In Our View: The Grand Challenge

Robert Kennedy: Overall, getting ourselves back to full productivity by overcoming supply chain issues, the pandemic malaise, and lack of interaction with colleagues is a huge challenge for all of us. Another big challenge is creating a more diverse workforce. We’ll do better if we develop all the talent and include all the voices working on problems.

Speaking in terms of more scientific issues, I work in a few different areas. For metabolomics, there are many large challenges. We still can only identify a fraction of the compounds that are detected in a given sample. Tracking “features” is much less interesting than tracking known chemicals, so we really need better ways to identify compounds in complex mixtures. It is difficult because the ultimate validation requires having authentic compounds to test. Another issue is analyzing the large data sets generated. For example, in most fields it is straightforward to do a power analysis to determine the sample size needed – this simple issue has not yet been worked out for metabolomics. 

Graham Cooks: The biggest challenge for any field of research is to have fresh material to explore. We are fortunate in MS to have a wide remit, but this role has a limited lifetime, involving well-defined stages: first it is ignored, then it is embraced enthusiastically, next it is widely used, and finally it becomes routine. We have seen this happen to MS applied to proteins over the past 20 years. The fundamental science of ions – the chemistry and physics of ions – is a more reliable and central concern for MS. This topic includes fabricating new materials and surfaces with ions, synthesizing in a “make-to-measure” fashion small quantities of drug candidates for biological testing and the fundamental spectroscopy of ions.

Jeremy Nicholson: Understanding human disease complexity at the individual and population level and the global dynamics of disease is and will continue to be of paramount importance. Many of the world’s greatest problems are underpinned by analytical sciences – although this is not always obvious – from the Human Genome Project, to understanding global warming, to personalized medicine. 

These problems all require measurement and modeling of complex systems to inform us about the changing nature of the world we live in – and how to plan for the future. Humanity currently faces unprecedented environmental changes, upon which we find superimposed new pandemic threats and the emergence of zoonotic pathogens that challenge our healthcare systems and our economies. These changes are with us for good – or as long as human population growth continues to outstrip our resources. And so there are major challenges for analytical science to come up with new metrics and underpinning mechanisms of change that will hopefully enable the creation of mitigation strategies going forward. 

Ruedi Aebersold: In our field of MS-based proteomics, the progress achieved over the past two decades or so has been absolutely astounding, and it is the result of the advances realized in analytical chemistry and instrumentation. Today, the pace of progress in generating ever increasing quantity and quality of data continues unbroken. I think the biggest challenge we are facing in our field is the extraction of new biological or clinical knowledge from these data. The computational challenges of the future will focus on questions such as: how do specific proteins operate in the context of the whole proteome? How does the proteome as a whole – in terms of abundance and interactions of proteins – respond to genetic or environmental perturbations? Which observed proteomic changes are causal and which are consequential in terms of changes in their functional state, for example. These and related questions are at the core of the emerging field of personalized/precision medicine.

Jenny Brodbelt: Advances in analytical methods have allowed new data to be collected and new results to be generated in record-breaking time. Integrating all this data and new results into useful information is a daunting challenge, and it requires big team efforts, involving scientists from different disciplines. It can be difficult to maximize the value of all of this new data and end up with significant impact.

Alejandro Cifuentes: In 2021 and beyond, we will still be trying to comprehend the huge complexity of the interaction of food ingredients and our body, and although I believe we will come to a better understanding of the microbiome’s role here, we will have to wait a good number of years before we see the expected benefits from this discipline. In terms of technological advances, we will look for better analytical instruments – faster, more sensitive, with higher resolution – and cheaper. As a main challenge, we will have to solve our current limitations in data treatment. How can we overcome bottlenecks and integrate huge amounts of data generated by existing analytical techniques from different levels of expression – genomics, transcriptomics, proteomics, metabolomics? And more importantly, how will we transform this data into useful biological information? 

Erin Baker: I believe the current biggest challenge in analytical chemistry is the rapid evaluation of all the multidimensional or multi-omic data that is collected. Automation and rapid data acquisition rates are allowing analytical platforms to collect thousands of datasets each day. However, assessing these datasets quickly is extremely difficult – my students often work in the lab for a week and evaluate data for months to determine molecular significance, as well as the environmental and biological connections.

Claire Eyers: For so long, the field of proteomics has relied on identification of peptides as a proxy for gene expression, which is fine, but limited. Biology is not that simple, with protein function being critically dependent on dynamic regulatory modifications. Our single biggest challenge therefore is moving away from trying to understand biological systems based purely on protein “identification,” towards understanding the complement of differentially modified protein isoforms, or proteoforms, that truly regulate function.

Paul Haddad: Despite our collective efforts, chromatographic methods (especially liquid chromatography) are limited by the available peak capacity. Current levels of peak capacity in LC systems are insufficient to enable the resolution of complex samples – especially those of biological origin. The future challenge is to achieve substantially increased peak capacity (for example to 1,000,000) and this is likely to be achieved through the use of 3D systems, which pose immense technological challenges.

Martin Gilar: I think it is the challenge of LC education. Universities are educating very few chromatographers. The pharma, biopharma, and chemical industries are struggling to attract LC experts. Overall, the level of analytical expertise is declining at a frightening speed. This trend, combined with the development of new biotherapies, puts stress on the industry. Part of the solution is the development of robust and easy-to-use instruments, as well as software guided approaches to LC method development. Still, somebody will have to pick up the education slack – and this may be an opportunity for the next generation of entrepreneurs.

Gary Hieftje: In my opinion, the biggest challenge facing analytical science, indeed science in general, is the intrusion of politics into science, its conduct, and its findings. Although this problem is not new, it has become more serious and dangerous in the past several years. No doubt, the ongoing COVID-19 pandemic has exacerbated things. Both sides of many political questions claim to have science on their team, when neither has data to support its position.  Preliminary findings are asserted to be absolute truth because “science says so,” and statistics are frequently misunderstood, applied to inadequate data sets, or intentionally distorted. As always, scientists themselves are likely the only ones who can introduce reason into this fray.  They must be truthful, thorough, and objective, and resist espousing popular positions despite the lure of notoriety and research funding.

Candice Ulmer: The single biggest challenge facing the field in 2021 is establishing diverse, equitable, and inclusive work environments that are supportive of all individuals regardless of race, ethnicity, national origin, gender/gender identity/sexual orientation, age, religion, culture, and disabilities, to mention but a few. This effort is going to require effective strategies to recruit/retain personnel, data-driven DEI training tools, and enforced corrective actions for issues encountered.

Nicole Pamme: Keeping our scientific communities together across the globe, whilst also managing scientific exchange in a climate friendly way is the biggest challenge. It’s been hard to discuss and meet in person in recent months. Online conferences can go some way, and they have many advantages, but they cannot quite replace meeting and discussing in person. How do we best balance this?

Roy Goodacre: I think one of the biggest challenges facing analytical sciences in 2021, and indeed beyond, is to make what we do scientifically understandable to the public. Numerical values mean very little to some people, and few people grasp the concepts of scale, especially when one is talking in orders of magnitude. I strongly believe that analytical scientists must report the results in a manner that is comprehensible – giving the true meaning of the findings.  Education is key here.

Ian Wilson: For 2021, I think the biggest challenge is getting the labs working at pace again, rather than just marking time and doing only the MUST DO tasks. We must get our ambition back. COVID-19 knocked us all off course, and we need grit and determination to catch up with all of the things that didn’t get done.