Cell Reports Methods最新文献_第6页

A co-culture system of macrophages with breast cancer tumoroids to study cell interactions and therapeutic responses. 用于研究细胞相互作用和治疗反应的巨噬细胞与乳腺癌肿瘤细胞共培养系统。

IF 4.3

Cell Reports Methods Pub Date : 2024-06-17 Epub Date: 2024-06-10 DOI: 10.1016/j.crmeth.2024.100792

Antonella Raffo-Romero, Lydia Ziane-Chaouche, Sophie Salomé-Desnoulez, Nawale Hajjaji, Isabelle Fournier, Michel Salzet, Marie Duhamel

{"title":"A co-culture system of macrophages with breast cancer tumoroids to study cell interactions and therapeutic responses.","authors":"Antonella Raffo-Romero, Lydia Ziane-Chaouche, Sophie Salomé-Desnoulez, Nawale Hajjaji, Isabelle Fournier, Michel Salzet, Marie Duhamel","doi":"10.1016/j.crmeth.2024.100792","DOIUrl":"10.1016/j.crmeth.2024.100792","url":null,"abstract":"3D tumoroids have revolutionized in vitro/ex vivo cancer biology by recapitulating the complex diversity of tumors. While tumoroids provide new insights into cancer development and treatment response, several limitations remain. As the tumor microenvironment, especially the immune system, strongly influences tumor development, the absence of immune cells in tumoroids may lead to inappropriate conclusions. Macrophages, key players in tumor progression, are particularly challenging to integrate into the tumoroids. In this study, we established three optimized and standardized methods for co-culturing human macrophages with breast cancer tumoroids: a semi-liquid model and two matrix-embedded models tailored for specific applications. We then tracked interactions and macrophage infiltration in these systems using flow cytometry and light sheet microscopy and showed that macrophages influenced not only tumoroid molecular profiles but also chemotherapy response. This underscores the importance of increasing the complexity of 3D models to more accurately reflect in vivo conditions.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11228374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141307004","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}

引用次数: 0

DNA-PAINT adaptors make for efficient multiplexing. DNA-PAINT 适配器可实现高效的多路复用。

IF 4.3

Cell Reports Methods Pub Date : 2024-06-17 DOI: 10.1016/j.crmeth.2024.100801

Matthew D Lycas, Suliana Manley

引用次数: 0

ElecFeX is a user-friendly toolbox for efficient feature extraction from single-cell electrophysiological recordings. ElecFeX 是一个用户友好型工具箱，用于从单细胞电生理记录中高效提取特征。

IF 4.3

Cell Reports Methods Pub Date : 2024-06-17 Epub Date: 2024-06-06 DOI: 10.1016/j.crmeth.2024.100791

Xinyue Ma, Loïs S Miraucourt, Haoyi Qiu, Mengyi Xu, Erik P Cook, Arjun Krishnaswamy, Reza Sharif-Naeini, Anmar Khadra

{"title":"ElecFeX is a user-friendly toolbox for efficient feature extraction from single-cell electrophysiological recordings.","authors":"Xinyue Ma, Loïs S Miraucourt, Haoyi Qiu, Mengyi Xu, Erik P Cook, Arjun Krishnaswamy, Reza Sharif-Naeini, Anmar Khadra","doi":"10.1016/j.crmeth.2024.100791","DOIUrl":"10.1016/j.crmeth.2024.100791","url":null,"abstract":"Characterizing neurons by their electrophysiological phenotypes is essential for understanding the neural basis of behavioral and cognitive functions. Technological developments have enabled the collection of hundreds of neural recordings; this calls for new tools capable of performing feature extraction efficiently. To address the urgent need for a powerful and accessible tool, we developed ElecFeX, an open-source MATLAB-based toolbox that (1) has an intuitive graphical user interface, (2) provides customizable measurements for a wide range of electrophysiological features, (3) processes large-size datasets effortlessly via batch analysis, and (4) yields formatted output for further analysis. We implemented ElecFeX on a diverse set of neural recordings; demonstrated its functionality, versatility, and efficiency in capturing electrical features; and established its significance in distinguishing neuronal subgroups across brain regions and species. ElecFeX is thus presented as a user-friendly toolbox to benefit the neuroscience community by minimizing the time required for extracting features from their electrophysiological datasets.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11228277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141288708","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}

引用次数: 0

Improved detection of tryptic immunoglobulin variable region peptides by chromatographic and gas-phase fractionation techniques. 利用色谱和气相分馏技术改进对胰蛋白酶免疫球蛋白可变区肽的检测。

IF 4.3

Cell Reports Methods Pub Date : 2024-06-17 Epub Date: 2024-06-10 DOI: 10.1016/j.crmeth.2024.100795

Christoph Stingl, Martijn M VanDuijn, Thomas Dejoie, Peter A E Sillevis Smitt, Theo M Luider

{"title":"Improved detection of tryptic immunoglobulin variable region peptides by chromatographic and gas-phase fractionation techniques.","authors":"Christoph Stingl, Martijn M VanDuijn, Thomas Dejoie, Peter A E Sillevis Smitt, Theo M Luider","doi":"10.1016/j.crmeth.2024.100795","DOIUrl":"10.1016/j.crmeth.2024.100795","url":null,"abstract":"The polyclonal repertoire of circulating antibodies potentially holds valuable information about an individual's humoral immune state. While bottom-up proteomics is well suited for serum proteomics, the vast number of antibodies and dynamic range of serum challenge this analysis. To acquire the serum proteome more comprehensively, we incorporated high-field asymmetric waveform ion-mobility spectrometry (FAIMS) or two-dimensional chromatography into standard trypsin-based bottom-up proteomics. Thereby, the number of variable region (VR)-related spectra increased 1.7-fold with FAIMS and 10-fold with chromatography fractionation. To match antibody VRs to spectra, we combined de novo searching and BLAST alignment. Validation of this approach showed that, as peptide length increased, the de novo accuracy decreased and BLAST performance increased. Through in silico calculations on antibody repository sequences, we determined the uniqueness of tryptic VR peptides and their suitability as antibody surrogate. Approximately one-third of these peptides were unique, and about one-third of all antibodies contained at least one unique peptide.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11228375/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141307006","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}

引用次数: 0

Development of an efficient, effective, and economical technology for proteome analysis. 开发高效、有效、经济的蛋白质组分析技术。

IF 4.3

Cell Reports Methods Pub Date : 2024-06-17 Epub Date: 2024-06-11 DOI: 10.1016/j.crmeth.2024.100796

Katherine R Martin, Ha T Le, Ahmed Abdelgawad, Canyuan Yang, Guotao Lu, Jessica L Keffer, Xiaohui Zhang, Zhihao Zhuang, Papa Nii Asare-Okai, Clara S Chan, Mona Batish, Yanbao Yu

{"title":"Development of an efficient, effective, and economical technology for proteome analysis.","authors":"Katherine R Martin, Ha T Le, Ahmed Abdelgawad, Canyuan Yang, Guotao Lu, Jessica L Keffer, Xiaohui Zhang, Zhihao Zhuang, Papa Nii Asare-Okai, Clara S Chan, Mona Batish, Yanbao Yu","doi":"10.1016/j.crmeth.2024.100796","DOIUrl":"10.1016/j.crmeth.2024.100796","url":null,"abstract":"We present an efficient, effective, and economical approach, named E3technology, for proteomics sample preparation. By immobilizing silica microparticles into the polytetrafluoroethylene matrix, we develop a robust membrane medium, which could serve as a reliable platform to generate proteomics-friendly samples in a rapid and low-cost fashion. We benchmark its performance using different formats and demonstrate them with a variety of sample types of varied complexity, quantity, and volume. Our data suggest that E3technology provides proteome-wide identification and quantitation performance equivalent or superior to many existing methods. We further propose an enhanced single-vessel approach, named E4technology, which performs on-filter in-cell digestion with minimal sample loss and high sensitivity, enabling low-input and low-cell proteomics. Lastly, we utilized the above technologies to investigate RNA-binding proteins and profile the intact bacterial cell proteome.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11228373/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141311920","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}

引用次数: 0

Recapitulating the tumor microenvironment in a dish, one cell type at a time. 在培养皿中重现肿瘤微环境，一次只重现一种细胞类型。

IF 4.3

Cell Reports Methods Pub Date : 2024-06-17 DOI: 10.1016/j.crmeth.2024.100800

Benjamin N Ostendorf

引用次数: 0

Bioengineering methods for vascularizing organoids. 血管化器官组织的生物工程方法。

IF 4.3

Cell Reports Methods Pub Date : 2024-06-17 Epub Date: 2024-05-16 DOI: 10.1016/j.crmeth.2024.100779

Peter N Nwokoye, Oscar J Abilez

{"title":"Bioengineering methods for vascularizing organoids.","authors":"Peter N Nwokoye, Oscar J Abilez","doi":"10.1016/j.crmeth.2024.100779","DOIUrl":"10.1016/j.crmeth.2024.100779","url":null,"abstract":"Organoids, self-organizing three-dimensional (3D) structures derived from stem cells, offer unique advantages for studying organ development, modeling diseases, and screening potential therapeutics. However, their translational potential and ability to mimic complex in vivo functions are often hindered by the lack of an integrated vascular network. To address this critical limitation, bioengineering strategies are rapidly advancing to enable efficient vascularization of organoids. These methods encompass co-culturing organoids with various vascular cell types, co-culturing lineage-specific organoids with vascular organoids, co-differentiating stem cells into organ-specific and vascular lineages, using organoid-on-a-chip technology to integrate perfusable vasculature within organoids, and using 3D bioprinting to also create perfusable organoids. This review explores the field of organoid vascularization, examining the biological principles that inform bioengineering approaches. Additionally, this review envisions how the converging disciplines of stem cell biology, biomaterials, and advanced fabrication technologies will propel the creation of increasingly sophisticated organoid models, ultimately accelerating biomedical discoveries and innovations.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140960031","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

Systematic biases in reference-based plasma cell-free DNA fragmentomic profiling. 基于参考文献的血浆无细胞 DNA 片段分析中的系统性偏差。

IF 4.3

Cell Reports Methods Pub Date : 2024-06-17 Epub Date: 2024-06-11 DOI: 10.1016/j.crmeth.2024.100793

Xiaoyi Liu, Mengqi Yang, Dingxue Hu, Yunyun An, Wanqiu Wang, Huizhen Lin, Yuqi Pan, Jia Ju, Kun Sun

{"title":"Systematic biases in reference-based plasma cell-free DNA fragmentomic profiling.","authors":"Xiaoyi Liu, Mengqi Yang, Dingxue Hu, Yunyun An, Wanqiu Wang, Huizhen Lin, Yuqi Pan, Jia Ju, Kun Sun","doi":"10.1016/j.crmeth.2024.100793","DOIUrl":"10.1016/j.crmeth.2024.100793","url":null,"abstract":"Plasma cell-free DNA (cfDNA) fragmentation patterns are emerging directions in cancer liquid biopsy with high translational significance. Conventionally, the cfDNA sequencing reads are aligned to a reference genome to extract their fragmentomic features. In this study, through cfDNA fragmentomics profiling using different reference genomes on the same datasets in parallel, we report systematic biases in such conventional reference-based approaches. The biases in cfDNA fragmentomic features vary among races in a sample-dependent manner and therefore might adversely affect the performances of cancer diagnosis assays across multiple clinical centers. In addition, to circumvent the analytical biases, we develop Freefly, a reference-free approach for cfDNA fragmentomics profiling. Freefly runs ∼60-fold faster than the conventional reference-based approach while generating highly consistent results. Moreover, cfDNA fragmentomic features reported by Freefly can be directly used for cancer diagnosis. Hence, Freefly possesses translational merit toward the rapid and unbiased measurement of cfDNA fragmentomics.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11228372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141311877","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}

引用次数: 0

Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques. 利用机器学习技术优化几何约束心脏器官的设计。

IF 4.3

Cell Reports Methods Pub Date : 2024-06-17 DOI: 10.1016/j.crmeth.2024.100798

Andrew Kowalczewski, Shiyang Sun, Nhu Y Mai, Yuanhui Song, Plansky Hoang, Xiyuan Liu, Huaxiao Yang, Zhen Ma

{"title":"Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques.","authors":"Andrew Kowalczewski, Shiyang Sun, Nhu Y Mai, Yuanhui Song, Plansky Hoang, Xiyuan Liu, Huaxiao Yang, Zhen Ma","doi":"10.1016/j.crmeth.2024.100798","DOIUrl":"10.1016/j.crmeth.2024.100798","url":null,"abstract":"Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The convergence of organoid technology, tissue engineering, and artificial intelligence (AI) could potentially enhance our understanding of the design principles for organoid engineering. In this study, we utilized micropatterning techniques to create a designer library of 230 cardiac organoids with 7 geometric designs. We employed manifold learning techniques to analyze single organoid heterogeneity based on 10 physiological parameters. We clustered and refined the cardiac organoids based on their functional similarity using unsupervised machine learning approaches, thus elucidating unique functionalities associated with geometric designs. We also highlighted the critical role of calcium transient rising time in distinguishing organoids based on geometric patterns and clustering results. This integration of organoid engineering and machine learning enhances our understanding of structure-function relationships in cardiac organoids, paving the way for more controlled and optimized organoid design.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11228370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421116","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}

引用次数: 0

GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions. GENIX 可对单细胞 RNA 测序进行比较网络分析，揭示治疗干预的特征。

IF 4.3

Cell Reports Methods Pub Date : 2024-06-17 Epub Date: 2024-06-10 DOI: 10.1016/j.crmeth.2024.100794

Nima Nouri, Giorgio Gaglia, Hamid Mattoo, Emanuele de Rinaldis, Virginia Savova

{"title":"GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions.","authors":"Nima Nouri, Giorgio Gaglia, Hamid Mattoo, Emanuele de Rinaldis, Virginia Savova","doi":"10.1016/j.crmeth.2024.100794","DOIUrl":"10.1016/j.crmeth.2024.100794","url":null,"abstract":"Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects and is susceptible to false positives. We present GENIX (gene expression network importance examination), a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. We benchmark GENIX using both synthetic and experimental datasets, including analysis of influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) from recovered COVID-19 patients. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11228368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141307005","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}

引用次数: 0