Cell Reports Methods最新文献_第5页

Fatecode enables cell fate regulator prediction using classification-supervised autoencoder perturbation. Fatecode 利用分类监督自动编码器扰动技术实现细胞命运调节器预测。

IF 4.3

Cell Reports Methods Pub Date : 2024-07-15 Epub Date: 2024-07-09 DOI: 10.1016/j.crmeth.2024.100819

Mehrshad Sadria, Anita Layton, Sidhartha Goyal, Gary D Bader

{"title":"Fatecode enables cell fate regulator prediction using classification-supervised autoencoder perturbation.","authors":"Mehrshad Sadria, Anita Layton, Sidhartha Goyal, Gary D Bader","doi":"10.1016/j.crmeth.2024.100819","DOIUrl":"10.1016/j.crmeth.2024.100819","url":null,"abstract":"Cell reprogramming, which guides the conversion between cell states, is a promising technology for tissue repair and regeneration, with the ultimate goal of accelerating recovery from diseases or injuries. To accomplish this, regulators must be identified and manipulated to control cell fate. We propose Fatecode, a computational method that predicts cell fate regulators based only on single-cell RNA sequencing (scRNA-seq) data. Fatecode learns a latent representation of the scRNA-seq data using a deep learning-based classification-supervised autoencoder and then performs in silico perturbation experiments on the latent representation to predict genes that, when perturbed, would alter the original cell type distribution to increase or decrease the population size of a cell type of interest. We assessed Fatecode's performance using simulations from a mechanistic gene-regulatory network model and scRNA-seq data mapping blood and brain development of different organisms. Our results suggest that Fatecode can detect known cell fate regulators from single-cell transcriptomics datasets.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141580965","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

Leveraging a self-cleaving peptide for tailored control in proximity labeling proteomics. 在近距离标记蛋白质组学中利用自裂解肽进行定制控制。

IF 4.3

Cell Reports Methods Pub Date : 2024-07-15 Epub Date: 2024-07-09 DOI: 10.1016/j.crmeth.2024.100818

Louis Delhaye, George D Moschonas, Daria Fijalkowska, Annick Verhee, Delphine De Sutter, Tessa Van de Steene, Margaux De Meyer, Hanna Grzesik, Laura Van Moortel, Karolien De Bosscher, Thomas Jacobs, Sven Eyckerman

{"title":"Leveraging a self-cleaving peptide for tailored control in proximity labeling proteomics.","authors":"Louis Delhaye, George D Moschonas, Daria Fijalkowska, Annick Verhee, Delphine De Sutter, Tessa Van de Steene, Margaux De Meyer, Hanna Grzesik, Laura Van Moortel, Karolien De Bosscher, Thomas Jacobs, Sven Eyckerman","doi":"10.1016/j.crmeth.2024.100818","DOIUrl":"10.1016/j.crmeth.2024.100818","url":null,"abstract":"Protein-protein interactions play an important biological role in every aspect of cellular homeostasis and functioning. Proximity labeling mass spectrometry-based proteomics overcomes challenges typically associated with other methods and has quickly become the current state of the art in the field. Nevertheless, tight control of proximity-labeling enzymatic activity and expression levels is crucial to accurately identify protein interactors. Here, we leverage a T2A self-cleaving peptide and a non-cleaving mutant to accommodate the protein of interest in the experimental and control TurboID setup. To allow easy and streamlined plasmid assembly, we built a Golden Gate modular cloning system to generate plasmids for transient expression and stable integration. To highlight our T2A Split/link design, we applied it to identify protein interactions of the glucocorticoid receptor and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid and non-structural protein 7 (NSP7) proteins by TurboID proximity labeling. Our results demonstrate that our T2A split/link provides an opportune control that builds upon previously established control requirements in the field.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141580966","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

SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer. SingleCellGGM 可从单细胞转录组中识别基因表达程序，并促进通用细胞标签转移。

IF 4.3

Cell Reports Methods Pub Date : 2024-07-15 Epub Date: 2024-07-05 DOI: 10.1016/j.crmeth.2024.100813

Yupu Xu, Yuzhou Wang, Shisong Ma

{"title":"SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer.","authors":"Yupu Xu, Yuzhou Wang, Shisong Ma","doi":"10.1016/j.crmeth.2024.100813","DOIUrl":"10.1016/j.crmeth.2024.100813","url":null,"abstract":"Gene co-expression analysis of single-cell transcriptomes, aiming to define functional relationships between genes, is challenging due to excessive dropout values. Here, we developed a single-cell graphical Gaussian model (SingleCellGGM) algorithm to conduct single-cell gene co-expression network analysis. When applied to mouse single-cell datasets, SingleCellGGM constructed networks from which gene co-expression modules with highly significant functional enrichment were identified. We considered the modules as gene expression programs (GEPs). These GEPs enable direct cell-type annotation of individual cells without cell clustering, and they are enriched with genes required for the functions of the corresponding cells, sometimes at levels greater than 10-fold. The GEPs are conserved across datasets and enable universal cell-type label transfer across different studies. We also proposed a dimension-reduction method through averaging by GEPs for single-cell analysis, enhancing the interpretability of results. Thus, SingleCellGGM offers a unique GEP-based perspective to analyze single-cell transcriptomes and reveals biological insights shared by different single-cell datasets.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141545326","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

A mammalian model reveals inorganic polyphosphate channeling into the nucleolus and induction of a hyper-condensate state. 哺乳动物模型揭示了无机多磷酸进入核仁的通道和诱导高凝状态。

IF 4.3

Cell Reports Methods Pub Date : 2024-07-15 Epub Date: 2024-07-08 DOI: 10.1016/j.crmeth.2024.100814

Filipy Borghi, Cristina Azevedo, Errin Johnson, Jemima J Burden, Adolfo Saiardi

{"title":"A mammalian model reveals inorganic polyphosphate channeling into the nucleolus and induction of a hyper-condensate state.","authors":"Filipy Borghi, Cristina Azevedo, Errin Johnson, Jemima J Burden, Adolfo Saiardi","doi":"10.1016/j.crmeth.2024.100814","DOIUrl":"10.1016/j.crmeth.2024.100814","url":null,"abstract":"Inorganic polyphosphate (polyP) is a ubiquitous polymer that controls fundamental processes. To overcome the absence of a genetically tractable mammalian model, we developed an inducible mammalian cell line expressing Escherichia coli polyphosphate kinase 1 (EcPPK1). Inducing EcPPK1 expression prompted polyP synthesis, enabling validation of polyP analytical methods. Virtually all newly synthesized polyP accumulates within the nucleus, mainly in the nucleolus. The channeled polyP within the nucleolus results in the redistribution of its markers, leading to altered rRNA processing. Ultrastructural analysis reveals electron-dense polyP structures associated with a hyper-condensed nucleolus resulting from an exacerbation of the liquid-liquid phase separation (LLPS) phenomena controlling this membraneless organelle. The selective accumulation of polyP in the nucleoli could be interpreted as an amplification of polyP channeling to where its physiological function takes place. Indeed, quantitative analysis of several mammalian cell lines confirms that endogenous polyP accumulates within the nucleolus.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294840/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141564708","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

Directly selecting cell-type marker genes for single-cell clustering analyses. 直接选择细胞类型标记基因进行单细胞聚类分析。

IF 4.3

Cell Reports Methods Pub Date : 2024-07-15 Epub Date: 2024-07-08 DOI: 10.1016/j.crmeth.2024.100810

Zihao Chen, Changhu Wang, Siyuan Huang, Yang Shi, Ruibin Xi

引用次数: 0

Tongue orthotopic xenografts to study fusion-negative rhabdomyosarcoma invasion and metastasis in live animals. 用舌头正位异种移植物研究融合阴性横纹肌肉瘤在活体动物中的侵袭和转移。

IF 4.3

Cell Reports Methods Pub Date : 2024-07-15 Epub Date: 2024-07-03 DOI: 10.1016/j.crmeth.2024.100802

Sarah M Hammoudeh, Yeap Ng, Bih-Rong Wei, Thomas D Madsen, Mukesh P Yadav, R Mark Simpson, Roberto Weigert, Paul A Randazzo

{"title":"Tongue orthotopic xenografts to study fusion-negative rhabdomyosarcoma invasion and metastasis in live animals.","authors":"Sarah M Hammoudeh, Yeap Ng, Bih-Rong Wei, Thomas D Madsen, Mukesh P Yadav, R Mark Simpson, Roberto Weigert, Paul A Randazzo","doi":"10.1016/j.crmeth.2024.100802","DOIUrl":"10.1016/j.crmeth.2024.100802","url":null,"abstract":"PAX3/7 fusion-negative rhabdomyosarcoma (FN-RMS) is a childhood mesodermal lineage malignancy with a poor prognosis for metastatic or relapsed cases. Limited understanding of advanced FN-RMS is partially attributed to the absence of sequential invasion and dissemination events and the challenge in studying cell behavior, using, for example, non-invasive intravital microscopy (IVM), in currently used xenograft models. Here, we developed an orthotopic tongue xenograft model of FN-RMS to study cell behavior and the molecular basis of invasion and metastasis using IVM. FN-RMS cells are retained in the tongue and invade locally into muscle mysial spaces and vascular lumen, with evidence of hematogenous dissemination to the lungs and lymphatic dissemination to lymph nodes. Using IVM of tongue xenografts reveals shifts in cellular phenotype, migration to blood and lymphatic vessels, and lymphatic intravasation. Insight from this model into tumor invasion and metastasis at the tissue, cellular, and subcellular level can guide new therapeutic avenues for advanced FN-RMS.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141535543","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

PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis. PANAMA 支持高灵敏度双纳米流 LC-MS 代谢组学和蛋白质组学分析。

IF 4.3

Cell Reports Methods Pub Date : 2024-07-15 Epub Date: 2024-07-02 DOI: 10.1016/j.crmeth.2024.100803

Weiwei Lin, Fatemeh Mousavi, Benjamin C Blum, Christian F Heckendorf, Matthew Lawton, Noah Lampl, Ryan Hekman, Hongbo Guo, Mark McComb, Andrew Emili

{"title":"PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis.","authors":"Weiwei Lin, Fatemeh Mousavi, Benjamin C Blum, Christian F Heckendorf, Matthew Lawton, Noah Lampl, Ryan Hekman, Hongbo Guo, Mark McComb, Andrew Emili","doi":"10.1016/j.crmeth.2024.100803","DOIUrl":"10.1016/j.crmeth.2024.100803","url":null,"abstract":"High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141499166","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

Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients. 通过交叉关注，对 130 名肺癌患者的有限全息成像临床数据进行深度学习。

IF 4.3

Cell Reports Methods Pub Date : 2024-07-15 Epub Date: 2024-07-08 DOI: 10.1016/j.crmeth.2024.100817

Suraj Verma, Giuseppe Magazzù, Noushin Eftekhari, Thai Lou, Alex Gilhespy, Annalisa Occhipinti, Claudio Angione

{"title":"Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients.","authors":"Suraj Verma, Giuseppe Magazzù, Noushin Eftekhari, Thai Lou, Alex Gilhespy, Annalisa Occhipinti, Claudio Angione","doi":"10.1016/j.crmeth.2024.100817","DOIUrl":"10.1016/j.crmeth.2024.100817","url":null,"abstract":"Deep-learning tools that extract prognostic factors derived from multi-omics data have recently contributed to individualized predictions of survival outcomes. However, the limited size of integrated omics-imaging-clinical datasets poses challenges. Here, we propose two biologically interpretable and robust deep-learning architectures for survival prediction of non-small cell lung cancer (NSCLC) patients, learning simultaneously from computed tomography (CT) scan images, gene expression data, and clinical information. The proposed models integrate patient-specific clinical, transcriptomic, and imaging data and incorporate Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway information, adding biological knowledge within the learning process to extract prognostic gene biomarkers and molecular pathways. While both models accurately stratify patients in high- and low-risk groups when trained on a dataset of only 130 patients, introducing a cross-attention mechanism in a sparse autoencoder significantly improves the performance, highlighting tumor regions and NSCLC-related genes as potential biomarkers and thus offering a significant methodological advancement when learning from small imaging-omics-clinical samples.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141564662","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

"Forcing" new interpretations of molecular tension sensor studies. "迫使 "对分子张力传感器研究做出新的解释。

IF 4.3

Cell Reports Methods Pub Date : 2024-07-15 DOI: 10.1016/j.crmeth.2024.100821

Matthew R Pawlak, Adam T Smiley, Wendy R Gordon

引用次数: 0

A practical introduction to holo-omics. 整体组学实用入门。

IF 4.3

Cell Reports Methods Pub Date : 2024-07-15 Epub Date: 2024-07-09 DOI: 10.1016/j.crmeth.2024.100820

Iñaki Odriozola, Jacob A Rasmussen, M Thomas P Gilbert, Morten T Limborg, Antton Alberdi

引用次数: 0