Cell Reports MethodsPub Date : 2024-12-16Epub Date: 2024-12-03DOI: 10.1016/j.crmeth.2024.100911
Cyril J Peter, Aman Agarwal, Risa Watanabe, Bibi S Kassim, Xuedi Wang, Tova Y Lambert, Behnam Javidfar, Viviana Evans, Travis Dawson, Maya Fridrikh, Kiran Girdhar, Panos Roussos, Sathiji K Nageshwaran, Nadejda M Tsankova, Robert P Sebra, Mitchell R Vollger, Andrew B Stergachis, Dan Hasson, Schahram Akbarian
{"title":"Single chromatin fiber profiling and nucleosome position mapping in the human brain.","authors":"Cyril J Peter, Aman Agarwal, Risa Watanabe, Bibi S Kassim, Xuedi Wang, Tova Y Lambert, Behnam Javidfar, Viviana Evans, Travis Dawson, Maya Fridrikh, Kiran Girdhar, Panos Roussos, Sathiji K Nageshwaran, Nadejda M Tsankova, Robert P Sebra, Mitchell R Vollger, Andrew B Stergachis, Dan Hasson, Schahram Akbarian","doi":"10.1016/j.crmeth.2024.100911","DOIUrl":"10.1016/j.crmeth.2024.100911","url":null,"abstract":"<p><p>We apply a single-molecule chromatin fiber sequencing (Fiber-seq) protocol designed for amplification-free cell-type-specific mapping of the regulatory architecture at nucleosome resolution along extended ∼10-kb chromatin fibers to neuronal and non-neuronal nuclei sorted from human brain tissue. Specifically, application of this method enables the resolution of cell-selective promoter and enhancer architectures on single fibers, including transcription factor footprinting and position mapping, with sequence-specific fixation of nucleosome arrays flanking transcription start sites and regulatory motifs. We uncover haplotype-specific chromatin patterns, multiple regulatory elements cis-aligned on individual fibers, and accessible chromatin at 20,000 unique sites encompassing retrotransposons and other repeat sequences hitherto \"unmappable\" by short-read epigenomic sequencing. Overall, we show that Fiber-seq is applicable to human brain tissue, offering sharp demarcation of nucleosome-depleted regions at sites of open chromatin in conjunction with multi-kilobase nucleosomal positioning at single-fiber resolution on a genome-wide scale.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"100911"},"PeriodicalIF":4.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11704683/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142781313","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}
{"title":"Expanding the landscape of antibody discovery.","authors":"Shelbe Johnson, Brandon J DeKosky","doi":"10.1016/j.crmeth.2024.100936","DOIUrl":"10.1016/j.crmeth.2024.100936","url":null,"abstract":"<p><p>Library:library screening technologies hold substantial promise for paired antibody:antigen discovery, but challenges have persisted. In this issue of Cell Reports Methods, Wagner et al. introduce a method that combines antibody-ribosome-mRNA complexes, antigen cell surface display, and single-cell RNA sequencing to successfully screen diverse antibody gene libraries against a library of viral receptor proteins.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":"4 12","pages":"100936"},"PeriodicalIF":4.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11704609/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142847767","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}
{"title":"Enhancing immuno-oncology investigations through multidimensional decoding of tumor microenvironment with IOBR 2.0.","authors":"Dongqiang Zeng, Yiran Fang, Wenjun Qiu, Peng Luo, Shixiang Wang, Rongfang Shen, Wenchao Gu, Xiatong Huang, Qianqian Mao, Gaofeng Wang, Yonghong Lai, Guangda Rong, Xi Xu, Min Shi, Zuqiang Wu, Guangchuang Yu, Wangjun Liao","doi":"10.1016/j.crmeth.2024.100910","DOIUrl":"10.1016/j.crmeth.2024.100910","url":null,"abstract":"<p><p>The use of large transcriptome datasets has greatly improved our understanding of the tumor microenvironment (TME) and helped develop precise immunotherapies. The growing application of multi-omics, single-cell RNA sequencing (scRNA-seq), and spatial transcriptome sequencing has led to many new insights, yet these findings still require clinical validation in large cohorts. To advance multi-omics integration in TME research, we have upgraded the Immuno-Oncology Biological Research (IOBR) package to IOBR 2.0, restructuring and standardizing its analytical workflow. IOBR 2.0 offers six modules for TME analysis based on multi-omics data, including data preprocessing, TME estimation, TME infiltration pattern identification, cellular interaction analysis, genome and TME interaction, and feature visualization, as well as modeling. Additionally, IOBR 2.0 enables constructing gene signatures and reference matrices from scRNA-seq data for TME deconvolution. The user-friendly pipeline provides comprehensive insights into tumor-immune interactions, and a detailed GitBook(https://iobr.github.io/book/) offers a complete manual and analysis guide for each module.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"100910"},"PeriodicalIF":4.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11704618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142772889","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}
{"title":"Controlled aggregative assembly to form self-organizing macroscopic human intestine from induced pluripotent stem cells.","authors":"Junichi Takahashi, Hady Yuki Sugihara, Shu Kato, Sho Kawasaki, Sayaka Nagata, Ryuichi Okamoto, Tomohiro Mizutani","doi":"10.1016/j.crmeth.2024.100930","DOIUrl":"10.1016/j.crmeth.2024.100930","url":null,"abstract":"<p><p>Human intestinal organoids (HIOs) derived from human pluripotent stem cells (hPSCs) are promising resources for intestinal regenerative therapy as they recapitulate both endodermal and mesodermal components of the intestine. However, due to their hPSC-line-dependent mesenchymal development and spherical morphology, HIOs have limited applicability beyond basic research and development. Here, we demonstrate the incorporation of separately differentiated mesodermal and mid/hindgut cells into assembled spheroids to stabilize mesenchymal growth in HIOs. In parallel, we generate tubular intestinal constructs (assembled human intestinal tubules [a-HITs]) by leveraging the high aggregative property of assembled spheroids. Through rotational culture in a bioreactor, a-HITs self-organize to develop epithelium and supportive mesenchyme. Upon mesenteric transplantation, a-HITs mature into centimeter-scale tubular intestinal tissue with complex architectures. Our aggregation- and suspension-based approach offers basic technology for engineering tubular intestinal tissue from hPSCs, which could be ultimately applied to the generation of the human intestine for clinical application.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"100930"},"PeriodicalIF":4.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11704612/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814496","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}
Cell Reports MethodsPub Date : 2024-12-16Epub Date: 2024-11-27DOI: 10.1016/j.crmeth.2024.100909
Benjamin P Sharpe, Liliya A Nazlamova, Carmen Tse, David A Johnston, Jaya Thomas, Rhianna Blyth, Oliver J Pickering, Ben Grace, Jack Harrington, Rushda Rajak, Matthew Rose-Zerilli, Zoe S Walters, Tim J Underwood
{"title":"Patient-derived tumor organoid and fibroblast assembloid models for interrogation of the tumor microenvironment in esophageal adenocarcinoma.","authors":"Benjamin P Sharpe, Liliya A Nazlamova, Carmen Tse, David A Johnston, Jaya Thomas, Rhianna Blyth, Oliver J Pickering, Ben Grace, Jack Harrington, Rushda Rajak, Matthew Rose-Zerilli, Zoe S Walters, Tim J Underwood","doi":"10.1016/j.crmeth.2024.100909","DOIUrl":"10.1016/j.crmeth.2024.100909","url":null,"abstract":"<p><p>The tumor microenvironment (TME) comprises all non-tumor elements of cancer and strongly influences disease progression and phenotype. To understand tumor biology and accurately test new therapeutic strategies, representative models should contain both tumor cells and normal cells of the TME. Here, we describe and characterize co-culture tumor-derived organoids and cancer-associated fibroblasts (CAFs), a major component of the TME, in matrix-embedded assembloid models of esophageal adenocarcinoma (EAC). We demonstrate that the assembloid models faithfully recapitulate the differentiation status of EAC and different CAF phenotypes found in the EAC patient TME. We evaluate cell phenotypes by combining tissue-clearing techniques with whole-mount immunofluorescence and histology, providing a practical framework for the characterization of cancer assembloids.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"100909"},"PeriodicalIF":4.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11704619/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142751578","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}
{"title":"A statistical approach for systematic identification of transition cells from scRNA-seq data.","authors":"Yuanxin Wang, Merve Dede, Vakul Mohanty, Jinzhuang Dou, Ziyi Li, Ken Chen","doi":"10.1016/j.crmeth.2024.100913","DOIUrl":"10.1016/j.crmeth.2024.100913","url":null,"abstract":"<p><p>Decoding cellular state transitions is crucial for understanding complex biological processes in development and disease. While recent advancements in single-cell RNA sequencing (scRNA-seq) offer insights into cellular trajectories, existing tools primarily study expressional rather than regulatory state shifts. We present CellTran, a statistical approach utilizing paired-gene expression correlations to detect transition cells from scRNA-seq data without explicitly resolving gene regulatory networks. Applying our approach to various contexts, including tissue regeneration, embryonic development, preinvasive lesions, and humoral responses post-vaccination, reveals transition cells and their distinct gene expression profiles. Our study sheds light on the underlying molecular mechanisms driving cellular state transitions, enhancing our ability to identify therapeutic targets for disease interventions.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"100913"},"PeriodicalIF":4.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11704623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792441","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}
Cell Reports MethodsPub Date : 2024-12-16Epub Date: 2024-11-26DOI: 10.1016/j.crmeth.2024.100908
Vanessa Pahl, Paul Lubrano, Felicia Troßmann, Daniel Petras, Hannes Link
{"title":"Intact protein barcoding enables one-shot identification of CRISPRi strains and their metabolic state.","authors":"Vanessa Pahl, Paul Lubrano, Felicia Troßmann, Daniel Petras, Hannes Link","doi":"10.1016/j.crmeth.2024.100908","DOIUrl":"10.1016/j.crmeth.2024.100908","url":null,"abstract":"<p><p>Detecting strain-specific barcodes with mass spectrometry can facilitate the screening of genetically engineered bacterial libraries. Here, we introduce intact protein barcoding, a method to measure protein-based library barcodes and metabolites using flow injection mass spectrometry (FI-MS). Protein barcodes are based on ubiquitin with N-terminal tags of six amino acids. We demonstrate that FI-MS detects intact ubiquitin proteins and identifies the mass of N-terminal barcodes. In the same analysis, we measured relative concentrations of primary metabolites. We constructed six ubiquitin-barcoded CRISPR interference (CRISPRi) strains targeting metabolic enzymes and analyzed their metabolic profiles and ubiquitin barcodes. FI-MS detected barcodes and distinct metabolome changes in CRISPRi-targeted pathways. We demonstrate the scalability of intact protein barcoding by measuring 132 ubiquitin barcodes in microtiter plates. These results show that intact protein barcoding enables fast and simultaneous detection of library barcodes and intracellular metabolites, opening up new possibilities for mass spectrometry-based barcoding.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"100908"},"PeriodicalIF":4.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11704613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142740797","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}
Cell Reports MethodsPub Date : 2024-12-16Epub Date: 2024-12-09DOI: 10.1016/j.crmeth.2024.100915
Yubin Lin, Alexander Silverman-Dultz, Madeline Bailey, Daniel J Cohen
{"title":"A programmable, open-source robot that scratches cultured tissues to investigate cell migration, healing, and tissue sculpting.","authors":"Yubin Lin, Alexander Silverman-Dultz, Madeline Bailey, Daniel J Cohen","doi":"10.1016/j.crmeth.2024.100915","DOIUrl":"10.1016/j.crmeth.2024.100915","url":null,"abstract":"<p><p>Despite the widespread popularity of the \"scratch assay,\" where a pipette is dragged manually through cultured tissue to create a gap to study cell migration and healing, it carries significant drawbacks. Its heavy reliance on manual technique can complicate quantification, reduce throughput, and limit the versatility and reproducibility. We present an open-source, low-cost, accessible, robotic scratching platform that addresses all of the core issues. Compatible with nearly all standard cell culture dishes and usable directly in a sterile culture hood without specialized training, our robot makes highly reproducible scratches in a variety of complex cultured tissues with high throughput. Moreover, the robot demonstrates precise removal of tissues for sculpting arbitrary tissue and wound shapes, enabling complex co-culture experiments. This system significantly improves the usefulness of the conventional scratch assay and opens up new possibilities in complex tissue engineering for realistic wound healing and migration research.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"100915"},"PeriodicalIF":4.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11704611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808083","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}
{"title":"WEST is an ensemble method for spatial transcriptomics analysis.","authors":"Jiazhang Cai, Huimin Cheng, Shushan Wu, Wenxuan Zhong, Guo-Cheng Yuan, Ping Ma","doi":"10.1016/j.crmeth.2024.100886","DOIUrl":"10.1016/j.crmeth.2024.100886","url":null,"abstract":"<p><p>Spatial transcriptomics is a groundbreaking technology, enabling simultaneous profiling of gene expression and spatial orientation within biological tissues. Yet when analyzing spatial transcriptomics data, effective integration of expression and spatial information poses considerable analytical challenges. Although many methods have been developed to address this issue, many are platform specific and lack the general applicability to analyze diverse datasets. In this article, we propose a method called the weighted ensemble method for spatial transcriptomics (WEST) that utilizes ensemble techniques to improve the performance and robustness of spatial transcriptomics data analytics. We compare the performance of WEST with six methods on both synthetic and real-world datasets. WEST represents a significant advance in detecting spatial domains, offering improved accuracy and flexibility compared to existing methods, making it a valuable tool for spatial transcriptomics data analytics.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"100886"},"PeriodicalIF":4.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11705770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142606501","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}
{"title":"Generation of self-renewing neuromesodermal progenitors with neuronal and skeletal muscle bipotential from human embryonic stem cells.","authors":"Pingxin Sun, Yuan Yuan, Zhuman Lv, Xinlu Yu, Haoxin Ma, Shulong Liang, Jiqianzhu Zhang, Jiangbo Zhu, Junyu Lu, Chunyan Wang, Le Huan, Caixia Jin, Chao Wang, Wenlin Li","doi":"10.1016/j.crmeth.2024.100897","DOIUrl":"10.1016/j.crmeth.2024.100897","url":null,"abstract":"<p><p>Progress has been made in generating spinal cord and trunk derivatives from neuromesodermal progenitors (NMPs). However, maintaining the self-renewal of NMPs in vitro remains a challenge. In this study, we developed a cocktail of small molecules and growth factors that induces human embryonic stem cells to produce self-renewing NMPs (srNMPs) under chemically defined conditions. These srNMPs maintain the state of neuromesodermal progenitors in prolonged culture and have the potential to generate mesodermal cells and neurons, even at the single-cell level. Additionally, suspended srNMP aggregates can spontaneously differentiate into all tissue types of early embryonic trunks. Furthermore, transplanted srNMP-derived muscle satellite cells or progenitors of motor neurons were integrated into skeletal muscle or the spinal cord, respectively, and contributed to regeneration in mouse models. In summary, srNMPs hold great promise for applications in developmental biology and as renewable cell sources for cell therapy for trunk and spinal cord injuries.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"100897"},"PeriodicalIF":4.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11705767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142606449","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}