1
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Tasca P, van den Berg BM, Rabelink TJ, Wang G, Heijs B, van Kooten C, de Vries APJ, Kers J. Application of spatial-omics to the classification of kidney biopsy samples in transplantation. Nat Rev Nephrol 2024; 20:755-766. [PMID: 38965417 DOI: 10.1038/s41581-024-00861-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/06/2024] [Indexed: 07/06/2024]
Abstract
Improvement of long-term outcomes through targeted treatment is a primary concern in kidney transplant medicine. Currently, the validation of a rejection diagnosis and subsequent treatment depends on the histological assessment of allograft biopsy samples, according to the Banff classification system. However, the lack of (early) disease-specific tissue markers hinders accurate diagnosis and thus timely intervention. This challenge mainly results from an incomplete understanding of the pathophysiological processes underlying late allograft failure. Integration of large-scale multimodal approaches for investigating allograft biopsy samples might offer new insights into this pathophysiology, which are necessary for the identification of novel therapeutic targets and the development of tailored immunotherapeutic interventions. Several omics technologies - including transcriptomic, proteomic, lipidomic and metabolomic tools (and multimodal data analysis strategies) - can be applied to allograft biopsy investigation. However, despite their successful application in research settings and their potential clinical value, several barriers limit the broad implementation of many of these tools into clinical practice. Among spatial-omics technologies, mass spectrometry imaging, which is under-represented in the transplant field, has the potential to enable multi-omics investigations that might expand the insights gained with current clinical analysis technologies.
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Affiliation(s)
- Paola Tasca
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Transplant Center, Leiden University Medical Center, Leiden, the Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
| | - Bernard M van den Berg
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Ton J Rabelink
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands
- The Novo Nordisk Foundation Center for Stem Cell Medicine (Renew), Leiden University Medical Center, Leiden, the Netherlands
| | - Gangqi Wang
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands
- The Novo Nordisk Foundation Center for Stem Cell Medicine (Renew), Leiden University Medical Center, Leiden, the Netherlands
| | - Bram Heijs
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
- Bruker Daltonics GmbH & Co. KG, Bremen, Germany
| | - Cees van Kooten
- Leiden Transplant Center, Leiden University Medical Center, Leiden, the Netherlands
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Aiko P J de Vries
- Leiden Transplant Center, Leiden University Medical Center, Leiden, the Netherlands.
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands.
| | - Jesper Kers
- Leiden Transplant Center, Leiden University Medical Center, Leiden, the Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Pathology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
- Center for Analytical Sciences Amsterdam, Van't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, the Netherlands
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2
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Li Y, Du Y, Li R, Zhong W, Zou X, Li L, Xu L, Wu L, Che X. Spatial transcriptomics in pancreatic cancer: Advances, prospects and challenges. Crit Rev Oncol Hematol 2024; 203:104430. [PMID: 38942220 DOI: 10.1016/j.critrevonc.2024.104430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 06/20/2024] [Accepted: 06/24/2024] [Indexed: 06/30/2024] Open
Abstract
Pancreatic cancer remains one of the deadliest malignancies with an overall 5-year survival rate of 13 %. This dismal fact can be partly attributed to currently limited understanding of tumor heterogeneity and immune microenvironment. Traditional bulk-sequencing techniques overlook the diversity of tumor cells, while single-cell sequencing disorganizes the position localizing of cells in tumor microenvironment. The advent of spatial transcriptomics (ST) presents a novel solution by integrating location and whole transcript expression information. This technology allows for detailed observation of spatio-temporal changes across various cell subtypes within the pancreatic tumor microenvironment, providing insights into their potential functions. This review offers an overview of recent studies implementing ST in pancreatic cancer research, highlighting its instrumental role in investigating the heterogeneity and functions of tumor cells, stromal cells, and immune cells. On the basis, we also prospected and summarized the clinical application scenarios, technical limitations and challenges of ST technology in pancreatic cancer.
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Affiliation(s)
- Yunlong Li
- Department of Pancreatic and Gastric Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yongxing Du
- Department of Pancreatic and Gastric Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Rui Li
- BGI, Shenzhen 518083, China; BGI Research, Shenzhen 518083, China; Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen 518083, China
| | - Wenhui Zhong
- Department of Pancreatic and Gastric Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xuanxuan Zou
- BGI, Shenzhen 518083, China; BGI Research, Chongqing 401329, China; BGI Research, Shenzhen 518083, China
| | - Liji Li
- College of Life Sciences, South China Agricultural University, Guangzhou 510642, China
| | - Lin Xu
- Department of Hepatobiliary and Pancreatic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518172, China
| | - Liang Wu
- BGI, Shenzhen 518083, China; BGI Research, Chongqing 401329, China; BGI Research, Shenzhen 518083, China.
| | - Xu Che
- Department of Pancreatic and Gastric Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; Department of Hepatobiliary and Pancreatic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518172, China.
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3
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Xiu YH, Sun SL, Zhou BW, Wan Y, Tang H, Long HX. DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax. Methods 2024; 231:226-236. [PMID: 39413889 DOI: 10.1016/j.ymeth.2024.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 09/26/2024] [Accepted: 10/04/2024] [Indexed: 10/18/2024] Open
Abstract
Although spatial transcriptomics data provide valuable insights into gene expression profiles and the spatial structure of tissues, most studies rely solely on gene expression information, underutilizing the spatial data. To fully leverage the potential of spatial transcriptomics and graph neural networks, the DGSI (Deep Graph Structure Infomax) model is proposed. This innovative graph data processing model uses graph convolutional neural networks and employs an unsupervised learning approach. It maximizes the mutual information between graph-level and node-level representations, emphasizing flexible sampling and aggregation of nodes and their neighbors. This effectively captures and incorporates local information from nodes into the overall graph structure. Additionally, this paper developed the DGSIST framework, an unsupervised cell clustering method that integrates the DGSI model, SVD dimensionality reduction algorithm, and k-means++ clustering algorithm. This aims to identify cell types accurately. DGSIST fully uses spatial transcriptomics data and outperforms existing methods in accuracy. Demonstrations of DGSIST's capability across various tissue types and technological platforms have shown its effectiveness in accurately identifying spatial domains in multiple tissue sections. Compared to other spatial clustering methods, DGSIST excels in cell clustering and effectively eliminates batch effects without needing batch correction. DGSIST excels in spatial clustering analysis, spatial variation identification, and differential gene expression detection and directly applies to graph analysis tasks, such as node classification, link prediction, or graph clustering. Anticipation lies in the contribution of the DGSIST framework to a deeper understanding of the spatial organizational structures of diseases such as cancer.
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Affiliation(s)
- Yu-Han Xiu
- College of Information Science Technology, Hainan Normal University, HaiKou City 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, HaiKou City 571158, China
| | - Si-Lin Sun
- College of Information Science Technology, Hainan Normal University, HaiKou City 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, HaiKou City 571158, China
| | - Bing-Wei Zhou
- College of Information Science Technology, Hainan Normal University, HaiKou City 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, HaiKou City 571158, China
| | - Ying Wan
- School of Basic Medical Sciences, Southwest Medical University, Luzhou 646000, China
| | - Hua Tang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou 646000, China; Medical Engineering & Medical Informatics Integration and Transformational Medicine Key Laboratory of Luzhou City, Luzhou 646000, China.
| | - Hai-Xia Long
- College of Information Science Technology, Hainan Normal University, HaiKou City 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, HaiKou City 571158, China.
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4
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Gong D, Arbesfeld-Qiu JM, Perrault E, Bae JW, Hwang WL. Spatial oncology: Translating contextual biology to the clinic. Cancer Cell 2024; 42:1653-1675. [PMID: 39366372 DOI: 10.1016/j.ccell.2024.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 08/01/2024] [Accepted: 09/06/2024] [Indexed: 10/06/2024]
Abstract
Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.
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Affiliation(s)
- Dennis Gong
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jeanna M Arbesfeld-Qiu
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard University, Graduate School of Arts and Sciences, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Ella Perrault
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard University, Graduate School of Arts and Sciences, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Jung Woo Bae
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - William L Hwang
- Center for Systems Biology, Department of Radiation Oncology, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard University, Graduate School of Arts and Sciences, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA.
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5
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Walker M, Federico E, Espinoza JL, Dupont CL. Unveiling Molecular Diversity in Cerebral Thrombi via Spatial Transcriptomics. Stroke 2024; 55:e266-e268. [PMID: 39206540 PMCID: PMC11421970 DOI: 10.1161/strokeaha.124.047907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Affiliation(s)
- Melanie Walker
- Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA
- Stroke and Applied Neurosciences Center, University of Washington School of Medicine, Seattle, WA
- Departments of Genomic Medicine, Human Health & Disease and Human Microbiome, J. Craig Venter Institute, La Jolla, CA
| | - Emma Federico
- Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA
- Stroke and Applied Neurosciences Center, University of Washington School of Medicine, Seattle, WA
| | - Josh L. Espinoza
- Departments of Genomic Medicine, Human Health & Disease and Human Microbiome, J. Craig Venter Institute, La Jolla, CA
| | - Christopher L. Dupont
- Departments of Genomic Medicine, Human Health & Disease and Human Microbiome, J. Craig Venter Institute, La Jolla, CA
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6
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Kumaran G, Carroll L, Muirhead N, Bottomley MJ. How Can Spatial Transcriptomic Profiling Advance Our Understanding of Skin Diseases? J Invest Dermatol 2024:S0022-202X(24)01926-2. [PMID: 39177547 DOI: 10.1016/j.jid.2024.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/23/2024] [Accepted: 07/04/2024] [Indexed: 08/24/2024]
Abstract
Spatial transcriptomic (ST) profiling is the mapping of gene expression within cell populations with preservation of positional context and represents an exciting new approach to develop our understanding of local and regional influences upon skin biology in health and disease. With the ability to probe from a few hundred transcripts to the entire transcriptome, multiple ST approaches are now widely available. In this paper, we review the ST field and discuss its application to dermatology. Its potential to advance our understanding of skin biology in health and disease is highlighted through the illustrative examples of 3 research areas: cutaneous aging, tumorigenesis, and psoriasis.
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Affiliation(s)
- Girishkumar Kumaran
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Liam Carroll
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | | | - Matthew J Bottomley
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
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7
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Wang W, Liu X, Wang DC. Single-cell and spatial alterations of neural cells and circuits in clinical and translational medicine. Clin Transl Med 2024; 14:e1696. [PMID: 38812092 PMCID: PMC11136700 DOI: 10.1002/ctm2.1696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 04/28/2024] [Indexed: 05/31/2024] Open
Abstract
The spatiotemporal heterogeneity of neurons, circuits and regulators is being uncovered at a single-cell level, from single-cell gene expression to functional regulations. The classifications, architectonics and functional communications amongst neural cells and circuits within the brain can be clearly delineated using single-cell multiomics and transomics. This Editorial highlights the spatiotemporal heterogeneity of neurons and circuits as well as regulators, initiates the translation of neuronal diversity and spatial organisation at single-cell levels into clinical considerations, and enables the discovery and development of new therapies for neurological diseases. It is predicted that single-cell and spatial multiomics will be integrated with metabolomic profiles and corresponding gene epigenetic modifications. The interactions amongst DNAs, RNAs and proteins in a cell provide details of intracellular functional regulations and new opportunities for the translation of temporospatial diversity of neural cell subtypes/states into clinical practice. The application of single-cell multiomics with four-dimensional genome to the human pathological brain will lead us to a new milestone of the diagnosis and treatment.
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Affiliation(s)
- William Wang
- Office of Clinical and Translational MedicineShanghaiChina
| | - Xuanqi Liu
- Shanghai Institute of Clinical BioinformaticsZhongshan HospitalShanghai Medical CollegeFudan UniversityShanghaiChina
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8
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Alexandrov T, Saez‐Rodriguez J, Saka SK. Enablers and challenges of spatial omics, a melting pot of technologies. Mol Syst Biol 2023; 19:e10571. [PMID: 37842805 PMCID: PMC10632737 DOI: 10.15252/msb.202110571] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 10/17/2023] Open
Abstract
Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.
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Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- BioInnovation InstituteCopenhagenDenmark
| | - Julio Saez‐Rodriguez
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Sinem K Saka
- Genome Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
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Peng L, He X, Peng X, Li Z, Zhang L. STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and k-sums clustering. Comput Biol Med 2023; 166:107440. [PMID: 37738898 DOI: 10.1016/j.compbiomed.2023.107440] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/15/2023] [Accepted: 08/29/2023] [Indexed: 09/24/2023]
Abstract
BACKGROUND Spatial transcriptomics technologies fully utilize spatial location information, tissue morphological features, and transcriptional profiles. Integrating these data can greatly advance our understanding about cell biology in the morphological background. METHODS We developed an innovative spatial clustering method called STGNNks by combining graph neural network, denoising auto-encoder, and k-sums clustering. First, spatial resolved transcriptomics data are preprocessed and a hybrid adjacency matrix is constructed. Next, gene expressions and spatial context are integrated to learn spots' embedding features by a deep graph infomax-based graph convolutional network. Third, the learned features are mapped to a low-dimensional space through a zero-inflated negative binomial (ZINB)-based denoising auto-encoder. Fourth, a k-sums clustering algorithm is developed to identify spatial domains by combining k-means clustering and the ratio-cut clustering algorithms. Finally, it implements spatial trajectory inference, spatially variable gene identification, and differentially expressed gene detection based on the pseudo-space-time method on six 10x Genomics Visium datasets. RESULTS We compared our proposed STGNNks method with five other spatial clustering methods, CCST, Seurat, stLearn, Scanpy and SEDR. For the first time, four internal indicators in the area of machine learning, that is, silhouette coefficient, the Davies-Bouldin index, the Caliniski-Harabasz index, and the S_Dbw index, were used to measure the clustering performance of STGNNks with CCST, Seurat, stLearn, Scanpy and SEDR on five spatial transcriptomics datasets without labels (i.e., Adult Mouse Brain (FFPE), Adult Mouse Kidney (FFPE), Human Breast Cancer (Block A Section 2), Human Breast Cancer (FFPE), and Human Lymph Node). And two external indicators including adjusted Rand index (ARI) and normalized mutual information (NMI) were applied to evaluate the performance of the above six methods on Human Breast Cancer (Block A Section 1) with real labels. The comparison experiments elucidated that STGNNks obtained the smallest Davies-Bouldin and S_Dbw values and the largest Silhouette Coefficient, Caliniski-Harabasz, ARI and NMI, significantly outperforming the above five spatial transcriptomics analysis algorithms. Furthermore, we detected the top six spatially variable genes and the top five differentially expressed genes in each cluster on the above five unlabeled datasets. And the pseudo-space-time tree plot with hierarchical layout demonstrated a flow of Human Breast Cancer (Block A Section 1) progress in three clades branching from three invasive ductal carcinoma regions to multiple ductal carcinoma in situ sub-clusters. CONCLUSION We anticipate that STGNNks can efficiently improve spatial transcriptomics data analysis and further boost the diagnosis and therapy of related diseases. The codes are publicly available at https://github.com/plhhnu/STGNNks.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China; College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Xianzhi He
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Xinhuai Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Zejun Li
- School of Computer Science, Hunan Institute of Technology, Hengyang, 421002, Hunan, China.
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China.
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10
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Robles-Remacho A, Sanchez-Martin RM, Diaz-Mochon JJ. Spatial Transcriptomics: Emerging Technologies in Tissue Gene Expression Profiling. Anal Chem 2023; 95:15450-15460. [PMID: 37814884 PMCID: PMC10603609 DOI: 10.1021/acs.analchem.3c02029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/21/2023] [Indexed: 10/11/2023]
Abstract
In this Perspective, we discuss the current status and advances in spatial transcriptomics technologies, which allow high-resolution mapping of gene expression in intact cell and tissue samples. Spatial transcriptomics enables the creation of high-resolution maps of gene expression patterns within their native spatial context, adding an extra layer of information to the bulk sequencing data. Spatial transcriptomics has expanded significantly in recent years and is making a notable impact on a range of fields, including tissue architecture, developmental biology, cancer, and neurodegenerative and infectious diseases. The latest advancements in spatial transcriptomics have resulted in the development of highly multiplexed methods, transcriptomic-wide analysis, and single-cell resolution utilizing diverse technological approaches. In this Perspective, we provide a detailed analysis of the molecular foundations behind the main spatial transcriptomics technologies, including methods based on microdissection, in situ sequencing, single-molecule FISH, spatial capturing, selection of regions of interest, and single-cell or nuclei dissociation. We contextualize the detection and capturing efficiency, strengths, limitations, tissue compatibility, and applications of these techniques as well as provide information on data analysis. In addition, this Perspective discusses future directions and potential applications of spatial transcriptomics, highlighting the importance of the continued development to promote widespread adoption of these techniques within the research community.
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Affiliation(s)
- Agustín Robles-Remacho
- GENYO.
Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustracion,
114. 18016 Granada, Spain
- Department
of Medicinal and Organic Chemistry, School of Pharmacy, University of Granada, Campus Cartuja s/n, 18071 Granada, Spain
- Instituto
de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
| | - Rosario M. Sanchez-Martin
- GENYO.
Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustracion,
114. 18016 Granada, Spain
- Department
of Medicinal and Organic Chemistry, School of Pharmacy, University of Granada, Campus Cartuja s/n, 18071 Granada, Spain
- Instituto
de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
| | - Juan J. Diaz-Mochon
- GENYO.
Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Avenida de la Ilustracion,
114. 18016 Granada, Spain
- Department
of Medicinal and Organic Chemistry, School of Pharmacy, University of Granada, Campus Cartuja s/n, 18071 Granada, Spain
- Instituto
de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
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11
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Piwecka M, Rajewsky N, Rybak-Wolf A. Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease. Nat Rev Neurol 2023; 19:346-362. [PMID: 37198436 PMCID: PMC10191412 DOI: 10.1038/s41582-023-00809-y] [Citation(s) in RCA: 58] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/31/2023] [Indexed: 05/19/2023]
Abstract
In the past decade, single-cell technologies have proliferated and improved from their technically challenging beginnings to become common laboratory methods capable of determining the expression of thousands of genes in thousands of cells simultaneously. The field has progressed by taking the CNS as a primary research subject - the cellular complexity and multiplicity of neuronal cell types provide fertile ground for the increasing power of single-cell methods. Current single-cell RNA sequencing methods can quantify gene expression with sufficient accuracy to finely resolve even subtle differences between cell types and states, thus providing a great tool for studying the molecular and cellular repertoire of the CNS and its disorders. However, single-cell RNA sequencing requires the dissociation of tissue samples, which means that the interrelationships between cells are lost. Spatial transcriptomic methods bypass tissue dissociation and retain this spatial information, thereby allowing gene expression to be assessed across thousands of cells within the context of tissue structural organization. Here, we discuss how single-cell and spatially resolved transcriptomics have been contributing to unravelling the pathomechanisms underlying brain disorders. We focus on three areas where we feel these new technologies have provided particularly useful insights: selective neuronal vulnerability, neuroimmune dysfunction and cell-type-specific treatment response. We also discuss the limitations and future directions of single-cell and spatial RNA sequencing technologies.
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Affiliation(s)
- Monika Piwecka
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Nikolaus Rajewsky
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Agnieszka Rybak-Wolf
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrueck Center for Molecular Medicine, Berlin, Germany.
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12
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Fang Y, Peng Z, Wang Y, Yuan X, Gao K, Fan R, Liu R, Liu Y, Zhang H, Xie Z, Jiang W. Improvements and challenges of tissue preparation for spatial transcriptome analysis of skull base tumors. Heliyon 2023; 9:e14133. [PMID: 36938455 PMCID: PMC10018477 DOI: 10.1016/j.heliyon.2023.e14133] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/09/2023] Open
Abstract
Background Spatial transcriptome (ST) provides molecular profiles of tumor cells at the spatial level, which brings new progress to the research of tumors and the tumor microenvironment. This study summarizes the experiences and lessons learned in the spatial section preparation of two different pathological types of nose and skull base tumors at our institution, with the aim of offering guidelines to researchers to avoid wasting precious samples and provide a basis for the application of ST in clinical practice. Methods Frozen tissue blocks from patients with squamous cell carcinoma and adenocarcinoma of the nose and skull base diagnosed at our institution were prepared. The effects of different procedures and pathological tissue types on slide quality were explored and evaluated using RNA integrity number (RIN) and HE scores as criteria. The effects of different RIN values on ST sequencing data were explored. Results A total of 43 samples were obtained from 26 patients, including 22 with squamous carcinomas and 21 with adenocarcinomas. Thirteen samples with satisfactory RNA quality control and good histological morphology were sequenced for ST. Sample isolation time <15 min and abandonment of snap-frozen isopentane significantly improved RNA quality (p = 0.004, p < 0.0001) and histomorphological integrity (p = 0.02, p = 0.02). Selection of a suitable tissue RNA extraction kit was critical for RNA quality (p < 0.0001). No difference between 6 ≤ RIN <7 and RIN >7 in ST sequencing results was found, indicating that RIN ≥6 can be used as a criterion for qualified RNA quality control. Therefore, fresh tissues washed as soon as possible with cold PBS and then dried using OCT for snap freezing are currently the best method for preparing spatial sections of nose and skull base tumor tissues of different pathological types. Conclusion This study is the first to investigate the feasibility of applying ST to different pathological types of nose and skull base tumors and to demonstrate the widespread application of ST in tumors. Rational optimization of spatial slide preparation procedures and exploration of individualized pre-sequencing protocols are used as the first stage to ensure the quality of spatial sequencing and lay the foundation for subsequent spatial analysis.
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Affiliation(s)
- Yan Fang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Zhouying Peng
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Yumin Wang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Xiaotian Yuan
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Kelei Gao
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Ruohao Fan
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Ruijie Liu
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Yalan Liu
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Hua Zhang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Zhihai Xie
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Weihong Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Corresponding author. Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
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Ya D, Zhang Y, Cui Q, Jiang Y, Yang J, Tian N, Xiang W, Lin X, Li Q, Liao R. Application of spatial transcriptome technologies to neurological diseases. Front Cell Dev Biol 2023; 11:1142923. [PMID: 36936681 PMCID: PMC10020196 DOI: 10.3389/fcell.2023.1142923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023] Open
Abstract
Spatial transcriptome technology acquires gene expression profiles while retaining spatial location information, it displays the gene expression properties of cells in situ. Through the investigation of cell heterogeneity, microenvironment, function, and cellular interactions, spatial transcriptome technology can deeply explore the pathogenic mechanisms of cell-type-specific responses and spatial localization in neurological diseases. The present article overviews spatial transcriptome technologies based on microdissection, in situ hybridization, in situ sequencing, in situ capture, and live cell labeling. Each technology is described along with its methods, detection throughput, spatial resolution, benefits, and drawbacks. Furthermore, their applications in neurodegenerative disease, neuropsychiatric illness, stroke and epilepsy are outlined. This information can be used to understand disease mechanisms, pick therapeutic targets, and establish biomarkers.
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Affiliation(s)
- Dongshan Ya
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Yingmei Zhang
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Qi Cui
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Yanlin Jiang
- Department of Pharmacology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Jiaxin Yang
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Ning Tian
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Wenjing Xiang
- Department of Neurology ward 2, Guilin People’s Hospital, Guilin, China
| | - Xiaohui Lin
- Department of Geriatrics, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Qinghua Li
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Rujia Liao
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- *Correspondence: Rujia Liao,
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Li L, Liu Y, Liu X, Zheng N, Gu Y, Song Y, Wang X. Regulatory roles of external cholesterol in human airway epithelial mitochondrial function through STARD3 signalling. Clin Transl Med 2022; 12:e902. [PMID: 35678098 PMCID: PMC9178408 DOI: 10.1002/ctm2.902] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/11/2022] [Accepted: 05/11/2022] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Hypercholesterolemia is found in patients with chronic lung inflammation, during which airway epithelial cells play important roles in maintenance of inflammatory responses to pathogens. The present study aims at molecular mechanisms by which cholesterol changes airway epithelial sensitivity in response to smoking. METHODS Human bronchial epithelial cells (HBEs) were stimulated with cigarette smoke extract (CSE) and mice were exposed to CS/lipopolysaccharide (LPS) as models in vitro and in vivo. Severe COPD patients and healthy volunteers were also enrolled and the level of cholesterol in plasma was detected by metabolomics. Filipin III and elisa kits were used to stain free cholesterol. Mitochondrial function was detected by mitotracker green, mitotracker green, and Seahorse. Mitochondrial morphology was detected by high content screening and electron microscopy. The mRNA and protein levels of mitochondrial dynamics-related proteins were detected by RT-qPCR and Western blot,respectively. BODIPY 493/503 was used to stain lipid droplets. Lipidomics was used to detect intracellular lipid components. The mRNA level of interleukin (IL)-6 and IL-8 were detected by RT-qPCR. RESULTS We found that the cholesterol overload was associated with chronic obstructive pulmonary disease (COPD) and airway epithelia-driven inflammation, evidenced by hypercholesterolemia in patients with COPD and preclinical models, alteration of lipid metabolism-associated genes in CSE-induced airway epithelia and production of ILs. External cholesterol altered airway epithelial sensitivity of inflammation in response to CSE, through the regulation of STARD3-MFN2 pathway, cholesterol re-distribution, altered transport and accumulation of cholesterol, activities of lipid transport regulators and disorder of mitochondrial function and dynamics. MFN2 down-regulation increased airway epithelial sensitivity and production of ILs after smoking, at least partially by injuring fatty acid oxidation and activating mTOR phosphorylation. CONCLUSIONS Our data provide new insights for understanding molecular mechanisms of cholesterol-altered airway epithelial inflammation and for developing diagnostic biomarkers and therapeutic targets to improve patient outcomes.
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Affiliation(s)
- Liyang Li
- Department of Pulmonary and Critical Care Medicine, Zhongshan HospitalFudan University Shanghai Medical CollegeShanghaiChina
| | - Yifei Liu
- Center of Molecular Diagnosis and TherapyThe Second Hospital of Fujian Medical UniversityQuanzhouFujianChina
| | - Xuanqi Liu
- Shanghai Institute of Clinical BioinformaticsShanghaiChina
| | - Nannan Zheng
- Department of Pulmonary and Critical Care Medicine, Zhongshan HospitalFudan University Shanghai Medical CollegeShanghaiChina
| | - Yutong Gu
- Department of Pulmonary and Critical Care Medicine, Zhongshan HospitalFudan University Shanghai Medical CollegeShanghaiChina
| | - Yuanlin Song
- Department of Pulmonary and Critical Care Medicine, Zhongshan HospitalFudan University Shanghai Medical CollegeShanghaiChina
| | - Xiangdong Wang
- Department of Pulmonary and Critical Care Medicine, Zhongshan HospitalFudan University Shanghai Medical CollegeShanghaiChina
- Shanghai Institute of Clinical BioinformaticsShanghaiChina
- Shanghai Engineering Research for AI Technology for Cardiopulmonary DiseasesShanghaiChina
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15
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Yan F, Li Z, Powell CA, Wang X. Forward single-cell sequencing into clinical application: Understanding of ageing and rejuvenation from clinical observation to single-cell solution. Clin Transl Med 2022; 12:e827. [PMID: 35593205 PMCID: PMC9121316 DOI: 10.1002/ctm2.827] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 04/05/2022] [Indexed: 12/20/2022] Open
Affiliation(s)
- Furong Yan
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University Shanghai Medical College, Shanghai, China.,Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Shanghai, China
| | - Zhangping Li
- The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Charles A Powell
- Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Xiangdong Wang
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University Shanghai Medical College, Shanghai, China.,Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Shanghai, China
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16
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Zhang L, Chen D, Song D, Liu X, Zhang Y, Xu X, Wang X. Clinical and translational values of spatial transcriptomics. Signal Transduct Target Ther 2022; 7:111. [PMID: 35365599 PMCID: PMC8972902 DOI: 10.1038/s41392-022-00960-w] [Citation(s) in RCA: 62] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 03/04/2022] [Accepted: 03/09/2022] [Indexed: 02/06/2023] Open
Abstract
The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-seq) acts as a pivotal component to bridge the pathological phenomes of human tissues with molecular alterations, defining in situ intercellular molecular communications and knowledge on spatiotemporal molecular medicine. The present article overviews the development of ST and aims to evaluate clinical and translational values for understanding molecular pathogenesis and uncovering disease-specific biomarkers. We compare the advantages and disadvantages of sequencing- and imaging-based technologies and highlight opportunities and challenges of ST. We also describe the bioinformatics tools necessary on dissecting spatial patterns of gene expression and cellular interactions and the potential applications of ST in human diseases for clinical practice as one of important issues in clinical and translational medicine, including neurology, embryo development, oncology, and inflammation. Thus, clear clinical objectives, designs, optimizations of sampling procedure and protocol, repeatability of ST, as well as simplifications of analysis and interpretation are the key to translate ST from bench to clinic.
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Affiliation(s)
- Linlin Zhang
- Zhongshan Hospital, Department of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Shanghai, 200000, China
| | - Dongsheng Chen
- Suzhou Institute of Systems Medicine, Suzhou, 215123, Jiangsu, China
| | - Dongli Song
- Zhongshan Hospital, Department of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Shanghai, 200000, China
| | - Xiaoxia Liu
- Zhongshan Hospital, Department of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Shanghai, 200000, China
| | - Yanan Zhang
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen, 518055, China
| | - Xun Xu
- BGI-Shenzhen, Shenzhen, 518083, China.
| | - Xiangdong Wang
- Zhongshan Hospital, Department of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Shanghai, 200000, China.
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