Author

Alexis L. Bergsma

Van Andel Institute

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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Neuroscience· 3

Signature Assays, Organisms & Methods

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Approach:experimental assay· 2 Organism:mouse· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1

Frequent Co-Authors

Michael X. HendersoniD· 2 shared Patrik BrundiniD· 2 shared Allison LindquistiD· 1 shared Andrea ParhamiD· 1 shared Brundin Lena· 1 shared Caio M. MassariiD· 1 shared Darren J. MooreiD· 1 shared Dylan J. Dues· 1 shared

Activity by Year

2022-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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2 shared papersNeuroscience

Michael X. HendersonORCID

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3 papers

Recent·Most cited

Neuropathology in an α-synuclein preformed fibril mouse model occurs independent of the Parkinson's disease-linked lysosomal ATP13A2 protein

Caio M. Massari, Dylan J. Dues, Alexis L. Bergsma, Kayla A. Sipple, Maxwell A. Frye, Erin T. Williams, Darren J. Moore

This study shows that α-synuclein pathology spreads independently of ATP13A2 in a mouse model, indicating ATP13A2 is not critical for -syn clearance or propagation.

Neurobiology of Disease · Oct 13, 2024Peer-reviewedNeuroscience

Human Endogenous Retrovirus Expression is Dynamically Regulated in Parkinson's Disease

Juozas Gordevičius, Thomas M. Goralski, Alexis L. Bergsma, Andrea Parham, Emily Kuhn, Lindsay K. Meyerdirk, Mitch McDonald, Milda Milčiūtė, Elizabeth I. van Putten, Lee L. Marshall, Patrik Brundin, Lena Brundin, Viviane Labrie, Michael X. Henderson, John Andrew Pospisilik

This study identifies up-regulation of human endogenous retrovirus (HERV) transcripts in Parkinson's disease (PD) across multiple tissues and blood, linking TE activation to PD pathogenesis and potential treatment monitoring.

bioRxiv · Nov 3, 2023 · PreprintNeuroscience

A novel automated morphological analysis of microglia activation using a deep learning assisted model

Lucas A. Stetzik, Gabriela Mercado, Lindsey Smith, George Sonia, Quansah Emmanuel, Katarzyna Müller Luda, Schulz Emily, Meyerdirk Lindsay, Allison Lindquist, Alexis L. Bergsma, Russell G. Jones, Brundin Lena, Michael X. Henderson, Pospisilik John Andrew, Patrik Brundin

This paper introduces a deep learning model for automated microglia morphology analysis, validated across mouse disease models and available on a commercial platform.

bioRxiv · Mar 11, 2022 · PreprintNeuroscience