This work was supported by NIH Grant 1R21AG031965-01A1. M. K. is an Ellison Medical Foundation New Scholar in Aging.

Part 1: Preparation of aging cultures
<ol>
	<li>Streak strains of interest from frozen stocks onto YEPD agar plates (1% yeast extract, 2% bacto-peptone, 2% agar, 2% glucose).</li>
	<li>Incubate the cells at 30&#730;C for 48 hours or until single colonies appear.</li>
	<li>Pick single colonies and inoculate into 5 mL of YEPD liquid medium (1% yeast extract, 2% bacto-peptone, 2% glucose) in test tubes.</li>
	<li>Grow cultures overnight at 30&#730;C while maintaining constant agitation using either a shaker or roller drum.</li>
	<li>Inoculate 5 mL of synthetic complete (SC) medium (Table 1) with 50 &#181;L of the overnight culture. Generally three SC cultures are prepared for each strain to provide triplicate biological replication of the life span analysis for each strain being examined.</li>
	<li>Maintain the cultures at 30&#730;C with constant agitation on a roller drum for the entire experiment (generally 2 or more weeks).</li>
</ol>
Part 2: Taking a viability age-point
After two days of culture in SC media, the cells should be in stationary phase and the first age-point is ready to be taken. Subsequent age-points should be taken every 2-3 days for a minimum of two weeks. For each age point:
<ol>
	<li>Prepare the Bioscreen 100-well Honeycomb plates for inoculation by filling each well with 145 &#181;L of YEPD. Be sure to leave at least one well filled with only YEPD and no cells for data analysis later.</li>
	<li>Remove the aging cultures from the incubator.</li>
	<li>Briefly vortex the first culture to be inoculated into the Honeycomb plate, while being careful not to spill any of the culture.</li>
	<li>Remove 5 &#181;L of the mixed culture and pipette it into the first well of the Honeycomb plate. Flame the mouth of each test tube before and after removing the 5 &#181;L aliquot.</li>
	<li>Repeat this procedure for each aging culture being sure to note the well position corresponding to each culture. Identical well positions should be used for each subsequent age-point throughout the entire experiment.</li>
	<li>Replace the cultures into the 30&#730;C incubator when finished with the inoculations.</li>
</ol>
Part 3: Loading the Bioscreen C MBR machine
<ol>
	<li>Expose the incubator compartment by lifting the lid and remove the cover to the sample tray.</li>
	<li>Insert the newly inoculated Honeycomb plate into sample tray (use the left slot if you are only reading one plate).</li>
	<li>Replace the cover to the sample tray and lower the lid to the incubator compartment.</li>
	<li>Check to make sure the heat transfer fluid is above the minimum fill level. If low, add more using a 1000 &#181;L pipette with the heat transfer fluid provided.</li>
	<li>Using the Bioscreen software &#8220;EZExperiment&#8221;, set the following parameters to obtain suitable growth curves for Saccharomyces cerevisiae:
	<ul>
		<li>Number of samples: Enter the number of wells with media or 200</li>
		<li>Filter: 420-580nm, Wideband</li>
		<li>Temperature: 30&#730;C</li>
		<li>Experiment Length: 1 Day, 0 hours, 0 seconds</li>
		<li>Measurement Interval: 30 minutes</li>
		<li>Shaking: On, Continuous shaking, High</li>
	</ul>
	</li>
	<li>Click &#8220;Start&#8221; to begin readings.</li>
</ol>
Part 4: Data Analysis
Typically, six age-points are taken over the course of two weeks. The age-points are taken at days 2, 4, 6, 9, 11, and 13. Depending on the experimental design and strains being tested, it may be desirable to take age-points more or less frequently or for longer than 2 weeks. It is important to load the Honeycomb plate in the same order for every age-point such that each aging culture corresponds to the same well position at every age-point, as this will make data analysis much easier.
<ol>
	<li>Obtain the output files from the Bioscreen C MBR machine. The software &#8220;EZExperiment&#8221; will output the Bioscreen data as a tab-delimited file that is compatible with Microsoft Excel as well as other software. The first column shows at what time the OD measurement was taken during the experiment, with subsequent columns representing each well in the Bioscreen Honeycomb plates (Figure 1).</li>
	<li>Delete the first OD reading from every column. This reading is &#8220;noise&#8221;.</li>
	<li>Normalize the data by subtracting the OD value of the well with YEPD alone from all OD values in each column. This removes the background absorbance by the media.</li>
	<li>Plot the outgrowth curves. The curves will shift as a function of age (Figure 2). For example, by plotting the outgrowth curves from column 1 (well 1 of the Honeycomb plate) over the six time points, there is a distinct rightward shift of the curves over time.</li>
	<li>Calculate the doubling time (&#948;) for each well based on the growth kinetics of the day 2 age-point. The equation used to calculate doubling time is: 
	<img alt="Equation" src="/files/ftp_upload/1156/equat.png" /> 
	where OD1 and OD2 represent successive OD measurements and t1 and t2 being the time between measurements. Calculate doubling times only between OD values of 0.2 to 0.5. The average of these values is the doubling time for that well. Calculate doubling times only between OD values of 0.2 to 0.5. The average of these values is the doubling time for that well. Most wild-type yeast strains should give a doubling time value between 85 to 90 minutes.</li>
	<li>For each age-point, calculate the time shift (&#8710;t) in the outgrowth curves relative to the initial age-point (day 2). An easy way to do this is to determine the difference in the length of time it took for each well to reach an OD of 0.3 between the initial age-point and each subsequent age-point. The time that a particular well reached an OD of 0.3 can be calculated from the linear regression equation corresponding to ln(OD) as a function of time between the two time-points bracketing OD=0.3.</li>
	<li>Calculate the fraction surviving at each age-point in order to generate a survival curve (Figure 3). Define the initial age-point (day 2) to be 100% viability. For each successive age-point calculate the percent survival using the equation: 
	<img alt="Second Equation" height="115" src="/files/ftp_upload/1156/equat2.png" width="245" /> 
	where sn is the survival percentage, &#8710;tn is the time shift, and &#948;n is the doubling time.</li>
	<li>Generate survival curves (Figure 3B) as desired for each well (or replicate wells) by plotting the fraction (or percent) of viable cells as a function of age.</li>
	<li>Calculate the survival integral (SI) for each well. SI is defined as the area under the survival curve and can be estimated by the formula: 
	<img alt="Equation 3" height="79" src="/files/ftp_upload/1156/equat3.png" width="400" /> 
	where agen is the age-point (e.g. 2, 4, 6, 9, 11, and 13) and sn is the survival value at that age-point.</li>
	<li>Determine statistical parameters of replicate wells from the SI and survival data. Common statistical parameters of interest include mean, median, and variance for each set of biological replicates. A t-test or similar analysis can be used for pairwise comparison of SI for different experimental and control groups. It may also be desirable to generate survival curves, as described in 4.8 from averaged biological replicates.</li>
</ol>
Part 5: Representative results
At the completion of the experiment, you will have plotted the survival curve and performed data analysis sufficient to determine the chronological aging potential for several different strains or conditions. If performed properly, the growth curves obtained from the Bioscreen C MBR machine should look similar to those shown in Figure 2 and the resulting survival curves should resemble those shown in Figure 3. Generally, wild-type cells cultured under the conditions described here will have a median chronological life span on the order of 7 days. Substantial variation in survival is observed in different strains and under some conditions, such as growth in 0.05% glucose media, median survival can exceed 30 days.
<img alt="" src="/files/ftp_upload/1156/1156fig1.png" /> 
Figure 1. Data output from the Bioscreen program &#8220;EZExperiment&#8221;. Column A displays the time at which an absorbance reading was taken. Successive columns represent the wells of the Honeycomb plate inoculated with cells taken from aging cultures.
<img alt="" src="/files/ftp_upload/1156/1156fig2.png" /> 
Figure 2. Outgrowth curves from a single biological replicate over the course of an experiment. There is a distinct shift in the curves over time as cells in the aging culture lose viability. The length of time between the initial time point (day 2) and a successive time point determines viability at that particular age.
<img alt="" src="/files/ftp_upload/1156/1156fig3a.png" /><img alt="" src="/files/ftp_upload/1156/1156fig3b.png" /> 
Figure 3. A) A survival curve generated using the outgrowth data from Figure 1. The day 2 time point is set as the 100% viability point. B) Final survival curves of two strains tested in the same experiment. These survival curves represent the average viabilities of three biological replicates for each strain. Error bars represent standard deviation within biological replicates. The shaded area under the survival curve represents the survival integral (SI) for strain 1.

<table border="1">
	<tbody>
		<tr>
			<td colspan="2">Table 1. Synthetic Defined Medium Used for Chronological Aging Studies (strain background BY4743)</td>
		</tr>
		<tr>
			<td>Component</td>
			<td>Concentration (g/L)</td>
		</tr>
		<tr>
			<td>D-glucose</td>
			<td>20</td>
		</tr>
		<tr>
			<td>Yeast nitrogen base (-AA/AS)</td>
			<td>1.7</td>
		</tr>
		<tr>
			<td>(NH4)2SO4</td>
			<td>5.0</td>
		</tr>
		<tr>
			<td>Adenine</td>
			<td>0.04</td>
		</tr>
		<tr>
			<td>L-Arginine</td>
			<td>0.02</td>
		</tr>
		<tr>
			<td>L-Aspartic acid</td>
			<td>0.1</td>
		</tr>
		<tr>
			<td>L-Glutamic acid</td>
			<td>0.1</td>
		</tr>
		<tr>
			<td>L-Histidine</td>
			<td>0.1</td>
		</tr>
		<tr>
			<td>L-Leucine</td>
			<td>0.3</td>
		</tr>
		<tr>
			<td>L-Lysine</td>
			<td>0.03</td>
		</tr>
		<tr>
			<td>L-Methionine</td>
			<td>0.02</td>
		</tr>
		<tr>
			<td>L-Phenylalanine</td>
			<td>0.05</td>
		</tr>
		<tr>
			<td>L-Serine</td>
			<td>0.375</td>
		</tr>
		<tr>
			<td>L-Threonine</td>
			<td>0.2</td>
		</tr>
		<tr>
			<td>L-Tryptophan</td>
			<td>0.04</td>
		</tr>
		<tr>
			<td>L-Tyrosine</td>
			<td>0.03</td>
		</tr>
		<tr>
			<td>L-Valine</td>
			<td>0.15</td>
		</tr>
		<tr>
			<td>Uracil</td>
			<td>0.1</td>
		</tr>
	</tbody>
</table>
Note: This recipe accounts for auxotrophies in diploid BY4743 strain. Amino acid auxotrophies should be compensated for by adding a 5X final concentration to the synthetic complete medium.

<ol>
	<li>Steinkraus, K. A., Kaeberlein, M., Kennedy, B. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Replicative+aging+in+yeast:+the+means+to+the+end.">Replicative aging in yeast: the means to the end.</a> Annu Rev Cell Dev Biol. 24, 29 (2008).</li><li>Kaeberlein, M., Conn, P. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Handbook of models for human aging. , 109 (2006).</li><li>Fabrizio, P., Longo, V. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+chronological+life+span+of+Saccharomyces+cerevisiae.">The chronological life span of Saccharomyces cerevisiae.</a> Aging Cell. 2 (2), 73 (2003).</li><li>Murakami, C. J., Burtner, C. R., Kennedy, B. K., Kaeberlein, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+method+for+high-throughput+quantitative+analysis+of+yeast+chronological+life+span.">A method for high-throughput quantitative analysis of yeast chronological life span.</a> J Gerontol A Biol Sci Med Sci. 63 (2), 113 (2008).</li><li>Piper, P. W., Harris, N. L., MacLean, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Preadaptation+to+efficient+respiratory+maintenance+is+essential+both+for+maximal+longevity+and+the+retention+of+replicative+potential+in+chronologically+ageing+yeast.">Preadaptation to efficient respiratory maintenance is essential both for maximal longevity and the retention of replicative potential in chronologically ageing yeast.</a> Mech Ageing Dev. 127 (9), 733 (2006).</li><li>Fabrizio, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Superoxide+is+a+mediator+of+an+altruistic+aging+program+in+Saccharomyces+cerevisiae.">Superoxide is a mediator of an altruistic aging program in Saccharomyces cerevisiae.</a> J Cell Biol. 166 (7), 1055 (2004).</li></ol>

The high-throughput chronological life span assay described here is an effective method for quantifying the aging potential of large numbers of strains with high accuracy and precision. The primary advance of this method over classical methods for determining survival by counting colony-forming units (e.g. see 3) is the use of a shaker/incubator/plate reading device such as the Bioscreen C MBR machine to obtain high-resolution growth curves at each age-point. In direct comparison with low-throughput chronolog...

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<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">	
		
		<div>
		<table border="1">
		<thead>
		<tr>
		<th>Material Name</th>
		<th>Type</th>
		<th>Company</th>
		<th>Catalogue Number</th>
		<th>Comment</th>
		</tr>
		</thead>
		<tbody>
		
<tr>
<td>Bacto Peptone</td>
<td>Reagent</td>
<td>BD</td>
<td>211677</td>
<td>&nbsp;</td>
<tr>
<td>Bacto Yeast Extract</td>
<td>Reagent</td>
<td>BD</td>
<td>288620</td>
<td>&nbsp;</td>
<tr>
<td>Difco Agar</td>
<td>Reagent</td>
<td>BD</td>
<td>214530</td>
<td>&nbsp;</td>
<tr>
<td>Yeast Nitrogen Base w/o A.A. and A.S.</td>
<td>Reagent</td>
<td>MidSci</td>
<td>J630-500G</td>
<td>&nbsp;</td>
<tr>
<td>Amino Acids</td>
<td>Reagent</td>
<td>Sigma</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<tr>
<td>Ammonium Sulfate</td>
<td>Reagent</td>
<td>Spectrum</td>
<td>AM185</td>
<td>&nbsp;</td>
<tr>
<td>Dextrose</td>
<td>Reagent</td>
<td>Fisher</td>
<td>D16-10</td>
<td>&nbsp;</td>
<tr>
<td>Bioscreen C MBR machine</td>
<td>Tool</td>
<td>Growth Curves USA</td>
<td>5101370</td>
<td>&nbsp;</td>
<tr>
<td>Bioscreen 100-well Honeycomb plate</td>
<td>Tool</td>
<td>Growth Curves USA</td>
<td>9502550</td>
<td>&nbsp;</td>		</tbody>
		</table>
		</div>

quantifying yeast chronological life span by outgrowth of aged cells

The budding yeast Saccharomyces cerevisiae has proven to be an important model organism in the field of aging research 1. The replicative and chronological life spans are two established paradigms used to study aging in yeast. Replicative aging is defined as the number of daughter cells a single yeast mother cell produces before senescence; chronological aging is defined by the length of time cells can survive in a non-dividing, quiescence-like state 2. We have developed a high-throughput method for quantitative measurement of chronological life span. This method involves aging the cells in a defined medium under agitation and at constant temperature. At each age-point, a sub-population of cells is removed from the aging culture and inoculated into rich growth medium. A high-resolution growth curve is then obtained for this sub-population of aged cells using a Bioscreen C MBR machine. An algorithm is then applied to determine the relative proportion of viable cells in each sub-population based on the growth kinetics at each age-point. This method requires substantially less time and resources compared to other chronological lifespan assays while maintaining reproducibility and precision. The high-throughput nature of this assay should allow for large-scale genetic and chemical screens to identify novel longevity modifiers for further testing in more complex organisms.

Watch this Scientific Journal Video about Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells at JoVE.com

Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells

Chronological aging in yeast refers to the loss of cell viability associated with time in stationary phase. Here we describe a high-throughput method for quantitatively determining yeast chronological life span.

The budding yeast Saccharomyces cerevisiae has proven to be an important model organism in the field of aging research 1. The replicative and ...

quantifying-yeast-chronological-life-span-by-outgrowth-of-aged-cells

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16.5K Views.  University of Washington. Chronological aging in yeast refers to the loss of cell viability associated with time in stationary phase. Here we describe a high-throughput method for quantitatively determining yeast chronological life span.

Video: Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells

A Rapid Technique for the Visualization of Live Immobilized Yeast Cells

We present here a simple, rapid, and extremely flexible technique for the immobilization and visualization of growing yeast cells by epifluorescence microscopy. The technique is equally suited for visualization of static yeast populations, or time courses experiments up to ten hours in length. My microscopy investigates epigenetic inheritance at the silent mating loci in S. cerevisiae. There are two silent mating loci, HML and HMR, which are normally not expressed as they are packaged in heterochromatin. In the sir1 mutant background silencing is weakened such that each locus can either be in the expressed or silenced epigenetic state, so in the population as a whole there is a mix of cells of different epigenetic states for both HML and HMR. My microscopy demonstrated that there is no relationship between the epigenetic state of HML and HMR in an individual cell. sir1 cells stochastically switch epigenetic states, establishing silencing at a previously expressed locus or expressing a previously silenced locus. My time course microscopy tracked individual sir1 cells and their offspring to score the frequency of each of the four possible epigenetic switches, and thus the stability of each of the epigenetic states in sir1 cells. See also  Xu et al., Mol. Cell 2006.

A rapid technique for the visualization of growing immobilized yeast cells, here applied to fluorescent reporters at the silent mating loci HML and HMR

We present here a simple, rapid, and extremely flexible technique for the immobilization and visualization of growing yeast cells by epifluorescence ...

Measuring Caenorhabditis elegans Life Span on Solid Media

Aging is a degenerative process characterized by a progressive deterioration of cellular components and organelles resulting in mortality. The nematode Caenorhabditis elegans has emerged as a principal model used to study the biology of aging. Because virtually every biological subsystem undergoes functional decline with increasing age, life span is the primary endpoint of interest when considering total rate of aging. In nematodes, life span is typically defined as the number of days an animal remains responsive to external stimuli. Nematodes can be propagated either in liquid media or on solid media in plates, and techniques have been developed for measuring life span under both conditions. Here we present a generalized protocol for measuring life span of nematodes maintained on solid nematode growth media and fed a diet of UV-killed bacteria. These procedures can easily be adapted to assay life span under various common conditions, including a diet consisting of live bacteria, dietary restriction, and RNA interference.

Measuring Caenorhabditis elegans Life Span on Solid Media

In this article we present a general protocol for measuring life span of nematodes maintained on solid media with UV-killed bacterial food.

Aging is a degenerative process characterized by a progressive deterioration of cellular components and organelles resulting in mortality. The nematode ...

Measuring Replicative Life Span in the Budding Yeast

Aging is a degenerative process characterized by a progressive deterioration of cellular components and organelles resulting in mortality. The budding yeast Saccharomyces cerevisiae has been used extensively to study the biology of aging, and several determinants of yeast longevity have been shown to be conserved in multicellular eukaryotes, including worms, flies, and mice 1. Due to the lack of easily quantified age-associated phenotypes, aging in yeast has been assayed almost exclusively by measuring the life span of cells in different contexts, with two different life span paradigms in common usage 2. Chronological life span refers to the length of time that a mother cell can survive in a non-dividing, quiescence-like state, and is proposed to serve as a model for aging of post-mitotic cells in multicellular eukaryotes. Replicative life span, in contrast, refers the number of daughter cells produced by a mother cell prior to senescence, and is thought to provide a model of aging in mitotically active cells. Here we present a generalized protocol for measuring the replicative life span of budding yeast mother cells. The goal of the replicative life span assay is to determine how many times each mother cell buds. The mother and daughter cells can be easily differentiated by an experienced researcher using a standard light microscope (total magnification 160X), such as the Zeiss Axioscope 40 or another comparable model. Physical separation of daughter cells from mother cells is achieved using a manual micromanipulator equipped with a fiber-optic needle. Typical laboratory yeast strains produce 20-30 daughter cells per mother and one life span experiment requires 2-3 weeks.

In this article we present a general protocol for measuring the replicative life span of yeast mother cells.

Aging is a degenerative process characterized by a progressive deterioration of cellular components and organelles resulting in mortality. The budding ...

Purification of Mitochondria from Yeast Cells

Mitochondria are the main site of ATP production during aerobic metabolism in eukaryotic non-photosynthetic cells1. These complex organelles also play essential roles in apoptotic cell death2, cell survival3, mammalian development4, neuronal development and function4, intracellular signalling5, and longevity regulation6. Our understanding of these complex biological processes controlled by mitochondria relies on robust methods for assessing their morphology, their protein and lipid composition, the integrity of their DNA, and their numerous vital functions. The budding yeast Saccharomyces cerevisiae, a genetically and biochemically manipulable unicellular eukaryote with annotated genome and well-defined proteome, is a valuable model for studying the molecular and cellular mechanisms underlying essential biological functions of mitochondria. For these types of studies, it is crucial to have highly pure mitochondria. Here we present a detailed description of a rapid and effective method for purification of yeast mitochondria. This method enables the isolation of highly pure mitochondria that are essentially free of contamination by other organelles and retain their structural and functional integrity after their purification. Mitochondria purified by this method are suitable for cell-free reconstitution of essential mitochondrial processes and can be used for the analysis of mitochondrial structure and functions, mitochondrial proteome and lipidome, and mitochondrial DNA.

We describe a rapid and effective method for purification of mitochondria from the yeast Saccharomyces cerevisiae. This method enables the high-yield isolation of pure mitochondria that are essentially free of contamination by other organelles and retain their structural and functional integrity after their purification.

Mitochondria are the main site of ATP production during aerobic metabolism in eukaryotic non-photosynthetic cells1. These complex organelles also play ...

Measuring Caenorhabditis elegans Life Span in 96 Well Microtiter Plates

Lifespan is a biological process regulated by several genetic pathways. One strategy to investigate the biology of aging is to study animals that harbor mutations in components of age-regulatory pathways. If these mutations perturb the function of the age-regulatory pathway and therefore alter the lifespan of the entire organism, they provide important mechanistic insights1-3.
Another strategy to investigate the regulation of lifespan is to use small molecules to perturb age-regulatory pathways. To date, a number of molecules are known to extend lifespan in various model organisms and are used as tools to study the biology of aging4-16. The number of molecules identified thus far is small compared to the genetic "toolset" that is available to study the biology of aging.
Caenorhabditis elegans is one of the principle models used to study aging because of its excellent genetics and short lifespan of three weeks. More recently, C.elegans has emerged as a model organism for phenotype based drug screens5,7,16-20 because of its small size and its ability to grow in microtiter plates.
Here we present an assay to measure C.elegans lifespan in 96 well microtiter plates. The assay was developed and successfully used to screen large libraries for molecules that extend C.elegans lifespan7. The reliability of the assay was evaluated in multiple tests: first, by measuring the lifespan of wild type animals grown at different temperatures; second, by measuring the lifespan of mutants with altered lifespans; third, by measuring changes in lifespan in response to different concentrations of the antidepressant Mirtazepine. Mirtazepine has previously been shown to extend lifespan in C.elegans7. The results of these tests show that the assay is able to replicate previous findings from other assays and is quantitative. The microtiter format also makes this lifespan assay compatible with automated liquid handling systems and allows integration into automated platforms.

Measuring Caenorhabditis elegans Life Span in 96 Well Microtiter Plates

In this protocol we present a method to measure Caenorhabditis elegans lifespan in 96 well microtiter plates.

Lifespan is a biological process regulated by several genetic pathways. One strategy to investigate the biology of aging is to study animals that harbor ...

Quantitative Live Cell Fluorescence-microscopy Analysis of Fission Yeast

Several microscopy techniques are available today that can detect a specific protein within the cell. During the last decade live cell imaging using fluorochromes like Green Fluorescent Protein (GFP) directly attached to the protein of interest has become increasingly popular 1. Using GFP and similar fluorochromes the subcellular localisations and movements of proteins can be detected in a fluorescent microscope. Moreover, also the subnuclear localisation of a certain region of a chromosome can be studied using this technique. GFP is fused to the Lac Repressor protein (LacR) and ectopically expressed in the cell where tandem repeats of the lacO sequence has been inserted into the region of interest on the chromosome2. The LacR-GFP will bind to the lacO repeats and that area of the genome will be visible as a green dot in the fluorescence microscope. Yeast is especially suited for this type of manipulation since homologous recombination is very efficient and thereby enables targeted integration of the lacO repeats and engineered fusion proteins with GFP 3. Here we describe a quantitative method for live cell analysis of fission yeast. Additional protocols for live cell analysis of fission yeast can be found, for example on how to make a movie of the meiotic chromosomal behaviour 4. In this particular experiment we focus on subnuclear organisation and how it is affected during gene induction. We have labelled a gene cluster, named Chr1, by the introduction of lacO binding sites in the vicinity of the genes. The gene cluster is enriched for genes that are induced early during nitrogen starvation of fission yeast 5. In the strain the nuclear membrane (NM) is labelled by the attachment of mCherry to the NM protein Cut11 giving rise to a red fluorescent signal. The Spindle Pole body (SPB) compound Sid4 is fused to Red Fluorescent Protein (Sid4-mRFP) 6. In vegetatively growing yeast cells the centromeres are always attached to the SPB that is embedded in the NM 7. The SPB is identified as a large round structure in the NM. By imaging before and 20 minutes after depletion of the nitrogen source we can determine the distance between the gene cluster (GFP) and the NM/SPB. The mean or median distances before and after nitrogen depletion are compared and we can thus quantify whether or not there is a shift in subcellular localisation of the gene cluster after nitrogen depletion.

The fission yeast, Schizosaccharomyces pombe, is a good model system to study basic cellular processes. Here we describe a method to perform quantitative live cell analysis of fission yeast. In this particular experiment we focus on organisation of the genome within the cell nucleus, but the method can also be used to study cytosolic factors.

Several microscopy techniques are available today that can detect a specific protein within the cell. During the last decade live cell imaging using ...

Temporal Quantification of MAPK Induced Expression in Single Yeast Cells

The quantification of gene expression at the single cell level uncovers novel regulatory mechanisms obscured in measurements performed at the population level. Two methods based on microscopy and flow cytometry are presented to demonstrate how such data can be acquired. The expression of a fluorescent reporter induced upon activation of the high osmolarity glycerol MAPK pathway in yeast is used as an example. The specific advantages of each method are highlighted. Flow cytometry measures a large number of cells (10,000) and provides a direct measure of the dynamics of protein expression independent of the slow maturation kinetics of the fluorescent protein. Imaging of living cells by microscopy is by contrast limited to the measurement of the matured form of the reporter in fewer cells. However, the data sets generated by this technique can be extremely rich thanks to the combinations of multiple reporters and to the spatial and temporal information obtained from individual cells. The combination of these two measurement methods can deliver new insights on the regulation of protein expression by signaling pathways.

Two complementary methods based on flow cytometry and microscopy are presented which enable the quantification, at the single cell level, of the dynamics of gene expression induced by the activation of a MAPK pathway in yeast.

The quantification of gene expression at the  single cell level uncovers novel regulatory mechanisms obscured in measurements  performed at the population ...

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

Through a delicate balance between quiescence and proliferation, self renewal and production of differentiated progeny, hematopoietic stem cells (HSCs) maintain the turnover of all mature blood cell lineages. The coordination of the complex signals leading to specific HSC fates relies upon the interaction between HSCs and the intricate bone marrow microenvironment, which is still poorly understood[1-2].
We describe how by combining a newly developed specimen holder for stable animal positioning with multi-step confocal and two-photon in vivo imaging techniques, it is possible to obtain high-resolution 3D stacks containing HSPCs and their surrounding niches and to monitor them over time through multi-point time-lapse imaging. High definition imaging allows detecting ex vivo labeled hematopoietic stem and progenitor cells (HSPCs) residing within the bone marrow. Moreover, multi-point time-lapse 3D imaging, obtained with faster acquisition settings, provides accurate information about HSPC movement and the reciprocal interactions between HSPCs and stroma cells.
Tracking of HSPCs in relation to GFP positive osteoblastic cells is shown as an exemplary application of this method. This technique can be utilized to track any appropriately labeled hematopoietic or stromal cell of interest within the mouse calvarium bone marrow space.

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

The nature of the interactions between hematopoietic stem and progenitor cells (HSPCs) and bone marrow niches is poorly understood. Custom hardware modifications and a multi-step acquisition protocol allow the use of two-photon and confocal microscopy to image ex vivo labeled HSPCs homed within bone marrow areas, tracking interactions and movement.

Through a delicate balance between quiescence and proliferation, self renewal and production of differentiated progeny, hematopoietic stem cells (HSCs) ...

Generation and Culture of Blood Outgrowth Endothelial Cells from Human Peripheral Blood

Historically, the limited availability of primary endothelial cells from patients with vascular disorders has hindered the study of the molecular mechanisms underlying endothelial dysfunction in these individuals. However, the recent identification of blood outgrowth endothelial cells (BOECs), generated from circulating endothelial progenitors in adult peripheral blood, may circumvent this limitation by offering an endothelial-like, primary cell surrogate for patient-derived endothelial cells. Beyond their value to understanding endothelial biology and disease modeling, BOECs have potential uses in endothelial cell transplantation therapies. They are also a suitable cellular substrate for the generation of induced pluripotent stem cells (iPSCs) via nuclear reprogramming, offering a number of advantages over other cell types. We describe a method for the reliable generation, culture and characterization of BOECs from adult peripheral blood for use in these and other applications. This approach (i) allows for the generation of patient-specific endothelial cells from a relatively small volume of adult peripheral blood and (ii) produces cells that are highly similar to primary endothelial cells in morphology, cell signaling and gene expression.

This protocol allows for the reliable generation and characterization of blood outgrowth endothelial cells (BOECs) from a small volume of adult peripheral blood. BOECs can be used as a surrogate for endothelial cells from patients with vascular disorders and as a substrate for the generation of induced pluripotent stem cells.

Historically, the limited availability of primary endothelial cells from patients with vascular disorders has hindered the study of the molecular ...

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Quantitatively capturing developmental processes is crucial to derive mechanistic models and key to identify and describe mutant phenotypes. Here protocols are presented for preparing embryos and adult C. elegans animals for short- and long-term time-lapse microscopy and methods for tracking and quantification of developmental processes. The methods presented are all based on C. elegans strains available from the Caenorhabditis Genetics Center and on open-source software that can be easily implemented in any laboratory independently of the microscopy system used. A reconstruction of a 3D cell-shape model using the modelling software IMOD, manual tracking of fluorescently-labeled subcellular structures using the multi-purpose image analysis program Endrov, and an analysis of cortical contractile flow using PIVlab (Time-Resolved Digital Particle Image Velocimetry Tool for MATLAB) are shown. It is discussed how these methods can also be deployed to quantitatively capture other developmental processes in different models, e.g., cell tracking and lineage tracing, tracking of vesicle flow.

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Here it is shown how to track and quantify developmental processes in C. elegans. The methods presented are based on open-source tools that can be easily implemented. It is demonstrated how to reconstruct 3D cell-shape models, how to manually track subcellular structures, and how to analyze cortical contractile flow.