1
|
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.
Collapse
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
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Masatti L, Marchetti M, Pirrotta S, Spagnol G, Corrà A, Ferrari J, Noventa M, Saccardi C, Calura E, Tozzi R. The unveiled mosaic of intra-tumor heterogeneity in ovarian cancer through spatial transcriptomic technologies: A systematic review. Transl Res 2024; 273:104-114. [PMID: 39111726 DOI: 10.1016/j.trsl.2024.08.001] [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] [Received: 04/13/2024] [Revised: 07/16/2024] [Accepted: 08/01/2024] [Indexed: 08/12/2024]
Abstract
Epithelial ovarian cancer is a significant global health issue among women. Diagnosis and treatment pose challenges due to difficulties in predicting patient responses to therapy, primarily stemming from gaps in understanding tumor chemoresistance mechanisms. Recent advancements in transcriptomic technologies like single-cell RNA sequencing and spatial transcriptomics have greatly improved our understanding of ovarian cancer intratumor heterogeneity and tumor microenvironment composition. Spatial transcriptomics, in particular, comprises a plethora of technologies that enable the detection of hundreds of transcriptomes and their spatial distribution within a histological section, facilitating the study of cell types, states, and interactions within the tumor and its microenvironment. Studies investigating the spatial distribution of gene expression in ovarian cancer masses have identified specific features that impact prognosis and therapy outcomes. Emerging evidence suggests that specific spatial patterns of tumor cells and their immune and non-immune microenvironment significantly influence therapy response, as well as the behavior and progression of primary tumors and metastatic sites. The importance of spatially contextualizing ovarian cancer transcriptomes is underscored by these findings, which will advance our understanding and therapeutic approaches for this complex disease.
Collapse
Affiliation(s)
- Laura Masatti
- Department of Biology, University of Padova, Padova, Italy
| | - Matteo Marchetti
- Department of Gynecology and Obstetrics, Division of Women and Children, Padova University Hospital, Padova, Italy
| | | | - Giulia Spagnol
- Department of Gynecology and Obstetrics, Division of Women and Children, Padova University Hospital, Padova, Italy
| | - Anna Corrà
- Department of Biology, University of Padova, Padova, Italy; Fondazione Istituto di Ricerca Pediatrica Città della Speranza, Padova, Italy
| | - Jacopo Ferrari
- Department of Gynecology and Obstetrics, Division of Women and Children, Padova University Hospital, Padova, Italy
| | - Marco Noventa
- Department of Gynecology and Obstetrics, Division of Women and Children, Padova University Hospital, Padova, Italy
| | - Carlo Saccardi
- Department of Gynecology and Obstetrics, Division of Women and Children, Padova University Hospital, Padova, Italy
| | - Enrica Calura
- Department of Biology, University of Padova, Padova, Italy.
| | - Roberto Tozzi
- Department of Gynecology and Obstetrics, Division of Women and Children, Padova University Hospital, Padova, Italy
| |
Collapse
|
4
|
Abstract
The ability to localize hundreds of macromolecules to discrete locations, structures and cell types in a tissue is a powerful approach to understand the cellular and spatial organization of an organ. Spatially resolved transcriptomic technologies enable mapping of transcripts at single-cell or near single-cell resolution in a multiplex manner. The rapid development of spatial transcriptomic technologies has accelerated the pace of discovery in several fields, including nephrology. Its application to preclinical models and human samples has provided spatial information about new cell types discovered by single-cell sequencing and new insights into the cell-cell interactions within neighbourhoods, and has improved our understanding of the changes that occur in response to injury. Integration of spatial transcriptomic technologies with other omics methods, such as proteomics and spatial epigenetics, will further facilitate the generation of comprehensive molecular atlases, and provide insights into the dynamic relationships of molecular components in homeostasis and disease. This Review provides an overview of current and emerging spatial transcriptomic methods, their applications and remaining challenges for the field.
Collapse
Affiliation(s)
- Sanjay Jain
- Division of Nephrology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
| | - Michael T Eadon
- Division of Nephrology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
| |
Collapse
|
5
|
Ariotta V, Azzalini E, Canzonieri V, Hautaniemi S, Bonin S. Comparative Analysis of Gene Expression Analysis Methods for RNA in Situ Hybridization Images. J Mol Diagn 2024; 26:931-942. [PMID: 39068989 DOI: 10.1016/j.jmoldx.2024.06.010] [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: 02/29/2024] [Revised: 05/27/2024] [Accepted: 06/26/2024] [Indexed: 07/30/2024] Open
Abstract
Gene expression analysis is pivotal in cancer research and clinical practice. Although traditional methods lack spatial context, RNA in situ hybridization (RNA-ISH) is a powerful technique that retains spatial tissue information. Here, RNAscope score, RT-droplet digital PCR, and automated QuantISH and QuPath were used for quantifying RNA-ISH expression values from formalin-fixed, paraffin-embedded samples. The methods were compared using high-grade serous ovarian carcinoma samples, focusing on CCNE1, WFDC2, and PPIB genes. The findings demonstrate good concordance between automated methods and RNAscope, with RT-droplet digital PCR showing less concordance. Additionally, QuantISH exhibits robust performance, even for low-expressed genes like CCNE1, showcasing its modular design and enhancing accessibility as a viable alternative for gene expression analysis.
Collapse
Affiliation(s)
- Valeria Ariotta
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Eros Azzalini
- Department of Medical Sciences, University of Trieste, Trieste, Italy
| | - Vincenzo Canzonieri
- Department of Medical Sciences, University of Trieste, Trieste, Italy; Pathology Unit, Centro di Riferimento Oncologico IRCCS, Aviano-National Cancer Institute, Pordenone, Italy
| | - Sampsa Hautaniemi
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Serena Bonin
- Department of Medical Sciences, University of Trieste, Trieste, Italy.
| |
Collapse
|
6
|
Zhang Y, Yu B, Ming W, Zhou X, Wang J, Chen D. SpaTopic: A statistical learning framework for exploring tumor spatial architecture from spatially resolved transcriptomic data. SCIENCE ADVANCES 2024; 10:eadp4942. [PMID: 39331720 PMCID: PMC11430467 DOI: 10.1126/sciadv.adp4942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 08/21/2024] [Indexed: 09/29/2024]
Abstract
Tumor tissues exhibit a complex spatial architecture within the tumor microenvironment (TME). Spatially resolved transcriptomics (SRT) is promising for unveiling the spatial structures of the TME at both cellular and molecular levels, but identifying pathology-relevant spatial domains remains challenging. Here, we introduce SpaTopic, a statistical learning framework that harmonizes spot clustering and cell-type deconvolution by integrating single-cell transcriptomics and SRT data. Through topic modeling, SpaTopic stratifies the TME into spatial domains with coherent cellular organization, facilitating refined annotation of the spatial architecture with improved performance. We assess SpaTopic across various tumor types and show accurate prediction of tertiary lymphoid structures and tumor boundaries. Moreover, marker genes derived from SpaTopic are transferrable and can be applied to mark spatial domains in other datasets. In addition, SpaTopic enables quantitative comparison and functional characterization of spatial domains across SRT datasets. Overall, SpaTopic presents an innovative analytical framework for exploring, comparing, and interpreting tumor SRT data.
Collapse
Affiliation(s)
- Yuelei Zhang
- Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
| | - Bianjiong Yu
- Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
| | - Wenxuan Ming
- Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
| | - Xiaolong Zhou
- Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
| | - Jin Wang
- Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
| | - Dijun Chen
- Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
- Central Laboratory of Stomatology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China
- Chemistry and Biomedicine Innovation Center, Nanjing University, Nanjing, China
| |
Collapse
|
7
|
Xiao Y, Li Y, Zhao H. Spatiotemporal metabolomic approaches to the cancer-immunity panorama: a methodological perspective. Mol Cancer 2024; 23:202. [PMID: 39294747 PMCID: PMC11409752 DOI: 10.1186/s12943-024-02113-9] [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: 07/03/2024] [Accepted: 09/05/2024] [Indexed: 09/21/2024] Open
Abstract
Metabolic reprogramming drives the development of an immunosuppressive tumor microenvironment (TME) through various pathways, contributing to cancer progression and reducing the effectiveness of anticancer immunotherapy. However, our understanding of the metabolic landscape within the tumor-immune context has been limited by conventional metabolic measurements, which have not provided comprehensive insights into the spatiotemporal heterogeneity of metabolism within TME. The emergence of single-cell, spatial, and in vivo metabolomic technologies has now enabled detailed and unbiased analysis, revealing unprecedented spatiotemporal heterogeneity that is particularly valuable in the field of cancer immunology. This review summarizes the methodologies of metabolomics and metabolic regulomics that can be applied to the study of cancer-immunity across single-cell, spatial, and in vivo dimensions, and systematically assesses their benefits and limitations.
Collapse
Affiliation(s)
- Yang Xiao
- Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, 400044, China
| | - Yongsheng Li
- Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, 400044, China.
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing, 400030, China.
| | - Huakan Zhao
- Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, 400044, China.
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing, 400030, China.
| |
Collapse
|
8
|
Rajachandran S, Xu Q, Cao Q, Zhang X, Chen F, Mangiameli SM, Chen H. Subcellular Level Spatial Transcriptomics with PHOTON. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.10.612328. [PMID: 39314454 PMCID: PMC11419108 DOI: 10.1101/2024.09.10.612328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
The subcellular localization of RNA is closely linked to its function. Many RNA species are partitioned into organelles and other subcellular compartments for storage, processing, translation, or degradation. Thus, capturing the subcellular spatial distribution of RNA would directly contribute to the understanding of RNA functions and regulation. Here, we present PHOTON (Photoselection of Transcriptome over Nanoscale), a method which combines high resolution imaging with high throughput sequencing to achieve spatial transcriptome profiling at subcellular resolution. We demonstrate PHOTON as a versatile tool to accurately capture the transcriptome of target cell types in situ at the tissue level such as granulosa cells in the ovary, as well as RNA content within subcellular compartments such as the nucleolus and the stress granule. Using PHOTON, we also reveal the functional role of m6A modification on mRNA partitioning into stress granules. These results collectively demonstrate that PHOTON is a flexible and generalizable platform for understanding subcellular molecular dynamics through the transcriptomic lens.
Collapse
Affiliation(s)
- Shreya Rajachandran
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qianlan Xu
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qiqi Cao
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xin Zhang
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Fei Chen
- Gene Regulation Observatory, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Sarah M. Mangiameli
- Gene Regulation Observatory, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Haiqi Chen
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| |
Collapse
|
9
|
Analyzing submicron spatial transcriptomics data at their original resolution. Nat Methods 2024:10.1038/s41592-024-02416-1. [PMID: 39271936 DOI: 10.1038/s41592-024-02416-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
|
10
|
Liang W, Zhu Z, Xu D, Wang P, Guo F, Xiao H, Hou C, Xue J, Zhi X, Ran R. The burgeoning spatial multi-omics in human gastrointestinal cancers. PeerJ 2024; 12:e17860. [PMID: 39285924 PMCID: PMC11404479 DOI: 10.7717/peerj.17860] [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: 03/27/2024] [Accepted: 07/14/2024] [Indexed: 09/19/2024] Open
Abstract
The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body's three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers.
Collapse
Affiliation(s)
- Weizheng Liang
- Central Laboratory, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei province, China
| | - Zhenpeng Zhu
- Department of Surgery, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei Province, China
- Hebei North University, Zhangjiakou, Hebei Province, China
| | - Dandan Xu
- Central Laboratory, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei province, China
| | - Peng Wang
- Department of Surgery, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei Province, China
- Hebei North University, Zhangjiakou, Hebei Province, China
| | - Fei Guo
- Department of Surgery, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei Province, China
| | - Haoshan Xiao
- Department of Surgery, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei Province, China
- Hebei North University, Zhangjiakou, Hebei Province, China
| | - Chenyang Hou
- Department of Surgery, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei Province, China
- Hebei North University, Zhangjiakou, Hebei Province, China
| | - Jun Xue
- Department of Surgery, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei Province, China
| | - Xuejun Zhi
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei province, China
| | - Rensen Ran
- Central Laboratory, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei province, China
- Department of Chemical Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| |
Collapse
|
11
|
Lin H, Yang X, Ye S, Huang L, Mu W. Antigen escape in CAR-T cell therapy: Mechanisms and overcoming strategies. Biomed Pharmacother 2024; 178:117252. [PMID: 39098176 DOI: 10.1016/j.biopha.2024.117252] [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: 05/13/2024] [Revised: 07/30/2024] [Accepted: 07/31/2024] [Indexed: 08/06/2024] Open
Abstract
Chimeric antigen receptor T (CAR-T) cell therapy has shown promise in treating hematological malignancies and certain solid tumors. However, its efficacy is often hindered by negative relapses resulting from antigen escape. This review firstly elucidates the mechanisms underlying antigen escape during CAR-T cell therapy, including the enrichment of pre-existing target-negative tumor clones, antigen gene mutations or alternative splicing, deficits in antigen processing, antigen redistribution, lineage switch, epitope masking, and trogocytosis-mediated antigen loss. Furthermore, we summarize various strategies to overcome antigen escape, evaluate their advantages and limitations, and propose future research directions. Thus, we aim to provide valuable insights to enhance the effectiveness of CAR-T cell therapy.
Collapse
Affiliation(s)
- Haolong Lin
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Immunotherapy Research Center for Hematologic Diseases of Hubei Province, Wuhan, Hubei 430030, China
| | - Xiuxiu Yang
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Immunotherapy Research Center for Hematologic Diseases of Hubei Province, Wuhan, Hubei 430030, China
| | - Shanwei Ye
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Immunotherapy Research Center for Hematologic Diseases of Hubei Province, Wuhan, Hubei 430030, China
| | - Liang Huang
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Immunotherapy Research Center for Hematologic Diseases of Hubei Province, Wuhan, Hubei 430030, China; State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China.
| | - Wei Mu
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Immunotherapy Research Center for Hematologic Diseases of Hubei Province, Wuhan, Hubei 430030, China.
| |
Collapse
|
12
|
Ding J, Li L, Lu Q, Venegas J, Wang Y, Wu L, Jin W, Wen H, Liu R, Tang W, Dai X, Li Z, Zuo W, Chang Y, Lei YL, Shang L, Danaher P, Xie Y, Tang J. SpatialCTD: A Large-Scale Tumor Microenvironment Spatial Transcriptomic Dataset to Evaluate Cell Type Deconvolution for Immuno-Oncology. J Comput Biol 2024; 31:871-885. [PMID: 39117342 DOI: 10.1089/cmb.2024.0532] [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: 08/10/2024] Open
Abstract
Recent technological advancements have enabled spatially resolved transcriptomic profiling but at a multicellular resolution that is more cost-effective. The task of cell type deconvolution has been introduced to disentangle discrete cell types from such multicellular spots. However, existing benchmark datasets for cell type deconvolution are either generated from simulation or limited in scale, predominantly encompassing data on mice and are not designed for human immuno-oncology. To overcome these limitations and promote comprehensive investigation of cell type deconvolution for human immuno-oncology, we introduce a large-scale spatial transcriptomic deconvolution benchmark dataset named SpatialCTD, encompassing 1.8 million cells and 12,900 pseudo spots from the human tumor microenvironment across the lung, kidney, and liver. In addition, SpatialCTD provides more realistic reference than those generated from single-cell RNA sequencing (scRNA-seq) data for most reference-based deconvolution methods. To utilize the location-aware SpatialCTD reference, we propose a graph neural network-based deconvolution method (i.e., GNNDeconvolver). Extensive experiments show that GNNDeconvolver often outperforms existing state-of-the-art methods by a substantial margin, without requiring scRNA-seq data. To enable comprehensive evaluations of spatial transcriptomics data from flexible protocols, we provide an online tool capable of converting spatial transcriptomic data from various platforms (e.g., 10× Visium, MERFISH, and sci-Space) into pseudo spots, featuring adjustable spot size. The SpatialCTD dataset and GNNDeconvolver implementation are available at https://github.com/OmicsML/SpatialCTD, and the online converter tool can be accessed at https://omicsml.github.io/SpatialCTD/.
Collapse
Affiliation(s)
- Jiayuan Ding
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Lingxiao Li
- Boston University, Boston, Massachusetts, USA
| | - Qiaolin Lu
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Julian Venegas
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Yixin Wang
- Department of Bioengineering, Stanford University, Palo Alto, California, USA
| | - Lidan Wu
- NanoString Technologies, Seattle, USA
| | - Wei Jin
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Hongzhi Wen
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Renming Liu
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Wenzhuo Tang
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, USA
| | - Xinnan Dai
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Zhaoheng Li
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Wangyang Zuo
- Department of Computer Science, Zhejiang University of Technology, Hangzhou, China
| | - Yi Chang
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Yu Leo Lei
- Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, USA
- University of Michigan Rogel Cancer Center, Ann Arbor, USA
| | - Lulu Shang
- Department of Biostatistics, MD Anderson Cancer Center, Houston, USA
| | | | - Yuying Xie
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, USA
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, USA
| | - Jiliang Tang
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| |
Collapse
|
13
|
You Y, Fu Y, Li L, Zhang Z, Jia S, Lu S, Ren W, Liu Y, Xu Y, Liu X, Jiang F, Peng G, Sampath Kumar A, Ritchie ME, Liu X, Tian L. Systematic comparison of sequencing-based spatial transcriptomic methods. Nat Methods 2024; 21:1743-1754. [PMID: 38965443 PMCID: PMC11399101 DOI: 10.1038/s41592-024-02325-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 05/29/2024] [Indexed: 07/06/2024]
Abstract
Recent developments of sequencing-based spatial transcriptomics (sST) have catalyzed important advancements by facilitating transcriptome-scale spatial gene expression measurement. Despite this progress, efforts to comprehensively benchmark different platforms are currently lacking. The extant variability across technologies and datasets poses challenges in formulating standardized evaluation metrics. In this study, we established a collection of reference tissues and regions characterized by well-defined histological architectures, and used them to generate data to compare 11 sST methods. We highlighted molecular diffusion as a variable parameter across different methods and tissues, significantly affecting the effective resolutions. Furthermore, we observed that spatial transcriptomic data demonstrate unique attributes beyond merely adding a spatial axis to single-cell data, including an enhanced ability to capture patterned rare cell states along with specific markers, albeit being influenced by multiple factors including sequencing depth and resolution. Our study assists biologists in sST platform selection, and helps foster a consensus on evaluation standards and establish a framework for future benchmarking efforts that can be used as a gold standard for the development and benchmarking of computational tools for spatial transcriptomic analysis.
Collapse
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.
| |
Collapse
|
14
|
Advancing sequencing-based spatial transcriptomics with a comprehensive benchmarking study. Nat Methods 2024; 21:1589-1590. [PMID: 38977827 DOI: 10.1038/s41592-024-02326-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
|
15
|
Castro-Mendoza PB, Weaver CM, Chang W, Medalla M, Rockland KS, Lowery L, McDonough E, Varghese M, Hof PR, Meyer DE, Luebke JI. Proteomic features of gray matter layers and superficial white matter of the rhesus monkey neocortex: comparison of prefrontal area 46 and occipital area 17. Brain Struct Funct 2024; 229:1495-1525. [PMID: 38943018 PMCID: PMC11374833 DOI: 10.1007/s00429-024-02819-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 06/08/2024] [Indexed: 06/30/2024]
Abstract
In this novel large-scale multiplexed immunofluorescence study we comprehensively characterized and compared layer-specific proteomic features within regions of interest of the widely divergent dorsolateral prefrontal cortex (A46) and primary visual cortex (A17) of adult rhesus monkeys. Twenty-eight markers were imaged in rounds of sequential staining, and their spatial distribution precisely quantified within gray matter layers and superficial white matter. Cells were classified as neurons, astrocytes, oligodendrocytes, microglia, or endothelial cells. The distribution of fibers and blood vessels were assessed by quantification of staining intensity across regions of interest. This method revealed multivariate similarities and differences between layers and areas. Protein expression in neurons was the strongest determinant of both laminar and regional differences, whereas protein expression in glia was more important for intra-areal laminar distinctions. Among specific results, we observed a lower glia-to-neuron ratio in A17 than in A46 and the pan-neuronal markers HuD and NeuN were differentially distributed in both brain areas with a lower intensity of NeuN in layers 4 and 5 of A17 compared to A46 and other A17 layers. Astrocytes and oligodendrocytes exhibited distinct marker-specific laminar distributions that differed between regions; notably, there was a high proportion of ALDH1L1-expressing astrocytes and of oligodendrocyte markers in layer 4 of A17. The many nuanced differences in protein expression between layers and regions observed here highlight the need for direct assessment of proteins, in addition to RNA expression, and set the stage for future protein-focused studies of these and other brain regions in normal and pathological conditions.
Collapse
Affiliation(s)
- Paola B Castro-Mendoza
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, 02118, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, 02215, USA
| | - Christina M Weaver
- Department of Mathematics, Franklin and Marshall College, Lancaster, PA, 17604, USA
| | - Wayne Chang
- Yale School of Medicine, 333 Cedar St, New Haven, CT, 06510, USA
| | - Maria Medalla
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, 02118, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, 02215, USA
| | - Kathleen S Rockland
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Lisa Lowery
- GE HealthCare Technology and Innovation Center, Niskayuna, NY, 12309, USA
| | | | - Merina Varghese
- Nash Family Department of Neuroscience, Friedman Brain Institute, and Center for Discovery and Innovation, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA
| | - Patrick R Hof
- Nash Family Department of Neuroscience, Friedman Brain Institute, and Center for Discovery and Innovation, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA
| | - Dan E Meyer
- GE HealthCare Technology and Innovation Center, Niskayuna, NY, 12309, USA
| | - Jennifer I Luebke
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, 02118, USA.
- Center for Systems Neuroscience, Boston University, Boston, MA, 02215, USA.
| |
Collapse
|
16
|
Fan G, Xie T, Tang L, Li L, Han X, Shi Y. The co-location of CD14+APOE+ cells and MMP7+ tumour cells contributed to worse immunotherapy response in non-small cell lung cancer. Clin Transl Med 2024; 14:e70009. [PMID: 39187937 PMCID: PMC11347392 DOI: 10.1002/ctm2.70009] [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: 04/23/2024] [Revised: 08/13/2024] [Accepted: 08/15/2024] [Indexed: 08/28/2024] Open
Abstract
Intra-tumour immune infiltration is a crucial determinant affecting immunotherapy response in non-small cell lung cancer (NSCLC). However, its phenotype and related spatial structure have remained elusive. To overcome these restrictions, we undertook a comprehensive study comprising spatial transcriptomic (ST) data (28 712 spots from six samples). We identified two distinct intra-tumour infiltration patterns: immune exclusion (characterised by myeloid cells) and immune activation (characterised by plasma cells). The immune exclusion and immune activation signatures showed adverse and favourable roles in NSCLC patients' survival, respectively. Notably, CD14+APOE+ cells were recognised as the main cell type in immune exclusion samples, with increased epithelial‒mesenchymal transition and decreased immune activities. The co-location of CD14+APOE+ cells and MMP7+ tumour cells was observed in both ST and bulk transcriptomics data, validated by multiplex immunofluorescence performed on 20 NSCLC samples. The co-location area exhibited the upregulation of proliferation-related pathways and hypoxia activities. This co-localisation inhibited T-cell infiltration and the formation of tertiary lymphoid structures. Both CD14+APOE+ cells and MMP7+ tumour cells were associated with worse survival. In an immunotherapy cohort from the ORIENT-3 clinical trial, NSCLC patients who responded unfavourably exhibited higher infiltration of CD14+APOE+ cells and MMP7+ tumour cells. Within the co-location area, the MK, SEMA3 and Macrophage migration inhibitory factor (MIF) signalling pathway was most active in cell‒cell communication. This study identified immune exclusion and activation patterns in NSCLC and the co-location of CD14+APOE+ cells and MMP7+ tumour cells as contributors to immune resistance.
Collapse
Affiliation(s)
- Guangyu Fan
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeBeijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted DrugsBeijingChina
| | - Tongji Xie
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeBeijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted DrugsBeijingChina
| | - Le Tang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeBeijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted DrugsBeijingChina
| | - Lin Li
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences & Peking Union Medical CollegeBeijingChina
| | - Xiaohong Han
- Clinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Clinical PK & PD Investigation for Innovative DrugsChinese Academy of Medical Sciences & Peking Union Medical CollegeBeijingChina
| | - Yuankai Shi
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeBeijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted DrugsBeijingChina
| |
Collapse
|
17
|
Kumar R, Kolloli A, Subbian S, Kaushal D, Shi L, Tyagi S. Imaging the Architecture of Granulomas Induced by Mycobacterium tuberculosis Infection with Single-molecule Fluorescence In Situ Hybridization. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2024; 213:526-537. [PMID: 38912840 PMCID: PMC11407750 DOI: 10.4049/jimmunol.2300068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 05/30/2024] [Indexed: 06/25/2024]
Abstract
Granulomas are an important hallmark of Mycobacterium tuberculosis infection. They are organized and dynamic structures created when immune cells assemble around the sites of infection in the lungs that locally restrict M. tuberculosis growth and the host's inflammatory responses. The cellular architecture of granulomas is traditionally studied by immunofluorescence labeling of surface markers on the host cells. However, very few Abs are available for model animals used in tuberculosis research, such as nonhuman primates and rabbits, and secreted immunological markers such as cytokines cannot be imaged in situ using Abs. Furthermore, traditional phenotypic surface markers do not provide sufficient resolution for the detection of the many subtypes and differentiation states of immune cells. Using single-molecule fluorescence in situ hybridization (smFISH) and its derivatives, amplified smFISH and iterative smFISH, we developed a platform for imaging mRNAs encoding immune markers in rabbit and macaque tuberculosis granulomas. Multiplexed imaging for several mRNA and protein markers was followed by quantitative measurement of the expression of these markers in single cells. An analysis of the combinatorial expressions of these markers allowed us to classify the cells into several subtypes, and to chart their densities within granulomas. For one mRNA target, hypoxia-inducible factor-1α, we imaged its mRNA and protein in the same cells, demonstrating the specificity of the probes. This method paves the way for defining granular differentiation states and cell subtypes from transcriptomic data, identifying key mRNA markers for these cell subtypes, and then locating the cells in the spatial context of granulomas.
Collapse
Affiliation(s)
| | | | - Selvakumar Subbian
- Public Health Research Institute
- Department of Medicine, New Jersey Medical School, Rutgers University, Newark, NJ
| | - Deepak Kaushal
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX
| | - Lanbo Shi
- Public Health Research Institute
- Department of Medicine, New Jersey Medical School, Rutgers University, Newark, NJ
| | - Sanjay Tyagi
- Public Health Research Institute
- Department of Medicine, New Jersey Medical School, Rutgers University, Newark, NJ
| |
Collapse
|
18
|
Zaman F, Smith ML, Balagopal A, Durand CM, Redd AD, Tobian AAR. Spatial technologies to evaluate the HIV-1 reservoir and its microenvironment in the lymph node. mBio 2024; 15:e0190924. [PMID: 39058091 PMCID: PMC11324018 DOI: 10.1128/mbio.01909-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2024] Open
Abstract
The presence of the HIV-1 reservoir, a group of immune cells that contain intact, integrated, and replication-competent proviruses, is a major challenge to cure HIV-1. HIV-1 reservoir cells are largely unaffected by the cytopathic effects of viruses, antiviral immune responses, or antiretroviral therapy (ART). The HIV-1 reservoir is seeded early during HIV-1 infection and augmented during active viral replication. CD4+ T cells are the primary target for HIV-1 infection, and recent studies suggest that memory T follicular helper cells within the lymph node, more precisely in the B cell follicle, harbor integrated provirus, which contribute to viral rebound upon ART discontinuation. The B cell follicle, more specifically the germinal center, possesses a unique environment because of its distinct property of being partly immune privileged, potentially allowing HIV-1-infected cells within the lymph nodes to be protected from CD8+ T cells. This modified immune response in the germinal center of the follicle is potentially explained by the exclusion of CD8+ T cells and the presence of T regulatory cells at the junction of the follicle and extrafollicular region. The proviral makeup of HIV-1-infected cells is similar in lymph nodes and blood, suggesting trafficking between these compartments. Little is known about the cell-to-cell interactions, microenvironment of HIV-1-infected cells in the follicle, and trafficking between the lymph node follicle and other body compartments. Applying a spatiotemporal approach that integrates genomics, transcriptomics, and proteomics to investigate the HIV-1 reservoir and its neighboring cells in the lymph node has promising potential for informing HIV-1 cure efforts.
Collapse
Affiliation(s)
- Fatima Zaman
- Department of
Pathology, Johns Hopkins University School of
Medicine, Baltimore,
Maryland, USA
| | - Melissa L. Smith
- Department of
Biochemistry and Molecular Genetics, University of Louisville School of
Medicine, Louisville,
Kentucky, USA
| | - Ashwin Balagopal
- Division of Infectious
Diseases, Department of Medicine, Johns Hopkins
University, Baltimore,
Maryland, USA
| | - Christine M. Durand
- Division of Infectious
Diseases, Department of Medicine, Johns Hopkins
University, Baltimore,
Maryland, USA
| | - Andrew D. Redd
- Division of Infectious
Diseases, Department of Medicine, Johns Hopkins
University, Baltimore,
Maryland, USA
- Laboratory of
Immunoregulation, National Institute of Allergy and Infectious Diseases,
National Institutes of Health,
Bethesda, Maryland, USA
- Institute of
Infectious Disease and Molecular Medicine, University of Cape
Town, Cape Town,
South Africa
| | - Aaron A. R. Tobian
- Department of
Pathology, Johns Hopkins University School of
Medicine, Baltimore,
Maryland, USA
- Division of Infectious
Diseases, Department of Medicine, Johns Hopkins
University, Baltimore,
Maryland, USA
| |
Collapse
|
19
|
Huang X, Zhao X, Li Y, Feng Y, Zhang G, Wang Q, Xu C. Combining Bulk and Single Cell RNA-Sequencing Data to Identify Hub Genes of Fibroblasts in Dilated Cardiomyopathy. J Inflamm Res 2024; 17:5375-5388. [PMID: 39161677 PMCID: PMC11330748 DOI: 10.2147/jir.s470860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Accepted: 08/02/2024] [Indexed: 08/21/2024] Open
Abstract
Background Dilated cardiomyopathy (DCM) is the second leading cause of heart failure, with intricate pathophysiological underpinnings. In order to shed fresh light on the mechanistic research of DCM, we combined bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data to examine significant cells and genes implicated in the disease. Methods This analysis employed publicly accessible bulk RNA-seq and scRNA-seq DCM datasets. The scRNA-seq data underwent normalization, principal component, and t-distribution stochastic neighbor embedding analysis. Cell-to-cell communication networks and activity analysis were conducted using CellChat. Utilizing enrichment analysis, the marker genes' role in the active cells was evaluated. After screening by limma software and weighted gene co-expression network analysis, the differentially expressed genes (DEGs) served as hub genes. Furthermore, these hub genes were subjected to immunological studies, transcription factor expression, and gene set enrichment. Lastly, the expression of the four hub genes and their connection to DCM were verified using the rat models. Results Fibroblasts and monocytes were chosen as hub cells from among the eight identified cell clusters; their marker genes intersected with DEGs to yield six hub genes. In addition, the six hub genes and the essential module genes intersected to yield four essential genes (ASPN, SFRP4, LUM, and FRZB) that were connected to the Wnt signaling pathway and highly expressed in fibroblast. The four hub DEGs had an expression pattern in the DCM rat model experiment results that was in line with the findings of the bioinformatics study. Additionally, there was a strong correlation between decreased cardiac function and the up-regulation of ASPN, SFRP4, LUM, and FRZB. Conclusion Ultimately, bulk RNA-seq and scRNA-seq data identified fibroblasts and monocytes as the main cell types implicated in DCM. The highly expressed genes ASPN, FRZB, LUM, and SFRP4 in fibroblasts may aid in the mechanistic investigation of DCM.
Collapse
Affiliation(s)
- Xiaoyan Huang
- Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People’s Hospital, Xi’an, People’s Republic of China
- Shaanxi Engineering Research Center of Cell Immunology, Shaanxi Provincial People’s Hospital, Xi’an, People’s Republic of China
| | - Xiangrong Zhao
- Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People’s Hospital, Xi’an, People’s Republic of China
- Shaanxi Engineering Research Center of Cell Immunology, Shaanxi Provincial People’s Hospital, Xi’an, People’s Republic of China
| | - Yaping Li
- Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People’s Hospital, Xi’an, People’s Republic of China
- Shaanxi Engineering Research Center of Cell Immunology, Shaanxi Provincial People’s Hospital, Xi’an, People’s Republic of China
| | - Yangmeng Feng
- Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People’s Hospital, Xi’an, People’s Republic of China
- Shaanxi Engineering Research Center of Cell Immunology, Shaanxi Provincial People’s Hospital, Xi’an, People’s Republic of China
| | - Guoan Zhang
- Department of Cardiovascular Surgery, Shaanxi Provincial People’s Hospital, Xi’an, People’s Republic of China
| | - Qiyu Wang
- Department of Graduate School, Yan’an University, Yan’an, People’s Republic of China
| | - Cuixiang Xu
- Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People’s Hospital, Xi’an, People’s Republic of China
- Shaanxi Engineering Research Center of Cell Immunology, Shaanxi Provincial People’s Hospital, Xi’an, People’s Republic of China
| |
Collapse
|
20
|
Hu Y, Xie M, Li Y, Rao M, Shen W, Luo C, Qin H, Baek J, Zhou XM. Benchmarking clustering, alignment, and integration methods for spatial transcriptomics. Genome Biol 2024; 25:212. [PMID: 39123269 PMCID: PMC11312151 DOI: 10.1186/s13059-024-03361-0] [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: 03/12/2024] [Accepted: 07/30/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Spatial transcriptomics (ST) is advancing our understanding of complex tissues and organisms. However, building a robust clustering algorithm to define spatially coherent regions in a single tissue slice and aligning or integrating multiple tissue slices originating from diverse sources for essential downstream analyses remains challenging. Numerous clustering, alignment, and integration methods have been specifically designed for ST data by leveraging its spatial information. The absence of comprehensive benchmark studies complicates the selection of methods and future method development. RESULTS In this study, we systematically benchmark a variety of state-of-the-art algorithms with a wide range of real and simulated datasets of varying sizes, technologies, species, and complexity. We analyze the strengths and weaknesses of each method using diverse quantitative and qualitative metrics and analyses, including eight metrics for spatial clustering accuracy and contiguity, uniform manifold approximation and projection visualization, layer-wise and spot-to-spot alignment accuracy, and 3D reconstruction, which are designed to assess method performance as well as data quality. The code used for evaluation is available on our GitHub. Additionally, we provide online notebook tutorials and documentation to facilitate the reproduction of all benchmarking results and to support the study of new methods and new datasets. CONCLUSIONS Our analyses lead to comprehensive recommendations that cover multiple aspects, helping users to select optimal tools for their specific needs and guide future method development.
Collapse
Affiliation(s)
- Yunfei Hu
- Department of Computer Science, Vanderbilt University, 37235, Nashville, USA
| | - Manfei Xie
- Department of Biomedical Engineering, Vanderbilt University, 37235, Nashville, USA
| | - Yikang Li
- Department of Biomedical Engineering, Vanderbilt University, 37235, Nashville, USA
| | - Mingxing Rao
- Department of Computer Science, Vanderbilt University, 37235, Nashville, USA
| | - Wenjun Shen
- Department of Bioinformatics, Shantou University Medical College, 515041, Shantou, China
| | - Can Luo
- Department of Biomedical Engineering, Vanderbilt University, 37235, Nashville, USA
| | - Haoran Qin
- Department of Computer Science, Vanderbilt University, 37235, Nashville, USA
| | - Jihoon Baek
- Department of Computer Science, Vanderbilt University, 37235, Nashville, USA
| | - Xin Maizie Zhou
- Department of Computer Science, Vanderbilt University, 37235, Nashville, USA.
- Department of Biomedical Engineering, Vanderbilt University, 37235, Nashville, USA.
| |
Collapse
|
21
|
Isnard P, Li D, Xuanyuan Q, Wu H, Humphreys BD. Histopathological-Based Analysis of Human Kidney Spatial Transcriptomics Data: Toward Precision Pathology. THE AMERICAN JOURNAL OF PATHOLOGY 2024:S0002-9440(24)00274-8. [PMID: 39097165 DOI: 10.1016/j.ajpath.2024.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 06/04/2024] [Accepted: 06/26/2024] [Indexed: 08/05/2024]
Abstract
The application of spatial transcriptomics (ST) technologies is booming and has already yielded important insights across many different tissues and disease models. In nephrology, ST technologies have helped to decipher the cellular and molecular mechanisms at work in kidney diseases and have allowed the recent creation of spatially anchored human kidney atlases in healthy and diseased kidney tissues. During ST data analysis, the obtained computationally annotated clusters are often superimposed on a histologic image without their initial identification being based on the morphologic and spatial analyses of the tissues and lesions. In this study, we conduct a histopathologic-based analysis of ST data on a human kidney sample corresponding as closely as possible to the reality of the interpretation of a kidney biopsy sample in a health care or research context. This study shows the feasibility of a morphology-based approach to interpreting ST data, helping to improve our understanding of the lesion phenomena at work in chronic kidney disease at both the cellular and the molecular level. Finally, we show that our newly identified pathology-based clusters can be accurately projected onto other slides from nephrectomy or needle biopsy samples. They thus serve as a reference for analyzing other kidney tissues, paving the way for the future of molecular microscopy and precision pathology.
Collapse
Affiliation(s)
- Pierre Isnard
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Dian Li
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Qiao Xuanyuan
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Haojia Wu
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Benjamin D Humphreys
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, Missouri; Department of Developmental Biology, Washington University in St. Louis, St. Louis, Missouri.
| |
Collapse
|
22
|
Mao G, Yang Y, Luo Z, Lin C, Xie P. SpatialQC: automated quality control for spatial transcriptome data. Bioinformatics 2024; 40:btae458. [PMID: 39051702 PMCID: PMC11333854 DOI: 10.1093/bioinformatics/btae458] [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: 02/14/2024] [Revised: 07/04/2024] [Accepted: 07/24/2024] [Indexed: 07/27/2024] Open
Abstract
SUMMARY The advent of spatial transcriptomics has revolutionized our understanding of the spatial heterogeneity in tissues, providing unprecedented insights into the cellular and molecular mechanisms underlying biological processes. Although quality control (QC) critical for downstream data analyses, there is currently a lack of specialized tools for one-stop spatial transcriptome QC. Here, we introduce SpatialQC, a one-stop QC pipeline, which generates comprehensive QC reports and produces clean data in an interactive fashion. SpatialQC is widely applicable to spatial transcriptomic techniques. AVAILABILITY AND IMPLEMENTATION source code and user manuals are available via https://github.com/mgy520/spatialQC, and deposited on Zenodo (https://doi.org/10.5281/zenodo.12634669).
Collapse
Affiliation(s)
- Guangyao Mao
- Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing 210000, China
- Co-innovation Center of Neuroregeneration, Nantong University, Nantong 226000, China
| | - Yi Yang
- Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing 210000, China
| | - Zhuojuan Luo
- Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing 210000, China
- Co-innovation Center of Neuroregeneration, Nantong University, Nantong 226000, China
- Center of Reproductive Medicine, Fujian Maternity and Child Health Hospital, Fuzhou 350000, China
- Shenzhen Research Institute, Southeast University, Shenzhen, China
| | - Chengqi Lin
- Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing 210000, China
- Co-innovation Center of Neuroregeneration, Nantong University, Nantong 226000, China
- Center of Reproductive Medicine, Fujian Maternity and Child Health Hospital, Fuzhou 350000, China
- Shenzhen Research Institute, Southeast University, Shenzhen, China
| | - Peng Xie
- School of Biological Science & Medical Engineering, Southeast University, Nanjing 518000, China
| |
Collapse
|
23
|
Lu H, Suo Z, Lin J, Cong Y, Liu Z. Monocyte-macrophages modulate intestinal homeostasis in inflammatory bowel disease. Biomark Res 2024; 12:76. [PMID: 39095853 PMCID: PMC11295551 DOI: 10.1186/s40364-024-00612-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 07/04/2024] [Indexed: 08/04/2024] Open
Abstract
BACKGROUND Monocytes and macrophages play an indispensable role in maintaining intestinal homeostasis and modulating mucosal immune responses in inflammatory bowel disease (IBD). Although numerous studies have described macrophage properties in IBD, the underlying mechanisms whereby the monocyte-macrophage lineage modulates intestinal homeostasis during gut inflammation remain elusive. MAIN BODY In this review, we decipher the cellular and molecular mechanisms governing the generation of intestinal mucosal macrophages and fill the knowledge gap in understanding the origin, maturation, classification, and functions of mucosal macrophages in intestinal niches, particularly the phagocytosis and bactericidal effects involved in the elimination of cell debris and pathogens. We delineate macrophage-mediated immunoregulation in the context of producing pro-inflammatory and anti-inflammatory cytokines, chemokines, toxic mediators, and macrophage extracellular traps (METs), and participating in the modulation of epithelial cell proliferation, angiogenesis, and fibrosis in the intestine and its accessory tissues. Moreover, we emphasize that the maturation of intestinal macrophages is arrested at immature stage during IBD, and the deficiency of MCPIP1 involves in the process via ATF3-AP1S2 signature. In addition, we confirmed the origin potential of IL-1B+ macrophages and defined C1QB+ macrophages as mature macrophages. The interaction crosstalk between the intestine and the mesentery has been described in this review, and the expression of mesentery-derived SAA2 is upregulated during IBD, which contributes to immunoregulation of macrophage. Moreover, we also highlight IBD-related susceptibility genes (e.g., RUNX3, IL21R, GTF2I, and LILRB3) associated with the maturation and functions of macrophage, which provide promising therapeutic opportunities for treating human IBD. CONCLUSION In summary, this review provides a comprehensive, comprehensive, in-depth and novel description of the characteristics and functions of macrophages in IBD, and highlights the important role of macrophages in the molecular and cellular process during IBD.
Collapse
Affiliation(s)
- Huiying Lu
- Department of Gastroenterology, Huaihe Hospital of Henan University, Henan Province, Kaifeng, 475000, China
- Center for Inflammatory Bowel Disease Research and Department of Gastroenterology, Shanghai Tenth People's Hospital of Tongji University, No. 301 Yanchang Road, Shanghai, 200072, China
| | - Zhimin Suo
- Department of Gastroenterology, Huaihe Hospital of Henan University, Henan Province, Kaifeng, 475000, China
| | - Jian Lin
- Center for Inflammatory Bowel Disease Research and Department of Gastroenterology, Shanghai Tenth People's Hospital of Tongji University, No. 301 Yanchang Road, Shanghai, 200072, China
| | - Yingzi Cong
- Division of Gastroenterology and Hepatology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
- Center for Human Immunology, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Zhanju Liu
- Center for Inflammatory Bowel Disease Research and Department of Gastroenterology, Shanghai Tenth People's Hospital of Tongji University, No. 301 Yanchang Road, Shanghai, 200072, China.
| |
Collapse
|
24
|
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.
| |
Collapse
|
25
|
Zhang J, Huang Y, Tan X, Wang Z, Cheng R, Zhang S, Chen Y, Jiang F, Tan W, Deng X, Li F. Integrated analysis of multiple transcriptomic approaches and machine learning integration algorithms reveals high endothelial venules as a prognostic immune-related biomarker in bladder cancer. Int Immunopharmacol 2024; 136:112184. [PMID: 38824904 DOI: 10.1016/j.intimp.2024.112184] [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: 03/15/2024] [Revised: 04/19/2024] [Accepted: 04/28/2024] [Indexed: 06/04/2024]
Abstract
BACKGROUND Despite the availability of established surgical and chemotherapy options, the treatment of bladder cancer (BCa) patients remains challenging. While immunotherapy has emerged as a promising approach, its benefits are limited to a subset of patients. The exploration of additional targets to enhance the efficacy of immunotherapy is a valuable research direction. METHOD High endothelial venules (HEV) ssGSEA analysis was conducted using BEST. Through the utilization of R packages Limma, Seurat, SingleR, and Harmony, analyses were performed on spatial transcriptomics, bulk RNA-sequencing (bulk RNA-seq), and single-cell RNA sequencing (scRNA-seq) data, yielding HEV-related genes (HEV.RGs). Molecular subtyping analysis based on HEV.RGs was conducted using R package MOVICS, and various machine learning-integrated algorithm was employed to construct prognostic model. LDLRAD3 was validated through subcutaneous tumor formation in mice, HEV induction, Western blot, and qPCR. RESULTS A correlation between higher HEV levels and improved immune response and prognosis was revealed by HEV ssGSEA analysis in BCa patients receiving immunotherapy. HEV.RGs were identified in subsequent transcriptomic analyses. Based on these genes, BCa patients were stratified into two molecular clusters with distinct survival and immune infiltration patterns using various clustering-integrated algorithm. Prognostic model was developed using multiple machine learning-integrated algorithm. Low LDLRAD3 expression may promote HEV generation, leading to enhanced immunotherapy efficacy, as suggested by bulk RNA-seq, scRNA-seq analyses, and experimental validation of LDLRAD3. CONCLUSIONS HEV served as a predictive factor for immune response and prognosis in BCa patients receiving immunotherapy. LDLRAD3 represented a potential target for HEV induction and enhancing the efficacy of immunotherapy.
Collapse
Affiliation(s)
- Jinge Zhang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Yuan Huang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Xing Tan
- Department of Nanfang Hospital Administration Office, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Zihuan Wang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Ranyang Cheng
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Shenlan Zhang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Yuwen Chen
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Feifan Jiang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Wanlong Tan
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China.
| | - Xiaolin Deng
- Department of Urology, Ganzhou People's Hospital, Ganzhou, PR China.
| | - Fei Li
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China.
| |
Collapse
|
26
|
Fortner A, Bucur O. Multiplexed spatial transcriptomics methods and the application of expansion microscopy. Front Cell Dev Biol 2024; 12:1378875. [PMID: 39105173 PMCID: PMC11298486 DOI: 10.3389/fcell.2024.1378875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 06/10/2024] [Indexed: 08/07/2024] Open
Abstract
While spatial transcriptomics has undeniably revolutionized our ability to study cellular organization, it has driven the development of a great number of innovative transcriptomics methods, which can be classified into in situ sequencing (ISS) methods, in situ hybridization (ISH) techniques, and next-generation sequencing (NGS)-based sequencing with region capture. These technologies not only refine our understanding of cellular processes, but also open up new possibilities for breakthroughs in various research domains. One challenge of spatial transcriptomics experiments is the limitation of RNA detection due to optical crowding of RNA in the cells. Expansion microscopy (ExM), characterized by the controlled enlargement of biological specimens, offers a means to achieve super-resolution imaging, overcoming the diffraction limit inherent in conventional microscopy and enabling precise visualization of RNA in spatial transcriptomics methods. In this review, we elaborate on ISS, ISH and NGS-based spatial transcriptomic protocols and on how performance of these techniques can be extended by the combination of these protocols with ExM. Moving beyond the techniques and procedures, we highlight the broader implications of transcriptomics in biology and medicine. These include valuable insight into the spatial organization of gene expression in cells within tissues, aid in the identification and the distinction of cell types and subpopulations and understanding of molecular mechanisms and intercellular changes driving disease development.
Collapse
Affiliation(s)
- Andra Fortner
- Medical School, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany
- Victor Babes National Institute of Pathology, Bucharest, Romania
| | - Octavian Bucur
- Victor Babes National Institute of Pathology, Bucharest, Romania
- Genomics Research and Development Institute, Bucharest, Romania
| |
Collapse
|
27
|
Zhang H, Lu KH, Ebbini M, Huang P, Lu H, Li L. Mass spectrometry imaging for spatially resolved multi-omics molecular mapping. NPJ IMAGING 2024; 2:20. [PMID: 39036554 PMCID: PMC11254763 DOI: 10.1038/s44303-024-00025-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 06/21/2024] [Indexed: 07/23/2024]
Abstract
The recent upswing in the integration of spatial multi-omics for conducting multidimensional information measurements is opening a new chapter in biological research. Mapping the landscape of various biomolecules including metabolites, proteins, nucleic acids, etc., and even deciphering their functional interactions and pathways is believed to provide a more holistic and nuanced exploration of the molecular intricacies within living systems. Mass spectrometry imaging (MSI) stands as a forefront technique for spatially mapping the metabolome, lipidome, and proteome within diverse tissue and cell samples. In this review, we offer a systematic survey delineating different MSI techniques for spatially resolved multi-omics analysis, elucidating their principles, capabilities, and limitations. Particularly, we focus on the advancements in methodologies aimed at augmenting the molecular sensitivity and specificity of MSI; and depict the burgeoning integration of MSI-based spatial metabolomics, lipidomics, and proteomics, encompassing the synergy with other imaging modalities. Furthermore, we offer speculative insights into the potential trajectory of MSI technology in the future.
Collapse
Affiliation(s)
- Hua Zhang
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Kelly H. Lu
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Malik Ebbini
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Penghsuan Huang
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Haiyan Lu
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Lingjun Li
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706 USA
- Lachman Institute for Pharmaceutical Development, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
- Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
| |
Collapse
|
28
|
Zhao Q, Shao H, Zhang T. Single-cell RNA sequencing in ovarian cancer: revealing new perspectives in the tumor microenvironment. Am J Transl Res 2024; 16:3338-3354. [PMID: 39114691 PMCID: PMC11301471 DOI: 10.62347/smsg9047] [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: 03/25/2024] [Accepted: 06/30/2024] [Indexed: 08/10/2024]
Abstract
Single-cell sequencing technology has emerged as a pivotal tool for unraveling the complexities of the ovarian tumor microenvironment (TME), which is characterized by its cellular heterogeneity and intricate cell-to-cell interactions. Ovarian cancer (OC), known for its high lethality among gynecologic malignancies, presents significant challenges in treatment and diagnosis, partly due to the complexity of its TME. The application of single-cell sequencing in ovarian cancer research has enabled the detailed characterization of gene expression profiles at the single-cell level, shedding light on the diverse cell populations within the TME, including cancer cells, stromal cells, and immune cells. This high-resolution mapping has been instrumental in understanding the roles of these cells in tumor progression, invasion, metastasis, and drug resistance. By providing insight into the signaling pathways and cell-to-cell communication mechanisms, single-cell sequencing facilitates the identification of novel therapeutic targets and the development of personalized medicine approaches. This review summarizes the advancement and application of single-cell sequencing in studying the stromal components and the broader TME in OC, highlighting its implications for improving diagnosis, treatment strategies, and understanding of the disease's underlying biology.
Collapse
Affiliation(s)
- Qiannan Zhao
- Department of Clinical Laboratory, Yantaishan HospitalYantai 264003, Shandong, P. R. China
| | - Huaming Shao
- Department of Medical Laboratory, Qingdao West Coast Second HospitalQingdao 266500, Shandong, P. R. China
| | - Tianmei Zhang
- Department of Gynecology, Yantaishan HospitalYantai 264003, Shandong, P. R. China
| |
Collapse
|
29
|
Liao L, Martin PCN, Kim H, Panahandeh S, Won KJ. Data enhancement in the age of spatial biology. Adv Cancer Res 2024; 163:39-70. [PMID: 39271267 DOI: 10.1016/bs.acr.2024.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
Unveiling the intricate interplay of cells in their native environment lies at the heart of understanding fundamental biological processes and unraveling disease mechanisms, particularly in complex diseases like cancer. Spatial transcriptomics (ST) offers a revolutionary lens into the spatial organization of gene expression within tissues, empowering researchers to study both cell heterogeneity and microenvironments in health and disease. However, current ST technologies often face limitations in either resolution or the number of genes profiled simultaneously. Integrating ST data with complementary sources, such as single-cell transcriptomics and detailed tissue staining images, presents a powerful solution to overcome these limitations. This review delves into the computational approaches driving the integration of spatial transcriptomics with other data types. By illuminating the key challenges and outlining the current algorithmic solutions, we aim to highlight the immense potential of these methods to revolutionize our understanding of cancer biology.
Collapse
Affiliation(s)
- Linbu Liao
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Denmark; Samuel Oschin Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Patrick C N Martin
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Hyobin Kim
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Sanaz Panahandeh
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Kyoung Jae Won
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
| |
Collapse
|
30
|
Ghannam A, Hahn V, Fan J, Tasevski S, Moughni S, Li G, Zhang Z. Sex-specific and cell-specific regulation of ER stress and neuroinflammation after traumatic brain injury in juvenile mice. Exp Neurol 2024; 377:114806. [PMID: 38701941 DOI: 10.1016/j.expneurol.2024.114806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 04/14/2024] [Accepted: 04/30/2024] [Indexed: 05/06/2024]
Abstract
Endoplasmic reticulum (ER) stress and neuroinflammation play an important role in secondary brain damage after traumatic brain injury (TBI). Due to the complex brain cytoarchitecture, multiple cell types are affected by TBI. However, cell type-specific and sex-specific responses to ER stress and neuroinflammation remain unclear. Here we investigated differential regulation of ER stress and neuroinflammatory pathways in neurons and microglia during the acute phase post-injury in a mouse model of impact acceleration TBI in both males and females. We found that TBI resulted in significant weight loss only in males, and sensorimotor impairment and depressive-like behaviors in both males and females at the acute phase post-injury. By concurrently isolating neurons and microglia from the same brain sample of the same animal, we were able to evaluate the simultaneous responses in neurons and microglia towards ER stress and neuroinflammation in both males and females. We discovered that the ER stress and anti-inflammatory responses were significantly stronger in microglia, especially in female microglia, compared with the male and female neurons. Whereas the degree of phosphorylated-tau (pTau) accumulation was significantly higher in neurons, compared with the microglia. In conclusion, TBI resulted in behavioral deficits and cell type-specific and sex-specific responses to ER stress and neuroinflammation, and abnormal protein accumulation at the acute phase after TBI in immature mice.
Collapse
Affiliation(s)
- Amanda Ghannam
- Department of Natural Sciences, College of Arts, Sciences, and Letters, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, United States of America.
| | - Victoria Hahn
- Department of Natural Sciences, College of Arts, Sciences, and Letters, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, United States of America.
| | - Jie Fan
- Department of Natural Sciences, College of Arts, Sciences, and Letters, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, United States of America.
| | - Stefanie Tasevski
- Department of Natural Sciences, College of Arts, Sciences, and Letters, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, United States of America.
| | - Sara Moughni
- Department of Natural Sciences, College of Arts, Sciences, and Letters, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, United States of America.
| | - Gengxin Li
- Statistics, Department of Mathematics and Statistics, College of Arts, Sciences, and Letters, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, United States of America.
| | - Zhi Zhang
- Department of Natural Sciences, College of Arts, Sciences, and Letters, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, United States of America.
| |
Collapse
|
31
|
Karpov OA, Stotland A, Raedschelders K, Chazarin B, Ai L, Murray CI, Van Eyk JE. Proteomics of the heart. Physiol Rev 2024; 104:931-982. [PMID: 38300522 PMCID: PMC11381016 DOI: 10.1152/physrev.00026.2023] [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: 07/03/2023] [Revised: 12/25/2023] [Accepted: 01/14/2024] [Indexed: 02/02/2024] Open
Abstract
Mass spectrometry-based proteomics is a sophisticated identification tool specializing in portraying protein dynamics at a molecular level. Proteomics provides biologists with a snapshot of context-dependent protein and proteoform expression, structural conformations, dynamic turnover, and protein-protein interactions. Cardiac proteomics can offer a broader and deeper understanding of the molecular mechanisms that underscore cardiovascular disease, and it is foundational to the development of future therapeutic interventions. This review encapsulates the evolution, current technologies, and future perspectives of proteomic-based mass spectrometry as it applies to the study of the heart. Key technological advancements have allowed researchers to study proteomes at a single-cell level and employ robot-assisted automation systems for enhanced sample preparation techniques, and the increase in fidelity of the mass spectrometers has allowed for the unambiguous identification of numerous dynamic posttranslational modifications. Animal models of cardiovascular disease, ranging from early animal experiments to current sophisticated models of heart failure with preserved ejection fraction, have provided the tools to study a challenging organ in the laboratory. Further technological development will pave the way for the implementation of proteomics even closer within the clinical setting, allowing not only scientists but also patients to benefit from an understanding of protein interplay as it relates to cardiac disease physiology.
Collapse
Affiliation(s)
- Oleg A Karpov
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Aleksandr Stotland
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Koen Raedschelders
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Blandine Chazarin
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Lizhuo Ai
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Christopher I Murray
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| | - Jennifer E Van Eyk
- Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States
| |
Collapse
|
32
|
Nössing C, Herek P, Shariat SF, Berger W, Englinger B. Advances in preclinical assessment of therapeutic targets for bladder cancer precision medicine. Curr Opin Urol 2024; 34:251-257. [PMID: 38602053 PMCID: PMC11155291 DOI: 10.1097/mou.0000000000001177] [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: 04/12/2024]
Abstract
PURPOSE OF REVIEW Bladder cancer incidence is on the rise, and until recently, there has been little to no change in treatment regimens over the last 40 years. Hence, it is imperative to work on strategies and approaches to untangle the complexity of intra- and inter-tumour heterogeneity of bladder cancer with the aim of improving patient-specific care and treatment outcomes. The focus of this review is therefore to highlight novel targets, advances, and therapy approaches for bladder cancer patients. RECENT FINDINGS The success of combining an antibody-drug conjugate (ADC) with immunotherapy has been recently hailed as a game changer in treating bladder cancer patients. Hence, interest in other ADCs as a treatment option is also rife. Furthermore, strategies to overcome chemoresistance to standard therapy have been described recently. In addition, other studies showed that targeting genomic alterations (e.g. mutations in FGFR3 , DNA damage repair genes and loss of the Y chromosome) could also be helpful as prognostic and treatment stratification biomarkers. The use of single-cell RNA sequencing approaches has allowed better characterisation of the tumour microenvironment and subsequent identification of novel targets. Functional precision medicine could be another avenue to improve and guide personalized treatment options. SUMMARY Several novel preclinical targets and treatment options have been described recently. The validation of these advances will lead to the development and implementation of robust personalized treatment regimens for bladder cancer patients.
Collapse
Affiliation(s)
| | - Paula Herek
- Department of Urology, Comprehensive Cancer Center
- Center for Cancer Research and Comprehensive Cancer Center, Medical University of Vienna, Austria
| | - Shahrokh F. Shariat
- Department of Urology, Comprehensive Cancer Center
- Department of Urology, Weill Cornell Medical College, New York, New York
- Department of Urology, University of Texas Southwestern, Dallas, Texas, USA
- Department of Urology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
- Institute for Urology, University of Jordan, Amman, Jordan
- Research center for Evidence Medicine, Urology Department, Tabriz University of Medical Sciences, Tabriz, Iran
- Karl Landsteiner Institute of Urology and Andrology, Vienna, Austria
| | - Walter Berger
- Center for Cancer Research and Comprehensive Cancer Center, Medical University of Vienna, Austria
| | - Bernhard Englinger
- Department of Urology, Comprehensive Cancer Center
- Center for Cancer Research and Comprehensive Cancer Center, Medical University of Vienna, Austria
| |
Collapse
|
33
|
Chen R, Nie P, Wang J, Wang GZ. Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1865. [PMID: 38972934 DOI: 10.1002/wrna.1865] [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: 01/31/2024] [Revised: 05/05/2024] [Accepted: 05/14/2024] [Indexed: 07/09/2024]
Abstract
The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.
Collapse
Affiliation(s)
- Renrui Chen
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Pengxing Nie
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jing Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Guang-Zhong Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| |
Collapse
|
34
|
Ohn J, Seo MK, Park J, Lee D, Choi H. SpatialSPM: statistical parametric mapping for the comparison of gene expression pattern images in multiple spatial transcriptomic datasets. Nucleic Acids Res 2024; 52:e51. [PMID: 38676948 PMCID: PMC11194061 DOI: 10.1093/nar/gkae293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 03/19/2024] [Accepted: 04/05/2024] [Indexed: 04/29/2024] Open
Abstract
Spatial transcriptomic (ST) techniques help us understand the gene expression levels in specific parts of tissues and organs, providing insights into their biological functions. Even though ST dataset provides information on the gene expression and its location for each sample, it is challenging to compare spatial gene expression patterns across tissue samples with different shapes and coordinates. Here, we propose a method, SpatialSPM, that reconstructs ST data into multi-dimensional image matrices to ensure comparability across different samples through spatial registration process. We demonstrated the applicability of this method by kidney and mouse olfactory bulb datasets as well as mouse brain ST datasets to investigate and directly compare gene expression in a specific anatomical region of interest, pixel by pixel, across various biological statuses. Beyond traditional analyses, SpatialSPM is capable of generating statistical parametric maps, including T-scores and Pearson correlation coefficients. This feature enables the identification of specific regions exhibiting differentially expressed genes across tissue samples, enhancing the depth and specificity of ST studies. Our approach provides an efficient way to analyze ST datasets and may offer detailed insights into various biological conditions.
Collapse
Affiliation(s)
| | | | | | | | - Hongyoon Choi
- Portrai, Inc., Seoul 03136, Republic of Korea
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| |
Collapse
|
35
|
Wehrle E, Günther D, Mathavan N, Singh A, Müller R. Protocol for preparing formalin-fixed paraffin-embedded musculoskeletal tissue samples from mice for spatial transcriptomics. STAR Protoc 2024; 5:102986. [PMID: 38555590 PMCID: PMC10998190 DOI: 10.1016/j.xpro.2024.102986] [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: 11/26/2023] [Revised: 01/29/2024] [Accepted: 03/11/2024] [Indexed: 04/02/2024] Open
Abstract
Here, we present a protocol for using spatial transcriptomics in bone and multi-tissue musculoskeletal formalin-fixed paraffin-embedded (FFPE) samples from mice. We describe steps for tissue harvesting, sample preparation, paraffin embedding, and FFPE sample selection. We detail procedures for sectioning and placement on spatial slides prior to imaging, decrosslinking, library preparation, and final analyses of the sequencing data. The complete protocol takes ca. 18 days for mouse femora with adjacent muscle; of this time, >50% is required for mineralized tissue decalcification. For complete details on the use and execution of this protocol, please refer to Wehrle et al.1 and Mathavan et al.2.
Collapse
Affiliation(s)
- Esther Wehrle
- Institute for Biomechanics, ETH Zurich, Zurich, Switzerland; AO Research Institute Davos, Davos Platz, Switzerland.
| | - Denise Günther
- Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
| | | | - Amit Singh
- Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
| | - Ralph Müller
- Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
| |
Collapse
|
36
|
Chen J, Zhou M, Wu W, Zhang J, Li Y, Li D. STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics. ARXIV 2024:arXiv:2406.06393v2. [PMID: 38947920 PMCID: PMC11213178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Recent advances in multi-modal algorithms have driven and been driven by the increasing availability of large image-text datasets, leading to significant strides in various fields, including computational pathology. However, in most existing medical image-text datasets, the text typically provides high-level summaries that may not sufficiently describe sub-tile regions within a large pathology image. For example, an image might cover an extensive tissue area containing cancerous and healthy regions, but the accompanying text might only specify that this image is a cancer slide, lacking the nuanced details needed for in-depth analysis. In this study, we introduce STimage-1K4M, a novel dataset designed to bridge this gap by providing genomic features for sub-tile images. STimage-1K4M contains 1,149 images derived from spatial transcriptomics data, which captures gene expression information at the level of individual spatial spots within a pathology image. Specifically, each image in the dataset is broken down into smaller sub-image tiles, with each tile paired with 15,000 - 30,000 dimensional gene expressions. With 4,293,195 pairs of sub-tile images and gene expressions, STimage-1K4M offers unprecedented granularity, paving the way for a wide range of advanced research in multi-modal data analysis an innovative applications in computational pathology, and beyond.
Collapse
Affiliation(s)
| | | | - Wenrong Wu
- University of North Carolina at Chapel Hill
| | | | - Yun Li
- University of North Carolina at Chapel Hill
| | - Didong Li
- University of North Carolina at Chapel Hill
| |
Collapse
|
37
|
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.
Collapse
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.
| |
Collapse
|
38
|
Chen Q, Zhang S, Liu W, Sun X, Luo Y, Sun X. Application of emerging technologies in ischemic stroke: from clinical study to basic research. Front Neurol 2024; 15:1400469. [PMID: 38915803 PMCID: PMC11194379 DOI: 10.3389/fneur.2024.1400469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 05/24/2024] [Indexed: 06/26/2024] Open
Abstract
Stroke is a primary cause of noncommunicable disease-related death and disability worldwide. The most common form, ischemic stroke, is increasing in incidence resulting in a significant burden on patients and society. Urgent action is thus needed to address preventable risk factors and improve treatment methods. This review examines emerging technologies used in the management of ischemic stroke, including neuroimaging, regenerative medicine, biology, and nanomedicine, highlighting their benefits, clinical applications, and limitations. Additionally, we suggest strategies for technological development for the prevention, diagnosis, and treatment of ischemic stroke.
Collapse
Affiliation(s)
- Qiuyan Chen
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
| | - Shuxia Zhang
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
| | - Wenxiu Liu
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
| | - Xiao Sun
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
| | - Yun Luo
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
| | - Xiaobo Sun
- Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Innovative Drug Discovery of Traditional Chinese Medicine (Natural Medicine) and Translational Medicine, Beijing, China
- Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, China
| |
Collapse
|
39
|
Chen X, Lin J, Wang Y, Zhang W, Xie W, Zheng Z, Wong KC. HE2Gene: image-to-RNA translation via multi-task learning for spatial transcriptomics data. Bioinformatics 2024; 40:btae343. [PMID: 38837395 PMCID: PMC11164830 DOI: 10.1093/bioinformatics/btae343] [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: 09/22/2023] [Revised: 05/06/2024] [Accepted: 05/25/2024] [Indexed: 06/07/2024] Open
Abstract
MOTIVATION Tissue context and molecular profiling are commonly used measures in understanding normal development and disease pathology. In recent years, the development of spatial molecular profiling technologies (e.g. spatial resolved transcriptomics) has enabled the exploration of quantitative links between tissue morphology and gene expression. However, these technologies remain expensive and time-consuming, with subsequent analyses necessitating high-throughput pathological annotations. On the other hand, existing computational tools are limited to predicting only a few dozen to several hundred genes, and the majority of the methods are designed for bulk RNA-seq. RESULTS In this context, we propose HE2Gene, the first multi-task learning-based method capable of predicting tens of thousands of spot-level gene expressions along with pathological annotations from H&E-stained images. Experimental results demonstrate that HE2Gene is comparable to state-of-the-art methods and generalizes well on an external dataset without the need for re-training. Moreover, HE2Gene preserves the annotated spatial domains and has the potential to identify biomarkers. This capability facilitates cancer diagnosis and broadens its applicability to investigate gene-disease associations. AVAILABILITY AND IMPLEMENTATION The source code and data information has been deposited at https://github.com/Microbiods/HE2Gene.
Collapse
Affiliation(s)
- Xingjian Chen
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
- Department of Computer Science, City University of Hong Kong, Kowloog Tong 999077, Hong Kong SAR
| | - Jiecong Lin
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Department of Pathology, Harvard Medical School, Boston, MA 02129, USA
- Department of Computer Science, The University of Hong Kong, Pokfulam 999077, Hong Kong SAR
| | - Yuchen Wang
- Department of Computer Science, City University of Hong Kong, Kowloog Tong 999077, Hong Kong SAR
| | - Weitong Zhang
- Department of Computer Science, City University of Hong Kong, Kowloog Tong 999077, Hong Kong SAR
| | - Weidun Xie
- Department of Computer Science, City University of Hong Kong, Kowloog Tong 999077, Hong Kong SAR
| | - Zetian Zheng
- Department of Computer Science, City University of Hong Kong, Kowloog Tong 999077, Hong Kong SAR
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Kowloog Tong 999077, Hong Kong SAR
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen 518057, China
| |
Collapse
|
40
|
Wan T, Fu C, Peng J, Lu J, Li P, Zhuo J. Repairing the in situ hybridization missing data in the hippocampus region by using a 3D residual U-Net model. BIOMEDICAL OPTICS EXPRESS 2024; 15:3541-3554. [PMID: 38867784 PMCID: PMC11166418 DOI: 10.1364/boe.522078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 03/31/2024] [Accepted: 04/22/2024] [Indexed: 06/14/2024]
Abstract
The hippocampus is a critical brain region. Transcriptome data provides valuable insights into the structure and function of the hippocampus at the gene level. However, transcriptome data is often incomplete. To address this issue, we use the convolutional neural network model to repair the missing voxels in the hippocampus region, based on Allen institute coronal slices in situ hybridization (ISH) dataset. Moreover, we analyze the gene expression correlation between coronal and sagittal dataset in the hippocampus region. The results demonstrated that the trend of gene expression correlation between the coronal and sagittal datasets remained consistent following the repair of missing data in the coronal ISH dataset. In the last, we use repaired ISH dataset to identify novel genes specific to hippocampal subregions. Our findings demonstrate the accuracy and effectiveness of using deep learning method to repair ISH missing data. After being repaired, ISH has the potential to improve our comprehension of the hippocampus's structure and function.
Collapse
Affiliation(s)
- Tong Wan
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China
- Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China
| | - Changping Fu
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China
- Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China
| | - Jiinbo Peng
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China
- Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China
| | - Jinling Lu
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China
- Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China
- Britton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Science, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou 215100, China
| | - Pengcheng Li
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China
- Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China
- Britton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Science, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou 215100, China
| | - JunJie Zhuo
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China
- Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China
| |
Collapse
|
41
|
Valihrach L, Zucha D, Abaffy P, Kubista M. A practical guide to spatial transcriptomics. Mol Aspects Med 2024; 97:101276. [PMID: 38776574 DOI: 10.1016/j.mam.2024.101276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 04/30/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024]
Abstract
Spatial transcriptomics is revolutionizing modern biology, offering researchers an unprecedented ability to unravel intricate gene expression patterns within tissues. From pioneering techniques to newly commercialized platforms, the field of spatial transcriptomics has evolved rapidly, ushering in a new era of understanding across various disciplines, from developmental biology to disease research. This dynamic expansion is reflected in the rapidly growing number of technologies and data analysis techniques developed and introduced. However, the expanding landscape presents a considerable challenge for researchers, especially newcomers to the field, as staying informed about these advancements becomes increasingly complex. To address this challenge, we have prepared an updated review with a particular focus on technologies that have reached commercialization and are, therefore, accessible to a broad spectrum of potential new users. In this review, we present the fundamental principles of spatial transcriptomic methods, discuss the challenges in data analysis, provide insights into experimental considerations, offer information about available resources for spatial transcriptomics, and conclude with a guide for method selection and a forward-looking perspective. Our aim is to serve as a guiding resource for both experienced users and newcomers navigating the complex realm of spatial transcriptomics in this era of rapid development. We intend to equip researchers with the necessary knowledge to make informed decisions and contribute to the cutting-edge research that spatial transcriptomics offers.
Collapse
Affiliation(s)
- Lukas Valihrach
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic; Department of Cellular Neurophysiology, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic.
| | - Daniel Zucha
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic; Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, Czech Republic
| | - Pavel Abaffy
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic
| | - Mikael Kubista
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic.
| |
Collapse
|
42
|
Chu LX, Wang WJ, Gu XP, Wu P, Gao C, Zhang Q, Wu J, Jiang DW, Huang JQ, Ying XW, Shen JM, Jiang Y, Luo LH, Xu JP, Ying YB, Chen HM, Fang A, Feng ZY, An SH, Li XK, Wang ZG. Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine. Mil Med Res 2024; 11:31. [PMID: 38797843 PMCID: PMC11129507 DOI: 10.1186/s40779-024-00537-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.
Collapse
Affiliation(s)
- Liu-Xi Chu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Wen-Jia Wang
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xin-Pei Gu
- School of Pharmaceutical Sciences, Guangdong Provincial Key Laboratory of New Drug Screening, Southern Medical University, Guangzhou, 510515, China
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China
| | - Ping Wu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Chen Gao
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Quan Zhang
- Integrative Muscle Biology Laboratory, Division of Regenerative and Rehabilitative Sciences, University of Tennessee Health Science Center, Memphis, TN, 38163, United States
| | - Jia Wu
- Key Laboratory for Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Da-Wei Jiang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jun-Qing Huang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China
| | - Xin-Wang Ying
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jia-Men Shen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi Jiang
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Li-Hua Luo
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 324025, Zhejiang, China
| | - Jun-Peng Xu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi-Bo Ying
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Hao-Man Chen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Ao Fang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Zun-Yong Feng
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore, 119074, Singapore.
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore.
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), Singapore, 138673, Singapore.
| | - Shu-Hong An
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China.
| | - Xiao-Kun Li
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
| | - Zhou-Guang Wang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China.
| |
Collapse
|
43
|
Holze H, Talarmain L, Fennell KA, Lam EY, Dawson MA, Vassiliadis D. Analysis of synthetic cellular barcodes in the genome and transcriptome with BARtab and bartools. CELL REPORTS METHODS 2024; 4:100763. [PMID: 38670101 PMCID: PMC11133760 DOI: 10.1016/j.crmeth.2024.100763] [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: 12/20/2023] [Revised: 02/25/2024] [Accepted: 04/03/2024] [Indexed: 04/28/2024]
Abstract
Cellular barcoding is a lineage-tracing methodology that couples heritable synthetic barcodes to high-throughput sequencing, enabling the accurate tracing of cell lineages across a range of biological contexts. Recent studies have extended these methods by incorporating lineage information into single-cell or spatial transcriptomics readouts. Leveraging the rich biological information within these datasets requires dedicated computational tools for dataset pre-processing and analysis. Here, we present BARtab, a portable and scalable Nextflow pipeline, and bartools, an open-source R package, designed to provide an integrated end-to-end cellular barcoding analysis toolkit. BARtab and bartools contain methods to simplify the extraction, quality control, analysis, and visualization of lineage barcodes from population-level, single-cell, and spatial transcriptomics experiments. We showcase the utility of our integrated BARtab and bartools workflow via the analysis of exemplar bulk, single-cell, and spatial transcriptomics experiments containing cellular barcoding information.
Collapse
Affiliation(s)
- Henrietta Holze
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Laure Talarmain
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Katie A Fennell
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Enid Y Lam
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Mark A Dawson
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia; The University of Melbourne Centre for Cancer Research, The University of Melbourne, Melbourne, VIC 3000, Australia.
| | - Dane Vassiliadis
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia.
| |
Collapse
|
44
|
Cao J, Li C, Cui Z, Deng S, Lei T, Liu W, Yang H, Chen P. Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery. Theranostics 2024; 14:2946-2968. [PMID: 38773973 PMCID: PMC11103497 DOI: 10.7150/thno.95908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/25/2024] [Indexed: 05/24/2024] Open
Abstract
Recent advancements in modern science have provided robust tools for drug discovery. The rapid development of transcriptome sequencing technologies has given rise to single-cell transcriptomics and single-nucleus transcriptomics, increasing the accuracy of sequencing and accelerating the drug discovery process. With the evolution of single-cell transcriptomics, spatial transcriptomics (ST) technology has emerged as a derivative approach. Spatial transcriptomics has emerged as a hot topic in the field of omics research in recent years; it not only provides information on gene expression levels but also offers spatial information on gene expression. This technology has shown tremendous potential in research on disease understanding and drug discovery. In this article, we introduce the analytical strategies of spatial transcriptomics and review its applications in novel target discovery and drug mechanism unravelling. Moreover, we discuss the current challenges and issues in this research field that need to be addressed. In conclusion, spatial transcriptomics offers a new perspective for drug discovery.
Collapse
Affiliation(s)
- Junxian Cao
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Caifeng Li
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Zhao Cui
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Shiwen Deng
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Tong Lei
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Wei Liu
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Hongjun Yang
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Peng Chen
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| |
Collapse
|
45
|
Li R, Du K, Zhang C, Shen X, Yun L, Wang S, Li Z, Sun Z, Wei J, Li Y, Guo B, Sun C. Single-cell transcriptome profiling reveals the spatiotemporal distribution of triterpenoid saponin biosynthesis and transposable element activity in Gynostemma pentaphyllum shoot apexes and leaves. FRONTIERS IN PLANT SCIENCE 2024; 15:1394587. [PMID: 38779067 PMCID: PMC11109411 DOI: 10.3389/fpls.2024.1394587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/24/2024] [Indexed: 05/25/2024]
Abstract
Gynostemma pentaphyllum (Thunb.) Makino is an important producer of dammarene-type triterpenoid saponins. These saponins (gypenosides) exhibit diverse pharmacological benefits such as anticancer, antidiabetic, and immunomodulatory effects, and have major potential in the pharmaceutical and health care industries. Here, we employed single-cell RNA sequencing (scRNA-seq) to profile the transcriptomes of more than 50,000 cells derived from G. pentaphyllum shoot apexes and leaves. Following cell clustering and annotation, we identified five major cell types in shoot apexes and four in leaves. Each cell type displayed substantial transcriptomic heterogeneity both within and between tissues. Examining gene expression patterns across various cell types revealed that gypenoside biosynthesis predominantly occurred in mesophyll cells, with heightened activity observed in shoot apexes compared to leaves. Furthermore, we explored the impact of transposable elements (TEs) on G. pentaphyllum transcriptomic landscapes. Our findings the highlighted the unbalanced expression of certain TE families across different cell types in shoot apexes and leaves, marking the first investigation of TE expression at the single-cell level in plants. Additionally, we observed dynamic expression of genes involved in gypenoside biosynthesis and specific TE families during epidermal and vascular cell development. The involvement of TE expression in regulating cell differentiation and gypenoside biosynthesis warrant further exploration. Overall, this study not only provides new insights into the spatiotemporal organization of gypenoside biosynthesis and TE activity in G. pentaphyllum shoot apexes and leaves but also offers valuable cellular and genetic resources for a deeper understanding of developmental and physiological processes at single-cell resolution in this species.
Collapse
Affiliation(s)
- Rucan Li
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ke Du
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chuyi Zhang
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaofeng Shen
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lingling Yun
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shu Wang
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ziqin Li
- College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Zhiying Sun
- College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Jianhe Wei
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ying Li
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Baolin Guo
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chao Sun
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| |
Collapse
|
46
|
Sonawane AR, Pucéat M, Jo H. Editorial: Single-cell OMICs analyses in cardiovascular diseases. Front Cardiovasc Med 2024; 11:1413184. [PMID: 38770014 PMCID: PMC11102967 DOI: 10.3389/fcvm.2024.1413184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 04/23/2024] [Indexed: 05/22/2024] Open
Affiliation(s)
- Abhijeet Rajendra Sonawane
- Center for Interdisciplinary Cardiovascular Sciences and Center for Excellences in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Michel Pucéat
- INSERM, Cardiovascular and Nutrition Center (C2VN), Aix-Marseille University, Marseille, France
| | - Hanjoong Jo
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States
| |
Collapse
|
47
|
Loh JJ, Ma S. Hallmarks of cancer stemness. Cell Stem Cell 2024; 31:617-639. [PMID: 38701757 DOI: 10.1016/j.stem.2024.04.004] [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: 12/31/2023] [Revised: 03/11/2024] [Accepted: 04/03/2024] [Indexed: 05/05/2024]
Abstract
Cancer stemness is recognized as a key component of tumor development. Previously coined "cancer stem cells" (CSCs) and believed to be a rare population with rigid hierarchical organization, there is good evidence to suggest that these cells exhibit a plastic cellular state influenced by dynamic CSC-niche interplay. This revelation underscores the need to reevaluate the hallmarks of cancer stemness. Herein, we summarize the techniques used to identify and characterize the state of these cells and discuss their defining and emerging hallmarks, along with their enabling and associated features. We also highlight potential future directions in this field of research.
Collapse
Affiliation(s)
- Jia-Jian Loh
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Stephanie Ma
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; State Key Laboratory of Liver Research, The University of Hong Kong, Hong Kong SAR, China; Laboratory of Synthetic Chemistry and Chemical Biology, Hong Kong Science and Technology Park, Hong Kong SAR, China; Centre for Translational and Stem Cell Biology, Hong Kong Science and Technology Park, Hong Kong SAR, China.
| |
Collapse
|
48
|
Chen Q, Fan X. Potential role of the protein interactome in translating TCM theory and clinical practice into modern biomedical knowledge. Chin J Nat Med 2024; 22:385-386. [PMID: 38796212 DOI: 10.1016/s1875-5364(24)60635-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Indexed: 05/28/2024]
Affiliation(s)
- Qian Chen
- 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 314100, 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 314100, China.
| |
Collapse
|
49
|
Stouffer KM, Trouvé A, Younes L, Kunst M, Ng L, Zeng H, Anant M, Fan J, Kim Y, Chen X, Rue M, Miller MI. Cross-modality mapping using image varifolds to align tissue-scale atlases to molecular-scale measures with application to 2D brain sections. Nat Commun 2024; 15:3530. [PMID: 38664422 PMCID: PMC11045777 DOI: 10.1038/s41467-024-47883-4] [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: 04/13/2023] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
This paper explicates a solution to building correspondences between molecular-scale transcriptomics and tissue-scale atlases. This problem arises in atlas construction and cross-specimen/technology alignment where specimens per emerging technology remain sparse and conventional image representations cannot efficiently model the high dimensions from subcellular detection of thousands of genes. We address these challenges by representing spatial transcriptomics data as generalized functions encoding position and high-dimensional feature (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling regions as homogeneous random fields with unknown transcriptomic feature distribution. We solve simultaneously for the minimizing geodesic diffeomorphism of coordinates through LDDMM and for these latent feature densities. We map tissue-scale mouse brain atlases to gene-based and cell-based transcriptomics data from MERFISH and BARseq technologies and to histopathology and cross-species atlases to illustrate integration of diverse molecular and cellular datasets into a single coordinate system as a means of comparison and further atlas construction.
Collapse
Affiliation(s)
- Kaitlin M Stouffer
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
- Centre Borelli, ENS Paris-Saclay, Gif-sur-yvette, France.
| | - Alain Trouvé
- Centre Borelli, ENS Paris-Saclay, Gif-sur-yvette, France
| | - Laurent Younes
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | | | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Manjari Anant
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jean Fan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, Penn State University, College of Medicine, State College, PA, USA
| | - Xiaoyin Chen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Mara Rue
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Michael I Miller
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
| |
Collapse
|
50
|
Lu H, Zhang J, Cao Y, Wu S, Wei Y, Yin R. Advances in applications of artificial intelligence algorithms for cancer-related miRNA research. Zhejiang Da Xue Xue Bao Yi Xue Ban 2024; 53:231-243. [PMID: 38650448 PMCID: PMC11057993 DOI: 10.3724/zdxbyxb-2023-0511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 01/30/2024] [Indexed: 04/25/2024]
Abstract
MiRNAs are a class of small non-coding RNAs, which regulate gene expression post-transcriptionally by partial complementary base pairing. Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in bioinformatic research. Compared to traditional bioinformatic tools, miRNA target prediction tools based on artificial intelligence algorithms have higher accuracy, and can successfully predict subcellular localization and redistribution of miRNAs to deepen our understanding. Additionally, the construction of clinical models based on artificial intelligence algorithms could significantly improve the mining efficiency of miRNA used as biomarkers. In this article, we summarize recent development of bioinformatic miRNA tools based on artificial intelligence algorithms, focusing on the potential of machine learning and deep learning in cancer-related miRNA research.
Collapse
Affiliation(s)
- Hongyu Lu
- School of Pharmacy, Jiangsu University, Zhenjiang 212013, Jiangsu Province, China.
| | - Jia Zhang
- School of Pharmacy, Jiangsu University, Zhenjiang 212013, Jiangsu Province, China
| | - Yixin Cao
- Department of Medical Oncology, Affiliated Hospital of Jiangsu University, Zhenjiang 212013, Jiangsu Province, China
| | - Shuming Wu
- School of Pharmacy, Jiangsu University, Zhenjiang 212013, Jiangsu Province, China
| | - Yuan Wei
- School of Pharmacy, Jiangsu University, Zhenjiang 212013, Jiangsu Province, China.
| | - Runting Yin
- School of Pharmacy, Jiangsu University, Zhenjiang 212013, Jiangsu Province, China.
| |
Collapse
|