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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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Liu Y, Yang C. Computational methods for alignment and integration of spatially resolved transcriptomics data. Comput Struct Biotechnol J 2024; 23:1094-1105. [PMID: 38495555 PMCID: PMC10940867 DOI: 10.1016/j.csbj.2024.03.002] [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: 01/06/2024] [Revised: 03/02/2024] [Accepted: 03/04/2024] [Indexed: 03/19/2024] Open
Abstract
Most of the complex biological regulatory activities occur in three dimensions (3D). To better analyze biological processes, it is essential not only to decipher the molecular information of numerous cells but also to understand how their spatial contexts influence their behavior. With the development of spatially resolved transcriptomics (SRT) technologies, SRT datasets are being generated to simultaneously characterize gene expression and spatial arrangement information within tissues, organs or organisms. To fully leverage spatial information, the focus extends beyond individual two-dimensional (2D) slices. Two tasks known as slices alignment and data integration have been introduced to establish correlations between multiple slices, enhancing the effectiveness of downstream tasks. Currently, numerous related methods have been developed. In this review, we first elucidate the details and principles behind several representative methods. Then we report the testing results of these methods on various SRT datasets, and assess their performance in representative downstream tasks. Insights into the strengths and weaknesses of each method and the reasons behind their performance are discussed. Finally, we provide an outlook on future developments. The codes and details of experiments are now publicly available at https://github.com/YangLabHKUST/SRT_alignment_and_integration.
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Affiliation(s)
- Yuyao Liu
- Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing, China
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China
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3
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Han S, Xu Q, Du Y, Tang C, Cui H, Xia X, Zheng R, Sun Y, Shang H. Single-cell spatial transcriptomics in cardiovascular development, disease, and medicine. Genes Dis 2024; 11:101163. [PMID: 39224111 PMCID: PMC11367031 DOI: 10.1016/j.gendis.2023.101163] [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: 03/27/2023] [Revised: 10/17/2023] [Accepted: 10/29/2023] [Indexed: 09/04/2024] Open
Abstract
Cardiovascular diseases (CVDs) impose a significant burden worldwide. Despite the elucidation of the etiology and underlying molecular mechanisms of CVDs by numerous studies and recent discovery of effective drugs, their morbidity, disability, and mortality are still high. Therefore, precise risk stratification and effective targeted therapies for CVDs are warranted. Recent improvements in single-cell RNA sequencing and spatial transcriptomics have improved our understanding of the mechanisms and cells involved in cardiovascular phylogeny and CVDs. Single-cell RNA sequencing can facilitate the study of the human heart at remarkably high resolution and cellular and molecular heterogeneity. However, this technique does not provide spatial information, which is essential for understanding homeostasis and disease. Spatial transcriptomics can elucidate intracellular interactions, transcription factor distribution, cell spatial localization, and molecular profiles of mRNA and identify cell populations causing the disease and their underlying mechanisms, including cell crosstalk. Herein, we introduce the main methods of RNA-seq and spatial transcriptomics analysis and highlight the latest advances in cardiovascular research. We conclude that single-cell RNA sequencing interprets disease progression in multiple dimensions, levels, perspectives, and dynamics by combining spatial and temporal characterization of the clinical phenome with multidisciplinary techniques such as spatial transcriptomics. This aligns with the dynamic evolution of CVDs (e.g., "angina-myocardial infarction-heart failure" in coronary artery disease). The study of pathways for disease onset and mechanisms (e.g., age, sex, comorbidities) in different patient subgroups should improve disease diagnosis and risk stratification. This can facilitate precise individualized treatment of CVDs.
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Affiliation(s)
- Songjie Han
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Qianqian Xu
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Yawen Du
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Chuwei Tang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Herong Cui
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Xiaofeng Xia
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Rui Zheng
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Yang Sun
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Hongcai Shang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
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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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5
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Matchett KP, Paris J, Teichmann SA, Henderson NC. Spatial genomics: mapping human steatotic liver disease. Nat Rev Gastroenterol Hepatol 2024; 21:646-660. [PMID: 38654090 DOI: 10.1038/s41575-024-00915-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/28/2024] [Indexed: 04/25/2024]
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease) is a leading cause of chronic liver disease worldwide. MASLD can progress to metabolic dysfunction-associated steatohepatitis (MASH, formerly known as non-alcoholic steatohepatitis) with subsequent liver cirrhosis and hepatocellular carcinoma formation. The advent of current technologies such as single-cell and single-nuclei RNA sequencing have transformed our understanding of the liver in homeostasis and disease. The next frontier is contextualizing this single-cell information in its native spatial orientation. This understanding will markedly accelerate discovery science in hepatology, resulting in a further step-change in our knowledge of liver biology and pathobiology. In this Review, we discuss up-to-date knowledge of MASLD development and progression and how the burgeoning field of spatial genomics is driving exciting new developments in our understanding of human liver disease pathogenesis and therapeutic target identification.
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Affiliation(s)
- Kylie P Matchett
- Centre for Inflammation Research, Institute for Regeneration and Repair, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, UK
| | - Jasmin Paris
- Centre for Inflammation Research, Institute for Regeneration and Repair, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, UK
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Cambridge, UK
- Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK
| | - Neil C Henderson
- Centre for Inflammation Research, Institute for Regeneration and Repair, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, UK.
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
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6
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Guo Y, Ren C, He Y, Wu Y, Yang X. Deciphering the spatiotemporal transcriptional landscape of intestinal diseases (Review). Mol Med Rep 2024; 30:157. [PMID: 38994768 PMCID: PMC11258600 DOI: 10.3892/mmr.2024.13281] [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: 11/21/2023] [Accepted: 04/19/2024] [Indexed: 07/13/2024] Open
Abstract
The intestines are the largest barrier organ in the human body. The intestinal barrier plays a crucial role in maintaining the balance of the intestinal environment and protecting the intestines from harmful bacterial invasion. Single‑cell RNA sequencing technology allows the detection of the different cell types in the intestine in two dimensions and the exploration of cell types that have not been fully characterized. The intestinal mucosa is highly complex in structure, and its proper functioning is linked to multiple structures in the proximal‑distal intestinal and luminal‑mucosal axes. Spatial localization is at the core of the efforts to explore the interactions between the complex structures. Spatial transcriptomics (ST) is a method that allows for comprehensive tissue analysis and the acquisition of spatially separated genetic information from individual cells, while preserving their spatial location and interactions. This approach also prevents the loss of fragile cells during tissue disaggregation. The emergence of ST technology allows us to spatially dissect enzymatic processes and interactions between multiple cells, genes, proteins and signals in the intestine. This includes the exchange of oxygen and nutrients in the intestine, different gradients of microbial populations and the role of extracellular matrix proteins. This regionally precise approach to tissue studies is gaining more acceptance and is increasingly applied in the investigation of disease mechanisms related to the gastrointestinal tract. Therefore, this review summarized the application of ST in gastrointestinal diseases.
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Affiliation(s)
- Yajing Guo
- School of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, P.R. China
| | - Chao Ren
- Graduate School, Hunan University of Traditional Chinese Medicine, Changsha, Hunan 410208, P.R. China
| | - Yuxi He
- Department of Digestive Medicine, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing 400021, P.R. China
| | - Yue Wu
- Department of Digestive Medicine, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing 400021, P.R. China
| | - Xiaojun Yang
- Department of Digestive Medicine, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing 400021, P.R. China
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7
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Zhu S, Kubota N, Wang S, Wang T, Xiao G, Hoshida Y. STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics. Nat Commun 2024; 15:7559. [PMID: 39214995 PMCID: PMC11364663 DOI: 10.1038/s41467-024-51728-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 08/14/2024] [Indexed: 09/04/2024] Open
Abstract
In in situ capturing-based spatial transcriptomics, spots of the same size and printed at fixed locations cannot precisely capture the randomly-located single cells, therefore inherently failing to profile transcriptome at the single-cell level. To this end, we present STIE, an Expectation Maximization algorithm that aligns the spatial transcriptome to its matched histology image-based nuclear morphology and recovers missing cells from ~70% gap area, thereby achieving the real single-cell level and whole-slide scale deconvolution, convolution, and clustering for both low- and high-resolution spots. STIE characterizes cell-type-specific gene expression and demonstrates outperforming concordance with true cell-type-specific transcriptomic signatures than the other spot- and subspot-level methods. Furthermore, STIE reveals the single-cell level insights, for instance, lower actual spot resolution than its reported spot size, unbiased evaluation of cell type colocalization, superior power of high-resolution spot in distinguishing nuanced cell types, and spatial cell-cell interactions at the single-cell level other than spot level.
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Affiliation(s)
- Shijia Zhu
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA.
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Naoto Kubota
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Shidan Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Tao Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yujin Hoshida
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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8
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Chang Y, Liu J, Jiang Y, Ma A, Yeo YY, Guo Q, McNutt M, Krull JE, Rodig SJ, Barouch DH, Nolan GP, Xu D, Jiang S, Li Z, Liu B, Ma Q. Graph Fourier transform for spatial omics representation and analyses of complex organs. Nat Commun 2024; 15:7467. [PMID: 39209833 PMCID: PMC11362340 DOI: 10.1038/s41467-024-51590-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 08/08/2024] [Indexed: 09/04/2024] Open
Abstract
Spatial omics technologies decipher functional components of complex organs at cellular and subcellular resolutions. We introduce Spatial Graph Fourier Transform (SpaGFT) and apply graph signal processing to a wide range of spatial omics profiling platforms to generate their interpretable representations. This representation supports spatially variable gene identification and improves gene expression imputation, outperforming existing tools in analyzing human and mouse spatial transcriptomics data. SpaGFT can identify immunological regions for B cell maturation in human lymph nodes Visium data and characterize variations in secondary follicles using in-house human tonsil CODEX data. Furthermore, it can be integrated seamlessly into other machine learning frameworks, enhancing accuracy in spatial domain identification, cell type annotation, and subcellular feature inference by up to 40%. Notably, SpaGFT detects rare subcellular organelles, such as Cajal bodies and Set1/COMPASS complexes, in high-resolution spatial proteomics data. This approach provides an explainable graph representation method for exploring tissue biology and function.
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Affiliation(s)
- Yuzhou Chang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Jixin Liu
- School of Mathematics, Shandong University, 250100, Jinan, China
| | - Yi Jiang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA
- Program in Virology, Division of Medical Sciences, Harvard Medical School, Boston, MA, 20115, USA
| | - Qi Guo
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
| | - Megan McNutt
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
| | - Jordan E Krull
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Scott J Rodig
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, 02115, USA
- Department of Pathology, Brigham & Women's Hospital, Boston, MA, 02115, USA
| | - Dan H Barouch
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, 02139, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Sizun Jiang
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA
- Program in Virology, Division of Medical Sciences, Harvard Medical School, Boston, MA, 20115, USA
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, 02115, USA
| | - Zihai Li
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Bingqiang Liu
- School of Mathematics, Shandong University, 250100, Jinan, China.
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA.
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA.
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9
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Liu X, Peng T, Xu M, Lin S, Hu B, Chu T, Liu B, Xu Y, Ding W, Li L, Cao C, Wu P. Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications. J Hematol Oncol 2024; 17:72. [PMID: 39182134 PMCID: PMC11344930 DOI: 10.1186/s13045-024-01596-9] [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: 05/22/2024] [Accepted: 08/09/2024] [Indexed: 08/27/2024] Open
Abstract
The emergence of spatial multi-omics has helped address the limitations of single-cell sequencing, which often leads to the loss of spatial context among cell populations. Integrated analysis of the genome, transcriptome, proteome, metabolome, and epigenome has enhanced our understanding of cell biology and the molecular basis of human diseases. Moreover, this approach offers profound insights into the interactions between intracellular and intercellular molecular mechanisms involved in the development, physiology, and pathogenesis of human diseases. In this comprehensive review, we examine current advancements in multi-omics technologies, focusing on their evolution and refinement over the past decade, including improvements in throughput and resolution, modality integration, and accuracy. We also discuss the pivotal contributions of spatial multi-omics in revealing spatial heterogeneity, constructing detailed spatial atlases, deciphering spatial crosstalk in tumor immunology, and advancing translational research and cancer therapy through precise spatial mapping.
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Affiliation(s)
- Xiaojie Liu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ting Peng
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Miaochun Xu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shitong Lin
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Bai Hu
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Tian Chu
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Binghan Liu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yashi Xu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wencheng Ding
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Li Li
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Canhui Cao
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Peng Wu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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10
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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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11
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Kumaran G, Carroll L, Muirhead N, Bottomley MJ. How Can Spatial Transcriptomic Profiling Advance Our Understanding of Skin Diseases? J Invest Dermatol 2024:S0022-202X(24)01926-2. [PMID: 39177547 DOI: 10.1016/j.jid.2024.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/23/2024] [Accepted: 07/04/2024] [Indexed: 08/24/2024]
Abstract
Spatial transcriptomic (ST) profiling is the mapping of gene expression within cell populations with preservation of positional context and represents an exciting new approach to develop our understanding of local and regional influences upon skin biology in health and disease. With the ability to probe from a few hundred transcripts to the entire transcriptome, multiple ST approaches are now widely available. In this paper, we review the ST field and discuss its application to dermatology. Its potential to advance our understanding of skin biology in health and disease is highlighted through the illustrative examples of 3 research areas: cutaneous aging, tumorigenesis, and psoriasis.
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Affiliation(s)
- Girishkumar Kumaran
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Liam Carroll
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | | | - Matthew J Bottomley
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
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12
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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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13
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Poovathingal S, Davie K, Borm LE, Vandepoel R, Poulvellarie N, Verfaillie A, Corthout N, Aerts S. Nova-ST: Nano-patterned ultra-dense platform for spatial transcriptomics. CELL REPORTS METHODS 2024; 4:100831. [PMID: 39111312 DOI: 10.1016/j.crmeth.2024.100831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 06/07/2024] [Accepted: 07/11/2024] [Indexed: 08/22/2024]
Abstract
Spatial transcriptomics workflows using barcoded capture arrays are commonly used for resolving gene expression in tissues. However, existing techniques are either limited by capture array density or are cost prohibitive for large-scale atlasing. We present Nova-ST, a dense nano-patterned spatial transcriptomics technique derived from randomly barcoded Illumina sequencing flow cells. Nova-ST enables customized, low-cost, flexible, and high-resolution spatial profiling of large tissue sections. Benchmarking on mouse brain sections demonstrates significantly higher sensitivity compared to existing methods at a reduced cost.
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Affiliation(s)
- Suresh Poovathingal
- VIB Center for AI & Computational Biology (VIB.AI), 3000 Leuven, Belgium; VIB-KU Leuven Center for Brain & Disease Research, CBD Technologies, Single Cell & Microfluidics Expertise Unit, 3000 Leuven, Belgium; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA.
| | - Kristofer Davie
- VIB Center for AI & Computational Biology (VIB.AI), 3000 Leuven, Belgium; VIB-KU Leuven Center for Brain & Disease Research, CBD Technologies, Single Cell Bioinformatics Expertise Unit, 3000 Leuven, Belgium
| | - Lars E Borm
- VIB Center for AI & Computational Biology (VIB.AI), 3000 Leuven, Belgium; VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium; Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium
| | - Roel Vandepoel
- VIB Center for AI & Computational Biology (VIB.AI), 3000 Leuven, Belgium; VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium; Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium
| | - Nicolas Poulvellarie
- VIB-KU Leuven Center for Brain & Disease Research, CBD Technologies, Neuroengineering Expertise Unit, 3000 Leuven, Belgium
| | | | - Nikky Corthout
- Bio Imaging Core, VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium
| | - Stein Aerts
- VIB Center for AI & Computational Biology (VIB.AI), 3000 Leuven, Belgium; VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA; Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium.
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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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Zhang D, Rodríguez-Kirby LAR, Lin Y, Song M, Wang L, Wang L, Kanatani S, Jimenez-Beristain T, Dang Y, Zhong M, Kukanja P, Wang S, Chen XL, Gao F, Wang D, Xu H, Lou X, Liu Y, Chen J, Sestan N, Uhlén P, Kriegstein A, Zhao H, Castelo-Branco G, Fan R. Spatial dynamics of mammalian brain development and neuroinflammation by multimodal tri-omics mapping. RESEARCH SQUARE 2024:rs.3.rs-4814866. [PMID: 39184075 PMCID: PMC11343178 DOI: 10.21203/rs.3.rs-4814866/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
The ability to spatially map multiple layers of the omics information over different time points allows for exploring the mechanisms driving brain development, differentiation, arealization, and alterations in disease. Herein we developed and applied spatial tri-omic sequencing technologies, DBiT ARP-seq (spatial ATAC-RNA-Protein-seq) and DBiT CTRP-seq (spatial CUT&Tag-RNA-Protein-seq) together with multiplexed immunofluorescence imaging (CODEX) to map spatial dynamic remodeling in brain development and neuroinflammation. A spatiotemporal tri-omic atlas of the mouse brain was obtained at different stages from postnatal day P0 to P21, and compared to the regions of interest in the human developing brains. Specifically, in the cortical area, we discovered temporal persistence and spatial spreading of chromatin accessibility for the layer-defining transcription factors. In corpus callosum, we observed dynamic chromatin priming of myelin genes across the subregions. Together, it suggests a role for layer specific projection neurons to coordinate axonogenesis and myelination. We further mapped the brain of a lysolecithin (LPC) neuroinflammation mouse model and observed common molecular programs in development and neuroinflammation. Microglia, exhibiting both conserved and distinct programs for inflammation and resolution, are transiently activated not only at the core of the LPC lesion, but also at distal locations presumably through neuronal circuitry. Thus, this work unveiled common and differential mechanisms in brain development and neuroinflammation, resulting in a valuable data resource to investigate brain development, function and disease.
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Affiliation(s)
- Di Zhang
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
- These authors contributed equally
| | - Leslie A Rubio Rodríguez-Kirby
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
- These authors contributed equally
| | - Yingxin Lin
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - Mengyi Song
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco; San Francisco, CA 94143, USA
- Department of Neurology, University of California San Francisco; San Francisco, CA 94143, USA
| | - Li Wang
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco; San Francisco, CA 94143, USA
- Department of Neurology, University of California San Francisco; San Francisco, CA 94143, USA
| | - Lijun Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - Shigeaki Kanatani
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Tony Jimenez-Beristain
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Yonglong Dang
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Mei Zhong
- Yale Stem Cell Center and Department of Cell Biology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Petra Kukanja
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Shaohui Wang
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco; San Francisco, CA 94143, USA
- Department of Neurology, University of California San Francisco; San Francisco, CA 94143, USA
| | - Xinyi Lisa Chen
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - Fu Gao
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Dejiang Wang
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Hang Xu
- Binformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Xing Lou
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Yang Liu
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Jinmiao Chen
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Binformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Nenad Sestan
- Department of Neuroscience, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Per Uhlén
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Arnold Kriegstein
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco; San Francisco, CA 94143, USA
- Department of Neurology, University of California San Francisco; San Francisco, CA 94143, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Gonçalo Castelo-Branco
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Human and Translational Immunology Program, Yale School of Medicine, New Haven, CT 06520, USA
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16
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Abdulraouf A, Jiang W, Xu Z, Zhang Z, Isakov S, Raihan T, Zhou W, Cao J. Optics-free Spatial Genomics for Mapping Mouse Brain Aging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.06.606712. [PMID: 39149282 PMCID: PMC11326199 DOI: 10.1101/2024.08.06.606712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Spatial transcriptomics has revolutionized our understanding of cellular network dynamics in aging and disease by enabling the mapping of molecular and cellular organization across various anatomical locations. Despite these advances, current methods face significant challenges in throughput and cost, limiting their utility for comprehensive studies. To address these limitations, we introduce IRISeq (Imaging Reconstruction using Indexed Sequencing), a optics-free spatial transcriptomics platform that eliminates the need for predefined capture arrays or extensive imaging, allowing for the rapid and cost-effective processing of multiple tissue sections simultaneously. Its capacity to reconstruct images based solely on sequencing local DNA interactions allows for profiling of tissues without size constraints and across varied resolutions. Applying IRISeq, we examined gene expression and cellular dynamics in thirty brain regions of both adult and aged mice, uncovering region-specific changes in gene expression associated with aging. Further cell type-centric analysis further identified age-related cell subtypes and intricate changes in cell interactions that are distinct to certain spatial niches, emphasizing the unique aspects of aging in different brain regions. The affordability and simplicity of IRISeq position it as a versatile tool for mapping region-specific gene expression and cellular interactions across various biological systems.
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Affiliation(s)
- Abdulraouf Abdulraouf
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- The Tri-Institutional M.D-Ph.D Program, New York, NY, USA
- The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
- These authors contributed equally: Abdulraouf Abdulraouf, Weirong Jiang
| | - Weirong Jiang
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- These authors contributed equally: Abdulraouf Abdulraouf, Weirong Jiang
| | - Zihan Xu
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
| | - Zehao Zhang
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
| | - Samuel Isakov
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
| | - Tanvir Raihan
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
| | - Wei Zhou
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- Senior author
| | - Junyue Cao
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- Senior author
- Lead Contact
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17
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Hu C, Borji M, Marrero GJ, Kumar V, Weir JA, Kammula SV, Macosko EZ, Chen F. Scalable imaging-free spatial genomics through computational reconstruction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.05.606465. [PMID: 39149311 PMCID: PMC11326198 DOI: 10.1101/2024.08.05.606465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Tissue organization arises from the coordinated molecular programs of cells. Spatial genomics maps cells and their molecular programs within the spatial context of tissues. However, current methods measure spatial information through imaging or direct registration, which often require specialized equipment and are limited in scale. Here, we developed an imaging-free spatial transcriptomics method that uses molecular diffusion patterns to computationally reconstruct spatial data. To do so, we utilize a simple experimental protocol on two dimensional barcode arrays to establish an interaction network between barcodes via molecular diffusion. Sequencing these interactions generates a high dimensional matrix of interactions between different spatial barcodes. Then, we perform dimensionality reduction to regenerate a two-dimensional manifold, which represents the spatial locations of the barcode arrays. Surprisingly, we found that the UMAP algorithm, with minimal modifications can faithfully successfully reconstruct the arrays. We demonstrated that this method is compatible with capture array based spatial transcriptomics/genomics methods, Slide-seq and Slide-tags, with high fidelity. We systematically explore the fidelity of the reconstruction through comparisons with experimentally derived ground truth data, and demonstrate that reconstruction generates high quality spatial genomics data. We also scaled this technique to reconstruct high-resolution spatial information over areas up to 1.2 centimeters. This computational reconstruction method effectively converts spatial genomics measurements to molecular biology, enabling spatial transcriptomics with high accessibility, and scalability.
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Affiliation(s)
- Chenlei Hu
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Mehdi Borji
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Vipin Kumar
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jackson A Weir
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Biological and Biomedical Sciences Program, Harvard University, Cambridge, MA, USA
| | | | - Evan Z Macosko
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Fei Chen
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
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18
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Li B, Bao F, Hou Y, Li F, Li H, Deng Y, Dai Q. Tissue characterization at an enhanced resolution across spatial omics platforms with deep generative model. Nat Commun 2024; 15:6541. [PMID: 39095360 PMCID: PMC11297205 DOI: 10.1038/s41467-024-50837-5] [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: 12/09/2023] [Accepted: 07/22/2024] [Indexed: 08/04/2024] Open
Abstract
Recent advances in spatial omics have expanded the spectrum of profiled molecular categories beyond transcriptomics. However, many of these technologies are constrained by limited spatial resolution, hindering our ability to deeply characterize intricate tissue architectures. Existing computational methods primarily focus on the resolution enhancement of transcriptomics data, lacking the adaptability to address the emerging spatial omics technologies that profile various omics types. Here, we introduce soScope, a unified generative framework designed to enhance data quality and spatial resolution for molecular profiles obtained from diverse spatial technologies. soScope aggregates multimodal tissue information from omics, spatial relations and images, and jointly infers omics profiles at enhanced resolutions with omics-specific modeling through distribution priors. With comprehensive evaluations on diverse spatial omics platforms, including Visium, Xenium, spatial-CUT&Tag, and slide-DNA/RNA-seq, soScope improves performances in identifying biologically meaningful intestine and kidney architectures, revealing embryonic heart structure that cannot be resolved at the original resolution and correcting sample and technical biases arising from sequencing and sample processing. Furthermore, soScope extends to spatial multiomics technology spatial-CITE-seq and spatial ATAC-RNA-seq, leveraging cross-omics reference for simultaneous multiomics enhancement. soScope provides a versatile tool to improve the utilization of continually expanding spatial omics technologies and resources.
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Affiliation(s)
- Bohan Li
- School of Artificial Intelligence, Beihang University, Beijing, China
| | - Feng Bao
- Beijing National Research Center for Information Science and Technology (BNRist), Beijing, China
- Institute for Brain and Cognitive Science, Tsinghua University (THUIBCS), Beijing, China
- Department of Automation, Tsinghua University, Beijing, China
| | - Yimin Hou
- School of Artificial Intelligence, Beihang University, Beijing, China
| | - Fengji Li
- School of Artificial Intelligence, Beihang University, Beijing, China
| | - Hongjue Li
- School of Artificial Intelligence, Beihang University, Beijing, China
| | - Yue Deng
- School of Artificial Intelligence, Beihang University, Beijing, China.
| | - Qionghai Dai
- Beijing National Research Center for Information Science and Technology (BNRist), Beijing, China.
- Institute for Brain and Cognitive Science, Tsinghua University (THUIBCS), Beijing, China.
- Department of Automation, Tsinghua University, Beijing, China.
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19
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Dezem FS, Arjumand W, DuBose H, Morosini NS, Plummer J. Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives. Annu Rev Biomed Data Sci 2024; 7:131-153. [PMID: 38768396 DOI: 10.1146/annurev-biodatasci-102523-103640] [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: 05/22/2024]
Abstract
Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative to the organization of the molecular microenvironment of tissue samples in normal and disease states. Spatial omics can be categorized into three major modalities: (a) next-generation sequencing-based assays, (b) imaging-based spatially resolved transcriptomics approaches including in situ hybridization/in situ sequencing, and (c) imaging-based spatial proteomics. These modalities allow assessment of transcripts and proteins at a cellular level, generating large and computationally challenging datasets. The lack of standardized computational pipelines to analyze and integrate these nonuniform structured data has made it necessary to apply artificial intelligence and machine learning strategies to best visualize and translate their complexity. In this review, we summarize the currently available techniques and computational strategies, highlight their advantages and limitations, and discuss their future prospects in the scientific field.
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Affiliation(s)
- Felipe Segato Dezem
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Wani Arjumand
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Hannah DuBose
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Natalia Silva Morosini
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Jasmine Plummer
- Department of Cellular and Molecular Biology and Comprehensive Cancer Center, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
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20
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Tian T, Zhang J, Lin X, Wei Z, Hakonarson H. Dependency-aware deep generative models for multitasking analysis of spatial omics data. Nat Methods 2024; 21:1501-1513. [PMID: 38783067 DOI: 10.1038/s41592-024-02257-y] [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: 01/02/2023] [Accepted: 03/25/2024] [Indexed: 05/25/2024]
Abstract
Spatially resolved transcriptomics (SRT) technologies have significantly advanced biomedical research, but their data analysis remains challenging due to the discrete nature of the data and the high levels of noise, compounded by complex spatial dependencies. Here, we propose spaVAE, a dependency-aware, deep generative spatial variational autoencoder model that probabilistically characterizes count data while capturing spatial correlations. spaVAE introduces a hybrid embedding combining a Gaussian process prior with a Gaussian prior to explicitly capture spatial correlations among spots. It then optimizes the parameters of deep neural networks to approximate the distributions underlying the SRT data. With the approximated distributions, spaVAE can contribute to several analytical tasks that are essential for SRT data analysis, including dimensionality reduction, visualization, clustering, batch integration, denoising, differential expression, spatial interpolation, resolution enhancement and identification of spatially variable genes. Moreover, we have extended spaVAE to spaPeakVAE and spaMultiVAE to characterize spatial ATAC-seq (assay for transposase-accessible chromatin using sequencing) data and spatial multi-omics data, respectively.
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Affiliation(s)
- Tian Tian
- School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, Hubei, China
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jie Zhang
- National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China
| | - Xiang Lin
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA
| | - Zhi Wei
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.
| | - Hakon Hakonarson
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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21
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Franzén L, Olsson Lindvall M, Hühn M, Ptasinski V, Setyo L, Keith BP, Collin A, Oag S, Volckaert T, Borde A, Lundeberg J, Lindgren J, Belfield G, Jackson S, Ollerstam A, Stamou M, Ståhl PL, Hornberg JJ. Mapping spatially resolved transcriptomes in human and mouse pulmonary fibrosis. Nat Genet 2024; 56:1725-1736. [PMID: 38951642 PMCID: PMC11319205 DOI: 10.1038/s41588-024-01819-2] [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: 12/20/2023] [Accepted: 05/30/2024] [Indexed: 07/03/2024]
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with poor prognosis and limited treatment options. Efforts to identify effective treatments are thwarted by limited understanding of IPF pathogenesis and poor translatability of available preclinical models. Here we generated spatially resolved transcriptome maps of human IPF (n = 4) and bleomycin-induced mouse pulmonary fibrosis (n = 6) to address these limitations. We uncovered distinct fibrotic niches in the IPF lung, characterized by aberrant alveolar epithelial cells in a microenvironment dominated by transforming growth factor beta signaling alongside predicted regulators, such as TP53 and APOE. We also identified a clear divergence between the arrested alveolar regeneration in the IPF fibrotic niches and the active tissue repair in the acutely fibrotic mouse lung. Our study offers in-depth insights into the IPF transcriptional landscape and proposes alveolar regeneration as a promising therapeutic strategy for IPF.
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Affiliation(s)
- Lovisa Franzén
- Safety Sciences, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden
| | - Martina Olsson Lindvall
- Safety Sciences, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Michael Hühn
- Translational Science and Experimental Medicine, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Victoria Ptasinski
- Safety Sciences, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Laura Setyo
- Pathology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Benjamin P Keith
- Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Astrid Collin
- Animal Science and Technology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Steven Oag
- Animal Science and Technology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Thomas Volckaert
- Bioscience In Vivo, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Annika Borde
- Bioscience In Vivo, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Joakim Lundeberg
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden
| | - Julia Lindgren
- Translational Genomics, Centre for Genomics Research, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Graham Belfield
- Translational Genomics, Centre for Genomics Research, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Sonya Jackson
- Late-Stage Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Anna Ollerstam
- Safety Sciences, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Marianna Stamou
- Safety Sciences, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden.
| | - Patrik L Ståhl
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden.
| | - Jorrit J Hornberg
- Safety Sciences, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
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22
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Yu Q, Tian R, Jin X, Wu L. DAIS: a method for identifying spatial domains based on density clustering of spatial omics data. J Genet Genomics 2024; 51:884-887. [PMID: 38599516 DOI: 10.1016/j.jgg.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 03/17/2024] [Accepted: 04/02/2024] [Indexed: 04/12/2024]
Affiliation(s)
- Qichao Yu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI Research, Shenzhen, Guangdong 518083, China; BGI Research, Chongqing 401329, China
| | - Ru Tian
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI Research, Shenzhen, Guangdong 518083, China; BGI Research, Chongqing 401329, China
| | - Xin Jin
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI Research, Shenzhen, Guangdong 518083, China.
| | - Liang Wu
- BGI Research, Shenzhen, Guangdong 518083, China; BGI Research, Chongqing 401329, China.
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23
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Zhang D, Rubio Rodríguez-Kirby LA, Lin Y, Song M, Wang L, Wang L, Kanatani S, Jimenez-Beristain T, Dang Y, Zhong M, Kukanja P, Wang S, Chen XL, Gao F, Wang D, Xu H, Lou X, Liu Y, Chen J, Sestan N, Uhlén P, Kriegstein A, Zhao H, Castelo-Branco G, Fan R. Spatial dynamics of mammalian brain development and neuroinflammation by multimodal tri-omics mapping. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.28.605493. [PMID: 39091821 PMCID: PMC11291146 DOI: 10.1101/2024.07.28.605493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
The ability to spatially map multiple layers of the omics information over different time points allows for exploring the mechanisms driving brain development, differentiation, arealization, and alterations in disease. Herein we developed and applied spatial tri-omic sequencing technologies, DBiT ARP-seq (spatial ATAC-RNA-Protein-seq) and DBiT CTRP-seq (spatial CUT&Tag-RNA-Protein-seq) together with multiplexed immunofluorescence imaging (CODEX) to map spatial dynamic remodeling in brain development and neuroinflammation. A spatiotemporal tri-omic atlas of the mouse brain was obtained at different stages from postnatal day P0 to P21, and compared to the regions of interest in the human developing brains. Specifically, in the cortical area, we discovered temporal persistence and spatial spreading of chromatin accessibility for the layer-defining transcription factors. In corpus callosum, we observed dynamic chromatin priming of myelin genes across the subregions. Together, it suggests a role for layer specific projection neurons to coordinate axonogenesis and myelination. We further mapped the brain of a lysolecithin (LPC) neuroinflammation mouse model and observed common molecular programs in development and neuroinflammation. Microglia, exhibiting both conserved and distinct programs for inflammation and resolution, are transiently activated not only at the core of the LPC lesion, but also at distal locations presumably through neuronal circuitry. Thus, this work unveiled common and differential mechanisms in brain development and neuroinflammation, resulting in a valuable data resource to investigate brain development, function and disease.
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Affiliation(s)
- Di Zhang
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
- These authors contributed equally
| | - Leslie A Rubio Rodríguez-Kirby
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
- These authors contributed equally
| | - Yingxin Lin
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - Mengyi Song
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco; San Francisco, CA 94143, USA
- Department of Neurology, University of California San Francisco; San Francisco, CA 94143, USA
| | - Li Wang
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco; San Francisco, CA 94143, USA
- Department of Neurology, University of California San Francisco; San Francisco, CA 94143, USA
| | - Lijun Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - Shigeaki Kanatani
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Tony Jimenez-Beristain
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Yonglong Dang
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Mei Zhong
- Yale Stem Cell Center and Department of Cell Biology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Petra Kukanja
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Shaohui Wang
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco; San Francisco, CA 94143, USA
- Department of Neurology, University of California San Francisco; San Francisco, CA 94143, USA
| | - Xinyi Lisa Chen
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - Fu Gao
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Dejiang Wang
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Hang Xu
- Binformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Xing Lou
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Yang Liu
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Jinmiao Chen
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Binformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Nenad Sestan
- Department of Neuroscience, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Per Uhlén
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Arnold Kriegstein
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco; San Francisco, CA 94143, USA
- Department of Neurology, University of California San Francisco; San Francisco, CA 94143, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Gonçalo Castelo-Branco
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Human and Translational Immunology Program, Yale School of Medicine, New Haven, CT 06520, USA
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24
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Schott M, León-Periñán D, Splendiani E, Strenger L, Licha JR, Pentimalli TM, Schallenberg S, Alles J, Samut Tagliaferro S, Boltengagen A, Ehrig S, Abbiati S, Dommerich S, Pagani M, Ferretti E, Macino G, Karaiskos N, Rajewsky N. Open-ST: High-resolution spatial transcriptomics in 3D. Cell 2024; 187:3953-3972.e26. [PMID: 38917789 DOI: 10.1016/j.cell.2024.05.055] [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: 01/04/2024] [Revised: 04/05/2024] [Accepted: 05/30/2024] [Indexed: 06/27/2024]
Abstract
Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.
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Affiliation(s)
- Marie Schott
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Daniel León-Periñán
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Elena Splendiani
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany; Department of Experimental Medicine, Sapienza University, Rome, Italy
| | - Leon Strenger
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Jan Robin Licha
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Tancredi Massimo Pentimalli
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Simon Schallenberg
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität Berlin, 10117 Berlin, Germany
| | - Jonathan Alles
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Sarah Samut Tagliaferro
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Anastasiya Boltengagen
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Sebastian Ehrig
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany
| | - Stefano Abbiati
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany; IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Steffen Dommerich
- Department of Otorhinolaryngology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, Berlin 13353, Germany
| | - Massimiliano Pagani
- IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy; Department of Medical Biotechnology and Translational Medicine, Università degli Studi, Milan, Italy
| | | | - Giuseppe Macino
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany; Department of Cellular Biotechnologies and Hematology, La Sapienza University of Rome, 00161 Rome, Italy.
| | - Nikos Karaiskos
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany.
| | - Nikolaus Rajewsky
- Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology (BIMSB), Max-Delbrück-Centrum for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str. 28, 10115 Berlin, Germany; Charité - Universitätsmedizin, Charitéplatz 1, 10117 Berlin, Germany; German Center for Cardiovascular Research (DZHK), Site Berlin, Berlin, Germany; NeuroCure Cluster of Excellence, Berlin, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany; National Center for Tumor Diseases (NCT), Site Berlin, Berlin, Germany.
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25
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Liu X, Shi J, Jiao Y, An J, Tian J, Yang Y, Zhuo L. Integrated multi-omics with machine learning to uncover the intricacies of kidney disease. Brief Bioinform 2024; 25:bbae364. [PMID: 39082652 PMCID: PMC11289682 DOI: 10.1093/bib/bbae364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/20/2024] [Accepted: 07/17/2024] [Indexed: 08/03/2024] Open
Abstract
The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.
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Affiliation(s)
| | | | | | | | | | | | - Li Zhuo
- Corresponding author. Department of Nephrology, China-Japan Friendship Hospital, Beijing 100029, China; China-Japan Friendship Clinic Medical College, Beijing University of Chinese Medicine, 100029 Beijing, China. E-mail:
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26
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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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27
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Yan C, Zhu Y, Chen M, Yang K, Cui F, Zou Q, Zhang Z. Integration tools for scRNA-seq data and spatial transcriptomics sequencing data. Brief Funct Genomics 2024; 23:295-302. [PMID: 38267084 DOI: 10.1093/bfgp/elae002] [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: 08/02/2023] [Revised: 09/26/2023] [Accepted: 01/03/2024] [Indexed: 01/26/2024] Open
Abstract
Numerous methods have been developed to integrate spatial transcriptomics sequencing data with single-cell RNA sequencing (scRNA-seq) data. Continuous development and improvement of these methods offer multiple options for integrating and analyzing scRNA-seq and spatial transcriptomics data based on diverse research inquiries. However, each method has its own advantages, limitations and scope of application. Researchers need to select the most suitable method for their research purposes based on the actual situation. This review article presents a compilation of 19 integration methods sourced from a wide range of available approaches, serving as a comprehensive reference for researchers to select the suitable integration method for their specific research inquiries. By understanding the principles of these methods, we can identify their similarities and differences, comprehend their applicability and potential complementarity, and lay the foundation for future method development and understanding. This review article presents 19 methods that aim to integrate scRNA-seq data and spatial transcriptomics data. The methods are classified into two main groups and described accordingly. The article also emphasizes the incorporation of High Variance Genes in annotating various technologies, aiming to obtain biologically relevant information aligned with the intended purpose.
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Affiliation(s)
- Chaorui Yan
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Yanxu Zhu
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Miao Chen
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Kainan Yang
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Feifei Cui
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Zilong Zhang
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
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28
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Serrano K, Tedeschi F, Andersen SU, Scheller HV. Unraveling plant-microbe symbioses using single-cell and spatial transcriptomics. TRENDS IN PLANT SCIENCE 2024:S1360-1385(24)00152-3. [PMID: 38991926 DOI: 10.1016/j.tplants.2024.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 06/12/2024] [Accepted: 06/19/2024] [Indexed: 07/13/2024]
Abstract
Plant-microbe symbioses require intense interaction and genetic coordination to successfully establish in specific cell types of the host and symbiont. Traditional RNA-seq methodologies lack the cellular resolution to fully capture these complexities, but single-cell and spatial transcriptomics (ST) are now allowing scientists to probe symbiotic interactions at an unprecedented level of detail. Here, we discuss the advantages that novel spatial and single-cell transcriptomic technologies provide in studying plant-microbe endosymbioses and highlight key recent studies. Finally, we consider the remaining limitations of applying these approaches to symbiosis research, which are mainly related to the simultaneous capture of both plant and microbial transcripts within the same cells.
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Affiliation(s)
- Karen Serrano
- Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA; DOE Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
| | - Francesca Tedeschi
- Department of Molecular Biology and Genetics, Aarhus University, Universitetsbyen 81, DK-8000 Aarhus C, Denmark
| | - Stig U Andersen
- Department of Molecular Biology and Genetics, Aarhus University, Universitetsbyen 81, DK-8000 Aarhus C, Denmark.
| | - Henrik V Scheller
- Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA; DOE Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA.
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29
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Kwon Y, Woo J, Yu F, Williams SM, Markillie LM, Moore RJ, Nakayasu ES, Chen J, Campbell-Thompson M, Mathews CE, Nesvizhskii AI, Qian WJ, Zhu Y. Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583367. [PMID: 38496682 PMCID: PMC10942300 DOI: 10.1101/2024.03.04.583367] [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
Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ~3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.
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Affiliation(s)
- Yumi Kwon
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Jongmin Woo
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, United States
| | - Sarah M. Williams
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Lye Meng Markillie
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ronald J. Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ernesto S. Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Jing Chen
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Martha Campbell-Thompson
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Clayton E. Mathews
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Alexey I. Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ying Zhu
- Department of Proteomic and Genomic Technologies, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States
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30
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Wang L, Xu W, Zhang S, Gundberg GC, Zheng CR, Wan Z, Mustafina K, Caliendo F, Sandt H, Kamm R, Weiss R. Sensing and guiding cell-state transitions by using genetically encoded endoribonuclease-mediated microRNA sensors. Nat Biomed Eng 2024:10.1038/s41551-024-01229-z. [PMID: 38982158 DOI: 10.1038/s41551-024-01229-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 05/11/2024] [Indexed: 07/11/2024]
Abstract
Precisely sensing and guiding cell-state transitions via the conditional genetic activation of appropriate differentiation factors is challenging. Here we show that desired cell-state transitions can be guided via genetically encoded sensors, whereby endogenous cell-state-specific miRNAs regulate the translation of a constitutively transcribed endoribonuclease, which, in turn, controls the translation of a gene of interest. We used this approach to monitor several cell-state transitions, to enrich specific cell types and to automatically guide the multistep differentiation of human induced pluripotent stem cells towards a haematopoietic lineage via endothelial cells as an intermediate state. Such conditional activation of gene expression is durable and resistant to epigenetic silencing and could facilitate the monitoring of cell-state transitions in physiological and pathological conditions and eventually the 'rewiring' of cell-state transitions for applications in organoid-based disease modelling, cellular therapies and regenerative medicine.
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Affiliation(s)
- Lei Wang
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Bioengineering, Northeastern University, Boston, MA, USA.
- Department of Biology, Northeastern University, Boston, MA, USA.
| | - Wenlong Xu
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shun Zhang
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
- State Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Gregory C Gundberg
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Christine R Zheng
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Zhengpeng Wan
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kamila Mustafina
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Fabio Caliendo
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hayden Sandt
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Roger Kamm
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ron Weiss
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
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31
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Wen Y, Yang H, Hong Y. Transcriptomic Approaches to Cardiomyocyte-Biomaterial Interactions: A Review. ACS Biomater Sci Eng 2024; 10:4175-4194. [PMID: 38934720 DOI: 10.1021/acsbiomaterials.4c00303] [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: 06/28/2024]
Abstract
Biomaterials, essential for supporting, enhancing, and repairing damaged tissues, play a critical role in various medical applications. This Review focuses on the interaction of biomaterials and cardiomyocytes, emphasizing the unique significance of transcriptomic approaches in understanding their interactions, which are pivotal in cardiac bioengineering and regenerative medicine. Transcriptomic approaches serve as powerful tools to investigate how cardiomyocytes respond to biomaterials, shedding light on the gene expression patterns, regulatory pathways, and cellular processes involved in these interactions. Emerging technologies such as bulk RNA-seq, single-cell RNA-seq, single-nucleus RNA-seq, and spatial transcriptomics offer promising avenues for more precise and in-depth investigations. Longitudinal studies, pathway analyses, and machine learning techniques further improve the ability to explore the complex regulatory mechanisms involved. This review also discusses the challenges and opportunities of utilizing transcriptomic techniques in cardiomyocyte-biomaterial research. Although there are ongoing challenges such as costs, cell size limitation, sample differences, and complex analytical process, there exist exciting prospects in comprehensive gene expression analyses, biomaterial design, cardiac disease treatment, and drug testing. These multimodal methodologies have the capacity to deepen our understanding of the intricate interaction network between cardiomyocytes and biomaterials, potentially revolutionizing cardiac research with the aim of promoting heart health, and they are also promising for studying interactions between biomaterials and other cell types.
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Affiliation(s)
- Yufeng Wen
- Department of Bioengineering, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Huaxiao Yang
- Department of Biomedical Engineering, University of North Texas, Denton, Texas 76207, United States
| | - Yi Hong
- Department of Bioengineering, University of Texas at Arlington, Arlington, Texas 76019, United States
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32
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Zhang J, Xia W, Zhou J, Qin S, Lin L, Zhao T, Wang H, Mi C, Hu Y, Chen Z, Zhu T, Yang X, Zhang T, Xia G, Ke Y, Wang C. Participation of preovulatory follicles in the activation of primordial follicles in mouse ovaries. Int J Biol Sci 2024; 20:3863-3880. [PMID: 39113716 PMCID: PMC11302884 DOI: 10.7150/ijbs.95020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 06/29/2024] [Indexed: 08/10/2024] Open
Abstract
The mechanisms behind the selection and initial recruitment of primordial follicles (PmFs) from the non-growing PmF pool during each estrous cycle in females remain largely unknown. This study demonstrates that PmFs closest to the ovulatory follicle are preferentially activated in mouse ovaries under physiological conditions. PmFs located within 40 μm of the ovulatory follicles were more likely to be activated compared to those situated further away during the peri-ovulation period. Repeated superovulation treatments accelerated the depletion of the PmF reserve, whereas continuous suppression of ovulation delayed PmF reserve consumption. Spatial transcriptome sequencing of peri-ovulatory follicles revealed that ovulation primarily induces the degradation and remodeling of the extracellular matrix (ECM). This ECM degradation reduces mechanical stress around PmFs, thereby triggering their activation. Specifically, Cathepsin L (CTSL), a cysteine proteinase and lysosomal enzyme involved in ECM degradation, initiates the activation of PmFs adjacent to ovulatory follicles in a distance-dependent manner. These findings highlight the link between ovulation and selective PmF activation, and underscore the role of CTSL in this process under physiological conditions.
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Affiliation(s)
- Jingwen Zhang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Wenzhe Xia
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Jiaqi Zhou
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
- School of Life Sciences and Medicine, Shandong University of Technology, Shandong 255049, China
| | - Shaogang Qin
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Lin Lin
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Ting Zhao
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Huarong Wang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
- Department of Obstetrics and Gynecology, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510000, China
| | - Chen Mi
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Yifan Hu
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Zixuan Chen
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
- Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Tianhua Zhu
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Xinyu Yang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Tuo Zhang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
- Transformation Engineering Research Center of Chronic Disease Diagnosis and Treatment, Department of Physiology, College of Basic Medicine, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Guoliang Xia
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Yuwen Ke
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Chao Wang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
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33
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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 2024:10.1038/s41592-024-02325-3. [PMID: 38965443 DOI: 10.1038/s41592-024-02325-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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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34
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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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35
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Wang C, Qiu J, Liu M, Wang Y, Yu Y, Liu H, Zhang Y, Han L. Microfluidic Biochips for Single-Cell Isolation and Single-Cell Analysis of Multiomics and Exosomes. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2401263. [PMID: 38767182 PMCID: PMC11267386 DOI: 10.1002/advs.202401263] [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: 02/02/2024] [Revised: 04/26/2024] [Indexed: 05/22/2024]
Abstract
Single-cell multiomic and exosome analyses are potent tools in various fields, such as cancer research, immunology, neuroscience, microbiology, and drug development. They facilitate the in-depth exploration of biological systems, providing insights into disease mechanisms and aiding in treatment. Single-cell isolation, which is crucial for single-cell analysis, ensures reliable cell isolation and quality control for further downstream analyses. Microfluidic chips are small lightweight systems that facilitate efficient and high-throughput single-cell isolation and real-time single-cell analysis on- or off-chip. Therefore, most current single-cell isolation and analysis technologies are based on the single-cell microfluidic technology. This review offers comprehensive guidance to researchers across different fields on the selection of appropriate microfluidic chip technologies for single-cell isolation and analysis. This review describes the design principles, separation mechanisms, chip characteristics, and cellular effects of various microfluidic chips available for single-cell isolation. Moreover, this review highlights the implications of using this technology for subsequent analyses, including single-cell multiomic and exosome analyses. Finally, the current challenges and future prospects of microfluidic chip technology are outlined for multiplex single-cell isolation and multiomic and exosome analyses.
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Affiliation(s)
- Chao Wang
- Institute of Marine Science and TechnologyShandong UniversityQingdao266237China
| | - Jiaoyan Qiu
- Institute of Marine Science and TechnologyShandong UniversityQingdao266237China
| | - Mengqi Liu
- Institute of Marine Science and TechnologyShandong UniversityQingdao266237China
| | - Yihe Wang
- Institute of Marine Science and TechnologyShandong UniversityQingdao266237China
| | - Yang Yu
- Department of PeriodontologySchool and Hospital of StomatologyCheeloo College of MedicineShandong UniversityJinan250100China
| | - Hong Liu
- State Key Laboratory of Crystal MaterialsShandong UniversityJinan250100China
| | - Yu Zhang
- Institute of Marine Science and TechnologyShandong UniversityQingdao266237China
| | - Lin Han
- Institute of Marine Science and TechnologyShandong UniversityQingdao266237China
- Shandong Engineering Research Center of Biomarker and Artificial Intelligence ApplicationJinan250100China
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36
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Sun K, Liu X, Lan X. A single-cell atlas of chromatin accessibility in mouse organogenesis. Nat Cell Biol 2024; 26:1200-1211. [PMID: 38977846 DOI: 10.1038/s41556-024-01435-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 04/29/2024] [Indexed: 07/10/2024]
Abstract
Organogenesis is a highly complex and precisely regulated process. Here we profiled the chromatin accessibility in >350,000 cells derived from 13 mouse embryos at four developmental stages from embryonic day (E) 10.5 to E13.5 by SPATAC-seq in a single experiment. The resulting atlas revealed the status of 830,873 candidate cis-regulatory elements in 43 major cell types. By integrating the chromatin accessibility atlas with the previous transcriptomic dataset, we characterized cis-regulatory sequences and transcription factors associated with cell fate commitment, such as Nr5a2 in the development of gastrointestinal tract, which was preliminarily supported by the in vivo experiment in zebrafish. Finally, we integrated this atlas with the previous single-cell chromatin accessibility dataset from 13 adult mouse tissues to delineate the developmental stage-specific gene regulatory programmes within and across different cell types and identify potential molecular switches throughout lineage development. This comprehensive dataset provides a foundation for exploring transcriptional regulation in organogenesis.
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Affiliation(s)
- Keyong Sun
- School of Medicine, Tsinghua University, Beijing, China
- Peking-Tsinghua-NIBS Joint Graduate Program, Tsinghua University, Beijing, China
- Tsinghua-Peking Center for Life Sciences, Beijing, China
| | - Xin Liu
- Tsinghua-Peking Center for Life Sciences, Beijing, China
- School of Life Sciences, Tsinghua University, Beijing, China
| | - Xun Lan
- School of Medicine, Tsinghua University, Beijing, China.
- Peking-Tsinghua-NIBS Joint Graduate Program, Tsinghua University, Beijing, China.
- Tsinghua-Peking Center for Life Sciences, Beijing, China.
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China.
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37
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Liu Y, Sundah NR, Ho NRY, Shen WX, Xu Y, Natalia A, Yu Z, Seet JE, Chan CW, Loh TP, Lim BY, Shao H. Bidirectional linkage of DNA barcodes for the multiplexed mapping of higher-order protein interactions in cells. Nat Biomed Eng 2024; 8:909-923. [PMID: 38898172 DOI: 10.1038/s41551-024-01225-3] [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: 01/28/2023] [Accepted: 05/05/2024] [Indexed: 06/21/2024]
Abstract
Capturing the full complexity of the diverse hierarchical interactions in the protein interactome is challenging. Here we report a DNA-barcoding method for the multiplexed mapping of pairwise and higher-order protein interactions and their dynamics within cells. The method leverages antibodies conjugated with barcoded DNA strands that can bidirectionally hybridize and covalently link to linearize closely spaced interactions within individual 3D protein complexes, encoding and decoding the protein constituents and the interactions among them. By mapping protein interactions in cancer cells and normal cells, we found that tumour cells exhibit a larger diversity and abundance of protein complexes with higher-order interactions. In biopsies of human breast-cancer tissue, the method accurately identified the cancer subtype and revealed that higher-order protein interactions are associated with cancer aggressiveness.
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Affiliation(s)
- Yu Liu
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Noah R Sundah
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Nicholas R Y Ho
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
| | - Wan Xiang Shen
- Department of Pharmacy and Pharmaceutical Sciences, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Yun Xu
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Auginia Natalia
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Zhonglang Yu
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Ju Ee Seet
- Department of Pathology, National University Hospital, Singapore, Singapore
| | - Ching Wan Chan
- Department of Surgery, National University Hospital, Singapore, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Tze Ping Loh
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Laboratory Medicine, National University Hospital, Singapore, Singapore
| | - Brian Y Lim
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore.
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore.
| | - Huilin Shao
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore.
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore.
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore.
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38
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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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Liao X, Scheidereit E, Kuppe C. New tools to study renal fibrogenesis. Curr Opin Nephrol Hypertens 2024; 33:420-426. [PMID: 38587103 PMCID: PMC11139246 DOI: 10.1097/mnh.0000000000000988] [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: 04/09/2024]
Abstract
PURPOSE OF REVIEW Kidney fibrosis is a key pathological aspect and outcome of chronic kidney disease (CKD). The advent of multiomic analyses using human kidney tissue, enabled by technological advances, marks a new chapter of discovery in fibrosis research of the kidney. This review highlights the rapid advancements of single-cell and spatial multiomic techniques that offer new avenues for exploring research questions related to human kidney fibrosis development. RECENT FINDINGS We recently focused on understanding the origin and transition of myofibroblasts in kidney fibrosis using single-cell RNA sequencing (scRNA-seq) [1] . We analysed cells from healthy human kidneys and compared them to patient samples with CKD. We identified PDGFRα+/PDGFRβ+ mesenchymal cells as the primary cellular source of extracellular matrix (ECM) in human kidney fibrosis. We found several commonly shared cell states of fibroblasts and myofibroblasts and provided insights into molecular regulators. Novel single-cell and spatial multiomics tools are now available to shed light on cell lineages, the plasticity of kidney cells and cell-cell communication in fibrosis. SUMMARY As further single-cell and spatial multiomic approaches are being developed, opportunities to apply these methods to human kidney tissues expand similarly. Careful design and optimisation of the multiomic experiments are needed to answer questions related to cell lineages, plasticity and cell-cell communication in kidney fibrosis.
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Affiliation(s)
- Xian Liao
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany
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40
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Zhang L, Sagan A, Qin B, Hu B, Osmanbeyoglu HU. STAN, a computational framework for inferring spatially informed transcription factor activity across cellular contexts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.26.600782. [PMID: 38979296 PMCID: PMC11230390 DOI: 10.1101/2024.06.26.600782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Transcription factors (TFs) drive significant cellular changes in response to environmental cues and intercellular signaling. Neighboring cells influence TF activity and, consequently, cellular fate and function. Spatial transcriptomics (ST) captures mRNA expression patterns across tissue samples, enabling characterization of the local microenvironment. However, these datasets have not been fully leveraged to systematically estimate TF activity governing cell identity. Here, we present STAN ( S patially informed T ranscription factor A ctivity N etwork), a linear mixed-effects computational method that predicts spot-specific, spatially informed TF activities by integrating curated TF-target gene priors, mRNA expression, spatial coordinates, and morphological features from corresponding imaging data. We tested STAN using lymph node, breast cancer, and glioblastoma ST datasets to demonstrate its applicability by identifying TFs associated with specific cell types, spatial domains, pathological regions, and ligand-receptor pairs. STAN augments the utility of ST to reveal the intricate interplay between TFs and spatial organization across a spectrum of cellular contexts.
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41
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Long Y, Ang KS, Sethi R, Liao S, Heng Y, van Olst L, Ye S, Zhong C, Xu H, Zhang D, Kwok I, Husna N, Jian M, Ng LG, Chen A, Gascoigne NRJ, Gate D, Fan R, Xu X, Chen J. Deciphering spatial domains from spatial multi-omics with SpatialGlue. Nat Methods 2024:10.1038/s41592-024-02316-4. [PMID: 38907114 DOI: 10.1038/s41592-024-02316-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 05/20/2024] [Indexed: 06/23/2024]
Abstract
Advances in spatial omics technologies now allow multiple types of data to be acquired from the same tissue slice. To realize the full potential of such data, we need spatially informed methods for data integration. Here, we introduce SpatialGlue, a graph neural network model with a dual-attention mechanism that deciphers spatial domains by intra-omics integration of spatial location and omics measurement followed by cross-omics integration. We demonstrated SpatialGlue on data acquired from different tissue types using different technologies, including spatial epigenome-transcriptome and transcriptome-proteome modalities. Compared to other methods, SpatialGlue captured more anatomical details and more accurately resolved spatial domains such as the cortex layers of the brain. Our method also identified cell types like spleen macrophage subsets located at three different zones that were not available in the original data annotations. SpatialGlue scales well with data size and can be used to integrate three modalities. Our spatial multi-omics analysis tool combines the information from complementary omics modalities to obtain a holistic view of cellular and tissue properties.
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Affiliation(s)
- Yahui Long
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Kok Siong Ang
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Raman Sethi
- Binformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Sha Liao
- BGI-Shenzhen, Shenzhen, China
- BGI Research-Southwest, BGI, Chongqing, China
| | - Yang Heng
- BGI-Shenzhen, Shenzhen, China
- BGI Research-Southwest, BGI, Chongqing, China
| | - Lynn van Olst
- The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Shuchen Ye
- Binformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Chengwei Zhong
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Hang Xu
- Binformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Di Zhang
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Immanuel Kwok
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Nazihah Husna
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Immunology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Min Jian
- BGI-Shenzhen, Shenzhen, China
- BGI Research Asia-Pacific, BGI, Singapore, Singapore
| | - Lai Guan Ng
- Shanghai Immune Therapy Institute, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Shanghai, China
| | - Ao Chen
- BGI-Shenzhen, Shenzhen, China
- BGI Research-Southwest, BGI, Chongqing, China
- JFL-BGI STOmics Center, Jinfeng Laboratory, Chongqing, China
| | - Nicholas R J Gascoigne
- Immunology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Cancer Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - David Gate
- The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Xun Xu
- BGI-Shenzhen, Shenzhen, China
| | - Jinmiao Chen
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
- Binformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
- Immunology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Center for Computational Biology and Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore.
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42
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Lv X, Wang B, Liu K, Li MJ, Yi X, Wu X. Decoding heterogeneous and coordinated tissue architecture in glioblastoma using spatial transcriptomics. iScience 2024; 27:110064. [PMID: 38947514 PMCID: PMC11214485 DOI: 10.1016/j.isci.2024.110064] [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: 01/18/2024] [Revised: 05/05/2024] [Accepted: 05/17/2024] [Indexed: 07/02/2024] Open
Abstract
Glioblastoma multiforme (GBM) is one of the most lethal brain tumors, characterized by profound heterogeneity. While single-cell transcriptomic studies have revealed extensive intra-tumor heterogeneity, shed light on intra-tumor diversity, spatial intricacies remain largely unexplored. Leveraging clinical GBM specimens, this study employs spatial transcriptomics technology to delve into gene expression heterogeneity. Our investigation unveils a significant enrichment of tissue stem cell signature in regions bordering necrosis and the peritumoral area, positively correlated with the mesenchymal subtype signature. Moreover, upregulated genes in these regions are linked with extracellular matrix (ECM)-receptor interaction, proteoglycans, as well as vascular endothelial growth factor (VEGF) and angiopoietin-Tie (ANGPT) signaling pathways. In contrast, signatures related to glycogen metabolism and oxidative phosphorylation show no relevance to pathological zoning, whereas creatine metabolism signature is notably exclusive to vascular-enriched areas. These spatial profiles not only offer valuable references but also pave the way for future in-depth functional and mechanistic investigations into GBM progression.
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Affiliation(s)
- Xuejiao Lv
- State Key Laboratory of Experimental Hematology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Department of Cell Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China
| | - Bo Wang
- Department of Neurosurgery, Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin Huanhu Hospital, Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgical Institute, No. 6 Jizhao Road, Tianjin 300350, China
| | - Kunlun Liu
- Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Mulin Jun Li
- Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Xianfu Yi
- Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Xudong Wu
- State Key Laboratory of Experimental Hematology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Department of Cell Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
- Tianjin Key Laboratory of Spine and Spinal Cord, Tianjin Medical University General Hospital, Tianjin, China
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43
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Kim H, Kim KE, Madan E, Martin P, Gogna R, Rhee HW, Won KJ. Unveiling contact-mediated cellular crosstalk. Trends Genet 2024:S0168-9525(24)00132-X. [PMID: 38906738 DOI: 10.1016/j.tig.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 06/23/2024]
Abstract
Cell-cell interactions orchestrate complex functions in multicellular organisms, forming a regulatory network for diverse biological processes. Their disruption leads to disease states. Recent advancements - including single-cell sequencing and spatial transcriptomics, coupled with powerful bioengineering and molecular tools - have revolutionized our understanding of how cells respond to each other. Notably, spatial transcriptomics allows us to analyze gene expression changes based on cell proximity, offering a unique window into the impact of cell-cell contact. Additionally, computational approaches are being developed to decipher how cell contact governs the symphony of cellular responses. This review explores these cutting-edge approaches, providing valuable insights into deciphering the intricate cellular changes influenced by cell-cell communication.
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Affiliation(s)
- Hyobin Kim
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West, Hollywood, CA, USA
| | - Kwang-Eun Kim
- Department of Convergence Medicine, Yonsei University Wonju College of Medicine, Wonju, South Korea; Department of Chemistry, Seoul National University, Seoul, South Korea
| | - Esha Madan
- Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA; School of Medicine, Institute of Molecular Medicine, Virginia Commonwealth University, Richmond, VA, USA; Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Patrick Martin
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West, Hollywood, CA, USA
| | - Rajan Gogna
- Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA; School of Medicine, Institute of Molecular Medicine, Virginia Commonwealth University, Richmond, VA, USA; Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Hyun-Woo Rhee
- Department of Chemistry, Seoul National University, Seoul, South Korea.
| | - Kyoung-Jae Won
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West, Hollywood, CA, USA.
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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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45
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Li Y, Zhang J, Gao X, Zhang QC. Tissue module discovery in single-cell-resolution spatial transcriptomics data via cell-cell interaction-aware cell embedding. Cell Syst 2024; 15:578-592.e7. [PMID: 38823396 DOI: 10.1016/j.cels.2024.05.001] [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: 05/25/2023] [Revised: 01/08/2024] [Accepted: 05/07/2024] [Indexed: 06/03/2024]
Abstract
Computational methods are desired for single-cell-resolution spatial transcriptomics (ST) data analysis to uncover spatial organization principles for how individual cells exert tissue-specific functions. Here, we present ST data analysis via interaction-aware cell embedding (SPACE), a deep-learning method for cell-type identification and tissue module discovery from single-cell-resolution ST data by learning a cell representation that captures its gene expression profile and interactions with its spatial neighbors. SPACE identified spatially informed cell subtypes defined by their special spatial distribution patterns and distinct proximal-interacting cell types. SPACE also automatically discovered "cell communities"-tissue modules with discernible boundaries and a uniform spatial distribution of constituent cell types. For each cell community, SPACE outputs a characteristic proximal cell-cell interaction network associated with physiological processes, which can be used to refine ligand-receptor-based intercellular signaling analyses. We envision that SPACE can be used in large-scale ST projects to understand how proximal cell-cell interactions contribute to emergent biological functions within cell communities. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Yuzhe Li
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Jinsong Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China; Shanghai Qi Zhi Institute, Shanghai 200030, China
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia; KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia; BioMap, Beijing 100086, China.
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China.
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Lv Z, Jiang S, Kong S, Zhang X, Yue J, Zhao W, Li L, Lin S. Advances in Single-Cell Transcriptome Sequencing and Spatial Transcriptome Sequencing in Plants. PLANTS (BASEL, SWITZERLAND) 2024; 13:1679. [PMID: 38931111 PMCID: PMC11207393 DOI: 10.3390/plants13121679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/31/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024]
Abstract
"Omics" typically involves exploration of the structure and function of the entire composition of a biological system at a specific level using high-throughput analytical methods to probe and analyze large amounts of data, including genomics, transcriptomics, proteomics, and metabolomics, among other types. Genomics characterizes and quantifies all genes of an organism collectively, studying their interrelationships and their impacts on the organism. However, conventional transcriptomic sequencing techniques target population cells, and their results only reflect the average expression levels of genes in population cells, as they are unable to reveal the gene expression heterogeneity and spatial heterogeneity among individual cells, thus masking the expression specificity between different cells. Single-cell transcriptomic sequencing and spatial transcriptomic sequencing techniques analyze the transcriptome of individual cells in plant or animal tissues, enabling the understanding of each cell's metabolites and expressed genes. Consequently, statistical analysis of the corresponding tissues can be performed, with the purpose of achieving cell classification, evolutionary growth, and physiological and pathological analyses. This article provides an overview of the research progress in plant single-cell and spatial transcriptomics, as well as their applications and challenges in plants. Furthermore, prospects for the development of single-cell and spatial transcriptomics are proposed.
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Affiliation(s)
- Zhuo Lv
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.L.); (S.J.); (S.K.); (X.Z.); (J.Y.); (W.Z.); (L.L.)
- Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
- College of Life Science, Nanjing Forestry University, Nanjing 210037, China
| | - Shuaijun Jiang
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.L.); (S.J.); (S.K.); (X.Z.); (J.Y.); (W.Z.); (L.L.)
- Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
- College of Life Science, Nanjing Forestry University, Nanjing 210037, China
| | - Shuxin Kong
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.L.); (S.J.); (S.K.); (X.Z.); (J.Y.); (W.Z.); (L.L.)
- Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
- College of Life Science, Nanjing Forestry University, Nanjing 210037, China
| | - Xu Zhang
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.L.); (S.J.); (S.K.); (X.Z.); (J.Y.); (W.Z.); (L.L.)
- Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
- College of Life Science, Nanjing Forestry University, Nanjing 210037, China
| | - Jiahui Yue
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.L.); (S.J.); (S.K.); (X.Z.); (J.Y.); (W.Z.); (L.L.)
- Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
- College of Life Science, Nanjing Forestry University, Nanjing 210037, China
| | - Wanqi Zhao
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.L.); (S.J.); (S.K.); (X.Z.); (J.Y.); (W.Z.); (L.L.)
- Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
- College of Life Science, Nanjing Forestry University, Nanjing 210037, China
| | - Long Li
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.L.); (S.J.); (S.K.); (X.Z.); (J.Y.); (W.Z.); (L.L.)
- Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
| | - Shuyan Lin
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.L.); (S.J.); (S.K.); (X.Z.); (J.Y.); (W.Z.); (L.L.)
- Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
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Ji B, Chen J, Gong H, Li X. Streamlined Full-Length Total RNA Sequencing of Paraformaldehyde-Fixed Brain Tissues. Int J Mol Sci 2024; 25:6504. [PMID: 38928210 PMCID: PMC11204141 DOI: 10.3390/ijms25126504] [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: 04/25/2024] [Revised: 06/04/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
Paraformaldehyde (PFA) fixation is the preferred method for preserving tissue architecture for anatomical and pathological observations. Meanwhile, PFA reacts with the amine groups of biomolecules to form chemical cross-linking, which preserves RNA within the tissue. This has great prospects for RNA sequencing to characterize the molecular underpinnings after anatomical and pathological observations. However, RNA is inaccessible due to cross-linked adducts forming between RNA and other biomolecules in prolonged PFA-fixed tissue. It is also difficult to perform reverse transcription and PCR, resulting in low sequencing sensitivity and reduced reproducibility. Here, we developed a method to perform RNA sequencing in PFA-fixed tissue, which is easy to use, cost-effective, and allows efficient sample multiplexing. We employ cross-link reversal to recover RNA and library construction using random primers without artificial fragmentation. The yield and quality of recovered RNA significantly increased through our method, and sequencing quality metrics and detected genes did not show any major differences compared with matched fresh samples. Moreover, we applied our method for gene expression analysis in different regions of the mouse brain and identified unique gene expression profiles with varied functional implications. We also find significant dysregulation of genes involved in Alzheimer's disease (AD) pathogenesis within the medial septum (MS)/vertical diagonal band of Broca (VDB) of the 5×FAD mouse brain. Our method can thus increase the performance of high-throughput RNA sequencing with PFA-fixed samples and allows longitudinal studies of small tissue regions isolated by their in situ context.
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Affiliation(s)
- Bingqing Ji
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; (B.J.); (J.C.); (H.G.)
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jiale Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; (B.J.); (J.C.); (H.G.)
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; (B.J.); (J.C.); (H.G.)
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, HUST-Suzhou Institute for Brainsmatics, JITRI, Chinese Academy of Medical Sciences, Suzhou 215125, China
| | - Xiangning Li
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, HUST-Suzhou Institute for Brainsmatics, JITRI, Chinese Academy of Medical Sciences, Suzhou 215125, China
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou 570228, China
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Zuo C, Xia J, Chen L. Dissecting tumor microenvironment from spatially resolved transcriptomics data by heterogeneous graph learning. Nat Commun 2024; 15:5057. [PMID: 38871687 DOI: 10.1038/s41467-024-49171-7] [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: 06/10/2023] [Accepted: 05/22/2024] [Indexed: 06/15/2024] Open
Abstract
Spatially resolved transcriptomics (SRT) has enabled precise dissection of tumor-microenvironment (TME) by analyzing its intracellular molecular networks and intercellular cell-cell communication (CCC). However, lacking computational exploration of complicated relations between cells, genes, and histological regions, severely limits the ability to interpret the complex structure of TME. Here, we introduce stKeep, a heterogeneous graph (HG) learning method that integrates multimodality and gene-gene interactions, in unraveling TME from SRT data. stKeep leverages HG to learn both cell-modules and gene-modules by incorporating features of diverse nodes including genes, cells, and histological regions, allows for identifying finer cell-states within TME and cell-state-specific gene-gene relations, respectively. Furthermore, stKeep employs HG to infer CCC for each cell, while ensuring that learned CCC patterns are comparable across different cell-states through contrastive learning. In various cancer samples, stKeep outperforms other tools in dissecting TME such as detecting bi-potent basal populations, neoplastic myoepithelial cells, and metastatic cells distributed within the tumor or leading-edge regions. Notably, stKeep identifies key transcription factors, ligands, and receptors relevant to disease progression, which are further validated by the functional and survival analysis of independent clinical data, thereby highlighting its clinical prognostic and immunotherapy applications.
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Affiliation(s)
- Chunman Zuo
- Institute of Artificial Intelligence, Shanghai Engineering Research Center of Industrial Big Data and Intelligent System, Donghua University, Shanghai, 201620, China.
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130022, China.
| | - Junjie Xia
- Institute of Artificial Intelligence, Shanghai Engineering Research Center of Industrial Big Data and Intelligent System, Donghua University, Shanghai, 201620, China
- Department of Applied Mathematics, Donghua University, Shanghai, 201620, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, 200031, China.
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 310024, China.
- West China Biomedical Big Data Center, Med-X center for informatics, West China Hospital, Sichuan University, Chengdu, 610041, China.
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Kim HS, Xiao Y, Chen X, He S, Im J, Willner MJ, Finlayson MO, Xu C, Zhu H, Choi SJ, Mosharov EV, Kim H, Xu B, Leong KW. Chronic Opioid Treatment Arrests Neurodevelopment and Alters Synaptic Activity in Human Midbrain Organoids. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400847. [PMID: 38549185 PMCID: PMC11151039 DOI: 10.1002/advs.202400847] [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: 01/23/2024] [Indexed: 06/06/2024]
Abstract
Understanding the impact of long-term opioid exposure on the embryonic brain is critical due to the surging number of pregnant mothers with opioid dependency. However, this has been limited by human brain inaccessibility and cross-species differences in animal models. Here, a human midbrain model is established that uses hiPSC-derived midbrain organoids to assess cell-type-specific responses to acute and chronic fentanyl treatment and fentanyl withdrawal. Single-cell mRNA sequencing of 25,510 cells from organoids in different treatment groups reveals that chronic fentanyl treatment arrests neuronal subtype specification during early midbrain development and alters synaptic activity and neuron projection. In contrast, acute fentanyl treatment increases dopamine release but does not significantly alter gene expression related to cell lineage development. These results provide the first examination of the effects of opioid exposure on human midbrain development at the single-cell level.
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Affiliation(s)
- Hye Sung Kim
- Department of Biomedical EngineeringColumbia UniversityNew YorkNY10027USA
- Institute of Tissue Regeneration Engineering (ITREN)Dankook UniversityCheonan31116Republic of Korea
- Mechanobiology Dental Medicine Research CenterDankook UniversityCheonan31116Republic of Korea
- Department of Nanobiomedical Science and BK21 PLUS NBM Global Research Center for Regenerative MedicineDankook UniversityCheonan31116Republic of Korea
| | - Yang Xiao
- Department of Biomedical EngineeringColumbia UniversityNew YorkNY10027USA
| | - Xuejing Chen
- Department of Biomedical EngineeringColumbia UniversityNew YorkNY10027USA
- Department of PhysicsTsinghua UniversityBeijing100084China
| | - Siyu He
- Department of Biomedical EngineeringColumbia UniversityNew YorkNY10027USA
| | - Jongwon Im
- Department of Biomedical EngineeringColumbia UniversityNew YorkNY10027USA
| | - Moshe J. Willner
- Department of Biomedical EngineeringColumbia UniversityNew YorkNY10027USA
| | - Michael O. Finlayson
- Single Cell Analysis CoreJP Sulzberger Columbia Genome CenterColumbia University Irving Medical CenterNew YorkNY10032USA
| | - Cong Xu
- Department of Biomedical EngineeringColumbia UniversityNew YorkNY10027USA
| | - Huixiang Zhu
- Department of PsychiatryColumbia University Medical CenterNew YorkNY10032USA
| | - Se Joon Choi
- Department of PsychiatryColumbia University Medical CenterNew YorkNY10032USA
- Division of Molecular TherapeuticsNew York State Psychiatric InstituteNew YorkNY10032USA
| | - Eugene V. Mosharov
- Department of PsychiatryColumbia University Medical CenterNew YorkNY10032USA
- Division of Molecular TherapeuticsNew York State Psychiatric InstituteNew YorkNY10032USA
| | - Hae‐Won Kim
- Institute of Tissue Regeneration Engineering (ITREN)Dankook UniversityCheonan31116Republic of Korea
- Mechanobiology Dental Medicine Research CenterDankook UniversityCheonan31116Republic of Korea
- Department of Nanobiomedical Science and BK21 PLUS NBM Global Research Center for Regenerative MedicineDankook UniversityCheonan31116Republic of Korea
| | - Bin Xu
- Department of PsychiatryColumbia University Medical CenterNew YorkNY10032USA
| | - Kam W. Leong
- Department of Biomedical EngineeringColumbia UniversityNew YorkNY10027USA
- Department of Systems BiologyColumbia University Irving Medical CenterNew YorkNY10032USA
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50
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Zhang Y, Lee RY, Tan CW, Guo X, Yim WWY, Lim JC, Wee FY, Yang WU, Kharbanda M, Lee JYJ, Ngo NT, Leow WQ, Loo LH, Lim TK, Sobota RM, Lau MC, Davis MJ, Yeong J. Spatial omics techniques and data analysis for cancer immunotherapy applications. Curr Opin Biotechnol 2024; 87:103111. [PMID: 38520821 DOI: 10.1016/j.copbio.2024.103111] [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: 07/27/2023] [Revised: 03/01/2024] [Accepted: 03/03/2024] [Indexed: 03/25/2024]
Abstract
In-depth profiling of cancer cells/tissues is expanding our understanding of the genomic, epigenomic, transcriptomic, and proteomic landscape of cancer. However, the complexity of the cancer microenvironment, particularly its immune regulation, has made it difficult to exploit the potential of cancer immunotherapy. High-throughput spatial omics technologies and analysis pipelines have emerged as powerful tools for tackling this challenge. As a result, a potential revolution in cancer diagnosis, prognosis, and treatment is on the horizon. In this review, we discuss the technological advances in spatial profiling of cancer around and beyond the central dogma to harness the full benefits of immunotherapy. We also discuss the promise and challenges of spatial data analysis and interpretation and provide an outlook for the future.
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Affiliation(s)
- Yue Zhang
- Duke-NUS Medical School, Singapore 169856, Singapore
| | - Ren Yuan Lee
- Yong Loo Lin School of Medicine, National University of Singapore, 169856 Singapore; Singapore Thong Chai Medical Institution, Singapore 169874, Singapore
| | - Chin Wee Tan
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia
| | - Xue Guo
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Willa W-Y Yim
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Jeffrey Ct Lim
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Felicia Yt Wee
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - W U Yang
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Malvika Kharbanda
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia
| | - Jia-Ying J Lee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore
| | - Nye Thane Ngo
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Wei Qiang Leow
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Lit-Hsin Loo
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore
| | - Tony Kh Lim
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Radoslaw M Sobota
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Mai Chan Lau
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A⁎STAR), Singapore 138648, Singapore
| | - Melissa J Davis
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia; immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia; Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Joe Yeong
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore; Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore.
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