Cell Reports Methods最新文献_第2页

High-throughput bioprinting to produce micropatterned neuroepithelial tissues and model TSC2-deficient brain malformations. 利用高通量生物打印技术制造微图案化神经上皮组织和tsc2缺陷脑畸形模型。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-17 DOI: 10.1016/j.crmeth.2025.101177

Negin Imani Farahani, Kenneth Kin Lam Wong, George Allen, Abhimanyu Minhas, Lisa Lin, Shama Nazir, Lisa M Julian

{"title":"High-throughput bioprinting to produce micropatterned neuroepithelial tissues and model TSC2-deficient brain malformations.","authors":"Negin Imani Farahani, Kenneth Kin Lam Wong, George Allen, Abhimanyu Minhas, Lisa Lin, Shama Nazir, Lisa M Julian","doi":"10.1016/j.crmeth.2025.101177","DOIUrl":"10.1016/j.crmeth.2025.101177","url":null,"abstract":"In vitro human pluripotent stem cell (hPSC)-derived models have been crucial in advancing our understanding of the mechanisms underlying neurodevelopment, though knowledge of the earliest stages of brain formation is lacking. Micropatterning of cell populations as they transition from pluripotency through the process of neurulation can produce self-assembled neuroepithelial tissues (NETs) with precise spatiotemporal control, enhancing the fidelity of hPSC models to the early developing human brain and their use in phenotypic assessments. Here, we introduce an accessible, customizable, and scalable method to produce self-assembled NETs using bioprinting to rapidly deposit reproducibly sized extracellular matrix droplets. Matrix addition to the media provides a scaffold that promotes 3D tissue folding, reflecting neural tube development. We demonstrate that these scaffolded NETs (scNETs) exhibit key architectural and biological features of the human brain during normal and abnormal development-notably, hyperproliferation and structural malformations induced by TSC2 deficiency-and provide a robust drug screening tool.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101177"},"PeriodicalIF":4.5,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145087422","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

Computational tracking of cell origins using CellSexID from single-cell transcriptomes. 利用单细胞转录组的CellSexID对细胞起源进行计算跟踪。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-17 DOI: 10.1016/j.crmeth.2025.101181

Huilin Tai, Qian Li, Jingtao Wang, Jiahui Tan, Bowen Zhao, Ryann Lang, Basil J Petrof, Jun Ding

{"title":"Computational tracking of cell origins using CellSexID from single-cell transcriptomes.","authors":"Huilin Tai, Qian Li, Jingtao Wang, Jiahui Tan, Bowen Zhao, Ryann Lang, Basil J Petrof, Jun Ding","doi":"10.1016/j.crmeth.2025.101181","DOIUrl":"https://doi.org/10.1016/j.crmeth.2025.101181","url":null,"abstract":"Cell tracking in chimeric models is essential yet challenging in developmental biology, regenerative medicine, and transplantation research. Current methods like fluorescent labeling and genetic barcoding are technically demanding, costly, and often impractical for dynamic tissues. We present CellSexID, a computational framework that uses sex as a surrogate marker for cell-origin inference. By training machine-learning models on single-cell transcriptomic data, CellSexID accurately predicts individual cell sex, enabling in silico distinction between donor and recipient cells in sex-mismatched settings. The model identifies minimal sex-linked gene sets through ensemble feature selection and has been validated using public datasets and experimental flow sorting, confirming biological relevance. We demonstrate CellSexID's applicability beyond chimeric models, including organ transplantation and sample demultiplexing. As a practical alternative to physical labeling, CellSexID facilitates precise cell tracking and supports diverse biomedical applications where mixed cellular populations need to be distinguished.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101181"},"PeriodicalIF":4.5,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145087386","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

Developmental transformations of Purkinje cells tracked by DNA electrokinetic mobility. DNA电动力移动追踪浦肯野细胞的发育转化。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-15 Epub Date: 2025-08-26 DOI: 10.1016/j.crmeth.2025.101143

Cheryl Brandenburg, Garrett W Crutcher, Andrea J Romanowski, Sarah G Donofrio, Lita R Duraine, Richard N A Owusu-Mensah, Benjamin H Cooper, Izumi Sugihara, Gene J Blatt, Roy V Sillitoe, Alexandros Poulopoulos

{"title":"Developmental transformations of Purkinje cells tracked by DNA electrokinetic mobility.","authors":"Cheryl Brandenburg, Garrett W Crutcher, Andrea J Romanowski, Sarah G Donofrio, Lita R Duraine, Richard N A Owusu-Mensah, Benjamin H Cooper, Izumi Sugihara, Gene J Blatt, Roy V Sillitoe, Alexandros Poulopoulos","doi":"10.1016/j.crmeth.2025.101143","DOIUrl":"10.1016/j.crmeth.2025.101143","url":null,"abstract":"Brain development begins with neurogenesis in progenitor zones and ends with expansive, intricately-patterned cellular diversity in the adult brain. We took advantage of bioelectric interactions between DNA and embryonic tissue to perform \"stereo-tracking,\" a developmental targeting strategy that differentially labels cells at different depths within progenitor zones. This 3D labeling was achieved by delivery of plasmids with distinct electrokinetic mobilities in utero. We applied stereo-tracking with light sheet imaging in the cerebellum and identified that Purkinje cells follow embryonically committed developmental trajectories, linking distinct progenitor zone subfields to the mature topography of the cerebellar cortex. We additionally identified an unexpected subcellular structure on the axon initial segment of Purkinje cells that we termed \"axon bubbles.\" These structures were revealed by glycosylphosphatidylinositol (GPI)-linked surface labeling and confirmed by electron microscopy. Our findings demonstrate organization of neural progenitor zones in three dimensions, exemplifying the potential of stereo-tracking to uncover new biology within developing systems.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101143"},"PeriodicalIF":4.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144971557","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

Comparison of single-cell RNA-seq methods to enable transcriptome profiling of neutrophils in clinical samples. 比较单细胞RNA-seq方法，使临床样品中性粒细胞转录组分析。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-15 DOI: 10.1016/j.crmeth.2025.101173

Klas Hatje, Kim Schneider, Sabrina Danilin, Fabian Koechl, Nicolas Giroud, Laurent Juglair, Daniel Marbach, Philip Knuckles, Tobias Bergauer, Matteo Metruccio, Alba Garrido, Jitao David Zhang, Marc Sultan, Emma Bell

引用次数: 0

Kontextual reframes analysis of spatial omics data and reveals consistent cell relationships across images. Kontextual重构了空间组学数据的分析，揭示了图像间一致的细胞关系。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-15 DOI: 10.1016/j.crmeth.2025.101175

Farhan Ameen, Nick Robertson, David M Lin, Shila Ghazanfar, Ellis Patrick

{"title":"Kontextual reframes analysis of spatial omics data and reveals consistent cell relationships across images.","authors":"Farhan Ameen, Nick Robertson, David M Lin, Shila Ghazanfar, Ellis Patrick","doi":"10.1016/j.crmeth.2025.101175","DOIUrl":"https://doi.org/10.1016/j.crmeth.2025.101175","url":null,"abstract":"Spatial proteomic and transcriptomic technologies enable high-throughput phenotyping of cells in situ, enabling quantification of spatial relationships among diverse cell populations. However, the experimental design choice of which regions of a tissue will be imaged can greatly impact the interpretation of spatial quantifications. That is, spatial relationships identified in one region of interest may not be interpreted consistently across other regions. To address this challenge, we introduce Kontextual, a method that considers alternative frames of reference for contextualizing spatial relationships. These contexts may represent landmarks, spatial domains, or groups of functionally similar cells that are consistent across regions. By modeling spatial relationships between cells relative to these contexts, Kontextual produces robust spatial quantifications that are not confounded by the region selected. We demonstrate in spatial proteomics and transcriptomics datasets that modeling spatial relationships this way is biologically meaningful and can improve the prediction of patient prognosis in a classification setting.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101175"},"PeriodicalIF":4.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076260","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

SuperGLUE facilitates an explainable training framework for multi-modal data analysis. SuperGLUE为多模态数据分析提供了一个可解释的培训框架。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-15 Epub Date: 2025-09-05 DOI: 10.1016/j.crmeth.2025.101167

Tianyu Liu, Jia Zhao, Hongyu Zhao

引用次数: 0

Toward universal immunofluorescence normalization for multiplex tissue imaging with UniFORM. 统一组织成像的通用免疫荧光归一化。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-15 Epub Date: 2025-09-08 DOI: 10.1016/j.crmeth.2025.101172

Kunlun Wang, Kaoutar Ait-Ahmad, Sam Kupp, Zachary Sims, Eric Cramer, Zeynep Sayar, Jessica Yu, Melissa H Wong, Gordon B Mills, S Ece Eksi, Young Hwan Chang

{"title":"Toward universal immunofluorescence normalization for multiplex tissue imaging with UniFORM.","authors":"Kunlun Wang, Kaoutar Ait-Ahmad, Sam Kupp, Zachary Sims, Eric Cramer, Zeynep Sayar, Jessica Yu, Melissa H Wong, Gordon B Mills, S Ece Eksi, Young Hwan Chang","doi":"10.1016/j.crmeth.2025.101172","DOIUrl":"10.1016/j.crmeth.2025.101172","url":null,"abstract":"We present UniFORM, a non-parametric, Python-based pipeline for normalizing multiplex tissue imaging (MTI) data at both the feature and pixel levels. UniFORM employs an automated rigid landmark registration method tailored to the distributional characteristics of MTI, with UniFORM operating without prior distributional assumptions and handling both unimodal and bimodal patterns. By aligning the biologically invariant negative populations, UniFORM removes technical variation while preserving tissue-specific expression patterns in positive populations. Benchmarked on three MTI platforms, UniFORM consistently outperforms existing methods in mitigating batch effects while maintaining biological signal fidelity. This is evidenced by improved marker distribution alignment and positive population preservation, enhanced k-nearest neighbor batch effect test (kBET) and silhouette scores, and more coherent downstream analyses, such as uniform manifold approximation and projection (UMAP) visualizations and Leiden clustering. UniFORM also offers an optional guided fine-tuning mode for complex or heterogeneous datasets. While optimized for fluorescence-based MTI, its scalable design supports broad applications for MTI data normalization, enabling accurate and biologically meaningful interpretations.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101172"},"PeriodicalIF":4.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145030848","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

Sensitive and specific affinity purification-mass spectrometry assisted by PafA-mediated proximity labeling. 敏感和特异性亲和纯化-质谱辅助pafa介导的接近标记。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-15 Epub Date: 2025-09-08 DOI: 10.1016/j.crmeth.2025.101166

Shihan Luo, Lijuan Xie, Lin Yang, Zheyao Hu, Lei Wang, Yueqin Wang, Qingqing Li, Shujuan Guo, Shengce Tao, Hewei Jiang

{"title":"Sensitive and specific affinity purification-mass spectrometry assisted by PafA-mediated proximity labeling.","authors":"Shihan Luo, Lijuan Xie, Lin Yang, Zheyao Hu, Lei Wang, Yueqin Wang, Qingqing Li, Shujuan Guo, Shengce Tao, Hewei Jiang","doi":"10.1016/j.crmeth.2025.101166","DOIUrl":"10.1016/j.crmeth.2025.101166","url":null,"abstract":"While affinity purification-mass spectrometry (AP-MS) has significantly advanced protein-protein interaction (PPI) studies, its limitations in detecting weak, transient, and membrane-associated interactions remain. To address these challenges, we introduced a proteomic method termed affinity purification coupled proximity labeling-mass spectrometry (APPLE-MS), which combines the high specificity of Twin-Strep tag enrichment with PafA-mediated proximity labeling. This method achieves improved sensitivity while maintaining high specificity (4.07-fold over AP-MS). APPLE-MS also revealed the dynamic mitochondrial interactome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ORF9B during antiviral responses, while endogenous PIN1 profiling uncovered novel roles in DNA replication. Notably, APPLE-MS enabled in situ mapping of GLP-1 receptor complexes, demonstrating its unique capabilities for membrane PPI studies. This versatile method advances interactome research by providing comprehensive, physiologically relevant PPI networks, opening new opportunities for mechanistic discovery and therapeutic targeting.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101166"},"PeriodicalIF":4.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145030866","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

Methods, applications, and computational challenges in bait capture enrichment. 鱼饵捕获富集的方法、应用和计算挑战。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-15 DOI: 10.1016/j.crmeth.2025.101174

Jonathan E Bravo, Kimberly J Newsom, Noelle Noyes, Christina Boucher

引用次数: 0

A machine learning tool with light-based image analysis for automatic classification of 3D pain behaviors. 基于光的图像分析的机器学习工具，用于3D疼痛行为的自动分类。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-15 Epub Date: 2025-08-27 DOI: 10.1016/j.crmeth.2025.101145

Omer Barkai, Biyao Zhang, Bruna Lenfers Turnes, Maryam Arab, David A Yarmolinsky, Zihe Zhang, Lee B Barrett, Clifford J Woolf

{"title":"A machine learning tool with light-based image analysis for automatic classification of 3D pain behaviors.","authors":"Omer Barkai, Biyao Zhang, Bruna Lenfers Turnes, Maryam Arab, David A Yarmolinsky, Zihe Zhang, Lee B Barrett, Clifford J Woolf","doi":"10.1016/j.crmeth.2025.101145","DOIUrl":"10.1016/j.crmeth.2025.101145","url":null,"abstract":"Detailed assessment of pain-related behaviors in animals is essential for both exploring pain mechanisms and evaluating analgesic efficacy. While pose estimation tools have advanced automated behavior analysis, current existing algorithms often do not account for an animal's body-part contact intensity with-and distance from-the surface, a critical nuance for measuring certain pain-related responses like paw withdrawal (\"flinching\"). These subtle responses continue to require time-consuming and subjective human scoring. Here, we present BAREfoot (behavior with automatic recognition and evaluation), a supervised machine learning (ML) algorithm that combines pose estimation with light-based analysis of body-part contact and elevation to automatically detect pain behaviors in freely moving mice. We show the utility and accuracy of this algorithm for capturing a range of pain-related behavioral bouts using a bottom-up animal behavior platform and its application for robust drug screening. This open-source algorithm is adaptable for detecting diverse behaviors across species and experimental platforms.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101145"},"PeriodicalIF":4.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144971590","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