Automated proteomic sample preparation: The key component for high throughput and quantitative mass spectrometry analysis

Abstract

Sample preparation for mass spectrometry-based proteomics has many tedious and time-consuming steps that can introduce analytical errors. In particular, the steps around the proteolytic digestion of protein samples are prone to inconsistency. One route for reliable sample processing is the development and optimization of a workflow utilizing an automated liquid handling workstation. Diligent assessment of the sample type, protocol design, reagents, and incubation conditions can significantly improve the speed and consistency of preparation. When combining robust liquid chromatography-mass spectrometry with either discovery or targeted methods, automated sample preparation facilitates increased throughput and reproducible quantitation of biomarker candidates. These improvements in analysis are also essential to process the large patient cohorts necessary to validate a candidate biomarker for potential clinical use. This article reviews the steps in the workflow, optimization strategies, and known applications in clinical, pharmaceutical, and research fields that demonstrate the broad utility for improved automation of sample preparation in the proteomic field.

Keywords: automation; high throughput; optimization; proteomics; quantitative mass spectrometry; robot; sample preparation.

FIGURE 1

Schematic of the proteomic sample preparation process. Top panel summarizes sample type, different automated processes, and MS acquisition methods. Bottom panel illustrates the basic steps in proteomic sample preparation

FIGURE 2

The human proteome illustrated by biological, functional, structural, and sequence complexity. The total number of proteins and their isoforms reported by Swiss‐Prot database; and total proteins associated with eight large categories of biological function (https://www.nextprot.org/about/statistics)

FIGURE 3

Automated proteomic sample preparation evaluation. Left panel, CV% assessment of three distinct digestion conditions are shown: manual digestion (red), unoptimized automated digestion (green), and optimized automated digestion (blue). The reproducibility was assessed by area ratio (light native/heavy SIL) and the CV% was calculated with 4–8 digestion replicates of β‐gal spiked into pooled healthy human plasma. Plasma samples were denatured, reduced, alkylated, and digested with trypsin. Average area ratio CV% β‐gal and human serum albumin were calculated from peak area light (native/heavy SIL). MRM data was acquired by an 6500QTRAP. Right panel, the CV% assessment of automated solid phase extraction (24‐well) is shown. β‐gal and human serum albumin SIL peptides spiked into predigestion healthy human plasma pool, then desalted by a positive pressure apparatus integrated into a controlled i7 automated workstation. SIL heavy peptide signals after desalting were used to calculate CV%. MRM data was acquired similarly by LC‐MS. CV% was calculated from seven repeated injections. SIL, stable isotope labeled