MAPS: machine-assisted phenotype scoring enables rapid functional assessment of genetic variants by high-content microscopy

• Main Authors: Chao, J.T (Author), Loewen, C.J.R (Author), Roskelley, C.D (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: Ability testing; Classification (of information); Cost effectiveness; Cost-effective methods; Deep learning; Diseases; Experimental conditions; Functionalizations; High-content screening; High-throughput microscopy; human; Humans; image processing; Image Processing, Computer-Assisted; Learning services; Machine learning; microscopy; Microscopy; Optical projectors; phenotype; Phenotype; Protein subcellular localization; Single-cell phenotyping; software; Software; Software toolkits; Subcellular localizations; Tumor suppressors; Tumors; Well testing; workflow; Workflow
• Online Access: View Fulltext in Publisher

 Background: Genetic testing is widely used in evaluating a patient’s predisposition to hereditary diseases. In the case of cancer, when a functionally impactful mutation (i.e. genetic variant) is identified in a disease-relevant gene, the patient is at elevated risk of developing a lesion in their lifetime. Unfortunately, as the rate and coverage of genetic testing has accelerated, our ability to assess the functional status of new variants has fallen behind. Therefore, there is an urgent need for more practical, streamlined and cost-effective methods for classifying variants. Results: To directly address this issue, we designed a new approach that uses alterations in protein subcellular localization as a key indicator of loss of function. Thus, new variants can be rapidly functionalized using high-content microscopy (HCM). To facilitate the analysis of the large amounts of imaging data, we developed a new software toolkit, named MAPS for machine-assisted phenotype scoring, that utilizes deep learning to extract and classify cell-level features. MAPS helps users leverage cloud-based deep learning services that are easy to train and deploy to fit their specific experimental conditions. Model training is code-free and can be done with limited training images. Thus, MAPS allows cell biologists to easily incorporate deep learning into their image analysis pipeline. We demonstrated an effective variant functionalization workflow that integrates HCM and MAPS to assess missense variants of PTEN, a tumor suppressor that is frequently mutated in hereditary and somatic cancers. Conclusions: This paper presents a new way to rapidly assess variant function using cloud deep learning. Since most tumor suppressors have well-defined subcellular localizations, our approach could be widely applied to functionalize variants of uncertain significance and help improve the utility of genetic testing. © 2021, The Author(s).