Probabilistic cell typing enables fine mapping of closely related cell types in situ.
Xiaoyan Qian,Kenneth D. Harris,Thomas Hauling,Thomas Hauling,Dimitris Nicoloutsopoulos,Ana B. Muñoz-Manchado,Nathan G. Skene,Nathan G. Skene,Jens Hjerling-Leffler,Mats Nilsson +9 more
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TLDR
This work introduces probabilistic cell typing by in situ sequencing (pciSeq), an approach that leverages previous scRNA-seq classification to identify cell types using multiplexed in situ RNA detection to spatially map cell types accurately in the mouse hippocampus and isocortex.Abstract:
Understanding the function of a tissue requires knowing the spatial organization of its constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has revealed the genome-wide expression patterns that define its many, closely related neuronal types, but cannot reveal their spatial arrangement. Here we introduce probabilistic cell typing by in situ sequencing (pciSeq), an approach that leverages previous scRNA-seq classification to identify cell types using multiplexed in situ RNA detection. We applied this method by mapping the inhibitory neurons of mouse hippocampal area CA1, for which ground truth is available from extensive previous work identifying their laminar organization. Our method identified these neuronal classes in a spatial arrangement matching ground truth, and further identified multiple classes of isocortical pyramidal cell in a pattern matching their known organization. This method will allow identifying the spatial organization of closely related cell types across the brain and other tissues.read more
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Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer's Disease.
Wei Ting Chen,Wei Ting Chen,Ashley Lu,Ashley Lu,Katleen Craessaerts,Katleen Craessaerts,Benjamin Pavie,Carlo Sala Frigerio,Carlo Sala Frigerio,Carlo Sala Frigerio,Nikky Corthout,Xiaoyan Qian,Jana Lalakova,Malte Kühnemund,Iryna Voytyuk,Iryna Voytyuk,Leen Wolfs,Leen Wolfs,Renzo Mancuso,Renzo Mancuso,Evgenia Salta,Evgenia Salta,Sriram Balusu,Sriram Balusu,An Snellinx,An Snellinx,Sebastian Munck,Aleksandra Jurek,José Fernández Navarro,Takaomi C. Saido,Inge Huitinga,Inge Huitinga,Joakim Lundeberg,Mark Fiers,Mark Fiers,Mark Fiers,Bart De Strooper,Bart De Strooper,Bart De Strooper +38 more
TL;DR: Genome-wide spatial transcriptomics analysis provides an unprecedented approach to untangle the dysregulated cellular network in the vicinity of pathogenic hallmarks of AD and other brain diseases.
Journal ArticleDOI
A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart
Michaela Asp,Stefania Giacomello,Ludvig Larsson,Chenglin Wu,Daniel Fürth,Xiaoyan Qian,Eva Wärdell,Joaquin Custodio,Johan Reimegård,Fredrik Salmén,Cecilia Österholm,Patrik L. Ståhl,Erik Sundström,Elisabet Åkesson,Olaf Bergmann,Magda Bienko,Agneta Månsson-Broberg,Mats Nilsson,Christer Sylvén,Joakim Lundeberg +19 more
TL;DR: A molecular approach is presented that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains.
Journal ArticleDOI
Exploring tissue architecture using spatial transcriptomics.
TL;DR: Spatial transcriptomics can also be used for hypothesis testing using experimental designs that compare time points or conditions, including genetic or environmental perturbations as mentioned in this paper, and is naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.
Journal ArticleDOI
Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
TL;DR: In this paper, a suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue.
Journal ArticleDOI
Spatially Resolved Transcriptomes-Next Generation Tools for Tissue Exploration.
TL;DR: In this review, available spatial transcriptomics methods are described and their applications as well as their strengths and weaknesses are discussed.
References
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Journal ArticleDOI
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Amit Zeisel,Ana B. Muñoz-Manchado,Simone Codeluppi,Peter Lönnerberg,Gioele La Manno,Anna Juréus,Sueli Marques,Hermany Munguba,Liqun He,Christer Betsholtz,Christer Betsholtz,Charlotte Rolny,Gonçalo Castelo-Branco,Jens Hjerling-Leffler,Sten Linnarsson +14 more
TL;DR: Large-scale single-cell RNA sequencing is used to classify cells in the mouse somatosensory cortex and hippocampal CA1 region and found 47 molecularly distinct subclasses, comprising all known major cell types in the cortex.
Journal ArticleDOI
Globally optimal stitching of tiled 3D microscopic image acquisitions
TL;DR: This work developed a method that, based on the Fourier Shift Theorem, computes all possible translations between pairs of 3D images, yielding the best overlap in terms of the cross-correlation measure and subsequently finds the globally optimal configuration of the whole group of3D images.
Journal ArticleDOI
Molecular Architecture of the Mouse Nervous System.
Amit Zeisel,Hannah Hochgerner,Peter Lönnerberg,Anna Johnsson,Fatima Memic,Job van der Zwan,Martin Häring,Emelie Braun,Lars E. Borm,Gioele La Manno,Simone Codeluppi,Alessandro Furlan,Kawai Lee,Nathan G. Skene,Kenneth D. Harris,Jens Hjerling-Leffler,Ernest Arenas,Patrik Ernfors,Ulrika Marklund,Sten Linnarsson +19 more
TL;DR: RNA sequencing of half a million single cells was used to create a detailed census of cell types in the mouse nervous system and mapped cell types spatially and derived a hierarchical, data-driven taxonomy.
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