Interdisciplinary Avenues: Part 1
The 2024 Power List tell us about important problems that could – and should – be tackled through interdisciplinary work
| 5 min read | Discussion
Daniel W. Armstrong
Much, perhaps most, analytical research is interdisciplinary in nature. Depending on the task, it can involve materials science, pharmacology, biology, computer science, etc. Currently, we are working with mathematicians, biologists, spectroscopists, organic chemists, inorganic chemists and engineers on various different projects. One interesting “bio-analytical” research project involves the means by which insects selectively acquire resistance to pesticides by developing symbiotic relationships with gut bacteria that can detoxify certain compounds. Such research involves having the wherewithal to grow hundreds of different bacteria, being able to analyze their degradation of dozens of pesticides and finally to mine genomic databases for possible relationships. Obviously, the biological aspects of such a project are huge and essential.
Juergen Popp
I do not want to mention a specific example but rather our research in general optical health technology, i.e. utilizing optical analytical approaches for medical diagnosis and therapy. Optical health technologies is a prime example of interdisciplinary research and requires the interaction of a wide range of disciplines, such as chemists, physicists, physicians, computer scientists, engineers, etc. One of the most important lessons we have learnt as technologists over the last 20-25 years is the need to involve the end user, i.e. the medical profession, in research from the outset. There is no point in researching a "cool" optical analytical method that is not needed clinically. Translational optical analytical research must always be driven by unmet medical need and requires the successful interdisciplinary interaction of technologists and clinicians from the outset.
Ruedi Aebersold
The fundamental problem in molecular biology and medicine is how the molecules that constitute a living organism, their properties, attributes and interactions determine phenotypes. Solving this problem can only be achieved through interdisciplinary work involving biologists/clinicians, analytical scientists and data scientists.
Chad Mirkin
Structural nanomedicine is a new framework for preparing highly potent medicines by taking into account the structural presentation of the different components that define a particular medicine. A good example of this approach involves vaccine development where we are utilizing spherical nucleic acid (SNA) nanoconstructs as modular entities to explore how the structural presentation of adjuvant and antigen impact therapeutic response in the context of many cancer models. Progress in this area will be spurred by interdisciplinary projects that involve researchers from the fields of chemistry, biology, data science, medicine, engineering, and materials science.
Erin Baker
Interdisciplinary work is extremely important for tackling the complex challenges of the 21st century. We commonly work with experts in microbiology, toxicology, new machine learning techniques, and advanced statistical approaches to assess vital biological and environmental problems facing the world. This combination of expertise and the use of our advanced analytical methods has enabled the discovery of new molecules, pathways and perturbations which would not have been uncovered without the knowledge of all researchers involved in the studies.
Lingjun Li
Creating a region-specific and cell type-specific biomolecular atlas of the brain of Alzheimer’s disease to gain molecular insights into aging and age-related diseases. This highly interdisciplinary project would require a multifaceted approach including a suite of spatial and single cell multi-omics measurements enabled by mass spectrometry imaging, animal models of aging and Alzheimer’s disease and clinical specimens, as well as computational tool development and machine learning approaches and classical biochemical and molecular approaches for biomolecule validation, co-localization, and multidimensional correlation in the context of neurodegenerative disease.
Gunda Köllensperger
Analytical science today is inherently linked to computational method development. E.g. without key contributions from data scientists, the success of non-targeted analysis by high resolution mass spectrometry would not have been possible. From the start, the open-source philosophy fostered the wide adoption of high-resolution mass spectrometry as a discovery tool in many application fields. In turn, years of community efforts deploying open-source harmonized omics-data and spectral libraries form today the basis for emerging computational developments shifting paradigms on what we can learn from data. Prime examples are strategies based on repository scale data analysis uncovering unprecedented chemical and phenotypic associations of knowns and unknown small molecules. In the latter case, “reverse metabolomics” searches for spectra of known molecules across repositories and links their occurrence to meta-data. This new way of hypothesis generation will for sure impact future metabolomics study design and accelerate research. Now, that the ideas are out and there for the community, the input of experimental analytical chemists is still needed as repositories and spectral libraries are still not overly populated. The more curated data and meta-data are deployed by the community, the better compound annotation tools will become and the more unexpected associations will be discovered.
J. Michael Ramsey
The U.S. National Academies recently published a report entitled, “Charting a Future for Sequencing RNA and Its Modifications: A New Era for Biology and Medicine (2024),”. This report describes the need for direct sequencing of all types of RNA to develop a more complete understanding of RNAs role in biology, coined the Rnome. There are more than 170 known molecular modifications to the bases contained in RNA biopolymers. Today RNA is sequenced by the technique of RNAseq, where RNA is converted to cDNA which is then sequenced using next generation sequencing. This sequencing strategy is blind to the molecular modifications of the nucleobases that make up RNA, thus missing important information. A program to develop methods for direct sequencing of RNA is more challenging than genome sequencing due in no small part to the number of modifications that occur compared to DNA and the relative instability of RNA. Like the human genome project, multidisciplinary teams will be required to tackle the Rnome. Teams containing talents in biology, biochemistry, enzymology, mass spectrometry, chemical separations, nanofluidics, computer science, AI, and more will undoubtedly be needed for such a grand challenge project.
Ljiljana Pasa-Tolic
Solving complex problems today increasingly involves a multidisciplinary approach in conjunction with advanced analytical techniques. There are countless examples. All ecosystems, including human ecosystems, are complex networks of organisms interacting with each other and their abiotic surroundings. We tend to simplify, divide and conquer, but in the process, we lose valuable information about how the system works, ability to predict and ultimately control biology. Addressing the complexity challenge requires not only quantum leaps in measurement science (throughput, sensitivity, depth of coverage, real-time and in-situ measurements, etc.), but also effective collaboration across disciplines. And advancing team science requires effective implementation of a recognition system that celebrates individual and team contributions equally!
