| 1. |
- Baker, Ethan A. G., et al.
(author)
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In silico tissue generation and power analysis for spatial omics
- 2023
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In: Nature Methods. - : Springer Nature. - 1548-7091 .- 1548-7105. ; 20:3, s. 424-
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Journal article (peer-reviewed)abstract
- As spatially resolved multiplex profiling of RNA and proteins becomes more prominent, it is increasingly important to understand the statistical power available to test specific hypotheses when designing and interpreting such experiments. Ideally, it would be possible to create an oracle that predicts sampling requirements for generalized spatial experiments. However, the unknown number of relevant spatial features and the complexity of spatial data analysis make this challenging. Here, we enumerate multiple parameters of interest that should be considered in the design of a properly powered spatial omics study. We introduce a method for tunable in silico tissue (IST) generation and use it with spatial profiling data sets to construct an exploratory computational framework for spatial power analysis. Finally, we demonstrate that our framework can be applied across diverse spatial data modalities and tissues of interest. While we demonstrate ISTs in the context of spatial power analysis, these simulated tissues have other potential use cases, including spatial method benchmarking and optimization. This paper presents a statistical framework for power analysis of spatial omics studies, facilitated by an in silico tissue-generation method.
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| 2. |
- Vickovic, Sanja, et al.
(author)
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High-definition spatial transcriptomics for in situ tissue profiling
- 2019
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In: Nature Methods. - : NATURE PUBLISHING GROUP. - 1548-7091 .- 1548-7105. ; 16:10, s. 987-
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Journal article (peer-reviewed)abstract
- Spatial and molecular characteristics determine tissue function, yet high-resolution methods to capture both concurrently are lacking. Here, we developed high-definition spatial transcriptomics, which captures RNA from histological tissue sections on a dense, spatially barcoded bead array. Each experiment recovers several hundred thousand transcriptcoupled spatial barcodes at 2-mu m resolution, as demonstrated in mouse brain and primary breast cancer. This opens the way to high-resolution spatial analysis of cells and tissues.
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| 3. |
- Vickovic, Sanja, et al.
(author)
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Three-dimensional spatial transcriptomics uncovers cell type dynamics in the rheumatoid arthritis synovium
- 2024
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Other publication (other academic/artistic)abstract
- The inflamed rheumatic joint is a highly heterogeneous and complex tissue with dynamic recruitment and expansion of multiple cell types that interact in multifaceted ways within a localized area. Rheumatoid arthritis synovium has primarily been studied either by immunostaining or by molecular profiling after tissue homogenization. Here, we use Spatial Transcriptomics to study local cellular interactions at the site of chronic synovial inflammation. We report comprehensive spatial RNA-seq data coupled to quantitative and cell type-specific chemokine-driven dynamics at and around organized structures of infiltrating leukocyte cells in the synovium.
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| 4. |
- Vickovic, Sanja, et al.
(author)
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Three-dimensional spatial transcriptomics uncovers cell type localizations in the human rheumatoid arthritis synovium
- 2022
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In: Communications Biology. - : Springer Nature. - 2399-3642. ; 5:1
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Journal article (peer-reviewed)abstract
- The inflamed rheumatic joint is a highly heterogeneous and complex tissue with dynamic recruitment and expansion of multiple cell types that interact in multifaceted ways within a localized area. Rheumatoid arthritis synovium has primarily been studied either by immunostaining or by molecular profiling after tissue homogenization. Here, we use Spatial Transcriptomics, where tissue-resident RNA is spatially labeled in situ with barcodes in a transcriptome-wide fashion, to study local tissue interactions at the site of chronic synovial inflammation. We report comprehensive spatial RNA-Seq data coupled to cell type-specific localization patterns at and around organized structures of infiltrating leukocyte cells in the synovium. Combining morphological features and high-throughput spatially resolved transcriptomics may be able to provide higher statistical power and more insights into monitoring disease severity and treatment-specific responses in seropositive and seronegative rheumatoid arthritis. Sanja Vickovic et al. use spatial transcriptomics to probe the local synovial tissue interactions in rheumatoid arthritis (RA) patients. Their results provide a valuable resource to understand the spatial organisation of cell populations in the synovium in the context of RA-associated inflammation.
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