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Abstract

Developmental Biology

Spatial High-resolution Analysis of Gene Expression Levels in Tendons

Published: March 8th, 2024

DOI:

10.3791/65852

Abstract

In recent years, many protocols have been developed for high-resolution transcriptomics in many different medical and biology fields. However, matrix-rich tissues, and specifically, tendons were left behind due to their low cell number, low RNA amount per cell, and high matrix content, which made them complicated to analyze. One of the recent and most important single-cell tools is the spatial analysis of gene expression levels in tendons. These RNA spatial tools have specifically high importance in tendons to locate specific cells of new and unknown populations, validate single-cell RNA-seq results, and add histological context to the single-cell RNA-seq data. These new methods will enable the analysis of RNA in cells with exceptional sensitivity and the detection of single-molecule RNA targets at the single-cell level, which will help to molecularly characterize tendons and promote tendon research.

In this method paper, we will focus on the available methods to analyze spatial gene expression levels on histological sections by using novel in situ hybridization assays to detect target RNA within intact cells at single-cell levels. First, we will focus on how to prepare the tendon tissue for the different available assays and how to amplify target-specific signals without background noise but with high sensitivity and high specificity. Then, the paper will describe specific permeabilization methods, the different probe designs, and the signal amplification strategies currently available. These unique methods of analyzing transcription levels of different genes in single-cell resolution will enable the identification and characterization of the tendon tissue cells in young and aged populations of various animal models and human tendon tissues. This method will also help analyze gene expression levels in other matrix-rich tissues such as bones, cartilage, and ligaments.

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Keywords Spatial Transcriptomics

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