This article utilizes single-cell resolution spatial analysis across multiple solid tumors to characterize antigen-presenting cancer-associated fibroblasts (apCAFs). The study reveals two distinct apCAF populations—M-apCAFs (mesothelial-related) and F-apCAFs (fibrocyte-related)—that exhibit different spatial localizations within the tumor microenvironment. Specifically, the F-apCAFs are found predominantly in lymphocyte-enriched niches, while the M-apCAFs associate closely with cancer cells. A major functional finding is that both apCAF populations are the primary stromal source for secreted phosphoprotein 1 (SPP1), a protein known to drive tumor progression and therapy resistance. Further functional experiments in mouse models of peritoneal metastasis and pancreatic cancer demonstrate that targeting or knocking out host SPP1 significantly inhibits tumor growth and metastasis. The authors conclude that these spatial and molecular distinctions provide an important framework for understanding CAF heterogeneity and suggest that anti-SPP1 therapy may overcome therapeutic resistance.

References:

• Chen X, Zhou Z, Xie L, et al. Single-cell resolution spatial analysis of antigen-presenting cancer-associated fibroblast niches[J]. Cancer Cell, 2025.

The article introduces a novel genetic engineering approach called selection gene drives to proactively combat the challenge of drug resistance in cancer treatment. This system is designed to redirect tumor evolution by modifying cancer cells with a dual-switch genetic circuit. The first switch provides a controllable fitness advantage under targeted therapy, promoting the engineered cells' expansion, while the second switch is a therapeutic payload that, when engaged, eliminates both the engineered and native drug-resistant cells via a bystander effect. Through the use of stochastic and spatial evolutionary models, the researchers established the necessary design criteria for this system, demonstrating in both in vitro and mouse models that this forward-engineering strategy can successfully eradicate diverse forms of pre-existing genetic resistance in tumors. The work highlights the modularity of the design and its potential to be a powerful framework for future evolution-guided anticancer therapy.

References:

• Leighow S M, Reynolds J A, Sokirniy I, et al. Programming tumor evolution with selection gene drives to proactively combat drug resistance[J]. Nature Biotechnology, 2025, 43(5): 737-751.

The source addresses the critical limitations in evaluating single-cell profile embeddings, which are widely used for characterizing cell types and states in biological investigations. To demonstrate the incompleteness of existing quality assessment metrics, the researchers introduce a model called Islander, a three-layer perceptron that outperforms established integration methods on standard metrics but severely distorts actual biological structures by creating separate cell "islands." This flaw reveals that current evaluation methods, such as those within the scIB framework, prioritize inter-batch mixing and tight cell clustering at the expense of preserving subtle biological continua or hierarchies. To provide a robust solution, the authors propose a new, complementary metric named scGraph, which evaluates how effectively an embedding preserves the similarities and relationships between cell types across different datasets. The text ultimately asserts that employing both the established scIB metrics and the novel scGraph is essential for a comprehensive evaluation framework that accurately measures quality while ensuring that meaningful biological variation is not obscured by overcorrection.

References:

• Wang H, Leskovec J, Regev A. Limitations of cell embedding metrics assessed using drifting islands[J]. Nature Biotechnology, 2025: 1-4.

The paper introduces a novel computational approach called the single-cell Polygenic Risk Score (scPRS), which is designed to overcome the limitations of conventional risk prediction by accounting for cellular and molecular heterogeneity in complex human diseases. This methodology integrates single-cell epigenome profiling, specifically scATAC-seq data, with a Graph Neural Network (GNN) to calculate genetic risk scores at the individual cell level. Researchers applied scPRS to four major conditions, including Type 2 Diabetes, Alzheimer Disease, and Hypertrophic Cardiomyopathy, consistently demonstrating superior predictive performance compared to established polygenic score methods. Critically, scPRS is capable of prioritizing and identifying disease-relevant cell types, such as specific pancreatic cells in T2D or microglia in AD. Furthermore, the model uncovers cell-type-specific genetic regulatory programs, allowing for the fine-mapping of causal risk variants and genes associated with disease pathogenesis. Experimental validation confirmed that genetic variations pinpointed by scPRS impact essential cellular functions, supporting the model's high resolution and biological interpretability.

References:

• Zhang S, Shu H, Zhou J, et al. Single-cell polygenic risk scores dissect cellular and molecular heterogeneity of complex human diseases[J]. Nature Biotechnology, 2025: 1-17.

The source details the creation of a comprehensive spatiotemporal atlas of the mouse colon to characterize the complex tissue and molecular changes that occur during aging across various anatomical regions and life stages. This extensive resource was built by integrating data from roughly 1,500 tissue sections analyzed by spatial transcriptomics (ST) and over 400,000 profiles gathered through single nucleus RNA-sequencing (snRNA-seq). To synthesize this multimodal data, the authors developed a novel hierarchical Bayesian model called cSplotch, which accurately infers cell-type-specific gene expression by integrating molecular and histological information. The analysis identified significant molecular and cellular gradients along the longitudinal (proximal-distal) axis and the crypt axis that correspond to functional specialization within the organ. Temporal analysis revealed that aging is associated with a pervasive decline in secretory goblet cells and an expansion of progenitor cells, ultimately leading to a pervasive inflammatory state in the mucosal lining.

References:

• Daly A C, Cambuli F, Äijö T, et al. Tissue and cellular spatiotemporal dynamics in colon aging[J]. Nature biotechnology, 2025: 1-13.

The paper introduces PRISM (profiling of RNA in situ through single-round imaging), a new high-multiplex fluorescent in situ hybridization (FISH) method designed to make spatial RNA imaging more accessible and efficient by overcoming the limitations of conventional microscopy. PRISM expands coding capacity by using color-intensity barcoding within a single round of staining, allowing for up to 64-plex detection without complex, expensive fluidics-dependent instrumentation. The article demonstrates PRISM’s robustness by applying it to create detailed spatial and quasi-3D atlases of diverse tissues, including mouse embryos, mouse brain sections, and human hepatocellular carcinoma (HCC). These applications reveal intricate biological details, such as cellular heterogeneity, developmental progression, and the complex microscale organization of the HCC tumor microenvironment.

References:

• Chang T, Zhao S, Deng K, et al. High-plex spatial RNA imaging in one round with conventional microscopes using color-intensity barcodes[J]. Nature Biotechnology, 2025: 1-13.

The resource details the creation of a functional genomics atlas of over 148,000 regulatory regions in neural stem cells (NSCs) using ChIP-STARR-seq to understand gene regulation in neurodevelopment. Based on this atlas, the authors developed BRAIN-MAGNET, a convolutional neural network (CNN) that predicts non-coding regulatory element (NCRE) activity directly from DNA sequences. Critically, BRAIN-MAGNET is validated to prioritize functional non-coding variants linked to neurological diseases by identifying nucleotides essential for NCRE function, often outperforming existing variant prioritization tools in GWAS and rare disease contexts. The research employs rigorous experimental and computational methods to interpret the regulatory genome, including analyzing Transcription Factor (TF) motifs and epigenetic priming of enhancers.

References:

• Deng R, Perenthaler E, Nikoncuk A, et al. BRAIN-MAGNET: A functional genomics atlas for interpretation of non-coding variants[J]. Cell, 2025.

The article introduces Systema, a new evaluation framework designed to improve the prediction of transcriptional responses to genetic perturbations in functional genomics. The authors argue that existing computational methods for predicting these responses are often misleadingly successful because they predominantly capture systematic variation-consistent transcriptional differences between perturbed and control cells-rather than perturbation-specific effects. To address this overestimation of performance, Systema shifts the point of reference from the control cell centroid to the perturbed cell centroid, making evaluation more robust against these systemic biases. Using Systema, the research shows that predicting the effects of unseen genetic perturbations is more difficult than previously indicated by standard metrics, though some models, like fine-tuned scGPT, demonstrate a limited ability to infer coarse-grained, biologically meaningful perturbation effects. This work highlights the need for heterogeneous gene panels and reference-independent metrics to accurately assess the predictive power and biological utility of perturbation response models.

References:

• Viñas Torné R, Wiatrak M, Piran Z, et al. Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation[J]. Nature Biotechnology, 2025: 1-10.

The paper introduces novel imaging platforms, Deep-STARmap and Deep-RIBOmap, designed for scalable, single-cell spatial transcriptomics and translatomics within thick, three-dimensional (3D) tissue blocks, overcoming limitations of traditional thin-sectioning methods. The researchers detail the technological advancements, including scalable probe synthesis and robust complementary DNA crosslinking, which enable the quantification of thousands of gene transcripts and their corresponding translation activities. The utility of these methods is demonstrated through comprehensive studies in mouse brain tissue, combining molecular profiling with neuronal morphology tracing, and in human cutaneous squamous cell carcinoma (cSCC) to analyze tumor–immune cell interactions in 3D. Ultimately, the new platforms provide a more accurate and quantitative understanding of gene expression and cellular organization within complex biological structures, offering significant potential for advancing neuroscience and oncology research.

References:

• Sui X, Lo J A, Luo S, et al. Scalable spatial single-cell transcriptomics and translatomics in 3D thick tissue blocks[J]. Nature Methods, 2025: 1-11.

The paper introduces a disease-agnostic genome-editing strategy called Prime Editing-mediated Readthrough of Premature Termination Codons (PERT). This technique uses prime editing to permanently convert a redundant endogenous transfer RNA (tRNA) into an optimized suppressor tRNA (sup-tRNA) to rescue diseases caused by premature stop codons (nonsense mutations). The authors detail their iterative screening process to identify the most potent sup-tRNA variants and optimize the prime editing components for efficient, one-time genomic installation. Crucially, they demonstrate that this approach is effective in human cell models of diseases like Batten disease, Tay–Sachs disease, and cystic fibrosis, and it successfully rescues disease pathology in a mouse model of Hurler syndrome, all while showing no detectable toxicity or off-target effects on the transcriptome or proteome. PERT offers a generalized therapeutic solution that may bypass the need to develop a unique genome-editing treatment for every single pathogenic mutation.

References:

• Pierce S E, Erwood S, Oye K, et al. Prime editing-installed suppressor tRNAs for disease-agnostic genome editing[J]. Nature, 2025: 1-12.