Related Experiment Video
Updated: Feb 8, 2026

09:18
Using Microfluidic Devices to Measure Lifespan and Cellular Phenotypes in Single Budding Yeast Cells
Published on: March 30, 2017
8.2K
Yeast lifespan variation correlates with cell growth and SIR2 expression
Jessica T Smith1, Jill W White2, Huzefa Dungrawala3
1Department of Cell Biology and Biochemistry, Texas Tech University Health Sciences Center, Lubbock, TX, United States of America.
Plos One
|July 7, 2018
Summary
Cellular growth rates and SIR2 expression influence yeast lifespan variation. Larger cells and faster growth correlate with shorter lifespans, while moderate SIR2 increases extend it.
Area of Science:
- Cellular and Molecular Biology
- Aging Research
- Yeast Genetics
Background:
- Yeast replicative lifespan assays show significant cell-to-cell variation.
- Microfluidic studies suggest growth and gene expression heterogeneity contribute to aging differences.
Purpose of the Study:
- To investigate factors contributing to lifespan variation in yeast using traditional plate assays.
- To correlate cellular birth size and inter-generational growth rates with replicative lifespan.
Main Methods:
- Analysis of a large dataset of replicative lifespan data from traditional yeast plate assays.
- Correlation of birth size, inter-generational growth rates, and SIR2 expression with lifespan.
Main Results:
- Short-lived yeast cells often senesce with a budded morphology.
- Large birth size and high inter-generational growth rates significantly reduced lifespan.
- SIR2 expression levels correlated with lifespan and growth; reduced in large cells, increased in small cells.
Conclusions:
- Cellular growth rates and SIR2 expression are key contributors to lifespan heterogeneity in individual yeast cells.
- Moderate increases in SIR2 expression are associated with reduced growth and extended lifespan.
Related Concept Videos
Yeast Signaling
17.3K
Yeasts are single-celled organisms, but unlike bacteria, they are eukaryotes (cells with a nucleus). Cell signaling in yeast is similar to signaling in other eukaryotic cells. A ligand, such as a protein or a small molecule released from a yeast cell, attaches to a receptor on the cell surface. The binding stimulates second-messenger kinases to activate or inactivate transcription factors that further regulate gene expression. Many of the yeast intracellular signaling cascades have similar...
17.3K
What is Variation?
18.6K
Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
18.6K
Correlations
36.4K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
36.4K
Correlation and Causation
42.8K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
42.8K
Variation
8.0K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
8.0K
Correlation
15.2K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
15.2K

