Maximilian T. Strauss

7.1k total citations · 5 hit papers
49 papers, 4.0k citations indexed

About

Maximilian T. Strauss is a scholar working on Molecular Biology, Biophysics and Spectroscopy. According to data from OpenAlex, Maximilian T. Strauss has authored 49 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Molecular Biology, 19 papers in Biophysics and 14 papers in Spectroscopy. Recurrent topics in Maximilian T. Strauss's work include Advanced Fluorescence Microscopy Techniques (18 papers), Advanced biosensing and bioanalysis techniques (17 papers) and Advanced Proteomics Techniques and Applications (14 papers). Maximilian T. Strauss is often cited by papers focused on Advanced Fluorescence Microscopy Techniques (18 papers), Advanced biosensing and bioanalysis techniques (17 papers) and Advanced Proteomics Techniques and Applications (14 papers). Maximilian T. Strauss collaborates with scholars based in Germany, Denmark and United States. Maximilian T. Strauss's co-authors include Ralf Jungmann, Florian Schueder, Thomas Schlichthaerle, Matthias Mann, Joerg Schnitzbauer, Alexander Auer, Wenfeng Zeng, Peng Yin, Chanchal Kumar and Philipp E. Geyer and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Angewandte Chemie International Edition.

In The Last Decade

Maximilian T. Strauss

49 papers receiving 4.0k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Super-resolution microscopy with DNA-PAINT

2017 642 citations Maximilian T. Strauss, Thomas Schlichthaerle et al. profile →
Programmable self-assembly of three-dimensional nanostructures from 10,000 unique components

2017 329 citations Maximilian T. Strauss, Florian Schueder et al. Nature profile →
Artificial intelligence for proteomics and biomarker discovery

2021 216 citations Matthias Mann, Chanchal Kumar et al. Cell Systems profile →
Ångström-resolution fluorescence microscopy

2023 156 citations S. Reinhardt, Luciano A. Masullo et al. Nature profile →
Noninvasive proteomic biomarkers for alcohol-related liver disease

2022 154 citations Lili Niu, Maja Thiele et al. Nature Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Maximilian T. Strauss Germany	33	2.6k	1.2k	840	594	492	49	4.0k
Brian P. English United States	21	3.1k 1.2×	1.4k 1.2×	532 0.6×	265 0.4×	351 0.7×	29	4.7k
Jiji Chen United States	32	2.3k 0.9×	1.1k 1.0×	937 1.1×	204 0.3×	200 0.4×	52	4.3k
Luc Reymond Switzerland	28	3.6k 1.4×	1.1k 1.0×	553 0.7×	400 0.7×	250 0.5×	45	5.4k
Ryota Iino Japan	40	4.2k 1.6×	843 0.7×	1.2k 1.4×	276 0.5×	572 1.2×	108	6.0k
Chirlmin Joo Netherlands	36	5.3k 2.0×	988 0.9×	1.1k 1.3×	152 0.3×	242 0.5×	89	6.6k
Florian Schueder Germany	25	2.1k 0.8×	1.5k 1.3×	922 1.1×	80 0.1×	688 1.4×	41	3.6k
Steven F. Lee United Kingdom	32	1.4k 0.5×	876 0.8×	561 0.7×	89 0.1×	356 0.7×	88	3.4k
Ivan R. Corrêa United States	34	4.0k 1.5×	949 0.8×	395 0.5×	207 0.3×	180 0.4×	98	5.5k
Thomas M. Jovin Germany	32	2.4k 0.9×	2.4k 2.1×	1.3k 1.5×	212 0.4×	422 0.9×	55	5.0k
Prabuddha Sengupta United States	32	2.6k 1.0×	1.1k 0.9×	779 0.9×	103 0.2×	370 0.8×	46	4.2k

Countries citing papers authored by Maximilian T. Strauss

Since Specialization

Citations

This map shows the geographic impact of Maximilian T. Strauss'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 Maximilian T. Strauss with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maximilian T. Strauss more than expected).

Fields of papers citing papers by Maximilian T. Strauss

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maximilian T. Strauss. 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 Maximilian T. Strauss. The network helps show where Maximilian T. Strauss may publish in the future.

Co-authorship network of co-authors of Maximilian T. Strauss

This figure shows the co-authorship network connecting the top 25 collaborators of Maximilian T. Strauss. A scholar is included among the top collaborators of Maximilian T. Strauss 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 Maximilian T. Strauss. Maximilian T. Strauss is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Wallmann, Georg, Patricia Skowronek, Marvin Thielert, et al.. (2025). AlphaDIA enables DIA transfer learning for feature-free proteomics. Nature Biotechnology. 1 indexed citations

2.

Strauss, Maximilian T., Isabell Bludau, Wen‐Feng Zeng, et al.. (2024). AlphaPept: a modern and open framework for MS-based proteomics. Nature Communications. 15(1). 2168–2168. 21 indexed citations

3.

Strauss, Maximilian T., Xiang Zheng, Andreas Mund, et al.. (2024). Spatial characterization and stratification of colorectal adenomas by deep visual proteomics. iScience. 27(9). 110620–110620. 4 indexed citations

4.

Reinhardt, S., Luciano A. Masullo, Rafal Kowalewski, et al.. (2023). Ångström-resolution fluorescence microscopy. Nature. 617(7962). 711–716. 156 indexed citations breakdown →

5.

Bludau, Isabell, et al.. (2023). AlphaPeptStats: an open-source Python package for automated and scalable statistical analysis of mass spectrometry-based proteomics. Bioinformatics. 39(8). 10 indexed citations

6.

Santos, Alberto, Ana R. Colaço, Annelaura Bach Nielsen, et al.. (2022). A knowledge graph to interpret clinical proteomics data. Nature Biotechnology. 40(5). 692–702. 138 indexed citations

7.

Zeng, Wen‐Feng, Xie‐Xuan Zhou, Sander Willems, et al.. (2022). AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics. Nature Communications. 13(1). 7238–7238. 101 indexed citations

8.

Niu, Lili, Maja Thiele, Philipp E. Geyer, et al.. (2022). Noninvasive proteomic biomarkers for alcohol-related liver disease. Nature Medicine. 28(6). 1277–1287. 154 indexed citations breakdown →

9.

Mann, Matthias, Chanchal Kumar, Wenfeng Zeng, & Maximilian T. Strauss. (2021). Artificial intelligence for proteomics and biomarker discovery. Cell Systems. 12(8). 759–770. 216 indexed citations breakdown →

10.

Voytik, Eugenia, Isabell Bludau, Sander Willems, et al.. (2021). AlphaMap: an open-source Python package for the visual annotation of proteomics data with sequence-specific knowledge. Bioinformatics. 38(3). 849–852. 12 indexed citations

11.

Winter, Sebastian Virreira, Özge Karayel, Maximilian T. Strauss, et al.. (2021). Urinary proteome profiling for stratifying patients with familial Parkinson’s disease. EMBO Molecular Medicine. 13(3). e13257–e13257. 84 indexed citations

12.

Meier, Florian, Niklas Köhler, Andreas‐David Brunner, et al.. (2021). Deep learning the collisional cross sections of the peptide universe from a million experimental values. Nature Communications. 12(1). 1185–1185. 95 indexed citations

13.

Willems, Sander, Eugenia Voytik, Patricia Skowronek, Maximilian T. Strauss, & Matthias Mann. (2021). AlphaTims: Indexing Trapped Ion Mobility Spectrometry–TOF Data for Fast and Easy Accession and Visualization. Molecular & Cellular Proteomics. 20. 100149–100149. 23 indexed citations

14.

Geyer, Philipp E., Sophia Doll, Sebastian Virreira Winter, et al.. (2021). High‐resolution serum proteome trajectories in COVID‐19 reveal patient‐specific seroconversion. EMBO Molecular Medicine. 13(8). e14167–e14167. 88 indexed citations

15.

Klingner, Christoph, Thomas Schlichthaerle, Maximilian T. Strauss, et al.. (2021). Quantitative single-protein imaging reveals molecular complex formation of integrin, talin, and kindlin during cell adhesion. Nature Communications. 12(1). 919–919. 39 indexed citations

16.

Schlichthaerle, Thomas, Maximilian T. Strauss, Florian Schueder, et al.. (2019). Direct Visualization of Single Nuclear Pore Complex Proteins Using Genetically‐Encoded Probes for DNA‐PAINT. Angewandte Chemie. 131(37). 13138–13142. 14 indexed citations

17.

Schlichthaerle, Thomas, Maximilian T. Strauss, Florian Schueder, et al.. (2019). Direct Visualization of Single Nuclear Pore Complex Proteins Using Genetically‐Encoded Probes for DNA‐PAINT. Angewandte Chemie International Edition. 58(37). 13004–13008. 75 indexed citations

18.

Strauss, Sebastian, Philipp C. Nickels, Maximilian T. Strauss, et al.. (2018). Modified aptamers enable quantitative sub-10-nm cellular DNA-PAINT imaging. Nature Methods. 15(9). 685–688. 129 indexed citations

19.

Auer, Alexander, Maximilian T. Strauss, Sebastian Malkusch, et al.. (2018). Correlative Single-Molecule FRET and DNA-PAINT Imaging. Nano Letters. 18(7). 4626–4630. 48 indexed citations

20.

Schlichthaerle, Thomas, Alexandra S. Eklund, Florian Schueder, et al.. (2018). Ortsspezifische Funktionalisierung von Affimeren für die DNA‐PAINT‐Mikroskopie. Angewandte Chemie. 130(34). 11226–11230. 11 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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