Suppr超能文献

衰老细胞分泌组的研究方法

Methods to Investigate the Secretome of Senescent Cells.

作者信息

Samiminemati Afshin, Aprile Domenico, Siniscalco Dario, Di Bernardo Giovanni

机构信息

Department of Experimental Medicine, Biotechnology, and Molecular Biology Section, Luigi Vanvitelli Campania University, 80138 Naples, Italy.

Sbarro Health Research Organization, Temple University, Philadelphia, PA 19122, USA.

出版信息

Methods Protoc. 2024 Jul 2;7(4):52. doi: 10.3390/mps7040052.

Abstract

The word "secretome" was first used to describe the proteins that cells secrete under different circumstances; however, recent studies have proven the existence of other molecules such as RNA and chemical compounds in the secretome. The study of secretome has significance for the diagnosis and treatment of disease as it provides insight into cellular functions, including immune responses, development, and homeostasis. By halting cell division, cellular senescence plays a role in both cancer defense and aging by secreting substances known as senescence-associated secretory phenotypes (SASP). A variety of techniques could be used to analyze the secretome: protein-based approaches like mass spectrometry and protein microarrays, nucleic acid-based methods like RNA sequencing, microarrays, and in silico prediction. Each method offers unique advantages and limitations in characterizing secreted molecules. Top-down and bottom-up strategies for thorough secretome analysis are became possible by mass spectrometry. Understanding cellular function, disease causes, and proper treatment targets is aided by these methodologies. Their approaches, benefits, and drawbacks will all be discussed in this review.

摘要

“分泌组”一词最初用于描述细胞在不同情况下分泌的蛋白质;然而,最近的研究已证实分泌组中还存在其他分子,如RNA和化合物。分泌组的研究对疾病的诊断和治疗具有重要意义,因为它有助于深入了解细胞功能,包括免疫反应、发育和体内平衡。通过阻止细胞分裂,细胞衰老通过分泌称为衰老相关分泌表型(SASP)的物质在癌症防御和衰老过程中发挥作用。可使用多种技术分析分泌组:基于蛋白质的方法,如质谱分析和蛋白质微阵列;基于核酸的方法,如RNA测序、微阵列和计算机预测。每种方法在表征分泌分子方面都有独特的优势和局限性。质谱分析使全面分析分泌组的自上而下和自下而上策略成为可能。这些方法有助于理解细胞功能、疾病病因和合适的治疗靶点。本综述将讨论它们的方法、优点和缺点。

相似文献

1
Methods to Investigate the Secretome of Senescent Cells.
Methods Protoc. 2024 Jul 2;7(4):52. doi: 10.3390/mps7040052.
3
Tumor Secretome to Adoptive Cellular Immunotherapy: Reduce Me Before I Make You My Partner.
Front Immunol. 2021 Aug 10;12:717850. doi: 10.3389/fimmu.2021.717850. eCollection 2021.
5
Keeping the senescence secretome under control: Molecular reins on the senescence-associated secretory phenotype.
Exp Gerontol. 2016 Sep;82:39-49. doi: 10.1016/j.exger.2016.05.010. Epub 2016 May 25.
8
The lysosomal proteome of senescent cells contributes to the senescence secretome.
Aging Cell. 2022 Oct;21(10):e13707. doi: 10.1111/acel.13707. Epub 2022 Sep 10.
9
Assessing Functional Roles of the Senescence-Associated Secretory Phenotype (SASP).
Methods Mol Biol. 2019;1896:45-55. doi: 10.1007/978-1-4939-8931-7_6.
10
Dynamic and scalable assessment of the senescence-associated secretory phenotype (SASP).
Methods Cell Biol. 2024;181:181-195. doi: 10.1016/bs.mcb.2022.10.005. Epub 2022 Dec 5.

引用本文的文献

本文引用的文献

4
Cell Secretome Strategies for Controlled Drug Delivery and Wound-Healing Applications.
Polymers (Basel). 2022 Jul 20;14(14):2929. doi: 10.3390/polym14142929.
5
Unconventional Pathways of Protein Secretion: Mammals . Plants.
Front Cell Dev Biol. 2022 Apr 28;10:895853. doi: 10.3389/fcell.2022.895853. eCollection 2022.
6
Application of MALDI-TOF MS for identification of environmental bacteria: A review.
J Environ Manage. 2022 Mar 1;305:114359. doi: 10.1016/j.jenvman.2021.114359. Epub 2021 Dec 24.
7
Mechanisms and Regulation of Cellular Senescence.
Int J Mol Sci. 2021 Dec 6;22(23):13173. doi: 10.3390/ijms222313173.
9
Towards Secretome Standardization: Identifying Key Ingredients of MSC-Derived Therapeutic Cocktail.
Stem Cells Int. 2021 Aug 26;2021:3086122. doi: 10.1155/2021/3086122. eCollection 2021.
10
iTRAQ-based proteomics of testicular interstitial fluid during aging in mice.
Theriogenology. 2021 Nov;175:44-53. doi: 10.1016/j.theriogenology.2021.08.034. Epub 2021 Aug 31.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验