Quick 96FASP for high throughput quantitative proteome analysis

Abstract

Filter aided sample preparation (FASP) is becoming a central method for proteomic sample cleanup and peptide generation prior to LC-MS analysis. We previously adapted this method to a 96-well filter plate, and applied to prepare protein digests from cell lysate and body fluid samples in a high throughput quantitative manner. While the 96FASP approach is scalable and can handle multiple samples simultaneously, two key advantages compared to single FASP, it is also time-consuming. The centrifugation-based liquid transfer on the filter plate takes 3~5 times longer than single filter. To address this limitation, we now present a quick 96FASP (named q96FASP) approach that, relying on the use of filter membranes with a large MWCO size (~30 kDa), significantly reduces centrifugal times. We show that q96FASP allows the generation of protein digests derived from whole cell lysates and body fluids in a quality similar to that of the single FASP method. Processing a sample in multiple wells in parallel, we observed excellent experimental repeatability by label-free quantitation approach. We conclude that the q96FASP approach promises to be a promising cost- and time-effective method for shotgun proteomics and will be particularly useful in large scale biomarker discovery studies.

Assessment and refinement of sample preparation methods for deep and quantitative plant proteome profiling

“…We chose the UA-FASP method for further testing because 1) it resulted in the largest recovery of protein (Table 1), 2) enabled detection of a similar total number of peptides and proteins as the phenol-FASP method (Figure 1), and 3) it does not require the use of hazardous phenol. Furthermore, because the UA-FASP method does not require complicated phase separation it should be compatible with recently described approaches for high-throughput sample preparation using 96-well filter plates (37,38). Ultimately, using the UA-FASP method we were able to robustly and reproducibly quantify over ten thousand proteins from each analyzed leaf sample.…”

Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data

“…depletion and/or fractionation) and limitations in data acquisition throughput. More recently, efforts to generate large sample biobanks for proteomic analysis 19,20 , the introduction of automated and high-throughput sample preparation workflows [21][22][23] , and improvements in liquid chromatography have facilitated larger cohort studies 23 . Some developments combine rapid sample preparation protocols, multiplexing strategies, automated platforms and optimized HPLC setups 21,[24][25][26] .…”

Computational Approaches in Proteomics

“…In contrast to TD and MD proteomics analysis, for BU data analysis, a deconvolution step is not required when implementing ESI, due to the rare generation of double-and triple-charged fragment ions (36). Mass spectra raw data are commonly processed by Proteome Discoverer or MaxQuant platforms using several search engines, such as Sequest, Mascot, Andromeda, X!Tandem and COMET, usually against UniProt databases (37)(38)(39). MaxQuant software can also determine protein quantitation and estimate the error of PTM false localization.…”

“…MaxQuant software can also determine protein quantitation and estimate the error of PTM false localization. For downstream correlation and clustering analysis, the identified proteins are commonly processed in the Perseus platform (38,40,41). To reduce data complexity, principal component analysis (PCA) has been the method of choice, and also to identify the relatedness of the differentially expressed proteins within and among samples (39,41).…”