Biology subjects

Lasch, P.

Publications and source records attributed to Lasch, P..

9 recordsLinked to original sources

bioRxiv2026

MicrobeMS - A MATLAB Toolbox for Microbial Identification Based on Mass Spectrometry

1.Over the last two decades, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF MS) has become the standard method for identifying bacteria and has found a wide range of applications, especially in clinical microbiology. The methods high taxonomic resolution, minimal sample preparation, and complete, ready-to-use commercial systems, which include instrumentation, experimental protocols, spectral databases, and identification analysis software, were key factors in the success of MALDI-ToF MS as the standard for identifying microorganisms in routine diagnostic laboratories. However, despite the availability of these commercial solutions, there is also a growing need for efficient, cost-effective, vendor-neutral databases and analysis tools. These tools would enable the compilation of user-defined mass spectral databases and the testing of new analysis methods and algorithms, particularly in an academic context. To this end, MicrobeMS software has been developed to cover all stages of MALDI-ToF MS-based identification analysis. MicrobeMS is an easy-to-use desktop application for analyzing mass spectra from microorganisms and performing tasks related to spectrum database compilation. It includes routines for direct data import and export, biomarker peak searches, management of spectrum metadata, testing of spectrum quality, supervised and unsupervised identification analysis and intuitive result display. MicrobeMS is implemented in MATLAB and is freely available as MATLAB pcode for Windows and Linux, as well as a standalone application. Over the last fifteen years, the software has undergone continuous development and is now used routinely in various settings at the Centre for Biological Threats and Special Pathogens (ZBS) at the Robert Koch Institute (RKI) in Berlin, Germany, for example in supporting spectrum database compilation, to identify special or rare pathogenic bacteria by advanced identification analysis concepts, or to test in silico MALDI-ToF MS databases derived from microbial genomes. In this software publication the versatility and capabilities of MicrobeMS are demonstrated using a test data set from highly pathogenic bacteria (HPB) which has been obtained as part of a published European Union (EU)-funded External Quality Assurance Exercise (EQAE). MicrobeMS and HPB test data can both be downloaded from https://wiki.microbe-ms.com/. The goal of this software publication is twofold: to raise awareness of MicrobeMS within the scientific community and to encourage the testing of the software and custom-developed MALDI-ToF MS databases of the RKI, which are published at the ZENODO data repository (https://doi.org/10.5281/zenodo.7702374).

bioinformatics↗

bioRxiv2024

Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics

Antimicrobial resistance (AMR) is an increasing challenge for therapy of bacterial infections. Currently, patient treatment is guided by antimicrobial susceptibility testing (AST) using phenotypic assays and species identification by MALDI-ToF biotyping. Bacterial phenotype prediction using omics technologies could offer several advantages over current diagnostic methods. It would allow species identification and AST to be combined in a single measurement, it would eliminate the need for secondary cultivation and could enable the prediction of phenotypes beyond AMR, such as virulence. In this study, the potential of proteomics for clinical microbiology was evaluated in an analysis of 126 clinical isolates covering 16 species including all ESKAPE genera and 29 of the most common AMR determinants. For this purpose, a flexible workflow was developed, which enables to report the AMR phenotype and the species of primary cultures within 2h. Proteomics provided high specificity (99.9%) and sensitivity (94.4 %) for AMR detection, while allowing species identification from very large sequence databases with high accuracy. The results show, that proteomics is well suited for phenotyping clinical bacterial isolates and has the potential to become a valuable diagnostic tool for clinical microbiology in the future.

microbiology↗

bioRxiv2022

Optimizing data points per protein increases protein identifications while maintaining quantitative precision in short gradient data-independent acquisition proteomics

The combination of short liquid chromatography (LC) gradients and data independent acquisition (DIA) by mass spectrometry (MS) has proven its huge potential for high-throughput proteomics. This methodology benefits from the speed of the latest generation of mass spectrometers, which enable short MS cycle times needed to provide sufficient sampling of sharp LC peaks. However, the optimization of isolation window schemes resulting in a certain number of data points per peak (DPPP) is understudied, although it is one of the most important parameters for the outcome of this methodology. In this study, we show that substantially reducing the number of DPPP for short gradient DIA massively increases protein identifications while maintaining quantitative precision. A deeper analysis of the underlying effects shows that a reduction of DPPP increases the selectivity of the data analysis, allowing fragment ions to be tracked over a longer period of the LC gradient. This in turn increases the number of precursors identified per protein, keeping the number of data points per protein nearly constant even at long cycle times. When proteins are inferred from its precursors, quantitative precision is maintained at low DPPP while greatly increasing proteomic depth. This strategy enabled us quantifying 6018 HeLa proteins (> 80,000 precursor identifications) with coefficients of variation below 20% in 30 min using a Q Exactive Orbitrap mass spectrometer, which corresponds to a throughput of 29 samples per day. This indicates that the potential of high-throughput DIA-MS has not been fully exploited yet.

biochemistry↗

bioRxiv2022

Perchlorate-Specific Proteomic Stress Responses of Debaryomyces hansenii Could Enable Microbial Survival in Martian Brines

If life exists on Mars, it would face several challenges including the presence of perchlorates, which destabilize biomacromolecules by inducing chaotropic stress. However, little is known about perchlorate toxicity for microorganism on the cellular level. Here we present the first proteomic investigation on the perchlorate-specific stress responses of the halotolerant yeast Debaryomyces hansenii and compare these to generally known salt stress adaptations. We found that the responses to NaCl and NaClO4-induced stresses share many common metabolic features, e.g., signaling pathways, elevated energy metabolism, or osmolyte biosynthesis. However, several new perchlorate-specific stress responses could be identified, such as protein glycosylation and cell wall remodulations, presumably in order to stabilize protein structures and the cell envelope. These stress responses would also be relevant for life on Mars, which - given the environmental conditions - likely developed chaotropic defense strategies such as stabilized confirmations of biomacromolecules and the formation of cell clusters.

cell biology↗

bioRxiv2020

Universal antimicrobial resistance detection from clinical bacterial isolates using proteomics

Antimicrobial resistance (AMR) poses an increasing challenge for therapy and clinical management of bacterial infections. Currently, antimicrobial resistance detection often relies on phenotypic assays, which are performed independently from species identification. Although genomics-based approaches are increasingly being proposed as possible alternatives for resistance detection, the analysis of proteins should be superior to gene or transcript sequencing when it comes to phenotype prediction from molecular data as the actual resistance against antibiotics is almost exclusively mediated by proteins. In this study, we present a unbiased proteomics workflow for detecting both, bacterial species and AMR related proteins in the absence of secondary antibiotic cultivation in less than 4 h from a primary culture. The method was validated using a sample cohort of 7 bacterial species and 11 AMR determinants represented by 13 protein isoforms which resulted in a sensitivity of 98 % (100 % with vancomycin inference) and a specificity of 100 % with respect to AMR determinants. This proof-of concept study suggests a high application potential of untargeted proteomics in clinical microbiology.

microbiology↗

bioRxiv2020

Optimization of data-independent acquisition using predicted libraries for deep and accurate proteome profiling

In silico spectral library prediction of all possible peptides from whole organisms has a great potential for improving proteome profiling by data-independent acquisition (DIA) and extending its scope of application. In combination with other recent improvements in the field of mass spectrometry (MS)-based proteomics, including sample preparation, peptide separation and data analysis, we aimed to uncover the full potential of such an advanced DIA strategy by optimization of the data acquisition. The results demonstrate that the combination of high-quality in silico libraries, reproducible and high-resolution peptide separation using micro-pillar array columns as well as neural network supported data analysis enables the use of long MS scan cycles without impairing the quantification performance. The study demonstrates that mean coefficient of variations of 4 % were obtained even at only 1.5 data points per peak (full width at half maximum) across different gradient lengths, which in turn improved proteome coverage up to more than 8000 proteins from HeLa cells using empirically-corrected libraries and more than 7000 proteins using a whole human in silico predicted library. These data were obtained using a Q Exactive orbitrap mass spectrometer with moderate scanning speed (12 Hz) and perform very well in comparison to recent studies using more advanced MS instruments, which underline the high potential of this optimization strategy for various applications in clinical proteomics, microbiology and molecular biology.

systems biology↗

bioRxiv2019

Evaluation of MALDI-ToF Mass Spectrometry for Rapid Detection of Cereulide from Bacillus cereus Cultures

1.Bacillus cereus plays an often unrecognized role in food borne diseases. Food poisoning caused by this pathogen is manifested by either diarrhea or emesis. While different enterotoxins have been linked to the diarrheal type of B. cereus infections, the emetic toxin cereulide is responsible for the second type. Due to the relatively high prevalence of cereulide associated food poisoning, methods for simple and reliable detection of cereulide producing strains are of utmost importance. Currently, liquid-chromatography coupled to mass spectrometry (LC-MS) is used for sensitive, specific and quantitative cereulide detection, but this technique requires specialized LC-MS equipment, which is often not available in microbiology routine diagnostic laboratories. The last decade has witnessed the advent of matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-ToF MS) as a simple, rapid and cost-efficient technique for identification of microbial pathogens in routine diagnostics. Just recently, two different studies reported on the application of MALDI-ToF MS for either the differentiation of emetic and non-emetic strains of B. cereus or for direct detection of cereulide from bacterial colony smears. However, no method evaluation and optimization was performed in frame of these studies. Thus, additional investigations on the selectivity and sensitivity of MALDI-TOF MS for cereulide detection are needed before implementation of this method in routine diagnostics can be considered. These aspects prompted us to investigate open or controversial issues and to systematically test sample preparation methods, commonly used for microbial identification for their suitability to detect the emetic toxin directly from bacteria. Based on our experimental findings we propose a MALDI-ToF MS workflow that allows identification of B. cereus and sensitive detection of cereulide in parallel, using standard, linear-mode MALDI-ToF MS equipment. The experimental protocol is based on the well-established ethanol/formic acid extraction method and offers, if required, possibilities for further characterization by more sophisticated LC-MS-based methods. In summary, the ease of use and the achieved level of analytical sensitivity as well as the wide-spread availability of standard MALDI-ToF MS equipment in clinical microbiological laboratories provides a promising tool to improve and to facilitate routine diagnostics of B. cereus associated food intoxications.

microbiology↗

bioRxiv2019

Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data

1.Over the past decade, modern methods of mass spectrometry (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. While MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem mass spectrometry (LC-MS2) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be very time-consuming, especially if the experimental LC-MS2 data are tested against sequence databases covering a broad panel of different microbiological taxa. In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. MS1 data are then extracted and systematically tested against an in silico library of peptide mass data compiled in house. The library has been computed from the UniProt Knowledgebase Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from spectral distances between experimental and in silico peptide mass data and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient - less than two minutes per sample - and has been successfully tested by a set of 19 different microbial pathogens. The approach is rapid, accurate and automatable and holds great potential for future microbiological applications.

microbiology↗

bioRxiv2018

Sample Preparation by Easy Extraction and Digestion (SPEED) - A Universal, Rapid, and Detergent-free Protocol for Proteomics based on Acid Extraction

Mass spectrometry is the method of choice for deep and comprehensive analysis of proteomes and has become a key technology to support the progress in life science and biomedicine. However, sample preparation in proteomics is not standardized and contributes to a lack of reproducibility. The main challenge is to extract all proteins in a manner that enables efficient digestion into peptides and is compatible with subsequent mass spectrometric analysis. Current methods are based on the idea of removing detergents or chaotropic agents during sample processing, which are essential for protein extraction but interfere with digestion and LC-MS. These multi-step preparations are prone to losses, biases and contaminations, while being time-consuming and labor-intensive.\n\nWe report a universal detergent-free method, named Sample Preparation by Easy Extraction and Digestion (SPEED), which is based on a simple three-step procedure, acidification, neutralization and digestion. SPEED is a one-pot method for peptide generation from various sources and is easily applicable even for lysis-resistant sample types as pure trifluoroacetic acid (TFA) is used for highly efficient protein extraction. SPEED-based sample processing is highly reproducible, provides exceptional peptide yields and enables preparation even of tissue samples with less than 15 min hands-on time and without any special equipment. Evaluation of SPEED performance revealed, that the number of quantified proteins and the quantitative reproducibility are superior compared to the well-established sample processing protocols FASP, ISD-Urea and SP3 for various sample types, including human cells, bacteria and tissue, even at low protein starting amounts.

systems biology↗