Divergence in cellular markers observed in single-cell transcriptomics datasets between cultured primary trabecular meshwork cells and tissues.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-14 DOI:10.1038/s41597-025-04528-5
Alice Tian, Sangbae Kim, Hasna Baidouri, Jin Li, Xuesen Cheng, Janice Vranka, Yumei Li, Rui Chen, VijayKrishna Raghunathan
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Abstract

The trabecular meshwork within the outflow apparatus is critical in maintaining intraocular pressure homeostasis. In vitro studies employing primary cell cultures of the human trabecular meshwork (hTM) have conventionally served as surrogates for investigating the pathobiology of TM dysfunction. Despite its abundant use, translation of outcomes from in vitro studies to ex vivo and/or in vivo studies remains a challenge. Given the cell heterogeneity, performing single-cell RNA sequencing comparing primary hTM cell cultures to hTM tissue may provide important insights on cellular identity and translatability, as such an approach has not been reported before. In this study, we assembled a total of 14 primary hTM in vitro samples across passages 1-4, including 4 samples from individuals diagnosed with glaucoma. This dataset offers a comprehensive transcriptomic resource of primary hTM in vitro scRNA-seq data to study global changes in gene expression in comparison to cells in tissue in situ. We have performed extensive preprocessing and quality control, allowing the research community to access and utilize this public resource.

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在培养的原代小梁网细胞和组织的单细胞转录组学数据集中观察到细胞标记的差异。
流出器内的小梁网对维持眼压稳态至关重要。利用人小梁网(hTM)原代细胞培养物的体外研究通常被用作研究小梁网功能障碍病理生物学的替代方法。尽管其广泛使用,但将体外研究的结果转化为离体和/或体内研究仍然是一个挑战。考虑到细胞的异质性,进行单细胞RNA测序比较原代热媒细胞培养物和热媒组织可能提供关于细胞身份和可翻译性的重要见解,因为这种方法以前没有报道过。在这项研究中,我们在1-4代中收集了14个原代体外hTM样本,其中4个样本来自青光眼患者。该数据集提供了一个全面的原代hTM体外scRNA-seq数据转录组学资源,用于研究与原位组织细胞相比基因表达的全局变化。我们已经进行了广泛的预处理和质量控制,允许研究界访问和利用这一公共资源。
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来源期刊
Scientific Data
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.
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