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Rajendran R, Beck RC, Waskasi MM, Kelly BD, Bauer DR. Digital analysis of the prostate tumor microenvironment with high-order chromogenic multiplexing. J Pathol Inform 2024; 15:100352. [PMID: 38186745 PMCID: PMC10770522 DOI: 10.1016/j.jpi.2023.100352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/30/2023] [Accepted: 11/16/2023] [Indexed: 01/09/2024] Open
Abstract
As our understanding of the tumor microenvironment grows, the pathology field is increasingly utilizing multianalyte diagnostic assays to understand important characteristics of tumor growth. In clinical settings, brightfield chromogenic assays represent the gold-standard and have developed significant trust as the first-line diagnostic method. However, conventional brightfield tests have been limited to low-order assays that are visually interrogated. We have developed a hybrid method of brightfield chromogenic multiplexing that overcomes these limitations and enables high-order multiplex assays. However, how compatible high-order brightfield multiplexed images are with advanced analytical algorithms has not been extensively evaluated. In the present study, we address this gap by developing a novel 6-marker prostate cancer assay that targets diverse aspects of the tumor microenvironment such as prostate-specific biomarkers (PSMA and p504s), immune biomarkers (CD8 and PD-L1), a prognostic biomarker (Ki-67), as well as an adjunctive diagnostic biomarker (basal cell cocktail) and apply the assay to 143 differentially graded adenocarcinoma prostate tissues. The tissues were then imaged on our spectroscopic multiplexing imaging platform and mined for proteomic and spatial features that were correlated with cancer presence and disease grade. Extracted features were used to train a UMAP model that differentiated healthy from cancerous tissue with an accuracy of 89% and identified clusters of cells based on cancer grade. For spatial analysis, cell-to-cell distances were calculated for all biomarkers and differences between healthy and adenocarcinoma tissues were studied. We report that p504s positive cells were at least 2× closer to cells expressing PD-L1, CD8, Ki-67, and basal cell in adenocarcinoma tissues relative to the healthy control tissues. These findings offer a powerful insight to understand the fingerprint of the prostate tumor microenvironment and indicate that high-order chromogenic multiplexing is compatible with digital analysis. Thus, the presented chromogenic multiplexing system combines the clinical applicability of brightfield assays with the emerging diagnostic power of high-order multiplexing in a digital pathology friendly format that is well-suited for translational studies to better understand mechanisms of tumor development and growth.
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Affiliation(s)
- Rahul Rajendran
- Roche Diagnostics Solutions, (Ventana Medical Systems, Inc.), Tucson, AZ, USA
| | - Rachel C. Beck
- Roche Diagnostics Solutions, (Ventana Medical Systems, Inc.), Tucson, AZ, USA
| | - Morteza M. Waskasi
- Roche Diagnostics Solutions, (Ventana Medical Systems, Inc.), Tucson, AZ, USA
| | - Brian D. Kelly
- Roche Diagnostics Solutions, (Ventana Medical Systems, Inc.), Tucson, AZ, USA
| | - Daniel R. Bauer
- Roche Diagnostics Solutions, (Ventana Medical Systems, Inc.), Tucson, AZ, USA
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2
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Song B, Wang X, Qin L, Hussain S, Liang W. Brain gliomas: Diagnostic and therapeutic issues and the prospects of drug-targeted nano-delivery technology. Pharmacol Res 2024; 206:107308. [PMID: 39019336 DOI: 10.1016/j.phrs.2024.107308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 07/12/2024] [Accepted: 07/12/2024] [Indexed: 07/19/2024]
Abstract
Glioma is the most common intracranial malignant tumor, with severe difficulty in treatment and a low patient survival rate. Due to the heterogeneity and invasiveness of tumors, lack of personalized clinical treatment design, and physiological barriers, it is often difficult to accurately distinguish gliomas, which dramatically affects the subsequent diagnosis, imaging treatment, and prognosis. Fortunately, nano-delivery systems have demonstrated unprecedented capabilities in diagnosing and treating gliomas in recent years. They have been modified and surface modified to efficiently traverse BBB/BBTB, target lesion sites, and intelligently release therapeutic or contrast agents, thereby achieving precise imaging and treatment. In this review, we focus on nano-delivery systems. Firstly, we provide an overview of the standard and emerging diagnostic and treatment technologies for glioma in clinical practice. After induction and analysis, we focus on summarizing the delivery methods of drug delivery systems, the design of nanoparticles, and their new advances in glioma imaging and treatment in recent years. Finally, we discussed the prospects and potential challenges of drug-delivery systems in diagnosing and treating glioma.
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Affiliation(s)
- Baoqin Song
- School of Pharmaceutical Sciences & Institute of Materia Medica, Shandong First Medical University & Shandong Academy of Medical Sciences, National Key Laboratory of Advanced Drug Delivery and Release Systems, Key Laboratory for Biotechnology Drugs of National Health Commission (Shandong Academy of Medical Sciences), Key Lab for Rare & Uncommon Diseases of Shandong Province, Jinan, Shandong 250117, China
| | - Xiu Wang
- School of Pharmaceutical Sciences & Institute of Materia Medica, Shandong First Medical University & Shandong Academy of Medical Sciences, National Key Laboratory of Advanced Drug Delivery and Release Systems, Key Laboratory for Biotechnology Drugs of National Health Commission (Shandong Academy of Medical Sciences), Key Lab for Rare & Uncommon Diseases of Shandong Province, Jinan, Shandong 250117, China.
| | - Lijing Qin
- School of Pharmaceutical Sciences & Institute of Materia Medica, Shandong First Medical University & Shandong Academy of Medical Sciences, National Key Laboratory of Advanced Drug Delivery and Release Systems, Key Laboratory for Biotechnology Drugs of National Health Commission (Shandong Academy of Medical Sciences), Key Lab for Rare & Uncommon Diseases of Shandong Province, Jinan, Shandong 250117, China
| | - Shehbaz Hussain
- School of Pharmaceutical Sciences & Institute of Materia Medica, Shandong First Medical University & Shandong Academy of Medical Sciences, National Key Laboratory of Advanced Drug Delivery and Release Systems, Key Laboratory for Biotechnology Drugs of National Health Commission (Shandong Academy of Medical Sciences), Key Lab for Rare & Uncommon Diseases of Shandong Province, Jinan, Shandong 250117, China
| | - Wanjun Liang
- School of Pharmaceutical Sciences & Institute of Materia Medica, Shandong First Medical University & Shandong Academy of Medical Sciences, National Key Laboratory of Advanced Drug Delivery and Release Systems, Key Laboratory for Biotechnology Drugs of National Health Commission (Shandong Academy of Medical Sciences), Key Lab for Rare & Uncommon Diseases of Shandong Province, Jinan, Shandong 250117, China.
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3
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Swain AK, Pandit V, Sharma J, Yadav P. SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and domain identification in spatial transcriptomics. Commun Biol 2024; 7:639. [PMID: 38796505 PMCID: PMC11127982 DOI: 10.1038/s42003-024-06349-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 05/17/2024] [Indexed: 05/28/2024] Open
Abstract
Efficiently mapping of cell types in situ remains a major challenge in spatial transcriptomics. Most spot deconvolution tools ignore spatial coordinate information and perform extremely slow on large datasets. Here, we introduce SpatialPrompt, a spatially aware and scalable tool for spot deconvolution and domain identification. SpatialPrompt integrates gene expression, spatial location, and single-cell RNA sequencing (scRNA-seq) dataset as reference to accurately infer cell-type proportions of spatial spots. SpatialPrompt uses non-negative ridge regression and graph neural network to efficiently capture local microenvironment information. Our extensive benchmarking analysis on Visium, Slide-seq, and MERFISH datasets demonstrated superior performance of SpatialPrompt over 15 existing tools. On mouse hippocampus dataset, SpatialPrompt achieves spot deconvolution and domain identification within 2 minutes for 50,000 spots. Overall, domain identification using SpatialPrompt was 44 to 150 times faster than existing methods. We build a database housing 40 plus curated scRNA-seq datasets for seamless integration with SpatialPrompt for spot deconvolution.
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Affiliation(s)
- Asish Kumar Swain
- Department of Bioscience & Bioengineering, Indian Institute of Technology, Jodhpur, Rajasthan, 342030, India
| | - Vrushali Pandit
- Department of Bioscience & Bioengineering, Indian Institute of Technology, Jodhpur, Rajasthan, 342030, India
| | - Jyoti Sharma
- Department of Bioscience & Bioengineering, Indian Institute of Technology, Jodhpur, Rajasthan, 342030, India
| | - Pankaj Yadav
- Department of Bioscience & Bioengineering, Indian Institute of Technology, Jodhpur, Rajasthan, 342030, India.
- School of Artificial Intelligence and Data Science, Indian Institute of Technology, Jodhpur, Rajasthan, 342030, India.
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4
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Yeo S, Schrader AW, Lee J, Asadian M, Han HS. Spot-Based Global Registration for Subpixel Stitching of Single-Molecule Resolution Images for Tissue-Scale Spatial Transcriptomics. Anal Chem 2024; 96:6517-6522. [PMID: 38621224 PMCID: PMC11076048 DOI: 10.1021/acs.analchem.3c05686] [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] [Indexed: 04/17/2024]
Abstract
Single-molecule imaging at the tissue scale has revolutionized our understanding of biology by providing unprecedented insight into the molecular expression of individual cells and their spatial organization within tissues. However, achieving precise image stitching at the single-molecule level remains a challenge, primarily due to heterogeneous background signals and dim labeling signals in single-molecule images. This paper introduces Spot-Based Global Registration (SBGR), a novel strategy that shifts the focus from raw images to identified molecular spots for high-resolution image alignment. The use of spot-based data enables straightforward and robust evaluation of the credibility of estimated translations and stitching performance. The method outperforms existing image-based stitching methods, achieving subpixel accuracy (83 ± 36 nm) with exceptional consistency. Furthermore, SBGR incorporates a mechanism to surgically remove duplicate spots in overlapping regions, maximizing information recovery from duplicate measurements. In conclusion, SBGR emerges as a robust and accurate solution for stitching single-molecule resolution images in tissue-scale spatial transcriptomics, offering versatility and potential for high-resolution spatial analysis.
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Affiliation(s)
- Seokjin Yeo
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Alex W Schrader
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Juyeon Lee
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Marisa Asadian
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Hee-Sun Han
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
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5
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Marmarelis MG, Littman R, Battaglin F, Niedzwiecki D, Venook A, Ambite JL, Galstyan A, Lenz HJ, Ver Steeg G. q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics. Commun Biol 2024; 7:400. [PMID: 38565955 PMCID: PMC11255321 DOI: 10.1038/s42003-024-06104-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 03/25/2024] [Indexed: 04/04/2024] Open
Abstract
Unlocking the full dimensionality of single-cell RNA sequencing data (scRNAseq) is the next frontier to a richer, fuller understanding of cell biology. We introduce q-diffusion, a framework for capturing the coexpression structure of an entire library of genes, improving on state-of-the-art analysis tools. The method is demonstrated via three case studies. In the first, q-diffusion helps gain statistical significance for differential effects on patient outcomes when analyzing the CALGB/SWOG 80405 randomized phase III clinical trial, suggesting precision guidance for the treatment of metastatic colorectal cancer. Secondly, q-diffusion is benchmarked against existing scRNAseq classification methods using an in vitro PBMC dataset, in which the proposed method discriminates IFN-γ stimulation more accurately. The same case study demonstrates improvements in unsupervised cell clustering with the recent Tabula Sapiens human atlas. Finally, a local distributional segmentation approach for spatial scRNAseq, driven by q-diffusion, yields interpretable structures of human cortical tissue.
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Affiliation(s)
- Myrl G Marmarelis
- Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA.
| | - Russell Littman
- University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Francesca Battaglin
- Keck School of Medicine, University of Southern California, 1975 Zonal Ave., Los Angeles, CA, 90033, USA
| | | | - Alan Venook
- University of California San Francisco, San Francisco, CA, 94143, USA
| | - Jose-Luis Ambite
- Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA
| | - Aram Galstyan
- Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA
| | - Heinz-Josef Lenz
- Keck School of Medicine, University of Southern California, 1975 Zonal Ave., Los Angeles, CA, 90033, USA
| | - Greg Ver Steeg
- Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA
- University of California Riverside, Riverside, CA, 92521, USA
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6
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Ahn S, Lee HS. Applicability of Spatial Technology in Cancer Research. Cancer Res Treat 2024; 56:343-356. [PMID: 38291743 PMCID: PMC11016655 DOI: 10.4143/crt.2023.1302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 01/29/2024] [Indexed: 02/01/2024] Open
Abstract
This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.
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Affiliation(s)
- Sangjeong Ahn
- Department of Pathology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
- Artificial Intelligence Center, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
- Department of Medical Informatics, Korea University College of Medicine, Seoul, Korea
| | - Hye Seung Lee
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
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7
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Yan K, Liu Q, Huang R, Jiang Y, Bian Z, Li S, Li L, Shen F, Tsuneyama K, Zhang Q, Lian Z, Guan H, Xu B. Spatial transcriptomics reveals prognosis-associated cellular heterogeneity in the papillary thyroid carcinoma microenvironment. Clin Transl Med 2024; 14:e1594. [PMID: 38426403 PMCID: PMC10905537 DOI: 10.1002/ctm2.1594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 01/28/2024] [Accepted: 02/05/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Papillary thyroid carcinoma (PTC) is the most common malignant endocrine tumour, and its incidence and prevalence are increasing considerably. Cellular heterogeneity in the tumour microenvironment is important for PTC prognosis. Spatial transcriptomics is a powerful technique for cellular heterogeneity study. METHODS In conjunction with a clinical pathologist identification method, spatial transcriptomics was employed to characterise the spatial location and RNA profiles of PTC-associated cells within the tissue sections. The spatial RNA-clinical signature genes for each cell type were extracted and applied to outlining the distribution regions of specific cells on the entire section. The cellular heterogeneity of each cell type was further revealed by ContourPlot analysis, monocle analysis, trajectory analysis, ligand-receptor analysis and Gene Ontology enrichment analysis. RESULTS The spatial distribution region of tumour cells, typical and atypical follicular cells (FCs and AFCs) and immune cells were accurately and comprehensively identified in all five PTC tissue sections. AFCs were identified as a transitional state between FCs and tumour cells, exhibiting a higher resemblance to the latter. Three tumour foci were shared among all patients out of the 13 observed. Notably, tumour foci No. 2 displayed elevated expression levels of genes associated with lower relapse-free survival in PTC patients. We discovered key ligand-receptor interactions, including LAMB3-ITGA2, FN1-ITGA3 and FN1-SDC4, involved in the transition of PTC cells from FCs to AFCs and eventually to tumour cells. High expression of these patterns correlated with reduced relapse-free survival. In the tumour immune microenvironment, reduced interaction between myeloid-derived TGFB1 and TGFBR1 in tumour focus No. 2 contributed to tumourigenesis and increased heterogeneity. The spatial RNA-clinical analysis method developed here revealed prognosis-associated cellular heterogeneity in the PTC microenvironment. CONCLUSIONS The occurrence of tumour foci No. 2 and three enhanced ligand-receptor interactions in the AFC area/tumour foci reduced the relapse-free survival of PTC patients, potentially leading to improved prognostic strategies and targeted therapies for PTC patients.
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Affiliation(s)
- Kai Yan
- Guangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina
| | - Qing‐Zhi Liu
- Chronic Disease LaboratoryInstitutes for Life SciencesSouth China University of TechnologyGuangzhouChina
| | - Rong‐Rong Huang
- Guangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina
| | - Yi‐Hua Jiang
- Guangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and ApplicationGuangzhouChina
| | - Zhen‐Hua Bian
- School of Biomedical Sciences and EngineeringSouth China University of TechnologyGuangzhou International CampusGuangzhouChina
| | - Si‐Jin Li
- Department of Thyroid SurgeryGuangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Liang Li
- Medical Research InstituteGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Fei Shen
- Department of Thyroid SurgeryGuangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
| | - Koichi Tsuneyama
- Department of Pathology and Laboratory MedicineInstitute of Biomedical SciencesTokushima University Graduate SchoolTokushimaJapan
| | - Qing‐Ling Zhang
- Department of PathologyGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Zhe‐Xiong Lian
- Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Haixia Guan
- Department of EndocrinologyGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Bo Xu
- Department of Thyroid SurgeryGuangzhou First People's HospitalSouth China University of TechnologyGuangzhouChina
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8
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Hao M, Luo E, Chen Y, Wu Y, Li C, Chen S, Gao H, Bian H, Gu J, Wei L, Zhang X. STEM enables mapping of single-cell and spatial transcriptomics data with transfer learning. Commun Biol 2024; 7:56. [PMID: 38184694 PMCID: PMC10771471 DOI: 10.1038/s42003-023-05640-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 11/27/2023] [Indexed: 01/08/2024] Open
Abstract
Profiling spatial variations of cellular composition and transcriptomic characteristics is important for understanding the physiology and pathology of tissues. Spatial transcriptomics (ST) data depict spatial gene expression but the currently dominating high-throughput technology is yet not at single-cell resolution. Single-cell RNA-sequencing (SC) data provide high-throughput transcriptomic information at the single-cell level but lack spatial information. Integrating these two types of data would be ideal for revealing transcriptomic landscapes at single-cell resolution. We develop the method STEM (SpaTially aware EMbedding) for this purpose. It uses deep transfer learning to encode both ST and SC data into a unified spatially aware embedding space, and then uses the embeddings to infer SC-ST mapping and predict pseudo-spatial adjacency between cells in SC data. Semi-simulation and real data experiments verify that the embeddings preserved spatial information and eliminated technical biases between SC and ST data. We apply STEM to human squamous cell carcinoma and hepatic lobule datasets to uncover the localization of rare cell types and reveal cell-type-specific gene expression variation along a spatial axis. STEM is powerful for mapping SC and ST data to build single-cell level spatial transcriptomic landscapes, and can provide mechanistic insights into the spatial heterogeneity and microenvironments of tissues.
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Affiliation(s)
- Minsheng Hao
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Erpai Luo
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Yixin Chen
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Yanhong Wu
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Chen Li
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Sijie Chen
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Haoxiang Gao
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Haiyang Bian
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Jin Gu
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Lei Wei
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Xuegong Zhang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China.
- School of Life Sciences and School of Medicine, Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China.
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9
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Pierantoni L, Reis RL, Silva-Correia J, Oliveira JM, Heavey S. Spatial -omics technologies: the new enterprise in 3D breast cancer models. Trends Biotechnol 2023; 41:1488-1500. [PMID: 37544843 DOI: 10.1016/j.tibtech.2023.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 06/28/2023] [Accepted: 07/06/2023] [Indexed: 08/08/2023]
Abstract
The fields of tissue bioengineering, -omics, and spatial biology are advancing rapidly, each offering the opportunity for a paradigm shift in breast cancer research. However, to date, collaboration between these fields has not reached its full potential. In this review, we describe the most recently generated 3D breast cancer models regarding the biomaterials and technological platforms employed. Additionally, their biological evaluation is reported, highlighting their advantages and limitations. Specifically, we focus on the most up-to-date -omics and spatial biology techniques, which can generate a deeper understanding of the biological relevance of bioengineered 3D breast cancer in vitro models, thus paving the way towards truly clinically relevant microphysiological systems, improved drug development success rates, and personalised medicine approaches.
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Affiliation(s)
- Lara Pierantoni
- 3B's Research Group, I3Bs - Research Institute on Biomaterials, Biodegradables and Biomimetics of University of Minho, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, AvePark, Zona Industrial da Gandra, Barco, Guimarães 4805-017, Portugal; ICVS/3B's - PT Government Associated Laboratory, Braga/Guimarães, Portugal.
| | - Rui L Reis
- 3B's Research Group, I3Bs - Research Institute on Biomaterials, Biodegradables and Biomimetics of University of Minho, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, AvePark, Zona Industrial da Gandra, Barco, Guimarães 4805-017, Portugal; ICVS/3B's - PT Government Associated Laboratory, Braga/Guimarães, Portugal
| | - Joana Silva-Correia
- 3B's Research Group, I3Bs - Research Institute on Biomaterials, Biodegradables and Biomimetics of University of Minho, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, AvePark, Zona Industrial da Gandra, Barco, Guimarães 4805-017, Portugal; ICVS/3B's - PT Government Associated Laboratory, Braga/Guimarães, Portugal
| | - Joaquim M Oliveira
- 3B's Research Group, I3Bs - Research Institute on Biomaterials, Biodegradables and Biomimetics of University of Minho, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, AvePark, Zona Industrial da Gandra, Barco, Guimarães 4805-017, Portugal; ICVS/3B's - PT Government Associated Laboratory, Braga/Guimarães, Portugal
| | - Susan Heavey
- Division of Surgery & Interventional Science, University College London, London, UK
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10
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Velleuer E, Domínguez-Hüttinger E, Rodríguez A, Harris LA, Carlberg C. Concepts of multi-level dynamical modelling: understanding mechanisms of squamous cell carcinoma development in Fanconi anemia. Front Genet 2023; 14:1254966. [PMID: 38028610 PMCID: PMC10652399 DOI: 10.3389/fgene.2023.1254966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Fanconi anemia (FA) is a rare disease (incidence of 1:300,000) primarily based on the inheritance of pathogenic variants in genes of the FA/BRCA (breast cancer) pathway. These variants ultimately reduce the functionality of different proteins involved in the repair of DNA interstrand crosslinks and DNA double-strand breaks. At birth, individuals with FA might present with typical malformations, particularly radial axis and renal malformations, as well as other physical abnormalities like skin pigmentation anomalies. During the first decade of life, FA mostly causes bone marrow failure due to reduced capacity and loss of the hematopoietic stem and progenitor cells. This often makes hematopoietic stem cell transplantation necessary, but this therapy increases the already intrinsic risk of developing squamous cell carcinoma (SCC) in early adult age. Due to the underlying genetic defect in FA, classical chemo-radiation-based treatment protocols cannot be applied. Therefore, detecting and treating the multi-step tumorigenesis process of SCC in an early stage, or even its progenitors, is the best option for prolonging the life of adult FA individuals. However, the small number of FA individuals makes classical evidence-based medicine approaches based on results from randomized clinical trials impossible. As an alternative, we introduce here the concept of multi-level dynamical modelling using large, longitudinally collected genome, proteome- and transcriptome-wide data sets from a small number of FA individuals. This mechanistic modelling approach is based on the "hallmarks of cancer in FA", which we derive from our unique database of the clinical history of over 750 FA individuals. Multi-omic data from healthy and diseased tissue samples of FA individuals are to be used for training constituent models of a multi-level tumorigenesis model, which will then be used to make experimentally testable predictions. In this way, mechanistic models facilitate not only a descriptive but also a functional understanding of SCC in FA. This approach will provide the basis for detecting signatures of SCCs at early stages and their precursors so they can be efficiently treated or even prevented, leading to a better prognosis and quality of life for the FA individual.
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Affiliation(s)
- Eunike Velleuer
- Department of Cytopathology, Heinrich Heine University, Düsseldorf, Germany
- Center for Child and Adolescent Health, Helios Klinikum, Krefeld, Germany
| | - Elisa Domínguez-Hüttinger
- Departamento Düsseldorf Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad México, Mexico
| | - Alfredo Rodríguez
- Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad México, Mexico
- Instituto Nacional de Pediatría, Ciudad México, Mexico
| | - Leonard A. Harris
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR, United States
- Interdisciplinary Graduate Program in Cell and Molecular Biology, University of Arkansas, Fayetteville, AR, United States
- Cancer Biology Program, Winthrop P Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Carsten Carlberg
- Institute of Animal Reproduction and Food Research, Polish Academy of Sciences, Olsztyn, Poland
- School of Medicine, Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
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11
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Wang Q, Zhi Y, Zi M, Mo Y, Wang Y, Liao Q, Zhang S, Gong Z, Wang F, Zeng Z, Guo C, Xiong W. Spatially Resolved Transcriptomics Technology Facilitates Cancer Research. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302558. [PMID: 37632718 PMCID: PMC10602551 DOI: 10.1002/advs.202302558] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/16/2023] [Indexed: 08/28/2023]
Abstract
Single cell RNA sequencing (scRNA-seq) provides a great convenience for studying tumor occurrence and development for its ability to study gene expression at the individual cell level. However, patient-derived tumor tissues are composed of multiple types of cells including tumor cells and adjacent non-malignant cells such as stromal cells and immune cells. The spatial locations of various cells in situ tissues plays a pivotal role in the occurrence and development of tumors, which cannot be elucidated by scRNA-seq alone. Spatially resolved transcriptomics (SRT) technology emerges timely to explore the unrecognized relationship between the spatial background of a particular cell and its functions, and is increasingly used in cancer research. This review provides a systematic overview of the SRT technologies that are developed, in particular the more widely used cutting-edge SRT technologies based on next-generation sequencing (NGS). In addition, the main achievements by SRT technologies in precisely unveiling the underappreciated spatial locations on gene expression and cell function with unprecedented high-resolution in cancer research are emphasized, with the aim of developing more effective clinical therapeutics oriented to a deeper understanding of the interaction between tumor cells and surrounding non-malignant cells.
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Affiliation(s)
- Qian Wang
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
| | - Yuan Zhi
- Department of Oral and Maxillofacial SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaHunan410012P. R. China
| | - Moxin Zi
- Department of Oral and Maxillofacial SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaHunan410012P. R. China
| | - Yongzhen Mo
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
- Department of Otolaryngology Head and Neck SurgeryXiangya HospitalCentral South UniversityChangshaHunan410008P. R. China
| | - Yumin Wang
- Department of Otolaryngology Head and Neck SurgeryXiangya HospitalCentral South UniversityChangshaHunan410008P. R. China
| | - Qianjin Liao
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
| | - Shanshan Zhang
- Department of Otolaryngology Head and Neck SurgeryXiangya HospitalCentral South UniversityChangshaHunan410008P. R. China
| | - Zhaojian Gong
- Department of Oral and Maxillofacial SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaHunan410012P. R. China
| | - Fuyan Wang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
| | - Zhaoyang Zeng
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
| | - Can Guo
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
| | - Wei Xiong
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
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12
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Allen C, Chang Y, Neelon B, Chang W, Kim HJ, Li Z, Ma Q, Chung D. A Bayesian multivariate mixture model for high throughput spatial transcriptomics. Biometrics 2023; 79:1775-1787. [PMID: 35895854 PMCID: PMC10134739 DOI: 10.1111/biom.13727] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 07/18/2022] [Indexed: 01/11/2023]
Abstract
High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single-cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we seek to identify sub-populations of cells within a tissue sample that may inform biological phenomena. Existing computational methods either ignore the spatial heterogeneity in gene expression profiles, fail to account for important statistical features such as skewness, or are heuristic-based network clustering methods that lack the inferential benefits of statistical modeling. To address this gap, we develop SPRUCE: a Bayesian spatial multivariate finite mixture model based on multivariate skew-normal distributions, which is capable of identifying distinct cellular sub-populations in HST data. We further implement a novel combination of Pólya-Gamma data augmentation and spatial random effects to infer spatially correlated mixture component membership probabilities without relying on approximate inference techniques. Via a simulation study, we demonstrate the detrimental inferential effects of ignoring skewness or spatial correlation in HST data. Using publicly available human brain HST data, SPRUCE outperforms existing methods in recovering expertly annotated brain layers. Finally, our application of SPRUCE to human breast cancer HST data indicates that SPRUCE can distinguish distinct cell populations within the tumor microenvironment. An R package spruce for fitting the proposed models is available through The Comprehensive R Archive Network.
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Affiliation(s)
- Carter Allen
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, U.S.A
- The Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, U.S.A
| | - Yuzhou Chang
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, U.S.A
- The Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, U.S.A
| | - Brian Neelon
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, U.S.A
| | - Won Chang
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, U.S.A
| | - Hang J. Kim
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, U.S.A
| | - Zihai Li
- The Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, U.S.A
| | - Qin Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, U.S.A
- The Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, U.S.A
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, U.S.A
- The Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, U.S.A
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13
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Ottaiano A, Ianniello M, Santorsola M, Ruggiero R, Sirica R, Sabbatino F, Perri F, Cascella M, Di Marzo M, Berretta M, Caraglia M, Nasti G, Savarese G. From Chaos to Opportunity: Decoding Cancer Heterogeneity for Enhanced Treatment Strategies. BIOLOGY 2023; 12:1183. [PMID: 37759584 PMCID: PMC10525472 DOI: 10.3390/biology12091183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 08/24/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023]
Abstract
Cancer manifests as a multifaceted disease, characterized by aberrant cellular proliferation, survival, migration, and invasion. Tumors exhibit variances across diverse dimensions, encompassing genetic, epigenetic, and transcriptional realms. This heterogeneity poses significant challenges in prognosis and treatment, affording tumors advantages through an increased propensity to accumulate mutations linked to immune system evasion and drug resistance. In this review, we offer insights into tumor heterogeneity as a crucial characteristic of cancer, exploring the difficulties associated with measuring and quantifying such heterogeneity from clinical and biological perspectives. By emphasizing the critical nature of understanding tumor heterogeneity, this work contributes to raising awareness about the importance of developing effective cancer therapies that target this distinct and elusive trait of cancer.
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Affiliation(s)
- Alessandro Ottaiano
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Monica Ianniello
- AMES, Centro Polidiagnostico Strumentale srl, Via Padre Carmine Fico 24, 80013 Casalnuovo Di Napoli, Italy; (M.I.); (R.R.); (R.S.); (G.S.)
| | - Mariachiara Santorsola
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Raffaella Ruggiero
- AMES, Centro Polidiagnostico Strumentale srl, Via Padre Carmine Fico 24, 80013 Casalnuovo Di Napoli, Italy; (M.I.); (R.R.); (R.S.); (G.S.)
| | - Roberto Sirica
- AMES, Centro Polidiagnostico Strumentale srl, Via Padre Carmine Fico 24, 80013 Casalnuovo Di Napoli, Italy; (M.I.); (R.R.); (R.S.); (G.S.)
| | - Francesco Sabbatino
- Oncology Unit, Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy;
| | - Francesco Perri
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Marco Cascella
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Massimiliano Di Marzo
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Massimiliano Berretta
- Department of Clinical and Experimental Medicine, University of Messina, 98122 Messina, Italy;
| | - Michele Caraglia
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, Via Luigi De Crecchio 7, 80138 Naples, Italy;
| | - Guglielmo Nasti
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Giovanni Savarese
- AMES, Centro Polidiagnostico Strumentale srl, Via Padre Carmine Fico 24, 80013 Casalnuovo Di Napoli, Italy; (M.I.); (R.R.); (R.S.); (G.S.)
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14
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Zhou Y, Jiang X, Wang X, Huang J, Li T, Jin H, He J. Promise of spatially resolved omics for tumor research. J Pharm Anal 2023; 13:851-861. [PMID: 37719191 PMCID: PMC10499658 DOI: 10.1016/j.jpha.2023.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 07/01/2023] [Accepted: 07/06/2023] [Indexed: 09/19/2023] Open
Abstract
Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures. By interacting with the microenvironment, tumor cells undergo dynamic changes in gene expression and metabolism, resulting in spatiotemporal variations in their capacity for proliferation and metastasis. In recent years, the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis. By preserving location information while obtaining a large number of gene and molecular data, spatially resolved metabolomics (SRM) and spatially resolved transcriptomics (SRT) approaches can offer new ideas and reliable tools for the in-depth study of tumors. This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques, as well as a review of their applications in cancer-related fields.
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Affiliation(s)
- Yanhe Zhou
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Xinyi Jiang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Xiangyi Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Jianpeng Huang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Tong Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Hongtao Jin
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100050, China
- NMPA Key Laboratory for Safety Research and Evaluation of Innovative Drug, Beijing, 10050, China
| | - Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- NMPA Key Laboratory for Safety Research and Evaluation of Innovative Drug, Beijing, 10050, China
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15
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Xu T, Zhu E, Zhang C, Calandrelli R, Lin P, Zhong S. High-Resolution Characterization of Human Brain Cortex with High-Fidelity Spatial Transcriptomic Slides (HiFi-Slides). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.12.544625. [PMID: 37398363 PMCID: PMC10312654 DOI: 10.1101/2023.06.12.544625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Spatial transcriptomic tools and platforms help researchers to inspect tissues and cells with fine details of how they differentiate in expressions and how they orient themselves. With the higher resolution we get and higher throughput of expression targets, spatial analysis can truly become the core player for cell clustering, migration study, and, eventually, the novel model for pathological study. We present the demonstration of HiFi-slide, a whole transcriptomic sequencing technique that recycles used sequenced-by-synthesis flow cell surfaces to a high-resolution spatial mapping tool that can be directly applied to tissue cell gradient analysis, gene expression analysis, cell proximity analysis, and other cellular-level spatial studies.
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16
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Park H, Jo SH, Lee RH, Macks CP, Ku T, Park J, Lee CW, Hur JK, Sohn CH. Spatial Transcriptomics: Technical Aspects of Recent Developments and Their Applications in Neuroscience and Cancer Research. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2206939. [PMID: 37026425 PMCID: PMC10238226 DOI: 10.1002/advs.202206939] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 03/10/2023] [Indexed: 06/04/2023]
Abstract
Spatial transcriptomics is a newly emerging field that enables high-throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems. By transitioning from conventional biological studies to "in situ" biology, spatial transcriptomics can provide transcriptome-scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and discussions regarding future directions of spatial multi-omics and their expanding roles in biomedical applications are emphasized.
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Affiliation(s)
- Han‐Eol Park
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
- School of Biological SciencesSeoul National UniversitySeoul08826Republic of Korea
| | - Song Hyun Jo
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Rosalind H. Lee
- School of Life SciencesGwangju Institute of Science and Technology (GIST)Gwangju61005Republic of Korea
| | - Christian P. Macks
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
| | - Taeyun Ku
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Jihwan Park
- School of Life SciencesGwangju Institute of Science and Technology (GIST)Gwangju61005Republic of Korea
| | - Chung Whan Lee
- Department of ChemistryGachon UniversitySeongnamGyeonggi‐do13120Republic of Korea
| | - Junho K. Hur
- Department of GeneticsCollege of MedicineHanyang UniversitySeoul04763Republic of Korea
| | - Chang Ho Sohn
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
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17
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Li X, Huang W, Xu X, Zhang HY, Shi Q. Deciphering tissue heterogeneity from spatially resolved transcriptomics by the autoencoder-assisted graph convolutional neural network. Front Genet 2023; 14:1202409. [PMID: 37303949 PMCID: PMC10248005 DOI: 10.3389/fgene.2023.1202409] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 05/09/2023] [Indexed: 06/13/2023] Open
Abstract
Spatially resolved transcriptomics (SRT) provides an unprecedented opportunity to investigate the complex and heterogeneous tissue organization. However, it is challenging for a single model to learn an effective representation within and across spatial contexts. To solve the issue, we develop a novel ensemble model, AE-GCN (autoencoder-assisted graph convolutional neural network), which combines the autoencoder (AE) and graph convolutional neural network (GCN), to identify accurate and fine-grained spatial domains. AE-GCN transfers the AE-specific representations to the corresponding GCN-specific layers and unifies these two types of deep neural networks for spatial clustering via the clustering-aware contrastive mechanism. In this way, AE-GCN accommodates the strengths of both AE and GCN for learning an effective representation. We validate the effectiveness of AE-GCN on spatial domain identification and data denoising using multiple SRT datasets generated from ST, 10x Visium, and Slide-seqV2 platforms. Particularly, in cancer datasets, AE-GCN identifies disease-related spatial domains, which reveal more heterogeneity than histological annotations, and facilitates the discovery of novel differentially expressed genes of high prognostic relevance. These results demonstrate the capacity of AE-GCN to unveil complex spatial patterns from SRT data.
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18
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Perrault EN, Shireman JM, Ali ES, Lin P, Preddy I, Park C, Budhiraja S, Baisiwala S, Dixit K, James CD, Heiland DH, Ben-Sahra I, Pott S, Basu A, Miska J, Ahmed AU. Ribonucleotide reductase regulatory subunit M2 drives glioblastoma TMZ resistance through modulation of dNTP production. SCIENCE ADVANCES 2023; 9:eade7236. [PMID: 37196077 PMCID: PMC10191446 DOI: 10.1126/sciadv.ade7236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 04/13/2023] [Indexed: 05/19/2023]
Abstract
During therapy, adaptations driven by cellular plasticity are partly responsible for driving the inevitable recurrence of glioblastoma (GBM). To investigate plasticity-induced adaptation during standard-of-care chemotherapy temozolomide (TMZ), we performed in vivo single-cell RNA sequencing in patient-derived xenograft (PDX) tumors of GBM before, during, and after therapy. Comparing single-cell transcriptomic patterns identified distinct cellular populations present during TMZ therapy. Of interest was the increased expression of ribonucleotide reductase regulatory subunit M2 (RRM2), which we found to regulate dGTP and dCTP production vital for DNA damage response during TMZ therapy. Furthermore, multidimensional modeling of spatially resolved transcriptomic and metabolomic analysis in patients' tissues revealed strong correlations between RRM2 and dGTP. This supports our data that RRM2 regulates the demand for specific dNTPs during therapy. In addition, treatment with the RRM2 inhibitor 3-AP (Triapine) enhances the efficacy of TMZ therapy in PDX models. We present a previously unidentified understanding of chemoresistance through critical RRM2-mediated nucleotide production.
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Affiliation(s)
- Ella N. Perrault
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Jack M. Shireman
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Eunus S. Ali
- Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Peiyu Lin
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Isabelle Preddy
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Cheol Park
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Shreya Budhiraja
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Shivani Baisiwala
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Karan Dixit
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - C. David James
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Northwestern Medicine Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Dieter H Heiland
- Microenvironment and Immunology Research Laboratory, Medical-Center, University of Freiburg, Freiburg, Germany
- Department of Neurosurgery, Medical-Center, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK), partner site Freiburg, Freiburg, Germany
| | - Issam Ben-Sahra
- Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Sebastian Pott
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Anindita Basu
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Jason Miska
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Atique U. Ahmed
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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19
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Kalliara E, Belfrage E, Gullberg U, Drott K, Ek S. Spatially Guided and Single Cell Tools to Map the Microenvironment in Cutaneous T-Cell Lymphoma. Cancers (Basel) 2023; 15:cancers15082362. [PMID: 37190290 DOI: 10.3390/cancers15082362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/12/2023] [Accepted: 04/15/2023] [Indexed: 05/17/2023] Open
Abstract
Mycosis fungoides (MF) and Sézary syndrome (SS) are two closely related clinical variants of cutaneous T-cell lymphomas (CTCL). Previously demonstrated large patient-to-patient and intra-patient disease heterogeneity underpins the importance of personalized medicine in CTCL. Advanced stages of CTCL are characterized by dismal prognosis, and the early identification of patients who will progress remains a clinical unmet need. While the exact molecular events underlying disease progression are poorly resolved, the tumor microenvironment (TME) has emerged as an important driver. In particular, the Th1-to-Th2 shift in the immune response is now commonly identified across advanced-stage CTCL patients. Herein, we summarize the role of the TME in CTCL evolution and the latest studies in deciphering inter- and intra-patient heterogeneity. We introduce spatially resolved omics as a promising technology to advance immune-oncology efforts in CTCL. We propose the combined implementation of spatially guided and single-cell omics technologies in paired skin and blood samples. Such an approach will mediate in-depth profiling of phenotypic and molecular changes in reactive immune subpopulations and malignant T cells preceding the Th1-to-Th2 shift and reveal mechanisms underlying disease progression from skin-limited to systemic disease that collectively will lead to the discovery of novel biomarkers to improve patient prognostication and the design of personalized treatment strategies.
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Affiliation(s)
- Eirini Kalliara
- Department of Immunotechnology, Faculty of Engineering (LTH), University of Lund, 223 63 Lund, Sweden
| | - Emma Belfrage
- Department of Dermatology and Venereology, Skane University Hospital (SUS), 205 02 Lund, Sweden
| | - Urban Gullberg
- Department of Hematology and Transfusion Medicine, Skane University Hospital (SUS), 205 02 Lund, Sweden
| | - Kristina Drott
- Department of Hematology and Transfusion Medicine, Skane University Hospital (SUS), 205 02 Lund, Sweden
| | - Sara Ek
- Department of Immunotechnology, Faculty of Engineering (LTH), University of Lund, 223 63 Lund, Sweden
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20
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Ru B, Huang J, Zhang Y, Aldape K, Jiang P. Estimation of cell lineages in tumors from spatial transcriptomics data. Nat Commun 2023; 14:568. [PMID: 36732531 PMCID: PMC9895078 DOI: 10.1038/s41467-023-36062-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 01/13/2023] [Indexed: 02/04/2023] Open
Abstract
Spatial transcriptomics (ST) technology through in situ capturing has enabled topographical gene expression profiling of tumor tissues. However, each capturing spot may contain diverse immune and malignant cells, with different cell densities across tissue regions. Cell type deconvolution in tumor ST data remains challenging for existing methods designed to decompose general ST or bulk tumor data. We develop the Spatial Cellular Estimator for Tumors (SpaCET) to infer cell identities from tumor ST data. SpaCET first estimates cancer cell abundance by integrating a gene pattern dictionary of copy number alterations and expression changes in common malignancies. A constrained regression model then calibrates local cell densities and determines immune and stromal cell lineage fractions. SpaCET provides higher accuracy than existing methods based on simulation and real ST data with matched double-blind histopathology annotations as ground truth. Further, coupling cell fractions with ligand-receptor coexpression analysis, SpaCET reveals how intercellular interactions at the tumor-immune interface promote cancer progression.
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Affiliation(s)
- Beibei Ru
- Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jinlin Huang
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Yu Zhang
- Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Kenneth Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peng Jiang
- Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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21
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Ospina O, Soupir A, Fridley BL. A Primer on Preprocessing, Visualization, Clustering, and Phenotyping of Barcode-Based Spatial Transcriptomics Data. Methods Mol Biol 2023; 2629:115-140. [PMID: 36929076 DOI: 10.1007/978-1-0716-2986-4_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Recent developments in spatially resolved transcriptomics (ST) have resulted in a large number of studies characterizing the architecture of tissues, the spatial distribution of cell types, and their interactions. Furthermore, ST promises to enable the discovery of more accurate drug targets while also providing a better understanding of the etiology and evolution of complex diseases. The analysis of ST brings similar challenges as seen in other gene expression assays such as scRNA-seq; however, there is the additional spatial information that warrants the development of suitable algorithms for the quality control, preprocessing, visualization, and other discovery-enabling approaches (e.g., clustering, cell phenotyping). In this chapter, we review some of the existing algorithms to perform these analytical tasks and highlight some of the unmet analytical challenges in the analysis of ST data. Given the diversity of available ST technologies, we focus this chapter on the analysis of barcode-based RNA quantitation techniques.
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Affiliation(s)
- Oscar Ospina
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Alex Soupir
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Brooke L Fridley
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
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22
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Haase C, Gustafsson K, Mei S, Yeh SC, Richter D, Milosevic J, Turcotte R, Kharchenko PV, Sykes DB, Scadden DT, Lin CP. Image-seq: spatially resolved single-cell sequencing guided by in situ and in vivo imaging. Nat Methods 2022; 19:1622-1633. [PMID: 36424441 PMCID: PMC9718684 DOI: 10.1038/s41592-022-01673-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 10/03/2022] [Indexed: 11/26/2022]
Abstract
Tissue function depends on cellular organization. While the properties of individual cells are increasingly being deciphered using powerful single-cell sequencing technologies, understanding their spatial organization and temporal evolution remains a major challenge. Here, we present Image-seq, a technology that provides single-cell transcriptional data on cells that are isolated from specific spatial locations under image guidance, thus preserving the spatial information of the target cells. It is compatible with in situ and in vivo imaging and can document the temporal and dynamic history of the cells being analyzed. Cell samples are isolated from intact tissue and processed with state-of-the-art library preparation protocols. The technique therefore combines spatial information with highly sensitive RNA sequencing readouts from individual, intact cells. We have used both high-throughput, droplet-based sequencing as well as SMARTseq-v4 library preparation to demonstrate its application to bone marrow and leukemia biology. We discovered that DPP4 is a highly upregulated gene during early progression of acute myeloid leukemia and that it marks a more proliferative subpopulation that is confined to specific bone marrow microenvironments. Furthermore, the ability of Image-seq to isolate viable, intact cells should make it compatible with a range of downstream single-cell analysis tools including multi-omics protocols.
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Affiliation(s)
- Christa Haase
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA.
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA.
| | - Karin Gustafsson
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Stem Cell Institute, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Shenglin Mei
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shu-Chi Yeh
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA
- Department of Orthopaedics, Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, NY, USA
| | - Dmitry Richter
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Jelena Milosevic
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Raphaël Turcotte
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Peter V Kharchenko
- Harvard Stem Cell Institute, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Altos Labs, San Diego, CA, USA
| | - David B Sykes
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Stem Cell Institute, Cambridge, MA, USA
| | - David T Scadden
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Stem Cell Institute, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Charles P Lin
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA.
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA.
- Harvard Stem Cell Institute, Cambridge, MA, USA.
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23
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Li L, Sun C, Sun Y, Dong Z, Wu R, Sun X, Zhang H, Jiang W, Zhou Y, Cen X, Cai S, Xia H, Zhu Y, Guo T, Piatkevich KD. Spatially resolved proteomics via tissue expansion. Nat Commun 2022; 13:7242. [PMID: 36450705 PMCID: PMC9712279 DOI: 10.1038/s41467-022-34824-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 11/04/2022] [Indexed: 12/12/2022] Open
Abstract
Spatially resolved proteomics is an emerging approach for mapping proteome heterogeneity of biological samples, however, it remains technically challenging due to the complexity of the tissue microsampling techniques and mass spectrometry analysis of nanoscale specimen volumes. Here, we describe a spatially resolved proteomics method based on the combination of tissue expansion with mass spectrometry-based proteomics, which we call Expansion Proteomics (ProteomEx). ProteomEx enables quantitative profiling of the spatial variability of the proteome in mammalian tissues at ~160 µm lateral resolution, equivalent to the tissue volume of 0.61 nL, using manual microsampling without the need for custom or special equipment. We validated and demonstrated the utility of ProteomEx for streamlined large-scale proteomics profiling of biological tissues including brain, liver, and breast cancer. We further applied ProteomEx for identifying proteins associated with Alzheimer's disease in a mouse model by comparative proteomic analysis of brain subregions.
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Affiliation(s)
- Lu Li
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.13402.340000 0004 1759 700XCollege of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310024 Zhejiang China
| | - Cuiji Sun
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Yaoting Sun
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Zhen Dong
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Runxin Wu
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.21107.350000 0001 2171 9311Whiting School of Engineering, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218 USA
| | - Xiaoting Sun
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Hanbin Zhang
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Wenhao Jiang
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Yan Zhou
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Xufeng Cen
- grid.13402.340000 0004 1759 700XDepartment of Biochemistry & Molecular Medical Center, Zhejiang University School of Medicine, Hangzhou, 310058 China
| | - Shang Cai
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Hongguang Xia
- grid.13402.340000 0004 1759 700XDepartment of Biochemistry & Molecular Medical Center, Zhejiang University School of Medicine, Hangzhou, 310058 China ,grid.452661.20000 0004 1803 6319Research Center for Clinical Pharmacy & Key Laboratory for Drug Evaluation and Clinical Research of Zhejiang Province, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003 China ,grid.13402.340000 0004 1759 700XZhejiang Laboratory for Systems & Precision Medicine, Zhejiang University Medical Center, 1369 West Wenyi Road, Hangzhou, 311121 China
| | - Yi Zhu
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Tiannan Guo
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Key Laboratory of Structural Biology of Zhejiang Province, Westlake University, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
| | - Kiryl D. Piatkevich
- grid.494629.40000 0004 8008 9315Research Center for Industries of the Future and School of Life Sciences, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China ,grid.494629.40000 0004 8008 9315Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024 Zhejiang China
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24
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Yamazaki M, Hosokawa M, Matsunaga H, Arikawa K, Takamochi K, Suzuki K, Hayashi T, Kambara H, Takeyama H. Integrated spatial analysis of gene mutation and gene expression for understanding tumor diversity in formalin-fixed paraffin-embedded lung adenocarcinoma. Front Oncol 2022; 12:936190. [PMID: 36505794 PMCID: PMC9731154 DOI: 10.3389/fonc.2022.936190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/31/2022] [Indexed: 11/26/2022] Open
Abstract
Introduction A deeper understanding of intratumoral heterogeneity is essential for prognosis prediction or accurate treatment plan decisions in clinical practice. However, due to the cross-links and degradation of biomolecules within formalin-fixed paraffin-embedded (FFPE) specimens, it is challenging to analyze them. In this study, we aimed to optimize the simultaneous extraction of mRNA and DNA from microdissected FFPE tissues (φ = 100 µm) and apply the method to analyze tumor diversity in lung adenocarcinoma before and after erlotinib administration. Method Two magnetic beads were used for the simultaneous extraction of mRNA and DNA. The decross-linking conditions were evaluated for gene mutation and gene expression analyses of microdissected FFPE tissues. Lung lymph nodes before treatment and lung adenocarcinoma after erlotinib administration were collected from the same patient and were preserved as FFPE specimens for 4 years. Gene expression and gene mutations between histologically classified regions of lung adenocarcinoma (pre-treatment tumor in lung lymph node biopsies and post-treatment tumor, normal lung, tumor stroma, and remission stroma, in resected lung tissue) were compared in a microdissection-based approach. Results Using the optimized simultaneous extraction of DNA and mRNA and whole-genome amplification, we detected approximately 4,000-10,000 expressed genes and the epidermal growth factor receptor (EGFR) driver gene mutations from microdissected FFPE tissues. We found the differences in the highly expressed cancer-associated genes and the positive rate of EGFR exon 19 deletions among the tumor before and after treatment and tumor stroma, even though they were collected from tumors of the same patient or close regions of the same specimen. Conclusion Our integrated spatial analysis method would be applied to various FFPE pathology specimens providing area-specific gene expression and gene mutation information.
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Affiliation(s)
- Miki Yamazaki
- Department of Life Science and Medical Bioscience, Waseda University, Tokyo, Japan,Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan
| | - Masahito Hosokawa
- Department of Life Science and Medical Bioscience, Waseda University, Tokyo, Japan,Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan,Research Organization for Nano and Life Innovation, Waseda University, Tokyo, Japan,Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, Waseda University, Tokyo, Japan
| | - Hiroko Matsunaga
- Research Organization for Nano and Life Innovation, Waseda University, Tokyo, Japan
| | - Koji Arikawa
- Research Organization for Nano and Life Innovation, Waseda University, Tokyo, Japan
| | - Kazuya Takamochi
- Department of Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Kenji Suzuki
- Department of Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Takuo Hayashi
- Department of Human Pathology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Hideki Kambara
- Research Organization for Nano and Life Innovation, Waseda University, Tokyo, Japan,Frontier BioSystems Inc., Tokyo, Japan
| | - Haruko Takeyama
- Department of Life Science and Medical Bioscience, Waseda University, Tokyo, Japan,Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan,Research Organization for Nano and Life Innovation, Waseda University, Tokyo, Japan,Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, Waseda University, Tokyo, Japan,*Correspondence: Haruko Takeyama,
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25
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Overbey EG, Das S, Cope H, Madrigal P, Andrusivova Z, Frapard S, Klotz R, Bezdan D, Gupta A, Scott RT, Park J, Chirko D, Galazka JM, Costes SV, Mason CE, Herranz R, Szewczyk NJ, Borg J, Giacomello S. Challenges and considerations for single-cell and spatially resolved transcriptomics sample collection during spaceflight. CELL REPORTS METHODS 2022; 2:100325. [PMID: 36452864 PMCID: PMC9701605 DOI: 10.1016/j.crmeth.2022.100325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have experienced rapid development in recent years. The findings of spaceflight-based scRNA-seq and SRT investigations are likely to improve our understanding of life in space and our comprehension of gene expression in various cell systems and tissue dynamics. However, compared to their Earth-based counterparts, gene expression experiments conducted in spaceflight have not experienced the same pace of development. Out of the hundreds of spaceflight gene expression datasets available, only a few used scRNA-seq and SRT. In this perspective piece, we explore the growing importance of scRNA-seq and SRT in space biology and discuss the challenges and considerations relevant to robust experimental design to enable growth of these methods in the field.
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Affiliation(s)
- Eliah G. Overbey
- Weill Cornell Medicine, New York, NY, USA
- Institute for Computational Biomedicine, New York, NY, USA
| | - Saswati Das
- Department of Biochemistry, Atal Bihari Vajpayee Institute of Medical Sciences & Dr. Ram Manohar Lohia Hospital, New Delhi, India
| | - Henry Cope
- School of Medicine, University of Nottingham, Derby DE22 3DT, UK
| | - Pedro Madrigal
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Zaneta Andrusivova
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Solène Frapard
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Rebecca Klotz
- KBR, Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA 94035, USA
| | - Daniela Bezdan
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen 72076, Germany
- NGS Competence Center Tübingen (NCCT), University of Tübingen, Tübingen, German
- yuri GmbH, Meckenbeuren, Germany
| | | | - Ryan T. Scott
- KBR, Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA 94035, USA
| | | | | | - Jonathan M. Galazka
- Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA 94035, USA
| | - Sylvain V. Costes
- Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA 94035, USA
| | - Christopher E. Mason
- Weill Cornell Medicine, New York, NY, USA
- Institute for Computational Biomedicine, New York, NY, USA
- The Feil Family Brain and Mind Research Institute, New York, NY, USA
- The WorldQuant Initiative for Quantitative Prediction, New York, NY, USA
| | - Raul Herranz
- Centro de Investigaciones Biológicas Margarita Salas (CSIC), Madrid 28040, Spain
| | - Nathaniel J. Szewczyk
- School of Medicine, University of Nottingham, Derby DE22 3DT, UK
- Department of Biomedical Sciences, Heritage College of Osteopathic Medicine, Ohio University, Athens, OH 45701, USA
| | - Joseph Borg
- Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta, Msida, Malta
| | - Stefania Giacomello
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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26
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Yu Q, Jiang M, Wu L. Spatial transcriptomics technology in cancer research. Front Oncol 2022; 12:1019111. [PMID: 36313703 PMCID: PMC9606570 DOI: 10.3389/fonc.2022.1019111] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/21/2022] [Indexed: 08/25/2023] Open
Abstract
In recent years, spatial transcriptomics (ST) technologies have developed rapidly and have been widely used in constructing spatial tissue atlases and characterizing spatiotemporal heterogeneity of cancers. Currently, ST has been used to profile spatial heterogeneity in multiple cancer types. Besides, ST is a benefit for identifying and comprehensively understanding special spatial areas such as tumor interface and tertiary lymphoid structures (TLSs), which exhibit unique tumor microenvironments (TMEs). Therefore, ST has also shown great potential to improve pathological diagnosis and identify novel prognostic factors in cancer. This review presents recent advances and prospects of applications on cancer research based on ST technologies as well as the challenges.
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Affiliation(s)
- Qichao Yu
- Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Miaomiao Jiang
- Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Liang Wu
- Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
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27
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Fang S, Chen B, Zhang Y, Sun H, Liu L, Liu S, Li Y, Xu X. Computational Approaches and Challenges in Spatial Transcriptomics. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022:S1672-0229(22)00129-2. [PMID: 36252814 PMCID: PMC10372921 DOI: 10.1016/j.gpb.2022.10.001] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 09/08/2022] [Accepted: 10/09/2022] [Indexed: 01/19/2023]
Abstract
The development of spatial transcriptomics (ST) technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs. The large-scale data generated by these ST technologies, which contain spatial gene expression information, have elicited the need for spatially resolved approaches to meet the requirements of computational and biological data interpretation. These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression, correcting the inner batch effect and loss of expression to improve the data quality, conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels, and conducting multi-omics integration analysis to provide an extensible framework toward the in-depth understanding of biological processes. However, algorithms designed specifically for ST technologies to meet these requirements are still in their infancy. Here, we review computational approaches to these problems in light of corresponding issues and challenges, and present forward-looking insights into algorithm development.
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28
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Akhoundova D, Rubin MA. Clinical application of advanced multi-omics tumor profiling: Shaping precision oncology of the future. Cancer Cell 2022; 40:920-938. [PMID: 36055231 DOI: 10.1016/j.ccell.2022.08.011] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/22/2022] [Accepted: 08/11/2022] [Indexed: 12/17/2022]
Abstract
Next-generation DNA sequencing technology has dramatically advanced clinical oncology through the identification of therapeutic targets and molecular biomarkers, leading to the personalization of cancer treatment with significantly improved outcomes for many common and rare tumor entities. More recent developments in advanced tumor profiling now enable dissection of tumor molecular architecture and the functional phenotype at cellular and subcellular resolution. Clinical translation of high-resolution tumor profiling and integration of multi-omics data into precision treatment, however, pose significant challenges at the level of prospective validation and clinical implementation. In this review, we summarize the latest advances in multi-omics tumor profiling, focusing on spatial genomics and chromatin organization, spatial transcriptomics and proteomics, liquid biopsy, and ex vivo modeling of drug response. We analyze the current stages of translational validation of these technologies and discuss future perspectives for their integration into precision treatment.
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Affiliation(s)
- Dilara Akhoundova
- Department for BioMedical Research, University of Bern, 3008 Bern, Switzerland; Department of Medical Oncology, Inselspital, University Hospital of Bern, 3010 Bern, Switzerland
| | - Mark A Rubin
- Department for BioMedical Research, University of Bern, 3008 Bern, Switzerland; Bern Center for Precision Medicine, Inselspital, University Hospital of Bern, 3008 Bern, Switzerland.
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Marco Salas S, Yuan X, Sylven C, Nilsson M, Wählby C, Partel G. De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks. PLoS Comput Biol 2022; 18:e1010366. [PMID: 35960757 PMCID: PMC9401155 DOI: 10.1371/journal.pcbi.1010366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 08/24/2022] [Accepted: 07/06/2022] [Indexed: 11/21/2022] Open
Abstract
With the emergence of high throughput single cell techniques, the understanding of the molecular and cellular diversity of mammalian organs have rapidly increased. In order to understand the spatial organization of this diversity, single cell data is often integrated with spatial data to create probabilistic cell maps. However, targeted cell typing approaches relying on existing single cell data achieve incomplete and biased maps that could mask the true diversity present in a tissue slide. Here we applied a de novo technique to spatially resolve and characterize cellular diversity of in situ sequencing data during human heart development. We obtained and made accessible well defined spatial cell-type maps of fetal hearts from 4.5 to 9 post conception weeks, not biased by probabilistic cell typing approaches. With our analysis, we could characterize previously unreported molecular diversity within cardiomyocytes and epicardial cells and identified their characteristic expression signatures, comparing them with specific subpopulations found in single cell RNA sequencing datasets. We further characterized the differentiation trajectories of epicardial cells, identifying a clear spatial component on it. All in all, our study provides a novel technique for conducting de novo spatial-temporal analyses in developmental tissue samples and a useful resource for online exploration of cell-type differentiation during heart development at sub-cellular image resolution.
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Affiliation(s)
- Sergio Marco Salas
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - Xiao Yuan
- Department of Information Technology and Science for Life Laboratory Uppsala University, Uppsala, Sweden
| | - Christer Sylven
- Department of Medicine, Karolinska Institutet, Huddinge, Stockholm, Sweden
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - Carolina Wählby
- Department of Information Technology and Science for Life Laboratory Uppsala University, Uppsala, Sweden
| | - Gabriele Partel
- Department of Information Technology and Science for Life Laboratory Uppsala University, Uppsala, Sweden
- Laboratory of Multi-omic Integrative Bioinformatics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB-KU Leuven Center for Brain & Disease Research, Laboratory of Computational Biology, Department of Human Genetics, Leuven, Belgium
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Li Q, Zhang X, Ke R. Spatial Transcriptomics for Tumor Heterogeneity Analysis. Front Genet 2022; 13:906158. [PMID: 35899203 PMCID: PMC9309247 DOI: 10.3389/fgene.2022.906158] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 05/31/2022] [Indexed: 12/12/2022] Open
Abstract
The molecular heterogeneity of cancer is one of the major causes of drug resistance that leads to treatment failure. Thus, better understanding the heterogeneity of cancer will contribute to more precise diagnosis and improved patient outcomes. Although single-cell sequencing has become an important tool for investigating tumor heterogeneity recently, it lacks the spatial information of analyzed cells. In this regard, spatial transcriptomics holds great promise in deciphering the complex heterogeneity of cancer by providing localization-indexed gene expression information. This study reviews the applications of spatial transcriptomics in the study of tumor heterogeneity, discovery of novel spatial-dependent mechanisms, tumor immune microenvironment, and matrix microenvironment, as well as the pathological classification and prognosis of cancer. Finally, future challenges and opportunities for spatial transcriptomics technology’s applications in cancer are also discussed.
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31
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Bridges K, Miller-Jensen K. Mapping and Validation of scRNA-Seq-Derived Cell-Cell Communication Networks in the Tumor Microenvironment. Front Immunol 2022; 13:885267. [PMID: 35572582 PMCID: PMC9096838 DOI: 10.3389/fimmu.2022.885267] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 03/25/2022] [Indexed: 01/25/2023] Open
Abstract
Recent advances in single-cell technologies, particularly single-cell RNA-sequencing (scRNA-seq), have permitted high throughput transcriptional profiling of a wide variety of biological systems. As scRNA-seq supports inference of cell-cell communication, this technology has and continues to anchor groundbreaking studies into the efficacy and mechanism of novel immunotherapies for cancer treatment. In this review, we will highlight methods developed to infer inter- and intracellular signaling from scRNA-seq and discuss how they have contributed to studies of immunotherapeutic intervention in the tumor microenvironment (TME). However, a central challenge remains in validating the hypothesized cell-cell interactions. Therefore, this review will also cover strategies for integration of these scRNA-seq-derived interaction networks with existing experimental and computational approaches. Integration of these networks with imaging, protein secretion measurements, and network analysis and mathematical modeling tools addresses challenges that remain with scRNA-seq to enhance studies of immunosuppressive and immunotherapy-altered signaling in the TME.
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Affiliation(s)
- Kate Bridges
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States
| | - Kathryn Miller-Jensen
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, United States
- Systems Biology Institute, Yale University, New Haven, CT, United States
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Yan H, Shi J, Dai Y, Li X, Wu Y, Zhang J, Gu Z, Zhang C, Leng J. Technique integration of single-cell RNA sequencing with spatially resolved transcriptomics in the tumor microenvironment. Cancer Cell Int 2022; 22:155. [PMID: 35440049 PMCID: PMC9020011 DOI: 10.1186/s12935-022-02580-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 04/08/2022] [Indexed: 12/05/2022] Open
Abstract
Background The tumor microenvironment contributes to tumor initiation, growth, invasion, and metastasis. The tumor microenvironment is heterogeneous in cellular and acellular components, particularly structural features and their gene expression at the inter-and intra-tumor levels. Main text Single-cell RNA sequencing profiles single-cell transcriptomes to reveal cell proportions and trajectories while spatial information is lacking. Spatially resolved transcriptomics redeems this lack with limited coverage or depth of transcripts. Hence, the integration of single-cell RNA sequencing and spatial data makes the best use of their strengths, having insights into exploring diverse tissue architectures and interactions in a complicated network. We review applications of integrating the two methods, especially in cellular components in the tumor microenvironment, showing each role in cancer initiation and progression, which provides clinical relevance in prognosis, optimal treatment, and potential therapeutic targets. Conclusion The integration of two approaches may break the bottlenecks in the spatial resolution of neighboring cell subpopulations in cancer, and help to describe the signaling circuitry about the intercommunication and its exact mechanisms in producing different types and malignant stages of tumors.
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Affiliation(s)
- Hailan Yan
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China.,National Clinical Research Center for Obstetric & Gynecologic Diseases, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China
| | - Jinghua Shi
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China.,National Clinical Research Center for Obstetric & Gynecologic Diseases, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China
| | - Yi Dai
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China.,National Clinical Research Center for Obstetric & Gynecologic Diseases, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China
| | - Xiaoyan Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China.,National Clinical Research Center for Obstetric & Gynecologic Diseases, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China
| | - Yushi Wu
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China.,National Clinical Research Center for Obstetric & Gynecologic Diseases, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China
| | - Jing Zhang
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China.,National Clinical Research Center for Obstetric & Gynecologic Diseases, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China
| | - Zhiyue Gu
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China.,National Clinical Research Center for Obstetric & Gynecologic Diseases, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China
| | - Chenyu Zhang
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China.,National Clinical Research Center for Obstetric & Gynecologic Diseases, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China
| | - Jinhua Leng
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China. .,National Clinical Research Center for Obstetric & Gynecologic Diseases, No.1 Shuaifuyuan Dongcheng District, Beijing, 100730, China.
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Islimye E, Girard V, Gould AP. Functions of Stress-Induced Lipid Droplets in the Nervous System. Front Cell Dev Biol 2022; 10:863907. [PMID: 35493070 PMCID: PMC9047859 DOI: 10.3389/fcell.2022.863907] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 03/22/2022] [Indexed: 12/12/2022] Open
Abstract
Lipid droplets are highly dynamic intracellular organelles that store neutral lipids such as cholesteryl esters and triacylglycerols. They have recently emerged as key stress response components in many different cell types. Lipid droplets in the nervous system are mostly observed in vivo in glia, ependymal cells and microglia. They tend to become more numerous in these cell types and can also form in neurons as a consequence of ageing or stresses involving redox imbalance and lipotoxicity. Abundant lipid droplets are also a characteristic feature of several neurodegenerative diseases. In this minireview, we take a cell-type perspective on recent advances in our understanding of lipid droplet metabolism in glia, neurons and neural stem cells during health and disease. We highlight that a given lipid droplet subfunction, such as triacylglycerol lipolysis, can be physiologically beneficial or harmful to the functions of the nervous system depending upon cellular context. The mechanistic understanding of context-dependent lipid droplet functions in the nervous system is progressing apace, aided by new technologies for probing the lipid droplet proteome and lipidome with single-cell type precision.
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34
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Zeng Z, Li Y, Li Y, Luo Y. Statistical and machine learning methods for spatially resolved transcriptomics data analysis. Genome Biol 2022; 23:83. [PMID: 35337374 PMCID: PMC8951701 DOI: 10.1186/s13059-022-02653-7] [Citation(s) in RCA: 55] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 03/15/2022] [Indexed: 01/28/2023] Open
Abstract
The recent advancement in spatial transcriptomics technology has enabled multiplexed profiling of cellular transcriptomes and spatial locations. As the capacity and efficiency of the experimental technologies continue to improve, there is an emerging need for the development of analytical approaches. Furthermore, with the continuous evolution of sequencing protocols, the underlying assumptions of current analytical methods need to be re-evaluated and adjusted to harness the increasing data complexity. To motivate and aid future model development, we herein review the recent development of statistical and machine learning methods in spatial transcriptomics, summarize useful resources, and highlight the challenges and opportunities ahead.
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Affiliation(s)
- Zexian Zeng
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100084, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100084, China
- Department of Data Sciences, Dana Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
| | - Yawei Li
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Yiming Li
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Yuan Luo
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Northwestern University Clinical and Translational Sciences Institute, Chicago, IL, 60611, USA.
- Institute for Augmented Intelligence in Medicine, Northwestern University, Chicago, IL, 60611, USA.
- Center for Health Information Partnerships, Northwestern University, Chicago, IL, 60611, USA.
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35
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Li XY, Shen Y, Zhang L, Guo X, Wu J. Understanding initiation and progression of hepatocellular carcinoma through single cell sequencing. Biochim Biophys Acta Rev Cancer 2022; 1877:188720. [PMID: 35304295 DOI: 10.1016/j.bbcan.2022.188720] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 02/06/2023]
Abstract
Unsatisfied clinical outcome drives to better understand hepatic carcinogenesis, microenvironment and escape of immune surveillance in hepatocellular carcinoma (HCC). Single cell RNA sequencing (scRNA-Seq) has generated enormous data to pinpoint pathophysiologic alterations in tumor microenvironment (TME) or trace lineage development in cancer stem cells (CSCs), circulating tumor cells (CTCs), and subsets of immune cells, such as exhausting T cells, tumor-associated macrophages (TAMs), dendritic cells or other lineages. New insights have significantly advanced current understanding in progression, poor responses to molecular-targeted therapeutics or immune checkpoint inhibitors, metastasis in both basic research and clinical practice. The present review intends to cover a basic workflow of the scRNA-seq technology, existing limitations and improvement areas. Moreover, in-depth understanding in TME, exhausting T cells, CSCs, CTCs, tumor-associated macrophages, dendritic cells in HCC facilitates implementation of personalized and precise therapy in an era of availability with an array of systemic regimens.
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Affiliation(s)
- Xin-Yue Li
- Dept. of Medical Microbiology & Parasitology, MOE/NHC/CAMS Key Laboratory of Medical Molecular Virology, School of Basic Medical Sciences, Fudan University Shanghai Medical College, Shanghai 200032, China
| | - Yue Shen
- Dept. of Gastroenterology & Hepatology, Zhongshan Hospital of Fudan University, Shanghai 200032, China
| | - Li Zhang
- Dept. of Medical Microbiology & Parasitology, MOE/NHC/CAMS Key Laboratory of Medical Molecular Virology, School of Basic Medical Sciences, Fudan University Shanghai Medical College, Shanghai 200032, China
| | - Xiao Guo
- Dept. of Medical Microbiology & Parasitology, MOE/NHC/CAMS Key Laboratory of Medical Molecular Virology, School of Basic Medical Sciences, Fudan University Shanghai Medical College, Shanghai 200032, China; Pathogenic Research Core Facility, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China
| | - Jian Wu
- Dept. of Medical Microbiology & Parasitology, MOE/NHC/CAMS Key Laboratory of Medical Molecular Virology, School of Basic Medical Sciences, Fudan University Shanghai Medical College, Shanghai 200032, China; Dept. of Gastroenterology & Hepatology, Zhongshan Hospital of Fudan University, Shanghai 200032, China; Shanghai Institute of Liver Diseases, Fudan University Shanghai Medical College, Shanghai 200032, China.
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36
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Ospina OE, Wilson CM, Soupir AC, Berglund A, Smalley I, Tsai KY, Fridley BL. spatialGE: quantification and visualization of the tumor microenvironment heterogeneity using spatial transcriptomics. Bioinformatics 2022; 38:2645-2647. [PMID: 35258565 PMCID: PMC9890305 DOI: 10.1093/bioinformatics/btac145] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 02/04/2022] [Accepted: 03/07/2022] [Indexed: 02/05/2023] Open
Abstract
SUMMARY Spatially resolved transcriptomics promises to increase our understanding of the tumor microenvironment and improve cancer prognosis and therapies. Nonetheless, analytical methods to explore associations between the spatial heterogeneity of the tumor and clinical data are not available. Hence, we have developed spatialGE, a software that provides visualizations and quantification of the tumor microenvironment heterogeneity through gene expression surfaces, spatial heterogeneity statistics that can be compared against clinical information, spot-level cell deconvolution and spatially informed clustering, all using a new data object to store data and resulting analyses simultaneously. AVAILABILITY AND IMPLEMENTATION The R package and tutorial/vignette are available at https://github.com/FridleyLab/spatialGE. A script to reproduce the analyses in this manuscript is available in Supplementary information. The Thrane study data included in spatialGE was made available from the public available from the website https://www.spatialresearch.org/resources-published-datasets/doi-10-1158-0008-5472-can-18-0747/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Oscar E Ospina
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Christopher M Wilson
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Alex C Soupir
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Anders Berglund
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Inna Smalley
- Department of Tumor Biology, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Kenneth Y Tsai
- Department of Anatomic Pathology, Moffitt Cancer Center, Tampa, FL 33612, USA
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Liu X, Jiang Y, Song D, Zhang L, Xu G, Hou R, Zhang Y, Chen J, Cheng Y, Liu L, Xu X, Chen G, Wu D, Chen T, Chen A, Wang X. Clinical challenges of tissue preparation for spatial transcriptome. Clin Transl Med 2022; 12:e669. [PMID: 35083877 PMCID: PMC8792118 DOI: 10.1002/ctm2.669] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/16/2021] [Accepted: 11/17/2021] [Indexed: 02/06/2023] Open
Abstract
Spatial transcriptomics is considered as an important part of spatiotemporal molecular images to bridge molecular information with clinical images. Of those potentials and opportunities, the excellent quality of human sample preparation and handling will ensure the precise and reliable information generated from clinical spatial transcriptome. The present study aims at defining potential factors that might influence the quality of spatial transcriptomics in lung cancer, para-cancer, or normal tissues, pathological images of sections and the RNA integrity before spatial transcriptome sequencing. We categorised potential influencing factors from clinical aspects, including patient selection, pathological definition, surgical types, sample harvest, temporary preservation conditions and solutions, frozen approaches, transport and storage conditions and duration. We emphasis on the relationship between the combination of histological scores with RNA integrity number (RIN) and the unique molecular identifier (UMI), which is determines the quality of of spatial transcriptomics; however, we did not find significantly relevance between them. Our results showed that isolated times and dry conditions of sample are critical for the UMI and the quality of spatial transcriptomic samples. Thus, clinical procedures of sample preparation should be furthermore optimised and standardised as new standards of operation performance for clinical spatial transcriptome. Our data suggested that the temporary preservation time and condition of samples at operation room should be within 30 min and in 'dry' status. The direct cryo-preservation within OCT media for human lung sample is recommended. Thus, we believe that clinical spatial transcriptome will be a decisive approach and bridge in the development of spatiotemporal molecular images and provide new insights for understanding molecular mechanisms of diseases at multi-orientations.
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Affiliation(s)
- Xiaoxia Liu
- Department of Pulmonary and Critical Care MedicineInstitute for Clinical ScienceShanghai Institute of Clinical BioinformaticsZhongshan Hospital of Fudan UniversityShanghai Engineering Research for AI Technology for Cardiopulmonary DiseasesShanghaiChina
| | - Yujia Jiang
- BGIShenzhenChina
- BGI College & Henan Institute of Medical and Pharmaceutical SciencesZhengzhou UniversityZhengzhouChina
| | - Dongli Song
- Department of Pulmonary and Critical Care MedicineInstitute for Clinical ScienceShanghai Institute of Clinical BioinformaticsZhongshan Hospital of Fudan UniversityShanghai Engineering Research for AI Technology for Cardiopulmonary DiseasesShanghaiChina
- Jinshan Hospital Centre for Tumor Diagnosis and TherapyFudan University Shanghai Medical CollegeShanghaiChina
| | - Linlin Zhang
- Department of Pulmonary and Critical Care MedicineInstitute for Clinical ScienceShanghai Institute of Clinical BioinformaticsZhongshan Hospital of Fudan UniversityShanghai Engineering Research for AI Technology for Cardiopulmonary DiseasesShanghaiChina
| | - Guang Xu
- Institute of Computer ScienceFudan UniversityShanghaiChina
| | - Rui Hou
- Shanghai Biotechnology CorporationShanghaiChina
| | - Yong Zhang
- Department of Pulmonary and Critical Care MedicineInstitute for Clinical ScienceShanghai Institute of Clinical BioinformaticsZhongshan Hospital of Fudan UniversityShanghai Engineering Research for AI Technology for Cardiopulmonary DiseasesShanghaiChina
| | - Jian Chen
- Shanghai Lung Cancer CenterShanghai Chest HospitalShanghai Jiao Tong UniversityShanghaiChina
| | - Yunfeng Cheng
- Jinshan Hospital Centre for Tumor Diagnosis and TherapyFudan University Shanghai Medical CollegeShanghaiChina
| | | | | | - Gang Chen
- Department of PathologyZhongshan Hospital, Fudan UniversityShanghaiChina
| | - Duojiao Wu
- Department of Pulmonary and Critical Care MedicineInstitute for Clinical ScienceShanghai Institute of Clinical BioinformaticsZhongshan Hospital of Fudan UniversityShanghai Engineering Research for AI Technology for Cardiopulmonary DiseasesShanghaiChina
- Jinshan Hospital Centre for Tumor Diagnosis and TherapyFudan University Shanghai Medical CollegeShanghaiChina
| | - Tianxiang Chen
- Shanghai Lung Cancer CenterShanghai Chest HospitalShanghai Jiao Tong UniversityShanghaiChina
| | | | - Xiangdong Wang
- Department of Pulmonary and Critical Care MedicineInstitute for Clinical ScienceShanghai Institute of Clinical BioinformaticsZhongshan Hospital of Fudan UniversityShanghai Engineering Research for AI Technology for Cardiopulmonary DiseasesShanghaiChina
- Jinshan Hospital Centre for Tumor Diagnosis and TherapyFudan University Shanghai Medical CollegeShanghaiChina
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Valdebenito-Maturana B, Guatimosim C, Carrasco MA, Tapia JC. Spatially Resolved Expression of Transposable Elements in Disease and Somatic Tissue with SpatialTE. Int J Mol Sci 2021; 22:ijms222413623. [PMID: 34948421 PMCID: PMC8708317 DOI: 10.3390/ijms222413623] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 12/10/2021] [Accepted: 12/13/2021] [Indexed: 11/23/2022] Open
Abstract
Spatial transcriptomics (ST) is transforming the way we can study gene expression and its regulation through position-specific resolution within tissues. However, as in bulk RNA-Seq, transposable elements (TEs) are not being studied due to their highly repetitive nature. In recent years, TEs have been recognized as important regulators of gene expression, and thus, TE expression analysis in a spatially resolved manner could further help to understand their role in gene regulation within tissues. We present SpatialTE, a tool to analyze TE expression from ST datasets and show its application in somatic and diseased tissues. The results indicate that TEs have spatially regulated expression patterns and that their expression profiles are spatially altered in ALS disease, indicating that TEs might perform differential regulatory functions within tissue organs. We have made SpatialTE publicly available as open-source software under an MIT license.
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Affiliation(s)
- Braulio Valdebenito-Maturana
- Núcleo Científico Multidisciplinario, School of Medicine, Universidad de Talca, Campus Talca, Talca 3460000, Chile;
| | - Cristina Guatimosim
- Departamento de Morfologia, ICB, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil;
| | - Mónica Alejandra Carrasco
- School of Medicine, Universidad de Talca, Campus Talca, Talca 3460000, Chile
- Correspondence: (M.A.C.); (J.C.T.)
| | - Juan Carlos Tapia
- School of Medicine, Universidad de Talca, Campus Talca, Talca 3460000, Chile
- Correspondence: (M.A.C.); (J.C.T.)
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Basu A, Budhraja A, Juwayria, Abhilash D, Gupta I. Novel omics technology driving translational research in precision oncology. ADVANCES IN GENETICS 2021; 108:81-145. [PMID: 34844717 DOI: 10.1016/bs.adgen.2021.08.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
In this review, we summarize the current challenges faced by cancer researchers and motivate the use of novel genomics solutions. We follow this up with a comprehensive overview of three recent genomics technologies: liquid biopsy, single-cell RNA sequencing and spatial transcriptomics. We discuss a few representative protocols/assays for each technology along with their strengths, weaknesses, optimal use-cases, and their current stage of clinical deployment by summarizing trial data. We focus on how these technologies help us develop a better understanding of cancer as a rapidly evolving heterogeneous genetic disease that modulates its immediate microenvironment leading to systemic macro-level changes in the patient body. We summarize the review with a flowchart that integrates these three technologies in the existing workflows of clinicians and researchers toward robust detection, accurate diagnosis, and precision oncology.
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Affiliation(s)
- Anubhav Basu
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi, India
| | - Anshul Budhraja
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi, India
| | - Juwayria
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi, India
| | - Dasari Abhilash
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi, India
| | - Ishaan Gupta
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi, India.
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40
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Ahmed R, Augustine R, Valera E, Ganguli A, Mesaeli N, Ahmad IS, Bashir R, Hasan A. Spatial mapping of cancer tissues by OMICS technologies. Biochim Biophys Acta Rev Cancer 2021; 1877:188663. [PMID: 34861353 DOI: 10.1016/j.bbcan.2021.188663] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 11/15/2021] [Accepted: 11/26/2021] [Indexed: 12/14/2022]
Abstract
Spatial mapping of heterogeneity in gene expression in cancer tissues can improve our understanding of cancers and help in the rapid detection of cancers with high accuracy and reliability. Significant advancements have been made in recent years in OMICS technologies, which possess the strong potential to be applied in the spatial mapping of biopsy tissue samples and their molecular profiling to a single-cell level. The clinical application of OMICS technologies in spatial profiling of cancer tissues is also advancing. The current review presents recent advancements and prospects of applying OMICS technologies to the spatial mapping of various analytes in cancer tissues. We benchmark the current state of the art in the field to advance existing OMICS technologies for high throughput spatial profiling. The factors taken into consideration include spatial resolution, types of biomolecules, number of different biomolecules that can be detected from the same assay, labeled versus label-free approaches, and approximate time required for each assay. Further advancements are still needed for the widespread application of OMICs technologies in performing fast and high throughput spatial mapping of cancer tissues as well as their effective use in research and clinical applications.
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Affiliation(s)
- Rashid Ahmed
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha 2713, Qatar; Biomedical Research Center (BRC), Qatar University, Doha 2713, Qatar; Nick Holonyak Jr. Micro and Nanotechnology Laboratory, University of Illinois at Urbana Champaign, IL, USA
| | - Robin Augustine
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha 2713, Qatar; Biomedical Research Center (BRC), Qatar University, Doha 2713, Qatar
| | - Enrique Valera
- Nick Holonyak Jr. Micro and Nanotechnology Laboratory, University of Illinois at Urbana Champaign, IL, USA; Department of Bioengineering, University of Illinois at Urbana Champaign, IL, USA
| | - Anurup Ganguli
- Nick Holonyak Jr. Micro and Nanotechnology Laboratory, University of Illinois at Urbana Champaign, IL, USA; Department of Bioengineering, University of Illinois at Urbana Champaign, IL, USA
| | - Nasrin Mesaeli
- Department of Biochemistry, Weill Cornell Medicine in Qatar, Qatar Foundation, Doha, Qatar
| | - Irfan S Ahmad
- Nick Holonyak Jr. Micro and Nanotechnology Laboratory, University of Illinois at Urbana Champaign, IL, USA
| | - Rashid Bashir
- Nick Holonyak Jr. Micro and Nanotechnology Laboratory, University of Illinois at Urbana Champaign, IL, USA; Department of Bioengineering, University of Illinois at Urbana Champaign, IL, USA; Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
| | - Anwarul Hasan
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha 2713, Qatar; Biomedical Research Center (BRC), Qatar University, Doha 2713, Qatar.
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41
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Xiang Y, Sugimura R. Single-Cell Approaches to Deconvolute the Development of HSCs. Cells 2021; 10:2876. [PMID: 34831099 PMCID: PMC8616492 DOI: 10.3390/cells10112876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/19/2021] [Accepted: 10/23/2021] [Indexed: 12/19/2022] Open
Abstract
Hematopoietic stem cells (HSCs) play a core role in blood development. The ability to efficiently produce HSCs from various pluripotent stem cell sources is the Holy Grail in the hematology field. However, in vitro or in vivo HSC production remains low, which may be attributable to the lack of understanding of hematopoiesis. Here, we review the recent progress in this area and introduce advanced technologies, such as single-cell RNA-seq, spatial transcriptomics, and molecular barcoding, which may help to acquire missing information about HSC generation. We finally discuss unresolved questions, the answers to which may be conducive to HSC production, providing a promising path toward HSC-based immunotherapies.
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Affiliation(s)
| | - Ryohichi Sugimura
- Li Ka Shing Faculty of Medicine, School of Biomedical Sciences, University of Hong Kong, Hong Kong SAR, China;
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42
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Longo SK, Guo MG, Ji AL, Khavari PA. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat Rev Genet 2021; 22:627-644. [PMID: 34145435 PMCID: PMC9888017 DOI: 10.1038/s41576-021-00370-8] [Citation(s) in RCA: 385] [Impact Index Per Article: 128.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/29/2021] [Indexed: 02/07/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies.
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Affiliation(s)
- Sophia K. Longo
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA,Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Margaret G. Guo
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA,Stanford Cancer Institute, Stanford University, Stanford, CA, USA,Program in Biomedical Informatics, Stanford University, Stanford, CA, USA
| | - Andrew L. Ji
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA,Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Paul A. Khavari
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA,Stanford Cancer Institute, Stanford University, Stanford, CA, USA,Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
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43
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Ahn SW, Ferland B, Jonas OH. An Interactive Pipeline for Quantitative Histopathological Analysis of Spatially Defined Drug Effects in Tumors. J Pathol Inform 2021; 12:34. [PMID: 34760331 PMCID: PMC8529341 DOI: 10.4103/jpi.jpi_17_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 05/10/2021] [Accepted: 05/10/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Tumor heterogeneity is increasingly being recognized as a major source of variability in the histopathological assessment of drug responses. Quantitative analysis of immunohistochemistry (IHC) and immunofluorescence (IF) images using biomarkers that capture spatialpatterns of distinct tumor biology and drug concentration in tumors is of high interest to the field. METHODS We have developed an image analysis pipeline to measure drug response using IF and IHC images along spatial gradients of local drug release from a tumor-implantable drug delivery microdevice. The pipeline utilizes a series of user-interactive python scripts and CellProfiler pipelines with custom modules to perform image and spatial analysis of regions of interest within whole-slide images. RESULTS Worked examples demonstrate that intratumor measurements such as apoptosis, cell proliferation, and immune cell population density can be quantitated in a spatially and drug concentration-dependent manner, establishing in vivo profiles of pharmacodynamics and pharmacokinetics in tumors. CONCLUSIONS Spatial image analysis of tumor response along gradients of local drug release is achievable in high throughput. The major advantage of this approach is the use of spatially aware annotation tools to correlate drug gradients with drug effects in tumors in vivo.
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Affiliation(s)
- Sebastian W Ahn
- Department of Radiology, Laboratory for Bio-Micro Devices, Brigham and Women’s Hospital, Boston, MA, USA
| | - Benjamin Ferland
- Department of Radiology, Laboratory for Bio-Micro Devices, Brigham and Women’s Hospital, Boston, MA, USA
| | - Oliver H Jonas
- Department of Radiology, Laboratory for Bio-Micro Devices, Brigham and Women’s Hospital, Boston, MA, USA
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44
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Atta L, Fan J. Computational challenges and opportunities in spatially resolved transcriptomic data analysis. Nat Commun 2021; 12:5283. [PMID: 34489425 PMCID: PMC8421472 DOI: 10.1038/s41467-021-25557-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 08/18/2021] [Indexed: 12/19/2022] Open
Abstract
Spatially resolved transcriptomic data demand new computational analysis methods to derive biological insights. Here, we comment on these associated computational challenges as well as highlight the opportunities for standardized benchmarking metrics and data-sharing infrastructure in spurring innovation moving forward.
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Affiliation(s)
- Lyla Atta
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Medical Scientist Training Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jean Fan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
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45
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Niida A, Mimori K, Shibata T, Miyano S. Modeling colorectal cancer evolution. J Hum Genet 2021; 66:869-878. [PMID: 33986478 PMCID: PMC8384629 DOI: 10.1038/s10038-021-00930-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/09/2021] [Accepted: 04/13/2021] [Indexed: 11/27/2022]
Abstract
Understanding cancer evolution provides a clue to tackle therapeutic difficulties in colorectal cancer. In this review, together with related works, we will introduce a series of our studies, in which we constructed an evolutionary model of colorectal cancer by combining genomic analysis and mathematical modeling. In our model, multiple subclones were generated by driver mutation acquisition and subsequent clonal expansion in early-stage tumors. Among the subclones, the one obtaining driver copy number alterations is endowed with malignant potentials to constitute a late-stage tumor in which extensive intratumor heterogeneity is generated by the accumulation of neutral mutations. We will also discuss how to translate our understanding of cancer evolution to a solution to the problem related to therapeutic resistance: mathematical modeling suggests that relapse caused by acquired resistance could be suppressed by utilizing clonal competition between sensitive and resistant clones. Considering the current rate of technological development, modeling cancer evolution by combining genomic analysis and mathematical modeling will be an increasingly important approach for understanding and overcoming cancer.
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Affiliation(s)
- Atsushi Niida
- Laboratory of Molecular Medicine, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
| | - Koshi Mimori
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, Japan
| | - Tatsuhiro Shibata
- Laboratory of Molecular Medicine, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Division of Cancer Genomics, National Cancer Center Research Institute, Tokyo, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
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46
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Comba A, Faisal SM, Varela ML, Hollon T, Al-Holou WN, Umemura Y, Nunez FJ, Motsch S, Castro MG, Lowenstein PR. Uncovering Spatiotemporal Heterogeneity of High-Grade Gliomas: From Disease Biology to Therapeutic Implications. Front Oncol 2021; 11:703764. [PMID: 34422657 PMCID: PMC8377724 DOI: 10.3389/fonc.2021.703764] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/19/2021] [Indexed: 12/13/2022] Open
Abstract
Glioblastomas (GBM) are the most common and aggressive tumors of the central nervous system. Rapid tumor growth and diffuse infiltration into healthy brain tissue, along with high intratumoral heterogeneity, challenge therapeutic efficacy and prognosis. A better understanding of spatiotemporal tumor heterogeneity at the histological, cellular, molecular, and dynamic levels would accelerate the development of novel treatments for this devastating brain cancer. Histologically, GBM is characterized by nuclear atypia, cellular pleomorphism, necrosis, microvascular proliferation, and pseudopalisades. At the cellular level, the glioma microenvironment comprises a heterogeneous landscape of cell populations, including tumor cells, non-transformed/reactive glial and neural cells, immune cells, mesenchymal cells, and stem cells, which support tumor growth and invasion through complex network crosstalk. Genomic and transcriptomic analyses of gliomas have revealed significant inter and intratumoral heterogeneity and insights into their molecular pathogenesis. Moreover, recent evidence suggests that diverse dynamics of collective motion patterns exist in glioma tumors, which correlate with histological features. We hypothesize that glioma heterogeneity is not stochastic, but rather arises from organized and dynamic attributes, which favor glioma malignancy and influences treatment regimens. This review highlights the importance of an integrative approach of glioma histopathological features, single-cell and spatially resolved transcriptomic and cellular dynamics to understand tumor heterogeneity and maximize therapeutic effects.
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Affiliation(s)
- Andrea Comba
- Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, United States.,Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, United States.,Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Syed M Faisal
- Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, United States.,Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, United States.,Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Maria Luisa Varela
- Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, United States.,Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, United States.,Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Todd Hollon
- Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Wajd N Al-Holou
- Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Yoshie Umemura
- Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Felipe J Nunez
- Laboratory of Molecular and Cellular Therapy, Fundación Instituto Leloir, Buenos Aires, Argentina
| | - Sebastien Motsch
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States
| | - Maria G Castro
- Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, United States.,Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, United States.,Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Pedro R Lowenstein
- Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, United States.,Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, United States.,Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, United States
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47
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Jeong HY, Ham IH, Lee SH, Ryu D, Son SY, Han SU, Kim TM, Hur H. Spatially distinct reprogramming of the tumor microenvironment based on tumor invasion in diffuse-type gastric cancers. Clin Cancer Res 2021; 27:6529-6542. [PMID: 34385296 DOI: 10.1158/1078-0432.ccr-21-0792] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 07/14/2021] [Accepted: 08/10/2021] [Indexed: 12/09/2022]
Abstract
PURPOSE Histological features of diffuse-type gastric cancer (GC) indicate that the tumor microenvironment (TME) may substantially impact tumor invasiveness. However, cellular components and molecular features associated with cancer invasiveness in the TME of diffuse-type GCs are poorly understood. EXPERIMENTAL DESIGN We performed single-cell RNA-sequencing (scRNA-seq) using tissue samples from superficial and deep invasive layers of cancerous and paired normal tissues freshly harvested from five patients with diffuse-type GC. The scRNA-seq results were validated by immunohistochemistry and duplex in situ hybridization (ISH) in formalin-fixed paraffin-embedded tissues. RESULTS Seven major cell types were identified. Fibroblasts, endothelial cells, and myeloid cells were categorised as being enriched in the deep layers. Cell type-specific clustering further revealed that the superficial-to-deep layer transition is associated with enrichment in inflammatory endothelial cells and fibroblasts with upregulated CCL2 transcripts. Immunohistochemistry and duplex ISH revealed the distribution of the major cell types and CCL2-expressing endothelial cells and fibroblasts, indicating tumor invasion. Elevation of CCL2 levels along the superficial-to-deep layer axis revealed the immunosuppressive immune cell sub-types that may contribute to tumor cell aggressiveness in the deep invasive layers of diffuse-type GC. The analyses of public datasets revealed the high-level co-expression of stromal cell-specific genes and that CCL2 correlated with poor survival outcomes in GC patients. CONCLUSIONS This study reveals the spatial reprogramming of the TME that may underlie invasive tumor potential in diffuse-type GC. This TME profiling across tumor layers suggests new targets, such as CCL2, that can modify the TME to inhibit tumor progression in diffuse-type GC.
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Affiliation(s)
- Hye Young Jeong
- Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea
| | - In-Hye Ham
- Department of Surgery, Ajou University School of Medicine
| | - Sung Hak Lee
- Department of Hospital Pathology, Catholic University of Korea
| | - Daeun Ryu
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea
| | - Sang-Yong Son
- Department of Surgery, Ajou University School of Medicine
| | - Sang-Uk Han
- Department of Surgery, Ajou University School of Medicine
| | - Tae-Min Kim
- Department of Medical Informatics, Catholic University of Korea
| | - Hoon Hur
- Department of Surgery, Ajou University School of Medicine
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48
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Davis-Marcisak EF, Deshpande A, Stein-O'Brien GL, Ho WJ, Laheru D, Jaffee EM, Fertig EJ, Kagohara LT. From bench to bedside: Single-cell analysis for cancer immunotherapy. Cancer Cell 2021; 39:1062-1080. [PMID: 34329587 PMCID: PMC8406623 DOI: 10.1016/j.ccell.2021.07.004] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/16/2021] [Accepted: 07/02/2021] [Indexed: 01/04/2023]
Abstract
Single-cell technologies are emerging as powerful tools for cancer research. These technologies characterize the molecular state of each cell within a tumor, enabling new exploration of tumor heterogeneity, microenvironment cell-type composition, and cell state transitions that affect therapeutic response, particularly in the context of immunotherapy. Analyzing clinical samples has great promise for precision medicine but is technically challenging. Successfully identifying predictors of response requires well-coordinated, multi-disciplinary teams to ensure adequate sample processing for high-quality data generation and computational analysis for data interpretation. Here, we review current approaches to sample processing and computational analysis regarding their application to translational cancer immunotherapy research.
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Affiliation(s)
- Emily F Davis-Marcisak
- McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins School of Medicine, 550 N Broadway, Suite 1101E, Baltimore, MD 21205, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 1650 Orleans Street, Room 485, Baltimore, MD 21287, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Immunotherapy Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Atul Deshpande
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 1650 Orleans Street, Room 485, Baltimore, MD 21287, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Immunotherapy Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Genevieve L Stein-O'Brien
- McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins School of Medicine, 550 N Broadway, Suite 1101E, Baltimore, MD 21205, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 1650 Orleans Street, Room 485, Baltimore, MD 21287, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Immunotherapy Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Won J Ho
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 1650 Orleans Street, Room 485, Baltimore, MD 21287, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Immunotherapy Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel Laheru
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 1650 Orleans Street, Room 485, Baltimore, MD 21287, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Immunotherapy Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elizabeth M Jaffee
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 1650 Orleans Street, Room 485, Baltimore, MD 21287, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Immunotherapy Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elana J Fertig
- McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins School of Medicine, 550 N Broadway, Suite 1101E, Baltimore, MD 21205, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 1650 Orleans Street, Room 485, Baltimore, MD 21287, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Immunotherapy Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Luciane T Kagohara
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 1650 Orleans Street, Room 485, Baltimore, MD 21287, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Immunotherapy Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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49
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Rad HS, Rad HS, Shiravand Y, Radfar P, Arpon D, Warkiani ME, O'Byrne K, Kulasinghe A. The Pandora's box of novel technologies that may revolutionize lung cancer. Lung Cancer 2021; 159:34-41. [PMID: 34304051 DOI: 10.1016/j.lungcan.2021.06.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/19/2021] [Accepted: 06/27/2021] [Indexed: 01/10/2023]
Abstract
Non-small cell lung cancer (NSCLC) is one of the most common cancers globally and has a 5-year survival rate ~20%. Immunotherapies have demonstrated long-term and durable responses in NSCLC patients, although they appear to be effective in only a subset of patients. A more comprehensive understanding of the underlying tumour biology may contribute to identifying those patients likely to achieve optimal outcomes. Profiling the tumour microenvironment (TME) has shown to be beneficial in addressing fundamental tumour-immune cell interactions. Advances in multiplexing immunohistochemistry and molecular barcoding has led to recent advances in profiling genes and proteins in NSCLC. Here, we review the recent advancements in spatial profiling technologies for the analysis of NSCLC tissue samples to gain new insights and therapeutic options for NSCLC. The combination of spatial transcriptomics combined with advanced imaging is likely to lead to deep insights into NSCLC tissue biology, which can be a powerful tool to predict likelihood of response to therapy.
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Affiliation(s)
- Habib Sadeghi Rad
- Queensland University of Technology, Centre for Genomics and Personalised Health, Cancer and Ageing Research Program, School of Biomedical Sciences, Faculty of Health, Woolloongabba, QLD, Australia; Translational Research Institute, Woolloongabba, QLD, Australia
| | - Hamid Sadeghi Rad
- School of Medicine, Golestan University of Medical Sciences, Golestan, Iran
| | - Yavar Shiravand
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Naples, Italy
| | - Payar Radfar
- University of Technology Sydney, Sydney, NSW, Australia
| | - David Arpon
- Translational Research Institute, Woolloongabba, QLD, Australia; Princess Alexandra Hospital, Woolloongabba, QLD, Australia
| | | | - Ken O'Byrne
- Queensland University of Technology, Centre for Genomics and Personalised Health, Cancer and Ageing Research Program, School of Biomedical Sciences, Faculty of Health, Woolloongabba, QLD, Australia; Translational Research Institute, Woolloongabba, QLD, Australia; Princess Alexandra Hospital, Woolloongabba, QLD, Australia
| | - Arutha Kulasinghe
- Queensland University of Technology, Centre for Genomics and Personalised Health, Cancer and Ageing Research Program, School of Biomedical Sciences, Faculty of Health, Woolloongabba, QLD, Australia; Translational Research Institute, Woolloongabba, QLD, Australia; Princess Alexandra Hospital, Woolloongabba, QLD, Australia.
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50
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Scatena C, Murtas D, Tomei S. Cutaneous Melanoma Classification: The Importance of High-Throughput Genomic Technologies. Front Oncol 2021; 11:635488. [PMID: 34123788 PMCID: PMC8193952 DOI: 10.3389/fonc.2021.635488] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 03/30/2021] [Indexed: 02/06/2023] Open
Abstract
Cutaneous melanoma is an aggressive tumor responsible for 90% of mortality related to skin cancer. In the recent years, the discovery of driving mutations in melanoma has led to better treatment approaches. The last decade has seen a genomic revolution in the field of cancer. Such genomic revolution has led to the production of an unprecedented mole of data. High-throughput genomic technologies have facilitated the genomic, transcriptomic and epigenomic profiling of several cancers, including melanoma. Nevertheless, there are a number of newer genomic technologies that have not yet been employed in large studies. In this article we describe the current classification of cutaneous melanoma, we review the current knowledge of the main genetic alterations of cutaneous melanoma and their related impact on targeted therapies, and we describe the most recent high-throughput genomic technologies, highlighting their advantages and disadvantages. We hope that the current review will also help scientists to identify the most suitable technology to address melanoma-related relevant questions. The translation of this knowledge and all actual advancements into the clinical practice will be helpful in better defining the different molecular subsets of melanoma patients and provide new tools to address relevant questions on disease management. Genomic technologies might indeed allow to better predict the biological - and, subsequently, clinical - behavior for each subset of melanoma patients as well as to even identify all molecular changes in tumor cell populations during disease evolution toward a real achievement of a personalized medicine.
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Affiliation(s)
- Cristian Scatena
- Division of Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Daniela Murtas
- Department of Biomedical Sciences, Section of Cytomorphology, University of Cagliari, Cagliari, Italy
| | - Sara Tomei
- Omics Core, Integrated Genomics Services, Research Department, Sidra Medicine, Doha, Qatar
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