1
|
Zhang C, Wang L, Shi Q. Computational modeling for deciphering tissue microenvironment heterogeneity from spatially resolved transcriptomics. Comput Struct Biotechnol J 2024; 23:2109-2115. [PMID: 38800634 PMCID: PMC11126885 DOI: 10.1016/j.csbj.2024.05.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 05/15/2024] [Accepted: 05/16/2024] [Indexed: 05/29/2024] Open
Abstract
Spatial transcriptomics techniques, while measuring gene expression, retain spatial location information, aiding in situ studies of organismal tissue architecture and the progression of pathological processes. These techniques generate vast amounts of omics data, necessitating the development of computational methods to reveal the underlying tissue microenvironment heterogeneity. The main directions in spatial transcriptomics data analysis are spatial domain detection and spatial deconvolution, which can identify spatial functional regions and parse the distribution of cell types in spatial transcriptomics data by integrating single-cell transcriptomics data. In these two research directions, many computational methods have been successively proposed. This article will categorize them into three types: machine learning-based methods, probabilistic models-based methods, and deep learning-based methods. It will list and discuss the representative algorithms of each type along with their advantages and disadvantages and describe the datasets and evaluation metrics used to assess these computational methods, facilitating researchers in selecting suitable computational methods according to their research needs. Finally, combining the latest technological developments and the advantages and disadvantages of current algorithms, this article will look forward to the future directions of computational method development.
Collapse
Affiliation(s)
- Chuanchao Zhang
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, Hangzhou 310024; University of Chinese Academy of Sciences, China
| | - Lequn Wang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qianqian Shi
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Engineering Technology Research Center of Agricultural Big Data, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| |
Collapse
|
2
|
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
|
3
|
Huang J, Fu X, Zhang Z, Xie Y, Liu S, Wang Y, Zhao Z, Peng Y. A graph self-supervised residual learning framework for domain identification and data integration of spatial transcriptomics. Commun Biol 2024; 7:1123. [PMID: 39266614 PMCID: PMC11393357 DOI: 10.1038/s42003-024-06814-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 08/30/2024] [Indexed: 09/14/2024] Open
Abstract
Spatial transcriptomics (ST) technologies allow for comprehensive characterization of gene expression patterns in the context of tissue microenvironment. However, accurately identifying domains with spatial coherence in both gene expression and histology in situ and effectively integrating data from multi-sample remains challenging. Here, we propose ResST, a graph self-supervised residual learning model based on graph neural network and Margin Disparity Discrepancy (MDD) theory. ResST aggregates gene expression, biological effects, spatial location, and morphological information to capture nonlinear relationships between a cell and surrounding cells for spatial domain identification. Also, ResST integrates multiple ST datasets and aligns latent embeddings based on MDD theory for correcting batch effects. Results show that ResST identifies continuous spatial domains at a finer scale in ten ST datasets acquired with different technologies. Moreover, ResST efficiently integrated data from multiple tissue sections vertically or horizontally while correcting batch effects. Overall, ResST demonstrates exceptional performance in analyzing ST datasets.
Collapse
Affiliation(s)
- Jinjin Huang
- Henan Key Laboratory for Pharmacology of Liver Diseases, BGI College & Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Xiaoqian Fu
- Henan Key Laboratory for Pharmacology of Liver Diseases, BGI College & Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
- Academy of Medical Science, Zhengzhou University, Zhengzhou, China
| | - Zhuangli Zhang
- Henan Key Laboratory for Pharmacology of Liver Diseases, BGI College & Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Yinfeng Xie
- Henan Key Laboratory for Pharmacology of Liver Diseases, BGI College & Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Shangkun Liu
- Henan Key Laboratory for Pharmacology of Liver Diseases, BGI College & Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
- Academy of Medical Science, Zhengzhou University, Zhengzhou, China
| | - Yarong Wang
- Henan Key Laboratory for Pharmacology of Liver Diseases, BGI College & Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Zhihong Zhao
- Henan Key Laboratory for Pharmacology of Liver Diseases, BGI College & Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Youmei Peng
- Henan Key Laboratory for Pharmacology of Liver Diseases, BGI College & Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
| |
Collapse
|
4
|
Maciejewski K, Czerwinska P. Scoping Review: Methods and Applications of Spatial Transcriptomics in Tumor Research. Cancers (Basel) 2024; 16:3100. [PMID: 39272958 DOI: 10.3390/cancers16173100] [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: 07/19/2024] [Revised: 08/30/2024] [Accepted: 08/30/2024] [Indexed: 09/15/2024] Open
Abstract
Spatial transcriptomics (ST) examines gene expression within its spatial context on tissue, linking morphology and function. Advances in ST resolution and throughput have led to an increase in scientific interest, notably in cancer research. This scoping study reviews the challenges and practical applications of ST, summarizing current methods, trends, and data analysis techniques for ST in neoplasm research. We analyzed 41 articles published by the end of 2023 alongside public data repositories. The findings indicate cancer biology is an important focus of ST research, with a rising number of studies each year. Visium (10x Genomics, Pleasanton, CA, USA) is the leading ST platform, and SCTransform from Seurat R library is the preferred method for data normalization and integration. Many studies incorporate additional data types like single-cell sequencing and immunohistochemistry. Common ST applications include discovering the composition and function of tumor tissues in the context of their heterogeneity, characterizing the tumor microenvironment, or identifying interactions between cells, including spatial patterns of expression and co-occurrence. However, nearly half of the studies lacked comprehensive data processing protocols, hindering their reproducibility. By recommending greater transparency in sharing analysis methods and adapting single-cell analysis techniques with caution, this review aims to improve the reproducibility and reliability of future studies in cancer research.
Collapse
Affiliation(s)
- Kacper Maciejewski
- Undergraduate Research Group "Biobase", Poznan University of Medical Sciences, 61-701 Poznan, Poland
| | - Patrycja Czerwinska
- Undergraduate Research Group "Biobase", Poznan University of Medical Sciences, 61-701 Poznan, Poland
- Department of Cancer Immunology, Poznan University of Medical Sciences, 61-866 Poznan, Poland
- Department of Diagnostics and Cancer Immunology, Greater Poland Cancer Centre, 61-866 Poznan, Poland
| |
Collapse
|
5
|
Liu X, Peng T, Xu M, Lin S, Hu B, Chu T, Liu B, Xu Y, Ding W, Li L, Cao C, Wu P. Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications. J Hematol Oncol 2024; 17:72. [PMID: 39182134 PMCID: PMC11344930 DOI: 10.1186/s13045-024-01596-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 08/09/2024] [Indexed: 08/27/2024] Open
Abstract
The emergence of spatial multi-omics has helped address the limitations of single-cell sequencing, which often leads to the loss of spatial context among cell populations. Integrated analysis of the genome, transcriptome, proteome, metabolome, and epigenome has enhanced our understanding of cell biology and the molecular basis of human diseases. Moreover, this approach offers profound insights into the interactions between intracellular and intercellular molecular mechanisms involved in the development, physiology, and pathogenesis of human diseases. In this comprehensive review, we examine current advancements in multi-omics technologies, focusing on their evolution and refinement over the past decade, including improvements in throughput and resolution, modality integration, and accuracy. We also discuss the pivotal contributions of spatial multi-omics in revealing spatial heterogeneity, constructing detailed spatial atlases, deciphering spatial crosstalk in tumor immunology, and advancing translational research and cancer therapy through precise spatial mapping.
Collapse
Affiliation(s)
- Xiaojie Liu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ting Peng
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Miaochun Xu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shitong Lin
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Bai Hu
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Tian Chu
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Binghan Liu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yashi Xu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wencheng Ding
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Li Li
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Canhui Cao
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Peng Wu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| |
Collapse
|
6
|
Liu L, Chen A, Li Y, Mulder J, Heyn H, Xu X. Spatiotemporal omics for biology and medicine. Cell 2024; 187:4488-4519. [PMID: 39178830 DOI: 10.1016/j.cell.2024.07.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 07/05/2024] [Accepted: 07/23/2024] [Indexed: 08/26/2024]
Abstract
The completion of the Human Genome Project has provided a foundational blueprint for understanding human life. Nonetheless, understanding the intricate mechanisms through which our genetic blueprint is involved in disease or orchestrates development across temporal and spatial dimensions remains a profound scientific challenge. Recent breakthroughs in cellular omics technologies have paved new pathways for understanding the regulation of genomic elements and the relationship between gene expression, cellular functions, and cell fate determination. The advent of spatial omics technologies, encompassing both imaging and sequencing-based methodologies, has enabled a comprehensive understanding of biological processes from a cellular ecosystem perspective. This review offers an updated overview of how spatial omics has advanced our understanding of the translation of genetic information into cellular heterogeneity and tissue structural organization and their dynamic changes over time. It emphasizes the discovery of various biological phenomena, related to organ functionality, embryogenesis, species evolution, and the pathogenesis of diseases.
Collapse
Affiliation(s)
| | - Ao Chen
- BGI Research, Shenzhen 518083, China
| | | | - Jan Mulder
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Holger Heyn
- Centro Nacional de Análisis Genómico (CNAG), Barcelona, Spain
| | - Xun Xu
- BGI Research, Hangzhou 310030, China; BGI Research, Shenzhen 518083, China.
| |
Collapse
|
7
|
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
|
8
|
Ramirez A, Orcutt-Jahns BT, Pascoe S, Abraham A, Remigio B, Thomas N, Meyer AS. Integrative, high-resolution analysis of single cells across experimental conditions with PARAFAC2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.29.605698. [PMID: 39131377 PMCID: PMC11312543 DOI: 10.1101/2024.07.29.605698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Effective tools for exploration and analysis are needed to extract insights from large-scale single-cell measurement data. However, current techniques for handling single-cell studies performed across experimental conditions (e.g., samples, perturbations, or patients) require restrictive assumptions, lack flexibility, or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that the tensor decomposition method PARAFAC2 (Pf2) enables the dimensionality reduction of single-cell data across conditions. We demonstrate these benefits across two distinct contexts of single-cell RNA-sequencing (scRNA-seq) experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus (SLE) patient samples. By isolating relevant gene modules across cells and conditions, Pf2 enables straightforward associations of gene variation patterns across specific patients or perturbations while connecting each coordinated change to certain cells without pre-defining cell types. The theoretical grounding of Pf2 suggests a unified framework for many modeling tasks associated with single-cell data. Thus, Pf2 provides an intuitive universal dimensionality reduction approach for multi-sample single-cell studies across diverse biological contexts.
Collapse
Affiliation(s)
- Andrew Ramirez
- Department of Bioengineering, University of California, Los Angeles (UCLA), CA, USA
| | | | - Sean Pascoe
- Department of Bioengineering, University of California, Los Angeles (UCLA), CA, USA
- Department of Molecular Biosciences, Northwestern University, Evanston, IL, USA
| | - Armaan Abraham
- Department of Bioengineering, University of California, Los Angeles (UCLA), CA, USA
| | | | | | - Aaron S. Meyer
- Department of Bioengineering, University of California, Los Angeles (UCLA), CA, USA
- Jonsson Comprehensive Cancer Center, UCLA, CA, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, UCLA, CA, USA
| |
Collapse
|
9
|
Ospina OE, Manjarres-Betancur R, Gonzalez-Calderon G, Soupir AC, Smalley I, Tsai K, Markowitz J, Vallebuona E, Berglund A, Eschrich S, Yu X, Fridley BL. spatialGE: A user-friendly web application to democratize spatial transcriptomics analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.27.601050. [PMID: 39005315 PMCID: PMC11244876 DOI: 10.1101/2024.06.27.601050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, its potential is often underutilized due to the advanced data analysis and programming skills required. To address this, we present spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provides a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enables comparative analysis among samples and supports various ST technologies. We demonstrate the utility of spatialGE through its application in studying the tumor microenvironment of melanoma brain metastasis and Merkel cell carcinoma. Our results highlight the ability of spatialGE to identify spatial gene expression patterns and enrichments, providing valuable insights into the tumor microenvironment and its utility in democratizing ST data analysis for the wider scientific community.
Collapse
Affiliation(s)
- Oscar E. Ospina
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | | | | | - Alex C. Soupir
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Inna Smalley
- Department of Metabolism and Physiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Kenneth Tsai
- Department of Pathology, Moffitt Cancer Center, Tampa, FL, USA
| | - Joseph Markowitz
- Department of Cutaneous Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Ethan Vallebuona
- Department of Metabolism and Physiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Anders Berglund
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Steven Eschrich
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Xiaoqing Yu
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Brooke L. Fridley
- Division of Health Services and Outcomes Research, Children’s Mercy, Kansas City, MO, USA
| |
Collapse
|
10
|
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
|
11
|
Huang R, Kratka CE, Pea J, McCann C, Nelson J, Bryan JP, Zhou LT, Russo DD, Zaniker EJ, Gandhi AH, Shalek AK, Cleary B, Farhi SL, Duncan FE, Goods BA. Single-cell and spatiotemporal profile of ovulation in the mouse ovary. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.20.594719. [PMID: 38826447 PMCID: PMC11142086 DOI: 10.1101/2024.05.20.594719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Ovulation is a spatiotemporally coordinated process that involves several tightly controlled events, including oocyte meiotic maturation, cumulus expansion, follicle wall rupture and repair, and ovarian stroma remodeling. To date, no studies have detailed the precise window of ovulation at single-cell resolution. Here, we performed parallel single-cell RNA-seq and spatial transcriptomics on paired mouse ovaries across an ovulation time course to map the spatiotemporal profile of ovarian cell types. We show that major ovarian cell types exhibit time-dependent transcriptional states enriched for distinct functions and have specific localization profiles within the ovary. We also identified gene markers for ovulation-dependent cell states and validated these using orthogonal methods. Finally, we performed cell-cell interaction analyses to identify ligand-receptor pairs that may drive ovulation, revealing previously unappreciated interactions. Taken together, our data provides a rich and comprehensive resource of murine ovulation that can be mined for discovery by the scientific community.
Collapse
|
12
|
Zhang Y, Lee RY, Tan CW, Guo X, Yim WWY, Lim JC, Wee FY, Yang WU, Kharbanda M, Lee JYJ, Ngo NT, Leow WQ, Loo LH, Lim TK, Sobota RM, Lau MC, Davis MJ, Yeong J. Spatial omics techniques and data analysis for cancer immunotherapy applications. Curr Opin Biotechnol 2024; 87:103111. [PMID: 38520821 DOI: 10.1016/j.copbio.2024.103111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 03/01/2024] [Accepted: 03/03/2024] [Indexed: 03/25/2024]
Abstract
In-depth profiling of cancer cells/tissues is expanding our understanding of the genomic, epigenomic, transcriptomic, and proteomic landscape of cancer. However, the complexity of the cancer microenvironment, particularly its immune regulation, has made it difficult to exploit the potential of cancer immunotherapy. High-throughput spatial omics technologies and analysis pipelines have emerged as powerful tools for tackling this challenge. As a result, a potential revolution in cancer diagnosis, prognosis, and treatment is on the horizon. In this review, we discuss the technological advances in spatial profiling of cancer around and beyond the central dogma to harness the full benefits of immunotherapy. We also discuss the promise and challenges of spatial data analysis and interpretation and provide an outlook for the future.
Collapse
Affiliation(s)
- Yue Zhang
- Duke-NUS Medical School, Singapore 169856, Singapore
| | - Ren Yuan Lee
- Yong Loo Lin School of Medicine, National University of Singapore, 169856 Singapore; Singapore Thong Chai Medical Institution, Singapore 169874, Singapore
| | - Chin Wee Tan
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia
| | - Xue Guo
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Willa W-Y Yim
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Jeffrey Ct Lim
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Felicia Yt Wee
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - W U Yang
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Malvika Kharbanda
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia
| | - Jia-Ying J Lee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore
| | - Nye Thane Ngo
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Wei Qiang Leow
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Lit-Hsin Loo
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore
| | - Tony Kh Lim
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Radoslaw M Sobota
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Mai Chan Lau
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A⁎STAR), Singapore 138648, Singapore
| | - Melissa J Davis
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia; immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia; Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Joe Yeong
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore; Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore.
| |
Collapse
|
13
|
Yang S, Wang Z, Wang C, Li C, Wang B. Comparative Evaluation of Machine Learning Models for Subtyping Triple-Negative Breast Cancer: A Deep Learning-Based Multi-Omics Data Integration Approach. J Cancer 2024; 15:3943-3957. [PMID: 38911381 PMCID: PMC11190774 DOI: 10.7150/jca.93215] [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: 12/14/2023] [Accepted: 05/19/2024] [Indexed: 06/25/2024] Open
Abstract
Objective: Triple-negative breast cancer (TNBC) poses significant diagnostic challenges due to its aggressive nature. This research develops an innovative deep learning (DL) model based on the latest multi-omics data to enhance the accuracy of TNBC subtype and prognosis prediction. The study focuses on addressing the constraints of prior studies by showcasing a model with substantial advancements in data integration, statistical performance, and algorithmic optimization. Methods: Breast cancer-related molecular characteristic data, including mRNA, miRNA, gene mutations, DNA methylation, and magnetic resonance imaging (MRI) images, were retrieved from the TCGA and TCIA databases. This study not only compared single-omics with multi-omics machine learning models but also applied Bayesian optimization to innovatively optimize the neural network structure of a DL model for multi-omics data. Results: The DL model for multi-omics data significantly outperformed single-omics models in subtype prediction, achieving a 98.0% accuracy in cross-validation, 97.0% in the validation set, and 91.0% in an external test set. Additionally, the MRI radiomics model showed promising performance, especially with the training set; however, a decrease in performance during transfer testing underscored the advantages of the DL model for multi-omics data in data consistency and digital processing. Conclusion: Our multi-omics DL model presents notable innovations in statistical performance and transfer learning capability, bearing significant clinical relevance for TNBC classification and prognosis prediction. While the MRI radiomics model proved effective, it requires further optimization for cross-dataset application to enhance accuracy and consistency. Our findings offer new insights into improving TNBC classification and prognosis through multi-omics data and DL algorithms.
Collapse
Affiliation(s)
| | | | | | | | - Binjie Wang
- Department of Imaging, Huaihe Hospital of Henan University, Kaifeng 475000, P. R. China
| |
Collapse
|
14
|
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
|
15
|
Ospina OE, Soupir AC, Manjarres-Betancur R, Gonzalez-Calderon G, Yu X, Fridley BL. Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models. Sci Rep 2024; 14:10967. [PMID: 38744956 PMCID: PMC11094014 DOI: 10.1038/s41598-024-61758-0] [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: 02/01/2024] [Accepted: 05/09/2024] [Indexed: 05/16/2024] Open
Abstract
Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or "niches" followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon's rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).
Collapse
Affiliation(s)
- Oscar E Ospina
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Alex C Soupir
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Xiaoqing Yu
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Brooke L Fridley
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
- Biostatistics and Epidemiology Core, Division of Health Services & Outcomes Research, Children's Mercy, Kansas City, MO, USA.
| |
Collapse
|
16
|
Schmidt M, Avagyan S, Reiche K, Binder H, Loeffler-Wirth H. A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections. Curr Issues Mol Biol 2024; 46:4701-4720. [PMID: 38785552 PMCID: PMC11119626 DOI: 10.3390/cimb46050284] [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: 03/25/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.
Collapse
Affiliation(s)
- Maria Schmidt
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
| | - Susanna Avagyan
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia
| | - Kristin Reiche
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Perlickstrasse 1, 04103 Leipzig, Germany
- Institute for Clinical Immunology, University Hospital of Leipzig, 04103 Leipzig, Germany
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia
| | - Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
| |
Collapse
|
17
|
Marmarelis MG, Littman R, Battaglin F, Niedzwiecki D, Venook A, Ambite JL, Galstyan A, Lenz HJ, Ver Steeg G. q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics. Commun Biol 2024; 7:400. [PMID: 38565955 PMCID: PMC11255321 DOI: 10.1038/s42003-024-06104-w] [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: 06/23/2023] [Accepted: 03/25/2024] [Indexed: 04/04/2024] Open
Abstract
Unlocking the full dimensionality of single-cell RNA sequencing data (scRNAseq) is the next frontier to a richer, fuller understanding of cell biology. We introduce q-diffusion, a framework for capturing the coexpression structure of an entire library of genes, improving on state-of-the-art analysis tools. The method is demonstrated via three case studies. In the first, q-diffusion helps gain statistical significance for differential effects on patient outcomes when analyzing the CALGB/SWOG 80405 randomized phase III clinical trial, suggesting precision guidance for the treatment of metastatic colorectal cancer. Secondly, q-diffusion is benchmarked against existing scRNAseq classification methods using an in vitro PBMC dataset, in which the proposed method discriminates IFN-γ stimulation more accurately. The same case study demonstrates improvements in unsupervised cell clustering with the recent Tabula Sapiens human atlas. Finally, a local distributional segmentation approach for spatial scRNAseq, driven by q-diffusion, yields interpretable structures of human cortical tissue.
Collapse
Affiliation(s)
- Myrl G Marmarelis
- Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA.
| | - Russell Littman
- University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Francesca Battaglin
- Keck School of Medicine, University of Southern California, 1975 Zonal Ave., Los Angeles, CA, 90033, USA
| | | | - Alan Venook
- University of California San Francisco, San Francisco, CA, 94143, USA
| | - Jose-Luis Ambite
- Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA
| | - Aram Galstyan
- Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA
| | - Heinz-Josef Lenz
- Keck School of Medicine, University of Southern California, 1975 Zonal Ave., Los Angeles, CA, 90033, USA
| | - Greg Ver Steeg
- Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA
- University of California Riverside, Riverside, CA, 92521, USA
| |
Collapse
|
18
|
Haviv D, Remšík J, Gatie M, Snopkowski C, Takizawa M, Pereira N, Bashkin J, Jovanovich S, Nawy T, Chaligne R, Boire A, Hadjantonakis AK, Pe'er D. The covariance environment defines cellular niches for spatial inference. Nat Biotechnol 2024:10.1038/s41587-024-02193-4. [PMID: 38565973 DOI: 10.1038/s41587-024-02193-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 02/28/2024] [Indexed: 04/04/2024]
Abstract
A key challenge of analyzing data from high-resolution spatial profiling technologies is to suitably represent the features of cellular neighborhoods or niches. Here we introduce the covariance environment (COVET), a representation that leverages the gene-gene covariate structure across cells in the niche to capture the multivariate nature of cellular interactions within it. We define a principled optimal transport-based distance metric between COVET niches that scales to millions of cells. Using COVET to encode spatial context, we developed environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA sequencing data into a latent space. ENVI includes two decoders: one to impute gene expression across the spatial modality and a second to project spatial information onto single-cell data. ENVI can confer spatial context to genomics data from single dissociated cells and outperforms alternatives for imputing gene expression on diverse spatial datasets.
Collapse
Affiliation(s)
- Doron Haviv
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Ján Remšík
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mohamed Gatie
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Catherine Snopkowski
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meril Takizawa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | | | - Tal Nawy
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ronan Chaligne
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Adrienne Boire
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anna-Katerina Hadjantonakis
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Howard Hughes Medical Institute, New York, NY, USA.
| |
Collapse
|
19
|
Danishuddin, Khan S, Kim JJ. Spatial transcriptomics data and analytical methods: An updated perspective. Drug Discov Today 2024; 29:103889. [PMID: 38244672 DOI: 10.1016/j.drudis.2024.103889] [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: 10/31/2023] [Revised: 01/01/2024] [Accepted: 01/15/2024] [Indexed: 01/22/2024]
Abstract
Spatial transcriptomics (ST) is a newly emerging field that integrates high-resolution imaging and transcriptomic data to enable the high-throughput analysis of the spatial localization of transcripts in diverse biological systems. The rapid progress in this field necessitates the development of innovative computational methods to effectively tackle the distinct challenges posed by the analysis of ST data. These platforms, integrating AI techniques, offer a promising avenue for understanding disease mechanisms and expediting drug discovery. Despite significant advances in the development of ST data analysis techniques, there is an ongoing need to enhance these models for increased biological relevance. In this review, we briefly discuss the ST-related databases and current deep-learning-based models for spatial transcriptome data analyses and highlight their roles and future perspectives in biomedical applications.
Collapse
Affiliation(s)
- Danishuddin
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea.
| | - Shawez Khan
- National Center for Cancer Immune Therapy (CCIT-DK), Department of Oncology, Copenhagen University Hospital, Herlev, Denmark
| | - Jong Joo Kim
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea.
| |
Collapse
|
20
|
Zhang B, Li M, Kang Q, Deng Z, Qin H, Su K, Feng X, Chen L, Liu H, Fang S, Zhang Y, Li Y, Brix S, Xu X. Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images. GIGABYTE 2024; 2024:gigabyte110. [PMID: 38434932 PMCID: PMC10905256 DOI: 10.46471/gigabyte.110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/13/2024] [Indexed: 03/05/2024] Open
Abstract
In spatially resolved transcriptomics, Stereo-seq facilitates the analysis of large tissues at the single-cell level, offering subcellular resolution and centimeter-level field-of-view. Our previous work on StereoCell introduced a one-stop software using cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. With advancements allowing the acquisition of cell boundary information, such as cell membrane/wall staining images, we updated our software to a new version, STCellbin. Using cell nuclei staining images, STCellbin aligns cell membrane/wall staining images with spatial gene expression maps. Advanced cell segmentation ensures the detection of accurate cell boundaries, leading to more reliable single-cell spatial gene expression profiles. We verified that STCellbin can be applied to mouse liver (cell membranes) and Arabidopsis seed (cell walls) datasets, outperforming other methods. The improved capability of capturing single-cell gene expression profiles results in a deeper understanding of the contribution of single-cell phenotypes to tissue biology. Availability & Implementation The source code of STCellbin is available at https://github.com/STOmics/STCellbin.
Collapse
Affiliation(s)
- Bohan Zhang
- BGI Research, Shenzhen, 518083, China
- BGI Research, Beijing, 102601, China
| | - Mei Li
- BGI Research, Shenzhen, 518083, China
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | | | | | - Hua Qin
- BGI Research, Beijing, 102601, China
| | - Kui Su
- BGI Research, Shenzhen, 518083, China
| | | | | | | | | | | | | | - Susanne Brix
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Xun Xu
- BGI Research, Shenzhen, 518083, China
| |
Collapse
|
21
|
Van Kleunen L, Ahmadian M, Post MD, Wolsky RJ, Rickert C, Jordan K, Hu J, Richer JK, Marjon NA, Behbakht K, Sikora MJ, Bitler BG, Clauset A. The spatial structure of the tumor immune microenvironment can explain and predict patient response in high-grade serous carcinoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.26.577350. [PMID: 38352574 PMCID: PMC10862769 DOI: 10.1101/2024.01.26.577350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Despite ovarian cancer being the deadliest gynecological malignancy, there has been little change to therapeutic options and mortality rates over the last three decades. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes but are limited by a lack of spatial understanding. We performed multiplexed ion beam imaging (MIBI) on 83 human high-grade serous carcinoma tumors - one of the largest protein-based, spatially-intact, single-cell resolution tumor datasets assembled - and used statistical and machine learning approaches to connect features of the TIME spatial organization to patient outcomes. Along with traditional clinical/immunohistochemical attributes and indicators of TIME composition, we found that several features of TIME spatial organization had significant univariate correlations and/or high relative importance in high-dimensional predictive models. The top performing predictive model for patient progression-free survival (PFS) used a combination of TIME composition and spatial features. Results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.
Collapse
Affiliation(s)
- Lucy Van Kleunen
- Department of Computer Science, University of Colorado, Boulder, USA
| | - Mansooreh Ahmadian
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Miriam D Post
- Department of Pathology, The University of Colorado Anschutz Medical Campus
| | - Rebecca J Wolsky
- Department of Pathology, The University of Colorado Anschutz Medical Campus
| | - Christian Rickert
- Department of Immunology and Microbiology, The University of Colorado Anschutz Medical Campus
| | - Kimberly Jordan
- Department of Immunology and Microbiology, The University of Colorado Anschutz Medical Campus
| | - Junxiao Hu
- Department of Pediatrics, Cancer Center Biostatistics Core, University of Colorado Anschutz Medical Campus, CO, USA
| | - Jennifer K. Richer
- Department of Pathology, The University of Colorado Anschutz Medical Campus
| | - Nicole A. Marjon
- Department of OB/GYN, The University of Colorado Anschutz Medical Campus
| | - Kian Behbakht
- Department of OB/GYN, The University of Colorado Anschutz Medical Campus
| | - Matthew J. Sikora
- Department of Pathology, The University of Colorado Anschutz Medical Campus
| | - Benjamin G. Bitler
- Department of OB/GYN, The University of Colorado Anschutz Medical Campus
| | - Aaron Clauset
- Department of Computer Science, University of Colorado, Boulder, USA
- BioFrontiers Institute, University of Colorado, Boulder, CO, USA
- Santa Fe Institute, Santa Fe, NM, USA
| |
Collapse
|
22
|
Tao Y, Sun X, Wang F. BiGATAE: a bipartite graph attention auto-encoder enhancing spatial domain identification from single-slice to multi-slices. Brief Bioinform 2024; 25:bbae045. [PMID: 38385877 PMCID: PMC10883416 DOI: 10.1093/bib/bbae045] [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: 11/12/2023] [Revised: 01/10/2024] [Accepted: 01/23/2024] [Indexed: 02/23/2024] Open
Abstract
Recent advancements in spatial transcriptomics technology have revolutionized our ability to comprehensively characterize gene expression patterns within the tissue microenvironment, enabling us to grasp their functional significance in a spatial context. One key field of research in spatial transcriptomics is the identification of spatial domains, which refers to distinct regions within the tissue where specific gene expression patterns are observed. Diverse methodologies have been proposed, each with its unique characteristics. As the availability of spatial transcriptomics data continues to expand, there is a growing need for methods that can integrate information from multiple slices to discover spatial domains. To extend the applicability of existing single-slice analysis methods to multi-slice clustering, we introduce BiGATAE (Bipartite Graph Attention Auto Encoder) that leverages gene expression information from adjacent tissue slices to enhance spatial transcriptomics data. BiGATAE comprises two steps: aligning slices to generate an adjacency matrix for different spots in consecutive slices and constructing a bipartite graph. Subsequently, it utilizes a graph attention network to integrate information across different slices. Then it can seamlessly integrate with pre-existing techniques. To evaluate the performance of BiGATAE, we conducted benchmarking analyses on three different datasets. The experimental results demonstrate that for existing single-slice clustering methods, the integration of BiGATAE significantly enhances their performance. Moreover, single-slice clustering methods integrated with BiGATAE outperform methods specifically designed for multi-slice integration. These results underscore the proficiency of BiGATAE in facilitating information transfer across multiple slices and its capacity to broaden the applicability and sustainability of pre-existing methods.
Collapse
Affiliation(s)
- Yuhao Tao
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan University Handan Street, 200433 Shanghai, China
| | - Xiaoang Sun
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan University Handan Street, 200433 Shanghai, China
| | - Fei Wang
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan University Handan Street, 200433 Shanghai, China
| |
Collapse
|
23
|
Lv T, Zhang Y, Li M, Kang Q, Fang S, Zhang Y, Brix S, Xu X. EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics. Gigascience 2024; 13:giad097. [PMID: 38373746 PMCID: PMC10939424 DOI: 10.1093/gigascience/giad097] [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: 06/01/2023] [Revised: 09/12/2023] [Accepted: 10/13/2023] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND The emergence of high-resolved spatial transcriptomics (ST) has facilitated the research of novel methods to investigate biological development, organism growth, and other complex biological processes. However, high-resolved and whole transcriptomics ST datasets require customized imputation methods to improve the signal-to-noise ratio and the data quality. FINDINGS We propose an efficient and adaptive Gaussian smoothing (EAGS) imputation method for high-resolved ST. The adaptive 2-factor smoothing of EAGS creates patterns based on the spatial and expression information of the cells, creates adaptive weights for the smoothing of cells in the same pattern, and then utilizes the weights to restore the gene expression profiles. We assessed the performance and efficiency of EAGS using simulated and high-resolved ST datasets of mouse brain and olfactory bulb. CONCLUSIONS Compared with other competitive methods, EAGS shows higher clustering accuracy, better biological interpretations, and significantly reduced computational consumption.
Collapse
Affiliation(s)
- Tongxuan Lv
- BGI Research, Shenzhen 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Mei Li
- BGI Research, Shenzhen 518083, China
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | | | - Shuangsang Fang
- BGI Research, Shenzhen 518083, China
- BGI Research, Beijing 102601, China
| | | | | | - Xun Xu
- BGI Research, Shenzhen 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| |
Collapse
|
24
|
Shimonov S, Cunningham JM, Talmon R, Aizenbud L, Desai SJ, Rimm D, Schalper K, Kluger H, Kluger Y. SORBET: Automated cell-neighborhood analysis of spatial transcriptomics or proteomics for interpretable sample classification via GNN. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.30.573739. [PMID: 38260586 PMCID: PMC10802254 DOI: 10.1101/2023.12.30.573739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Spatially resolved transcriptomics or proteomics data have the potential to contribute fundamental insights into the mechanisms underlying physiologic and pathological processes. However, analysis of these data capable of relating spatial information, multiplexed markers, and their observed phenotypes remains technically challenging. To analyze these relationships, we developed SORBET, a deep learning framework that leverages recent advances in graph neural networks (GNN). We apply SORBET to predict tissue phenotypes, such as response to immunotherapy, across different disease processes and different technologies including both spatial proteomics and transcriptomics methods. Our results show that SORBET accurately learns biologically meaningful relationships across distinct tissue structures and data acquisition methods. Furthermore, we demonstrate that SORBET facilitates understanding of the spatially-resolved biological mechanisms underlying the inferred phenotypes. In sum, our method facilitates mapping between the rich spatial and marker information acquired from spatial 'omics technologies to emergent biological phenotypes. Moreover, we provide novel techniques for identifying the biological processes that comprise the predicted phenotypes.
Collapse
|
25
|
Robles-Remacho A, Sanchez-Martin RM, Diaz-Mochon JJ. Spatial Transcriptomics: Emerging Technologies in Tissue Gene Expression Profiling. Anal Chem 2023; 95:15450-15460. [PMID: 37814884 PMCID: PMC10603609 DOI: 10.1021/acs.analchem.3c02029] [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: 05/10/2023] [Accepted: 09/21/2023] [Indexed: 10/11/2023]
Abstract
In this Perspective, we discuss the current status and advances in spatial transcriptomics technologies, which allow high-resolution mapping of gene expression in intact cell and tissue samples. Spatial transcriptomics enables the creation of high-resolution maps of gene expression patterns within their native spatial context, adding an extra layer of information to the bulk sequencing data. Spatial transcriptomics has expanded significantly in recent years and is making a notable impact on a range of fields, including tissue architecture, developmental biology, cancer, and neurodegenerative and infectious diseases. The latest advancements in spatial transcriptomics have resulted in the development of highly multiplexed methods, transcriptomic-wide analysis, and single-cell resolution utilizing diverse technological approaches. In this Perspective, we provide a detailed analysis of the molecular foundations behind the main spatial transcriptomics technologies, including methods based on microdissection, in situ sequencing, single-molecule FISH, spatial capturing, selection of regions of interest, and single-cell or nuclei dissociation. We contextualize the detection and capturing efficiency, strengths, limitations, tissue compatibility, and applications of these techniques as well as provide information on data analysis. In addition, this Perspective discusses future directions and potential applications of spatial transcriptomics, highlighting the importance of the continued development to promote widespread adoption of these techniques within the research community.
Collapse
Affiliation(s)
- Agustín Robles-Remacho
- GENYO.
Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustracion,
114. 18016 Granada, Spain
- Department
of Medicinal and Organic Chemistry, School of Pharmacy, University of Granada, Campus Cartuja s/n, 18071 Granada, Spain
- Instituto
de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
| | - Rosario M. Sanchez-Martin
- GENYO.
Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustracion,
114. 18016 Granada, Spain
- Department
of Medicinal and Organic Chemistry, School of Pharmacy, University of Granada, Campus Cartuja s/n, 18071 Granada, Spain
- Instituto
de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
| | - Juan J. Diaz-Mochon
- GENYO.
Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustracion,
114. 18016 Granada, Spain
- Department
of Medicinal and Organic Chemistry, School of Pharmacy, University of Granada, Campus Cartuja s/n, 18071 Granada, Spain
- Instituto
de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
| |
Collapse
|
26
|
Shireman JM, Cheng L, Goel A, Garcia DM, Partha S, Quiñones-Hinojosa A, Kendziorski C, Dey M. Spatial transcriptomics in glioblastoma: is knowing the right zip code the key to the next therapeutic breakthrough? Front Oncol 2023; 13:1266397. [PMID: 37916170 PMCID: PMC10618006 DOI: 10.3389/fonc.2023.1266397] [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: 07/24/2023] [Accepted: 09/27/2023] [Indexed: 11/03/2023] Open
Abstract
Spatial transcriptomics, the technology of visualizing cellular gene expression landscape in a cells native tissue location, has emerged as a powerful tool that allows us to address scientific questions that were elusive just a few years ago. This technological advance is a decisive jump in the technological evolution that is revolutionizing studies of tissue structure and function in health and disease through the introduction of an entirely new dimension of data, spatial context. Perhaps the organ within the body that relies most on spatial organization is the brain. The central nervous system's complex microenvironmental and spatial architecture is tightly regulated during development, is maintained in health, and is detrimental when disturbed by pathologies. This inherent spatial complexity of the central nervous system makes it an exciting organ to study using spatial transcriptomics for pathologies primarily affecting the brain, of which Glioblastoma is one of the worst. Glioblastoma is a hyper-aggressive, incurable, neoplasm and has been hypothesized to not only integrate into the spatial architecture of the surrounding brain, but also possess an architecture of its own that might be actively remodeling the surrounding brain. In this review we will examine the current landscape of spatial transcriptomics in glioblastoma, outline novel findings emerging from the rising use of spatial transcriptomics, and discuss future directions and ultimate clinical/translational avenues.
Collapse
Affiliation(s)
- Jack M. Shireman
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
| | - Lingxin Cheng
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Amiti Goel
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
| | - Diogo Moniz Garcia
- Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, United States
| | - Sanil Partha
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
| | | | - Christina Kendziorski
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Mahua Dey
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
| |
Collapse
|
27
|
Cheng M, Jiang Y, Xu J, Mentis AFA, Wang S, Zheng H, Sahu SK, Liu L, Xu X. Spatially resolved transcriptomics: a comprehensive review of their technological advances, applications, and challenges. J Genet Genomics 2023; 50:625-640. [PMID: 36990426 DOI: 10.1016/j.jgg.2023.03.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/11/2023] [Accepted: 03/16/2023] [Indexed: 03/29/2023]
Abstract
The ability to explore life kingdoms is largely driven by innovations and breakthroughs in technology, from the invention of the microscope 350 years ago to the recent emergence of single-cell sequencing, by which the scientific community has been able to visualize life at an unprecedented resolution. Most recently, the Spatially Resolved Transcriptomics (SRT) technologies have filled the gap in probing the spatial or even three-dimensional organization of the molecular foundation behind the molecular mysteries of life, including the origin of different cellular populations developed from totipotent cells and human diseases. In this review, we introduce recent progresses and challenges on SRT from the perspectives of technologies and bioinformatic tools, as well as the representative SRT applications. With the currently fast-moving progress of the SRT technologies and promising results from early adopted research projects, we can foresee the bright future of such new tools in understanding life at the most profound analytical level.
Collapse
Affiliation(s)
| | - Yujia Jiang
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
| | | | | | - Shuai Wang
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Sunil Kumar Sahu
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China; State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, Guangdong 518083, China
| | - Longqi Liu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Xun Xu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China; BGI-Shenzhen, Shenzhen, Guangdong 518103, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, Guangdong 518120, China.
| |
Collapse
|
28
|
Guo L, Li Y, Qi Y, Huang Z, Han K, Liu X, Liu X, Xu M, Fan G. VT3D: a visualization toolbox for 3D transcriptomic data. J Genet Genomics 2023; 50:713-719. [PMID: 37054878 DOI: 10.1016/j.jgg.2023.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 04/03/2023] [Accepted: 04/04/2023] [Indexed: 04/15/2023]
Abstract
Data visualization empowers researchers to communicate their results that support scientific reasoning in an intuitive way. Three-dimension (3D) spatially resolved transcriptomic atlases constructed from multi-view and high-dimensional data have rapidly emerged as a powerful tool to unravel spatial gene expression patterns and cell type distribution in biological samples, revolutionizing the understanding of gene regulatory interactions and cell niches. However, limited accessible tools for data visualization impede the potential impact and application of this technology. Here we introduce VT3D, a visualization toolbox that allows users to explore 3D transcriptomic data, enabling gene expression projection to any 2D plane of interest, 2D virtual slice creation and visualization, and interactive 3D data browsing with surface model plots. In addition, it can either work on personal devices in standalone mode or be hosted as a web-based server. We apply VT3D to multiple datasets produced by the most popular techniques, including both sequencing-based approaches (Stereo-seq, spatial transcriptomics, and Slide-seq) and imaging-based approaches (MERFISH and STARMap), and successfully build a 3D atlas database that allows interactive data browsing. We demonstrate that VT3D bridges the gap between researchers and spatially resolved transcriptomics, thus accelerating related studies such as embryogenesis and organogenesis processes. The source code of VT3D is available at https://github.com/BGI-Qingdao/VT3D, and the modeled atlas database is available at http://www.bgiocean.com/vt3d_example.
Collapse
Affiliation(s)
- Lidong Guo
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong 266555, China
| | - Yao Li
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong 266555, China
| | - Yanwei Qi
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong 266555, China
| | - Zhi Huang
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong 266555, China
| | - Kai Han
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong 266555, China
| | - Xiaobin Liu
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong 266555, China
| | - Xin Liu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mengyang Xu
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong 266555, China; BGI-Shenzhen, Shenzhen, Guangdong 518083, China.
| | - Guangyi Fan
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong 266555, China; BGI-Shenzhen, Shenzhen, Guangdong 518083, China; State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, Guangdong 518083, China.
| |
Collapse
|
29
|
García-Ruiz S, Reynolds RH, Grant-Peters M, Gustavsson EK, Fairbrother-Browne A, Chen Z, Brenton JW, Ryten M. aws-s3-integrity-check: an open-source bash tool to verify the integrity of a dataset stored on Amazon S3. GIGABYTE 2023; 2023:gigabyte87. [PMID: 37637773 PMCID: PMC10448181 DOI: 10.46471/gigabyte.87] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 08/17/2023] [Indexed: 08/29/2023] Open
Abstract
Amazon Simple Storage Service (Amazon S3) is a widely used platform for storing large biomedical datasets. Unintended data alterations can occur during data writing and transmission, altering the original content and generating unexpected results. However, no open-source and easy-to-use tool exists to verify end-to-end data integrity. Here, we present aws-s3-integrity-check, a user-friendly, lightweight, and reliable bash tool to verify the integrity of a dataset stored in an Amazon S3 bucket. Using this tool, we only needed ∼114 min to verify the integrity of 1,045 records ranging between 5 bytes and 10 gigabytes and occupying ∼935 gigabytes of the Amazon S3 cloud. Our aws-s3-integrity-check tool also provides file-by-file on-screen and log-file-based information about the status of each integrity check. To our knowledge, this tool is the only open-source one that allows verifying the integrity of a dataset uploaded to the Amazon S3 Storage quickly, reliably, and efficiently. The tool is freely available for download and use at https://github.com/SoniaRuiz/aws-s3-integrity-check and https://hub.docker.com/r/soniaruiz/aws-s3-integrity-check.
Collapse
Affiliation(s)
- Sonia García-Ruiz
- Department of Genetics and Genomic Medicine Research & Teaching, UCL GOS Institute of Child Health, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK
| | - Regina Hertfelder Reynolds
- Department of Genetics and Genomic Medicine Research & Teaching, UCL GOS Institute of Child Health, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK
| | - Melissa Grant-Peters
- Department of Genetics and Genomic Medicine Research & Teaching, UCL GOS Institute of Child Health, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK
| | - Emil Karl Gustavsson
- Department of Genetics and Genomic Medicine Research & Teaching, UCL GOS Institute of Child Health, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK
| | - Aine Fairbrother-Browne
- Department of Genetics and Genomic Medicine Research & Teaching, UCL GOS Institute of Child Health, London, UK
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King’s College London, London, UK
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, UCL, London, UK
| | - Zhongbo Chen
- Department of Genetics and Genomic Medicine Research & Teaching, UCL GOS Institute of Child Health, London, UK
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, UCL, London, UK
| | - Jonathan William Brenton
- Department of Genetics and Genomic Medicine Research & Teaching, UCL GOS Institute of Child Health, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK
| | - Mina Ryten
- Department of Genetics and Genomic Medicine Research & Teaching, UCL GOS Institute of Child Health, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK
| |
Collapse
|
30
|
Wang G, Yao Y, Huang H, Zhou J, Ni C. Multiomics technologies for comprehensive tumor microenvironment analysis in triple-negative breast cancer under neoadjuvant chemotherapy. Front Oncol 2023; 13:1131259. [PMID: 37284197 PMCID: PMC10239824 DOI: 10.3389/fonc.2023.1131259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 05/08/2023] [Indexed: 06/08/2023] Open
Abstract
Triple-negative breast cancer (TNBC) is one of the most aggressive breast cancer subtypes and is characterized by abundant infiltrating immune cells within the microenvironment. As standard care, chemotherapy remains the fundamental neoadjuvant treatment in TNBC, and there is increasing evidence that supplementation with immune checkpoint inhibitors may potentiate the therapeutic efficiency of neoadjuvant chemotherapy (NAC). However, 20-60% of TNBC patients still have residual tumor burden after NAC and require additional chemotherapy; therefore, it is critical to understand the dynamic change in the tumor microenvironment (TME) during treatment to help improve the rate of complete pathological response and long-term prognosis. Traditional methods, including immunohistochemistry, bulk tumor sequencing, and flow cytometry, have been applied to elucidate the TME of breast cancer, but the low resolution and throughput may overlook key information. With the development of diverse high-throughput technologies, recent reports have provided new insights into TME alterations during NAC in four fields, including tissue imaging, cytometry, next-generation sequencing, and spatial omics. In this review, we discuss the traditional methods and the latest advances in high-throughput techniques to decipher the TME of TNBC and the prospect of translating these techniques to clinical practice.
Collapse
Affiliation(s)
- Gang Wang
- Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
| | - Yao Yao
- Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Tumor Microenvironment and Immune Therapy of Zhejiang Province, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, China
| | - Huanhuan Huang
- Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Tumor Microenvironment and Immune Therapy of Zhejiang Province, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, China
| | - Jun Zhou
- Department of Breast Surgery, Affiliated Hangzhou First People’s Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Chao Ni
- Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Tumor Microenvironment and Immune Therapy of Zhejiang Province, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, China
| |
Collapse
|
31
|
Xing Y, Zan C, Liu L. Recent advances in understanding neuronal diversity and neural circuit complexity across different brain regions using single-cell sequencing. Front Neural Circuits 2023; 17:1007755. [PMID: 37063385 PMCID: PMC10097998 DOI: 10.3389/fncir.2023.1007755] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 02/16/2023] [Indexed: 04/18/2023] Open
Abstract
Neural circuits are characterized as interconnecting neuron networks connected by synapses. Some kinds of gene expression and/or functional changes of neurons and synaptic connections may result in aberrant neural circuits, which has been recognized as one crucial pathological mechanism for the onset of many neurological diseases. Gradual advances in single-cell sequencing approaches with strong technological advantages, as exemplified by high throughput and increased resolution for live cells, have enabled it to assist us in understanding neuronal diversity across diverse brain regions and further transformed our knowledge of cellular building blocks of neural circuits through revealing numerous molecular signatures. Currently published transcriptomic studies have elucidated various neuronal subpopulations as well as their distribution across prefrontal cortex, hippocampus, hypothalamus, and dorsal root ganglion, etc. Better characterization of brain region-specific circuits may shed light on new pathological mechanisms involved and assist in selecting potential targets for the prevention and treatment of specific neurological disorders based on their established roles. Given diverse neuronal populations across different brain regions, we aim to give a brief sketch of current progress in understanding neuronal diversity and neural circuit complexity according to their locations. With the special focus on the application of single-cell sequencing, we thereby summarize relevant region-specific findings. Considering the importance of spatial context and connectivity in neural circuits, we also discuss a few published results obtained by spatial transcriptomics. Taken together, these single-cell sequencing data may lay a mechanistic basis for functional identification of brain circuit components, which links their molecular signatures to anatomical regions, connectivity, morphology, and physiology. Furthermore, the comprehensive characterization of neuron subtypes, their distributions, and connectivity patterns via single-cell sequencing is critical for understanding neural circuit properties and how they generate region-dependent interactions in different context.
Collapse
Affiliation(s)
- Yu Xing
- Department of Neurology, Beidahuang Industry Group General Hospital, Harbin, China
| | - Chunfang Zan
- Institute for Stroke and Dementia Research (ISD), LMU Klinikum, Ludwig-Maximilian-University (LMU), Munich, Germany
| | - Lu Liu
- Munich Medical Research School (MMRS), LMU Klinikum, Ludwig-Maximilian-University (LMU), Munich, Germany
- *Correspondence: Lu Liu, ,
| |
Collapse
|