Emanuela Gionfriddo, Associate Professor of Chemistry, University at Buffalo, The State University of New York, USA
Looking five to ten years ahead, I believe the miniaturization of analytical platforms will have a major impact on analytical science by making in situ, in vivo, and on-site measurements increasingly accessible. The long-term potential is to make sophisticated chemical measurements increasingly accessible and practical for routine use, including outside highly specialized laboratory settings. Combined with artificial intelligence, this could further expand our ability to predict analytical performance, optimize workflows, and interpret increasingly complex data. However, I believe AI will have the greatest impact when it complements, rather than replaces, the analytical scientist’s fundamental understanding of analytical processes and instrumentation.
Edward Sisco, Research Chemist, U.S. National Institute of Standards and Technology, USA
I think over the next five to ten years we will see two trends significantly impact analytical science – bringing the lab to point of need and next-generation chemometrics. As technology advances and we miniaturize instrumentation without sacrificing performance, the ability to make high-quality, robust chemical measurements at the point of need will increase rapidly. This will have major impacts on everything from healthcare to food safety to environmental monitoring. I think this will also empower more citizen scientists to make their own measurements at scale, and I am interested to see how that impacts researchers.
In terms of next-generation chemometrics, I think we are going to see a movement away from throwing data into a bunch of models to see which one sticks. Instead, we will move towards more meaningful, novel approaches that not only maximize the amount of information we can squeeze out of a measurement but also enable us to better contextualize that information within a larger space. For instance, in forensic drug analysis, the goal is often to identify the presence of a drug based on chemical measurements by largely visual interpretation of data. Having objective ways to come to that conclusion would be immensely beneficial to the field. Taking that same measurement and then being able to determine how that drug was synthesized, whether other recently analyzed samples likely came from the same batch of drugs or the same trafficking route, and what the potential harm of that sample is relative to other samples, would provide immensely helpful additional insights. Realizing this will require increased adoption of standardized methods across laboratories in order to build out larger pools of data.
Adrián de la Fuente Ballesteros, Postdoctoral Researcher in Analytical Chemistry, TESEA Research Group, University of Valladolid, Spain
Beyond AI, I think one of the biggest trends will be making analytical science more inclusive. Of course, high-resolution instruments and chemical fingerprinting will provide much more comprehensive information, but they also require expensive equipment and advanced data analysis. I think we sometimes overlook the fact that these advances shouldn't increase the gap between well-funded laboratories and those with limited resources. Recent conflicts, economic crises, and natural disasters have shown that access to analytical technologies is still very unequal. That's why I believe developing affordable methods, open-source tools, citizen science initiatives, and promoting open knowledge will be just as important as developing new technologies over the next decade.
Frederik André Hansen, Postdoctoral Fellow (Tenure Track), Department of Pharmacy, University of Oslo, Norway
Looking beyond AI, I believe the environmental footprint of analytical laboratories will come under increasing scrutiny and may eventually become subject to stricter regulation. Many routine laboratories still rely on legacy methods that consume large volumes of organic solvents, while the widespread use of single-use plastic consumables contributes substantially to laboratory waste. I expect the field to continue moving toward more sustainable approaches, such as 1 mm ID HPLC columns and miniaturized sample preparation techniques. These technologies have now reached a level of robustness and reliability that will likely enable their broader adoption in routine analytical laboratories in the near future.
Zoë Ayres, Head of Laboratory Facilities, The Open University, UK
I see an ever-increasing interest in sustainable practices in analytical science. I think that this has huge power as a community to drive analytical instrument companies to design more environmentally sustainable instrumentation, and of course, the more market demand for this is realised, that in turn drives innovation when it comes to designing more environmentally friendly analytical equipment.
Jelle De Vos, Senior Scientist, RIC Group, Belgium
Chromatography can be considered a mature field, but nevertheless technological innovations can still impact the experimental approach. Functionalized packed-bed columns that can be tailored to the users’ desires by immobilizing the preferred ligand for that particular study are becoming commercially available. This was previously only available at specialized R&D laboratories and academic labs. The new column fabrication techniques, like lithography or additive manufacturing make it possible to explore novel bed geometries and on-chip workflows. As a result, users can highly tune their instrumental set-up by designing the perfect hydraulic flow path. This will be an exciting future direction of the field: tailored chromatography-based workflows specific to the problem at hand, especially for solving questions arising in life sciences.
We cannot look away from the disruptive effects specialized machine learning tools already have in all fields of science, but a major drawback is that it’s the data quality and the speed at which we can provide it that ultimately dictates the reliability of the model. I think that chromatography will become one of the key tools to deliver data about the chemical nature of newly predicted drug candidates, but will only reach its full potential when analytics are on-line integrated at every stage of the discovery cycle and beyond.
Jonathan Maurer, Postdoctoral Researcher, Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Switzerland
We are increasingly seeing therapeutic modalities being combined: an antibody for its specificity, an LNP enabling delivery, a nucleic acid for the therapeutic effect… No single analytical technique can fully characterize all the critical quality attributes of such complex systems. I therefore expect multidimensional liquid chromatography to become increasingly important both in R&D and in quality control and routine laboratories. Combined with automation and machine learning to explore large experimental spaces rapidly, it could make analytical method development far more powerful and ambitious.
Ansgar Korf, CEO and Co-Founder, mzio GmbH, Germany
Untargeted measurement becoming the default rather than the exception. Targeted assays were trusted because they were manageable, and untargeted work was exploratory. Software has now caught up with the data volumes, so that trade-off is disappearing. Targeted methods will remain essential wherever a validated answer is required, but they answer the question you designed them for and nothing else. Untargeted data can be revisited years later to answer questions that did not exist at the time of acquisition. That turns every dataset from a one-off deliverable into a lasting resource, and I think the cumulative effect on analytical science will be larger than any individual technical advance.
Nicole Strittmatter, Professor of Analytical Chemistry, Department of Biosciences, TUM School of Natural Sciences, Technical University of Munich, Germany
We often celebrate the next generation of instruments, but I think the bigger challenge now lies in understanding the wealth of information they already provide. As analytical technologies continue to evolve, I hope the coming decade will bring equally transformative advances but also democratisation in data analysis, interpretation, and integration, enabling us to fully realise the potential of these powerful methods.
Aleksandra Binek, Lead Scientist, Mass Spectrometry Imaging and Single-Cell Proteomics; Project Scientist, Cedars-Sinai Proteomics and Metabolomics Core, Cedars-Sinai Medical Center, USA
The greatest impact will come from computational advances that allows the integration of multimodal spatial and single-cell measurements. The field has reached a stage in technology, where we can combine powerful analytical approaches with increasingly sensitive and specific instrumentation to construct and recreate the approximations of biological systems. The unprecedented technical resolution render complex multi-omics read-outs which will require adaptive computational models that can help answer questions where the analytes are, which cells they come from, and what environments and cells they interact with.
Guinevere Lageveen-Kammeijer, Assistant Professor of Analytical Biochemistry, University of Groningen, the Netherlands
I think one of the biggest trends will be our ability to extract far richer molecular information from dramatically less material. Single-cell analysis is perhaps the most obvious example, but it goes beyond sensitivity. Simply detecting a molecule will increasingly not be enough, we want to distinguish isomers, linkages and subtle structural differences that can completely change the biology.
For glycoscience, that is incredibly exciting. Glycans have largely been missing from the single-cell revolution, and bringing glycomics into this space could reveal an entirely new layer of cellular heterogeneity. It also means that separation science will become increasingly important. Mass spectrometry is extraordinarily powerful, but it cannot resolve every form of molecular complexity on its own. I may be slightly biased, but I think this creates a very exciting future for CE-MS!
At the same time, these technologies will generate an enormous wealth of information. The next challenge will be finding the hidden gems: integrating glycomics with other molecular layers and using computational tools and AI to uncover patterns we could never recognize manually. But the analytical scientist remains essential. We still need to understand where our data comes from, what its limitations are, and whether an exciting pattern actually makes chemical and biological sense.
Nicholas M. Riley, Assistant Professor, University of Washington, USA
As a bioanalytical chemist, my first thoughts go to the connection between advancing analytical measurements and generating biological insight. Glycoproteomics, for example, has continued to advance in its ability to catalogue the glycoproteome, which has shown us that there are concerted changes to the milieu of the glycocalyx. But, we still have a long we to go to reliably quantify these changes and translate them to functional insights. See this as a bigger trend in the analytical sciences: as we can measure with more precision, accuracy, and throughput, how do we combine this new level of data quality with experiments that help us translate that information to knowledge about biological function? I hope we, as a field, see the next 10 years as the era in which we capitalized on our ever-advancing measurement skills to resolve some of the critical biological questions that drive our analytical innovations.
Michael Marty, Associate Professor, Department of Chemistry, The University of Texas at Austin, USA
One thing that has transformed our lab is cheap gene synthesis. In the past, we would waste months on molecular biology. Now, it is cheaper, faster, and much more reliable to just order your gene already in the plasmid you want. You can design essentially anything, and it shows up a few days later ready to go. On the most basic level, it can be cheaper now to produce a protein in-house than to buy it commercially. But, it also enables more advanced protein design to be a regular part of the workflow. We have found it very helpful to be able to custom design proteins to fit a particular analytical need. As AI-guided protein design continues to get better, I think we will see more analytical applications driven by bespoke proteins and custom gene synthesis.
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