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1. SpaceX: gene co-expression network estimation for spatial transcriptomics

   https://doi.org/10.1093/bioinformatics/btac645  

   Acharyya, Satwik ; Zhou, Xiang ; Baladandayuthapani, Veerabhadran ; Kendziorski, ed., Christina ( September 2022 , Bioinformatics)

   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) frommore »the tumor region for the breast cancer data.

   The SpaceX R-package is available at github.com/bayesrx/SpaceX.

   Supplementary data are available at Bioinformatics online.

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2. Space: the final frontier — achieving single-cell, spatially resolved transcriptomics in plants

   https://doi.org/10.1042/ETLS20200274  

   Gurazada, Sai Guna ; Cox, Kevin L. ; Czymmek, Kirk J. ; Meyers, Blake C. ( February 2021 , Emerging Topics in Life Sciences)

   Jez, Joseph M. ; Topp, Christopher N. (Ed.)

   Single-cell RNA-seq is a tool that generates a high resolution of transcriptional data that can be used to understand regulatory networks in biological systems. In plants, several methods have been established for transcriptional analysis in tissue sections, cell types, and/or single cells. These methods typically require cell sorting, transgenic plants, protoplasting, or other damaging or laborious processes. Additionally, the majority of these technologies lose most or all spatial resolution during implementation. Those that offer a high spatial resolution for RNA lack breadth in the number of transcripts characterized. Here, we briefly review the evolution of spatial transcriptomics methods and we highlight recent advances and current challenges in sequencing, imaging, and computational aspects toward achieving 3D spatial transcriptomics of plant tissues with a resolution approaching single cells. We also provide a perspective on the potential opportunities to advance this novel methodology in plants.
3. Scan-IT: Domain segmentation of spatial transcriptomics images by graph neural network

   Cang, Zixuan ; Ning, Xinyi ; Nie, Annika ; Xu, Min ; Zhang, Jing ( November 2021 , British Machine Vision Conference)

   Complex biological tissues consist of numerous cells in a highly coordinated manner and carry out various biological functions. Therefore, segmenting a tissue into spatial and functional domains is critically important for understanding and controlling the biological functions. The emerging spatial transcriptomic technologies allow simultaneous measurements of thousands of genes with precise spatial information, providing an unprecedented opportunity for dissecting biological tissues. However, how to utilize such noisy, sparse, and high dimensional data for tissue segmentation remains a major challenge. Here, we develop a deep learning-based method, named SCAN-IT by transforming the spatial domain identification problem into an image segmentation problem, with cells mimicking pixels and expression values of genes within a cell representing the color channels. Specifically, SCAN-IT relies on geometric modeling, graph neural networks, and an informatics approach, DeepGraphInfomax. We demonstrate that SCAN-IT can handle datasets from a wide range of spatial transcriptomics techniques, including the ones with high spatial resolution but low gene coverage as well as those with low spatial resolution but high gene coverage. We show that SCAN-IT outperforms state-of-the-art methods using a benchmark dataset with ground truth domain annotations.
4. Spatial Transcriptomics as a Novel Approach to Redefine Electrical Stimulation Safety

   https://doi.org/10.3389/fnins.2022.937923  

   Whitsitt, Quentin A. ; Koo, Beomseo ; Celik, Mahmut Emin ; Evans, Blake M. ; Weiland, James D. ; Purcell, Erin K. ( July 2022 , Frontiers in Neuroscience)

   Current standards for safe delivery of electrical stimulation to the central nervous system are based on foundational studies which examined post-mortem tissue for histological signs of damage. This set of observations and the subsequently proposed limits to safe stimulation, termed the “Shannon limits,” allow for a simple calculation (using charge per phase and charge density) to determine the intensity of electrical stimulation that can be delivered safely to brain tissue. In the three decades since the Shannon limits were reported, advances in molecular biology have allowed for more nuanced and detailed approaches to be used to expand current understanding of the physiological effects of stimulation. Here, we demonstrate the use of spatial transcriptomics (ST) in an exploratory investigation to assess the biological response to electrical stimulation in the brain. Electrical stimulation was delivered to the rat visual cortex with either acute or chronic electrode implantation procedures. To explore the influence of device type and stimulation parameters, we used carbon fiber ultramicroelectrode arrays (7 μm diameter) and microwire electrode arrays (50 μm diameter) delivering charge and charge density levels selected above and below reported tissue damage thresholds (range: 2–20 nC, 0.1–1 mC/cm 2 ). Spatial transcriptomics was performed using Visium Spatialmore »Gene Expression Slides (10x Genomics, Pleasanton, CA, United States), which enabled simultaneous immunohistochemistry and ST to directly compare traditional histological metrics to transcriptional profiles within each tissue sample. Our data give a first look at unique spatial patterns of gene expression that are related to cellular processes including inflammation, cell cycle progression, and neuronal plasticity. At the acute timepoint, an increase in inflammatory and plasticity related genes was observed surrounding a stimulating electrode compared to a craniotomy control. At the chronic timepoint, an increase in inflammatory and cell cycle progression related genes was observed both in the stimulating vs. non-stimulating microwire electrode comparison and in the stimulating microwire vs. carbon fiber comparison. Using the spatial aspect of this method as well as the within-sample link to traditional metrics of tissue damage, we demonstrate how these data may be analyzed and used to generate new hypotheses and inform safety standards for stimulation in cortex.« less
5. Spatiotemporal dynamics of the tomato fruit transcriptome under prolonged water stress

   https://doi.org/10.1093/plphys/kiac445  

   Nicolas, Philippe ; Shinozaki, Yoshihito ; Powell, Adrian ; Philippe, Glenn ; Snyder, Stephen I. ; Bao, Kan ; Zheng, Yi ; Xu, Yimin ; Courtney, Lance ; Vrebalov, Julia ; et al ( September 2022 , Plant Physiology)

   Water availability influences all aspects of plant growth and development; however, most studies of plant responses to drought have focused on vegetative organs, notably roots and leaves. Far less is known about the molecular bases of drought acclimation responses in fruits, which are complex organs with distinct tissue types. To obtain a more comprehensive picture of the molecular mechanisms governing fruit development under drought, we profiled the transcriptomes of a spectrum of fruit tissues from tomato (Solanum lycopersicum), spanning early growth through ripening and collected from plants grown under varying intensities of water stress. In addition, we compared transcriptional changes in fruit with those in leaves to highlight different and conserved transcriptome signatures in vegetative and reproductive organs. We observed extensive and diverse genetic reprogramming in different fruit tissues and leaves, each associated with a unique response to drought acclimation. These included major transcriptional shifts in the placenta of growing fruit and in the seeds of ripe fruit related to cell growth and epigenetic regulation, respectively. Changes in metabolic and hormonal pathways, such as those related to starch, carotenoids, jasmonic acid, and ethylene metabolism, were associated with distinct fruit tissues and developmental stages. Gene coexpression network analysis provided furthermore »insights into the tissue-specific regulation of distinct responses to water stress. Our data highlight the spatiotemporal specificity of drought responses in tomato fruit and indicate known and unrevealed molecular regulatory mechanisms involved in drought acclimation, during both vegetative and reproductive stages of development.

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