A5014948456- Advanced Proteomics Techniques and Applications
- Mass Spectrometry Techniques and Applications
- Metabolomics and Mass Spectrometry Studies
- Single-cell and spatial transcriptomics
- Machine Learning in Bioinformatics
- Evolution and Genetic Dynamics
- Advanced Biosensing Techniques and Applications
- RNA Research and Splicing
- RNA modifications and cancer
- Cell Image Analysis Techniques
- Gene Regulatory Network Analysis
- Protein Structure and Dynamics
- Microbial Metabolic Engineering and Bioproduction
- vaccines and immunoinformatics approaches
- Photosynthetic Processes and Mechanisms
- Bioinformatics and Genomic Networks
- Bacterial Genetics and Biotechnology
- Chemokine receptors and signaling
- Endoplasmic Reticulum Stress and Disease
- thermodynamics and calorimetric analyses
- Boron Compounds in Chemistry
- Genomics and Phylogenetic Studies
- Radiation Detection and Scintillator Technologies
- Vibrio bacteria research studies
- Enzyme Structure and Function
Max Planck Institute of Biochemistry
2020-2025
Center for Systems Biology
2021-2024
Harvard University
2021-2024
Ludwig-Maximilians-Universität München
2014-2023
Boston University
2022
Technical University of Munich
2018-2019
Machine learning and in particular deep (DL) are increasingly important mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility fragment intensities of a peptide just from amino acid sequence with good accuracy. However, is very rapidly developing field new neural network architectures frequently appearing, which challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, modular Python framework built on PyTorch...
Single-cell proteomics by mass spectrometry is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion complex tissues would greatly enhance insights. Here we describe single-cell Deep Visual Proteomics (scDVP), technology that integrates high-content imaging, laser microdissection multiplexed spectrometry. scDVP resolves context-dependent, spatial proteome murine hepatocytes at...
Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited proteomic depth, throughput, robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated complete dimethyl labeling bulk or single-cell samples, without losing depth. Lys-N digestion enables five-plex quantification MS1 MS2 level. Because channels are...
Abstract In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora different computational tools can process the MS data to derive peptide and protein identification quantification. However, during last years there has been dramatic progress in computer science, including collaboration that have transformed research industry. To leverage these advances, we develop...
Abstract Single-cell technologies are revolutionizing biology but today mainly limited to imaging and deep sequencing 1–3 . However, proteins the main drivers of cellular function in-depth characterization individual cells by mass spectrometry (MS)-based proteomics would thus be highly valuable complementary 4,5 Chemical labeling-based single-cell approaches introduce hundreds into MS, direct analysis single has not yet reached necessary sensitivity, robustness quantitative accuracy answer...
Recent advances in mass spectrometry-based proteomics enable the acquisition of increasingly large datasets within relatively short times, which exposes bottlenecks bioinformatics pipeline. Although peptide identification is already scalable, most label-free quantification (LFQ) algorithms scale quadratic or cubic with sample numbers, may even preclude analysis large-scale data. Here we introduce directLFQ, a ratio-based approach for normalization and calculation protein intensities. It...
Amino acid metabolism is crucial for inflammatory processes during atherogenesis. The endogenous amino homoarginine a robust biomarker cardiovascular outcome and mortality with high levels being protective. However, the underlying mechanisms remain elusive. We investigated effect of supplementation on atherosclerotic plaque development particular focus inflammation.Female ApoE-deficient mice were supplemented (14 mg/L) in drinking water starting 2 weeks before continuing throughout 6-week...
Abstract Mass spectrometry (MS)-based proteomics continues to evolve rapidly, opening more and application areas. The scale of data generated on novel instrumentation acquisition strategies pose a challenge bioinformatic analysis. Search engines need make optimal use the for biological discoveries while remaining statistically rigorous, transparent performant. Here we present alphaDIA, modular open-source search framework independent (DIA) proteomics. We developed feature-free identification...
ABSTRACT In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making their efficient analysis a principal challenge. There is plethora different computational tools that process the MS data and derive peptide protein identification quantification. During last decade, there has been dramatic progress in computer science software engineering, including collaboration have transformed research industry. To leverage these...
Mass spectrometry based proteomics is the method of choice for quantifying genome-wide differential changes protein expression in a wide range biological and biomedical applications. Protein need to be reliably derived from many measured peptide intensities their corresponding fold changes. These vary considerably given protein. Numerous instrumental setups aim reduce this variability, whereas current computational methods only implicitly account problem. We introduce new method, MS-EmpiRe,...
Bacteria reorganize their physiology upon entry to stationary phase. What part of this reorganization improves starvation survival is a difficult question because the change in includes global proteome, envelope, and metabolism cell. In work, we used several trade-offs between fast growth long statistically score over 2,000 Escherichia coli proteins for correlation with death rate. The combined ranking allowed us narrow down set that positively correlate validate causal role subset proteins....
Abstract Quantitative readout is essential in proteomics, yet current bioinformatics methods lack a framework to handle the inherent multi-level nature of data (fragments, MS1 isotopes, charge states, modifications, peptides and genes). We present AlphaQuant, which introduces tree-based quantification . This approach organizes quantitative into hierarchical tree across levels. It allows differential analyses at fragment level, recovering up 50-fold more regulated proteins compared...
Abstract Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited proteomic depth, throughput robustness, a challenge that we address here by streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated complete dimethyl labeling bulk or single-cell samples, without losing depth. In single runs mammalian cells, three-plex analysis tryptic peptides...
In clinical ion beam therapy, protons as well heavier ions such carbon are used for treatment. For protons, β+-emitters only induced by fragmentation reactions in the target (target fragmentation), whereas heavy ions, they additionally fragmentations of projectile (further referred to autoactivation). An approach utilizing these processes treatment verfication, comparing measured Positron Emission Tomography (PET) data predictions from Monte Carlo simulations, has already been clinically...
Abstract Single-cell proteomics by mass spectrometry (MS) is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion complex tissues would greatly enhance insights. Here we describe single-cell Deep Visual Proteomics (scDVP), technology that integrates high-content imaging, laser microdissection multiplexed MS. scDVP resolves context-dependent, spatial proteome murine hepatocytes at...
Abstract Machine learning and in particular deep (DL) are increasingly important mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility fragment intensities of a peptide just from amino acid sequence with good accuracy. However, is very rapidly developing field new neural network architectures frequently appearing, which challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, modular Python framework built on...
Abstract The majority of microbes on earth, whether they live in the ocean, soil or animals, are not growing, but instead struggling to survive starvation 1–6 . Some genes and environmental conditions affecting survival have been identified 7–13 , despite almost a century study 14–16 we do know which processes lead irreversible loss viability, maintenance counteract them how lifespan is determined from balance these opposing processes. Here, used time-lapse microscopy capture characterize...
1 Abstract Mass spectrometry based proteomics is the method of choice for quantifying genome-wide differential changes proteins in a wide range biological and biomedical applications. Protein need to be reliably derived from large number measured peptide intensities their corresponding fold changes. These vary considerably given protein. Numerous instrumental setups aim reduce this variability, while current computational methods only implicitly account problem. We introduce new method,...
Spectral libraries play a central role in the analysis of data-independent-acquisition (DIA) proteomics experiments. A main assumption current spectral library tools is that single characteristic intensity pattern (CIP) suffices to describe fragmentation peptide particular charge state (peptide pair). However, we find this often not case. We carry out systematic evaluation variability over public repositories and in-house data sets. show widespread partly occurs under fixed experimental...
ABSTRACT Recent advances in mass spectrometry (MS)-based proteomics enable the acquisition of increasingly large datasets within relatively short times, which exposes bottlenecks bioinformatics pipeline. Whereas peptide identification is already scalable, most label-free quantification (LFQ) algorithms scale quadratic or cubic with sample numbers, may even preclude analysis large-scale data. Here we introduce directLFQ, a ratiobased approach for normalization and calculation protein...
ABSTRACT While absolute quantification is challenging in high-throughput measurements, changes of features between conditions can often be determined with high precision. Therefore, analysis fold the standard method, but often, a doubly differential required. Differential alternative splicing an example analysis, i.e. for different isoforms gene. EmpiRe quantitative approach various kinds omics data based on appropriate biological objects. Empirical error distributions these are estimated...
Summary Alternative splicing can substantially diversify biological cell states and influence cellular function. The functional impact of has to be estimated at protein level, typically by mass spectrometry (MS) -based proteomics. Although this technology measures increasingly large peptides sets, distinguishing isoform-specific are rare, limiting detection quantification splicing. We introduce MS-EmpiReS, a quantification-based computational approach for differential alternative in...