Author

Lisa Maria Steinheuer

University Hospital BonnORCID 0000-0002-2329-801XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Cell Biology· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Organism:human· 2 Assay:scRNA-seq· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:cell-cell communication· 1

Frequent Co-Authors

Kevin ThurleyiD· 2 shared Ananthi Kumar· 1 shared Brett G.M. HughesiD· 1 shared Denise RommeliD· 1 shared Dilan PathiranaiD· 1 shared Dillon CorvinoiD· 1 shared Eva BartokiD· 1 shared Felix BenderiD· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersCell Biology

Kevin ThurleyORCID

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3 papers

Recent·Most cited

Cell BiologybioRxivPreprintOct 29, 2026

Mechanical stress-induced release of mitochondrial double-stranded RNA activates RIG-I to drive osteogenic differentiation in aortic disease

Madeleine Graef, Felix Bender, Svenja Dudek…·Gunther Hartmann

This study shows mechanical stress releases mitochondrial dsRNA, activating RIG-I to drive osteogenic differentiation and calcification in aortic disease, with JAK/STAT inhibition offering therapeutic potential.

DOI 10.64898/2026.10.28.762541

humanmouse

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ImmunologyEuropean Journal of ImmunologyPeer-reviewedNov 12, 2024

Type I interferon drives a cellular state inert to TCR-stimulation and could impede effective T-cell differentiation in cancer

Dillon Corvino, Martin Druce Batstone, Brett G.M. Hughes…·Tobias Bald

The paper identifies a subset of CD8+ TILs in HPV-HNSCC enriched for interferon-stimulated genes that are transcriptionally inert to TCR stimulation and may hinder T-cell differentiation.

DOI 10.1002/eji.202451371

humanscRNA-seq

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BioinformaticsbioRxivPreprintApr 2, 2021

Benchmarking scRNA-seq imputation tools with respect to network inference highlights deficits in performance at high levels of sparsity

Lisa Maria Steinheuer, Sebastian Canzler, Jörg Hackermüller A

This study benchmarks scRNA-seq imputation tools for network inference, finding they perform well on low/moderate sparsity but fail at high sparsity.

6 citations

scRNA-seqbenchmark study

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