This work was supported by National Institute of General Medical Sciences Grant R01 GM125943, National Geographic Grant CP-044R-17; Icelandic Research Fund Grant 152336-051; and University of Illinois at Chicago startup funds. Also, we thank the following contributors: Dr. Amanda Bulman for assistance with MALDI-TOF MS protein acquisition parameters; Dr. Terry Moore and Dr. Atul Jain for recrystallizing alpha-cyano-4-hydroxycinnamic acid matrix (CHCA).

<ol>
	<li>Sandrin, T. R., Goldstein, J. E., Schumaker, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MALDI+TOF+MS+profiling+of+bacteria+at+the+strain+level:+A+review.">MALDI TOF MS profiling of bacteria at the strain level: A review.</a> Mass Spectrometry Reviews. 32 (3), 188-217 (2013).</li><li>Cain, T. C., Lubman, D. M., Weber, W. J., Vertes, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differentiation+of+bacteria+using+protein+profiles+from+matrix-assisted+laser+desorption/ionization+time-of-flight+mass+spectrometry.">Differentiation of bacteria using protein profiles from matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.</a> Rapid Communications in Mass Spectrometry. 8 (12), 1026-1030 (1994).</li><li>Holland, R. D., Wilkes, J. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+identification+of+intact+whole+bacteria+based+on+spectral+patterns+using+matrix-assisted+laser+desorption/ionization+with+time-of-flight+mass+spectrometry.">Rapid identification of intact whole bacteria based on spectral patterns using matrix-assisted laser desorption/ionization with time-of-flight mass spectrometry.</a> Rapid Communications in Mass Spectrometry. 10 (10), 1227-1232 (1996).</li><li>Rahi, P., Prakash, O., Shouche, Y. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Matrix-assisted+laser+desorption/ionization+time-of-flight+mass-spectrometry+(MALDI-TOF+MS)+based+microbial+identifications:+challenges+and+scopes+for+microbial+ecologists.">Matrix-assisted laser desorption/ionization time-of-flight mass-spectrometry (MALDI-TOF MS) based microbial identifications: challenges and scopes for microbial ecologists.</a> Frontiers in Microbiology. 7, 1359 (2016).</li><li>Silva, R., Lopes, N. P., Silva, D. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Application+of+MALDI+mass+spectrometry+in+natural+products+analysis.">Application of MALDI mass spectrometry in natural products analysis.</a> Planta Medica. 82, 671-689 (2016).</li><li>Clark, C. M., Costa, M. S., Sanchez, L. M., Murphy, B. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Coupling+MALDI-TOF+mass+spectrometry+protein+and+specialized+metabolite+analyses+to+rapidly+discriminate+bacterial+function.">Coupling MALDI-TOF mass spectrometry protein and specialized metabolite analyses to rapidly discriminate bacterial function.</a> Proceedings of the National Academy of Sciences of the United States of America. 115 (19), 4981-4986 (2018).</li><li>Freiwald, A., Sauer, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phylogenetic+classification+and+identification+of+bacteria+by+mass+spectrometry.">Phylogenetic classification and identification of bacteria by mass spectrometry.</a> Nature Protocols. 4 (5), 732-742 (2009).</li><li>Schulthess, B., Bloemberg, G. V., Zbinden, R., B&#246;ttger, E. C., Hombach, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+the+Bruker+MALDI+Biotyper+for+identification+of+Gram-positive+rods:+development+of+a+diagnostic+algorithm+for+the+clinical+laboratory.">Evaluation of the Bruker MALDI Biotyper for identification of Gram-positive rods: development of a diagnostic algorithm for the clinical laboratory.</a> Journal of Clinical Microbiology. 52 (4), 1089-1097 (2014).</li><li>Schumann, P., Maier, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MALDI-TOF+mass+spectrometry+applied+to+classification+and+identification+of+bacteria.">MALDI-TOF mass spectrometry applied to classification and identification of bacteria.</a> Methods in Microbiology. 41, 275-306 (2014).</li><li>Chambers, M. C., Maclean, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+cross-platform+toolkit+for+mass+spectrometry+and+proteomics.">A cross-platform toolkit for mass spectrometry and proteomics.</a> Nature Biotechnology. 30 (10), 918-920 (2012).</li><li>Kessner, D., Chambers, M., Burke, R., Agus, D., Mallick, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ProteoWizard:+open+source+software+for+rapid+proteomics+tools+development.">ProteoWizard: open source software for rapid proteomics tools development.</a> Bioinformatics. 24 (21), 2534 (2008).</li><li>Martens, L., Chambers, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=mzML-a+community+standard+for+mass+spectrometry+data.">mzML-a community standard for mass spectrometry data.</a> Molecular &#38; Cellular Proteomics. 10 (1), (2011).</li></ol>

The authors have nothing to disclose.

The IDBac protocol details bacterial protein and specialized metabolite data acquisition and analysis of up to 384 bacterial isolates in 4 h by a single researcher. With IDBac there is no need to extract DNA from bacterial isolates or generate specialized metabolite extracts from liquid fermentation broths and analyze them using chromatographic methods. Instead, protein and specialized metabolite data are gathered by simply spreading material from bacterial colonies directly onto a MALDI target plate. This greatly reduces the time and cost associated with alternative techniques such as 16S rRNA gene sequencing and LCMS9.
It is important to add a matrix blank and calibration spots to the MALDI plate, and we recommend using an appropriate number of replicates to ensure reproducibility and statistical confidence. The numbers of replicates will be experiment-dependent. For example, if a user intends to differentiate thousands of colonies from a collection of environmental diversity plates, fewer replicates may be necessary (our lab collects three technical replicates per colony). Alternatively, if a user wishes to create a custom database of strains from specific bacterial taxa to rapidly determine sub-species classifications of unknown isolates, then more replicates are appropriate (our lab collects eight biological replicates per strain).
IDBac is a tool for rapidly differentiating highly-related bacterial isolates based on putative taxonomic information and specialized metabolite production. It can complement or serve as a precursor to orthogonal methods such as in-depth genetic analyses, studies involving metabolite production and function, or characterization of specialized metabolite structure by Nuclear Magnetic Resonance spectroscopy and/or LC-MS/MS.
Specialized metabolite production (IDBac MANs) is highly susceptible to bacterial growth conditions, especially using different media, which is a potential limitation of the method. However these traits may be exploited by the user, as IDBac can readily generate MANs showing the differences in specialized metabolite production under a variety of growth conditions. It is important to note that while specialized metabolite fingerprints may vary by growth condition, we have previously shown that protein fingerprints remain relatively stable across these variables (see Clark et al.6). When dealing with environmental diversity plates, we recommend purifying bacterial isolates prior to analysis in order to reduce possible contributions from neighboring bacterial cross-talk.
Finally, the lack of a searchable public database of protein MS fingerprints is a major shortcoming in the use of this method to classify unknown environmental bacteria. We created IDBac with this in mind, and included automated conversion of data into a community-accepted open-source format (mzML)10,11,12 and designed the software to allow searching, sharing, and creation of custom databases. We are in the process of creating a large public database (&#62;10,000 fully characterized strains), which will allow for the classification of some isolates to the species-level, including links to GenBank accession numbers when available.
IDBac is open source and the code is available for anyone to customize their data analysis and visualization needs. We recommend that users consult an extensive body of literature (Sauer et al.7, Silva et al.5) to help support and design their experimental goals. We host a forum for discussion at: https://groups.google.com/forum/#!forum/idbac and a means to report issues with the software at: https://github.com/chasemc/IDBacApp/issues.

A major barrier to researchers who study bacterial function is the ability to quickly and simultaneously assess the taxonomic identity of a microorganism and its capacity to produce specialized metabolites. This has prevented significant advances in understanding the relationship between bacterial phylogeny and specialized metabolite production in the majority of bacteria isolated from the environment. Although MS-based methods that use protein fingerprints to group and identify bacteria are well described1,2,3,4, these studies have generally been performed on small groups of isolates, in a species-specific manner. Importantly, information on specialized metabolite production, a major driver of microbial function in the environment, has remained unincorporated in these studies. Silva et al.5 recently provided a comprehensive history detailing the underuse of MALDI-TOF MS to analyze specialized metabolites and the shortage of software to relieve current bioinformatics bottlenecks. In order to address these shortcomings, we created IDBac, a bioinformatics pipeline that integrates both linear and reflectron modes of MALDI-TOF MS6. This allows users to rapidly visualize and differentiate bacterial isolates based on both protein and specialized metabolite MS fingerprints, respectively.
IDBac is cost-effective, high-throughput, and designed for the lay user. It is freely available (chasemc.github.io/IDBac), and only requires access to a MALDI-TOF mass spectrometer (reflectron mode will be required for specialized metabolite analysis). Sample preparation relies on the simple &#8220;extended direct transfer&#8221; method7,8 and data are collected with consecutive linear and reflectron acquisitions on a single MALDI-target spot. With IDBac, it is possible to analyze the putative phylogeny and specialized metabolite production of hundreds of colonies in under four hours, including sample preparation, data acquisition, and data visualization. This presents a significant time and cost advantage over traditional methods of identifying bacteria (such as gene sequencing), and analyzing metabolic output (liquid chromatography-mass spectrometry [LCMS] and similar chromatographic methods).
Using data obtained in linear mode analysis, IDBac employs hierarchical clustering to represent the relatedness of protein spectra. Since the spectra mostly represent ionized ribosomal proteins, they provide a representation of the phylogenetic diversity present in a sample. In addition, IDBac incorporates reflectron mode data to display specialized metabolite fingerprints as Metabolite Association Networks (MANs). MANs are bipartite networks that allow for easy visualization of shared and unique metabolite production between bacterial isolates. The IDBac platform allows researchers to analyze both protein and specialized metabolite data in tandem but also individually if only one data-type is acquired. Importantly, IDBac processes raw data from Bruker and Xiamen instruments, as well as txt, tab, csv, mzXML, and mzML. This eliminates the need for manual conversion and formatting of data sets, and significantly reduces the risk of user error or mishandling of MS data.&#160;

<table><tbody><tr><td>Acetonitrile</td><td>Fisher</td><td>60-002-65</td><td>LC-MS Ultra CHROMASOLV</td></tr><tr><td>Autoflex Speed LEF MALDI-TOF instrument</td><td>Bruker Daltonics</td><td></td><td></td></tr><tr><td>Bruker Daltonics Bacterial test standard</td><td>Fisher</td><td>NC0884024</td><td>Bruker Daltonics 8604530</td></tr><tr><td>Bruker Peptide Calibration standard</td><td>Fisher</td><td>NC9846988</td><td>Bruker Daltonics 8206195</td></tr><tr><td>Formic Acid</td><td>Fisher Chemical</td><td>A117-50</td><td>99.5+%, Optima LC/MS Grade</td></tr><tr><td>MALDI-TOF target Plate</td><td>Bruker Daltonics</td><td></td><td></td></tr><tr><td>Methanol</td><td>Fisher Chemical</td><td>A456-500</td><td>Optima LC/MS Grade</td></tr><tr><td>Toothpicks</td><td></td><td></td><td>any is ok</td></tr><tr><td>Trifluoroacetic acid</td><td>Fisher</td><td>AC293810010</td><td>99.5%, for biochemistry, ACROS Organics</td></tr><tr><td>Water</td><td>VWR</td><td>7732-18-5</td><td>LC-MS</td></tr><tr><td>&amp;alpha;-Cyano-4-hydroxycinnamic acid</td><td>Sigma</td><td>28166-41-8</td><td>(C2020-25G) &amp;ge;98% (TLC), powder</td></tr></tbody></table>

using the open-source maldi tof-ms idbac pipeline for analysis of microbial protein and specialized metabolite data

In order to visualize the relationship between bacterial phylogeny and specialized metabolite production of bacterial colonies growing on nutrient agar, we developed IDBac&#8212;a low-cost and high-throughput matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) bioinformatics pipeline. IDBac software is designed for non-experts, is freely available, and capable of analyzing a few to thousands of bacterial colonies. Here, we present procedures for the preparation of bacterial colonies for MALDI-TOF MS analysis, MS instrument operation, and data processing and visualization in IDBac. In particular, we instruct users how to cluster bacteria into dendrograms based on protein MS fingerprints and interactively create Metabolite Association Networks (MANs) from specialized metabolite data.

We analyzed six strains of Micromonospora chokoriensis and two strains of Bacillus subtilis, which were previously characterized6, using data available at DOI: 10.5281/zenodo.2574096. Following directions in the Starting with Raw Data tab, we selected the option Click here to convert Bruker files and followed the IDBac-provided instructions for each dataset (Figure 14).
After the automated conversion and preprocessing/peak-peaking steps were completed, we proceeded to create a new combined IDBac experiment by transferring samples from the two experiments into a single experiment containing both Bacillus and Micromonosopora samples (Figure 15). The resulting analysis involved comparing protein spectra using mirror plots, as pictured in Figure 16, which was useful for evaluating spectra quality and adjusting peak-picking settings. Figure 17 displays a screenshot of the protein clustering results with default settings selected. The dendrogram was colored by adjusting the threshold on the plot (appears as a dotted line). Of note is the clear separation between genera, with both M. chokoriensis and B. subtilis isolates clustering separately.
Figure 18, Figure 19, and Figure 20 highlight the ability to generate MANs of user-selected regions by clicking and dragging across the protein dendrogram. With this we were able to rapidly create MANs to compare only the B. subtilis strains (Figure 18), only the M. chokoriensis strains (Figure 19), and all the strains simultaneously (Figure 20). The primary function of these networks is to provide researchers with a broad overview of the degree of specialized metabolite overlap between bacteria. With these data in hand, researchers now have the capacity to make informed decisions from only a small amount of material scraped from a bacterial colony.
<img alt="Figure 14" class="xfigimg" src="/files/ftp_upload/59219/59219fig14v2.jpg" /> 
Figure 14: Spectra processing. Downloaded Bruker autoFlex spectra were converted and processed using IDBac. <a href="https://www.jove.com/files/ftp_upload/59219/59219fig14v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 15" class="xfigimg" src="/files/ftp_upload/59219/59219fig15v2.jpg" /> 
Figure 15: Combined IDBac experiment. Because the Micromonospora and Bacillus spectra were collected on different MALDI target plates, the two experiments were subsequently combined into a single experiment-&#8220;Bacillus_Micromonsopora&#8221;. This was done within the &#8220;Work with Previous Experiments&#8221; tab, following directions within the menu &#8220;Transfer samples from previous experiments to new/other experiments&#8221;. <a href="https://www.jove.com/files/ftp_upload/59219/59219fig15v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 16" class="xfigimg" src="/files/ftp_upload/59219/59219fig16v2.jpg" /> 
Figure 16: Comparison. Micromonspora and Bacillus spectra were compared using the mirror plots within the &#8220;Protein Data Analysis&#8221; page. Ultimately, default peak settings were chosen. <a href="https://www.jove.com/files/ftp_upload/59219/59219fig16v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 17" class="xfigimg" src="/files/ftp_upload/59219/59219fig17v2.jpg" /> 
Figure 17: Hierarchical clustering. Hierarchical clustering, using default settings, correctly grouped Bacillus and Micromonospora isolates. The dendrogram was colored by &#8220;cutting&#8221; the dendrogram at an arbitrary height (displayed as a dashed-line) and 100 bootstraps used to show confidence in branching. <a href="https://www.jove.com/files/ftp_upload/59219/59219fig17v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 18" class="xfigimg" src="/files/ftp_upload/59219/59219fig18v2.jpg" /> 
Figure 18: MAN created by selecting the Bacillus sp. strains from the protein dendrogram showed differential production of specialized metabolites. <a href="https://www.jove.com/files/ftp_upload/59219/59219fig18v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 19" class="xfigimg" src="/files/ftp_upload/59219/59219fig19v2.jpg" /> 
Figure 19: MAN created by selecting the six Micromonospora sp. strains from the protein dendrogram showed differential production of specialized metabolites. <a href="https://www.jove.com/files/ftp_upload/59219/59219fig19v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 20" class="xfigimg" src="/files/ftp_upload/59219/59219fig20v2.jpg" /> 
Figure 20: MAN of Bacillus sp. and Micromonospora sp. strains showing a differential production of specialized metabolites. <a href="https://www.jove.com/files/ftp_upload/59219/59219fig20v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Using the Open-Source MALDI TOF-MS IDBac Pipeline for Analysis of Microbial Protein and Specialized Metabolite Data at JoVE.com

Using the Open-Source MALDI TOF-MS IDBac Pipeline for Analysis of Microbial Protein and Specialized Metabolite Data

IDBac is an open-source mass spectrometry-based bioinformatics pipeline that integrates data from both intact protein and specialized metabolite spectra, collected on cell material scraped from bacterial colonies. The pipeline allows researchers to rapidly organize hundreds to thousands of bacterial colonies into putative taxonomic groups, and further differentiate them based on specialized metabolite production.

In order to visualize the relationship between bacterial phylogeny and specialized metabolite production of bacterial colonies growing on nutrient agar, ...

using-open-source-maldi-tof-ms-idbac-pipeline-for-analysis-microbial

Research

JoVE Journal

Biochemistry

19.3K Views.  University of Illinois at Chicago. IDBac koppelt massaspects gegevens uit eiwit- en gespecialiseerde metabolietgebieden van onbekende bacteriële isolaten om snel onderscheid te maken tussen isolaten op basis van zowel hun identiteit als hun potentiële milieufunctie. Onze office source software breidt het nut van bestaande MALDI TOF gebaseerde microbiële identificatie strategieën. Deze uitbreiding omvat analyse van gespecialiseerde stofwisseling als een extra manier om onderscheid te maken tussen kolonies met vergelijkbare ...

Video: Using the Open-Source MALDI TOF-MS IDBac Pipeline for Analysis of Microbial Protein and Specialized Metabolite Data

Low Molecular Weight Protein Enrichment on Mesoporous Silica Thin Films for Biomarker Discovery

The identification of circulating biomarkers holds great potential for non invasive approaches in early diagnosis and prognosis, as well as for the monitoring of therapeutic efficiency.1-3 The circulating low molecular weight proteome (LMWP) composed of small proteins shed from tissues and cells or peptide fragments derived from the proteolytic degradation of larger proteins, has been associated with the pathological condition in patients and likely reflects the state of disease.4,5 Despite these potential clinical applications, the use of Mass Spectrometry (MS) to profile the LMWP from biological fluids has proven to be very challenging due to the large dynamic range of protein and peptide concentrations in serum.6 Without sample pre-treatment, some of the more highly abundant proteins obscure the detection of low-abundance species in serum/plasma. Current proteomic-based approaches, such as two-dimensional polyacrylamide gel-electrophoresis (2D-PAGE) and shotgun proteomics methods are labor-intensive, low throughput and offer limited suitability for clinical applications.7-9 Therefore, a more effective strategy is needed to isolate LMWP from blood and allow the high throughput screening of clinical samples. 
Here, we present a fast, efficient and reliable multi-fractionation system based on mesoporous silica chips to specifically target and enrich LMWP.10,11 Mesoporous silica (MPS) thin films with tunable features at the nanoscale were fabricated using the triblock copolymer template pathway. Using different polymer templates and polymer concentrations in the precursor solution, various pore size distributions, pore structures, connectivity and surface properties were determined and applied for selective recovery of low mass proteins. The selective parsing of the enriched peptides into different subclasses according to their physicochemical properties will enhance the efficiency of recovery and detection of low abundance species. In combination with mass spectrometry and statistic analysis, we demonstrated the correlation between the nanophase characteristics of the mesoporous silica thin films and the specificity and efficacy of low mass proteome harvesting. The results presented herein reveal the potential of the nanotechnology-based technology to provide a powerful alternative to conventional methods for LMWP harvesting from complex biological fluids. Because of the ability to tune the material properties, the capability for low-cost production, the simplicity and rapidity of sample collection, and the greatly reduced sample requirements for analysis, this novel nanotechnology will substantially impact the field of proteomic biomarker research and clinical proteomic assessment.

We developed a technology based on mesoporous silica thin film for the selective recovery of low molecular weight proteins and peptides from human serum. The physico-chemical properties of our mesoporous chips were finely tuned to provide substantial control in peptide enrichment and consequently profile the serum proteome for diagnostic purposes.

The identification of circulating biomarkers holds great potential for non invasive approaches in early diagnosis and prognosis, as well as for the ...

Bioengineering

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

Environment

Automated Sample Multiplexing by using Combined Precursor Isotopic Labeling and Isobaric Tagging (cPILOT)

We have introduced a high throughput quantitative proteomics workflow, combined precursor isotopic labeling and isobaric tagging (cPILOT) capable of multiplexing up to 22 or 24 samples with tandem mass tags or isobaric N,N-dimethyl leucine isobaric tags, respectively, in a single experiment. This enhanced sample multiplexing considerably reduces mass spectrometry acquisition times and increases the utility of the expensive commercial isobaric reagents. However, the manual process of sample handling and pipetting steps in the strategy can be labor intensive, time consuming, and introduce sample loss and quantitative error. These limitations can be overcome through the incorporation of automation. Here we transferred the manual cPILOT protocol to an automated liquid handling device that can prepare large sample numbers (i.e., 96 samples) in parallel. Overall, automation increases feasibility and reproducibility of cPILOT and allows for broad usage by other researchers with comparable automation devices.

Automated Sample Multiplexing by using Combined Precursor Isotopic Labeling and Isobaric Tagging cPILOT

Combined precursor isotopic labeling and isobaric tagging (cPILOT) is an enhanced sample multiplexing strategy that is capable of increasing the number of samples that can be analyzed simultaneously with available isobaric tags. Incorporation of a robotic platform has greatly increased experimental throughput, reproducibility, and quantitative accuracy.

We have introduced a high throughput quantitative proteomics workflow, combined precursor isotopic labeling and isobaric tagging (cPILOT) capable of ...

Chemistry

Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

There are a number of rare and, therefore, insufficiently described bacterial pathogens which are reported to cause severe infections especially in immunocompromised patients. In most cases only few data, mostly published as case reports, are available which investigate the role of such pathogens as an infectious agent. Therefore, in order to clarify the pathogenic character of such microorganisms, it is necessary to conduct epidemiologic studies which include large numbers of these bacteria. The methods used in such a surveillance study have to meet the following criteria: the identification of the strains has to be accurate according to the valid nomenclature, they should be easy to handle (robustness), economical in routine diagnostics and they have to generate comparable results among different laboratories. Generally, there are three strategies for identifying bacterial strains in a routine setting: 1) phenotypic identification characterizing the biochemical and metabolic properties of the bacteria, 2) molecular techniques such as 16S rRNA gene sequencing and 3) mass spectrometry as a novel proteome based approach. Since mass spectrometry and molecular approaches are the most promising tools for identifying a large variety of bacterial species, these two methods are described. Advances, limitations and potential problems when using these techniques are discussed.

Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and molecular techniques (16S rRNA gene sequencing) permit the identification of rare bacterial pathogens in routine diagnostics. The goal of this protocol lies in the combination of both techniques which leads to more accurate and reliable data.

There are a number of rare and, therefore, insufficiently described bacterial pathogens which are reported to cause severe infections especially in ...

Immunology and Infection

Identification of Antibacterial Immunity Proteins in Escherichia coli using MALDI-TOF-TOF-MS/MS and Top-Down Proteomic Analysis

This protocol identifies the immunity proteins of the bactericidal enzymes: colicin E3 and bacteriocin, produced by a pathogenic Escherichia coli strain using antibiotic induction, and identified by MALDI-TOF-TOF tandem mass spectrometry and top-down proteomic analysis with software developed in-house. The immunity protein of colicin E3 (Im3) and the immunity protein of bacteriocin (Im-Bac) were identified from prominent b- and/or y-type fragment ions generated by the polypeptide backbone cleavage (PBC) on the C-terminal side of aspartic acid, glutamic acid, and asparagine residues by the aspartic acid effect fragmentation mechanism. The software rapidly scans in silico protein sequences derived from the whole genome sequencing of the bacterial strain. The software also iteratively removes amino acid residues of a protein sequence in the event that the mature protein sequence is truncated. A single protein sequence possessed mass and fragment ions consistent with those detected for each immunity protein. The candidate sequence was then manually inspected to confirm that all detected fragment ions could be assigned. The N-terminal methionine of Im3 was post-translationally removed, whereas Im-Bac had the complete sequence. In addition, we found that only two or three non-complementary fragment ions formed by PBC are necessary to identify the correct protein sequence. Finally, a promoter (SOS box) was identified upstream of the antibacterial and immunity genes in a plasmid genome of the bacterial strain.

Identification of Antibacterial Immunity Proteins in Escherichia coli using MALDI-TOF-TOF-MS/MS and Top-Down Proteomic Analysis

Here we present a protocol for the rapid identification of proteins produced by genomically sequenced pathogenic bacteria using MALDI-TOF-TOF tandem mass spectrometry and top-down proteomic analysis with software developed in-house. Metastable protein ions fragment because of the aspartic acid effect and this specificity is exploited for protein identification.

This protocol identifies the immunity proteins of the bactericidal enzymes: colicin E3 and bacteriocin, produced by a pathogenic Escherichia coli strain ...

Investigation of Microbial Cooperation via Imaging Mass Spectrometry Analysis of Bacterial Colonies Grown on Agar and in Tissue During Infection

Understanding the metabolic consequences of microbial interactions that occur during infection presents a unique challenge to the field of biomedical imaging. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry represents a label-free, in situ imaging modality capable of generating spatial maps for a wide variety of metabolites. While thinly sectioned tissue samples are now routinely analyzed via this technology, imaging mass spectrometry analyses of non-traditional substrates, such as bacterial colonies commonly grown on agar in microbiology research, remain challenging due to the high water content and uneven topography of these samples. This paper demonstrates a sample preparation workflow to allow for imaging mass spectrometry analyses of these sample types. This process is exemplified using bacterial co-culture macrocolonies of two gastrointestinal pathogens: Clostridioides difficile and Enterococcus faecalis. Studying microbial interactions in this well-defined agar environment is also shown to complement tissue studies aimed at understanding microbial metabolic cooperation between these two pathogenic organisms in mouse models of infection. Imaging mass spectrometry analyses of the amino acid metabolites arginine and ornithine are presented as representative data. This method is broadly applicable to other analytes, microbial pathogens or diseases, and tissue types where a spatial measure of cellular or tissue biochemistry is desired.

Investigation of Microbial Cooperation via Imaging Mass Spectrometry Analysis of Bacterial Colonies Grown on Agar and in Tissue During Infection

A novel sample preparation method is demonstrated for the analysis of agar-based, bacterial macrocolonies via matrix-assisted laser desorption/ionization imaging mass spectrometry.

Understanding the metabolic consequences of microbial interactions that occur during infection presents a unique challenge to the field of biomedical ...

Dithranol as a Matrix for Matrix Assisted Laser Desorption/Ionization Imaging on a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer

Mass spectrometry imaging (MSI) determines the spatial localization and distribution patterns of compounds on the surface of a tissue section, mainly using MALDI (matrix&#160;assisted laser desorption/ionization)-based analytical techniques. New matrices for small-molecule MSI, which can improve the analysis of low-molecular weight (MW) compounds, are needed. These matrices should provide increased analyte signals while decreasing MALDI background signals. In addition, the use of ultrahigh-resolution instruments, such as Fourier transform ion cyclotron resonance (FTICR) mass spectrometers, has&#160;the ability to resolve analyte signals from matrix signals, and this can partially overcome many problems associated with the background originating from the MALDI matrix. The reduction in the intensities of the metastable matrix clusters by FTICR MS can also help to overcome some of the interferences associated with matrix peaks on other instruments. High-resolution instruments such as the FTICR mass spectrometers are advantageous as they can produce distribution patterns of many compounds simultaneously while still providing confidence in chemical identifications. Dithranol (DT; 1,8-dihydroxy-9,10-dihydroanthracen-9-one) has previously been reported as a MALDI matrix for tissue imaging. In this work, a protocol for the use of DT for MALDI imaging of endogenous lipids from the surfaces of mammalian tissue sections, by positive-ion MALDI-MS, on an ultrahigh-resolution hybrid quadrupole FTICR instrument has been provided.

Dithranol (DT; 1,8-dihydroxy-9,10-dihydroanthracen-9-one) has previously been reported as a MALDI matrix for tissue imaging of small molecules; protocols for the use of DT for the MALDI imaging of endogenous lipids on the surface of tissue sections&#160;by positive-ion MALDI-MS on an ultrahigh-resolution quadrupole-FTICR instrument are provided here.

Mass spectrometry imaging (MSI) determines the spatial localization and distribution patterns of compounds on the surface of a tissue section, mainly ...

Biology

Use of MALDI-TOF Mass Spectrometry and a Custom Database to Characterize Bacteria Indigenous to a Unique Cave Environment (Kartchner Caverns, AZ, USA)

MALDI-TOF mass spectrometry has been shown to be a rapid and reliable tool for identification of bacteria at the genus and species, and in some cases, strain levels. Commercially available and open source software tools have been developed to facilitate identification; however, no universal/standardized data analysis pipeline has been described in the literature. Here, we provide a comprehensive and detailed demonstration of bacterial identification procedures using a MALDI-TOF mass spectrometer. Mass spectra were collected from 15 diverse bacteria isolated from Kartchner Caverns, AZ, USA, and identified by 16S rDNA sequencing. Databases were constructed in BioNumerics 7.1. Follow-up analyses of mass spectra were performed, including cluster analyses, peak matching, and statistical analyses. Identification was performed using blind-coded samples randomly selected from these 15 bacteria. Two identification methods are presented: similarity coefficient-based and biomarker-based methods. Results show that both identification methods can identify the bacteria to the species level.

Use of MALDI-TOF Mass Spectrometry and a Custom Database to Characterize Bacteria Indigenous to a Unique Cave Environment Kartchner Caverns, AZ, USA

This work details procedures for rapid identification of bacteria using MALDI-TOF MS. The identification procedures include spectrum acquisition, database construction, and follow up analyses. Two identification methods, similarity coefficient-based and biomarker-based methods, are presented.

MALDI-TOF mass spectrometry has been shown to be a rapid and reliable tool for identification of bacteria at the genus and species, and in some cases, ...

High-throughput and Deep-proteome Profiling by 16-plex Tandem Mass Tag Labeling Coupled with Two-dimensional Chromatography and Mass Spectrometry

Isobaric tandem mass tag (TMT) labeling is widely used in proteomics because of its high multiplexing capacity and deep proteome coverage. Recently, an expanded 16-plex TMT method has been introduced, which further increases the throughput of proteomic studies. In this manuscript, we present an optimized protocol for 16-plex TMT-based deep-proteome profiling, including protein sample preparation, enzymatic digestion, TMT labeling reaction, two-dimensional reverse-phase liquid chromatography (LC/LC) fractionation, tandem mass spectrometry (MS/MS), and computational data processing. The crucial quality control steps and improvements in the process specific for the 16-plex TMT analysis are highlighted. This multiplexed process offers a powerful tool for profiling a variety of complex samples such as cells, tissues, and clinical specimens. More than 10,000 proteins and posttranslational modifications such as phosphorylation, methylation, acetylation, and ubiquitination in highly complex biological samples from up to 16 different samples can be quantified in a single experiment, providing a potent tool for basic and clinical research.

Presented here is an optimized high-throughput protocol developed with 16-plex tandem mass tag reagents, enabling quantitative proteome profiling of biological samples. Extensive basic pH fractionation and high-resolution LC-MS/MS mitigate ratio compression and provide deep proteome coverage.

Isobaric tandem mass tag (TMT) labeling is widely used in proteomics because of its high multiplexing capacity and deep proteome coverage. Recently, an ...

Sample Preparation for Metabolic Profiling using MALDI Mass Spectrometry Imaging

Metabolomics, the study to identify and quantify small molecules and metabolites present in an experimental sample, has emerged as an important tool to investigate the biological activities during development and diseases. Metabolomics approaches&#160;are widely employed in the study of cancer, nutrition/diet, diabetes, and other physiological and pathological conditions involving metabolic processes. An advantageous tool that aids in metabolomic profiling advocated in this paper is matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI). Its ability to detect metabolites in situ without labeling, structural modifications, or other specialized reagents, such as those used in immunostaining, makes MALDI MSI a unique tool in advancing methodologies relevant in the field of metabolomics. An appropriate sample preparation process is critical to yield optimal results and will be the focus of this paper.

The goal of this protocol is to provide detailed guidance on the sample preparation when planning for experiments using MALDI MSI to maximize metabolic and molecular detection in biological samples.

Metabolomics, the study to identify and quantify small molecules and metabolites present in an experimental sample, has emerged as an important tool to ...

Using the Open-Source MALDI TOF-MS IDBac Pipeline for Analysis of Microbial Protein and Specialized Metabolite Data

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