
Strength in numbers: quantitative single‐molecule RNA detection assays
Author(s) -
Gaspar Imre,
Ephrussi Anne
Publication year - 2015
Publication title -
wiley interdisciplinary reviews: developmental biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.779
H-Index - 45
eISSN - 1759-7692
pISSN - 1759-7684
DOI - 10.1002/wdev.170
Subject(s) - nucleic acid , rna , computational biology , biology , gene expression , gene , transcription (linguistics) , population , cytoplasm , transcriptome , microbiology and biotechnology , genetics , philosophy , linguistics , demography , sociology
Gene expression is a fundamental process that underlies development, homeostasis, and behavior of organisms. The fact that it relies on nucleic acid intermediates, which can specifically interact with complementary probes, provides an excellent opportunity for studying the multiple steps—transcription, RNA processing, transport, translation, degradation, and so forth—through which gene function manifests. Over the past three decades, the toolbox of nucleic acid science has expanded tremendously, making high‐precision in situ detection of DNA and RNA possible. This has revealed that many—probably the vast majority of—transcripts are distributed within the cytoplasm or the nucleus in a nonrandom fashion. With the development of microscopy techniques we have learned not only about the qualitative localization of these molecules but also about their absolute numbers with great precision. Single‐molecule techniques for nucleic acid detection have been transforming our views of biology with elementary power: cells are not average members of their population but are highly distinct individuals with greatly and suddenly changing gene expression, and this behavior of theirs can be measured, modeled, and thus predicted and, finally, comprehended. WIREs Dev Biol 2015, 4:135–150. doi: 10.1002/wdev.170 This article is categorized under: Establishment of Spatial and Temporal Patterns > Cytoplasmic Localization Gene Expression and Transcriptional Hierarchies > Quantitative Methods and Models Technologies > Analysis of the Transcriptome