Sergio Triana

1.8k total citations · 1 hit paper
15 papers, 737 citations indexed

About

Sergio Triana is a scholar working on Molecular Biology, Epidemiology and Pharmacology. According to data from OpenAlex, Sergio Triana has authored 15 papers receiving a total of 737 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 5 papers in Epidemiology and 4 papers in Pharmacology. Recurrent topics in Sergio Triana's work include Nail Diseases and Treatments (5 papers), Single-cell and spatial transcriptomics (4 papers) and Fungal Biology and Applications (4 papers). Sergio Triana is often cited by papers focused on Nail Diseases and Treatments (5 papers), Single-cell and spatial transcriptomics (4 papers) and Fungal Biology and Applications (4 papers). Sergio Triana collaborates with scholars based in Germany, United States and Netherlands. Sergio Triana's co-authors include Theodore Alexandrov, Steeve Boulant, Megan L. Stanifer, Carmon Kee, Luca Rappez, Prasad Phapale, Mathias Heikenwälder, Katja Ovchinnikova, Rose M. Gathungu and Mira Stadler and has published in prestigious journals such as Nature Communications, Nature Methods and Scientific Reports.

In The Last Decade

Sergio Triana

15 papers receiving 727 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

SpaceM reveals metabolic states of single cells

2021 231 citations Luca Rappez, Mira Stadler et al. Nature Methods profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sergio Triana Germany	9	300	263	147	119	102	15	737
James W. Boles United States	10	335 1.1×	213 0.8×	101 0.7×	110 0.9×	19 0.2×	14	685
Pramod Kumar Tiwari India	18	359 1.2×	198 0.8×	148 1.0×	70 0.6×	14 0.1×	59	832
Xiaoling Su China	16	354 1.2×	87 0.3×	84 0.6×	52 0.4×	69 0.7×	41	585
Che A. Stafford Germany	12	537 1.8×	99 0.4×	158 1.1×	446 3.7×	39 0.4×	16	908
Deb N. Chakravarti United States	17	355 1.2×	79 0.3×	108 0.7×	238 2.0×	92 0.9×	44	862
Liam Cassidy Germany	16	774 2.6×	353 1.3×	128 0.9×	50 0.4×	187 1.8×	31	1.1k
Holger A. Lindner Germany	16	437 1.5×	265 1.0×	114 0.8×	278 2.3×	9 0.1×	42	1.0k
Jason L. Larabee United States	15	288 1.0×	160 0.6×	56 0.4×	94 0.8×	31 0.3×	37	574
Jingfang Mu China	13	546 1.8×	504 1.9×	87 0.6×	298 2.5×	8 0.1×	30	1.2k
Kosuke Takeya Japan	14	332 1.1×	66 0.3×	76 0.5×	109 0.9×	19 0.2×	43	681

Countries citing papers authored by Sergio Triana

Since Specialization

Citations

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

Fields of papers citing papers by Sergio Triana

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sergio Triana

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

All Works

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15 of 15 papers shown

Martins, Leila R., Lina Sieverling, Cihan Erkut, et al.. (2024). Single-cell division tracing and transcriptomics reveal cell types and differentiation paths in the regenerating lung. Nature Communications. 15(1). 2246–2246. 7 indexed citations

Treveil, Agatha, Dezső Módos, Matthew Madgwick, et al.. (2022). Mapping the epithelial–immune cell interactome upon infection in the gut and the upper airways. npj Systems Biology and Applications. 8(1). 15–15. 4 indexed citations

Laise, Pasquale, Megan L. Stanifer, Xiaoyun Sun, et al.. (2022). A model for network-based identification and pharmacological targeting of aberrant, replication-permissive transcriptional programs induced by viral infection. Communications Biology. 5(1). 714–714. 3 indexed citations

Reimer, Katharina C., Jitske Jansen, Gijs J. Overheul, et al.. (2022). Using human iPSC-derived kidney organoids to decipher SARS-CoV-2 pathology on single cell level. STAR Protocols. 3(3). 101612–101612. 2 indexed citations

Rappez, Luca, Mira Stadler, Sergio Triana, et al.. (2021). SpaceM reveals metabolic states of single cells. Nature Methods. 18(7). 799–805. 231 indexed citations breakdown →

Triana, Sergio, Megan L. Stanifer, Mohammed Shahraz, et al.. (2021). Single‐cell transcriptomics reveals immune response of intestinal cell types to viral infection. Molecular Systems Biology. 17(7). e9833–e9833. 36 indexed citations

Triana, Sergio, Carlos Ramírez, Carmon Kee, et al.. (2021). Single‐cell analyses reveal SARS‐CoV‐2 interference with intrinsic immune response in the human gut. Molecular Systems Biology. 17(4). e10232–e10232. 69 indexed citations

Stanifer, Megan L., Carmon Kee, Mirko Cortese, et al.. (2020). Critical Role of Type III Interferon in Controlling SARS-CoV-2 Infection in Human Intestinal Epithelial Cells. Cell Reports. 32(1). 107863–107863. 226 indexed citations

Triana, Sergio, Silvia Restrepo, Han A. B. Wösten, et al.. (2020). New Therapeutic Candidates for the Treatment of Malassezia pachydermatis -Associated Infections. Scientific Reports. 10(1). 4860–4860. 6 indexed citations

10.

Ramírez, Adriana Marcela Celis, Adolfo Amézquita, Sergio Triana, et al.. (2020). Analysis of Malassezia Lipidome Disclosed Differences Among the Species and Reveals Presence of Unusual Yeast Lipids. Frontiers in Cellular and Infection Microbiology. 10. 338–338. 39 indexed citations

11.

Rappez, Luca, et al.. (2019). PySpacell: A Python Package for Spatial Analysis of Cell Images. Cytometry Part A. 97(3). 288–295. 8 indexed citations

12.

Triana, Sergio, Hans de Cock, Robin A. Ohm, et al.. (2017). Lipid Metabolic Versatility in Malassezia spp. Yeasts Studied through Metabolic Modeling. Frontiers in Microbiology. 8. 1772–1772. 33 indexed citations

13.

Ramírez, Adriana Marcela Celis, Aurin M. Vos, Sergio Triana, et al.. (2017). Highly efficient transformation system for Malassezia furfur and Malassezia pachydermatis using Agrobacterium tumefaciens-mediated transformation. Journal of Microbiological Methods. 134. 1–6. 27 indexed citations

14.

Triana, Sergio, et al.. (2017). Malassezia spp. beyond The Mycobiota. Data Archiving and Networked Services (DANS). 3(3). 1–10. 20 indexed citations

15.

Triana, Sergio, Robin A. Ohm, Han A. B. Wösten, et al.. (2015). Draft Genome Sequence of the Animal and Human Pathogen Malassezia pachydermatis Strain CBS 1879. Genome Announcements. 3(5). 26 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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