Author

M. T. Eadon

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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Cell Biology· 2 Physiology· 1 Scientific Communication & Education· 1

Signature Assays, Organisms & Methods

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Organism:human· 4 Research Focus:cell type annotation· 3 Approach:deep learning· 2 Assay:imaging· 2 Approach:statistical model· 2 Research Focus:batch correction / integration· 1 Assay:bulk RNA-seq· 1 Research Focus:cell-cell communication· 1

Frequent Co-Authors

K. W. Dunn· 3 shared P. C. Dagher· 3 shared S. Winfree· 3 shared T. M. El-Achkar· 3 shared D. Barwinska· 2 shared J. B. Hodgin· 2 shared J. Luo· 2 shared K. J. Kelly· 2 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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3 shared papersPhysiology

K. W. Dunn

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Author profile →

4 papers

Recent·Most cited

miRNA and mRNA Signatures in Human Acute Kidney Injury Tissue

D. Janosevic, T. De Luca, R. M. Ferreira, D. L. Gisch, T. Hato, J. Luo, Y. Yang, J. B. Hodgin, P. C. Dagher, M. T. Eadon

This study identifies miRNA and mRNA signatures in human AKI tissue, revealing their roles in inflammation, immune activation, and epithelial-to-mesenchymal transition, and highlights the importance of using appropriate reference controls.

bioRxiv · Sep 11, 2023Cell Biology

Integrated cytometry with machine learning applied to high-content imaging of human kidney tissue for in-situ cell classification and neighborhood analysis

S. Winfree, A. T. McNutt, S. Khochare, T. J. Borgard, D. Barwinska, A. R. Sabo, M. J. Ferkowicz, J. C. Williams, J. E. Lingeman, C. J. Gulbronson, K. J. Kelly, T. A. Sutton, P. C. Dagher, M. T. Eadon, K. W. Dunn, T. M. El-Achkar

The paper introduces VTEA, a software tool that integrates machine learning and cytometry analysis for high-content imaging of human kidney tissue to classify cells and analyze their spatial neighborhoods.

bioRxiv · Dec 27, 2021Physiology

A Multimodal and Integrated Approach to Interrogate Human Kidney Biopsies with Rigor and Reproducibility: The Kidney Precision Medicine Project

T. M. El-Achkar, M. T. Eadon, R. Menon, B. B. Lake, T. K. Sigdel, T. Alexandrov, S. Parikh, G. Zhang, D. Dobi, K. W. Dunn, E. A. Otto, C. R. Anderton, J. M. Carson, J. Luo, C. Park, , For the Kidney Precision Medicine Project

The paper describes a pipeline for generating 3D molecular atlases of human kidney biopsies using multi-OMICS and imaging technologies, emphasizing quality control and reproducibility across institutions.

bioRxiv · Aug 23, 2020 · PreprintScientific Communication & Education

In situ classification of cell types in human kidney tissue using 3D nuclear staining

A. Woloshuk, S. Khochare, A. F. Almulhim, A. McNutt, D. Dean, D. Barwinska, M. Ferkowicz, M. T. Eadon, K. J. Kelly, K. W. Dunn, M. A. Hasan, T. M. El-Achkar, S. Winfree

This paper develops a deep learning method using 3D nuclear staining to classify cell types in human kidney tissue without requiring specific markers.

bioRxiv · Jun 24, 2020Cell Biology