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Title: Spatially Resolved Transcriptomic Analysis of the Germinating Barley Grain
Seeds, which provide a major source of calories for humans, are a unique stage of a flowering plant’s lifecycle. During seed germination the embryo reactivates rapidly and goes through major developmental transitions to become a seedling. This requires extensive and complex spatiotemporal coordination of cell and tissue activity. Existing gene expression profiling methods, such as laser capture microdissection followed by RNA-seq and single-cell RNA7 seq, suffer from either low throughput or the loss of spatial information about the cells analysed. Spatial transcriptomics methods couple high throughput analysis of gene expression simultaneously with the ability to record the spatial location of each individual region analysed. We developed a spatial transcriptomics workflow for germinating barley grain to better understand the spatiotemporal control of gene expression within individual seed cell types. More than 14,000 genes were differentially regulated across 0, 1, 3, 6 and 24 hours after imbibition. This approach enabled us to observe that many functional categories displayed specific spatial expression patterns that could be resolved at a sub-tissue level. Individual aquaporin gene family members, important for water and ion transport, had specific spatial expression patterns over time, as well as genes related to cell wall modification, membrane transport and transcription factors. Using spatial autocorrelation algorithms, we were able to identify auxin transport genes that had increasingly focused expression within subdomains of the embryo over germination time, suggestive of a role in establishment of the embryo axis. Together, our data provides an unprecedented spatially resolved cellular map for barley grain germination and specific genes to target for functional genomics to define cellular restricted processes in tissues during germination. The data can be viewed at  more » « less
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National Science Foundation
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  1. Abstract

    Seeds are a vital source of calories for humans and a unique stage in the life cycle of flowering plants. During seed germination, the embryo undergoes major developmental transitions to become a seedling. Studying gene expression in individual seed cell types has been challenging due to the lack of spatial information or low throughput of existing methods. To overcome these limitations, a spatial transcriptomics workflow was developed for germinating barley grain. This approach enabled high-throughput analysis of spatial gene expression, revealing specific spatial expression patterns of various functional gene categories at a sub-tissue level. This study revealed over 14 000 genes differentially regulated during the first 24 h after imbibition. Individual genes, such as the aquaporin gene family, starch degradation, cell wall modification, transport processes, ribosomal proteins and transcription factors, were found to have specific spatial expression patterns over time. Using spatial autocorrelation algorithms, we identified auxin transport genes that had increasingly focused expression within subdomains of the embryo over time, suggesting their role in establishing the embryo axis. Overall, our study provides an unprecedented spatially resolved cellular map for barley germination and identifies specific functional genomics targets to better understand cellular restricted processes during germination. The data can be viewed at

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  2. Abstract Motivation

    The analysis of spatially resolved transcriptome enables the understanding of the spatial interactions between the cellular environment and transcriptional regulation. In particular, the characterization of the gene–gene co-expression at distinct spatial locations or cell types in the tissue enables delineation of spatial co-regulatory patterns as opposed to standard differential single gene analyses. To enhance the ability and potential of spatial transcriptomics technologies to drive biological discovery, we develop a statistical framework to detect gene co-expression patterns in a spatially structured tissue consisting of different clusters in the form of cell classes or tissue domains.


    We develop SpaceX (spatially dependent gene co-expression network), a Bayesian methodology to identify both shared and cluster-specific co-expression network across genes. SpaceX uses an over-dispersed spatial Poisson model coupled with a high-dimensional factor model which is based on a dimension reduction technique for computational efficiency. We show via simulations, accuracy gains in co-expression network estimation and structure by accounting for (increasing) spatial correlation and appropriate noise distributions. In-depth analysis of two spatial transcriptomics datasets in mouse hypothalamus and human breast cancer using SpaceX, detected multiple hub genes which are related to cognitive abilities for the hypothalamus data and multiple cancer genes (e.g. collagen family) from the tumor region for the breast cancer data.

    Availability and implementation

    The SpaceX R-package is available at

    Supplementary information

    Supplementary data are available at Bioinformatics online.

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