Cell Reports Methods最新文献

Genome-wide profiling of unmodified DNA using methyltransferase-directed tagging and enrichment. 使用甲基转移酶定向标记和富集对未修饰DNA进行全基因组分析。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-29 DOI: 10.1016/j.crmeth.2025.101187

Luca Tosti, Calum Mould, Imogen Gatehouse, Anthony C Smith, Krystian Ubych, Valentina Miano, Peter W Laird, Jack Kennefick, Robert K Neely

{"title":"Genome-wide profiling of unmodified DNA using methyltransferase-directed tagging and enrichment.","authors":"Luca Tosti, Calum Mould, Imogen Gatehouse, Anthony C Smith, Krystian Ubych, Valentina Miano, Peter W Laird, Jack Kennefick, Robert K Neely","doi":"10.1016/j.crmeth.2025.101187","DOIUrl":"https://doi.org/10.1016/j.crmeth.2025.101187","url":null,"abstract":"We present \"Active-Seq\" (azide click tagging for in vitro epigenomic sequencing), a base-conversion-free technology that enables the isolation of DNA containing unmodified CpG sites using a mutated bacterial methyltransferase enzyme and a synthetically prepared cofactor analog. Active-Seq is a robust epigenomic profiling platform with a simple and streamlined workflow, performed in tandem with sequencing library preparation and compatible with DNA input quantities as low as 1 ng. We establish a baseline for the performance of Active-Seq using model DNA oligos and further validate it against gold-standard whole-genome bisulfite sequencing data. We show robust performance of the platform across tissue-derived DNA and demonstrate enrichment of DNA at unmethylated, cell-type-specific marker regions of the epigenome, laying the foundation for the future application of this technology in tissue deconvolution applications. Finally, we apply the technology to cell-free DNA samples, outlining an approach for tumor-informed disease profiling in patients with colorectal cancer.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101187"},"PeriodicalIF":4.5,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201608","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

Reference-guided assembly of metagenomes with MetaCompass. 参考引导组装宏基因组与MetaCompass。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-26 DOI: 10.1016/j.crmeth.2025.101186

Tu Luan, Victoria P Cepeda-Espinoza, Bo Liu, Zac Bowen, Ujjwal Ayyangar, Mathieu Almeida, Sergey Koren, Todd J Treangen, Adam Porter, Mihai Pop

引用次数: 0

Metabolic profiling of antigen-specific CD8⁺ T cells by spectral flow cytometry. 用光谱流式细胞术分析抗原特异性CD8+ T细胞的代谢谱。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-26 DOI: 10.1016/j.crmeth.2025.101185

Nils Mülling, J Fréderique de Graaf, Graham A Heieis, Kristina Boss, Benjamin Wilde, Bart Everts, Ramon Arens

{"title":"Metabolic profiling of antigen-specific CD8+ T cells by spectral flow cytometry.","authors":"Nils Mülling, J Fréderique de Graaf, Graham A Heieis, Kristina Boss, Benjamin Wilde, Bart Everts, Ramon Arens","doi":"10.1016/j.crmeth.2025.101185","DOIUrl":"https://doi.org/10.1016/j.crmeth.2025.101185","url":null,"abstract":"Cytotoxic CD8+ T cells are essential mediators of immune responses against viral infections and tumors. Upon antigen encounter, antigen-specific CD8+ T cells undergo clonal expansion and produce effector cytokines, processes that require dynamic metabolic adaptation. However, profiling antigen-specific T cells at single-cell resolution remains technically challenging. We present a spectral flow cytometry-based workflow enabling metabolic profiling of antigen-specific CD8+ T cells identified via major histocompatibility complex (MHC) class I tetramers or CD137 upregulation. The approach integrates the analysis of metabolic protein expression to infer pathway activity, uptake of fluorescent probes to measure functional metabolism and metabolite utilization, and assays evaluating cellular energy metabolism. Applied to human and mouse samples, this method defined the metabolic profiles of cytomegalovirus-, SARS-CoV-2-, and tumor-specific CD8+ T cells across distinct activation states and tissues. By detailing each component of the workflow, we provide practical guidance for applying metabolic spectral flow cytometry to dissect disease mechanisms and therapeutic responses.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101185"},"PeriodicalIF":4.5,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145182218","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

In silico methods for drug-target interaction prediction. 药物-靶标相互作用预测的计算机方法。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-24 DOI: 10.1016/j.crmeth.2025.101184

Xiaoqing Ru, Lifeng Xu, Wu Han, Quan Zou

引用次数: 0

A statistical physics approach to integrating multi-omics data for disease-module detection. 整合多组学数据用于疾病模块检测的统计物理方法。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-19 DOI: 10.1016/j.crmeth.2025.101183

Xu-Wen Wang, Min Hyung Ryu, Michael H Cho, Peter Castaldi, Craig P Hersh, Edwin K Silverman, Yang-Yu Liu

引用次数: 0

The Brownian dynamics simulator PyRID for reacting and interacting particles written in Python. 用Python编写的用于反应和相互作用粒子的布朗动力学模拟器PyRID。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-18 DOI: 10.1016/j.crmeth.2025.101182

Moritz Becker, Nahid Safari, Christian Tetzlaff

{"title":"The Brownian dynamics simulator PyRID for reacting and interacting particles written in Python.","authors":"Moritz Becker, Nahid Safari, Christian Tetzlaff","doi":"10.1016/j.crmeth.2025.101182","DOIUrl":"https://doi.org/10.1016/j.crmeth.2025.101182","url":null,"abstract":"Recent advances in molecular biology have led to large-scale datasets providing new insights into the molecular organization of cells. To fully exploit their potential, computer simulations are essential to gain in-depth understanding of molecular principles. We developed the Python reaction interaction diffusion simulator (PyRID), a Python-based reaction-diffusion simulator designed for the efficient simulation of molecular biological systems. PyRID incorporates unimolecular and bimolecular reactions as well as pair interactions and simulation of individual interacting proteins to polydisperse molecular assemblies. It supports mesh-based compartments and surface diffusion of particles, enabling analyses of interactions between (trans)membrane proteins with intra- and extracellular proteins. Distinctively, PyRID uses hierarchical grids for polydisperse systems, supports rigid bead models, and calculates diffusion tensors internally. Validation against theoretical results and established models confirms PyRID's accuracy in reproducing key physical properties. PyRID is written entirely in Python, making it accessible to the broader scientific community, facilitating customization and integration into diverse research workflows.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101182"},"PeriodicalIF":4.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145092547","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

A deep learning pipeline for accurate and automated restoration, segmentation, and quantification of dendritic spines. 一个深度学习管道，用于准确和自动的恢复，分割和量化树突棘。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-18 DOI: 10.1016/j.crmeth.2025.101179

Sergio Bernal-Garcia, Alexa P Schlotter, Daniela B Pereira, Aleksandra J Recupero, Franck Polleux, Luke A Hammond

{"title":"A deep learning pipeline for accurate and automated restoration, segmentation, and quantification of dendritic spines.","authors":"Sergio Bernal-Garcia, Alexa P Schlotter, Daniela B Pereira, Aleksandra J Recupero, Franck Polleux, Luke A Hammond","doi":"10.1016/j.crmeth.2025.101179","DOIUrl":"10.1016/j.crmeth.2025.101179","url":null,"abstract":"Quantification of dendritic spines is essential for studying synaptic connectivity, yet most current approaches require manual adjustments or the combination of multiple software tools for optimal results. Here, we present restoration enhanced spine and neuron analysis (RESPAN), an open-source pipeline integrating state-of-the-art deep learning for image restoration, segmentation, and analysis in an easily deployable, user-friendly interface. Leveraging content-aware restoration to enhance signal, contrast, and isotropic resolution further enhances RESPAN's robust detection of spines, dendritic branches, and soma across a wide variety of samples, including challenging datasets with limited signal, such as rapid volumetric imaging and in vivo two-photon microscopy. Extensive validation against expert annotations and comparison with other software demonstrate RESPAN's superior accuracy and reproducibility across multiple imaging modalities. RESPAN offers significant improvements in usability over currently available approaches, streamlining and democratizing access to a combination of advanced capabilities through an accessible resource for the neuroscience community.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101179"},"PeriodicalIF":4.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145092494","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

A real-time all-optical interface for dynamic perturbation of neural activity during behavior. 一个实时全光接口，用于在行为过程中对神经活动进行动态扰动。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-18 DOI: 10.1016/j.crmeth.2025.101180

Zihui Zhang, Patrycja Dzialecka, Lloyd E Russell, Riccardo Ratto, Christina Buetfering, Oliver M Gauld, David R Selviah, Michael Häusser

引用次数: 0

Comprehensive noise reduction in single-cell data with the RECODE platform. 利用RECODE平台对单细胞数据进行全面降噪。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-17 DOI: 10.1016/j.crmeth.2025.101178

Yusuke Imoto

引用次数: 0

SpliPath enhances disease gene discovery in case-control analyses of rare splice-altering genetic variants. SpliPath在罕见剪接改变基因变异的病例对照分析中增强了疾病基因的发现。

IF 4.5

Cell Reports Methods Pub Date : 2025-09-17 DOI: 10.1016/j.crmeth.2025.101176

Yan Wang, Charlotte van Dijk, Ilia Timpanaro, Paul Hop, Brendan Kenna, Maarten Kooyman, Eleonora Aronica, R Jeroen Pasterkamp, Leonard H van den Berg, Johnathan Cooper-Knock, Jan H Veldink, Kevin Kenna

{"title":"SpliPath enhances disease gene discovery in case-control analyses of rare splice-altering genetic variants.","authors":"Yan Wang, Charlotte van Dijk, Ilia Timpanaro, Paul Hop, Brendan Kenna, Maarten Kooyman, Eleonora Aronica, R Jeroen Pasterkamp, Leonard H van den Berg, Johnathan Cooper-Knock, Jan H Veldink, Kevin Kenna","doi":"10.1016/j.crmeth.2025.101176","DOIUrl":"https://doi.org/10.1016/j.crmeth.2025.101176","url":null,"abstract":"We developed SpliPath as a generalizable framework to discover disease associations mediated by rare variants that induce experimentally supported mRNA splicing defects. Our approach integrates components of burden tests (BTs), traditional splicing quantitative trait locus (sQTL) analyses, and sequence-to-function AI models (SpliceAI and Pangolin). Central to the workings of SpliPath is our concept of collapsed rare variant splicing QTL (crsQTL). crsQTL groups rare variants that are predicted to alter splicing in the same way, specifically by linking them to shared splice junctions observed in independent (unpaired) RNA sequencing (RNA-seq) datasets. We demonstrate the utility of SpliPath through applications in amyotrophic lateral sclerosis (ALS). Through this, we showcase scenarios where SpliPath detects genetic associations that cannot be recovered by more simplistic combinations of BT and SpliceAI. We also nominate crsQTL for splice defects detected in large-scale analyses of ALS patient tissue.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101176"},"PeriodicalIF":4.5,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145087405","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