Allele-level visualization of transcription and chromatin by high-throughput imaging.
Faisal AlmansourAdib KeikhosraviGianluca PegoraroTom MisteliPublished in: bioRxiv : the preprint server for biology (2024)
The spatial arrangement of the genome within the nucleus is a pivotal aspect of cellular organization and function with implications for gene expression and regulation. While all genome organization features, such as loops, domains, and radial positioning, are non-random, they are characterized by a high degree of single-cell variability. Imaging approaches are ideally suited to visualize, measure, and study single-cell heterogeneity in genome organization. Here, we describe two methods for the detection of DNA and RNA of individual gene alleles by fluorescence in situ hybridization (FISH) in a high-throughput format. We have optimized combined DNA/RNA FISH approaches either using simultaneous or sequential detection. These optimized DNA and RNA FISH protocols, implemented in a 384-well plate format alongside automated image and data analysis, enable accurate detection of chromatin loci and their gene expression status across a large cell population with allele-level resolution. We successfully visualized MYC and EGFR DNA and RNA in multiple cell types, and we determined the radial position of active and inactive MYC and EGFR alleles. These optimized DNA/RNA detection approaches are versatile and sensitive tools for mapping of chromatin features and gene activity at the single-allele level and at high throughput.
Keyphrases
- single cell
- high throughput
- gene expression
- genome wide
- nucleic acid
- single molecule
- circulating tumor
- rna seq
- dna methylation
- cell free
- transcription factor
- high resolution
- loop mediated isothermal amplification
- real time pcr
- small cell lung cancer
- dna damage
- data analysis
- label free
- epidermal growth factor receptor
- circulating tumor cells
- tyrosine kinase
- deep learning
- stem cells
- ultrasound guided
- machine learning
- photodynamic therapy
- genome wide identification