1
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Zhou X, Seow WY, Ha N, Cheng TH, Jiang L, Boonruangkan J, Goh JJL, Prabhakar S, Chou N, Chen KH. Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes. Nat Commun 2024; 15:2342. [PMID: 38491027 PMCID: PMC10943009 DOI: 10.1038/s41467-024-46669-y] [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: 08/11/2023] [Accepted: 03/04/2024] [Indexed: 03/18/2024] Open
Abstract
High-dimensional, spatially resolved analysis of intact tissue samples promises to transform biomedical research and diagnostics, but existing spatial omics technologies are costly and labor-intensive. We present Fluorescence In Situ Hybridization of Cellular HeterogeneIty and gene expression Programs (FISHnCHIPs) for highly sensitive in situ profiling of cell types and gene expression programs. FISHnCHIPs achieves this by simultaneously imaging ~2-35 co-expressed genes (clustered into modules) that are spatially co-localized in tissues, resulting in similar spatial information as single-gene Fluorescence In Situ Hybridization (FISH), but with ~2-20-fold higher sensitivity. Using FISHnCHIPs, we image up to 53 modules from the mouse kidney and mouse brain, and demonstrate high-speed, large field-of-view profiling of a whole tissue section. FISHnCHIPs also reveals spatially restricted localizations of cancer-associated fibroblasts in a human colorectal cancer biopsy. Overall, FISHnCHIPs enables fast, robust, and scalable cell typing of tissues with normal physiology or undergoing pathogenesis.
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Affiliation(s)
- Xinrui Zhou
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Wan Yi Seow
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Norbert Ha
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Teh How Cheng
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Lingfan Jiang
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Jeeranan Boonruangkan
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Jolene Jie Lin Goh
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Shyam Prabhakar
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Nigel Chou
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore.
| | - Kok Hao Chen
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore.
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2
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Yafi MA, Hisham MHH, Grisanti F, Martin JF, Rahman A, Samee MAH. scGIST: gene panel design for spatial transcriptomics with prioritized gene sets. Genome Biol 2024; 25:57. [PMID: 38408997 PMCID: PMC10895727 DOI: 10.1186/s13059-024-03185-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/14/2024] [Indexed: 02/28/2024] Open
Abstract
A critical challenge of single-cell spatial transcriptomics (sc-ST) technologies is their panel size. Being based on fluorescence in situ hybridization, they are typically limited to panels of about a thousand genes. This constrains researchers to build panels from only the marker genes of different cell types and forgo other genes of interest, e.g., genes encoding ligand-receptor complexes or those in specific pathways. We propose scGIST, a constrained feature selection tool that designs sc-ST panels prioritizing user-specified genes without compromising cell type detection accuracy. We demonstrate scGIST's efficacy in diverse use cases, highlighting it as a valuable addition to sc-ST's algorithmic toolbox.
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Affiliation(s)
- Mashrur Ahmed Yafi
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh
| | - Md Hasibul Husain Hisham
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh
| | - Francisco Grisanti
- Department of Integrative Physiology, Baylor College of Medicine, Houston, 77030, TX, USA
| | - James F Martin
- Department of Integrative Physiology, Baylor College of Medicine, Houston, 77030, TX, USA
- Cardiomyocyte Renewal Laboratory, Texas Heart Institute, Houston, 77030, TX, USA
| | - Atif Rahman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh.
| | - Md Abul Hassan Samee
- Department of Integrative Physiology, Baylor College of Medicine, Houston, 77030, TX, USA.
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3
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Zhang Y, Petukhov V, Biederstedt E, Que R, Zhang K, Kharchenko PV. Gene panel selection for targeted spatial transcriptomics. Genome Biol 2024; 25:35. [PMID: 38273415 PMCID: PMC10811939 DOI: 10.1186/s13059-024-03174-1] [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: 03/24/2023] [Accepted: 01/12/2024] [Indexed: 01/27/2024] Open
Abstract
Targeted spatial transcriptomics hold particular promise in analyzing complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is their reliance on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method performing gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.
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Affiliation(s)
- Yida Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Neurobiology, Duke University, Durham, NC, USA
| | - Viktor Petukhov
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Evan Biederstedt
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Richard Que
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Kun Zhang
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA.
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4
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Wahle P, Brancati G, Harmel C, He Z, Gut G, Del Castillo JS, Xavier da Silveira Dos Santos A, Yu Q, Noser P, Fleck JS, Gjeta B, Pavlinić D, Picelli S, Hess M, Schmidt GW, Lummen TTA, Hou Y, Galliker P, Goldblum D, Balogh M, Cowan CS, Scholl HPN, Roska B, Renner M, Pelkmans L, Treutlein B, Camp JG. Multimodal spatiotemporal phenotyping of human retinal organoid development. Nat Biotechnol 2023; 41:1765-1775. [PMID: 37156914 PMCID: PMC10713453 DOI: 10.1038/s41587-023-01747-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 03/13/2023] [Indexed: 05/10/2023]
Abstract
Organoids generated from human pluripotent stem cells provide experimental systems to study development and disease, but quantitative measurements across different spatial scales and molecular modalities are lacking. In this study, we generated multiplexed protein maps over a retinal organoid time course and primary adult human retinal tissue. We developed a toolkit to visualize progenitor and neuron location, the spatial arrangements of extracellular and subcellular components and global patterning in each organoid and primary tissue. In addition, we generated a single-cell transcriptome and chromatin accessibility timecourse dataset and inferred a gene regulatory network underlying organoid development. We integrated genomic data with spatially segmented nuclei into a multimodal atlas to explore organoid patterning and retinal ganglion cell (RGC) spatial neighborhoods, highlighting pathways involved in RGC cell death and showing that mosaic genetic perturbations in retinal organoids provide insight into cell fate regulation.
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Affiliation(s)
- Philipp Wahle
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Giovanna Brancati
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Christoph Harmel
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Zhisong He
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Gabriele Gut
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | | | - Aline Xavier da Silveira Dos Santos
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Qianhui Yu
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Pascal Noser
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Jonas Simon Fleck
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Bruno Gjeta
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Dinko Pavlinić
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Simone Picelli
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Max Hess
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Gregor W Schmidt
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Tom T A Lummen
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Yanyan Hou
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Patricia Galliker
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - David Goldblum
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Marton Balogh
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Cameron S Cowan
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
| | - Hendrik P N Scholl
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Botond Roska
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Magdalena Renner
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Lucas Pelkmans
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
| | - Barbara Treutlein
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
| | - J Gray Camp
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland.
- Department of Ophthalmology, University of Basel, Basel, Switzerland.
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
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5
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Zhang C, Duan ZW, Xu YP, Liu J, Li HD. FEED: a feature selection method based on gene expression decomposition for single cell clustering. Brief Bioinform 2023; 24:bbad389. [PMID: 37935617 DOI: 10.1093/bib/bbad389] [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: 06/12/2023] [Revised: 08/31/2023] [Accepted: 09/22/2023] [Indexed: 11/09/2023] Open
Abstract
Single-cell clustering is a critical step in biological downstream analysis. The clustering performance could be effectively improved by extracting cell-type-specific genes. The state-of-the-art feature selection methods usually calculate the importance of a single gene without considering the information contained in the gene expression distribution. Moreover, these methods ignore the intrinsic expression patterns of genes and heterogeneity within groups of different mean expression levels. In this work, we present a Feature sElection method based on gene Expression Decomposition (FEED) of scRNA-seq data, which selects informative genes to enhance clustering performance. First, the expression levels of genes are decomposed into multiple Gaussian components. Then, a novel gene correlation calculation method is proposed to measure the relationship between genes from the perspective of distribution. Finally, a permutation-based approach is proposed to determine the threshold of gene importance to obtain marker gene subsets. Compared with state-of-the-art feature selection methods, applying FEED on various scRNA-seq datasets including large datasets followed by different common clustering algorithms results in significant improvements in the accuracy of cell-type identification. The source codes for FEED are freely available at https://github.com/genemine/FEED.
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Affiliation(s)
- Chao Zhang
- School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
| | - Zhi-Wei Duan
- School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
| | - Yun-Pei Xu
- School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
| | - Jin Liu
- School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
| | - Hong-Dong Li
- School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
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6
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Yang W, Xu M, Xu S, Guan Q, Geng S, Wang J, Wei W, Xu H, Liu Y, Meng Y, Gao MQ. Single-cell RNA reveals a tumorigenic microenvironment in the interface zone of human breast tumors. Breast Cancer Res 2023; 25:100. [PMID: 37644609 PMCID: PMC10463980 DOI: 10.1186/s13058-023-01703-7] [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: 06/02/2023] [Accepted: 08/22/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND The interface zone, area around invasive carcinoma, can be thought of as the actual tissue of the tumor microenvironment with precedent alterations for tumor invasion. However, the heterogeneity and characteristics of the microenvironment in the interface area have not yet been thoroughly explored. METHODS For in vitro studies, single-cell RNA sequencing (scRNA-seq) was used to characterize the cells from the tumor zone, the normal zone and the interface zone with 5-mm-wide belts between the tumor invasion front and the normal zone. Through scRNA-seq data analysis, we compared the cell types and their transcriptional characteristics in the different zones. Pseudotime, cell-cell communication and pathway analysis were performed to characterize the zone-specific microenvironment. Cell proliferation, wound healing and clone formation experiments explored the function of differentially expressed gene BMPR1B, which were confirmed by tumor models in vivo. RESULTS After screening, 88,548 high-quality cells were obtained and identified. Regulatory T cells, M2 macrophages, angiogenesis-related mast cells, stem cells with weak DNA repair ability, endothelial cells with angiogenic activity, fibroblasts with collagen synthesis and epithelial cells with proliferative activity form a unique tumorigenic microenvironment in the interface zone. Cell-cell communication analysis revealed that there are special ligand-receptor pairs between different cell types in the interface zone, which protects endothelial cell apoptosis and promotes epithelial cell proliferation and migration, compared to the normal zone. Compared with the normal zone, the highly expressed BMPR1B gene promotes the tumorigenic ability of cancer cells in the interface zone. CONCLUSIONS Our work identified a unique tumorigenic microenvironment of the interface zone and allowed for deeper insights into the tumor microenvironment of breast cancer that will serve as a helpful resource for advancing breast cancer diagnosis and therapy.
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Affiliation(s)
- Wei Yang
- College of Life Sciences, Northwest University, Xi'an, China
| | - Meiyu Xu
- College of Life Sciences, Northwest University, Xi'an, China
| | - Shuoqi Xu
- College of Life Sciences, Northwest University, Xi'an, China
| | - Qingxian Guan
- College of Life Sciences, Northwest University, Xi'an, China
| | - Shuaiming Geng
- College of Life Sciences, Northwest University, Xi'an, China
| | - Juanhong Wang
- Department of Pathology, Xi'an No.3 Hospital, The Affiliated Hospital of Northwest University, Xi'an, China
| | - Wei Wei
- Department of Pathology, Xi'an No.3 Hospital, The Affiliated Hospital of Northwest University, Xi'an, China
| | - Hongwei Xu
- Basic Medical College, Qingdao University, Qingdao, China
| | - Ying Liu
- Basic Medical College, Qingdao University, Qingdao, China
| | - Yong Meng
- School of Medicine, Northwest University, Xi'an, China
| | - Ming-Qing Gao
- School of Medicine, Northwest University, Xi'an, China.
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7
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Bressan D, Battistoni G, Hannon GJ. The dawn of spatial omics. Science 2023; 381:eabq4964. [PMID: 37535749 PMCID: PMC7614974 DOI: 10.1126/science.abq4964] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/30/2023] [Indexed: 08/05/2023]
Abstract
Spatial omics has been widely heralded as the new frontier in life sciences. This term encompasses a wide range of techniques that promise to transform many areas of biology and eventually revolutionize pathology by measuring physical tissue structure and molecular characteristics at the same time. Although the field came of age in the past 5 years, it still suffers from some growing pains: barriers to entry, robustness, unclear best practices for experimental design and analysis, and lack of standardization. In this Review, we present a systematic catalog of the different families of spatial omics technologies; highlight their principles, power, and limitations; and give some perspective and suggestions on the biggest challenges that lay ahead in this incredibly powerful-but still hard to navigate-landscape.
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Affiliation(s)
- Dario Bressan
- CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom
| | - Giorgia Battistoni
- CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom
| | - Gregory J. Hannon
- CRUK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom
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8
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Covert I, Gala R, Wang T, Svoboda K, Sümbül U, Lee SI. Predictive and robust gene selection for spatial transcriptomics. Nat Commun 2023; 14:2091. [PMID: 37045821 PMCID: PMC10097645 DOI: 10.1038/s41467-023-37392-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 03/16/2023] [Indexed: 04/14/2023] Open
Abstract
A prominent trend in single-cell transcriptomics is providing spatial context alongside a characterization of each cell's molecular state. This typically requires targeting an a priori selection of genes, often covering less than 1% of the genome, and a key question is how to optimally determine the small gene panel. We address this challenge by introducing a flexible deep learning framework, PERSIST, to identify informative gene targets for spatial transcriptomics studies by leveraging reference scRNA-seq data. Using datasets spanning different brain regions, species, and scRNA-seq technologies, we show that PERSIST reliably identifies panels that provide more accurate prediction of the genome-wide expression profile, thereby capturing more information with fewer genes. PERSIST can be adapted to specific biological goals, and we demonstrate that PERSIST's binarization of gene expression levels enables models trained on scRNA-seq data to generalize with to spatial transcriptomics data, despite the complex shift between these technologies.
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Affiliation(s)
- Ian Covert
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Rohan Gala
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Tim Wang
- HHMI Janelia Research Campus, Ashburn, VA, USA
| | - Karel Svoboda
- HHMI Janelia Research Campus, Ashburn, VA, USA
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Uygar Sümbül
- Allen Institute for Brain Science, Seattle, WA, USA.
| | - Su-In Lee
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
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9
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Zhang Y, Petukhov V, Biederstedt E, Que R, Zhang K, Kharchenko PV. Gene panel selection for targeted spatial transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.03.527053. [PMID: 36993340 PMCID: PMC10054990 DOI: 10.1101/2023.02.03.527053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Targeted spatial transcriptomics hold particular promise in analysis of complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is that they rely on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method to perform gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.
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Affiliation(s)
- Yida Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Neurobiology, Duke University, Durham, NC, USA
| | - Viktor Petukhov
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Evan Biederstedt
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Richard Que
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Kun Zhang
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA
| | - Peter V. Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA
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10
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Spatial RNA sequencing methods show high resolution of single cell in cancer metastasis and the formation of tumor microenvironment. Biosci Rep 2023; 43:232194. [PMID: 36459212 PMCID: PMC9950536 DOI: 10.1042/bsr20221680] [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: 08/04/2022] [Revised: 11/30/2022] [Accepted: 12/02/2022] [Indexed: 12/03/2022] Open
Abstract
Cancer metastasis often leads to death and therapeutic resistance. This process involves the participation of a variety of cell components, especially cellular and intercellular communications in the tumor microenvironment (TME). Using genetic sequencing technology to comprehensively characterize the tumor and TME is therefore key to understanding metastasis and therapeutic resistance. The use of spatial transcriptome sequencing enables the localization of gene expressions and cell activities in tissue sections. By examining the localization change as well as gene expression of these cells, it is possible to characterize the progress of tumor metastasis and TME formation. With improvements of this technology, spatial transcriptome sequencing technology has been extended from local regions to whole tissues, and from single sequencing technology to multimodal analysis combined with a variety of datasets. This has enabled the detection of every single cell in tissue slides, with high resolution, to provide more accurate predictive information for tumor treatments. In this review, we summarize the results of recent studies dealing with new multimodal methods and spatial transcriptome sequencing methods in tumors to illustrate recent developments in the imaging resolution of micro-tissues.
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11
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Liu J, Tran V, Vemuri VNP, Byrne A, Borja M, Kim YJ, Agarwal S, Wang R, Awayan K, Murti A, Taychameekiatchai A, Wang B, Emanuel G, He J, Haliburton J, Oliveira Pisco A, Neff NF. Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing. Life Sci Alliance 2022; 6:6/1/e202201701. [PMID: 36526371 PMCID: PMC9760489 DOI: 10.26508/lsa.202201701] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 12/23/2022] Open
Abstract
Spatial transcriptomics extends single-cell RNA sequencing (scRNA-seq) by providing spatial context for cell type identification and analysis. Imaging-based spatial technologies such as multiplexed error-robust fluorescence in situ hybridization (MERFISH) can achieve single-cell resolution, directly mapping single-cell identities to spatial positions. MERFISH produces a different data type than scRNA-seq, and a technical comparison between the two modalities is necessary to ascertain how to best integrate them. We performed MERFISH on the mouse liver and kidney and compared the resulting bulk and single-cell RNA statistics with those from the Tabula Muris Senis cell atlas and from two Visium datasets. MERFISH quantitatively reproduced the bulk RNA-seq and scRNA-seq results with improvements in overall dropout rates and sensitivity. Finally, we found that MERFISH independently resolved distinct cell types and spatial structure in both the liver and kidney. Computational integration with the Tabula Muris Senis atlas did not enhance these results. We conclude that MERFISH provides a quantitatively comparable method for single-cell gene expression and can identify cell types without the need for computational integration with scRNA-seq atlases.
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Affiliation(s)
| | | | | | | | | | | | | | - Ruofan Wang
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Kyle Awayan
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Abhishek Murti
- School of Medicine, University of California, San Francisco, CA, USA
| | | | - Bruce Wang
- School of Medicine, University of California, San Francisco, CA, USA
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