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Lee Y, Xu Y, Gao P, Chen J. TENET: Triple-enhancement based graph neural network for cell-cell interaction network reconstruction from spatial transcriptomics. J Mol Biol 2024; 436:168543. [PMID: 38508302 DOI: 10.1016/j.jmb.2024.168543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 03/03/2024] [Accepted: 03/13/2024] [Indexed: 03/22/2024]
Abstract
Cellular communication relies on the intricate interplay of signaling molecules, forming the Cell-cell Interaction network (CCI) that coordinates tissue behavior. Researchers have shown the capability of shallow neural networks in reconstructing CCI, given molecules' abundance in the Spatial Transcriptomics (ST) data. When encountering situations such as sparse connections in CCI and excessive noise, the susceptibility of shallow networks to these factors significantly impacts the accuracy of CCI reconstruction, resulting in subpar results. To reconstruct a more comprehensive and accurate CCI, we propose a novel method named Triple-Enhancement based Graph Neural Network (TENET). In TENET, three progressive enhancement mechanisms build upon each other, creating a cumulative effect. This approach can ensure the ability to capture valuable features in limited data and amplify the noise signal to facilitate the denoising effect. Additionally, the whole architecture guides the decoding reconstruction phase with integrated knowledge, which leverages the accumulated insights from each stage of enhancement to ensure a refined and comprehensive CCI reconstruction. The presented TENET has been implemented and tested on both real and synthetic ST datasets. Averagely, the CCI reconstruction using TENET achieves a 9.61% improvement in Average Precision (AP) and a 7.32% improvement in Area Under the Receiver Operating Characteristic (AUROC) compared to the existing state-of-the-art (SOTA) method. The source code and data are available at https://github.com/Yujian-Lee/TENET.
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Affiliation(s)
- Yujian Lee
- Guangdong Provincial Key Laboratory IRADS, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China; Department of Computer Science, Hong Kong Baptist University, Hong Kong Special Administrative Region; Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China
| | - Yongqi Xu
- Department of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
| | - Peng Gao
- Department of Computer Science, Hong Kong Baptist University, Hong Kong Special Administrative Region; Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China
| | - Jiaxing Chen
- Guangdong Provincial Key Laboratory IRADS, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China; Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China.
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2
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Bhuva DD, Tan CW, Salim A, Marceaux C, Pickering MA, Chen J, Kharbanda M, Jin X, Liu N, Feher K, Putri G, Tilley WD, Hickey TE, Asselin-Labat ML, Phipson B, Davis MJ. Library size confounds biology in spatial transcriptomics data. Genome Biol 2024; 25:99. [PMID: 38637899 PMCID: PMC11025268 DOI: 10.1186/s13059-024-03241-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 04/09/2024] [Indexed: 04/20/2024] Open
Abstract
Spatial molecular data has transformed the study of disease microenvironments, though, larger datasets pose an analytics challenge prompting the direct adoption of single-cell RNA-sequencing tools including normalization methods. Here, we demonstrate that library size is associated with tissue structure and that normalizing these effects out using commonly applied scRNA-seq normalization methods will negatively affect spatial domain identification. Spatial data should not be specifically corrected for library size prior to analysis, and algorithms designed for scRNA-seq data should be adopted with caution.
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Affiliation(s)
- Dharmesh D Bhuva
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia.
- Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3052, Australia.
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, 3010, Australia.
| | - Chin Wee Tan
- Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, 3010, Australia
- The University of Queensland Fraser Institute, The University of Queensland, Woolloongabba, QLD, 4102, Australia
| | - Agus Salim
- Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3052, Australia
- Melbourne School of Population and Global Health, School of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Claire Marceaux
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, 3010, Australia
- Personalised Oncology Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3052, Australia
| | - Marie A Pickering
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia
| | - Jinjin Chen
- Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Malvika Kharbanda
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia
- Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Xinyi Jin
- Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Ning Liu
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia
- Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Kristen Feher
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia
- Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Givanna Putri
- Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Wayne D Tilley
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia
| | - Theresa E Hickey
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia
| | - Marie-Liesse Asselin-Labat
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, 3010, Australia
- Personalised Oncology Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3052, Australia
| | - Belinda Phipson
- Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Melissa J Davis
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia
- Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, 3010, Australia
- The University of Queensland Fraser Institute, The University of Queensland, Woolloongabba, QLD, 4102, Australia
- Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, 3010, Australia
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3
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Wang J, Li B, Luo M, Huang J, Zhang K, Zheng S, Zhang S, Zhou J. Progression from ductal carcinoma in situ to invasive breast cancer: molecular features and clinical significance. Signal Transduct Target Ther 2024; 9:83. [PMID: 38570490 PMCID: PMC10991592 DOI: 10.1038/s41392-024-01779-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 04/05/2024] Open
Abstract
Ductal carcinoma in situ (DCIS) represents pre-invasive breast carcinoma. In untreated cases, 25-60% DCIS progress to invasive ductal carcinoma (IDC). The challenge lies in distinguishing between non-progressive and progressive DCIS, often resulting in over- or under-treatment in many cases. With increasing screen-detected DCIS in these years, the nature of DCIS has aroused worldwide attention. A deeper understanding of the biological nature of DCIS and the molecular journey of the DCIS-IDC transition is crucial for more effective clinical management. Here, we reviewed the key signaling pathways in breast cancer that may contribute to DCIS initiation and progression. We also explored the molecular features of DCIS and IDC, shedding light on the progression of DCIS through both inherent changes within tumor cells and alterations in the tumor microenvironment. In addition, valuable research tools utilized in studying DCIS including preclinical models and newer advanced technologies such as single-cell sequencing, spatial transcriptomics and artificial intelligence, have been systematically summarized. Further, we thoroughly discussed the clinical advancements in DCIS and IDC, including prognostic biomarkers and clinical managements, with the aim of facilitating more personalized treatment strategies in the future. Research on DCIS has already yielded significant insights into breast carcinogenesis and will continue to pave the way for practical clinical applications.
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Affiliation(s)
- Jing Wang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Baizhou Li
- Department of Pathology, the Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Meng Luo
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
- Department of Plastic Surgery, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jia Huang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Kun Zhang
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shu Zheng
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Suzhan Zhang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China.
| | - Jiaojiao Zhou
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China.
- Cancer Center, Zhejiang University, Hangzhou, China.
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4
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Hashemi Gheinani A, Kim J, You S, Adam RM. Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment. Nat Rev Urol 2024; 21:214-242. [PMID: 37604982 DOI: 10.1038/s41585-023-00805-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2023] [Indexed: 08/23/2023]
Abstract
The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.
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Affiliation(s)
- Ali Hashemi Gheinani
- Department of Urology, Boston Children's Hospital, Boston, MA, USA
- Department of Surgery, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Urology, Inselspital, Bern, Switzerland
- Department for BioMedical Research, University of Bern, Bern, Switzerland
| | - Jina Kim
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sungyong You
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Rosalyn M Adam
- Department of Urology, Boston Children's Hospital, Boston, MA, USA.
- Department of Surgery, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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5
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Lei L, Han K, Wang Z, Shi C, Wang Z, Dai R, Zhang Z, Wang M, Guo Q. Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics. Brief Bioinform 2024; 25:bbae173. [PMID: 38627939 PMCID: PMC11021349 DOI: 10.1093/bib/bbae173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 03/03/2024] [Accepted: 04/02/2024] [Indexed: 04/19/2024] Open
Abstract
The latest breakthroughs in spatially resolved transcriptomics technology offer comprehensive opportunities to delve into gene expression patterns within the tissue microenvironment. However, the precise identification of spatial domains within tissues remains challenging. In this study, we introduce AttentionVGAE (AVGN), which integrates slice images, spatial information and raw gene expression while calibrating low-quality gene expression. By combining the variational graph autoencoder with multi-head attention blocks (MHA blocks), AVGN captures spatial relationships in tissue gene expression, adaptively focusing on key features and alleviating the need for prior knowledge of cluster numbers, thereby achieving superior clustering performance. Particularly, AVGN attempts to balance the model's attention focus on local and global structures by utilizing MHA blocks, an aspect that current graph neural networks have not extensively addressed. Benchmark testing demonstrates its significant efficacy in elucidating tissue anatomy and interpreting tumor heterogeneity, indicating its potential in advancing spatial transcriptomics research and understanding complex biological phenomena.
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Affiliation(s)
- Lixin Lei
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Kaitai Han
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Zijun Wang
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Chaojing Shi
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Zhenghui Wang
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Ruoyan Dai
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Zhiwei Zhang
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Mengqiu Wang
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Qianjin Guo
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
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6
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Liang Q, Soto LS, Haymaker C, Chen K. Interpretable Spatial Gradient Analysis for Spatial Transcriptomics Data. bioRxiv 2024:2024.03.19.585725. [PMID: 38562886 PMCID: PMC10983986 DOI: 10.1101/2024.03.19.585725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intratumor heterogeneity and can influence tumor invasion, progression, and response to treatment. Here we report Local Spatial Gradient Inference (LSGI), a computational framework that systematically identifies spatial locations with prominent, interpretable STGs from spatial transcriptomic (ST) data. To achieve so, LSGI scrutinizes each sliding window employing non-negative matrix factorization (NMF) combined with linear regression. With LSGI, we demonstrated the identification of spatially proximal yet opposite directed pathway gradients in a glioblastoma dataset. We further applied LSGI to 87 tumor ST datasets reported from nine published studies and identified both pan-cancer and tumor-type specific pathways with gradated expression patterns, such as epithelial mesenchymal transition, MHC complex, and hypoxia. The local gradients were further categorized according to their association to tumor-TME (tumor microenvironment) interface, highlighting the pathways related to spatial transcriptional intratumoral heterogeneity. We conclude that LSGI enables highly interpretable STG analysis which can reveal novel insights in tumor biology from the increasingly reported tumor ST datasets.
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Affiliation(s)
- Qingnan Liang
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center
| | - Luisa Solis Soto
- Department of Translational Molecular Pathology, UT MD Anderson Cancer Center
| | - Cara Haymaker
- Department of Translational Molecular Pathology, UT MD Anderson Cancer Center
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center
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Carrillo-Perez F, Pizurica M, Zheng Y, Nandi TN, Madduri R, Shen J, Gevaert O. Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models. Nat Biomed Eng 2024:10.1038/s41551-024-01193-8. [PMID: 38514775 DOI: 10.1038/s41551-024-01193-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 02/29/2024] [Indexed: 03/23/2024]
Abstract
Training machine-learning models with synthetically generated data can alleviate the problem of data scarcity when acquiring diverse and sufficiently large datasets is costly and challenging. Here we show that cascaded diffusion models can be used to synthesize realistic whole-slide image tiles from latent representations of RNA-sequencing data from human tumours. Alterations in gene expression affected the composition of cell types in the generated synthetic image tiles, which accurately preserved the distribution of cell types and maintained the cell fraction observed in bulk RNA-sequencing data, as we show for lung adenocarcinoma, kidney renal papillary cell carcinoma, cervical squamous cell carcinoma, colon adenocarcinoma and glioblastoma. Machine-learning models pretrained with the generated synthetic data performed better than models trained from scratch. Synthetic data may accelerate the development of machine-learning models in scarce-data settings and allow for the imputation of missing data modalities.
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Affiliation(s)
- Francisco Carrillo-Perez
- Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, School of Medicine, Stanford, CA, USA
| | - Marija Pizurica
- Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, School of Medicine, Stanford, CA, USA
- Internet technology and Data science Lab (IDLab), Ghent University, Ghent, Belgium
| | - Yuanning Zheng
- Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, School of Medicine, Stanford, CA, USA
| | - Tarak Nath Nandi
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
| | - Ravi Madduri
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
| | - Jeanne Shen
- Department of Pathology, Stanford University, School of Medicine, Palo Alto, CA, USA
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, School of Medicine, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, School of Medicine, Stanford, CA, USA.
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8
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Lu Y, Oliva M, Pierce BL, Liu J, Chen LS. Integrative cross-omics and cross-context analysis elucidates molecular links underlying genetic effects on complex traits. Nat Commun 2024; 15:2383. [PMID: 38493154 PMCID: PMC10944527 DOI: 10.1038/s41467-024-46675-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
Genetic effects on functionally related 'omic' traits often co-occur in relevant cellular contexts, such as tissues. Motivated by the multi-tissue methylation quantitative trait loci (mQTLs) and expression QTLs (eQTLs) analysis, we propose X-ING (Cross-INtegrative Genomics) for cross-omics and cross-context integrative analysis. X-ING takes as input multiple matrices of association statistics, each obtained from different omics data types across multiple cellular contexts. It models the latent binary association status of each statistic, captures the major association patterns among omics data types and contexts, and outputs the posterior mean and probability for each input statistic. X-ING enables the integration of effects from different omics data with varying effect distributions. In the multi-tissue cis-association analysis, X-ING shows improved detection and replication of mQTLs by integrating eQTL maps. In the trans-association analysis, X-ING reveals an enrichment of trans-associations in many disease/trait-relevant tissues.
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Affiliation(s)
- Yihao Lu
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Meritxell Oliva
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
- Genomics Research Center, AbbVie, North Chicago, IL, USA
| | - Brandon L Pierce
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Jin Liu
- School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen, China.
| | - Lin S Chen
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA.
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9
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Atta L, Clifton K, Anant M, Aihara G, Fan J. Gene count normalization in single-cell imaging-based spatially resolved transcriptomics. bioRxiv 2024:2023.08.30.555624. [PMID: 37693542 PMCID: PMC10491191 DOI: 10.1101/2023.08.30.555624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals. Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue regions or cell types, we demonstrate how normalization methods based on detected gene counts per cell differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream analyses including differential gene expression, gene fold change, and spatially variable gene analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissue's component cell types. These effects are not observed with normalization approaches that do not use detected gene counts for gene expression magnitude adjustment, such as with cell volume or cell area normalization. We recommend using non-gene count-based normalization approaches when feasible and evaluating gene panel representativeness before using gene count-based normalization methods if necessary. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data.
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Affiliation(s)
- Lyla Atta
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
| | - Kalen Clifton
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
| | - Manjari Anant
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Gohta Aihara
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
| | - Jean Fan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
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10
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Li R, Chen X, Yang X. Navigating the landscapes of spatial transcriptomics: How computational methods guide the way. Wiley Interdiscip Rev RNA 2024; 15:e1839. [PMID: 38527900 DOI: 10.1002/wrna.1839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/24/2024] [Accepted: 03/04/2024] [Indexed: 03/27/2024]
Abstract
Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single-cell, multi-cellular, or sub-cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi-modal high-throughput data source, which poses new challenges for the development of analytical methods for data-mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever-evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization.
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Affiliation(s)
- Runze Li
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xu Chen
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xuerui Yang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
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11
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Zhang L, Xiong Z, Xiao M. A Review of the Application of Spatial Transcriptomics in Neuroscience. Interdiscip Sci 2024:10.1007/s12539-024-00603-4. [PMID: 38374297 DOI: 10.1007/s12539-024-00603-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/29/2023] [Accepted: 01/02/2024] [Indexed: 02/21/2024]
Abstract
Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.
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Affiliation(s)
- Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Zhenqi Xiong
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Ming Xiao
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
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12
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He F, Yu X, Zhang J, Cui J, Tang L, Zou S, Pu J, Ran P. Biomass-related PM 2.5 induced inflammatory microenvironment via IL-17F/IL-17RC axis. Environ Pollut 2024; 342:123048. [PMID: 38036089 DOI: 10.1016/j.envpol.2023.123048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 10/13/2023] [Accepted: 11/23/2023] [Indexed: 12/02/2023]
Abstract
Biomass exposure is a significant environmental risk factor for COPD, but the underlying mechanisms have not yet been fully elucidated. Inflammatory microenvironment has been shown to drive the development of many chronic diseases. Pollution exposure can cause increased levels of inflammatory factors in the lungs, leading to an inflammatory microenvironment which is prevalent in COPD. Our findings revealed that IL-17F was elevated in COPD, while exposure to biomass led to increased expression of IL-17F in both alveolar epithelial and macrophage cells in mice. Blocking IL-17F could alleviate the lung inflammation induced by seven days of biomass exposure in mice. We employed a transwell co-culture system to simulate the microenvironment and investigate the interactions between MLE-12 and MH-S cells. We demonstrated that anti-IL-17F antibody attenuated the inflammatory responses induced by BRPM2.5 in MLE-12 and MH-S co-cultured with BRPM2.5-MLE-12, which reduced inflammatory changes in microenvironment. We found that IL-17RC, an important receptor for IL-17F, played a key role in the interactions. Knockout of IL-17RC in MH-S resulted in inhibited IL-17F signaling and attenuated inflammatory response after MH-S co-culture with BRPM2.5-MLE-12. Our investigation suggests that BRPM2.5 induces lung epithelial-macrophage interactions via IL-17F/IL-17RC axis regulating the inflammatory response. These results may provide a novel strategy for effective prevention and treatment of biomass-related COPD.
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Affiliation(s)
- Fang He
- School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, Guangdong, 510000, China; State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510000, China
| | - Xiaoyuan Yu
- School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, Guangdong, 510000, China
| | - Jiahuan Zhang
- State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510000, China
| | - Jieda Cui
- State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510000, China; Guangzhou National Laboratory, No.9 XingDaoHuanBei Road, Guangzhou International BioIsland, Guangzhou, Guangdong, 510000, China
| | - Lei Tang
- School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, Guangdong, 510000, China
| | - Siqi Zou
- School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, Guangdong, 510000, China
| | - Jinding Pu
- Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510000, China
| | - Pixin Ran
- State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510000, China; Guangzhou National Laboratory, No.9 XingDaoHuanBei Road, Guangzhou International BioIsland, Guangzhou, Guangdong, 510000, China.
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13
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Guo X, Chen L. From G1 to M: a comparative study of methods for identifying cell cycle phases. Brief Bioinform 2024; 25:bbad517. [PMID: 38261342 PMCID: PMC10805071 DOI: 10.1093/bib/bbad517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/08/2023] [Accepted: 12/13/2023] [Indexed: 01/24/2024] Open
Abstract
Accurate identification of cell cycle phases in single-cell RNA-sequencing (scRNA-seq) data is crucial for biomedical research. Many methods have been developed to tackle this challenge, employing diverse approaches to predict cell cycle phases. In this review article, we delve into the standard processes in identifying cell cycle phases within scRNA-seq data and present several representative methods for comparison. To rigorously assess the accuracy of these methods, we propose an error function and employ multiple benchmarking datasets encompassing human and mouse data. Our evaluation results reveal a key finding: the fit between the reference data and the dataset being analyzed profoundly impacts the effectiveness of cell cycle phase identification methods. Therefore, researchers must carefully consider the compatibility between the reference data and their dataset to achieve optimal results. Furthermore, we explore the potential benefits of incorporating benchmarking data with multiple known cell cycle phases into the analysis. Merging such data with the target dataset shows promise in enhancing prediction accuracy. By shedding light on the accuracy and performance of cell cycle phase prediction methods across diverse datasets, this review aims to motivate and guide future methodological advancements. Our findings offer valuable insights for researchers seeking to improve their understanding of cellular dynamics through scRNA-seq analysis, ultimately fostering the development of more robust and widely applicable cell cycle identification methods.
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Affiliation(s)
- Xinyu Guo
- Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, United States
| | - Liang Chen
- Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, United States
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14
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Kiessling P, Kuppe C. Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases. Genome Med 2024; 16:14. [PMID: 38238823 PMCID: PMC10795303 DOI: 10.1186/s13073-024-01282-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 01/02/2024] [Indexed: 01/22/2024] Open
Abstract
Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.
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Affiliation(s)
- Paul Kiessling
- Department of Nephrology, Rheumatology, and Clinical Immunology, University Hospital RWTH Aachen, Aachen, Germany
| | - Christoph Kuppe
- Department of Nephrology, Rheumatology, and Clinical Immunology, University Hospital RWTH Aachen, Aachen, Germany.
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15
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Mason K, Sathe A, Hess PR, Rong J, Wu CY, Furth E, Susztak K, Levinsohn J, Ji HP, Zhang N. Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions. Genome Biol 2024; 25:14. [PMID: 38217002 PMCID: PMC10785550 DOI: 10.1186/s13059-023-03159-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 12/22/2023] [Indexed: 01/14/2024] Open
Abstract
Existing methods for analysis of spatial transcriptomic data focus on delineating the global gene expression variations of cell types across the tissue, rather than local gene expression changes driven by cell-cell interactions. We propose a new statistical procedure called niche-differential expression (niche-DE) analysis that identifies cell-type-specific niche-associated genes, which are differentially expressed within a specific cell type in the context of specific spatial niches. We further develop niche-LR, a method to reveal ligand-receptor signaling mechanisms that underlie niche-differential gene expression patterns. Niche-DE and niche-LR are applicable to low-resolution spot-based spatial transcriptomics data and data that is single-cell or subcellular in resolution.
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Affiliation(s)
- Kaishu Mason
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, USA
| | - Anuja Sathe
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Paul R Hess
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, USA
| | - Jiazhen Rong
- Genomics and Computational Biology Graduate Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Chi-Yun Wu
- The Gladstone Institute, San Francisco, USA
| | - Emma Furth
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Katalin Susztak
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jonathan Levinsohn
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hanlee P Ji
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nancy Zhang
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, USA.
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16
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Yuan Z. MENDER: fast and scalable tissue structure identification in spatial omics data. Nat Commun 2024; 15:207. [PMID: 38182575 PMCID: PMC10770058 DOI: 10.1038/s41467-023-44367-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 12/11/2023] [Indexed: 01/07/2024] Open
Abstract
Tissue structure identification is a crucial task in spatial omics data analysis, for which increasingly complex models, such as Graph Neural Networks and Bayesian networks, are employed. However, whether increased model complexity can effectively lead to improved performance is a notable question in the field. Inspired by the consistent observation of cellular neighborhood structures across various spatial technologies, we propose Multi-range cEll coNtext DEciphereR (MENDER), for tissue structure identification. Applied on datasets of 3 brain regions and a whole-brain atlas, MENDER, with biology-driven design, offers substantial improvements over modern complex models while automatically aligning labels across slices, despite using much less running time than the second-fastest. MENDER's identification power allows the uncovering of previously overlooked spatial domains that exhibit strong associations with brain aging. MENDER's scalability makes it freely appliable on a million-level brain spatial atlas. MENDER's discriminative power enables the differentiation of breast cancer patient subtypes obscured by single-cell analysis.
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Affiliation(s)
- Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, 200433, China.
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17
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Wang H, Huang R, Nelson J, Gao C, Tran M, Yeaton A, Felt K, Pfaff KL, Bowman T, Rodig SJ, Wei K, Goods BA, Farhi SL. Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues. bioRxiv 2023:2023.12.07.570603. [PMID: 38106230 PMCID: PMC10723440 DOI: 10.1101/2023.12.07.570603] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Emerging imaging spatial transcriptomics (iST) platforms and coupled analytical methods can recover cell-to-cell interactions, groups of spatially covarying genes, and gene signatures associated with pathological features, and are thus particularly well-suited for applications in formalin fixed paraffin embedded (FFPE) tissues. Here, we benchmarked the performance of three commercial iST platforms on serial sections from tissue microarrays (TMAs) containing 23 tumor and normal tissue types for both relative technical and biological performance. On matched genes, we found that 10x Xenium shows higher transcript counts per gene without sacrificing specificity, but that all three platforms concord to orthogonal RNA-seq datasets and can perform spatially resolved cell typing, albeit with different false discovery rates, cell segmentation error frequencies, and with varying degrees of sub-clustering for downstream biological analyses. Taken together, our analyses provide a comprehensive benchmark to guide the choice of iST method as researchers design studies with precious samples in this rapidly evolving field.
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Affiliation(s)
- Huan Wang
- Spatial Technology Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
| | - Ruixu Huang
- Thayer School of Engineering, Molecular and Systems Biology, and Program in Quantitative Biomedical Sciences at Dartmouth College, Hanover, NH 03755, USA
| | - Jack Nelson
- Spatial Technology Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
| | - Ce Gao
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02215, USA
| | - Miles Tran
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02215, USA
| | - Anna Yeaton
- Present affiliation: Immunai, New York, NY 10016, USA
| | - Kristen Felt
- ImmunoProfile, Brigham & Women’s Hospital and Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Kathleen L. Pfaff
- Center for Immuno-Oncology, Tissue Biomarker Laboratory, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Teri Bowman
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02215, USA
| | - Scott J. Rodig
- Center for Immuno-Oncology, Tissue Biomarker Laboratory, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02215, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02215, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02215, USA
| | - Brittany A. Goods
- Thayer School of Engineering, Molecular and Systems Biology, and Program in Quantitative Biomedical Sciences at Dartmouth College, Hanover, NH 03755, USA
| | - Samouil L. Farhi
- Spatial Technology Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
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18
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Li C, Thijssen J, Kroes T, de Boer M, Abdelaal T, Höllt T, Lelieveldt B. SpaceWalker enables interactive gradient exploration for spatial transcriptomics data. Cell Rep Methods 2023; 3:100645. [PMID: 37972590 PMCID: PMC10753200 DOI: 10.1016/j.crmeth.2023.100645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 09/01/2023] [Accepted: 10/25/2023] [Indexed: 11/19/2023]
Abstract
In spatial transcriptomics (ST) data, biologically relevant features such as tissue compartments or cell-state transitions are reflected by gene expression gradients. Here, we present SpaceWalker, a visual analytics tool for exploring the local gradient structure of 2D and 3D ST data. The user can be guided by the local intrinsic dimensionality of the high-dimensional data to define seed locations, from which a flood-fill algorithm identifies transcriptomically similar cells on the fly, based on the high-dimensional data topology. In several use cases, we demonstrate that the spatial projection of these flooded cells highlights tissue architectural features and that interactive retrieval of gene expression gradients in the spatial and transcriptomic domains confirms known biology. We also show that SpaceWalker generalizes to several different ST protocols and scales well to large, multi-slice, 3D whole-brain ST data while maintaining real-time interaction performance.
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Affiliation(s)
- Chang Li
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands
| | - Julian Thijssen
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands
| | - Thomas Kroes
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands
| | - Mitchell de Boer
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands
| | - Tamim Abdelaal
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands; Delft Bioinformatics Lab, INSY, TU Delft, 2628 XE Delft, the Netherlands
| | - Thomas Höllt
- Computer Graphics and Visualization, INSY, TU Delft, 2628 XE Delft, the Netherlands
| | - Boudewijn Lelieveldt
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands; Delft Bioinformatics Lab, INSY, TU Delft, 2628 XE Delft, the Netherlands.
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19
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Guo B, Huuki-Myers LA, Grant-Peters M, Collado-Torres L, Hicks SC. escheR: unified multi-dimensional visualizations with Gestalt principles. Bioinform Adv 2023; 3:vbad179. [PMID: 38107654 PMCID: PMC10723033 DOI: 10.1093/bioadv/vbad179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/14/2023] [Accepted: 12/04/2023] [Indexed: 12/19/2023]
Abstract
Summary The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide an open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows. Availability and implementation The open source R package escheR is freely available on Bioconductor (https://bioconductor.org/packages/escheR).
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Affiliation(s)
- Boyi Guo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, United States
| | - Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, United States
| | - Melissa Grant-Peters
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, WC1N 1EH, United Kingdom
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, United States
| | - Leonardo Collado-Torres
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, United States
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, United States
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, United States
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, 21218, United States
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, United States
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, 21218, United States
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20
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Wang T, Song Z, Zhao X, Wu Y, Wu L, Haghparast A, Wu H. Spatial transcriptomic analysis of the mouse brain following chronic social defeat stress. Exploration (Beijing) 2023; 3:20220133. [PMID: 38264685 PMCID: PMC10742195 DOI: 10.1002/exp.20220133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 09/03/2023] [Indexed: 01/25/2024]
Abstract
Depression is a highly prevalent and disabling mental disorder, involving numerous genetic changes that are associated with abnormal functions in multiple regions of the brain. However, there is little transcriptomic-wide characterization of chronic social defeat stress (CSDS) to comprehensively compare the transcriptional changes in multiple brain regions. Spatial transcriptomics (ST) was used to reveal the spatial difference of gene expression in the control, resilient (RES) and susceptible (SUS) mouse brains, and annotated eight anatomical brain regions and six cell types. The gene expression profiles uncovered that CSDS leads to gene synchrony changes in different brain regions. Then it was identified that inhibitory neurons and synaptic functions in multiple regions were primarily affected by CSDS. The brain regions Hippocampus (HIP), Isocortex, and Amygdala (AMY) present more pronounced transcriptional changes in genes associated with depressive psychiatric disorders than other regions. Signalling communication between these three brain regions may play a critical role in susceptibility to CSDS. Taken together, this study provides important new insights into CSDS susceptibility at the ST level, which offers a new approach for understanding and treating depression.
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Affiliation(s)
- Ting Wang
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Zhihong Song
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Xin Zhao
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Yan Wu
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Liying Wu
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
| | - Abbas Haghparast
- Neuroscience Research Center, School of MedicineShahid Beheshti University of Medical SciencesTehranIran
| | - Haitao Wu
- Department of NeurobiologyBeijing Institute of Basic Medical SciencesBeijingChina
- Key Laboratory of Neuroregeneration, Co‐innovation Center of NeuroregenerationNantong UniversityNantongChina
- Chinese Institute for Brain ResearchBeijingChina
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21
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London CA, Gardner H, Zhao S, Knapp DW, Utturkar SM, Duval DL, Chambers MR, Ostrander E, Trent JM, Kuffel G. Leading the pack: Best practices in comparative canine cancer genomics to inform human oncology. Vet Comp Oncol 2023; 21:565-577. [PMID: 37778398 DOI: 10.1111/vco.12935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/17/2023] [Accepted: 08/18/2023] [Indexed: 10/03/2023]
Abstract
Pet dogs develop spontaneous cancers at a rate estimated to be five times higher than that of humans, providing a unique opportunity to study disease biology and evaluate novel therapeutic strategies in a model system that possesses an intact immune system and mirrors key aspects of human cancer biology. Despite decades of interest, effective utilization of pet dog cancers has been hindered by a limited repertoire of necessary cellular and molecular reagents for both in vitro and in vivo studies, as well as a dearth of information regarding the genomic landscape of these cancers. Recently, many of these critical gaps have been addressed through the generation of a highly annotated canine reference genome, the creation of several tools necessary for multi-omic analysis of canine tumours, and the development of a centralized repository for key genomic and associated clinical information from canine cancer patients, the Integrated Canine Data Commons. Together, these advances have catalysed multidisciplinary efforts designed to integrate the study of pet dog cancers more effectively into the translational continuum, with the ultimate goal of improving human outcomes. The current review summarizes this recent progress and provides a guide to resources and tools available for comparative study of pet dog cancers.
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Affiliation(s)
- Cheryl A London
- Cummings School of Veterinary Medicine, Tufts University, North Grafton, Massachusetts, USA
| | - Heather Gardner
- Cummings School of Veterinary Medicine, Tufts University, North Grafton, Massachusetts, USA
| | - Shaying Zhao
- University of Georgia Cancer Center, University of Georgia, Athens, Georgia, USA
| | - Deborah W Knapp
- College of Veterinary Medicine, Purdue University, West Lafayette, Indiana, USA
| | - Sagar M Utturkar
- Purdue Institute for Cancer Research, Purdue University, West Lafayette, Indiana, USA
| | - Dawn L Duval
- College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, Colorado, USA
| | | | - Elaine Ostrander
- Cancer Genetics and Comparative Genomics Branch, National Cancer Institute, Bethesda, Maryland, USA
| | - Jeffrey M Trent
- Translational Genomics Research Institute, Phoenix, Arizona, USA
| | - Gina Kuffel
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
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22
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Zhang X, Cao Q, Rajachandran S, Grow EJ, Evans M, Chen H. Dissecting mammalian reproduction with spatial transcriptomics. Hum Reprod Update 2023; 29:794-810. [PMID: 37353907 PMCID: PMC10628492 DOI: 10.1093/humupd/dmad017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 05/15/2023] [Indexed: 06/25/2023] Open
Abstract
BACKGROUND Mammalian reproduction requires the fusion of two specialized cells: an oocyte and a sperm. In addition to producing gametes, the reproductive system also provides the environment for the appropriate development of the embryo. Deciphering the reproductive system requires understanding the functions of each cell type and cell-cell interactions. Recent single-cell omics technologies have provided insights into the gene regulatory network in discrete cellular populations of both the male and female reproductive systems. However, these approaches cannot examine how the cellular states of the gametes or embryos are regulated through their interactions with neighboring somatic cells in the native tissue environment owing to tissue disassociations. Emerging spatial omics technologies address this challenge by preserving the spatial context of the cells to be profiled. These technologies hold the potential to revolutionize our understanding of mammalian reproduction. OBJECTIVE AND RATIONALE We aim to review the state-of-the-art spatial transcriptomics (ST) technologies with a focus on highlighting the novel biological insights that they have helped to reveal about the mammalian reproductive systems in the context of gametogenesis, embryogenesis, and reproductive pathologies. We also aim to discuss the current challenges of applying ST technologies in reproductive research and provide a sneak peek at what the field of spatial omics can offer for the reproduction community in the years to come. SEARCH METHODS The PubMed database was used in the search for peer-reviewed research articles and reviews using combinations of the following terms: 'spatial omics', 'fertility', 'reproduction', 'gametogenesis', 'embryogenesis', 'reproductive cancer', 'spatial transcriptomics', 'spermatogenesis', 'ovary', 'uterus', 'cervix', 'testis', and other keywords related to the subject area. All relevant publications until April 2023 were critically evaluated and discussed. OUTCOMES First, an overview of the ST technologies that have been applied to studying the reproductive systems was provided. The basic design principles and the advantages and limitations of these technologies were discussed and tabulated to serve as a guide for researchers to choose the best-suited technologies for their own research. Second, novel biological insights into mammalian reproduction, especially human reproduction revealed by ST analyses, were comprehensively reviewed. Three major themes were discussed. The first theme focuses on genes with non-random spatial expression patterns with specialized functions in multiple reproductive systems; The second theme centers around functionally interacting cell types which are often found to be spatially clustered in the reproductive tissues; and the thrid theme discusses pathological states in reproductive systems which are often associated with unique cellular microenvironments. Finally, current experimental and computational challenges of applying ST technologies to studying mammalian reproduction were highlighted, and potential solutions to tackle these challenges were provided. Future directions in the development of spatial omics technologies and how they will benefit the field of human reproduction were discussed, including the capture of cellular and tissue dynamics, multi-modal molecular profiling, and spatial characterization of gene perturbations. WIDER IMPLICATIONS Like single-cell technologies, spatial omics technologies hold tremendous potential for providing significant and novel insights into mammalian reproduction. Our review summarizes these novel biological insights that ST technologies have provided while shedding light on what is yet to come. Our review provides reproductive biologists and clinicians with a much-needed update on the state of art of ST technologies. It may also facilitate the adoption of cutting-edge spatial technologies in both basic and clinical reproductive research.
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Affiliation(s)
- Xin Zhang
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qiqi Cao
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Shreya Rajachandran
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Edward J Grow
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Melanie Evans
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Haiqi Chen
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Singh-Varma A, Shah AM, Liu S, Zamora R, Monga SP, Vodovotz Y. Defining spatiotemporal gene modules in liver regeneration using Analytical Dynamic Visual Spatial Omics Representation (ADViSOR). Hepatol Commun 2023; 7:e0289. [PMID: 37889540 PMCID: PMC10615476 DOI: 10.1097/hc9.0000000000000289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 08/23/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND The liver is the only organ with the ability to regenerate following surgical or toxicant insults, and partial hepatectomy serves as an experimental model of liver regeneration (LR). Dynamic changes in gene expression occur from the periportal to pericentral regions of the liver following partial hepatectomy; thus, spatial transcriptomics, combined with a novel computational pipeline (ADViSOR [Analytic Dynamic Visual Spatial Omics Representation]), was employed to gain insights into the spatiotemporal molecular underpinnings of LR. METHODS ADViSOR, comprising Time-Interval Principal Component Analysis and sliding dynamic hypergraphs, was applied to spatial transcriptomics data on 100 genes assayed serially through LR, including key components of the Wnt/β-catenin pathway at critical timepoints after partial hepatectomy. RESULTS This computational pipeline identified key functional modules demonstrating cell signaling and cell-cell interactions, inferring shared regulatory mechanisms. Specifically, ADViSOR analysis suggested that macrophage-mediated inflammation is a critical component of early LR and confirmed prior studies showing that Ccnd1, a hepatocyte proliferative gene, is regulated by the Wnt/β-catenin pathway. These findings were subsequently validated through protein localization, which provided further confirmation and novel insights into the spatiotemporal changes in the Wnt/β-catenin pathway during LR. CONCLUSIONS Thus, ADViSOR may yield novel insights in other complex, spatiotemporal contexts.
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Affiliation(s)
- Anya Singh-Varma
- Department of Pathology, Division of Experimental Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Pittsburgh Liver Research Center, University of Pittsburgh Medical Center and University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Medicine, Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Ashti M. Shah
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Silvia Liu
- Department of Pathology, Division of Experimental Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Pittsburgh Liver Research Center, University of Pittsburgh Medical Center and University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ruben Zamora
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Center for Inflammation and Regeneration Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Satdarshan P. Monga
- Department of Pathology, Division of Experimental Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Pittsburgh Liver Research Center, University of Pittsburgh Medical Center and University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Medicine, Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Center for Inflammation and Regeneration Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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24
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Pickard K, Stephenson E, Mitchell A, Jardine L, Bacon CM. Location, location, location: mapping the lymphoma tumor microenvironment using spatial transcriptomics. Front Oncol 2023; 13:1258245. [PMID: 37869076 PMCID: PMC10586500 DOI: 10.3389/fonc.2023.1258245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/19/2023] [Indexed: 10/24/2023] Open
Abstract
Lymphomas are a heterogenous group of lymphoid neoplasms with a wide variety of clinical presentations. Response to treatment and prognosis differs both between and within lymphoma subtypes. Improved molecular and genetic profiling has increased our understanding of the factors which drive these clinical dynamics. Immune and non-immune cells within the lymphoma tumor microenvironment (TME) can both play a key role in antitumor immune responses and conversely also support lymphoma growth and survival. A deeper understanding of the lymphoma TME would identify key lymphoma and immune cell interactions which could be disrupted for therapeutic benefit. Single cell RNA sequencing studies have provided a more comprehensive description of the TME, however these studies are limited in that they lack spatial context. Spatial transcriptomics provides a comprehensive analysis of gene expression within tissue and is an attractive technique in lymphoma to both disentangle the complex interactions between lymphoma and TME cells and improve understanding of how lymphoma cells evade the host immune response. This article summarizes current spatial transcriptomic technologies and their use in lymphoma research to date. The resulting data has already enriched our knowledge of the mechanisms and clinical impact of an immunosuppressive TME in lymphoma and the accrual of further studies will provide a fundamental step in the march towards personalized medicine.
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Affiliation(s)
- Keir Pickard
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Haematology Department, Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Emily Stephenson
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Alex Mitchell
- Haematology Department, Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Laura Jardine
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Haematology Department, Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Chris M. Bacon
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Cellular Pathology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
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25
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Li Z, Chen X, Zhang X, Jiang R, Chen S. Latent feature extraction with a prior-based self-attention framework for spatial transcriptomics. Genome Res 2023; 33:1757-1773. [PMID: 37903634 PMCID: PMC10691543 DOI: 10.1101/gr.277891.123] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/19/2023] [Indexed: 11/01/2023]
Abstract
Rapid advances in spatial transcriptomics (ST) have revolutionized the interrogation of spatial heterogeneity and increase the demand for comprehensive methods to effectively characterize spatial domains. As a prerequisite for ST data analysis, spatial domain characterization is a crucial step for downstream analyses and biological implications. Here we propose a prior-based self-attention framework for spatial transcriptomics (PAST), a variational graph convolutional autoencoder for ST, which effectively integrates prior information via a Bayesian neural network, captures spatial patterns via a self-attention mechanism, and enables scalable application via a ripple walk sampler strategy. Through comprehensive experiments on data sets generated by different technologies, we show that PAST can effectively characterize spatial domains and facilitate various downstream analyses, including ST visualization, spatial trajectory inference and pseudotime analysis. Also, we highlight the advantages of PAST for multislice joint embedding and automatic annotation of spatial domains in newly sequenced ST data. Compared with existing methods, PAST is the first ST method that integrates reference data to analyze ST data. We anticipate that PAST will open up new avenues for researchers to decipher ST data with customized reference data, which expands the applicability of ST technology.
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Affiliation(s)
- Zhen Li
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaoyang Chen
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xuegong Zhang
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Rui Jiang
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
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26
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Velten B, Stegle O. Principles and challenges of modeling temporal and spatial omics data. Nat Methods 2023; 20:1462-1474. [PMID: 37710019 DOI: 10.1038/s41592-023-01992-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/31/2023] [Indexed: 09/16/2023]
Abstract
Studies with temporal or spatial resolution are crucial to understand the molecular dynamics and spatial dependencies underlying a biological process or system. With advances in high-throughput omic technologies, time- and space-resolved molecular measurements at scale are increasingly accessible, providing new opportunities to study the role of timing or structure in a wide range of biological questions. At the same time, analyses of the data being generated in the context of spatiotemporal studies entail new challenges that need to be considered, including the need to account for temporal and spatial dependencies and compare them across different scales, biological samples or conditions. In this Review, we provide an overview of common principles and challenges in the analysis of temporal and spatial omics data. We discuss statistical concepts to model temporal and spatial dependencies and highlight opportunities for adapting existing analysis methods to data with temporal and spatial dimensions.
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Affiliation(s)
- Britta Velten
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK.
- Centre for Organismal Studies (COS) and Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany.
| | - Oliver Stegle
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK.
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
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27
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Shi X, Zhu J, Long Y, Liang C. Identifying spatial domains of spatially resolved transcriptomics via multi-view graph convolutional networks. Brief Bioinform 2023; 24:bbad278. [PMID: 37544658 DOI: 10.1093/bib/bbad278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/27/2023] [Accepted: 07/14/2023] [Indexed: 08/08/2023] Open
Abstract
MOTIVATION Recent advances in spatially resolved transcriptomics (ST) technologies enable the measurement of gene expression profiles while preserving cellular spatial context. Linking gene expression of cells with their spatial distribution is essential for better understanding of tissue microenvironment and biological progress. However, effectively combining gene expression data with spatial information to identify spatial domains remains challenging. RESULTS To deal with the above issue, in this paper, we propose a novel unsupervised learning framework named STMGCN for identifying spatial domains using multi-view graph convolution networks (MGCNs). Specifically, to fully exploit spatial information, we first construct multiple neighbor graphs (views) with different similarity measures based on the spatial coordinates. Then, STMGCN learns multiple view-specific embeddings by combining gene expressions with each neighbor graph through graph convolution networks. Finally, to capture the importance of different graphs, we further introduce an attention mechanism to adaptively fuse view-specific embeddings and thus derive the final spot embedding. STMGCN allows for the effective utilization of spatial context to enhance the expressive power of the latent embeddings with multiple graph convolutions. We apply STMGCN on two simulation datasets and five real spatial transcriptomics datasets with different resolutions across distinct platforms. The experimental results demonstrate that STMGCN obtains competitive results in spatial domain identification compared with five state-of-the-art methods, including spatial and non-spatial alternatives. Besides, STMGCN can detect spatially variable genes with enriched expression patterns in the identified domains. Overall, STMGCN is a powerful and efficient computational framework for identifying spatial domains in spatial transcriptomics data.
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Affiliation(s)
- Xuejing Shi
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
| | - Juntong Zhu
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
| | - Yahui Long
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, 138648, Singapore
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
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28
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Charitakis N, Salim A, Piers AT, Watt KI, Porrello ER, Elliott DA, Ramialison M. Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods. Genome Biol 2023; 24:209. [PMID: 37723583 PMCID: PMC10506280 DOI: 10.1186/s13059-023-03045-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 08/21/2023] [Indexed: 09/20/2023] Open
Abstract
Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing number of published tools designed to define SVG sets currently lack benchmarking methods to accurately assess performance. This study compares results of 6 purpose-built packages for SVG identification across 9 public and 5 simulated datasets and highlights discrepancies between results. Additional tools for generation of simulated data and development of benchmarking methods are required to improve methods for identifying SVGs.
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Affiliation(s)
- Natalie Charitakis
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
| | - Agus Salim
- Melbourne School of Population and Global Health, University of Melbourne, Bouverie St, Carlton, VIC, 3053, Australia
- School of Mathematics and Statistics, University of Melbourne, Swanston Street, Parkville, VIC, 3010, Australia
| | - Adam T Piers
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Melbourne Centre for Cardiovascular Genomics and Regenerative Medicine, The Royal Children's Hospital, Melbourne, VIC, 3052, Australia
| | - Kevin I Watt
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Department of Anatomy and Physiology, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia
- Department of Diabetes, Monash University, Alfred Centre, Commercial Road, Melbourne, VIC, 3004, Australia
| | - Enzo R Porrello
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Anatomy and Physiology, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
| | - David A Elliott
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
| | - Mirana Ramialison
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
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29
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Chew V. Unveiling the hidden battlefield: dissecting the invasive zone in liver cancer. Cell Res 2023; 33:651-652. [PMID: 37488306 PMCID: PMC10474255 DOI: 10.1038/s41422-023-00855-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/26/2023] Open
Affiliation(s)
- Valerie Chew
- Translational Immunology Institute (TII), SingHealth-DukeNUS Academic Medical Centre, Singapore, Singapore.
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30
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Magoulopoulou A, Salas SM, Tiklová K, Samuelsson ER, Hilscher MM, Nilsson M. Padlock Probe-Based Targeted In Situ Sequencing: Overview of Methods and Applications. Annu Rev Genomics Hum Genet 2023; 24:133-150. [PMID: 37018847 DOI: 10.1146/annurev-genom-102722-092013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Elucidating spatiotemporal changes in gene expression has been an essential goal in studies of health, development, and disease. In the emerging field of spatially resolved transcriptomics, gene expression profiles are acquired with the tissue architecture maintained, sometimes at cellular resolution. This has allowed for the development of spatial cell atlases, studies of cell-cell interactions, and in situ cell typing. In this review, we focus on padlock probe-based in situ sequencing, which is a targeted spatially resolved transcriptomic method. We summarize recent methodological and computational tool developments and discuss key applications. We also discuss compatibility with other methods and integration with multiomic platforms for future applications.
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Affiliation(s)
- Anastasia Magoulopoulou
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Sergio Marco Salas
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Katarína Tiklová
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Erik Reinhold Samuelsson
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Markus M Hilscher
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Mats Nilsson
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
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31
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Liu Z, Wu D, Zhai W, Ma L. SONAR enables cell type deconvolution with spatially weighted Poisson-Gamma model for spatial transcriptomics. Nat Commun 2023; 14:4727. [PMID: 37550279 PMCID: PMC10406862 DOI: 10.1038/s41467-023-40458-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 07/25/2023] [Indexed: 08/09/2023] Open
Abstract
Recent advancements in spatial transcriptomic technologies have enabled the measurement of whole transcriptome profiles with preserved spatial context. However, limited by spatial resolution, the measured expressions at each spot are often from a mixture of multiple cells. Computational deconvolution methods designed for spatial transcriptomic data rarely make use of the valuable spatial information as well as the neighboring similarity information. Here, we propose SONAR, a Spatially weighted pOissoN-gAmma Regression model for cell-type deconvolution with spatial transcriptomic data. SONAR directly models the raw counts of spatial transcriptomic data and applies a geographically weighted regression framework that incorporates neighboring information to enhance local estimation of regional cell type composition. In addition, SONAR applies an additional elastic weighting step to adaptively filter dissimilar neighbors, which effectively prevents the introduction of local estimation bias in transition regions with sharp boundaries. We demonstrate the performance of SONAR over other state-of-the-art methods on synthetic data with various spatial patterns. We find that SONAR can accurately map region-specific cell types in real spatial transcriptomic data including mouse brain, human heart and human pancreatic ductal adenocarcinoma. We further show that SONAR can reveal the detailed distributions and fine-grained co-localization of immune cells within the microenvironment at the tumor-normal tissue margin in human liver cancer.
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Affiliation(s)
- Zhiyuan Liu
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China
- University of the Chinese Academy of Sciences, 100049, Beijing, China
| | - Dafei Wu
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China
| | - Weiwei Zhai
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China.
- University of the Chinese Academy of Sciences, 100049, Beijing, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, 650223, Kunming, China.
| | - Liang Ma
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China.
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32
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He M, Liu K, Cao J, Chen Q. An update on the role and potential mechanisms of clock genes regulating spermatogenesis: A systematic review of human and animal experimental studies. Rev Endocr Metab Disord 2023; 24:585-610. [PMID: 36792803 DOI: 10.1007/s11154-022-09783-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/25/2022] [Indexed: 02/17/2023]
Abstract
Circadian clocks can be traced in nearly all life kingdoms, with the male reproductive system no exception. However, our understanding of the circadian clock in spermatogenesis seems to fall behind other scenarios. The present review aims to summarize the current knowledge about the role and especially the potential mechanisms of clock genes in spermatogenesis regulation. Accumulating studies have revealed rhythmic oscillation in semen parameters and some physiological events of spermatogenesis. Disturbing the clock gene expression by genetic mutations or environmental changes will also notably damage spermatogenesis. On the other hand, the mechanisms of spermatogenetic regulation by clock genes remain largely unclear. Some recent studies, although not revealing the entire mechanisms, indeed attempted to shed light on this issue. Emerging clues hinted that gonadal hormones, retinoic acid signaling, homologous recombination, and the chromatoid body might be involved in the regulation of spermatogenesis by clock genes. Then we highlight the challenges and the promising directions for future studies so as to stimulate attention to this critical field which has not gained adequate concern.
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Affiliation(s)
- Mengchao He
- Key Lab of Medical Protection for Electromagnetic Radiation, Ministry of Education of China, Institute of Toxicology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, 400038, China
| | - Kun Liu
- Center for Disease Control and Prevention of Southern Theatre Command, Guangzhou, 510630, China
| | - Jia Cao
- Key Lab of Medical Protection for Electromagnetic Radiation, Ministry of Education of China, Institute of Toxicology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, 400038, China.
| | - Qing Chen
- Key Lab of Medical Protection for Electromagnetic Radiation, Ministry of Education of China, Institute of Toxicology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, 400038, China.
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33
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Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, Lücken MD, Strobl DC, Henao J, Curion F, Schiller HB, Theis FJ. Best practices for single-cell analysis across modalities. Nat Rev Genet 2023; 24:550-572. [PMID: 37002403 PMCID: PMC10066026 DOI: 10.1038/s41576-023-00586-w] [Citation(s) in RCA: 106] [Impact Index Per Article: 106.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 04/03/2023]
Abstract
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
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Affiliation(s)
- Lukas Heumos
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Anna C Schaar
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany
| | - Christopher Lance
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Paediatrics, Dr von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Anastasia Litinetskaya
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Felix Drost
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Luke Zappia
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Malte D Lücken
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany
| | - Daniel C Strobl
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Juan Henao
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
| | - Fabiola Curion
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Herbert B Schiller
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany.
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Qu F, Li W, Xu J, Zhang R, Ke J, Ren X, Meng X, Qin L, Zhang J, Lu F, Zhou X, Luo X, Zhang Z, Wang M, Wu G, Pei D, Chen J, Cui G, Suo S, Peng G. Three-dimensional molecular architecture of mouse organogenesis. Nat Commun 2023; 14:4599. [PMID: 37524711 PMCID: PMC10390492 DOI: 10.1038/s41467-023-40155-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 07/16/2023] [Indexed: 08/02/2023] Open
Abstract
Mammalian embryos exhibit sophisticated cellular patterning that is intricately orchestrated at both molecular and cellular level. It has recently become apparent that cells within the animal body display significant heterogeneity, both in terms of their cellular properties and spatial distributions. However, current spatial transcriptomic profiling either lacks three-dimensional representation or is limited in its ability to capture the complexity of embryonic tissues and organs. Here, we present a spatial transcriptomic atlas of all major organs at embryonic day 13.5 in the mouse embryo, and provide a three-dimensional rendering of molecular regulation for embryonic patterning with stacked sections. By integrating the spatial atlas with corresponding single-cell transcriptomic data, we offer a detailed molecular annotation of the dynamic nature of organ development, spatial cellular interactions, embryonic axes, and divergence of cell fates that underlie mammalian development, which would pave the way for precise organ engineering and stem cell-based regenerative medicine.
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Affiliation(s)
- Fangfang Qu
- Center for Cell Lineage and Atlas, Bioland Laboratory, Guangzhou, China
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Medical University, 510005, Guangzhou, Guangdong, China
- Guangzhou Laboratory, 510005, Guangzhou, Guangdong, China
| | - Wenjia Li
- Center for Cell Lineage and Atlas, Bioland Laboratory, Guangzhou, China
- Guangzhou Laboratory, 510005, Guangzhou, Guangdong, China
- The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, 510005, Guangzhou, Guangdong, China
| | - Jian Xu
- Center for Cell Lineage and Atlas, Bioland Laboratory, Guangzhou, China
| | - Ruifang Zhang
- Center for Cell Lineage and Atlas, Bioland Laboratory, Guangzhou, China
| | - Jincan Ke
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, 510530, Guangzhou, China
| | - Xiaodie Ren
- Center for Cell Lineage and Atlas, Bioland Laboratory, Guangzhou, China
| | - Xiaogao Meng
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, 510530, Guangzhou, China
- Life Science and Medicine, University of Science and Technology of China, 230026, Hefei, Anhui, China
| | - Lexin Qin
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, 510530, Guangzhou, China
| | - Jingna Zhang
- Center for Cell Lineage and Atlas, Bioland Laboratory, Guangzhou, China
| | - Fangru Lu
- Center for Cell Lineage and Atlas, Bioland Laboratory, Guangzhou, China
| | - Xin Zhou
- Center for Cell Lineage and Atlas, Bioland Laboratory, Guangzhou, China
| | - Xi Luo
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, 510530, Guangzhou, China
| | - Zhen Zhang
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, 510530, Guangzhou, China
| | - Minhan Wang
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, 510530, Guangzhou, China
| | - Guangming Wu
- Center for Cell Lineage and Atlas, Bioland Laboratory, Guangzhou, China
- Guangzhou Laboratory, 510005, Guangzhou, Guangdong, China
- School of Basic Medical Sciences, Guangzhou Medical University, 510005, Guangzhou, Guangdong, China
| | - Duanqing Pei
- Laboratory of Cell Fate Control, School of Life Sciences, Westlake University, Hangzhou, China
| | - Jiekai Chen
- Center for Cell Lineage and Atlas, Bioland Laboratory, Guangzhou, China
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, 510530, Guangzhou, China
| | - Guizhong Cui
- Center for Cell Lineage and Atlas, Bioland Laboratory, Guangzhou, China.
- Guangzhou Laboratory, 510005, Guangzhou, Guangdong, China.
- School of Basic Medical Sciences, Guangzhou Medical University, 510005, Guangzhou, Guangdong, China.
| | - Shengbao Suo
- Guangzhou Laboratory, 510005, Guangzhou, Guangdong, China.
- The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, 510005, Guangzhou, Guangdong, China.
| | - Guangdun Peng
- Center for Cell Lineage and Atlas, Bioland Laboratory, Guangzhou, China.
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, 510530, Guangzhou, China.
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Carrillo-Perez F, Pizurica M, Zheng Y, Nandi TN, Madduri R, Shen J, Gevaert O. RNA-to-image multi-cancer synthesis using cascaded diffusion models. bioRxiv 2023:2023.01.13.523899. [PMID: 36711711 PMCID: PMC9882105 DOI: 10.1101/2023.01.13.523899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Data scarcity presents a significant obstacle in the field of biomedicine, where acquiring diverse and sufficient datasets can be costly and challenging. Synthetic data generation offers a potential solution to this problem by expanding dataset sizes, thereby enabling the training of more robust and generalizable machine learning models. Although previous studies have explored synthetic data generation for cancer diagnosis, they have predominantly focused on single modality settings, such as whole-slide image tiles or RNA-Seq data. To bridge this gap, we propose a novel approach, RNA-Cascaded-Diffusion-Model or RNA-CDM, for performing RNA-to-image synthesis in a multi-cancer context, drawing inspiration from successful text-to-image synthesis models used in natural images. In our approach, we employ a variational auto-encoder to reduce the dimensionality of a patient's gene expression profile, effectively distinguishing between different types of cancer. Subsequently, we employ a cascaded diffusion model to synthesize realistic whole-slide image tiles using the latent representation derived from the patient's RNA-Seq data. Our results demonstrate that the generated tiles accurately preserve the distribution of cell types observed in real-world data, with state-of-the-art cell identification models successfully detecting important cell types in the synthetic samples. Furthermore, we illustrate that the synthetic tiles maintain the cell fraction observed in bulk RNA-Seq data and that modifications in gene expression affect the composition of cell types in the synthetic tiles. Next, we utilize the synthetic data generated by RNA-CDM to pretrain machine learning models and observe improved performance compared to training from scratch. Our study emphasizes the potential usefulness of synthetic data in developing machine learning models in sarce-data settings, while also highlighting the possibility of imputing missing data modalities by leveraging the available information. In conclusion, our proposed RNA-CDM approach for synthetic data generation in biomedicine, particularly in the context of cancer diagnosis, offers a novel and promising solution to address data scarcity. By generating synthetic data that aligns with real-world distributions and leveraging it to pretrain machine learning models, we contribute to the development of robust clinical decision support systems and potential advancements in precision medicine.
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36
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Li JSY, Raghubar AM, Matigian NA, Ng MSY, Rogers NM, Mallett AJ. The Utility of Spatial Transcriptomics for Solid Organ Transplantation. Transplantation 2023; 107:1463-1471. [PMID: 36584371 DOI: 10.1097/tp.0000000000004466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Spatial transcriptomics (ST) measures and maps transcripts within intact tissue sections, allowing the visualization of gene activity within the spatial organization of complex biological systems. This review outlines advances in genomic sequencing technologies focusing on in situ sequencing-based ST, including applications in transplant and relevant nontransplant settings. We describe the experimental and analytical pipelines that underpin the current generation of spatial technologies. This context is important for understanding the potential role ST may play in expanding our knowledge, including in organ transplantation, and the important caveats/limitations when interpreting the vast data output generated by such methodological platforms.
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Affiliation(s)
- Jennifer S Y Li
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, NSW, Australia
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Arti M Raghubar
- Kidney Health Service, Royal Brisbane and Women's Hospital, QLD, Australia
- Conjoint Internal Medicine Laboratory, Pathology Queensland, Health Support Queensland, QLD, Australia
- Department of Anatomical Pathology, Pathology Queensland, Health Support Queensland, QLD, Australia
- Faculty of Medicine, University of Queensland, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, QLD, Australia
| | - Nicholas A Matigian
- QCIF Facility for Advanced Bioinformatics, The University of Queensland, QLD, Australia
| | - Monica S Y Ng
- Kidney Health Service, Royal Brisbane and Women's Hospital, QLD, Australia
- Conjoint Internal Medicine Laboratory, Pathology Queensland, Health Support Queensland, QLD, Australia
- Faculty of Medicine, University of Queensland, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, QLD, Australia
- Nephrology Department, Princess Alexandra Hospital, QLD, Australia
| | - Natasha M Rogers
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, NSW, Australia
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
- Department of Renal Medicine, Westmead Hospital, Westmead, NSW, Australia
| | - Andrew J Mallett
- Faculty of Medicine, University of Queensland, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, QLD, Australia
- College of Medicine and Dentistry, James Cook University, QLD, Australia
- Department of Renal Medicine, Townsville University Hospital, QLD, Australia
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37
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Guo B, Huuki-Myers LA, Grant-Peters M, Collado-Torres L, Hicks SC. escheR: Unified multi-dimensional visualizations with Gestalt principles. bioRxiv 2023:2023.03.18.533302. [PMID: 36993732 PMCID: PMC10055209 DOI: 10.1101/2023.03.18.533302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide an open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows.
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Affiliation(s)
- Boyi Guo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, MD, USA
| | - Louise A. Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Melissa Grant-Peters
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | | | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, MD, USA
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38
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He S, Shao W, Chen SC, Wang T, Gibson MC. Spatial transcriptomics reveals a cnidarian segment polarity program in Nematostella vectensis. Curr Biol 2023:S0960-9822(23)00676-0. [PMID: 37315559 DOI: 10.1016/j.cub.2023.05.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 04/16/2023] [Accepted: 05/18/2023] [Indexed: 06/16/2023]
Abstract
During early animal evolution, the emergence of axially polarized segments was central to the diversification of complex bilaterian body plans. Nevertheless, precisely how and when segment polarity pathways arose remains obscure. Here, we demonstrate the molecular basis for segment polarization in developing larvae of the sea anemone Nematostella vectensis. Utilizing spatial transcriptomics, we first constructed a 3D gene expression atlas of developing larval segments. Capitalizing on accurate in silico predictions, we identified Lbx and Uncx, conserved homeodomain-containing genes that occupy opposing subsegmental domains under the control of both bone morphogenetic protein (BMP) signaling and the Hox-Gbx cascade. Functionally, Lbx mutagenesis eliminated all molecular evidence of segment polarization at the larval stage and caused an aberrant mirror-symmetric pattern of retractor muscles (RMs) in primary polyps. These results demonstrate the molecular basis for segment polarity in a non-bilaterian animal, suggesting that polarized metameric structures were present in the Cnidaria-Bilateria common ancestor over 600 million years ago.
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Affiliation(s)
- Shuonan He
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Wanqing Shao
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA; Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | | | - Ting Wang
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA; Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Matthew C Gibson
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA; Department of Anatomy and Cell Biology, The University of Kansas School of Medicine, Kansas City, KS 66160, USA.
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Tekkela S, Theocharidis G, McGrath JA, Onoufriadis A. Spatial transcriptomics in human skin research. Exp Dermatol 2023. [PMID: 37150587 DOI: 10.1111/exd.14827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/17/2023] [Accepted: 04/21/2023] [Indexed: 05/09/2023]
Abstract
Spatial transcriptomics is a revolutionary technique that enables researchers to characterise tissue architecture and localisation of gene expression. A plethora of technologies that map gene expression are currently being developed, aiming to facilitate spatially resolved, high-dimensional assessment of gene transcription in the context of human skin research. Knowing which gene is expressed by which cell and in which location within skin, facilitates understanding of skin function and dysfunction in both health and disease. In this review, we summarise the available spatial transcriptomic methods and we describe their application to a broad spectrum of dermatological diseases.
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Affiliation(s)
- Stavroula Tekkela
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Georgios Theocharidis
- Joslin-Beth Israel Deaconess Foot Center and The Rongxiang Xu, MD, Center for Regenerative Therapeutics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - John A McGrath
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Alexandros Onoufriadis
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, UK
- Laboratory of Medical Biology and Genetics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Delgado L, Navarrete M. Shining the Light on Astrocytic Ensembles. Cells 2023; 12:1253. [PMID: 37174653 PMCID: PMC10177371 DOI: 10.3390/cells12091253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/22/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
While neurons have traditionally been considered the primary players in information processing, the role of astrocytes in this mechanism has largely been overlooked due to experimental constraints. In this review, we propose that astrocytic ensembles are active working groups that contribute significantly to animal conduct and suggest that studying the maps of these ensembles in conjunction with neurons is crucial for a more comprehensive understanding of behavior. We also discuss available methods for studying astrocytes and argue that these ensembles, complementarily with neurons, code and integrate complex behaviors, potentially specializing in concrete functions.
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Affiliation(s)
| | - Marta Navarrete
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), 28002 Madrid, Spain
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Heydari AA, Sindi SS. Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing. Biophys Rev (Melville) 2023; 4:011306. [PMID: 38505815 PMCID: PMC10903438 DOI: 10.1063/5.0091135] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 12/19/2022] [Indexed: 03/21/2024]
Abstract
Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. Data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially resolved data (such as spatial resolution, sensitivity, and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering, among others. However, DL models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially resolved transcriptomics while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight domains in which we anticipate transformational DL applications.
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Huang J, Qin F, Lai X, Yang T, Yu J, Wei C, Wei L, Li J. Exploring heterogeneity of tumor immune cells and adrenal cells in aldosterone-producing adenomas using single-cell RNA-seq and investigating differences by sex. Heliyon 2023; 9:e14357. [PMID: 36942259 PMCID: PMC10024085 DOI: 10.1016/j.heliyon.2023.e14357] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 02/28/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
The mechanism behind the higher incidence of aldosterone-producing adenoma (APA) in women compared to men is not yet understood. In this study, we utilized single-cell RNA sequencing (scRNA-seq) to investigate the immune cell infiltration and adrenal cell characteristics in APA. Our findings revealed a high presence of immune cells in the tumor microenvironment, with macrophages and T lymphocytes being the most prevalent. Comparison of infiltrating cells between males and females showed that female CD8+T cells had stronger cytotoxic and inflammation-related functions, while female myeloid cells had more enrichment in inflammatory pathways. Additionally, we found that female adrenal cells had greater upregulation of immune-related and antigen presentation pathways. Furthermore, our analysis revealed that zona glomerulosa (ZG) cells had a higher capability for aldosterone synthesis. These results provide a deeper understanding of the APA microenvironment in patients of different sexes and offer new insights into the onset of APA.
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Affiliation(s)
- Jing Huang
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Fei Qin
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Xiaomei Lai
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Tingting Yang
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Jie Yu
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Chaoping Wei
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Lixia Wei
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China
| | - Jianling Li
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China
- Mobile Post-doctoral Stations of Guangxi Medical University, Nanning, Guangxi 530021, China
- Corresponding author. Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China.
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Yuan Z, Pan W, Zhao X, Zhao F, Xu Z, Li X, Zhao Y, Zhang MQ, Yao J. SODB facilitates comprehensive exploration of spatial omics data. Nat Methods 2023; 20:387-399. [PMID: 36797409 DOI: 10.1038/s41592-023-01773-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 01/06/2023] [Indexed: 02/18/2023]
Abstract
Spatial omics technologies generate wealthy but highly complex datasets. Here we present Spatial Omics DataBase (SODB), a web-based platform providing both rich data resources and a suite of interactive data analytical modules. SODB currently maintains >2,400 experiments from >25 spatial omics technologies, which are freely accessible as a unified data format compatible with various computational packages. SODB also provides multiple interactive data analytical modules, especially a unique module, Spatial Omics View (SOView). We conduct comprehensive statistical analyses and illustrate the utility of both basic and advanced analytical modules using multiple spatial omics datasets. We demonstrate SOView utility with brain spatial transcriptomics data and recover known anatomical structures. We further delineate functional tissue domains with associated marker genes that were obscured when analyzed using previous methods. We finally show how SODB may efficiently facilitate computational method development. The SODB website is https://gene.ai.tencent.com/SpatialOmics/ . The command-line package is available at https://pysodb.readthedocs.io/en/latest/ .
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Affiliation(s)
- Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Tencent AI Lab, Shenzhen, China.
| | - Wentao Pan
- Tencent AI Lab, Shenzhen, China
- Shenzhen International Graduate School, Tsinghua University, Shenzen, China
| | | | - Fangyuan Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | - Xiu Li
- Shenzhen International Graduate School, Tsinghua University, Shenzen, China
| | - Yi Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, USA.
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Xun Z, Ding X, Zhang Y, Zhang B, Lai S, Zou D, Zheng J, Chen G, Su B, Han L, Ye Y. Reconstruction of the tumor spatial microenvironment along the malignant-boundary-nonmalignant axis. Nat Commun 2023; 14:933. [PMID: 36806082 PMCID: PMC9941488 DOI: 10.1038/s41467-023-36560-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 02/06/2023] [Indexed: 02/22/2023] Open
Abstract
Although advances in spatial transcriptomics (ST) enlarge to unveil spatial landscape of tissues, it remains challenging to delineate pathology-relevant and cellular localizations, and interactions exclusive to a spatial niche (e.g., tumor boundary). Here, we develop Cottrazm, integrating ST with hematoxylin and eosin histological image, and single-cell transcriptomics to delineate the tumor boundary connecting malignant and non-malignant cell spots in tumor tissues, deconvolute cell-type composition at spatial location, and reconstruct cell type-specific gene expression profiles at sub-spot level. We validate the performance of Cottrazm along the malignant-boundary-nonmalignant spatial axis. We identify specific macrophage and fibroblast subtypes localized around tumor boundary that interacted with tumor cells to generate a structural boundary, which limits T cell infiltration and promotes immune exclusion in tumor microenvironment. In this work, Cottrazm provides an integrated tool framework to dissect the tumor spatial microenvironment and facilitates the discovery of functional biological insights, thereby identifying therapeutic targets in oncologic ST datasets.
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Affiliation(s)
- Zhenzhen Xun
- Center for Immune-Related Diseases at Shanghai Institute of Immunology, Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Immunology, State Key Laboratory of Oncogenes and Related Genes, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xinyu Ding
- Center for Immune-Related Diseases at Shanghai Institute of Immunology, Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Immunology, State Key Laboratory of Oncogenes and Related Genes, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yao Zhang
- Department of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Benyan Zhang
- Department of Pathology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Shujing Lai
- Shanghai Institute of Immunology, State Key Laboratory of Oncogenes and Related Genes, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Duowu Zou
- Department of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Junke Zheng
- Hongqiao International Institute of Medicine, Shanghai Tongren Hospital, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Faculty of Basic Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Guoqiang Chen
- State Key Laboratory of Oncogenes and Related Genes, and Research Unit of Stress and Cancer, Chinese Academy of Medical Sciences, Shanghai Cancer Institute, Renji hospital, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, 200127, China
| | - Bing Su
- Center for Immune-Related Diseases at Shanghai Institute of Immunology, Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Immunology, State Key Laboratory of Oncogenes and Related Genes, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Leng Han
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, 77030, USA.
| | - Youqiong Ye
- Center for Immune-Related Diseases at Shanghai Institute of Immunology, Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai Institute of Immunology, State Key Laboratory of Oncogenes and Related Genes, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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Abstract
The human lung cellular portfolio, traditionally characterized by cellular morphology and individual markers, is highly diverse, with over 40 cell types and a complex branching structure highly adapted for agile airflow and gas exchange. While constant during adulthood, lung cellular content changes in response to exposure, injury, and infection. Some changes are temporary, but others are persistent, leading to structural changes and progressive lung disease. The recent advance of single-cell profiling technologies allows an unprecedented level of detail and scale to cellular measurements, leading to the rise of comprehensive cell atlas styles of reporting. In this review, we chronical the rise of cell atlases and explore their contributions to human lung biology in health and disease.
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Affiliation(s)
- Taylor S Adams
- Section of Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut, USA;
| | - Arnaud Marlier
- Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, Connecticut, USA
| | - Naftali Kaminski
- Section of Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut, USA;
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He J, Deng C, Krall L, Shan Z. ScRNA-seq and ST-seq in liver research. Cell Regen 2023; 12:11. [PMID: 36732412 PMCID: PMC9895469 DOI: 10.1186/s13619-022-00152-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 11/11/2022] [Indexed: 02/04/2023]
Abstract
Spatial transcriptomics, which combine gene expression data with spatial information, has quickly expanded in recent years. With application of this method in liver research, our knowledge about liver development, regeneration, and diseases have been greatly improved. While this field is moving forward, a variety of problems still need to be addressed, including sensitivity, limited capacity to obtain exact single-cell information, data processing methods, as well as others. Methods like single-cell RNA sequencing (scRNA-seq) are usually used together with spatial transcriptome sequencing (ST-seq) to clarify cell-specific gene expression. In this review, we explore how advances of scRNA-seq and ST-seq, especially ST-seq, will pave the way to new opportunities to investigate fundamental questions in liver research. Finally, we will discuss the strengths, limitations, and future perspectives of ST-seq in liver research.
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Affiliation(s)
- Jia He
- grid.440773.30000 0000 9342 2456State Key Laboratory of Conservation and Utilization of Bio-resources in Yunnan and Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, 650091 China
| | - Chengxiang Deng
- grid.440773.30000 0000 9342 2456State Key Laboratory of Conservation and Utilization of Bio-resources in Yunnan and Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, 650091 China
| | - Leonard Krall
- grid.440773.30000 0000 9342 2456State Key Laboratory of Conservation and Utilization of Bio-resources in Yunnan and Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, 650091 China
| | - Zhao Shan
- State Key Laboratory of Conservation and Utilization of Bio-resources in Yunnan and Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, 650091, China.
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Cang Z, Zhao Y, Almet AA, Stabell A, Ramos R, Plikus MV, Atwood SX, Nie Q. Screening cell-cell communication in spatial transcriptomics via collective optimal transport. Nat Methods 2023; 20:218-228. [PMID: 36690742 PMCID: PMC9911355 DOI: 10.1038/s41592-022-01728-4] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 11/21/2022] [Indexed: 01/24/2023]
Abstract
Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing datasets provide unprecedented opportunities to dissect cell-cell communication (CCC). However, incorporation of the spatial information and complex biochemical processes required in the reconstruction of CCC remains a major challenge. Here, we present COMMOT (COMMunication analysis by Optimal Transport) to infer CCC in spatial transcriptomics, which accounts for the competition between different ligand and receptor species as well as spatial distances between cells. A collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. Furthermore, we introduce downstream analysis tools to infer spatial signaling directionality and genes regulated by signaling using machine learning models. We apply COMMOT to simulation data and eight spatial datasets acquired with five different technologies to show its effectiveness and robustness in identifying spatial CCC in data with varying spatial resolutions and gene coverages. Finally, COMMOT identifies new CCCs during skin morphogenesis in a case study of human epidermal development.
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Affiliation(s)
- Zixuan Cang
- Department of Mathematics and Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC, USA
| | - Yanxiang Zhao
- Department of Mathematics, The George Washington University, Washington, DC, USA
| | - Axel A Almet
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA
| | - Adam Stabell
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
| | - Raul Ramos
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
| | - Maksim V Plikus
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
| | - Scott X Atwood
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA.
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA.
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Justin Margret J, Jain SK. Overview of gene expression techniques with an emphasis on vitamin D related studies. Curr Med Res Opin 2023; 39:205-217. [PMID: 36537177 DOI: 10.1080/03007995.2022.2159148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Each cell controls when and how its genes must be expressed for proper function. Every function in a cell is driven by signaling molecules through various regulatory cascades. Different cells in a multicellular organism may express very different sets of genes, even though they contain the same DNA. The set of genes expressed in a cell determines the set of proteins and functional RNAs it contains, giving it its unique properties. Malfunction in gene expression harms the cell and can lead to the development of various disease conditions. The use of rapid high-throughput gene expression profiling unravels the complexity of human disease at various levels. Peripheral blood mononuclear cells (PBMC) have been used frequently to understand gene expression homeostasis in various disease conditions. However, more studies are required to validate whether PBMC gene expression patterns accurately reflect the expression of other cells or tissues. Vitamin D, which is responsible for a multitude of health consequences, is also an immune modulatory hormone with major biological activities in the innate and adaptive immune systems. Vitamin D exerts its diverse biological effects in target tissues by regulating gene expression and its deficiency, is recognized as a public health problem worldwide. Understanding the genetic factors that affect vitamin D has the potential benefit that it will make it easier to identify individuals who require supplementation. Different technological advances in gene expression can be used to identify and assess the severity of disease and aid in the development of novel therapeutic interventions. This review focuses on different gene expression approaches and various clinical studies of vitamin D to investigate the role of gene expression in identifying the molecular signature of the disease.
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Affiliation(s)
- Jeffrey Justin Margret
- Department of Pediatrics, Louisiana State University Health Sciences Center-Shreveport, Shreveport, LA, USA
| | - Sushil K Jain
- Department of Pediatrics, Louisiana State University Health Sciences Center-Shreveport, Shreveport, LA, USA
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Armstrong AR, Tóth F, Carlson CS, Kim HKW, Johnson CP. Effects of acute femoral head ischemia on the growth plate and metaphysis in a piglet model of Legg-Calvé-Perthes disease. Osteoarthritis Cartilage 2023; 31:766-774. [PMID: 36696941 DOI: 10.1016/j.joca.2023.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 12/27/2022] [Accepted: 01/17/2023] [Indexed: 01/23/2023]
Abstract
OBJECTIVE To determine the effects of acute (≤7 days) femoral head ischemia on the proximal femoral growth plate and metaphysis in a piglet model of Legg-Calvé-Perthes disease (LCPD). We hypothesized that qualitative and quantitative histological assessment would identify effects of ischemia on endochondral ossification. DESIGN Unilateral femoral head ischemia was surgically induced in piglets, and femurs were collected for histological assessment at 2 (n = 7) or 7 (n = 5) days post-ischemia. Samples were assessed qualitatively, and histomorphometry of the growth plate zones and primary spongiosa was performed. In a subset of samples at 7 days, hypertrophic chondrocytes were quantitatively assessed and immunohistochemistry for TGFβ1 and Indian hedgehog was performed. RESULTS By 2 days post-ischemia, there was significant thinning of the proliferative and hypertrophic zones, by 63 μm (95% CI -103, -22) and -19 μm (95% CI -33, -5), respectively. This thinning persisted at 7 days post-ischemia. Likewise, at 7 days post-ischemia, the primary spongiosa was thinned to absent by an average of 311 μm (95% CI -542, -82) in all ischemic samples. TGFβ1 expression was increased in the hypertrophic zone at 7 days post-ischemia. CONCLUSIONS Alterations to the growth plate zones and metaphysis occurred by 2 days post-ischemia and persisted at 7 days post-ischemia. Our findings suggest that endochondral ossification may be disrupted at an earlier time point than previously reported and that growth disruption may occur in the piglet model as occurs in some children with LCPD.
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Affiliation(s)
- A R Armstrong
- Department of Veterinary Clinical Sciences, University of Minnesota, St. Paul, MN, USA.
| | - F Tóth
- Department of Veterinary Clinical Sciences, University of Minnesota, St. Paul, MN, USA.
| | - C S Carlson
- Department of Veterinary Clinical Sciences, University of Minnesota, St. Paul, MN, USA.
| | - H K W Kim
- Center for Excellence in Hip, Scottish Rite for Children, Dallas, TX, USA; Department of Orthopedic Surgery, UT Southwestern Medical Center, Dallas, TX, USA.
| | - C P Johnson
- Department of Veterinary Clinical Sciences, University of Minnesota, St. Paul, MN, USA; Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA.
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50
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Yue L, Liu F, Hu J, Yang P, Wang Y, Dong J, Shu W, Huang X, Wang S. A guidebook of spatial transcriptomic technologies, data resources and analysis approaches. Comput Struct Biotechnol J 2023; 21:940-955. [PMID: 38213887 PMCID: PMC10781722 DOI: 10.1016/j.csbj.2023.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 01/13/2023] [Accepted: 01/14/2023] [Indexed: 01/18/2023] Open
Abstract
Advances in transcriptomic technologies have deepened our understanding of the cellular gene expression programs of multicellular organisms and provided a theoretical basis for disease diagnosis and therapy. However, both bulk and single-cell RNA sequencing approaches lose the spatial context of cells within the tissue microenvironment, and the development of spatial transcriptomics has made overall bias-free access to both transcriptional information and spatial information possible. Here, we elaborate development of spatial transcriptomic technologies to help researchers select the best-suited technology for their goals and integrate the vast amounts of data to facilitate data accessibility and availability. Then, we marshal various computational approaches to analyze spatial transcriptomic data for various purposes and describe the spatial multimodal omics and its potential for application in tumor tissue. Finally, we provide a detailed discussion and outlook of the spatial transcriptomic technologies, data resources and analysis approaches to guide current and future research on spatial transcriptomics.
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Affiliation(s)
- Liangchen Yue
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Feng Liu
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
| | - Jiongsong Hu
- University of South China, Hengyang, Hunan 421001, China
| | - Pin Yang
- Anhui Medical University, Hefei 230022, Anhui, China
| | - Yuxiang Wang
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Junguo Dong
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Wenjie Shu
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Xingxu Huang
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310029, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Shengqi Wang
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
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