<p><strong>In the week of March 30, the following webinars await you in the field of liquid phase and especially HPLC and LC/MS.</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 500 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Bruker: MALDI Imaging Becomes IR-resistible: Going Multimodal for Advanced Biomedical Insights</strong></h5><ul><li>Mo, 30.3.2026 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5971">Registration</a></li></ul><p>Explore advanced multimodal imaging with MALDI, QCL-based IR, and fluorescence microscopy to achieve ROI-focused molecular insights in complex tissues.</p><h5><strong>2. Shimadzu Corporation: Environmental PFAS Testing: Introducing Next-Generation Mass Spectrometry Solutions</strong></h5><ul><li>Tu, 31.3.2026 09:30 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6026">Registration</a></li></ul><p>Explore advanced PFAS analysis strategies combining LC-MS/MS and MALDI-TOF screening to improve sensitivity, efficiency, and regulatory readiness in environmental testing.</p><h5><strong>3. Tosoh Bioscience: Tosoh Tuesday - Scaling Up from Process Development to GMP Production with Pre-packed Columns</strong></h5><ul><li>Tu, 31.3.2026 15:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6089">Registration</a></li></ul><p>This webinar will introduce SkillPak™ MAX pre-packed columns and present performance data using TOYOPEARL® Super A, a novel Protein A resin.</p><h5><strong>4. Phenomenex: Recipe for Separation: Crafting Your Perfect HPLC Method</strong></h5><ul><li>We, 1.4.2026 10:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6091">Registration</a></li></ul><p>Discover how to properly use and maintain your column to maximize its performance and lifespan.</p>

Webinars LabRulezLCMS Week 14/2026

News from LabRulezLCMS Library - Week 14, 2026

<p><strong>This study presents a novel MS/MS filtering tree approach for distinguishing regio- and stereoisomeric bile acids in untargeted LC-MS/MS data. By leveraging intensity ratios of closely related fragment ions, the method overcomes limitations of traditional spectral matching, enabling more accurate isomer annotation.</strong></p><p>A user-friendly web-based application was developed to facilitate implementation without coding expertise. Applied to public datasets, the workflow revealed diet-associated bile acid patterns across mammalian species and enabled identification of previously uncharacterized compounds, demonstrating its potential for large-scale metabolomics studies.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05677"><strong>MS/MS Mass Spectrometry Filtering Tree for Bile Acid Regio- and Stereoisomer Annotation</strong></a></h4><p>Ipsita Mohanty, Shipei Xing, Vanessa Castillo, Julius Agongo, Abubaker Patan, Yasin El Abiead, Helena Mannochio-Russo, Wilhan D. Gonçalves Nunes, Jasmine Zemlin, Itzhak Mizrahi, Dionicio Siegel, Mingxun Wang, Lee R. Hagey, and Pieter C. Dorrestein*</p><p>Anal. Chem. 2026, 98, 6, 4571–4584</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.analchem.5c05677">https://doi.org/10.1021/acs.analchem.5c05677</a></p><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/"><strong>CC-BY 4.0</strong></a></h6></blockquote><p><strong>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.</strong></p><div><p>Cholesterol-derived steroid molecules, known as bile acids, have been detected in nearly every organ where they have been studied. <a href="javascript:void(0);">(1)</a> Alterations in bile acid composition and dysbiosis have been linked to various health conditions, including neurocognitive developmental disorders, cancer, infections, and metabolic diseases such as diabetes and inflammatory bowel disease (IBD). <a href="javascript:void(0);">(2−8)</a> In the last 5 years, the number of detected bile acids in metabolomics experiments <a href="javascript:void(0);">(1,9−15)</a> has increased by the 1000s and possibly approaching 10,000s - most of which have not yet been fully structurally characterized. <a href="javascript:void(0);">(1,6,16−19)</a> We believe this is only the tip of the iceberg and that in the next 5 years this number will continue to increase. The scale of the bile acid pool and the challenge of accurately detecting and describing their structure truly represent an exciting frontier for analytical and computational scientists who continue to push the boundaries of what is possible.</p><p>In humans, bile acids are predominantly derived from cholesterol through pathways that exist not only in the liver as previously thought but also in many other organs including kidneys and brain. <a href="javascript:void(0);">(16,20,21)</a> These pathways shorten the side chain, producing a carboxyl terminus on the remaining 24-carbon steroid structure, which consists of four interconnected rings and side chain, collectively referred to here as the “core” (colored in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05677#fig1">Figure 1</a>a). The core of bile acids undergoes multiple hydroxylations, a process typically associated with the liver but also occurring in other organs such as the brain, kidney, and spleen. <a href="javascript:void(0);">(16)</a> Considering hydroxylation at known carbon positions on bile acids reported from human samples (as starred in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05677#fig1">Figure 1</a>a, <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c05677/suppl_file/ac5c05677_si_002.xlsx">Supplementary Table S1</a>) along with stereoisomers and allo versus nonallo A/B ring isomerization at C5, approximately 1,800 candidate mono-, di-, and trihydroxy C<sub>24</sub> bile acid cores are theoretically possible (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05677#fig1">Figure 1</a>a). This number further expands exponentially in the gut.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_6_4571_4584_Figure_1_MS_MS_fragmentation_of_bile_acid_isomers_9bfcc8779d.jpg" alt="Anal. Chem. 2026, 98, 6, 4571–4584: Figure 1. MS/MS fragmentation of bile acid isomers. (a) Structure of bile acids highlighting mono-, di-, and trihydroxylated steroid cores, with experimentally observed potential hydroxylation sites on the steroid core indicated by red stars. (b) MS/MS fragmentation spectra of the regioisomers, taurochenodeoxycholic (TCDCA) acid and taurodeoxycholic acid (TDCA), illustrating a low-intensity mass region containing ions unique to each isomer. (c) Enlarged view of the MS/MS fragmentation spectra for taurochenodeoxycholic and taurodeoxycholic acids, emphasizing the ion pair used to calculate relative intensity ratios for differentiating these isomers." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_6_4571_4584_Figure_1_MS_MS_fragmentation_of_bile_acid_isomers_9bfcc8779d.jpg 245w," sizes="100vw" width="2100" height="890"></p><p>Recently, we developed MS/MS fragmentation-based filters using a Mass Spectrometry Query Language (MassQL), a query language that allows filtering of data patterns <a href="javascript:void(0);">(49)</a> to retrieve all MS/MS spectra from the Orbitrap data sets in GNPS/MassIVE repository containing bile acid-specific fragment ions. After merging similar spectra, this resulted in a reference library of 21,549 MS/MS spectra. <a href="javascript:void(0);">(1)</a> This library includes MS/MS spectra of known and many yet-to-be structurally characterized bile acid candidates that have already been detected from public metabolomics data repositories. This also includes MS/MS of multiple ion forms such as different adducts, in-source fragments, and multimers, <a href="javascript:void(0);">(1)</a> which all help in the annotation of bile acids. This MassQL library enables the annotation of the number of hydroxyl groups on the bile acid core, as well as the identification of potential modifications, and ∼ 62% of the atomic compositions are annotated. Other spectral “modifications” that do not have a distinct atomic composition may be different adducts or multiple combinations of adducts, in source fragments, and multimers. However, the diagnostic bile acid MS/MS fragments used to build this reference library do not allow for the differentiation between regio- and stereoisomers. Also, MS/MS spectral matching with the candidate library using the cosine metric often overlooks finer details within fragment ions, as minor peak intensity changes or low-intensity fragment ions have a minimal influence on overall spectral match scores (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05677#fig1">Figure 1</a>b).</p><p>Based on our manual inspection of hundreds of bile acid MS/MS spectra, we observed and hypothesized that information in the low-intensity MS/MS fragments, including specific ion pairs, their intensity ratios, and unique ions, could be used to distinguish the steroid core isomers. As proof of concept in leveraging MS/MS data to enable further refinement of the stereo and regiochemistry of the hydroxylations of the bile acids cores, we developed a MassQL-based filtering tree utilizing key marker ions and their intensity ratios within narrow <i>m</i>/<i>z</i> windows to propose regio- and stereoisomer assignments of bile acids solely from MS/MS data. In this study, we focus on bile acid amidates, a class of structures found in humans and other animals.</p><h4><strong>Experimental Section</strong></h4><h5><strong>MS/MS Data Acquisition of Taurine-Conjugated Bile Acids</strong></h5><p>We generated one mM stock solutions of our in-house collection of 48 taurine-conjugated bile acids in 100% MeOH. The standards used in this study were obtained from the bile acid collection of the late Dr. Alan Hofmann’s laboratory at UCSD. The Dorrestein laboratory inherited these standards after his demise. The standard samples were injected (5 μL) into a Vanquish ultrahigh-performance liquid chromatography (UHPLC) system coupled to a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/559"><strong>Q-Exactive quadrupole Orbitrap mass spectrometer</strong></a> (Thermo Fisher Scientific, Waltham, MA). A <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1509"><strong>Kinetex polar C18 column</strong></a> (2.1 × 100 mm, 2.6 μm particle size, 100 A pore size; Phenomenex, Torrance) was employed with a SecurityGuard C18 column (2.1 mm ID) at 40 °C column temperature. The mobile phases (0.5 mL/min) were 0.1% formic acid in both water (A) and ACN (B) with the following gradient: 0–0.5 min 5%B, 0.5–1.1 min 5–25%B, 1.1–7.5 min 25–40%B, 7.5–8.5 min 40–99%B, 8.5–10 min 99%B, 10–12 min 5%B. The mass spectrometer was operated in positive heated electrospray ionization with the following parameters: sheath gas flow, 53 AU; auxiliary gas flow, 14 AU; sweep gas flow, 3 AU; auxiliary gas temperature, 400 °C; spray voltage, 3.5 kV; inlet capillary temperature, 269 °C; S-lens level, 50 V. MS1 scan was performed at <i>m</i>/<i>z</i> 150–1500 with the following parameters: resolution, 35,000 at <i>m</i>/<i>z</i> 200; maximum ion injection time, 100 ms; automatic gain control (AGC) target, 1E6. Up to 5 MS/MS spectra per MS1 scan were recorded under the data-dependent mode (dd-MS<sup>2</sup>) with the following parameters: resolution, 17,500 at <i>m</i>/<i>z</i> 200; maximum ion injection time, 150 ms; AGC target, 5.0E5; MS/MS precursor isolation window, <i>m</i>/<i>z</i> 1; isolation offset, <i>m</i>/<i>z</i> 0.5; normalized collision energy (NCE) of 45%; minimum AGC for MS/MS spectrum, 2.5E4; apex trigger, 2 to 5 s; dynamic precursor exclusion, 8 s. Fragmentation was performed in the HCD cell using nitrogen as the collision gas. The data was deposited in GNPS/MassIVE and is publicly available at MSV000092003. The MS/MS spectra are also added now to the GNPS spectral library - BILELIB19 (<a target="_blank" rel="noopener noreferrer" href="https://gnps.ucsd.edu/ProteoSAFe/gnpslibrary.jsp?library=BILELIB19">https://gnps.ucsd.edu/ProteoSAFe/gnpslibrary.jsp?library=BILELIB19</a>)</p><h4><strong>Results and Discussion</strong></h4><h5><strong>Retrospective Bile Acid Isomer Profiling in a Public Data Set of Animals with Different Diets</strong></h5><p>To illustrate the utility of the MassQL multistep filtering trees in retrospectively analyzing data in public repositories, we selected a publicly available data set containing feces from 40 animals (13 mammalian species) from the Zoological Center Tel Aviv-Ramat Gan, Israel. <a href="javascript:void(0);">(62)</a> The data set consisted of 3 herbivores, 23 omnivores, and 13 carnivores (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05677#fig4">Figure 4</a>a). We selected this data set as we had previously confirmed detection of 6 previously uncharacterized polyamine-conjugated bile acids that we could not confirm with retention times as none of the standards we had synthesized matched, although their MS/MS did. We showed that their levels were higher in carnivores compared to herbivores and omnivores; <a href="javascript:void(0);">(1)</a> however, we were still unable to resolve the regio- and stereochemistry of all possible isomers of polyamine-conjugated bile acids. To further dive into the diversity of bile acids, we matched the MS/MS spectra of this study against the GNPS spectral libraries, including the candidate bile acid MS/MS reference library that we had previously created from the repository scale analysis. <a href="javascript:void(0);">(1)</a> This revealed 993 MS/MS spectral matches to bile acids. We next applied the multistep MassQL queries to resolve the regio- and stereochemistry of the bile acids. The steps described in this section follow the same filtering approach as outlined for the dihydroxy bile acid filtering tree (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05677#fig2">Figure 2</a>).&nbsp;</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_6_4571_4584_Figure_2_Development_of_MSMS_fragmentation_based_Mass_QL_filtering_tree_2227b688a9.jpg" alt="Anal. Chem. 2026, 98, 6, 4571–4584: Figure 2. Development of MS/MS fragmentation-based MassQL filtering tree. Sequential MS/MS fragmentation-based filtering tree designed to classify regio- and stereoisomers of dihydroxylated bile acids. Structures at each filtering step are shown, with terminal bins color-coded for clarity." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_6_4571_4584_Figure_2_Development_of_MSMS_fragmentation_based_Mass_QL_filtering_tree_2227b688a9.jpg 183w," sizes="100vw" width="1000" height="852"></p><p>Step 1 excludes MS/MS spectral matches to candidate bile acids where the experimental spectra lacked the two diagnostic MS/MS ions for bile acids. Steroids and lipids often exhibit similar MS/MS spectra to bile acids, sometimes yielding high cosine similarity scores (&gt;0.7), yet they lack key low-intensity diagnostic ions. Due to the low intensity of these ions, their presence or absence typically does not significantly affect spectral matching scores. However, our MassQL isomer filters in Step 1 identified these MS/MS spectra to reveal that 543 MS/MS spectra had the two characteristic fragment ions for mono-, di-, and trihydroxy bile acids (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05677#fig4">Figure 4</a>a). A portion of the 543 MS/MS spectra are adducts, in-source fragments, and multimers, overall reducing the number of unique bile acid structures. Based on retention time and peak shape correlation analysis in MZmine4, <a href="javascript:void(0);">(63)</a> a strategy that allows the discovery of ion forms associated with a specific molecule, we estimate these to represent 402 bile acids. Even though the data contained evidence for hundreds of unique bile acids, the stereo- and regiochemistry of hydroxylations were yet to be defined.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_6_4571_4584_Figure_4_Filtering_of_bile_acid_isomers_in_an_untargeted_LC_MS_MS_data_set_d7f119b028.jpg" alt="Anal. Chem. 2026, 98, 6, 4571–4584: Figure 4. Filtering of bile acid isomers in an untargeted LC-MS/MS data set. Representative example for using the MassQL filters for identifying bile acid isomers. (a) Overview of study design. Fecal samples from herbivores (n = 3), omnivores (n = 23), and carnivores (n = 13) were analyzed by LC-MS/MS and processed using MZmine4 and FBMN on GNPS2. Stepwise MassQL filtering refined candidate bile acids from 543 features (Step 1) to 96 (Steps 2 and 3) and 49 stereo and regiochemistry assigned MS/MS spectra (Step 4). Differential abundance of bile acids between herbivores/omnivores and carnivores is illustrated in the volcano plots (b) at Step 1 and (c) Step 4 of the dihydroxy filtering tree. The annotation of a previously uncharacterized bile acid was refined from (OH)2-N-acetyl-putrescine to 3,12α-N-acetyl-putrescine." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_6_4571_4584_Figure_4_Filtering_of_bile_acid_isomers_in_an_untargeted_LC_MS_MS_data_set_d7f119b028.jpg 243w," sizes="100vw" width="1819" height="1170"></p><p>The application of our MassQL filtering tree allowed us to further refine structural details within the bile acid core. After Step 2 and Step 3 of the MassQL filtering, we separated the amidated mono-, di-, and trihydroxylated bile acids from their ketones and unconjugated versions to give 96 bile acids (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05677#fig4">Figure 4</a>a). A univariate analysis at Step 1 of bile acid annotations showed a higher abundance of almost all bile acids in the carnivores compared to herbivores and omnivores combined (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05677#fig4">Figure 4</a>b). As we move down the MassQL filtering tree to Step 4, we can now hypothesize the regio- and, in many cases, the stereochemistry of their hydroxyl positions. With the current implementation of the tree, we can predict the bile acid cores of 49 MS/MS spectra (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05677#fig4">Figure 4</a>a). The highest number of spectral matches that could be assigned are dihydroxylated bile acids, symbolized here as (OH)<sub>2</sub> (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05677#fig4">Figure 4</a>c). These were categorized into individual bins of 3,12α-(OH)<sub>2</sub>, 3,7-(OH)<sub>2</sub>; 3,6-(OH)<sub>2</sub>; 3,12β-(OH)<sub>2</sub>; 7,12β-(OH)<sub>2</sub> and 3,6-(OH)<sub>2</sub> isomers following the application of the dihydroxy isomer filtering tree.</p><h4><strong>Conclusion</strong></h4><p>In conclusion, this study introduces the concept of using a MassQL MS/MS fragmentation-based filtering tree approach to enable precise regio- and stereochemical differentiation of bile acid isomers, which were subsequently validated against reference standards. The method expands the ability to annotate bile acids and has led to the discovery of bile acid deoxycholyl-<i>N</i>-acetyl-putrescine, which had previously been detected in the feces of carnivores but not structurally characterized. By making annotated MS/MS spectra publicly available and enabling the MassQL queries, this approach enhances bile acid analysis, offering innovative tools for exploring their metabolic roles and biological significance, including retro-analysis of regio- and stereochemical isomer variations in existing public LC-MS/MS data.</p></div>

MS/MS Mass Spectrometry Filtering Tree for Bile Acid Regio- and Stereoisomer Annotation

<p><strong>In the week of April 6, the following webinars await you in the field of liquid phase and especially HPLC and LC/MS.</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 500 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Waters Corporation: Fit-for-purpose workflows for inositol stereoisomer profiling in dietary supplements and foods using single and tandem mass spectrometry</strong></h5><ul><li>Tu, 7.4.2026 19:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6030">Registration</a></li></ul><p>The aim of this webinar is to present analytical procedures capable of simultaneously separating and quantifying free inositol stereoisomers in both simple matrices and complex matrices.</p><h5><strong>2. Thermo Fisher Scientific: Transforming Fuel Testing Performance with Combustion Ion Chromatography</strong></h5><ul><li>We, 8.4.2026 07:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6093">Registration</a></li></ul><p>Join us for an interactive session on how Combustion Ion Chromatography (C-IC) is transforming fuel testing performance in modern laboratories.</p><h5><strong>3. SCIEX: Getting started with LC-MS/MS for PFAS</strong></h5><ul><li>We, 8.4.2026 10:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6102">Registration</a></li></ul><p>Learn how to build LC-MS/MS methods for PFAS analysis, achieve ultra-low detection limits, and gain practical skills for environmental and food testing in this expert-led webinar.</p><h5><strong>4. Phenomenex: Extraction Face-Off: Choosing Between SLE and SPE</strong></h5><ul><li>We, 8.4.2026 10:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6092">Registration</a></li></ul><p>Understand how SLE and SPE work and how they are different from each other.</p><h5><strong>5. Waters Corporation: RADIAN™ ASAP Mass Spectrometry for the Detection and Analysis of Explosive Traces</strong></h5><ul><li>We, 8.4.2026 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6031">Registration</a></li></ul><p>Explore how mass spectrometry enables rapid detection of trace explosives. Learn how RADIAN™ ASAP extends forensic and security workflows, supporting future screening technologies and ion-mobility innovation.</p><h5><strong>6. Technology Networks: Label Free Quantitation: New Insights Into High-Throughput Proteomic Workflows</strong></h5><ul><li>Th, 9.4.2026 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5969">Registration</a></li></ul><p>Discover how next-generation LC–MS and the ZenoTOF 8600 enable robust, high-throughput LFQ proteomics, backed by insights from a large multi-laboratory benchmark study.</p>

Webinars LabRulezLCMS Week 15/2026

News from LabRulezLCMS Library - Week 15, 2026

<p><strong>In the week of April 13, the following webinars await you in the field of liquid phase and especially HPLC and LC/MS.</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 500 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Agilent Technologies: The Journey of Safer Science through Safer Labs</strong></h5><ul><li>Tu, 14.4.2026 07:30 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6104">Registration</a></li></ul><p>Agilent Safer Labs webinar highlights best practices in lab safety, including handling, storage, and instrumentation, helping professionals build safer, more efficient laboratory environments.</p><h5><strong>2. Phenomenex: Strategies to Mitigate Sporadic PFAS Contamination in Analytical Workflows</strong></h5><ul><li>We, 15.4.2026 10:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6095">Registration</a></li></ul><p>Discover how PFAS contamination enters LC-MS workflows and learn practical strategies to identify sources, prevent false positives, and ensure reliable PFAS analysis.</p><h5><strong>3. SCIEX: Tracking irrigation-related contaminants in water and soils (QTOF-MS)</strong></h5><ul><li>We, 15.4.2026 10:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6033">Registration</a></li></ul><p>Join this webinar to hear about benefits of ZenoTOF 7600 system for the analysis of pharmaceuticals and pesticides in wastewater, soils and crops.</p><h5><strong>4. Thermo Fisher Scientific: Imaging and top-down analysis of intact protein complexes in tissue</strong></h5><ul><li>Th, 16.4.2026 12:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6059">Registration</a></li></ul><p>Join this webinar to hear about latest methods and advances for probing intact protein complexes in tissue using in situ native mass spectrometry.</p><h5><strong>5. C&amp;EN: Precision Lab Solutions for High-End Optics and Coatings</strong></h5><ul><li>Th, 16.4.2026 17:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6096">Registration</a></li></ul><p>Join this presentation and open discussion with industry-leading experts Patrick Muller and Kieran Evans – leaders in UV/Vis and IR analytical methods and testing techniques.</p><h5><strong>6. Agilent Technologies: Accelerating GLP-1 Analog Impurity Profiling with the Agilent Pro IQ Plus Followed by Detailed Sequence Analysis using Electron Capture Dissociation</strong></h5><ul><li>Th, 16.4.2026 19:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5997">Registration</a></li></ul><p>Accelerate GLP-1 impurity profiling with single quad screening and LC/Q-TOF ExD analysis. Learn how ECD improves sequence confirmation and modification localization.</p><h5><strong>7. Agilent Technologies: Quantifying GLP-1 Peptides in Plasma: Bioanalytical Workflows for PK and Development</strong></h5><ul><li>Fr, 17.4.2026 11:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5944">Registration</a></li></ul><p>Peptide 360 Episode 3 webinar on LC-MS bioanalysis of GLP-1 peptides in plasma for PK studies and development decisions.</p>

Webinars LabRulezLCMS Week 16/2026

<p><strong>This study introduces atmospheric solids analysis probe high-resolution tandem mass spectrometry with cyclic ion mobility as a novel approach for characterizing Asian lacquers. Applied to 27 historical samples from Japan, China, Vietnam, and Myanmar, the method enabled classification by geographic origin based on phenol and benzenediol derivatives.</strong></p><p>In addition to lacquer identification, the technique simultaneously detected pigments such as arsenic sulfides and associated resins, providing comprehensive chemical insight. This approach expands analytical capabilities for cultural heritage studies, supporting restoration, conservation, and art historical research.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572"><strong>Characterization of Asian lacquers by atmospheric solids analysis probe high resolution tandem mass spectrometry coupled with cyclic ion mobility separation</strong></a></h4><p>Vojtěch Zemek, Radek Ryšánek, Petra Krejčí, Lukáš Kučera, Jana Nádvorníková, Adéla Tůmová, Helena Heroldová, Adriana Stříbrná, Petr Bednář</p><p>Journal of Cultural Heritage, 77, 2026, 187-196</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1016/j.culher.2025.11.012">https://doi.org/10.1016/j.culher.2025.11.012</a></p><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/"><strong>CC-BY 4.0</strong></a></h6></blockquote><p><strong>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.</strong></p><div><p>In recent years, an expansion of ambient pressure ionization mass spectrometry in various areas is observed [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0024"><span>24</span></a>,<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0025"><span>25</span></a>]. Atmospheric solids analysis probe mass spectrometry (ASAP-MS) is a powerful technique for direct analysis of even very complex samples, such as polymers [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0026"><span>26</span></a>], foods [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0027"><span>27</span></a>], or petrochemical sources and products [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0024"><span>24</span></a>]. ASAP ionization technique is a modification of atmospheric pressure chemical modification (APCI). In conventional arrangement, the sample is attached to a sampling probe tip (a standard melting point capillary is inserted into powder or liquid sample that sticks to its surface) and the probe is consequently placed into the ion source (commonly adapted ESI or APCI source). The analytes are released into the gas phase using hot gas (nitrogen) and ionized in the corona discharge. The formed ions (quasimolecular ions, radical cations or radical anions) are then transferred to the evacuated part of mass spectrometer [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0024"><span>24</span></a>,<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0025"><span>25</span></a>,<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0028"><span>28</span></a>]. Note that especially at higher vaporization temperatures a portion of the analytes is pyrolyzed and a mixture of parent ions and pyrolysis product ions is commonly observed in the ASAP-MS spectra [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0029"><span>29</span></a>]. The main feature of this direct MS mode is a fast detection of the formed products (including those with a higher mass and perhaps a shorter lifetime). In contrast, due to chromatographic separation in py-GC/MS, only relatively stable and volatile products are detected (the chromatographic step provides an additional selectivity here). In addition, electron impact ionization produces significantly different spectra profiles compared to the atmospheric pressure chemical ionization ongoing in the ASAP source. Both techniques are to a certain extent complementary [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0024"><span>24</span></a>,<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0025"><span>25</span></a>,<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0030"><span>30</span></a>].</p><p>There is a direct possibility of coupling mass spectrometry with ion mobility spectrometry (IMS) to significantly improve the selectivity of analysis (and, to certain extent, to compensate the advantage of the chromatographic step in py-GC/MS while maintaining the direct MS analysis speed). During an ion mobility spectrometry experiment, ions of analytes are flying through the ion mobility cell (e.g., a drift tube or a closed-loop path within a traveling wave ion guide in case of the cyclic ion mobility cell) filled with buffer gas (e.g. helium). In this process, ions collide with the buffer gas. These collisions slow down the ions differently based on their size, shape and charge. Ion mobility spectrometry therefore allows separation of the present analytes in the gas phase according to their collision cross sections (CCS). It also allows separation of isomeric compounds and filtration of the matrix signals, resulting in a higher quality of the MS and MS/MS spectra [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0031"><span>[31]</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0032"><span>[32]</span></a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0033"><span>[33]</span></a>].</p><p>ASAP-MS has a high potential for the study of protective and decorative layers on the surfaces of historical objects. To the best of our knowledge, however, ASAP-MS and ASAP-IMS-MS have never been used for characterization of lacquers. This paper deals with the development of ASAP-MS method for characterization of Asian lacquers and its application for classification of oriental historical objects.</p><h4><strong>3. Material and methods</strong></h4><h5><strong>3.3. Instrumentation and experimental setup</strong></h5><p>Analyses were performed using <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/202"><strong>Waters Cyclic IMS high resolution tandem mass spectrometer</strong></a> with an integrated cyclic ion mobility cell and atmospheric solids analysis probe (ASAP) used as ion source (Waters, Milford, MA, USA). Samples (∼ 0.1–0.5 mg) were inserted into a melting point capillary (G119/32, manufacturer SAMCO, United Kingdom) through an in-lab radially drilled hole (hole diameter: approx. 1 mm; distance from the bottom end approximately 5 mm). Solution of 2′,4′,6′-trihydroxyacetophenone monohydrate (THAP) in acetone was used as a standard for lock mass correction (<i>c</i> = 1 mg.ml<sup>-1</sup>). The m/z values were calculated as the means from three repeated measurements of appropriate samples. Approx. 1 μl of standard was deposited onto the capillary using a small pipette tip. Following experimental parameters were used: desolvation temperature of 600 °C, source temperature of 100 °C, corona “current” mode (corona current 2.0 μA), cone voltage of 30 V, source offset of 10 V, cone gas flow of 30 l/min, desolvation gas flow of 500 l/min, nebulizer gas pressure of 6 bar and reference capillary voltage of 2.5 kV. As for the ion mobility separation setup, a cyclic ion mobility cell was used in the following sequence: 1. Inject (10 ms), 2. Separate (37 ms), 3. Inject to Pre-Store (0.50 ms), 4. Eject (0.50 ms), 5. Reinject from Pre-Store (0.63 ms), 6. Eject and Acquire (13.20 ms). TW static height was 11 V, TW ramp start height was 15 V and TW ramp end height was 35 V. Acquisition time was 3 min for the MS scan and 1 min for the MS/MS scan used for identification of analytes. In the MS/MS mode, collision energy 30 V was used for pentadecyl and heptadecylcatechols, 35 V for phenols substituted with (CH<sub>2</sub>)<sub>10</sub>C<sub>6</sub>H<sub>5</sub> and 40 V for catechols substituted with (CH<sub>2</sub>)<sub>12</sub>C<sub>6</sub>H<sub>5</sub> and phenols substituted with CH<sub>2</sub>CO(CH<sub>2</sub>)<sub>10</sub>C<sub>6</sub>H<sub>5</sub>, respectively. Scan time 1 s was used for both scans. At the end of each working day, sample V was measured for confirmation that the instrument measured consistently. Values of m/z were calculated by MassLynx software (version 4.2, SCN 1016) after lock mass correction using the signal of protonated molecule of THAP. Theoretical mass of particular analyte was used for lock mass correction in the MS/MS spectra. Centroid top at 80 % and areas of peaks were used for accurate mass and intensity of signals calculation. The maximal accepted difference from theoretical mass (dtm) of signals was 10 mDa. Peak areas were used to compare the abundance of identified analytes among particular samples. Mobilograms from cyclic ion mobility experiments were produced by DriftScope v2.9 software. In mobilograms, spectra were generated by integration at Full Width at Half Maximum (FWHM) of peaks at drift time 1.72 ms.</p><h4><strong>4. Results and discussion</strong></h4><h5><strong>4.1. Utilization of ASAP-MS</strong></h5><p>In the ASAP-MS spectra of the studied lacquers, signals of a several compounds were observed (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#fig0002"><span>Fig. 2</span></a>). Signals of radical cations as well as protonated molecules of identified analytes were easily visible. Usually a slightly higher abundance of cation radicals compared to protonated molecules was observed (spectra of all samples are listed in Supplementary material B). The processes observed in the ASAP-MS spectra involve the release of alkyl phenol/alkyl catechol monomer(s) and cleavages of alkyl chain(s) after the carbon linked to the phenol/catechol ring (i.e. cleavage after the first, second and third alkyl carbons) and elimination of water either from polymer or released monomers (the pyrolytic/collision-induced fragmentation processes are discussed below in detail). The analysis of provided samples revealed the most important markers of specific types of lacquers.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Journal_of_Cultural_Heritage_77_2026_187_196_Fig_2_Spectra_of_lacquers_prepared_from_Toxicodendron_vernicifluum_Toxicodendron_succedaneum_and_Gluta_usitata_samples_U_V_and_B_0200c24c06.jpg" alt="Journal of Cultural Heritage, 77, 2026, 187-196: Fig. 2. Spectra of lacquers prepared from Toxicodendron vernicifluum, Toxicodendron succedaneum and Gluta usitata (samples U, V and B)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Journal_of_Cultural_Heritage_77_2026_187_196_Fig_2_Spectra_of_lacquers_prepared_from_Toxicodendron_vernicifluum_Toxicodendron_succedaneum_and_Gluta_usitata_samples_U_V_and_B_0200c24c06.jpg 245w," sizes="100vw" width="2750" height="1269"></p><h5><strong>4.1.4. Study of MS/MS spectra of characteristic markers</strong></h5><p>For all the standard materials, the most important markers were subjected to fragmentation after the collision induced dissociation (CID) in the Trap collision cell of the mass spectrometer (MS/MS experiments; all fragmentation spectra and observed diagnostic fragments including differences from theoretical masses are given in Supplementary material B).</p><p>Signals of fragments with an intensity higher than the specified percentage of well known fragments arising from studied compounds were investigated and identified. Diagnostic fragments are given in Table B.2, Supplementary material B. The threshold signal was set for different compounds. For heptadecyl- and pentadecylcatechols, the threshold signal was defined as 5 % of the signal of dihydroxytropylium that is always an integral component of their collision spectra. For heptadecenyl- and pentadecenylcatechols the threshold signal was 2.5 % of dihydroxytropylium. For pentadecatrienyl catechol it was 100 % of dihydroxytropylium while for dodecylphenylcatechol it was 1 % of dihydroxytropylium. In case of 1-(3-hydroxyphenyl)-12-phenyldodecan-2-one, the threshold signal was defined as 17 % of the signal of hydroxyphenylethan-2-one. Finally, for 10-decylphenylphenol and 12-dodecylphenylphenol, 50 % of the signal of methylphenol was used as the threshold signal. These fragments were used for confirmation of the presence of lacquer compounds in routine measurements (at least one MS/MS spectrum was measured in each sample). The threshold signals were suggested and set in order to reach a sufficient yield of diagnostic fragments for unambiguous identification of individual markers. These signals are intended to be used in long term measurement in practice.</p><div><p>Fragmentation of heptadecylcatechols with 0-3 double bonds (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#fig0003"><span>Fig. 3</span></a>a-d) is similar to that of pentadecylcatechols with same number of double bonds. In all cases, the signals of the characteristic fragments are well separated from the signals of the matrix.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Journal_of_Cultural_Heritage_77_2026_187_196_Fig_3_Fragmentation_spectra_of_heptadecylcatechols_possessing_0_3_double_bonds_6149330bc3.jpg" alt="Journal of Cultural Heritage, 77, 2026, 187-196: Fig. 3. Fragmentation spectra of heptadecylcatechols possessing 0-3 double bonds, (MS/MS; m/z of parent ions are: a) 342.2559, b) 344.2715, c) 346.2872, d) 348.3028)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Journal_of_Cultural_Heritage_77_2026_187_196_Fig_3_Fragmentation_spectra_of_heptadecylcatechols_possessing_0_3_double_bonds_6149330bc3.jpg 232w," sizes="100vw" width="3000" height="2017"></p><h5><strong>4.2. Principal component analysis</strong></h5><p>Excellent separation of particular types of Asian lacquers in Score Plot (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#fig0005"><span>Fig. 5</span></a>a-d) was obtained by Principal Component Analysis based on the benzenediol and phenol derivatives described above. The Score Plot contains a set of 20 samples obtained from the Náprstek Museum Prague as well as other samples (BBI, BBO, SBI, SBO, U, V and B) in which lacquer compounds were detected (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#tbl0002"><span>Table 2</span></a>). Note that in three other samples from The Náprstek Museum Prague, the lacquer compounds were not found at all.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Journal_of_Cultural_Heritage_77_2026_187_196_Fig_5_Score_plot_and_bi_plot_from_PCA_analysis_of_lacquers_taken_from_historic_objects_df82e0592a.jpg" alt="Journal of Cultural Heritage, 77, 2026, 187-196: Fig. 5. Score plot and bi-plot from PCA analysis of lacquers taken from historic objects, a. whole Score PCA plot, b. zoom of the T. succedaneum area, c. zoom of the T. vernicifluum area, d. zoom of the G. usitata area, e. whole PCA bi-plot, f. zoom of the T. succedaneum area in the PCA bi-plot, g. zoom of the T. vernicifluum area in the PCA bi-plot, h. zoom of the G. usitata area in the PCA bi-plot." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Journal_of_Cultural_Heritage_77_2026_187_196_Fig_5_Score_plot_and_bi_plot_from_PCA_analysis_of_lacquers_taken_from_historic_objects_df82e0592a.jpg 156w," sizes="100vw" width="3000" height="2994"></p><p>The significance of suggested markers for the studied lacquers across the whole data matrix was confirmed. The effect of particular markers is reflected in related bi-plots (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#fig0005"><span>Fig. 5</span></a>e-h). Qualitatively, the results are in accordance with the former study obtained by py-GC/MS [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1296207425002572#bib0001"><span>1</span></a>]. Since both cation-radicals and protonated molecules are formed in ASAP source with significant yield of both ion forms, their sum is an input into the data matrix for each substance (e.g., for pentadecenyl catechol, where the input is the sum of intensities I<sub>(m/z</sub> <sub>=</sub> <sub>318)</sub>+ <i>I</i><sub>(m/z</sub> <sub>=</sub> <sub>319)</sub>), see PCA matrix in Supplementary material C. Measured m/z values of the analyzed compounds are in good agreement with the theoretical values calculated for specific elemental compositions (noted differences from the theoretical mass, dtm is in all cases less than or equal to 10 mDa).</p><h4><strong>5. Conclusion</strong></h4><p>The combination of atmospheric solids analysis probe with high resolution tandem mass spectrometry proved to be a fast and effective tool for analysis of historical Asian lacquer objects, their resolution and classification. The developed method is based on profiling of phenol and benezenediols derivatives. The fragmentation of alkylphenols and alkylbenzene diols after collision activated dissociation in Trap and Transfer collision cells was described. Scission of alkyl chains and elimination of water represent the main fragmentation processes important for identification. Cyclic ion mobility separation appeared to be a useful tool for “cleaning” of signals of the lacquer ions from the matrix signals allowing their better identification from the fragmentation spectra. Moreover, ASAP-MS and MS/MS allowed detection of diterpenic resins, triterpenic resins and arsenic sulphide pigments present along with Asian lacquers. Principal Component Analysis provided complete separation of samples into three clusters of Asian lacquers in Score Plot. A simple classification approach based on direct comparison of newly suggested classification parameters (CPs) allows reliable differentiation of lacquer samples according to their provenance. The developed method is now implemented in routine controls of lacquered objects and could be applied to the analysis of resins and ambers obtained from archaeological findings.</p></div></div>

Characterization of Asian lacquers by atmospheric solids analysis probe high resolution tandem mass spectrometry coupled with cyclic ion mobility separation

News from LabRulezLCMS Library - Week 16, 2026

<p><strong>This study introduces LCMS-Net, a deep learning model designed for direct analysis of raw LC-HRMS data without the need for manual preprocessing. By modeling spatial features of the data, the approach improves reproducibility and detection performance in untargeted metabolomics workflows.</strong></p><p>LCMS-Net demonstrated improved performance in forensic cause-of-death screening and colon cancer detection, outperforming existing methods while reducing batch effects across instruments. Its simplified architecture and lack of pretraining requirements enable faster and more efficient data analysis, highlighting its potential for routine metabolomics and forensic applications.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05404"><strong>LCMS-Net: Deep Learning for Raw High Resolution Mass Spectrometry Data Applied to Forensic Cause-of-Death Screening</strong></a></h4><p>Lisa M. Menacher, Liam J. Ward, Fredrik Heintz, Henrik Green, and Oleg Sysoev*</p><p>Anal. Chem. 2026, 98, 9, 6589–6597</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.analchem.5c05404">https://doi.org/10.1021/acs.analchem.5c05404</a></p><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/"><strong>CC-BY 4.0</strong></a></h6></blockquote><p><strong>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.</strong></p><div><p>Metabolomics is the study of all low-molecular-weight substances in a biological specimen. <a href="javascript:void(0);">(1)</a> As this is closely linked to cellular functions and biochemical processes, it offers a comprehensive view of the current physiological state of an organism. This makes metabolomics a promising tool for a broad range of applications, including diagnostics, precision medicine, and toxicology. <a href="javascript:void(0);">(1−3)</a> However, large-scale large-scale liquid chromatography-high resolution mass spectrometry (LC-HRMS) studies are difficult due to the complex and labor-intensive data preprocessing. This typically involves peak picking, peak alignment, gap filling, and normalization, to simplify the data structure, extract biologically relevant features, and improve the interpretability for subsequent analyses. <a href="javascript:void(0);">(4−7)</a> Although tools like XCMS or MZmine offer widely used implementations of this workflow, the preprocessing of LC-HRMS data remains challenging, for a number of reasons: (1) The preprocessing of LC-HRMS data can be time-consuming due to computational constraints for large datasets. <a href="javascript:void(0);">(4,8,9)</a> (2) Domain knowledge is necessary to select suitable parameters for the used preprocessing software, and thus to obtain reliable features. <a href="javascript:void(0);">(4,7,8)</a> (3) The reproducibility of the extracted features is often limited across software libraries, especially for low-abundant metabolites. <a href="javascript:void(0);">(4,8)</a> (4) Preprocessing tools may fail to detect some metabolites entirely, resulting in the loss of potentially important information. <a href="javascript:void(0);">(4)</a></p><p>In the past few years, deep learning models have emerged as promising alternatives to the traditional preprocessing workflow for LC-HRMS data. While many publications in this field have been focused on enhancing a particular step of the preprocessing, such as peak identification or peak alignment, end-to-end deep learning has also gained popularity. <a href="javascript:void(0);">(9,10)</a> In end-to-end deep learning, a model is trained to extract useful information directly from raw data without explicit feature engineering. Often, this is less time-consuming than conventional methods and improves the predictive accuracy of the downstream task. This was demonstrated by Cadow et al., who converted raw MS data from prostate biopsies into pseudoimages through binning. They then applied pretrained deep learning models for image processing to extract feature vectors from the pseudoimages and trained a classifier to discriminate between tumor and healthy cases. <a href="javascript:void(0);">(11)</a> A similar approach was also used by Shen et al. to predict the gestational age of pregnant women. To address data scarcity, they proposed a data augmentation strategy that simulates retention time (RT) drifts during the data acquisition. As a result, several pseudoimages can be produced from a single LC-HRMS sample, which substantially increases the size of the training set. <a href="javascript:void(0);">(12)</a> Furthermore, Wang et al. developed an end-to-end deep learning model to detect esophageal squamous cell carcinoma based on raw LC-HRMS data. By decomposing a sample into multiple tiles instead of just one down-scaled pseudoimage, their approach preserves more details of the raw LC-HRMS data. <a href="javascript:void(0);">(13)</a> Most recently, Deng et al. used an ensemble of pretrained image classification models for lung cancer detection. Their approach enables more robust predictions for multiclass classification tasks. Furthermore, model explainability was addressed through the analysis of contributing factors. <a href="javascript:void(0);">(14)</a> Although, these studies collectively highlight the potential of end-to-end deep learning for metabolomics-based analyses, they also show its limitations. For example, the use of pretrained architectures for image classification may be flawed due to the substantially different topology of LC-HRMS data and natural images. Natural images typically contain hierarchically structured objects with geometrical properties such as size or patterns while objects in LC-HRMS pseudoimages are essentially represented as one-dimensional peaks along the retention time axis and are sharply localized in <i>m</i>/<i>z</i>-value. <a href="javascript:void(0);">(6,15)</a> Existing deep learning methods for metabolomics primarily use 2D-convolutions and thus do not take into account such spatial properties of LC-HRMS data. Consequently, there remains a need for more robust methods that are specifically tailored to LC-HRMS data. This is particularly relevant for real-world applications, where batch effects and diverse study populations are common and difficult to mitigate.</p><p>This study presents LCMS-Net, a fully automated end-to-end deep learning model for the analysis of LC-HRMS data. By directly processing the raw data and taking into account its spatial properties by using 1D-convolutions, LCMS-Net mitigates the need for time-intensive, manual feature engineering and improves the classification performance of downstream tasks. This is demonstrated through comprehensive benchmarking for two different applications, forensic CoD screening and colon cancer detection. To the best of our knowledge, it is the first time end-to-end deep learning is applied to post-mortem metabolomics and in the context of forensic science. Furthermore, LCMS-Net achieves robust results even in the presence of severe batch effects, as shown by training and evaluating the CoD screening model with data from different measurement instruments. Lastly, LCMS-Net does not rely on large pretrained deep learning models, and thus requires less computational resources than previous end-to-end learning approaches for the analysis of LC-HRMS data.</p><h4><strong>Methods</strong></h4><h5><strong>Cause-of-Death Screening Dataset</strong></h5><p>The cause-of-death screening study was approved by the Swedish Ethical Review Authority (Dnr 2019–04530 &amp; Dnr 2025–0249–02). All samples were collected from autopsy cases admitted to the Swedish National Board of Forensic Medicine between July 2017 and December 2024, which had undergone routine toxicological screening via HRMS. Screening was performed from femoral whole blood samples, in accordance with standardized procedures reported previously. <a href="javascript:void(0);">(32)</a> Sample separation was performed using gradient elution on a C18 column (150 mm × 2.1 mm, 1.8 μm; <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1952"><strong>Waters Acquity HSS T3 column</strong></a>, Water Sverige AB, Sweden). MS-data was collected in positive mode and the total acquisition time for each sample was 12 min. Samples admitted between July 2017 and November 2020 were run using an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=10&amp;search=6540+Q-TOF"><strong>Agilent 6540 Q-TOF system</strong></a> in MS-mode only, and samples admitted afterward using an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=10&amp;search=6546+Q-TOF"><strong>Agilent 6546 Q-TOF system</strong></a> with data-dependent acquisition mode (autoMSMS). Aside from these differences in acquisition strategy, source settings and TOF parameters (e.g., gas temperatures, voltages, and mass ranges) were kept consistent between platforms. Henceforth, we will refer to the former as <i>Dataset A</i> and the latter as <i>Dataset B</i> for clarity.</p><p>Dataset A was primarily used for the implementation and evaluation of LCMS-Net and includes 4282 autopsy cases processed in over 641 analytical runs. All cases belong to one of the following five CoD groups: acidosis (<i>n</i> = 100), drug intoxication (<i>n</i> = 1385), ischemic heart disease (IHD) (<i>n</i> = 1362), hanging (<i>n</i> = 1200), and pneumonia (<i>n</i> = 235). A detail summary of the inclusion criteria and study population was previously described by Ward et al. <a href="javascript:void(0);">(26)</a> Dataset B was used to evaluate the sensitivity of LCMS-Net to batch effects and includes additional 5485 autopsy cases with the same inclusion criteria and CoD groups as Dataset A.</p><h4><strong>Overview of LCMS-Net</strong></h4><p>LCMS-Net is a end-to-end deep learning model for the analysis of raw LC-HRMS data. First, a data preparation pipeline transforms the raw LC-HRMS data into pseudoimages. Afterward, convolutional neural networks (CNNs) specifically designed to take the spatial properties of LC-HRMS data into account are used to extract relevant features for the downstream task. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05404#fig1">Figure 1</a> shows an overview of this workflow for CoD screening, as well as the architecture of LCMS-Net.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_9_6589_6597_Figure_1_Overview_of_LCMS_Net_applied_to_Co_D_screening_6db285c40d.jpg" alt="Anal. Chem. 2026, 98, 9, 6589–6597: Figure 1. Overview of LCMS-Net applied to CoD screening. Binning is used to reduce the size of the raw LC-HRMS data and to ensure equally shaped input matrices. Afterward, the input matrices are processed by an ensemble of 1D-CNNs consisting of a depthwise 1D convolutional layer, followed by max pooling and spatial dropout. The results of the CNNs are flattened and dense layers are used to predict the class labels of the input data." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_9_6589_6597_Figure_1_Overview_of_LCMS_Net_applied_to_Co_D_screening_6db285c40d.jpg 238w," sizes="100vw" width="1500" height="986"></p><h4><strong>Results and Discussion</strong></h4><h5><strong>Evaluation of Prediction Performance for CoD Screening</strong></h5><p>First, LCMS-Net was evaluated on the CoD screening dataset (Dataset A). <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05404#fig2">Figure 2</a> shows the results of the model comparison for the CoD screening dataset. With an average accuracy of 70.8%, LCMS-Net outperformed all four benchmark models trained on preprocessed LC-HRMS data. In comparison, our previously published OPLS-DA model for CoD screening achieved only 65.0% accuracy (<i>p</i>-value = 0.0010). Furthermore, LCMS-Net also achieved a better F1-score and sensitivity than these models, while maintaining a high specificity score. The deep learning benchmark model, DeepMSProfiler, achieved a similar accuracy to LCMS-Net. However, LCMS-Net outperformed DeepMSProfiler in terms of both F1-score (<i>p</i>-value = 0.0122) and sensitivity (<i>p</i>-value = 0.0010). This indicates that our method maintains a better balance between true positives and false positives across the five CoD groups, which is especially important for high-stakes applications like forensic science. It should be noted that traditional cross-validation was used for the estimation of p-values, as our purpose was to compare models with fixed hyperparameters. When instead comparing frameworks (e.g., RF vs SVM), nested cross-validation would be more appropriate.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_9_6589_6597_Figure_2_Evaluation_of_Co_D_screening_prediction_performance_of_LCMS_Net_in_comparison_to_benchmark_models_11eef68bb7.jpg" alt="Anal. Chem. 2026, 98, 9, 6589–6597: Figure 2. Evaluation of CoD screening prediction performance of LCMS-Net in comparison to benchmark models. The prediction performance of a classifier is measured by accuracy, macro F1-score, macro sensitivity, and macro specificity over five model runs. The error bars represent the standard deviations between model runs." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_9_6589_6597_Figure_2_Evaluation_of_Co_D_screening_prediction_performance_of_LCMS_Net_in_comparison_to_benchmark_models_11eef68bb7.jpg 245w," sizes="100vw" width="1500" height="709"></p><p>Notably, LCMS-Net achieves its high classification performance without pretraining on natural images. This shows that although transfer learning is often useful, it may fail if the source (e.g., natural images) and target data distribution (e.g., LC-HRMS data) are too different. Such domain mismatch can lead to negative transfer, where features from a pretrained model negatively impact the classification performance of the downstream task, resulting in a lower accuracy than training a simpler model from scratch. <a href="javascript:void(0);">(42)</a> By employing an architecture tailored to LC-HRMS data, LCMS-Net is also computationally more efficient, with fewer than 500,000 trainable parameters compared to over 7 million trainable parameters in DeepMSProfiler. <a href="javascript:void(0);">(14)</a> This makes our method accessible to researchers with limited computational resources.</p><h5><strong>Application for Colon Cancer Detection</strong></h5><p>Lastly, we have tested the potential of LCMS-Net in another application domain, by assessing its prediction performance on the CRC dataset. The cancer detection task has been previously used by Deng et al. to show that DeepMSProfiler can be successfully applied to different application areas, making it a suitable benchmark task. We trained a separate instance of LCMS-Net for the model evaluation on the CRC dataset due to differences in sample preparation, analytical protocols, and instrument setup compared to the CoD screening dataset. The hyperparameter settings remained unchanged, as we assumed similar structural properties for both datasets.</p><p>LCMS-Net outperforms DeepMSProfiler with an F1-score of 97.3% compared to 95.5% on the colon cancer detection task. Furthermore, sensitivity and specificity improvements of 2.8% were observed. These results underline the relevance of LCMS-Net for other application areas than CoD screening. A detailed overview of the evaluation metrics for the colon cancer prediction can be found in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c05404/suppl_file/ac5c05404_si_001.pdf">Supporting Table 7</a>.</p><h4><strong>Conclusion</strong></h4><p>Current preprocessing workflows for untargeted metabolomics are often time-consuming, require extensive domain knowledge, lack reproducibility, or fail to detect some metabolites entirely. LCMS-Net offers an alternative end-to-end approach by operating directly on the raw LC-HRMS data and explicitly modeling its spatial properties. The deep learning model enables faster processing and does not require manual parameter selection. Furthermore, it has been shown through two case studies, CoD screening and cancer detection, that our method extracts a higher amount of relevant information from the raw data and thus outperforms existing models for the analysis of metabolomics data. Specifically, LCMS-Net achieves an average F1-score of 65.5% for CoD screening and 97.3% for colon cancer detection. The previous state-of-the-art models achieved 56.1% (OPLS-DA) and 95.5% (DeepMSProfiler) respectively. The prediction performance of LCMS-Net is consistent even when applied to data from different measurement instruments than those used for the training of the deep learning model, showing its robustness toward batch effects. However, the interpretability of LCMS-Net remains limited and future development is needed to assess which metabolites are learned by the model and which remain undetected. Despite this, LCMS-Net opens up new opportunities for large-scale analyses of LC-HRMS data, potentially driving innovation across numerous application domains.</p></div>

LCMS-Net: Deep Learning for Raw High Resolution Mass Spectrometry Data Applied to Forensic Cause-of-Death Screening

<p><strong>In the week of April 20, the following webinars await you in the field of liquid phase and especially HPLC and LC/MS.</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 500 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Metrohm: Copper deposition baths: Advanced analytical solutions for development and monitoring</strong></h5><ul><li>Tu, 21.4.2026 09:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6123">Registration</a></li></ul><p>Discover a multi-method approach to copper plating bath analysis combining titration, CVS, XRF, and pH measurement for improved process control, data reliability, and production quality.</p><h5><strong>2. Bruker: Exploring the Caenorhabditis elegans lipidome in 4D</strong></h5><ul><li>Tu, 21.4.2026 10:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6106">Registration</a></li></ul><p>Combining chromatography, trapped ion mobility, accurate mass and fragment information enables confident discovery and annotation of novel lipids in this key model organism.</p><h5><strong>3. Technology Networks: Episode 3: PFAS in Food – Addressing Contamination in the Food Supply Chain</strong></h5><ul><li>Tu, 21.4.2026 17:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6108">Registration</a></li></ul><p>PFAS contamination in food is an emerging analytical and regulatory challenge, especially as testing expands beyond water into complex food and animal protein matrices.</p><h5><strong>4. Agilent Technologies: Getting the Most from Your Agilent LCMS System</strong></h5><ul><li>Tu, 21.4.2026 17:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5740">Registration</a></li></ul><p>In this webinar, we will discuss best practices and preventative maintenance tips to help maximize your instrument’s uptime.</p><h5><strong>5. Thermo Fisher Scientific: Mass spectrometry strategies to power drug discovery</strong></h5><ul><li>Tu, 21.4.2026 19:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6097">Registration</a></li></ul><p>In this first master class session, explore how advanced mass spectrometry strategies are powering the next generation of drug discovery through chemoproteomics.</p><h5><strong>6. SCIEX: PFAS first steps: A roundtable for new analysts</strong></h5><ul><li>We, 22.4.2026 10:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6103">Registration</a></li></ul><p>PFAS roundtable webinar for new analysts covering troubleshooting, workflow optimization, and regulatory readiness with expert insights from LC-MS specialists and industry professionals.</p><h5><strong>7. SelectScience: What’s in your hydrogen? Analytical toolkits for fuel purity analysis</strong></h5><ul><li>We, 22.4.2026 10:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6055">Registration</a></li></ul><p>Join this dual-session webinar on hydrogen impurity testing and ISO 14687 fuel quality. Learn how GC, GC-MS, IC, and FTIR workflows support safe, compliant hydrogen production and fuel-cell applications.</p><h5><strong>8. Separation Science: Testing PFAS in Biosolids: From Sample Preparation to Interference Separation</strong></h5><ul><li>We, 22.4.2026 11:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6124">Registration</a></li></ul><p>Learn how robust analytical approaches can deliver confident results, even in the most challenging wastewater and biosolid samples.</p><h5><strong>9. Thermo Fisher Scientific: Automated direct mRNA sequence mapping using online partial RNase T1 digestion and 2D LC-MS</strong></h5><ul><li>We, 22.4.2026 11:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6110">Registration</a></li></ul><p>This webinar highlights a fully automated 2D LC-MS workflow for rapid, high-coverage mRNA characterization.</p><h5><strong>10. SCIEX: Unlocking SCIEX OS software: automating acquisition, analysis, and 21 CFR part 11 compliance reporting</strong></h5><ul><li>We, 22.4.2026 15:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6125">Registration</a></li></ul><p>Join us to discover how the powerful capabilities of SCIEX OS Software help laboratories improve efficiency, reduce manual steps, and maintain data integrity.</p><h5><strong>11. Thermo Fisher Scientific: From experts to applications: Screening protein-drug interactions with HT-PELSA</strong></h5><ul><li>We, 22.4.2026 19:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6098">Registration</a></li></ul><p>Part 2 of the Chemoproteomics Master Class explores HT-PELSA with Orbitrap Astral MS, showing peptide-level ligand binding analysis for target discovery, specificity, and drug pipeline insights.</p><h5><strong>12. Agilent Technologies: Pesticide Residues &amp; Fumigants Edition</strong></h5><ul><li>Th, 23.4.2026 08:30 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5955">Registration</a></li></ul><p>Food Safety Webinar Series – Part 2: Pesticide Residues &amp; Fumigants Edition. Explore LC-MS/MS and GC-MS/MS workflows, QuEChERS extraction, and regulatory reporting under Codex and EU frameworks.</p><h5><strong>13. Agilent Technologies: Enhancing Peptide and Oligonucleotide Analysis with Ultra Inert HPLC Technology</strong></h5><ul><li>Th, 23.4.2026 11:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6134">Registration</a></li></ul><p>Learn how ultra-inert HPLC columns improve LC analysis of peptides and oligonucleotides by minimizing metal interactions and enhancing peak shape, sensitivity, and reproducibility.</p><h5><strong>14. Thermo Fisher Scientific: Chromeleon Admin Console III: Data management and automation</strong></h5><ul><li>Th, 23.4.2026 11:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6126">Registration</a></li></ul><p>In this practical session, we’ll dive into more Administration Console capabilities that help you manage and standardize your setup at scale.</p><h5><strong>15. Waters Corporation: Informatics series #31 Empower Advanced Custom Fields - Examples</strong></h5><ul><li>Th, 23.4.2026 11:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5923">Registration</a></li></ul><p>Learn how to use Advanced Custom Fields in Empower to streamline calculations, improve reporting, and ensure validation-ready, compliant data processing.</p><h5><strong>16. Waters Corporation: A Multi-Residue Approach - Pesticides, Pharmaceuticals, and Personal Care Products</strong></h5><ul><li>Th, 23.4.2026 15:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6112">Registration</a></li></ul><p>Navigating challenges and opportunities for environmental testing labs under the revised EU Water Framework Directive.</p><h5><strong>17. Waters Corporation: Analysis of Glyphosate, Glyphosinate and AMPA in water without the need of derivatisation</strong></h5><ul><li>Th, 23.4.2026 15:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5926">Registration</a></li></ul><p>Learn about required method conditions and instrumentation for Glyphosate, Glyphosinate and AMPA analysis in water without the need of derivatisation.</p><h5><strong>18. Thermo Fisher Scientific: Insights from an IC specialist: Are you using the right eluent?</strong></h5><ul><li>Th, 23.4.2026 16:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6127">Registration</a></li></ul><p>Join the IC Ask the Experts session to learn practical eluent management strategies, optimize ion chromatography workflows, and get answers to your real-world analytical challenges.</p><h5><strong>19. Waters Corporation: Managing Process Performance in Stage 3 Process Validation: Regulatory Expectations for Continued Process Verification</strong></h5><ul><li>Th, 23.4.2026 17:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6060">Registration</a></li></ul><p>Join this webinar to gain practical insights into regulatory expectations for CPV and strengthen your approach to maintaining process performance throughout the product lifecycle.</p><h5><strong>20. Thermo Fisher Scientific: High-throughput activity based protein profiling - Systematic mapping of covalent drug-protein interactions on a proteome-wide scale</strong></h5><ul><li>Th, 23.4.2026 19:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6099">Registration</a></li></ul><p>Part 3 of the Chemoproteomics Master Class highlights activity-based proteome profiling and covalent screening to accelerate target identification and drug discovery using scalable MS workflows.</p><h5><strong>21. Agilent Technologies: Advancing and Characterizing Next-Gen Antibody Conjugates: Insights from BBB-Targeting Antibody Platforms</strong></h5><ul><li>Fr, 24.4.2026 08:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/6128">Registration</a></li></ul><p>Discover next-generation antibody therapeutics, BBB-targeting platforms, and LC-QTOF workflows for advanced characterization of complex antibody-drug and oligonucleotide conjugates.</p>

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Webinars LabRulezLCMS Week 17/2026

LCMS-Net: Deep Learning for Raw High Resolution Mass Spectrometry Data Applied to Forensic Cause-of-Death Screening

News from LabRulezLCMS Library - Week 16, 2026

Characterization of Asian lacquers by atmospheric solids analysis probe high resolution tandem mass spectrometry coupled with cyclic ion mobility separation

Webinars LabRulezLCMS Week 16/2026

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MS/MS Mass Spectrometry Filtering Tree for Bile Acid Regio- and Stereoisomer Annotation

News from LabRulezLCMS Library - Week 14, 2026

Webinars LabRulezLCMS Week 14/2026

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