WebAtlas pipeline for integrated single cell and spatial transcriptomic data

Li, Tong; Horsfall, David; Basurto-Lozada, Daniela; Roberts, Kenny; Prete, Martin; Lawrence, John E G; He, Peng; Tuck, Elisabeth; Moore, Josh; Ghazanfar, Shila; Teichmann, Sarah; Haniffa, Muzlifah; Bayraktar, Omer Ali

doi:10.1101/2023.05.19.541329

Cited by 7 publications

(4 citation statements)

References 32 publications

(12 reference statements)

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“…To place cells in their anatomical context at high resolution, we applied a nearest neighbour technique to combine this integrated single cell atlas with our 90-gene in-situ sequencing (ISS) dataset for the embryonic hindlimb 21 (see methods). This analysis revealed clear spatial segregation between mesenchymal-derived cell types, such as muscle, cartilage and periosteum, as well as co-location of vascular endothelial cells and macrophages.…”

Section: Resultsmentioning

confidence: 99%

“…Here, we set out to profile human skeletal development by performing single-cell RNA sequencing on individual long bones dissected from first trimester human hind limbs. We combine this data with that of previous studies to produce a detailed atlas of endochondral ossification 19–21 . We then use this atlas as a reference for evaluating a series of in vitro chondrogenesis protocols, finding substantial variability in the cell states produced by the different protocols, all of which exhibited some off-target differentiation.…”

Section: Introductionmentioning

confidence: 99%

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Single cell transcriptomics reveals chondrocyte differentiation dynamicsin vivoandin vitro

Lawrence,

Woods,

Roberts

et al. 2023

Preprint

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SummaryThe consistent production ofin vitrochondrocytes that faithfully recapitulatein vivodevelopment would be of great benefit for musculoskeletal disease modelling and regenerative medicine. Current efforts are often limited by off-target differentiation, resulting in a heterogeneous product. Furthermore, the lack of comparison to human embryonic tissue, precludes detailed evaluation ofin vitrocells. Here, we perform single-cell RNA sequencing of embryonic long bones dissected from first trimester hind limbs from a range of gestational ages. We combine this with publicly available data to form a detailed atlas of endochondral ossification, which we then use to evaluate a series of publishedin vitrochondrogenesis protocols, finding substantial variability in cell states produced by each. We apply single-nuclear RNA sequencing to one protocol to enable direct comparison betweenin vitroandin vivo,and perform trajectory alignment between the two to reveal differentiation dynamics at the single-cell level, shedding new light on off-target differentiationin vitro. Using this information, we inhibit the activity of FOXO1, a transcription factor predicted to be active in embryonic bone development and in chondrogenic cellsin vitro, and increase chondrocyte transcriptsin vitro.This study therefore presents a new framework for evaluating tissue engineering protocols, using single-cell data from human development to drive improvement and bring the prospect of true engineered cartilage closer to reality.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Single cell transcriptomics reveals chondrocyte differentiation dynamicsin vivoandin vitro

Lawrence,

Woods,

Roberts

et al. 2023

Preprint

Self Cite

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“…We also thank I. Gold, M. Keller, N. Gehlenborg, T. Li and O. Bayraktar for discussions and initial implementations concerning JavaScript interoperability for remote data visualization with Vitessce 22 , in particular as part of the WebAltas pipeline 27 . In addition we thank M. Klein for his valuable contributions to napari-spatialdata during a hackathon in Heidelberg (April 2023); F. Wünnemann for his contributions on spatialdata-io in another hackathon in Heidelberg (July 2023); Q. Blampey for contributions to spatialdata-io; A. Shmatko for contribution to implementation of the napari lasso tool; A.…”

Section: Acknowledgementsmentioning

confidence: 99%

SpatialData: an open and universal data framework for spatial omics

Marconato,

Palla,

Yamauchi

et al. 2024

Nat Methods

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Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study.

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“…Computational integration of different data modalities remains a challenge, as well as common frameworks for the analysis of spatially resolved omics data. Recent pipelines were able to convert and harmonize data sets from multiple single-cell and spatial technologies in the developing human lower limb, creating a joint embedding of 3 modalities ( Li et al 2023 ), and others were able to bring the diversity of spatial data in a common data representation and provide a common set of analysis and interactive visualization tools ( Palla et al 2022 ). Comprehensive benchmarking studies to evaluate performance of each method provide valuable information and guidelines for the development of more effective methods.…”

Section: Conclusion and Future Perspectivementioning

confidence: 99%

Redefining Mucosal Inflammation with Spatial Genomics

Caetano,

Sharpe

2024

J Dent Res

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The human oral mucosa contains one of the most complex cellular systems that are essential for normal physiology and defense against a wide variety of local pathogens. Evolving techniques and experimental systems have helped refine our understanding of this complex cellular network. Current single-cell RNA sequencing methods can resolve subtle differences between cell types and states, thus providing a great tool for studying the molecular and cellular repertoire of the oral mucosa in health and disease. However, it requires the dissociation of tissue samples, which means that the interrelationships between cells are lost. Spatial transcriptomic methods bypass tissue dissociation and retain this spatial information, thereby allowing gene expression to be assessed across thousands of cells within the context of tissue structural organization. Here, we discuss the contribution of spatial technologies in shaping our understanding of this complex system. We consider the impact on identifying disease cellular neighborhoods and how space defines cell state. We also discuss the limitations and future directions of spatial sequencing technologies with recent advances in machine learning. Finally, we offer a perspective on open questions about mucosal homeostasis that these technologies are well placed to address.

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WebAtlas pipeline for integrated single cell and spatial transcriptomic data

Cited by 7 publications

References 32 publications

Single cell transcriptomics reveals chondrocyte differentiation dynamicsin vivoandin vitro

Single cell transcriptomics reveals chondrocyte differentiation dynamicsin vivoandin vitro

SpatialData: an open and universal data framework for spatial omics

Redefining Mucosal Inflammation with Spatial Genomics

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