Georgi Tushev

4.3k total citations · 2 hit papers
19 papers, 2.8k citations indexed

About

Georgi Tushev is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cell Biology. According to data from OpenAlex, Georgi Tushev has authored 19 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 5 papers in Cellular and Molecular Neuroscience and 5 papers in Cell Biology. Recurrent topics in Georgi Tushev's work include RNA Research and Splicing (8 papers), RNA and protein synthesis mechanisms (6 papers) and RNA modifications and cancer (4 papers). Georgi Tushev is often cited by papers focused on RNA Research and Splicing (8 papers), RNA and protein synthesis mechanisms (6 papers) and RNA modifications and cancer (4 papers). Georgi Tushev collaborates with scholars based in Germany, United States and Netherlands. Georgi Tushev's co-authors include Erin M. Schuman, Caspar Glock, Susanne tom Dieck, Iván J. Cajigas, Sivakumar Sambandan, Anne Biever, Güney Akbalık, Irena Vlatkovic, Irina Epstein and Xi Wang and has published in prestigious journals such as Science, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Georgi Tushev

18 papers receiving 2.8k citations

Hit Papers

Neural circular RNAs are derived from synaptic genes and ... 2012 2026 2016 2021 2015 2012 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
Georgi Tushev Germany 16 2.3k 972 521 275 173 19 2.8k
Olivia Bermingham‐McDonogh United States 29 1.5k 0.7× 270 0.3× 378 0.7× 178 0.6× 244 1.4× 42 2.5k
Henri Tiedge United States 34 3.3k 1.4× 1.1k 1.1× 662 1.3× 180 0.7× 155 0.9× 63 3.8k
Stefanie Otto United States 9 1.0k 0.4× 440 0.5× 577 1.1× 235 0.9× 111 0.6× 10 1.5k
Haydn M. Prosser United Kingdom 24 1.5k 0.7× 311 0.3× 284 0.5× 172 0.6× 201 1.2× 39 2.4k
Alain Dabdoub United States 25 1.7k 0.8× 323 0.3× 317 0.6× 215 0.8× 363 2.1× 49 2.9k
Debra L. Silver United States 28 2.0k 0.9× 373 0.4× 550 1.1× 618 2.2× 102 0.6× 54 3.0k
Marion Coolen France 19 949 0.4× 496 0.5× 185 0.4× 343 1.2× 79 0.5× 28 1.4k
Jan H. Lui United States 15 2.1k 0.9× 525 0.5× 738 1.4× 294 1.1× 322 1.9× 17 3.1k
Takeshi Yoshimatsu United States 20 1.4k 0.6× 144 0.1× 624 1.2× 368 1.3× 128 0.7× 46 1.8k
Marta Florio Germany 16 1.9k 0.8× 253 0.3× 417 0.8× 258 0.9× 155 0.9× 17 2.5k

Countries citing papers authored by Georgi Tushev

Since Specialization
Citations

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

Fields of papers citing papers by Georgi Tushev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Georgi Tushev

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

All Works

19 of 19 papers shown
1.
Ciirdaeva, Elena, et al.. (2025). An integrated transcriptomic and proteomic map of the mouse hippocampus at synaptic resolution. Nature Communications. 16(1). 7942–7942.
2.
Fusco, Claudia M., Ashley M. Bourke, Georgi Tushev, et al.. (2025). Neuronal processes contain the essential components for the late steps of ribosome biogenesis. Proceedings of the National Academy of Sciences. 122(31). e2502424122–e2502424122. 1 indexed citations
3.
Oostrum, Marc van, Stefano L. Giandomenico, Susanne tom Dieck, et al.. (2023). The proteomic landscape of synaptic diversity across brain regions and cell types. Cell. 186(24). 5411–5427.e23. 52 indexed citations
4.
Spacek, Martin A., et al.. (2022). In vivo extracellular recordings of thalamic and cortical visual responses reveal V1 connectivity rules. Proceedings of the National Academy of Sciences. 119(41). e2207032119–e2207032119. 5 indexed citations
5.
Hain, David, Tatiana Gallego‐Flores, Elena Ciirdaeva, et al.. (2022). Molecular diversity and evolution of neuron types in the amniote brain. Science. 377(6610). eabp8202–eabp8202. 40 indexed citations
6.
Pérez, J., Susanne tom Dieck, Beatriz Alvarez‐Castelao, et al.. (2021). Subcellular sequencing of single neurons reveals the dendritic transcriptome of GABAergic interneurons. eLife. 10. 47 indexed citations
7.
Glock, Caspar, Anne Biever, Georgi Tushev, et al.. (2021). The translatome of neuronal cell bodies, dendrites, and axons. Proceedings of the National Academy of Sciences. 118(43). 89 indexed citations
8.
Biever, Anne, Caspar Glock, Georgi Tushev, et al.. (2020). Monosomes actively translate synaptic mRNAs in neuronal processes. Science. 367(6477). 171 indexed citations
9.
Wang, Xi, Xintian You, Julian D. Langer, et al.. (2019). Full-length transcriptome reconstruction reveals a large diversity of RNA and protein isoforms in rat hippocampus. Nature Communications. 10(1). 5009–5009. 42 indexed citations
10.
Tosches, Maria Antonietta, et al.. (2018). Evolution of pallium, hippocampus, and cortical cell types revealed by single-cell transcriptomics in reptiles. Science. 360(6391). 881–888. 292 indexed citations
11.
12.
Sambandan, Sivakumar, Güney Akbalık, Lisa Kochen, et al.. (2017). Activity-dependent spatially localized miRNA maturation in neuronal dendrites. Science. 355(6325). 634–637. 138 indexed citations
13.
Hanus, Cyril, Georgi Tushev, Sakshi Garg, et al.. (2016). Unconventional secretory processing diversifies neuronal ion channel properties. eLife. 5. 90 indexed citations
14.
Akbalık, Güney, Georgi Tushev, Sivakumar Sambandan, et al.. (2016). Visualization of newly synthesized neuronal RNA in vitro and in vivo using click-chemistry. RNA Biology. 14(1). 20–28. 28 indexed citations
15.
You, Xintian, Irena Vlatkovic, Ana Babić, et al.. (2015). Neural circular RNAs are derived from synaptic genes and regulated by development and plasticity. Nature Neuroscience. 18(4). 603–610. 916 indexed citations breakdown →
16.
Epstein, Irina, et al.. (2013). Alternative polyadenylation and differential expression of Shank mRNAs in the synaptic neuropil. Philosophical Transactions of the Royal Society B Biological Sciences. 369(1633). 20130137–20130137. 21 indexed citations
17.
Hinz, Flora I., Mark Aizenberg, Georgi Tushev, & Erin M. Schuman. (2013). Protein Synthesis-Dependent Associative Long-Term Memory in Larval Zebrafish. Journal of Neuroscience. 33(39). 15382–15387. 42 indexed citations
18.
Tushev, Georgi, Lisa Kochen, Belquis Nassim-Assir, et al.. (2013). Deep Sequencing and High-Resolution Imaging Reveal Compartment-Specific Localization of Bdnf mRNA in Hippocampal Neurons. Science Signaling. 6(306). rs16–rs16. 35 indexed citations
19.
Cajigas, Iván J., et al.. (2012). The Local Transcriptome in the Synaptic Neuropil Revealed by Deep Sequencing and High-Resolution Imaging. Neuron. 74(3). 453–466. 526 indexed citations breakdown →

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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