Squidpy: a scalable framework for spatial omics analysis

Palla, Giovanni; Spitzer, Hannah; Klein, Michal; Fischer, David; Schaar, Anna C.; Kuemmerle, Louis B.; Rybakov, Sergei; Ibarra, Ignacio L.; Holmberg, Olle; Virshup, Isaac; Lotfollahi, Mohammad; Richter, Sabrina; Theis, Fabian J.

doi:10.1038/s41592-021-01358-2

Cited by 444 publications

(470 citation statements)

References 59 publications

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“…Given the relevance of scRNA-seq to spatial data, and how spatial data are often analyzed like scRNA-seq data in exploratory data analysis (EDA), popular scRNA-seq EDA ecosystems, such as Seurat 32 , SCANPY (which spatical single-cell analysis in Python (Squidpy) is built on) 117 , and SingleCellExperiment (extended by SpatialExperiment) 118 , have added functionalities for spatial data, such as updates to data containers and functions to facilitate visualization of gene expression and cell or spot metadata at spatial locations (Section 7.2 of Supplementary Information). EDA packages dedicated to spatial data with beautiful graphics and good documentation have also been written, such as Giotto 119 and STUtility 120 .…”

Section: Discussionmentioning

confidence: 99%

Museum of spatial transcriptomics

2022

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The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors, depends on the spatial organization of their cells. In the past decade, high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To comprehensively document spatial gene expression technologies and data-analysis methods, we present a curated review of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field, such as usage of experimental techniques, species, tissues studied, and computational approaches used. Our Review places current methods in a historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/.

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Section: Discussionmentioning

confidence: 99%

Museum of spatial transcriptomics

2022

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“…Alongside experimental development, computational frameworks have been developed in order to analyze these new data sets [11][12][13][14][15][16][17] . However, it still remains challenging to accurately recover spatial domains especially when these domains contain a multitude of cell types.…”

Section: Discussionmentioning

confidence: 99%

“…The recovery of anatomical structures containing multiple cell types becomes extremely arduous and generally has relied on manual isolation. One solution to this challenge is to link anatomical territories from companion hematoxylin and eosin (H&E) staining images to their spatial transcriptomic assay [14][15][16][17][18] . However, techniques such as Slide-Seq 9,10 do not provide these companion images.…”

Section: Main Textmentioning

confidence: 99%

Vesalius: high-resolution in silico anatomization of Spatial Transcriptomic data using Image Analysis

Martin

Lövkvist

Hong

et al. 2021

Preprint

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Characterization of tissue architecture is important to understand cell function and mechanisms in vivo. Spatial transcriptomics (ST), by recovering the transcriptome of a cell or tissue section while maintaining the two-dimensional location of the cell or tissue, can provide remarkable insights into the cellular function in association with the tissue architecture. Still, however, automatic identification of tissue architecture is challenging. Here, we present Vesalius to decipher tissue anatomy by embracing various image analysis techniques for high-resolution ST data. Vesalius recovers known and even previously uncharacterized anatomical structures in mouse brain and embryos. Intriguingly, Vesalius identifies spatially expressed genes linked to the morphology of tissue structures. Vesalius is a tool to perform high-resolution in silico anatomization and molecular characterization from ST data.

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“…gr . co_occurrence function (Palla et al 2022). Briefly, the ratio describes the normalized probability of locating a cell type i in a radius of size d , conditioned on the existence of a cell type j : …”

Section: Supplementary Textmentioning

confidence: 99%

Concordance of MERFISH Spatial Transcriptomics with Bulk and Single-cell RNA Sequencing

Liu

Tran

Vemuri

et al. 2022

Preprint

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Spatial transcriptomics extends single cell RNA sequencing (scRNA-seq) technologies by providing spatial context for cell type identification and analysis. In particular, imaging-based spatial technologies such as Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH) can achieve single-cell resolution, allowing for the direct mapping of single cell identities to spatial positions. Nevertheless, because MERFISH produces an intrinsically different data type than scRNA-seq methods, a technical comparison between the two modalities is necessary to ascertain how best to integrate them. Here, we used the Vizgen MERSCOPE platform to perform MERFISH on mouse liver and kidney tissues and compared the resulting bulk and single-cell RNA statistics with those from the existing Tabula Muris Senis cell atlas. We found that MERFISH produced measurements that quantitatively reproduced the bulk RNA-seq and scRNA-seq results, with some minor differences in overall gene dropout rates and single-cell transcript count statistics. Finally, we explored the ability of MERFISH to identify cell types, and found that it could independently resolve distinct cell types and spatial structure in both liver and kidney. Computational integration with the Tabula Muris Senis atlas using scVI and scANVI did not noticeably enhance these results. We conclude that compared to scRNA-seq, MERFISH provides a quantitatively comparable method for measuring single-cell gene expression, and that efficient gene panel design allows for robust identification of cell types with intact spatial information without the need for computational integration with scRNA-seq reference atlases.

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Squidpy: a scalable framework for spatial omics analysis

Cited by 444 publications

References 59 publications

Museum of spatial transcriptomics

Museum of spatial transcriptomics

Vesalius: high-resolution in silico anatomization of Spatial Transcriptomic data using Image Analysis

Concordance of MERFISH Spatial Transcriptomics with Bulk and Single-cell RNA Sequencing

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