Single-cell analyses reveal increased gene expression variability in human neurodevelopmental conditions

The American Journal of Human Genetics, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

SummaryInterindividual variation in phenotypic penetrance and severity is found many neurodevelopmental conditions, although the underlying mechanisms remain largely unresolved. Within individuals, homogeneous cell types (i.e., genetically identical similar environments) can differ molecule abundance. Here, we investigate hypothesis that conditions drive increased variability gene expression, not just differential expression. Leveraging independent single-cell single-nucleus RNA sequencing datasets derived from human brain-relevant tissue types, identify a significant increase expression driven by autosomal aneuploidy trisomy 21 (T21) as well autism-associated chromodomain helicase DNA binding protein 8 (CHD8) haploinsufficiency. Our analyses are consistent with global and, part, stochastic variability, which uncoupled changes transcript Highly variable genes tend to be cell-type specific modest enrichment for repressive H3K27me3, while least more likely constrained associated active histone marks. results indicate brain potential contribute diverse outcomes. These findings also provide scaffold understanding disease, essential deeper insights into genotype-phenotype relationships.Graphical abstract

Language: Английский

Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data

Nucleic Acids Research, Journal Year: 2025, Volume and Issue: 53(7)

Published: March 31, 2025

Bursty gene expression is characterized by two intuitive parameters, burst frequency and size, the cell-cycle dependence of which has not been extensively profiled at transcriptome level. In this study, we estimate parameters per allele in G1 G2/M phases for thousands mouse genes fitting mechanistic models to messenger RNA count data, obtained sequencing single cells whose position inferred using a deep-learning method. We find that upon DNA replication, median approximately halves, while size remains mostly unchanged. Genome-wide distributions parameter ratios between are broad, indicating substantial heterogeneity transcriptional regulation. also observe significant negative correlation ratios, suggesting regulatory processes do independently control parameters. show accurately must explicitly account copy number variation extrinsic noise due coupling transcription cell age across cycle, but corrections technical imperfect capture molecules experiments less critical.

Language: Английский

Exploring transcription modalities from bimodal, single-cell RNA sequencing data

NAR Genomics and Bioinformatics, Journal Year: 2024, Volume and Issue: 6(4)

Published: Sept. 28, 2024

Abstract There is a growing interest in generating bimodal, single-cell RNA sequencing (RNA-seq) data for studying biological pathways. These are predominantly utilized understanding phenotypic trajectories using velocities; however, the shape information encoded two-dimensional resolution of such not yet exploited. In this paper, we present an elliptical parametrization RNA-seq data, from which derived statistics that reveal four different modalities. modalities can be interpreted as manifestations changes rates splicing, transcription or degradation. We performed our analysis on cell cycle and colorectal cancer dataset. both datasets, found genes picked up by differential gene expression (DGEA), consequently unnoticed, visibly delineate phenotypes. This indicates that, addition to DGEA, searching exhibit discovered could aid recovering set phenotypes apart. For communities biomarkers cellular phenotyping, bimodal broaden search space genes, furthermore, allow incorporating processing into regulatory analyses.

Language: Английский