Rong Fan

Patient T cells modified with chimeric antigen receptors (CARs) have shown remarkable therapeutic success in previously incurable cancers. However, despite its success in hematological malignancies, CAR-T therapy has shown only modest efficacy in solid tumors. The medical field has focused on developing new molecular designs that enhance CAR-T persistence, prevent exhaustion in the tumor microenvironment (TME), and increase the specificity of the CAR-T cell to various tumor antigens. However, much less effort has been placed on enhancing CAR-T cells’ invasion into and migration through the stromal matrix of the TME, where they can elicit tumor-directed cytotoxicity. Here, we engineered CAR-T cells with synthetic cytokine receptors targeting inflammatory cytokines produced by the cells themselves, which increase their speed through collagen I matrices. We show that this enhanced motility is driven by increased CAR-T cell translocation through confined microchannels via cellular deformation, together with elevated migratory speeds in proximity to neighboring cells, suggesting improved responsiveness to local cues. These migration-enhanced CAR-T cells significantly reduce both primary tumor size and distant organ metastasis in triple-negative breast cancer (TNBC) mouse models compared to standard CAR-T cells. These results support molecular design strategies that exploit mechanisms to improve cellular therapies for metastatic solid tumors.

Single-cell atlases have transformed our understanding of mammalian tissue biology, yet the diverse noncoding “dark transcriptome” remains largely uncharted. Here, we present the Dark Transcriptome Mouse Organogenesis Cell Atlas (DT-MOCA), comprising 276,455 cells across embryonic days 11 to 18 and jointly capturing protein-coding transcripts and five major classes of noncoding RNAs through single-nucleus total RNA sequencing, establishing the first comprehensive catalog of noncoding RNAs across major cell types and developmental lineages. We found that long noncoding RNA and microRNA profiles distinguished major cell types and developmental lineages with resolution comparable to messenger RNA, whereas transfer RNA, small nuclear RNA, and small nucleolar RNA programs were less cell type-discriminative and more broadly shared. We further resolve noncoding RNA expression patterns as lineage-specific, temporally modulated, or coordinately regulated by cell identity and developmental stage. We observed that temporally regulated long noncoding RNAs were evolutionarily younger than those associated with cell identity. We further generate a catalog of the change of noncoding RNAs cross all 11 major developmental trajectories. It revealed lineage-specific coordination between transfer RNA supply and codon demand during musculoskeletal organogenesis as well as dynamic microRNA and small nucleolar RNA programs during the central nervous system development. We observed microRNAs converge on regulators of proliferation and gliogenesis and shared induction of Snord116 defining a developmental window relevant to Prader–Willi syndrome. Together, this atlas provides a comprehensive catalog of the developmental dark transcriptome and reveals how noncoding RNA programs are organized across cell identity, developmental time, and lineage progression.

We present the first spatial tri-omic (proteomic, transcriptomic, and epigenomic) taxonomy of human hippocampus, addressing two key challenges in single-cell and spatial transcriptomics: visualizing cell morphology and epigenetic gene regulation in situ. By designing and multiplex imaging a novel panel of 25 protein targets, we generated the first large scale spatial proteomics map of the hippocampus at single cell resolution (330 nanometers). Through integrated spatial proteomic and transcriptomic analyses, we identified canonical hippocampal subfields and distinct cell types within the dentate gyrus and cornu Ammonis (CA). RNA synthesis rates were the highest in the CA pyramidal neurons than any other hippocampal subfields. To map gene regulation, we applied state-of-art spatial ATAC-seq to identify open chromatin regions and cis-regulatory elements, revealing how chromatin landscapes differ across hippocampal subfields. These regional differences help explain why certain neurons are more excitable and recruited for circuit formation, as their chromatin is open and responsive to transcription factors. Together, these approaches created the most comprehensive spatial atlas of the human hippocampus to date, consisting of over 1.6 million single cells from 16 individuals. This study demonstrates the use of spatial proteomics and epigenomics to investigate the molecular neuropathology of human brain in both healthy and diseased states.

Cellular function depends on the spatial organization of cells and biomolecules within the tissue microenvironment. Advances in spatial omics have enabled profiling of molecular features such as transcriptome, proteome and epigenome, and there has been rapid progress of imaging-based approaches to study spatial three-dimensional (3D) genome organization. Here we present Spatial-ATAC-Hi-C, a microfluidic‑based platform for genome-wide, spatially resolved joint-profiling of 3D genome organization and chromatin accessibility on tissue slides. Applied to mouse and human brains, Spatial-ATAC-Hi-C revealed distinct chromatin architecture and gene regulatory programs in neuronal and non-neuronal populations in their native tissue context. In glioblastoma and astrocytoma samples, we detected spatially resolved 3D genome alterations, copy number variations and structural variations across tumor regions, revealing clinically relevant oncogenic events and clonal heterogeneity. By co-profiling of genome architecture and chromatin accessibility while preserving tissue architecture, Spatial-ATAC-Hi-C provides a powerful tool for studying spatial gene regulation in human biology and disease.

Metabolism is fundamental to cell function, yet its activities vary across tissue environments. Resolving these processes in situ at single-cell resolution is crucial for understanding physiology in health and disease. However, existing methods lack biochemical specificity or direct linkage to cell identity. Here we report a method, Raman Enhanced Delineation of Cell Atlases in Tissues (REDCAT), an all-optical platform integrating Raman scattering microscopy and high-plex immunofluorescence to co-map metabolism and cell types. REDCAT achieves subcellular profiling of protein, lipid, nuclear metabolites and redox metabolism in human tissues. In lymph nodes, it revealed cell-type-specific metabolic specialization. In lymphoma, REDCAT uncovered profound reprogramming and transitional states during tumor transformation. In the liver, it resolved zonation-dependent metabolic gradients. By linking cell identity to spatial metabolic states, REDCAT provides a framework for studying immunity and cancer, offering a path to deciphering the metabolic basis of disease.

CD19-targeted chimeric antigen receptor (CAR) T cell therapy achieves high initial response rates in B-cell acute lymphoblastic leukemia (B-ALL), yet half of patients relapse within one year. Pre-infusion product composition decoded by single-cell RNA sequencing (scRNA-seq) carries information predictive of long-term CAR T persistence, but extracting this information from individual patients typically requires highly sophisticated bioinformatics expert annotation, limiting clinical translation. Here, we evaluate whether single-cell foundation models (scFMs) can extract clinically actionable information from engineered CAR T products. We applied four scFMs (scGPT, scFoundation, CellPLM and UCE), including fine-tuned versions of scGPT and scFoundation, to paired basal and CD19-stimulated pre-infusion CAR T products from 33 pediatric patients with B-ALL. Although annotation accuracy declined relative to healthy peripheral blood references, scFM-derived cell composition stratified patients with long-duration B-cell aplasia with a leave-one-out cross-validated area under the receiver operating characteristic curve of 0.879 (95% confidence interval, 0.742–0.986). Notably, foundation-model-identified cell proportion analysis matched or exceeded expert annotations for several predictive features, demonstrating that accurate clinical prediction may not require perfect per-cell annotation to begin with. CD8+XCL1/2+ cells were further identified as the biomarker consistently associated with durable CAR T persistence across models under CD19 stimulation, whereas other candidate populations showed limited reproducibility. Finally, we translate these findings into a locally deployable decision-support AI agent that predicts the probability of sustained CAR T persistence from pre-infusion CAR T scRNA-seq data.

CD19-targeted chimeric antigen receptor (CAR) T cell therapy achieves high initial response rates in B-cell acute lymphoblastic leukemia (B-ALL), yet half of patients relapse within one year. Pre-infusion product composition decoded by single-cell RNA sequencing (scRNA-seq) carries information predictive of long-term CAR T persistence, but extracting this information from individual patients typically requires highly sophisticated bioinformatics expert annotation, limiting clinical translation. Here, we evaluate whether single-cell foundation models (scFMs) can extract clinically actionable information from engineered CAR T products. We applied four scFMs (scGPT, scFoundation, CellPLM and UCE), including fine-tuned versions of scGPT and scFoundation, to paired basal and CD19-stimulated pre-infusion CAR T products from 33 pediatric patients with B-ALL. Although annotation accuracy declined relative to healthy peripheral blood references, scFM-derived cell composition stratified patients with long-duration B-cell aplasia with a leave-one-out cross-validated area under the receiver operating characteristic curve of 0.879 (95% confidence interval, 0.742–0.986). Notably, foundation-model-identified cell proportion analysis matched or exceeded expert annotations for several predictive features, demonstrating that accurate clinical prediction may not require perfect per-cell annotation to begin with. CD8+XCL1/2+ cells were further identified as the biomarker consistently associated with durable CAR T persistence across models under CD19 stimulation, whereas other candidate populations showed limited reproducibility. Finally, we translate these findings into a locally deployable decision-support AI agent that predicts the probability of sustained CAR T persistence from pre-infusion CAR T scRNA-seq data.

SUMMARY: Glycosylation is a fundamental process regulating cellular function, tissue organization, and disease progression. However, comprehensive glycan profiling at single-cell spatial resolution remains largely inaccessible, particularly in clinical archival tissues. Here we develop spatial-GPT, a multimodal platform for simultaneous profiling of glycans, proteins, and/or transcripts in archival formalin-fixed paraffin-embedded (FFPE) tissues. Using a panel of 30 DNA-encoded lectins recognizing major mammalian glycan motifs and structural classes, sequencing-based spatial-GPT (DBiT-GPT) mapped the spatial glycome, proteome, and transcriptome across 16 human liver specimens encompassing steatosis, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC), leading to identification of spatial glyco-codes – combinatorial glycan states associated with distinct cellular identities, tissue features, and pathological processes. Unexpectedly, glyco-codes alone were sufficient to resolve major cell types, disease states, and HCC subtypes, revealing a previously unappreciated level of biological information encoded within the tissue glycome. Spatial glycomics uncovered tumor-like glyco-codes in premalignant regions, suggesting that glycan reprogramming may precede overt malignant transformation. Using imaging-based single-cell spatial glycan-protein profiling (CODEX-GP), we track glyco-codes across the whole-tissue architecture of 3 representative HCC samples. We further examined the glyco-codes across more than 300 patient specimens and quantified cell-type- and disease-specific glyco-codes as well as glycan-defined immune-evasion, T-cell-exhaustion, and steato-fibrotic niches. Together, these findings establish spatial glyco-codes as a previously unrecognized layer of tissue organization that encodes cellular identity, tissue function, and disease progression. The ability of glyco-codes to distinguish major liver pathologies across independent patient cohorts further highlights their potential as a new class of molecular histopathology biomarkers.

Summary: Min, Schweizer, and colleagues use artificial intelligence–powered deep visual proteomics to generate a spatial proteomic atlas of pancreatic cancer precursor evolution, revealing that major metabolic and inflammatory reprogramming occurs long before overt histologic transformation. More broadly, the study highlights the emerging potential of spatial proteomics and multiomics to bridge histopathology with molecular pathology and precision oncology. See related article by Min et al., p. 1323