Spatial Transcriptomics in Neuroscience

Spatial Transcriptomics in Neuroscience Identifying and characterizing the multitude of cell types that make up the brain is fundamental to understanding their function in health and disease. Single-cell RNA sequencing (scRNA-seq) approaches can provide transcriptomic profiles of individual cells isolated from brain tissue. However, the spatial context of these cells is vital to truly understand their function. This infographic will explore how spatial transcriptomics is being used in neuroscience and its applications across different neuroscience subdisciplines. What is spatial transcriptomics? Spatial transcriptomics involves using technologies that enable gene expression data to be analyzed in a positional context. Spatial transcriptomics holds unmatched capabilities for linking single-cell transcriptomic profile with connectional, morphological, physiological and functional properties while providing cellular locations within Spatially resolved transcriptomics was the tissue. Nature’s “Method of the Year” in 2020 Spatial transcriptomics techniques Spatial transcriptomic technologies vary in complexity, throughput and resolution. There are generally two main categories of methods: 1. Imaging based-methods In situ hybridization (ISH) In situ sequencing (ISS) rRNA molecules are visualized in situ, rather mRNA is sequenced directly in a section than being extracted from cells. Labeled of fixed tissue or cell, preserving its probes (complimentary RNA sequences) morphology. This method was originally are hybridized to transcripts of interest based on padlock probes targeting known within the sample which can be visualized. RNA sequences. Method variations have now been developed, including using Fluorescence in situ hybridization (FISH) is fluorescent probes and barcode-based one commonly used method. methods. Hybridization Sequencing erse transcriptio Reverse transcription Hybridization Amplificatio Sequencing transcription Amplificatio Fixation Hybridization bridizat Fluorescence microscopy Lysis RNA extraction The spatial resolution of current methods can vary from subcellular to regional level with there often a trade-off between resolution and throughput. Method complexity makes data and protocol sharing more difficult. A range of complexities emerge when integrating spatially resolved omics data due to technical concerns during sample collection such as tissue gaps, distortions introduced during sectioning and tears. These technical limitations pose challenges for spatial transcriptomics data generation, processing, analysis and interpretation. Reverse transcription Hybridization Amplification Sequencing 1. Improving resolution without compromising throughput 2. A diverse range of methodologies 3. Harmonizing spatially resolved data across omics layers Sponsored by Fixation Hybridization Lysis Fluore luorescence microscopy Lysis RNA extraction The spatial resolution of current methods can vary from subcellular to regional level with there often a trade-off between resolution and throughput. Method complexity makes data and protocol sharing more difficult. A range of complexities emerge when integrating spatially resolved omics data due to technical concerns during sample collection such as tissue gaps, distortions introduced during sectioning and tears. These technical limitations pose challenges for spatial transcriptomics data generation, processing, analysis and interpretation. Reverse transcription Hybridization Amplification Sequencing 1. Improving resolution without compromising throughput 2. A diverse range of methodologies 3. Harmonizing spatially resolved data across omics layers Sponsored by Fixation Hybridization Lysis Fluorescence microscopy Lysis RNA extraction The spatial resolution of current methods can vary from subcellular to regional level with there often a trade-off between resolution and throughput. Method complexity makes data and protocol sharing more difficult. A range of complexities emerge when integrating spatially resolved omics data due to technical concerns during sample collection such as tissue gaps, distortions introduced during sectioning and tears. These technical limitations pose challenges for spatial transcriptomics data generation, processing, analysis and interpretation. Reverse transcription Hybridization Amplification Sequencing 1. Improving resolution without compromising throughput 2. A diverse range of methodologies 3. Harmonizing spatially resolved data across omics layers Sponsored by Fixation Hybridization Lysis Fluorescenc microscopy 2. Sequencing-based methods Microdissection In situ capture Regions of interest are isolated from a Involves capturing and barcoding sample, placed in individual test tubes and transcripts within the tissue. Sequencing is the RNA is extracted and sequenced. then performed outside the tissue. Tissue mRNA Lysis Spatial ID Slide Sequencing Lysis RNA extraction The spatial resolution of current methods can vary from subcellular to regional level with there often a trade-off between resolution and throughput. Method complexity makes data and protocol sharing more difficult. A range of complexities emerge when integrating spatially resolved omics data due to technical concerns during sample collection such as tissue gaps, distortions introduced during sectioning and tears. These technical limitations pose challenges for spatial transcriptomics data generation, processing, analysis and interpretation. Reverse transcription Hybridization Amplification Sequencing 1. Improving resolution without compromising throughput 2. A diverse range of methodologies 3. Harmonizing spatially resolved data across omics layers Sponsored by Fixation Hybridization Lysis Fluorescence microscopy RNA extraction NA extraction The spatial resolution of current methods can vary from subcellular to regional level with there often a trade-off between resolution and throughput. Method complexity makes data and protocol sharing more difficult. A range of complexities emerge when integrating spatially resolved omics data due to technical concerns during sample collection such as tissue gaps, distortions introduced during sectioning and tears. These technical limitations pose challenges for spatial transcriptomics data generation, processing, analysis and interpretation. Reverse transcription Hybridization Amplification Sequencing 1. Improving resolution without compromising throughput 2. A diverse range of methodologies 3. Harmonizing spatially resolved data across omics layers Sponsored by Fixation Hybridization Lysis Fluorescence microscopy RNA extraction Applications of spatial transcriptomics in neuroscience One major challenge within neuroscience is systematically understanding the extraordinary diversity of brain cell types and how they contribute to brain function. Below are some areas of neuroscience that have been explored using spatial transcriptomics techniques: Mapping the mammalian primary motor cortex As part of the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative - Cell Census Network (BICCN), neuroscientists have generated a comprehensive multimodal cell census and atlas of the mammalian primary motor cortex. Zhang et al. used MERFISH to generate a molecularly defined and spatially resolved cell atlas of the mouse primary motor cortex. They profiled ~300,000 cells in the What is the primary motor cortex? mouse primary motor cortex and its The primary motor cortex is the region adjacent areas, identifying 95 neuronal of the cerebral cortex involved in the and non-neuronal cell clusters. The planning, control and execution of data revealed a complex spatial map in voluntary movements. which excitatory and inhibitory neuronal clusters adopted laminar organizations. Notably, intratelencephalic neurons, the largest branch of neurons in the primary motor cortex, formed a large continuous gradient along the cortical depth axis, in which the gene expression of individual cells correlated with their cortical depths. Brain cell type identification Neuronal cells are the most basic structural and functional units of the nervous system. Zeisel et al. examined the mouse nervous system to understand the diversity of neuronal cell types using RNA sequencing data from 500,000 single cells and mapped cell types spatially. The authors identified seven distinct, regionally restricted astrocyte types that obeyed developmental boundaries and correlated with the spatial distribution of key glutamate and glycine neurotransmitters. Mapping different disease states Alzheimer’s disease (AD) is a common and irreversible neurodegenerative disease characterized by extensive synaptic loss. Sadick et al. used single-nuclei RNA sequencing (snRNA seq) to characterize astrocytes and oligodendrocytes from apolipoprotein human AD and age- and genotype-matched non-symptomatic brains. Spatial transcriptomics techniques were used to localize heterogeneous astrocyte subtypes to specific regions of brain tissue across samples. Results revealed that astrocytes and oligodendrocytes have altered and heterogeneous transcriptomes in AD, in addition to astrocyte inflammation responses mimicking some AD associated gene expression changes. Spatial transcriptomics may also provide insights into malignant primary and secondary brain tumors through understanding the complex cell types within the tumor microenvironment and how they interact with each other. Sleep deprivation Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention and metabolism. Vanrobaeys et al. used spatial transcriptomics techniques to define the impact of a brief period of sleep deprivation across the brain in male mice. Sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Applications of spatial transcriptomics in neuroscience Despite the explosion of technological innovation in spatial transcriptomics in recent years, some of the technical limitations of current approaches remain: 1. Improving 3. Harmonizing resolution without 2. A diverse range spatially resolved compromising of methodologies data across omics throughput layers The spatial resolution of Method complexity makes A range of complexities current methods can data and protocol sharing emerge when integrating vary from subcellular to more difficult spatially resolved omics regional level with there data due to technical often a trade-off between concerns during sample resolution and throughput. collection such as tissue gaps, distortions introduced during sectioning and tears. These technical limitations pose challenges for spatial transcriptomics data generation, processing, analysis and interpretation. Sponsored by