Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. Data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially resolved data (such as spatial resolution, sensitivity, and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering, among others. However, DL models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially resolved transcriptomics while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight domains in which we anticipate transformational DL applications.
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Organizing your space: The potential for integrating spatial transcriptomics and 3D imaging data in plants
Abstract Plant cells communicate information for the regulation of development and responses to external stresses. A key form of this communication is transcriptional regulation, accomplished via complex gene networks operating both locally and systemically. To fully understand how genes are regulated across plant tissues and organs, high resolution, multi-dimensional spatial transcriptional data must be acquired and placed within a cellular and organismal context. Spatial transcriptomics (ST) typically provides a two-dimensional spatial analysis of gene expression of tissue sections that can be stacked to render three-dimensional data. For example, X-ray and light-sheet microscopy provide sub-micron scale volumetric imaging of cellular morphology of tissues, organs, or potentially entire organisms. Linking these technologies could substantially advance transcriptomics in plant biology and other fields. Here, we review advances in ST and 3D microscopy approaches and describe how these technologies could be combined to provide high resolution, spatially organized plant tissue transcript mapping.
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- Award ID(s):
- 1945854
- NSF-PAR ID:
- 10342475
- Date Published:
- Journal Name:
- Plant Physiology
- Volume:
- 188
- Issue:
- 2
- ISSN:
- 0032-0889
- Page Range / eLocation ID:
- 703 to 712
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.
Results 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 github.com/bayesrx/SpaceX.
Supplementary information Supplementary data are available at Bioinformatics online.
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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.more » « less
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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.more » « less
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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 Spatial 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.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/plphys/kiab508
