Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

How many human proteoforms are there?

Despite decades of accumulated knowledge about proteins and their post-translational modifications (PTMs), numerous questions remain regarding their molecular composition and biological function. One of the most fundamental queries is the extent to which the combinations of DNA-, RNA- and PTM-level variations explode the complexity of the human proteome. Here, we outline what we know from current databases and measurement strategies including mass spectrometry-based proteomics. In doing so, we examine prevailing notions about the number of modifications displayed on human proteins and how they combine to generate the protein diversity underlying health and disease. We frame central issues regarding determination of protein-level variation and PTMs, including some paradoxes present in the field today. We use this framework to assess existing data and to ask the question, "How many distinct primary structures of proteins (proteoforms) are created from the 20,300 human genes?" We also explore prospects for improving measurements to better regularize protein-level biology and efficiently associate PTMs to function and phenotype

Longitudinal Transcriptomic, Proteomic, and Metabolomic Response of Citrus sinensis to Diaphorina citri Inoculation of Candidatus Liberibacter asiaticus

Huanglongbing (HLB) is a fatal citrus disease that is currently threatening citrus varieties worldwide. One putative causative agent, Candidatus Liberibacter asiaticus (CLas), is vectored by Diaphorina citri, known as the Asian citrus psyllid (ACP). Understanding the details of CLas infection in HLB disease has been hindered by its Candidatus nature and the inability to confidently detect it in diseased trees during the asymptomatic stage. To identify early changes in citrus metabolism in response to inoculation of CLas using its natural psyllid vector, leaves from Madam Vinous sweet orange (Citrus sinensis (L.) Osbeck) trees were exposed to CLas-positive ACP or CLas-negative ACP and longitudinally analyzed using transcriptomics (RNA sequencing), proteomics (liquid chromatography-tandem mass spectrometry; data available in Dryad: 10.25338/B83H1Z), and metabolomics (proton nuclear magnetic resonance). At 4 weeks postexposure (wpe) to psyllids, the initial HLB plant response was primarily to the ACP and, to a lesser extent, the presence or absence of CLas. Additionally, analysis of 4, 8, 12, and 16 wpe identified 17 genes and one protein as consistently differentially expressed between leaves exposed to CLas-positive ACP versus CLas-negative ACP. This study informs identification of early detection molecular targets and contributes to a broader understanding of vector-transmitted plant pathogen interactions

C-H cyanation of 6-ring N-containing heteroaromatics

Heteroaromatic nitriles are important compounds in drug discovery, both for their prevalence in the clinic and due to the diverse range of transformations they can undergo. As such, efficient and reliable methods to access them have the potential for far-reaching impact across synthetic chemistry and the biomedical sciences. Herein, we report an approach to heteroaromatic C-H cyanation through triflic anhydride activation, nucleophilic addition of cyanide, followed by elimination of trifluoromethanesulfinate to regenerate the cyanated heteroaromatic ring. This one-pot protocol is simple to perform, is applicable to a broad range of decorated 6-ring N-containing heterocycles, and has been shown to be suitable for late-stage functionalization of complex drug-like architectures

Modeling peptide fragmentation with dynamic Bayesian networks for peptide identification

Motivation: Tandem mass spectrometry (MS/MS) is an indispensable technology for identification of proteins from complex mixtures. Proteins are digested to peptides that are then identified by their fragmentation patterns in the mass spectrometer. Thus, at its core, MS/MS protein identification relies on the relative predictability of peptide fragmentation. Unfortunately, peptide fragmentation is complex and not fully understood, and what is understood is not always exploited by peptide identification algorithms

Initial recommendations for performing, benchmarking, and reporting single-cell proteomics experiments

Analyzing proteins from single cells by tandem mass spectrometry (MS) has become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition, and data analysis. Broadly accepted community guidelines and standardized metrics will enhance rigor, data quality, and alignment between laboratories. Here we propose best practices, quality controls, and data reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics.Comment: Supporting website: https://single-cell.net/guideline

Improved quality control processing of peptide-centric LC-MS proteomics data

Motivation: In the analysis of differential peptide peak intensities (i.e. abundance measures), LC-MS analyses with poor quality peptide abundance data can bias downstream statistical analyses and hence the biological interpretation for an otherwise high-quality dataset. Although considerable effort has been placed on assuring the quality of the peptide identification with respect to spectral processing, to date quality assessment of the subsequent peptide abundance data matrix has been limited to a subjective visual inspection of run-by-run correlation or individual peptide components. Identifying statistical outliers is a critical step in the processing of proteomics data as many of the downstream statistical analyses [e.g. analysis of variance (ANOVA)] rely upon accurate estimates of sample variance, and their results are influenced by extreme values

A mass accuracy sensitive probability based scoring algorithm for database searching of tandem mass spectrometry data

<p>Abstract</p> <p>Background</p> <p>Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has become one of the most used tools in mass spectrometry based proteomics. Various algorithms have since been developed to automate the process for modern high-throughput LC-MS/MS experiments.</p> <p>Results</p> <p>A probability based statistical scoring model for assessing peptide and protein matches in tandem MS database search was derived. The statistical scores in the model represent the probability that a peptide match is a random occurrence based on the number or the total abundance of matched product ions in the experimental spectrum. The model also calculates probability based scores to assess protein matches. Thus the protein scores in the model reflect the significance of protein matches and can be used to differentiate true from random protein matches.</p> <p>Conclusion</p> <p>The model is sensitive to high mass accuracy and implicitly takes mass accuracy into account during scoring. High mass accuracy will not only reduce false positives, but also improves the scores of true positive matches. The algorithm is incorporated in an automated database search program MassMatrix.</p

Low density lipoprotein and liposome mediated uptake and cytotoxic effect of N4-octadecyl-1-β-D-arabinofuranosylcytosine in Daudi lymphoma cells

Low density lipoprotein (LDL) receptor-mediated uptake and cytotoxic effects of N4-octadecyl-1-beta-D-arabinofuranosylcytosine (NOAC) were studied in Daudi lymphoma cells. NOAC was either incorporated into LDL or liposomes to compare specific and unspecific uptake mechanisms. Binding of LDL to Daudi cells was not altered after NOAC incorporation (K(D) 60 nM). Binding of liposomal NOAC was not saturable with increasing concentrations. Specific binding of NOAC-LDL to Daudi cells was five times higher than to human lymphocytes. LDL receptor binding could be blocked and up- or down-regulated. Co-incubation with colchicine reduced NOAC-LDL uptake by 36%. These results suggested that NOAC-LDL is taken up via the LDL receptor pathway. In an in vitro cytotoxicity test, the IC50 of NOAC-LDL was about 160 microM, whereas with liposomal NOAC the IC50 was 40 microM. Blocking the LDL receptors with empty LDL protected 50% of the cells from NOAC cytotoxicity. The cellular distribution of NOAC-LDL or NOAC-liposomes differed only in the membrane and nuclei fraction with 13% and 6% respectively. Although it is more convenient to prepare NOAC-liposomes as compared to the loading of LDL particles with the drug, the receptor-mediated uptake of NOAC-LDL provides an interesting rationale for the specific delivery of the drug to tumours that express elevated numbers of LDL receptors