Sushmita Chatterjee

1.2k total citations · 1 hit paper
18 papers, 847 citations indexed

About

Sushmita Chatterjee is a scholar working on Molecular Biology, Oncology and Immunology. According to data from OpenAlex, Sushmita Chatterjee has authored 18 papers receiving a total of 847 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 4 papers in Oncology and 4 papers in Immunology. Recurrent topics in Sushmita Chatterjee's work include RNA Interference and Gene Delivery (7 papers), Advanced biosensing and bioanalysis techniques (3 papers) and Cancer, Hypoxia, and Metabolism (2 papers). Sushmita Chatterjee is often cited by papers focused on RNA Interference and Gene Delivery (7 papers), Advanced biosensing and bioanalysis techniques (3 papers) and Cancer, Hypoxia, and Metabolism (2 papers). Sushmita Chatterjee collaborates with scholars based in Israel, India and South Korea. Sushmita Chatterjee's co-authors include Edo Kon, Preeti Sharma, Dan Peer, Srinivas Ramishetti, Inbal Hazan‐Halevy, Gonna Somu Naidu, Abhijit De, Meir Goldsmith, Niels Dammes and Dganit Danino and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Advanced Materials and PLoS ONE.

In The Last Decade

Sushmita Chatterjee

18 papers receiving 829 citations

Hit Papers

Endosomal escape: A bottleneck for LNP-mediated therapeutics 2024 2026 2025 2024 50 100 150 200

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sushmita Chatterjee Israel 13 623 153 131 114 91 18 847
Jilian R. Melamed United States 14 638 1.0× 121 0.8× 155 1.2× 175 1.5× 60 0.7× 22 884
Shan Guan China 19 796 1.3× 156 1.0× 137 1.0× 134 1.2× 149 1.6× 51 1.2k
Tongren Yang China 13 835 1.3× 188 1.2× 90 0.7× 198 1.7× 72 0.8× 18 1.1k
Dan Peer Israel 12 540 0.9× 93 0.6× 97 0.7× 151 1.3× 66 0.7× 24 776
Nuphar Veiga Israel 9 851 1.4× 163 1.1× 144 1.1× 109 1.0× 103 1.1× 10 1.0k
Michaela Jeong South Korea 8 656 1.1× 121 0.8× 88 0.7× 75 0.7× 65 0.7× 11 816
Jerry Leung Canada 13 837 1.3× 153 1.0× 127 1.0× 132 1.2× 41 0.5× 23 1.1k
Elisa Schrader Echeverri United States 10 560 0.9× 96 0.6× 81 0.6× 77 0.7× 45 0.5× 20 738
Fazhan Wang China 16 320 0.5× 104 0.7× 103 0.8× 78 0.7× 80 0.9× 31 613
Hiroki Tanaka Japan 19 630 1.0× 234 1.5× 100 0.8× 97 0.9× 73 0.8× 50 890

Countries citing papers authored by Sushmita Chatterjee

Since Specialization
Citations

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

Fields of papers citing papers by Sushmita Chatterjee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sushmita Chatterjee

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

All Works

18 of 18 papers shown
1.
Chatterjee, Sushmita, et al.. (2024). Endosomal escape: A bottleneck for LNP-mediated therapeutics. Proceedings of the National Academy of Sciences. 121(11). e2307800120–e2307800120. 233 indexed citations breakdown →
2.
Naidu, Gonna Somu, Seok‐Beom Yong, Srinivas Ramishetti, et al.. (2023). A Combinatorial Library of Lipid Nanoparticles for Cell Type‐Specific mRNA Delivery. Advanced Science. 10(19). e2301929–e2301929. 73 indexed citations
3.
Chatterjee, Sushmita, Gonna Somu Naidu, Inbal Hazan‐Halevy, et al.. (2023). Therapeutic gene silencing of CKAP5 leads to lethality in genetically unstable cancer cells. Science Advances. 9(14). eade4800–eade4800. 15 indexed citations
4.
Yong, Seok‐Beom, Srinivas Ramishetti, Meir Goldsmith, et al.. (2022). Dual‐Targeted Lipid Nanotherapeutic Boost for Chemo‐Immunotherapy of Cancer. Advanced Materials. 34(13). e2106350–e2106350. 54 indexed citations
5.
Yong, Seok‐Beom, Srinivas Ramishetti, Meir Goldsmith, et al.. (2022). Dual‐Targeted Lipid Nanotherapeutic Boost for Chemo‐Immunotherapy of Cancer (Adv. Mater. 13/2022). Advanced Materials. 34(13). 3 indexed citations
6.
Goldsmith, Meir, Srinivas Ramishetti, Nuphar Veiga, et al.. (2021). Therapeutic inhibitory RNA in head and neck cancer via functional targeted lipid nanoparticles. Journal of Controlled Release. 337. 378–389. 41 indexed citations
7.
Singh, Manu Smriti, Srinivas Ramishetti, Dalit Landesman‐Milo, et al.. (2021). Therapeutic Gene Silencing Using Targeted Lipid Nanoparticles in Metastatic Ovarian Cancer. Small. 17(19). e2100287–e2100287. 46 indexed citations
8.
Singh, Manu Smriti, Srinivas Ramishetti, Dalit Landesman‐Milo, et al.. (2021). Gene Silencing: Therapeutic Gene Silencing Using Targeted Lipid Nanoparticles in Metastatic Ovarian Cancer (Small 19/2021). Small. 17(19). 1 indexed citations
9.
Kon, Edo, Inbal Hazan‐Halevy, Daniel Rosenblum, et al.. (2020). Resveratrol Enhances mRNA and siRNA Lipid Nanoparticles Primary CLL Cell Transfection. Pharmaceutics. 12(6). 520–520. 18 indexed citations
10.
Ramishetti, Srinivas, Inbal Hazan‐Halevy, Sushmita Chatterjee, et al.. (2020). A Combinatorial Library of Lipid Nanoparticles for RNA Delivery to Leukocytes. Advanced Materials. 32(12). e1906128–e1906128. 181 indexed citations
11.
Ramishetti, Srinivas, Inbal Hazan‐Halevy, Sushmita Chatterjee, et al.. (2020). RNA Delivery: A Combinatorial Library of Lipid Nanoparticles for RNA Delivery to Leukocytes (Adv. Mater. 12/2020). Advanced Materials. 32(12). 4 indexed citations
12.
Chatterjee, Sushmita, et al.. (2019). Mannose glycosylation is an integral step for NIS localization and function in human breast cancer cells. Journal of Cell Science. 132(20). 6 indexed citations
13.
Singh, Manu Smriti, Meir Goldsmith, Sushmita Chatterjee, et al.. (2019). An ovarian spheroid based tumor model that represents vascularized tumors and enables the investigation of nanomedicine therapeutics. Nanoscale. 12(3). 1894–1903. 21 indexed citations
14.
Kelkar, Madhura, Bhushan Thakur, Abhishek Derle, et al.. (2017). Tumor suppressor protein p53 exerts negative transcriptional regulation on human sodium iodide symporter gene expression in breast cancer. Breast Cancer Research and Treatment. 164(3). 603–615. 13 indexed citations
15.
16.
Chatterjee, Sushmita. (2014). Applications of lentiviral vectors in molecular imaging. Frontiers in bioscience. 19(6). 835–835. 3 indexed citations
17.
Chatterjee, Sushmita, Renu Malhotra, Frency Varghese, et al.. (2013). Quantitative Immunohistochemical Analysis Reveals Association between Sodium Iodide Symporter and Estrogen Receptor Expression in Breast Cancer. PLoS ONE. 8(1). e54055–e54055. 55 indexed citations
18.
Chatterjee, Sushmita, et al.. (2013). Long chain lipid based tamoxifen NLC. Part II: Pharmacokinetic, biodistribution and in vitro anticancer efficacy studies. International Journal of Pharmaceutics. 454(1). 584–592. 66 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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