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SCAMPR: Single-Cell Automated Multiplex Pipeline for RNA Quantification and Spatial Mapping

Ali Marandi Ghoddousi, R.; Levitt, P.; Kamitakahara, A.; Magalong, V.

2022-03-26 neuroscience
10.1101/2022.03.23.485552 bioRxiv
Show abstract

Spatial gene expression, achieved classically through in situ hybridization, is a fundamental tool for topographic phenotyping of cell types in the nervous system. Newly developed techniques allow for the visualization of multiple mRNAs at single-cell resolution, greatly expanding the ability to link gene expression to tissue topography. Yet, methods for efficient and accurate quantification and analysis of high dimensional in situ hybridization are limited. To this end, the Single-Cell Automated Multiplex Pipeline for RNA (SCAMPR) was developed, facilitating rapid and accurate segmentation of neuronal cell bodies using a dual immunohistochemistry-RNAscope protocol and quantification of low and high abundance mRNA signals using open-source image processing and automated segmentation tools. Proof of principle using SCAMPR focused on spatial mapping of gene expression by peripheral (vagal nodose) and central (visual cortex) neurons. The analytical effectiveness of SCAMPR is demonstrated by identifying the impact of early life stress on differential gene expression by vagal neuron subtypes. MotivationQuantitative analysis of spatial mRNA expression in neurons can lack accuracy and be both computationally and time intensive. Existing methods that rely on nuclear labeling (DAPI) to distinguish adjoining cells lack the precision to detect mRNA expression in the cytoplasm. In addition, quantification methods that rely on puncta counts can generate large, variable datasets that potentially undercount highly expressed mRNAs. To overcome these methodological barriers, we developed the SCAMPR pipeline that allows for fast, accurate segmentation of neuronal cell body boundaries, topographic gene expression mapping, and high dimensional quantification and analysis of mRNA expression in tissue sections.

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