Cell systemsPub Date : 2026-09-16Epub Date: 2026-08-18DOI: 10.1016/j.cels.2026.101702
Alyssa Jasmine Chiang, Nicholas Csicsery, Richard O'Laughlin, Leo Baumgart, Elizabeth Stasiowski, Phuc Nguyen, Austin Doughty, Myra Ashraf, Raegan Mink, Elina C Olson, Michael Ferry, Adam M Feist, Nan Hao, Karsten Zengler, Jeff Hasty
{"title":"A diversified consortium of engineered biosensors enables multi-target quantification.","authors":"Alyssa Jasmine Chiang, Nicholas Csicsery, Richard O'Laughlin, Leo Baumgart, Elizabeth Stasiowski, Phuc Nguyen, Austin Doughty, Myra Ashraf, Raegan Mink, Elina C Olson, Michael Ferry, Adam M Feist, Nan Hao, Karsten Zengler, Jeff Hasty","doi":"10.1016/j.cels.2026.101702","DOIUrl":"10.1016/j.cels.2026.101702","url":null,"abstract":"<p><p>Whole-cell biosensors (WCBs) offer rapid, cost-effective monitoring of environmental contamination. Efforts to optimize detection of isolated target analytes under laboratory conditions have achieved vastly improved performance and set the stage for WCB deployment in complex environments. We propose a framework that leverages cross-reactivity of single-target WCBs to quantify multiple targets using supervised machine learning. Specifically, we engineer six sensors for heavy metal contaminants in laboratory E. coli. We then evolve the strain to generate five chassis with improved growth in seawater conditions and transform them with the sensors to create a set of 30 variants. The variant responses are characterized with microfluidics, revealing significant diversity. Leveraging this diversity, we combinatorially quantify multiple analytes with a machine learning model that takes an in silico consortium of response inputs and outperforms single-target WCBs in over 90% of test samples. These results form a generalizable framework that facilitates WCB translation toward settings beyond the laboratory. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101702"},"PeriodicalIF":7.7,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell systemsPub Date : 2026-09-16Epub Date: 2026-08-19DOI: 10.1016/j.cels.2026.101699
Kamyar Keshavarz, Mo R Ebrahimkhani
{"title":"Synthetic tissue ecology: Ecological control of developmental self-organization across scales.","authors":"Kamyar Keshavarz, Mo R Ebrahimkhani","doi":"10.1016/j.cels.2026.101699","DOIUrl":"10.1016/j.cels.2026.101699","url":null,"abstract":"<p><p>Embryonic development builds tissues and organs through the self-organization of heterogeneous cells across spatial and temporal scales, analogous to an ecosystem. Although ecological systems are often stochastic and multistable, embryogenesis is remarkably reproducible. We argue that this contrast is not a contradiction. Developmental robustness arises from evolutionarily filtered ecological interactions embedded within hierarchically organized, multiscale architectures that restrict accessible collective states. Competition, cooperation, niche construction, and density-dependent coupling are not metaphors but core interaction dynamics that shape tissues in vivo. In vitro systems frequently lack these multiscale constraints, allowing more permissive ecological dynamics and increased variability. We propose \"synthetic tissue ecology\" as a conceptual and engineering framework that treats development as a stabilized regime of ecological organization. By shifting focus to interaction architectures and constraints that direct cross-scale self-organization, this framework enables the decoding of mesoscale modules and tissue-coupling principles while supporting the engineering of robust organoids, embryoids, and regenerative systems.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101699"},"PeriodicalIF":7.7,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell systemsPub Date : 2026-09-16Epub Date: 2026-06-18DOI: 10.1016/j.cels.2026.101644
James A Briggs, Janghwan Cho, Daniel Strebinger, Julia Joung, Tatum W Braun, Rhiannon K Macrae, Hojun Li, Feng Zhang
{"title":"Pooled combinatorial screening identifies transcription factor sets that drive hematopoietic progenitor-like cell fate.","authors":"James A Briggs, Janghwan Cho, Daniel Strebinger, Julia Joung, Tatum W Braun, Rhiannon K Macrae, Hojun Li, Feng Zhang","doi":"10.1016/j.cels.2026.101644","DOIUrl":"10.1016/j.cels.2026.101644","url":null,"abstract":"<p><p>Mammalian cells can be directed toward specific fates by overexpression of transcription factors (TFs). However, discovering and optimizing which TFs in combination produce a state of interest remains challenging. Here, we develop a scalable screening platform that addresses this challenge by combining high multiplicity of infection (MOI), pooled delivery of barcoded TF open reading frames (ORFs), data augmentation, targeted cell enrichments, and single-cell transcriptomic readouts. As proof of principle, we apply the platform to optimize the generation of hematopoietic stem and progenitor-like cells (HSPCs) from human embryonic stem cells. Our data demonstrate technical performance across a range of key metrics and reveal a richly structured reprogramming fitness landscape over millions of TF combinations. In silico optimization of HSPC-similarity metrics over this landscape revealed two TF combinations that demonstrate superior potency in generating naive multipotent hematopoietic progenitors relative to gold-standard controls. This study demonstrates a powerful approach for data-driven cell fate engineering using complex combinatorial perturbations.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101644"},"PeriodicalIF":7.7,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Evaluation of anti-tumor drugs by simulating physiological absorption and metabolism with a multi-organoid system.","authors":"Chuyu Xiao, Yaqiong Guo, Chunlin Li, Yuan Liu, Ting Wang, Haitao Liu, Yalong Wang, Xiyue Chen, Mengxian Zhang, Ronghui Tan, Zhi Fang, Huidong Liu, Zhicheng Yao, Xiong Lei, Jianhua Qin, Ye-Guang Chen","doi":"10.1016/j.cels.2026.101734","DOIUrl":"https://doi.org/10.1016/j.cels.2026.101734","url":null,"abstract":"<p><p>Although patient-derived tumor organoids are widely applied for evaluating anti-tumor drugs, the lack of a physiologically relevant environment limits drug assessment. Intestinal absorption and liver metabolism are critical for drug efficacy but cannot be recapitulated in single-organoid models. Here, we establish an intestine-liver-tumor multi-organoid system to simulate in vivo absorption and metabolism, which allows for more physiologically relevant evaluations of drug efficacy and toxicity. Under fluidic conditions, the system maintains the viability of intestinal cells and liver organoids, which exhibit superior cellular diversity and close transcriptional resemblance to native tissues, with enhanced differentiation and metabolism. The system reveals the decreased effectiveness of the liver-deactivated drugs docetaxel and 5-FU, and the increased efficacy of the liver-activated drugs capecitabine and ifosfamide, on tumor organoids, which cannot be revealed by tumor organoid models alone. With gravity-driven flow and multi-organ interactions, this system recapitulates drug absorption and metabolism, thus providing a platform for accurate evaluations of metabolism-dependent drugs and advancing precision medicine.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101734"},"PeriodicalIF":7.7,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148932026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell systemsPub Date : 2026-09-02DOI: 10.1016/j.cels.2026.101717
Yodai Takei, Jordan A Lay, James M Linton, Duncan M Chadly, Yoshiki Ochiai, Ron Hadas, Andrew A Perez, Mario R Blanco, Paola Laurino, Mitchell Guttman, Michael B Elowitz
{"title":"Genome-wide chromatin recording resolves dynamic cell state changes.","authors":"Yodai Takei, Jordan A Lay, James M Linton, Duncan M Chadly, Yoshiki Ochiai, Ron Hadas, Andrew A Perez, Mario R Blanco, Paola Laurino, Mitchell Guttman, Michael B Elowitz","doi":"10.1016/j.cels.2026.101717","DOIUrl":"10.1016/j.cels.2026.101717","url":null,"abstract":"<p><p>Understanding how the chromatin state of a cell influences its future behavior is a major challenge throughout biology. However, most chromatin profiling methods are limited to endpoint assays. Here, we present LagTag, a method for recovery of earlier and endpoint chromatin states in the same mammalian cells. In this approach, transient activity of bacterial adenine methyltransferase fusions records the DNA-binding profiles of chromatin-associated proteins of interest at earlier time points. Subsequent tagmentation and sequencing recover the earlier chromatin profile from adenine methylation profiles, alongside endpoint profiles of endogenous chromatin-associated proteins. We verified that LagTag profiles aligned with those from established methods in mouse and human cells. We then applied LagTag to record and recover dynamic chromatin state transitions during mouse embryonic stem cell differentiation, capturing transcriptional signatures from pre- and post-differentiation time points within the same cell population. LagTag thus provides a foundation for temporally resolved chromatin profiling. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101717"},"PeriodicalIF":7.7,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13540299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148883046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell systemsPub Date : 2026-08-28DOI: 10.1016/j.cels.2026.101710
Yu-Chen Cheng, Hyemin Gu, Thomas O McDonald, Wenbo Wu, Shubham Tripathi, Cristina Guarducci, Douglas Russo, Daniel L Abravanel, Madeline Bailey, Yue Wang, Yun Zhang, Yannis Pantazis, Herbert Levine, Rinath Jeselsohn, Markos A Katsoulakis, Franziska Michor
{"title":"PROFET predicts continuous gene expression dynamics from scRNA-seq data to elucidate heterogeneity of cancer treatment responses.","authors":"Yu-Chen Cheng, Hyemin Gu, Thomas O McDonald, Wenbo Wu, Shubham Tripathi, Cristina Guarducci, Douglas Russo, Daniel L Abravanel, Madeline Bailey, Yue Wang, Yun Zhang, Yannis Pantazis, Herbert Levine, Rinath Jeselsohn, Markos A Katsoulakis, Franziska Michor","doi":"10.1016/j.cels.2026.101710","DOIUrl":"10.1016/j.cels.2026.101710","url":null,"abstract":"<p><p>Single-cell RNA sequencing (scRNA-seq) profiles cellular heterogeneity but captures only static snapshots, limiting inference of gene expression dynamics. We developed PROFET (particle-based reconstruction of generative force-matched expression trajectories), a framework that reconstructs continuous, nonlinear single-cell trajectories from sparsely sampled scRNA-seq time series. PROFET combines a particle-based gradient-flow algorithm with simulation-free force matching to accurately infer cellular dynamics. Across mouse and human in vitro datasets and an in vivo axolotl regeneration dataset, PROFET achieved 2.6-12.5× lower prediction error than ten state-of-the-art trajectory inference methods. Applying PROFET to newly generated scRNA-seq data from a palbociclib-treated MCF7 cell line and three published breast cancer patient datasets, we reconstructed treatment-response trajectories and identified a resistant cell subpopulation exhibiting large phenotypic shifts and enrichment of the surface markers UNC5B, TLR3, PCDH19, PROCR, SLITRK6, and SEMA6B. PROFET provides a biologically grounded framework for reconstructing cell-state dynamics from static single-cell data across development, regeneration, and therapeutic response. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101710"},"PeriodicalIF":7.7,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13528695/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148852126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell systemsPub Date : 2026-08-19Epub Date: 2026-06-23DOI: 10.1016/j.cels.2026.101645
Deepash Kothiwal, Aaron W Kollasch, Murali Anuganti, Nicholas Hollmer, Anita Ghosh, Roushu Zhang, Haiying Li, Steffanie B Paul, Ruitong Li, Yvrick Zagar, Mina Abdollahi, Zachary Anderson, Filmawit Belay, Matt Salotto, Sophia Ulmer, Youssef Atef Abdelalim, Aditi Kachare, Satyendra Kumar, Mahesh Vangala, Chang Yang, Alain Chedotal, Joseph G Jardine, Andre A R Teixeira, Deborah J Moshinsky, Haisun Zhu, Shaotong Zhu, Timothy A Springer, Debora S Marks, Rob Meijers
{"title":"High-throughput machine learning-aided antibody discovery for cell surface antigens.","authors":"Deepash Kothiwal, Aaron W Kollasch, Murali Anuganti, Nicholas Hollmer, Anita Ghosh, Roushu Zhang, Haiying Li, Steffanie B Paul, Ruitong Li, Yvrick Zagar, Mina Abdollahi, Zachary Anderson, Filmawit Belay, Matt Salotto, Sophia Ulmer, Youssef Atef Abdelalim, Aditi Kachare, Satyendra Kumar, Mahesh Vangala, Chang Yang, Alain Chedotal, Joseph G Jardine, Andre A R Teixeira, Deborah J Moshinsky, Haisun Zhu, Shaotong Zhu, Timothy A Springer, Debora S Marks, Rob Meijers","doi":"10.1016/j.cels.2026.101645","DOIUrl":"10.1016/j.cels.2026.101645","url":null,"abstract":"<p><p>Machine learning (ML) has the potential to revolutionize antibody design and selection, but its success depends on access to well-curated datasets of antibody-antigen interactions. We developed a synthetic Fab yeast display library optimized for seamless integration with ML processes, focusing on sequence diversity within the complementary determining region heavy chain CDRH3 loop. The library incorporates key sequence features derived from human B cell repertoires captured in a compact antigen recognition module (ARM) format. Built with the VH1-69 heavy chain and four light chains, the library was evaluated against ten human and murine cell surface antigens, including programmed cell death ligand 1 (PD-L1), T cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domains (TIGIT), and roundabout guidance receptor 1 (ROBO1). This approach yielded hundreds of antibodies with robust biophysical properties, some of which were validated by flow cytometry and immunohistochemistry. Furthermore, ML analysis identified additional antibodies for ROBO2 and PD-L2 from the aggregate sequencing data. The publicly available dataset establishes an ML-compatible framework designed to accelerate and streamline antibody discovery and development. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101645"},"PeriodicalIF":7.7,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148310775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell systemsPub Date : 2026-08-19Epub Date: 2026-06-30DOI: 10.1016/j.cels.2026.101655
Lateef Aliyu, Junneng Wen, Sonali Sunsunwal, Jinke Wu, Noam Greenshtein, Effram Wei, Parastoo Azadi, Robert Price, Michael Betenbaugh
{"title":"Glycoform engineering of a mammalian platform to sculpt a humanized recombinant bioscavenger.","authors":"Lateef Aliyu, Junneng Wen, Sonali Sunsunwal, Jinke Wu, Noam Greenshtein, Effram Wei, Parastoo Azadi, Robert Price, Michael Betenbaugh","doi":"10.1016/j.cels.2026.101655","DOIUrl":"10.1016/j.cels.2026.101655","url":null,"abstract":"<p><p>This study assessed three mammalian expression platforms-human embryonic kidney (HEK293), Chinese hamster ovary (CHO), and HepG2 cells-for their ability to produce recombinant human butyrylcholinesterase (rHuBChE), a promising bioscavenger for organophosphate (OP) poisoning treatment. Key challenges remain in producing rHuBChE with a glycosylation profile similar to that of the native human plasma enzyme butyrylcholinesterase (HuBChE), which is critical for its stability and in vivo half-life. HEK293 cells showed the most promising results, prompting extensive glycoengineering targeting over ten glycosylation-related genes to optimize the glycan profile. This involved knocking out genes associated with fucosylation, enhancing sialylation, and reducing glycan branching. Glycan analysis revealed a substantial reduction in aberrant glycan structures, improved sialylation, and a glycoprofile closely resembling that of plasma-derived HuBChE. These comprehensive modifications to glycosyltransferases and nucleotide sugar donor levels are expected to enhance pharmacokinetics and reduce immunogenicity, positioning rHuBChE as a viable candidate for further preclinical and clinical development against OP poisoning and other medical emergencies. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101655"},"PeriodicalIF":7.7,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell systemsPub Date : 2026-08-19Epub Date: 2026-06-26DOI: 10.1016/j.cels.2026.101651
Wei He, Jen-Wei Huang, Yalong Wang, Samuel B Hayward, Giuseppe Leuzzi, Rongjie Fu, Shuyue Wang, Alina Vaitsiankova, Yiwen Chen, Mark T Bedford, Raphael Guerois, Alberto Ciccia, Han Xu
{"title":"Deciphering protein mutation-phenotype linkages from CRISPR-based tiling mutagenesis screens.","authors":"Wei He, Jen-Wei Huang, Yalong Wang, Samuel B Hayward, Giuseppe Leuzzi, Rongjie Fu, Shuyue Wang, Alina Vaitsiankova, Yiwen Chen, Mark T Bedford, Raphael Guerois, Alberto Ciccia, Han Xu","doi":"10.1016/j.cels.2026.101651","DOIUrl":"10.1016/j.cels.2026.101651","url":null,"abstract":"<p><p>CRISPR-based high-throughput mutagenesis screens enable systematic mapping of mutations to phenotypes, yet deciphering mutation-phenotype links remains challenging. Here, we present ProTiler-Mut, a versatile computational framework that leverages tiling mutagenesis screens, which introduce variants across entire protein sequences, to analyze mutation effects at the levels of residues, substructures, and protein-protein interactions (PPIs). Applying ProTiler-Mut to multi-condition base-editing (BE) screens targeting DNA damage response proteins and T cell regulators, we define a separation-of-function (SoF) category beyond the conventional loss-of-function (LoF) and gain-of-function (GoF) classes, where SoF mutations show the strongest enrichment for ClinVar-annotated pathogenic variants. ProTiler-Mut also identifies candidate substructures that enable functional inference of unscreened pathogenic mutations and prioritizes candidate phenotype-associated PPIs potentially disrupted by functional variants. Using ProTiler-Mut, in cells with elevated programmed cell death 1 (PD-1) expression, we identify pathogenic GoF mutations that constitute a substructure that may disrupt mitogen-activated protein kinase (MAPK)1-RSK1 interactions and lead to MAPK activation. Finally, we show that ProTiler-Mut is applicable across different mutagenesis screening platforms. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101651"},"PeriodicalIF":7.7,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13463601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148341426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell systemsPub Date : 2026-08-19Epub Date: 2026-08-13DOI: 10.1016/j.cels.2026.101704
Malte Mederacke, Kevin A Yamauchi, Nikolaos Doumpas, Laura Schaumann, Jonathan Sperl, Thomas Weikert, D Merrill Dane, Connie C W Hsia, Maurice Pradella, Jens Bremerich, Roman Vetter, Dagmar Iber
{"title":"The emergence of the fractal bronchial tree.","authors":"Malte Mederacke, Kevin A Yamauchi, Nikolaos Doumpas, Laura Schaumann, Jonathan Sperl, Thomas Weikert, D Merrill Dane, Connie C W Hsia, Maurice Pradella, Jens Bremerich, Roman Vetter, Dagmar Iber","doi":"10.1016/j.cels.2026.101704","DOIUrl":"10.1016/j.cels.2026.101704","url":null,"abstract":"<p><p>The fractal design of the bronchial tree, as described by the Hess-Murray law, facilitates energy-efficient lung ventilation, yet its developmental origins remain unclear. Here, we quantify the rearrangement of the embryonic bronchial tree into its fractal architecture using SkelePlex, a new neural network-based image processing pipeline, and elucidate the biophysical principles governing its formation. We find that the branch shapes are such that pressure drop, shear, axial, and hoop stresses are equal across all branches. The seemingly random diameters of sister branches reflect the different number of tips they connect to in the asymmetric lung tree. By analyzing lungs after pneumonectomy and those affected by chronic obstructive pulmonary disease (COPD), we show that the same principles persist in adulthood and disease, potentially providing quantitative biomarkers for disease progression and therapeutic guidance.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"101704"},"PeriodicalIF":7.7,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148726511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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