Anneli KruveAssociate Professor

About me

About me

My fields of study are focused on the fundamentals and applications of mass spectrometry. Specifically, on establishing (semi-)quantitative non-targeted analysis and applying ion mobility separation for complex structural characterization.

I graduated in 2011 from the University of Tartu and continued my studies as a post-doc in Techion, Israel. I was a Humboldt fellow at Freie Universität Berlin (2017–2018) and has supervised several doctoral students at the University of Tartu. I joined Stockholm University in 2019 and I am in charge of the mass spectrometry laboratory.

I was selected in 2018 to be among the Top 40 under 40 power list by the Analytical Scientist.

Research

I use modelling and machine learning to understand ionization processes in electrospray (ESI) and developing semi-quantitative non-targeted analysis methodology.

On-going projects

Quantification is crucial in all branches of analytical chemistry: environmental analysis, metabolomics, and monitoring food contaminants. The method of choice often is mass spectrometry, which guarantees low detection limits and high selectivity. However, targeted analysis is often tedious and limited in their coverage as standards are needed to evaluate the vast differences in ionization efficiencies of different compounds in the electrospray ionization source. The non-targeted analysis with mass spectrometry has been developed to allow detection of contaminants, metabolites, etc. without the use of standard compounds. Still, the lack of quantitative information remains one of the major bottlenecks in non-targeted metabolomics. We develop novel strategies for non-targeted quantification using conventional LC-MS and machine learning approaches. We apply these methods to obtain quantitative results in a number of applications from emerging contaminants in water to metabolites in cell cultures.

Recent  publications and a link to Diva for the complete list

1. Kruve, A.; Caprice, K.; Lavendomme, R.; Wollschlager, J.M.; Schoder, S.; Schroder, H.V.; Nitschke, J.R.; Cougnon, F.B.L.; Schalley, C.A. Ion-Mobility Mass Spectrometry for the Rapid Determination of the Topology of Interlocked and Knotted Molecules doi 10.1002/anie.201904541
2. Kruve, A. Semi-Quantitative Non-Target Analysis of Water with LC/HRMS: How Far Are We? Rapid. Commun. Mass Spectrom. Accepted for Publication. Rapid Commun. Mass Spectrom. 2019, 33 (3), 54–63.
3. Liigand, P.; Liigand, J.; Cuyckens, F.; Vreeken, R. J.; Kruve, A. Ionisation Efficiencies Can Be Predicted in Complicated Biological Matrices: A Proof of Concept. Anal. Chim. Acta 2018, 1032, 68–74.
4. Liigand, P.; Kaupmees, K.; Haav, K.; Liigand, J.; Leito, I.; Girod, M.; Antoine, R.; Kruve, A. Think Negative: Finding the Best Electrospray Ionization/MS Mode for Your Analyte. Anal. Chem. 2017, 89 (11), 5665–5668.
5. Kruve, A.; Kaupmees, K. Predicting ESI/MS Signal Change for Anions in Different Solvents. Anal. Chem. 2017, 89 (9), 5079–5086.

Visiting address Arrheniuslaboratorierna A 337, Svante Arrhenius väg, Stockholm

External website: kruvelab.com

ORCID 0000-0001-9725-3351, http://orcid.org/0000-0001-9725-3351

Research projects

Ongoing

Completed

MS2Tox: Deep Learning for Automated Prediction of the Endocrine Disruptive Potency of Chemicals

MS2Tox: Deep Learning for Automated Prediction of the Endocrine Disruptive Potency of Chemicals in Complex Mixtures

Design of chemical reagents for fast and sensitive detection of pesticides in water and food

The focus of this project is on sustainable development of chemical reagents for detection of glyphosate.

Quantification of contaminants with LC/HRMS and graph-based machine learning

Understanding the toxicity of real-world complex mixtures, and identification of the most toxic chemicals is an essential starting point for the intelligent design of water treatment solutions and chemical regulation.

RapMixTox

The Swedish Government recently launched a comprehensive report on the future of chemical risk management accounting for combination effects and assessing chemicals in groups.

Self-learning artificial intelligence for detection of toxic chemicals in recycled materials

Machine learning could contribute enormously is identifying small potentially toxic chemicals from big data acquired from the analysis of recycled materials with LC/ESI/HRMS.

Tandem development of waste textile recycling process and chemical screening for a non-toxic

Tandem development of waste textile recycling process and chemical screening for a non-toxic- Re:Start. While recycling of textiles is identified as an urgent need, only 1% of textile is recycled globally today.

Toxicity guided inverse design of materials for environmental remediation

Environmental remediation of water and soil as well as clean-up of waste streams relies on separation of pollutants followed by their transformation to non-hazardous products.