Biology subjects

Krieger, J. R.

Publications and source records attributed to Krieger, J. R..

4 recordsLinked to original sources

bioRxiv2024

TIMS2Rescore: A DDA-PASEF optimized data-driven rescoring pipeline based on MS2Rescore

The high throughput analysis of proteins with mass spectrometry (MS) is highly valuable for understanding human biology, discovering disease biomarkers, identifying therapeutic targets, and exploring pathogen interactions. To achieve these goals, specialized proteomics subfields - such as plasma proteomics, immunopeptidomics, and metaproteomics - must tackle specific analytical challenges, such as an increased identification ambiguity compared to routine proteomics experiments. Technical advancements in MS instrumentation can counter these issues by acquiring more discerning information at higher sensitivity levels, as is exemplified by the incorporation of ion mobility and parallel accumulation - serial fragmentation (PASEF) technologies in timsTOF instruments. In addition, AI-based bioinformatics solutions can help overcome ambiguity issues by integrating more data into the identification workflow. Here, we introduce TIMS2Rescore, a data-driven rescoring workflow optimized for DDA-PASEF data from timsTOF instruments. This platform includes new timsTOF MS2PIP spectrum prediction models and IM2Deep, a new deep learning-based peptide ion mobility predictor. Furthermore, to fully streamline data throughput, TIMS2Rescore directly accepts Bruker raw mass spectrometry data, and search results from ProteoScape and many other search engines, including MS Amanda and PEAKS. We showcase TIMS2Rescore performance on plasma proteomics, immunopeptidomics (HLA class I and II), and metaproteomics data sets. TIMS2Rescore is open-source and freely available at https://github.com/compomics/tims2rescore.

bioinformatics↗

bioRxiv2024

High-throughput proteome profiling with low variation in a multi-center study using dia-PASEF

High throughput proteomics is gaining increasing traction as it facilitates screening of large sample cohorts required in clinical research and systems biology studies. Recent developments in mass spectrometry-based proteomics resulted in improved hardware and software providing deep proteome coverage, robustness, and scale accessible to a wide range of laboratories. Here, we benchmark dia-PASEF, a data-independent acquisition scheme that integrates trapped ion mobility with high scan speed, with a high-resolution time-of-flight mass analyzer (timsTOF HT) for the deep proteome analysis of a human cell line applying short 5-minute gradients. To show intra-and interlaboratory reproducibility, we performed a multi-laboratory study including 11 sites. We demonstrate that on average 7,072 protein groups and 99,835 peptides were identified in human chronic myelogenous leukemia cells on the timsTOF HT with low variation. Our results underline that dia-PASEF data acquisition combined with reproducible chromatography enables high robustness and data consistency across instruments and laboratories, which is a prerequisite for translational biomedical insights.

systems biology↗

bioRxiv2021

A Facile Immunopeptidomics Workflow for Capturing the HLA-I Ligandome with PEAKS XPro

Identifying antigens displayed specifically on tumour cell surfaces by human leukocyte antigen (HLA) proteins is important for the development of immunotherapies and cancer vaccines. The difficulty in capturing an HLA ligandome stems from the fact that many HLA ligands are derived from splicing events or contain mutations, hindering their identification in a standard database search. To address this challenge, we developed an immunopeptidomics workflow with PEAKS XPro that uses de novo sequencing to uncover such peptides and identifies mutations for neoantigen discovery. We demonstrate the utility of this workflow by re-analyzing HLA-I ligandome datasets and reveal a vast diversity in peptide sequences among clones derived from a colorectal cancer tumour. Over 8000 peptides predicted to bind HLA-I molecules were identified by de novo sequencing only (not found in the UniProt database) and make up over 50% of identified peptides from each sample. Lastly, tumour-specific mutations and consensus sequence motif characteristics are defined. This workflow is widely applicable to any immunopeptidomic mass spectrometry dataset and does not require custom database generation for neoantigen discovery.

bioinformatics↗

bioRxiv2019

Evosep One Enables Robust Quantitative Deep Proteome Coverage using Tandem Mass Tags while Significantly Reducing Instrument Time

The balance between comprehensively analyzing the proteome and using valuable mass spectrometry time is a genuine challenge in the field of proteomics. Multidimensional fractionation strategies have significantly increased proteome coverage, but often at the cost of increased mass analysis time, despite advances in mass spectrometer acquisition rates. Recently, the Evosep One liquid chromatography system was shown to analyze peptide samples in a high throughput manner without sacrificing in depth proteomics coverage. We demonstrate incorporation of Evosep One technology into our multiplexing workflow for quantitative analysis of tandem mass tag (TMT)-labeled non-small cell lung carcinoma (NSCLC) patient-derived xenografts (PDXs). Using a 30 samples per day Evosep workflow, >12,000 proteins were identified in 48 hours of mass spectrometry time, which is comparable to the number of proteins identified by our conventional concatenated EASY-nLC workflow in 67.5 hours. Shorter Evosep gradient lengths reduced the number of protein identifications by 10%, while decreasing mass analysis time by 50%. Thus, our Evosep workflow enables quantitative analysis of multiplexed samples in less time without conceding depth of proteome coverage.

systems biology↗