bims-metlip 2020-06-14 papers

bims-metlip

Biomed News

on Methods and protocols in metabolomics and lipidomics

Issue of 2020–06–14
fourteen papers selected by
Sofia Costa, Cold Spring Harbor Laboratory

9-Plex ultra high performance liquid chromatography tandem mass spectrometry determination of free hydroxyl polycyclic aromatic hydrocarbons in human plasma and urine.
Fast Quantification without Conventional Chromatography, the Growing Power of Mass Spectrometry.
Identification of Trenbolone Metabolites Using Hydrogen Isotope Ratio Mass Spectrometry and Liquid Chromatography/High Accuracy/High Resolution Mass Spectrometry for Doping Control Analysis.
QSRR Automator: A Tool for Automating Retention Time Prediction in Lipidomics and Metabolomics.
NMR and MS-based Stable Isotope-Resolved Metabolomics and Applications in Cancer Metabolism.
MetaboKit: a comprehensive data extraction tool for untargeted metabolomics.
Stationary-phase optimized selectivity in supercritical fluid chromatography using a customized Phase OPtimized Liquid Chromatography kit: comparison of different prediction approaches.
Normalized retention time for scheduled liquid chromatography-multistage mass spectrometry analysis of epitranscriptomic modifications.
Lemna minor studies under various storage periods using extended-polarity extraction and metabolite non-target screening analysis.
Phosphorus NMR and its application to metabolomics.
Simultaneous analysis of retinol, six carotenoids, two tocopherols, and coenzyme Q10 from human plasma by HPLC.
Collision Cross Section Conformational Analyses of Bile Acids via Ion Mobility-Mass Spectrometry.
Single-Cell Classification Using Mass Spectrometry Through Interpretable Machine Learning.
Peak detection for MALDI mass spectrometry imaging data using sparse frame multipliers.

J Chromatogr A. 2020 Jul 19. pii: S0021-9673(20)30434-9. [Epub ahead of print]1623 461182

9-Plex ultra high performance liquid chromatography tandem mass spectrometry determination of free hydroxyl polycyclic aromatic hydrocarbons in human plasma and urine.

Luping Sun, Shuyun Zhu, Zhenjia Zheng, Jing Sun, Xian-En Zhao, Huwei Liu.

  Hydroxyl-polycyclic aromatic hydrocarbons (OH-PAHs) are biomarkers for assessing the exposure levels of polycyclic aromatic hydrocarbons (PAHs). A series of stable isotope mass tags (SIMT-332/338/346/349/351/354/360/363/374/377) were firstly designed and synthesized to perform multiplexed stable isotope labeling derivatization (MSILD) of OH-PAHs in human plasma and urine. Their derivatives were enriched and purified by magnetic dispersive solid phase extraction (MDSPE) using prepared Fe3O4/GO and then determined by ultra high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) in multiple reaction monitoring mode. 9-Plexed MSILD reagents were prepared using pipemidic acid as core structure with different isotope mass tags, in which carbonyl chloride group was used to label OH-PAHs. The SIMT-346 labeled OH-PAHs standards were used as internal standards, which can greatly increase the quantitative accuracy. 9-Plex labeled nine different real samples can be quantified by UHPLC-MS/MS in a single run. Under optimized MSILD-MDSPE conditions, good linearities of seven OH-PAHs were obtained with satisfactory coefficient of determination R2 > 0.991. Limits of detection (LODs) of seven OH-PAHs were from 0.1 to 0.5 pg/mL, and limits of quantitation (LOQs) ranged from 0.5 to 2.0 pg/mL. The intra- and inter-day precisions ranged in 2.3-12.4% with accuracies in the range of 91.7-108.4%. Acceptable results of matrix effect (89.7-105.7%) and derivatization efficiency (> 96.4%) were obtained. In short, the developed method has been proved to be high-throughput, sensitive, accurate and easy-handling. This method was applied for the measurement of seven free OH-PAHs in human urine and plasma, and expected to provide technical support for the evaluation of PAHs exposure levels in humans.

Keywords:  Derivatization; Exposome; High throughput; Hydroxyl polycyclic aromatic hydrocarbons; Magnetic dispersive solid phase extraction; Multiplexed tags chemical isotope labeling

DOI:  https://doi.org/10.1016/j.chroma.2020.461182
Anal Chem. 2020 Jun 08.

Fast Quantification without Conventional Chromatography, the Growing Power of Mass Spectrometry.

George N Gachumi, Randy W Purves, Carsten Hopf, Anas El-Aneed.

Mass spectrometry (MS) in hyphenated techniques is widely accepted as the gold standard quantitative tool in life sciences. However, MS possesses intrinsic analytical capabilities that allow it to be a stand-alone quantitative technique, particularly with current technological advancements. MS has a great potential for simplifying quantitative analysis without the need for tedious chromatographic separation. Its selectivity relies on multistage MS analysis (MSn), including tandem mass spectrometry (MS/MS), as well as the ever-growing advancements of high-resolution MS instruments. This perspective describes various analytical platforms that utilize MS as a stand-alone quantitative technique namely, flow injection analysis (FIA), matrix assisted laser desorption ionization (MALDI) including MALDI-MS imaging, and ion mobility, particularly high-field asymmetric waveform ion mobility spectrometry (FAIMS). When MS alone is not capable of providing reliable quantitative data, instead of conventional liquid chromatography (LC)-MS, the use of a guard column (i.e., fast chromatography) may be sufficient for quantification. Although the omission of a chromatographic separation simplifies the analytical process, extra procedures may be needed during sample preparation and clean-up to address the issue of matrix effects. The discussion of this manuscript focusses on key parameters underlying the uniqueness of each technique for its application in quantitative analysis without the need for a chromatographic separation. In addition, the potential for each analytical strategy and its challenges are discussed as well as improvements needed to render them as mainstream quantitative analytical tools. Overcoming the hurdles for fully validating a quantitative method will allow MS alone to eventually become an indispensable quantitative tool for clinical and toxicological studies.

DOI: https://doi.org/10.1021/acs.analchem.0c00877
Front Chem. 2020 ;8 435

Identification of Trenbolone Metabolites Using Hydrogen Isotope Ratio Mass Spectrometry and Liquid Chromatography/High Accuracy/High Resolution Mass Spectrometry for Doping Control Analysis.

Marlen Putz, Thomas Piper, Mario Thevis.

  Trenbolone is a synthetic anabolic-androgenic steroid, which has been misused for performance enhancement in sports. The detection of trenbolone doping in routine sports drug testing programs is complex as methods utilizing gas chromatography/mass spectrometry are complicated by unspecific derivatization products and artifacts, and liquid chromatography/mass spectrometry-based assays have shown to allow for comparably high limits-of-detection only. The number of previously reported metabolites in human urine is limited, and most analytical methods rely on targeting epitrenbolone, trenbolone glucuronide, and epitrenbolone glucuronide. In order to probe for the presence of additional trenbolone metabolites and to re-investigate the metabolism, an elimination study was conducted. One single dose of 10 mg of 5-fold deuterated trenbolone was administered to a healthy male volunteer and urine samples were collected for 30 days. For sample processing, published protocols were combined considering unconjugated, glucuronic acid-, sulfo- and alkaline-labile conjugated steroid metabolites. The sample preparation strategy consisted of solid-phase extractions, liquid-liquid extractions, metabolite de-conjugation, HPLC fractionation, and derivatization. Analytical methods included gas chromatography/thermal conversion/hydrogen isotope ratio mass spectrometry combined with single quadrupole mass spectrometry as well as liquid chromatography/high accuracy/high resolution mass spectrometry of the hydrolyzed and non-hydrolyzed samples. Twenty deuterium-labeled metabolites were identified including glucuronic acid-, sulfo- and potential cysteine-conjugates, and characterized by parallel reaction monitoring experiments yielding corresponding product ion mass spectra. Main metabolites were attributed to trenbolone-diol and potential trenbolone-diketone derivatives excreted as glucuronic acid and sulfo-conjugated analytes with detection windows of 5, respectively 6 days. Further characterization was conducted with pseudo MS3 experiments of the intact conjugates and by comparison of resulting product ion mass spectra with reference material.

Keywords:  gas chromatography thermal conversion isotope ratio mass spectrometry (GC-TC-IRMS); human metabolism; in vivo metabolism; liquid chromatography high resolution mass spectrometry (LC-HRMS); phase-II conjugates; pseudo MS3 product ion mass spectra; sports drug testing; steroids

DOI:  https://doi.org/10.3389/fchem.2020.00435
Metabolites. 2020 Jun 09. pii: E237. [Epub ahead of print]10(6):

QSRR Automator: A Tool for Automating Retention Time Prediction in Lipidomics and Metabolomics.

Bradley C Naylor, J Leon Catrow, J Alan Maschek, James E Cox.

  The use of retention time is often critical for the identification of compounds in metabolomic and lipidomic studies. Standards are frequently unavailable for the retention time measurement of many metabolites, thus the ability to predict retention time for these compounds is highly valuable. A number of studies have applied machine learning to predict retention times, but applying a published machine learning model to different lab conditions is difficult. This is due to variation between chromatographic equipment, methods, and columns used for analysis. Recreating a machine learning model is likewise difficult without a dedicated bioinformatician. Herein we present QSRR Automator, a software package to automate retention time prediction model creation and demonstrate its utility by testing data from multiple chromatography columns from previous publications and in-house work. Analysis of these data sets shows similar accuracy to published models, demonstrating the software's utility in metabolomic and lipidomic studies.

Keywords:  automation; lipidomics; machine learning; metabolomics; retention time prediction

DOI:  https://doi.org/10.3390/metabo10060237
Trends Analyt Chem. 2019 Nov;pii: 115322. [Epub ahead of print]120

NMR and MS-based Stable Isotope-Resolved Metabolomics and Applications in Cancer Metabolism.

Andrew N Lane, Richard M Higashi, Teresa W-M Fan.

  There is considerable interest in defining metabolic reprogramming in human diseases, which is recognized as a hallmark of human cancer. Although radiotracers have a long history in specific metabolic studies, stable isotope-enriched precursors coupled with modern high resolution mass spectrometry and NMR spectroscopy have enabled systematic mapping of metabolic networks and fluxes in cells, tissues and living organisms including humans. These analytical platforms are high in information content, are complementary and cross-validating in terms of compound identification, quantification, and isotope labeling pattern analysis of a large number of metabolites simultaneously. Furthermore, new developments in chemoselective derivatization and in vivo spectroscopy enable tracking of labile/low abundance metabolites and metabolic kinetics in real-time. Here we review developments in Stable Isotope Resolved Metabolomics (SIRM) and recent applications in cancer metabolism using a wide variety of stable isotope tracers that probe both broad and specific aspects of cancer metabolism required for proliferation and survival.

Keywords:  NMR; SIRM; cancer metabolism; mass spectrometry; model systems

DOI:  https://doi.org/10.1016/j.trac.2018.11.020
Mol Omics. 2020 Jun 10.

MetaboKit: a comprehensive data extraction tool for untargeted metabolomics.

Pradeep Narayanaswamy, Guoshou Teo, Jin Rong Ow, Adam Lau, Philipp Kaldis, Stephen Tate, Hyungwon Choi.

We have developed MetaboKit, a comprehensive software package for compound identification and relative quantification in mass spectrometry-based untargeted metabolomics analysis. In data dependent acquisition (DDA) analysis, MetaboKit constructs a customized spectral library with compound identities from reference spectral libraries, adducts, dimers, in-source fragments (ISF), MS/MS fragmentation spectra, and more importantly the retention time information unique to the chromatography system used in the experiment. Using the customized library, the software performs targeted peak integration for precursor ions in DDA analysis and for precursor and product ions in data independent acquisition (DIA) analysis. With its stringent identification algorithm requiring matches by both MS and MS/MS data, MetaboKit provides identification results with significantly greater specificity than the competing software packages without loss in sensitivity. The proposed MS/MS-based screening of ISFs also reduces the chance of unverifiable identification of ISFs considerably. MetaboKit's quantification module produced peak area values highly correlated with known concentrations in a DIA analysis of the metabolite standards at both MS1 and MS2 levels. Moreover, the analysis of Cdk1Liv-/- mouse livers showed that MetaboKit can identify a wide range of lipid species and their ISFs, and quantitatively reconstitute the well-characterized fatty liver phenotype in these mice. In DIA data, the MS1-level and MS2-level peak area data produced similar fold change estimates in the differential abundance analysis, and the MS2-level peak area data allowed for quantitative comparisons in compounds whose precursor ion chromatogram was too noisy for peak integration.

DOI: https://doi.org/10.1039/d0mo00030b
Anal Bioanal Chem. 2020 Jun 12.

Stationary-phase optimized selectivity in supercritical fluid chromatography using a customized Phase OPtimized Liquid Chromatography kit: comparison of different prediction approaches.

Yasmine Grooten, Petra Riasová, Inês Salsinha, Debby Mangelings, Yvan Vander Heyden.

  The use of stationary-phase optimized selectivity in liquid chromatography (SOS-LC) was shown to be successful for HPLC to analyze complex mixtures using a Phase OPtimized Liquid Chromatography (POPLC) kit. This commercial kit contains five stationary-phase types of varying lengths, which can be coupled to offer an improved separation of compounds. Recently, stationary-phase optimized selectivity supercritical fluid chromatography (SOS-SFC) has been introduced, transferring the methodology to SFC. In this study, the applicability of a customized POPLC expert kit for isocratic SFC runs was explored. Five stationary-phase chemistries were selected as potentially most suitable for achiral separations of polar compounds: aminopropyl (amino), cyanopropyl (CN), diol, ethylpyridine (EP), and silica. The retention factors (k) on the individual stationary phases were used for the prediction of the best stationary-phase combination, based on the POPLC algorithm (via the included software). As an alternative, the best column combination was predicted using multiple linear regression (MLR) models on the results obtained from a simplex centroid mixture design with only three stationary-phase types (amino, silica, and EP). A third approach applied the isocratic POPLC algorithm on the same three stationary-phase data. The proposed combinations were assembled and tested. The predicted and experimental retention factors were compared. The predictions based on the POPLC algorithm provided a stationary phase showing a complete separation of the mixture. The stationary phase suggested by the MLR models, on the other hand, showed co-elution of two compounds, due to an unexpected experimental retention shift. Overall, the customized POPLC kit showed good potential to be applied in SFC. Graphical abstract.

Keywords:  Achiral SFC; Coupled systems; Polar stationary phases; Retention prediction; Stationary-phase optimization

DOI:  https://doi.org/10.1007/s00216-020-02739-w
J Chromatogr A. 2020 Jul 19. pii: S0021-9673(20)30433-7. [Epub ahead of print]1623 461181

Normalized retention time for scheduled liquid chromatography-multistage mass spectrometry analysis of epitranscriptomic modifications.

Gwendolyn Gonzalez, Yuxiang Cui, Pengcheng Wang, Yinsheng Wang.

Investigations into post-transcriptional modifications of RNA and their regulatory proteins have revealed pivotal roles of these modifications in cellular functions. A robust method for the quantitative analysis of modified nucleosides in RNA may facilitate the assessment about their functions in RNA biology and disease etiology. Here, we developed a sensitive nano-liquid chromatography-multistage mass spectrometry (nLC-MS3) method for profiling simultaneously 27 modified ribonucleosides. We employed normalized retention time (iRT) and scheduled selected-reaction monitoring (SRM) to achieve high-throughput analysis, where we assigned iRT values for modified ribonucleosides based on their relative elution times with respect to the four canonical ribonucleosides. The iRT scores allowed for reliable predictions of retention times for modified ribonucleosides with the use of two types of stationary phase materials and various mobile phase gradients. The method enabled the identification of 20 modified ribonucleosides with the use of the enzymatic digestion mixture of 2.5 ng total RNA and facilitated robust quantification of modified cytidine derivatives in total RNA. Together, we established a scheduled SRM-based method for high-throughput analysis of modified ribonucleosides with the use of a few nanograms of RNA.

DOI: https://doi.org/10.1016/j.chroma.2020.461181
J Pharm Biomed Anal. 2020 May 28. pii: S0731-7085(20)31248-6. [Epub ahead of print]188 113362

Lemna minor studies under various storage periods using extended-polarity extraction and metabolite non-target screening analysis.

Rofida Wahman, Johanna Graßmann, Andrés Sauvêtre, Peter Schröder, Thomas Letzel.

  Plant metabolomic studies cover a broad band of compounds, including various functional groups with different polarities and other physiochemical properties. For this reason, specific optimized methods are needed in order to enable efficient and non-destructive extraction of molecules over a large range of LogD values. This study presents a simple and efficient extraction procedure for Lemna minor samples demonstrating polarity extension of the molecular range. The Lemna samples chosen were kept under the following storage conditions: 1) fresh, 2) stored for a few days at -80 °C, and 3) stored for 6 months at -80 °C. The samples were extracted using five specifically chosen solvents: 100 % ethanol, 100 % methanol (MeOH), acidic 90 % MeOH (MeOH-water-formic acid (FAC) (90:9.5:0.5, v/v/v), MeOH-water (50:50, v/v), and 100 % water. The final extraction procedure was conducted subject to three solvent conditions, and the subsequent polarity-extended analysis was applied for Lemna minor samples using RPLC-HILIC-ESI-TOF-MS. The extraction yield is in descending order (acidic 90 % MeOH), 50 % MeOH, 100 % water and 100 % MeOH. The results displayed significant molecular differences, both in the extracts investigated and in the fresh Lemna samples, compared to stored samples, in terms of the extraction yield and reducing contents as well as the number of features. The storage of Lemna minor resulted in changes to the fingerprint of its metabolites as the reducing contents increased. The comparisons enable a direct view of molecule characterizations, in terms of their polarity, molecular mass, and signal intensity. This parametric information would appear ideal for further statistical data analysis. Consequently, the extraction procedure and the analysis/data evaluation are highly suitable for the so-called extended-polarity non-target screening procedure.

Keywords:  Extended polarity chromatographic separation; Extended polarity extraction method; Lemna minor; Metabolomics; Non-target screening; RPLC-HILIC-ESI-TOF-MS analysis; Storage effect

DOI:  https://doi.org/10.1016/j.jpba.2020.113362
Anal Chem. 2020 Jun 12.

Phosphorus NMR and its application to metabolomics.

Fatema Bhinderwala, Paula Evans, Kaleb Jones, Benjamin R Laws, Thomas Smith, Martha D Morton, Robert Powers.

Stable isotopes are routinely employed by NMR metabolomics to highlight specific metabolic processes and to monitor pathway flux. 13C-carbon and 15N-nitrogen labeled nutrients are convenient sources of isotope tracers and are commonly added as supplements to a variety of biological systems ranging from cell cultures to animal models. Unlike 13C and 15N, 31P-phosphourous is a naturally abundant and NMR active isotope that doesn't require an external supplemental source. To date, 31P NMR has seen limited usage in metabolomics because of a lack of reference spectra, difficulties in sample preparation, and an absence of two-dimensional (2D) NMR experiments. But, 31P NMR has the potential of expanding the coverage of the metabolome by detecting phosphorous-containing metabolites. Phosphorylated metabolites regulate key cellular processes, serve as a surrogate for intracellular pH conditions, and provides a measure of a cell's metabolic energy and redox state, among other processes. Thus, incorporating 31P NMR into a metabolomics investigation will enable the detection of these key cellular processes. To facilitate the application of 31P NMR in metabolomics, we present a unified protocol that allows for the simultaneous and efficient detection of 1H-, 13C-, 15N- and 31P-labeled metabolites. The protocol includes the application of a 2D 1H-31P HSQC-TOCSY experiment to detect 31P-labeled metabolites from heterogeneous biological mixtures, methods for sample preparation to detect 1H-, 13C-, 15N- and 31P-labeled metabolites from a single NMR sample, and a dataset of one-dimensional (1D) 31P NMR and 2D 1H-31P HSQC-TOCSY spectra of 38 common phosphorus-containing metabolites to assist in metabolite assignments.

DOI: https://doi.org/10.1021/acs.analchem.0c00591
J Chromatogr B Analyt Technol Biomed Life Sci. 2020 May 29. pii: S1570-0232(19)31400-X. [Epub ahead of print]1151 122158

Simultaneous analysis of retinol, six carotenoids, two tocopherols, and coenzyme Q10 from human plasma by HPLC.

Lysiane Boulet, Brigitte Alex, Nadège Clavey, Julie Martinez, Véronique Ducros.

Lipophilic antioxidant determination is of relevance in health and diseases. Several HPLC methods exists but rare are those including coenzyme Q10 with carotenoids, retinol and tocopherols. Here a single-step extraction was proposed for the detection of retinol, α and γ-tocopherols, lutein, zeaxanthin, trans-ß-carotene, α-carotene, ß-cryptoxanthin and lycopene as well as coenzyme Q10. A single HPLC column was used and UV-vis diode array detection was performed. Echinenone, alpha-tocopherol nicotinate and coenzyme Q4 were employed as internal standards. Intra-assay and inter-assay precision were respectively 1.4-7.9% and 2.2-15.8%. Accuracy was validated using SRM 968e. LOD (limit of detection) and LOQ (limit of quantification) obtained were sufficient for nutritional epidemiological study and routine clinical application.

DOI: https://doi.org/10.1016/j.jchromb.2020.122158
J Am Soc Mass Spectrom. 2020 Jun 11.

Collision Cross Section Conformational Analyses of Bile Acids via Ion Mobility-Mass Spectrometry.

James C Poland, Katrina L Leaptrot, Stacy D Sherrod, Charles Robb Flynn, John A McLean.

Bile acids serve as one of the most important classes of biological molecules in the gastrointestinal system. Due to their structural similarity, bile acids have historically been difficult to accurately annotate in complex biological matrices using mass spectrometry. They often have identical or nominally similar mass-to-charge ratios and similar fragmentation patterns that make identification by mass spectrometry arduous, normally involving chemical derivatization and separation via liquid chromatography. Here, we demonstrate the use of drift tube ion mobility (DTIM) to derive collision cross section (CCS) values in nitrogen drift gas (DTCCSN2) for use as an additional descriptor to facilitate expedited bile acid identification. We also explore trends in DTIM measurements and detail structural characteristics for differences in DTCCSN2 values between subclasses of bile acid molecules.

DOI: https://doi.org/10.1021/jasms.0c00015
Anal Chem. 2020 Jun 10.

Single-Cell Classification Using Mass Spectrometry Through Interpretable Machine Learning.

Yuxuan Richard Xie, Daniel Castro, Sara Bell, Stanislav S Rubakhin, Jonathan V Sweedler.

The brain consists of organized ensembles of cells that exhibit distinct morphologies, cellular connectivity and dynamic biochemistries that control the executive functions of an organism. However, the relationships between chemical heterogeneity, cell function, and phenotype are not always understood. Recent advancements in matrix-assisted laser desorption/ionization mass spectrometry have enabled the high-throughput, multiplexed chemical analysis of single cells, capable of resolving hundreds of molecules in each mass spectrum. We developed a machine learning workflow to classify single cells according to their mass spectra based on cell groups of interest (GOI), e.g., neurons vs. astrocytes. Three datasets from various cell groups were acquired on three different mass spectrometer platforms representing thousands of individual cell spectra were collected and used to validate the single cell classification workflow. The trained models achieved >80% classification accuracy and were subjected to the recently developed instance-based model interpretation framework, SHAP (SHapley Additive exPlanations), which locally assigns feature importance for each single-cell spectrum. SHAP values were used for both local and global interpretations of our datasets, preserving the chemical heterogeneity uncovered by the single-cell analysis while offering the ability to perform supervised analysis. The top contributing mass features to each of the GOI were ranked and selected using mean absolute SHAP values, highlighting the values that are specific to the defined GOI. Our approach provides insight into discriminating the chemical profiles of the single cells through interpretable machine learning, facilitating downstream analysis and validation.

DOI: https://doi.org/10.1021/acs.analchem.0c01660
J Proteomics. 2020 Jun 09. pii: S1874-3919(20)30220-7. [Epub ahead of print] 103852

Peak detection for MALDI mass spectrometry imaging data using sparse frame multipliers.

Florian Lieb, Tobias Boskamp, Hans-Georg Stark.

  MALDI mass spectrometry imaging (MALDI MSI) is a spatially resolved analytical tool for biological tissue analysis by measuring mass-to-charge ratios of ionized molecules. With increasing spatial and mass resolution of MALDI MSI data, appropriate data analysis and interpretation is getting more and more challenging. A reliable separation of important peaks from noise (aka peak detection) is a prerequisite for many subsequent processing steps and should be as accurate as possible. We propose a novel peak detection algorithm based on sparse frame multipliers, which can be applied to raw MALDI MSI data without prior preprocessing. The accuracy is evaluated on a simulated data set in comparison with state-of-the-art algorithms. These results also show the proposed method's robustness to baseline and noise effects. In addition, the method is evaluated on real MALDI-TOF data sets, whereby spatial information can be included in the peak picking process. SIGNIFICANCE: The field of proteomics, in particular MALDI Imaging, encompasses huge amounts of data. The processing and preprocessing of this data in order to segment or classify spatial structures of certain peptides or isotope patterns can hence be cumbersome and includes several independent processing steps. In this work, we propose a simple peak-picking algorithm to quickly analyze large raw MALDI Imaging data sets, which has a better sensitivity than current state-of-the-art algorithms. Further, it is possible to get an overall overview of the entire data set showing the most significant and spatially localized peptide structures and, hence, contributes all data driven evaluation of MALDI Imaging data.

Keywords:  Frame multiplier; MALDI imaging; Peak picking

DOI:  https://doi.org/10.1016/j.jprot.2020.103852