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Library-based single-cell analysis of CAR signaling reveals drivers of in vivo persistence

Cell Systems 2025年第5期16卷 101260页

作者： Perez, Caleb R. Garmilla, Andrea Nilsson, Avlant Baghdassarian, Hratch M. Gordon, Khloe S. Lima, Louise G. Smith, Blake E. Maus, Marcela V. Lauffenburger, Douglas A. Birnbaum, Michael E. Koch Institute for Integrative Cancer Research Cambridge MA United States Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA United States Singapore-MIT Alliance for Research and Technology Centre Singapore Singapore Program in Immunology Harvard Medical School Boston MA United States Department of Biology and Biological Engineering Chalmers University of Technology Gothenburg Sweden Kranz Family Center for Cancer Research and Cellular Immunotherapy Program Massachusetts General Hospital Charlestown MA United States Department of Medicine Harvard Medical School Boston MA United States Ragon Institute of MIT MGH and Harvard Cambridge MA United States

The anti-tumor function of engineered T cells expressing chimeric antigen receptors (CARs) is dependent on signals transduced through intracellular signaling domains (ICDs). Different ICDs are known to drive distinct phenotypes, but systematic investigations into how ICD architectures direct T cell function—particularly at the molecular level—are lacking. Here, we use single-cell sequencing to map diverse signaling inputs to transcriptional outputs, focusing on a defined library of clinically relevant ICD architectures. Informed by these observations, we functionally characterize transcriptionally distinct ICD variants across various contexts to build comprehensive maps from ICD composition to phenotypic output. We identify a unique tonic signaling signature associated with a subset of ICD architectures that drives durable in vivo persistence and efficacy in liquid, but not solid, tumors. Our findings work toward decoding CAR signaling design principles, with implications for the rational design of next-generation ICD architectures optimized for in vivo function. © 2025 Elsevier Inc.

关键词： CAR T cells chimeric antigen receptors immunotherapy intracellular signaling domains persistence pooled screens single-cell RNA sequencing T cell signaling T cells tonic signaling

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Novel presentation and pathophysiology of heavy parasitic burdens in Weddell seals (Leptonychotes weddellii) during sedation

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BMC Veterinary Research 2025年第1期21卷 1-12页

作者： Shero, Michelle R. Burek-Huntington, Kathy McCorkell, Robert Nadler, Steven A. Rzucidlo, Caroline L. Klink, Amy C. Hindle, Allyson G. Burns, Jennifer M. Johnson, Shawn Biology Department Woods Hole Oceanographic Institution Woods Hole MA United States Alaska Veterinary Pathology Services Eagle River AK United States Faculty of Veterinary Medicine University of Calgary Calgary Canada Department of Entomology and Nematology University of California Davis Davis CA United States MIT-WHOI Joint Program in Oceanography/Applied Ocean Science & Engineering Woods Hole and Cambridge MA United States School of Life Sciences University of Nevada Las Vegas Las Vegas NV United States Department of Biological Sciences Texas Tech University Lubbock TX United States Sea Change Health Sunnyvale CA United States

Background: Marine mammals act as sentinel species, with top predators’ overall health reflecting their ecosystem, integrated across multiple trophic levels. Yet apparently healthy wild animals may have significant subclinical pathology that goes undetected due to unknown medical histories. Marine mammals, particularly phocid seals, often suffer from heavy parasite burdens. While there are documented cases of severe respiratory infections resulting in complications during sedation, there have been no reports of gastrointestinal parasites contributing to poor outcomes during examinations requiring sedation or anesthesia. This report describes two unique presentations of high intestinal parasite loads that purportedly predisposed Weddell seals (Leptonychotes weddellii) to complications under sedation, and characterizes underlying pathology. Case presentation: Two adult female Weddell seals exhibited prolonged apnea and vomiting while under intravenous sedation, that led to aspiration and mortality despite resuscitation attempts. Post-mortem examination revealed a severe Diphyllobothrium tapeworm impaction in the duodenum, with the parasitic mass causing a partial or complete obstruction. In both cases, the stomach was remarkably distended, suggesting the parasitic mass slowed gastric emptying. Both animals’ stomachs contained a high parasite burden with roundworms embedded into the mucosa. Histological analysis identified underlying pathological conditions that were likely parasite related, including chronic pneumonia associated with lungworm infestations, reactive, depleted and fibrosed lymph nodes, granulomatous lymphadenitis and hepatitis. Further examination in one of the animals revealed severe gastritis and necrotizing duodenitis at the site of the cestode infection. Conclusions: To our knowledge, this is the first description of a significant gastrointestinal parasitic impaction being linked to acute distress during sedation in a marine mammal. We provide an in

关键词： Cestode Duodenitis Gastritis Inflammation Nematode Obstruction Parasite Pinniped Weddell seal

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Reinforcement learning for efficient and robust multi-setpoint and multi-trajectory tracking in bioprocesses

arXiv

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arXiv 2025年

作者： Espinel-Ríos, Sebastián Avalos, José L. Chanona, Ehecatl Antonio del Rio Zhang, Dongda Biomedical Manufacturing Program Commonwealth Scientific and Industrial Research Organisation Clayton Australia Department of Chemical and Biological Engineering Princeton University Princeton United States Omenn-Darling Bioengineering Institute Princeton University Princeton United States The Andlinger Center for Energy and the Environment Princeton University Princeton United States High Meadows Environmental Institute Princeton University Princeton United States Department of Chemical Engineering Imperial College London London United Kingdom Department of Chemical Engineering University of Manchester Manchester United Kingdom

Efficient and robust bioprocess control is essential for maximizing performance and adaptability in advanced biotechnological systems. In this work, we present a reinforcement-learning framework for multi-setpoint and multi-trajectory tracking. Tracking multiple setpoints and time-varying trajectories in reinforcement learning is challenging due to the complexity of balancing multiple objectives, a difficulty further exacerbated by system uncertainties such as uncertain initial conditions and stochastic dynamics. This challenge is relevant, e.g., in bioprocesses involving microbial consortia, where precise control over population compositions is required. We introduce a novel return function based on multiplicative reciprocal saturation functions, which explicitly couples reward gains to the simultaneous satisfaction of multiple references. Through a case study involving light-mediated cybergenetic growth control in microbial consortia, we demonstrate via computational experiments that our approach achieves faster convergence, improved stability, and superior control compliance compared to conventional quadratic-cost-based return functions. Moreover, our method enables tuning of the saturation function’s parameters, shaping the learning process and policy updates. By incorporating system uncertainties, our framework also demonstrates robustness, a key requirement in industrial bioprocessing. Overall, this work advances reinforcement-learning-based control strategies in bioprocess engineering, with implications in the broader field of process and systems engineering. © 2025, CC BY.

关键词： Trajectories

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Temporal resolution of spike coding in feedforward networks with signal convergence and divergence

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PLoS Computational Biology 2025年第4 APRIL期21卷 e1012971页

作者： Mobille, Zach Sikandar, Usama Bin Sponberg, Simon Choi, Hannah School of Mathematics Georgia Institute of Technology AtlantaGA United States Interdisciplinary Graduate Program in Quantitative Biosciences Georgia Institute of Technology AtlantaGA United States School of Physics Georgia Institute of Technology AtlantaGA United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States School of Biological Sciences Georgia Institute of Technology AtlantaGA United States

Convergent and divergent structures in the networks that make up biological brains are found across many species and brain regions at various spatial scales. Neurons in these networks fire action potentials, or "spikes," whose precise timing is becoming increasingly appreciated as large sources of information about both sensory input and motor output. In this work, we investigate the extent to which feedforward convergent/divergent network structure is related to the gain in information of spike timing representations over spike count representations. While previous theories on coding in convergent and divergent networks have largely neglected the role of precise spike timing, our model and analyses place this aspect at the forefront. For a suite of stimuli with different timescales, we demonstrate that structural bottlenecks–small groups of neurons post-synaptic to network convergence–have a stronger preference for spike timing codes than expansion layers created by structural divergence. We further show that this relationship can be generalized across different spike-generating models and measures of coding capacity, implying a potentially fundamental link between network structure and coding strategy using spikes. Additionally, we found that a simple network model based on convergence and divergence ratios of a hawkmoth (Manduca sexta) nervous system can reproduce the relative contribution of spike timing information in its motor output, providing testable predictions on optimal temporal resolutions of spike coding across the moth sensory-motor pathway at both the single-neuron and population levels. © 2025 Mobille et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Neurons

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Connectivity of single neurons classifies cell subtypes in mouse brains

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Nature Methods 2025年第4期22卷 861-873页

作者： Liu, Lijuan Yun, Zhixi Manubens-Gil, Linus Chen, Hanbo Xiong, Feng Dong, Hongwei Zeng, Hongkui Hawrylycz, Michael Ascoli, Giorgio A. Peng, Hanchuan New Cornerstone Science Laboratory SEU-ALLEN Joint Center Institute for Brain and Intelligence Southeast University Nanjing China School of Biological Science & Medical Engineering Southeast University Nanjing China Tencent AI Lab Bellevue WA United States UCLA Brain Research and Artificial Intelligence Nexus Department of Neurobiology David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States Allen Institute for Brain Science Seattle WA United States Center for Neural Informatics Bioengineering Department and Neuroscience Program Krasnow Institute for Advanced Study George Mason University Fairfax VA United States Shanghai Academy of Natural Sciences Fudan University Shanghai China

Classification of single neurons at a brain-wide scale is a way to characterize the structural and functional organization of brains. Here we acquired and standardized a large morphology database of 20,158 mouse neurons and generated a potential connectivity map of single neurons based on their dendritic and axonal arbors. With such an anatomy–morphology–connectivity mapping, we defined neuron connectivity subtypes for neurons in 31 brain regions. We found that cell types defined by connectivity show distinct separation from each other. Within this context, we were able to characterize the diversity in secondary motor cortical neurons, and subtype connectivity patterns in thalamocortical pathways. Our findings underscore the importance of connectivity in characterizing the modularity of brain anatomy at the single-cell level. These results highlight that connectivity subtypes supplement conventionally recognized transcriptomic cell types, electrophysiological cell types and morphological cell types as factors to classify cell classes and their identities. © The Author(s) 2025.

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5-Methyluridine is Ubiquitous in Pseudomonas aeruginosa tRNA and Modulates Antimicrobial Resistance and Virulence

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Journal of Molecular Biology 2025年 169020页

作者： Chittrakanwong, Jurairat Chen, Ruixi Wu, Junzhou Demott, Michael S. Sun, Jingjing Phatinuwat, Kamonwan Jaroensuk, Juthamas Atichartpongkul, Sopapan Mongkolsuk, Skorn Begley, Thomas Dedon, Peter C. Fuangthong, Mayuree Program in Applied Biological Sciences Chulabhorn Graduate Institute Bangkok Thailand Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA United States Antimicrobial Resistance Interdisciplinary Research Group Singapore-MIT Alliance for Research and Technology Singapore Singapore Center for Environmental Health Sciences Massachusetts Institute of Technology Cambridge MA United States Laboratory of Biotechnology Chulabhorn Research Institute Bangkok Thailand Center of Excellence on Environmental Health and Toxicology (EHT) Bangkok Thailand The RNA Institute and Department of Biological Sciences University at Albany Albany NY United States

Building on decades of work in characterizing the dozens of RNA modifications in the microbial epitranscriptome, recent advances in analytical technology and genetics have revealed systems-level functions for many tRNA modifications. The tRNA (uracil-5-)-methyltransferase TrmA and its product, 5-methyl uridine (m⁵U) at position 54 in the T-loop, however, has not been linked to a specific phenotype. Here, we defined the functional and biological roles of TrmA in Pseudomonas aeruginosa (PA14), a major multidrug-resistant pathogen. Surprisingly, though TrmA was found to site-specifically catalyze m⁵U54 on all PA14 tRNAs, loss of TrmA had no effect on the levels of any of 36 tRNA modifications except m⁵U and had minimal effects on multiple phenotypic parameters, including growth rate, morphology, motility, and biofilm formation. However, loss of TrmA conferred a striking polymyxin antibiotic resistance. mRNA and tRNA profiling and proteomics analyses revealed that TrmA regulates the expression of codon-biased gene families at the level of translation, including components of a type III secretion system (T3SS). Loss of TrmA upregulated T3SS, leading to increased macrophage IL-1β in bacterial challenge tests. Altogether, these results revealed novel biological functions of TrmA and its roles in modulating gene expression at multiple levels in P. aeruginosa. © 2025 Elsevier Ltd

关键词： codon usage Pseudomonas aeruginosa translation TrmA tRNA modification

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Cover Feature: Compartmentalized Sesquiterpenoid Biosynthesis and Functionalization in theChlamydomonas reinhardtiiPlastid (ChemBioChem 5/2025)

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ChemBioChem 2025年第5期26卷

作者： Sergio Gutiérrez Sebastian Overmans Gordon B. Wellman Kyle J. Lauersen Bioengineering Program Biological Environmental Sciences and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

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Transfer Learning Enables Robust Prediction of Cellular Toxicity from Environmental Micro- and Nanoplastics

SSRN

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SSRN 2025年

作者： Chen, Kunpeng Li, Keyuan Canchola, Alexa Liu, Jinyong Liu, Qing Chou, Wei-Chun Department of Environmental Sciences University of California RiversideCA92521 United States Environmental Toxicology Graduate Program University of California RiversideCA92521 United States Department of Chemical and Environmental Engineering University of California RiversideCA92521 United States Department of Biological Sciences Clemson University ClemsonSC29634 United States University of Florida United States

Micro- and nanoplastics (MNPs) are emerging pollutants that accumulate in ecosystems, food chains, and the human body, raising concerns about human health risks. However, understanding their toxicity remains challenging due to limited experimental data and the poor performance of conventional machine learning models on small, fragmented datasets. To address this, we developed a transfer learning-based quantitative structure-activity relationship (QSAR) model to predict MNP-induced cytotoxicity. Our approach leverages a three-layer neural network pre-trained on a large nanoparticle dataset and fine-tuned on three small MNP datasets. By incorporating experimental annotations (e.g., dose, exposure time) and quantum chemistry descriptors, the model enhances predictive reliability despite data scarcity. The transfer learning QSAR model achieved high performance (ROC-AUC = 0.88, balanced accuracy = 0.83), outperforming traditional methods. Feature importance analysis identified surface functionalization and dose as critical predictors of cytotoxicity. The results also suggest that nanoplastics may induce cytotoxicity more rapidly than microplastics, even with shorter exposure durations. This study demonstrates the utility of transfer learning in environmental toxicology and supports its use for emerging contaminants where data are limited. Future work will focus on expanding dataset diversity and refining models to improve generalizability for regulatory and risk assessment applications. © 2025, The Authors. All rights reserved.

关键词： Multilayer neural networks

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OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells

Advanced Intelligent Systems

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Advanced Intelligent Systems 2025年第3期7卷

作者： Chen, Po-Yuan Ko, Tai-Ming Institute of Bioinformatics and Systems Biology College of Engineering Bioscience National Yang Ming Chiao Tung University Hsinchu300 Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei115 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei115 Taiwan Institute of Statistical Science Academia Sinica Taipei115 Taiwan Department of Biological Science and Technology College of Engineering Bioscience National Yang Ming Chiao Tung University Hsinchu300 Taiwan National Yang Ming Chiao Tung University Hsinchu300 Taiwan School of Pharmacy College of Pharmacy Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung807 Taiwan

Oxidative stress, characterized by an imbalance between reactive oxygen species (ROS) and antioxidants, plays a pivotal role in inflammatory responses associated with both chronic diseases and acute injuries. In this study, OXidative Stress PREDictor (OxSpred), a supervised learning model tailored to accurately annotate the oxidative stress state of innate immune cells at the single-cell level, is introduced. Compared to the traditional gene-set-variation-analysis-based enrichment method, OxSpred demonstrates superior accuracy with an area under the receiver operating characteristic curve of 0.89 and offers interpretable embeddings with significant biological relevance. Using the predicted ROS states, precise elucidation and interpretation of the roles of novel innate immune cell subtypes can be achieved. Overall, OxSpred enhances the utility of single-cell transcriptomic datasets by providing a robust in silico method for determining intracellular oxidative stress states, thereby enriching the understanding of innate immune cell functions during inflammation. © 2024 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH.

关键词： Cells

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Emulating complex synapses using interlinked proton conductors

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Physical Review Applied 2025年第1期23卷 014027-014027页

作者： Lifu Zhang Ji-An Li Yang Hu Jie Jiang Rongjie Lai Marcus K. Benna Jian Shi Department of Materials Science and Engineering Rensselaer Polytechnic Institute Troy New York 12180 USA Neurosciences Graduate Program University of California San Diego La Jolla California 92093 USA Department of Mathematics Purdue University West Lafayette Indiana 47907 USA Department of Neurobiology School of Biological Sciences University of California at San Diego La Jolla California 92093 USA Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute Troy New York 12180 USA

In terms of energy efficiency and computational speed, neuromorphic electronics based on nonvolatile memory devices are expected to be one of most promising hardware candidates for future artificial intelligence (AI). However, catastrophic forgetting, networks rapidly overwriting previously learned weights when learning new tasks, remains a pivotal obstacle in either digital or analog AI chips for unleashing the true power of brainlike computing. To address catastrophic forgetting in the context of online memory storage, a complex synapse model (the Benna-Fusi model) was proposed recently [M. K. Benna and S. Fusi, Nat. Neurosci. 19, 1697 (2016)], the synaptic weight and internal variables of which evolve following diffusion dynamics. In this work, by designing a proton transistor with a series of charge-diffusion-controlled storage components, we have experimentally realized the Benna-Fusi artificial complex synapse. Memory consolidation from coupled storage components is revealed by both numerical simulations and experimental observations. Different memory timescales for the complex synapse are engineered by the diffusion length of charge carriers and the capacity and number of coupled storage components. The advantages of the demonstrated complex synapse for both memory capacity and memory consolidation are revealed by neural network simulations of face-familiarity detection. Our experimental realization of the complex synapse suggests a promising approach to enhance memory capacity and to enable continual learning.

关键词： Nonvolatile storage

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