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Sudhakar M, Vignesh H, Natarajan KN. Crosstalk between tumor and microenvironment: Insights from spatial transcriptomics. Adv Cancer Res 2024; 163:187-222. [PMID: 39271263 DOI: 10.1016/bs.acr.2024.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
Cancer is a dynamic disease, and clonal heterogeneity plays a fundamental role in tumor development, progression, and resistance to therapies. Single-cell and spatial multimodal technologies can provide a high-resolution molecular map of underlying genomic, epigenomic, and transcriptomic alterations involved in inter- and intra-tumor heterogeneity and interactions with the microenvironment. In this review, we provide a perspective on factors driving cancer heterogeneity, tumor evolution, and clonal states. We briefly describe spatial transcriptomic technologies and summarize recent literature that sheds light on the dynamical interactions between tumor states, cell-to-cell communication, and remodeling local microenvironment.
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Affiliation(s)
- Malvika Sudhakar
- DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Harie Vignesh
- DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
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2
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Andersson A, Behanova A, Avenel C, Windhager J, Malmberg F, Wählby C. Points2Regions: Fast, interactive clustering of imaging-based spatial transcriptomics data. Cytometry A 2024. [PMID: 38958502 DOI: 10.1002/cyto.a.24884] [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: 02/19/2024] [Revised: 05/30/2024] [Accepted: 06/13/2024] [Indexed: 07/04/2024]
Abstract
Imaging-based spatial transcriptomics techniques generate data in the form of spatial points belonging to different mRNA classes. A crucial part of analyzing the data involves the identification of regions with similar composition of mRNA classes. These biologically interesting regions can manifest at different spatial scales. For example, the composition of mRNA classes on a cellular scale corresponds to cell types, whereas compositions on a millimeter scale correspond to tissue-level structures. Traditional techniques for identifying such regions often rely on complementary data, such as pre-segmented cells, or lengthy optimization. This limits their applicability to tasks on a particular scale, restricting their capabilities in exploratory analysis. This article introduces "Points2Regions," a computational tool for identifying regions with similar mRNA compositions. The tool's novelty lies in its rapid feature extraction by rasterizing points (representing mRNAs) onto a pyramidal grid and its efficient clustering using a combination of hierarchical andk $$ k $$ -means clustering. This enables fast and efficient region discovery across multiple scales without relying on additional data, making it a valuable resource for exploratory analysis. Points2Regions has demonstrated performance similar to state-of-the-art methods on two simulated datasets, without relying on segmented cells, while being several times faster. Experiments on real-world datasets show that regions identified by Points2Regions are similar to those identified in other studies, confirming that Points2Regions can be used to extract biologically relevant regions. The tool is shared as a Python package integrated into TissUUmaps and a Napari plugin, offering interactive clustering and visualization, significantly enhancing user experience in data exploration.
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Affiliation(s)
- Axel Andersson
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Andrea Behanova
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Christophe Avenel
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Jonas Windhager
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Filip Malmberg
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Carolina Wählby
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
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Lin S, Cui Y, Zhao F, Yang Z, Song J, Yao J, Zhao Y, Qian BZ, Zhao Y, Yuan Z. Complete spatially resolved gene expression is not necessary for identifying spatial domains. CELL GENOMICS 2024; 4:100565. [PMID: 38781966 PMCID: PMC11228956 DOI: 10.1016/j.xgen.2024.100565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/29/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024]
Abstract
Spatially resolved transcriptomics (SRT) technologies have revolutionized the study of tissue organization. We introduce a graph convolutional network with an attention and positive emphasis mechanism, termed BINARY, relying exclusively on binarized SRT data to accurately delineate spatial domains. BINARY outperforms existing methods across various SRT data types while using significantly less input information. Our study suggests that precise gene expression quantification may not always be essential, inspiring further exploration of the broader applications of spatially resolved binarized gene expression data.
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Affiliation(s)
- Senlin Lin
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Yan Cui
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China
| | - Fangyuan Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Zhidong Yang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800, Australia
| | | | - Yu Zhao
- AI Lab, Tencent, Shenzhen, China
| | - Bin-Zhi Qian
- Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, The Human Phenome Institute, Zhangjiang-Fudan International Innovation Center, Fudan University, Shanghai, China
| | - Yi Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
| | - Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China.
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Yang M, Ji B, Luo Q, Jiang T, Yang X. Laser axial scanning microdissection for high-efficiency dissection from uneven biological samples. BIOMEDICAL OPTICS EXPRESS 2024; 15:3795-3806. [PMID: 38867797 PMCID: PMC11166427 DOI: 10.1364/boe.523954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/04/2024] [Accepted: 05/04/2024] [Indexed: 06/14/2024]
Abstract
Fast and efficient separation of target samples is crucial for the application of laser-assisted microdissection in the molecular biology research field. Herein, we developed a laser axial scanning microdissection (LASM) system with an 8.6 times extended depth of focus by using an electrically tunable lens. We showed that the ablation quality of silicon wafers at different depths became homogenous after using our system. More importantly, for those uneven biological tissue sections within a height difference of no more than 19.2 µm, we have demonstrated that the targets with a size of microns at arbitrary positions can be dissected efficiently without additional focusing and dissection operations. Besides, dissection experiments on various biological samples with different embedding methods, which were widely adopted in biological experiments, also have shown the feasibility of our system.
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Affiliation(s)
- Minjun Yang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
| | - BingQing Ji
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Qingming Luo
- School of Biomedical Engineering, Hainan University, Haikou 570228, China
| | - Tao Jiang
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou 215123, China
| | - Xiaoquan Yang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou 215123, China
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Magoulopoulou A, Salas SM, Tiklová K, Samuelsson ER, Hilscher MM, Nilsson M. Padlock Probe-Based Targeted In Situ Sequencing: Overview of Methods and Applications. Annu Rev Genomics Hum Genet 2023; 24:133-150. [PMID: 37018847 DOI: 10.1146/annurev-genom-102722-092013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Elucidating spatiotemporal changes in gene expression has been an essential goal in studies of health, development, and disease. In the emerging field of spatially resolved transcriptomics, gene expression profiles are acquired with the tissue architecture maintained, sometimes at cellular resolution. This has allowed for the development of spatial cell atlases, studies of cell-cell interactions, and in situ cell typing. In this review, we focus on padlock probe-based in situ sequencing, which is a targeted spatially resolved transcriptomic method. We summarize recent methodological and computational tool developments and discuss key applications. We also discuss compatibility with other methods and integration with multiomic platforms for future applications.
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Affiliation(s)
- Anastasia Magoulopoulou
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Sergio Marco Salas
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Katarína Tiklová
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Erik Reinhold Samuelsson
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Markus M Hilscher
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Mats Nilsson
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
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Sallinger K, Gruber M, Müller CT, Bonstingl L, Pritz E, Pankratz K, Gerger A, Smolle MA, Aigelsreiter A, Surova O, Svedlund J, Nilsson M, Kroneis T, El-Heliebi A. Spatial tumour gene signature discriminates neoplastic from non-neoplastic compartments in colon cancer: unravelling predictive biomarkers for relapse. J Transl Med 2023; 21:528. [PMID: 37543577 PMCID: PMC10403907 DOI: 10.1186/s12967-023-04384-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 07/22/2023] [Indexed: 08/07/2023] Open
Abstract
BACKGROUND Opting for or against the administration of adjuvant chemotherapy in therapeutic management of stage II colon cancer remains challenging. Several studies report few survival benefits for patients treated with adjuvant therapy and additionally revealing potential side effects of overtreatment, including unnecessary exposure to chemotherapy-induced toxicities and reduced quality of life. Predictive biomarkers are urgently needed. We, therefore, hypothesise that the spatial tissue composition of relapsed and non-relapsed colon cancer stage II patients reveals relevant biomarkers. METHODS The spatial tissue composition of stage II colon cancer patients was examined by a novel spatial transcriptomics technology with sub-cellular resolution, namely in situ sequencing. A panel of 176 genes investigating specific cancer-associated processes such as apoptosis, proliferation, angiogenesis, stemness, oxidative stress, hypoxia, invasion and components of the tumour microenvironment was designed to examine differentially expressed genes in tissue of relapsed versus non-relapsed patients. Therefore, FFPE slides of 10 colon cancer stage II patients either classified as relapsed (5 patients) or non-relapsed (5 patients) were in situ sequenced and computationally analysed. RESULTS We identified a tumour gene signature that enables the subclassification of tissue into neoplastic and non-neoplastic compartments based on spatial expression patterns obtained through in situ sequencing. We developed a computational tool called Genes-To-Count (GTC), which automates the quantification of in situ signals, accurately mapping their position onto the spatial tissue map and automatically identifies neoplastic and non-neoplastic tissue compartments. The GTC tool was used to quantify gene expression of biological processes upregulated within the neoplastic tissue in comparison to non-neoplastic tissue and within relapsed versus non-relapsed stage II colon patients. Three differentially expressed genes (FGFR2, MMP11 and OTOP2) in the neoplastic tissue compartments of relapsed patients in comparison to non-relapsed patients were identified predicting recurrence in stage II colon cancer. CONCLUSIONS In depth spatial in situ sequencing showed potential to provide a deeper understanding of the underlying mechanisms involved in the recurrence of disease and revealed novel potential predictive biomarkers for disease relapse in colon cancer stage II patients. Our open-access GTC-tool allowed us to accurately capture the tumour compartment and quantify spatial gene expression in colon cancer tissue.
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Affiliation(s)
- Katja Sallinger
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Centre, Medical University of Graz, Graz, Austria
- Center for Biomarker Research in Medicine (CBmed), Graz, Austria
| | - Michael Gruber
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Centre, Medical University of Graz, Graz, Austria
| | - Christin-Therese Müller
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Centre, Medical University of Graz, Graz, Austria
| | - Lilli Bonstingl
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Centre, Medical University of Graz, Graz, Austria
- Center for Biomarker Research in Medicine (CBmed), Graz, Austria
| | - Elisabeth Pritz
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Centre, Medical University of Graz, Graz, Austria
| | - Karin Pankratz
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Centre, Medical University of Graz, Graz, Austria
| | - Armin Gerger
- Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Maria Anna Smolle
- Department of Orthopaedics and Trauma, Medical University of Graz, Graz, Austria
| | - Ariane Aigelsreiter
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Olga Surova
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 17165, Solna, Sweden
| | - Jessica Svedlund
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 17165, Solna, Sweden
- 10x Genomics, Life City, Solnavägen 3H, 113 63, Stockholm, Sweden
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 17165, Solna, Sweden
| | - Thomas Kroneis
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Centre, Medical University of Graz, Graz, Austria
- Center for Biomarker Research in Medicine (CBmed), Graz, Austria
| | - Amin El-Heliebi
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Centre, Medical University of Graz, Graz, Austria.
- Center for Biomarker Research in Medicine (CBmed), Graz, Austria.
- Biotechmed, Graz, Austria.
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Li J, Zhou J, Xia Y, Rui Y, Yang X, Xie G, Jiang G, Wang H. Rolling circle extension-assisted loop-mediated isothermal amplification (Rol-LAMP) method for locus-specific and visible detection of RNA N6-methyladenosine. Nucleic Acids Res 2023; 51:e51. [PMID: 36971119 PMCID: PMC10201442 DOI: 10.1093/nar/gkad200] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 02/17/2023] [Accepted: 03/09/2023] [Indexed: 08/26/2023] Open
Abstract
N6-methyladenosine (m6A) is the most prevalent RNA modification in eukaryotic mRNAs. Currently available detection methods for locus-specific m6A marks rely on RT-qPCR, radioactive methods, or high-throughput sequencing. Here, we develop a non-qPCR, ultrasensitive, isothermal, and naked-eye visible method for m6A detection based on rolling circle amplification (RCA) and loop-mediated isothermal amplification (LAMP), named m6A-Rol-LAMP, to verify putative m6A sites in transcripts obtained from the high-throughput data. When padlock probes hybridize to the potential m6A sites on targets, they are converted to circular form by DNA ligase in the absence of m6A modification, while m6A modification hinders the sealing of padlock probes. Subsequently, Bst DNA polymerase-mediated RCA and LAMP allow the amplification of the circular padlock probe to achieve the locus-specific detection of m6A. Following optimization and validation, m6A-Rol-LAMP can ultra-sensitively and quantitatively determine the existence of m6A modification on a specific target site as low as 100 amol under isothermal conditions. Detections of m6A can be performed on rRNA, mRNA, lincRNA, lncRNA and pre-miRNA from biological samples with naked-eye observations after dye incubation. Together, we provide a powerful tool for locus-specific detection of m6A, which can simply, quickly, sensitively, specifically, and visually determine putative m6A modification on RNA.
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Affiliation(s)
- Jiexin Li
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, Guangdong510006, China
| | - Jiawang Zhou
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, Guangdong510006, China
| | - Yan Xia
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, Guangdong510006, China
| | - Yalan Rui
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, Guangdong510006, China
| | - Xianyuan Yang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, Guangdong510006, China
| | - Guoyou Xie
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, Guangdong510006, China
| | - Guanmin Jiang
- Department of Clinical Laboratory, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai 2528000, Guangdong, China
| | - Hongsheng Wang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, Guangdong510006, China
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Pielawski N, Andersson A, Avenel C, Behanova A, Chelebian E, Klemm A, Nysjö F, Solorzano L, Wählby C. TissUUmaps 3: Improvements in interactive visualization, exploration, and quality assessment of large-scale spatial omics data. Heliyon 2023; 9:e15306. [PMID: 37131430 PMCID: PMC10149187 DOI: 10.1016/j.heliyon.2023.e15306] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/30/2023] [Accepted: 04/03/2023] [Indexed: 05/04/2023] Open
Abstract
Background and objectives Spatially resolved techniques for exploring the molecular landscape of tissue samples, such as spatial transcriptomics, often result in millions of data points and images too large to view on a regular desktop computer, limiting the possibilities in visual interactive data exploration. TissUUmaps is a free, open-source browser-based tool for GPU-accelerated visualization and interactive exploration of 107+ data points overlaying tissue samples. Methods Herein we describe how TissUUmaps 3 provides instant multiresolution image viewing and can be customized, shared, and also integrated into Jupyter Notebooks. We introduce new modules where users can visualize markers and regions, explore spatial statistics, perform quantitative analyses of tissue morphology, and assess the quality of decoding in situ transcriptomics data. Results We show that thanks to targeted optimizations the time and cost associated with interactive data exploration were reduced, enabling TissUUmaps 3 to handle the scale of today's spatial transcriptomics methods. Conclusion TissUUmaps 3 provides significantly improved performance for large multiplex datasets as compared to previous versions. We envision TissUUmaps to contribute to broader dissemination and flexible sharing of largescale spatial omics data.
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Affiliation(s)
- Nicolas Pielawski
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Axel Andersson
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Christophe Avenel
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Andrea Behanova
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Eduard Chelebian
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Anna Klemm
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Fredrik Nysjö
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Leslie Solorzano
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Carolina Wählby
- Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
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Heydari AA, Sindi SS. Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing. BIOPHYSICS REVIEWS 2023; 4:011306. [PMID: 38505815 PMCID: PMC10903438 DOI: 10.1063/5.0091135] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 12/19/2022] [Indexed: 03/21/2024]
Abstract
Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. Data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially resolved data (such as spatial resolution, sensitivity, and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering, among others. However, DL models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially resolved transcriptomics while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight domains in which we anticipate transformational DL applications.
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10
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Scalable in situ single-cell profiling by electrophoretic capture of mRNA using EEL FISH. Nat Biotechnol 2023; 41:222-231. [PMID: 36138169 PMCID: PMC9931581 DOI: 10.1038/s41587-022-01455-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 08/01/2022] [Indexed: 12/17/2022]
Abstract
Methods to spatially profile the transcriptome are dominated by a trade-off between resolution and throughput. Here we develop a method named Enhanced ELectric Fluorescence in situ Hybridization (EEL FISH) that can rapidly process large tissue samples without compromising spatial resolution. By electrophoretically transferring RNA from a tissue section onto a capture surface, EEL speeds up data acquisition by reducing the amount of imaging needed, while ensuring that RNA molecules move straight down toward the surface, preserving single-cell resolution. We apply EEL on eight entire sagittal sections of the mouse brain and measure the expression patterns of up to 440 genes to reveal complex tissue organization. Moreover, EEL can be used to study challenging human samples by removing autofluorescent lipofuscin, enabling the spatial transcriptome of the human visual cortex to be visualized. We provide full hardware specifications, all protocols and complete software for instrument control, image processing, data analysis and visualization.
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11
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Yue L, Liu F, Hu J, Yang P, Wang Y, Dong J, Shu W, Huang X, Wang S. A guidebook of spatial transcriptomic technologies, data resources and analysis approaches. Comput Struct Biotechnol J 2023; 21:940-955. [PMID: 38213887 PMCID: PMC10781722 DOI: 10.1016/j.csbj.2023.01.016] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 01/13/2023] [Accepted: 01/14/2023] [Indexed: 01/18/2023] Open
Abstract
Advances in transcriptomic technologies have deepened our understanding of the cellular gene expression programs of multicellular organisms and provided a theoretical basis for disease diagnosis and therapy. However, both bulk and single-cell RNA sequencing approaches lose the spatial context of cells within the tissue microenvironment, and the development of spatial transcriptomics has made overall bias-free access to both transcriptional information and spatial information possible. Here, we elaborate development of spatial transcriptomic technologies to help researchers select the best-suited technology for their goals and integrate the vast amounts of data to facilitate data accessibility and availability. Then, we marshal various computational approaches to analyze spatial transcriptomic data for various purposes and describe the spatial multimodal omics and its potential for application in tumor tissue. Finally, we provide a detailed discussion and outlook of the spatial transcriptomic technologies, data resources and analysis approaches to guide current and future research on spatial transcriptomics.
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Affiliation(s)
- Liangchen Yue
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Feng Liu
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
| | - Jiongsong Hu
- University of South China, Hengyang, Hunan 421001, China
| | - Pin Yang
- Anhui Medical University, Hefei 230022, Anhui, China
| | - Yuxiang Wang
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Junguo Dong
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Wenjie Shu
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Xingxu Huang
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310029, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Shengqi Wang
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
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12
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Moffitt JR, Lundberg E, Heyn H. The emerging landscape of spatial profiling technologies. Nat Rev Genet 2022; 23:741-759. [PMID: 35859028 DOI: 10.1038/s41576-022-00515-3] [Citation(s) in RCA: 143] [Impact Index Per Article: 71.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/16/2022] [Indexed: 01/04/2023]
Abstract
Improved scale, multiplexing and resolution are establishing spatial nucleic acid and protein profiling methods as a major pillar for cellular atlas building of complex samples, from tissues to full organisms. Emerging methods yield omics measurements at resolutions covering the nano- to microscale, enabling the charting of cellular heterogeneity, complex tissue architectures and dynamic changes during development and disease. We present an overview of the developing landscape of in situ spatial genome, transcriptome and proteome technologies, exemplify their impact on cell biology and translational research, and discuss current challenges for their community-wide adoption. Among many transformative applications, we envision that spatial methods will map entire organs and enable next-generation pathology.
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Affiliation(s)
- Jeffrey R Moffitt
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, USA.,Department of Microbiology, Harvard Medical School, Boston, MA, USA
| | - Emma Lundberg
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden.,Department of Bioengineering, Stanford University, Stanford, CA, USA.,Department of Pathology, Stanford University, Stanford, CA, USA.,Chan Zuckerberg Biohub, San Francisco, San Francisco, CA, USA
| | - Holger Heyn
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain. .,Universitat Pompeu Fabra (UPF), Barcelona, Spain.
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Hilscher MM, Langseth CM, Kukanja P, Yokota C, Nilsson M, Castelo-Branco G. Spatial and temporal heterogeneity in the lineage progression of fine oligodendrocyte subtypes. BMC Biol 2022; 20:122. [PMID: 35610641 PMCID: PMC9131697 DOI: 10.1186/s12915-022-01325-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 05/09/2022] [Indexed: 11/11/2022] Open
Abstract
Background Oligodendrocytes are glial cells that support and insulate axons in the central nervous system through the production of myelin. Oligodendrocytes arise throughout embryonic and early postnatal development from oligodendrocyte precursor cells (OPCs), and recent work demonstrated that they are a transcriptional heterogeneous cell population, but the regional and functional implications of this heterogeneity are less clear. Here, we apply in situ sequencing (ISS) to simultaneously probe the expression of 124 marker genes of distinct oligodendrocyte populations, providing comprehensive maps of the corpus callosum, cingulate, motor, and somatosensory cortex in the brain, as well as gray matter (GM) and white matter (WM) regions in the spinal cord, at postnatal (P10), juvenile (P20), and young adult (P60) stages. We systematically compare the abundances of these populations and investigate the neighboring preference of distinct oligodendrocyte populations. Results We observed that oligodendrocyte lineage progression is more advanced in the juvenile spinal cord compared to the brain, corroborating with previous studies. We found myelination still ongoing in the adult corpus callosum while it was more advanced in the cortex. Interestingly, we also observed a lateral-to-medial gradient of oligodendrocyte lineage progression in the juvenile cortex, which could be linked to arealization, as well as a deep-to-superficial gradient with mature oligodendrocytes preferentially accumulating in the deeper layers of the cortex. The ISS experiments also exposed differences in abundances and population dynamics over time between GM and WM regions in the brain and spinal cord, indicating regional differences within GM and WM, and we found that neighboring preferences of some oligodendroglia populations are altered from the juvenile to the adult CNS. Conclusions Overall, our ISS experiments reveal spatial heterogeneity of oligodendrocyte lineage progression in the brain and spinal cord and uncover differences in the timing of oligodendrocyte differentiation and myelination, which could be relevant to further investigate functional heterogeneity of oligodendroglia, especially in the context of injury or disease. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-022-01325-z.
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Affiliation(s)
- Markus M Hilscher
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 65, Solna, Sweden.
| | | | - Petra Kukanja
- Laboratory of Molecular Neurobiology, Department Medical Biochemistry and Biophysics, Karolinska Institutet, Biomedicum, 17177, Stockholm, Sweden
| | - Chika Yokota
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 65, Solna, Sweden
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 65, Solna, Sweden
| | - Gonçalo Castelo-Branco
- Laboratory of Molecular Neurobiology, Department Medical Biochemistry and Biophysics, Karolinska Institutet, Biomedicum, 17177, Stockholm, Sweden.
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Langseth CM, Gyllborg D, Miller JA, Close JL, Long B, Lein ES, Hilscher MM, Nilsson M. Comprehensive in situ mapping of human cortical transcriptomic cell types. Commun Biol 2021; 4:998. [PMID: 34429496 PMCID: PMC8384853 DOI: 10.1038/s42003-021-02517-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 07/22/2021] [Indexed: 12/18/2022] Open
Abstract
The ability to spatially resolve the cellular architecture of human cortical cell types over informative areas is essential to understanding brain function. We combined in situ sequencing gene expression data and single-nucleus RNA-sequencing cell type definitions to spatially map cells in sections of the human cortex via probabilistic cell typing. We mapped and classified a total of 59,816 cells into all 75 previously defined subtypes to create a first spatial atlas of human cortical cells in their native position, their abundances and genetic signatures. We also examined the precise within- and across-layer distributions of all the cell types and provide a resource for the cell atlas community. The abundances and locations presented here could serve as a reference for further studies, that include human brain tissues and disease applications at the cell type level.
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Affiliation(s)
| | - Daniel Gyllborg
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | | | | | - Brian Long
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Markus M Hilscher
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.
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Matisse: a MATLAB-based analysis toolbox for in situ sequencing expression maps. BMC Bioinformatics 2021; 22:391. [PMID: 34332548 PMCID: PMC8325818 DOI: 10.1186/s12859-021-04302-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 07/19/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A range of spatially resolved transcriptomic methods has recently emerged as a way to spatially characterize the molecular and cellular diversity of a tissue. As a consequence, an increasing number of computational techniques are developed to facilitate data analysis. There is also a need for versatile user friendly tools that can be used for a de novo exploration of datasets. RESULTS Here we present MATLAB-based Analysis toolbox for in situ sequencing (ISS) expression maps (Matisse). We demonstrate Matisse by characterizing the 2-dimensional spatial expression of 119 genes profiled in a mouse coronal section, exploring different levels of complexity. Additionally, in a comprehensive analysis, we further analyzed expression maps from a second technology, osmFISH, targeting a similar mouse brain region. CONCLUSION Matisse proves to be a valuable tool for initial exploration of in situ sequencing datasets. The wide set of tools integrated allows for simple analysis, using the position of individual reads, up to more complex clustering and dimensional reduction approaches, taking cellular content into account. The toolbox can be used to analyze one or several samples at a time, even from different spatial technologies, and it includes different segmentation approaches that can be useful in the analysis of spatially resolved transcriptomic datasets.
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