Sascha Martens

19.9k total citations · 4 hit papers
70 papers, 6.8k citations indexed

About

Sascha Martens is a scholar working on Epidemiology, Cell Biology and Molecular Biology. According to data from OpenAlex, Sascha Martens has authored 70 papers receiving a total of 6.8k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Epidemiology, 39 papers in Cell Biology and 38 papers in Molecular Biology. Recurrent topics in Sascha Martens's work include Autophagy in Disease and Therapy (51 papers), Cellular transport and secretion (24 papers) and Endoplasmic Reticulum Stress and Disease (15 papers). Sascha Martens is often cited by papers focused on Autophagy in Disease and Therapy (51 papers), Cellular transport and secretion (24 papers) and Endoplasmic Reticulum Stress and Disease (15 papers). Sascha Martens collaborates with scholars based in Austria, Germany and United States. Sascha Martens's co-authors include Harvey T. McMahon, Gabriele Zaffagnini, Michael M. Kozlov, Dorotea Fracchiolla, Marta Walczak, Julia Romanov, Jonathan C. Howard, Claudine Kraft, Alberto Danieli and Eleonora Turco and has published in prestigious journals such as Science, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Sascha Martens

69 papers receiving 6.8k citations

Hit Papers

Phosphorylation of OPTN by TBK1 enhances its bin... 2008 2026 2014 2020 2016 2008 2016 2012 100 200 300 400 500

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sascha Martens Austria 37 3.8k 3.7k 2.3k 709 678 70 6.8k
Claudia Puri United Kingdom 37 3.2k 0.8× 3.4k 0.9× 2.7k 1.2× 457 0.6× 385 0.6× 56 7.0k
Michael J. Clague United Kingdom 51 2.6k 0.7× 8.8k 2.4× 3.6k 1.6× 1.1k 1.5× 232 0.3× 131 11.5k
Sylvie Urbé United Kingdom 49 2.4k 0.6× 8.7k 2.3× 4.4k 2.0× 1.1k 1.5× 253 0.4× 92 11.5k
Hemmo Meyer Germany 38 2.3k 0.6× 5.5k 1.5× 4.3k 1.9× 484 0.7× 211 0.3× 91 8.1k
Eric Spooner United States 43 1.6k 0.4× 5.6k 1.5× 1.1k 0.5× 1.6k 2.3× 418 0.6× 58 8.4k
Janice Griffith Netherlands 38 1.3k 0.3× 3.9k 1.1× 2.3k 1.0× 1.4k 2.0× 168 0.2× 46 6.5k
Christian Ungermann Germany 56 2.1k 0.6× 6.6k 1.8× 6.5k 2.9× 392 0.6× 217 0.3× 149 10.0k
Nicholas A. Bright United Kingdom 43 1.5k 0.4× 4.5k 1.2× 4.5k 2.0× 779 1.1× 151 0.2× 73 8.7k
Thomas J. Melia United States 35 1.6k 0.4× 3.6k 1.0× 2.8k 1.3× 219 0.3× 131 0.2× 62 5.2k
Maria Antonietta De Matteis Italy 49 1.5k 0.4× 5.9k 1.6× 4.9k 2.2× 693 1.0× 141 0.2× 129 9.5k

Countries citing papers authored by Sascha Martens

Since Specialization
Citations

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

Fields of papers citing papers by Sascha Martens

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sascha Martens. 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 Sascha Martens. The network helps show where Sascha Martens may publish in the future.

Co-authorship network of co-authors of Sascha Martens

This figure shows the co-authorship network connecting the top 25 collaborators of Sascha Martens. A scholar is included among the top collaborators of Sascha Martens 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 Sascha Martens. Sascha Martens 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.
Adriaenssens, Elias, Justyna Sawa‐Makarska, Thanh Ngoc Nguyen, et al.. (2025). Reconstitution of BNIP3/NIX-mitophagy initiation reveals hierarchical flexibility of the autophagy machinery. Nature Cell Biology. 27(8). 1272–1287. 2 indexed citations
2.
Fracchiolla, Dorotea, et al.. (2025). The rapidly expanding role of LC3-interacting regions in autophagy. The Journal of Cell Biology. 224(8). 1 indexed citations
3.
Baumann, Verena, et al.. (2024). Faa1 membrane binding drives positive feedback in autophagosome biogenesis via fatty acid activation. The Journal of Cell Biology. 223(7). 4 indexed citations
4.
Martens, Sascha, et al.. (2024). The Role of ATG9 Vesicles in Autophagosome Biogenesis. Journal of Molecular Biology. 436(15). 168489–168489. 22 indexed citations
5.
Adriaenssens, Elias, Thanh Ngoc Nguyen, Justyna Sawa‐Makarska, et al.. (2024). Control of mitophagy initiation and progression by the TBK1 adaptors NAP1 and SINTBAD. Nature Structural & Molecular Biology. 31(11). 1717–1731. 17 indexed citations
6.
Ferrari, Luca, Bernd Bauer, Martina Schuschnig, et al.. (2024). Tau fibrils evade autophagy by excessive p62 coating and TAX1BP1 exclusion. Science Advances. 10(24). eadm8449–eadm8449. 16 indexed citations
7.
Danieli, Alberto, et al.. (2023). Sequestration of translation initiation factors in p62 condensates. Cell Reports. 42(12). 113583–113583. 5 indexed citations
8.
Martens, Sascha, et al.. (2023). Damaged mitochondria recruit the effector NEMO to activate NF-κB signaling. Molecular Cell. 83(17). 3188–3204.e7. 50 indexed citations
9.
Picchianti, Lorenzo, Víctor Sánchez de Medina Hernández, Ni Zhan, et al.. (2023). Shuffled ATG8 interacting motifs form an ancestral bridge between UFMylation and autophagy. The EMBO Journal. 42(10). e112053–e112053. 29 indexed citations
10.
Bauer, Bernd, Sascha Martens, & Luca Ferrari. (2023). Aggrephagy at a glance. Journal of Cell Science. 136(10). 25 indexed citations
11.
Adriaenssens, Elias, Luca Ferrari, & Sascha Martens. (2022). Orchestration of selective autophagy by cargo receptors. Current Biology. 32(24). R1357–R1371. 66 indexed citations
12.
Jensen, Liv, Shanlin Rao, Martina Schuschnig, et al.. (2022). Membrane curvature sensing and stabilization by the autophagic LC3 lipidation machinery. Science Advances. 8(50). eadd1436–eadd1436. 30 indexed citations
13.
Kataura, Tetsushi, Elsje G. Otten, Yoana Rabanal‐Ruiz, et al.. (2022). NDP52 acts as a redox sensor in PINK1/Parkin‐mediated mitophagy. The EMBO Journal. 42(5). e111372–e111372. 40 indexed citations
14.
Fracchiolla, Dorotea, Chunmei Chang, James H. Hurley, & Sascha Martens. (2020). A PI3K-WIPI2 positive feedback loop allosterically activates LC3 lipidation in autophagy. The Journal of Cell Biology. 219(7). 72 indexed citations
15.
Sawa‐Makarska, Justyna, Verena Baumann, Nicolas Coudevylle, et al.. (2020). Reconstitution of autophagosome nucleation defines Atg9 vesicles as seeds for membrane formation. Science. 369(6508). 188 indexed citations
16.
Reiter, Wolfgang, Thorsten Brach, Daniel Papinski, et al.. (2014). Hrr25 kinase promotes selective autophagy by phosphorylating the cargo receptor A tg19. EMBO Reports. 15(8). 862–870. 73 indexed citations
17.
Groffen, Alexander J., Sascha Martens, L. Niels Cornelisse, et al.. (2010). Doc2b Is a High-Affinity Ca 2+ Sensor for Spontaneous Neurotransmitter Release. Science. 327(5973). 1614–1618. 253 indexed citations
18.
Lynch, Kara L., et al.. (2008). Synaptotagmin-1 Utilizes Membrane Bending and SNARE Binding to Drive Fusion Pore Expansion. Molecular Biology of the Cell. 19(12). 5093–5103. 99 indexed citations
19.
Martens, Sascha, Michael M. Kozlov, & Harvey T. McMahon. (2007). How Synaptotagmin Promotes Membrane Fusion. Science. 316(5828). 1205–1208. 428 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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