Author

T. N. Arrey

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biochemistry· 4 Microbiology· 1

Signature Assays, Organisms & Methods

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Assay:proteomics· 4 Approach:experimental assay· 3 Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Organism:human· 2 Organism:bacteria (other)· 1 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1

Frequent Co-Authors

A. Makarov· 2 shared A. Pashkova· 2 shared E. Damoc· 2 shared A. d. V. Martinez· 1 shared A. Droit· 1 shared A. Harder· 1 shared A. Peterson· 1 shared B. C. Searle· 1 shared

Activity by Year

2019-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 15 mutual co-author links

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2 shared papersBiochemistry

A. Makarov

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Author profile →

5 papers

Recent·Most cited

Paradigm shift in biomarker translation: a pipeline to generate clinical grade biomarker candidates from DIA-MS discovery

Q. Fu, M. Vegesna, N. Sundararaman, E. Damoc, T. N. Arrey, A. Pashkova, E. Mengesha, P. Debbas, S. Joung, D. Li, S. Cheng, J. Braun, D. P. B. McGovern, C. Murray, Y. Xuan, J. E. Van Eyk

This paper introduces TEAQ, a software tool that selects clinical-grade biomarker candidates from DIA-MS data by evaluating protein quantification metrics in a discovery cohort.

bioRxiv · Mar 20, 2024 · PreprintBiochemistry

Challenging the Astral mass analyzer - up to 5300 proteins per single-cell at unseen quantitative accuracy to study cellular heterogeneity.

J. A. Bubis, T. N. Arrey, E. Damoc, B. Delanghe, J. Slovakova, T. M. Sommer, H. Kagawa, P. Pichler, N. C. Rivron, K. Mechtler, M. Matzinger

This paper introduces a high-throughput single-cell proteomics workflow achieving 5300 protein groups per cell with high accuracy, enabling the study of cellular heterogeneity and lineage differentiation.

bioRxiv · Feb 1, 2024 · PreprintBiochemistry

Evaluating the performance of the Astral mass analyzer for quantitative proteomics using data independent acquisition

L. R. Heil, N. E. Damoc, T. N. Arrey, A. Pashkova, E. Denisov, J. Petzoldt, A. Peterson, C. Hsu, B. C. Searle, N. Shulman, M. Riffle, B. Connolly, B. X. MacLean, P. M. Remes, M. Senko, , M. J. MacCoss

The paper evaluates the Astral mass analyzer's performance in quantitative proteomics, showing it quantifies 5 times more peptides per unit time than existing Orbitrap mass spectrometers and detects over 5,000 plasma proteins.

bioRxiv · Jun 3, 2023 · PreprintBiochemistry

A Compact Quadrupole-Orbitrap Mass Spectrometer with FAIMS Interface Improves Proteome Coverage in Short LC Gradients

D. B. Bekker-Jensen, A. d. V. Martinez, S. Steigerwald, P. Ruether, K. Fort, T. N. Arrey, A. Harder, A. Makarov, J. V. Olsen

bioRxiv · Nov 30, 2019Biochemistry

Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning

F. Roux-Dalvai, C. Gotti, M. Leclercq, M.-C. Helie, M. Boissinot, T. N. Arrey, C. Dauly, F. Fournier, I. Kelly, J. Marcoux, J. Bestman-Smith, M. G. Bergeron, A. Droit

This paper develops a method using LC-MS/MS and machine learning to rapidly identify bacterial species in clinical samples without culture, achieving 97% accuracy for UTI pathogens.

bioRxiv · May 10, 2019Microbiology