Author

Thomas R. Kiehl

Neural Stem Cell InstituteORCID 0000-0002-1559-6391Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:differential expression· 2 Organism:mouse· 2 Assay:scRNA-seq· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Research Focus:database / resource· 1

Frequent Co-Authors

Farhad FarjoodiD· 2 shared Sally TempleiD· 2 shared Caitlin E. HilliD· 1 shared Charles Bergeron· 1 shared Christina A. Allen· 1 shared Elizabeth S. FisheriD· 1 shared Matthew A. Amarante· 1 shared Mo LiuiD· 1 shared

Activity by Year

2022-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersNeuroscience

Farhad FarjoodORCID

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3 papers

Recent·Most cited

NeurosciencebioRxivPreprintSep 10, 2023

Adult Mouse Leptomeninges Exhibit Regional and Age-related Cellular Heterogeneity and Gene Expression Differences Implicating Mental Disorders

Christina A. Allen, Susan K. Goderie, Mo Liu…·Sally Temple

The paper analyzes regional and age-related differences in adult mouse leptomeninges using snucRNA-seq, revealing gene expression patterns linked to mental disorders.

DOI 10.1101/2023.09.10.557097

mousescRNA-seq

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NeuroscienceJournal of NeuroinflammationPeer-reviewedNov 4, 2022

Single Cell Profiling of CD45+ Spinal Cord Cells Reveals Microglial and B Cell Heterogeneity and Crosstalk Following Spinal Cord Injury

Elizabeth S. Fisher, Matthew A. Amarante, Natasha Lowry…·Thomas R. Kiehl

The paper uses single-cell RNA sequencing to identify immune cell populations and interactions in mouse spinal cord injury, revealing microglial and B cell heterogeneity and crosstalk.

25 citations

mousescRNA-seq

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NeurosciencebioRxivPreprintMay 27, 2022

Identification and assessment of systematic measurement error on electrophysiological recordings of neural cell cultures

Tyler Stone, Thomas R. Kiehl, Charles Bergeron

The paper identifies systematic measurement errors in MEA recordings and shows how they affect data interpretation, proposing methods for detection and correction.

DOI 10.1101/2022.05.27.493606

electronic health recordsstatistical model

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