Cell Reports Methods最新文献_第8页

Accelerated production of human epithelial organoids in a miniaturized spinning bioreactor. 在微型旋转生物反应器中加速生产人类上皮细胞器官组织。

IF 4.3

Cell Reports Methods Pub Date : 2024-11-18 DOI: 10.1016/j.crmeth.2024.100903

Shicheng Ye, Ary Marsee, Gilles S van Tienderen, Mohammad Rezaeimoghaddam, Hafsah Sheikh, Roos-Anne Samsom, Eelco J P de Koning, Sabine Fuchs, Monique M A Verstegen, Luc J W van der Laan, Frans van de Vosse, Jos Malda, Keita Ito, Bart Spee, Kerstin Schneeberger

{"title":"Accelerated production of human epithelial organoids in a miniaturized spinning bioreactor.","authors":"Shicheng Ye, Ary Marsee, Gilles S van Tienderen, Mohammad Rezaeimoghaddam, Hafsah Sheikh, Roos-Anne Samsom, Eelco J P de Koning, Sabine Fuchs, Monique M A Verstegen, Luc J W van der Laan, Frans van de Vosse, Jos Malda, Keita Ito, Bart Spee, Kerstin Schneeberger","doi":"10.1016/j.crmeth.2024.100903","DOIUrl":"10.1016/j.crmeth.2024.100903","url":null,"abstract":"Conventional static culture of organoids necessitates weekly manual passaging and results in nonhomogeneous exposure of organoids to nutrients, oxygen, and toxic metabolites. Here, we developed a miniaturized spinning bioreactor, RPMotion, specifically optimized for accelerated and cost-effective culture of epithelial organoids under homogeneous conditions. We established tissue-specific RPMotion settings and standard operating protocols for the expansion of human epithelial organoids derived from the liver, intestine, and pancreas. All organoid types proliferated faster in the bioreactor (5.2-fold, 3-fold, and 4-fold, respectively) compared to static culture while keeping their organ-specific phenotypes. We confirmed that the bioreactor is suitable for organoid establishment directly from biopsies and for long-term expansion of liver organoids. Furthermore, we showed that after accelerated expansion, liver organoids can be differentiated into hepatocyte-like cells in the RPMotion bioreactor. In conclusion, this miniaturized bioreactor enables work-, time-, and cost-efficient organoid culture, holding great promise for organoid-based fundamental and translational research and development.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":"4 11","pages":"100903"},"PeriodicalIF":4.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11705766/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677130","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

Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL. 利用 FLORAL 增强微生物特征选择的可扩展对数比率套索回归。

IF 4.3

Cell Reports Methods Pub Date : 2024-11-18 Epub Date: 2024-11-07 DOI: 10.1016/j.crmeth.2024.100899

Teng Fei, Tyler Funnell, Nicholas R Waters, Sandeep S Raj, Mirae Baichoo, Keimya Sadeghi, Anqi Dai, Oriana Miltiadous, Roni Shouval, Meng Lv, Jonathan U Peled, Doris M Ponce, Miguel-Angel Perales, Mithat Gönen, Marcel R M van den Brink

{"title":"Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL.","authors":"Teng Fei, Tyler Funnell, Nicholas R Waters, Sandeep S Raj, Mirae Baichoo, Keimya Sadeghi, Anqi Dai, Oriana Miltiadous, Roni Shouval, Meng Lv, Jonathan U Peled, Doris M Ponce, Miguel-Angel Perales, Mithat Gönen, Marcel R M van den Brink","doi":"10.1016/j.crmeth.2024.100899","DOIUrl":"10.1016/j.crmeth.2024.100899","url":null,"abstract":"Identifying predictive biomarkers of patient outcomes from high-throughput microbiome data is of high interest, while existing computational methods do not satisfactorily account for complex survival endpoints, longitudinal samples, and taxa-specific sequencing biases. We present FLORAL, an open-source tool to perform scalable log-ratio lasso regression and microbial feature selection for continuous, binary, time-to-event, and competing risk outcomes, with compatibility for longitudinal microbiome data as time-dependent covariates. The proposed method adapts the augmented Lagrangian algorithm for a zero-sum constraint optimization problem while enabling a two-stage screening process for enhanced false-positive control. In extensive simulation and real-data analyses, FLORAL achieved consistently better false-positive control compared to other lasso-based approaches and better sensitivity over popular differential abundance testing methods for datasets with smaller sample sizes. In a survival analysis of allogeneic hematopoietic cell transplant recipients, FLORAL demonstrated considerable improvement in microbial feature selection by utilizing longitudinal microbiome data over solely using baseline microbiome data.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"100899"},"PeriodicalIF":4.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11705925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142606500","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

iSubGen generates integrative disease subtypes by pairwise similarity assessment. iSubGen 通过成对相似性评估生成综合疾病亚型。

IF 4.3

Cell Reports Methods Pub Date : 2024-11-18 Epub Date: 2024-10-23 DOI: 10.1016/j.crmeth.2024.100884

Natalie S Fox, Mao Tian, Alexander L Markowitz, Syed Haider, Constance H Li, Paul C Boutros

{"title":"iSubGen generates integrative disease subtypes by pairwise similarity assessment.","authors":"Natalie S Fox, Mao Tian, Alexander L Markowitz, Syed Haider, Constance H Li, Paul C Boutros","doi":"10.1016/j.crmeth.2024.100884","DOIUrl":"10.1016/j.crmeth.2024.100884","url":null,"abstract":"There are myriad types of biomedical data-molecular, clinical images, and others. When a group of patients with the same underlying disease exhibits similarities across multiple types of data, this is called a subtype. Existing subtyping approaches struggle to handle diverse data types with missing information. To improve subtype discovery, we exploited changes in the correlation-structure between different data types to create iSubGen, an algorithm for integrative subtype generation. iSubGen can accommodate any feature that can be compared with a similarity metric to create subtypes versatilely. It can combine arbitrary data types for subtype discovery, such as merging genetic, transcriptomic, proteomic, and pathway data. iSubGen recapitulates known subtypes across multiple cancers even with substantial missing data and identifies subtypes with distinct clinical behaviors. It performs equally with or superior to other subtyping methods, offering greater stability and robustness to missing data and flexibility to new data types. It is available at https://cran.r-project.org/web/packages/iSubGen.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"100884"},"PeriodicalIF":4.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11705582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509369","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

Exploring protein natural diversity in environmental microbiomes with DeepMetagenome. 利用 DeepMetagenome 探索环境微生物组中蛋白质的天然多样性。

IF 4.3

Cell Reports Methods Pub Date : 2024-11-18 Epub Date: 2024-11-07 DOI: 10.1016/j.crmeth.2024.100896

Xiaofang Li, Jun Zhang, Dan Ma, Xiaofei Fan, Xin Zheng, Yong-Xin Liu

引用次数: 0

Opto-chemogenetic inhibition of L-type Ca_V1 channels in neurons through a membrane-assisted molecular linkage. 通过膜辅助分子连接对神经元中的 L 型 CaV1 通道进行光化学抑制。

IF 4.3

Cell Reports Methods Pub Date : 2024-11-18 Epub Date: 2024-11-07 DOI: 10.1016/j.crmeth.2024.100898

Jinli Geng, Yaxiong Yang, Boying Li, Zhen Yu, Shuang Qiu, Wen Zhang, Shixin Gao, Nan Liu, Yi Liu, Bo Wang, Yubo Fan, Chengfen Xing, Xiaodong Liu

{"title":"Opto-chemogenetic inhibition of L-type CaV1 channels in neurons through a membrane-assisted molecular linkage.","authors":"Jinli Geng, Yaxiong Yang, Boying Li, Zhen Yu, Shuang Qiu, Wen Zhang, Shixin Gao, Nan Liu, Yi Liu, Bo Wang, Yubo Fan, Chengfen Xing, Xiaodong Liu","doi":"10.1016/j.crmeth.2024.100898","DOIUrl":"10.1016/j.crmeth.2024.100898","url":null,"abstract":"Genetically encoded inhibitors of CaV1 channels that operate via C-terminus-mediated inhibition (CMI) have been actively pursued. Here, we advance the design of CMI peptides by proposing a membrane-anchoring tag that is sufficient to link the inhibitory modules to the target channel as well as chemical and optogenetic modes of system control. We designed and implemented the constitutive and inducible CMI modules with appropriate dynamic ranges for the short and long variants of CaV1.3, both naturally occurring in neurons. Upon optical (near-infrared-responsive nanoparticles) and/or chemical (rapamycin) induction of FRB/FKBP binding, the designed peptides translocated onto the membrane via FRB-Ras, where the physical linkage requirement for CMI could be satisfied. The peptides robustly produced acute, potent, and specific inhibitions on both recombinant and neuronal CaV1 activities, including Ca2+ influx-neuritogenesis coupling. Validated through opto-chemogenetic induction, this prototype demonstrates Ca2+ channel modulation via membrane-assisted molecular linkage, promising broad applicability to diverse membrane proteins.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"100898"},"PeriodicalIF":4.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11705922/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142606454","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

MEA-NAP: A flexible network analysis pipeline for neuronal 2D and 3D organoid multielectrode recordings. MEA-NAP：用于神经元二维和三维类器官多电极记录的灵活网络分析管道

IF 4.3

Cell Reports Methods Pub Date : 2024-11-18 Epub Date: 2024-11-08 DOI: 10.1016/j.crmeth.2024.100901

Timothy P H Sit, Rachael C Feord, Alexander W E Dunn, Jeremi Chabros, David Oluigbo, Hugo H Smith, Lance Burn, Elise Chang, Alessio Boschi, Yin Yuan, George M Gibbons, Mahsa Khayat-Khoei, Francesco De Angelis, Erik Hemberg, Martin Hemberg, Madeline A Lancaster, Andras Lakatos, Stephen J Eglen, Ole Paulsen, Susanna B Mierau

{"title":"MEA-NAP: A flexible network analysis pipeline for neuronal 2D and 3D organoid multielectrode recordings.","authors":"Timothy P H Sit, Rachael C Feord, Alexander W E Dunn, Jeremi Chabros, David Oluigbo, Hugo H Smith, Lance Burn, Elise Chang, Alessio Boschi, Yin Yuan, George M Gibbons, Mahsa Khayat-Khoei, Francesco De Angelis, Erik Hemberg, Martin Hemberg, Madeline A Lancaster, Andras Lakatos, Stephen J Eglen, Ole Paulsen, Susanna B Mierau","doi":"10.1016/j.crmeth.2024.100901","DOIUrl":"10.1016/j.crmeth.2024.100901","url":null,"abstract":"Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics-patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and thus can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches. VIDEO ABSTRACT.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"100901"},"PeriodicalIF":4.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11706071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142628067","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

Quantifying tumor specificity using Bayesian probabilistic modeling for drug and immunotherapeutic target discovery. 利用贝叶斯概率模型量化肿瘤特异性，以发现药物和免疫治疗靶点。

IF 4.3

Cell Reports Methods Pub Date : 2024-11-18 Epub Date: 2024-11-07 DOI: 10.1016/j.crmeth.2024.100900

Guangyuan Li, Daniel Schnell, Anukana Bhattacharjee, Mark Yarmarkovich, Nathan Salomonis

{"title":"Quantifying tumor specificity using Bayesian probabilistic modeling for drug and immunotherapeutic target discovery.","authors":"Guangyuan Li, Daniel Schnell, Anukana Bhattacharjee, Mark Yarmarkovich, Nathan Salomonis","doi":"10.1016/j.crmeth.2024.100900","DOIUrl":"10.1016/j.crmeth.2024.100900","url":null,"abstract":"In diseases such as cancer, the design of new therapeutic strategies requires extensive, costly, and unfortunately sometimes deadly testing to reveal life threatening off-target effects. We hypothesized that the disease specificity of targets can be systematically learned for all genes by jointly evaluating complementary molecular measurements of healthy tissues using a hierarchical Bayesian modeling approach. Our method, BayesTS, integrates protein and gene expression evidence and includes tunable parameters to moderate tissue essentiality. Applied to all protein coding genes, BayesTS outperforms alternative strategies to define therapeutic targets and nominates previously unknown targets while allowing for incorporation of new types of modalities. To expand target repertoires, we show that extension of BayesTS to splicing antigens and combinatorial target pairs results in more specific targets for therapy. We expect that BayesTS will facilitate improved target prioritization for oncology drug development, ultimately leading to the discovery of more effective and safer treatments.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"100900"},"PeriodicalIF":4.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11705768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142606457","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

Tumor-associated antigen prediction using a single-sample gene expression state inference algorithm. 使用单样本基因表达状态推断算法预测肿瘤相关抗原。

IF 4.3

Cell Reports Methods Pub Date : 2024-11-18 DOI: 10.1016/j.crmeth.2024.100906

Xinpei Yi, Hongwei Zhao, Shunjie Hu, Liangqing Dong, Yongchao Dou, Jing Li, Qiang Gao, Bing Zhang

{"title":"Tumor-associated antigen prediction using a single-sample gene expression state inference algorithm.","authors":"Xinpei Yi, Hongwei Zhao, Shunjie Hu, Liangqing Dong, Yongchao Dou, Jing Li, Qiang Gao, Bing Zhang","doi":"10.1016/j.crmeth.2024.100906","DOIUrl":"10.1016/j.crmeth.2024.100906","url":null,"abstract":"We developed a Bayesian-based algorithm to infer gene expression states in individual samples and incorporated it into a workflow to identify tumor-associated antigens (TAAs) across 33 cancer types using RNA sequencing (RNA-seq) data from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA). Our analysis identified 212 candidate TAAs, with 78 validated in independent RNA-seq datasets spanning seven cancer types. Eighteen of these TAAs were further corroborated by proteomics data, including 10 linked to liver cancer. We predicted that 38 peptides derived from these 10 TAAs would bind strongly to HLA-A02, the most common HLA allele. Experimental validation confirmed significant binding affinity and immunogenicity for 21 of these peptides. Notably, approximately 64% of liver tumors expressed one or more TAAs associated with these 21 peptides, positioning them as promising candidates for liver cancer therapies, such as peptide vaccines or T cell receptor (TCR)-T cell treatments. This study highlights the power of integrating computational and experimental approaches to discover TAAs for immunotherapy.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":"4 11","pages":"100906"},"PeriodicalIF":4.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11705763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677228","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

Optimized full-spectrum flow cytometry panel for deep immunophenotyping of murine lungs. 用于小鼠肺部深度免疫分型的全谱流式细胞仪优化面板。

IF 4.3

Cell Reports Methods Pub Date : 2024-11-18 Epub Date: 2024-10-30 DOI: 10.1016/j.crmeth.2024.100885

Zora Baumann, Carsten Wiethe, Cinja M Vecchi, Veronica Richina, Telma Lopes, Mohamed Bentires-Alj

引用次数: 0

Elucidating the spatiotemporal dynamics of glucose metabolism with genetically encoded fluorescent biosensors. 利用基因编码荧光生物传感器阐明葡萄糖代谢的时空动态。

IF 4.3

Cell Reports Methods Pub Date : 2024-11-18 Epub Date: 2024-11-12 DOI: 10.1016/j.crmeth.2024.100904

Xie Li, Xueyi Wen, Weitao Tang, Chengnuo Wang, Yaqiong Chen, Yi Yang, Zhuo Zhang, Yuzheng Zhao

{"title":"Elucidating the spatiotemporal dynamics of glucose metabolism with genetically encoded fluorescent biosensors.","authors":"Xie Li, Xueyi Wen, Weitao Tang, Chengnuo Wang, Yaqiong Chen, Yi Yang, Zhuo Zhang, Yuzheng Zhao","doi":"10.1016/j.crmeth.2024.100904","DOIUrl":"10.1016/j.crmeth.2024.100904","url":null,"abstract":"Glucose metabolism has been well understood for many years, but some intriguing questions remain regarding the subcellular distribution, transport, and functions of glycolytic metabolites. To address these issues, a living cell metabolic monitoring technology with high spatiotemporal resolution is needed. Genetically encoded fluorescent sensors can achieve specific, sensitive, and spatiotemporally resolved metabolic monitoring in living cells and in vivo, and dozens of glucose metabolite sensors have been developed recently. Here, we highlight the importance of tracking specific intermediate metabolites of glycolysis and glycolytic flux measurements, monitoring the spatiotemporal dynamics, and quantifying metabolite abundance. We then describe the working principles of fluorescent protein sensors and summarize the existing biosensors and their application in understanding glucose metabolism. Finally, we analyze the remaining challenges in developing high-quality biosensors and the huge potential of biosensor-based metabolic monitoring at multiple spatiotemporal scales.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"100904"},"PeriodicalIF":4.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11705769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142627927","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