This work was supported by the AFM-Telethon to E.V. (#21545) and by the Ospedale San Raffaele (OSR) Seed Grant to S.Z. (ZAMBRA5X1000). Dr. Jean-Yves Tinevez from the Image Analysis Hub of the Institut Pasteur is acknowledged for publicly sharing his &#34;Simple Tracker&#34; MATLAB routines.

All procedures involving animal subjects were approved by the San Raffaele Institutional Animal Care and Use Committee.
1. Dissection of mouse hindlimb muscles
<ol>
	<li>Sterilize tweezers and scissors (both straight and curved) by autoclaving.</li>
	<li>Prepare and filter (0.22 &#181;m) all the media (blocking medium, digestion medium, proliferating medium, and differentiating medium) before starting the experiment (see Table of Materials).</li>
	<li>Put 5 mL of phosphate-buffered saline (PBS) in a 35 mm Petri dish for muscle collection.</li>
	<li>Sacrifice the mouse by cervical dislocation or by using CO2 and sterilize the skin with ethanol. Remove the skin from the hindlimbs and upper limbs by using scissors and tweezers, to facilitate the isolation of muscles. 
	NOTE: Muscles can be isolated from neonatal or adult mice. Neonatal mice have an increased number of satellite cells compared to adult mice. However, the total number of satellite cells that can be isolated from a single adult mouse is sufficient to perform the experiment described below.</li>
	<li>Isolate tibialis, soleus, extensor digitorum longus (EDL), gastrocnemius, quadriceps, and triceps, and put them in the Petri dish containing PBS. Leave the dish on ice. Carefully remove tendons and fat with sterilized scissors and tweezers.</li>
</ol>
2. Isolation of primary myoblasts
NOTE: All the procedures for cell culture are done in sterile conditions.
<ol>
	<li>Remove the muscles from the PBS. Cut and mince the muscles, by using sterile curved scissors, until a uniform mass is obtained. Put the muscle pieces into a 50 mL tube.</li>
	<li>Add 10 mL of the digestion medium to the muscle pieces and incubate at 37 &#176;C for 30 min under strong agitation (250 min-1) in a water bath, to enzymatically digest the muscles.</li>
	<li>Add 10 mL of Dulbecco&#8217;s modified Eagle&#8217;s medium (DMEM) containing 10% fetal bovine serum (FBS), 1% glutamine, 1% penicillin/streptomycin, and 1% gentamicin (blocking medium) to stop the digestion.</li>
	<li>Centrifuge at 40 x g for 5 min and collect the supernatant in a 50 mL tube. Save the pellet on ice. 
	NOTE: After the centrifugation, the supernatant contains some satellite cells, blood cells, endothelial cells, and fibroblasts, while the pellet contains pieces of undigested muscle and connective tissue. To increase the number of isolated cells, the pellet must be subjected to additional steps of digestion as described below.</li>
	<li>Centrifuge the supernatant at 650 x g for 5 min. Discard the supernatant and resuspend the pellet in 1 mL of DMEM containing 10% FBS. Keep the resuspended pellet on ice.</li>
	<li>Repeat steps 2.2&#8211;2.5 2x for the rest of the pellet obtained in step 2.4 and add the subsequently resuspended pellets to the first resuspended pellet conserved on ice.</li>
	<li>Pass the resuspended pellets through a 70 &#181;m filter and then through a 40 &#181;m filter. Add 15 mL of DMEM with 10% FBS, and centrifuge at 650 x g for 5 min.</li>
	<li>Resuspend the cell pellet in 3 mL of red blood cell lysis buffer to deplete the red blood cells. Incubate it for 5 min at room temperature. Add 40 mL of PBS to stop the lysis of the red blood cells, and centrifuge at 650 x g for 5 min. Remove the supernatant and resuspend the pellet in 5 mL of DMEM with 10% FBS.</li>
	<li>As preplating step, plate the cells in 20 mL of proliferation medium in a 150 mm uncoated Petri dish. After 1 h of incubation in a cell culture incubator (37 &#176;C, 5% CO2), collect the medium. 
	NOTE: Fibroblasts, attaching to the plate, are then discarded, while satellite cells remain in suspension.</li>
	<li>Repeat step 2.9 3x and, at the last preplating step, collect the medium which contains the satellite cells in suspension.</li>
	<li>Centrifuge at 650 x g for 5 min.</li>
	<li>While the cells are centrifuging, coat two 150 mm Petri dishes with collagen (10 mL of a 0.1% solution in 0.1 M acetic acid). To do so, put the collagen on the plate, incubate for 5 min, and remove it. Let the plate dry for 30 min.</li>
	<li>Discard the supernatant obtained in step 2.11 and resuspend the cell pellet in 40 mL of proliferation medium (Table of Materials). Plate the cells on two collagen-coated 150 mm Petri dishes (20 mL per dish) and keep the cells in a cell culture incubator (37 &#176;C, 5% CO2, 5% O2) in proliferation medium for 2&#8211;3 days with 1 &#181;g/mL of doxycycline to induce H2B-GFP expression. 
	NOTE: This step allows the proliferation of myoblasts to obtain a higher number of cells for further assays. Cell density is maintained very low to avoid a spontaneous differentiation of myoblasts into myotubes.</li>
</ol>
3. Myoblast proliferation and differentiation assays
NOTE: When myoblast density is high, some cells initiate elongation, and then, it is necessary to split the cells. Generally, it is possible to split cells 2x&#8211;3x without affecting their phenotype.
<ol>
	<li>Discard the medium, wash the cells with 5 mL of PBS, add 2 mL of trypsin (1x), and incubate the plate at 37 &#176;C in a cell incubator for 5 min. Check under the microscope and, when round cells detach, add 5 mL of DMEM with 10% FBS to inactivate the trypsin. Count the number of cells (usually it is possible to obtain about 1 x 106 cells/mouse). 
	NOTE: Incubation with trypsin for 5 min is usually enough to detach the proliferating myoblasts.</li>
	<li>Subject the cells to one of the assays described below.
	<ol>
		<li>Proliferation assay 
		<ol>
			<li>Plate 50,000 cells/well in 1 mL/well of proliferation medium in a collagen-coated 12-well plate and incubate the plated cells in a cell culture incubator (37 &#176;C, 5% CO2). Fix and count the cells at 24, 48, or 72 h. 
			NOTE: The medium is replaced every 48 h to avoid nutrient exhaustion.</li>
		</ol>
		</li>
		<li>Differentiation assay 
		<ol>
			<li>Coat a 12-well plate with 350 &#181;L of differentiation medium containing basement membrane matrix (1:100). Incubate the plate at 37 &#176;C for 30 min and remove the medium.</li>
			<li>Plate 200,000 cells/well in the differentiation medium in the matrix-coated plate. Incubate the cells in a cell culture incubator (37 &#176;C, 5% CO2, 5% O2) until they adhere to the plate (2 h are generally enough for the cells to adhere and to start differentiation).</li>
			<li>To assess differentiation, perform staining for myosin heavy chain and nuclei as previously described11. Alternatively, perform live-imaging analyses as described below.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
4. Live-imaging of myoblast differentiation and nuclear tracking
<ol>
	<li>For live cell imaging, put the cells, obtained in section 3.2.2 and plated in a 12-well plate, under a confocal microscope, equipped with an incubation system to maintain the cells at 37 &#176;C in 5% CO2.</li>
	<li>Use a 20x dry objective (0.7 NA) to get fields of view with hundreds of cells, enough to monitor myofiber formation. H2B-GFP cells can be imaged with a low-intensity 488 nm Argon laser. 
	NOTE: An open pinhole ensures that the image depth (~10 &#181;m) contains the cell thickness so that cells are kept in focus throughout the experiment. Using a confocal microscope is advantageous since it improves the signal-to-noise ratio of the images, although wide-field microscopy could also be used. Myofiber formation can be monitored through the transmission channel.</li>
	<li>Acquire images of 16-bit and 1,024 x 1,024 pixels per frame every 6 min for 16 h. For each acquired position, generate a multiframe.tif file (see example in the Supplementary Files). 
	NOTE: In the present protocol, commercial software associated with the microscope (Table of Materials) has been used; use the appropriate software to achieve the same goal when using other microscopes.</li>
	<li>Download the software provided as a .zip supplementary file. 
	NOTE: The routines are scripts that must be run in MATLAB. The software is an adaptation of a published software12 optimized for the tracking of the nuclei during myotube formation. This software is divided into two parts: the first one identifies the nuclei in each frame by segmenting them, while the second one generates &#8220;tracks&#8221; (trajectories) of nuclei movement. The complete code for nuclear segmentation and tracking and a step-by-step guide to getting started are provided in the Supplementary Files.</li>
	<li>Extract the zip file and save it in a desired path on the personal computer (PC) in use. Perform all the below passages on a PC with the necessary software already installed. Note that the .zip file is composed of three folders as mentioned below. 
	NOTE: Segmentation Routines contains the functions to be run for the segmentation. Tracking Routines, instead, contains the functions required for the tracking. Example Segmentation Tracking provides the basic scripts and files to get acquainted with the system. The software used for segmentation and tracking of the nuclei runs properly in MATLAB, R2015a or later versions.</li>
	<li>To segment the nuclei from the .tif file, name the file, for example, NameFile.tif.
	<ol>
		<li>Open the Example Segmentation Tracking folder and click on DoSegmentation.m. This will open the command window in MATLAB.</li>
		<li>Check the Current Folder window on the left to see all the elements in the Example of Segmentation Tracking folder. Double-click on DoSegmentation.m. 
		NOTE: Detailed information on the modifications that have to be done in the script can be found in the StepbyStep.txt file. It is mandatory to modify the script so that the variable filenameinput is NameFile.tif and folderinput is the folder where the .tif file is contained.</li>
		<li>Run the script (by clicking on the green Run button). The result of the segmentation will be saved as a file.mat (SegmentedNameFile.mat), while the segmentation of the nuclei can be visualized in the output figure. 
		NOTE: Parameters for the segmentation, described in the script, can be modified if needed. The quality of the segmentation can be checked using the script CheckSegmentation.m (see the StepbyStep.txt file). Based on this, if the quality of the segmentation is poor (only a few nuclei detected), adjust the parameters of the segmentation, clearly indicated in the DoSegmentation.m script, and repeat the procedure.</li>
	</ol>
	</li>
	<li>To generate the tracks (i.e., the trajectories of each of the nuclei in time), using the nuclear positions obtained in the segmentation, run the script GenerateTracks.m in the same working folder. Be sure to add the proper segmentation file as an input, obtained before (see the StepbyStep.txt file for further information). 
	NOTE: As an output, the user will obtain a file TrackedNameFile.mat, with a matrix for the x-coordinates and another matrix for the y-coordinates of the generated tracks.</li>
	<li>Evaluate the quality of the tracks using CheckTracking.m (see the StepbyStep.txt file for further information). Use the output figure of this last passage to help visualize each nuclei position in time. Check on the left for green crosses that indicate each segmented nucleus. To select one specific nucleus, use the lower scrollbar. Note that the selected nucleus will be circled in red, while on the right, the trajectory of the selected cell can be followed in time (by using the upper scrollbar).</li>
</ol>

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A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cellular+dynamics+in+the+muscle+satellite+cell+niche.">Cellular dynamics in the muscle satellite cell niche.</a> EMBO reports. 14 (12), 1062-1072 (2013).</li><li>Mauro, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Satellite+cell+of+skeletal+muscle+fibers.">Satellite cell of skeletal muscle fibers.</a> The Journal of Biophysical and Biochemical Cytology. 9, 493-495 (1961).</li><li>Motohashi, N., Asakura, Y., Asakura, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Isolation,+culture,+and+transplantation+of+muscle+satellite+cells.">Isolation, culture, and transplantation of muscle satellite cells.</a> Journal of Visualized Experiments. 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(120), e55047 (2017).</li><li>Roman, W., Gomes, E. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nuclear+positioning+in+skeletal+muscle.">Nuclear positioning in skeletal muscle.</a> Seminars in Cell &amp; Developmental Biology. , (2017).</li><li>Pimentel, M. R., Falcone, S., Cadot, B., Gomes, E. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+Vitro+Differentiation+of+Mature+Myofibers+for+Live+Imaging.">In Vitro Differentiation of Mature Myofibers for Live Imaging.</a> Journal of Visualized Experiments. (119), e55141 (2017).</li><li>Foudi, A., 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=Analysis+of+histone+2B-GFP+retention+reveals+slowly+cycling+hematopoietic+stem+cells.">Analysis of histone 2B-GFP retention reveals slowly cycling hematopoietic stem cells.</a> Nature Biotechnology. 27 (1), 84-90 (2009).</li><li>Tirone, M., 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=High+mobility+group+box+1+orchestrates+tissue+regeneration+via+CXCR4.">High mobility group box 1 orchestrates tissue regeneration via CXCR4.</a> The Journal of Experimental Medicine. 215 (1), 303-318 (2018).</li><li>Zambrano, S., De Toma, I., Piffer, A., Bianchi, M. E., Agresti, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NF-kappaB+oscillations+translate+into+functionally+related+patterns+of+gene+expression.">NF-kappaB oscillations translate into functionally related patterns of gene expression.</a> eLife. 5, e09100 (2016).</li><li>Adamski, D., 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=Effects+of+Hoechst+33342+on+C2C12+and+PC12+cell+differentiation.">Effects of Hoechst 33342 on C2C12 and PC12 cell differentiation.</a> FEBS Letters. 581 (16), 3076-3080 (2007).</li><li>Otsu, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Threshold+Selection+Method+from+Gray-Level+Histograms.">A Threshold Selection Method from Gray-Level Histograms.</a> IEEE Transactions on Systems, Man, and Cybernetics. 9 (1), 62-66 (1979).</li><li>. Simpel Tracker - File Exchange - MATLAB Central Available from: <a target="_blank" href="https://it.mathworks.com/matlabcentral/fileexchange/34040-simple-tracker">https://it.mathworks.com/matlabcentral/fileexchange/34040-simple-tracker</a> (2016)</li><li>Burkard, R., Dell'Amico, M., Martello, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Assignment Problems, Revised Reprint. , (2009).</li><li>Falcone, S., 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=N-WASP+is+required+for+Amphiphysin-2/BIN1-dependent+nuclear+positioning+and+triad+organization+in+skeletal+muscle+and+is+involved+in+the+pathophysiology+of+centronuclear+myopathy.">N-WASP is required for Amphiphysin-2/BIN1-dependent nuclear positioning and triad organization in skeletal muscle and is involved in the pathophysiology of centronuclear myopathy.</a> EMBO Molecular Medicine. 6 (11), 1455-1475 (2014).</li><li>Syverud, B. C., Lee, J. D., VanDusen, K. W., Larkin, L. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Isolation+and+Purification+of+Satellite+Cells+for+Skeletal+Muscle+Tissue+Engineering.">Isolation and Purification of Satellite Cells for Skeletal Muscle Tissue Engineering.</a> Journal of Regenerative Medicine. 3 (2), (2014).</li><li>Liu, L., Cheung, T. H., Charville, G. W., Rando, T. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Isolation+of+skeletal+muscle+stem+cells+by+fluorescence-activated+cell+sorting.">Isolation of skeletal muscle stem cells by fluorescence-activated cell sorting.</a> Nature Protocols. 10 (10), 1612-1624 (2015).</li></ol>

The authors have nothing to disclose.

Muscle fibers (myofibers) are multinucleated cells that are formed by the fusion of muscle precursors cells (myoblasts) derived from muscle stem cells (satellite cells) that undergo proliferation and differentiation2,3,4. To assess myoblast differentiation, the common procedure consists of culturing myoblasts in differentiating medium and fixing the cells at different time points to perform immunofluorescence staining for MyHC, a marker of differentiation, and staining of nuclei with DNA-intercalating molecules (e.g., Hoechst)5. This method is useful to evaluate myoblast differentiation by measuring different parameters, such as the differentiation index (number of MyHC-positive cells/total cell number) or the fusion index (number of myotubes with at least two nuclei/total cell number). However, myoblast fusion and myotube formation are highly dynamic processes that rely on the motility of the nuclei8. Indeed, nuclear positioning within myofibers is required for proper muscle regeneration and function17. The mobility of myoblasts and of their nuclei might turn out to be a critical parameter to evaluate myoblast differentiation and to uncover novel defects in muscular disorders. Hence, it is essential to develop novel procedures to study cell behavior and nuclear movement during myoblast differentiation and myofiber formation.
Here, we describe a method that enables scientists to follow myoblast differentiation and myotube formation by live cell imaging and to perform quantitative analyses from the automatic tracking of nuclei. The advantage of this method is the possibility to track during the differentiation of fluorescent nuclei of myoblasts, isolated from H2B-GFP mice, without any cell transfection or additional staining. The H2B-GFP mice are commercially available and, consequently, easily accessible. Alternatively, it is possible to isolate the myoblasts from other transgenic mice that express a fluorescent protein in the nucleus or to transfect the cells isolated from wild-type mice to express a fluorescent protein in the nucleus, as previously described9. These different options further extend the potential applications of the method presented here.
The current protocol relies on the culture of primary myoblasts, and consequently, a high variability might be observed in the following experimental procedures, also due to the potential presence of nonmyogenic cells after the isolation from muscles18. To overcome this limitation, it is possible to isolate myoblasts by cell sorting as previously described19. Another critical point in the method described here is the difficulty to generate a perfect tracking of nuclei when cells are close to each other, such as during myotube formation. However, the software routines discussed here allow scientists to visually inspect the quality of the tracking and, if necessary, to select a subset of cells of interest for a subsequent analysis. Further improvement of the software might be required to provide a complete characterization of nuclear dynamics during myofiber formation.
In conclusion, this method is useful to provide quantitative insight into nuclei dynamics during myoblast differentiation by comparing different conditions, as we reported with Hoechst incubation. This example illustrates the ability to extract meaningful dynamic data from time-lapse experiments.

Skeletal muscle is the largest tissue in the human body, totaling 35%-40% of body mass1. Satellite cells are muscle stem cells, anatomically characterized by their position (juxtaposed to the plasma membrane, underneath the basal lamina of muscle fibers), that give rise to proliferating myoblasts (myogenic progenitor cells), which eventually differentiate and integrate into existing myofibers and/or fuse to form new myofibers2,3,4. Their discovery and the progress in the study of their biology has led to significant insights into muscle development and regeneration.
Protocols to isolate and differentiate myoblasts into myotubes have been developed many years ago and are still widely used to study skeletal muscle differentiation5,6,7. However, most of these methods represent static procedures that rely on the analysis of fixed cells and, consequently, do not allow scientists to fully explore highly dynamic processes, such as myoblast fusion and myofiber maturation. The most striking example is nuclear positioning, which is tightly regulated, with nuclei initially in the center of the myofiber and, then, located at the periphery after myofiber maturation8,9. Live imaging is the most appropriate technique to obtain further insights into such a peculiar phenomenon.
Here, we describe a method that enables scientists to record myoblast differentiation and myotube formation by time-lapse microscopy and to perform quantitative analyses from the automatic tracking of myoblast nuclei. This method provides a high-throughput quantitative characterization of nuclear dynamics and myoblast behavior during differentiation and fusion. The protocol is divided into four different parts, namely (1) the collection of muscles from the hindlimbs of mice, (2) the isolation of primary myoblasts that consists in mechanical and enzymatic digestion, (3) myoblast proliferation and differentiation, and (4) live imaging to track nuclei within the first 16 h of myoblast differentiation.
In the following procedure, myoblasts are isolated from H2B-GFP mice and treated with 1 &#181;g/mL of doxycycline to induce H2B-GFP expression, as previously described10. Alternatively, it is possible to isolate the myoblasts from other transgenic mice that express a fluorescent protein in the nucleus or to transfect the cells isolated from wild-type mice to express a fluorescent protein in the nucleus, as described by Pimentel et al.9.

<table>
	<tbody>
		<tr>
			<td>chicken embryos extract</td>
			<td>Seralab</td>
			<td>CE-650-J</td>
			<td></td>
		</tr>
		<tr>
			<td>collagenase</td>
			<td>Sigma</td>
			<td>C9263-1G</td>
			<td>125units/mg</td>
		</tr>
		<tr>
			<td>collagen from calf skin</td>
			<td>Sigma</td>
			<td>C8919</td>
			<td></td>
		</tr>
		<tr>
			<td>dispase</td>
			<td>Gibco</td>
			<td>17105-041</td>
			<td>1.78 units/mg</td>
		</tr>
		<tr>
			<td>doxyciclin</td>
			<td>Sigma</td>
			<td>D1515</td>
			<td></td>
		</tr>
		<tr>
			<td>DMEM</td>
			<td>Sigma</td>
			<td>D5671</td>
			<td></td>
		</tr>
		<tr>
			<td>fetal bovine serum</td>
			<td>Life technologies</td>
			<td>10270106</td>
			<td></td>
		</tr>
		<tr>
			<td>gentamicin</td>
			<td>Sigma</td>
			<td>G1397</td>
			<td></td>
		</tr>
		<tr>
			<td>Horse serum</td>
			<td>Invitrogen</td>
			<td>16050-098</td>
			<td></td>
		</tr>
		<tr>
			<td>Hoechst</td>
			<td>life technologies</td>
			<td>33342</td>
			<td></td>
		</tr>
		<tr>
			<td>IMDM</td>
			<td>Sigma</td>
			<td>I3390</td>
			<td></td>
		</tr>
		<tr>
			<td>L-glutamine</td>
			<td>Sigma</td>
			<td>G7513</td>
			<td></td>
		</tr>
		<tr>
			<td>Matrigel</td>
			<td>Corning</td>
			<td>356231</td>
			<td></td>
		</tr>
		<tr>
			<td>penicillin-streptomycin</td>
			<td>Sigma</td>
			<td>P0781</td>
			<td></td>
		</tr>
		<tr>
			<td>red blood cells lysis medium</td>
			<td>Biolegend</td>
			<td>420301</td>
			<td></td>
		</tr>
		<tr>
			<td>Digestion medium</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>collagenase</td>
			<td>40 mg</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>dispase</td>
			<td>70 mg</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>PBS</td>
			<td>20 ml</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>filtered 0.22um</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Blocking medium</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>DMEM</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Fetal bovine serum</td>
			<td>10%</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>L-glutammine</td>
			<td>1%</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>penicillin-streptomycin</td>
			<td>1%</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>gentamicin</td>
			<td>1 &#8240;</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>filtered 0.22um</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>proliferation medium</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>IMDM</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Fetal bovine serum</td>
			<td>20%</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>L-glutammine</td>
			<td>1%</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>penicillin-streptomycin</td>
			<td>1%</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>gentamicin</td>
			<td>1 &#8240;</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>chichen embryo extract</td>
			<td>3%</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>filtered 0.22um</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>differentiation medium</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>IMDM</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Horse serum</td>
			<td>2%</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>L-glutammine</td>
			<td>1%</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>penicillin-streptomycin</td>
			<td>1%</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>gentamicin</td>
			<td>1 &#8240;</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>chichen embryo extract</td>
			<td>1%</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>filtered 0.22um</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

exploiting live imaging to track nuclei during myoblast differentiation and fusion

Nuclear positioning within cells is important for multiple cellular processes in development and regeneration. The most intriguing example of nuclear positioning occurs during skeletal muscle differentiation. Muscle fibers (myofibers) are multinucleated cells formed by the fusion of muscle precursor cells (myoblasts) derived from muscle stem cells (satellite cells) that undergo proliferation and differentiation. Correct nuclear positioning within myofibers is required for the proper muscle regeneration and function. The common procedure to assess myoblast differentiation and myofiber formation relies on fixed cells analyzed by immunofluorescence, which impedes the study of nuclear movement and cell behavior over time. Here, we describe a method for the analysis of myoblast differentiation and myofiber formation by live cell imaging. We provide a software for automated nuclear tracking to obtain a high-throughput quantitative characterization of nuclear dynamics and myoblast behavior (i.e., the trajectory) during differentiation and fusion.

To automatically follow nuclear movement during myoblast differentiation in live imaging, the nuclei should preferentially be fluorescently labeled. It is important to note that using DNA-intercalating molecules is not feasible because these molecules interfere with the proliferation and differentiation of primary myoblasts13. As an example, proliferation and differentiation have been analyzed in primary myoblasts cultured with or without Hoechst (Figure 1). It is evident that proliferation (Figure 1A, B) and differentiation (Figure 1C, middle panel) are strongly impaired in myoblasts cultured with Hoechst 33342. Conversely, myoblasts isolated from H2B-GFP mice and cultured with doxycycline have nuclei with green fluorescence and differentiate similarly to myoblasts isolated from wild-type mice (Figure 1C, right and left panels, respectively).
Live cell imaging with primary myoblasts expressing the H2B-GFP protein allows the tracking of nuclei during differentiation (Figure 2 and Supplementary Video 1). Merged images of transmission and GFP channels at the initial (Figure 2A) and final time points (Figure 2B) during the differentiation of H2B-GFP myoblasts allow scientists to identify myotubes and, consequently, nuclei that end up integrating into a myotube (e.g., the nucleus in the blue circle) or nuclei that do not fuse into a myotube (e.g., the nucleus in the red circle).
To extract information on nuclei/cell movements from the live cell imaging data, it is possible to use the provided software to track the trajectories of the nuclei. The software uses the image from the H2B-GFP channel (the nuclei; Figure 3A) to create a mask and to segment the nuclei in each frame. For nuclear segmentation in a frame, a &#34;conservative&#34; threshold is selected using Otsu&#39;s method on the image after Gaussian filtering14. Next, objects are roughly segmented; to get a finer segmentation, each object is masked again using a threshold based on the average in its bounding box. A watershed transform is then used to separate nearby objects (Figure 3B). In our hands, one watershed transform has been unable to separate most nearby nuclei (Figure 3B, *inset). Thus, we used the area to select the larger objects in the image and apply a new watershed transformation (Figure 3B, **inset), which is able to separate additional nearby nuclei. With this approach, most of the nuclei are identified in the image (Figure 3C).
To track the nuclei, we improved the routines by incorporating the tracking routines published by Tinevez15. In short, the tracking algorithm links nuclei detected in consecutive frames with the aim of minimizing the sum of the distances between the position of each nucleus in a frame and the position of the same nucleus in the next one, using the &#34;Hungarian algorithm&#34; for such minimization16. The step is repeated in order to link unlinked nuclei that are up to a certain maximum number of frames away. A threshold for the maximum distance between the position of an object in a frame and the next one is also established. For the data presented here, a maximum number of five frames and a maximum distance of 20 pixels per step give a high number of correct tracks (Figure 3D). Importantly, we can use these routines to check the quality of the tracking. It is also possible to use this script to find cells that have a given behavior, for example forming (or not) a myofiber, and subsequently analyze the dynamical features of the set of cells of interest.
The applied software generates the tracks with the x- and y-coordinates for each cell in frame n, <img alt="Equation 1" src="/files/ftp_upload/58888/58888eq1.jpg" />, for hundreds of cells (Figure 3D). We can use such information to extract information about nuclei motion. As an example, the total displacement between the initial frame (n = 0) and the final frame N is defined as follows.
Displacement of a trajectory= ||&#160;<img alt="Equation 2" src="/files/ftp_upload/58888/58888eq2.jpg" />&#160;||
Here, &#34;||&#183;||&#34; stands for the norm of the vector (Figure 4A).
The velocity of each nucleus in frame n is as follows. 
<img alt="Equation 3" src="/files/ftp_upload/58888/58888eq3.jpg" />
Then, we can define the length of a trajectory for a given cell as follows.
Length of trajectory=&#160;<img alt="Equation 4" src="/files/ftp_upload/58888/58888eq4.jpg" />
As an example of how these simple parameters can already give relevant information, we computed the length of nuclei trajectories of myoblasts incubated with or without Hoechst. It appears that the total length of the trajectories is slightly higher for cells stained with Hoechst, although the difference is not significant (Figure 4B). However, when we computed the total displacement during a time lapse, we observed that the total displacement of cells stained with Hoechst was significantly smaller than that of unstained cells (Figure 4C). This indicates that the movement of stained cells has a lower directionality. To further strengthen this hypothesis, we quantified the directionality of the motion of a cell by computing the angle of the velocity in each frame (Figure 4A).
<img alt="Equation 5" src="/files/ftp_upload/58888/58888eq5.jpg" />
We also quantified the variation of the angle between frames.
<img alt="Equation 6" src="/files/ftp_upload/58888/58888eq6.jpg" style="opacity: 0.9;" />
The total angle variation along a trajectory can then be defined as follows.
Total angle variation=&#160;<img alt="Equation 7" src="/files/ftp_upload/58888/58888eq7.jpg" />
As predicted, the total angle variation for the unstained cells is significantly smaller compared to cells stained with Hoechst (Figure 4D). Hence, this simple measure of directionality, obtained from a straightforward calculation, indicates that the myoblasts stained with Hoechst are less prone to maintain the direction of their displacement, which reflects their impaired ability in forming myotubes (Figure 1).
In short, live cell imaging data generated as described here provide a quantitative link between nuclear dynamics and the ability to form myotubes and can be used as input for a high-throughput quantitative characterization of nuclear dynamics and myoblast behavior during differentiation and fusion.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58888/58888fig1.jpg" /> 
Figure 1: Incubation with Hoechst interferes with the proliferation and differentiation of myoblasts. (A) Representative images of primary myoblasts stained with Hoechst after 24 h in proliferation medium (left panel) or cultured for 24 h in proliferation medium with Hoechst (right panel), and (B) the relative quantification. Data are the mean &#177; SEM, and the statistical significance was assessed with Student&#39;s t-test. *P &#60; 0.05. (C) Representative images of primary wild-type myoblasts, cultured for 24 h in differentiating medium without (left panel) or with Hoechst (middle panel), and of H2B-GFP myoblasts cultured for 24 h in differentiating medium (right panel) and stained with Hoechst (blue) and an anti-myosin heavy chain antibody (MyHC; red). The scale bars = 500 &#181;m. <a href="https://www.jove.com/files/ftp_upload/58888/58888fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58888/58888fig2.jpg" /> 
Figure 2: Live imaging of H2B-GFP myoblasts during differentiation. Merged images of transmission and GFP channels at (A) the initial (t = 0 h) and (B) final time points (t = 16 h) during the differentiation of H2B-GFP myoblasts (20x objective). Some examples of myotubes are highlighted with black arrows in panel B. The scale bars = 50 &#181;m. (C) Magnified dotted areas from panels A and B in which it is possible to identify a single nucleus that ends up integrating into a myotube (in blue) or not (in red) at t = 0 h, t = 8 h, and t = 16 h. The scale bars = 50 &#181;m. <a href="https://www.jove.com/files/ftp_upload/58888/58888fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58888/58888fig3.jpg" /> 
Figure 3: Segmentation of H2B-GFP myoblasts tracking during differentiation. (A) Example of a frame of the time lapses showing the nuclei of approximately 400 cells. (B)&#160;Example of nuclei masking and segmentation. Bright objects are segmented using both a global and a local threshold, and a watershed transformation is applied to separate nearby nuclei. Nearby nuclei might be difficult to segment (*inset), so a second watershed-based segmentation is performed in objects of large areas to segment a higher number of nuclei (**inset). (C)&#160;Detected nuclear positions (red crosses), that are later used in the tracking algorithm. (D) Tracks generated using the tracking algorithm that minimizes the sum of the distances between each object&#39;s position in two consecutive frames. A maximum possible value of such distance for each object is provided as an input that allows scientists to link nuclei separated by more than one frame. The scale bars = 50 &#181;m. <a href="https://www.jove.com/files/ftp_upload/58888/58888fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58888/58888fig4.jpg" /> 
Figure 4: Quantitative analyses of nuclei dynamics uncover the impaired ability of myoblasts incubated with Hoechst to maintain cell directionality. (A)&#160;Description of parameter measurements.It is possible to define the velocity of a nucleus by considering the position in two consecutive frames. The trajectory angle and the angle variation can then be readily calculated from the positions as indicated in the scheme. (B)&#160;Distribution of the trajectories length, (C) total displacement, and (D) variation of the trajectories angle of myoblasts incubated without Hoechst (green line) or with Hoechst (blue line). The two distributions are not significantly different, while the total displacement is significantly higher, and the variation of the angle is significantly lower for unstained cells (one-sided Kolmogorov-Smirnov test, *p &#60; 0.05 and **p &#60; 0.005). <a href="https://www.jove.com/files/ftp_upload/58888/58888fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Video 1: Live imaging of H2B-GFP myoblasts during differentiation. Imaging of H2B-GFP myoblasts cultured in differentiating medium from the initial (t = 0 h) to final time points (t = 16 h) using transmission and GFP channels (20x objective). <a href="https://www.jove.com/files/ftp_upload/58888/Supplementary_video.mp4">Please click here to download this file.</a>

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Exploiting Live Imaging to Track Nuclei During Myoblast Differentiation and Fusion

Skeletal muscle differentiation is a highly dynamic process, which particularly relies on nuclear positioning. Here, we describe a method to track nuclei movements by live cell imaging during myoblast differentiation and myotube formation and to perform a quantitative characterization of nuclei dynamics by extracting information from automatic tracking.

Nuclear positioning within cells is important for multiple cellular processes in development and regeneration. The most intriguing example of nuclear ...

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Fibroblast Transformation and Muscle Stem Cells

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8.0K Views.  Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute. Skeletal muscle differentiation is a highly dynamic process, which particularly relies on nuclear positioning. Here, we describe a method to track nuclei movements by live cell imaging during myoblast differentiation and myotube formation and to perform a quantitative characterization of nuclei dynamics by extracting information from automatic tracking.

Video: Exploiting Live Imaging to Track Nuclei During Myoblast Differentiation and Fusion

Mouse Mammary Epithelial Cells form Mammospheres During Lactogenic Differentiation

A phenotypic measure commonly used to determine the degree of lactogenic differentiation in mouse mammary epithelial cell cultures is the formation of dome shaped cell structures referred to as mammospheres 1. The HC11 cell line has been employed as a model system for the study of regulation of mammary lactogenic differentiation both in vitro and in vivo 2. The HC11 cells differentiate and synthesize milk proteins in response to treatment with lactogenic hormones. Following the growth of HC11 mouse mammary epithelial cells to confluence, lactogenic differentiation was induced by the addition of a combination of lactogenic hormones including dexamethasone, insulin, and prolactin, referred to as DIP. The HC11 cells induced to differentiate were photographed at times up to 120 hours post induction of differentiation and the number of mammospheres that appeared in each culture was enumerated. The size of the individual mammospheres correlates with the degree of differentiation and this is depicted in the images of the differentiating cells.

HC11 lactogenic differentiation can be characterized by the formation of domed structures referred to as mammospheres. The structures can be enumerated by phase contrast microscopy to aid in quantifying lactogenic differentiation.

A phenotypic measure commonly used to determine the degree of lactogenic differentiation in mouse mammary epithelial cell cultures is the formation of ...

Live Imaging Of Drosophila melanogaster Embryonic Hemocyte Migrations

Many studies address cell migration using in vitro methods, whereas the physiologically relevant environment is that of the organism itself. Here we present a protocol for the mounting of Drosophila melanogaster embryos and subsequent live imaging of fluorescently labeled hemocytes, the embryonic macrophages of this organism. Using the Gal4-uas system1 we drive the expression of a variety of genetically encoded, fluorescently tagged markers in hemocytes to follow their developmental dispersal throughout the embryo. Following collection of embryos at the desired stage of development, the outer chorion is removed and the embryos are then mounted in halocarbon oil between a hydrophobic, gas-permeable membrane and a glass coverslip for live imaging. In addition to gross migratory parameters such as speed and directionality, higher resolution imaging coupled with the use of fluorescent reporters of F-actin and microtubules can provide more detailed information concerning the dynamics of these cytoskeletal components.

Live Imaging Of Drosophila melanogaster Embryonic Hemocyte Migrations

Drosophila hemocytes disperse over the entirety of the developing embryo. This protocol demonstrates how to mount and image these migrations using embryos with fluorescently labelled hemocytes.

Many studies address cell migration using in vitro methods, whereas the physiologically relevant environment is that of the organism itself. Here we ...

Fluorescent Labeling of COS-7 Expressing SNAP-tag Fusion Proteins for Live Cell Imaging

SNAP-tag and CLIP-tag protein labeling systems enable the specific, covalent attachment of molecules, including fluorescent dyes, to a protein of interest in live cells. These systems offer a broad selection of fluorescent substrates optimized for a range of imaging instrumentation. Once cloned and expressed, the tagged protein can be used with a variety of substrates for numerous downstream applications without having to clone again. There are two steps to using this system: cloning and expression of the protein of interest as a SNAP-tag fusion, and labeling of the fusion with the SNAP-tag substrate of choice. The SNAP-tag is a small protein based on human O6-alkylguanine-DNA-alkyltransferase (hAGT), a DNA repair protein. SNAP-tag labels are dyes conjugated to guanine or chloropyrimidine leaving groups via a benzyl linker. In the labeling reaction, the substituted benzyl group of the substrate is covalently attached to the SNAP-tag. CLIP-tag is a modified version of SNAP-tag, engineered to react with benzylcytosine rather than benzylguanine derivatives. When used in conjunction with SNAP-tag, CLIP-tag enables the orthogonal and complementary labeling of two proteins simultaneously in the same cells.

SNAP-tag and CLIP-tag protein labeling systems enable the specific, covalent attachment of molecules, including fluorescent dyes, to a protein of interest in live cells. Once cloned and expressed, the tagged protein can be used with a variety of substrates for numerous downstream applications without having to clone again.

SNAP-tag and CLIP-tag protein labeling systems enable the specific, covalent attachment of molecules, including fluorescent dyes, to a protein of interest ...

Preparation of Developing and Adult Drosophila Brains and Retinae for Live Imaging

The Drosophila brain and visual system are widely utilized model systems to study neuronal development, function and degeneration. Here we show three preparations of the brain and visual system that cover the range from the developing eye disc-brain complex in the developing pupae to individual eye and brain dissection from adult flies. All protocols are optimized for the live culture of the preparations. However, we also present the conditions for fixed tissue immunohistochemistry where applicable. Finally, we show live imaging conditions for these preparations using conventional and resonant 4D confocal live imaging in a perfusion chamber. Together, these protocols provide a basis for live imaging on different time scales ranging from functional intracellular assays on the scale of minutes to developmental or degenerative processes on the scale of many hours.

Preparation of Developing and Adult Drosophila Brains and Retinae for Live Imaging

This protocol describes three Drosophila preparations: 1) adult brain dissection, 2) adult retina dissection and 3) developing eye disc- brain complexes dissection. Emphasis is laid on special preparation techniques and conditions for live imaging, although all preparations can be used for fixed tissue immunohistochemistry.

The Drosophila brain and visual system are widely utilized model systems to study neuronal development, function and degeneration. Here we show three ...

Determining the Contribution of the Energy Systems During Exercise

One of the most important aspects of the metabolic demand is the relative contribution of the energy systems to the total energy required for a given physical activity. Although some sports are relatively easy to be reproduced in a laboratory (e.g., running and cycling), a number of sports are much more difficult to be reproduced and studied in controlled situations. This method presents how to assess the differential contribution of the energy systems in sports that are difficult to mimic in controlled laboratory conditions. The concepts shown here can be adapted to virtually any sport.
The following physiologic variables will be needed: rest oxygen consumption, exercise oxygen consumption, post-exercise oxygen consumption, rest plasma lactate concentration and post-exercise plasma peak lactate. To calculate the contribution of the aerobic metabolism, you will need the oxygen consumption at rest and during the exercise. By using the trapezoidal method, calculate the area under the curve of oxygen consumption during exercise, subtracting the area corresponding to the rest oxygen consumption. To calculate the contribution of the alactic anaerobic metabolism, the post-exercise oxygen consumption curve has to be adjusted to a mono or a bi-exponential model (chosen by the one that best fits). Then, use the terms of the fitted equation to calculate anaerobic alactic metabolism, as follows: ATP-CP metabolism = A1 (mL . s-1) x t1 (s). Finally, to calculate the contribution of the lactic anaerobic system, multiply peak plasma lactate by 3 and by the athlete&#x2019;s body mass (the result in mL is then converted to L and into kJ).
The method can be used for both continuous and intermittent exercise. This is a very interesting approach as it can be adapted to exercises and sports that are difficult to be mimicked in controlled environments. Also, this is the only available method capable of distinguishing the contribution of three different energy systems. Thus, the method allows the study of sports with great similarity to real situations, providing desirable ecological validity to the study.

This protocol allows researchers focused on exercise and sports sciences to determine the relative contribution of three different energy systems to the total energy expenditure during a large variety of exercises.

One of the most important aspects of the metabolic demand is the relative contribution of the energy systems to the total energy required for a given ...

Fluorescence-based Measurement of Store-operated Calcium Entry in Live Cells: from Cultured Cancer Cell to Skeletal Muscle Fiber

Store operated Ca2+ entry (SOCE), earlier termed capacitative Ca2+ entry, is a tightly regulated mechanism for influx of extracellular Ca2+ into cells to replenish depleted endoplasmic reticulum (ER) or sarcoplasmic reticulum (SR) Ca2+ stores1,2. Since Ca2+ is a ubiquitous second messenger, it is not surprising to see that SOCE plays important roles in a variety of cellular processes, including proliferation, apoptosis, gene transcription and motility. Due to its wide occurrence in nearly all cell types, including epithelial cells and skeletal muscles, this pathway has received great interest3,4. However, the heterogeneity of SOCE characteristics in different cell types and the physiological function are still not clear5-7.
The functional channel properties of SOCE can be revealed by patch-clamp studies, whereas a large body of knowledge about this pathway has been gained by fluorescence-based intracellular Ca2+ measurements because of its convenience and feasibility for high-throughput screening. The objective of this report is to summarize a few fluorescence-based methods to measure the activation of SOCE in monolayer cells, suspended cells and muscle fibers5,8-10. The most commonly used of these fluorescence methods is to directly monitor the dynamics of intracellular Ca2+ using the ratio of F340nm and F380nm (510 nm for emission wavelength) of the ratiometric Ca2+ indicator Fura-2. To isolate the activity of unidirectional SOCE from intracellular Ca2+ release and Ca2+ extrusion, a Mn2+ quenching assay is frequently used. Mn2+ is known to be able to permeate into cells via SOCE while it is impervious to the surface membrane extrusion processes or to ER uptake by Ca2+ pumps due to its very high affinity with Fura-2. As a result, the quenching of Fura-2 fluorescence induced by the entry of extracellular Mn2+ into the cells represents a measurement of activity of SOCE9. Ratiometric measurement and the Mn+2 quenching assays can be performed on a cuvette-based spectrofluorometer in a cell population mode or in a microscope-based system to visualize single cells. The advantage of single cell measurements is that individual cells subjected to gene manipulations can be selected using GFP or RFP reporters, allowing studies in genetically modified or mutated cells. The spatiotemporal characteristics of SOCE in structurally specialized skeletal muscle can be achieved in skinned muscle fibers by simultaneously monitoring the fluorescence of two low affinity Ca2+ indicators targeted to specific compartments of the muscle fiber, such as Fluo-5N in the SR and Rhod-5N in the transverse tubules9,11,12.

The extent of store-operated Ca2+ entry (SOCE) can be monitored using fluorescent Ca2+ indicators. Mn2+ quenching of such indicators assays SOCE in cultured cells and skeletal muscle fibers. A technique allowing spatial and temporal resolution of SOCE by confocal imaging of mechanically skinned muscle fibers is also described.

Store operated Ca2+ entry (SOCE), earlier termed capacitative Ca2+ entry, is a tightly regulated mechanism for influx of extracellular Ca2+ into cells to ...

4D Imaging of Protein Aggregation in Live Cells

One of the key tasks of any living cell is maintaining the proper folding of newly synthesized proteins in the face of ever-changing environmental conditions and an intracellular environment that is tightly packed, sticky, and hazardous to protein stability1. The ability to dynamically balance protein production, folding and degradation demands highly-specialized quality control machinery, whose absolute necessity is observed best when it malfunctions. Diseases such as ALS, Alzheimer's, Parkinson's, and certain forms of Cystic Fibrosis have a direct link to protein folding quality control components2, and therefore future therapeutic development requires a basic understanding of underlying processes. Our experimental challenge is to understand how cells integrate damage signals and mount responses that are tailored to diverse circumstances.
The primary reason why protein misfolding represents an existential threat to the cell is the propensity of incorrectly folded proteins to aggregate, thus causing a global perturbation of the crowded and delicate intracellular folding environment1. The folding health, or "proteostasis," of the cellular proteome is maintained, even under the duress of aging, stress and oxidative damage, by the coordinated action of different mechanistic units in an elaborate quality control system3,4. A specialized machinery of molecular chaperones can bind non-native polypeptides and promote their folding into the native state1, target them for degradation by the ubiquitin-proteasome system5, or direct them to protective aggregation inclusions6-9.
In eukaryotes, the cytosolic aggregation quality control load is partitioned between two compartments8-10: the juxtanuclear quality control compartment (JUNQ) and the insoluble protein deposit (IPOD) (Figure 1 - model). Proteins that are ubiquitinated by the protein folding quality control machinery are delivered to the JUNQ, where they are processed for degradation by the proteasome. Misfolded proteins that are not ubiquitinated are diverted to the IPOD, where they are actively aggregated in a protective compartment.
Up until this point, the methodological paradigm of live-cell fluorescence microscopy has largely been to label proteins and track their locations in the cell at specific time-points and usually in two dimensions. As new technologies have begun to grant experimenters unprecedented access to the submicron scale in living cells, the dynamic architecture of the cytosol has come into view as a challenging new frontier for experimental characterization. We present a method for rapidly monitoring the 3D spatial distributions of multiple fluorescently labeled proteins in the yeast cytosol over time. 3D timelapse (4D imaging) is not merely a technical challenge; rather, it also facilitates a dramatic shift in the conceptual framework used to analyze cellular structure.
We utilize a cytosolic folding sensor protein in live yeast to visualize distinct fates for misfolded proteins in cellular aggregation quality control, using rapid 4D fluorescent imaging. The temperature sensitive mutant of the Ubc9 protein10-12 (Ubc9ts) is extremely effective both as a sensor of cellular proteostasis, and a physiological model for tracking aggregation quality control. As with most ts proteins, Ubc9ts is fully folded and functional at permissive temperatures due to active cellular chaperones. Above 30 &deg;C, or when the cell faces misfolding stress, Ubc9ts misfolds and follows the fate of a native globular protein that has been misfolded due to mutation, heat denaturation, or oxidative damage. By fusing it to GFP or other fluorophores, it can be tracked in 3D as it forms Stress Foci, or is directed to JUNQ or IPOD.

Cellular viability depends on timely and efficient management of protein misfolding. Here we describe a method for visualizing the different potential fates of a misfolded protein: refolding, degradation, or sequestration in inclusions. We demonstrate the use of a folding sensor, Ubc9ts, for monitoring proteostasis and aggregation quality control in live cells using 4D microscopy.

One of the key tasks of any living cell is maintaining the proper folding of newly synthesized proteins in the face of ever-changing environmental ...

Live Imaging of Apoptotic Cell Clearance during Drosophila Embryogenesis

The proper elimination of unwanted or aberrant cells through apoptosis and subsequent phagocytosis (apoptotic cell clearance) is crucial for normal development in all metazoan organisms. Apoptotic cell clearance is a highly dynamic process intimately associated with cell death; unengulfed apoptotic cells are barely seen in vivo under normal conditions. In order to understand the different steps of apoptotic cell clearance and to compare 'professional' phagocytes - macrophages and dendritic cells to 'non-professional' - tissue-resident neighboring cells, in vivo live imaging of the process is extremely valuable. Here we describe a protocol for studying apoptotic cell clearance in live Drosophila embryos. To follow the dynamics of different steps in phagocytosis we use specific markers for apoptotic cells and phagocytes. In addition, we can monitor two phagocyte systems in parallel: 'professional' macrophages and 'semi-professional' glia in the developing central nervous system (CNS). The method described here employs the Drosophila embryo as an excellent model for real time studies of apoptotic cell clearance.

Live Imaging of Apoptotic Cell Clearance during Drosophila Embryogenesis

Here we describe an effective method for studying dynamics of apoptotic cell clearance in vivo. This method employs live Drosophila embryos as a powerful model for monitoring phagocytosis of apoptotic cells using specific labeling of apoptotic cells and phagocytes.

The  proper elimination of unwanted or aberrant cells through apoptosis and  subsequent phagocytosis (apoptotic cell clearance) is crucial for normal  ...

Live Cell Fluorescence Microscopy to Observe Essential Processes During Microbial Cell Growth

Core cellular processes such as DNA replication and segregation, protein synthesis, cell wall biosynthesis, and cell division rely on the function of proteins which are essential for bacterial survival. A series of target-specific dyes can be used as probes to better understand these processes. Staining with lipophilic dyes enables the observation of membrane structure, visualization of lipid microdomains, and detection of membrane blebs. Use of fluorescent-d-amino acids (FDAAs) to probe the sites of peptidoglycan biosynthesis can indicate potential defects in cell wall biogenesis or cell growth patterning. Finally, nucleic acid stains can indicate possible defects in DNA replication or chromosome segregation. Cyanine DNA stains label living cells and are suitable for time-lapse microscopy enabling real-time observations of nucleoid morphology during cell growth. Protocols for cell labeling can be applied to protein depletion mutants to identify defects in membrane structure, cell wall biogenesis, or chromosome segregation. Furthermore, time-lapse microscopy can be used to monitor morphological changes as an essential protein is removed and can provide additional insights into protein function. For example, the depletion of essential cell division proteins results in filamentation or branching, whereas the depletion of cell growth proteins may cause cells to become shorter or rounder. Here, protocols for cell growth, target-specific labeling, and time-lapse microscopy are provided for the bacterial plant pathogen Agrobacterium tumefaciens. Together, target-specific dyes and time-lapse microscopy enable characterization of essential processes in A. tumefaciens. Finally, the protocols provided can be readily modified to probe essential processes in other bacteria.

Understanding the function of essential processes in bacteria is challenging. Fluorescence microscopy with target-specific dyes can provide key insights into microbial cell growth and cell cycle progression. Here, Agrobacterium tumefaciens is used as a model bacterium to highlight methods for live cell imaging for characterization of essential processes.

Core cellular processes such as DNA replication and segregation, protein synthesis, cell wall biosynthesis, and cell division rely on the function of ...

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression

Precise measurements of between- and within-strain heterogeneity in microbial growth rates are essential for understanding genetic and environmental inputs into stress tolerance, pathogenicity, and other key components of fitness. This manuscript describes a microscope-based assay that tracks approximately 105&#160;Saccharomyces cerevisiae microcolonies per experiment. After automated time-lapse imaging of yeast immobilized in a multiwell plate, microcolony growth rates are easily analyzed with custom image-analysis software. For each microcolony, expression and localization of fluorescent proteins and survival of acute stress can also be monitored. This assay allows precise estimation of strains&#39; average growth rates, as well as comprehensive measurement of heterogeneity in growth, gene expression, and stress tolerance within clonal populations.

Yeast growth phenotypes are precisely measured through highly parallel time-lapse imaging of immobilized cells growing into microcolonies. Simultaneously, stress tolerance, protein expression, and protein localization can be monitored, generating integrated datasets to study how environmental and genetic differences, as well as gene-expression heterogeneity among isogenic cells, modulate growth.

Precise measurements of between- and within-strain heterogeneity in microbial growth rates are essential for understanding genetic and environmental ...