Bone Proteomics Method Optimization for Forensic Investigations

Abstract

The application of proteomic analysis to forensic skeletal remains has gained significant interest in improving biological and chronological estimations in medico-legal investigations. To enhance the applicability of these analyses to forensic casework, it is crucial to maximize throughput and proteome recovery while minimizing interoperator variability and laboratory-induced post-translational protein modifications (PTMs). This work compared different workflows for extracting, purifying, and analyzing bone proteins using liquid chromatography with tandem mass spectrometry (LC-MS)/MS including an in-StageTip protocol previously optimized for forensic applications and two protocols using novel suspension-trap technology (S-Trap) and different lysis solutions. This study also compared data-dependent acquisition (DDA) with data-independent acquisition (DIA). By testing all of the workflows on 30 human cortical tibiae samples, S-Trap workflows resulted in increased proteome recovery with both lysis solutions tested and in decreased levels of induced deamidations, and the DIA mode resulted in greater sensitivity and window of identification for the identification of lower-abundance proteins, especially when open-source software was utilized for data processing in both modes. The newly developed S-Trap protocol is, therefore, suitable for forensic bone proteomic workflows and, particularly when paired with DIA mode, can offer improved proteomic outcomes and increased reproducibility, showcasing its potential in forensic proteomics and contributing to achieving standardization in bone proteomic analyses for forensic applications.

Keywords: acquisition modes; bone proteomics; forensic science; mass spectrometry; protein extraction.

Figure 1

Outline of the three study workflows investigated including the number of samples (1–30), in which biological replicates are compared, extraction protocol, and mass spectrometric acquisition mode. Further details of the extraction protocols are outlined in Table S1. “Workflow One” is a comparison between protein extraction techniques. “Workflow Two” is a lysis buffer comparison using the novel S-Trap Protocol. “Workflow Three” is an investigation between DDA and DIA acquisition modes.

Figure 2

Summary outline flowchart of DDA and DIA proteomics data analysis following MaxQuant and DIA-NN workflows. Standardization of data occurred only for Workflow 3.

Figure 3

(A) Principal component analysis (PCA) of 66 proteins found within the Procopio and Buckley versus S-Trap experiment subgroups for each of the human skeletal tibiae specimens. Axes 1 and 2 explain 87.7% of the variance. (B) Heatmap between Workflow One subgroups “Procopio and Buckley” and “S-Trap” for proteins. Scale is in normalized abundance.

Figure 4

(A) PCA of 14 peptides found within the ZipTip versus S-Trap experiment subgroups for each of the human skeletal tibia specimens that have had PTM to specific amino acid residues. Axes 1 and 2 explain 68% of the variance. (B) Boxplot of deamidation ratios (%) in the sample groups of Procopio and Buckley vs S-Trap. (C) Boxplot of oxidation ratios (%) in the sample groups of Procopio and Buckley vs S-Trap.

Figure 5

(A) PCA of 112 proteins found within the GuHCl/Tris versus SDS experiment subgroups for each of the human skeletal tibiae specimens. Axes 1 and 2 explain 61.5% of the variance. (B) Heatmap between Workflow Two subgroups “GuHCl/Tris” and “SDS” for proteins. Scale is in normalized abundance (i.e., unitless).

Figure 6

(A) PCA of 21 peptides found within the GuHCl/Tris versus SDS experiment subgroups for each of the human skeletal tibiae specimens that have had PTM to specific amino acid residues. Axes 1 and 2 explain 45.4% of the variance. (B) Boxplot of deamidation ratios (%) in the sample groups of GuHCl/Tris vs SDS. (C) Boxplot of oxidation ratios (%) in the sample groups of GuHCl/Tris vs SDS.

Figure 7

(A) PCA of 22 proteins found within the DDA versus DIA experiment subgroups for each of the specimens. Axes 1 and 2 explain 64.9% of the variance. The centroids are represented by the larger circle and triangle icons. (B) Heatmap with Euclidean hierarchical clustering between Workflow Three subgroups “DDA” and “DIA” for proteins.