Author
National Institute of Neurological Disorders and StrokeORCID 0000-0002-8461-6210Unclaimed ORCID profile · follow to get notified of new preprints
6 papers
Recent·Most cited
Kaya J.E. Matson, Cecilia R. Petruconis, Amber Hernandez, Jay R. Dave, Suradip Das, Erika L. F. Holzbaur
This study shows STMN2 regulates microtubule dynamics and axonal resilience in human motor neurons, linking cytoskeletal homeostasis to ALS pathology.
bioRxiv · Nov 9, 2026 · PreprintCell Biology
Archana Yadav, Kaya J.E. Matson, Li Li, Isabelle Hua, Joana Petrescu, Kristy Kang, Mor R. Alkaslasi, Dylan I. Lee, Saadia Hasan, Ahmad Galuta, Annemarie Dedek, Sara Ameri, Jessica Parnell, Mohammad M. Alshardan, Feras Abbas Qumqumji, , Ariel J. Levine
This paper presents a cellular taxonomy of the adult human spinal cord using single nucleus RNA-sequencing and spatial transcriptomics, identifying 29 glial and 35 neuronal clusters and highlighting motoneuron vulnerability to ALS.
Neuron · Feb 1, 2023Peer-reviewedNeuroscience
Jordan W. Squair, Matthieu Gautier, Claudia Kathe, Mark A. Anderson, Nicholas D. James, Thomas Haynes Hutson, Rémi Hudelle, Taha Qaiser, Kaya J.E. Matson, Quentin Barraud, Ariel J. Levine, Gioele La Manno, Michael A. Skinnider, Grégoire Courtine
This study evaluates differential expression methods in single-cell transcriptomics, finding that many produce false discoveries due to ignoring biological variation.
Nature Communications · Sep 28, 2021Peer-reviewedBioinformatics
Kaya J.E. Matson, Daniel E. Russ, Claudia Kathe, Dragan Maric, Isabelle Hua, Jonathan Krynitsky, Randall H. Pursley, Anupama Sathyamurthy, Jordan W. Squair, Grégoire Courtine, Ariel J. Levine
The paper profiles single nucleus RNA-seq data from spinal cord tissue after injury to identify repair-related cellular mechanisms and potential therapeutic targets.
bioRxiv · Apr 28, 2021 · PreprintNeuroscience
Daniel E. Russ, Ryan B. Patterson Cross, Li Li, Stephanie C. Koch, Kaya J.E. Matson, Ariel J. Levine
The paper harmonizes six spinal cord single-cell studies to create a unified atlas, revealing cell type organization and optimizing computational classification based on transcriptomic data.
bioRxiv · Sep 3, 2020 · PreprintNeuroscience
Michael A. Skinnider, Jordan W. Squair, Claudia Kathe, Mark A. Anderson, Matthieu Gautier, Kaya J.E. Matson, Marco Milano, Thomas Haynes Hutson, Quentin Barraud, Aaron Alexander Phillips, Leonard J. Foster, Gioele La Manno, Ariel J. Levine, Grégoire Courtine
The paper introduces a machine-learning method, Augur, to identify cell types most responsive to biological perturbations in single-cell data and validates it on multiple datasets.
Nature Biotechnology · Jul 20, 2020Peer-reviewedGenomics