Cell Reports Methods最新文献_第3页

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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142606454","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

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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142628067","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

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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142606457","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

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":"https://doi.org/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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677228","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

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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142627927","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

Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data. 利用单细胞 RNA-seq 数据对大块组织中的细胞丰度进行独立于聚类的估算。

IF 4.3

Cell Reports Methods Pub Date : 2024-11-18 DOI: 10.1016/j.crmeth.2024.100905

Rachael G Aubin, Javier Montelongo, Robert Hu, Elijah Gunther, Patrick Nicodemus, Pablo G Camara

{"title":"Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data.","authors":"Rachael G Aubin, Javier Montelongo, Robert Hu, Elijah Gunther, Patrick Nicodemus, Pablo G Camara","doi":"10.1016/j.crmeth.2024.100905","DOIUrl":"10.1016/j.crmeth.2024.100905","url":null,"abstract":"Single-cell RNA sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes such as cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in phenotypic resolution and functionality with respect to regression-based methods. Using ConDecon, we discover the implication of neurodegenerative microglia inflammatory pathways in the mesenchymal transformation of pediatric ependymoma and characterize their spatial trajectories of activation. The generality of this approach enables the deconvolution of other data modalities, such as bulk ATAC-seq data.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":"4 11","pages":"100905"},"PeriodicalIF":4.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677226","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

Development of fluorescence lifetime biosensors for ATP, cAMP, citrate, and glucose using the mTurquoise2-based platform. 利用基于 mTurquoise2 的平台开发 ATP、cAMP、柠檬酸盐和葡萄糖的荧光寿命生物传感器。

IF 4.3

Cell Reports Methods Pub Date : 2024-11-18 DOI: 10.1016/j.crmeth.2024.100902

Chongxia Zhong, Satoshi Arai, Yasushi Okada

{"title":"Development of fluorescence lifetime biosensors for ATP, cAMP, citrate, and glucose using the mTurquoise2-based platform.","authors":"Chongxia Zhong, Satoshi Arai, Yasushi Okada","doi":"10.1016/j.crmeth.2024.100902","DOIUrl":"https://doi.org/10.1016/j.crmeth.2024.100902","url":null,"abstract":"Single-fluorescent protein (FP)-based FLIM (fluorescence lifetime imaging microscopy) biosensors can visualize intracellular processes quantitatively. They require a single wavelength for detection, which facilitates multi-color imaging. However, their development has been limited by the absence of a general design framework and complex screening processes. In this study, we engineered FLIM biosensors for ATP (adenosine triphosphate), cAMP (cyclic adenosine monophosphate), citrate, and glucose by inserting each sensing domain into mTurquoise2 (mTQ2) between Tyr-145 and Phe-146 using peptide linkers. Fluorescence intensity-based screening yielded FLIM biosensors with a 0.5 to 1.0 ns dynamic range upon analyte binding, demonstrating that the mTQ2(1-145)-GT-X-EF-mTQ2(146-238) backbone is a versatile platform for FLIM biosensors, allowing for simple intensity-based screening while providing dual-functional biosensors for both FLIM and intensity-based imaging. As a proof of concept, we monitored cAMP and Ca2+ dynamics simultaneously in living cells by dual-color imaging. Our results complement recent studies, establishing mTQ2 as a valuable framework for developing FLIM biosensors.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":"4 11","pages":"100902"},"PeriodicalIF":4.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677227","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

Recovering single-cell expression profiles from spatial transcriptomics with scResolve. 利用 scResolve 从空间转录组学中恢复单细胞表达谱。

IF 4.3

Cell Reports Methods Pub Date : 2024-10-21 Epub Date: 2024-09-25 DOI: 10.1016/j.crmeth.2024.100864

Hao Chen, Young Je Lee, Jose A Ovando-Ricardez, Lorena Rosas, Mauricio Rojas, Ana L Mora, Ziv Bar-Joseph, Jose Lugo-Martinez

引用次数: 0