Dataset Information

Accurate inference of genome-wide spatial expression with iSpatial.

ABSTRACT: Spatially resolved transcriptomic analyses can reveal molecular insights underlying tissue structure and context-dependent cell-cell or cell-environment interaction. Because of the current technical limitation, obtaining genome-wide spatial transcriptome at single-cell resolution is challenging. Here, we developed a new algorithm named iSpatial to derive the spatial pattern of the entire transcriptome by integrating spatial transcriptomic and single-cell RNA-seq datasets. Compared to other existing methods, iSpatial has higher accuracy in predicting gene expression and spatial distribution. Furthermore, it reduces false-positive and false-negative signals in the original datasets. By testing iSpatial with multiple spatial transcriptomic datasets, we demonstrate its wide applicability to datasets from different tissues and by different techniques. Thus, we provide a computational approach to reveal spatial organization of the entire transcriptome at single-cell resolution. With numerous high-quality datasets available in the public domain, iSpatial provides a unique way to understand the structure and function of complex tissues and disease processes.

SUBMITTER: Zhang C

PROVIDER: S-EPMC9417177 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Accurate inference of genome-wide spatial expression with iSpatial.

Zhang Chao C Chen Renchao R Zhang Yi Y

Science advances 20220826 34

Spatially resolved transcriptomic analyses can reveal molecular insights underlying tissue structure and context-dependent cell-cell or cell-environment interaction. Because of the current technical limitation, obtaining genome-wide spatial transcriptome at single-cell resolution is challenging. Here, we developed a new algorithm named iSpatial to derive the spatial pattern of the entire transcriptome by integrating spatial transcriptomic and single-cell RNA-seq datasets. Compared to other exist ...[more]

PMID: 36026447

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Project description:BACKGROUND: Rice is highly sensitive to drought, and the effect of drought may vary with the different genotypes and development stages. Genome-wide gene expression profiling was used as the initial point to dissect molecular genetic mechanism of this complex trait and provide valuable information for the improvement of drought tolerance in rice. Affymetrix rice genome array containing 48,564 japonica and 1,260 indica sequences was used to analyze the gene expression pattern of rice exposed to drought stress. The transcriptome from leaf, root, and young panicle at three developmental stages was comparatively analyzed combined with bioinformatics exploring drought stress related cis-elements. RESULTS: There were 5,284 genes detected to be differentially expressed under drought stress. Most of these genes were tissue- or stage-specific regulated by drought. The tissue-specific down-regulated genes showed distinct function categories as photosynthesis-related genes prevalent in leaf, and the genes involved in cell membrane biogenesis and cell wall modification over-presented in root and young panicle. In a drought environment, several genes, such as GA2ox, SAP15, and Chitinase III, were regulated in a reciprocal way in two tissues at the same development stage. A total of 261 transcription factor genes were detected to be differentially regulated by drought stress. Most of them were also regulated in a tissue- or stage-specific manner. A cis-element containing special CGCG box was identified to over-present in the upstream of 55 common induced genes, and it may be very important for rice plants responding to drought environment. CONCLUSIONS: Genome-wide gene expression profiling revealed that most of the drought differentially expressed genes (DEGs) were under temporal and spatial regulation, suggesting a crosstalk between various development cues and environmental stimuli. The identification of the differentially regulated DEGs, including TF genes and unique candidate cis-element for drought responsiveness, is a very useful resource for the functional dissection of the molecular mechanism in rice responding to environment stress.

Dataset Information

Accurate inference of genome-wide spatial expression with iSpatial.

Publications

Accurate inference of genome-wide spatial expression with iSpatial.

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