Definitive Screening Designs to Optimize Library-Free DIA-MS Identification and Quantification of Neuropeptides.
Definitive Screening Designs to Optimize Library-Free DIA-MS Identification and Quantification of Neuropeptides.
复制标题
确定的筛选设计,以优化无文库的DIA-MS识别和神经肽的定量。
DOI:
10.1021/acs.jproteome.3c00088
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发表时间:
2023-05-05
影响因子:
4.4
通讯作者:
Li, Lingjun
中科院分区:
文献类型:
--
作者:
Phetsanthad, Ashley;Carr, Austin V.;Fields, Lauren;Li, Lingjun
关键词:
Method optimization is crucial for successful mass spectrometry (MS) analysis. However, extensive method assessments, altering various parameters individually, are rarely performed due to practical limitations regarding time and sample quantity. To maximize sample space for optimization while maintaining reasonable instrumentation requirements, a definitive screening design (DSD) is leveraged for systematic optimization of data-independent acquisition (DIA) parameters to maximize crustacean neuropeptide identifications. While DSDs require several injections, a library-free methodology enables surrogate sample usage for comprehensive optimization of MS parameters to assess biomolecules from limited samples. We identified several parameters contributing significant first- or second-order effects to method performance, and the DSD model predicted ideal values to implement. These increased reproducibility and detection capabilities enabled the identification of 461 peptides, compared to 375 and 262 peptides identified through data-dependent acquisition (DDA) and a published DIA method for crustacean neuropeptides, respectively. Herein, we demonstrate a DSD optimization workflow, using standard material, not reliant on spectral libraries for the analysis of any low abundance molecules from previous samples of limited availability. This extends the DIA method to low abundance isoforms dysregulated or only detectable in disease samples, thus improving characterization of previously inaccessible biomolecules, such as neuropeptides. Data are available via ProteomeXchange with identifier PXD038520.
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影响因子:
4.4
作者:
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通讯作者:
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影响因子:
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DOI:
10.1074/mcp.ra117.000314
发表时间:
2017-12
期刊:
Molecular & cellular proteomics : MCP
影响因子:
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Kraemer, Thomas
DOI:
10.1016/j.jpba.2015.10.005
发表时间:
2016-01-25
影响因子:
3.4
作者:
Jadhav, Sushant Bhimrao;Kumar, C. Kiran;Bhosale, P. N.
通讯作者:
Bhosale, P. N.