often intimately linked to dysregulated anti-tumor immune function of T cells. Identifying key metabolic genes that affect immune cell interactions thus holds promise for uncovering potential therapeutic targets in the treatment of breast cancer. This study leverages single-cell transcriptomic data from breast cancer to investigate tumor-specific T-cell subsets and their interacting subnetworks in the TME during cancer progression. We further assess the metabolic pathway activities of tumor-specifically activated T-cell subsets. The results reveal that metabolic pathways involved in insulin synthesis, secretion, degradation, as well as fructose catabolism, significantly influence multiple T cell interactions. By integrating the metabolic pathways that significantly up-regulate T cells in tumors and influence their interactions, we identify key abnormal metabolic genes associated with T-cell collaboration and further develop a breast cancer risk assessment model. Additionally, using gene expression profiles of prognosis-related genes significantly associated with aberrant metabolism and drug IC50 values, we predict targeted drugs, yielding potential candidates like GSK-J4 and PX-12. This study integrate the analysis of abnormal T-cell interactions and metabolic pathway abnormalities in the breast cancer TME, elucidating their roles in cancer progression and providing leads for novel breast cancer therapeutic strategies.

The high heterogeneity within and between breast cancer patients complicates treatment determination and prognosis assessment. Treatment decision-making is influenced by various factors, such as tumor subtype, histological grade, and genotype, necessitating personalized treatment strategies. Prognostic outcomes vary significantly depending on patient-specific conditions. As a critical branch of artificial intelligence, machine learning efficiently handles large datasets and automates decision-making processes. The introduction of machine learning offers new solutions for breast cancer treatment selection and prognosis assessment. In the field of cancer therapy, traditional methods for predicting treatment and survival outcomes often rely on single or few biomarkers, limiting their ability to capture the complexity of biological processes comprehensively. Machine learning analyzes patients' multi-omic data and the intricate patterns of variations during cancer initiation and progression to predict patients' survival and treatment outcomes. Consequently, it facilitates the selection of appropriate therapeutic interventions to implement early intervention and improve treatment efficacy for patients. Here, we first introduce common machine learning methods, and then elaborate on the application of machine learning in the field of survival prediction and prognosis from two aspects: evaluating survival and predicting treatment outcomes for breast cancer patients. The aim is to provide breast cancer patients with precise treatment strategies to improve therapeutic outcomes and quality of life.

With the rapid development of high-throughput sequencing technology in the past decade, an increasing number of sequencing methods targeting different types of DNA damage have been developed and widely used in the field. These technologies not only help to elucidate the dynamic processes of repair pathways corresponding to different types of lesions, understand the underlying mechanisms of key factors and identify new hotspots prone to damage, but also greatly advanced our knowledge of crucial physiological processes such as meiotic homologous recombination, antibody generation and cytosine demethylation. These advancements hold significant potential for broader applications in exploring disease initiation and drug development. However, understanding and selecting the appropriate techniques have become difficult. This article reviews the main sequencing detection methods for the most common DNA lesions and introduce their principles, thereby providing valuable insights for the selection, application, further development and optimization of these technologies.

Colorectal cancer (CRC), a malignancy affecting the colon and rectum, ranks as the third most common cancer worldwide and the second leading cause of cancer-related deaths. Early detection of CRC is crucial for preventing metastasis, reducing mortality, improving prognosis, and enhancing patients' quality of life. Genetic factors play a significant role in CRC development, accounting for up to 35% of the disease risk. Genome-wide association studies have identified several genetic loci associated with CRC risk. However, these studies often lack direct evidence of causality. While traditional blood biomarkers such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) are widely used for CRC diagnosis and monitoring, their sensitivity and accuracy in early diagnosis are limited. Thus, there is a pressing need to develop new biomarkers that reflect the genetic background of CRC to improve early detection and diagnostic accuracy. In addition, understanding the genetic mechanisms underlying these biomarkers is essential for elucidating CRC pathogenesis and developing precise personalized treatment strategies. Mendelian randomization (MR) analysis, as an emerging epidemiological tool, can accurately assess the causal relationship between genetic variations and diseases by reducing confounding biases in observational studies. MR analysis has been applied in evaluating the causal impact of various blood biomarkers on CRC risk, shedding lights on the potential causal relationships between these biomarkers and CRC pathogenesis in the context of genetic background. In this review, we summarize the applications of MR analysis in studies of blood biomarkers for CRC, aiming to enhance the early diagnosis and personalized treatment of CRC.

In recent years, statistics and machine learning methods have been widely used to analyze the relationship between human gut microbial metagenome and metabolic diseases, which is of great significance for the functional annotation and development of microbial communities. In this study, we proposed a new and scalable framework for image enhancement and deep learning of gut metagenome, which could be used in the classification of human metabolic diseases. Each data sample in three representative human gut metagenome datasets was transformed into image and enhanced, and put into the machine learning models of logistic regression (LR), support vector machine (SVM), Bayesian network (BN) and random forest (RF), and the deep learning models of multilayer perceptron (MLP) and convolutional neural network (CNN). The accuracy performance of the overall evaluation model for disease prediction was verified by accuracy (A), accuracy (P), recall (R), F1 score (F1), area under ROC curve (AUC) and 10 fold cross-validation. The results showed that the overall performance of MLP model was better than that of CNN, LR, SVM, BN, RF and PopPhy-CNN, and the performance of MLP and CNN models was further improved after data enhancement (random rotation and adding salt-and-pepper noise). The accuracy of MLP model in disease prediction was further improved by 4%-11%, F1 by 1%-6% and AUC by 5%-10%. The above results showed that human gut metagenome image enhancement and deep learning could accurately extract microbial characteristics and effectively predict the host disease phenotype. The source code and datasets used in this study can be publicly accessed in https://github.com/HuaXWu/GM_ML_Classification.git.

GULP1 is an engulfment adaptor protein containing a phosphotyrosine-binding (PTB) domain, and existing studies have shown that it can promote glucose uptake in 3T3-L1 adipocytes. To further explore key metabolically related differential genes downstream of GULP1, this study conducted transcriptome analysis on adipocytes and skeletal muscle cells overexpressing GULP1. Subsequently, abnormally expressed genes were subjected to bioinformatic analysis, and real-time fluorescent quantitative PCR (qRT-PCR) was used for mutual validation with transcriptome sequencing. The results indicated that, with a threshold of P < 0.05 and |Log₂FoldChange| ≥ 1 for screening differentially expressed genes, compared with control cells, there were 278 upregulated and 263 downregulated genes in adipocytes overexpressing GULP1. Metabolism-related GO (Gene Ontology) terms included cholesterol biosynthetic process, cholesterol metabolic process, response to lipopolysaccharide, lipid metabolic process, etc. A total of 52 metabolically related differentially expressed genes were enriched in 10 KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, with lipid metabolism being highly enriched. In skeletal muscle cells overexpressing GULP1, there were 280 upregulated and 302 downregulated genes, with metabolism-related GO terms including hormone metabolic process, response to lipopolysaccharide, one-carbon metabolic process, etc. A total of 86 metabolically related differentially expressed genes were enriched in 10 KEGG pathways, with amino acid metabolism, lipid metabolism, and carbohydrate metabolism being highly enriched. GULP1's biological functions are extensive, including lipid metabolism and oncology. This study, through transcriptomics and bioinformatic analysis, identified key metabolically related differential genes downstream of GULP1, obtained metabolically related differential genes and signaling pathways after GULP1 overexpression, providing important theoretical basis for future research on GULP1 downstream target genes.

With the release of large-scale genomic resources from ancient and modern populations, advancements in computational biology tools, and the enhancement of data mining capabilities, the field of genomics is undergoing a revolutionary transformation. These advancements and changes have not only significantly deepened our understanding of the complex evolutionary processes of human origins, migration, and admixture but have also unveiled the impact of these processes on human health and disease. They have accelerated research into the genetic basis of human health and disease and provided new avenues for uncovering the evolutionary trajectories recorded in the human genome related to population history and disease genetics. The ancestral recombination graph (ARG) reconstructs the evolutionary relationships between genomic segments by analyzing recombination events and coalescence patterns across different regions of the genome. An ARG provides a record of all coalescence and recombination events since the divergence of the sequences under study and specifies a complete genealogy at each genomic position, which is the ideal data structure for genomic analysis. Here, we review the theoretical foundations and research advancements of the ARG, and explore its translational applications and future prospects across various disciplines, including forensic genomics, population genetics, evolutionary medicine, and medical genomics. Our goal is to promote the application of this technique in genomic research, thereby deepening our understanding of the human genome.

Illustrating molecular mechanisms of human embryonic development has always been one of the most significant challenges in biology. The scarcity of human embryo samples, the difficulty in dissecting embryo samples, and the complex structures of human organs are the major obstacles in studying human embryogenesis. In recent years, with the rapid advancement of single-cell technology, humans can systematically analyze the dynamic changes in differentiation at various stages of the central dogma and achieve observation and research with spatial information. This has accelerated the progress in constructing a human developmental cell atlas, ultimately allowing us to depict the cell ontology, fate trajectories, and three-dimensional dynamic changes of human development. In this review, we first introduce the single-cell technologies used to construct the atlas, then summarize the latest progress in human developmental cell atlas, followed by identifying the main problems and challenges in this field so far. Finally, we discuss how to utilize the human developmental cell atlas to address key biological and medical issues. This review provides guidance for the optimal use of single-cell omics technology in constructing and applying a human developmental cell atlas.

Expression quantitative trait loci (eQTL) represent genetic variants that regulate gene expression levels. eQTL analysis has become a crucial method for identifying the functional roles of disease-associated genetic variants in the post-genome-wide association study (GWAS) era, yielding numerous significant discoveries. Traditional eQTL analysis relies on whole-genome sequencing combined with bulk RNA-seq, which obscures gene expression differences between cells and thus fails to identify cell type- or state-dependent eQTL. This limitation makes it challenging to elucidate the roles of disease-associated genetic variants under specific conditions. In recent years, with the development and widespread application of single-cell RNA sequencing (scRNA-seq) technology, scRNA-seq-based eQTL (sc-eQTL) research has emerged as a focal point. The advantage of this approach lies in its ability to leverage the resolution and granularity of single-cell sequencing to uncover eQTL that are dependent on cell type, cell state, and cellular dynamics. This significantly enhances our ability to analyze genetic variants associated with gene expression. Consequently, it holds substantial significance for advancing our understanding of the formation of complex organs and the mechanisms underlying disease onset, progression, intervention, and treatment. This review comprehensively examines the recent advancements in sc-eQTL studies, focusing on their development, experimental design strategies, modeling approaches, and current challenges. The aim is to offer researchers novel perspectives for identifying disease-associated loci and elucidating gene regulatory mechanisms.