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Related Experiment Video

Updated: Jan 8, 2026

Single-cell Profiling of Developing and Mature Retinal Neurons

Single-cell Profiling of Developing and Mature Retinal Neurons

Published on: April 19, 2012

Decoding cell-class specific roles of non-coding variants in human retina.

Leah S VandenBosch¹, Amy S Leonardson^1,2, Timothy J Cherry^3,4,5

¹Center for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute, Seattle, WA, USA.

Scientific Reports

|December 11, 2025

Summary

This summary is machine-generated.

Machine learning models predict how genetic variants affect inherited retinal diseases (IRDs). Using single nucleus epigenomic data, these models accurately identify functional impacts on regulatory elements, prioritizing variants for further study.

Keywords:

Cis-regulatory element Machine learning Non-coding variants Retinal disease

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Area of Science:

Genomics
Molecular Biology
Computational Biology

Background:

Non-coding variants in cis-regulatory elements are implicated in inherited retinal diseases (IRDs).
Functional characterization of these regulatory variants is a significant challenge in understanding IRD pathogenesis.
Identifying impactful variants is crucial for diagnosing and potentially treating IRDs.

Purpose of the Study:

To develop and validate machine learning (ML) models for predicting the functional impact of non-coding variants on retinal cis-regulatory elements.
To enhance the identification and prioritization of disease-causing variants in inherited retinal diseases.
To leverage single nucleus ATAC-seq data for cell-class-specific variant effect prediction.

Main Methods:

Implemented a gapped k-mer support vector machine (SVM) approach trained on single nucleus ATAC-seq data from human retina.
Developed 18 distinct ML models to predict variant impact on 39,437 cell-class-specific regulatory elements.
Utilized Variant Impact Prediction (VIP) scores and correlated them with massively parallel reporter assays (MPRAs) for validation.

Main Results:

Achieved prediction accuracy (AUROC) over 90% with high cell class specificity for the developed ML models.
VIP scores effectively highlighted sequences within regulatory elements, including transcription factor binding motifs, sensitive to mutation.
MPRA correlations confirmed the cell-class-specific predictive power of VIP scores for single nucleotide variants and indels.

Conclusions:

Single nucleus epigenomic data can be effectively used to predict the functional impact of non-coding sequence variants.
The developed ML models and VIP scores provide a powerful tool for rapid prioritization of patient variants for functional analysis in IRDs.
This approach advances the understanding of genetic contributions to inherited retinal diseases and facilitates variant interpretation.