Jason Lohmueller

5.0k total citations · 2 hit papers
27 papers, 3.2k citations indexed

About

Jason Lohmueller is a scholar working on Molecular Biology, Oncology and Genetics. According to data from OpenAlex, Jason Lohmueller has authored 27 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 14 papers in Oncology and 6 papers in Genetics. Recurrent topics in Jason Lohmueller's work include CAR-T cell therapy research (13 papers), CRISPR and Genetic Engineering (7 papers) and Advanced biosensing and bioanalysis techniques (6 papers). Jason Lohmueller is often cited by papers focused on CAR-T cell therapy research (13 papers), CRISPR and Genetic Engineering (7 papers) and Advanced biosensing and bioanalysis techniques (6 papers). Jason Lohmueller collaborates with scholars based in United States, China and Switzerland. Jason Lohmueller's co-authors include S. F. Schaffner, Patrick Varilly, Ben Fry, Pardis C. Sabeti, Pamela A. Silver, Steven A. McCarroll, Chris Cotsapas, Rachelle Gaudet, Elizabeth H. Byrne and Xiaohui Xie and has published in prestigious journals such as Nature, Science and Journal of the American Chemical Society.

In The Last Decade

Jason Lohmueller

25 papers receiving 3.1k citations

Hit Papers

Genome-wide detection and characterization of positive se... 2006 2026 2012 2019 2007 2006 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jason Lohmueller United States 15 1.7k 1.3k 362 331 219 27 3.2k
Rémy Bruggmann Switzerland 37 799 0.5× 1.6k 1.3× 152 0.4× 323 1.0× 276 1.3× 116 3.9k
Elizabeth H. Byrne United States 7 1.3k 0.8× 776 0.6× 132 0.4× 304 0.9× 230 1.1× 11 2.8k
Bruce Whitelaw United Kingdom 38 3.0k 1.8× 3.5k 2.7× 254 0.7× 253 0.8× 183 0.8× 160 5.2k
David Roazen United States 2 1.8k 1.0× 2.1k 1.6× 335 0.9× 240 0.7× 617 2.8× 2 4.3k
Ami Levy‐Moonshine United States 5 1.9k 1.1× 2.2k 1.8× 339 0.9× 256 0.8× 659 3.0× 5 4.5k
Mauricio O. Carneiro United States 6 2.0k 1.2× 2.6k 2.0× 345 1.0× 269 0.8× 694 3.2× 6 5.0k
Cornel Fraefel Switzerland 34 1.8k 1.1× 1.8k 1.4× 395 1.1× 626 1.9× 232 1.1× 150 4.0k
Khalid Shakir United States 2 1.8k 1.1× 2.1k 1.6× 336 0.9× 240 0.7× 626 2.9× 2 4.3k
Danika L. Bannasch United States 32 1.2k 0.7× 1.5k 1.1× 446 1.2× 211 0.6× 196 0.9× 106 3.3k
Isaäc J. Nijman Netherlands 36 1.9k 1.1× 2.6k 2.1× 410 1.1× 388 1.2× 633 2.9× 81 5.1k

Countries citing papers authored by Jason Lohmueller

Since Specialization
Citations

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

Fields of papers citing papers by Jason Lohmueller

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jason Lohmueller

This figure shows the co-authorship network connecting the top 25 collaborators of Jason Lohmueller. A scholar is included among the top collaborators of Jason Lohmueller 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 Jason Lohmueller. Jason Lohmueller 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.
Lohmueller, Jason, et al.. (2025). Conditional Control of Benzylguanine Reaction with the Self-Labeling SNAP-tag Protein. Bioconjugate Chemistry. 36(3). 540–548. 1 indexed citations
2.
McGovern, Andrew, et al.. (2025). Small-molecule control of CAR T cells. Nature Reviews Chemistry. 9(12). 809–825.
4.
Lohmueller, Jason, et al.. (2024). Preventative Cancer Vaccine-Elicited Human Anti-MUC1 Antibodies Have Multiple Effector Functions. Antibodies. 13(4). 85–85.
5.
Ruffo, Elisa, et al.. (2023). Post-translational covalent assembly of CAR and synNotch receptors for programmable antigen targeting. Nature Communications. 14(1). 2463–2463. 45 indexed citations
6.
Lontos, Konstantinos, Yiyang Wang, Andrew Frisch, et al.. (2023). Metabolic reprogramming via an engineered PGC-1α improves human chimeric antigen receptor T-cell therapy against solid tumors. Journal for ImmunoTherapy of Cancer. 11(3). e006522–e006522. 59 indexed citations
7.
Singh, Amrik, et al.. (2023). Abstract 898: Generation and validation of anti-linker monoclonal antibodies for the detection of surface expressed scFv-based CARs. Cancer Research. 83(7_Supplement). 898–898. 1 indexed citations
8.
Kataoka, Shunsuke, et al.. (2021). The costimulatory activity of Tim-3 requires Akt and MAPK signaling and its recruitment to the immune synapse. Science Signaling. 14(687). 33 indexed citations
9.
Ruffo, Elisa, et al.. (2021). Preclinical development of universal SNAP-CAR T cell therapy. The Journal of Immunology. 206(1_Supplement). 67.11–67.11. 1 indexed citations
10.
Eisenberg, Seth, Amy Powers, Jason Lohmueller, et al.. (2021). 112 Tumor-specific reactivity and effector function of chimeric antigen receptor engineered macrophages targeting MUC1. SHILAP Revista de lepidopterología. A122–A122. 6 indexed citations
11.
Lohmueller, Jason & Olivera J. Finn. (2017). Current modalities in cancer immunotherapy: Immunomodulatory antibodies, CARs and vaccines. Pharmacology & Therapeutics. 178. 31–47. 73 indexed citations
12.
Cascio, Sandra, Joshua Sciurba, Xue Jia, et al.. (2017). Abnormally glycosylated MUC1 establishes a positive feedback circuit of inflammatory cytokines, mediated by NF-κB p65 and EzH2, in colitis-associated cancer. Oncotarget. 8(62). 105284–105298. 17 indexed citations
13.
Lienert, Florian, Jason Lohmueller, Abhishek D. Garg, & Pamela A. Silver. (2014). Synthetic biology in mammalian cells: next generation research tools and therapeutics. Nature Reviews Molecular Cell Biology. 15(2). 95–107. 228 indexed citations
14.
Lohmueller, Jason, et al.. (2013). Protein Scaffold-Activated Protein Trans-Splicing in Mammalian Cells. Journal of the American Chemical Society. 135(20). 7713–7719. 24 indexed citations
15.
Garg, Abhishek, et al.. (2012). Engineering synthetic TAL effectors with orthogonal target sites. Nucleic Acids Research. 40(15). 7584–7595. 122 indexed citations
16.
Leisner, Madeleine, Leonidas Bleris, Jason Lohmueller, Zhen Xie, & Yaakov Benenson. (2011). MicroRNA Circuits for Transcriptional Logic. Methods in molecular biology. 813. 169–186. 11 indexed citations
17.
Leisner, Madeleine, Leonidas Bleris, Jason Lohmueller, Zhen Xie, & Yaakov Benenson. (2010). Rationally designed logic integration of regulatory signals in mammalian cells. Nature Nanotechnology. 5(9). 666–670. 90 indexed citations
18.
Sabeti, Pardis C., Patrick Varilly, Ben Fry, et al.. (2007). Genome-wide detection and characterization of positive selection in human populations. Nature. 449(7164). 913–918. 1403 indexed citations breakdown →
19.
Lohmueller, Jason, John Cumbers, M Schmidt, et al.. (2007). Progress toward construction and modelling of a tri-stable toggle switch in E. coli. 1(1). 25–28. 4 indexed citations
20.
Sabeti, Pardis C., S. F. Schaffner, Ben Fry, et al.. (2006). Positive Natural Selection in the Human Lineage. Science. 312(5780). 1614–1620. 802 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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