Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
Functional & integrative genomics
A multi-scale graph frequency network for structural and functional region analysis in spatial transcriptomics
Spatial transcriptomics enables the systematic exploration of how gene expression patterns are organized within intact tissues, yet effective analysis remains difficult due to the complexity of spatial dependencies and multi-scale tissue architectures. Here, we present the Spatial Graph Frequency Network (SGFN), a deep learning framework that integrates graph signal processing, graph attention, and contrastive learning to jointly model spatial topology and molecular features. Central to SGFN is a frequency-domain enhancement module that decomposes spatial graphs into multi-scale spectral components using the Laplacian eigenbasis, complemented by adaptive wavelet denoising when the retained graph-frequency sequence length permits valid decomposition. Evaluation across diverse biological systems-including the human dorsolateral prefrontal cortex, mouse brain, human breast cancer, osmFISH, MERFISH, STARmap, mouse embryonic development, head and neck angiosarcoma, and brain metastasis-shows that SGFN achieves improved or competitive performance relative to representative baseline methods in reference-based benchmarks, and identifies biologically coherent spatial or functional regions in unlabeled datasets supported by marker-gene, spatial-autocorrelation, cell-type-colocalization, and pathway-enrichment evidence. SGFN accurately reconstructed cortical layer architecture in the human brain, delineated immune and metabolic modules in tumors, and revealed spatiotemporal trajectories during embryogenesis. By combining interpretable frequency-domain representations with data-driven learning, SGFN provides a unified computational framework for decoding tissue organization and molecular heterogeneity, advancing the understanding of developmental, physiological, and pathological spatial systems.
7 July 2026
Read appraisal →Developmental cell
A machine learning-based megakaryocyte identification system uncovers resident organs, markers, and functional diversity
The organ- and stage-specific diversity of megakaryocytes (MKs) has prompted a reassessment of their distribution and functions. By integrating single-cell transcriptomic data across multiple organs and developmental stages, we identified previously unreported MK and platelet markers, including Tnik, a key regulator of MK function and platelet production. Using these markers alongside established ones, we developed a machine learning-based MK identification system (MKIDS) that enables MK detection in the brain, heart, and placenta in mice and humans. Functional studies demonstrated that brain-resident MKs are essential for neural development, underscoring organ-specific roles of MKs in regulating tissue development and function. Transcriptomic integration of MKs across organs and stages, with functional validation, revealed a developmental shift in platelet production-from a mitochondria-low to a mitochondria-enriched subpopulation. Our findings offer a transformative perspective on the MK system, highlighting its cellular diversity, functional complexity, and developmental dynamics.
12 June 2026
Read appraisal →ACS synthetic biology
Prompt-Only Gene-Circuit Modeling with a Large Language Model Simulates the synNotch Spheroids
Morphogenesis emerges from gene regulatory circuits that translate biochemical cues into spatially organized cell fates; yet, constructing quantitative simulations of these processes remains technically demanding. Here, we present a prompt-only pipeline in which a large language model (LLM) automatically generates CompuCell3D (CC3D) simulations directly from natural-language biological specifications. Using the canonical synNotch spheroid assay as a benchmark, we show that the LLM-generated code captures key features of contact-dependent signaling, cadherin-mediated adhesion, and multicellular sorting dynamics without manual programming. Baseline simulations exhibited the expected qualitative ordering of inducer-responder activation but revealed fragmentation into multiple spheroids, implicating premature reciprocal signaling as a kinetic failure mode. Incorporating a cumulative contact-duration threshold for synNotch activation rescued robust single-spheroid formation, suggesting that sustained cell-cell interactions are a critical design principle for this pattern. Our results establish conversational LLM guidance as a powerful accelerator for multiscale modeling, collapsing the steep learning curve of CC3D into a dialog-driven workflow. This framework enables nonspecialists to test gene-circuit hypotheses in silico and provides a generalizable route toward rapid, mechanism-level exploration of developmental design principles.
16 May 2026
Read appraisal →Stem cells (Dayton, Ohio)
Quiescence in the resting zone of the growth plate: a systematic review
Postnatal skeletal growth in childhood and adolescence depends on cartilage organs called (epiphyseal) growth plates. Studies in the last decade have identified populations of skeletal stem cells within mouse growth plates' resting zones. While cellular quiescence is vital for the maintenance of many tissue-resident stem cell populations, the resting zone chondrocytes have been labeled "quiescent" for decades. However, the features of cellular quiescence that have been reported in the postnatal resting zone, how they were defined or experimentally assessed, and knowledge gaps relative to other quiescent cell types, remain to be well described. To address this, we conducted a systematic review, using the PRISMA guidelines, to identify studies of resting zone chondrocytes including the prefix "quiescen*." Definitions, keywords, chronological data and experimental findings were extracted. Our analysis demonstrated that, compared to those in other well-studied tissues, features of cellular quiescence in RZ chondrocytes remain poorly reported and underexplored, with limited molecular and functional characterization. Furthermore, while most identified studies reported changes in cell division parameters, integration between cues controlling resting zone cell quiescence is incomplete and heterogeneity among the various sub-populations of RZ cells/potential quiescent states is yet to be fully determined. This review identifies consensuses and knowledge gaps between studies and between quiescent RZ cells and those in other tissues and can act to enhance consistency and comparability in future studies of "quiescence" in the RZ chondrocytes.
30 Apr 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Machine learning multiscale collective cell dynamics: From single-cell characterization to multicellular monolayer modeling
Collective cell dynamics are fundamental to numerous physiological processes, including embryo development, tissue morphogenesis, immune response, and disease progression. Accurately modeling these dynamics across scales remains challenging, as traditional physics-based models usually rely on many unmeasurable parameters and unclear active physics, limiting their ability to capture both single-cell features and emergent multicellular behaviors. Here, we introduce a scale-adaptive hybrid machine learning (ML) framework, in which complementary physics-guided and physics-agnostic method each resolves an above key issue at single-cell and multicellular scales, respectively. The physics-guided ML method integrates physical models with experimental data to infer previously unmeasurable parameters, enabling more accurate characterization of single-cell shape and velocity features, but unknown active forces and inherent physical assumptions still constrain its ability to predict multicellular scale dynamics. In contrast, the physics-agnostic ML method bypasses explicit physical assumptions to directly model multicellular behaviors from historical state sequences. This method provides robust long-term predictions of coarse-grained density oscillations and waves observed experimentally, yet it suffers from high stochasticity when applied to single-cell scale modeling. By assigning each approach to the scale where it is most effective, our framework leverages their respective strengths while mitigating their limitations. This complementary strategy bridges the gap between theoretical modeling and experimental observations, offering a versatile computational paradigm for multiscale collective cell dynamics with broad potential in diverse physiological and pathological contexts.
27 Apr 2026
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