Mathias J. Gerl

5.6k total citations · 2 hit papers
44 papers, 3.6k citations indexed

About

Mathias J. Gerl is a scholar working on Molecular Biology, Cell Biology and Surgery. According to data from OpenAlex, Mathias J. Gerl has authored 44 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Molecular Biology, 11 papers in Cell Biology and 8 papers in Surgery. Recurrent topics in Mathias J. Gerl's work include Metabolomics and Mass Spectrometry Studies (15 papers), Lipid Membrane Structure and Behavior (10 papers) and Cellular transport and secretion (8 papers). Mathias J. Gerl is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (15 papers), Lipid Membrane Structure and Behavior (10 papers) and Cellular transport and secretion (8 papers). Mathias J. Gerl collaborates with scholars based in Germany, United States and Finland. Mathias J. Gerl's co-authors include Kai Simons, Christian Klose, Andrej Shevchenko, Júlio L. Sampaio, Michał A. Surma, Christer S. Ejsing, Hartmut Beug, Britta Brügger, Felix Meyenhofer and Charles Ferguson and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Mathias J. Gerl

43 papers receiving 3.6k citations

Hit Papers

Revitalizing membrane rafts: new tools and insights 2010 2026 2015 2020 2010 2023 250 500 750

Peers

Mathias J. Gerl
Federico Torta Singapore
Sarah Cohen United States
Martin Jakab Austria
Mischa Machius United States
Peter Hodder United States
Federico Torta Singapore
Mathias J. Gerl
Citations per year, relative to Mathias J. Gerl Mathias J. Gerl (= 1×) peers Federico Torta

Countries citing papers authored by Mathias J. Gerl

Since Specialization
Citations

This map shows the geographic impact of Mathias J. Gerl'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 Mathias J. Gerl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mathias J. Gerl more than expected).

Fields of papers citing papers by Mathias J. Gerl

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mathias J. Gerl. 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 Mathias J. Gerl. The network helps show where Mathias J. Gerl may publish in the future.

Co-authorship network of co-authors of Mathias J. Gerl

This figure shows the co-authorship network connecting the top 25 collaborators of Mathias J. Gerl. A scholar is included among the top collaborators of Mathias J. Gerl 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 Mathias J. Gerl. Mathias J. Gerl 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.
Tabassum, Rubina, Sanni Ruotsalainen, Mathias J. Gerl, et al.. (2025). Examining the link between 179 lipid species and 7 diseases using genetic predictors. EBioMedicine. 114. 105671–105671.
2.
Li, Jing, Liping Yang, Jun Song, et al.. (2024). Neurotensin accelerates atherosclerosis and increases circulating levels of short-chain and saturated triglycerides. Atherosclerosis. 392. 117479–117479. 1 indexed citations
3.
Mehl, Florence, Mathias J. Gerl, Céline Cruciani‐Guglielmacci, et al.. (2024). A multiorgan map of metabolic, signaling, and inflammatory pathways that coordinately control fasting glycemia in mice. iScience. 27(11). 111134–111134. 1 indexed citations
4.
Tabassum, Rubina, Sanni Ruotsalainen, Mathias J. Gerl, et al.. (2022). Lipidome‐ and Genome‐Wide Study to Understand Sex Differences in Circulatory Lipids. Journal of the American Heart Association. 11(19). e027103–e027103. 38 indexed citations
5.
Lauber, Chris, Mathias J. Gerl, Christian Klose, et al.. (2022). Lipidomic risk scores are independent of polygenic risk scores and can predict incidence of diabetes and cardiovascular disease in a large population cohort. PLoS Biology. 20(3). e3001561–e3001561. 29 indexed citations
6.
Matthiesen, Rune, Chris Lauber, Júlio L. Sampaio, et al.. (2021). Shotgun mass spectrometry-based lipid profiling identifies and distinguishes between chronic inflammatory diseases. EBioMedicine. 70. 103504–103504. 15 indexed citations
7.
Tam, Friederike I., Mathias J. Gerl, Christian Klose, et al.. (2021). Adverse Effects of Refeeding on the Plasma Lipidome in Young Individuals With Anorexia Nervosa?. Journal of the American Academy of Child & Adolescent Psychiatry. 60(12). 1479–1490. 11 indexed citations
8.
Nilsson, Peter M., Olle Melander, Mathias J. Gerl, et al.. (2021). Proteomic and Metabolomic Characterization of Metabolically Healthy Obesity: A Descriptive Study from a Swedish Cohort. Journal of Obesity. 2021. 1–9. 5 indexed citations
9.
Surma, Michał A., Mathias J. Gerl, Ronny Herzog, et al.. (2021). Mouse lipidomics reveals inherent flexibility of a mammalian lipidome. Scientific Reports. 11(1). 19364–19364. 54 indexed citations
10.
Penkert, Horst, Chris Lauber, Mathias J. Gerl, et al.. (2020). Plasma lipidomics of monozygotic twins discordant for multiple sclerosis. Annals of Clinical and Translational Neurology. 7(12). 2461–2466. 15 indexed citations
11.
Walther, Andreas, Carlo Vittorio Cannistraci, Kai Simons, et al.. (2018). Lipidomics in Major Depressive Disorder. Frontiers in Psychiatry. 9. 459–459. 52 indexed citations
12.
Klose, Christian, Mathias J. Gerl, Ronny Herzog, et al.. (2017). Large-scale human skin lipidomics by quantitative, high-throughput shotgun mass spectrometry. Scientific Reports. 7(1). 43761–43761. 62 indexed citations
13.
Körschen, Heinz G., Anke Penno, Andreas Rennhack, et al.. (2017). Identification of a feedback loop involving β-glucosidase 2 and its product sphingosine sheds light on the molecular mechanisms in Gaucher disease. Journal of Biological Chemistry. 292(15). 6177–6189. 18 indexed citations
14.
Ciucci, Sara, Yan Ge, Claudio Durán, et al.. (2017). Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies. Scientific Reports. 7(1). 43946–43946. 38 indexed citations
15.
Gerl, Mathias J., Júlio L. Sampaio, Severino Urban, et al.. (2012). Quantitative analysis of the lipidomes of the influenza virus envelope and MDCK cell apical membrane. The Journal of Cell Biology. 196(2). 213–221. 218 indexed citations
16.
Klose, Christian, Michał A. Surma, Mathias J. Gerl, et al.. (2012). Flexibility of a Eukaryotic Lipidome – Insights from Yeast Lipidomics. PLoS ONE. 7(4). e35063–e35063. 232 indexed citations
17.
Simons, Kai & Mathias J. Gerl. (2010). Revitalizing membrane rafts: new tools and insights. Nature Reviews Molecular Cell Biology. 11(10). 688–699. 994 indexed citations breakdown →
18.
Klemm, Robin W., Christer S. Ejsing, Michał A. Surma, et al.. (2009). Segregation of sphingolipids and sterols during formation of secretory vesicles at the trans-Golgi network. The Journal of Cell Biology. 185(4). 601–612. 316 indexed citations
19.
Lingwood, Daniel, Sebastian Schuck, Charles Ferguson, Mathias J. Gerl, & Kai Simons. (2009). Morphological homeostasis by autophagy. Autophagy. 5(7). 1039–1040. 5 indexed citations
20.
Schuck, Sebastian, Mathias J. Gerl, Agnes Ang, et al.. (2006). Rab10 is Involved in Basolateral Transport in Polarized Madin–Darby Canine Kidney Cells. Traffic. 8(1). 47–60. 103 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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