101
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Zhang D, Schroeder A, Yan H, Yang H, Hu J, Lee MYY, Cho KS, Susztak K, Xu GX, Feldman MD, Lee EB, Furth EE, Wang L, Li M. Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology. Nat Biotechnol 2024:10.1038/s41587-023-02019-9. [PMID: 38168986 PMCID: PMC11260191 DOI: 10.1038/s41587-023-02019-9] [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: 04/14/2023] [Accepted: 10/04/2023] [Indexed: 01/05/2024]
Abstract
Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.
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Affiliation(s)
- Daiwei Zhang
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Amelia Schroeder
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hanying Yan
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochen Yang
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jian Hu
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, USA
| | - Michelle Y Y Lee
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kyung S Cho
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - George X Xu
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael D Feldman
- Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Emma E Furth
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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102
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Sun C, Shao Y, Iqbal J. Insect Insights at the Single-Cell Level: Technologies and Applications. Cells 2023; 13:91. [PMID: 38201295 PMCID: PMC10777908 DOI: 10.3390/cells13010091] [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: 11/22/2023] [Revised: 12/23/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024] Open
Abstract
Single-cell techniques are a promising way to unravel the complexity and heterogeneity of transcripts at the cellular level and to reveal the composition of different cell types and functions in a tissue or organ. In recent years, advances in single-cell RNA sequencing (scRNA-seq) have further changed our view of biological systems. The application of scRNA-seq in insects enables the comprehensive characterization of both common and rare cell types and cell states, the discovery of new cell types, and revealing how cell types relate to each other. The recent application of scRNA-seq techniques to insect tissues has led to a number of exciting discoveries. Here we provide an overview of scRNA-seq and its application in insect research, focusing on biological applications, current challenges, and future opportunities to make new discoveries with scRNA-seq in insects.
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Affiliation(s)
- Chao Sun
- Analysis Center of Agrobiology and Environmental Sciences, Zhejiang University, Hangzhou 310058, China;
| | - Yongqi Shao
- Institute of Sericulture and Apiculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
| | - Junaid Iqbal
- Institute of Sericulture and Apiculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
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103
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Gu J, Iyer A, Wesley B, Taglialatela A, Leuzzi G, Hangai S, Decker A, Gu R, Klickstein N, Shuai Y, Jankovic K, Parker-Burns L, Jin Y, Zhang JY, Hong J, Niu S, Chou J, Landau DA, Azizi E, Chan EM, Ciccia A, Gaublomme JT. CRISPRmap: Sequencing-free optical pooled screens mapping multi-omic phenotypes in cells and tissue. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.26.572587. [PMID: 38234835 PMCID: PMC10793456 DOI: 10.1101/2023.12.26.572587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Pooled genetic screens are powerful tools to study gene function in a high-throughput manner. Typically, sequencing-based screens require cell lysis, which limits the examination of critical phenotypes such as cell morphology, protein subcellular localization, and cell-cell/tissue interactions. In contrast, emerging optical pooled screening methods enable the investigation of these spatial phenotypes in response to targeted CRISPR perturbations. In this study, we report a multi-omic optical pooled CRISPR screening method, which we have named CRISPRmap. Our method combines a novel in situ CRISPR guide identifying barcode readout approach with concurrent multiplexed immunofluorescence and in situ RNA detection. CRISPRmap barcodes are detected and read out through combinatorial hybridization of DNA oligos, enhancing barcode detection efficiency, while reducing both dependency on third party proprietary sequencing reagents and assay cost. Notably, we conducted a multi-omic base-editing screen in a breast cancer cell line on core DNA damage repair genes involved in the homologous recombination and Fanconi anemia pathways investigating how nucleotide variants in those genes influence DNA damage signaling and cell cycle regulation following treatment with ionizing radiation or DNA damaging agents commonly used for cancer therapy. Approximately a million cells were profiled with our multi-omic approach, providing a comprehensive phenotypic assessment of the functional consequences of the studied variants. CRISPRmap enabled us to pinpoint likely-pathogenic patient-derived mutations that were previously classified as variants of unknown clinical significance. Furthermore, our approach effectively distinguished barcodes of a pooled library in tumor tissue, and we coupled it with cell-type and molecular phenotyping by cyclic immunofluorescence. Multi-omic spatial analysis of how CRISPR-perturbed cells respond to various environmental cues in the tissue context offers the potential to significantly expand our understanding of tissue biology in both health and disease.
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Affiliation(s)
- Jiacheng Gu
- Department of Biological Sciences, Columbia University, NY, USA
| | - Abhishek Iyer
- Department of Biological Sciences, Columbia University, NY, USA
| | - Ben Wesley
- Department of Biological Sciences, Columbia University, NY, USA
| | - Angelo Taglialatela
- Department of Genetics and Development, Columbia University Irving Medical Center, NY, USA
| | - Giuseppe Leuzzi
- Department of Genetics and Development, Columbia University Irving Medical Center, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
- Institute for Cancer Genetics, Columbia University Irving Medical Center, NY, USA
| | - Sho Hangai
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
| | | | - Ruoyu Gu
- Department of Biological Sciences, Columbia University, NY, USA
| | | | - Yuanlong Shuai
- Department of Biological Sciences, Columbia University, NY, USA
| | - Kristina Jankovic
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
| | - Lucy Parker-Burns
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
| | - Yinuo Jin
- Department of Biomedical Engineering, Columbia University, NY, USA
| | - Jia Yi Zhang
- Department of Biomedical Engineering, Columbia University, NY, USA
| | - Justin Hong
- Department of Computer Science, Columbia University, NY, USA
| | - Steve Niu
- Weill Cornell Medicine, NY, USA
- Genentech Research and Early Development, CA, USA
| | - Jacqueline Chou
- Department of Biological Sciences, Columbia University, NY, USA
- Weill Cornell Medicine, NY, USA
| | - Dan A. Landau
- Weill Cornell Medicine, NY, USA
- New York Genome Center, NY, USA
| | - Elham Azizi
- Department of Biomedical Engineering, Columbia University, NY, USA
- Department of Computer Science, Columbia University, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, NY, USA
| | - Edmond M. Chan
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
- New York Genome Center, NY, USA
- Department of Medicine, Division of Hematology/Oncology, Columbia University Irving Medical Center, NY, USA
| | - Alberto Ciccia
- Department of Genetics and Development, Columbia University Irving Medical Center, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
- Institute for Cancer Genetics, Columbia University Irving Medical Center, NY, USA
| | - Jellert T. Gaublomme
- Department of Biological Sciences, Columbia University, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
- New York Genome Center, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, NY, USA
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104
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Zormpas E, Queen R, Comber A, Cockell SJ. Mapping the transcriptome: Realizing the full potential of spatial data analysis. Cell 2023; 186:5677-5689. [PMID: 38065099 DOI: 10.1016/j.cell.2023.11.003] [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: 03/30/2023] [Revised: 09/04/2023] [Accepted: 11/02/2023] [Indexed: 12/24/2023]
Abstract
RNA sequencing in situ allows for whole-transcriptome characterization at high resolution, while retaining spatial information. These data present an analytical challenge for bioinformatics-how to leverage spatial information effectively? Properties of data with a spatial dimension require special handling, which necessitate a different set of statistical and inferential considerations when compared to non-spatial data. The geographical sciences primarily use spatial data and have developed methods to analye them. Here we discuss the challenges associated with spatial analysis and examine how we can take advantage of practice from the geographical sciences to realize the full potential of spatial information in transcriptomic datasets.
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Affiliation(s)
- Eleftherios Zormpas
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Rachel Queen
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; Bioinformatics Support Unit, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Alexis Comber
- School of Geography and Leeds Institute for Data Analytics, University of Leeds, Leeds LS2 9NL, UK
| | - Simon J Cockell
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; School of Biomedical, Nutritional and Sport Sciences, Faculty of Medical Sciences, Newcastle upon Tyne NE2 4HH, UK.
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105
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Blain R, Couly G, Shotar E, Blévinal J, Toupin M, Favre A, Abjaghou A, Inoue M, Hernández-Garzón E, Clarençon F, Chalmel F, Mazaud-Guittot S, Giacobini P, Gitton Y, Chédotal A. A tridimensional atlas of the developing human head. Cell 2023; 186:5910-5924.e17. [PMID: 38070509 PMCID: PMC10783631 DOI: 10.1016/j.cell.2023.11.013] [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: 10/01/2023] [Accepted: 11/09/2023] [Indexed: 12/24/2023]
Abstract
The evolution and development of the head have long captivated researchers due to the crucial role of the head as the gateway for sensory stimuli and the intricate structural complexity of the head. Although significant progress has been made in understanding head development in various vertebrate species, our knowledge of early human head ontogeny remains limited. Here, we used advanced whole-mount immunostaining and 3D imaging techniques to generate a comprehensive 3D cellular atlas of human head embryogenesis. We present detailed developmental series of diverse head tissues and cell types, including muscles, vasculature, cartilage, peripheral nerves, and exocrine glands. These datasets, accessible through a dedicated web interface, provide insights into human embryogenesis. We offer perspectives on the branching morphogenesis of human exocrine glands and unknown features of the development of neurovascular and skeletomuscular structures. These insights into human embryology have important implications for understanding craniofacial defects and neurological disorders and advancing diagnostic and therapeutic strategies.
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Affiliation(s)
- Raphael Blain
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Gérard Couly
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Eimad Shotar
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France; Department of Interventional Neuroradiology, Pitié-Salpêtrière Hospital, Sorbonne Université, Paris, France
| | | | - Maryne Toupin
- INSERM, EHESP, Univ Rennes, Institut de recherche en santé, environnement et travail (Irset), UMR_S 1085, Rennes, France
| | - Anais Favre
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Ali Abjaghou
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Megumi Inoue
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | | | - Frédéric Clarençon
- Department of Interventional Neuroradiology, Pitié-Salpêtrière Hospital, Sorbonne Université, Paris, France
| | - Frédéric Chalmel
- INSERM, EHESP, Univ Rennes, Institut de recherche en santé, environnement et travail (Irset), UMR_S 1085, Rennes, France
| | - Séverine Mazaud-Guittot
- INSERM, EHESP, Univ Rennes, Institut de recherche en santé, environnement et travail (Irset), UMR_S 1085, Rennes, France
| | - Paolo Giacobini
- University of Lille, INSERM, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, 59000 Lille, France
| | - Yorick Gitton
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France.
| | - Alain Chédotal
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France; Institut de pathologie, Groupe Hospitalier Est, Hospices Civils de Lyon, Lyon, France; University Claude Bernard Lyon 1, MeLiS, CNRS UMR 5284, INSERM U1314, 69008 Lyon, France.
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106
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Yang ST, Zhang XF. ENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression prediction. Genome Biol 2023; 24:293. [PMID: 38129866 PMCID: PMC10734203 DOI: 10.1186/s13059-023-03139-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: 04/25/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Imaging-based spatial transcriptomics techniques provide valuable spatial and gene expression information at single-cell resolution. However, their current capability is restricted to profiling a limited number of genes per sample, resulting in most of the transcriptome remaining unmeasured. To overcome this challenge, we develop ENGEP, an ensemble learning-based tool that predicts unmeasured gene expression in spatial transcriptomics data by using multiple single-cell RNA sequencing datasets as references. ENGEP outperforms current state-of-the-art tools and brings biological insight by accurately predicting unmeasured genes. ENGEP has exceptional efficiency in terms of runtime and memory usage, making it scalable for analyzing large datasets.
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Affiliation(s)
- Shi-Tong Yang
- School of Mathematics and Statistics, Central China Normal University, Wuhan, China
- Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University, Wuhan, China
| | - Xiao-Fei Zhang
- School of Mathematics and Statistics, Central China Normal University, Wuhan, China.
- Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University, Wuhan, China.
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107
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Zhu S, Kubota N, Wang S, Wang T, Xiao G, Hoshida Y. Single-cell level deconvolution, convolution, and clustering in spatial transcriptomics by aligning spot level transcriptome to nuclear morphology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.17.572084. [PMID: 38187541 PMCID: PMC10769305 DOI: 10.1101/2023.12.17.572084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
In spot-based spatial transcriptomics, spots that are of the same size and printed at the fixed location cannot precisely capture the actual randomly located single cells, therefore failing to profile the transcriptome at the single-cell level. The current studies primarily focused on enhancing the spot resolution in size via computational imputation or technical improvement, however, they largely overlooked that single-cell resolution, i.e., resolution in cellular or even smaller size, does not equal single-cell level. Using both real and simulated spatial transcriptomics data, we demonstrated that even the high-resolution spatial transcriptomics still has a large number of spots partially covering multiple cells simultaneously, revealing the intrinsic non-single-cell level of spot-based spatial transcriptomics regardless of spot size. To this end, we present STIE, an EM algorithm that aligns the spatial transcriptome to its matched histology image-based nuclear morphology and recovers missing cells from up to ~70% gap area between spots via the nuclear morphological similarity and neighborhood information, thereby achieving the real single-cell level and whole-slide scale deconvolution/convolution and clustering for both low- and high-resolution spots. On both real and simulation spatial transcriptomics data, STIE characterizes the cell-type specific gene expression variation and demonstrates the outperforming concordance with the single-cell RNAseq-derived cell type transcriptomic signatures compared to the other spot- and subspot-level methods. Furthermore, STIE enabled us to gain novel insights that failed to be revealed by the existing methods due to the lack of single-cell level, for instance, lower actual spot resolution than its reported spot size, the additional contribution of cellular morphology to cell typing beyond transcriptome, unbiased evaluation of cell type colocalization, superior power of high-resolution spot in distinguishing nuanced cell types, and spatially resolved cell-cell interactions at the single-cell level other than spot level. The STIE code is publicly available as an R package at https://github.com/zhushijia/STIE.
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Affiliation(s)
- Shijia Zhu
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, USA
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Naoto Kubota
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Shidan Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Tao Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Yujin Hoshida
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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108
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Yang J, Jiang X, Jin KW, Shin S, Li Q. Bayesian Hidden Mark Interaction Model for Detecting Spatially Variable Genes in Imaging-Based Spatially Resolved Transcriptomics Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.17.572071. [PMID: 38168368 PMCID: PMC10760150 DOI: 10.1101/2023.12.17.572071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies on ad hoc kernels, or the energy-based Ising model, which requires gene expression to be measured on a lattice grid. To overcome these potential limitations, we developed a generalized energy-based framework to model gene expression measured from imaging-based SRT platforms, accommodating the irregular spatial distribution of measured cells. Our Bayesian model applies a zero-inflated negative binomial mixture model to dichotomize the raw count data, reducing noise. Additionally, we incorporate a geostatistical mark interaction model with a generalized energy function, where the interaction parameter is used to identify the spatial pattern. Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. We demonstrated the strength of our method on both simulated and real data. Our simulation study showed that our method captured various spatial patterns with high accuracy; moreover, analysis of a seqFISH dataset and a STARmap dataset established that our proposed method is able to identify genes with novel and strong spatial patterns.
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Affiliation(s)
- Jie Yang
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas, U.S.A
| | - Xi Jiang
- Department of Statistics and Data Science, Southern Methodist University, Dallas, Texas, U.S.A
| | - Kevin W. Jin
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, U.S.A
| | - Sunyoung Shin
- Department of Mathematics, Pohang University of Science and Technology, Pohang, South Korea
| | - Qiwei Li
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas, U.S.A
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109
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Liu Y, Jin Y, Azizi E, Blumberg AJ. Cellstitch: 3D cellular anisotropic image segmentation via optimal transport. BMC Bioinformatics 2023; 24:480. [PMID: 38102537 PMCID: PMC10724925 DOI: 10.1186/s12859-023-05608-2] [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: 06/28/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Spatial mapping of transcriptional states provides valuable biological insights into cellular functions and interactions in the context of the tissue. Accurate 3D cell segmentation is a critical step in the analysis of this data towards understanding diseases and normal development in situ. Current approaches designed to automate 3D segmentation include stitching masks along one dimension, training a 3D neural network architecture from scratch, and reconstructing a 3D volume from 2D segmentations on all dimensions. However, the applicability of existing methods is hampered by inaccurate segmentations along the non-stitching dimensions, the lack of high-quality diverse 3D training data, and inhomogeneity of image resolution along orthogonal directions due to acquisition constraints; as a result, they have not been widely used in practice. METHODS To address these challenges, we formulate the problem of finding cell correspondence across layers with a novel optimal transport (OT) approach. We propose CellStitch, a flexible pipeline that segments cells from 3D images without requiring large amounts of 3D training data. We further extend our method to interpolate internal slices from highly anisotropic cell images to recover isotropic cell morphology. RESULTS We evaluated the performance of CellStitch through eight 3D plant microscopic datasets with diverse anisotropic levels and cell shapes. CellStitch substantially outperforms the state-of-the art methods on anisotropic images, and achieves comparable segmentation quality against competing methods in isotropic setting. We benchmarked and reported 3D segmentation results of all the methods with instance-level precision, recall and average precision (AP) metrics. CONCLUSIONS The proposed OT-based 3D segmentation pipeline outperformed the existing state-of-the-art methods on different datasets with nonzero anisotropy, providing high fidelity recovery of 3D cell morphology from microscopic images.
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Affiliation(s)
- Yining Liu
- Department of Computer Science, Columbia University, New York, USA
- Irving Institute for Cancer Dynamics, New York, USA
| | - Yinuo Jin
- Department of Biomedical Engineering, Columbia University, New York, USA
- Irving Institute for Cancer Dynamics, New York, USA
| | - Elham Azizi
- Department of Computer Science, Columbia University, New York, USA.
- Department of Biomedical Engineering, Columbia University, New York, USA.
- Data Science Institute, Columbia University, New York, USA.
- Irving Institute for Cancer Dynamics, New York, USA.
| | - Andrew J Blumberg
- Department of Computer Science, Columbia University, New York, USA.
- Department of Mathematics, Columbia University, New York, USA.
- Irving Institute for Cancer Dynamics, New York, USA.
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110
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Jiang X, Dong L, Wang S, Wen Z, Chen M, Xu L, Xiao G, Li Q. Reconstructing Spatial Transcriptomics at the Single-cell Resolution with BayesDeep. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570715. [PMID: 38106214 PMCID: PMC10723442 DOI: 10.1101/2023.12.07.570715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Spatially resolved transcriptomics (SRT) techniques have revolutionized the characterization of molecular profiles while preserving spatial and morphological context. However, most next-generation sequencing-based SRT techniques are limited to measuring gene expression in a confined array of spots, capturing only a fraction of the spatial domain. Typically, these spots encompass gene expression from a few to hundreds of cells, underscoring a critical need for more detailed, single-cell resolution SRT data to enhance our understanding of biological functions within the tissue context. Addressing this challenge, we introduce BayesDeep, a novel Bayesian hierarchical model that leverages cellular morphological data from histology images, commonly paired with SRT data, to reconstruct SRT data at the single-cell resolution. BayesDeep effectively model count data from SRT studies via a negative binomial regression model. This model incorporates explanatory variables such as cell types and nuclei-shape information for each cell extracted from the paired histology image. A feature selection scheme is integrated to examine the association between the morphological and molecular profiles, thereby improving the model robustness. We applied BayesDeep to two real SRT datasets, successfully demonstrating its capability to reconstruct SRT data at the single-cell resolution. This advancement not only yields new biological insights but also significantly enhances various downstream analyses, such as pseudotime and cell-cell communication.
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Affiliation(s)
- Xi Jiang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A
- Department of Statistics and Data Science, Southern Methodist University, Dallas, Texas, U.S.A
| | - Lei Dong
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A
| | - Shidan Wang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A
| | - Zhuoyu Wen
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A
| | - Mingyi Chen
- Department of Pathology, The University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A
| | - Lin Xu
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A
- Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A
| | - Qiwei Li
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas, U.S.A
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111
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Li Z, Patel ZM, Song D, Yan G, Li JJ, Pinello L. Benchmarking computational methods to identify spatially variable genes and peaks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.02.569717. [PMID: 38076922 PMCID: PMC10705556 DOI: 10.1101/2023.12.02.569717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Spatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes. Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field. Here, we present a systematic evaluation of 14 methods using 60 simulated datasets generated by four different simulation strategies, 12 real-world transcriptomics, and three spatial ATAC-seq datasets. We find that spatialDE2 consistently outperforms the other benchmarked methods, and Moran's I achieves competitive performance in different experimental settings. Moreover, our results reveal that more specialized algorithms are needed to identify spatially variable peaks.
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Affiliation(s)
- Zhijian Li
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Zain M. Patel
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Dongyuan Song
- Interdepartmental Program of Bioinformatics, University of California, Los Angeles, CA, USA
| | - Guanao Yan
- Department of Statistics and Data Science, University of California, Los Angeles, CA, USA
| | - Jingyi Jessica Li
- Department of Statistics and Data Science, University of California, Los Angeles, CA, USA
| | - Luca Pinello
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
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112
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Walsh LA, Quail DF. Decoding the tumor microenvironment with spatial technologies. Nat Immunol 2023; 24:1982-1993. [PMID: 38012408 DOI: 10.1038/s41590-023-01678-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 10/10/2023] [Indexed: 11/29/2023]
Abstract
Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.
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Affiliation(s)
- Logan A Walsh
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada.
- Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
| | - Daniela F Quail
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada.
- Department of Physiology, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
- Department of Medicine, Division of Experimental Medicine, McGill University, Montreal, Quebec, Canada.
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113
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Binan L, Danquah S, Valakh V, Simonton B, Bezney J, Nehme R, Cleary B, Farhi SL. Simultaneous CRISPR screening and spatial transcriptomics reveals intracellular, intercellular, and functional transcriptional circuits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.30.569494. [PMID: 38076932 PMCID: PMC10705493 DOI: 10.1101/2023.11.30.569494] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Pooled optical screens have enabled the study of cellular interactions, morphology, or dynamics at massive scale, but have not yet leveraged the power of highly-plexed single-cell resolved transcriptomic readouts to inform molecular pathways. Here, we present Perturb-FISH, which bridges these approaches by combining imaging spatial transcriptomics with parallel optical detection of in situ amplified guide RNAs. We show that Perturb-FISH recovers intracellular effects that are consistent with Perturb-seq results in a screen of lipopolysaccharide response in cultured monocytes, and uncover new intercellular and density-dependent regulation of the innate immune response. We further pair Perturb-FISH with a functional readout in a screen of autism spectrum disorder risk genes, showing common calcium activity phenotypes in induced pluripotent stem cell derived astrocytes and their associated genetic interactions and dysregulated molecular pathways. Perturb-FISH is thus a generally applicable method for studying the genetic and molecular associations of spatial and functional biology at single-cell resolution.
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Affiliation(s)
- Loϊc Binan
- Spatial Technology Platform, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Serwah Danquah
- Spatial Technology Platform, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Vera Valakh
- Spatial Technology Platform, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Brooke Simonton
- Present address: The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. (Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA)
| | - Jon Bezney
- Present address: Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. (Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA)
| | - Ralda Nehme
- Spatial Technology Platform, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Brian Cleary
- Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA; Department of Biology, Boston University, Boston, MA, USA; Department of Biomedical Engineering, Boston University, Boston, MA, USA; Program in Bioinformatics, Boston University, Boston, MA, USA; Biological Design Center, Boston University, Boston, MA, USA
| | - Samouil L Farhi
- Spatial Technology Platform, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
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114
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Yin R, Xia K, Xu X. Spatial transcriptomics drives a new era in plant research. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 116:1571-1581. [PMID: 37651723 DOI: 10.1111/tpj.16437] [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: 05/05/2023] [Revised: 07/25/2023] [Accepted: 08/16/2023] [Indexed: 09/02/2023]
Abstract
SUMMARYThe plant community lags far behind the animal and human fields concerning the application of single‐cell methodologies. This is primarily due to the challenges associated with plant tissue dissection and the limitations of the available technologies. However, recent advances in spatial transcriptomics enable the study of single‐cells derived from plant tissues from a spatial perspective. This technology is already successfully used to identify cell types, reconstruct cell‐fate lineages, and reveal cell‐to‐cell interactions. Future technological advancements will overcome the challenges in sample processing, data analysis, and the integration of multiple‐omics technologies. Thanks to spatial transcriptomics, we anticipate several plant research projects to significantly advance our understanding of critical aspects of plant biology.
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Affiliation(s)
- Ruilian Yin
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 10049, China
- BGI Research, Shenzhen, 518083, China
| | - Keke Xia
- BGI Research, Shenzhen, 518083, China
| | - Xun Xu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 10049, China
- BGI Research, Shenzhen, 518083, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, BGI Research, Shenzhen, 518120, China
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115
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Toninelli M, Rossetti G, Pagani M. Charting the tumor microenvironment with spatial profiling technologies. Trends Cancer 2023; 9:1085-1096. [PMID: 37673713 DOI: 10.1016/j.trecan.2023.08.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/04/2023] [Accepted: 08/10/2023] [Indexed: 09/08/2023]
Abstract
In recent years technologies that can achieve readouts at cellular resolution such as single-cell RNA sequencing (scRNA-seq) have provided a comprehensive characterization of the cellular proportions and phenotypes that populate the tumor microenvironment (TME). However, because of the sample dissociation steps required by these protocols, they fail to capture information related to the intricate spatial context in which cells operate as well as their dense networks of interactions. Spatial profiling technologies have recently emerged as a valuable way to investigate the physical organization of cells crowding the TME in intact tissues. In this review we first discuss how spatial profiling technologies have propelled TME characterization, and then explore their potential to improve both diagnosis and prognosis for cancer patients in the clinic.
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Affiliation(s)
- Mattia Toninelli
- IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Grazisa Rossetti
- IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Massimiliano Pagani
- IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy; Department of Medical Biotechnology and Translational Medicine (BIOMETRA), Università degli Studi, Milan, Italy.
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116
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Cross AR, Gartner L, Hester J, Issa F. Opportunities for High-plex Spatial Transcriptomics in Solid Organ Transplantation. Transplantation 2023; 107:2464-2472. [PMID: 36944604 DOI: 10.1097/tp.0000000000004587] [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: 03/23/2023]
Abstract
The last 5 y have seen the development and widespread adoption of high-plex spatial transcriptomic technology. This technique detects and quantifies mRNA transcripts in situ, meaning that transcriptomic signatures can be sampled from specific cells, structures, lesions, or anatomical regions while conserving the physical relationships that exist within complex tissues. These methods now frequently implement next-generation sequencing, enabling the simultaneous measurement of many targets, up to and including the whole mRNA transcriptome. To date, spatial transcriptomics has been foremost used in the fields of neuroscience and oncology, but there is potential for its use in transplantation sciences. Transplantation has a clear dependence on biopsies for diagnosis, monitoring, and research. Transplant patients represent a unique cohort with multiple organs of interest, clinical courses, demographics, and immunosuppressive regimens. Obtaining high complexity data on the disease processes underlying rejection, tolerance, infection, malignancy, and injury could identify new opportunities for therapeutic intervention and biomarker identification. In this review, we discuss currently available spatial transcriptomic technologies and how they can be applied to transplantation.
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Affiliation(s)
- Amy R Cross
- Translational Research and Immunology Group, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
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117
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Zhang M, Pan X, Jung W, Halpern AR, Eichhorn SW, Lei Z, Cohen L, Smith KA, Tasic B, Yao Z, Zeng H, Zhuang X. Molecularly defined and spatially resolved cell atlas of the whole mouse brain. Nature 2023; 624:343-354. [PMID: 38092912 PMCID: PMC10719103 DOI: 10.1038/s41586-023-06808-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 10/31/2023] [Indexed: 12/17/2023]
Abstract
In mammalian brains, millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells have impeded our understanding of the molecular and cellular basis of brain function. Recent advances in spatially resolved single-cell transcriptomics have enabled systematic mapping of the spatial organization of molecularly defined cell types in complex tissues1-3, including several brain regions (for example, refs. 1-11). However, a comprehensive cell atlas of the whole brain is still missing. Here we imaged a panel of more than 1,100 genes in approximately 10 million cells across the entire adult mouse brains using multiplexed error-robust fluorescence in situ hybridization12 and performed spatially resolved, single-cell expression profiling at the whole-transcriptome scale by integrating multiplexed error-robust fluorescence in situ hybridization and single-cell RNA sequencing data. Using this approach, we generated a comprehensive cell atlas of more than 5,000 transcriptionally distinct cell clusters, belonging to more than 300 major cell types, in the whole mouse brain with high molecular and spatial resolution. Registration of this atlas to the mouse brain common coordinate framework allowed systematic quantifications of the cell-type composition and organization in individual brain regions. We further identified spatial modules characterized by distinct cell-type compositions and spatial gradients featuring gradual changes of cells. Finally, this high-resolution spatial map of cells, each with a transcriptome-wide expression profile, allowed us to infer cell-type-specific interactions between hundreds of cell-type pairs and predict molecular (ligand-receptor) basis and functional implications of these cell-cell interactions. These results provide rich insights into the molecular and cellular architecture of the brain and a foundation for functional investigations of neural circuits and their dysfunction in health and disease.
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Affiliation(s)
- Meng Zhang
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Xingjie Pan
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Won Jung
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Aaron R Halpern
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Stephen W Eichhorn
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Zhiyun Lei
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Limor Cohen
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | | | | | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Xiaowei Zhuang
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA.
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
- Department of Physics, Harvard University, Cambridge, MA, USA.
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118
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Wan X, Xiao J, Tam SST, Cai M, Sugimura R, Wang Y, Wan X, Lin Z, Wu AR, Yang C. Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope. Nat Commun 2023; 14:7848. [PMID: 38030617 PMCID: PMC10687049 DOI: 10.1038/s41467-023-43629-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 11/13/2023] [Indexed: 12/01/2023] Open
Abstract
The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), offer valuable insights, they face limitations either in cellular resolution or transcriptome-wide profiling. To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data and ST data using deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also accurately infers transcriptome-wide expression levels for image-based ST data. We demonstrate SpatialScope's utility through simulation studies and real data analysis from both seq-based and image-based ST approaches. SpatialScope provides spatial characterization of tissue structures at transcriptome-wide single-cell resolution, facilitating downstream analysis, including detecting cellular communication through ligand-receptor interactions, localizing cellular subtypes, and identifying spatially differentially expressed genes.
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Affiliation(s)
- Xiaomeng Wan
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Jiashun Xiao
- Shenzhen Research Institute of Big Data, Shenzhen, 518172, China
| | - Sindy Sing Ting Tam
- Division of Life Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
| | - Mingxuan Cai
- Department of Biostatistics, City University of Hong Kong, Hong Kong SAR, China
| | - Ryohichi Sugimura
- Li Ka Shing Faculty of Medicine, School of Biomedical Sciences, University of Hong Kong, Hong Kong SAR, China
| | - Yang Wang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications, The Hong Kong University of Science and Technology, Hong Kong SAR, China
- Big Data Bio-Intelligence Lab, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Xiang Wan
- Shenzhen Research Institute of Big Data, Shenzhen, 518172, China
| | - Zhixiang Lin
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Angela Ruohao Wu
- Division of Life Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China.
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
- Center for Aging Science, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
- State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
- Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
- Big Data Bio-Intelligence Lab, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
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119
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Handler K, Bach K, Borrelli C, Piscuoglio S, Ficht X, Acar IE, Moor AE. Fragment-sequencing unveils local tissue microenvironments at single-cell resolution. Nat Commun 2023; 14:7775. [PMID: 38012149 PMCID: PMC10681997 DOI: 10.1038/s41467-023-43005-8] [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: 02/23/2023] [Accepted: 10/27/2023] [Indexed: 11/29/2023] Open
Abstract
Cells collectively determine biological functions by communicating with each other-both through direct physical contact and secreted factors. Consequently, the local microenvironment of a cell influences its behavior, gene expression, and cellular crosstalk. Disruption of this microenvironment causes reciprocal changes in those features, which can lead to the development and progression of diseases. Hence, assessing the cellular transcriptome while simultaneously capturing the spatial relationships of cells within a tissue provides highly valuable insights into how cells communicate in health and disease. Yet, methods to probe the transcriptome often fail to preserve native spatial relationships, lack single-cell resolution, or are highly limited in throughput, i.e. lack the capacity to assess multiple environments simultaneously. Here, we introduce fragment-sequencing (fragment-seq), a method that enables the characterization of single-cell transcriptomes within multiple spatially distinct tissue microenvironments. We apply fragment-seq to a murine model of the metastatic liver to study liver zonation and the metastatic niche. This analysis reveals zonated genes and ligand-receptor interactions enriched in specific hepatic microenvironments. Finally, we apply fragment-seq to other tissues and species, demonstrating the adaptability of our method.
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Affiliation(s)
- Kristina Handler
- Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, 4056, Basel, Switzerland
| | - Karsten Bach
- Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, 4056, Basel, Switzerland
| | - Costanza Borrelli
- Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, 4056, Basel, Switzerland
| | - Salvatore Piscuoglio
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Visceral Surgery and Precision Medicine Research Laboratory, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Xenia Ficht
- Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, 4056, Basel, Switzerland
| | - Ilhan E Acar
- Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, 4056, Basel, Switzerland
| | - Andreas E Moor
- Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, 4056, Basel, Switzerland.
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120
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Zhang T, Zhang Z, Li L, Dong B, Wang G, Zhang D. GTAD: a graph-based approach for cell spatial composition inference from integrated scRNA-seq and ST-seq data. Brief Bioinform 2023; 25:bbad469. [PMID: 38127088 PMCID: PMC10734610 DOI: 10.1093/bib/bbad469] [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: 10/21/2023] [Revised: 11/20/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023] Open
Abstract
With the emergence of spatial transcriptome sequencing (ST-seq), research now heavily relies on the joint analysis of ST-seq and single-cell RNA sequencing (scRNA-seq) data to precisely identify cell spatial composition in tissues. However, common methods for combining these datasets often merge data from multiple cells to generate pseudo-ST data, overlooking topological relationships and failing to represent spatial arrangements accurately. We introduce GTAD, a method utilizing the Graph Attention Network for deconvolution of integrated scRNA-seq and ST-seq data. GTAD effectively captures cell spatial relationships and topological structures within tissues using a graph-based approach, enhancing cell-type identification and our understanding of complex tissue cellular landscapes. By integrating scRNA-seq and ST data into a unified graph structure, GTAD outperforms traditional 'pseudo-ST' methods, providing robust and information-rich results. GTAD performs exceptionally well with synthesized spatial data and accurately identifies cell spatial composition in tissues like the mouse cerebral cortex, cerebellum, developing human heart and pancreatic ductal carcinoma. GTAD holds the potential to enhance our understanding of tissue microenvironments and cellular diversity in complex bio-logical systems. The source code is available at https://github.com/zzhjs/GTAD.
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Affiliation(s)
- Tianjiao Zhang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Ziheng Zhang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Liangyu Li
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Benzhi Dong
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Dandan Zhang
- Department of Obstetrics and Gynecology, the First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
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121
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Xu H, Wang S, Fang M, Luo S, Chen C, Wan S, Wang R, Tang M, Xue T, Li B, Lin J, Qu K. SPACEL: deep learning-based characterization of spatial transcriptome architectures. Nat Commun 2023; 14:7603. [PMID: 37990022 PMCID: PMC10663563 DOI: 10.1038/s41467-023-43220-3] [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: 12/24/2022] [Accepted: 11/03/2023] [Indexed: 11/23/2023] Open
Abstract
Spatial transcriptomics (ST) technologies detect mRNA expression in single cells/spots while preserving their two-dimensional (2D) spatial coordinates, allowing researchers to study the spatial distribution of the transcriptome in tissues; however, joint analysis of multiple ST slices and aligning them to construct a three-dimensional (3D) stack of the tissue still remain a challenge. Here, we introduce spatial architecture characterization by deep learning (SPACEL) for ST data analysis. SPACEL comprises three modules: Spoint embeds a multiple-layer perceptron with a probabilistic model to deconvolute cell type composition for each spot in a single ST slice; Splane employs a graph convolutional network approach and an adversarial learning algorithm to identify spatial domains that are transcriptomically and spatially coherent across multiple ST slices; and Scube automatically transforms the spatial coordinate systems of consecutive slices and stacks them together to construct a 3D architecture of the tissue. Comparisons against 19 state-of-the-art methods using both simulated and real ST datasets from various tissues and ST technologies demonstrate that SPACEL outperforms the others for cell type deconvolution, for spatial domain identification, and for 3D alignment, thus showcasing SPACEL as a valuable integrated toolkit for ST data processing and analysis.
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Affiliation(s)
- Hao Xu
- Department of Oncology, The First Affiliated Hospital of USTC, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China
| | - Shuyan Wang
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- School of Data Science, University of Science and Technology of China, Hefei, 230027, China
| | - Minghao Fang
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
| | - Songwen Luo
- Department of Oncology, The First Affiliated Hospital of USTC, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China
| | - Chunpeng Chen
- Department of Oncology, The First Affiliated Hospital of USTC, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China
| | - Siyuan Wan
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- School of Data Science, University of Science and Technology of China, Hefei, 230027, China
| | - Rirui Wang
- Department of Oncology, The First Affiliated Hospital of USTC, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China
| | - Meifang Tang
- Department of Oncology, The First Affiliated Hospital of USTC, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China
| | - Tian Xue
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China
| | - Bin Li
- National Institute of Biological Sciences, Beijing, 102206, China.
| | - Jun Lin
- Department of Oncology, The First Affiliated Hospital of USTC, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China.
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China.
| | - Kun Qu
- Department of Oncology, The First Affiliated Hospital of USTC, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China.
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China.
- School of Data Science, University of Science and Technology of China, Hefei, 230027, China.
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122
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Fang Z, Liu T, Zheng R, A J, Yin M, Li M. stAA: adversarial graph autoencoder for spatial clustering task of spatially resolved transcriptomics. Brief Bioinform 2023; 25:bbad500. [PMID: 38189544 PMCID: PMC10772985 DOI: 10.1093/bib/bbad500] [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: 10/11/2023] [Revised: 11/22/2023] [Accepted: 12/11/2023] [Indexed: 01/09/2024] Open
Abstract
With the development of spatially resolved transcriptomics technologies, it is now possible to explore the gene expression profiles of single cells while preserving their spatial context. Spatial clustering plays a key role in spatial transcriptome data analysis. In the past 2 years, several graph neural network-based methods have emerged, which significantly improved the accuracy of spatial clustering. However, accurately identifying the boundaries of spatial domains remains a challenging task. In this article, we propose stAA, an adversarial variational graph autoencoder, to identify spatial domain. stAA generates cell embedding by leveraging gene expression and spatial information using graph neural networks and enforces the distribution of cell embeddings to a prior distribution through Wasserstein distance. The adversarial training process can make cell embeddings better capture spatial domain information and more robust. Moreover, stAA incorporates global graph information into cell embeddings using labels generated by pre-clustering. Our experimental results show that stAA outperforms the state-of-the-art methods and achieves better clustering results across different profiling platforms and various resolutions. We also conducted numerous biological analyses and found that stAA can identify fine-grained structures in tissues, recognize different functional subtypes within tumors and accurately identify developmental trajectories.
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Affiliation(s)
- Zhaoyu Fang
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Teng Liu
- Clinical Research Center (CRC), Medical Pathology Center (MPC), Cancer Early Detection and Treatment Center (CEDTC), Chongqing University Three Gorges Hospital, Chongqing University, Chongqing 404031, China
- Translational Medicine Research Center (TMRC), School of Medicine, Chongqing University, Chongqing 401331, China
| | - Ruiqing Zheng
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Jin A
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Mingzhu Yin
- Clinical Research Center (CRC), Medical Pathology Center (MPC), Cancer Early Detection and Treatment Center (CEDTC), Chongqing University Three Gorges Hospital, Chongqing University, Chongqing 404031, China
- Translational Medicine Research Center (TMRC), School of Medicine, Chongqing University, Chongqing 401331, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
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Polonsky M, Gerhardt LMS, Yun J, Koppitch K, Colón KL, Amrhein H, Zheng S, Yuan GC, Thomson M, Cai L, McMahon AP. Spatial transcriptomics defines injury-specific microenvironments in the adult mouse kidney and novel cellular interactions in regeneration and disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.22.568217. [PMID: 38045285 PMCID: PMC10690238 DOI: 10.1101/2023.11.22.568217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Kidney injury disrupts the intricate renal architecture and triggers limited regeneration, and injury-invoked inflammation and fibrosis. Deciphering molecular pathways and cellular interactions driving these processes is challenging due to the complex renal architecture. Here, we applied single cell spatial transcriptomics to examine ischemia-reperfusion injury in the mouse kidney. Spatial transcriptomics revealed injury-specific and spatially-dependent gene expression patterns in distinct cellular microenvironments within the kidney and predicted Clcf1-Crfl1 in a molecular interplay between persistently injured proximal tubule cells and neighboring fibroblasts. Immune cell types play a critical role in organ repair. Spatial analysis revealed cellular microenvironments resembling early tertiary lymphoid structures and identified associated molecular pathways. Collectively, this study supports a focus on molecular interactions in cellular microenvironments to enhance understanding of injury, repair and disease. One-Sentence Summary: Spatial transcriptomics predicted a molecular interplay amongst neighboring cell-types in the injured mammalian kidney Main Text.
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Chang T, Han W, Jiang M, Li J, Liao Z, Tang M, Zhang J, Shen J, Chen Z, Fei P, Ren X, Pang Y, Wang G, Wang J, Huang Y. Rapid and signal crowdedness-robust in situ sequencing through hybrid block coding. Proc Natl Acad Sci U S A 2023; 120:e2309227120. [PMID: 37963245 PMCID: PMC10666108 DOI: 10.1073/pnas.2309227120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 09/24/2023] [Indexed: 11/16/2023] Open
Abstract
Spatial transcriptomics technology has revolutionized our understanding of cell types and tissue organization, opening possibilities for researchers to explore transcript distributions at subcellular levels. However, existing methods have limitations in resolution, sensitivity, or speed. To overcome these challenges, we introduce SPRINTseq (Spatially Resolved and signal-diluted Next-generation Targeted sequencing), an innovative in situ sequencing strategy that combines hybrid block coding and molecular dilution strategies. Our method enables fast and sensitive high-resolution data acquisition, as demonstrated by recovering over 142 million transcripts using a 108-gene panel from 453,843 cells from four mouse brain coronal slices in less than 2 d. Using this advanced technology, we uncover the cellular and subcellular molecular architecture of Alzheimer's disease, providing additional information into abnormal cellular behaviors and their subcellular mRNA distribution. This improved spatial transcriptomics technology holds great promise for exploring complex biological processes and disease mechanisms.
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Affiliation(s)
- Tianyi Chang
- Biomedical Pioneering Innovation Center, Peking University, Beijing100871, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing100871, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing100871, China
| | - Wuji Han
- Biomedical Pioneering Innovation Center, Peking University, Beijing100871, China
- Yuanpei College, Peking University, Beijing100871, China
| | - Mengcheng Jiang
- Biomedical Pioneering Innovation Center, Peking University, Beijing100871, China
- Materials Science and Engineering, College of Engineering, Peking University, Beijing100871, China
| | - Jizhou Li
- School of Life Sciences, Tsinghua University, Beijing100084, China
| | - Zhizhao Liao
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing100871, China
- Changping Laboratory, Changping District, Beijing102206, China
| | - Mingchuan Tang
- Biomedical Pioneering Innovation Center, Peking University, Beijing100871, China
- Yuanpei College, Peking University, Beijing100871, China
| | - Jianyun Zhang
- Department of Oral Pathology, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing100081, China
| | - Jie Shen
- School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Capital Medical University, Beijing100069, China
| | - Zitian Chen
- Biomedical Pioneering Innovation Center, Peking University, Beijing100871, China
| | - Peng Fei
- School of Optical and Electronic Information – Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan430074, China
| | - Xianwen Ren
- Changping Laboratory, Changping District, Beijing102206, China
| | - Yuhong Pang
- Biomedical Pioneering Innovation Center, Peking University, Beijing100871, China
| | - Guanbo Wang
- Biomedical Pioneering Innovation Center, Peking University, Beijing100871, China
- Institute for Cell Analysis, Shenzhen Bay Laboratory, Guangdong528107, China
| | - Jianbin Wang
- School of Life Sciences, Tsinghua University, Beijing100084, China
| | - Yanyi Huang
- Biomedical Pioneering Innovation Center, Peking University, Beijing100871, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing100871, China
- Institute for Cell Analysis, Shenzhen Bay Laboratory, Guangdong528107, China
- College of Chemistry and Molecular Engineering, Beijing National Laboratory for Molecular Sciences, Peking University, Beijing100871, China
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125
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Wang J, Li J, Kramer ST, Su L, Chang Y, Xu C, Eadon MT, Kiryluk K, Ma Q, Xu D. Dimension-agnostic and granularity-based spatially variable gene identification using BSP. Nat Commun 2023; 14:7367. [PMID: 37963892 PMCID: PMC10645821 DOI: 10.1038/s41467-023-43256-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 11/03/2023] [Indexed: 11/16/2023] Open
Abstract
Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a non-parametric model by comparing gene expression pattens at two spatial granularities to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.
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Affiliation(s)
- Juexin Wang
- Department of BioHealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, Indianapolis, IN, 46202, USA.
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.
| | - Jinpu Li
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Skyler T Kramer
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Li Su
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Yuzhou Chang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Chunhui Xu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Michael T Eadon
- Department of Medicine, Indiana University, Indianapolis, IN, 46202, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA.
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA.
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA.
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126
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Zhang C, Dong K, Aihara K, Chen L, Zhang S. STAMarker: determining spatial domain-specific variable genes with saliency maps in deep learning. Nucleic Acids Res 2023; 51:e103. [PMID: 37811885 PMCID: PMC10639070 DOI: 10.1093/nar/gkad801] [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: 12/08/2022] [Revised: 08/26/2023] [Accepted: 09/19/2023] [Indexed: 10/10/2023] Open
Abstract
Spatial transcriptomics characterizes gene expression profiles while retaining the information of the spatial context, providing an unprecedented opportunity to understand cellular systems. One of the essential tasks in such data analysis is to determine spatially variable genes (SVGs), which demonstrate spatial expression patterns. Existing methods only consider genes individually and fail to model the inter-dependence of genes. To this end, we present an analytic tool STAMarker for robustly determining spatial domain-specific SVGs with saliency maps in deep learning. STAMarker is a three-stage ensemble framework consisting of graph-attention autoencoders, multilayer perceptron (MLP) classifiers, and saliency map computation by the backpropagated gradient. We illustrate the effectiveness of STAMarker and compare it with serveral commonly used competing methods on various spatial transcriptomic data generated by different platforms. STAMarker considers all genes at once and is more robust when the dataset is very sparse. STAMarker could identify spatial domain-specific SVGs for characterizing spatial domains and enable in-depth analysis of the region of interest in the tissue section.
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Affiliation(s)
- Chihao Zhang
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kangning Dong
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo 113-0033, Japan
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- School of Life Science and Technology, Shanghai Tech University, Shanghai 201210, China
- Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai, Guangdong 519031, China
| | - Shihua Zhang
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
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127
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Alexandrov T, Saez‐Rodriguez J, Saka SK. Enablers and challenges of spatial omics, a melting pot of technologies. Mol Syst Biol 2023; 19:e10571. [PMID: 37842805 PMCID: PMC10632737 DOI: 10.15252/msb.202110571] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 10/17/2023] Open
Abstract
Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.
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Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- BioInnovation InstituteCopenhagenDenmark
| | - Julio Saez‐Rodriguez
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Sinem K Saka
- Genome Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
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128
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Tang L, Huang ZP, Mei H, Hu Y. Insights gained from single-cell analysis of chimeric antigen receptor T-cell immunotherapy in cancer. Mil Med Res 2023; 10:52. [PMID: 37941075 PMCID: PMC10631149 DOI: 10.1186/s40779-023-00486-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 10/10/2023] [Indexed: 11/10/2023] Open
Abstract
Advances in chimeric antigen receptor (CAR)-T cell therapy have significantly improved clinical outcomes of patients with relapsed or refractory hematologic malignancies. However, progress is still hindered as clinical benefit is only available for a fraction of patients. A lack of understanding of CAR-T cell behaviors in vivo at the single-cell level impedes their more extensive application in clinical practice. Mounting evidence suggests that single-cell sequencing techniques can help perfect the receptor design, guide gene-based T cell modification, and optimize the CAR-T manufacturing conditions, and all of them are essential for long-term immunosurveillance and more favorable clinical outcomes. The information generated by employing these methods also potentially informs our understanding of the numerous complex factors that dictate therapeutic efficacy and toxicities. In this review, we discuss the reasons why CAR-T immunotherapy fails in clinical practice and what this field has learned since the milestone of single-cell sequencing technologies. We further outline recent advances in the application of single-cell analyses in CAR-T immunotherapy. Specifically, we provide an overview of single-cell studies focusing on target antigens, CAR-transgene integration, and preclinical research and clinical applications, and then discuss how it will affect the future of CAR-T cell therapy.
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Affiliation(s)
- Lu Tang
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Medical Center of Cell Therapy for Neoplastic Disease, Wuhan, 430022, China
- Key Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, 430022, China
| | - Zhong-Pei Huang
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Medical Center of Cell Therapy for Neoplastic Disease, Wuhan, 430022, China
- Key Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, 430022, China
| | - Heng Mei
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Medical Center of Cell Therapy for Neoplastic Disease, Wuhan, 430022, China.
- Key Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, 430022, China.
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Yu Hu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Medical Center of Cell Therapy for Neoplastic Disease, Wuhan, 430022, China.
- Key Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, 430022, China.
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
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129
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El Marrahi A, Lipreri F, Kang Z, Gsell L, Eroglu A, Alber D, Hausser J. NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology. Nat Commun 2023; 14:7182. [PMID: 37935691 PMCID: PMC10630431 DOI: 10.1038/s41467-023-42878-z] [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: 03/02/2023] [Accepted: 10/24/2023] [Indexed: 11/09/2023] Open
Abstract
Advances in multiplex histology allow surveying millions of cells, dozens of cell types, and up to thousands of phenotypes within the spatial context of tissue sections. This leads to a combinatorial challenge in (a) summarizing the cellular and phenotypic architecture of tissues and (b) identifying phenotypes with interesting spatial architecture. To address this, we combine ideas from community ecology and machine learning into niche-phenotype mapping (NIPMAP). NIPMAP takes advantage of geometric constraints on local cellular composition imposed by the niche structure of tissues in order to automatically segment tissue sections into niches and their interfaces. Projecting phenotypes on niches and their interfaces identifies previously-reported and previously-unreported spatially-driven phenotypes, concisely summarizes the phenotypic architecture of tissues, and reveals fundamental properties of tissue architecture. NIPMAP is applicable to both protein and RNA multiplex histology of healthy and diseased tissue. An open-source R/Python package implements NIPMAP.
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Affiliation(s)
- Anissa El Marrahi
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Fabio Lipreri
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Ziqi Kang
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Louise Gsell
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Alper Eroglu
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - David Alber
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Jean Hausser
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden.
- SciLifeLab; Solna, Stockholm, 171 65, Sweden.
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130
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Cheng Y, Hu M, Yang B, Jensen TB, Yang T, Yu R, Ma Z, Radda JSD, Jin S, Zang C, Wang S. Perturb-tracing enables high-content screening of multiscale 3D genome regulators. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.31.525983. [PMID: 36778402 PMCID: PMC9915657 DOI: 10.1101/2023.01.31.525983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Three-dimensional (3D) genome organization becomes altered during development, aging, and disease1-23, but the factors regulating chromatin topology are incompletely understood and currently no technology can efficiently screen for new regulators of multiscale chromatin organization. Here, we developed an image-based high-content screening platform (Perturb-tracing) that combines pooled CRISPR screen, a new cellular barcode readout method (BARC-FISH), and chromatin tracing. We performed a loss-of-function screen in human cells, and visualized alterations to their genome organization from 13,000 imaging target-perturbation combinations, alongside perturbation-paired barcode readout in the same single cells. Using 1.4 million 3D positions along chromosome traces, we discovered tens of new regulators of chromatin folding at different length scales, ranging from chromatin domains and compartments to chromosome territory. A subset of the regulators exhibited 3D genome effects associated with loop-extrusion and A-B compartmentalization mechanisms, while others were largely unrelated to these known 3D genome mechanisms. We found that the ATP-dependent helicase CHD7, the loss of which causes the congenital neural crest syndrome CHARGE24 and a chromatin remodeler previously shown to promote local chromatin openness25-27, counter-intuitively compacts chromatin over long range in different genomic contexts and cell backgrounds including neural crest cells, and globally represses gene expression. The DNA compaction effect of CHD7 is independent of its chromatin remodeling activity and does not require other protein partners. Finally, we identified new regulators of nuclear architectures and found a functional link between chromatin compaction and nuclear shape. Altogether, our method enables scalable, high-content identification of chromatin and nuclear topology regulators that will stimulate new insights into the 3D genome functions, such as global gene and nuclear regulation, in health and disease.
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Affiliation(s)
- Yubao Cheng
- Department of Genetics, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Mengwei Hu
- Department of Genetics, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Bing Yang
- Department of Genetics, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Tyler B Jensen
- Department of Genetics, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
- M.D.-Ph.D. Program, Yale University, New Haven, CT 06510, USA
| | - Tianqi Yang
- Department of Genetics, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Ruihuan Yu
- Department of Genetics, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Zhaoxia Ma
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Jonathan S D Radda
- Department of Genetics, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Shengyan Jin
- Department of Genetics, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Chongzhi Zang
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, 22908, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, 22908, USA
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA
- UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA, 22908, USA
| | - Siyuan Wang
- Department of Genetics, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
- Department of Cell Biology, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
- Yale Combined Program in the Biological and Biomedical Sciences, Yale University, New Haven, CT 06510, USA
- Molecular Cell Biology, Genetics and Development Program, Yale University, New Haven, CT 06510, USA
- Biochemistry, Quantitative Biology, Biophysics, and Structural Biology Program, Yale University, New Haven, CT 06510, USA
- M.D.-Ph.D. Program, Yale University, New Haven, CT 06510, USA
- Yale Center for RNA Science and Medicine, Yale University School of Medicine, New Haven, CT 06510, USA
- Yale Liver Center, Yale University School of Medicine, New Haven, CT 06510, USA
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131
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Zhang X, Cao Q, Rajachandran S, Grow EJ, Evans M, Chen H. Dissecting mammalian reproduction with spatial transcriptomics. Hum Reprod Update 2023; 29:794-810. [PMID: 37353907 PMCID: PMC10628492 DOI: 10.1093/humupd/dmad017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 05/15/2023] [Indexed: 06/25/2023] Open
Abstract
BACKGROUND Mammalian reproduction requires the fusion of two specialized cells: an oocyte and a sperm. In addition to producing gametes, the reproductive system also provides the environment for the appropriate development of the embryo. Deciphering the reproductive system requires understanding the functions of each cell type and cell-cell interactions. Recent single-cell omics technologies have provided insights into the gene regulatory network in discrete cellular populations of both the male and female reproductive systems. However, these approaches cannot examine how the cellular states of the gametes or embryos are regulated through their interactions with neighboring somatic cells in the native tissue environment owing to tissue disassociations. Emerging spatial omics technologies address this challenge by preserving the spatial context of the cells to be profiled. These technologies hold the potential to revolutionize our understanding of mammalian reproduction. OBJECTIVE AND RATIONALE We aim to review the state-of-the-art spatial transcriptomics (ST) technologies with a focus on highlighting the novel biological insights that they have helped to reveal about the mammalian reproductive systems in the context of gametogenesis, embryogenesis, and reproductive pathologies. We also aim to discuss the current challenges of applying ST technologies in reproductive research and provide a sneak peek at what the field of spatial omics can offer for the reproduction community in the years to come. SEARCH METHODS The PubMed database was used in the search for peer-reviewed research articles and reviews using combinations of the following terms: 'spatial omics', 'fertility', 'reproduction', 'gametogenesis', 'embryogenesis', 'reproductive cancer', 'spatial transcriptomics', 'spermatogenesis', 'ovary', 'uterus', 'cervix', 'testis', and other keywords related to the subject area. All relevant publications until April 2023 were critically evaluated and discussed. OUTCOMES First, an overview of the ST technologies that have been applied to studying the reproductive systems was provided. The basic design principles and the advantages and limitations of these technologies were discussed and tabulated to serve as a guide for researchers to choose the best-suited technologies for their own research. Second, novel biological insights into mammalian reproduction, especially human reproduction revealed by ST analyses, were comprehensively reviewed. Three major themes were discussed. The first theme focuses on genes with non-random spatial expression patterns with specialized functions in multiple reproductive systems; The second theme centers around functionally interacting cell types which are often found to be spatially clustered in the reproductive tissues; and the thrid theme discusses pathological states in reproductive systems which are often associated with unique cellular microenvironments. Finally, current experimental and computational challenges of applying ST technologies to studying mammalian reproduction were highlighted, and potential solutions to tackle these challenges were provided. Future directions in the development of spatial omics technologies and how they will benefit the field of human reproduction were discussed, including the capture of cellular and tissue dynamics, multi-modal molecular profiling, and spatial characterization of gene perturbations. WIDER IMPLICATIONS Like single-cell technologies, spatial omics technologies hold tremendous potential for providing significant and novel insights into mammalian reproduction. Our review summarizes these novel biological insights that ST technologies have provided while shedding light on what is yet to come. Our review provides reproductive biologists and clinicians with a much-needed update on the state of art of ST technologies. It may also facilitate the adoption of cutting-edge spatial technologies in both basic and clinical reproductive research.
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Affiliation(s)
- Xin Zhang
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qiqi Cao
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Shreya Rajachandran
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Edward J Grow
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Melanie Evans
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Haiqi Chen
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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132
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Sun S, Torok J, Mezias C, Ma D, Raj A. Spatial cell-type enrichment predicts mouse brain connectivity. Cell Rep 2023; 42:113258. [PMID: 37858469 DOI: 10.1016/j.celrep.2023.113258] [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: 12/12/2022] [Revised: 06/07/2023] [Accepted: 09/28/2023] [Indexed: 10/21/2023] Open
Abstract
A fundamental neuroscience topic is the link between the brain's molecular, cellular, and cytoarchitectonic properties and structural connectivity. Recent studies relate inter-regional connectivity to gene expression, but the relationship to regional cell-type distributions remains understudied. Here, we utilize whole-brain mapping of neuronal and non-neuronal subtypes via the matrix inversion and subset selection algorithm to model inter-regional connectivity as a function of regional cell-type composition with machine learning. We deployed random forest algorithms for predicting connectivity from cell-type densities, demonstrating surprisingly strong prediction accuracy of cell types in general, and particular non-neuronal cells such as oligodendrocytes. We found evidence of a strong distance dependency in the cell connectivity relationship, with layer-specific excitatory neurons contributing the most for long-range connectivity, while vascular and astroglia were salient for short-range connections. Our results demonstrate a link between cell types and connectivity, providing a roadmap for examining this relationship in other species, including humans.
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Affiliation(s)
- Shenghuan Sun
- Department of Radiology, University of California, San Francisco, San Francisco, CA, USA
| | - Justin Torok
- Department of Radiology, University of California, San Francisco, San Francisco, CA, USA
| | | | - Daren Ma
- Department of Radiology, University of California, San Francisco, San Francisco, CA, USA
| | - Ashish Raj
- Department of Radiology, University of California, San Francisco, San Francisco, CA, USA.
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133
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Meyers S, Demeyer S, Cools J. CRISPR screening in hematology research: from bulk to single-cell level. J Hematol Oncol 2023; 16:107. [PMID: 37875911 PMCID: PMC10594891 DOI: 10.1186/s13045-023-01495-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/21/2023] [Indexed: 10/26/2023] Open
Abstract
The CRISPR genome editing technology has revolutionized the way gene function is studied. Genome editing can be achieved in single genes or for thousands of genes simultaneously in sensitive genetic screens. While conventional genetic screens are limited to bulk measurements of cell behavior, recent developments in single-cell technologies make it possible to combine CRISPR screening with single-cell profiling. In this way, cell behavior and gene expression can be monitored simultaneously, with the additional possibility of including data on chromatin accessibility and protein levels. Moreover, the availability of various Cas proteins leading to inactivation, activation, or other effects on gene function further broadens the scope of such screens. The integration of single-cell multi-omics approaches with CRISPR screening open the path to high-content information on the impact of genetic perturbations at single-cell resolution. Current limitations in cell throughput and data density need to be taken into consideration, but new technologies are rapidly evolving and are likely to easily overcome these limitations. In this review, we discuss the use of bulk CRISPR screening in hematology research, as well as the emergence of single-cell CRISPR screening and its added value to the field.
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Affiliation(s)
- Sarah Meyers
- Center for Human Genetics, KU Leuven, Leuven, Belgium
- Center for Cancer Biology, VIB, Leuven, Belgium
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium
| | - Sofie Demeyer
- Center for Human Genetics, KU Leuven, Leuven, Belgium
- Center for Cancer Biology, VIB, Leuven, Belgium
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium
| | - Jan Cools
- Center for Human Genetics, KU Leuven, Leuven, Belgium.
- Center for Cancer Biology, VIB, Leuven, Belgium.
- Leuvens Kanker Instituut (LKI), KU Leuven - UZ Leuven, Leuven, Belgium.
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134
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Yao D, Binan L, Bezney J, Simonton B, Freedman J, Frangieh CJ, Dey K, Geiger-Schuller K, Eraslan B, Gusev A, Regev A, Cleary B. Scalable genetic screening for regulatory circuits using compressed Perturb-seq. Nat Biotechnol 2023:10.1038/s41587-023-01964-9. [PMID: 37872410 PMCID: PMC11035494 DOI: 10.1038/s41587-023-01964-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 08/22/2023] [Indexed: 10/25/2023]
Abstract
Pooled CRISPR screens with single-cell RNA sequencing readout (Perturb-seq) have emerged as a key technique in functional genomics, but they are limited in scale by cost and combinatorial complexity. In this study, we modified the design of Perturb-seq by incorporating algorithms applied to random, low-dimensional observations. Compressed Perturb-seq measures multiple random perturbations per cell or multiple cells per droplet and computationally decompresses these measurements by leveraging the sparse structure of regulatory circuits. Applied to 598 genes in the immune response to bacterial lipopolysaccharide, compressed Perturb-seq achieves the same accuracy as conventional Perturb-seq with an order of magnitude cost reduction and greater power to learn genetic interactions. We identified known and novel regulators of immune responses and uncovered evolutionarily constrained genes with downstream targets enriched for immune disease heritability, including many missed by existing genome-wide association studies. Our framework enables new scales of interrogation for a foundational method in functional genomics.
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Affiliation(s)
- Douglas Yao
- Program in Systems, Synthetic, and Quantitative Biology, Harvard University, Cambridge, MA, USA
| | - Loic Binan
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jon Bezney
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Brooke Simonton
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jahanara Freedman
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Chris J Frangieh
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kushal Dey
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | - Alexander Gusev
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Genentech, South San Francisco, CA, USA
| | - Brian Cleary
- Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA.
- Department of Biology, Boston University, Boston, MA, USA.
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
- Program in Bioinformatics, Boston University, Boston, MA, USA.
- Biological Design Center, Boston University, Boston, MA, USA.
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135
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Cohn DE, Forder A, Marshall EA, Vucic EA, Stewart GL, Noureddine K, Lockwood WW, MacAulay CE, Guillaud M, Lam WL. Delineating spatial cell-cell interactions in the solid tumour microenvironment through the lens of highly multiplexed imaging. Front Immunol 2023; 14:1275890. [PMID: 37936700 PMCID: PMC10627006 DOI: 10.3389/fimmu.2023.1275890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 10/11/2023] [Indexed: 11/09/2023] Open
Abstract
The growth and metastasis of solid tumours is known to be facilitated by the tumour microenvironment (TME), which is composed of a highly diverse collection of cell types that interact and communicate with one another extensively. Many of these interactions involve the immune cell population within the TME, referred to as the tumour immune microenvironment (TIME). These non-cell autonomous interactions exert substantial influence over cell behaviour and contribute to the reprogramming of immune and stromal cells into numerous pro-tumourigenic phenotypes. The study of some of these interactions, such as the PD-1/PD-L1 axis that induces CD8+ T cell exhaustion, has led to the development of breakthrough therapeutic advances. Yet many common analyses of the TME either do not retain the spatial data necessary to assess cell-cell interactions, or interrogate few (<10) markers, limiting the capacity for cell phenotyping. Recently developed digital pathology technologies, together with sophisticated bioimage analysis programs, now enable the high-resolution, highly-multiplexed analysis of diverse immune and stromal cell markers within the TME of clinical specimens. In this article, we review the tumour-promoting non-cell autonomous interactions in the TME and their impact on tumour behaviour. We additionally survey commonly used image analysis programs and highly-multiplexed spatial imaging technologies, and we discuss their relative advantages and limitations. The spatial organization of the TME varies enormously between patients, and so leveraging these technologies in future studies to further characterize how non-cell autonomous interactions impact tumour behaviour may inform the personalization of cancer treatment..
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Affiliation(s)
- David E. Cohn
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Aisling Forder
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Erin A. Marshall
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Emily A. Vucic
- Department of Biochemistry and Molecular Pharmacology, New York University (NYU) Langone Medical Center, New York, NY, United States
| | - Greg L. Stewart
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Kouther Noureddine
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - William W. Lockwood
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Calum E. MacAulay
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Martial Guillaud
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Wan L. Lam
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
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136
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Nagy-Pál P, Veres JM, Fekete Z, Karlócai MR, Weisz F, Barabás B, Reéb Z, Hájos N. Structural Organization of Perisomatic Inhibition in the Mouse Medial Prefrontal Cortex. J Neurosci 2023; 43:6972-6987. [PMID: 37640552 PMCID: PMC10586541 DOI: 10.1523/jneurosci.0432-23.2023] [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: 03/09/2023] [Revised: 08/11/2023] [Accepted: 08/18/2023] [Indexed: 08/31/2023] Open
Abstract
Perisomatic inhibition profoundly controls neural function. However, the structural organization of inhibitory circuits giving rise to the perisomatic inhibition in the higher-order cortices is not completely known. Here, we performed a comprehensive analysis of those GABAergic cells in the medial prefrontal cortex (mPFC) that provide inputs onto the somata and proximal dendrites of pyramidal neurons. Our results show that most GABAergic axonal varicosities contacting the perisomatic region of superficial (layer 2/3) and deep (layer 5) pyramidal cells express parvalbumin (PV) or cannabinoid receptor type 1 (CB1). Further, we found that the ratio of PV/CB1 GABAergic inputs is larger on the somatic membrane surface of pyramidal tract neurons in comparison with those projecting to the contralateral hemisphere. Our morphologic analysis of in vitro labeled PV+ basket cells (PVBC) and CCK/CB1+ basket cells (CCKBC) revealed differences in many features. PVBC dendrites and axons arborized preferentially within the layer where their soma was located. In contrast, the axons of CCKBCs expanded throughout layers, although their dendrites were found preferentially either in superficial or deep layers. Finally, using anterograde trans-synaptic tracing we observed that PVBCs are preferentially innervated by thalamic and basal amygdala afferents in layers 5a and 5b, respectively. Thus, our results suggest that PVBCs can control the local circuit operation in a layer-specific manner via their characteristic arborization, whereas CCKBCs rather provide cross-layer inhibition in the mPFC.SIGNIFICANCE STATEMENT Inhibitory cells in cortical circuits are crucial for the precise control of local network activity. Nevertheless, in higher-order cortical areas that are involved in cognitive functions like decision-making, working memory, and cognitive flexibility, the structural organization of inhibitory cell circuits is not completely understood. In this study we show that perisomatic inhibitory control of excitatory cells in the medial prefrontal cortex is performed by two types of basket cells endowed with different morphologic properties that provide inhibitory inputs with distinct layer specificity on cells projecting to disparate areas. Revealing this difference in innervation strategy of the two basket cell types is a key step toward understanding how they fulfill their distinct roles in cortical network operations.
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Affiliation(s)
- Petra Nagy-Pál
- Eötvös Loránd Research Network Institute of Experimental Medicine, 1083 Budapest, Hungary
- János Szentágothai School of Neurosciences, Semmelweis University, 1085 Budapest, Hungary
| | - Judit M Veres
- Eötvös Loránd Research Network Institute of Experimental Medicine, 1083 Budapest, Hungary
| | - Zsuzsanna Fekete
- Eötvös Loránd Research Network Institute of Experimental Medicine, 1083 Budapest, Hungary
- János Szentágothai School of Neurosciences, Semmelweis University, 1085 Budapest, Hungary
| | - Mária R Karlócai
- Eötvös Loránd Research Network Institute of Experimental Medicine, 1083 Budapest, Hungary
| | - Filippo Weisz
- Eötvös Loránd Research Network Institute of Experimental Medicine, 1083 Budapest, Hungary
| | - Bence Barabás
- Eötvös Loránd Research Network Institute of Experimental Medicine, 1083 Budapest, Hungary
- János Szentágothai School of Neurosciences, Semmelweis University, 1085 Budapest, Hungary
| | - Zsófia Reéb
- Eötvös Loránd Research Network Institute of Experimental Medicine, 1083 Budapest, Hungary
- Doctoral School of Biology, Institute of Biology, Eötvös Loránd University, 1117 Budapest, Hungary
| | - Norbert Hájos
- Eötvös Loránd Research Network Institute of Experimental Medicine, 1083 Budapest, Hungary
- Linda and Jack Gill Center for Molecular Bioscience, Indiana University Bloomington, Bloomington, Indiana 47405
- Program in Neuroscience, Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, Indiana 47405
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137
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Greenstreet L, Afanassiev A, Kijima Y, Heitz M, Ishiguro S, King S, Yachie N, Schiebinger G. DNA-GPS: A theoretical framework for optics-free spatial genomics and synthesis of current methods. Cell Syst 2023; 14:844-859.e4. [PMID: 37751737 DOI: 10.1016/j.cels.2023.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 04/19/2023] [Accepted: 08/25/2023] [Indexed: 09/28/2023]
Abstract
While single-cell sequencing technologies provide unprecedented insights into genomic profiles at the cellular level, they lose the spatial context of cells. Over the past decade, diverse spatial transcriptomics and multi-omics technologies have been developed to analyze molecular profiles of tissues. In this article, we categorize current spatial genomics technologies into three classes: optical imaging, positional indexing, and mathematical cartography. We discuss trade-offs in resolution and scale, identify limitations, and highlight synergies between existing single-cell and spatial genomics methods. Further, we propose DNA-GPS (global positioning system), a theoretical framework for large-scale optics-free spatial genomics that combines ideas from mathematical cartography and positional indexing. DNA-GPS has the potential to achieve scalable spatial genomics for multiple measurement modalities, and by eliminating the need for optical measurement, it has the potential to position cells in three-dimensions (3D).
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Affiliation(s)
- Laura Greenstreet
- Department of Mathematics, The University of British Columbia, Vancouver, BC, Canada
| | - Anton Afanassiev
- Department of Mathematics, The University of British Columbia, Vancouver, BC, Canada
| | - Yusuke Kijima
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada; Department of Aquatic Bioscience, The University of Tokyo, Tokyo, Japan
| | - Matthieu Heitz
- Department of Mathematics, The University of British Columbia, Vancouver, BC, Canada
| | - Soh Ishiguro
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Samuel King
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Nozomu Yachie
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada; Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan; Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Osaka, Japan; Graduate School of Media and Governance, Keio University, Fujisawa, Japan.
| | - Geoffrey Schiebinger
- Department of Mathematics, The University of British Columbia, Vancouver, BC, Canada; School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada.
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138
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Eichenberger BT, Griesbach E, Mitchell J, Chao JA. Following the Birth, Life, and Death of mRNAs in Single Cells. Annu Rev Cell Dev Biol 2023; 39:253-275. [PMID: 37843928 DOI: 10.1146/annurev-cellbio-022723-024045] [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: 10/18/2023]
Abstract
Recent advances in single-molecule imaging of mRNAs in fixed and living cells have enabled the lives of mRNAs to be studied with unprecedented spatial and temporal detail. These approaches have moved beyond simply being able to observe specific events and have begun to allow an understanding of how regulation is coupled between steps in the mRNA life cycle. Additionally, these methodologies are now being applied in multicellular systems and animals to provide more nuanced insights into the physiological regulation of RNA metabolism.
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Affiliation(s)
- Bastian T Eichenberger
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland;
- University of Basel, Basel, Switzerland
| | - Esther Griesbach
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland;
| | - Jessica Mitchell
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland;
| | - Jeffrey A Chao
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland;
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139
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Pan J, Chang Z, Zhang X, Dong Q, Zhao H, Shi J, Wang G. Research progress of single-cell sequencing in tuberculosis. Front Immunol 2023; 14:1276194. [PMID: 37901241 PMCID: PMC10611525 DOI: 10.3389/fimmu.2023.1276194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 09/29/2023] [Indexed: 10/31/2023] Open
Abstract
Tuberculosis is a major infectious disease caused by Mycobacterium tuberculosis infection. The pathogenesis and immune mechanism of tuberculosis are not clear, and it is urgent to find new drugs, diagnosis, and treatment targets. A useful tool in the quest to reveal the enigmas related to Mycobacterium tuberculosis infection and disease is the single-cell sequencing technique. By clarifying cell heterogeneity, identifying pathogenic cell groups, and finding key gene targets, the map at the single cell level enables people to better understand the cell diversity of complex organisms and the immune state of hosts during infection. Here, we briefly reviewed the development of single-cell sequencing, and emphasized the different applications and limitations of various technologies. Single-cell sequencing has been widely used in the study of the pathogenesis and immune response of tuberculosis. We review these works summarizing the most influential findings. Combined with the multi-molecular level and multi-dimensional analysis, we aim to deeply understand the blank and potential future development of the research on Mycobacterium tuberculosis infection using single-cell sequencing technology.
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Affiliation(s)
| | | | | | | | | | - Jingwei Shi
- Key Laboratory of Pathobiology Ministry of Education, College of Basic Medical Sciences/China-Japan Union Hospital of Jilin University, Jilin University, Changchun, China
| | - Guoqing Wang
- Key Laboratory of Pathobiology Ministry of Education, College of Basic Medical Sciences/China-Japan Union Hospital of Jilin University, Jilin University, Changchun, China
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140
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Chitra U, Arnold BJ, Sarkar H, Ma C, Lopez-Darwin S, Sanno K, Raphael BJ. Mapping the topography of spatial gene expression with interpretable deep learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561757. [PMID: 37873258 PMCID: PMC10592770 DOI: 10.1101/2023.10.10.561757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of this data complicates the analysis of spatial gene expression patterns such as gene expression gradients. We address these issues by deriving a topographic map of a tissue slice-analogous to a map of elevation in a landscape-using a novel quantity called the isodepth. Contours of constant isodepth enclose spatial domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in gene expression. We develop GASTON, an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gene expression gradients, and piecewise linear functions of the isodepth that model both continuous gradients and discontinuous spatial variation in the expression of individual genes. We validate GASTON by showing that it accurately identifies spatial domains and marker genes across several biological systems. In SRT data from the brain, GASTON reveals gradients of neuronal differentiation and firing, and in SRT data from a tumor sample, GASTON infers gradients of metabolic activity and epithelial-mesenchymal transition (EMT)-related gene expression in the tumor microenvironment.
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Affiliation(s)
- Uthsav Chitra
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Brian J. Arnold
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
| | - Hirak Sarkar
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Ludwig Cancer Institute, Princeton Branch, Princeton University, Princeton, NJ, USA
| | - Cong Ma
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | | | - Kohei Sanno
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
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141
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Pickard K, Stephenson E, Mitchell A, Jardine L, Bacon CM. Location, location, location: mapping the lymphoma tumor microenvironment using spatial transcriptomics. Front Oncol 2023; 13:1258245. [PMID: 37869076 PMCID: PMC10586500 DOI: 10.3389/fonc.2023.1258245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/19/2023] [Indexed: 10/24/2023] Open
Abstract
Lymphomas are a heterogenous group of lymphoid neoplasms with a wide variety of clinical presentations. Response to treatment and prognosis differs both between and within lymphoma subtypes. Improved molecular and genetic profiling has increased our understanding of the factors which drive these clinical dynamics. Immune and non-immune cells within the lymphoma tumor microenvironment (TME) can both play a key role in antitumor immune responses and conversely also support lymphoma growth and survival. A deeper understanding of the lymphoma TME would identify key lymphoma and immune cell interactions which could be disrupted for therapeutic benefit. Single cell RNA sequencing studies have provided a more comprehensive description of the TME, however these studies are limited in that they lack spatial context. Spatial transcriptomics provides a comprehensive analysis of gene expression within tissue and is an attractive technique in lymphoma to both disentangle the complex interactions between lymphoma and TME cells and improve understanding of how lymphoma cells evade the host immune response. This article summarizes current spatial transcriptomic technologies and their use in lymphoma research to date. The resulting data has already enriched our knowledge of the mechanisms and clinical impact of an immunosuppressive TME in lymphoma and the accrual of further studies will provide a fundamental step in the march towards personalized medicine.
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Affiliation(s)
- Keir Pickard
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Haematology Department, Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Emily Stephenson
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Alex Mitchell
- Haematology Department, Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Laura Jardine
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Haematology Department, Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Chris M. Bacon
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Cellular Pathology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
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142
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Li Z, Chen X, Zhang X, Jiang R, Chen S. Latent feature extraction with a prior-based self-attention framework for spatial transcriptomics. Genome Res 2023; 33:1757-1773. [PMID: 37903634 PMCID: PMC10691543 DOI: 10.1101/gr.277891.123] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/19/2023] [Indexed: 11/01/2023]
Abstract
Rapid advances in spatial transcriptomics (ST) have revolutionized the interrogation of spatial heterogeneity and increase the demand for comprehensive methods to effectively characterize spatial domains. As a prerequisite for ST data analysis, spatial domain characterization is a crucial step for downstream analyses and biological implications. Here we propose a prior-based self-attention framework for spatial transcriptomics (PAST), a variational graph convolutional autoencoder for ST, which effectively integrates prior information via a Bayesian neural network, captures spatial patterns via a self-attention mechanism, and enables scalable application via a ripple walk sampler strategy. Through comprehensive experiments on data sets generated by different technologies, we show that PAST can effectively characterize spatial domains and facilitate various downstream analyses, including ST visualization, spatial trajectory inference and pseudotime analysis. Also, we highlight the advantages of PAST for multislice joint embedding and automatic annotation of spatial domains in newly sequenced ST data. Compared with existing methods, PAST is the first ST method that integrates reference data to analyze ST data. We anticipate that PAST will open up new avenues for researchers to decipher ST data with customized reference data, which expands the applicability of ST technology.
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Affiliation(s)
- Zhen Li
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaoyang Chen
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xuegong Zhang
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Rui Jiang
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
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143
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Yuan Z, Yao J. Harnessing computational spatial omics to explore the spatial biology intricacies. Semin Cancer Biol 2023; 95:25-41. [PMID: 37400044 DOI: 10.1016/j.semcancer.2023.06.006] [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: 12/02/2022] [Revised: 05/09/2023] [Accepted: 06/19/2023] [Indexed: 07/05/2023]
Abstract
Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.
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Affiliation(s)
- Zhiyuan Yuan
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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144
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Shi H, He Y, Zhou Y, Huang J, Maher K, Wang B, Tang Z, Luo S, Tan P, Wu M, Lin Z, Ren J, Thapa Y, Tang X, Chan KY, Deverman BE, Shen H, Liu A, Liu J, Wang X. Spatial atlas of the mouse central nervous system at molecular resolution. Nature 2023; 622:552-561. [PMID: 37758947 PMCID: PMC10709140 DOI: 10.1038/s41586-023-06569-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/22/2023] [Indexed: 09/29/2023]
Abstract
Spatially charting molecular cell types at single-cell resolution across the 3D volume is critical for illustrating the molecular basis of brain anatomy and functions. Single-cell RNA sequencing has profiled molecular cell types in the mouse brain1,2, but cannot capture their spatial organization. Here we used an in situ sequencing method, STARmap PLUS3,4, to profile 1,022 genes in 3D at a voxel size of 194 × 194 × 345 nm3, mapping 1.09 million high-quality cells across the adult mouse brain and spinal cord. We developed computational pipelines to segment, cluster and annotate 230 molecular cell types by single-cell gene expression and 106 molecular tissue regions by spatial niche gene expression. Joint analysis of molecular cell types and molecular tissue regions enabled a systematic molecular spatial cell-type nomenclature and identification of tissue architectures that were undefined in established brain anatomy. To create a transcriptome-wide spatial atlas, we integrated STARmap PLUS measurements with a published single-cell RNA-sequencing atlas1, imputing single-cell expression profiles of 11,844 genes. Finally, we delineated viral tropisms of a brain-wide transgene delivery tool, AAV-PHP.eB5,6. Together, this annotated dataset provides a single-cell resource that integrates the molecular spatial atlas, brain anatomy and the accessibility to genetic manipulation of the mammalian central nervous system.
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Affiliation(s)
- Hailing Shi
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yichun He
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Yiming Zhou
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jiahao Huang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kamal Maher
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Computational and Systems Biology PhD Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brandon Wang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Zefang Tang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shuchen Luo
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Peng Tan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Morgan Wu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Zuwan Lin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Jingyi Ren
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yaman Thapa
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xin Tang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Ken Y Chan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin E Deverman
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hao Shen
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Albert Liu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jia Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
| | - Xiao Wang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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145
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Jung N, Kim TK. Spatial transcriptomics in neuroscience. Exp Mol Med 2023; 55:2105-2115. [PMID: 37779145 PMCID: PMC10618223 DOI: 10.1038/s12276-023-01093-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: 02/28/2023] [Revised: 07/07/2023] [Accepted: 07/09/2023] [Indexed: 10/03/2023] Open
Abstract
The brain is one of the most complex living tissue types and is composed of an exceptional diversity of cell types displaying unique functional connectivity. Single-cell RNA sequencing (scRNA-seq) can be used to efficiently map the molecular identities of the various cell types in the brain by providing the transcriptomic profiles of individual cells isolated from the tissue. However, the lack of spatial context in scRNA-seq prevents a comprehensive understanding of how different configurations of cell types give rise to specific functions in individual brain regions and how each distinct cell is connected to form a functional unit. To understand how the various cell types contribute to specific brain functions, it is crucial to correlate the identities of individual cells obtained through scRNA-seq with their spatial information in intact tissue. Spatial transcriptomics (ST) can resolve the complex spatial organization of cell types in the brain and their connectivity. Various ST tools developed during the past decade based on imaging and sequencing technology have permitted the creation of functional atlases of the brain and have pulled the properties of neural circuits into ever-sharper focus. In this review, we present a summary of several ST tools and their applications in neuroscience and discuss the unprecedented insights these tools have made possible.
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Affiliation(s)
- Namyoung Jung
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk, 37673, Republic of Korea
| | - Tae-Kyung Kim
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk, 37673, Republic of Korea.
- Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul, 03722, Republic of Korea.
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146
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Miao H, Kuruoğlu EE, Xu T. Non-homogeneous Poisson and renewal processes as spatial models for cancer mutation. Comput Biol Chem 2023; 106:107922. [PMID: 37499435 DOI: 10.1016/j.compbiolchem.2023.107922] [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: 10/27/2022] [Revised: 07/08/2023] [Accepted: 07/11/2023] [Indexed: 07/29/2023]
Abstract
Advances in sequencing technology assisted biologists in revealing signatures of DNA cancer mutation process and in demonstrating the mutagenesis behind. However, most of these signatures proposed by majority of work focus only on the type and frequency of mutations, without considering spatial information which is non-negligible in exploring mechanisms of mutation occurrence, e.g., Kataegis. Statistical characterization of the distance between consecutive mutations can give us relative spatial information; however, it ignores location information which is as important as distance information. In this work, we assume that DNA cancer mutations are location-dependent and that integrating the two variables, location and inter-distance, is beneficial to study DNA cancer mutation processes more accurately. Particularly, instead of following a specific distribution over the whole DNA sequence, we found out that the distribution of distance between successive mutations alternates between exponential and power-law distributions. Apart from this, the parameters of either of the two distributions vary with DNA locations. The cancers with kataegis phenomenon, a specific mutation pattern caused by abnormal activity of APOBEC protein family, are more likely to be accompanied by higher parameter values of distance distribution, implying higher occurrence rate of mutation. Therefore, we propose non-homogeneous Poisson and Renewal processes to spatially model DNA cancer mutations and to describe mutation patterns quantitatively and more accurately through a statistical perspective.
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Affiliation(s)
- Hengyuan Miao
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
| | - Ercan Engin Kuruoğlu
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China; Institute of Data and Information, Tsinghua University, Shenzhen, China; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
| | - Tao Xu
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China; Bio-intelligent Manufacturing and Living Matter Bioprinting Center, Research Institute of Tsinghua University in Shenzhen, Tsinghua University, Shenzhen, China; Department of Neurosurgery, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
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147
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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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148
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Cheng Z, Stefani C, Skillman T, Klimas A, Lee A, DiBernardo EF, Brown KM, Milman T, Wang Y, Gallagher BR, Lagree K, Jena BP, Pulido JS, Filler SG, Mitchell AP, Hiller NL, Lacy‐Hulbert A, Zhao Y. MicroMagnify: A Multiplexed Expansion Microscopy Method for Pathogens and Infected Tissues. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302249. [PMID: 37658522 PMCID: PMC10602566 DOI: 10.1002/advs.202302249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/29/2023] [Indexed: 09/03/2023]
Abstract
Super-resolution optical imaging tools are crucial in microbiology to understand the complex structures and behavior of microorganisms such as bacteria, fungi, and viruses. However, the capabilities of these tools, particularly when it comes to imaging pathogens and infected tissues, remain limited. MicroMagnify (µMagnify) is developed, a nanoscale multiplexed imaging method for pathogens and infected tissues that are derived from an expansion microscopy technique with a universal biomolecular anchor. The combination of heat denaturation and enzyme cocktails essential is found for robust cell wall digestion and expansion of microbial cells and infected tissues without distortion. µMagnify efficiently retains biomolecules suitable for high-plex fluorescence imaging with nanoscale precision. It demonstrates up to eightfold expansion with µMagnify on a broad range of pathogen-containing specimens, including bacterial and fungal biofilms, infected culture cells, fungus-infected mouse tone, and formalin-fixed paraffin-embedded human cornea infected by various pathogens. Additionally, an associated virtual reality tool is developed to facilitate the visualization and navigation of complex 3D images generated by this method in an immersive environment allowing collaborative exploration among researchers worldwide. µMagnify is a valuable imaging platform for studying how microbes interact with their host systems and enables the development of new diagnosis strategies against infectious diseases.
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Affiliation(s)
- Zhangyu Cheng
- Department of Biological SciencesCarnegie Mellon University4400 Fifth AvenuePittsburghPA15213USA
| | - Caroline Stefani
- Benaroya Research Institute at Virginia Mason1201 9th AveSeattleWA98101USA
| | | | - Aleksandra Klimas
- Department of Biological SciencesCarnegie Mellon University4400 Fifth AvenuePittsburghPA15213USA
| | - Aramchan Lee
- Department of Biological SciencesCarnegie Mellon University4400 Fifth AvenuePittsburghPA15213USA
| | - Emma F. DiBernardo
- Department of Biological SciencesCarnegie Mellon University4400 Fifth AvenuePittsburghPA15213USA
| | - Karina Mueller Brown
- Department of Biological SciencesCarnegie Mellon University4400 Fifth AvenuePittsburghPA15213USA
| | - Tatyana Milman
- Wills Eye Hospital and Jefferson University HospitalPhiladelphiaPA19107USA
| | - Yuhong Wang
- Department of Biological SciencesCarnegie Mellon University4400 Fifth AvenuePittsburghPA15213USA
| | - Brendan R. Gallagher
- Department of Biological SciencesCarnegie Mellon University4400 Fifth AvenuePittsburghPA15213USA
| | - Katherine Lagree
- Department of Biological SciencesCarnegie Mellon University4400 Fifth AvenuePittsburghPA15213USA
| | - Bhanu P. Jena
- Viron Molecular Medicine Institute201 Washington StreetBostonMA02201USA
- Department of PhysiologyWayne State University42 W Warren AveDetroitMI48202USA
- NanoBioScience InstituteWayne State University42 W Warren AveDetroitMI48202USA
- Center for Molecular Medicine & GeneticsSchool of MedicineWayne State University42 W Warren AveDetroitMI48202USA
| | - Jose S. Pulido
- Wills Eye Hospital and Jefferson University HospitalPhiladelphiaPA19107USA
| | - Scott G. Filler
- Lundquist Institute for Biomedical Innovation at Harbor‐UCLA Medical Center1124 W Carson StTorranceCA90502USA
- David Geffen School of Medicine at UCLA10833 Le Conte AveLos AngelesCA90095USA
| | - Aaron P. Mitchell
- Department of Biological SciencesCarnegie Mellon University4400 Fifth AvenuePittsburghPA15213USA
- Department of MicrobiologyUniversity of Georgia210 S Jackson streetAthensGA30602USA
| | - N. Luisa Hiller
- Department of Biological SciencesCarnegie Mellon University4400 Fifth AvenuePittsburghPA15213USA
| | - Adam Lacy‐Hulbert
- Benaroya Research Institute at Virginia Mason1201 9th AveSeattleWA98101USA
| | - Yongxin Zhao
- Department of Biological SciencesCarnegie Mellon University4400 Fifth AvenuePittsburghPA15213USA
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149
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Baysoy A, Bai Z, Satija R, Fan R. The technological landscape and applications of single-cell multi-omics. Nat Rev Mol Cell Biol 2023; 24:695-713. [PMID: 37280296 PMCID: PMC10242609 DOI: 10.1038/s41580-023-00615-w] [Citation(s) in RCA: 106] [Impact Index Per Article: 106.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2023] [Indexed: 06/08/2023]
Abstract
Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.
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Affiliation(s)
- Alev Baysoy
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Zhiliang Bai
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Rahul Satija
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA.
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150
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Jin K, Zhang Z, Zhang K, Viggiani F, Callahan C, Tang J, Aronow BJ, Shu J. Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.19.558548. [PMID: 37786667 PMCID: PMC10541596 DOI: 10.1101/2023.09.19.558548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Single-cell spatial transcriptomics such as in-situ hybridization or sequencing technologies can provide subcellular resolution that enables the identification of individual cell identities, locations, and a deep understanding of subcellular mechanisms. However, accurate segmentation and annotation that allows individual cell boundaries to be determined remains a major challenge that limits all the above and downstream insights. Current machine learning methods heavily rely on nuclei or cell body staining, resulting in the significant loss of both transcriptome depth and the limited ability to learn latent representations of spatial colocalization relationships. Here, we propose Bering, a graph deep learning model that leverages transcript colocalization relationships for joint noise-aware cell segmentation and molecular annotation in 2D and 3D spatial transcriptomics data. Graph embeddings for the cell annotation are transferred as a component of multi-modal input for cell segmentation, which is employed to enrich gene relationships throughout the process. To evaluate performance, we benchmarked Bering with state-of-the-art methods and observed significant improvement in cell segmentation accuracies and numbers of detected transcripts across various spatial technologies and tissues. To streamline segmentation processes, we constructed expansive pre-trained models, which yield high segmentation accuracy in new data through transfer learning and self-distillation, demonstrating the generalizability of Bering.
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Affiliation(s)
- Kang Jin
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- Department of Biomedical Informatics, University of Cincinnati, Cincinnati, OH, 45229, USA
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - Zuobai Zhang
- Mila - Québec AI Institute, Montréal, H2S 3H1, Québec, Canada
- Department of Computer Science and Operations Research, Université de Montréal, Montréal, H3T 1J4, Québec, Canada
| | - Ke Zhang
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - Francesca Viggiani
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - Claire Callahan
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - Jian Tang
- Mila - Québec AI Institute, Montréal, H2S 3H1, Québec, Canada
- Department of Decision Sciences, HEC Montréal, Montréal, H3T 2A7, Québec, Canada
- CIFAR AI Research Chair
| | - Bruce J Aronow
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- Department of Biomedical Informatics, University of Cincinnati, Cincinnati, OH, 45229, USA
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, 45221, USA
| | - Jian Shu
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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