2023 special issue on software tools and resources: accelerating research with new and evolving open source software

Proteomic

Amniotic-Fluid Stem Cells: Growth Dynamics and Differentiation Potential after a CD-117-Based Selection Procedure

Amniotic fluid (AF) has become an interesting source of fetal stem cells. However, AF contains heterogeneous and multiple, partially differentiated cell types. After isolation from the amniotic fluid, cells were characterized regarding their morphology and growth dynamics. They were sorted by magnetic associated cell sorting using the surface marker CD 117. In order to show stem cell characteristics such as pluripotency and to evaluate a possible therapeutic application of these cells, AF fluid-derived stem cells were differentiated along the adipogenic, osteogenic, and chondrogenic as well as the neuronal lineage under hypoxic conditions. Our findings reveal that magnetic associated cell sorting (MACS) does not markedly influence growth characteristics as demonstrated by the generation doubling time. There was, however, an effect regarding an altered adipogenic, osteogenic, and chondrogenic differentiation capacity in the selected cell fraction. In contrast, in the unselected cell population neuronal differentiation is enhanced

A Protein Standard That Emulates Homology for the Characterization of Protein Inference Algorithms

Proteomic

ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data.

This report presents the results from the 2016 Association of Biomolecular Resource Facilities Proteome Informatics Research Group (iPRG) study on proteoform inference and false discovery rate (FDR) estimation from bottom-up proteomics data. For this study, 3 replicate Q Exactive Orbitrap liquid chromatography-tandom mass spectrometry datasets were generated from each of

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

Insights from the first phosphopeptide challenge of the MS resource pillar of the HUPO human proteome project

Mass spectrometry has greatly improved the analysis of phosphorylation events in complex biological systems and on a large scale. Despite considerable progress, the correct identification of phosphorylated sites, their quantification, and their interpretation regarding physiological relevance remain challenging. The MS Resource Pillar of the Human Proteome Organization (HUPO) Human Proteome Project (HPP) initiated the Phosphopeptide Challenge as a resource to help the community evaluate methods, learn procedures and data analysis routines, and establish their own workflows by comparing results obtained from a standard set of 94 phosphopeptides (serine, threonine, tyrosine) and their nonphosphorylated counterparts mixed at different ratios in a neat sample and a yeast background. Participants analyzed both samples with their method(s) of choice to report the identification and site localization of these peptides, determine their relative abundances, and enrich for the phosphorylated peptides in the yeast background. We discuss the results from 22 laboratories that used a range of different methods, instruments, and analysis software. We reanalyzed submitted data with a single software pipeline and highlight the successes and challenges in correct phosphosite localization. All of the data from this collaborative endeavor are shared as a resource to encourage the development of even better methods and tools for diverse phosphoproteomic applications. All submitted data and search results were uploaded to MassIVE (littps://massive.ucsd.edu/) as data set MSV000085932 with ProteomeXchange identifier PXD020801.Proteomic

Quantitative Proteomic and Interaction Network Analysis of Cisplatin Resistance in HeLa Cells

Cisplatin along with other platinum based drugs are some of the most widely used chemotherapeutic agents. However drug resistance is a major problem for the successful chemotherapeutic treatment of cancer. Current evidence suggests that drug resistance is a multifactorial problem due to changes in the expression levels and activity of a wide number of proteins. A majority of the studies to date have quantified mRNA levels between drug resistant and drug sensitive cell lines. Unfortunately mRNA levels do not always correlate with protein expression levels due to post-transcriptional changes in protein abundance. Therefore global quantitative proteomics screens are needed to identify the protein targets that are differentially expressed in drug resistant cell lines. Here we employ a quantitative proteomics technique using stable isotope labeling with amino acids in cell culture (SILAC) coupled with mass spectrometry to quantify changes in protein levels between cisplatin resistant (HeLa/CDDP) and sensitive HeLa cells in an unbiased fashion. A total of 856 proteins were identified and quantified, with 374 displaying significantly altered expression levels between the cell lines. Expression level data was then integrated with a network of protein-protein interactions, and biological pathways to obtain a systems level view of proteome changes which occur with cisplatin resistance. Several of these proteins have been previously implicated in resistance towards platinum-based and other drugs, while many represent new potential markers or therapeutic targets