Dataset Information

Assessment and Refinement of Sample Preparation Methods for Deep and Quantitative Plant Proteome Profiling.

ABSTRACT: A major challenge in the field of proteomics is obtaining high-quality peptides for comprehensive proteome profiling by LC-MS. Here, evaluation and modification of a range of sample preparation methods using photosynthetically active Arabidopsis leaf tissue are done. It was found that inclusion of filter-aided sample preparation (FASP) based on filter digestion improves all protein extraction methods tested. Ultimately, a detergent-free urea-FASP approach that enables deep and robust quantification of leaf and root proteomes is shown. For example, from 4-day-old leaf tissue, up to 11 690 proteins were profiled from a single sample replicate. This method should be broadly applicable to researchers working with difficult to process plant samples.

SUBMITTER: Song G

PROVIDER: S-EPMC6296749 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Assessment and Refinement of Sample Preparation Methods for Deep and Quantitative Plant Proteome Profiling.

Song Gaoyuan G Hsu Polly Yingshan PY Walley Justin W JW

Proteomics 20180827 17

A major challenge in the field of proteomics is obtaining high-quality peptides for comprehensive proteome profiling by LC-MS. Here, evaluation and modification of a range of sample preparation methods using photosynthetically active Arabidopsis leaf tissue are done. It was found that inclusion of filter-aided sample preparation (FASP) based on filter digestion improves all protein extraction methods tested. Ultimately, a detergent-free urea-FASP approach that enables deep and robust quantificat ...[more]

PMID: 30035338

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Project description:Due to its outstanding throughput and analytical resolution, gel-free LC-based shotgun proteomics represents the gold standard of proteome analysis. Thereby, the efficiency of sample preparation dramatically affects the correctness and reliability of protein quantification. Thus, the steps of protein isolation, solubilization, and proteolysis represent the principal bottleneck of shotgun proteomics. The desired performance of the sample preparation protocols can be achieved by the application of detergents. However, these compounds ultimately compromise reverse-phase chromatographic separation and disrupt electrospray ionization. Filter-aided sample preparation (FASP) represents an elegant approach to overcome these limitations. Although this method is comprehensively validated for cell proteomics, its applicability to plants and compatibility with plant-specific protein isolation protocols remain to be confirmed. Thereby, the most important gap is the absence of the data on the linearity of underlying protein quantification methods for plant matrices. To fill this gap, we address here the potential of FASP in combination with two protein isolation protocols for quantitative analysis of pea (Pisum sativum) seed and Arabidopsis thaliana leaf proteomes by the shotgun approach. For this aim, in comprehensive spiking experiments with bovine serum albumin (BSA), we evaluated the linear dynamic range (LDR) of protein quantification in the presence of plant matrices. Furthermore, we addressed the interference of two different plant matrices in quantitative experiments, accomplished with two alternative sample preparation workflows in comparison to conventional FASP-based digestion of cell lysates, considered here as a reference. The spiking experiments revealed high sensitivities (LODs of up to 4 fmol) for spiked BSA and LDRs of at least 0.6 × 102. Thereby, phenol extraction yielded slightly better recoveries, whereas the detergent-based method showed better linearity. Thus, our results indicate the very good applicability of FASP to quantitative plant proteomics with only limited impact of the protein isolation technique on the method's overall performance.

Dataset Information

Assessment and Refinement of Sample Preparation Methods for Deep and Quantitative Plant Proteome Profiling.

Publications

Assessment and Refinement of Sample Preparation Methods for Deep and Quantitative Plant Proteome Profiling.

Similar Datasets

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