Fan Zhang

Cell- and tissue-type-specific DNA methylation markers provide epigenetic signatures of cellular identity and are widely used to infer cellular origin from bulk and single-cell methylomes. Existing resources mainly emphasize disease-associated biomarkers, methylome atlases, genomic annotations or RNA-based cell markers, leaving an unmet need for an integrated region-level resource dedicated to such markers. Here we present CellMethMarker ( http://gu-zhanglab.hfcas.ac.cn/CellMethMarker ), a curated database integrating 347,514 de novo markers derived from uniformly processed whole-genome and reduced-representation bisulfite sequencing datasets, 2,203,507 literature-derived marker records and harmonized metadata for 2,810 samples. CellMethMarker enables cell- and tissue-centered marker retrieval, coordinate-based search, genome-browser visualization, interactive methylation analysis and data download. In an independent single-cell methylome atlas, these markers recover the expected cell-type-associated methylation patterns, and a platform-matched reference built from them lowers the mean deconvolution error from 0.365 to 0.144. CellMethMarker therefore links region-level DNA methylation variation to cellular identity and supports methylome-based cell-type annotation and deconvolution.

Circulating tumor DNA is often scarce in plasma, and regional methylation summaries can obscure informative differences between individual molecules. Here we present ctDNA-BERT, a Transformer that scores individual bisulfite reads without predefined differentially methylated regions by jointly encoding CpG methylation state, nucleotide context and the fragment 3 ^′ end. Xenografts provide molecule-level tumor supervision. The resulting frozen scorer supported tumor-fraction estimation in synthetic and patient-derived xenograft mixtures and enriched tumor-concordant methylation and chromatin-accessibility signals in independent hepatocellular carcinoma and stage III pan-cancer plasma without cohort-specific retraining. Features derived from selected reads discriminated stage I cancers from healthy controls in a retrospective cohort of 1,477 specimens, achieving an out-of-fold area under the receiver operating characteristic curve of 0.884. By separating molecular scoring from sample-level inference, ctDNABERT provides a reusable read-level scoring layer for cell-free DNA methylation analysis.

DNA methylation is a fundamental epigenetic modification that regulates gene expression, maintains genomic integrity and defines cellular identity. Conventional bulk sequencing methods, such as reduced-representation bisulfite sequencing (RRBS), have elucidated genome-wide methylation landscapes but do not reveal cell-to-cell variation, limiting insights into methylation heterogeneity during development, differentiation and disease. Here, we present multiplexed single-cell RRBS (Msc-RRBS), a streamlined and scalable protocol for parallel DNA methylation profiling of hundreds of individual cells. All enzymatic reactions, including cell lysis, restriction digestion, end repair and adapter ligation, are carried out in a single microwell, which eliminates the need for buffer exchange and minimizes DNA loss. Short, indexed adapters are used to efficiently remove adapter dimers and enable effective purification of library DNA by magnetic bead clean-up. The workflow supports the generation of sequencing libraries from single cells, pooled cells or sub-nanogram amounts of purified genomic DNA. The full workflow, from cell sorting to sequencing-ready libraries and data analysis, requires ~6 d for one or more 96-well plates. Msc-RRBS thus provides an efficient, reproducible and high-throughput approach for dissecting cell-to-cell DNA methylation heterogeneity across diverse biological and disease contexts. The experiments require basic molecular biology skills, and primary data processing can be performed by someone with limited bioinformatics training. More advanced analyses need additional computational expertise.

Accurate prediction of drug–target affinity (DTA) is crucial for accelerating the discovery of drug candidates. Despite recent advances in machine learning methods, existing models often rely solely on either sequence information or protein pocket structures. This limitation prevents these models from fully capturing the critical three-dimensional (3D) physical information that governs molecular recognition, thus failing to meet the high-precision demands of drug discovery. To address this, we introduce HelixDTA, a deep learning framework featuring a parallel dual-branch architecture that independently learns representations from two distinct but complementary modalities: sequence context and complete target structures. By integrating sequence-derived features with structure-derived features at the prediction layer, this architecture preserves modality-specific insights while characterizing drug–target interactions more comprehensively. HelixDTA achieved strong and competitive performance on the Davis and KIBA benchmarks and maintained clear advantages across most similarity-based cold-start settings, demonstrating robust generalization to compounds with unseen scaffolds and novel protein targets. Furthermore, its built-in attention mechanism enhances interpretability by highlighting molecular regions that contribute to affinity prediction. Finally, a WRN case study demonstrates the potential use of HelixDTA for structure-aware allosteric candidate prioritization by integrating complete target-level structural context with docking-based pose inspection. In conclusion, HelixDTA offers a highly accurate, interpretable, and robust solution for DTA prediction. It highlights the value of integrating structural context with sequence-derived information and demonstrates significant potential to empower precision drug design and accelerate the drug discovery pipeline.

The B-cell receptor (BCR) repertoire encodes not only antigen-binding specificity but also intrinsic signatures reflecting B-cell functional states and differentiation trajectories. Deciphering the intricate sequence semantics embedded within these repertoires is pivotal for elucidating immune dynamics and expediting antibody discovery. Although single-cell sequencing provides high-resolution insights, its scalability and cost remain major obstacles, leaving population-level repertoire data underexploited. Furthermore, conventional bioinformatics approaches struggle to model the high-order, non-linear semantic dependencies inherent in antibody sequences. To address these challenges, we present BCRInsight, an antibody-specific pretrained language model that integrates a Transformer architecture with phenotype-aware contrastive learning. Pretrained on 80 million human BCR sequences, BCRInsight learns biologically meaningful contextual representations that encode subtle signatures of B-cell activation, maturation, and clonal evolution. Extensive benchmarking demonstrates that BCRInsight achieves state-of-the-art performance across multiple downstream tasks, particularly in paratope prediction. Further evaluation on diverse single-cell immune cohorts, including healthy, neoplastic, and viral infection states, reveals cross-scenario robustness and superior generalization relative to existing methods. Notably, attention-based analyses show that high-attention regions correspond closely to physical antigen-contact residues, highlighting emergent structural interpretability derived solely from self-supervised learning. Collectively, BCRInsight establishes a new paradigm for decoding the “language” of antibodies, offering a scalable and interpretable framework for computational immunology and rational antibody engineering.

Liver disease is a major threat to human health and life safety, primarily encompassing hepatitis caused by various factors (viral, alcoholic, fatty and autoimmune hepatitis), cirrhosis and liver cancer. Cytokines are molecules found on cell membranes that mediate the inflammation, immunity and a range of cellular functions, such as cell differentiation, proliferation, metastasis and apoptosis. In general, the delicate balance between pro- and anti-inflammatory effects is maintained by the body’s regulatory mechanisms. Once this balance is disturbed, complex chain reactions can occur, including systemic injury, tumor, multi-organ failure or death, resulting in the release of cytokines. This review mainly focuses on the classification, biological characteristics, pathogenesis, signaling pathways of various cytokines (interleukins, interferons, tumor necrosis factor, colony-stimulating factors, chemokines and growth factors) and their important roles in the occurrence and development of different liver diseases, mediating the immune responses, and further discusses the application prospects of cytokines in the clinical treatments of liver diseases.

Colorectal cancer (CRC) is a major global health concern, characterized by high morbidity and mortality rates. CRC progression involves intricate molecular networks that remain incompletely understood. In this study, we conducted an integrative multi-omics analysis of transcriptomic, proteomic, and metabolomic profiles from CRC tissues and matched normal adjacent tissues (NATs). Our analysis revealed 1,394 differentially expressed long non-Coding RNAs (lncRNAs), 2,788 genes, 548 proteins, and 91 metabolites. A significant interaction network comprising 22 lncRNAs, 14 mRNAs/proteins, and 9 metabolites was identified, among which lncRNA 60967.1 emerged as a pivotal regulator. Functional validation demonstrated that lncRNA 60967.1 is markedly downregulated in CRC cell lines and patient tissues. Overexpression of lncRNA 60967.1 restored expression of the tumor suppressor PLCD4 and increased levels of all-trans retinoic acid (ATRA). This modulation enhanced IFN-γ-induced apoptosis and increased expression of the IFN-γ receptor subunit IFNGR1, thereby partially reversing IFN-γ resistance. In murine models, lncRNA 60967.1 overexpression promoted immune cell infiltration and synergized with anti–PD-1 therapy to inhibit tumor growth. Collectively, our findings uncover a novel lncRNA-mRNA/protein-metabolite network, the lncRNA 60967.1-PLCD4-ATRA axis, that plays a critical role in CRC progression and immune modulation, offering promising therapeutic targets for improved treatment efficacy. Colorectal cancer (CRC) is characterized by substantial genetic and epigenetic heterogeneity, underscoring the need for novel therapeutic targets. While immunotherapy has led to significant advancements in cancer treatment, approximately 85% of CRC patients exhibit resistance due to different genetic and epigenetic features. Multi-omics approaches, which integrate data across genomic, proteomic, and metabolomic layers, have emerged as powerful tools for elucidating disease mechanisms. In this study, we conducted multi-omics analyses on tumor and adjacent normal tissues from 13 CRC patients. Complementary in vitro and in vivo experiments demonstrated that lncRNA 60967.1 regulates the PLCD4/ATRA axis and modulates the immune response to anti-PD-1 therapy, thereby promoting CRC progression. Our findings reveal a novel regulatory network involving lncRNA, PLCD4, and ATRA, providing a potential new target for CRC therapy.

Deoxyribonucleic acid (DNA) methylation plays a key role in gene regulation and is critical for development and human disease. Techniques such as whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) allow DNA methylation analysis at the genome scale, with Illumina NovaSeq 6000 and MGI Tech DNBSEQ-T7 being popular due to their efficiency and affordability. However, detailed comparative studies of their performance are not available. In this study, we constructed 60 WGBS and RRBS libraries for two platforms using different types of clinical samples and generated approximately 2.8 terabases of sequencing data. We systematically compared quality control metrics, genomic coverage, CpG methylation levels, intra- and interplatform correlations, and performance in detecting differentially methylated positions. Our results revealed that the DNBSEQ platform exhibited better raw read quality, although base quality recalibration indicated potential overestimation of base quality. The DNBSEQ platform also showed lower sequencing depth and less coverage uniformity in GC-rich regions than did the NovaSeq platform and tended to enrich methylated regions. Overall, both platforms demonstrated robust intra- and interplatform reproducibility for RRBS and WGBS, with NovaSeq performing better for WGBS, highlighting the importance of considering these factors when selecting a platform for bisulfite sequencing.