Farnaz Shamsi

3.1k total citations
28 papers, 1.1k citations indexed

About

Farnaz Shamsi is a scholar working on Physiology, Molecular Biology and Epidemiology. According to data from OpenAlex, Farnaz Shamsi has authored 28 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Physiology, 13 papers in Molecular Biology and 13 papers in Epidemiology. Recurrent topics in Farnaz Shamsi's work include Adipose Tissue and Metabolism (21 papers), Adipokines, Inflammation, and Metabolic Diseases (12 papers) and Lipid metabolism and biosynthesis (5 papers). Farnaz Shamsi is often cited by papers focused on Adipose Tissue and Metabolism (21 papers), Adipokines, Inflammation, and Metabolic Diseases (12 papers) and Lipid metabolism and biosynthesis (5 papers). Farnaz Shamsi collaborates with scholars based in United States, Denmark and Taiwan. Farnaz Shamsi's co-authors include Yu‐Hua Tseng, Chih‐Hao Wang, Matthew D. Lynes, Aaron M. Cypess, Tian Lian Huang, Cheryl Cero, Jonathan M. Dreyfuss, Tim J. Schulz, Ruidan Xue and Luiz Osório Leiria and has published in prestigious journals such as Cell, Nucleic Acids Research and Nature Medicine.

In The Last Decade

Farnaz Shamsi

26 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Farnaz Shamsi United States	16	781	409	342	192	146	28	1.1k
Naja Zenius Jespersen Denmark	11	1.1k 1.3×	496 1.2×	380 1.1×	271 1.4×	234 1.6×	15	1.4k
Ruidan Xue United States	13	1.3k 1.7×	735 1.8×	410 1.2×	320 1.7×	287 2.0×	14	1.6k
Amy L. Confides United States	13	452 0.6×	193 0.5×	283 0.8×	96 0.5×	184 1.3×	27	739
Serge Summermatter Switzerland	21	848 1.1×	162 0.4×	709 2.1×	39 0.2×	138 0.9×	25	1.3k
Jeong-Sun Ju United States	18	568 0.7×	674 1.6×	924 2.7×	100 0.5×	62 0.4×	31	1.7k
Yuko Kai Japan	11	917 1.2×	153 0.4×	995 2.9×	59 0.3×	197 1.3×	13	1.4k
Jessica Segalés Spain	15	582 0.7×	341 0.8×	1.4k 3.9×	56 0.3×	60 0.4×	16	1.7k
Hans P.M.M. Lauritzen United States	15	502 0.6×	203 0.5×	601 1.8×	76 0.4×	92 0.6×	17	998
Míriam Toledo Spain	20	972 1.2×	172 0.4×	817 2.4×	46 0.2×	144 1.0×	37	1.5k
Janne R. Hingst Denmark	15	682 0.9×	157 0.4×	949 2.8×	116 0.6×	172 1.2×	27	1.5k

Countries citing papers authored by Farnaz Shamsi

Since Specialization

Citations

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

Fields of papers citing papers by Farnaz Shamsi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Farnaz Shamsi

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

All Works

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20 of 20 papers shown

Corvera, Silvia, Akhila Rajan, Kristy L. Townsend, et al.. (2025). Advances in Adipose Tissue Biology. Endocrine Reviews. 47(1). 75–92.

Zheng, Rongbin, Yang Zhang, Tadataka Tsuji, et al.. (2025). MEBOCOST maps metabolite-mediated intercellular communications using single-cell RNA-seq. Nucleic Acids Research. 53(12). 5 indexed citations

Wang, Chih‐Hao, Tadataka Tsuji, Cheng‐Ying Yang, et al.. (2024). Endothelin 3/EDNRB signaling induces thermogenic differentiation of white adipose tissue. Nature Communications. 15(1). 7215–7215. 2 indexed citations

Shamsi, Farnaz. (2023). Methods for Single Cell Transcriptomic Analysis of Adipose Tissue. Methods in molecular biology. 2662. 241–249.

Gupta, Anushka, Vissarion Efthymiou, Sean D. Kodani, et al.. (2023). Mapping the transcriptional landscape of human white and brown adipogenesis using single-nuclei RNA-seq. Molecular Metabolism. 74. 101746–101746. 6 indexed citations

Shamsi, Farnaz, Rongbin Zheng, Li‐Lun Ho, Kaifu Chen, & Yu‐Hua Tseng. (2023). Comprehensive analysis of intercellular communication in the thermogenic adipose niche. Communications Biology. 6(1). 761–761. 10 indexed citations

Shamsi, Farnaz, Mary Piper, Li‐Lun Ho, et al.. (2021). Vascular smooth muscle-derived Trpv1+ progenitors are a source of cold-induced thermogenic adipocytes. Nature Metabolism. 3(4). 485–495. 71 indexed citations

Lundh, Morten, Ali Altıntaş, Odile Fabre, et al.. (2021). Cold-induction of afadin in brown fat supports its thermogenic capacity. Scientific Reports. 11(1). 9794–9794. 2 indexed citations

Cero, Cheryl, et al.. (2021). β3-Adrenergic receptors regulate human brown/beige adipocyte lipolysis and thermogenesis. JCI Insight. 6(11). 149 indexed citations

10.

Angueira, Anthony R., Alexander P. Sakers, Corey D. Holman, et al.. (2021). Defining the lineage of thermogenic perivascular adipose tissue. Nature Metabolism. 3(4). 469–484. 90 indexed citations

11.

Wang, Chih‐Hao, Morten Lundh, Accalia Fu, et al.. (2020). CRISPR-engineered human brown-like adipocytes prevent diet-induced obesity and ameliorate metabolic syndrome in mice. Science Translational Medicine. 12(558). 103 indexed citations

12.

Shwartz, Yulia, Meryem Gonzalez-Celeiro, H. Amalia Pasolli, et al.. (2020). Cell Types Promoting Goosebumps Form a Niche to Regulate Hair Follicle Stem Cells. Cell. 182(3). 578–593.e19. 96 indexed citations

13.

Pirouz, Mehdi, Chih‐Hao Wang, Qi Liu, et al.. (2020). The Perlman syndrome DIS3L2 exoribonuclease safeguards endoplasmic reticulum-targeted mRNA translation and calcium ion homeostasis. Nature Communications. 11(1). 2619–2619. 12 indexed citations

14.

Sato, Mari, Tadataka Tsuji, Xiaozhi Ren, et al.. (2020). Cell-autonomous light sensitivity via Opsin3 regulates fuel utilization in brown adipocytes. PLoS Biology. 18(2). e3000630–e3000630. 47 indexed citations

15.

Lundh, Morten, Marie S. Isidor, Kaja Plucińska, et al.. (2019). Afadin is a scaffold protein repressing insulin action via HDAC 6 in adipose tissue. EMBO Reports. 20(8). e48216–e48216. 19 indexed citations

16.

Gupta, Manoj, Dario F. De Jesus, Sevim Kahraman, et al.. (2018). Insulin receptor-mediated signaling regulates pluripotency markers and lineage differentiation. Molecular Metabolism. 18. 153–163. 20 indexed citations

17.

Lynes, Matthew D., Farnaz Shamsi, Elahu G. Sustarsic, et al.. (2018). Cold-Activated Lipid Dynamics in Adipose Tissue Highlights a Role for Cardiolipin in Thermogenic Metabolism. Cell Reports. 24(3). 781–790. 63 indexed citations

18.

Mo, Qianxing, Lisa A. Baer, Francis J. May, et al.. (2017). Identification and characterization of a supraclavicular brown adipose tissue in mice. JCI Insight. 2(11). 26 indexed citations

19.

Shamsi, Farnaz & Yu‐Hua Tseng. (2017). Protocols for Generation of Immortalized Human Brown and White Preadipocyte Cell Lines. Methods in molecular biology. 1566. 77–85. 21 indexed citations

20.

Shamsi, Farnaz, et al.. (2013). The roles of micro RNA in pancreas development and regeneration. Biomedical Reviews. 24(0). 57–57. 1 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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