1
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Junaid M, Lee EJ, Lim SB. Single-cell and spatial omics: exploring hypothalamic heterogeneity. Neural Regen Res 2025; 20:1525-1540. [PMID: 38993130 DOI: 10.4103/nrr.nrr-d-24-00231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 06/03/2024] [Indexed: 07/13/2024] Open
Abstract
Elucidating the complex dynamic cellular organization in the hypothalamus is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.
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Affiliation(s)
- Muhammad Junaid
- Department of Biochemistry & Molecular Biology, Ajou University School of Medicine, Suwon, South Korea
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
| | - Eun Jeong Lee
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
- Department of Brain Science, Ajou University School of Medicine, Suwon, South Korea
| | - Su Bin Lim
- Department of Biochemistry & Molecular Biology, Ajou University School of Medicine, Suwon, South Korea
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
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2
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Yan G, Hua SH, Li JJ. Categorization of 33 computational methods to detect spatially variable genes from spatially resolved transcriptomics data. ARXIV 2024:arXiv:2405.18779v4. [PMID: 38855546 PMCID: PMC11160866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 33 state-of-the-art methods, categorizing SVGs into three types: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.
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3
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Ren J, Luo S, Shi H, Wang X. Spatial omics advances for in situ RNA biology. Mol Cell 2024; 84:3737-3757. [PMID: 39270643 PMCID: PMC11455602 DOI: 10.1016/j.molcel.2024.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 07/07/2024] [Accepted: 08/02/2024] [Indexed: 09/15/2024]
Abstract
Spatial regulation of RNA plays a critical role in gene expression regulation and cellular function. Understanding spatially resolved RNA dynamics and translation is vital for bringing new insights into biological processes such as embryonic development, neurobiology, and disease pathology. This review explores past studies in subcellular, cellular, and tissue-level spatial RNA biology driven by diverse methodologies, ranging from cell fractionation, in situ and proximity labeling, imaging, spatially indexed next-generation sequencing (NGS) approaches, and spatially informed computational modeling. Particularly, recent advances have been made for near-genome-scale profiling of RNA and multimodal biomolecules at high spatial resolution. These methods enabled new discoveries into RNA's spatiotemporal kinetics, RNA processing, translation status, and RNA-protein interactions in cells and tissues. The evolving landscape of experimental and computational strategies reveals the complexity and heterogeneity of spatial RNA biology with subcellular resolution, heralding new avenues for RNA biology research.
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Affiliation(s)
- Jingyi Ren
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Shuchen Luo
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hailing Shi
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Xiao Wang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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4
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Wang N, Hong W, Wu Y, Chen Z, Bai M, Wang W, Zhu J. Next-generation spatial transcriptomics: unleashing the power to gear up translational oncology. MedComm (Beijing) 2024; 5:e765. [PMID: 39376738 PMCID: PMC11456678 DOI: 10.1002/mco2.765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 08/30/2024] [Accepted: 09/03/2024] [Indexed: 10/09/2024] Open
Abstract
The growing advances in spatial transcriptomics (ST) stand as the new frontier bringing unprecedented influences in the realm of translational oncology. This has triggered systemic experimental design, analytical scope, and depth alongside with thorough bioinformatics approaches being constantly developed in the last few years. However, harnessing the power of spatial biology and streamlining an array of ST tools to achieve designated research goals are fundamental and require real-world experiences. We present a systemic review by updating the technical scope of ST across different principal basis in a timeline manner hinting on the generally adopted ST techniques used within the community. We also review the current progress of bioinformatic tools and propose in a pipelined workflow with a toolbox available for ST data exploration. With particular interests in tumor microenvironment where ST is being broadly utilized, we summarize the up-to-date progress made via ST-based technologies by narrating studies categorized into either mechanistic elucidation or biomarker profiling (translational oncology) across multiple cancer types and their ways of deploying the research through ST. This updated review offers as a guidance with forward-looking viewpoints endorsed by many high-resolution ST tools being utilized to disentangle biological questions that may lead to clinical significance in the future.
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Affiliation(s)
- Nan Wang
- Cosmos Wisdom Biotech Co. LtdHangzhouChina
| | - Weifeng Hong
- Department of Radiation OncologyZhejiang Cancer HospitalHangzhouChina
- Hangzhou Institute of Medicine (HIM)Chinese Academy of SciencesHangzhouChina
- Zhejiang Key Laboratory of Radiation OncologyHangzhouChina
| | - Yixing Wu
- Department of Pulmonary and Critical Care MedicineZhongshan HospitalFudan UniversityShanghaiChina
| | - Zhe‐Sheng Chen
- Department of Pharmaceutical SciencesCollege of Pharmacy and Health SciencesInstitute for BiotechnologySt. John's UniversityQueensNew YorkUSA
| | - Minghua Bai
- Department of Radiation OncologyZhejiang Cancer HospitalHangzhouChina
- Hangzhou Institute of Medicine (HIM)Chinese Academy of SciencesHangzhouChina
- Zhejiang Key Laboratory of Radiation OncologyHangzhouChina
| | | | - Ji Zhu
- Department of Radiation OncologyZhejiang Cancer HospitalHangzhouChina
- Hangzhou Institute of Medicine (HIM)Chinese Academy of SciencesHangzhouChina
- Zhejiang Key Laboratory of Radiation OncologyHangzhouChina
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5
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Labade AS, Chiang ZD, Comenho C, Reginato PL, Payne AC, Earl AS, Shrestha R, Duarte FM, Habibi E, Zhang R, Church GM, Boyden ES, Chen F, Buenrostro JD. Expansion in situ genome sequencing links nuclear abnormalities to hotspots of aberrant euchromatin repression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.24.614614. [PMID: 39386718 PMCID: PMC11463693 DOI: 10.1101/2024.09.24.614614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/12/2024]
Abstract
Microscopy and genomics are both used to characterize cell function, but approaches to connect the two types of information are lacking, particularly at subnuclear resolution. While emerging multiplexed imaging methods can simultaneously localize genomic regions and nuclear proteins, their ability to accurately measure DNA-protein interactions is constrained by the diffraction limit of optical microscopy. Here, we describe expansion in situ genome sequencing (ExIGS), a technology that enables sequencing of genomic DNA and superresolution localization of nuclear proteins in single cells. We applied ExIGS to fibroblast cells derived from an individual with Hutchinson-Gilford progeria syndrome to characterize how variation in nuclear morphology affects spatial chromatin organization. Using this data, we discovered that lamin abnormalities are linked to hotspots of aberrant euchromatin repression that may erode cell identity. Further, we show that lamin abnormalities heterogeneously increase the repressive environment of the nucleus in tissues and aged cells. These results demonstrate that ExIGS may serve as a generalizable platform for connecting nuclear abnormalities to changes in gene regulation across disease contexts.
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6
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Wang JX, Zhou X. ELLA: Modeling Subcellular Spatial Variation of Gene Expression within Cells in High-Resolution Spatial Transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.23.614515. [PMID: 39386706 PMCID: PMC11463601 DOI: 10.1101/2024.09.23.614515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/12/2024]
Abstract
Spatial transcriptomic technologies are becoming increasingly high-resolution, enabling precise measurement of gene expression at the subcellular level. Here, we introduce a computational method called subcellular expression localization analysis (ELLA), for modeling the subcellular localization of mRNAs and detecting genes that display spatial variation within cells in high-resolution spatial transcriptomics. ELLA creates a unified cellular coordinate system to anchor diverse cell shapes and morphologies, utilizes a nonhomogeneous Poisson process to model spatial count data, leverages an expression gradient function to characterize subcellular expression patterns, and produces effective control of type I error and high statistical power. We illustrate the benefits of ELLA through comprehensive simulations and applications to four spatial transcriptomics datasets from distinct technologies, where ELLA not only identifies genes with distinct subcellular localization patterns but also associates these patterns with unique mRNA characteristics. Specifically, ELLA shows that genes enriched in the nucleus exhibit an abundance of long noncoding RNAs or protein-coding mRNAs, often characterized by longer gene lengths. Conversely, genes containing signal recognition peptides, encoding ribosomal proteins, or involved in membrane related activities tend to enrich in the cytoplasm or near the cellular membrane. Furthermore, ELLA reveals dynamic subcellular localization patterns during the cell cycle, with certain genes showing decreased nuclear enrichment in the G1 phase while others maintain their enrichment patterns throughout the cell cycle. Overall, ELLA represents a calibrated, powerful, robust, scalable, and versatile tool for modeling subcellular spatial expression variation across diverse high-resolution spatial transcriptomic platforms.
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7
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Asami S, Yin C, Garza LA, Kalhor R. Deconvolving organogenesis in space and time via spatial transcriptomics in thick tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.24.614640. [PMID: 39386671 PMCID: PMC11463617 DOI: 10.1101/2024.09.24.614640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/12/2024]
Abstract
Organ development is guided by a space-time landscape that constraints cell behavior. This landscape is challenging to characterize for the hair follicle - the most abundant mini organ - due to its complex microscopic structure and asynchronous development. We developed 3DEEP, a tissue clearing and spatial transcriptomic strategy for characterizing tissue blocks up to 400 µm in thickness. We captured 371 hair follicles at different stages of organogenesis in 1 mm 3 of skin of a 12-hour-old mouse with 6 million transcripts from 81 genes. From this single time point, we deconvoluted follicles by age based on whole-organ molecular pseudotimes to animate a stop-motion 3D atlas of follicle development along its trajectory. We defined molecular stages for hair follicle organogenesis and characterized the order of emergence for its structures, differential signaling dynamics at its top and bottom, morphogen shifts preceding and accompanying structural changes, and series of structural changes leading to the formation of its canal and opening. We further found that hair follicle stem cells and their niche are established and stratified early in organogenesis, before the formation of the hair bulb. Overall, this work demonstrates the power of increased depth of spatial transcriptomics to provide a four-dimensional analysis of organogenesis.
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8
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Kumar A, Schrader AW, Aggarwal B, Boroojeny AE, Asadian M, Lee J, Song YJ, Zhao SD, Han HS, Sinha S. Intracellular spatial transcriptomic analysis toolkit (InSTAnT). Nat Commun 2024; 15:7794. [PMID: 39242579 PMCID: PMC11379969 DOI: 10.1038/s41467-024-49457-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 06/04/2024] [Indexed: 09/09/2024] Open
Abstract
Imaging-based spatial transcriptomics technologies such as Multiplexed error-robust fluorescence in situ hybridization (MERFISH) can capture cellular processes in unparalleled detail. However, rigorous and robust analytical tools are needed to unlock their full potential for discovering subcellular biological patterns. We present Intracellular Spatial Transcriptomic Analysis Toolkit (InSTAnT), a computational toolkit for extracting molecular relationships from spatial transcriptomics data at single molecule resolution. InSTAnT employs specialized statistical tests and algorithms to detect gene pairs and modules exhibiting intriguing patterns of co-localization, both within individual cells and across the cellular landscape. We showcase the toolkit on five different datasets representing two different cell lines, two brain structures, two species, and three different technologies. We perform rigorous statistical assessment of discovered co-localization patterns, find supporting evidence from databases and RNA interactions, and identify associated subcellular domains. We uncover several cell type and region-specific gene co-localizations within the brain. Intra-cellular spatial patterns discovered by InSTAnT mirror diverse molecular relationships, including RNA interactions and shared sub-cellular localization or function, providing a rich compendium of testable hypotheses regarding molecular functions.
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Affiliation(s)
- Anurendra Kumar
- College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Alex W Schrader
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Bhavay Aggarwal
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | | | - Marisa Asadian
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - JuYeon Lee
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - You Jin Song
- Department of Cell and Developmental Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Sihai Dave Zhao
- Department of Statistics, University of Illinois Urbana-Champaign, Urbana, IL, 61820, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Hee-Sun Han
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Saurabh Sinha
- H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30318, USA.
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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9
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Polonsky M, Gerhardt LMS, Yun J, Koppitch K, Colón KL, Amrhein H, Wold B, Zheng S, Yuan GC, Thomson M, Cai L, McMahon AP. Spatial transcriptomics defines injury specific microenvironments and cellular interactions in kidney regeneration and disease. Nat Commun 2024; 15:7010. [PMID: 39237549 PMCID: PMC11377535 DOI: 10.1038/s41467-024-51186-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 08/01/2024] [Indexed: 09/07/2024] Open
Abstract
Kidney injury disrupts the intricate renal architecture and triggers limited regeneration, together with injury-invoked inflammation and fibrosis. Deciphering the molecular pathways and cellular interactions driving these processes is challenging due to the complex tissue structure. Here, we apply single cell spatial transcriptomics to examine ischemia-reperfusion injury in the mouse kidney. Spatial transcriptomics reveals injury-specific and spatially-dependent gene expression patterns in distinct cellular microenvironments within the kidney and predicts Clcf1-Crfl1 in a molecular interplay between persistently injured proximal tubule cells and their neighboring fibroblasts. Immune cell types play a critical role in organ repair. Spatial analysis identifies cellular microenvironments resembling early tertiary lymphoid structures and associated molecular pathways. Collectively, this study supports a focus on molecular interactions in cellular microenvironments to enhance understanding of injury, repair and disease.
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Affiliation(s)
- Michal Polonsky
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Louisa M S Gerhardt
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
- Fifth Department of Medicine, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany
| | - Jina Yun
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Kari Koppitch
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Katsuya Lex Colón
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Henry Amrhein
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Barbara Wold
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Shiwei Zheng
- Department of Genetics and Genomic Sciences and Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences and Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matt Thomson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Long Cai
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| | - Andrew P McMahon
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA.
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10
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You Y, Fu Y, Li L, Zhang Z, Jia S, Lu S, Ren W, Liu Y, Xu Y, Liu X, Jiang F, Peng G, Sampath Kumar A, Ritchie ME, Liu X, Tian L. Systematic comparison of sequencing-based spatial transcriptomic methods. Nat Methods 2024; 21:1743-1754. [PMID: 38965443 PMCID: PMC11399101 DOI: 10.1038/s41592-024-02325-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 05/29/2024] [Indexed: 07/06/2024]
Abstract
Recent developments of sequencing-based spatial transcriptomics (sST) have catalyzed important advancements by facilitating transcriptome-scale spatial gene expression measurement. Despite this progress, efforts to comprehensively benchmark different platforms are currently lacking. The extant variability across technologies and datasets poses challenges in formulating standardized evaluation metrics. In this study, we established a collection of reference tissues and regions characterized by well-defined histological architectures, and used them to generate data to compare 11 sST methods. We highlighted molecular diffusion as a variable parameter across different methods and tissues, significantly affecting the effective resolutions. Furthermore, we observed that spatial transcriptomic data demonstrate unique attributes beyond merely adding a spatial axis to single-cell data, including an enhanced ability to capture patterned rare cell states along with specific markers, albeit being influenced by multiple factors including sequencing depth and resolution. Our study assists biologists in sST platform selection, and helps foster a consensus on evaluation standards and establish a framework for future benchmarking efforts that can be used as a gold standard for the development and benchmarking of computational tools for spatial transcriptomic analysis.
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Affiliation(s)
- Yue You
- Guangzhou National Laboratory, Guangzhou, China
| | - Yuting Fu
- School of Life Sciences, Westlake University, Hangzhou, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Westlake Institute for Advanced Study, Hangzhou, China
| | - Lanxiang Li
- Guangzhou National Laboratory, Guangzhou, China
| | | | - Shikai Jia
- School of Life Sciences, Westlake University, Hangzhou, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Westlake Institute for Advanced Study, Hangzhou, China
| | - Shihong Lu
- Guangzhou National Laboratory, Guangzhou, China
| | - Wenle Ren
- Guangzhou National Laboratory, Guangzhou, China
| | - Yifang Liu
- School of Life Sciences, Westlake University, Hangzhou, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Westlake Institute for Advanced Study, Hangzhou, China
| | - Yang Xu
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Xiaojing Liu
- School of Life Sciences, Westlake University, Hangzhou, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Westlake Institute for Advanced Study, Hangzhou, China
| | - Fuqing Jiang
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, University of Chinese Academy of Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
| | - Guangdun Peng
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, University of Chinese Academy of Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
| | - Abhishek Sampath Kumar
- Department of Stem Cell and Regenerative Biology, Harvard University. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matthew E Ritchie
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Xiaodong Liu
- School of Life Sciences, Westlake University, Hangzhou, China.
- Research Center for Industries of the Future, Westlake University, Hangzhou, China.
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
- Westlake Institute for Advanced Study, Hangzhou, China.
| | - Luyi Tian
- Guangzhou National Laboratory, Guangzhou, China.
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, China.
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11
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Mante J, Groover KE, Pullen RM. Environmental community transcriptomics: strategies and struggles. Brief Funct Genomics 2024:elae033. [PMID: 39183066 DOI: 10.1093/bfgp/elae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 08/02/2024] [Accepted: 08/08/2024] [Indexed: 08/27/2024] Open
Abstract
Transcriptomics is the study of RNA transcripts, the portion of the genome that is transcribed, in a specific cell, tissue, or organism. Transcriptomics provides insight into gene expression patterns, regulation, and the underlying mechanisms of cellular processes. Community transcriptomics takes this a step further by studying the RNA transcripts from environmental assemblies of organisms, with the intention of better understanding the interactions between members of the community. Community transcriptomics requires successful extraction of RNA from a diverse set of organisms and subsequent analysis via mapping those reads to a reference genome or de novo assembly of the reads. Both, extraction protocols and the analysis steps can pose hurdles for community transcriptomics. This review covers advances in transcriptomic techniques and assesses the viability of applying them to community transcriptomics.
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Affiliation(s)
- Jeanet Mante
- Oak Ridge Associated Universities, Oak Ridge, 37831, TN, USA
| | - Kyra E Groover
- Department of Molecular Biosciences, University of Texas at Austin, Austin, 78705, TX, USA
| | - Randi M Pullen
- DEVCOM Army Research Laboratory, Adelphi, 20783, MD, USA
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12
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Liu L, Chen A, Li Y, Mulder J, Heyn H, Xu X. Spatiotemporal omics for biology and medicine. Cell 2024; 187:4488-4519. [PMID: 39178830 DOI: 10.1016/j.cell.2024.07.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 07/05/2024] [Accepted: 07/23/2024] [Indexed: 08/26/2024]
Abstract
The completion of the Human Genome Project has provided a foundational blueprint for understanding human life. Nonetheless, understanding the intricate mechanisms through which our genetic blueprint is involved in disease or orchestrates development across temporal and spatial dimensions remains a profound scientific challenge. Recent breakthroughs in cellular omics technologies have paved new pathways for understanding the regulation of genomic elements and the relationship between gene expression, cellular functions, and cell fate determination. The advent of spatial omics technologies, encompassing both imaging and sequencing-based methodologies, has enabled a comprehensive understanding of biological processes from a cellular ecosystem perspective. This review offers an updated overview of how spatial omics has advanced our understanding of the translation of genetic information into cellular heterogeneity and tissue structural organization and their dynamic changes over time. It emphasizes the discovery of various biological phenomena, related to organ functionality, embryogenesis, species evolution, and the pathogenesis of diseases.
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Affiliation(s)
| | - Ao Chen
- BGI Research, Shenzhen 518083, China
| | | | - Jan Mulder
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Holger Heyn
- Centro Nacional de Análisis Genómico (CNAG), Barcelona, Spain
| | - Xun Xu
- BGI Research, Hangzhou 310030, China; BGI Research, Shenzhen 518083, China.
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13
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Cui X, Dong X, Hu M, Zhou W, Shi W. Large field of view and spatial region of interest transcriptomics in fixed tissue. Commun Biol 2024; 7:1020. [PMID: 39164496 PMCID: PMC11335973 DOI: 10.1038/s42003-024-06694-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 08/07/2024] [Indexed: 08/22/2024] Open
Abstract
Expression profiling in spatially defined regions is crucial for systematically understanding tissue complexity. Here, we report a method of photo-irradiation for in-situ barcoding hybridization and ligation sequencing, named PBHL-seq, which allows targeted expression profiling from the photo-irradiated region of interest in intact fresh frozen and formalin fixation and paraffin embedding (FFPE) tissue samples. PBHL-seq uses photo-caged oligodeoxynucleotides for in situ reverse transcription followed by spatially targeted barcoding of cDNAs to create spatially indexed transcriptomes of photo-illuminated regions. We recover thousands of differentially enriched transcripts from different regions by applying PBHL-seq to OCT-embedded tissue (E14.5 mouse embryo and mouse brain) and FFPE mouse embryo (E15.5). We also apply PBHL-seq to the subcellular microstructures (cytoplasm and nucleus, respectively) and detect thousands of differential expression genes. Thus, PBHL-seq provides an accessible workflow for expression profiles from the region of interest in frozen and FFPE tissue at subcellular resolution with areas expandable to centimeter scale, while preserving the sample intact for downstream analysis to promote the development of transcriptomics.
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Affiliation(s)
- Xiaonan Cui
- Single Cell Systems Biology Laboratory, College of Marine Life Sciences, Ocean University of China, Qingdao, Shandong, 266100, China
| | - Xue Dong
- Single Cell Systems Biology Laboratory, College of Marine Life Sciences, Ocean University of China, Qingdao, Shandong, 266100, China
| | - Mengzhu Hu
- Single Cell Systems Biology Laboratory, College of Marine Life Sciences, Ocean University of China, Qingdao, Shandong, 266100, China
| | - Wenjian Zhou
- Single Cell Systems Biology Laboratory, College of Marine Life Sciences, Ocean University of China, Qingdao, Shandong, 266100, China
| | - Weiyang Shi
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
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14
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Liu B, Hu S, Wang X. Applications of single-cell technologies in drug discovery for tumor treatment. iScience 2024; 27:110486. [PMID: 39171294 PMCID: PMC11338156 DOI: 10.1016/j.isci.2024.110486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2024] Open
Abstract
Single-cell technologies have been known as advanced and powerful tools to study tumor biological systems at the single-cell resolution and are playing increasingly critical roles in multiple stages of drug discovery and development. Specifically, single-cell technologies can promote the discovery of drug targets, help high-throughput screening at single-cell level, and contribute to pharmacokinetic studies of anti-tumor drugs. Emerging single-cell analysis technologies have been developed to further integrating multidimensional single-cell molecular features, expanding the scale of single-cell data, profiling phenotypic impact of genes in single cell, and providing full-length coverage single-cell sequencing. In this review, we systematically summarized the applications of single-cell technologies in various sections of drug discovery for tumor treatment, including target identification, high-throughput drug screening, and pharmacokinetic evaluation and highlighted emerging single-cell technologies in providing in-depth understanding of tumor biology. Single-cell-technology-based drug discovery is expected to further optimize therapeutic strategies and improve clinical outcomes of tumor patients.
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Affiliation(s)
- Bingyu Liu
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
| | - Shunfeng Hu
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Xin Wang
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
- Taishan Scholars Program of Shandong Province, Jinan, Shandong 250021, China
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15
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Bhushan V, Nita-Lazar A. Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology. J Proteome Res 2024; 23:2700-2722. [PMID: 38451675 PMCID: PMC11296931 DOI: 10.1021/acs.jproteome.3c00839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.
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Affiliation(s)
- Vanya Bhushan
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Aleksandra Nita-Lazar
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
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16
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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17
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Fortner A, Bucur O. Multiplexed spatial transcriptomics methods and the application of expansion microscopy. Front Cell Dev Biol 2024; 12:1378875. [PMID: 39105173 PMCID: PMC11298486 DOI: 10.3389/fcell.2024.1378875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 06/10/2024] [Indexed: 08/07/2024] Open
Abstract
While spatial transcriptomics has undeniably revolutionized our ability to study cellular organization, it has driven the development of a great number of innovative transcriptomics methods, which can be classified into in situ sequencing (ISS) methods, in situ hybridization (ISH) techniques, and next-generation sequencing (NGS)-based sequencing with region capture. These technologies not only refine our understanding of cellular processes, but also open up new possibilities for breakthroughs in various research domains. One challenge of spatial transcriptomics experiments is the limitation of RNA detection due to optical crowding of RNA in the cells. Expansion microscopy (ExM), characterized by the controlled enlargement of biological specimens, offers a means to achieve super-resolution imaging, overcoming the diffraction limit inherent in conventional microscopy and enabling precise visualization of RNA in spatial transcriptomics methods. In this review, we elaborate on ISS, ISH and NGS-based spatial transcriptomic protocols and on how performance of these techniques can be extended by the combination of these protocols with ExM. Moving beyond the techniques and procedures, we highlight the broader implications of transcriptomics in biology and medicine. These include valuable insight into the spatial organization of gene expression in cells within tissues, aid in the identification and the distinction of cell types and subpopulations and understanding of molecular mechanisms and intercellular changes driving disease development.
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Affiliation(s)
- Andra Fortner
- Medical School, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany
- Victor Babes National Institute of Pathology, Bucharest, Romania
| | - Octavian Bucur
- Victor Babes National Institute of Pathology, Bucharest, Romania
- Genomics Research and Development Institute, Bucharest, Romania
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18
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Sudhakar M, Vignesh H, Natarajan KN. Crosstalk between tumor and microenvironment: Insights from spatial transcriptomics. Adv Cancer Res 2024; 163:187-222. [PMID: 39271263 DOI: 10.1016/bs.acr.2024.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
Cancer is a dynamic disease, and clonal heterogeneity plays a fundamental role in tumor development, progression, and resistance to therapies. Single-cell and spatial multimodal technologies can provide a high-resolution molecular map of underlying genomic, epigenomic, and transcriptomic alterations involved in inter- and intra-tumor heterogeneity and interactions with the microenvironment. In this review, we provide a perspective on factors driving cancer heterogeneity, tumor evolution, and clonal states. We briefly describe spatial transcriptomic technologies and summarize recent literature that sheds light on the dynamical interactions between tumor states, cell-to-cell communication, and remodeling local microenvironment.
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Affiliation(s)
- Malvika Sudhakar
- DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Harie Vignesh
- DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
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19
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Di Mauro F, Arbore G. Spatial Dissection of the Immune Landscape of Solid Tumors to Advance Precision Medicine. Cancer Immunol Res 2024; 12:800-813. [PMID: 38657223 PMCID: PMC11217735 DOI: 10.1158/2326-6066.cir-23-0699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 01/12/2024] [Accepted: 04/19/2024] [Indexed: 04/26/2024]
Abstract
Chemotherapeutics, radiation, targeted therapeutics, and immunotherapeutics each demonstrate clinical benefits for a small subset of patients with solid malignancies. Immune cells infiltrating the tumor and the surrounding stroma play a critical role in shaping cancer progression and modulating therapy response. They do this by interacting with the other cellular and molecular components of the tumor microenvironment. Spatial multi-omics technologies are rapidly evolving. Currently, such technologies allow high-throughput RNA and protein profiling and retain geographical information about the tumor microenvironment cellular architecture and the functional phenotype of tumor, immune, and stromal cells. An in-depth spatial characterization of the heterogeneous tumor immune landscape can improve not only the prognosis but also the prediction of therapy response, directing cancer patients to more tailored and efficacious treatments. This review highlights recent advancements in spatial transcriptomics and proteomics profiling technologies and the ways these technologies are being applied for the dissection of the immune cell composition in solid malignancies in order to further both basic research in oncology and the implementation of precision treatments in the clinic.
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Affiliation(s)
- Francesco Di Mauro
- Vita-Salute San Raffaele University, Milan, Italy.
- Experimental Immunology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
| | - Giuseppina Arbore
- Vita-Salute San Raffaele University, Milan, Italy.
- Experimental Immunology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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20
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Wu X, Xu W, Deng L, Li Y, Wang Z, Sun L, Gao A, Wang H, Yang X, Wu C, Zou Y, Yan K, Liu Z, Zhang L, Du G, Yang L, Lin D, Yue J, Wang P, Han Y, Fu Z, Dai J, Cao G. Spatial multi-omics at subcellular resolution via high-throughput in situ pairwise sequencing. Nat Biomed Eng 2024; 8:872-889. [PMID: 38745110 DOI: 10.1038/s41551-024-01205-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 04/01/2024] [Indexed: 05/16/2024]
Abstract
Technology for spatial multi-omics aids the discovery of new insights into cellular functions and disease mechanisms. Here we report the development and applicability of multi-omics in situ pairwise sequencing (MiP-seq), a method for the simultaneous detection of DNAs, RNAs, proteins and biomolecules at subcellular resolution. Compared with other in situ sequencing methods, MiP-seq enhances decoding capacity and reduces sequencing and imaging costs while maintaining the efficacy of detection of gene mutations, allele-specific expression and RNA modifications. MiP-seq can be integrated with in vivo calcium imaging and Raman imaging, which enabled us to generate a spatial multi-omics atlas of mouse brain tissues and to correlate gene expression with neuronal activity and cellular biochemical fingerprints. We also report a sequential dilution strategy for resolving optically crowded signals during in situ sequencing. High-throughput in situ pairwise sequencing may facilitate the multidimensional analysis of molecular and functional maps of tissues.
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Affiliation(s)
- Xiaofeng Wu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Weize Xu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Lulu Deng
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Yue Li
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Zhongchao Wang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Leqiang Sun
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Anran Gao
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Haoqi Wang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Xiaodan Yang
- School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chengchao Wu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Yanyan Zou
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Keji Yan
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Zhixiang Liu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Lingkai Zhang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Guohua Du
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Liyao Yang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Da Lin
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Junqiu Yue
- Department of Pathology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ping Wang
- Britton Chance Centre for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Centre for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Yunyun Han
- School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhenfang Fu
- Department of Pathology, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
| | - Jinxia Dai
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China.
| | - Gang Cao
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China.
- Faculty of Life and Health Sciences, and Shenzhen-Hong Kong Institute of Brain Science and The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- NMPA Key Laboratory for Research and Evaluation of Viral Vector Technology in Cell and Gene Therapy Medicinal Products, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
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21
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Sun ED, Ma R, Zou J. SPRITE: improving spatial gene expression imputation with gene and cell networks. Bioinformatics 2024; 40:i521-i528. [PMID: 38940132 PMCID: PMC11211834 DOI: 10.1093/bioinformatics/btae253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Spatially resolved single-cell transcriptomics have provided unprecedented insights into gene expression in situ, particularly in the context of cell interactions or organization of tissues. However, current technologies for profiling spatial gene expression at single-cell resolution are generally limited to the measurement of a small number of genes. To address this limitation, several algorithms have been developed to impute or predict the expression of additional genes that were not present in the measured gene panel. Current algorithms do not leverage the rich spatial and gene relational information in spatial transcriptomics. To improve spatial gene expression predictions, we introduce Spatial Propagation and Reinforcement of Imputed Transcript Expression (SPRITE) as a meta-algorithm that processes predictions obtained from existing methods by propagating information across gene correlation networks and spatial neighborhood graphs. RESULTS SPRITE improves spatial gene expression predictions across multiple spatial transcriptomics datasets. Furthermore, SPRITE predicted spatial gene expression leads to improved clustering, visualization, and classification of cells. SPRITE can be used in spatial transcriptomics data analysis to improve inferences based on predicted gene expression. AVAILABILITY AND IMPLEMENTATION The SPRITE software package is available at https://github.com/sunericd/SPRITE. Code for generating experiments and analyses in the manuscript is available at https://github.com/sunericd/sprite-figures-and-analyses.
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Affiliation(s)
- Eric D Sun
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, United States
| | - Rong Ma
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
| | - James Zou
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, United States
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22
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Jin Y, Zuo Y, Li G, Liu W, Pan Y, Fan T, Fu X, Yao X, Peng Y. Advances in spatial transcriptomics and its applications in cancer research. Mol Cancer 2024; 23:129. [PMID: 38902727 PMCID: PMC11188176 DOI: 10.1186/s12943-024-02040-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 06/10/2024] [Indexed: 06/22/2024] Open
Abstract
Malignant tumors have increasing morbidity and high mortality, and their occurrence and development is a complicate process. The development of sequencing technologies enabled us to gain a better understanding of the underlying genetic and molecular mechanisms in tumors. In recent years, the spatial transcriptomics sequencing technologies have been developed rapidly and allow the quantification and illustration of gene expression in the spatial context of tissues. Compared with the traditional transcriptomics technologies, spatial transcriptomics technologies not only detect gene expression levels in cells, but also inform the spatial location of genes within tissues, cell composition of biological tissues, and interaction between cells. Here we summarize the development of spatial transcriptomics technologies, spatial transcriptomics tools and its application in cancer research. We also discuss the limitations and challenges of current spatial transcriptomics approaches, as well as future development and prospects.
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Affiliation(s)
- Yang Jin
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuanli Zuo
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Gang Li
- Department of Thoracic Surgery, The Public Health Clinical Center of Chengdu, Chengdu, 610061, China
| | - Wenrong Liu
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yitong Pan
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Ting Fan
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xin Fu
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiaojun Yao
- Department of Thoracic Surgery, The Public Health Clinical Center of Chengdu, Chengdu, 610061, China.
| | - Yong Peng
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Frontier Medical Center, Tianfu Jincheng Laboratory, Chengdu, 610212, China.
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23
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Danino-Levi M, Goldberg T, Keter M, Akselrod N, Shprach-Buaron N, Safra M, Singer G, Alon S. Computational analysis of super-resolved in situ sequencing data reveals genes modified by immune-tumor contact events. RNA (NEW YORK, N.Y.) 2024; 30:749-759. [PMID: 38575346 PMCID: PMC11182005 DOI: 10.1261/rna.079801.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 03/18/2024] [Indexed: 04/06/2024]
Abstract
Cancer cells can manipulate immune cells and escape from the immune system response. Quantifying the molecular changes that occur when an immune cell touches a tumor cell can increase our understanding of the underlying mechanisms. Recently, it became possible to perform such measurements in situ-for example, using expansion sequencing, which enabled in situ sequencing of genes with super-resolution. We systematically examined whether individual immune cells from specific cell types express genes differently when in physical proximity to individual tumor cells. First, we demonstrated that a dense mapping of genes in situ can be used for the segmentation of cell bodies in 3D, thus improving our ability to detect likely touching cells. Next, we used three different computational approaches to detect the molecular changes that are triggered by proximity: differential expression analysis, tree-based machine learning classifiers, and matrix factorization analysis. This systematic analysis revealed tens of genes, in specific cell types, whose expression separates immune cells that are proximal to tumor cells from those that are not proximal, with a significant overlap between the different detection methods. Remarkably, an order of magnitude more genes are triggered by proximity to tumor cells in CD8 T cells compared to CD4 T cells, in line with the ability of CD8 T cells to directly bind major histocompatibility complex (MHC) class I on tumor cells. Thus, in situ sequencing of an individual biopsy can be used to detect genes likely involved in immune-tumor cell-cell interactions. The data used in this manuscript and the code of the InSituSeg, machine learning, cNMF, and Moran's I methods are publicly available at doi:10.5281/zenodo.7497981.
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Affiliation(s)
- Michal Danino-Levi
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
- Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Tal Goldberg
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
- Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Maya Keter
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Nikol Akselrod
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Noa Shprach-Buaron
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
- Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Modi Safra
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
- Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Gonen Singer
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Shahar Alon
- The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel
- Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan 5290002, Israel
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24
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Coppini A, Falconieri A, Mualem O, Nasrin SR, Roudon M, Saper G, Hess H, Kakugo A, Raffa V, Shefi O. Can repetitive mechanical motion cause structural damage to axons? Front Mol Neurosci 2024; 17:1371738. [PMID: 38912175 PMCID: PMC11191579 DOI: 10.3389/fnmol.2024.1371738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/23/2024] [Indexed: 06/25/2024] Open
Abstract
Biological structures have evolved to very efficiently generate, transmit, and withstand mechanical forces. These biological examples have inspired mechanical engineers for centuries and led to the development of critical insights and concepts. However, progress in mechanical engineering also raises new questions about biological structures. The past decades have seen the increasing study of failure of engineered structures due to repetitive loading, and its origin in processes such as materials fatigue. Repetitive loading is also experienced by some neurons, for example in the peripheral nervous system. This perspective, after briefly introducing the engineering concept of mechanical fatigue, aims to discuss the potential effects based on our knowledge of cellular responses to mechanical stresses. A particular focus of our discussion are the effects of mechanical stress on axons and their cytoskeletal structures. Furthermore, we highlight the difficulty of imaging these structures and the promise of new microscopy techniques. The identification of repair mechanisms and paradigms underlying long-term stability is an exciting and emerging topic in biology as well as a potential source of inspiration for engineers.
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Affiliation(s)
| | | | - Oz Mualem
- Faculty of Engineering, Bar Ilan Institute of Nanotechnologies and Advanced Materials, Gonda Brain Research Center, Bar Ilan University, Ramat Gan, Israel
| | - Syeda Rubaiya Nasrin
- Graduate School of Science, Division of Physics and Astronomy, Kyoto University, Kyoto, Japan
| | - Marine Roudon
- Department of Biomedical Engineering, Columbia University, New York, NY, United States
| | - Gadiel Saper
- Department of Biomedical Engineering, Columbia University, New York, NY, United States
| | - Henry Hess
- Department of Biomedical Engineering, Columbia University, New York, NY, United States
| | - Akira Kakugo
- Graduate School of Science, Division of Physics and Astronomy, Kyoto University, Kyoto, Japan
| | | | - Orit Shefi
- Faculty of Engineering, Bar Ilan Institute of Nanotechnologies and Advanced Materials, Gonda Brain Research Center, Bar Ilan University, Ramat Gan, Israel
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25
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Lee AT, Chang EF, Paredes MF, Nowakowski TJ. Large-scale neurophysiology and single-cell profiling in human neuroscience. Nature 2024; 630:587-595. [PMID: 38898291 DOI: 10.1038/s41586-024-07405-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 04/09/2024] [Indexed: 06/21/2024]
Abstract
Advances in large-scale single-unit human neurophysiology, single-cell RNA sequencing, spatial transcriptomics and long-term ex vivo tissue culture of surgically resected human brain tissue have provided an unprecedented opportunity to study human neuroscience. In this Perspective, we describe the development of these paradigms, including Neuropixels and recent brain-cell atlas efforts, and discuss how their convergence will further investigations into the cellular underpinnings of network-level activity in the human brain. Specifically, we introduce a workflow in which functionally mapped samples of human brain tissue resected during awake brain surgery can be cultured ex vivo for multi-modal cellular and functional profiling. We then explore how advances in human neuroscience will affect clinical practice, and conclude by discussing societal and ethical implications to consider. Potential findings from the field of human neuroscience will be vast, ranging from insights into human neurodiversity and evolution to providing cell-type-specific access to study and manipulate diseased circuits in pathology. This Perspective aims to provide a unifying framework for the field of human neuroscience as we welcome an exciting era for understanding the functional cytoarchitecture of the human brain.
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Affiliation(s)
- Anthony T Lee
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Mercedes F Paredes
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Tomasz J Nowakowski
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA.
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, USA.
- Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA.
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26
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Valihrach L, Zucha D, Abaffy P, Kubista M. A practical guide to spatial transcriptomics. Mol Aspects Med 2024; 97:101276. [PMID: 38776574 DOI: 10.1016/j.mam.2024.101276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 04/30/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024]
Abstract
Spatial transcriptomics is revolutionizing modern biology, offering researchers an unprecedented ability to unravel intricate gene expression patterns within tissues. From pioneering techniques to newly commercialized platforms, the field of spatial transcriptomics has evolved rapidly, ushering in a new era of understanding across various disciplines, from developmental biology to disease research. This dynamic expansion is reflected in the rapidly growing number of technologies and data analysis techniques developed and introduced. However, the expanding landscape presents a considerable challenge for researchers, especially newcomers to the field, as staying informed about these advancements becomes increasingly complex. To address this challenge, we have prepared an updated review with a particular focus on technologies that have reached commercialization and are, therefore, accessible to a broad spectrum of potential new users. In this review, we present the fundamental principles of spatial transcriptomic methods, discuss the challenges in data analysis, provide insights into experimental considerations, offer information about available resources for spatial transcriptomics, and conclude with a guide for method selection and a forward-looking perspective. Our aim is to serve as a guiding resource for both experienced users and newcomers navigating the complex realm of spatial transcriptomics in this era of rapid development. We intend to equip researchers with the necessary knowledge to make informed decisions and contribute to the cutting-edge research that spatial transcriptomics offers.
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Affiliation(s)
- Lukas Valihrach
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic; Department of Cellular Neurophysiology, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic.
| | - Daniel Zucha
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic; Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, Czech Republic
| | - Pavel Abaffy
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic
| | - Mikael Kubista
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic.
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27
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Xiao W, Halabi R, Lin CH, Nazim M, Yeom KH, Black DL. The lncRNA Malat1 is trafficked to the cytoplasm as a localized mRNA encoding a small peptide in neurons. Genes Dev 2024; 38:294-307. [PMID: 38688681 PMCID: PMC11146593 DOI: 10.1101/gad.351557.124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/12/2024] [Indexed: 05/02/2024]
Abstract
Synaptic function in neurons is modulated by local translation of mRNAs that are transported to distal portions of axons and dendrites. The metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is broadly expressed across cell types, almost exclusively as a nuclear long noncoding RNA. We found that in differentiating neurons, a portion of Malat1 RNA redistributes to the cytoplasm. Depletion of Malat1 using antisense oligonucleotides (ASOs) stimulates the expression of particular pre- and postsynaptic proteins, implicating Malat1 in their regulation. Neuronal Malat1 is localized in puncta of both axons and dendrites that costain with Staufen1 protein, similar to neuronal RNA granules formed by locally translated mRNAs. Ribosome profiling of cultured mouse cortical neurons identified ribosome footprints within a 5' region of Malat1 containing short open reading frames. The upstream-most reading frame (M1) of the Malat1 locus was linked to the GFP-coding sequence in mouse embryonic stem cells. When these gene-edited cells were differentiated into glutamatergic neurons, the M1-GFP fusion protein was expressed. Antibody staining for the M1 peptide confirmed its presence in wild-type neurons and showed that M1 expression was enhanced by synaptic stimulation with KCl. Our results indicate that Malat1 serves as a cytoplasmic coding RNA in the brain that is both modulated by and modulates synaptic function.
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Affiliation(s)
- Wen Xiao
- Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90095, USA
| | - Reem Halabi
- Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90095, USA
| | - Chia-Ho Lin
- Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90095, USA
| | - Mohammad Nazim
- Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90095, USA
| | - Kyu-Hyeon Yeom
- Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90095, USA
| | - Douglas L Black
- Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90095, USA;
- Molecular Biology Institute, University of California Los Angeles, Los Angeles, California 90095, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California Los Angeles, Los Angeles, California 90095, USA
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90095, USA
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28
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Hu X, van Sluijs B, García-Blay Ó, Stepanov Y, Rietrae K, Huck WTS, Hansen MMK. ARTseq-FISH reveals position-dependent differences in gene expression of micropatterned mESCs. Nat Commun 2024; 15:3918. [PMID: 38724524 PMCID: PMC11082235 DOI: 10.1038/s41467-024-48107-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 04/19/2024] [Indexed: 05/12/2024] Open
Abstract
Differences in gene-expression profiles between individual cells can give rise to distinct cell fate decisions. Yet how localisation on a micropattern impacts initial changes in mRNA, protein, and phosphoprotein abundance remains unclear. To identify the effect of cellular position on gene expression, we developed a scalable antibody and mRNA targeting sequential fluorescence in situ hybridisation (ARTseq-FISH) method capable of simultaneously profiling mRNAs, proteins, and phosphoproteins in single cells. We studied 67 (phospho-)protein and mRNA targets in individual mouse embryonic stem cells (mESCs) cultured on circular micropatterns. ARTseq-FISH reveals relative changes in both abundance and localisation of mRNAs and (phospho-)proteins during the first 48 hours of exit from pluripotency. We confirm these changes by conventional immunofluorescence and time-lapse microscopy. Chemical labelling, immunofluorescence, and single-cell time-lapse microscopy further show that cells closer to the edge of the micropattern exhibit increased proliferation compared to cells at the centre. Together these data suggest that while gene expression is still highly heterogeneous position-dependent differences in mRNA and protein levels emerge as early as 12 hours after LIF withdrawal.
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Affiliation(s)
- Xinyu Hu
- Institute for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, the Netherlands
- Oncode Institute, Nijmegen, The Netherlands
| | - Bob van Sluijs
- Institute for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, the Netherlands
| | - Óscar García-Blay
- Institute for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, the Netherlands
- Oncode Institute, Nijmegen, The Netherlands
| | - Yury Stepanov
- Institute for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, the Netherlands
| | - Koen Rietrae
- Institute for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, the Netherlands
| | - Wilhelm T S Huck
- Institute for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, the Netherlands.
| | - Maike M K Hansen
- Institute for Molecules and Materials, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, the Netherlands.
- Oncode Institute, Nijmegen, The Netherlands.
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29
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Yeo S, Schrader AW, Lee J, Asadian M, Han HS. Spot-Based Global Registration for Subpixel Stitching of Single-Molecule Resolution Images for Tissue-Scale Spatial Transcriptomics. Anal Chem 2024; 96:6517-6522. [PMID: 38621224 PMCID: PMC11076048 DOI: 10.1021/acs.analchem.3c05686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
Single-molecule imaging at the tissue scale has revolutionized our understanding of biology by providing unprecedented insight into the molecular expression of individual cells and their spatial organization within tissues. However, achieving precise image stitching at the single-molecule level remains a challenge, primarily due to heterogeneous background signals and dim labeling signals in single-molecule images. This paper introduces Spot-Based Global Registration (SBGR), a novel strategy that shifts the focus from raw images to identified molecular spots for high-resolution image alignment. The use of spot-based data enables straightforward and robust evaluation of the credibility of estimated translations and stitching performance. The method outperforms existing image-based stitching methods, achieving subpixel accuracy (83 ± 36 nm) with exceptional consistency. Furthermore, SBGR incorporates a mechanism to surgically remove duplicate spots in overlapping regions, maximizing information recovery from duplicate measurements. In conclusion, SBGR emerges as a robust and accurate solution for stitching single-molecule resolution images in tissue-scale spatial transcriptomics, offering versatility and potential for high-resolution spatial analysis.
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Affiliation(s)
- Seokjin Yeo
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Alex W Schrader
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Juyeon Lee
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Marisa Asadian
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Hee-Sun Han
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
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30
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Samadi Z, Askary A. Spatial motifs reveal patterns in cellular architecture of complex tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.08.588586. [PMID: 38645046 PMCID: PMC11030378 DOI: 10.1101/2024.04.08.588586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Spatial organization of cells is crucial to both proper physiological function of tissues and pathological conditions like cancer. Recent advances in spatial transcriptomics have enabled joint profiling of gene expression and spatial context of the cells. The outcome is an information rich map of the tissue where individual cells, or small regions, can be labeled based on their gene expression state. While spatial transcriptomics excels in its capacity to profile numerous genes within the same sample, most existing methods for analysis of spatial data only examine distribution of one or two labels at a time. These approaches overlook the potential for identifying higher-order associations between cell types - associations that can play a pivotal role in understanding development and function of complex tissues. In this context, we introduce a novel method for detecting motifs in spatial neighborhood graphs. Each motif represents a spatial arrangement of cell types that occurs in the tissue more frequently than expected by chance. To identify spatial motifs, we developed an algorithm for uniform sampling of paths from neighborhood graphs and combined it with a motif finding algorithm on graphs inspired by previous methods for finding motifs in DNA sequences. Using synthetic data with known ground truth, we show that our method can identify spatial motifs with high accuracy and sensitivity. Applied to spatial maps of mouse retinal bipolar cells and hypothalamic preoptic region, our method reveals previously unrecognized patterns in cell type arrangements. In some cases, cells within these spatial patterns differ in their gene expression from other cells of the same type, providing insights into the functional significance of the spatial motifs. These results suggest that our method can illuminate the substantial complexity of neural tissues, provide novel insight even in well studied models, and generate experimentally testable hypotheses.
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Affiliation(s)
- Zainalabedin Samadi
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, 90095, CA, USA
| | - Amjad Askary
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, 90095, CA, USA
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31
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Haviv D, Remšík J, Gatie M, Snopkowski C, Takizawa M, Pereira N, Bashkin J, Jovanovich S, Nawy T, Chaligne R, Boire A, Hadjantonakis AK, Pe'er D. The covariance environment defines cellular niches for spatial inference. Nat Biotechnol 2024:10.1038/s41587-024-02193-4. [PMID: 38565973 PMCID: PMC11445396 DOI: 10.1038/s41587-024-02193-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 02/28/2024] [Indexed: 04/04/2024]
Abstract
A key challenge of analyzing data from high-resolution spatial profiling technologies is to suitably represent the features of cellular neighborhoods or niches. Here we introduce the covariance environment (COVET), a representation that leverages the gene-gene covariate structure across cells in the niche to capture the multivariate nature of cellular interactions within it. We define a principled optimal transport-based distance metric between COVET niches that scales to millions of cells. Using COVET to encode spatial context, we developed environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA sequencing data into a latent space. ENVI includes two decoders: one to impute gene expression across the spatial modality and a second to project spatial information onto single-cell data. ENVI can confer spatial context to genomics data from single dissociated cells and outperforms alternatives for imputing gene expression on diverse spatial datasets.
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Affiliation(s)
- Doron Haviv
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Ján Remšík
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mohamed Gatie
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Catherine Snopkowski
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meril Takizawa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | | | - Tal Nawy
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ronan Chaligne
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Adrienne Boire
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anna-Katerina Hadjantonakis
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Howard Hughes Medical Institute, New York, NY, USA.
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32
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Hümpfer N, Thielhorn R, Ewers H. Expanding boundaries - a cell biologist's guide to expansion microscopy. J Cell Sci 2024; 137:jcs260765. [PMID: 38629499 PMCID: PMC11058692 DOI: 10.1242/jcs.260765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Abstract
Expansion microscopy (ExM) is a revolutionary novel approach to increase resolution in light microscopy. In contrast to super-resolution microscopy methods that rely on sophisticated technological advances, including novel instrumentation, ExM instead is entirely based on sample preparation. In ExM, labeled target molecules in fixed cells are anchored in a hydrogel, which is then physically enlarged by osmotic swelling. The isotropic swelling of the hydrogel pulls the labels apart from one another, and their relative organization can thus be resolved using conventional microscopes even if it was below the diffraction limit of light beforehand. As ExM can additionally benefit from the technical resolution enhancements achieved by super-resolution microscopy, it can reach into the nanometer range of resolution with an astoundingly low degree of error induced by distortion during the physical expansion process. Because the underlying chemistry is well understood and the technique is based on a relatively simple procedure, ExM is easily reproducible in non-expert laboratories and has quickly been adopted to address an ever-expanding spectrum of problems across the life sciences. In this Review, we provide an overview of this rapidly expanding new field, summarize the most important insights gained so far and attempt to offer an outlook on future developments.
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Affiliation(s)
- Nadja Hümpfer
- Department of Biology, Chemistry and Pharmacy, Institut für Chemie und Biochemie, Freie Universität Berlin, 14195 Berlin, Germany
| | - Ria Thielhorn
- Department of Biology, Chemistry and Pharmacy, Institut für Chemie und Biochemie, Freie Universität Berlin, 14195 Berlin, Germany
| | - Helge Ewers
- Department of Biology, Chemistry and Pharmacy, Institut für Chemie und Biochemie, Freie Universität Berlin, 14195 Berlin, Germany
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33
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Liffner B, Absalon S. Expansion microscopy of apicomplexan parasites. Mol Microbiol 2024; 121:619-635. [PMID: 37571814 DOI: 10.1111/mmi.15135] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 07/15/2023] [Accepted: 07/20/2023] [Indexed: 08/13/2023]
Abstract
Apicomplexan parasites comprise significant pathogens of humans, livestock and wildlife, but also represent a diverse group of eukaryotes with interesting and unique cell biology. The study of cell biology in apicomplexan parasites is complicated by their small size, and historically this has required the application of cutting-edge microscopy techniques to investigate fundamental processes like mitosis or cell division in these organisms. Recently, a technique called expansion microscopy has been developed, which rather than increasing instrument resolution like most imaging modalities, physically expands a biological sample. In only a few years since its development, a derivative of expansion microscopy known as ultrastructure-expansion microscopy (U-ExM) has been widely adopted and proven extremely useful for studying cell biology of Apicomplexa. Here, we review the insights into apicomplexan cell biology that have been enabled through the use of U-ExM, with a specific focus on Plasmodium, Toxoplasma and Cryptosporidium. Further, we summarize emerging expansion microscopy modifications and modalities and forecast how these may influence the field of parasite cell biology in future.
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Affiliation(s)
- Benjamin Liffner
- Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Sabrina Absalon
- Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, Indiana, USA
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34
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Mulholland EJ, Leedham SJ. Redefining clinical practice through spatial profiling: a revolution in tissue analysis. Ann R Coll Surg Engl 2024; 106:305-312. [PMID: 38555868 PMCID: PMC10981989 DOI: 10.1308/rcsann.2023.0091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/25/2023] [Indexed: 04/02/2024] Open
Abstract
Spatial biology, which combines molecular biology and advanced imaging, enhances our understanding of tissue cellular organisation. Despite its potential, spatial omics encounters challenges related to data complexity, computational requirements and standardisation of analysis. In clinical applications, spatial omics has the potential to revolutionise biomarker discovery, disease stratification and personalised treatments. It can identify disease-specific cell patterns, and could help risk stratify patients for clinical trials and disease-appropriate therapies. Although there are challenges in adopting it in clinical practice, spatial omics has the potential to significantly enhance patient outcomes. In this paper, we discuss the recent evolution of spatial biology, and its potential for improving our tissue level understanding and treatment of disease, to help advance precision and effectiveness in healthcare interventions.
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35
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Ren L, Huang D, Liu H, Ning L, Cai P, Yu X, Zhang Y, Luo N, Lin H, Su J, Zhang Y. Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review). Oncol Lett 2024; 27:152. [PMID: 38406595 PMCID: PMC10885005 DOI: 10.3892/ol.2024.14285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/19/2024] [Indexed: 02/27/2024] Open
Abstract
Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.
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Affiliation(s)
- Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Danni Huang
- Department of Radiology, Central South University Xiangya School of Medicine Affiliated Haikou People's Hospital, Haikou, Hainan 570208, P.R. China
| | - Hongjiang Liu
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Peiling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, Sichuan 610106, P.R. China
| | - Xiaolong Yu
- Hainan Yazhou Bay Seed Laboratory, Sanya Nanfan Research Institute, Material Science and Engineering Institute of Hainan University, Sanya, Hainan 572025, P.R. China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Nanchao Luo
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. China
| | - Jinsong Su
- Research Institute of Integrated Traditional Chinese Medicine and Western Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Yinghui Zhang
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
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36
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Kim H, Noh H. Signal Amplification by Spatial Concentration for Immunoassay on Cellulose Media. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2307556. [PMID: 38012537 DOI: 10.1002/smll.202307556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/13/2023] [Indexed: 11/29/2023]
Abstract
Immunoassay is one of the most common bioanalytical techniques from lab-based to point-of-care settings. Over time, various approaches have been developed to amplify signals for greater sensitivity. However, the need for effective, versatile, and simple signal amplification methods persists yet. This paper presents a novel signal amplification method for immunoassay that utilizes spatial concentration of a cellulose-based plate possessing sensor transducers, specifically gold nanoparticles. By modifying the dimensions of the plate, the density of nanoparticles increased, resulting in intensified color signals. The coating material, polydopamine, which is utilized to protect the gold nanoparticles. Chemical changes in nanocomposites are characterized using scanning electron microscopy, Raman spectroscopy, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and scanning electron microscopy. The application of this method to colorimetric quantification demonstrated great consistency across various concentrations of nanoparticles, with better reliability at lower concentration ranges. A model immunoassay is designed to evaluate the analytical performance. As a result, this method successfully corrected a false-negative result with a lowered Kd of 0.509 pmol per zone. This method shows strong signal enhancement capability that can correct false-negative signals in the immunoassays, with potential benefits including versatility, simplicity, low cost, and the ability to operate multiple plates simultaneously.
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Affiliation(s)
- Hyeokjung Kim
- Department of Optometry, Seoul National University of Science and Technology, Seoul, 01811, South Korea
| | - Hyeran Noh
- Department of Optometry, Seoul National University of Science and Technology, Seoul, 01811, South Korea
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37
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Wang G, Lee-Yow Y, Chang HY. Approaches to probe and perturb long noncoding RNA functions in diseases. Curr Opin Genet Dev 2024; 85:102158. [PMID: 38412563 PMCID: PMC10987257 DOI: 10.1016/j.gde.2024.102158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/15/2024] [Accepted: 01/27/2024] [Indexed: 02/29/2024]
Abstract
Long noncoding RNAs (lncRNAs) are a class of RNA molecules exceeding 200 nucleotides in length that lack long open-reading frames. Transcribed predominantly by RNA polymerase II (>500nt), lncRNAs can undergo splicing and are produced from various regions of the genome, including intergenic regions, introns, and in antisense orientation to protein-coding genes. Aberrations in lncRNA expression or function have been associated with a wide variety of diseases, including cancer, cardiovascular diseases, diabetes, and neurodegeneration. Despite the growing recognition of select lncRNAs as key players in cellular processes and diseases, several challenges obscure a comprehensive understanding of their functional landscape. Recent technological innovations, such as in sequencing, affinity-based techniques, imaging, and RNA perturbation, have advanced functional characterization and mechanistic understanding of disease-associated lncRNAs.
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Affiliation(s)
- Guiping Wang
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305, USA. https://twitter.com/@Guiping_W
| | - Yannick Lee-Yow
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305, USA. https://twitter.com/@yooaaooy
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305, USA.
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38
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Li K, Li J, Tao Y, Wang F. stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics. Brief Bioinform 2024; 25:bbae171. [PMID: 38628114 PMCID: PMC11021815 DOI: 10.1093/bib/bbae171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/11/2024] [Accepted: 04/01/2024] [Indexed: 04/19/2024] Open
Abstract
Spatial transcriptomics (ST) has become a powerful tool for exploring the spatial organization of gene expression in tissues. Imaging-based methods, though offering superior spatial resolutions at the single-cell level, are limited in either the number of imaged genes or the sensitivity of gene detection. Existing approaches for enhancing ST rely on the similarity between ST cells and reference single-cell RNA sequencing (scRNA-seq) cells. In contrast, we introduce stDiff, which leverages relationships between gene expression abundance in scRNA-seq data to enhance ST. stDiff employs a conditional diffusion model, capturing gene expression abundance relationships in scRNA-seq data through two Markov processes: one introducing noise to transcriptomics data and the other denoising to recover them. The missing portion of ST is predicted by incorporating the original ST data into the denoising process. In our comprehensive performance evaluation across 16 datasets, utilizing multiple clustering and similarity metrics, stDiff stands out for its exceptional ability to preserve topological structures among cells, positioning itself as a robust solution for cell population identification. Moreover, stDiff's enhancement outcomes closely mirror the actual ST data within the batch space. Across diverse spatial expression patterns, our model accurately reconstructs them, delineating distinct spatial boundaries. This highlights stDiff's capability to unify the observed and predicted segments of ST data for subsequent analysis. We anticipate that stDiff, with its innovative approach, will contribute to advancing ST imputation methodologies.
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Affiliation(s)
- Kongming Li
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan UniversityHandan Street, 200433 Shanghai, China
| | - Jiahao Li
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan UniversityHandan Street, 200433 Shanghai, China
| | - Yuhao Tao
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan UniversityHandan Street, 200433 Shanghai, China
| | - Fei Wang
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan UniversityHandan Street, 200433 Shanghai, China
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39
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Abay T, Stickels RR, Takizawa MT, Nalbant BN, Hsieh YH, Hwang S, Snopkowski C, Yu KKH, Abou-Mrad Z, Tabar V, Ludwig LS, Chaligné R, Satpathy AT, Lareau CA. Transcript-specific enrichment enables profiling rare cell states via scRNA-seq. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.587039. [PMID: 38586040 PMCID: PMC10996707 DOI: 10.1101/2024.03.27.587039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Single-cell genomics technologies have accelerated our understanding of cell-state heterogeneity in diverse contexts. Although single-cell RNA sequencing (scRNA-seq) identifies many rare populations of interest that express specific marker transcript combinations, traditional flow sorting limits our ability to enrich these populations for further profiling, including requiring cell surface markers with high-fidelity antibodies. Additionally, many single-cell studies require the isolation of nuclei from tissue, eliminating the ability to enrich learned rare cell states based on extranuclear protein markers. To address these limitations, we describe Programmable Enrichment via RNA Flow-FISH by sequencing (PERFF-seq), a scalable assay that enables scRNA-seq profiling of subpopulations from complex cellular mixtures defined by the presence or absence of specific RNA transcripts. Across immune populations (n = 141,227 cells) and fresh-frozen and formalin-fixed paraffin-embedded brain tissue (n = 29,522 nuclei), we demonstrate the sorting logic that can be used to enrich for cell populations via RNA-based cytometry followed by high-throughput scRNA-seq. Our approach provides a rational, programmable method for studying rare populations identified by one or more marker transcripts.
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Affiliation(s)
- Tsion Abay
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Pathology, Stanford University, Stanford CA, USA
- Program in Biological and Biomedical Sciences, Harvard University, Boston, MA, USA
| | - Robert R. Stickels
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Pathology, Stanford University, Stanford CA, USA
| | - Meril T. Takizawa
- Single-cell Analytics Innovation Lab, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Benan N. Nalbant
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yu-Hsin Hsieh
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Charité Universitätsmedizin Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
| | - Sidney Hwang
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Pathology, Stanford University, Stanford CA, USA
| | - Catherine Snopkowski
- Single-cell Analytics Innovation Lab, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kenny Kwok Hei Yu
- Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Zaki Abou-Mrad
- Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Viviane Tabar
- Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Leif S. Ludwig
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
| | - Ronan Chaligné
- Single-cell Analytics Innovation Lab, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ansuman T. Satpathy
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Pathology, Stanford University, Stanford CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | - Caleb A. Lareau
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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40
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Kalhor K, Chen CJ, Lee HS, Cai M, Nafisi M, Que R, Palmer CR, Yuan Y, Zhang Y, Li X, Song J, Knoten A, Lake BB, Gaut JP, Keene CD, Lein E, Kharchenko PV, Chun J, Jain S, Fan JB, Zhang K. Mapping human tissues with highly multiplexed RNA in situ hybridization. Nat Commun 2024; 15:2511. [PMID: 38509069 PMCID: PMC10954689 DOI: 10.1038/s41467-024-46437-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 02/23/2024] [Indexed: 03/22/2024] Open
Abstract
In situ transcriptomic techniques promise a holistic view of tissue organization and cell-cell interactions. There has been a surge of multiplexed RNA in situ mapping techniques but their application to human tissues has been limited due to their large size, general lower tissue quality and high autofluorescence. Here we report DART-FISH, a padlock probe-based technology capable of profiling hundreds to thousands of genes in centimeter-sized human tissue sections. We introduce an omni-cell type cytoplasmic stain that substantially improves the segmentation of cell bodies. Our enzyme-free isothermal decoding procedure allows us to image 121 genes in large sections from the human neocortex in <10 h. We successfully recapitulated the cytoarchitecture of 20 neuronal and non-neuronal subclasses. We further performed in situ mapping of 300 genes on a diseased human kidney, profiled >20 healthy and pathological cell states, and identified diseased niches enriched in transcriptionally altered epithelial cells and myofibroblasts.
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Affiliation(s)
- Kian Kalhor
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Chien-Ju Chen
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA, USA
| | - Ho Suk Lee
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of Electrical Engineering, University of California San Diego, La Jolla, CA, USA
| | - Matthew Cai
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Mahsa Nafisi
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Richard Que
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Carter R Palmer
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
- Program in Biomedical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Yixu Yuan
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Yida Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Jinghui Song
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Amanda Knoten
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Blue B Lake
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Altos Labs, San Diego, CA, USA
| | - Joseph P Gaut
- Department of Pathology and Immunology, Washington University School of Medicine, St.Louis, MO, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA
| | - Ed Lein
- Allen Institute for Brain Science, Seattle, WA, 98103, USA
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Altos Labs, San Diego, CA, USA
| | - Jerold Chun
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Sanjay Jain
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St.Louis, MO, USA
| | | | - Kun Zhang
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
- Altos Labs, San Diego, CA, USA.
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41
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Zhou X, Seow WY, Ha N, Cheng TH, Jiang L, Boonruangkan J, Goh JJL, Prabhakar S, Chou N, Chen KH. Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes. Nat Commun 2024; 15:2342. [PMID: 38491027 PMCID: PMC10943009 DOI: 10.1038/s41467-024-46669-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 03/04/2024] [Indexed: 03/18/2024] Open
Abstract
High-dimensional, spatially resolved analysis of intact tissue samples promises to transform biomedical research and diagnostics, but existing spatial omics technologies are costly and labor-intensive. We present Fluorescence In Situ Hybridization of Cellular HeterogeneIty and gene expression Programs (FISHnCHIPs) for highly sensitive in situ profiling of cell types and gene expression programs. FISHnCHIPs achieves this by simultaneously imaging ~2-35 co-expressed genes (clustered into modules) that are spatially co-localized in tissues, resulting in similar spatial information as single-gene Fluorescence In Situ Hybridization (FISH), but with ~2-20-fold higher sensitivity. Using FISHnCHIPs, we image up to 53 modules from the mouse kidney and mouse brain, and demonstrate high-speed, large field-of-view profiling of a whole tissue section. FISHnCHIPs also reveals spatially restricted localizations of cancer-associated fibroblasts in a human colorectal cancer biopsy. Overall, FISHnCHIPs enables fast, robust, and scalable cell typing of tissues with normal physiology or undergoing pathogenesis.
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Affiliation(s)
- Xinrui Zhou
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Wan Yi Seow
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Norbert Ha
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Teh How Cheng
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Lingfan Jiang
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Jeeranan Boonruangkan
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Jolene Jie Lin Goh
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Shyam Prabhakar
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Nigel Chou
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore.
| | - Kok Hao Chen
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore.
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42
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Burg MF, Zenkel T, Vystrčilová M, Oesterle J, Höfling L, Willeke KF, Lause J, Müller S, Fahey PG, Ding Z, Restivo K, Sridhar S, Gollisch T, Berens P, Tolias AS, Euler T, Bethge M, Ecker AS. Most discriminative stimuli for functional cell type clustering. ARXIV 2024:arXiv:2401.05342v2. [PMID: 38560735 PMCID: PMC10980086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron.
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Affiliation(s)
- Max F Burg
- International Max Planck Research School for Intelligent Systems, Tübingen, Germany
- Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany
- Tübingen AI Center, University of Tübingen, Germany
| | - Thomas Zenkel
- Institute of Ophthalmic Research, University of Tübingen, Germany
- Centre for Integrative Neuroscience, University of Tübingen, Germany
| | - Michaela Vystrčilová
- Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany
| | - Jonathan Oesterle
- Institute of Ophthalmic Research, University of Tübingen, Germany
- Centre for Integrative Neuroscience, University of Tübingen, Germany
| | - Larissa Höfling
- Institute of Ophthalmic Research, University of Tübingen, Germany
- Centre for Integrative Neuroscience, University of Tübingen, Germany
| | - Konstantin F Willeke
- International Max Planck Research School for Intelligent Systems, Tübingen, Germany
- Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany
- Institute for Bioinformatics and Medical Informatics, Tübingen University, Germany
| | - Jan Lause
- Tübingen AI Center, University of Tübingen, Germany
- Hertie Institute for AI in Brain Health, University of Tübingen, Germany
| | - Sarah Müller
- International Max Planck Research School for Intelligent Systems, Tübingen, Germany
- Tübingen AI Center, University of Tübingen, Germany
- Hertie Institute for AI in Brain Health, University of Tübingen, Germany
| | - Paul G Fahey
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Zhiwei Ding
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Kelli Restivo
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Shashwat Sridhar
- University Medical Center Göttingen, Department of Ophthalmology, Germany
- Bernstein Center for Computational Neuroscience Göttingen, Germany
| | - Tim Gollisch
- University Medical Center Göttingen, Department of Ophthalmology, Germany
- Bernstein Center for Computational Neuroscience Göttingen, Germany
- Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Germany
| | - Philipp Berens
- Tübingen AI Center, University of Tübingen, Germany
- Hertie Institute for AI in Brain Health, University of Tübingen, Germany
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Thomas Euler
- Institute of Ophthalmic Research, University of Tübingen, Germany
- Centre for Integrative Neuroscience, University of Tübingen, Germany
| | - Matthias Bethge
- Tübingen AI Center, University of Tübingen, Germany
- Centre for Integrative Neuroscience, University of Tübingen, Germany
| | - Alexander S Ecker
- Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
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43
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Shin TW, Wang H, Zhang C, An B, Lu Y, Zhang E, Lu X, Karagiannis ED, Kang JS, Emenari A, Symvoulidis P, Asano S, Lin L, Costa EK, Marblestone AH, Kasthuri N, Tsai LH, Boyden ES. Dense, Continuous Membrane Labeling and Expansion Microscopy Visualization of Ultrastructure in Tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.07.583776. [PMID: 38496681 PMCID: PMC10942445 DOI: 10.1101/2024.03.07.583776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Lipid membranes are key to the nanoscale compartmentalization of biological systems, but fluorescent visualization of them in intact tissues, with nanoscale precision, is challenging to do with high labeling density. Here, we report ultrastructural membrane expansion microscopy (umExM), which combines a novel membrane label and optimized expansion microscopy protocol, to support dense labeling of membranes in tissues for nanoscale visualization. We validated the high signal-to-background ratio, and uniformity and continuity, of umExM membrane labeling in brain slices, which supported the imaging of membranes and proteins at a resolution of ~60 nm on a confocal microscope. We demonstrated the utility of umExM for the segmentation and tracing of neuronal processes, such as axons, in mouse brain tissue. Combining umExM with optical fluctuation imaging, or iterating the expansion process, yielded ~35 nm resolution imaging, pointing towards the potential for electron microscopy resolution visualization of brain membranes on ordinary light microscopes.
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Affiliation(s)
- Tay Won Shin
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Hao Wang
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
- Picower Inst. for Learning and Memory, Cambridge
| | - Chi Zhang
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Bobae An
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Yangning Lu
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Elizabeth Zhang
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Xiaotang Lu
- Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, United States
| | | | - Jeong Seuk Kang
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Amauche Emenari
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Panagiotis Symvoulidis
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Shoh Asano
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Leanne Lin
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Emma K. Costa
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
| | | | - Adam H. Marblestone
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
- present address: Convergent Research
| | - Narayanan Kasthuri
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
- Department of Neurobiology, University of Chicago, Chicago, IL, USA
| | - Li-Huei Tsai
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
- Picower Inst. for Learning and Memory, Cambridge
| | - Edward S. Boyden
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
- Howard Hughes Medical Institute, Cambridge, MA 02139
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
- Koch Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
- Center for Neurobiological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
- K. Lisa Yang Center for Bionics, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
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44
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Chen J, Ke R. Spatial analysis toolkits for RNA in situ sequencing. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1842. [PMID: 38605484 DOI: 10.1002/wrna.1842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 04/13/2024]
Abstract
Spatial transcriptomics (ST) is featured by high-throughput gene expression profiling within their native cell and tissue context, offering a means to investigate gene regulatory networks in tissue microenvironment. In situ sequencing (ISS) is an imaging-based ST technology that simultaneously detects hundreds to thousands of genes at subcellular resolution. As a highly reproducible and robust technique, ISS has been widely adapted and undergone a series of technical iterations. As the interest in ISS-based spatial transcriptomic analysis grows, scalable and integrated data analysis workflows are needed to facilitate the applications of ISS in different research fields. This review presents the state-of-the-art bioinformatic toolkits for ISS data analysis, which covers the upstream and downstream analysis workflows, including image analysis, cell segmentation, clustering, functional enrichment, detection of spatially variable genes and cell clusters, spatial cell-cell interactions, and trajectory inference. To assist the community in choosing the right tools for their research, the application of each tool and its compatibility with ISS data are reviewed in detailed. Finally, future perspectives and challenges concerning how to integrate heterogeneous tools into a user-friendly analysis pipeline are discussed. This article is categorized under: RNA Methods > RNA Analyses In Vitro and In Silico.
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Affiliation(s)
- Jiayu Chen
- School of Medicine, Huaqiao University, Xiamen, Fujian, China
| | - Rongqin Ke
- School of Medicine, Huaqiao University, Xiamen, Fujian, China
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Li R, Chen X, Yang X. Navigating the landscapes of spatial transcriptomics: How computational methods guide the way. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1839. [PMID: 38527900 DOI: 10.1002/wrna.1839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/24/2024] [Accepted: 03/04/2024] [Indexed: 03/27/2024]
Abstract
Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single-cell, multi-cellular, or sub-cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi-modal high-throughput data source, which poses new challenges for the development of analytical methods for data-mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever-evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization.
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Affiliation(s)
- Runze Li
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xu Chen
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xuerui Yang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
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Sun ED, Ma R, Navarro Negredo P, Brunet A, Zou J. TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses. Nat Methods 2024; 21:444-454. [PMID: 38347138 DOI: 10.1038/s41592-024-02184-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 01/12/2024] [Indexed: 02/27/2024]
Abstract
Whole-transcriptome spatial profiling of genes at single-cell resolution remains a challenge. To address this limitation, spatial gene expression prediction methods have been developed to infer the spatial expression of unmeasured transcripts, but the quality of these predictions can vary greatly. Here we present Transcript Imputation with Spatial Single-cell Uncertainty Estimation (TISSUE) as a general framework for estimating uncertainty for spatial gene expression predictions and providing uncertainty-aware methods for downstream inference. Leveraging conformal inference, TISSUE provides well-calibrated prediction intervals for predicted expression values across 11 benchmark datasets. Moreover, it consistently reduces the false discovery rate for differential gene expression analysis, improves clustering and visualization of predicted spatial transcriptomics and improves the performance of supervised learning models trained on predicted gene expression profiles. Applying TISSUE to a MERFISH spatial transcriptomics dataset of the adult mouse subventricular zone, we identified subtypes within the neural stem cell lineage and developed subtype-specific regional classifiers.
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Affiliation(s)
- Eric D Sun
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Rong Ma
- Department of Statistics, Stanford University, Stanford, CA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Anne Brunet
- Department of Genetics, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Glenn Center for the Biology of Aging, Stanford University, Stanford, CA, USA
| | - James Zou
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
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Xiao W, Halabi R, Lin CH, Nazim M, Yeom KH, Black DL. The lncRNA Malat1 is trafficked to the cytoplasm as a localized mRNA encoding a small peptide in neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.01.578240. [PMID: 38352368 PMCID: PMC10862813 DOI: 10.1101/2024.02.01.578240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
Synaptic function is modulated by local translation of mRNAs that are transported to distal portions of axons and dendrites. The Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is broadly expressed across cell types, almost exclusively as a nuclear non-coding RNA. We found that in differentiating neurons, a portion of Malat1 RNA redistributes to the cytoplasm. Depletion of Malat1 from neurons stimulated expression of particular pre- and post- synaptic proteins, implicating Malat1 in their regulation. Neuronal Malat1 is localized to both axons and dendrites in puncta that co-stain with Staufen1 protein, similar to neuronal granules formed by locally translated mRNAs. Ribosome profiling of mouse cortical neurons identified ribosome footprints within a region of Malat1 containing short open reading frames. The upstream-most reading frame (M1) of the Malat1 locus was linked to the GFP coding sequence in mouse ES cells. When these gene-edited cells were differentiated into glutamatergic neurons, the M1-GFP fusion protein was expressed. Antibody staining for the M1 peptide confirmed its presence in wildtype neurons, and showed enhancement of M1 expression after synaptic stimulation with KCL. Our results indicate that Malat1 serves as a cytoplasmic coding RNA in the brain that is both modulated by and modulates synaptic function.
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Affiliation(s)
- Wen Xiao
- Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095
| | - Reem Halabi
- Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095
| | - Chia-Ho Lin
- Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095
| | - Mohammad Nazim
- Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095
| | - Kyu-Hyeon Yeom
- Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095
| | - Douglas L Black
- Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095
- Molecular Biology Institute, David Geffen School of Medicine, University of California, Los Angeles, CA 90095
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, David Geffen School of Medicine, University of California, Los Angeles, CA 90095
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, CA 90095
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Valdes PA, Yu CC(J, Aronson J, Ghosh D, Zhao Y, An B, Bernstock JD, Bhere D, Felicella MM, Viapiano MS, Shah K, Chiocca EA, Boyden ES. Improved immunostaining of nanostructures and cells in human brain specimens through expansion-mediated protein decrowding. Sci Transl Med 2024; 16:eabo0049. [PMID: 38295184 PMCID: PMC10911838 DOI: 10.1126/scitranslmed.abo0049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 01/10/2024] [Indexed: 02/02/2024]
Abstract
Proteins are densely packed in cells and tissues, where they form complex nanostructures. Expansion microscopy (ExM) variants have been used to separate proteins from each other in preserved biospecimens, improving antibody access to epitopes. Here, we present an ExM variant, decrowding expansion pathology (dExPath), that can expand proteins away from each other in human brain pathology specimens, including formalin-fixed paraffin-embedded (FFPE) clinical specimens. Immunostaining of dExPath-expanded specimens reveals, with nanoscale precision, previously unobserved cellular structures, as well as more continuous patterns of staining. This enhanced molecular staining results in observation of previously invisible disease marker-positive cell populations in human glioma specimens, with potential implications for tumor aggressiveness. dExPath results in improved fluorescence signals even as it eliminates lipofuscin-associated autofluorescence. Thus, this form of expansion-mediated protein decrowding may, through improved epitope access for antibodies, render immunohistochemistry more powerful in clinical science and, perhaps, diagnosis.
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Affiliation(s)
- Pablo A. Valdes
- Department of Neurosurgery, University of Texas Medical Branch, Galveston, TX, 77555
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA, 02115
- Media Arts and Sciences, MIT, Cambridge, MA, USA, 02115
| | - Chih-Chieh (Jay) Yu
- Media Arts and Sciences, MIT, Cambridge, MA, USA, 02115
- Department of Biological Engineering, MIT, MA, USA, 02139
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA, 02139
- RIKEN Center for Brain Science, Saitama, Japan, 351-0198
| | - Jenna Aronson
- Media Arts and Sciences, MIT, Cambridge, MA, USA, 02115
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA, 02139
- RIKEN Center for Brain Science, Saitama, Japan, 351-0198
| | - Debarati Ghosh
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA, 02139
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA, 02139
| | - Yongxin Zhao
- Media Arts and Sciences, MIT, Cambridge, MA, USA, 02115
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA, 15213
| | - Bobae An
- Media Arts and Sciences, MIT, Cambridge, MA, USA, 02115
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA, 02139
| | - Joshua D. Bernstock
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA, 02115
- Koch Institute, MIT, Cambridge, MA, USA, 02139
| | - Deepak Bhere
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA, 02115
- Department of Pathology, Microbiology and Immunology, School of Medicine Columbia, University of South Carolina, Columbia, SC, USA, 29209
- Center for Stem Cell and Translational Immunotherapy, Harvard Medical School/Brigham and Women’s Hospital, Boston, MA, USA, 02115
| | - Michelle M. Felicella
- Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA, 77555
| | - Mariano S. Viapiano
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA, 13210
| | - Khalid Shah
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA, 02115
- Center for Stem Cell and Translational Immunotherapy, Harvard Medical School/Brigham and Women’s Hospital, Boston, MA, USA, 02115
| | - E. Antonio Chiocca
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA, 02115
| | - Edward S. Boyden
- Media Arts and Sciences, MIT, Cambridge, MA, USA, 02115
- Department of Biological Engineering, MIT, MA, USA, 02139
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA, 02139
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA, 02139
- Koch Institute, MIT, Cambridge, MA, USA, 02139
- MIT Center for Neurobiological Engineering and K. Lisa Yang Center for Bionics, MIT, Cambridge, MA, USA, 02139
- Howard Hughes Medical Institute, Cambridge, MA, USA, 02139
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49
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Lee S, Kim G, Lee J, Lee AC, Kwon S. Mapping cancer biology in space: applications and perspectives on spatial omics for oncology. Mol Cancer 2024; 23:26. [PMID: 38291400 PMCID: PMC10826015 DOI: 10.1186/s12943-024-01941-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 01/12/2024] [Indexed: 02/01/2024] Open
Abstract
Technologies to decipher cellular biology, such as bulk sequencing technologies and single-cell sequencing technologies, have greatly assisted novel findings in tumor biology. Recent findings in tumor biology suggest that tumors construct architectures that influence the underlying cancerous mechanisms. Increasing research has reported novel techniques to map the tissue in a spatial context or targeted sampling-based characterization and has introduced such technologies to solve oncology regarding tumor heterogeneity, tumor microenvironment, and spatially located biomarkers. In this study, we address spatial technologies that can delineate the omics profile in a spatial context, novel findings discovered via spatial technologies in oncology, and suggest perspectives regarding therapeutic approaches and further technological developments.
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Affiliation(s)
- Sumin Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea
| | - Gyeongjun Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - JinYoung Lee
- Division of Engineering Science, University of Toronto, Toronto, Ontario, ON, M5S 3H6, Canada
| | - Amos C Lee
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul, 08826, Republic of Korea.
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
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50
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Cao J, Zheng Z, Sun D, Chen X, Cheng R, Lv T, An Y, Zheng J, Song J, Wu L, Yang C. Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing. Nat Biotechnol 2024:10.1038/s41587-023-02086-y. [PMID: 38228777 DOI: 10.1038/s41587-023-02086-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 12/04/2023] [Indexed: 01/18/2024]
Abstract
Spatial transcriptomics technologies with high resolution often lack high sensitivity in mRNA detection. Here we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq), which offers both high sensitivity and high resolution. Decoder-seq combines dendrimeric nanosubstrates with microfluidic coordinate barcoding to generate spatial arrays with a DNA density approximately ten times higher than previously reported methods while maintaining flexibility in resolution. We show that the high RNA capture efficiency of Decoder-seq improved the detection of lowly expressed olfactory receptor (Olfr) genes in mouse olfactory bulbs and contributed to the discovery of a unique layer enrichment pattern for two Olfr genes. The near-cellular resolution provided by Decoder-seq has enabled the construction of a spatial single-cell atlas of the mouse hippocampus, revealing dendrite-enriched mRNAs in neurons. When applying Decoder-seq to human renal cell carcinomas, we dissected the heterogeneous tumor microenvironment across different cancer subtypes and identified spatial gradient-expressed genes related to epithelial-mesenchymal transition with the potential to predict tumor prognosis and progression.
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Affiliation(s)
- Jiao Cao
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhong Zheng
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Di Sun
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xin Chen
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rui Cheng
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianpeng Lv
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu An
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junhua Zheng
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Jia Song
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Lingling Wu
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Chaoyong Yang
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, State Key Laboratory of Physical Chemical of Solid Surfaces, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China.
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