Laser Capture Proteomics: spatial tissue molecular profiling from the bench to personalized medicine.
Laser Capture Proteomics: spatial tissue molecular profiling from the bench to personalized medicine.
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DOI：
10.1080/14789450.2021.1984886
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发表时间：
2021-10
影响因子：
3.4
通讯作者：
Petricoin, Emanuel
中科院分区：
文献类型：
--
作者：
Liotta, Lance A.;Pappalardo, Philip A.;Carpino, Alan;Haymond, Amanda;Howard, Marissa;Espina, Virginia;Wulfkuhle, Julie;Petricoin, Emanuel
关键词：
激光捕获显微切割（LCM）使用激光在直接显微镜可视化下分离或捕获复杂异质组织切片中的特定目标细胞。最近，有一个激增的出版物使用LCM的组织空间分子谱相关的广泛的研究课题。我们总结了组织激光捕获蛋白质组学（LCP）的许多进展，使用质谱的发现，和蛋白质阵列的信号通路网络映射。这次审查强调：a）LCM磷酸化蛋白质组学从实验室到临床的过渡，用于个体化癌症治疗，和B）LCM单细胞分子分析的新兴前沿，其将蛋白质组学与基因组学和转录组学分析相结合。检索策略基于MeSH术语与专家细化的组合。LCM由一套丰富的仪器、方法学协议和分析性AI补充。用于基础研究和转化研究的软件。分辨率正在推进到组织单细胞水平。提出了LCM未来发展的愿景。新兴的LCM技术将数字和人工智能引导的远程成像与自动化和远程病理学相结合，以实现以前不可能实现的多组学分析。
Laser Capture Microdissection (LCM) uses a laser to isolate, or capture, specific cells of interest in a complex heterogeneous tissue section, under direct microscopic visualization. Recently, there has been a surge of publications using LCM for tissue spatial molecular profiling relevant to a wide range of research topics. We summarize the many advances in tissue Laser Capture Proteomics (LCP) using mass spectrometry for discovery, and protein arrays for signal pathway network mapping. This review emphasizes: a) transition of LCM phosphoproteomics from the lab to the clinic for individualized cancer therapy, and b) the emerging frontier of LCM single cell molecular analysis combining proteomics with genomic, and transcriptomic analysis. The search strategy was based on the combination of MeSH terms with expert refinement. LCM is complemented by a rich set of instruments, methodology protocols, and analytical A.I. (artificial intelligence) software for basic and translational research. Resolution is advancing to the tissue single cell level. A vision for the future evolution of LCM is presented. Emerging LCM technology is combining digital and AI guided remote imaging with automation, and telepathology, to a achieve multi-omic profiling that was not previously possible.
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DOI：
10.1073/pnas.1311647111
发表时间：
2014-02-04
影响因子：
11.1
作者：
Bajikar, Sameer S.;Fuchs, Christiane;Janes, Kevin A.
通讯作者：
Janes, Kevin A.
影响因子：
5.9
作者：
Andersson A;Bergenstråhle J;Asp M;Bergenstråhle L;Jurek A;Fernández Navarro J;Lundeberg J
通讯作者：
Lundeberg J
影响因子：
5.5
作者：
Cefalo CMA;Mezza T;Giaccari A;Kulkarni RN
通讯作者：
Kulkarni RN
影响因子：
16.6
作者：
Angelidis, Ilias;Simon, Lukas M.;Schiller, Herbert B.
通讯作者：
Schiller, Herbert B.
影响因子：
4.4
作者：
Davis, Simon;Scott, Connor;Fischer, Roman
通讯作者：
Fischer, Roman