Open-ST: High-resolution spatial transcriptomics in 3D

Schott, Marie; León-Periñán, Daniel; Splendiani, Elena; Strenger, Leon; Licha, Jan Robin; Pentimalli, Tancredi Massimo; Schallenberg, Simon; Alles, Jonathan; Samut Tagliaferro, Sarah; Boltengagen, Anastasiya; Ehrig, Sebastian; Abbiati, Stefano; Dommerich, Steffen; Pagani, Massimiliano; Ferretti, Elisabetta; Macino, Giuseppe; Karaiskos, Nikos; Rajewsky, Nikolaus

doi:10.1016/j.cell.2024.05.055

Cited by 17 publications

(2 citation statements)

References 74 publications

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“…units larger than a cell) spatial barcoding followed by sequencing. More recent sequencing-based approaches, such as Visium HD (5) and Open-ST (6) reached subcellular resolution, which greatly increased the amount of output data. Another category of spatial transcriptomics assays are based on combinatorial barcoding, probe-hybridisation and multiple rounds of image acquisition, and include Xenium (7) , CosMx (8) and MERSCOPE (9) .…”

Section: Introductionmentioning

confidence: 99%

Rapid and memory-efficient analysis and quality control of large spatial transcriptomics datasets

Kӧvér,

Vigilante

2024

Preprint

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The 10x Visium spatial transcriptomics platform has been widely adopted due to its established analysis pipelines, robust community support, and manageable data output. However, technologies like 10x Visium have the limitation of being low-resolution, and recently spatial transcriptomics platforms with subcellular resolution have proliferated. Such high-resolution datasets pose significant computational challenges for data analysis, with regards to memory requirement and processing speed. Here, we introduce Pseudovisium, a Python-based framework designed to facilitate the rapid and memory-efficient analysis, quality control and interoperability of high-resolution spatial transcriptomics data. This is achieved by mimicking the structure of 10x Visium through hexagonal binning of transcripts. Analysis of 47 publicly available datasets concluded that Pseudovisium increased data processing speed and reduced dataset size by more than an order of magnitude. At the same time, it preserved key biological signatures, such as spatially variable genes, enriched gene sets, cell populations, and gene-gene correlations. The Pseudovisium framework allows accurate simulation of Visium experiments, facilitating comparisons between technologies and guiding experimental design. Specifically, we found high concordance between Pseudovisium (derived from Xenium or CosMx) and Visium data from consecutive tissue slices. We further demonstrate Pseudovisium's utility by performing rapid quality control on large-scale datasets from Xenium, CosMx, and MERSCOPE platforms, identifying similar replicates, as well as potentially low-quality samples and probes. The common data format provided by Pseudovisium also enabled direct comparison of metrics across 6 spatial transcriptomics platforms and 59 datasets, revealing differences in transcript capture efficiency and quality. Lastly, Pseudovisium allows merging of datasets for joint analysis, as demonstrated by the identification of shared cell clusters and enriched gene sets in the mouse brain using data from multiple spatial platforms. By lowering the computational requirements and enhancing interoperability and reusability of spatial transcriptomics data, Pseudovisium democratizes analysis for wet-lab scientists and enables novel biological insights.

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Section: Introductionmentioning

confidence: 99%

Rapid and memory-efficient analysis and quality control of large spatial transcriptomics datasets

Kӧvér,

Vigilante

2024

Preprint

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“…The low spatial resolution of in-situ capture methods complicates the spatial analysis of single-cells, requiring deconvolution, imputation and/or integration with external singlecell transcriptomics resources [11][12][13] . However, recent advancements in spatial transcriptomics have revolutionized the field by offering full transcriptome profiling at nanometer resolution through profiling methods such as Stereo-seq, Seq-Scope, Open-ST, and Nova-ST [14][15][16][17] . These high-resolution techniques provide unique benefits, such as the ability to resolve transcriptome-wide expression at subcellular levels, in some cases in the sub-micron range.…”

Section: Introductionmentioning

confidence: 99%

Sainsc: a computational tool for segmentation-free analysis ofin-situcapture

Müller-Bötticher,

Tiesmeyer,

Eils

et al. 2024

Preprint

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Spatially resolved transcriptomics has become the method of choice to characterise the complexity of biomedical tissue samples. Until recently, scientists have been restricted to profiling methods with high spatial resolution but for a limited set of genes or methods that can profile transcriptome-wide but at low spatial resolution. Through recent developments, there are now methods which offer subcellular spatial resolution and full transcriptome coverage. However, utilizing the high spatial and gene resolution of these new methods remains elusive due to several factors including low detection efficiency, high computational cost and difficulties in delineating cell borders. Here we present Sainsc (Segmentation-free analysis ofin-situcapture data), which combines a cell-segmentation free approach with efficient data processing of transcriptome-wide nanometer resolution spatial data. Sainsc can generate cell-type maps with accurate cell-type assignment at a subcellular level, together with corresponding maps of the assignment scores that facilitate the interpretation in the local confidence of cell-type assignment. We demonstrate its utility and accuracy across different tissues and profiling methods. Compared to other methods, Sainsc requires lower computational resources and has scalable performance, enabling interactive data exploration. Sainsc is compatible with common data analysis frameworks and is available as open-source software in multiple programming languages.

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Next‐generation spatial transcriptomics: unleashing the power to gear up translational oncology

Wang,

Hong,

et al. 2024

MedComm

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The growing advances in spatial transcriptomics (ST) stand as the new frontier bringing unprecedented influences in the realm of translational oncology. This has triggered systemic experimental design, analytical scope, and depth alongside with thorough bioinformatics approaches being constantly developed in the last few years. However, harnessing the power of spatial biology and streamlining an array of ST tools to achieve designated research goals are fundamental and require real‐world experiences. We present a systemic review by updating the technical scope of ST across different principal basis in a timeline manner hinting on the generally adopted ST techniques used within the community. We also review the current progress of bioinformatic tools and propose in a pipelined workflow with a toolbox available for ST data exploration. With particular interests in tumor microenvironment where ST is being broadly utilized, we summarize the up‐to‐date progress made via ST‐based technologies by narrating studies categorized into either mechanistic elucidation or biomarker profiling (translational oncology) across multiple cancer types and their ways of deploying the research through ST. This updated review offers as a guidance with forward‐looking viewpoints endorsed by many high‐resolution ST tools being utilized to disentangle biological questions that may lead to clinical significance in the future.

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Open-ST: High-resolution spatial transcriptomics in 3D

Cited by 17 publications

References 74 publications

Rapid and memory-efficient analysis and quality control of large spatial transcriptomics datasets

Rapid and memory-efficient analysis and quality control of large spatial transcriptomics datasets

Sainsc: a computational tool for segmentation-free analysis ofin-situcapture

Next‐generation spatial transcriptomics: unleashing the power to gear up translational oncology

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