Author

J. Kranz

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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Bioengineering· 1 Immunology· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Approach:experimental assay· 2 Organism:human· 2 Research Focus:benchmarking· 1 Assay:CITE-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

C. Kuppe· 2 shared R. Kramann· 2 shared A. A. Dichter· 1 shared A.-K. Thebille· 1 shared C. F. Krebs· 1 shared C. Kurts· 1 shared D. Frank· 1 shared E. Fewings· 1 shared

Activity by Year

2020-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersImmunology

C. Kuppe

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Influence of condensation domains on activity and specificity of adenylation domains

J. Kranz, S. L. Wenski, A. A. Dichter, H. B. Bode, K. A. J. Bozhueyuek

The paper investigates the role of the C-A interface in NRPSs, showing that the C domain's influence on activity and specificity is mediated through its interaction with the A domain, not directly.

bioRxiv · Aug 23, 2021 · PreprintBioengineering

Dissecting CD8+ T cell pathology of severe SARS-CoV-2 infection by single-cell epitope mapping

F. Schreibing, M. Hannani, F. Ticconi, E. Fewings, J. S. Nagai, M. Begemann, C. Kuppe, I. Kurth, J. Kranz, D. Frank, T. M. Anslinger, P. Ziegler, T. Kraus, J. Enczmann, V. Balz, , R. Kramann

This study identifies an immunogenic CD8+ T cell epitope associated with severe SARS-CoV-2 infection and characterizes its pathological features.

bioRxiv · Mar 3, 2021 · PreprintImmunology

Deep learning-based molecular morphometrics for kidney biopsies

M. Zimmermann, M. Klaus, M. N. Wong, A.-K. Thebille, L. Gernhold, C. Kuppe, M. Halder, J. Kranz, N. Wanner, F. Braun, S. Wulf, T. Wiech, U. Panzer, C. F. Krebs, E. Hoxha, , V. G. Puelles

The paper develops a deep learning method for analyzing kidney biopsy images to quantify podocyte loss, identifying morphometric signatures in ANCA-GN patients for risk stratification.

bioRxiv · Aug 23, 2020Pathology