Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 10 appraisals
Cell reports methods
Data modeling the interplay between single-cell shape, single-cell protein expression, and tissue state.
While cell shape fundamentally governs tissue function, the underlying links between single-cell shape and protein expression have been difficult to resolve due to limitations in imaging multiplexing and population averaging. Here, we use multiplexed imaging and machine learning to investigate the coupling of cell shape and protein expression in heterogeneous human tissues. Our analysis establishes a universal, bi-directional link between a cell's shape and its protein expression across cell types, diseases, and disease states, amplified for cell state markers. Machine learning interpretability shows that shape features can potentially generate hypotheses of protein functions. Screening all protein-cell type pairs identified a subpopulation of large, p53-positive tumor cells across two cancers. Shape properties further enhanced graph neural network disease state prediction. Our results open the door to unraveling the intricate connections between protein expression, cell shape, tissue organization, and tissue state in a physiological context.
23 July 2026
Read appraisal →Journal of theoretical biology
Likelihood-free parameter inference for spatiotemporal stochastic biological models using neural posterior estimation
Cell migration is a key biological process underlying wound healing, tissue development, and cancer metastasis, yet calibrating mathematical models of migration to experimental data remains a major challenge. Scratch and barrier assays are widely used to study collective cell spreading, and agent-based random walk models provide a natural stochastic description of these experiments. However, parameter inference for such models is hampered by intractable likelihoods, forcing researchers to rely on Approximate Bayesian Computation, which introduces biases and tuning difficulties, or surrogate models that require potentially erroneous noise model specifications. Here, we overcome these limitations using neural posterior estimation, a simulation-based inference framework that learns the full posterior distribution directly from stochastic simulations without surrogate approximations or explicit noise model specifications. We deploy this framework on four progressively complex random walk models of barrier assay experiments describing in vitro cell migration: an isotropic baseline, a model with directional bias (chemotaxis), a model with cell proliferation, and a combined model incorporating both bias and proliferation. For each model, we demonstrate inference in two settings: using one-dimensional summary statistics (column counts), and using a convolutional neural network that enables inference directly from raw two-dimensional spatial data. Neural posterior estimation performs well across all four models, recovering biologically interpretable parameters (e.g. cell motility, directional bias, proliferation rates) from cases where classical surrogate-based methods are adequate through to the combined model where the interplay of multiple mechanisms renders surrogate approximations unreliable. We validate all posteriors using simulation-based calibration diagnostics and provide an open-source implementation of our pipeline to facilitate its adoption and extension to more complex, spatially-structured biological models.
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 →Chembiochem : a European journal of chemical biology
Mutant-Initiated Structure-Guided Refinement Enables Second-Generation Compact IscB Genome Editors
Compact RNA-guided nucleases such as IscB represent an attractive foundation for next-generation genome editors, yet their application in mammalian cells has been constrained by suboptimal activity. Here, instead of re-engineering enzymes, we establish a mutant-initiated, structure-guided optimization strategy to generate second-generation high-activity IscB editors. Using AlphaFold3 to model the engineered IscB*-ωRNA-DNA complex, we reveal remodeling of the nucleic-acid-binding interface induced by activity-enhancing substitutions. Guided by this predicted structure, we perform a focused mutational scan and identify V367 as an activity hotspot. Saturation mutagenesis at this position yields a single substitution, V367Y (IscB*-Act), which increases mean editing efficiency by 34% and achieves up to 2.1-fold improvement across endogenous targets in mammalian cells. Importantly, the V367Y substitution is transferable to an IscB-based adenine base editor, elevating A-to-G conversion by 68% on average and up to 4.46-fold at individual loci without altering the intrinsic editing window. Targeted off-target profiling at loci suggests that V367Y does not substantially increase off-target indels or A-to-G conversion. Together, our work demonstrates a practical framework for second-generation refinement of compact genome editors, bridging deep-learning-enabled structural prediction with interpretable protein engineering, and expands the functional potential of miniature IscB systems for both nuclease and base editing applications.
27 May 2026
Read appraisal →Apoptosis : an international journal on programmed cell death
Single-cell profiling and machine learning identify cuproptosis-related fibroblast subpopulations and fibrogenesis modulator AEBP1 in endometriosis
Endometriosis is characterized by progressive fibrosis and limited therapeutic options. Cuproptosis, a copper-dependent form of regulated cell death, has been implicated in multiple pathological conditions, but its relevance to fibroblast-mediated fibrotic progression in endometriosis remains unclear. Single-cell RNA sequencing data from normal, eutopic, and ectopic endometrial tissues were analyzed to assess cuproptosis-related gene (CRG) activity and fibroblast heterogeneity. Pseudotime analysis, cell-cell communication analysis and high-dimensional weighted gene co-expression network analysis were performed to identify disease-associated fibroblast states and candidate fibrosis-related genes. Machine learning approaches were applied to prioritize candidate hub genes. Functional validation was conducted in endometrial stromal cells, and a mouse model of endometriosis was used to assess the effects of tetrathiomolybdate (TTM), a copper chelator. Elevated CRG activity was enriched in a distinct fibroblast subpopulation with profibrotic transcriptional features. Network and machine learning analyses consistently prioritized AEBP1 as a candidate fibroblast-associated hub gene linked to cuproptosis-related signatures. In vitro, CuCl2 plus elesclomol treatment was associated with increased AEBP1 and fibrosis-related marker expression, accompanied by changes in β-catenin pathway-related proteins, whereas FDX1 or AEBP1 knockdown attenuated these effects. In vivo, TTM treatment reduced lesion burden, fibrotic marker expression and collagen deposition in ectopic lesions. Cuproptosis-related molecular alterations are associated with fibroblast activation and fibrotic progression in endometriosis. Targeting copper metabolism may have therapeutic potential in limiting lesion fibrosis.
19 May 2026
Read appraisal →Neuroinformatics
Instance-Aware Pseudo-Labeling and Class-Focused Contrastive Learning for Weakly Supervised Domain Adaptive Segmentation of Electron Microscopy
Annotation-efficient segmentation of the numerous mitochondria instances from various electron microscopy (EM) images is highly valuable for biological and neuroscience research. Although unsupervised domain adaptation (UDA) methods can help mitigate domain shifts and reduce the high costs of annotating each domain, they typically have relatively low performance in practical applications. Thus, we investigate weakly supervised domain adaptation (WDA) that utilizes additional sparse point labels on the target domain, which require minimal annotation effort and minimal expert knowledge. To take full use of the incomplete and imprecise point annotations, we introduce a multitask learning framework that jointly conducts segmentation and center detection with a novel cross-teaching mechanism and class-focused cross-domain contrastive learning. While leveraging unlabeled image regions is essential, we introduce segmentation self-training with a novel instance-aware pseudo-label (IPL) selection strategy. Unlike existing methods that typically rely on pixel-wise pseudo-label filtering, the IPL semantically selects reliable and diverse pseudo-labels with the help of the detection task. Comprehensive validations and comparisons on challenging datasets demonstrate that our method outperforms existing UDA and WDA methods, significantly narrowing the performance gap with the supervised upper bound. Furthermore, under the UDA setting, our method also achieves substantial improvements over other UDA techniques.
13 May 2026
Read appraisal →Analytical chemistry
TPP-Fabricated All-Fiber Nanoforce Sensor with Deep Learning Analysis Enables Ultrasensitive Cancer Cell Mechanophenotyping
Cellular mechanics provide sensitive, quantitative markers of cancer progression, yet routine nanoforce measurements remain constrained by the cost, footprint, and electromagnetic limitations of conventional platforms. Here we report an all-fiber nanoforce sensor (AFNS) that integrates a two-photon-polymerized (TPP) microcantilever on the end facet of a multimode fiber, establishing a compact and robust analytical strategy for ultrasensitive force measurements. The printed structure comprises a rigid base, a ∼2 μm-thick cantilever disk, and a probe tip. Finite-element analysis is employed to estimate the effective spring constant and to optimize mechanical compliance. Force-dependent multimode specklegrams are quantitatively decoded using an EfficientNet-based regression model, enabling highly accurate force readout (R2 = 0.9999; mean absolute error <0.2%). The sensor achieves a force resolution of 0.41 nN and maintains stable analytical performance over 100 repeated loading cycles, representing a rigorous repeatability assessment for this class of fiber-tip force sensors. The analytical capability of the AFNS is validated by quantifying the Young's modulus of polydimethylsiloxane and by mechanically phenotyping A549 and HepG2 cells at different invasion stages, showing strong agreement with atomic force microscopy. With low cost, replaceable probes, and a compact all-fiber configuration, this work provides a scalable analytical methodology for high-precision micro/nano-force characterization and quantitative cellular mechanics assays.
11 May 2026
Read appraisal →Protein science : a publication of the Protein Society
AlphaFold-driven discovery of oxysterol-binding protein-related protein-phosphoinositide 3-, 4-, and 5-phosphatase interactions using new generation confidence scores
Non-vesicular lipid transport contributes to the regulation of membrane composition and organelle function at membrane contact sites. OSBP-related proteins (ORPs) are central to this process, yet their interaction networks remain incompletely defined. Here, we systematically screened potential interactions between ORPs and phosphoinositide 3-, 4-, and 5-phosphatases using AlphaPulldown2, AlphaFold2-Multimer, and AlphaFold3. We established a protocol for model generation by combining AlphaFold2-Multimer predictions (including five-replicates) with an AlphaPulldown2 interaction screen across around 200 protein pairs, and with AlphaFold3 predictions including lipid-bound and multimeric assemblies. Interface confidence was assessed for consistency using the weighted ipTM + pTM metric, actifpTM, new generation ipSAE scoring, and FoldSeek-Multimer clustering. We further evaluated the protein pairs' biological plausibility based on subcellular localization data, in silico membrane insertion, evolutionary conservation via ConSurf, and protein binding interface analysis using the deep learning tool PeSTo. This integrative protocol uncovered functionally conserved binding modes in the SAC1 lipid phosphatase with the ORP family, particularly with ORP11, and predicted functionally relevant protein-lipid interfaces.
9 May 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
Read appraisal →Lab on a chip
Machine learning-driven single-cell phenotyping in size-controlled microenvironments via parallel deterministic droplet microfluidics
Understanding how individual cells respond to distinct physical microenvironments is critical for mechanobiology, cell therapy, and tissue engineering. Current single-cell encapsulation methods are often limited by Poisson loading and fixed droplet sizes, preventing parallel generation of multiple, size-specific microenvironments and constraining high-resolution phenotypic analyses. Here, we present a droplet microfluidic platform that enables deterministic single-cell encapsulation within microgels of multiple sizes from a single precursor stream, achieved through parallelized flow-focusing combined with cell-selective gelation. This system produces distinct microgel size regimes simultaneously, minimizing empty compartments and enabling direct, side-by-side comparisons of cellular behavior under controlled yet variable confinement. Using machine learning to analyze 3D morphological and cytoskeletal features, we reveal heterogeneous, size-dependent phenotypic responses and demonstrate that cellular phenotypes alone can predict microgel confinement across time. Together, these results establish a data-driven framework for mapping single-cell responses across engineered microenvironments and provide a scalable platform for predictive studies of mechanosensitive behavior in heterogeneous niches.
24 Apr 2026
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