<p><strong>What if you could assess a tumor's metastatic potential — without extensive sample prep — directly from tissue? Rachel Wood is working on exactly that.</strong></p><p><strong>Rachel Wood</strong> is a PhD candidate at Queen's University (Kingston, Ontario),&nbsp;working jointly in the labs of Dr. Richard Oleschuk (Chemistry) and Dr. Chris&nbsp;Nicoll (Pathology &amp; Molecular Medicine). Her research applies Ambient Ionization Mass Spectrometry — specifically the Liquid Microjunction Surface Sampling Probe &nbsp;(LMJ-SSP) and Desorption Electrospray Ionization (DESI) — to profile the metabolomics of non-metastatic and metastatic clear cell Renal Cell Carcinoma (ccRCC) cell lines. The long-term goal: a rapid, minimally invasive MS-based prognostic tool for kidney cancer.</p><p>This is the first reported use of LMJ-SSP to assess metastatic status in ccRCC —&nbsp;and early PCA results are already showing distinct separation between cell line&nbsp;<br>profiles.</p><h5><strong>In this episode, David and Rachel cover:</strong></h5><ul><li>Why <strong>ccRCC</strong> is such a resistant biomarker problem despite active genomic, proteomic, and metabolomic research</li><li>The clinical stakes of "<strong>diagnosed by accident</strong>" — and what a prognostic biomarker actually needs to do for a clinician</li><li>How AIMS complements (not replaces) LC-MS in a discovery-phase cancer workflow</li><li><strong>LMJ-SSP vs. DESI</strong>: what each technique sees that the other misses, including spatial resolution differences (~1mm vs. ~50µm)</li><li>Why Rachel runs LMJ-SSP on both a triple quad and a Q-TOF — and what each instrument contributes to the same research question</li><li>How her lab repurposed a 3D printer as a custom automated XYZ sampling stage with 50µm movement precision</li><li>Managing high-dimensional metabolomics data across two polarities, two cell lines, and two mass analyzers — the pipeline explained</li><li>What unsupervised machine learning (PCA) showed in early results</li><li>The path from cell lines to excised tumor tissue to, ultimately, liquid biopsies</li><li>Advice for analytical chemists considering work at the chemistry–oncology interface</li></ul><h3><strong>Video Transcription</strong></h3><p><strong>Kidney cancer remains a difficult biomarker problem, particularly when researchers try to distinguish tumors that are likely to behave aggressively from those that are not. In a recent episode of </strong><i><strong>Concentrating on Chromatography</strong></i><strong>, PhD researcher Rachel Wood discussed how she is combining rapid ambient ionization mass spectrometry, LMJ-SSP, DESI, multivariate data analysis, and conventional LC-MS workflows to investigate the molecular differences associated with clear cell renal cell carcinoma and its metastatic potential.</strong></p><h4><strong>Returning to the research question that started it all</strong></h4><p>Wood first encountered kidney cancer research during her undergraduate studies. Although she later moved into a more instrumentation-focused master’s project involving breast cancer, the subject of kidney cancer remained unfinished business.</p><p>When the opportunity arose to choose a PhD project, she returned to the field that had originally sparked her interest in research. She is now conducting her doctoral work at <strong>Queen’s University in Kingston, Ontario</strong>, in an interdisciplinary environment spanning chemistry, pathology, molecular medicine, computer science, and clinical research.</p><h4><strong>Why clear cell renal cell carcinoma is such a difficult biomarker problem</strong></h4><p>Wood’s research focuses on <strong>clear cell renal cell carcinoma (ccRCC)</strong>, which represents the majority of kidney cancer cases discussed in the interview.</p><p>The difficulty is not a lack of research effort. Genomics, proteomics, and metabolomics studies have all produced promising biomarker candidates. The challenge is that different studies often identify different sets of markers, with relatively little overlap between them.</p><p>Patient-to-patient biological variability further complicates the picture. A candidate biomarker panel that performs well in one cohort may not reproduce equally well in another. Selecting which features deserve further investigation therefore becomes a major analytical and biological challenge.</p><h4><strong>From incidental diagnosis to prognostic information</strong></h4><p>Another difficulty is the way kidney cancer is often discovered.</p><p>Rather than being detected through a dedicated screening program, tumors may be found incidentally during imaging performed for another reason. According to Wood, the ideal solution would be a diagnostic biomarker capable of detecting kidney cancer early. Such a marker is not currently available within the scope of the work she described.</p><p>That places additional importance on <strong>prognostic biomarkers</strong>. Once a tumor has been detected, clinicians need as much information as possible about how aggressive it may be and how likely it is to progress.</p><p>Wood’s current work is therefore not attempting to declare directly that a particular patient has metastatic disease. Instead, the goal is to understand <strong>what changes biologically as a tumor moves from a less aggressive to a more aggressive state</strong>.</p><p>That means asking which molecular pathways change, which mechanisms become altered, and which analytes may eventually provide useful indicators of tumor behavior.</p><h4><strong>AIMS and LC-MS answer different questions</strong></h4><p>A central theme of the interview was the relationship between rapid ambient ionization mass spectrometry and conventional LC-MS.</p><p>Wood is clear that <strong>AIMS is not intended to replace LC-MS</strong>. Instead, she sees the two approaches as complementary.</p><p>AIMS provides a rapid way to obtain a broad molecular overview of a sample with minimal preparation. This makes it especially useful during the discovery phase, when the goal is to gather as much molecular information as possible and identify features that distinguish biological groups.</p><p>LC-MS then becomes valuable when those features need to be separated, characterized more carefully, and quantified with greater confidence.</p><p>In Wood’s workflow, rapid MS screening can therefore help identify which analytes deserve closer attention before more targeted LC-MS investigations are performed.</p><h4><strong>What “minimal sample preparation” actually means</strong></h4><p>The amount of sample preparation required depends strongly on the type of material being analyzed.</p><p>Wood currently works mainly with <strong>cultured human cell lines</strong>, which means much of the preparation begins with growing the cells themselves. Depending on the experiment, the cells may then be chemically lysed or subjected to fixation, paraffin embedding, and sectioning before analysis by <strong>LMJ-SSP</strong> or <strong>DESI</strong>.</p><p>She also notes that the possibilities would be different for freshly excised tumor material. In an appropriate perioperative or biosafety laboratory setting, LMJ-SSP could potentially be used to analyze tumor margins more directly.</p><p>The practical meaning of “minimal preparation” is therefore highly dependent on the sample and the analytical question being asked.</p><h4><strong>Why combine LMJ-SSP and DESI?</strong></h4><p>Rather than choosing between LMJ-SSP and DESI, Wood uses the two approaches because they provide different types of information.</p><p><strong>LMJ-SSP</strong> offers rapid, localized analysis and can accommodate samples with uneven surface topography, which is particularly useful for excised tissue.</p><p><strong>DESI</strong>, by contrast, provides substantially higher spatial resolution. In the workflow described in the interview, DESI can achieve spatial resolution on the order of tens of micrometers, whereas LMJ-SSP operates at a much larger sampling scale depending on probe design.</p><p>That higher spatial resolution is important when moving from simplified cell-line models toward actual tumor tissue, where the molecular organization of different regions may reveal biologically relevant differences.</p><p>Combining both techniques therefore enables researchers to balance speed, surface compatibility, and spatial information.</p><h4><strong>Two mass analyzers for two parts of the same problem</strong></h4><p>Wood also uses two different mass spectrometers in her LMJ-SSP work.</p><p>Most of the discovery work is carried out using a <strong>triple quadrupole mass spectrometer</strong>, particularly because her project focuses strongly on lower-mass analytes and small molecules.</p><p>Once statistically important analytes have been identified, a <strong>Q-TOF</strong> instrument can be used for MS/MS experiments and tentative structural identification.</p><p>The two instruments therefore address different stages of the workflow: one supports broad discovery and comparison, while the other provides additional structural information needed to connect selected features with possible metabolic pathways.</p><h4><strong>Turning a 3D printer into an automated sampling platform</strong></h4><p>One of the most unusual elements of the project is the automated sampling system used with LMJ-SSP.</p><p>Historically, LMJ-SSP measurements could be performed manually or with custom-built XYZ positioning stages. Wood’s laboratory instead adapted a <strong>3D printer</strong> into an automated positioning system.</p><p>Custom software controls the printer and moves the sampling probe with precise control over its X, Y, and Z positions. According to Wood, the system enables movements down to approximately <strong>50 µm</strong> and also controls parameters such as the contact time between the probe and sample and the delay between consecutive sampling events.</p><p>This level of automation improves reproducibility because variables that would otherwise depend on manual operation can be controlled systematically.</p><p>Even the Z-position can be programmed, allowing the probe to operate at constant or varying heights depending on the sample surface.</p><h4><strong>Managing high-dimensional MS data</strong></h4><p>The experimental design creates a large data-analysis challenge.</p><p>Wood is working with multiple cell lines, both ionization polarities, and more than one mass analyzer. To maintain traceability, she analyzes the data in clearly defined subsets.</p><p>A typical workflow might begin with one mass analyzer and one polarity. Within that condition, the lower-aggressiveness and higher-aggressiveness cell models can then be compared directly.</p><p>The data processing pipeline includes steps such as:</p><ul><li>binning,</li><li>normalization,</li><li>transformation,</li><li>dimensionality reduction,</li><li>identification of discriminating molecular features.</li></ul><p>Once relevant features are identified, MS/MS analysis can be used to investigate them further. The same process is then repeated across other polarities and instruments.</p><h4><strong>Unsupervised machine learning reveals distinct molecular profiles</strong></h4><p>Machine learning is particularly valuable because the AIMS datasets are highly dimensional, containing very large numbers of molecular features across multiple samples.</p><p>Wood’s initial analysis has included <strong>principal component analysis (PCA)</strong>, an unsupervised dimensionality-reduction technique.</p><p>The early results are encouraging: the non-metastatic and metastatic cell-line models separate clearly in PCA space.</p><p>That does not itself identify a definitive biomarker panel, but it indicates that the two biological states have distinguishable molecular profiles even though both belong to the same kidney cancer subtype.</p><p>This provides an important foundation for identifying which specific metabolites or molecular pathways may be responsible for the separation.</p><h4><strong>Testing LMJ-SSP for metastatic status</strong></h4><p>A particularly novel aspect of Wood’s project is the use of LMJ-SSP to investigate differences associated with metastatic status.</p><p>She had previously worked on distinguishing healthy and cancerous tissues, where the biological differences are relatively large. Comparing two states within the same cancer type is more demanding because the molecular changes may be much subtler.</p><p>The scientific motivation, however, is strong. Tumors discovered at later stages carry greater concern for metastatic progression, making it important to understand the molecular changes associated with increasingly aggressive behavior.</p><p>For Wood, this made the methodological risk worthwhile: the analytical challenge directly addresses an area where better prognostic information could ultimately have clinical value.</p><h4><strong>A long-term goal: rapid MS-based prognostic testing</strong></h4><p>The long-term vision extends beyond cell-line research.</p><p>Wood is interested in whether rapid, minimally invasive mass spectrometry could eventually contribute to clinical decision-making.</p><p>One possibility would be identifying a diagnostic biomarker in a <strong>liquid biopsy</strong>, potentially using blood or urine. A urine-based marker capable of contributing to earlier detection would be particularly attractive for kidney cancer.</p><p>For prognostic applications, the goal would be slightly different. Once a tumor is discovered, molecular information could help characterize its likely behavior and provide clinicians with additional evidence when deciding between immediate treatment, surgery, or other management strategies.</p><p>Wood emphasizes that such a tool would not replace standard clinical workflows. Instead, it would provide additional molecular information to complement existing diagnostic and pathological approaches.</p><h4><strong>Why interdisciplinary work matters</strong></h4><p>Throughout the interview, Wood repeatedly returns to the importance of collaboration.</p><p>Her research sits at the intersection of analytical chemistry, pathology, oncology, computation, and clinical medicine. No single researcher can remain equally current in all of these fields.</p><p>Working closely with pathologists, computer scientists, surgeons, medical students, and other specialists therefore provides both scientific expertise and a way to stay connected with developments outside one’s immediate discipline.</p><p>For Wood, the biological context has also changed how she thinks about analytical chemistry. Developing a new instrument or method is important, but understanding <strong>who may ultimately benefit from that analytical capability</strong> makes the research more meaningful.</p><p>That patient-centered perspective is now an integral part of how she approaches the project.</p><h4><strong>An intersection full of opportunities</strong></h4><p>Wood describes the interface between analytical chemistry and oncology as an especially exciting area for young researchers.</p><p>The work can range from troubleshooting instrumentation to interpreting pathology, developing statistical methods, and collaborating across disciplines. That diversity creates constant opportunities to learn and to apply analytical chemistry to clinically relevant questions.</p><p>Her project illustrates that modern mass spectrometry research is increasingly about more than generating spectra. It involves connecting instrumentation, spatial sampling, multivariate statistics, machine learning, biological interpretation, and clinical needs into a single analytical strategy.</p><p>And in kidney cancer, where reliable prognostic biomarkers remain difficult to establish, that integrated approach may be particularly valuable.</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>

AIMS vs. LC-MS for Cancer Diagnostics: ccRCC Metabolomics with Rachel Wood

<p><strong>In the week of September 21, 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>6 000 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Waters Corporation: Strengthening SPE Workflows for Today’s Laboratory</strong></h5><ul><li>Mo, 21.9.2026 17:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6514">Registration</a></li></ul><p>Learn how to optimize SPE workflows with best practices for sorbent selection, recovery troubleshooting, and reducing matrix effects in modern analytical laboratories.</p><h5><strong>2. Agilent Technologies: From Crude to Candidate: Scaling Preparative Chromatography with Confidence</strong></h5><ul><li>Mo, 21.9.2026 18:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6498">Registration</a></li></ul><p>Learn how to scale preparative chromatography from crude sample to purified candidate, with practical strategies for method development, fraction collection, and method transfer.</p><h5><strong>3. SCIEX: Extending the power of LC MS/MS: advanced workflows with QTRAP®</strong></h5><ul><li>Tu, 22.9.2026 14:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6518">Registration</a></li></ul><p>Discover how MRM³ and QTRAP workflows provide greater selectivity and lower limits of quantification for LC-MS/MS analysis in complex matrices.</p><h5><strong>4. Technology Networks: Improving Efficiency in Routine PFAS Analysis Using EPA Methods 533 and 1633A</strong></h5><ul><li>Tu, 22.9.2026 16:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6517">Registration</a></li></ul><p>Explore how automated QC monitoring and the SCIEX novus V55 system support high-throughput PFAS quantitation using EPA Methods 533 and 1633A.</p><h5><strong>5. Phenomenex: Expanding the Evidence: New Data for More Reliable PFAS Analysis</strong></h5><ul><li>We, 23.9.2026 10:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6522">Registration</a></li></ul><p>Discover how new analytical data and contamination control strategies can improve baseline performance, sensitivity, and reliability in PFAS analysis.</p><h5><strong>6. SCIEX: How is capillary electrophoresis being used to analyze DNA and RNA</strong></h5><ul><li>We, 23.9.2026 11:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6521">Registration</a></li></ul><p>Explore how capillary electrophoresis supports DNA and RNA analysis, from plasmid quality assessment to mRNA impurity profiling, viral vector characterization, and oligonucleotide workflows.</p><h5><strong>7. Thermo Fisher Scientific: A novel monodisperse supermacroporous reversed-phase platform for comprehensive nucleic acid analysis across a broad size range</strong></h5><ul><li>We, 23.9.2026 11:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6476">Registration</a></li></ul><p>Discover how a novel monodisperse supermacroporous reversed-phase platform enables comprehensive nucleic acid analysis across a broad size range, from short oligonucleotides to mRNA.</p><h5><strong>8. Waters Corporation: The Next Frontier in Nitrosamines Testing: Detection and Risk Assessment in Packaging and IV Infusion Bags</strong></h5><ul><li>We, 23.9.2026 16:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6520">Registration</a></li></ul><p>Learn how LC-MS/MS supports nitrosamine detection and risk assessment in packaging materials while addressing extractables and leachables challenges in IV infusion bags.</p><h5><strong>9. MDPI: Membrane Technologies for Water and Food Sustainability</strong></h5><ul><li>We, 23.9.2026 17:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6561">Registration</a></li></ul><p>Explore membrane technologies for water and food sustainability, international research collaboration, and opportunities within the CYTED Program.</p><h5><strong>10. Agilent Technologies: Unlocking Lab Productivity: Automation Opportunities for LC/MS Laboratories</strong></h5><ul><li>We, 23.9.2026 18:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6495">Registration</a></li></ul><p>Discover how automation can increase productivity in LC/MS laboratories by reducing manual tasks, improving workflow efficiency, and supporting scalable analytical operations.</p><h5><strong>11. Thermo Fisher Scientific: Comparison of biocompatible and inert UHPLC systems for LC-UV quantitation of large RNA</strong></h5><ul><li>Th, 24.9.2026 11:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6477">Registration</a></li></ul><p>Learn how the Vanquish Amplify system minimizes metal interactions during LC-UV analysis of large RNA and explore the performance of biocompatible and inert UHPLC systems.</p><h5><strong>12. Agilent Technologies: Next-Generation Pathogen Genomics: The Long-Read Advantage</strong></h5><ul><li>Th, 24.9.2026 16:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6526">Registration</a></li></ul><p>Explore long-read sequencing and automated target enrichment in pathogen genomics and learn how Agilent SureSelect and Magnis NGS support improved pathogen detection.</p><h5><strong>13. Evosep Biosystems: Advancing Plasma Proteomics</strong></h5><ul><li>Th, 24.9.2026 16:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6523">Registration</a></li></ul><p>Discover advances in plasma proteomics and learn how high-throughput analytical workflows can accelerate biomarker discovery and cancer research.</p><h5><strong>14. Waters Corporation: Applications of Multi-Angle Light Scattering for Biotherapeutic Characterization</strong></h5><ul><li>Th, 24.9.2026 17:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/en-us/all/webinar/6430">Registration</a></li></ul><p>Learn how multi-angle light scattering (MALS) supports confident characterization of monoclonal antibodies, antibody-drug conjugates, and AAV viral vectors.</p>

Webinars LabRulezLCMS Week 39/2026

Forced degradation analysis of recombinant adeno-associated virus serotype 8 based on analytical anion exchange chromatography coupled to orthogonal characterization

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