Nat Commun. 2025 Apr 9;16(1):3351. doi: 10.1038/s41467-025-57894-4.

ABSTRACT

Despite extensive research, the gene regulatory architecture governing mammalian cell states remains poorly understood. Here we present an integrative systems biology approach to elucidate the network architecture of primed state pluripotency. Using an unbiased methodology, we identified and experimentally confirmed 132 transcription factors as master regulators (MRs) of mouse epiblast stem cell (EpiSC) pluripotency, many of which were further validated by CRISPR-mediated functional assays. To assemble a comprehensive regulatory network, we silenced each of the 132 MRs to assess their effects on the other MRs and their transcriptional targets, yielding a network of 1273 MR → MR interactions. Network architecture analyses revealed four functionally distinct MR modules (communities), and identified key Speaker and Mediator MRs based on their hierarchical rank and centrality. Our findings elucidate the de-centralized logic of a "communal interaction" model in which the balanced activities of four MR communities maintain primed state pluripotency.

PMID:40204698 | DOI:10.1038/s41467-025-57894-4

J Biotechnol. 2025 Apr 7:S0168-1656(25)00088-4. doi: 10.1016/j.jbiotec.2025.04.002. Online ahead of print.

ABSTRACT

Xylose isomerases (XI, EC 5.3.1.5) are key enzymes for the metabolism of pentoses by microorganisms. The importance of XIs goes beyond academic biochemical research and the catalysis of aldo-ketose conversion by XIs is among the most successful examples of industrial enzyme technology in a market that generates multibillion dollar annual revenues. Here we present an in-depth review of how structural information has contributed to the current understanding of XI catalysis, and discuss topics related to the ongoing efforts to elucidate key aspects of the catalytic mechanism. An overview of XI immobilization is also provided that illustrates how the discoveries in basic enzyme technology research can generate opportunities for novel uses of XI, and we review not only historical aspects but also more recent applications in HFCS, biofuels and other applications. The systems biology revolution will impact all aspects of XI research and application, and we finalize by reviewing the contemporary efforts of metabolic and protein engineering using XI and the future roles of the enzyme in the expanding bioeconomy.

PMID:40204218 | DOI:10.1016/j.jbiotec.2025.04.002

Dev Cell. 2025 Apr 4:S1534-5807(25)00157-1. doi: 10.1016/j.devcel.2025.03.010. Online ahead of print.

ABSTRACT

Cytokinesis challenges epithelial tissue homeostasis by generating forces that pull on neighboring cells. Junction reinforcement at the furrow in Xenopus epithelia regulates the speed of furrowing, suggesting that cytokinesis is subject to resistive forces from epithelial neighbors. We show that contractility factors accumulate near the furrow in neighboring cells, and increasing neighbor cell stiffness slows furrowing. Optogenetically increasing contractility in one or both neighbor cells slows furrowing or induces cytokinetic failure. Uncoupling mechanotransduction between dividing cells and their neighbors increases the furrow ingression rate, alters topological cell packing following cytokinesis, and impairs barrier function at the furrow. Computational modeling validates our findings and provides additional insights about epithelial mechanics during cytokinesis. We conclude that forces from the cytokinetic array must be carefully balanced with restraining forces generated by neighbor cells to regulate the speed and success of cytokinesis and maintain epithelial homeostasis.

PMID:40203834 | DOI:10.1016/j.devcel.2025.03.010

Microbiology (Reading). 2025 Apr;171(4). doi: 10.1099/mic.0.001553.

ABSTRACT

Cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapy has significantly changed the course of the disease in people with cystic fibrosis (CF) (pwCF). The approved triple therapy of elexacaftor, tezacaftor and ivacaftor (ETI), commercially known as Trikafta, increases CFTR channel function, leading to improvements in sweat chloride concentration, exercise capacity, body mass index, lung function and chronic respiratory symptoms. Because of this, the majority of pwCF are living longer and having fewer CF exacerbations. However, colonization with the common CF respiratory pathogens persists and remains a major cause of morbidity and mortality. Here, we review the current literature on the effect of ETI on the respiratory microbiota and discuss the challenges in addressing CF lung infections in the era of these new life-extending therapies.

PMID:40202901 | DOI:10.1099/mic.0.001553

PLoS One. 2025 Apr 9;20(4):e0319299. doi: 10.1371/journal.pone.0319299. eCollection 2025.

ABSTRACT

Traditional crack detection methods initially relied on manual observation, followed by instrument-assisted techniques. Today, road surface inspection leverages deep learning to achieve automated crack detection. However, in the domain of deep learning-based road surface damage classification, the heterogeneous and complex nature of road environments introduces significant background noise and unstructured features. These factors often undermine the robustness and generalization capability of models, thereby adversely affecting classification accuracy. To address this challenge, this research incorporates edge priors by integrating traditional edge detection techniques with deep convolutional neural networks (DCNNs). This paper proposes an innovative mechanism called Edge-Enhanced Multi-Scale Feature Fusion (EE-MSFF), which enhances edge information through multi-scale feature extraction, thereby mitigating the impact of complex backgrounds and improving the model's focus on crack regions. Specifically, the proposed mechanism leverages classical edge detection operators such as Sobel, Prewitt, and Laplacian to perform multi-scale edge information extraction during the feature extraction phase of the model. This process captures both local edge features and global structural information in crack regions, thereby enhancing the model's resistance to interference from complex backgrounds. By employing multi-scale receptive fields, the EE-MSFF mechanism facilitates hierarchical fusion of feature maps, guiding the model to learn edge information that is correlated with crack regions. This effectively strengthens the model's ability to perceive damaged pavement features in complex environments, improving classification accuracy and stability. In this study, the model underwent systematic training and validation on both the complex-background dataset RDD2020 and the simple-background dataset Concrete_Data_Week3. Experimental results demonstrate that the proposed model achieved a classification accuracy of 88.68% on the RDD2020 dataset and 99.5% on the Concrete_Data_Week3 dataset, where background interference is minimal. Furthermore, ablation studies were conducted to analyze the independent contributions of each module, highlighting the performance improvements associated with the integration of multi-scale edge features.

PMID:40203245 | DOI:10.1371/journal.pone.0319299

Nano Lett. 2025 Apr 9. doi: 10.1021/acs.nanolett.5c01435. Online ahead of print.

ABSTRACT

Point-of-care testing (POCT) with multiplexed capability, ultrahigh sensitivity, affordable smart devices, and user-friendly operation is critically needed for clinical diagnostics and food safety. This study presents a deep-learning-assisted microfluidic immunoassay platform that uses a smartphone-based imaging transcoding system, polystyrene microsphere-based encoding, and artificial-intelligence-assisted decoding. Microspheres of varying sizes act as multiprobes, with their quantities correlating to target concentrations after an immunoreaction and separation-filtration within the microfluidic chip. A smartphone with intelligent decoding software captures images of multiprobes from the chip and performs classification, counting, and concentration calculations. The "encoding-decoding" strategy and integrated microfluidic chip design allow these processes to be completed in simple steps, eliminating the need for additional immunomagnetic separation. As a proof of concept, this platform successfully detected multiple respiratory viruses and antibiotics in various real samples with high sensitivity within 30 min, demonstrating great potential as a smart, universal toolkit for next-generation POCT applications.

PMID:40203242 | DOI:10.1021/acs.nanolett.5c01435

PLoS One. 2025 Apr 9;20(4):e0320184. doi: 10.1371/journal.pone.0320184. eCollection 2025.

ABSTRACT

BACKGROUND: Head and neck cancer (HNC) becomes a vital global health burden. Accurate assessment of the disease burden plays an essential role in setting health priorities and guiding decision-making.

METHODS: This study explores data from the Global Burden of Disease (GBD) 2021 study, involving totally 204 countries during the period from 1980 to 2021. The analysis focuses on age-standardized incidence, mortality, and disability-adjusted life years (DALYs) for HNC. A Transformer-based model, HNCP-T, is used for the prediction of future trends from 2022 to 2030, quantified based on the estimated annual percentage change (EAPC).

RESULTS: The global age-standardized incidence rate (ASIR) for HNC has escalated between 1980 and 2021, with men bearing a higher burden than women. In addition, the burden rises with age and exhibits regional disparities, with the greatest impact on low-to-middle sociodemographic index (SDI) regions. Additionally, the model predicts a continued rise in ASIR (EAPC = 0.22), while the age-standardized death rate (ASDR) is shown to decrease more sharply for women (EAPC = -0.92) than men (EAPC = -0.54). The most rapid increase in ASIR is projected for low-to-middle SDI countries, while ASDR and DALY rates are found to decrease in different degrees across regions.

CONCLUSIONS: The current work offers a detailed analysis of the global burden of HNC based on the GBD 2021 dataset and demonstrates the accuracy of the HNCP-T model in predicting future trends. Significant regional and gender-based differences are found, with incidence rates rising, especially among women and in low-middle SDI regions. Furthermore, the results underscore the value of deep learning models in disease burden prediction, which can outperform traditional methods.

PMID:40203229 | DOI:10.1371/journal.pone.0320184

PLoS One. 2025 Apr 9;20(4):e0320816. doi: 10.1371/journal.pone.0320816. eCollection 2025.

ABSTRACT

Advancements in robotics and other technological innovations have accelerated the development of surgical procedures, increasing the demand for training environments that accurately replicate human anatomy. This study developed a system that utilizes the AutoSegmentator extension of 3D Slicer, based on nnU-Net, a state-of-the-art deep learning framework for automatic organ extraction, to import automatically extracted organ surface data into CAD software along with original DICOM-derived images. This system allows medical experts to manually refine the automatically extracted data, making it more accurate and closer to the ideal dataset. First, Python programming is used to automatically generate and save JPEG-format image data from DICOM data for display in Blender. Next, DICOM data imported into 3D Slicer is processed by AutoSegmentator to extract surface data of 104 organs in bulk, which is then exported in STL format. In Blender, a custom-developed Python script aligns the image data and organ surface data within the same 3D space, ensuring accurate spatial coordinates. By using Blender's CAD functionality within this space, the automatically extracted organ boundaries can be manually adjusted based on the image data, resulting in more precise organ surface data. Additionally, organs and blood vessels that cannot be automatically extracted can be newly created and added by referencing the image data. Through this process, a comprehensive anatomical dataset encompassing all required organs and blood vessels can be constructed. The dataset created with this system is easily customizable and can be applied to various surgical simulations, including 3D-printed simulators, hybrid simulators that incorporate animal organs, and surgical simulators utilizing augmented reality (AR). Furthermore, this system is built entirely using open-source, free software, providing high reproducibility, flexibility, and accessibility. By using this system, medical professionals can actively participate in the design and data processing of surgical simulation systems, leading to shorter development times and reduced costs.

PMID:40203219 | DOI:10.1371/journal.pone.0320816

Langmuir. 2025 Apr 9. doi: 10.1021/acs.langmuir.4c04969. Online ahead of print.

ABSTRACT

Geopolymer is regarded as a novel type of eco-friendly material that may replace cement. To improve the prediction accuracy of mechanical properties of fly ash-slag-based geopolymer (FASGG), as well as optimize material composition and mix design, this study utilizes seven key parameters as variables, and compressive and flexural strengths were as outputs. Deep learning techniques were applied to train and predict 600 sets of experimental data, developing a novel predictive model of MK-CNN-GRU, which integrated Maximal Information Coefficient-K-median algorithm, Convolutional Neural Network, and Gated Recurrent Unit algorithms. Results indicated that the ranking of input parameters which were related with compressive strength was curing age, Ca/Si ratio, fly ash-to-slag ratio, Si/Al ratio, water-to-binder ratio, alkali activator modulus, and alkali equivalent. Three classical models were selected as benchmarks for predicting compressive strength at different curing ages. The MK-CNN-GRU model could fully exploit the internal features of experimental data and learn its variation patterns, resulting in more stable predictive performance. An ablation study of the submodels confirms that MK-CNN-GRU model considers temporal dependencies, long- and short-term features, as well as local dependencies and hierarchical feature representations within the data. Experimental data suggested an exponential relationship between flexural and compressive strengths of FASGG. The predictions for flexural strength indicated that the MK-CNN-GRU model effectively captured variations, demonstrating good generalization ability and applicability. This model enhances the estimation accuracy regarding the mechanical behavior of FASGG, offering a theoretical framework for refining its composition and mix design.

PMID:40203137 | DOI:10.1021/acs.langmuir.4c04969

PLoS Comput Biol. 2025 Apr 9;21(4):e1012997. doi: 10.1371/journal.pcbi.1012997. Online ahead of print.

ABSTRACT

In the mouse distal terminal phalanx (P3), it remains mystery why amputation at less than 33% of the digit results in regeneration, while amputation exceeding 67% leads to non-regeneration. Unraveling the molecular mechanisms underlying this disparity could provide crucial insights for regenerative medicine. In this study, we aim to investigate the tissues within the wound bed to understand the tissue microenvironment associated with regenerative versus non-regenerative outcomes. We employed a P3-specific amputation model in mice, integrated with time-series RNA-seq and a macrophage assay challenged with pro- and anti-inflammatory cytokines, to explore these mechanisms. Our findings revealed that non-regenerative digits exhibit a greater intense early transcriptional response in the wound bed compared to regenerative ones. Furthermore, early macrophage phenotypes differ distinctly between regenerative and non-regenerative outcomes. Regenerative digits also display unique co-expression modules related to Bone Morphogenetic Protein 2 (Bmp2). The differentially expressed genes (DEGs) between regenerative and non-regenerative digits are enriched in targets of several transcription factors, such as HOXA11 and HOXD11 from the HOX gene family, showing a time-dependent pattern of enrichment. These transcription factors, known for their roles in bone regeneration, skeletal patterning, osteoblast activity, fracture healing, angiogenesis, and key signaling pathways, may act as master regulators of the regenerative gene signatures. Additionally, we developed a deep learning AI model capable of predicting post-amputation time and level from RNA-seq data, indicating that the regenerative probability may be "encoded" in the transcriptomic response to amputation.

PMID:40203060 | DOI:10.1371/journal.pcbi.1012997

PLoS One. 2025 Apr 9;20(4):e0315340. doi: 10.1371/journal.pone.0315340. eCollection 2025.

ABSTRACT

Deep learning, as a high-performance data analysis method, has demonstrated superior efficiency and accuracy in side-channel attacks compared to traditional methods. However, many existing models enhance accuracy by stacking network layers, leading to increased algorithmic and computational complexity, overfitting, low training efficiency, and limited feature extraction capabilities. Moreover, deep learning methods rely on data correlation, and the presence of noise tends to reduce this correlation, increasing the difficulty of attacks. To address these challenges, this paper proposes the application of an InceptionNet-based network structure for side-channel attacks. This network utilizes fewer training parameters. achieves faster convergence and demonstrates improved attack efficiency through parallel processing of input data. Additionally, a LU-Net-based network structure is proposed for denoising side-channel datasets. This network captures the characteristics of input signals through an encoder, reconstructs denoised signals using a decoder, and utilizes LSTM layers and skip connections to preserve the temporal coherence and spatial details of the signals, thereby achi-eving the purpose of denoising. Experimental evaluations were conducted on the ASCAD dataset and the DPA Contest v4 dataset for comparative studies. The results indicate that the deep learning attack model proposed in this paper effectively enhances side-channel attack performance. On the ASCAD dataset, the recovery of keys requires only 30 traces, and on the DPA Contest v4 dataset, only 1 trace is needed for key recovery. Furthermore, the proposed deep learning denoising model significantly reduces the impact of noise on side-channel attack performance, thereby improving efficiency.

PMID:40203055 | DOI:10.1371/journal.pone.0315340

PLoS One. 2025 Apr 9;20(4):e0320085. doi: 10.1371/journal.pone.0320085. eCollection 2025.

ABSTRACT

An enhancer is a specific DNA sequence typically located within a gene at upstream or downstream position and serves as a pivotal element in the regulation of eukaryotic gene transcription. Therefore, the recognition of enhancers is highly significant for comprehending gene expression regulatory systems. While some useful predictive models have been proposed, there are still deficiencies in these models. To address current limitations, we propose a model, DNABERT2-Enhancer, based on transformer architecture and deep learning, designed for the recognition of enhancers (classified as either enhancer or non-enhancer) and the identification of their activity (strong or weak enhancers). More specifically, DNABERT2-Enhancer is composed of a BERT model for extracting features and a CNN model for enhancers classification. Parameters of the BERT model are initialized by a pre-training DNABERT-2 language model. The enhancer recognition task is then fine-tuned through transfer learning to convert the original sequence into feature vectors. Subsequently, the CNN network is employed to learn the feature vector generated by BERT and produce the prediction results. In comparison with existing predictors utilizing the identical dataset, our approach demonstrates superior performance. This suggests that the model will be a useful instrument for academic research on the enhancer recognition.

PMID:40203028 | DOI:10.1371/journal.pone.0320085

PLoS One. 2025 Apr 9;20(4):e0321028. doi: 10.1371/journal.pone.0321028. eCollection 2025.

ABSTRACT

Campus walking environments significantly influence college students' daily lives and shape their subjective perceptions. However, previous studies have been constrained by limited sample sizes and inefficient, time-consuming methodologies. To address these limitations, we developed a deep learning framework to evaluate campus walking perceptions across four universities in China's Yangtze River Delta region. Utilizing 15,596 Baidu Street View Images (BSVIs), and perceptual ratings from 100 volunteers across four dimensions-aesthetics, security, depression, and vitality-we employed four machine learning models to predict perceptual scores. Our results demonstrate that the Random Forest (RF) model outperformed others in predicting aesthetics, security, and vitality, while linear regression was most effective for depression. Spatial analysis revealed that perceptions of aesthetics, security, and vitality were concentrated in landmark areas and regions with high pedestrian flow. Multiple linear regression analysis indicated that buildings exhibited stronger correlations with depression (β = 0.112) compared to other perceptual aspects. Moreover, vegetation (β = 0.032) and meadows (β = 0.176) elements significantly enhanced aesthetics. This study offers actionable insights for optimizing campus walking environments from a student-centered perspective, emphasizing the importance of spatial design and visual elements in enhancing students' perceptual experiences.

PMID:40203019 | DOI:10.1371/journal.pone.0321028

PLoS One. 2025 Apr 9;20(4):e0321233. doi: 10.1371/journal.pone.0321233. eCollection 2025.

NO ABSTRACT

PMID:40203015 | DOI:10.1371/journal.pone.0321233

Mol Biol Evol. 2025 Apr 9:msaf084. doi: 10.1093/molbev/msaf084. Online ahead of print.

ABSTRACT

Ancestral sequence reconstruction (ASR) is typically performed using homogeneous evolutionary models, which assume that the same substitution propensities affect all sites and lineages. These assumptions are routinely violated: heterogeneous structural and functional constraints favor different amino acids at different sites, and these constraints often change among lineages as epistatic substitutions accrue at other sites. To evaluate how violations of the homogeneity assumption affect ASR under realistic conditions, we developed site-specific substitution models and parameterized them using data from deep mutational scanning experiments on three protein families; we then used these models to perform ASR on the empirical alignments and on alignments simulated under heterogeneous conditions derived from the experiments. Extensive among-site and -lineage heterogeneity is present in these datasets, but the sequences reconstructed from empirical alignments are almost identical when heterogeneous or homogeneous models are used for ASR. Using models fit to DMS data from distantly related proteins in which mutational effects are very different also has a minimal impact on ASR. The rare differences occur primarily where phylogenetic signal is weak - at fast-evolving sites and nodes connected by long branches. When ASR is performed on simulated data, errors in the reconstructed sequences become more likely as branch lengths increase, but incorporating heterogeneity into the model does not improve accuracy. These data establish that ASR is robust to unincorporated realistic forms of evolutionary heterogeneity, because the primary determinant of ASR is phylogenetic signal, not the substitution model. The best way to improve accuracy is therefore not to develop more elaborate models but to apply ASR to densely sampled alignments that maximize phylogenetic signal at the nodes of interest.

PMID:40203289 | DOI:10.1093/molbev/msaf084

Proc Natl Acad Sci U S A. 2025 Apr 15;122(15):e2403083122. doi: 10.1073/pnas.2403083122. Epub 2025 Apr 9.

ABSTRACT

Wetting of liquid droplets on passive surfaces is ubiquitous in our daily lives, and the governing physical laws are well understood. When surfaces become active, however, the governing laws of wetting remain elusive. Here, we propose chemically active wetting as a class of active systems where the surface is active due to a binding process that is maintained away from equilibrium. We derive the corresponding nonequilibrium thermodynamic theory and show that active binding fundamentally changes the wetting behavior, leading to steady, nonequilibrium states with droplet shapes reminiscent of a pancake or a mushroom. The origin of such anomalous shapes can be explained by mapping to electrostatics, where pairs of binding sinks and sources correspond to electrostatic dipoles along the triple line. This is an example of a more general analogy, where localized chemical activity gives rise to a multipole field of the chemical potential. The underlying physics is relevant for cells, where droplet-forming proteins can bind to membranes accompanied by the turnover of biological fuels.

PMID:40203039 | DOI:10.1073/pnas.2403083122

STAR Protoc. 2025 Apr 8;6(2):103747. doi: 10.1016/j.xpro.2025.103747. Online ahead of print.

ABSTRACT

Activity tagging of neuronal ensembles has become an important tool in neuroscience. Here, we present a protocol for visualizing separate neuronal ensembles active during three distinct phases of a memory in transgenic mice. We describe steps to label active neurons using viral microinjection, inducing GFP expression under the robust activity marker (RAM) promoter, and transgenic mice, inducing tdTomato (TdT) expression, and immunohistochemical (IHC) visualization of endogenous cFos expression. We then detail procedures for preparation of tissue, imaging, and quantification of memory events. For complete details on the use and execution of this protocol, please refer to Lesuis et al.1.

PMID:40202842 | DOI:10.1016/j.xpro.2025.103747

STAR Protoc. 2025 Apr 8;6(2):103705. doi: 10.1016/j.xpro.2025.103705. Online ahead of print.

ABSTRACT

We present a protocol for obtaining cancer type and subtype predictions using a machine learning method (subSCOPE). We describe steps for data preparation, subSCOPE setup, and running subSCOPE inference on prepared data. The protocol supports five -omics data types as input (DNA methylation, gene expression, microRNA [miRNA] expression, point mutations, and copy-number variants) and allows individual cancer type and data type selection. For non-The Cancer Genome Atlas (TCGA) cancer samples, it provides subtype-level classification across 26 different TCGA cancer cohorts and 106 subtypes. For complete details on the use and execution of this protocol, please refer to Ellrott et al.1.

PMID:40202838 | DOI:10.1016/j.xpro.2025.103705

Mol Biol Cell. 2025 Apr 9:mbcE24090394. doi: 10.1091/mbc.E24-09-0394. Online ahead of print.

ABSTRACT

The tumor suppressor protein p53 plays a key role in the cellular response to DNA damage. In response to DNA double strand breaks (DSB), cultured cells exhibit oscillations of p53 levels, which impact gene expression and cell fate. The dynamics of p53 in-vivo have only been studied in fixed tissues or using reporters for p53's transcriptional activity. Here we established breast tumors expressing a fluorescent reporter for p53 levels and employed intravital imaging to quantify its dynamics in response to DSB in-vivo. Our findings revealed large heterogeneity among individual cells, with most cells exhibiting a single prolonged pulse. We then tested how p53 dynamics might change under high cell confluency, one factor that differs between cell culture and tissues. We revealed that highly confluent cultured breast cancer cells also show one broad p53 pulse instead of oscillations. Through mathematical modeling, sensitivity analysis and live cell imaging we identified low levels of the phosphatase Wip1, a transcriptional target and negative regulator of p53, as a key contributor to these dynamics. Since high cell confluency better reflects the microenvironment of tissues, the impact of cell confluency on p53 dynamics may have important consequences for cancerous tissues responding to DNA damage inducing therapies.

PMID:40202833 | DOI:10.1091/mbc.E24-09-0394

Mol Biol Cell. 2025 Apr 9:mbcE25030119. doi: 10.1091/mbc.E25-03-0119. Online ahead of print.

ABSTRACT

Pyroptosis represents one type of Programmed Cell Death (PCD). It is a form of inflammatory cell death that is canonically defined by caspase-1 cleavage and Gasdermin-mediated membrane pore formation. Caspase-1 initiates the inflammatory response (through IL-1β processing), and the N-terminal cleaved fragment of Gasdermin D polymerizes at the cell periphery forming pores to secrete pro-inflammatory markers. Cell morphology also changes in pyroptosis, with nuclear condensation and membrane rupture. However, recent research challenges canon, revealing a more complex secretome and morphological response in pyroptosis, including overlapping molecular characterization with other forms of cell death, such as apoptosis. Here, we take a multimodal, systems biology approach to characterize pyroptosis. We treated human Peripheral Blood Mononuclear Cells (PBMCs) with 36 different combinations of stimuli to induce pyroptosis or apoptosis. We applied both secretome profiling (nELISA) and high-content fluorescence microscopy (Cell Painting). To differentiate apoptotic, pyroptotic and control cells, we used canonical secretome markers and modified our Cell Painting assay to mark the N-terminus of Gasdermin-D. We trained hundreds of machine learning (ML) models to reveal intricate morphology signatures of pyroptosis that implicate changes across many different organelles and predict levels of many pro-inflammatory markers. Overall, our analysis provides a detailed map of pyroptosis which includes overlapping and distinct connections with apoptosis revealed through a mechanistic link between cell morphology and cell secretome.

PMID:40202832 | DOI:10.1091/mbc.E25-03-0119