Dataset Information

STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics.

ABSTRACT: In in situ capturing-based spatial transcriptomics, spots of the same size and printed at fixed locations cannot precisely capture the randomly-located single cells, therefore inherently failing to profile transcriptome at the single-cell level. To this end, we present STIE, an Expectation Maximization algorithm that aligns the spatial transcriptome to its matched histology image-based nuclear morphology and recovers missing cells from ~70% gap area, thereby achieving the real single-cell level and whole-slide scale deconvolution, convolution, and clustering for both low- and high-resolution spots. STIE characterizes cell-type-specific gene expression and demonstrates outperforming concordance with true cell-type-specific transcriptomic signatures than the other spot- and subspot-level methods. Furthermore, STIE reveals the single-cell level insights, for instance, lower actual spot resolution than its reported spot size, unbiased evaluation of cell type colocalization, superior power of high-resolution spot in distinguishing nuanced cell types, and spatial cell-cell interactions at the single-cell level other than spot level.

SUBMITTER: Zhu S

PROVIDER: S-EPMC11364663 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Publications

STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics.

Zhu Shijia S Kubota Naoto N Wang Shidan S Wang Tao T Xiao Guanghua G Hoshida Yujin Y

Nature communications 20240830 1

In in situ capturing-based spatial transcriptomics, spots of the same size and printed at fixed locations cannot precisely capture the randomly-located single cells, therefore inherently failing to profile transcriptome at the single-cell level. To this end, we present STIE, an Expectation Maximization algorithm that aligns the spatial transcriptome to its matched histology image-based nuclear morphology and recovers missing cells from ~70% gap area, thereby achieving the real single-cell level ...[more]

PMID: 39214995

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Project description:In spot-based spatial transcriptomics, spots that are of the same size and printed at the fixed location cannot precisely capture the actual randomly located single cells, therefore failing to profile the transcriptome at the single-cell level. The current studies primarily focused on enhancing the spot resolution in size via computational imputation or technical improvement, however, they largely overlooked that single-cell resolution, i.e., resolution in cellular or even smaller size, does not equal single-cell level. Using both real and simulated spatial transcriptomics data, we demonstrated that even the high-resolution spatial transcriptomics still has a large number of spots partially covering multiple cells simultaneously, revealing the intrinsic non-single-cell level of spot-based spatial transcriptomics regardless of spot size. To this end, we present STIE, an EM algorithm that aligns the spatial transcriptome to its matched histology image-based nuclear morphology and recovers missing cells from up to ~70% gap area between spots via the nuclear morphological similarity and neighborhood information, thereby achieving the real single-cell level and whole-slide scale deconvolution/convolution and clustering for both low- and high-resolution spots. On both real and simulation spatial transcriptomics data, STIE characterizes the cell-type specific gene expression variation and demonstrates the outperforming concordance with the single-cell RNAseq-derived cell type transcriptomic signatures compared to the other spot- and subspot-level methods. Furthermore, STIE enabled us to gain novel insights that failed to be revealed by the existing methods due to the lack of single-cell level, for instance, lower actual spot resolution than its reported spot size, the additional contribution of cellular morphology to cell typing beyond transcriptome, unbiased evaluation of cell type colocalization, superior power of high-resolution spot in distinguishing nuanced cell types, and spatially resolved cell-cell interactions at the single-cell level other than spot level. The STIE code is publicly available as an R package at https://github.com/zhushijia/STIE.

Dataset Information

STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics.

Publications

STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics.

Similar Datasets

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