Spatiotemporal multiomics uncover tumor ecosystem dynamics during metastatic colonization

July 30, 2026| Source: Science

The mechanisms underlying the interactions between disseminated tumor cells (DTCs) and their tissue microenvironment during metastatic colonization are currently poorly understood. We integrated multimodal single-cell and spatial profiling from liver cancer mouse models and human metastases to track the spatiotemporal dynamics of DTCs and their microenvironments from single-cell seeding to overt lung metastasis. We identified a residual population of quiescent Phgdh high DTCs that survived initial innate immune clearance and became transiently enriched in micrometastases. These cells shaped an immune-scarce microenvironment through PHGDH-dependent, H3K27me3-mediated epigenetic silencing of chemokine transcription, thereby promoting metastatic expansion. Cx3cr1 high interstitial macrophages were also transiently enriched before DTC expansion, creating an immune-privileged niche for metastatic outgrowth by recruiting immunosuppressive cells. Inactivating the PHGDH-H3K27me3 axis in DTCs or depleting interstitial macrophages restored immune surveillance and inhibited metastatic colonization. These findings provide insights into the development of micrometastasis-targeting regimens.

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Protein resource allocation across subcellular compartments revealed by large-scale absolute quantitative proteomics in yeast.

Subcellular compartmentalization within complex membrane-bound organelles is a hallmark of eukaryal life, enabling metabolic specialization through spatial segregation. Here, we compiled a dataset encompassing 300 absolute quantitative proteomes, the largest to date, across two yeast species under diverse experimental conditions. Leveraging big data analysis, formula fitting, and machine learning, quantitative correlations among protein abundance, resource allocation, and cellular phenotypes wer

Artificial IntelligenceNovember 3, 2026

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Built environments provide durable evidence for how ancient settlements were organized, expanded, and transformed over time. Yet quantitative approaches to documenting architectural variability are generally labor-intensive, limited to individual structures, or dependent on aggregate volumetric estimates. We present a scalable, machine-learning-based framework that shifts the unit of analysis to the individual building stone and combines two complementary datasets: low-altitude drone imagery of

Artificial IntelligenceNovember 3, 2026

In-context learning as a retraining-free paradigm for constitutive modeling.

Constitutive modeling is fundamental to computational solid mechanics, yet most existing approaches remain material-specific and require retraining or recalibration when materials change. Here we introduce In-Context Modeling (ICM), a retraining-free paradigm for constitutive modeling that infers constitutive behavior from physical context rather than material-specific optimization. Using hyperelasticity as a testbed, we demonstrate that ICM can be trained once on equilibrium deformation fields

Artificial IntelligenceOctober 29, 2026

Data-driven discovery and rapid, direct synthesis of MXenes.

MXenes are a rapidly expanding family of two-dimensional transition metal carbides and nitrides, yet scalable synthesis routes remain limited and historical reports suggest a broader, largely unexplored chemical space. We used machine learning-assisted database mining to uncover hidden multilayer MXenes. Screening reveals 33 previously synthesized but unrecognized MXene candidates. Guided by these findings, we synthesized seven multilayer MXenes using a rapid, scalable self-propagating high-temp

Artificial IntelligenceOctober 29, 2026

Intrinsic gestational timing governs human cerebellar development after preterm birth.

Intrinsic programs shape brain maturation; however, which of these programs are perturbed by prematurity as well as the molecular pathways involved remain unknown. The human cerebellum serves as a paradigm of extrauterine development-its accelerated growth and circuit formation align with the third trimester, a period disrupted by preterm delivery. Using multimodal datasets encompassing in vivo neuroimaging and neurodevelopmental outcomes with postmortem spatial-transcriptomic and histopathologi