WebAtlas pipeline for integrated single cell and spatial transcriptomic data

Single cell and spatial transcriptomics illuminate complementary features of tissues. However, online dissemination and exploration of integrated datasets is challenging due to the heterogeneity and scale of data. We introduce the WebAtlas pipeline for user-friendly sharing and interactive navigation of integrated datasets. WebAtlas unifies commonly used atlassing technologies into the cloud-optimised Zarr format and builds on Vitessce to enable remote data navigation. We showcase WebAtlas on the developing human lower limb to cross-query cell types and genes across single cell, sequencing- and imaging-based spatial transcriptomic data.

[1]  M. Gerstung,et al.  SpatialData: an open and universal data framework for spatial omics , 2023, bioRxiv.

[2]  David S. Fischer,et al.  The scverse project provides a computational ecosystem for single-cell omics data analysis , 2023, Nature Biotechnology.

[3]  B. Williams,et al.  A human embryonic limb cell atlas resolved in space and time , 2023, bioRxiv.

[4]  Fabian J Theis,et al.  OME-Zarr: a cloud-optimized bioimaging file format with international community support , 2023, bioRxiv.

[5]  Yi Zhao,et al.  SODB facilitates comprehensive exploration of spatial omics data , 2023, Nature Methods.

[6]  Carolyn A. Morrison,et al.  High resolution mapping of the breast cancer tumor microenvironment using integrated single cell, spatial and in situ analysis of FFPE tissue , 2022, bioRxiv.

[7]  Hernando M. Vergara,et al.  MoBIE: a Fiji plugin for sharing and exploration of multi-modal cloud-hosted big image data , 2022, bioRxiv.

[8]  Jeremy L. Muhlich,et al.  Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data , 2022, bioRxiv.

[9]  J. Marioni,et al.  StabMap: Mosaic single cell data integration using non-overlapping features , 2022, bioRxiv.

[10]  Fabian J Theis,et al.  Spatial components of molecular tissue biology , 2022, Nature Biotechnology.

[11]  M. Gerstung,et al.  Cell2location maps fine-grained cell types in spatial transcriptomics , 2022, Nature Biotechnology.

[12]  A. Brazma,et al.  The BioImage Archive - building a home for life-sciences microscopy data , 2021, bioRxiv.

[13]  Chris Allan,et al.  OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies , 2021, Nature Methods.

[14]  L. Yates,et al.  PoSTcode: Probabilistic image-based spatial transcriptomics decoder , 2021, bioRxiv.

[15]  B. Göttgens,et al.  Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis , 2021, Nature biotechnology.

[16]  Gustavo S. França,et al.  Exploring tissue architecture using spatial transcriptomics , 2021, Nature.

[17]  Gabriele Partel,et al.  TissUUmaps: interactive visualization of large-scale spatial gene expression and tissue morphology data , 2020, Bioinform..

[18]  Marius Pachitariu,et al.  Cellpose: a generalist algorithm for cellular segmentation , 2020, Nature Methods.

[19]  J. Marioni,et al.  Investigating higher order interactions in single cell data with scHOT , 2019, Nature Methods.

[20]  Martin Hjelmare,et al.  ImJoy: an open-source computational platform for the deep learning era , 2019, Nature Methods.

[21]  Fabian J Theis,et al.  SCANPY: large-scale single-cell gene expression data analysis , 2018, Genome Biology.

[22]  Bálint Antal,et al.  Image Data Resource: a bioimage data integration and publication platform , 2017, Nature Methods.

[23]  Philipp Otto,et al.  webKnossos: efficient online 3D data annotation for connectomics , 2017, Nature Methods.

[24]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[25]  E. Bradley,et al.  WNT5A regulates chondrocyte differentiation through differential use of the CaN/NFAT and IKK/NF-kappaB pathways. , 2010, Molecular endocrinology.

[26]  M. Paulsson,et al.  Expression of matrilins during maturation of mouse skeletal tissues. , 2002, Matrix biology : journal of the International Society for Matrix Biology.

[27]  Markus M. Hilscher,et al.  In Situ Sequencing: A High-Throughput, Multi-Targeted Gene Expression Profiling Technique for Cell Typing in Tissue Sections. , 2020, Methods in molecular biology.