<p><strong>🎤Dan Reddy </strong>(PhD candidate in the Department of Chemistry at Queen's University and a recipient of the NSERC Vanier Canada Graduate Scholarship. He was recently named one of the top 35 early-career scientists globally in the 2025 CAS Future Leaders program.)</p><p><strong>In this episode, we interview Daniel Reddy, 2025 CAS Future Leader and PhD candidate at Queen's University, about his groundbreaking research on automated mass spectrometry and dried matrix spots (DMS).</strong></p><p>Dan's work combines computer vision, 3D printer automation, and laser micromachining to revolutionize sample preparation—reducing CO₂ emissions by 28-fold and organic solvent use by 21-fold compared to traditional methods.</p><h4><strong>What You'll Learn:</strong></h4><ul><li>How to give a mass spectrometer "sight" and "taste" using computer vision and the LMJ-SSP (Liquid Microjunction Surface Sampling Probe)</li><li>The breakthrough technology behind Surface Energy Traps (SETs) for confining liquid droplets on paper substrates</li><li>Why dried matrix spots eliminate the need for cold-chain shipping and enable analysis of blood, urine, and saliva samples via standard mail</li><li>How DIY chemists are hacking 3D printers to build cost-effective autosamplers (replacing $10K+ systems)</li><li>The role of green chemistry and systems thinking in modernizing analytical methods</li><li>Why interdisciplinary collaboration (chemistry + computer science) is critical to innovation</li></ul><h4><strong>Key Topics:</strong></h4><ul><li>Dried Matrix Spots (DMS) for automated sample prep</li><li>Laser-micromachined Surface Energy Traps</li><li>Direct surface sampling mass spectrometry</li><li>Sustainability in analytical chemistry</li><li>3D printer customization for laboratory automation</li><li>The importance of science communication and community outreach</li></ul><h3><strong>Video Transcription</strong></h3><h4><span><strong>Introduction</strong></span></h4><p>Recent advances in analytical chemistry increasingly focus on simplifying workflows, improving sustainability, and enabling automation. In this context, the work of <span>Dan Reddy</span> centers on the concept of <i>“bringing mass spectrometry to life”</i> through the integration of automated sampling, computer vision, and dried matrix spot (DMS) methodologies.</p><p>The primary objective is to enable efficient and reproducible analysis of biological and liquid samples—such as blood, urine, saliva, or other solutions—by depositing them onto substrates, drying them, and extracting meaningful analytical information directly via mass spectrometry.</p><h4><span><strong>Conceptual Framework: Enabling “Sight” and “Taste” in Mass Spectrometry</strong></span></h4><p>A central innovation in this work is the conceptual transformation of mass spectrometry from a passive analytical tool into an active, semi-autonomous system.</p><ul><li><strong>“Sight”</strong> is introduced through <strong>camera-based computer vision</strong>, allowing the system to identify predefined patterns on a substrate.</li><li><strong>“Taste”</strong> is implemented via a <strong>liquid microjunction surface sampling probe (LMJSSP)</strong>, which enables direct extraction of analytes from surfaces.</li></ul><p>The LMJSSP consists of two concentric capillaries forming a continuously flowing microdroplet. When brought into contact with the sample surface, this droplet facilitates localized extraction, effectively enabling direct surface analysis without traditional chromatographic separation.</p><p>Combined with an automated positioning system (adapted from 3D printer mechanics), the platform can autonomously locate sampling sites and perform reproducible measurements.</p><h4><span><strong>Dried Matrix Spots as an Analytical Platform</strong></span></h4><h5><span><strong>Principle and Advantages</strong></span></h5><p>Dried matrix spots involve depositing a small volume of liquid sample onto a substrate (commonly paper), followed by drying. This approach offers several advantages:</p><ul><li>Reduced need for cold-chain transport</li><li>Simplified sample handling and storage</li><li>Compatibility with remote or decentralized sampling</li><li>Lower solvent consumption</li></ul><p>These features contribute significantly to greener analytical workflows and improved logistical efficiency.</p><h5><span><strong>Sustainability Impact</strong></span></h5><p>The implementation of DMS-based workflows has demonstrated:</p><ul><li>Up to <strong>28-fold reduction in CO₂ emissions</strong></li><li>Up to <strong>21-fold reduction in organic solvent use</strong></li></ul><p>These improvements arise from eliminating cold-chain logistics and minimizing solvent-intensive preparation steps.</p><h4><span><strong>Surface Energy Traps (SCTs): Controlled Micro-Sampling Environments</strong></span></h4><h5><span><strong>Definition and Fabrication</strong></span></h5><p>Surface Energy Traps (SCTs) are microstructured regions on a substrate designed to confine liquid droplets within defined boundaries.</p><p>The fabrication process involves:</p><ol><li>Coating paper with a <strong>hydrophobic layer</strong></li><li>Using <strong>laser ablation</strong> to selectively remove the coating in defined patterns</li><li>Creating <strong>hydrophilic circular regions</strong> (typically ~0.5 mm diameter)</li></ol><p>These patterned regions act as micro-wells that confine droplets spatially and prevent lateral diffusion.</p><h5><span><strong>Analytical Benefits</strong></span></h5><p>SCTs address key limitations of traditional dried matrix spots:</p><ul><li><strong>Improved spatial confinement</strong> of analytes</li><li><strong>Reduced diffusion variability</strong></li><li><strong>Enhanced reproducibility</strong></li><li><strong>Preconcentration effects</strong> due to controlled drying</li></ul><p>Additionally, the SCT diameter is smaller than the sampling probe, enabling near-complete analyte collection during surface extraction.</p><h4><span><strong>Drying Dynamics and Analyte Distribution</strong></span></h4><p>Traditional dried matrix spots often suffer from the <strong>“coffee-ring effect”</strong>, where analytes redistribute unevenly during drying. This leads to variability depending on sampling location.</p><p>The SCT approach mitigates these effects by:</p><ul><li>Limiting diffusion area</li><li>Maintaining analytes near the surface</li><li>Producing more uniform analyte distribution</li></ul><p>Preliminary investigations (e.g., via SEM imaging) indicate improved homogeneity, although further studies are needed to fully characterize drying kinetics and their influence on signal intensity.</p><h4><span><strong>Integration with Automated Sampling Systems</strong></span></h4><h5><span><strong>3D Printer-Based Positioning</strong></span></h5><p>A key engineering innovation is the repurposing of <strong>3D printer hardware</strong> for automated sample positioning:</p><ul><li>Provides cost-effective XYZ motion control</li><li>Enables programmable sampling routines</li><li>Supports integration with analytical instrumentation</li></ul><p>Custom software—developed in collaboration with computer engineers—allows users to define coordinates, dwell times, and sampling parameters.</p><h5><span><strong>Computer Vision Integration</strong></span></h5><p>The addition of computer vision enables:</p><ul><li>Pattern recognition (e.g., alignment markers near SCTs)</li><li>Autonomous targeting of sampling locations</li><li>Reduced operator intervention</li></ul><p>This transforms the system into a semi-autonomous analytical platform.</p><h4><span><strong>Comparison with Conventional Workflows</strong></span></h4><p>Compared to traditional LC-MS or GC-MS workflows, the presented approach offers:</p><div><div><figure class="table"><table><thead><tr><th>Aspect</th><th>Conventional Methods</th><th>SCT + LMJSSP Approach</th></tr></thead><tbody><tr><td>Sample prep</td><td>Multi-step, solvent-intensive</td><td>Minimal, direct surface sampling</td></tr><tr><td>Chromatography</td><td>Required</td><td>Potentially bypassed</td></tr><tr><td>Throughput</td><td>Moderate</td><td>High (automated)</td></tr><tr><td>Sustainability</td><td>Limited</td><td>Significantly improved</td></tr></tbody></table></figure></div></div><p>While chromatographic methods remain essential for many applications, this approach provides a complementary pathway for rapid screening and targeted analyses.</p><h4><span><strong>Green Chemistry and Systems Thinking</strong></span></h4><p>A notable aspect of this work is the integration of <strong>systems thinking</strong> into analytical chemistry. Inspired by engagement with organizations such as the <span>American Chemical Society</span> and green chemistry initiatives, the research emphasizes:</p><ul><li>Environmental impact reduction</li><li>Resource efficiency</li><li>Lifecycle considerations</li></ul><p>This perspective expands the role of analytical chemists beyond method performance to broader societal and environmental implications.</p><h4><span><strong>Future Outlook and Applications</strong></span></h4><p>The research is currently at an early stage but shows strong potential for:</p><ul><li><strong>Decentralized diagnostics</strong> (e.g., remote blood sampling)</li><li><strong>Environmental monitoring</strong></li><li><strong>High-throughput screening workflows</strong></li><li><strong>Field-deployable analytical systems</strong></li></ul><p>Further advancements are expected in:</p><ul><li>Substrate engineering</li><li>Automation robustness</li><li>Integration with advanced MS platforms (e.g., triple quadrupole systems)</li></ul><h4><span><strong>Conclusion</strong></span></h4><p>The integration of dried matrix spots, surface energy traps, and automated surface sampling represents a significant step toward more sustainable, efficient, and accessible analytical workflows.</p><p>By combining innovations in microfabrication, computer vision, and mass spectrometry, the work of <span>Dan Reddy</span> demonstrates how analytical chemistry can evolve toward smarter, greener, and more autonomous systems.</p><blockquote><p><strong>This text has been automatically transcribed from a video presentation using AI technology. It may contain inaccuracies and is not guaranteed to be 100% correct.</strong></p></blockquote><blockquote><h4><strong>Concentrating on Chromatography Podcast</strong></h4><p>Dive into the frontiers of chromatography, mass spectrometry, and sample preparation with host David Oliva. Each episode features candid conversations with leading researchers, industry innovators, and passionate scientists who are shaping the future of analytical chemistry. From decoding PFAS detection challenges to exploring the latest in AI-assisted liquid chromatography, this show uncovers practical workflows, sustainability breakthroughs, and the real-world impact of separation science. Whether you’re a chromatographer, lab professional, or researcher you'll discover inspiring content!</p><p>You can find <strong>Concentrating on Chromatography Podcast </strong>in podcast apps:</p><ul><li><a target="_blank" rel="noopener noreferrer" href="https://open.spotify.com/show/1wZg67rCjoGju4yhqxI3tL"><strong>Spotify</strong></a></li><li><a target="_blank" rel="noopener noreferrer" href="https://podcasts.apple.com/tz/podcast/concentrating-on-chromatography/id1788429185"><strong>Apple podcast</strong></a></li><li><a target="_blank" rel="noopener noreferrer" href="https://podscan.fm/podcasts/concentrating-on-chromatography"><strong>podscan</strong></a></li></ul><p>and on <a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/@ChromatographyTalk/videos"><strong>YouTube channel</strong></a></p></blockquote>

Mass Spectrometry, Surface Sampling and Dried Matrix Spots - Greener Chemistry and 3D Printing

<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

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Organomation, founded in 1959, designs and manufactures high-quality nitrogen evaporators and extraction systems. Known for innovation and durability, their lab instruments are used globally for efficient sample preparation with strong customer support.

Organomation

Phenomenex Axia Preparative LC Columns

Available in over 40 unique achiral and chiral selectivities, Axia advanced preparative column packings and column hardware designs offer several advantages.

<p class="cke-user_style-important"><strong>Guaranteed, Longest Lasting Preparative HPLC and SFC Column</strong></p><p>Available in over 40 unique achiral and chiral selectivities, Axia advanced preparative column packings and column hardware designs offer several advantages. Unlike traditional preparative HPLC column packing methods, the Axia packing method offers increased sorbent bed density for increased performance and eliminates media bed collapse as a source of premature column failure in preparative HPLC/SFC columns.</p><h3><strong>Axia Packed Preparative HPLC Columns</strong></h3><h4><strong>Axia Single Axial Compression Step</strong></h4><p>Axia packed preparative columns involve a single axial compression step, unlike conventional packed preparative columns. The ideal prep HPLC column bed density is custom calculated and automated for each specific media and column size. Computer control of the entire process ensures both proper bed density and column uniformity every time.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Phenomenex_Axia_Packing_Technology_1785ff0442.jpg" alt="Phenomenex: Axia Packing Technology" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Phenomenex_Axia_Packing_Technology_1785ff0442.jpg 245w," sizes="100vw" width="917" height="560"></p><h4><strong>Unmatched Column Reproducibility</strong></h4><p>The <strong>automated</strong> <strong>Axia™ packing system</strong> provides <strong>feedback control</strong> and <strong>infinite tuning</strong> of packing density for specific media characteristics such as mechanical strength and porosity. An optimum higher bed density can be consistently reproduced column-to-column. This directly translates into consistent efficiency and peak asymmetry measurements and decreases the column variability seen in traditionally packed preparative columns.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Phenomenex_Unmatched_Column_Reproducibility_a752be8dd5.jpg" alt="Phenomenex: Unmatched Column Reproducibility" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Phenomenex_Unmatched_Column_Reproducibility_a752be8dd5.jpg 245w," sizes="100vw" width="995" height="534"></p><h3><strong>Seamless Scalability to Increase Sample Load</strong></h3><h4><strong>Increase Column Length</strong></h4><p>Increase sample load without increasing your flow rate by using a longer column. With Axia technology, each preparative column is optimized for:</p><ul><li>Analytical-like efficiency</li><li>Long column lifetime</li><li>High sample load with high surface area media such as Kinetex, Aeris, Gemini, Luna, Luna Omega, or Synergi</li></ul><h4><strong>Increase Column ID</strong></h4><p>For maximizing load without increasing the run time, consider scaling up to a larger column ID. Axia packed columns provide the three important benefits you need.</p><ul><li>Reproducible performance across all column diameters</li><li>Increased throughput without sacrificing purity</li><li>High efficiency from analytical to preparative</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Phenomenex_Increase_Column_ID_a45d7f8aa3.jpg" alt="Phenomenex: Increase Column ID" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Phenomenex_Increase_Column_ID_a45d7f8aa3.jpg 156w," sizes="100vw" width="908" height="910"></p><h3><strong>Axia Technology Outperforms Traditional Packing Processes!</strong></h3><p>Because of the constant pressure placed on the integrated packing piston, Axia packed columns possess the dynamic capability of maintaining a consistent, homogeneous media bed. This results in superior column performance no matter which media selectivity you choose.</p><h3><strong>How to choose the right column</strong></h3><h5><strong>Acidic Compounds</strong></h5><ul><li>Kinetex XB-C18</li><li>Gemini C18</li></ul><h5><strong>Aromatic</strong></h5><ul><li>Luna Phenyl-Hexyl</li><li>Kinetex Biphenyl</li></ul><h5><strong>Closely Related Compounds</strong></h5><ul><li>Kinetex F5</li><li>Luna PFP(2)</li></ul><h5><strong>Extremely Hydrophobic</strong></h5><ul><li>Kinetex C8</li><li>Luna C8(2)</li></ul><h5><strong>Extremely Polar</strong></h5><ul><li>Luna HILIC(2)</li><li>Kinetex HILIC</li></ul><h5><strong>Hydrophobic Compounds</strong></h5><ul><li>Luna C18(2)</li><li>Kinetex C18</li></ul><h5><strong>Polar Acids</strong></h5><ul><li>Luna Omega Polar C18</li><li>Kinetex Polar C18</li></ul><h5><strong>Polar Bases</strong></h5><ul><li>Luna Omega PS C18</li><li>Kinetex PS C18</li></ul><h5><strong>Sugars</strong></h5><ul><li>Luna NH(2)</li></ul><h5><strong>Chiral</strong></h5><ul><li>Lux i-Amylose-3</li><li>Lux Cellulose-1</li></ul>

Products from category Preparative HPLC columns focused on PrepLC from Phenomenex - page 1

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