Allele-level visualization of transcription and chromatin by high-throughput imaging.

DNA/RNA FISH Genome organization High-throughput imaging In Situ Hybridization

Journal

Research square
Titre abrégé: Res Sq
Pays: United States
ID NLM: 101768035

Informations de publication

Date de publication:
22 Feb 2024
Historique:
pubmed: 11 3 2024
medline: 11 3 2024
entrez: 11 3 2024
Statut: epublish

Résumé

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

Identifiants

pubmed: 38464289
doi: 10.21203/rs.3.rs-3970096/v1
pmc: PMC10925428
pii:
doi:

Types de publication

Preprint

Langues

eng

Subventions

Organisme : Intramural NIH HHS
ID : ZIA BC010309
Pays : United States

Déclaration de conflit d'intérêts

Conflict of Interest The authors declare no conflicts of interest.

Auteurs

Faisal Almansour (F)

National Cancer Institute, National Institute of Health.

Adib Keikhosravi (A)

National Cancer Institute, National Institute of Health.

Gianluca Pegoraro (G)

National Cancer Institute, National Institute of Health.

Tom Misteli (T)

National Cancer Institute, National Institute of Health.

Classifications MeSH