Chieko Nakada

2.2k total citations · 1 hit paper
7 papers, 1.7k citations indexed

About

Chieko Nakada is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cell Biology. According to data from OpenAlex, Chieko Nakada has authored 7 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 3 papers in Cellular and Molecular Neuroscience and 2 papers in Cell Biology. Recurrent topics in Chieko Nakada's work include Lipid Membrane Structure and Behavior (4 papers), Force Microscopy Techniques and Applications (1 paper) and Cholesterol and Lipid Metabolism (1 paper). Chieko Nakada is often cited by papers focused on Lipid Membrane Structure and Behavior (4 papers), Force Microscopy Techniques and Applications (1 paper) and Cholesterol and Lipid Metabolism (1 paper). Chieko Nakada collaborates with scholars based in Japan, United States and United Kingdom. Chieko Nakada's co-authors include Takahiro Fujiwara, Akihiro Kusumi, Rinshi S. Kasai, Kenichi Suzuki, Hideji Murakoshi, Ken Ritchie, K. Murase, Ikuko Koyama‐Honda, Eric R. Prossnitz and Katsuyuki Murase and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Journal of Cell Biology and Nature Cell Biology.

In The Last Decade

Chieko Nakada

7 papers receiving 1.7k citations

Hit Papers

Paradigm Shift of the Plasma Membrane Concept from the Tw... 2005 2026 2012 2019 2005 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chieko Nakada Japan 6 1.3k 367 334 332 256 7 1.7k
Marija Vrljic United States 17 1.3k 1.0× 598 1.6× 261 0.8× 223 0.7× 214 0.8× 19 1.7k
Maïté Coppey‐Moisan France 28 1.4k 1.1× 454 1.2× 759 2.3× 330 1.0× 255 1.0× 58 2.6k
Reiner Peters Germany 30 2.4k 1.9× 359 1.0× 383 1.1× 257 0.8× 218 0.9× 63 3.3k
Akihiro Kusumi Japan 16 1.2k 1.0× 380 1.0× 288 0.9× 179 0.5× 307 1.2× 30 1.8k
Ikuko Koyama‐Honda Japan 20 1.8k 1.4× 938 2.6× 318 1.0× 429 1.3× 140 0.5× 36 3.1k
Veronika Mueller Germany 11 1.5k 1.2× 424 1.2× 720 2.2× 180 0.5× 279 1.1× 14 2.1k
Marc Tramier France 27 1.4k 1.1× 592 1.6× 748 2.2× 266 0.8× 142 0.6× 54 2.3k
Kevin Truong Canada 17 1.6k 1.2× 352 1.0× 278 0.8× 358 1.1× 66 0.3× 66 2.4k
Sally A. Kim United States 16 1.1k 0.9× 298 0.8× 521 1.6× 471 1.4× 133 0.5× 17 1.9k
David A. Holowka United States 17 1.5k 1.1× 268 0.7× 260 0.8× 87 0.3× 212 0.8× 32 1.9k

Countries citing papers authored by Chieko Nakada

Since Specialization
Citations

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

Fields of papers citing papers by Chieko Nakada

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chieko Nakada

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

All Works

7 of 7 papers shown
1.
Suzuki, Kazushi, Chieko Nakada, Matthew J. Daniels, et al.. (2018). Uninterrupted monitoring of drug effects in human-induced pluripotent stem cell-derived cardiomyocytes with bioluminescence Ca2+ microscopy. BMC Research Notes. 11(1). 313–313. 5 indexed citations
2.
Nakada, Chieko, et al.. (2013). ABCA1 dimer–monomer interconversion during HDL generation revealed by single-molecule imaging. Proceedings of the National Academy of Sciences. 110(13). 5034–5039. 90 indexed citations
3.
Kasai, Rinshi S., Kenichi Suzuki, Eric R. Prossnitz, et al.. (2011). Full characterization of GPCR monomer–dimer dynamic equilibrium by single molecule imaging. The Journal of Cell Biology. 192(3). 463–480. 273 indexed citations
4.
Morone, Nobuhiro, Chieko Nakada, Yasuhiro Umemura, Jiro Usukura, & Akihiro Kusumi. (2008). Chapter 12 Three-Dimensional Molecular Architecture of the Plasma-Membrane-Associated Cytoskeleton as Reconstructed by Freeze-Etch Electron Tomography. Methods in cell biology. 88. 207–236. 12 indexed citations
5.
Kusumi, Akihiro, Chieko Nakada, Ken Ritchie, et al.. (2005). Paradigm Shift of the Plasma Membrane Concept from the Two-Dimensional Continuum Fluid to the Partitioned Fluid: High-Speed Single-Molecule Tracking of Membrane Molecules. Annual Review of Biophysics and Biomolecular Structure. 34(1). 351–378. 875 indexed citations breakdown →
6.
Kusumi, Akihiro, H. Ike, Chieko Nakada, Katsuyuki Murase, & Takahiro Fujiwara. (2004). Single-molecule tracking of membrane molecules: plasma membrane compartmentalization and dynamic assembly of raft-philic signaling molecules. Seminars in Immunology. 17(1). 3–21. 185 indexed citations
7.
Nakada, Chieko, Kenneth Ritchie, Yuichi Oba, et al.. (2003). Accumulation of anchored proteins forms membrane diffusion barriers during neuronal polarization. Nature Cell Biology. 5(7). 626–632. 279 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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