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Series GSE254085 Query DataSets for GSE254085
Status Public on Aug 06, 2024
Title Noncoding Mendelian epigenomics - single cell multiome mouse cMN data
Organism Mus musculus
Experiment type Expression profiling by high throughput sequencing
Genome binding/occupancy profiling by high throughput sequencing
Summary Although Mendelian disorders are overwhelmingly attributed to protein-coding pathogenic variants, a majority of unsolved cases do not harbor obvious causal pathogenic variants in the coding sequence, suggesting a potential non-coding etiology. However, classification of pathogenicity in non-coding sequence remains prohibitive due to a vastly increased search space and the lack of a standardized rubric for interpretation. Here, we present an integrated single cell multiomic framework to nominate pathogenic non-coding variants for the congenital cranial dysinnervation disorders (CCDDs). The CCDDs are Mendelian neurodevelopmental disorders that result from aberrant development of cranial motor neurons in the embryonic brainstem. We created a non-coding reference atlas of single cell chromatin accessibility profiles for 86,089 embryonic mouse cranial motor neurons (cMNs). We found that high-quality single cell ATAC-seq (scATAC) profiles alone were a strong predictor of enhancement (64% in vivo validation rate). To further aid in interpretation, we integrated single cell histone modification and gene expression information to distinguish individual enhancers and their cognate genes. Relatively subtle differences in cellular composition of input data often led to substantial differences in predicted enhancer strength, cognate gene, and tissue of activity. Next, we mapped candidate non-coding variants from 899 whole genome sequences from 270 CCDD pedigrees to the murine cMN-specific regulatory elements and trained a machine learning classifier to accurately predict the functional effects of patient variants within these elements. We then performed high coverage scATACseq and site-specific footprinting analysis on an allelic series of CRISPR-humanised mice to validate our machine learning predictions and render important clues to the mode of pathogenicity. Finally, we performed peak- and gene-centric allelic aggregation to nominate non-coding variants, including those regulating MN1 and EBF3, respectively. Altogether this work extends non-coding variant analysis to Mendelian disease and presents a generalizable framework for nominating novel non-coding variants in other rare disorders.
 
Overall design Single cell multiome of mouse developing cranial motor neurons and spinal motor neurons at e11.5
 
Contributor(s) Engle E, Lee A
Citation(s) 39333082
Submission date Jan 24, 2024
Last update date Oct 15, 2024
Contact name Elizabeth C Engle
E-mail(s) elizabeth.engle@childrens.harvard.edu
Phone 6179194030
Organization name Boston Childrens Hospital
Street address 3 Blackfan Circle
City Boston
ZIP/Postal code 02115
Country USA
 
Platforms (1)
GPL19057 Illumina NextSeq 500 (Mus musculus)
Samples (4)
GSM8033171 CN347SMN_e11.5_R1_ATAC
GSM8033172 CN347SMN_e11.5_R1_GEX
GSM8033173 CN347SMN_e11.5_R2_ATAC
This SubSeries is part of SuperSeries:
GSE254090 A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders
Relations
BioProject PRJNA1068535

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Supplementary file Size Download File type/resource
GSE254085_RAW.tar 1.8 Gb (http)(custom) TAR (of BIGWIG, TAR)
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Raw data are available in SRA

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