Kim Ekroos

11.1k total citations · 3 hit papers
91 papers, 7.6k citations indexed

About

Kim Ekroos is a scholar working on Molecular Biology, Spectroscopy and Biochemistry. According to data from OpenAlex, Kim Ekroos has authored 91 papers receiving a total of 7.6k indexed citations (citations by other indexed papers that have themselves been cited), including 75 papers in Molecular Biology, 34 papers in Spectroscopy and 13 papers in Biochemistry. Recurrent topics in Kim Ekroos's work include Metabolomics and Mass Spectrometry Studies (46 papers), Mass Spectrometry Techniques and Applications (24 papers) and Advanced Proteomics Techniques and Applications (23 papers). Kim Ekroos is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (46 papers), Mass Spectrometry Techniques and Applications (24 papers) and Advanced Proteomics Techniques and Applications (23 papers). Kim Ekroos collaborates with scholars based in Germany, Finland and Norway. Kim Ekroos's co-authors include Andrej Shevchenko, Kai Simons, Christer S. Ejsing, Dimple Kauhanen, Eva Duchoslav, Gerhard Liebisch, Tuulia Sylvänne, Tore Skotland, Kirsten Sandvig and Júlio L. Sampaio and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Angewandte Chemie International Edition.

In The Last Decade

Kim Ekroos

87 papers receiving 7.5k citations

Hit Papers

Global analysis of the yeast lipidome by quantitative sho... 2003 2026 2010 2018 2009 2013 2003 250 500 750

Peers

Kim Ekroos
H. Alex Brown United States
Robert N. Cole United States
Robert H. Weiss United States
Michael Kinter United States
Birgit Schilling United States
Michael V. Milburn United States
Valerie B. O’Donnell United Kingdom
Wilhelm Haas United States
Oswald Quehenberger United States
Kim Ekroos
Citations per year, relative to Kim Ekroos Kim Ekroos (= 1×) peers Harald Köfeler

Countries citing papers authored by Kim Ekroos

Since Specialization
Citations

This map shows the geographic impact of Kim Ekroos's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kim Ekroos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kim Ekroos more than expected).

Fields of papers citing papers by Kim Ekroos

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kim Ekroos. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kim Ekroos. The network helps show where Kim Ekroos may publish in the future.

Co-authorship network of co-authors of Kim Ekroos

This figure shows the co-authorship network connecting the top 25 collaborators of Kim Ekroos. A scholar is included among the top collaborators of Kim Ekroos based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kim Ekroos. Kim Ekroos is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Skotland, Tore, Kim Ekroos, Alicia Llorente, & Kirsten Sandvig. (2025). Quantitative Lipid Analysis of Extracellular Vesicle Preparations: A Perspective. Journal of Extracellular Vesicles. 14(3). e70049–e70049. 3 indexed citations
2.
Hoffmann, Nils, Robert Ahrends, Erin Baker, et al.. (2025). Introduction of a lipidomics scoring system for data quality assessment. Journal of Lipid Research. 66(8). 100817–100817.
3.
Skotland, Tore, Kim Ekroos, Jeffrey G. McDonald, et al.. (2024). Pitfalls in lipid mass spectrometry of mammalian samples — a brief guide for biologists. Nature Reviews Molecular Cell Biology. 25(10). 759–760. 10 indexed citations
4.
Vandenbosch, Michiel, Nathan Heath Patterson, Marc Claesen, et al.. (2023). Toward Omics-Scale Quantitative Mass Spectrometry Imaging of Lipids in Brain Tissue Using a Multiclass Internal Standard Mixture. Analytical Chemistry. 95(51). 18719–18730. 20 indexed citations
5.
Höring, Marcus, et al.. (2022). Benchmarking One-Phase Lipid Extractions for Plasma Lipidomics. Analytical Chemistry. 94(36). 12292–12296. 39 indexed citations
6.
Baloni, Priyanka, Kwangsik Nho, Matthias Arnold, et al.. (2021). Investigating the importance of acylcarnitines in Alzheimer's disease.. PuSH - Publication Server of Helmholtz Zentrum München. 1 indexed citations
7.
Höring, Marcus, Kim Ekroos, Paul R.S. Baker, et al.. (2020). Correction of Isobaric Overlap Resulting from Sodiated Ions in Lipidomics. Analytical Chemistry. 92(16). 10966–10970. 24 indexed citations
8.
Ekroos, Kim, et al.. (2020). Lipid-based biomarkers for CVD, COPD, and aging – A translational perspective. Progress in Lipid Research. 78. 101030–101030. 33 indexed citations
9.
Liebisch, Gerhard, Robert Ahrends, Makoto Arita, et al.. (2019). Lipidomics needs more standardization. Nature Metabolism. 1(8). 745–747. 144 indexed citations
10.
Titz, Bjoern, Raffaella Maria Gadaleta, Giuseppe Lo Sasso, et al.. (2018). Proteomics and Lipidomics in Inflammatory Bowel Disease Research: From Mechanistic Insights to Biomarker Identification. International Journal of Molecular Sciences. 19(9). 2775–2775. 32 indexed citations
11.
Busnelli, Marco, Stefano Manzini, Mika Hilvo, et al.. (2017). Liver-specific deletion of the Plpp3 gene alters plasma lipid composition and worsens atherosclerosis in apoE−/− mice. Scientific Reports. 7(1). 44503–44503. 41 indexed citations
12.
Róg, Tomasz, Adam Orłowski, Moutusi Manna, et al.. (2016). Cholesterol Modulated Interdigitation of Long-Chain Sphingomyelin and Glycolipids. Biophysical Journal. 110(3). 578a–579a. 1 indexed citations
14.
Håversen, Liliana, Reza Mobini, Linda Andersson, et al.. (2016). ARAP2 promotes GLUT1-mediated basal glucose uptake through regulation of sphingolipid metabolism. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids. 1861(11). 1643–1651. 13 indexed citations
15.
Cheng, Jin M., Matti Suoniemi, Isabella Kardys, et al.. (2015). Plasma concentrations of molecular lipid species in relation to coronary plaque characteristics and cardiovascular outcome: Results of the ATHEROREMO-IVUS study. Atherosclerosis. 243(2). 560–566. 135 indexed citations
16.
Tarasov, Kirill V., et al.. (2014). High-content screening of yeast mutant libraries by shotgun lipidomics. Molecular BioSystems. 10(6). 1364–1376. 25 indexed citations
17.
Phuyal, Santosh, Tore Skotland, Nina P. Hessvik, et al.. (2014). The Ether Lipid Precursor Hexadecylglycerol Stimulates the Release and Changes the Composition of Exosomes Derived from PC-3 Cells. Journal of Biological Chemistry. 290(7). 4225–4237. 100 indexed citations
18.
Ejsing, Christer S., Júlio L. Sampaio, Vineeth Surendranath, et al.. (2009). Global analysis of the yeast lipidome by quantitative shotgun mass spectrometry. Proceedings of the National Academy of Sciences. 106(7). 2136–2141. 790 indexed citations breakdown →
19.
Kotronen, Anna, Vidya Velagapudi, Laxman Yetukuri, et al.. (2009). Serum saturated fatty acids containing triacylglycerols are better markers of insulin resistance than total serum triacylglycerol concentrations. Diabetologia. 52(4). 684–690. 157 indexed citations
20.
Yetukuri, Laxman, Kim Ekroos, Antonio Vidal‐Puig, & Matej Orešič. (2007). Informatics and computational strategies for the study of lipids. Molecular BioSystems. 4(2). 121–127. 168 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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