Sean R. Cutler

19.5k total citations · 5 hit papers
80 papers, 10.8k citations indexed

About

Sean R. Cutler is a scholar working on Plant Science, Molecular Biology and Biotechnology. According to data from OpenAlex, Sean R. Cutler has authored 80 papers receiving a total of 10.8k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Plant Science, 36 papers in Molecular Biology and 8 papers in Biotechnology. Recurrent topics in Sean R. Cutler's work include Plant Molecular Biology Research (37 papers), Plant Stress Responses and Tolerance (34 papers) and Plant nutrient uptake and metabolism (22 papers). Sean R. Cutler is often cited by papers focused on Plant Molecular Biology Research (37 papers), Plant Stress Responses and Tolerance (34 papers) and Plant nutrient uptake and metabolism (22 papers). Sean R. Cutler collaborates with scholars based in United States, Canada and Japan. Sean R. Cutler's co-authors include Pedro L. Rodrı́guez, Suzanne R. Abrams, Ruth Finkelstein, Sang‐Youl Park, Chris Somerville, David W. Ehrhardt, Peter McCourt, Joel S. Griffitts, Regina Antoni and Silvia Rubio and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Sean R. Cutler

77 papers receiving 10.7k citations

Hit Papers

Abscisic Acid: Emergence ... 2000 2026 2008 2017 2010 2009 2000 2009 2019 500 1000 1.5k 2.0k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sean R. Cutler United States 42 9.2k 5.0k 326 299 298 80 10.8k
Marc R. Knight United Kingdom 60 11.9k 1.3× 6.8k 1.4× 293 0.9× 379 1.3× 351 1.2× 105 13.6k
Keith Lindsey United Kingdom 57 6.6k 0.7× 5.4k 1.1× 158 0.5× 212 0.7× 286 1.0× 165 8.6k
Yasunari Fujita Japan 46 13.5k 1.5× 7.8k 1.6× 124 0.4× 285 1.0× 334 1.1× 79 15.0k
Paul M. Hasegawa United States 53 10.8k 1.2× 6.5k 1.3× 174 0.5× 507 1.7× 401 1.3× 109 13.2k
Klaus‐Dieter Scharf Germany 38 5.1k 0.6× 6.6k 1.3× 65 0.2× 396 1.3× 171 0.6× 59 8.5k
Peter McCourt Canada 46 6.2k 0.7× 3.4k 0.7× 80 0.2× 99 0.3× 981 3.3× 75 7.1k
Jianru Zuo China 51 7.0k 0.8× 5.9k 1.2× 49 0.2× 443 1.5× 147 0.5× 97 9.4k
Filip Rolland Belgium 36 7.6k 0.8× 4.7k 1.0× 55 0.2× 272 0.9× 237 0.8× 49 9.3k
Dae‐Jin Yun South Korea 74 12.3k 1.3× 10.0k 2.0× 89 0.3× 625 2.1× 377 1.3× 265 16.4k
Sang Yeol Lee South Korea 61 7.9k 0.9× 6.3k 1.3× 142 0.4× 504 1.7× 176 0.6× 213 10.5k

Countries citing papers authored by Sean R. Cutler

Since Specialization
Citations

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

Fields of papers citing papers by Sean R. Cutler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sean R. Cutler. 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 Sean R. Cutler. The network helps show where Sean R. Cutler may publish in the future.

Co-authorship network of co-authors of Sean R. Cutler

This figure shows the co-authorship network connecting the top 25 collaborators of Sean R. Cutler. A scholar is included among the top collaborators of Sean R. Cutler 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 Sean R. Cutler. Sean R. Cutler 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.
Park, Sang‐Youl, Francis C. Peterson, Jesús Beltrán, et al.. (2023). An orthogonalized PYR1-based CID module with reprogrammable ligand-binding specificity. Nature Chemical Biology. 20(1). 103–110. 20 indexed citations
2.
Li, Zongbo, Jesús Beltrán, Hao Tian, et al.. (2023). High-Performance Cannabinoid Sensor Empowered by Plant Hormone Receptors and Antifouling Magnetic Nanorods. ACS Sensors. 8(10). 3914–3922. 2 indexed citations
3.
Vaidya, Aditya S., et al.. (2022). Synthesis and characterization of abscisic acid receptor modulators. Methods in enzymology on CD-ROM/Methods in enzymology. 671. 435–470. 2 indexed citations
4.
Mega, Ryosuke, Fumitaka Abe, June‐Sik Kim, et al.. (2019). Tuning water-use efficiency and drought tolerance in wheat using abscisic acid receptors. Nature Plants. 5(2). 153–159. 234 indexed citations breakdown →
5.
Coego, Alberto, Jorge Lozano‐Juste, Esther Lechner, et al.. (2019). The MATH-BTB BPM3 and BPM5 subunits of Cullin3-RING E3 ubiquitin ligases target PP2CA and other clade A PP2Cs for degradation. Proceedings of the National Academy of Sciences. 116(31). 15725–15734. 62 indexed citations
6.
Kraus, Michael, et al.. (2019). Toward Development of Fluorescence-Quenching-Based Biosensors for Drought Stress in Plants. Analytical Chemistry. 91(24). 15644–15651. 9 indexed citations
7.
Vaidya, Aditya S., Francis C. Peterson, Dezi Elzinga, et al.. (2019). Dynamic control of plant water use using designed ABA receptor agonists. Science. 366(6464). 141 indexed citations
8.
Vaidya, Aditya S., Francis C. Peterson, Dmitry Yarmolinsky, et al.. (2017). A Rationally Designed Agonist Defines Subfamily IIIA Abscisic Acid Receptors As Critical Targets for Manipulating Transpiration. ACS Chemical Biology. 12(11). 2842–2848. 59 indexed citations
9.
Carland, Francine M., et al.. (2015). Novel Vein Patterns in Arabidopsis Induced by Small Molecules. PLANT PHYSIOLOGY. 170(1). 338–353. 10 indexed citations
10.
Vaidya, Aditya S., et al.. (2015). Chemical manipulation of plant water use. Bioorganic & Medicinal Chemistry. 24(3). 493–500. 51 indexed citations
11.
Lozano‐Juste, Jorge & Sean R. Cutler. (2014). Plant genome engineering in full bloom. Trends in Plant Science. 19(5). 284–287. 61 indexed citations
12.
Lumba, Shelley, Shigeo Toh, Louis‐François Handfield, et al.. (2014). A Mesoscale Abscisic Acid Hormone Interactome Reveals a Dynamic Signaling Landscape in Arabidopsis. Developmental Cell. 29(3). 360–372. 94 indexed citations
13.
Forde, Brian, Sean R. Cutler, Najia Zaman, & Patrick J. Krysan. (2013). Glutamate signalling via a MEKK1 kinase‐dependent pathway induces changes in Arabidopsis root architecture. The Plant Journal. 75(1). 1–10. 62 indexed citations
14.
Rosado, Abel, Glenn R. Hicks, Lorena Norambuena, et al.. (2011). Sortin1-Hypersensitive Mutants Link Vacuolar-Trafficking Defects and Flavonoid Metabolism in Arabidopsis Vegetative Tissues. Chemistry & Biology. 18(2). 187–197. 28 indexed citations
15.
Nishimura, Noriyuki, Ali Sarkeshik, Kazumasa Nito, et al.. (2009). PYR/PYL/RCAR family members are major in‐vivo ABI1 protein phosphatase 2C‐interacting proteins in Arabidopsis. The Plant Journal. 61(2). 290–299. 387 indexed citations
16.
Cutler, Sean R. & Dario Bonetta. (2009). Plant hormones : methods and protocols. Humana Press eBooks. 17 indexed citations
17.
Santiago, Julia, Florine Dupeux, Adam Round, et al.. (2009). The abscisic acid receptor PYR1 in complex with abscisic acid. Nature. 462(7273). 665–668. 405 indexed citations breakdown →
18.
DeBolt, Seth, Ryan Gutierrez, David W. Ehrhardt, et al.. (2007). Morlin, an inhibitor of cortical microtubule dynamics and cellulose synthase movement. Proceedings of the National Academy of Sciences. 104(14). 5854–5859. 122 indexed citations
19.
Ghassemian, Majid, Eiji Nambara, Sean R. Cutler, et al.. (2000). Regulation of Abscisic Acid Signaling by the Ethylene Response Pathway in Arabidopsis. The Plant Cell. 12(7). 1117–1126. 449 indexed citations
20.
Cutler, Sean R. & Chris Somerville. (1997). Cellulose synthesis: Cloning in silico. Current Biology. 7(2). R108–R111. 52 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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