Mass-spectrometry-based proteomics has become an important component of biological research. Numerous proteomics methods have been developed to identify and quantify the proteins in biological and clinical samples1, identify pathways affected by endogenous and exogenous perturbations2, and characterize protein complexes3. Despite successes, the interpretation of vast proteomics datasets remains a challenge. There have been several calls for improvements and standardization of proteomics data analysis frameworks, as well as for an application-programming interface for proteomics data access4,5. In response, we have developed the ProteoWizard Toolkit, a robust set of open-source, software libraries and applications designed to facilitate proteomics research. The libraries implement the first-ever, non-commercial, unified data access interface for proteomics, bridging field-standard open formats and all common vendor formats. In addition, diverse software classes enable rapid development of vendor-agnostic proteomics software. Additionally, ProteoWizard projects and applications, building upon the core libraries, are becoming standard tools for enabling significant proteomics inquiries.

A new matrix-assisted laser-desorption/ionization time-of-flight/time-of-flight mass spectrometer with the novel "LIFT" technique (MALDI LIFT-TOF/TOF MS) is described. This instrument provides high sensitivity (attomole range) for peptide mass fingerprints (PMF). It is also possible to analyze fragment ions generated by any one of three different modes of dissociation: laser-induced dissociation (LID) and high-energy collision-induced dissociation (CID) as real MS/MS techniques and in-source decay in the reflector mode of the mass analyzer (reISD) as a pseudo-MS/MS technique. Fully automated operation including spot picking from 2D gels, in-gel digestion, sample preparation on MALDI plates with hydrophilic/hydrophobic spot profiles and spectrum acquisition/processing lead to an identification rate of 66% after the PMF was obtained. The workflow control software subsequently triggered automated acquisition of multiple MS/MS spectra. This information, combined with the PMF increased the identification rate to 77%, thus providing data that allowed protein modifications and sequence errors in the protein sequence database to be detected. The quality of the MS/MS data allowed for automated de novo sequencing and protein identification based on homology searching.

Clinical laboratory testing for HER2 status in breast cancer tissues is critically important for therapeutic decision making. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a powerful tool for investigating proteins through the direct and morphology-driven analysis of tissue sections. We hypothesized that MALDI-IMS may determine HER2 status directly from breast cancer tissues. Breast cancer tissues (n = 48) predefined for HER2 status were subjected to MALDI-IMS, and protein profiles were obtained through direct analysis of tissue sections. Protein identification was performed by tissue microextraction and fractionation followed by top-down tandem mass spectrometry. A discovery and an independent validation set were used to predict HER2 status by applying proteomic classification algorithms. We found that specific protein/peptide expression changes strongly correlated with the HER2 overexpression. Among these, we identified m/z 8404 as cysteine-rich intestinal protein 1. The proteomic signature was able to accurately define HER2-positive from HER2-negative tissues, achieving high values for sensitivity of 83%, for specificity of 92%, and an overall accuracy of 89%. Our results underscore the potential of MALDI-IMS proteomic algorithms for morphology-driven tissue diagnostics such as HER2 testing and show that MALDI-IMS can reveal biologically significant molecular details from tissues which are not limited to traditional high-abundance proteins.