Scientists measure real rates of change in global gene expression levels for the first time in animal embryos

Expression profiles of selected genes over the first hours of development showing a wide range of dynamic expression timescales. 

An interdisciplinary group of researchers has shown for the first time that it is feasible to determine the rate of change of gene transcript levels at a global level in animal embryos. They did this by directly measuring absolute numbers of mRNA molecules per embryo at closely spaced time points during development.

Transcription is the first step in gene expression, where the genes on our DNA are copied into molecules called messenger RNAs (mRNAs). mRNA molecules contain the instructions for making proteins – the number of mRNAs from a given gene is a measure of the level of expression of that gene.

The work is significant because it lays the groundwork for the development of quantitative models of animal development, enabling the use of mathematical tools more commonly associated with the physical sciences to be applied in biological studies.

The study, by a team from the Francis Crick Institute, the University of California, Irvine and the Yale University School of Medicine, was carried out in Xenopus frogs, a commonly used model animal in biology.

Mike Gilchrist of the Crick (currently based at Mill Hill) explained: “Development is a complex process, generating a functional and correctly scaled organism from a single cell – the fertilised egg.  A quantitative model of development would have the potential to predict, for example, the consequences of having copies of gene variants which may be associated with disease, and would help us identify new genes that are critical for development.”

“Methods currently used for measuring gene expression generally rely on something called ‘relative normalisation’, which means that gene expression levels in a sample can only be estimated relative to other genes in the same sample. This can be misleading when comparing different samples, and in particular when making measurements over time. Real rates of change can only be determined from actual transcript numbers, and this gives us the kinetics of gene expression which we are interested in.”

Nick Owens, a post doc in Mike Gilchrist’s lab, who developed the computational analysis, said: “This study improves our ability to understand the way gene expression changes with time, and from this we gain insight into the logic of how gene expression is regulated. We find that gene expression during development is both remarkably dynamic and tightly controlled.”

“One outcome of our approach is that we find a characteristic timescale of changes in gene expression for each gene. Knowing whether a gene’s expression changes over minutes, hours or days has important implications for our understanding of the function of the gene. Short timescale genes shape development and long timescale genes manage the cellular machinery.”

Dr Gilchrist said: “This study suggests that we may improve significantly on the widely used analysis methods for determining gene expression levels from high throughput sequence data: absolute quantitation offers a much sounder basis for determining changes in gene expression level, a measure widely used to determine the consequence of genetic, chemical or physical disturbances in living systems.”

“A better understanding of development will have important implications for human health: failures in developmental processes that lead to congenital defects are currently the most common cause of infant mortality in the US and Europe. To understand the genetic causes of disease, we need to know which genes are involved in development, as well as when and where they act and how this changes with time. This work helps us to do this by providing the ‘when’ and by giving good estimates of actual transcript numbers and consequent transcription rates, measured over the whole embryo.”

The paper, Measuring Absolute RNA Copy Numbers at High Temporal Resolution Reveals Transcriptome Kinetics in Development, is published in Cell Reports.