Cell Reports MethodsPub Date : 2025-04-21Epub Date: 2025-04-11DOI: 10.1016/j.crmeth.2025.101026
Guangxin Su, Hanchen Wang, Ying Zhang, Marc R Wilkins, Pablo F Canete, Di Yu, Yang Yang, Wenjie Zhang
{"title":"Inferring gene regulatory networks by hypergraph generative model.","authors":"Guangxin Su, Hanchen Wang, Ying Zhang, Marc R Wilkins, Pablo F Canete, Di Yu, Yang Yang, Wenjie Zhang","doi":"10.1016/j.crmeth.2025.101026","DOIUrl":"https://doi.org/10.1016/j.crmeth.2025.101026","url":null,"abstract":"<p><p>We present hypergraph variational autoencoder (HyperG-VAE), a Bayesian deep generative model that leverages hypergraph representation to model single-cell RNA sequencing (scRNA-seq) data. The model features a cell encoder with a structural equation model to account for cellular heterogeneity and construct gene regulatory networks (GRNs) alongside a gene encoder using hypergraph self-attention to identify gene modules. The synergistic optimization of encoders via a decoder improves GRN inference, single-cell clustering, and data visualization, as validated by benchmarks. HyperG-VAE effectively uncovers gene regulation patterns and demonstrates robustness in downstream analyses, as shown in B cell development data from bone marrow. Gene set enrichment analysis of overlapping genes in predicted GRNs confirms the gene encoder's role in refining GRN inference. Offering an efficient solution for scRNA-seq analysis and GRN construction, HyperG-VAE also holds the potential for extending GRN modeling to temporal and multimodal single-cell omics.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":"5 4","pages":"101026"},"PeriodicalIF":4.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144050705","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}
Cell Reports MethodsPub Date : 2025-04-21Epub Date: 2025-04-14DOI: 10.1016/j.crmeth.2025.101027
Liam P Kelley, Song-Hua Hu, Sarah A Boswell, Peter K Sorger, Alison E Ringel, Marcia C Haigis
{"title":"Integrated analysis of transcriptional and metabolic responses to mitochondrial stress.","authors":"Liam P Kelley, Song-Hua Hu, Sarah A Boswell, Peter K Sorger, Alison E Ringel, Marcia C Haigis","doi":"10.1016/j.crmeth.2025.101027","DOIUrl":"https://doi.org/10.1016/j.crmeth.2025.101027","url":null,"abstract":"<p><p>Mitochondrial stress arises from a variety of sources, including mutations to mitochondrial DNA, the generation of reactive oxygen species, and an insufficient supply of oxygen or fuel. Mitochondrial stress induces a range of dedicated responses that repair damage and restore mitochondrial health. However, a systematic characterization of transcriptional and metabolic signatures induced by distinct types of mitochondrial stress is lacking. Here, we defined how primary human fibroblasts respond to a panel of mitochondrial inhibitors to trigger adaptive stress responses. Using metabolomic and transcriptomic analyses, we established integrated signatures of mitochondrial stress. We developed a tool, stress quantification using integrated datasets (SQUID), to deconvolute mitochondrial stress signatures from existing datasets. Using SQUID, we profiled mitochondrial stress in The Cancer Genome Atlas (TCGA) PanCancer Atlas, identifying a signature of pyruvate import deficiency in IDH1-mutant glioma. Thus, this study defines a tool to identify specific mitochondrial stress signatures, which may be applied to a range of systems.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":"5 4","pages":"101027"},"PeriodicalIF":4.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049826","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}
Cell Reports MethodsPub Date : 2025-04-21Epub Date: 2025-04-14DOI: 10.1016/j.crmeth.2025.101028
Mark T Miedel, Mahboubeh Varmazyad, Mengying Xia, Maria Mori Brooks, Dillon C Gavlock, Celeste Reese, Jaideep Behari, Alejandro Soto-Gutierrez, Albert Gough, D Lansing Taylor, Mark E Schurdak
{"title":"Validation of microphysiological systems for interpreting patient heterogeneity requires robust reproducibility analytics and experimental metadata.","authors":"Mark T Miedel, Mahboubeh Varmazyad, Mengying Xia, Maria Mori Brooks, Dillon C Gavlock, Celeste Reese, Jaideep Behari, Alejandro Soto-Gutierrez, Albert Gough, D Lansing Taylor, Mark E Schurdak","doi":"10.1016/j.crmeth.2025.101028","DOIUrl":"https://doi.org/10.1016/j.crmeth.2025.101028","url":null,"abstract":"<p><p>Multi-cell-type, 3D microphysiological systems (MPS) that recapitulate normal organ/organ system functions and the progression of diseases are being applied in drug discovery and development programs to enable precision medicine. A critical step for this application is to demonstrate the reproducibility of the MPS and its ability to identify biologic/clinical heterogeneity from experimental variability, which requires capturing detailed metadata associated with MPS studies as well as a strong analytical approach for assessing reproducibility. Detailed metadata ensure that identical study parameters are being compared when evaluating reproducibility. We have developed the Pittsburgh reproducibility protocol (PReP), which uses a set of common statistical metrics, the coefficient of variation (CV), ANOVA, and intraclass correlation coefficient (ICC), in a pipeline as a standard approach to evaluate the intra- and interstudy reproducibility of MPS performance. The PReP can be employed to identify biological/clinical heterogeneity relevant to precision medicine.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":"5 4","pages":"101028"},"PeriodicalIF":4.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144031422","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}
Cell Reports MethodsPub Date : 2025-04-21Epub Date: 2025-04-10DOI: 10.1016/j.crmeth.2025.101022
Yiwei Xiong, Jingtao Wang, Xiaoxiao Shang, Tingting Chen, Douglas D Fraser, Gregory J Fonseca, Simon Rousseau, Jun Ding
{"title":"Efficient and scalable construction of clinical variable networks for complex diseases with RAMEN.","authors":"Yiwei Xiong, Jingtao Wang, Xiaoxiao Shang, Tingting Chen, Douglas D Fraser, Gregory J Fonseca, Simon Rousseau, Jun Ding","doi":"10.1016/j.crmeth.2025.101022","DOIUrl":"https://doi.org/10.1016/j.crmeth.2025.101022","url":null,"abstract":"<p><p>Understanding the interplay among clinical variables-such as demographics, symptoms, and laboratory results-and their relationships with disease outcomes is critical for advancing diagnostics and understanding mechanisms in complex diseases. Existing methods fail to capture indirect or directional relationships, while existing Bayesian network learning methods are computationally expensive and only infer general associations without focusing on disease outcomes. Here we introduce random walk- and genetic algorithm-based network inference (RAMEN), a method for Bayesian network inference that uses absorbing random walks to prioritize outcome-relevant variables and a genetic algorithm for efficient network refinement. Applied to COVID-19 (Biobanque québécoise de la COVID-19), intensive care unit (ICU) septicemia (MIMIC-III), and COPD (CanCOLD) datasets, RAMEN reconstructs networks linking clinical markers to disease outcomes, such as elevated lactate levels in ICU patients. RAMEN demonstrates advantages in computational efficiency and scalability compared to existing methods. By modeling outcome-specific relationships, RAMEN provides a robust tool for uncovering critical disease mechanisms, advancing diagnostics, and enabling personalized treatment strategies.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":"5 4","pages":"101022"},"PeriodicalIF":4.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144039807","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}
Daniel Reumann, Martin Colombini, Paul Möseneder, Agnieszka Piszczek, Jürgen A Knoblich
{"title":"A cost- and time-efficient method for high-throughput cryoprocessing and tissue analysis using multiplexed tissue molds.","authors":"Daniel Reumann, Martin Colombini, Paul Möseneder, Agnieszka Piszczek, Jürgen A Knoblich","doi":"10.1016/j.crmeth.2025.101023","DOIUrl":"https://doi.org/10.1016/j.crmeth.2025.101023","url":null,"abstract":"<p><p>Cryosectioning remains the gold standard for antibody and transcriptomic/in situ hybridization tissue analysis. However, tissue processing is time-consuming and costly, limiting routine and diagnostic use. Currently, no commercially available protocols or products exist for multiplexing this process. Here, we introduce multiplexed tissue molds (MTMs) that enable high-throughput cryoprocessing-cutting costs and workload by up to 96% while permitting the processing of tissues of various sizes and origins. We demonstrate compatibility with heterogeneous tissues by processing 19 different adult mouse tissues in parallel. Furthermore, we process up to ∼110 neural organoids of different ages and sizes simultaneously and assess their neural differentiation marker expression. MTMs allow sectioning-based tissue analysis when labor, time, and cost are limiting factors. MTMs could be used to compare high specimen numbers in histopathological settings, organism-wide antigen and antibody targeting studies, high-throughput tissue screens, and defined tissue section positioning for, e.g., spatial transcriptomics experiments.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":"5 4","pages":"101023"},"PeriodicalIF":4.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144038579","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}
Cell Reports MethodsPub Date : 2025-03-24Epub Date: 2025-03-12DOI: 10.1016/j.crmeth.2025.100991
Wolu Chukwu, Siyun Lee, Alexander Crane, Shu Zhang, Sophie Webster, Oumayma Dakhama, Ipsa Mittra, Carlos Rauert, Marcin Imielinski, Rameen Beroukhim, Frank Dubois, Simona Dalin
{"title":"A sequence context-based approach for classifying tumor structural variants without paired normal samples.","authors":"Wolu Chukwu, Siyun Lee, Alexander Crane, Shu Zhang, Sophie Webster, Oumayma Dakhama, Ipsa Mittra, Carlos Rauert, Marcin Imielinski, Rameen Beroukhim, Frank Dubois, Simona Dalin","doi":"10.1016/j.crmeth.2025.100991","DOIUrl":"10.1016/j.crmeth.2025.100991","url":null,"abstract":"<p><p>Although several recent studies have characterized structural variants (SVs) in germline and cancer genomes independently, the genomic contexts of these SVs have not been comprehensively compared. We examined similarities and differences between 2 million germline and 115 thousand tumor SVs from a cohort of 963 patients from The Cancer Genome Atlas. We found significant differences in features related to their genomic sequences and localization that suggest differences between SV-generating processes and selective pressures. For example, our results show that features linked to transposon-mediated processes are associated with germline SVs, while somatic SVs more frequently show features characteristic of chromoanagenesis. These genomic differences enabled us to develop a classifier-the Germline and Tumor Structural Variant or \"the great GaTSV\" -that accurately distinguishes between germline and cancer SVs in tumor samples that lack a matched normal sample.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"100991"},"PeriodicalIF":4.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12049684/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626361","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}
Xueying Lyu, Russell Wing-Yeung Mok, Hoi-Ying Chan, Tina Suoangbaji, Qian Li, Fanhong Zeng, Renwen Long, Irene Oi-Lin Ng, Loey Lung-Yi Mak, Daniel Wai-Hung Ho
{"title":"AVID enables sensitive and accurate viral integration detection across human cancers.","authors":"Xueying Lyu, Russell Wing-Yeung Mok, Hoi-Ying Chan, Tina Suoangbaji, Qian Li, Fanhong Zeng, Renwen Long, Irene Oi-Lin Ng, Loey Lung-Yi Mak, Daniel Wai-Hung Ho","doi":"10.1016/j.crmeth.2025.101007","DOIUrl":"10.1016/j.crmeth.2025.101007","url":null,"abstract":"<p><p>Oncovirus infection is a key etiological risk factor of human cancers, which triggers virus integration in the host genome. Viral integration can lead to structural variation, gene dysfunction, and genome instability, promoting tumorigenesis. To support the investigation of virus-associated cancer and improve the detection of virus infection, we developed an algorithm called AVID (accurate viral integration detector) for viral integration detection. AVID was built by overcoming the existing detection limitations, enhancing sensitivity and accuracy, and expanding additional functions of viral integration detection. The performance of AVID was estimated in simulated datasets and experimentally validated datasets compared with other tools. To demonstrate its wide applicability, we also tested AVID on viral integration detection in multiple oncovirus-associated human cancers, including hepatocellular carcinoma (HCC), cervical cancer, and nasopharyngeal carcinoma. Taken together, our study developed an improved and applicable tool for viral integration detection and visualization to facilitate further exploration of virus-infected diseases.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":"5 3","pages":"101007"},"PeriodicalIF":4.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12049714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711473","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}
Sean Keeley, Miriam Fernández-Lajarín, David Bergemann, Nicolette John, Lily Parrott, Brittany E Andrea, Juan Manuel González-Rosa
{"title":"Rapid and robust generation of cardiomyocyte-specific crispants in zebrafish using the cardiodeleter system.","authors":"Sean Keeley, Miriam Fernández-Lajarín, David Bergemann, Nicolette John, Lily Parrott, Brittany E Andrea, Juan Manuel González-Rosa","doi":"10.1016/j.crmeth.2025.101003","DOIUrl":"10.1016/j.crmeth.2025.101003","url":null,"abstract":"<p><p>CRISPR-Cas9 has accelerated loss-of-function studies in zebrafish, but creating tissue-specific mutant lines is still labor intensive. While some tissue-specific Cas9 zebrafish lines exist, standardized methods for gene targeting, including guide RNA (gRNA) delivery, are lacking, limiting broader use in the community. To tackle these limitations, we develop a cardiomyocyte-specific Cas9 line, the cardiodeleter, that efficiently generates biallelic mutations in combination with gene-specific gRNAs. We create transposon-based guide shuttles that deliver gRNAs targeting a gene of interest while permanently labeling cells susceptible to becoming mutant. We validate this modular approach by deleting five genes (ect2, tnnt2a, cmlc2, amhc, and erbb2), resulting in the loss of the corresponding protein or phenocopy of established mutants. We provide detailed protocols for generating guide shuttles, facilitating the adoption of these techniques in the zebrafish community. Our approach enables rapid generation of tissue-specific crispants and analysis of mosaic phenotypes, making it a valuable tool for cell-autonomous studies and genetic screening.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":"5 3","pages":"101003"},"PeriodicalIF":4.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12049713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711481","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}
Ting An Lee, Jan Morlock, John Allan, Harrison Steel
{"title":"Directing microbial co-culture composition using cybernetic control.","authors":"Ting An Lee, Jan Morlock, John Allan, Harrison Steel","doi":"10.1016/j.crmeth.2025.101009","DOIUrl":"10.1016/j.crmeth.2025.101009","url":null,"abstract":"<p><p>We demonstrate a cybernetic approach to control the composition of a P. putida and E. coli co-culture that does not rely on genetic engineering to interface cells with computers. We first show how composition information can be extracted from different bioreactor measurements and then combined with a system model using an extended Kalman filter to generate accurate estimates of a noisy system. We then demonstrate that adjusting the culture temperature can drive the composition due to the species' different optimal temperatures. Using a proportional-integral control algorithm, we are able to track dynamic references with real-time noise rejection and independence from starting conditions such as inoculation ratio. We stabilize the co-culture for 7 days (∼250 generations) with the experiment ending before the cells could adapt out of the control. This cybernetic framework is broadly applicable, with different microbes' unique characteristics enabling robust control over diverse co-cultures.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":"5 3","pages":"101009"},"PeriodicalIF":4.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12049730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711476","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}
Daan B Boltje, Radim Skoupý, Clémence Taisne, Wiel H Evers, Arjen J Jakobi, Jacob P Hoogenboom
{"title":"Thickness- and quality-controlled fabrication of fluorescence-targeted frozen-hydrated lamellae.","authors":"Daan B Boltje, Radim Skoupý, Clémence Taisne, Wiel H Evers, Arjen J Jakobi, Jacob P Hoogenboom","doi":"10.1016/j.crmeth.2025.101004","DOIUrl":"10.1016/j.crmeth.2025.101004","url":null,"abstract":"<p><p>Cryogenic focused ion beam (FIB) milling is essential for fabricating thin lamella-shaped samples out of frozen-hydrated cells for high-resolution structure determination. Structural information can only be resolved at high resolution if the lamella thickness is between 100 and 200 nm. While the lamella fabrication workflow has improved significantly since its conception, quantitative, live feedback on lamella thickness, quality, and biological target inclusion remains lacking. Using coincident light microscopy integrated into the FIB scanning electron microscope (FIB-SEM), we present three strategies that enable accurate, live control during lamella fabrication. First, we combine four-dimensional (4D) STEM with fluorescence microscopy (FM) targeting to determine lamella thickness. Second, with reflected light microscopy (RLM), we screen target sites for ice contamination and monitor lamella thickness and protective Pt coating integrity during FIB milling. Third, we exploit thin-film interference for fine-grained feedback on thickness uniformity below 500 nm. Finally, we present a fluorescence-targeted, quality-controlled workflow for frozen-hydrated lamellae, benchmarked with excellent agreement with energy-filtered transmission electron microscopy (EFTEM) measurements and tomograms from electron cryotomography.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":"5 3","pages":"101004"},"PeriodicalIF":4.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12049727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711493","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}