Cell-based assay for ciliopathy patients to improve accurate diagnosis using ALPACA.
Cenna DoornbosRonald van BeekErnie M H F BongersDorien LugtenbergPeter H M KlarenLisenka E L M VissersRonald RoepmanMachteld M OudPublished in: European journal of human genetics : EJHG (2021)
Skeletal ciliopathies are a group of disorders caused by dysfunction of the cilium, a small signaling organelle present on nearly every vertebrate cell. This group of disorders is marked by genetic and clinical heterogeneity, which complicates accurate diagnosis. In this study, we developed a robust, standardized immunofluorescence approach to accurately diagnose a subset of these disorders. Hereto we determined and compared the cilium phenotype of healthy individuals to patients from three different ciliopathy subgroups, using skin-derived fibroblasts. The cilium phenotype assay consists of three parameters; (1) ciliogenesis, based on the presence or absence of cilium markers, (2) cilium length, measured by the combined signal of an axonemal and a cilium membrane marker, and (3) retrograde intraflagellar transport (IFT), quantified by the area of the ciliary tip. Analysis of the cilium phenotypic data yielded comparable and reproducible results and in addition, displayed identifiable clusters for healthy individuals and two ciliopathy subgroups, i.e. ATD and CED. Our results illustrate that standardized analysis of the cilium phenotype can be used to discriminate between ciliopathy subgroups. Therefore, we believe that standardization of functional assays analyzing cilium phenotypic data can provide additional proof for conclusive diagnosis of ciliopathies, which is essential for routine diagnostic care.
Keyphrases
- end stage renal disease
- ejection fraction
- newly diagnosed
- single cell
- chronic kidney disease
- high throughput
- cell therapy
- stem cells
- electronic health record
- gene expression
- mesenchymal stem cells
- high resolution
- big data
- patient reported outcomes
- dna methylation
- genome wide
- health insurance
- soft tissue
- clinical practice
- patient reported
- deep learning
- copy number