The emerging role of mass spectrometry-based proteomics in molecular pharming practices

Molecular pharming relies on the integration of foreign genes into a plant system for production of the desired recombinant protein. The speed, scalability, and lack of contaminating human pathogens highlights plants as an enticing and feasible system to produce diverse protein-based products, including vaccines, antibodies, and enzymes. However, limitations of expression levels, host defense responses, and production irregularities underscore distinct areas for improvement within the molecular pharming pipeline. Within the past five years, mass spectrometry-based proteomics has begun to address these critical areas and show promise in advancing our understanding of the complex biological systems driving molecular pharming. Further, opportunities to leverage comprehensive proteome profiling have surfaced to meet good manufacturing practice regulations and move biopharmaceuticals derived from plants into mainstream production.     

Targeted proteomics on its way to discovery

For a long time, targeted and discovery proteomics covered different corners of the detection spectrum, with targeted proteomics focused on small target sets. This changed with the recent advances in highly multiplexed analysis. While discovery proteomics still pushes higher numbers of identified and quantified proteins, the advances in targeted proteomics rose to cover large parts of less complex proteomes or proteomes with low protein detection counts due to dynamic range restrictions, like the blood proteome. These new developments will impact, especially on the field of biomarker discovery and the possibility of using targeted proteomics for diagnostic purposes.     

ProSight Annotator: Complete control and customization of protein entries in UniProt XML files

The effectiveness of any proteomics database search depends on the theoretical candidate information contained in the protein database. Unfortunately, candidate entries from protein databases such as UniProt rarely contain all the post-translational modifications (PTMs), disulfide bonds, or endogenous cleavages of interest to researchers. These omissions can limit discovery of novel and biologically important proteoforms. Conversely, searching for a specific proteoform becomes a computationally difficult task for heavily modified proteins. Both situations require updates to the database through user-annotated entries. Unfortunately, manually creating properly formatted UniProt Extensible Markup Language (XML) files is tedious and prone to errors. ProSight Annotator solves these issues by providing a graphical interface for adding user-defined features to UniProt-formatted XML files for better informed proteoform searches. It can be downloaded from http://prosightannotator.northwestern.edu .     

Improving the proteome coverage of Daphnia magna - implications for future ecotoxicoproteomics studies

Aquatic pollution is an increasing problem and requires extensive research efforts to understand associated consequences and to find suitable solutions. The crustacean Daphnia is a keystone species in lacustrine ecosystems by connecting primary producers with higher trophic levels. Therefore, Daphnia is perfectly suitable to investigate biological effects of freshwater pollution and is frequently used as an important model organism in ecotoxicology. The field of ecotoxicoproteomics has become increasingly prevalent, as proteins are important for an organism's physiology and respond rapidly to changing environmental conditions. However, one obstacle in proteome analysis of Daphnia is highly abundant proteins like vitellogenin, decreasing the analytical depth of proteome analysis. To improve proteome coverage in Daphnia, we established an easy-to-use procedure based on the LC-MS/MS of whole daphnids and the dissected Daphnia gut, which is the main tissue getting in contact with soluble and particulate pollutants, separately. Using a comprehensive spectral library, generated by gas-phase fractionation and a data-independent acquisition method, we identified 4621 and 5233 protein groups at high confidence (false discovery rate < 0.01) in Daphnia and Daphnia gut samples, respectively. By combining both datasets, a proteome coverage of 6027 proteins was achieved, demonstrating the effectiveness of our approach.     

An optimized MALDI MSI protocol for spatial detection of tryptic peptides in fresh frozen prostate tissue

MALDI MS imaging (MSI) is a powerful analytical tool for spatial peptide detection in heterogeneous tissues. Proper sample preparation is crucial to achieve high quality, reproducible measurements. Here we developed an optimized protocol for spatially resolved proteolytic peptide detection with MALDI time-of-flight MSI of fresh frozen prostate tissue sections. The parameters tested included four different tissue washes, four methods of protein denaturation, four methods of trypsin digestion (different trypsin densities, sprayers, and incubation times), and five matrix deposition methods (different sprayers, settings, and matrix concentrations). Evaluation criteria were the number of detected and excluded peaks, percentage of high mass peaks, signal-to-noise ratio, spatial localization, and average intensities of identified peptides, all of which were integrated into a weighted quality evaluation scoring system. Based on these scores, the optimized protocol included an ice-cold EtOH+H₂O wash, a 5 min heating step at 95°C, tryptic digestion incubated for 17h at 37°C and CHCA matrix deposited at a final amount of 1.8 μg/mm². Including a heat-induced protein denaturation step after tissue wash is a new methodological approach that could be useful also for other tissue types. This optimized protocol for spatial peptide detection using MALDI MSI facilitates future biomarker discovery in prostate cancer and may be useful in studies of other tissue types.     

A prostate cancer tissue specific spectral library for targeted proteomic analysis

Prostate cancer is the most common cancer in males worldwide. Mass spectrometry-based targeted proteomics has demonstrated great potential in quantifying proteins from formalin-fixed paraffin-embedded (FFPE) and (fresh) frozen biopsy tissues. Here we provide a comprehensive tissue-specific spectral library for targeted proteomic analysis of prostate tissue samples. Benign and malignant FFPE prostate tissue samples were processed into peptide samples by pressure cycling technology (PCT)-assisted sample preparation, and fractionated with high-pH reversed phase liquid chromatography (RPLC). Based on data-dependent acquisition (DDA) MS analysis using a TripleTOF 6600, we built a library containing 108,533 precursors, 84,198 peptides and 9384 unique proteins (1% FDR). The applicability of the library was demonstrated in prostate specimens.     

Analytical comparison of absolute quantification strategies to investigate the insulin signaling pathway in fat cells

So far, mass spectrometry-based targeted proteomics is the most sensitive approach to answer and address specific biological questions in an accurate and quantitative fashion. However, the data analysis design used for such quantification varies in the field leading to discrepancies in the reported values. In this study, different quantification strategies based on calibration curves were evaluated and compared. The best accuracy and coefficient of variation was achieved by ratio to ratio calibration curves. We applied the ratio to ratio quantification approach to analyze very low abundant insulin signaling proteins such as PIK3RA (0.10–0.93 fmol/μg), AKT1 (0.1–0.39 fmol/μg), and the insulin receptor (0.22–2.62 fmol/μg) in a fat cell model and demonstrated the adaptation of this pathway at different states of insulin sensitivity.     

A practical guide to interpreting and generating bottom-up proteomics data visualizations

Mass-spectrometry based bottom-up proteomics is the main method to analyze proteomes comprehensively and the rapid evolution of instrumentation and data analysis has made the technology widely available. Data visualization is an integral part of the analysis process and it is crucial for the communication of results. This is a major challenge due to the immense complexity of MS data. In this review, we provide an overview of commonly used visualizations, starting with raw data of traditional and novel MS technologies, then basic peptide and protein level analyses, and finally visualization of highly complex datasets and networks. We specifically provide guidance on how to critically interpret and discuss the multitude of different proteomics data visualizations. Furthermore, we highlight Python-based libraries and other open science tools that can be applied for independent and transparent generation of customized visualizations. To further encourage programmatic data visualization, we provide the Python code used to generate all data figures in this review on GitHub ( https://github.com/MannLabs/ProteomicsVisualization ).     

化学交联质谱技术的研究进展

化学交联质谱技术是解析蛋白质结构和研究蛋白质相互作用的重要工具。近5年以来,该技术在方法和应用上都取得了很大的进步。方法上,一方面可断裂交联剂与新型分离富集方法展现了较好的应用前景,另一方面更加高效的交联肽段搜索引擎和质量控制方法为交联质谱数据分析提供了有力的工具。应用上,一方面与冷冻电镜技术结合解析了大量蛋白质的结构,另一方面从研究蛋白质复合物的相互作用发展到研究全蛋白质组水平的相互作用网络。化学交联质谱技术在方法和应用上的蓬勃发展,体现了这一技术的重要作用。本文对化学交联质谱技术的各个环节进行了详细的综述,包括交联剂选择、交联反应、酶切、交联肽段富集、液质联用、交联肽段鉴定、质量控制和生物学应用,重点介绍了最近5年的研究进展。最后,讨论了化学交联质谱技术面临的挑战及未来的发展方向。