MSI Grand Challenges

MSI Grand Challenges


1. Use of ML for multiomics data integration

This research aims to address one of the key challenges in data analytics – data integration. While many state-of-the-art analytical instruments are deployed for obtaining measurements from a biological or a chemical system, these data are studied in their silos. Currently there are open-source tools and wrappers available to deploy many algorithms that integrate data from multiomic studies, often in biological systems. These often suffer from poor data quality, not by design but by nature of analytical sensitivity. Modern high-resolution methods are prone to introduction of noise due to sensitivity, often have missing chemical entities across different batches and other uncertainties in these measurements. Omics approachesthat measure proteins, lipids and metabolites in a biofluid or a microbial sample or cellular systems, are routinely used to discover phenotypic markers of perturbation or processes of interest. The most common analytical method used for multiomics is mass spectrometry (MS). These studies generate big data that are multidimensional
and multimodal.
This project will develop machine learning solutions to tackle raw and processed MS data despite its sparsity, heterogeneity and multicollinearity in measurements to uncover patterns within these data. To understand relationship between different omes it is common practice to combine these data to build predictive models. The resulting datasets are valuable resources for health research, process monitoring or therapeutics, yet their full utilisation tends to pose major challenges, because of data uncertainty, heterogeneity and incompleteness. Using methods such as neural networks, tree methods and probability based algorithms the project will develop tailored algorithms and workflows for robust feature selection and biomarker identification indicative of changes at molecular levels, from noisy, sparse MS data and other systemic metadata. Application of developed solution will be tested on data already available from on public repositories, UoM owned COVID-MS clinical data and other partner study data that are made available.

2. Use of ML for high fidelity data 'stitching' :

Time domain and frequency domain data are two of the main types of analytical measurements recorded. Fourier transform is one of the most common mathematical approaches used for interchanging data domains. A challenge lies in meaningful integration of chemical information about the same system collected on different platforms, even within the same laboratory. For example, analyses of unknown wastewater sample using gas chromatography-mass spectrometry and ion- mobility mass spectrometry will generate complementary information about the same sample. Routinely, these datasets are treated as two separate entities, leading to disjointed data reporting. Data stitching approach is often used in other disciplines to acquire and aggregate data from different sources. The project will explore already established methods in other disciplines e.g. multiarray integration or batch correction methods and build probability-based classifiers to explore high fidelity data stitching of analytical data. The approach will be focused on robust feature selection, feature matching and aggregation of high-fidelity data. The application of these approaches can be in the areas of multi-lab, multi-site studies including different analytical platforms, reducing costs of running expensive standards



Enhancing separation science to address Complex Systems, Multicomponent Analysis, and Dynamic Range Adaptability.

This research aims to address the forefront of sustainable separation science by tackling three pivotal challenges in separation science faced in industry when investigating complex mixtures. First, to delve into the intricacies of complex systems to enhance the refinement of separation technologies, ensuring they are both efficient and transferable to an industry and academic environment. Secondly, to develop cutting-edge measurement and simulation methodologies tailored for multicomponent mixtures, enabling precise control and optimization of separation processes. Lastly, to focus on the innovation of adaptive separation systems that are versatile enough to accommodate a diverse array of molecular structures, thereby increasing the resilience and adaptability for future demands.


  1. To gain an in-depth understanding of complex systems to enhance the efficiency and effectiveness of existing and emerging separation techniques.
  2. To develop innovative measurement and simulation methods tailored for the accurate analysis of multicomponent mixtures within intricate matrices.
  3. To design versatile and robust separation systems capable of accommodating the diverse dynamic range of structures present in complex systems.

Research Plan:

 Study of Complex Systems:

  • Conduct comprehensive literature reviews and engage in cross-disciplinary collaborations to map the current state of knowledge.
  • Perform experimental studies to elucidate the interactions and behaviours of compounds within complex matrices.
  • Integrate findings to refine and optimize separation processes, contributing to the accelerated development of compounds.

Advancement of Measurement and Simulation Techniques:

  1. Develop and validate advanced analytical methods for the precise characterization of multicomponent mixtures.
  2. Create and fine-tune computational models and simulations to predict separation outcomes, enhancing the predictability and scalability of separation methods.

Design of Dynamic Range-Adaptive Separation Systems:

  • Innovate and test new materials and technologies for chromatography columns that can adapt to a wide range of molecular sizes and affinities.
  • Explore the integration of multi-dimensional separations and hyphenated techniques to extend the dynamic range and resolving power of separation systems.

Expected Outcome

This research will deliver a suite of refined separation methodologies, advanced analytical tools, and adaptable technologies that will collectively fortify the capabilities for addressing multicomponent analysis in complex mixtures.



  1. Photonics based solutions for in-line/in-vivo monitoring of processes:

This challenge focusses on developing photonics-based solutions for in-line and in-vivo monitoring of processes across various fields including manufacturing and healthcare. In manufacturing, these solutions can be aimed at enabling non-invasive and real-time monitoring, facilitating precise control and optimization of processes by continuous monitoring of process parameters including chemical composition, temperature, and pressure within production lines with high sensitivity to ensure product quality and safety while minimizing waste and production downtime.

 In healthcare, this challenge can be aimed at developing tools for in-vivo monitoring of biological processes within tissues and organs in real-time, allowing clinicians to monitor physiological changes without invasive procedures to aid diagnosis, treatment monitoring, and personalized medicine, ultimately enhancing patient care and outcomes.


  1. Point of Use Sensors for quantitative biological measurements

 This challenge aims to develop Point-of-use sensors for quantitative biological measurements to enable rapid and accurate detection of biomolecules such as proteins, nucleic acids, and metabolites at the point of use. These sensors could facilitate timely diagnosis, treatment optimization, and disease monitoring to achieve faster decision-making and improved patient outcomes. These sensors can also be employed for environmental monitoring, food safety assessment, and infectious disease surveillance, enabling rapid response to emerging threats and facilitating proactive measures for public health protection.

The goal of the challenge is to advance sensor design, multiplexing capabilities, detection methods and their integration to address complex biological challenges to improve quality of life.




1 . Development, Evaluation and Application of Next Generation, Replacement or Novel Instrumentation for Sustainable (Green) Measurements.

This challenge is aimed at developing the next generation of analytical measurement technologies for laboratory, field or production environments which minimise environmental impact (including consumables and waste). Proposals could include (but are not limited to)

Next generation: e.g. miniaturisation of existing technologies.

Replacement  (i.e. existing technologies) that are ‘greener’ than typical / industry/regulatory accepted approaches but which need full evaluation and demonstration of analytical ‘equivalence’.

Novel technologies which have excellent ‘green’ credentials – offering significant advantages in minimising environmental impact as well as in measurement capability.


2 . Introduction of novel Measurement Technologies

This challenge is aimed at novel measurement technology which offers distinct advantages over existing techniques or which allows measurement of components previously challenging or impossible to measure by existing techniques. The technology may have already been developed but needs further evaluation for (new) industry applications or the technology itself may need further development and refinement. It may be that the instrumentation has yet to be developed but the proof of concept and or theory strongly supports the potential success. Lab and miniaturisation scales are in scope for this challenge.