Meghan Driscoll

2.4k total citations
26 papers, 1.0k citations indexed

About

Meghan Driscoll is a scholar working on Cell Biology, Biomedical Engineering and Biophysics. According to data from OpenAlex, Meghan Driscoll has authored 26 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Cell Biology, 11 papers in Biomedical Engineering and 10 papers in Biophysics. Recurrent topics in Meghan Driscoll's work include Cellular Mechanics and Interactions (21 papers), 3D Printing in Biomedical Research (8 papers) and Cell Image Analysis Techniques (7 papers). Meghan Driscoll is often cited by papers focused on Cellular Mechanics and Interactions (21 papers), 3D Printing in Biomedical Research (8 papers) and Cell Image Analysis Techniques (7 papers). Meghan Driscoll collaborates with scholars based in United States, Germany and France. Meghan Driscoll's co-authors include Wolfgang Losert, Gaudenz Danuser, John T. Fourkas, Erik S. Welf, Reto Fiolka, Xiaoyu Sun, Carole A. Parent, Kevin M. Dean, Julian Stopp and Jörg Renkawitz and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Meghan Driscoll

25 papers receiving 999 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Meghan Driscoll United States 16 527 344 315 217 96 26 1.0k
Nicolas Carpi France 11 946 1.8× 427 1.2× 497 1.6× 82 0.4× 85 0.9× 14 1.3k
Erik S. Welf United States 21 575 1.1× 389 1.1× 468 1.5× 470 2.2× 113 1.2× 34 1.4k
Yanlan Mao United Kingdom 19 1.1k 2.1× 400 1.2× 574 1.8× 113 0.5× 80 0.8× 37 1.6k
Maria Némethová Austria 13 734 1.4× 166 0.5× 437 1.4× 184 0.8× 56 0.6× 21 1.2k
Priyamvada Chugh United Kingdom 5 630 1.2× 165 0.5× 274 0.9× 113 0.5× 42 0.4× 6 818
Verena Ruprecht Austria 17 912 1.7× 504 1.5× 731 2.3× 228 1.1× 99 1.0× 30 1.7k
Claudio Collinet France 11 919 1.7× 243 0.7× 852 2.7× 142 0.7× 72 0.8× 14 1.5k
Martin Bergert Germany 11 933 1.8× 426 1.2× 485 1.5× 78 0.4× 60 0.6× 19 1.4k
Emmanuel Terriac France 19 503 1.0× 315 0.9× 458 1.5× 72 0.3× 275 2.9× 34 1.4k
Franziska Lautenschläger Germany 20 852 1.6× 596 1.7× 440 1.4× 102 0.5× 125 1.3× 43 1.6k

Countries citing papers authored by Meghan Driscoll

Since Specialization
Citations

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

Fields of papers citing papers by Meghan Driscoll

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Meghan Driscoll

This figure shows the co-authorship network connecting the top 25 collaborators of Meghan Driscoll. A scholar is included among the top collaborators of Meghan Driscoll 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 Meghan Driscoll. Meghan Driscoll 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.
Isogai, Tadamoto, Kevin M. Dean, Philippe Roudot, et al.. (2025). Direct Arp2/3-vinculin binding is required for pseudopod extension, but only on compliant substrates and in 3D. iScience. 28(6). 112623–112623.
2.
Driscoll, Meghan, Erik S. Welf, Andrew Weems, et al.. (2024). Proteolysis-free amoeboid migration of melanoma cells through crowded environments via bleb-driven worrying. Developmental Cell. 59(18). 2414–2428.e8. 12 indexed citations
3.
Weems, Andrew, Erik S. Welf, Meghan Driscoll, et al.. (2023). Blebs promote cell survival by assembling oncogenic signalling hubs. Nature. 615(7952). 517–525. 71 indexed citations
4.
Danuser, Gaudenz, et al.. (2023). Cellular harmonics for the morphology-invariant analysis of molecular organization at the cell surface. Nature Computational Science. 3(9). 777–788. 3 indexed citations
5.
Welf, Erik S., Christopher E. Miles, Etai Sapoznik, et al.. (2020). Actin-Membrane Release Initiates Cell Protrusions. Developmental Cell. 55(6). 723–736.e8. 57 indexed citations
6.
Renkawitz, Jörg, Aglaja Kopf, Julian Stopp, et al.. (2019). Nuclear positioning facilitates amoeboid migration along the path of least resistance. Nature. 568(7753). 546–550. 198 indexed citations
7.
Driscoll, Meghan, Erik S. Welf, Andrew R. Jamieson, et al.. (2019). Robust and automated detection of subcellular morphological motifs in 3D microscopy images. Nature Methods. 16(10). 1037–1044. 60 indexed citations
8.
Welf, Erik S., Meghan Driscoll, Kevin M. Dean, et al.. (2016). Quantitative Multiscale Cell Imaging in Controlled 3D Microenvironments. Developmental Cell. 36(4). 462–475. 55 indexed citations
9.
Schäfer, Claudia, Meghan Driscoll, Andrew T. Ludlow, et al.. (2016). Differential Kras V12 protein levels control a switch regulating lung cancer cell morphology and motility. PubMed. 2(3). 35004–35004. 8 indexed citations
10.
Sarkar, Sumona, Julián Candia, Stephen J. Florczyk, et al.. (2016). Machine learning based methodology to identify cell shape phenotypes associated with microenvironmental cues. Biomaterials. 104. 104–118. 40 indexed citations
11.
Sun, Xiaoyu, Meghan Driscoll, Satarupa Das, et al.. (2015). Asymmetric nanotopography biases cytoskeletal dynamics and promotes unidirectional cell guidance. Proceedings of the National Academy of Sciences. 112(41). 12557–12562. 66 indexed citations
12.
Driscoll, Meghan & Gaudenz Danuser. (2015). Quantifying Modes of 3D Cell Migration. Trends in Cell Biology. 25(12). 749–759. 43 indexed citations
13.
Driscoll, Meghan, et al.. (2014). Geometry-Driven Polarity in Motile Amoeboid Cells. PLoS ONE. 9(12). e113382–e113382. 23 indexed citations
14.
Wang, Chenlu, Sagar Chowdhury, Meghan Driscoll, et al.. (2014). The interplay of cell–cell and cell–substrate adhesion in collective cell migration. Journal of The Royal Society Interface. 11(100). 20140684–20140684. 24 indexed citations
15.
Losert, Wolfgang, et al.. (2013). Cellular Contact Guidance through Dynamic Sensing of Surface Topography. Biophysical Journal. 104(2). 148a–148a. 3 indexed citations
16.
Candia, Julián, Meghan Driscoll, Angélique Biancotto, et al.. (2013). From Cellular Characteristics to Disease Diagnosis: Uncovering Phenotypes with Supercells. PLoS Computational Biology. 9(9). e1003215–e1003215. 25 indexed citations
17.
Driscoll, Meghan, et al.. (2012). Automated image analysis of nuclear shape: What can we learn from a prematurely aged cell?. Aging. 4(2). 119–132. 46 indexed citations
18.
Driscoll, Meghan, Colin McCann, Tess Homan, et al.. (2011). Cell Shape Dynamics: From Waves to Migration. Bulletin of the American Physical Society. 2011. 1 indexed citations
19.
Driscoll, Meghan, John T. Fourkas, & Wolfgang Losert. (2011). Local and global measures of shape dynamics. Physical Biology. 8(5). 55001–55001. 25 indexed citations
20.
Li, Linjie, Meghan Driscoll, Ronald M. Hernández, et al.. (2008). Binary and Gray-Scale Patterning of Chemical Functionality on Polymer Films. Journal of the American Chemical Society. 130(41). 13512–13513. 10 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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