A scalable screening platform for phenotypic subtyping of ALS patient-derived fibroblasts Article Swipe
Karl Kumbier
,
Maike Roth
,
Zizheng Li
,
Julia R. Lazzari-Dean
,
C. Waters
,
Ping Huang
,
Vladislav A. Korobeynikov
,
Hemali Phatnani
,
Neil A. Shneider
,
Matthew P. Jacobson
,
Lani F. Wu
,
Steven J. Altschuler
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.09.27.509770
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.09.27.509770
A major challenge for understanding and treating Amyotrophic Lateral Sclerosis (ALS) is that most patients have no known genetic cause. Even within defined genetic subtypes, patients display considerable clinical heterogeneity. It is unclear how to identify subsets of ALS patients that share common molecular dysregulation or could respond similarly to treatment. Here, we developed a scalable microscopy and machine learning platform to phenotypically subtype readily available, primary patient-derived fibroblasts. Application of our platform identified robust signatures for the genetic subtype FUS-ALS, allowing cell lines to be scored along a spectrum from FUS-ALS to non-ALS. Our FUS-ALS phenotypic score negatively correlates with age of diagnosis and provides information that is distinct from transcript profiling. Interestingly, the FUS-ALS phenotypic score can be used to identify sporadic patient fibroblasts that have consistent pathway dysregulation with FUS-ALS. Further, we showcase how the score can be used to evaluate the effects of ASO treatment on patient fibroblasts. Our platform provides an approach to move from genetic to phenotypic subtyping and a first step towards rational selection of patient subpopulations for targeted therapies.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.09.27.509770
- https://www.biorxiv.org/content/biorxiv/early/2022/09/30/2022.09.27.509770.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4297516441
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4297516441Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2022.09.27.509770Digital Object Identifier
- Title
-
A scalable screening platform for phenotypic subtyping of ALS patient-derived fibroblastsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-28Full publication date if available
- Authors
-
Karl Kumbier, Maike Roth, Zizheng Li, Julia R. Lazzari-Dean, C. Waters, Ping Huang, Vladislav A. Korobeynikov, Hemali Phatnani, Neil A. Shneider, Matthew P. Jacobson, Lani F. Wu, Steven J. AltschulerList of authors in order
- Landing page
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https://doi.org/10.1101/2022.09.27.509770Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2022/09/30/2022.09.27.509770.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/09/30/2022.09.27.509770.full.pdfDirect OA link when available
- Concepts
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Subtyping, Phenotype, Amyotrophic lateral sclerosis, Genetic heterogeneity, Biology, Medicine, Computational biology, Bioinformatics, Pathology, Genetics, Disease, Gene, Computer science, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
- References (count)
-
41Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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