Marcello Maresca

3.1k total citations
36 papers, 1.7k citations indexed

About

Marcello Maresca is a scholar working on Molecular Biology, Physiology and Genetics. According to data from OpenAlex, Marcello Maresca has authored 36 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Molecular Biology, 4 papers in Physiology and 4 papers in Genetics. Recurrent topics in Marcello Maresca's work include CRISPR and Genetic Engineering (23 papers), RNA Interference and Gene Delivery (8 papers) and Advanced biosensing and bioanalysis techniques (7 papers). Marcello Maresca is often cited by papers focused on CRISPR and Genetic Engineering (23 papers), RNA Interference and Gene Delivery (8 papers) and Advanced biosensing and bioanalysis techniques (7 papers). Marcello Maresca collaborates with scholars based in Sweden, United Kingdom and Germany. Marcello Maresca's co-authors include Ning Guo, Yi Yang, Jun Fu, Axel Erler, Michelle J. Porritt, Pınar Akçakaya, Aengus Stewart, Katelynn R. Kazane, Beeke Wienert and Jonathan T. Vu and has published in prestigious journals such as Science, Nucleic Acids Research and Nature Communications.

In The Last Decade

Marcello Maresca

34 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marcello Maresca Sweden 21 1.4k 485 190 133 105 36 1.7k
Pengpeng Liu China 25 1.4k 1.0× 281 0.6× 61 0.3× 95 0.7× 119 1.1× 53 1.6k
Emma M. Schatoff United States 12 929 0.6× 272 0.6× 86 0.5× 34 0.3× 37 0.4× 18 1.3k
Elizabeth Frias Switzerland 4 1.1k 0.7× 265 0.5× 34 0.2× 69 0.5× 82 0.8× 5 1.2k
Eunji Kim South Korea 11 1.7k 1.2× 424 0.9× 21 0.1× 214 1.6× 220 2.1× 28 1.9k
Alyna Katti United States 7 672 0.5× 184 0.4× 77 0.4× 32 0.2× 40 0.4× 8 899
D. Dewran Koçak United States 8 1.5k 1.0× 298 0.6× 14 0.1× 120 0.9× 114 1.1× 9 1.6k
Naoko Fujimoto Japan 11 870 0.6× 193 0.4× 43 0.2× 36 0.3× 52 0.5× 16 1.1k
Elena Zelin United States 13 1.1k 0.8× 151 0.3× 89 0.5× 35 0.3× 16 0.2× 14 1.4k
Ulrich Elling Austria 17 1.1k 0.8× 231 0.5× 40 0.2× 61 0.5× 8 0.1× 37 1.4k
Erica A. Moehle United States 11 812 0.6× 173 0.4× 44 0.2× 76 0.6× 17 0.2× 14 929

Countries citing papers authored by Marcello Maresca

Since Specialization
Citations

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

Fields of papers citing papers by Marcello Maresca

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marcello Maresca

This figure shows the co-authorship network connecting the top 25 collaborators of Marcello Maresca. A scholar is included among the top collaborators of Marcello Maresca 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 Marcello Maresca. Marcello Maresca 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.
Maresca, Marcello, et al.. (2025). Prime Editing: Mechanistic Insights and DNA Repair Modulation. Cells. 14(4). 277–277. 4 indexed citations
2.
3.
Thom, George, Sandra Wimberger, Mike Firth, et al.. (2025). Dual inhibition of DNA-PK and Polϴ boosts precision of diverse prime editing systems. Nature Communications. 16(1). 4290–4290. 1 indexed citations
4.
Chalumeau, Anne, Guillaume Corre, Martin Peterka, et al.. (2025). A prime editing strategy to rewrite the γ-globin promoters and reactivate fetal hemoglobin for sickle cell disease. Blood. 146(22). 2641–2655. 1 indexed citations
5.
6.
Madeyski-Bengtson, Katja, Euan Gordon, George Thom, et al.. (2025). Modified pegRNAs mitigate scaffold-derived prime editing by-products. Nature Communications. 16(1). 3374–3374. 2 indexed citations
7.
Bravo, Jack P. K., Aikaterini Emmanouilidi, Margherita Francescatto, et al.. (2024). Engineered PsCas9 enables therapeutic genome editing in mouse liver with lipid nanoparticles. Nature Communications. 15(1). 9173–9173. 5 indexed citations
8.
Peterka, Martin, Nina Akrap, Songyuan Li, et al.. (2022). Harnessing DSB repair to promote efficient homology-dependent and -independent prime editing. Nature Communications. 13(1). 1240–1240. 33 indexed citations
9.
Ruiz, Mario, Henrik Palmgren, Marcus Henricsson, et al.. (2021). Extensive transcription mis-regulation and membrane defects in AdipoR2-deficient cells challenged with saturated fatty acids. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids. 1866(4). 158884–158884. 18 indexed citations
10.
Wimberger, Sandra, et al.. (2020). Improving Precise CRISPR Genome Editing by Small Molecules: Is there a Magic Potion?. Cells. 9(5). 1318–1318. 36 indexed citations
11.
Wienert, Beeke, Stacia K. Wyman, Chris D. Richardson, et al.. (2019). Unbiased detection of CRISPR off-targets in vivo using DISCOVER-Seq. Science. 364(6437). 286–289. 284 indexed citations
12.
Carreras, Alba, Luna Simona Pane, Roberto Nitsch, et al.. (2019). In vivo genome and base editing of a human PCSK9 knock-in hypercholesterolemic mouse model. BMC Biology. 17(1). 4–4. 64 indexed citations
13.
Fan, Jianjia, Wenchen Zhao, Jérôme Robert, et al.. (2018). Small molecule inducers of ABCA1 and apoE that act through indirect activation of the LXR pathway. Journal of Lipid Research. 59(5). 830–842. 37 indexed citations
14.
Gupta, Shailesh Kumar, Agata Wesolowska‐Andersen, Anna Kirstine Ringgaard, et al.. (2018). NKX6.1 induced pluripotent stem cell reporter lines for isolation and analysis of functionally relevant neuronal and pancreas populations. Stem Cell Research. 29. 220–231. 18 indexed citations
15.
Bjursell, Mikael, Michelle J. Porritt, Elke Ericson, et al.. (2018). Therapeutic Genome Editing With CRISPR/Cas9 in a Humanized Mouse Model Ameliorates α1-antitrypsin Deficiency Phenotype. EBioMedicine. 29. 104–111. 57 indexed citations
16.
Maresca, Marcello, Jun Fu, Maria Rostovskaya, et al.. (2011). Targeted isolation of cloned genomic regions by recombineering for haplotype phasing and isogenic targeting. Nucleic Acids Research. 39(20). e137–e137. 13 indexed citations
17.
Bird, Alexander W., Axel Erler, Jun Fu, et al.. (2011). High-efficiency counterselection recombineering for site-directed mutagenesis in bacterial artificial chromosomes. Nature Methods. 9(1). 103–109. 43 indexed citations
18.
Maresca, Marcello, Axel Erler, Jun Fu, et al.. (2010). Single-stranded heteroduplex intermediates in λ Red homologous recombination. BMC Molecular Biology. 11(1). 54–54. 97 indexed citations
19.
Ciotta, Giovanni, Helmut Hofemeister, Marcello Maresca, et al.. (2010). Recombineering BAC transgenes for protein tagging. Methods. 53(2). 113–119. 27 indexed citations
20.
Augui, Sandrine, Guillaume J. Filion, Elphège P. Nora, et al.. (2007). Sensing X Chromosome Pairs Before X Inactivation via a Novel X-Pairing Region of the Xic. Science. 318(5856). 1632–1636. 139 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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