Marvin Thielert

1.7k total citations · 1 hit paper
15 papers, 664 citations indexed

About

Marvin Thielert is a scholar working on Molecular Biology, Spectroscopy and Oncology. According to data from OpenAlex, Marvin Thielert has authored 15 papers receiving a total of 664 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 10 papers in Spectroscopy and 2 papers in Oncology. Recurrent topics in Marvin Thielert's work include Advanced Proteomics Techniques and Applications (9 papers), Single-cell and spatial transcriptomics (5 papers) and Mass Spectrometry Techniques and Applications (5 papers). Marvin Thielert is often cited by papers focused on Advanced Proteomics Techniques and Applications (9 papers), Single-cell and spatial transcriptomics (5 papers) and Mass Spectrometry Techniques and Applications (5 papers). Marvin Thielert collaborates with scholars based in Germany, Denmark and United States. Marvin Thielert's co-authors include Matthias Mann, Andreas‐David Brunner, Florian Meier, Constantin Ammar, Patricia Skowronek, Andreas Mund, Sabrina Richter, Florian A. Rosenberger, Oliver Raether and Fabian J. Theis and has published in prestigious journals such as Nature Biotechnology, Biochemistry and Nature Methods.

In The Last Decade

Marvin Thielert

12 papers receiving 655 citations

Hit Papers

Ultra‐high sensitivity mass spectrometry quantifies singl... 2022 2026 2023 2024 2022 50 100 150 200 250
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.

  1. Ultra‐high sensitivity mass spectrometry quantifies single‐cell proteome changes upon perturbation
    2022 294 citations Andreas‐David Brunner, Marvin Thielert et al. Molecular Systems Biology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marvin Thielert Germany 9 523 363 73 65 45 15 664
Jessica Lukowski United States 11 331 0.6× 276 0.8× 83 1.1× 31 0.5× 48 1.1× 22 548
René J. M. van Zeijl Netherlands 15 641 1.2× 671 1.8× 30 0.4× 48 0.7× 17 0.4× 16 887
Erik L. de Graaf Netherlands 15 573 1.1× 390 1.1× 43 0.6× 21 0.3× 43 1.0× 24 785
R. Gray Huffman United States 6 649 1.2× 514 1.4× 108 1.5× 90 1.4× 34 0.8× 7 794
Max Frank Switzerland 9 588 1.1× 410 1.1× 32 0.4× 19 0.3× 46 1.0× 13 833
Pey Yee Lee Malaysia 11 360 0.7× 193 0.5× 33 0.5× 13 0.2× 65 1.4× 18 542
Matthew The Germany 14 522 1.0× 382 1.1× 33 0.5× 14 0.2× 37 0.8× 27 652
Naga Rama Kothapalli United States 10 327 0.6× 202 0.6× 74 1.0× 23 0.4× 19 0.4× 13 564
Maria Reinecke Germany 6 318 0.6× 188 0.5× 55 0.8× 10 0.2× 43 1.0× 11 455
Alexander Hogrebe United States 6 584 1.1× 455 1.3× 24 0.3× 13 0.2× 60 1.3× 10 747

Countries citing papers authored by Marvin Thielert

Since Specialization
Citations

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

Fields of papers citing papers by Marvin Thielert

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marvin Thielert

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

All Works

15 of 15 papers shown
1.
Wallmann, Georg, et al.. (2026). Deep Learning-Driven Fragment Ion Selection for Improved Quantification in MS based Proteomics. bioRxiv (Cold Spring Harbor Laboratory).
2.
Wallmann, Georg, Patricia Skowronek, Marvin Thielert, et al.. (2025). AlphaDIA enables DIA transfer learning for feature-free proteomics. Nature Biotechnology. 1 indexed citations
3.
Thielert, Marvin, Maria Wahle, Constantin Ammar, et al.. (2025). Decoding adult murine pancreatic islet cell diversity through cell type-resolved proteomics and phosphoproteomics. Communications Biology. 8(1). 1483–1483.
4.
Thielert, Marvin, Anna-Lisa Lanz, Katharina Danhauser, et al.. (2025). Ontology-guided clustering enables proteomic analysis of rare pediatric disorders. EMBO Molecular Medicine. 17(7). 1842–1867.
5.
Thielert, Marvin, et al.. (2024). Spatial Proteomics of Single Hepatocytes with Multiplexed Data-Independent Acquisition (mDIA). Methods in molecular biology. 2817. 97–113. 3 indexed citations
6.
Nordmann, Thierry M., Lisa Schweizer, Andreas Metousis, et al.. (2023). A Standardized and Reproducible Workflow for Membrane Glass Slides in Routine Histology and Spatial Proteomics. Molecular & Cellular Proteomics. 22(10). 100643–100643. 4 indexed citations
7.
Thielert, Marvin, Constantin Ammar, Florian A. Rosenberger, et al.. (2023). Robust dimethyl‐based multiplex‐DIA doubles single‐cell proteome depth via a reference channel. Molecular Systems Biology. 19(9). e11503–e11503. 47 indexed citations
8.
Rosenberger, Florian A., Marvin Thielert, Maximilian T. Strauss, et al.. (2023). Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome. Nature Methods. 20(10). 1530–1536. 88 indexed citations
9.
Rosenberger, Florian A., Marvin Thielert, & Matthias Mann. (2023). Making single-cell proteomics biologically relevant. Nature Methods. 20(3). 320–323. 32 indexed citations
10.
Wahle, Maria, Marvin Thielert, Maximilian Zwiebel, et al.. (2023). IMBAS-MS Discovers Organ-Specific HLA Peptide Patterns in Plasma. Molecular & Cellular Proteomics. 23(1). 100689–100689. 10 indexed citations
11.
Skowronek, Patricia, Marvin Thielert, Eugenia Voytik, et al.. (2022). Rapid and In-Depth Coverage of the (Phospho-)Proteome With Deep Libraries and Optimal Window Design for dia-PASEF. Molecular & Cellular Proteomics. 21(9). 100279–100279. 105 indexed citations
12.
Brunner, Andreas‐David, Marvin Thielert, Catherine G. Vasilopoulou, et al.. (2022). Ultra‐high sensitivity mass spectrometry quantifies single‐cell proteome changes upon perturbation. Molecular Systems Biology. 18(3). e10798–e10798. 294 indexed citations breakdown →
13.
Skowronek, Patricia, Markus Lubeck, Georg Wallmann, et al.. (2022). Synchro-PASEF Allows Precursor-Specific Fragment Ion Extraction and Interference Removal in Data-Independent Acquisition. Molecular & Cellular Proteomics. 22(2). 100489–100489. 35 indexed citations
14.
Zhang, Xiaoyu, Marvin Thielert, Haoxin Li, & Benjamin F. Cravatt. (2021). SPIN4 Is a Principal Endogenous Substrate of the E3 Ubiquitin Ligase DCAF16. Biochemistry. 60(9). 637–642. 10 indexed citations
15.
Crowley, Vincent M., Marvin Thielert, & Benjamin F. Cravatt. (2021). Functionalized Scout Fragments for Site-Specific Covalent Ligand Discovery and Optimization. ACS Central Science. 7(4). 613–623. 35 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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