Analyzing Spatial Transcriptomics Data Using Giotto.

Spatial transcriptomic technologies have been developed rapidly in recent years. The addition of spatial context to expression data holds the potential to revolutionize many fields in biology. However, the lack of computational tools remains a bottleneck that is preventing the broader utilization of these technologies. Recently, we have developed Giotto as a comprehensive, generally applicable, and user-friendly toolbox for spatial transcriptomic data analysis and visualization. Giotto implements a rich set of algorithms to enable robust spatial data analysis. To help users get familiar with the Giotto environment and apply it effectively in analyzing new datasets, we will describe the detailed protocols for applying Giotto without any advanced programming skills. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Getting Giotto set up for use Basic Protocol 2: Pre-processing Basic Protocol 3: Clustering and cell-type identification Basic Protocol 4: Cell-type enrichment and deconvolution analyses Basic Protocol 5: Spatial structure analysis tools Basic Protocol 6: Spatial domain detection by using a hidden Markov random field model Support Protocol 1: Spatial proximity-associated cell-cell interactions Support Protocol 2: Assembly of a registered 3D Giotto object from 2D slices.

Advances in spatial transcriptomic data analysis.

Spatial transcriptomics is a rapidly growing field that promises to comprehensively characterize tissue organization and architecture at the single-cell or subcellular resolution. Such information provides a solid foundation for mechanistic understanding of many biological processes in both health and disease that cannot be obtained by using traditional technologies. The development of computational methods plays important roles in extracting biological signals from raw data. Various approaches have been developed to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical biases. Downstream analysis tools formulate spatial organization and cell-cell communications as quantifiable properties, and provide algorithms to derive such properties. Integrative pipelines further assemble multiple tools in one package, allowing biologists to conveniently analyze data from beginning to end. In this review, we summarize the state of the art of spatial transcriptomic data analysis methods and pipelines, and discuss how they operate on different technological platforms.

Targeting Morphine-Responsive Neurons: Generation of a Knock-In Mouse Line Expressing Cre Recombinase from the Mu-Opioid Receptor Gene Locus.

The mu-opioid receptor (MOR) modulates nociceptive pathways and reward processing, and mediates the strong analgesic and addictive properties of both medicinal as well as abused opioid drugs. MOR function has been extensively studied, and tools to manipulate or visualize the receptor protein are available. However, circuit mechanisms underlying MOR-mediated effects are less known, because genetic access to MOR-expressing neurons is lacking. Here we report the generation of a knock-in Oprm1-Cre mouse line, which allows targeting and manipulating MOR opioid-responsive neurons. A cDNA encoding a T2A cleavable peptide and Cre recombinase fused to enhanced green fluorescent protein (EGFP/Cre) was inserted downstream of the Oprm1 gene sequence. The resulting Oprm1-Cre line shows intact Oprm1 gene transcription. MOR and EGFP/Cre proteins are coexpressed in the same neurons, and localized in cytoplasmic and nuclear compartments, respectively. MOR signaling is unaltered, demonstrated by maintained DAMGO-induced G-protein activation, and in vivo MOR function is preserved as indicated by normal morphine-induced analgesia, hyperlocomotion, and sensitization. The Cre recombinase efficiently drives the expression of Cre-dependent reporter genes, shown by local virally mediated expression in the medial habenula and brain-wide fluorescence on breeding with tdTomato reporter mice, the latter showing a distribution patterns typical of MOR expression. Finally, we demonstrate that optogenetic activation of MOR neurons in the ventral tegmental area of Oprm1-Cre mice evokes strong avoidance behavior, as anticipated from the literature. The Oprm1-Cre line is therefore an excellent tool for both mapping and functional studies of MOR-positive neurons, and will be of broad interest for opioid, pain, and addiction research.

Nimodipine Reduces Dysfunction and Demyelination in Models of Multiple Sclerosis.

OBJECTIVE: Treatment of relapses in multiple sclerosis (MS) has not advanced beyond steroid use, which reduces acute loss of function, but has little effect on residual disability. Acute loss of function in an MS model (experimental autoimmune encephalomyelitis [EAE]) is partly due to central nervous system (CNS) hypoxia, and function can promptly improve upon breathing oxygen. Here, we investigate the cause of the hypoxia and whether it is due to a deficit in oxygen supply arising from impaired vascular perfusion. We also explore whether the CNS-selective vasodilating agent, nimodipine, may provide a therapy to restore function, and protect from demyelination in 2 MS models. METHODS: A variety of methods have been used to measure basic cardiovascular physiology, spinal oxygenation, mitochondrial function, and tissue perfusion in EAE. RESULTS: We report that the tissue hypoxia in EAE is associated with a profound hypoperfusion of the inflamed spinal cord. Treatment with nimodipine restores spinal oxygenation and can rapidly improve function. Nimodipine therapy also reduces demyelination in both EAE and a model of the early MS lesion. INTERPRETATION: Loss of function in EAE, and demyelination in EAE, and the model of the early MS lesion, seem to be due, at least in part, to tissue hypoxia due to local spinal hypoperfusion. Therapy to improve blood flow not only protects neurological function but also reduces demyelination. We conclude that nimodipine could be repurposed to offer substantial clinical benefit in MS. ANN NEUROL 2020 ANN NEUROL 2020;88:123-136.