Protocol for high-throughput semi-automated label-free- or TMT-based phosphoproteome profiling

Abstract

Tandem mass tags data-dependent acquisition (TMT-DDA) as well as data-independent acquisition-based label-free quantification (LFQ-DIA) have become the leading workflows to achieve deep proteome and phosphoproteome profiles. We present a modular pipeline for TMT-DDA and LFQ-DIA that integrates steps to perform scalable phosphoproteome profiling, including protein lysate extraction, clean-up, digestion, phosphopeptide enrichment, and TMT-labeling. We also detail peptide and/or phosphopeptide fractionation and pre-mass spectrometry desalting and provide researchers guidance on choosing the best workflow based on sample number and input. For complete details on the use and execution of this protocol, please refer to Koenig et al.¹ and Martínez-Val et al.².

Keywords: Mass Spectrometry; Protein Biochemistry; Proteomics.

Graphical abstract

Figure 1

Overview of automated PAC-based protein capture, clean-up and digestion (A and B) (A) KingFisher Flex /Apex layout (processing of up to 96 samples in parallel) or (B) KingFisher Duo /Presto layout (processing of up to 12 samples in parallel).

Figure 2

PAC with desalting digestion recovery for input amounts ranging from 100 μg to 500 μg HeLa lysate The protein concentration was measured using BCA and the peptide concentration was estimated after peptide desalting using absorbance at 280 nm.

Figure 3

Decision tree for helping in the selection of the ideal sample preparation approach, i.e., no fractionation with LFQ DIA or TMT-labeling followed by microflow fractionation (MF) or stage-tip fractionation (STF)

Figure 4

Schematic representation of the fraction concatenation after microflow fractionation (MF)

Figure 5

Overview of automated phosphopeptide enrichment (A and B) (A) KingFisher Flex /Apex layout (processing of up to 96 samples in parallel) or (B) KingFisher Duo Prime /Presto layout (processing of up to 12 samples in parallel).

Figure 6

Expected performance of MagReSyn Ti-IMAC HP and Zr-IMAC HP for TMT-based analysis from bulk HeLa lysate (A) Comparison between MagReSyn Ti-IMAC-HP and Zr-IMAC-HP beads for phosphopeptide enrichment. The data was acquired in DDA mode with 15k MS2 resolution and, using a 60SPD Evosep gradient for all amounts. The identifications represents the median of 3 replicates per conditions, the error-bars represent the standard deviation. (B) Barplot showing the phosphopeptide multiplicity as a function of the peptide input amount for both Ti-IMAC-HP and Zr-IMAC-HP. The multiplicity corresponds to the percentage of multiphosphorylated peptides. (C) Boxplot displaying the measured phosphopeptides intensity as a function of the peptide input amount for both Ti-IMAC-HP and Zr-IMAC-HP beads.

Figure 7

Expected performance of column and IMAC bead type DIA based phosphopeptide analysis using WHISPER 40SPD Evosep gradient (A) Comparative bar plot assessing the effect of the column type (Evosep (EV1112) vs. IonOpticks (AUR3-15075C18-TS)) on the number of phosphopeptides identified when using 2.5 μg of peptide input for phosphopeptide enrichment with MagReSyn Ti-IMAC-HP beads with the 40 SPD gradient (n = 3). (B) Comparative bar plot assessing the effect of the beads type (Ti-IMAC-HP vs. Zr-IMAC-HP) on the number of phosphopeptides (class I) identified when using 2.5 μg of peptide input for phosphopeptide enrichment using an IonOpticks column (n = 3) for phosphopeptide separation. Adapted from Martinez-Val et al.