Chenfei Wang

In the mammalian brain, adult neural stem cells (NSCs) located in the ventricular–subventricular zone (V–SVZ) of lateral ventricles and dentate gyrus continuously generate new neurons and glia throughout life. The adult NSCs in V–SVZ are derived from ventricular radial glial cells (RGCs) which become quiescent during embryonic development. However, the origin and establishment of this adult NSC pool remain elusive, primarily due to the challenge of tracing the lineage from embryonic RGCs to their quiescent adult successors. Here, by leveraging Vcam1—a marker continuously expressed from embryonic RGCs to adult NSCs, we unravel the dynamic process and regulatory mechanism underlying the fate bifurcation that gives rise to quiescent seed cells. We demonstrate that Vcam1+ embryonic RGCs directly contribute to the adult NSC pool. A subpopulation of quiescent Vcam1+ RGCs emerges early and is maintained throughout development, while a parallel population of proliferative Vcam1+ progenitors actively replenishes this quiescent reservoir. Through the combinatory analysis of single-cell multiomics and clonal lineage tracing of Vcam1+ cells, we identify the TGF-β/Smad3 pathway as a critical regulator of quiescence. Our work thus provides a mechanistic dissection of how the foundational adult NSC pool is established through precisely regulated fate decisions during embryogenesis, offering new insights into the principles of lifelong neurogenesis.

Mapping of protein-DNA interactions at single-cell resolution remains a central challenge in epigenomics, particularly for transcription factors (TFs), whose sparse binding limits reliable detection. Here, we establish DeChIC-seq (DNA Deaminase-based Chromatin Immuno-Conversion sequencing), a conversion-based strategy that uses a protein A-DddA_tox fusion to directly record protein-DNA interactions by inducing localized C-to-U conversions near antibody-bound chromatin. Retaining genome-wide background sequence information without immunoprecipitation, DeChIC-seq enables profiling of histone modifications and sensitive detection of TF binding. Integration with single-cell whole-genome amplification extends DeChIC-seq to single-cell applications (scDeChIC-seq), enabling chromatin profiling of individual cells. Applied to mouse embryogenesis, scDeChIC-seq resolves lineage-specific chromatin states through profiling of H3K4me3, CTCF, and RAD21 and sensitively detects TF binding, including that of NR5A2, TFAP2C, and KLF5, from extremely limited blastomere inputs. This underscores its strong potential for detecting TF-binding sites in scarce biological samples. DeChIC-seq establishes a conversion-based framework for chromatin profiling that enables mechanistic dissection of TF-driven gene regulation across rare cells, developmental systems, and disease contexts.

Tumor-infiltrating T cell plasticity dictates the efficacy of immune checkpoint blockade. While interferon-imprinted (ISG15+) T cells frequently emerge within the tumor microenvironment, their biological fate—whether acting as terminally exhausted sinks or active precursors—remains controversial. Integrating a pan-cancer atlas of 2.3 million cells across 39 malignancies with epigenomic, spatial, and in vivo profiling, we redefine ISG15+ T cells as an epigenetically poised, transitional reservoir. These stress-adapted cells maintain open chromatin at both cytotoxic and exhaustion loci. Spatially sequestered in the tumor core, they physically juxtapose with proliferating epithelial cells, where LGALS9-CD44 signaling acts as a tether enforcing this stalled state. Crucially, clonal tracking and in vivo models demonstrate that ICB breaks this spatial tethering, driving massive lineage diversion of ISG15+ clones into active effector pools. Consequently, baseline ISG15+ abundance represents a "spring-loaded" reservoir whose initial presence—and subsequent post-treatment clearance—robustly predicts ICB success.

Recent advances in spatial transcriptomics (ST) have brought unprecedented insights into cellular diversity and cell–cell interactions within their spatial context. High-resolution ST techniques, including barcoding-based and imaging-based platforms, have achieved remarkable subcellular resolution. However, precise cell segmentation remains a major challenge, hampering effective single-cell spatial analysis. Existing methods are often platform specific and lack scalability for datasets with large fields of view. Here we introduce Cellist, a new, multi-modal, cell-segmentation method that combines image and expression information, enabling comprehensive cell-level analyses. Applied to mouse brain Stereo-seq data, Cellist improves within-cell transcriptomic coherence compared to existing approaches. It further enhances spatial domain identification and cell-type annotation. Importantly, Cellist is compatible with various ST techniques including Seq-Scope, seqFISH+, STARmap and 10x Xenium, exhibiting robust performance and high computational efficiency across diverse ST platforms and biological systems. Finally, application to post-neoadjuvant immunotherapy, nonsmall cell lung-cancer samples reveals the spatial heterogeneity of tumor clones and identifies therapy response-related myeloid subtypes and structures. These findings highlight the potential of Cellist in enhancing the power of high-resolution ST techniques for characterizing intricate tissue architectures. Cellist is publicly available at https://github.com/wanglabtongji/Cellist.

Deciphering cellular responses to genetic perturbations is fundamental to modeling gene regulatory networks and understanding mechanisms that change cellular phenotypes. However, current computational approaches often fail to outperform simple baseline models, highlighting a critical bottleneck in their generalizability and robustness. Here, we present DeSCOPE, a lightweight conditional variational autoencoder framework for predicting genetic perturbation responses spanning transcriptomic, epigenomic, and broader multi-modal landscapes. We systematically benchmarked DeSCOPE across diverse datasets under two challenging out-of-distribution settings: unseen genes and unseen cell types. DeSCOPE uniquely surpasses simple baselines in the unseen gene scenario, and achieves substantially improved performance for unseen cell types while requiring fine-tuning with far fewer perturbed genes. Finally, DeSCOPE demonstrates superior performance in predicting combinatorial multi-gene perturbations. Overall, DeSCOPE serves as a versatile multi-modal virtual cell model that can effectively guide the design of therapeutic targets that change cellular phenotypes. DeSCOPE is available at https://github.com/wanglabtongji/DeSCOPE .

Metabolic reprogramming is a hallmark of cancer; however, the mechanisms driving metabolic heterogeneity across diverse cell types in the tumor microenviro

Single-strand break repair genes (sheet1), double-strand break repair genes (sheet2), and DNA methylation related genes (sheet3).

Expression of DSB repair genes especially resistant or non-resistant DSB repair genes in TCGA datasets with SSB repair genes mutation and deletion.