ISC 2026 - Day 2

<h4>What if you could compress 179 days of grueling laboratory workflow into just 14 hours?</h4>In this episode of ‪ChromatographyTalk‬, ‪Organomation‬ General Manager David Oliva sits down with analytical chemist Laura Penabad to discuss her groundbreaking new research in biocatalysis and high-throughput screening. Evolving engineered enzymes typically requires checking thousands of variants—a massive bottleneck when traditional Liquid Chromatography-Mass Spectrometry (LC-MS) runs take minutes per sample. Laura reveals how her team built a modular screening platform that shatters this limitation, running samples 128 times faster than standard methods without sacrificing analytical accuracy.Laura breaks down the "orchestra" of technologies making this possible, including coupling nanoliter droplet microfluidics with Cyclic Ion Mobility-Mass Spectrometry (cIM-MS) to separate complex chemical isomers in milliseconds. She also shares the chaotic but triumphant behind-the-scenes stories of her PhD journey—from presenting a "comically bad" early poster at ASMS to recruiting an undergraduate mechanical engineer to build custom automation software from scratch.If you are an early-career researcher, a mass spec enthusiast, or curious about the future of synthetic biology and machine learning in chemistry, this episode is packed with invaluable intuition and advice.<h3>Video Transcription</h3><h4>From Months to Hours: Droplet Ion Mobility–MS for Biocatalysis Screening</h4>High-throughput screening is central to directed enzyme evolution, but conventional analytical workflows can create a difficult compromise: extremely fast screens often provide limited structural information, while techniques such as LC-MS offer much richer chemical insight at the cost of throughput. In a recent interview, analytical chemist Laura described how her work with droplet-based cyclic ion mobility mass spectrometry is helping bridge that gap—providing rapid analysis while retaining the ability to distinguish structurally similar products.Her research grew from an initially modest proof of concept into a screening platform capable of analyzing a 96-well plate in roughly ten minutes. The resulting workflow was approximately 128 times faster than a conventional three-minute LC-MS analysis, opening new possibilities for directed evolution campaigns in which thousands of enzyme variants need to be evaluated.<h4>Directed evolution needs better analytical screening</h4>Biocatalysis takes advantage of enzymes as highly selective catalysts capable of performing reactions under relatively mild conditions. Directed evolution extends those capabilities by deliberately modifying enzyme sequences, expressing large numbers of variants, screening their activity, and using the best-performing mutants as starting points for subsequent rounds of evolution.The process can be repeated multiple times, progressively steering an enzyme toward improved activity, altered substrate specificity, or even chemistry that does not occur naturally.But the effectiveness of directed evolution depends heavily on the screening step. The more variants that can be analyzed—and the more useful chemical information that can be collected for each one—the more effectively researchers can explore the enormous sequence space available to an enzyme.For Laura, this created a clear analytical challenge: develop a screening platform that was substantially faster than conventional chromatographic analysis without sacrificing the structural discrimination needed for chemically similar products.<h4>From an imperfect first experiment to a working platform</h4>The project did not begin with a polished high-throughput workflow.Laura recalled looking back at her 2023 ASMS poster, where the early system was still working with standards rather than true isomeric samples. Droplet introduction was slow—around six seconds per droplet—and the signals were far from the ideal reproducible, square, flat-topped profiles expected from replicate droplets.Nevertheless, simply producing a droplet signal represented an important first milestone.The decisive change came when the project stopped being treated merely as a demonstration that droplet ion mobility–MS could work and instead became a deliberate effort to build a robust screening platform.Laura approached the problem in modules. First came reliable droplet introduction. Then the ion mobility separation method. Quantification became another independent component. Matrix effects were introduced later, gradually making the system resemble the conditions expected with real screening samples.Each layer was tested before another was added.This stepwise strategy became one of the central principles of the project: rather than attempting to optimize the entire system simultaneously, individual components were developed, challenged, and then assembled into an increasingly realistic workflow.<h4>Finding the middle ground between speed and structural information</h4>One of the biggest analytical problems in high-throughput screening is distinguishing products with the same nominal or exact mass.Constitutional isomers may contain the same atoms and therefore generate the same mass-to-charge ratio, while differing only in how functional groups are arranged within the molecular structure. Very rapid optical or mass-based screens may therefore struggle to distinguish them.Traditional LC-MS can solve many of these problems through chromatographic separation, but a several-minute analysis rapidly becomes impractical when thousands of enzyme variants must be screened.Ion mobility offered Laura’s team another option.Rather than separating compounds on a chromatographic timescale, ion mobility can differentiate ions according to their gas-phase behavior on a much shorter timescale. In the workflow described in the interview, this created a useful compromise: not the kilohertz throughput possible with some simple screening technologies, but dramatically faster structural differentiation than conventional LC-MS.The goal was therefore not simply to achieve the fastest possible screen, but to find a middle-throughput analytical regime with enough resolving power to answer chemically meaningful questions.<h4>A 96-well plate in about ten minutes</h4>That strategy eventually produced one of the most striking results of the project.The droplet cyclic ion mobility–MS platform processed a 96-well plate in roughly ten minutes, while maintaining quantitative results comparable with the conventional LC-MS workflow used for comparison. According to the interview, this represented an approximately 128-fold increase in analysis speed relative to a three-minute LC-MS run.Laura described the successful plate experiment less as a surprise than as confirmation that the modular development strategy had worked.Before reaching that point, the workflow had already been challenged through a sequence of increasingly demanding experiments: droplet formation, ion mobility separation, calibration, matrix-containing calibration, and cross-testing analytes at different concentrations. By the time the complete plate was analyzed, each individual part of the analytical system had already been tested under progressively more realistic conditions.<h4>What does that speed mean for directed evolution?</h4>The impact becomes even clearer when the workflow is considered at the scale of an entire directed-evolution campaign.Laura’s manuscript estimated that a five-round campaign involving 5,000 variants would require approximately 179 days using the conventional LC-MS workflow but only about 14 hours using the new platform.Compressing months of analytical work into less than a day has consequences beyond laboratory productivity.It allows researchers to screen more enzyme variants and therefore sample a larger portion of sequence space. But the resulting datasets may also become increasingly valuable for computational approaches.Rather than retaining information only about the best-performing enzyme variant, researchers can preserve data describing variants that performed poorly as well as those that succeeded. When genotype and phenotype information remain linked through the well-plate workflow, these larger datasets can potentially provide richer input for machine-learning models designed to guide subsequent rounds of enzyme evolution.Faster analysis therefore does more than accelerate an existing experiment—it can change the scale and design of the experiment itself.<h4>Balancing ion mobility resolution and signal</h4>Increasing cyclic ion mobility separation time can improve resolution, but it also introduces an important trade-off: additional passes can reduce signal.Laura optimized this balance experimentally by continuously infusing mixtures containing both isomers and progressively adjusting the ion mobility conditions. After tuning parameters such as wave height and velocity, increasing the separation to three passes provided baseline resolution at the high analyte concentrations used during method development.<blockquote>Concentration, however, also mattered.</blockquote>At higher ion populations, space-charge effects could broaden peaks and reduce the baseline separation. This meant that the optimal number of passes could not be selected independently of the concentration range required by the final assay.Her broader advice for optimization is therefore application-driven: decide first what the analytical method actually needs to accomplish.More speed will almost always require sacrificing something—resolution, signal intensity, quantitative precision, or another performance parameter. The key is to identify which compromise the application can tolerate and then optimize the system around that requirement.<h4>Solving droplet carryover with a practical compromise</h4>Droplet-based workflows present their own engineering challenges, including carryover between neighboring samples.In Laura’s system, analytes showed little tendency to partition into the fluorinated oil separating aqueous droplets within the Teflon tubing. The more troublesome carryover occurred closer to electrospray ionization, where a small amount of sample could remain associated with the interface and appear when the next droplet arrived.Rather than adding excessive engineering complexity to an already elaborate platform, the researchers adopted a pragmatic solution: a sacrificial wash droplet between analytical samples.It is an example of a recurring theme throughout the project. The objective was not to construct the theoretically most sophisticated droplet system, but to create a screening workflow that remained sufficiently robust, reproducible, and practical for large numbers of samples.<h4>High-throughput analysis also requires high-throughput data processing</h4>Accelerating the mass spectrometry experiment created another bottleneck: the data.The instrument produced a continuous intensity-versus-time trace rather than a neatly organized well-plate dataset. With small experiments, Laura could manually identify individual droplets and process the results in spreadsheets. That approach quickly became unrealistic as sample numbers increased.The solution emerged through collaboration with an undergraduate mentee, Aiden, who helped develop a dedicated software tool for processing the continuous traces.Laura defined how the software should behave and how the analytical information should be organized, while Aiden developed the computational implementation. The workflow itself was subsequently adjusted to make automated data processing more reliable—for example, by introducing marker columns that allowed the software to recognize the end of each plate row.The resulting tool can accept the acquired traces together with information about plate dimensions and replicates and automatically return parameters including peak identity, duration, and the corresponding well position. A later version also generated an automatically populated heat map.The experience illustrates how improving analytical throughput often requires simultaneous development of instrumentation, experimental design, and informatics. Making the measurement faster is useful only if the resulting data can be processed at a comparable speed.<h4>Mentoring became part of the scientific process</h4>The software project also reflects another theme Laura emphasized throughout the interview: the value of mentoring.Several younger researchers contributed directly to the development of the final platform. Laura described mentees not simply as additional hands in the laboratory but as scientists capable of proposing their own hypotheses and experimental ideas.Working with undergraduate researchers also influenced the way she organized experiments.Because their available laboratory time was limited, each task needed to have a clear purpose. That encouraged a more disciplined approach to experimental planning: instead of performing experiments simply because they could be done, the team had to ask which experiment would most efficiently answer the next important question.In that sense, mentoring became another mechanism for improving the research itself.<h4>The next step: continuous, automated screening</h4>Despite the dramatic increase in analytical speed, the current workflow still includes manual operations such as loading tubing and connecting the sample stream to the mass spectrometer.The next engineering challenge is therefore automation.Laura described ongoing work using the Venturi effect to pull samples directly from wells into the droplet system. Building on previous work in the laboratory with 96-well plates, she has already tested this approach with as many as 2,000 samples.The long-term direction is clear: continuous and robust droplet generation, automated handling of thousands of samples, rapid ion mobility separation, mass spectrometric detection, and automated reconstruction of the resulting data into plate-based outputs.Combining those elements could move the system closer to the type of large-scale screening required for increasingly ambitious biocatalysis campaigns.<h4>Learning mass spectrometry one problem at a time</h4>Laura also reflected on the broader experience of developing the platform during her PhD.Ion mobility and mass spectrometry initially appeared intimidating, particularly because they combine concepts from analytical chemistry, physical chemistry, separations, instrumentation, and electrical engineering. What eventually changed was not simply learning more facts, but developing the ability to move repeatedly between theory and experiment.An unexpected result sends the researcher back to the underlying theory. A better understanding suggests a modified experiment. That experiment creates another observation, gradually building intuition about how the system behaves.Her advice to early-career scientists is therefore to accept that this process takes time.Advanced analytical techniques may initially appear overwhelming, but understanding develops incrementally through repeated interaction between theory, experimentation, failure, and refinement. Equally important, she emphasized the role of the supportive ion mobility–mass spectrometry community in making a technically demanding field more approachable for young researchers.From the first inconsistent droplets to a platform capable of processing thousands of samples, that iterative mindset ultimately shaped the entire project.<blockquote>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.</blockquote><blockquote><h4>Concentrating on Chromatography Podcast</h4>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!You can find Concentrating on Chromatography Podcast in podcast apps:<ul><li><a target="_blank" rel="noopener noreferrer" href="https://open.spotify.com/show/1wZg67rCjoGju4yhqxI3tL">Spotify</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://podcasts.apple.com/tz/podcast/concentrating-on-chromatography/id1788429185">Apple podcast</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://podscan.fm/podcasts/concentrating-on-chromatography">podscan</a></li></ul>and on <a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/@ChromatographyTalk/videos">YouTube channel</a></blockquote>

Droplet Cyclic Ion Mobility Mass Spec: Compressing Months of Enzyme Screening Into Just 14 Hours

ISC 2026 - Day 3

Article

Events

Video

Scientists

Interview

The International Symposium on Chromatography (ISC) represents the oldest conference series focussing on separation science. ISC symposia have been organised since 1956 in each even year. ISC is internationally recognised as one of the premier meeting series for discussion of all modes of chromatography and separation science with a broad coverage of techniques and applications.

International Symposium on Chromatography - (ISC)

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

rHGH fragment or AOD-9604 (LCMS+CHNS) analysis

Combined LC-MS, CHNS and HPLC analysis of rHGH fragment 176–191 and AOD-9604 for identification, amount, peptide purity and contamination screening.

This analysis is designed to identify and determine the amount and purity of recombinant Human Growth Hormone fragment 176–191 and AOD-9604. It combines LC-MS, CHNS and HPLC to provide information about peptide identity, net peptide content, peptide purity and potential contamination with other peptides or degradation products.The test can detect and measure either target compound. It is therefore not necessary to know in advance whether the sample contains rHGH fragment 176–191 or AOD-9604.<h4>rHGH Fragment 176–191</h4>rHGH fragment 176–191, also known as somatropin 176–191, is a peptide corresponding to amino acids 176 to 191 of Human Growth Hormone. It consists of 16 amino acids and represents the C-terminal region of the HGH molecule.Its molecular weight is approximately 1,799 Da. It differs from AOD-9604 in the first amino acid of its sequence.<h4>AOD-9604</h4>AOD-9604 is a synthetically modified peptide derived from Human Growth Hormone fragment 176–191. Like the rHGH fragment, it contains 16 amino acids, but its sequence begins with tyrosine instead of phenylalanine.This structural difference allows the two peptides to be distinguished by LC-MS. The molecular weight of AOD-9604 is approximately 1,815 Da.<h4>Analytical Methods</h4><h5>LC-MS – Identification and Contamination Screening</h5>LC-MS is used to confirm the identity of the peptide in the sample. It also provides screening for detectable contamination with other peptides and peptide degradation products.<h5>CHNS – Net Peptide Content Determination</h5>CHNS elemental analysis measures the carbon, hydrogen, nitrogen and sulphur content of the sample. The measured values can be used to determine Net Peptide Content.Because the sample is combusted during CHNS analysis, the result is not affected by solubility issues that may complicate chromatographic measurement of peptide amount.<h5>HPLC – Peptide Purity Determination</h5>HPLC is used to determine peptide purity and the relative proportion of the main peptide component compared with chromatographically detectable impurities.<h4>What the Analysis Determines</h4><ul><li>Identity of rHGH fragment 176–191 or AOD-9604</li><li>Amount of peptide in the sample</li><li>Net peptide content determined by CHNS</li><li>Peptide purity determined by HPLC</li><li>Presence of other peptides or degradation products detected by LC-MS</li><li>Differentiation between rHGH fragment 176–191 and AOD-9604</li></ul><h4>Analysis Parameters</h4><ul><li>Target compounds: rHGH fragment – somatropin 176–191 and AOD-9604</li><li>CAS number of AOD-9604: 221231-10-3</li><li>Molecular weight of AOD-9604: 1,815 Da</li><li>AOD-9604 sequence: H-Tyr-Leu-Arg-Ile-Val-Gln-Cys(1)-Arg-Ser-Val-Glu-Gly-Ser-Cys(1)-Gly-Phe-OH</li><li>CAS number of rHGH fragment: 66004-57-7</li><li>Molecular weight of rHGH fragment: 1,799 Da</li><li>rHGH fragment sequence: H-Phe-Leu-Arg-Ile-Val-Gln-Cys(1)-Arg-Ser-Val-Glu-Gly-Ser-Cys(1)-Gly-Phe-OH</li><li>Identification method: LC-MS</li><li>Amount determination method: CHNS</li><li>Peptide purity method: HPLC</li><li>Detection limit: less than one twentieth of the usual dosage per vial</li><li>Result format for lyophilizates: mg per vial and peptide purity</li><li>Alternative result format: m/m purity and peptide purity</li><li>Minimum lyophilizate sample: one whole unopened vial containing the lyophilizate</li><li>Minimum raw API powder sample: 30 mg in a sealed glass vial to prevent moisture absorption</li><li>Average turnaround time: 7 business days</li></ul><blockquote><h5>Important Notice</h5>The alternative m/m purity result format is intended exclusively for raw API powders. For lyophilized samples, the standard result format provides the peptide amount in mg per vial together with peptide purity.</blockquote><h4>Additional Analyses</h4><h5>TFA Analysis</h5><ul><li>Detection and measurement of trifluoroacetic acid (TFA) in the sample. Results are reported in mg per vial.</li></ul><h5>Raw Data</h5><ul><li>Delivery of the available raw analytical data from the primary test and all applicable additional analyses.</li></ul><h5>Fentanyl Presence Analysis</h5><h5>Alternative Result Format – m/m Purity</h5><ul><li>Alternative reporting of the result as mass-by-mass purity. This option is intended exclusively for raw API powders.</li></ul><h5>Variance Testing</h5><ul><li>Analysis of multiple sample units to assess variation within a single batch. All submitted samples must originate from the same batch of the same product.</li></ul><h5>pH Measurement</h5><h5><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/2522">Endotoxin Analysis</a></h5><ul><li>Measurement of bacterial endotoxins, particularly lipopolysaccharides (LPS), in the sample. This analysis is available only for finished products and should not be ordered for raw powders.</li><li>The standard test provides accurate results up to 10 EU per vial. Values above 10 EU per vial are estimated and require an additional test with a different dilution for precise determination. Sending two vials is therefore recommended.</li><li>Where endotoxin levels above 10 EU per vial are expected, the method can be calibrated for the higher range in advance.</li></ul><h5>Additional Report</h5><ul><li>A separate additional laboratory report issued under a different client name.</li></ul><h5><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/2527">Sterility Testing</a></h5><ul><li>TAMC and TYMC testing checks the product for the presence of bacteria, yeasts and moulds. Additional unopened sample units must be submitted for analysis. The average turnaround time for this test is 14 days.</li></ul><h5><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/2525">Heavy Metals Analysis</a></h5><ul><li>Detection of arsenic, cadmium, lead and mercury in the sample. Testing for other metals can be arranged individually.</li></ul>

GCMS/LCMS screening for contamination

GCMS/LCMS screening for identification of contaminants in samples, with the analytical method selected according to sample type.

The GCMS/LCMS Screening for Contamination is a standalone laboratory service designed to identify potential contaminants in a sample using either gas chromatography–mass spectrometry (GCMS) or liquid chromatography–mass spectrometry (LCMS).The appropriate analytical method depends on the sample type. GCMS is used primarily for volatile and semi-volatile compounds, while LCMS is suitable for peptide-related contamination.<h4>About the Screening</h4><ul><li>GCMS screening uses the NIST 2014 mass spectral library to identify a broad range of potentially harmful substances in the product. Detection is limited to volatile and semi-volatile compounds and this method is not suitable for peptide samples. &nbsp;</li><li>LCMS screening is intended for peptide products and can detect contamination with other peptides or degraded peptide. Detection is limited to peptides included in the laboratory’s available reference list.</li></ul>Both methods provide identification of detected contaminants only. Quantitative determination of a detected substance can be offered separately.<h4>Key Features</h4><ul><li>Standalone contamination screening by GCMS or LCMS</li><li>Analytical method selected according to sample type</li><li>GCMS screening for volatile and semi-volatile contaminants</li><li>GCMS identification using the NIST 2014 mass spectral library</li><li>LCMS screening for peptide contamination and degraded peptide</li><li>Identification of detected contaminants</li><li>Quantitative determination available separately after identification</li><li>Suitable for tablets, capsules, liquid solutions, raw materials, and vials depending on the selected method</li><li>Average turnaround time of 7 business days</li></ul><h4>Analysis Parameters</h4><ul><li>Testing method: GCMS or LCMS</li><li>Amount of sample required: 1 tablet/capsule, 1 ml solution, 200 mg raw material, or 1 vial</li><li>Average turnaround time: 7 business days</li></ul><blockquote>Important Notice<ul><li>The analytical method is selected according to the type of sample and the expected contaminants.</li><li>GCMS detection is limited to volatile and semi-volatile compounds and cannot be used for peptide samples.</li><li>LCMS detection is limited to peptides included in the laboratory’s available reference list.</li><li>Both GCMS and LCMS screening provide identification only. Quantitative determination of a detected contaminant is not included in the standard analysis and can be offered separately.</li></ul></blockquote><h4>Optional Additional Analyses</h4>The following optional laboratory services are available:<ul><li>Additional Laboratory Report</li></ul>

Products focused on LC/MS - page 1

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Droplet Cyclic Ion Mobility Mass Spec: Compressing Months of Enzyme Screening Into Just 14 Hours

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