Paralysis occurring in amyotrophic lateral sclerosis (ALS) results from denervation of skeletal muscle as a consequence of motor neuron degeneration. Interactions between motor neurons and glia contribute to motor neuron loss, but the spatiotemporal ordering of molecular events that drive these processes in intact spinal tissue remains poorly understood. Here, we use spatial transcriptomics to obtain gene expression measurements of mouse spinal cords over the course of disease, as well as of postmortem tissue from ALS patients, to characterize the underlying molecular mechanisms in ALS. We identify pathway dynamics, distinguish regional differences between microglia and astrocyte populations at early time points, and discern perturbations in several transcriptional pathways shared between murine models of ALS and human postmortem spinal cords.

Spatial genomics technologies enable new approaches to study how cells interact and function in intact multicellular environments but present a host of technical and computational challenges. Here we describe Splotch, a novel computational framework for the analysis of spatially resolved transcriptomics data. Splotch aligns transcriptomics data from multiple tissue sections and timepoints to generate improved posterior estimates of gene expression. We demonstrate alignment of a large corpus of single-cell RNA-seq data into an automatically generated spatial-temporal coordinate and study optimal design for spatial transcriptomics experiments. Main:Unbiased spatial maps of gene expression are important resources for understanding diseases in intact tissue context 1-6 . Previous genome-wide studies have demonstrated the value of combining appropriate experimental designs with statistical and computational methods tailored to new genomic technologies 7,8 , and the study of spatial gene expression is no exception to this trend 9 . Here, we describe a method, Splotch, that combines integrative and spatiotemporal generative modeling (Supplementary Fig. 1) matched to the spatial transcriptomics (ST) technology 10,11 . Splotch enables interrogation of spatiotemporal genomics data by simultaneously using spatial, temporal and experimental (e.g. genotype) coordinates to improve probabilistic inference of spatially resolved gene expression quantities ( Fig. 1a; Methods).At its core, our generative model leverages ST data by using hierarchical experimental designs and spatial autocorrelation in measurements at two scales: 1) a tissue context predefined by manually or automatically designated anatomical regions and 2) the local neighborhood encoded automatically by the spatial transcriptomic technology (Supplementary Fig. 1; Methods). Importantly, the consideration of tissue contexts allows us to share information across tissue sections, enabling detection of reproducible spatiotemporal changes in disease. The choice of distinct tissue contexts, and thereby common coordinate system, is guided by the biological question, spatial resolution, and tissue of interest. To validate Splotch, we analyze a mouse lumbar spinal cord data set 2 (consisted of 76,136 ST spots across 1,165 tissue sections) and a mouse main olfactory bulb data set 11 (3,045 ST spots and 12 tissue sections) to demonstrate generalizability. Our spatial model provides a principled way to deal with missing data due to undersampling and, as a result, we are able to quantitate the spatial expression of 11,138 genes on the spinal cord data (and align these genes to the common coordinate), a huge improvement in the much smaller number of transcripts quantified in a single measurement in the median spinal cord ST experiment (1,415 genes and 2,227 unique transcripts per ST spot measurement prior to our Bayesian integration). Moreover, we demonstrate advantages of our approach, especially in the low signal-to-noise ratio (SNR) regime to power downstream analyses describe...

Paralysis occurring in amyotrophic lateral sclerosis (ALS) results from denervation of skeletal muscle as a consequence of motor neuron degeneration. Interactions between motor neurons and glia contribute to motor neuron loss, but the spatiotemporal ordering of molecular events that drive these processes in intact spinal tissue remains poorly understood. Here, we use spatial transcriptomics to obtain gene expression measurements of mouse spinal cords over the course of disease, as well as of postmortem tissue from ALS patients, to characterize the underlying molecular mechanisms in ALS. We identify novel pathway dynamics, regional differences between microglia and astrocyte populations at early time-points, and discern perturbations in several transcriptional pathways shared between murine models of ALS and human postmortem spinal cords.One Sentence SummaryAnalysis of the ALS spinal cord using Spatial Transcriptomics reveals spatiotemporal dynamics of disease driven gene regulation.

Genetic and genomic studies of brain disease increasingly demonstrate disease-associated interactions between the cell types of the brain. Increasingly complex and more physiologically relevant human-induced pluripotent stem cell (hiPSC)-based models better explore the molecular mechanisms underlying disease but also challenge our ability to resolve cell type-specific perturbations. Here, we report an extension of the RiboTag system, first developed to achieve cell type-restricted expression of epitope-tagged ribosomal protein (RPL22) in mouse tissue, to a variety of in vitro applications, including immortalized cell lines, primary mouse astrocytes, and hiPSC-derived neurons. RiboTag expression enables depletion of up to 87 percent of off-target RNA in mixed species co-cultures. Nonetheless, depletion efficiency varies across independent experimental replicates, particularly for hiPSC-derived motor neurons. The challenges and potential of implementing RiboTags in complex in vitro cultures are discussed.