A transcriptome data set for comparing skin, muscle and dorsal root ganglion between acute and chronic postsurgical pain rats.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-11-14 DOI:10.1038/s41597-024-04078-2

Xiao-Yan Meng, Lan Bu, Ling Shen, Kun-Ming Tao

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引用次数: 0

Abstract

Chronic postsurgical pain (CPSP), with a high prevalence and rising epidemic of opioids crisis, is typically derived from acute postoperative pain. Our knowledge on the forming of chronic pain mostly derives from mechanistic studies of pain processing in the brain and spinal cord circuits, yet most pharmacological interventions targeting CNS came to be unhelpful in preventing CPSP. Revealing the peripheral mechanisms behind the transition from acute to chronic pain after surgery could shine a light on the novel analgesic regimens. Based on two recognized animal models in simulation of acute and chronic postsurgical pain, we provide a next-generation RNA sequencing (RNA-seq) data set to evaluate the time-course transcriptomic variation in the tissue of skin, muscle and dorsal root ganglion (DRG) in these two pain models. The aim of this study is to identify the potential origin and mechanism of the persistent postoperative pain, and further to explore effective and safer analgesic regimens for surgical patients.

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用于比较急性和慢性术后疼痛大鼠皮肤、肌肉和背根神经节的转录组数据集。

慢性术后疼痛（CPSP）通常源于急性术后疼痛，其发病率高，阿片类药物危机日益严重。我们对慢性疼痛形成的认识主要来自于对大脑和脊髓回路疼痛处理的机理研究，然而大多数针对中枢神经系统的药物干预对预防 CPSP 毫无帮助。揭示手术后急性疼痛向慢性疼痛转变的外周机制，可以为新型镇痛方案提供启示。基于两种公认的模拟急性和慢性术后疼痛的动物模型，我们提供了新一代 RNA 测序（RNA-seq）数据集，以评估这两种疼痛模型中皮肤、肌肉和背根神经节（DRG）组织的时程转录组变化。这项研究的目的是确定术后持续疼痛的潜在起源和机制，并进一步探索对手术患者更有效、更安全的镇痛方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.