Cell Reports Methods最新文献

Dye-cycling DNA origami rotors for long-term tracking of transcription at base-pair resolution. 染料循环DNA折纸转子在碱基对分辨率转录的长期跟踪。

IF 5.8

Cell Reports Methods Pub Date : 2026-10-19 Epub Date: 2026-08-13 DOI: 10.1016/j.crmeth.2026.101550

Amanda L Wacker, Ryan J Fantasia, Brian Tenner, Boyu Liu, Jerry D Wu, Nicholas Monell, Pallav Kosuri

引用次数: 0

A scalable microarrayed organoid platform for precision modeling of intestinal epithelial damage and recovery. 一个可扩展的微阵列类器官平台，用于肠道上皮损伤和恢复的精确建模。

IF 5.8

Cell Reports Methods Pub Date : 2026-10-19 Epub Date: 2026-08-14 DOI: 10.1016/j.crmeth.2026.101551

Marine Meyer, Aline Roch, Juliane Dervaux, Laurent Ferrier, Evarist Planet, Julien Duc, Camilla Ceroni, Mariana Costa, Brice Chambrin, Jens Stolte, Sylviane Métairon, Abhilash Kannan, Sylke Hoehnel-Ka, Marine R-C Kraus, Patrick Descombes, Nathalie Brandenberg, Mario Noti, Omid Mashinchian

{"title":"A scalable microarrayed organoid platform for precision modeling of intestinal epithelial damage and recovery.","authors":"Marine Meyer, Aline Roch, Juliane Dervaux, Laurent Ferrier, Evarist Planet, Julien Duc, Camilla Ceroni, Mariana Costa, Brice Chambrin, Jens Stolte, Sylviane Métairon, Abhilash Kannan, Sylke Hoehnel-Ka, Marine R-C Kraus, Patrick Descombes, Nathalie Brandenberg, Mario Noti, Omid Mashinchian","doi":"10.1016/j.crmeth.2026.101551","DOIUrl":"10.1016/j.crmeth.2026.101551","url":null,"abstract":"Chronic inflammatory bowel diseases (IBDs), including Crohn's disease, are characterized by relapsing-remitting intestinal inflammation associated with epithelial barrier dysfunction. Existing preclinical models of IBD have limited capacity to quantitatively capture dynamic epithelial damage and recovery, which hinders the translation of research findings into new therapeutic approaches. Here, we establish a scalable, microarrayed 3D human intestinal organoid platform enabling high-throughput, single-organoid-resolution phenotyping of cytokine-induced epithelial injury and recovery. By integrating time-resolved morphological imaging, transcriptomics, permeability assays, and the intestinal organoid recovery score (IORS), we quantify injury-recovery trajectories and identify responder organoids following preventive or therapeutic intervention. CytoMix-challenged organoids display epithelial inflammatory programs with transcriptional overlap to Crohn's disease biopsy signatures. Dexamethasone, nicotinamide, and β-hydroxybutyrate improved epithelial recovery, with dexamethasone and nicotinamide modulating distinct inflammatory and barrier-associated pathways. Collectively, this single-organoid-resolution approach establishes a standardized platform for modeling epithelial inflammatory damage and recovery, enabling phenotype-driven precision medicine applications in gastrointestinal disease.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101551"},"PeriodicalIF":5.8,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148765292","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}

引用次数: 0

Modeling microbiome modulation of tumor metabolic networks to predict synergistic therapies. 模拟微生物组调节肿瘤代谢网络以预测协同治疗。

IF 5.8

Cell Reports Methods Pub Date : 2026-10-19 Epub Date: 2026-08-13 DOI: 10.1016/j.crmeth.2026.101549

Annie J Badenoch, Zeyang Pang, Carolina H Chung, Aaron Robida, Bretton Badenoch, Ritish Natesan, Layth Kakish, Jiahe Li, Sriram Chandrasekaran

{"title":"Modeling microbiome modulation of tumor metabolic networks to predict synergistic therapies.","authors":"Annie J Badenoch, Zeyang Pang, Carolina H Chung, Aaron Robida, Bretton Badenoch, Ritish Natesan, Layth Kakish, Jiahe Li, Sriram Chandrasekaran","doi":"10.1016/j.crmeth.2026.101549","DOIUrl":"10.1016/j.crmeth.2026.101549","url":null,"abstract":"Differences in microbiome composition profoundly influence drug response, yet methods to model the metabolic impact of microbes on host cells and therapeutics remain limited. We present a microbiome-aware computational framework combining machine learning and genome-scale metabolic models to predict combination therapies for colorectal cancer (CRC) in the presence of Fusobacterium nucleatum (Fn) and other pathogenic, probiotic, and commensal microbes. The model learned predictive metabolic flux signatures from 6,514 drug combination profiles in CRC cell lines and predicted synergistic drug combinations across both microbe-free and microbe-associated contexts. Model performance was supported through prospective comparison with newly reported drug combinations, in vitro drug synergy assays, microbiome co-culture experiments, and targeted metabolic perturbations of predicted pathway dependencies. Pharmacological perturbations in asymmetric co-cultures revealed phosphoinositol metabolism and cysteine transport as key determinants of Fn-dependent drug synergy. Together, this work introduces a scalable strategy for discovering microbiome-dependent combination therapies, including chemotherapies, immunotherapy, and probiotics.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101549"},"PeriodicalIF":5.8,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148759689","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}

引用次数: 0

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology. 多模态比对提高了计算病理学中基因组生物标志物预测的普遍性。

IF 5.8

Cell Reports Methods Pub Date : 2026-10-19 Epub Date: 2026-09-08 DOI: 10.1016/j.crmeth.2026.101578

Ekaterina Redekop, Eric Zimmermann, Ava P Amini, Alex X Lu, Neil Tenenholtz, James Hall, Lorin Crawford, Kristen A Severson

{"title":"Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.","authors":"Ekaterina Redekop, Eric Zimmermann, Ava P Amini, Alex X Lu, Neil Tenenholtz, James Hall, Lorin Crawford, Kristen A Severson","doi":"10.1016/j.crmeth.2026.101578","DOIUrl":"10.1016/j.crmeth.2026.101578","url":null,"abstract":"Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101578"},"PeriodicalIF":5.8,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148913588","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}

引用次数: 0

Deep mapping of structural perturbations to energetics enables precise TCR design. 结构扰动到能量学的深度映射使得精确的TCR设计成为可能。

IF 5.8

Cell Reports Methods Pub Date : 2026-10-19 Epub Date: 2026-08-20 DOI: 10.1016/j.crmeth.2026.101569

Shifu Luo, Songming Zhang, Ying Shi, Junjie Li, Jiaxin Cai, Ning-Yi Shao, Yi Pan, Jinyan Li

引用次数: 0

A sensitive method for profiling cell-type-specific secretomes of rare cells from complex tissues. 分析复杂组织中稀有细胞的细胞类型特异性分泌组的灵敏方法。

IF 5.8

Cell Reports Methods Pub Date : 2026-10-19 Epub Date: 2026-08-14 DOI: 10.1016/j.crmeth.2026.101552

Gergana Shipkovenska, Xuchao Lyu, Manalee Vishnu Surve, Joao A Paulo, Edward Chen, Vanshika Panchal, Brian Lin, Steven P Gygi, Wei Wei, Jonathan Z Long, Jayaraj Rajagopal

引用次数: 0

SwitchClass distinguishes baseline- and perturbation-aligned features across intermediate molecular states. SwitchClass区分了中间分子状态的基线和微扰特征。

IF 5.8

Cell Reports Methods Pub Date : 2026-10-19 Epub Date: 2026-08-12 DOI: 10.1016/j.crmeth.2026.101547

Di Xiao, Siqu Long, Pengyi Yang

{"title":"SwitchClass distinguishes baseline- and perturbation-aligned features across intermediate molecular states.","authors":"Di Xiao, Siqu Long, Pengyi Yang","doi":"10.1016/j.crmeth.2026.101547","DOIUrl":"10.1016/j.crmeth.2026.101547","url":null,"abstract":"Biological systems often exhibit intermediate molecular states under perturbation, showing changes that align with baseline condition or perturbed state. Capturing these complex patterns is critical for understanding molecular resilience and maladaptive persistence. We introduce SwitchClass, a label-switch classification framework that distinguishes features whose intermediate-state profiles align with baseline or perturbed condition. By training a dual classifier with inverted labels, SwitchClass computes a directional importance score (δ), which quantifies each feature's alignment across biological states. Applied to colorectal cancer proteomics, SwitchClass reveals proteins that normalize after therapy and those remaining dysregulated, uncovering partial molecular recovery. In dietary perturbation and reversal phosphoproteomics, it uncovers the phosphorylation sites linked to incomplete restoration of insulin signaling. In single-cell transcriptomes from COVID-19 patients with varying severities, it identifies cell-type-specific transcripts marking resolution or persistence of inflammatory activity. Together, these analyses demonstrate SwitchClass as an interpretable framework for mapping directional molecular changes in systems with intermediate states.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101547"},"PeriodicalIF":5.8,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148722933","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}

引用次数: 0

Deep learning-driven multiplexed mycotoxin detection via color-size encoded microbead imaging. 基于颜色大小编码微珠成像的深度学习驱动多路霉菌毒素检测。

IF 5.8

Cell Reports Methods Pub Date : 2026-10-19 Epub Date: 2026-08-14 DOI: 10.1016/j.crmeth.2026.101548

Xianfeng Lin, Lixin Kang, Jiaqi Feng, Meihua Dong, Nuo Duan, Zhouping Wang, Shijia Wu

{"title":"Deep learning-driven multiplexed mycotoxin detection via color-size encoded microbead imaging.","authors":"Xianfeng Lin, Lixin Kang, Jiaqi Feng, Meihua Dong, Nuo Duan, Zhouping Wang, Shijia Wu","doi":"10.1016/j.crmeth.2026.101548","DOIUrl":"10.1016/j.crmeth.2026.101548","url":null,"abstract":"Mycotoxins pose severe threats to food safety and public health, especially with synergistic toxicity from co-contamination. Herein, a deep learning-driven two-dimensional (2D)-encoded single microbead imaging decoding platform is developed for homogeneous analysis of four mycotoxins. The aptamer-recognition-triggered DNAzyme walker-hybridization chain reaction (Dz-HCR) cascade design enables direct fluorescent signal lighting and amplification on microbeads, avoiding complex separation and signal probe preparation. By leveraging two different-sized microbeads and two fluorophores modified at HCR hairpins, an ingenious color-size 2D-encoding strategy is established for high-throughput analysis. The YOLOv11 deep learning model enables rapid, accurate decoding and analysis of fluorescence images with multi-dimensional information, improving data processing efficiency. This platform exhibits high sensitivity (<1 pg/mL), wide linear ranges, and excellent specificity. The desirable spiked recovery (85.6%-114.0%) in maize and plant-based meat analogs and the consistency with HPLC-MS/MS confirm practical applicability. This method provides a promising strategy for high-throughput mycotoxin detection in food safety monitoring.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101548"},"PeriodicalIF":5.8,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148761897","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}

引用次数: 0

Strategic enhancement of Cas9 nickase-driven gene amplification. Cas9镍酶驱动基因扩增的战略性增强。

IF 5.8

Cell Reports Methods Pub Date : 2026-10-19 Epub Date: 2026-08-06 DOI: 10.1016/j.crmeth.2026.101544

Hiroaki Takesue, Satoshi Okada, Takashi Ito

引用次数: 0

Calibrated simulations for dynamic focusing of ultrasound through the temporal window. 超声通过时间窗动态聚焦的校准模拟。

IF 5.8

Cell Reports Methods Pub Date : 2026-10-19 Epub Date: 2026-08-14 DOI: 10.1016/j.crmeth.2026.101568

Ehsan Dadgar-Kiani, Vibha Hebbale, Malachy W Newman, Grace Attalla, John L Alvarez, Suraya Dunsford, Kevin A Caulfield, Cameron H Good, Andrew D Krystal, Leo P Sugrue, Joline M Fan, Elsa Fouragnan, Charles F Caskey, Samuel Pichardo, Kim Butts Pauly, Keith R Murphy

{"title":"Calibrated simulations for dynamic focusing of ultrasound through the temporal window.","authors":"Ehsan Dadgar-Kiani, Vibha Hebbale, Malachy W Newman, Grace Attalla, John L Alvarez, Suraya Dunsford, Kevin A Caulfield, Cameron H Good, Andrew D Krystal, Leo P Sugrue, Joline M Fan, Elsa Fouragnan, Charles F Caskey, Samuel Pichardo, Kim Butts Pauly, Keith R Murphy","doi":"10.1016/j.crmeth.2026.101568","DOIUrl":"10.1016/j.crmeth.2026.101568","url":null,"abstract":"Focused ultrasound can be delivered through the temporal window to modulate heterogeneously located brain areas. Acoustic simulations allow for safety assessments when dynamically targeting brain structures, but the mismatch between simulation and measured focal pressure can vary across the steerable range due to mechanically inaccurate assumptions made about the skull and transducer. Here, we describe efficient methods for simulation-measurement calibration using axisymmetric projections and sparse sampling across a 3D steerable subspace encompassing deep brain targets across 157 subjects. To address the simulation-reality mismatch in skull transmission, we used the measured and predicted pressure values through eight human temporal window fragments to derive an optimized bone attenuation coefficient. Collectively, the calibration framework and optimized temporal window coefficients can be used broadly across studies to improve the accuracy of reporting and dependent safety assessment for personalized neuromodulation treatments.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101568"},"PeriodicalIF":5.8,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148760408","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}

引用次数: 0