Proteomics for Low Cell Numbers: How to Optimize the Sample Preparation Workflow for Mass Spectrometry Analysis.
Proteomics for Low Cell Numbers: How to Optimize the Sample Preparation Workflow for Mass Spectrometry Analysis.
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DOI:
10.1021/acs.jproteome.1c00321
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发表时间:
2021-09-03
影响因子:
4.4
通讯作者:
Díez P
中科院分区:
文献类型:
--
作者:
Kassem S;van der Pan K;de Jager AL;Naber BAE;de Laat IF;Louis A;van Dongen JJM;Teodosio C;Díez P
Nowadays, massive genomics and transcriptomics data can be generated at the single-cell level. However, proteomics in this setting is still a big challenge. Despite the great improvements in sensitivity and performance of mass spectrometry instruments and the better knowledge on sample preparation processing, it is widely acknowledged that multistep proteomics workflows may lead to substantial sample loss, especially when working with paucicellular samples. Still, in clinical fields, frequently limited sample amounts are available for downstream analysis, thereby hampering comprehensive characterization at protein level. To aim at better protein and peptide recoveries, we compare existing and novel approaches in the multistep sample preparation protocols for mass spectrometry studies, from sample collection, cell lysis, protein quantification, and electrophoresis/staining to protein digestion, peptide recovery, and LC-MS/MS instruments. From this critical evaluation, we conclude that the recent innovations and technologies, together with high quality management of samples, make proteomics on paucicellular samples possible, which will have immediate impact for the proteomics community.
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DOI:
10.1016/s0021-9673(00)96972-3
发表时间:
1990-01-19
期刊:
JOURNAL OF CHROMATOGRAPHY
影响因子:
--
作者:
ALPERT, AJ
通讯作者:
ALPERT, AJ
影响因子:
7.4
作者:
Chen, Wei-Hao;Lee, Sheng-Chung;Chang, Huan-Cheng
通讯作者:
Chang, Huan-Cheng
DOI:
10.1007/978-1-4939-9240-9_2
发表时间:
2019-01-01
期刊:
SINGLE CELL METHODS: SEQUENCING AND PROTEOMICS
影响因子:
--
作者:
Braga, Felipe A. Vieira;Miragaia, Ricardo J.
通讯作者:
Miragaia, Ricardo J.
影响因子:
7.4
作者:
Alpert, Andrew J.
通讯作者:
Alpert, Andrew J.
影响因子:
7.4
作者:
Amantonico, Andrea;Urban, Pawel L.;Zenobi, Renato
通讯作者:
Zenobi, Renato