Megan Sykes

27.1k total citations · 4 hit papers
411 papers, 20.9k citations indexed

About

Megan Sykes is a scholar working on Immunology, Surgery and Hematology. According to data from OpenAlex, Megan Sykes has authored 411 papers receiving a total of 20.9k indexed citations (citations by other indexed papers that have themselves been cited), including 222 papers in Immunology, 161 papers in Surgery and 150 papers in Hematology. Recurrent topics in Megan Sykes's work include T-cell and B-cell Immunology (171 papers), Hematopoietic Stem Cell Transplantation (147 papers) and Xenotransplantation and immune response (135 papers). Megan Sykes is often cited by papers focused on T-cell and B-cell Immunology (171 papers), Hematopoietic Stem Cell Transplantation (147 papers) and Xenotransplantation and immune response (135 papers). Megan Sykes collaborates with scholars based in United States, Japan and Austria. Megan Sykes's co-authors include David H. Sachs, Yong‐Guang Yang, Thomas Wekerle, Thomas R. Spitzer, David T. Scadden, Markus Y. Mapara, Boris Nikolic, Tatsuo Kawai, David Dombkowski and Susan L. Saidman and has published in prestigious journals such as Nature, Science and New England Journal of Medicine.

In The Last Decade

Megan Sykes

397 papers receiving 20.6k citations

Hit Papers

Hematopoietic Stem Cell Quiescence Maintained by p21 cip1... 1995 2026 2005 2015 2000 2008 2012 1995 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Megan Sykes United States 75 10.7k 7.2k 6.6k 4.0k 3.6k 411 20.9k
David H. Sachs United States 87 14.1k 1.3× 13.2k 1.8× 4.3k 0.6× 5.3k 1.3× 6.9k 1.9× 697 32.0k
John E. Wagner United States 79 8.7k 0.8× 2.0k 0.3× 15.8k 2.4× 1.1k 0.3× 1.8k 0.5× 407 24.1k
Alejandro Aruffo United States 77 14.0k 1.3× 1.6k 0.2× 1.6k 0.2× 602 0.2× 1.7k 0.5× 196 25.3k
Robert S. Negrin United States 71 10.2k 1.0× 845 0.1× 6.9k 1.0× 560 0.1× 1.5k 0.4× 307 18.0k
Erik Thorsby Norway 62 8.2k 0.8× 3.0k 0.4× 1.3k 0.2× 576 0.1× 3.6k 1.0× 468 15.6k
Angela Panoskaltsis‐Mortari United States 69 10.5k 1.0× 1.6k 0.2× 5.2k 0.8× 320 0.1× 816 0.2× 275 16.2k
Rupert Handgretinger Germany 70 7.0k 0.7× 1.2k 0.2× 6.4k 1.0× 324 0.1× 2.2k 0.6× 493 19.0k
R. E. Billingham United States 52 5.0k 0.5× 2.3k 0.3× 913 0.1× 1.4k 0.4× 1.5k 0.4× 180 12.2k
Marcelo Fernández-Viña United States 52 5.0k 0.5× 588 0.1× 3.0k 0.4× 1.3k 0.3× 757 0.2× 235 8.3k
Yong‐Guang Yang China 50 3.8k 0.4× 2.5k 0.4× 1.6k 0.2× 207 0.1× 1.7k 0.5× 244 9.7k

Countries citing papers authored by Megan Sykes

Since Specialization
Citations

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

Fields of papers citing papers by Megan Sykes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Megan Sykes

This figure shows the co-authorship network connecting the top 25 collaborators of Megan Sykes. A scholar is included among the top collaborators of Megan Sykes 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 Megan Sykes. Megan Sykes 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.
Danzl, Nichole, Hao Wei Li, Elizabeth Waffarn, et al.. (2025). Follicular helper- and peripheral helper-like T cells drive autoimmune disease in human immune system mice. eLife. 13.
2.
Khosravi‐Maharlooei, Mohsen, Nichole Danzl, Hao Wei Li, et al.. (2024). Follicular helper- and peripheral helper-like T cells drive autoimmune disease in human immune system mice. eLife. 13. 2 indexed citations
3.
Wang, Hui, et al.. (2023). Origin, phenotype and autoimmune potential of T cells in human immune system mice receiving neonatal human thymus tissue. Frontiers in Immunology. 14. 1159341–1159341. 2 indexed citations
7.
Nguyen, Stéphanie, Giovanni Ferrari, Megan Sykes, et al.. (2023). Pitfalls and Future Directions of Contemporary Pediatric Valve Surgery: the Case for Living Valve Substitutes. Current Pediatrics Reports. 11(4). 180–192. 2 indexed citations
8.
Macedo, Camila, Alan F. Zahorchak, Xinyan Gu, et al.. (2023). Donor-derived regulatory dendritic cell infusion modulates effector CD8+T cell and NK cell responses after liver transplantation. Science Translational Medicine. 15(717). eadf4287–eadf4287. 18 indexed citations
9.
Wu, Yan, Elena Federzoni, Xiaodan Wang, et al.. (2022). CD47 cross-dressing by extracellular vesicles expressing CD47 inhibits phagocytosis without transmitting cell death signals. eLife. 11. 25 indexed citations
10.
DeWolf, Susan, Boris Grinshpun, Thomas Savage, et al.. (2018). Quantifying size and diversity of the human T cell alloresponse. JCI Insight. 3(15). 63 indexed citations
11.
Bardwell, Philip D., Ichiro Shimizu, Fabienne Haspot, et al.. (2011). B-Cell-Dependent Memory T Cells Impede Nonmyeloablative Mixed Chimerism Induction in Presensitized Mice. American Journal of Transplantation. 11(11). 2322–2331. 8 indexed citations
12.
Camirand, Geoffrey, Joël Rousseau, Nicolas Caron, et al.. (2008). Central Tolerance to Myogenic Cell Transplants Does Not Include Muscle Neoantigens. Transplantation. 85(12). 1791–1801. 9 indexed citations
13.
Yang, Yong‐Guang, James C. S. Wood, Ping Lan, et al.. (2004). Mouse retrovirus mediates porcine endogenous retrovirus transmission into human cells in long-term human-porcine chimeric mice. Journal of Clinical Investigation. 114(5). 695–700. 1 indexed citations
14.
Yang, Yong‐Guang, James C. S. Wood, Ping Lan, et al.. (2004). Mouse retrovirus mediates porcine endogenous retrovirus transmission into human cells in long-term human-porcine chimeric mice. Journal of Clinical Investigation. 114(5). 695–700. 34 indexed citations
15.
Abe, Masahiro, Pierre Theodore, Qi Jin, et al.. (2003). Stem cell activity of porcine c-kit+ hematopoietic cells. Experimental Hematology. 31(9). 833–840. 15 indexed citations
16.
Yang, Yong‐Guang, Lisa Garrett, Justin J. Sergio, et al.. (2000). Development and analysis of transgenic mice expressing porcine hematopoietic cytokines: a model for achieving durable porcine hematopoietic chimerism across an extensive xenogeneic barrier. Xenotransplantation. 7(1). 58–64. 22 indexed citations
17.
Zhao, Yong, Kirsten Swenson, Thomas Wekerle, et al.. (2000). The critical role of mouse CD4+cells in the rejection of highly disparate xenogeneic pig thymus grafts. Xenotransplantation. 7(2). 129–137. 16 indexed citations
18.
Zhao, Yong, Kirsten Swenson, Justin J. Sergio, & Megan Sykes. (1998). Pig MHC Mediates Positive Selection of Mouse CD4+ T Cells with a Mouse MHC-Restricted TCR in Pig Thymus Grafts. The Journal of Immunology. 161(3). 1320–1326. 35 indexed citations
19.
Emery, David W., Megan Sykes, David H. Sachs, & Christian LeGuern. (1994). Mixed swine/human long-term bone marrow cultures.. PubMed. 26(3). 1313–4. 9 indexed citations
20.
Sykes, Megan. (1990). Unusual T cell populations in adult murine bone marrow. Prevalence of CD3+CD4-CD8- and alpha beta TCR+NK1.1+ cells.. The Journal of Immunology. 145(10). 3209–3215. 168 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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