Roger Sciammas

4.6k total citations
50 papers, 3.6k citations indexed

About

Roger Sciammas is a scholar working on Immunology, Epidemiology and Transplantation. According to data from OpenAlex, Roger Sciammas has authored 50 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Immunology, 9 papers in Epidemiology and 9 papers in Transplantation. Recurrent topics in Roger Sciammas's work include T-cell and B-cell Immunology (32 papers), Immune Cell Function and Interaction (31 papers) and Immunotherapy and Immune Responses (11 papers). Roger Sciammas is often cited by papers focused on T-cell and B-cell Immunology (32 papers), Immune Cell Function and Interaction (31 papers) and Immunotherapy and Immune Responses (11 papers). Roger Sciammas collaborates with scholars based in United States, China and France. Roger Sciammas's co-authors include Harinder Singh, Jeffrey A. Bluestone, Anita S. Chong, Aaron R. Dinner, Mark M. Davis, Jianjun Chen, Hong Zhao, Jonathan H. Schatz, Louis M. Staudt and Arthur L. Shaffer and has published in prestigious journals such as Cell, Journal of Clinical Investigation and Nature Communications.

In The Last Decade

Roger Sciammas

49 papers receiving 3.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Roger Sciammas United States 26 2.3k 1.2k 452 332 249 50 3.6k
Jonathan C. Poe United States 37 4.2k 1.8× 917 0.8× 855 1.9× 440 1.3× 100 0.4× 62 5.8k
Nezih Cereb United States 20 3.2k 1.4× 599 0.5× 680 1.5× 277 0.8× 119 0.5× 63 3.8k
Terry I. Guinter United States 19 3.8k 1.6× 525 0.4× 953 2.1× 249 0.8× 98 0.4× 24 4.5k
Roberta Pelanda United States 35 3.2k 1.4× 1.5k 1.3× 774 1.7× 244 0.7× 61 0.2× 84 4.9k
Rudolf A. Manz Germany 36 3.7k 1.6× 1.1k 0.9× 805 1.8× 462 1.4× 139 0.6× 77 6.0k
Laura S. Grosmaire United States 24 3.9k 1.7× 807 0.7× 924 2.0× 263 0.8× 90 0.4× 39 4.9k
Louise J. McHeyzer‐Williams United States 26 3.7k 1.6× 700 0.6× 518 1.1× 447 1.3× 55 0.2× 39 4.6k
Patrice Douillard France 25 2.0k 0.8× 734 0.6× 351 0.8× 174 0.5× 108 0.4× 43 2.8k
Flavius Martin United States 27 4.8k 2.1× 1.1k 1.0× 582 1.3× 368 1.1× 62 0.2× 39 6.5k
Robert W. Karr United States 32 3.0k 1.3× 874 0.7× 299 0.7× 392 1.2× 80 0.3× 84 4.3k

Countries citing papers authored by Roger Sciammas

Since Specialization
Citations

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

Fields of papers citing papers by Roger Sciammas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Roger Sciammas

This figure shows the co-authorship network connecting the top 25 collaborators of Roger Sciammas. A scholar is included among the top collaborators of Roger Sciammas 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 Roger Sciammas. Roger Sciammas 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.
Sievert, Evelyn, et al.. (2025). Distinct plasmablast developmental intermediates produce graded expression of IgM secretory transcripts. Cell Reports. 44(2). 115283–115283.
2.
Zhao, Hongchang, Stella R. Hartono, Zheyuan Yu, et al.. (2022). Senataxin and RNase H2 act redundantly to suppress genome instability during class switch recombination. eLife. 11. 13 indexed citations
3.
Ochiai, Kyoko, Hiroki Shima, Tsuyoshi Ikura, et al.. (2021). Protocol for in vitro BCR-mediated plasma cell differentiation and purification of chromatin-associated proteins. STAR Protocols. 2(3). 100633–100633. 3 indexed citations
4.
Lee, Jihyung, Junyan Zhang, Young‐Jun Chung, et al.. (2020). Inhibition of IRF4 in dendritic cells by PRR-independent and -dependent signals inhibit Th2 and promote Th17 responses. eLife. 9. 27 indexed citations
5.
Withers, Sita S., Hong Chang, Justin A. Spanier, et al.. (2020). Development of canine PD-1/PD-L1 specific monoclonal antibodies and amplification of canine T cell function. PLoS ONE. 15(7). e0235518–e0235518. 30 indexed citations
6.
Chen, Jianjun, Jinghui Yang, Dengping Yin, et al.. (2020). Transplantation tolerance modifies donor-specific B cell fate to suppress de novo alloreactive B cells. Journal of Clinical Investigation. 130(7). 3453–3466. 17 indexed citations
7.
Veselits, Margaret, Azusa Tanaka, Yao-Qing Chen, et al.. (2017). Igβ ubiquitination activates PI3K signals required for endosomal sorting. The Journal of Experimental Medicine. 214(12). 3775–3790. 9 indexed citations
8.
Yang, Jinghui, James S. Young, Jianjun Chen, et al.. (2017). CTLA4-Ig in combination with FTY720 promotes allograft survival in sensitized recipients. JCI Insight. 2(9). 12 indexed citations
9.
Yang, Jinghui, Jianjun Chen, James S. Young, et al.. (2016). Tracing Donor-MHC Class II Reactive B cells in Mouse Cardiac Transplantation. Transplantation. 100(8). 1683–1691. 29 indexed citations
10.
Chong, Anita S. & Roger Sciammas. (2015). Memory B Cells in Transplantation. Transplantation. 99(1). 21–28. 42 indexed citations
11.
Veselits, Margaret, Azusa Tanaka, Stanley Lipkowitz, et al.. (2014). Recruitment of Cbl-b to B Cell Antigen Receptor Couples Antigen Recognition to Toll-Like Receptor 9 Activation in Late Endosomes. PLoS ONE. 9(3). e89792–e89792. 13 indexed citations
12.
13.
Williams, Jesse W., Melissa Y. Tjota, Bryan S. Clay, et al.. (2013). Transcription factor IRF4 drives dendritic cells to promote Th2 differentiation. Nature Communications. 4(1). 2990–2990. 310 indexed citations
14.
Ochiai, Kyoko, Mark Maienschein‐Cline, Giorgia Simonetti, et al.. (2013). Transcriptional Regulation of Germinal Center B and Plasma Cell Fates by Dynamical Control of IRF4. Immunity. 38(5). 918–929. 321 indexed citations
15.
Chen, Jianjun, Rebecca R. Pompano, Felix W. Santiago, et al.. (2013). The use of self-adjuvanting nanofiber vaccines to elicit high-affinity B cell responses to peptide antigens without inflammation. Biomaterials. 34(34). 8776–8785. 143 indexed citations
16.
Ochiai, Kyoko, Mark Maienschein‐Cline, Malay Mandal, et al.. (2012). A self-reinforcing regulatory network triggered by limiting IL-7 activates pre-BCR signaling and differentiation. Nature Immunology. 13(3). 300–307. 122 indexed citations
17.
Laslo, Peter, Chauncey J. Spooner, Aryeh Warmflash, et al.. (2006). Multilineage Transcriptional Priming and Determination of Alternate Hematopoietic Cell Fates. Cell. 126(4). 755–766. 487 indexed citations
18.
Sciammas, Roger & Jeffrey A. Bluestone. (1998). HSV-1 glycoprotein I-reactive TCR gamma delta cells directly recognize the peptide backbone in a conformationally dependent manner.. PubMed. 161(10). 5187–92. 43 indexed citations
19.
Sciammas, Roger, Raymond M. Johnson, Anne I. Sperling, et al.. (1994). Unique antigen recognition by a herpesvirus-specific TCR-gamma delta cell.. The Journal of Immunology. 152(11). 5392–5397. 142 indexed citations
20.
Sciammas, Roger, et al.. (1994). TCRγδ cells: Mysterious cells of the immune system. Immunologic Research. 13(4). 268–279. 19 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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