Name: lens-free video microscopy for the dynamic and quantitative analysis of adherent cell culture
Uploaded: 2018-02-23T14:00:00
Description: Watch this Scientific Journal Video about Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture at JoVE.com

The authors have nothing to acknowledge.

1. Cell Culture Monitoring Acquisition
<ol>
	<li>Grow HeLa cells in DMEM + glutamine (e.g., GlutaMAX) medium supplemented with 10% (v/v) heat-inactivated fetal calf serum and 1% penicillin and streptomycin.</li>
	<li>Coat 6-well glass bottom culture plates with fibronectin (25 &#181;g/mL) for 1 h. Then seed 2 x 104 cells per well.</li>
	<li>During the acquisition, change the medium every 3 days.</li>
	<li>For the time-lapse acquisition, use the video lens-free microscope (commercially available).<br...

<ol>
	<li>Zangle, T. A., Teitell, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Live-cell+mass+profiling:+an+emerging+approach+in+quantitative+biophysics.">Live-cell mass profiling: an emerging approach in quantitative biophysics.</a> Nat Methods. 11 (12), 1221-1228 (2014).</li><li>Popescu, G., Park, K., Mir, M., Bashir, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+technologies+for+measuring+single+cell+mass.">New technologies for measuring single cell mass.</a> Lab Chip. 14 (4), 646-652 (2014).</li><li>Reed, J., 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=Rapid,+massively+parallel+single-cell+drug+response+measurements+via+live+cell+interferometry.">Rapid, massively parallel single-cell drug response measurements via live cell interferometry.</a> Biophys J. 101 (5), 1025-1031 (2011).</li><li>Mir, 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=Optical+measurement+of+cycle-dependent+cell+growth.">Optical measurement of cycle-dependent cell growth.</a> Proc Natl Acad Sci. USA. 108 (32), 13124-13129 (2011).</li><li>Girshovitz, P., Shaked, N. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Generalized+cell+morphological+parameters+based+on+interferometric+phase+microscopy+and+their+application+to+cell+life+cycle+characterization.">Generalized cell morphological parameters based on interferometric phase microscopy and their application to cell life cycle characterization.</a> Biomed Opt Express. 3 (8), 1757-1773 (2012).</li><li>Kemper, B., Bauwens, A., Vollmer, A., Ketelhut, S., Langehanenberg, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Label-free+quantitative+cell+division+monitoring+of+endothelial+cells+by+digital+holographic+microscopy.">Label-free quantitative cell division monitoring of endothelial cells by digital holographic microscopy.</a> J Biomed Opt. 15 (3), (2010).</li><li>Mir, M., Bergamaschi, A., Katzenellenbogen, B. S., Popescu, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Highly+sensitive+quantitative+imaging+for+monitoring+single+cancer+cell+growth+kinetics+and+drug+response.">Highly sensitive quantitative imaging for monitoring single cancer cell growth kinetics and drug response.</a> PLoS One. 9 (2), 1-8 (2014).</li><li>Bon, P., Savatier, J., Merlin, M., Wattellier, B., Monneret, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optical+detection+and+measurement+of+living+cell+morphometric+features+with+single-shot+quantitative+phase+microscopy.">Optical detection and measurement of living cell morphometric features with single-shot quantitative phase microscopy.</a> J Biomed Opt. 17 (7), (2012).</li><li>Aknoun, 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=Living+cell+dry+mass+measurement+usinq+quantitative+phase+imaging+with+quadriwave+lateral+shearing+interferometry:+an+accuracy+and+sensitivity+discussion.">Living cell dry mass measurement usinq quantitative phase imaging with quadriwave lateral shearing interferometry: an accuracy and sensitivity discussion.</a> J Biomed Opt. 20 (1), 1-4 (2015).</li><li>Cotte, Y., 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=Marker-free+phase+nanoscopy.">Marker-free phase nanoscopy.</a> Nature Photonics. 7 (2), 113-117 (2013).</li><li>Choi, W., 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=Tomographic+phase+microscopy.">Tomographic phase microscopy.</a> Nat Methods. 4 (9), 717-719 (2007).</li><li>Kesavan, S. V., 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-throughput+monitoring+of+major+cell+functions+by+means+of+lensfree+video+microscopy.">High-throughput monitoring of major cell functions by means of lensfree video microscopy.</a> Sci Rep. 4, 1-11 (2014).</li><li>Zheng, G., Lee, S. A., Antebi, Y., Elowitz, M. B., Yang, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+ePetri+dish,+an+on-chip+cell+imaging+platform+based+on+subpixel+perspective+sweeping+microscopy+(SPSM).">The ePetri dish, an on-chip cell imaging platform based on subpixel perspective sweeping microscopy (SPSM).</a> Proc Natl Acad Sci USA. 108 (41), 16889-16894 (2011).</li><li>Pushkarsky, I., 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=Automated+single-cell+motility+analysis+on+a+chip+using+lensfree+microscopy.">Automated single-cell motility analysis on a chip using lensfree microscopy.</a> Sci Rep. 4, 4717 (2014).</li><li>Allier, C., 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=Imaging+of+dense+cell+cultures+by+multiwavelength+lens-free+video+microscopy.">Imaging of dense cell cultures by multiwavelength lens-free video microscopy.</a> Cytom Part A. 91 (5), 1-10 (2017).</li><li>Su, T. -. W., Seo, S., Erlinger, A., Ozcan, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+lensfree+imaging+and+characterization+of+a+heterogeneous+cell+solution+on+a+chip.">High-throughput lensfree imaging and characterization of a heterogeneous cell solution on a chip.</a> Biotechnol Bioeng. 102 (3), 856-868 (2009).</li><li>Delacroix, R., 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=Cerebrospinal+fluid+lens-free+microscopy:+a+new+tool+for+the+laboratory+diagnosis+of+meningitis.">Cerebrospinal fluid lens-free microscopy: a new tool for the laboratory diagnosis of meningitis.</a> Sci Rep. 7, 39893 (2017).</li><li>Tinevez, J. -. Y., 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=TrackMate:+an+open+and+extensible+platform+for+single-particle+tracking.">TrackMate: an open and extensible platform for single-particle tracking.</a> Methods. 115, 80-90 (2016).</li><li>Popescu, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optical+imaging+of+cell+mass+and+growth+dynamics.">Optical imaging of cell mass and growth dynamics.</a> Am J Physiol Physiol. 295 (2), 538-544 (2008).</li><li>Liu, P. Y., 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=Cell+refractive+index+for+cell+biology+and+disease+diagnosis:+past,+present+and+future.">Cell refractive index for cell biology and disease diagnosis: past, present and future.</a> Lab Chip. 16, 634-644 (2016).</li><li>Rapoport, D. H., Becker, T., Mamlouk, A. M., Schicktanz, S., Kruse, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+validation+algorithm+allows+for+automated+cell+tracking+and+the+extraction+of+biologically+meaningful+parameters.">A novel validation algorithm allows for automated cell tracking and the extraction of biologically meaningful parameters.</a> PLoS One. 6 (11), e27315 (2011).</li><li>Al-Kofahi, O., 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=Automated+cell+lineage+construction:+A+rapid+method+to+analyze+clonal+development+established+with+murine+neural+progenitor+cells.">Automated cell lineage construction: A rapid method to analyze clonal development established with murine neural progenitor cells.</a> Cell Cycle. 5 (3), 327-335 (2006).</li><li>Meijering, E., Dzyubachyk, O., Smal, I., van Cappellen, W. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tracking+in+cell+and+developmental+biology.">Tracking in cell and developmental biology.</a> Semin Cell Dev Biol. 20 (8), 894-902 (2009).</li><li>Posakony, J. W., England, J. M., Attardi, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mitochondrial+growth+and+division+during+the+cell+cycle+in+HeLa+cells.">Mitochondrial growth and division during the cell cycle in HeLa cells.</a> J Cell Biol. 74 (2), 468-491 (1977).</li><li>Zocchi, E., 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=Expression+of+CD38+Increases+Intracellular+Calcium+Concentration+and+Reduces+Doubling+Time+in+HeLa+and+3T3+Cells.">Expression of CD38 Increases Intracellular Calcium Concentration and Reduces Doubling Time in HeLa and 3T3 Cells.</a> J Biol Chem. 273 (14), 8017-8024 (1979).</li><li>Reitzer, L. J., Wice, B. M., Kennell, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evidence+that+glutamine,+not+sugar,+is+the+major+energy+source+for+cultured+HeLa+cells.">Evidence that glutamine, not sugar, is the major energy source for cultured HeLa cells.</a> J Biol Chem. 254 (8), 2669-2676 (1979).</li><li>Benedetti, A. D. E., Joshi-barve, S., Rinker-Schaeffer, C., Rhoads, R. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Expression+of+Antisense+RNA+against+Initiation+Factor+eIF-4E+mRNA+in+HeLa+Cells+Results+in+Lengthened+Cell+Division+Times,+Diminished+Translation+Rates,+and+Reduced+Levels+of+Both+eIF-4E+and+the+p220+Component+of+eIF-4F.">Expression of Antisense RNA against Initiation Factor eIF-4E mRNA in HeLa Cells Results in Lengthened Cell Division Times, Diminished Translation Rates, and Reduced Levels of Both eIF-4E and the p220 Component of eIF-4F.</a> Mol Cell Biol. 11 (11), 5435-5445 (1991).</li><li>Kumei, Y., Nakajima, T., Sato, A., Kamata, N., Enomoto, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reduction+of+G1+phase+duration+and+enhancement+of+c-myc+gene+expression+in+HeLa+cells+at+hypergravity.">Reduction of G1 phase duration and enhancement of c-myc gene expression in HeLa cells at hypergravity.</a> J Cell Sci. 93 (2), 221-226 (1989).</li><li>Ginzberg, M. B., Kafri, R., Kirschner, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+being+the+right+(cell)+size.">On being the right (cell) size.</a> Science. 348 (6236), 1245075 (2015).</li><li>Mathieu, E., 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=Time-lapse+lens-free+imaging+of+cell+migration+in+diverse+physical+microenvironments.">Time-lapse lens-free imaging of cell migration in diverse physical microenvironments.</a> Lab Chip. 16 (17), 3304-3316 (2016).</li></ol>

In this paper, we show that lens-free video microscopy can be used inside an incubator to capture the kinetics of thousands of cells. In order to describe the overall methodology we explained how a 2.7 day time-lapse acquisition of HeLa cells in culture can be analyzed with standard cell-tracking algorithms. The result is a dataset featuring 2.2 x 106 cell measurements and 10,584 cell cycle tracks. The acquisitions were performed on a culture of Hela cells with a relatively large cell-to-cell distance (cell de...

Monitoring cultured mammalian cells throughout several cell cycles and measuring accurately cell size and cell dry mass is a challenging task. Several label-free optical techniques are able to perform this task1,2: phase-shifting interferometry3, digital holographic microscopy (DHM)4,5,6,7, quadriwave lateral shearing interferometry8,9 and quantitative phase tomography10,11. These methods have led to many new insights into the understanding of the cell cycle of mammalian cells. However they are seldom coupled with automatic cell tracking algorithms and their throughput remains limited when measuring cell mass trajectories1 (N&#60;20 in respectively3,4,5,6). Hence a novel optical method is needed to measure cell mass trajectories with large statistics (N&#62;1000).
In this paper, we demonstrate the capability of lens-free video microscopy to simultaneously image thousands of cells directly inside the incubator, and then quantify single cell metrics along thousands of individual cell cycle tracks. Lens-free microscopy is a quantitative phase imaging technique which allows the acquisition of phase image of densely packed cells over a very large field of view (typically several tens of mm2, here 29.4 mm2)12,13,14,15. Several metrics at the single cell level are determined, e.g., cell area, cell dry mass, cell thickness, cell major axis length and cell aspect ratio12,15, from each image. Then, by applying a cell-tracking algorithm, these features can be plotted for every single cell as a function of the experiment time14,15. Furthermore, by detecting the occurrence of cell divisions in the cell tracks, it is possible to extract other important information such as the initial cell dry mass (just after the cell division), the final cell dry mass (just before the cell division) and the cell cycle duration, i.e., the time between two consecutive divisions15. All these measurements can be computed with very good statistics (N&#62;1000) since the large field of view would typically allow the analysis of 200 to 10,000 cells in a single lens-free acquisition.
In order to explain this methodology based on lens-free video microscopy, we describe the protocol to monitor and quantify a HeLa cell culture for 2.7 days. Data analysis is a four-step process based on multi-wavelength holographic reconstruction, cell-tracking, cell segmentation and cell division algorithms. Here it is shown that the spatial resolution and the relatively fast frame rate (one acquisition every 10 minutes) obtained with this lens-free video microscopy setup is compatible with standard cell-tracking algorithms. The full analysis of this dataset results in the measurement of 10,584 cell tracks over complete cell cycles.
To summarize, lens-free video microscopy is a powerful tool to automatically monitor thousands of unlabeled, unsynchronized, and unmodified cells per experiment; each cell being tracked over several cell cycles. Our measurements thus provide the mean value of several cell parameters, but more importantly, the inter-cell variability over a large population of cells.

<table border="0" cellpadding="0" cellspacing="0" width="675">
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:231px;">Cytonote lens-free video microscope&#160;</td>
			<td style="width:208px;">Iprasense</td>
			<td style="width:156px;"></td>
			<td style="width:80px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Horus acquisition software</td>
			<td>Iprasense</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">6-well glass bottom culture plates</td>
			<td>MatTek corporation</td>
			<td>Part No: P06G-0-14-F&#160;</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">DMEM + GlutaMAX medium&#160;</td>
			<td>Gibco</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">heat-inactivated fetal calf serum&#160;</td>
			<td>Eurobio</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">penicillin and streptomycin&#160;</td>
			<td>Gibco</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Fibronectin&#160;</td>
			<td>Sigma Aldrich</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Matlab, image processing toolbox&#160;</td>
			<td>Mathworks</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

lens-free video microscopy for the dynamic and quantitative analysis of adherent cell culture

Here, we demonstrate that lens-free video microscopy enables us to simultaneously capture the kinetics of thousands of cells directly inside the incubator and that it is possible to monitor and quantify single cells along several cell cycles. We describe the full protocol used to monitor and quantify a HeLa cell culture for 2.7 days. First, cell culture acquisition is performed with a lens-free video microscope, and then the data is analyzed following a four-step process: multi-wavelength holographic reconstruction, cell-tracking, cell segmentation and cell division detection algorithms. As a result, we show that it is possible to gather a dataset featuring more than 10,000 cell cycle tracks and more than 2 x 106 cell morphological measurements.

For the holographic reconstruction process, the light field is described by a scalar field A (where <img alt="Equation 4" src="/files/ftp_upload/56580/56580eq4v3.jpg" /> is the complex value of A on the plane at distance z from the sample, and lateral position <img alt="Equation 5" src="/files/ftp_upload/56580/56580eq5v3.jpg" /> and at wavelength &#955;). Light propagation is modeled by the Huygens-Fresnel theory which provides a propagator kernel <img alt="Equa...

Watch this Scientific Journal Video about Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture at JoVE.com

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Lens-free video microscopy enables us to monitor cell cultures directly inside the incubator. Here we describe the full protocol used to acquire and analyze a 2.7 day long acquisition of cultured HeLa cells, leading to a dataset of 2.2 x 106 measurements of individual cell morphology and 10584 cell cycle tracks.

Here, we demonstrate that lens-free video microscopy enables us to simultaneously capture the kinetics of thousands of cells directly inside the incubator ...

The overall goal of this methodology is to acquire and analyze the dynamics of the large population of adherent cells monitored by means of lens-free video microscopy. So, we think that this technique is interesting in many fields of biology and for studying cell proliferation or cell cystitis or drug screening, or different bio process monitoring. So this technique is label free and it's very simple.But it's very effective in studying large cell populations. What idea for this method, when we shifted from the field of detecting bacteria to the field of imaging mammalian cells. Then it was of use that the biologist, they will need time-lapse acquisition of the circuiture directly into the incubator.Then we built a customized lens-free video microscope that is compatible with the incubator and we developed a holographic reconstruction algorithm to get the picture of thousands of cells that are present in the very large field of view. Begin this procedure by coating a 6 well glass bottom plate with fibronectin at room temperature for 1 hour. After 1 hour, remove excess fibronectin and rinse out the 6 well glass bottom plate with PBS.Hela cells, grown in DMEM, plus glutamine medium, supplemented with 10%heat in activated fetal calf serum, and 1%penicillin and streptomycin are used in this demonstration. Seed twenty thousand cells per culture plate. It is important to use a container with a glass bottom.For the quality of the lens-free acquisition. Place the culture plate into the incubator on top of the lens-free video microscope. The lens-free video microscope used for this time-lapse acquisition is commercially available.It has a metal oxide semi-conductor image sensor with an imaging area of 29.4 square millimeters, and a pixel pitch of 1.67 micrometers. Multiple wave length illumination is provided by a multi-chip light emitting diode device located above a 150 micro meter pinhole at a distance of approximately 5 centimeters from the cells. Before starting the time-lapse acquisition, it's important to check that there is no condensation on the cover plate.Because condensation will degrade the image quality of the acquisition. If condensation has occurred on the cover of the culture plate, clean the cover. Control the video lens-free microscope with a commercially available acquisition software.Which performs both the time-lapse acquisition and the holographic reconstruction. Enter the required input parameters in the software interface. Set the frame rate, to 1 acquisition every 10 minutes, the cell culture type to adherent, and the duration of the experiment to 3 days.Start the time-lapse acquisition. This image shows an example of the full field of view of a raw acquisition in the blue channel on a 29.4 square millimeter area in one frame. Panel B is the detail of panel A, and panel C is the detail of panel B.The holographic reconstruction algorithm will automatically process the lens-free acquisition to obtain the phase image of the cell culture.An example image of cells before phase unwrapping is shown, followed by an image after phase unwrapping. Cell tracking is accomplished using the TrackMate algorithm, An open source Fiji plug-in for the automated tracking of single particles. After loading, the full time-lapse acquisition into Fiji and configuring the algorithms as described in the text protocol, set the input parameters.Set the estimated blob diameter to 15 pixels, the detector threshold to 0.25, the linking maximum distance to 15 pixels, the gap closing max distance to 15 pixels, and the number of spots in track to 3.5. The first step in the cell tracking algorithm is the detection of cells in the lens-free acquisition. Results of the cell tracking algorithm are shown, with all cell tracks shown over fifty frames, or about 8 hours.With a color code corresponding to the median velocity. The bottom panel is a detailed image of the yellow rectangle from the top panel. A representative video clip of cell tracking is shown here.At the end of the cell tracking process, select the analysis button to generate the results in the form of 3 text files. On the basis of this data, perform cell segmentation and detect cell divisions using dedicated algorithms, as detailed in the text protocol and supplementary code files. Compiling all single cell measurements over the full time-lapse acquisition, produces scatter plots of several metrics as a function of time.The total number of cells in the full field of view, increases from two thousand up to fifteen thousand in 2.7 days. Multiplied by the number of time points, this leads to a large data set of detected cells. Each characterized by its coordinates and morphological features, such as dry mass, area, average thickness, major access length, and aspect ratio.The combination of cell tracking and cell segmentation algorithms, enables kinetic measurements at the single cell level. This example shows the analysis of the cell track lasting about sixty-six hours, and exhibiting 4 cell divisions at the time points circled in yellow. Several metrics were computed as a function of time:cell surface area, dry mass, average thickness, and cell motility.The distribution of the cell cycle duration for 2.7 day observation was close to agouzian. With a mean of 16.5 hours. Cell cycle lengths, measured automatically, are well correlated to the ones measured manually.Other parameters were also examined, such as the cell initial dry mass, the final dry mass, and the average growth rate during the cell cycle. Once mastered, the complete data analysis of the time-lapse acquisition, can be done in a few hours, depending on the CPU configuration and the hardware performance. While attempting this procedure it's very important to carefully the initial cell concentration.If the initial cell concentration is too large, the cell track algorithm will fail at defining properly, the cell trajectories. But, if the cell concentration is too low, the statistics of the complete analysis will be also too low. Following this procedure, automated like cell tracking analyses can be performed to answer other questions in the fields of:biology, fundamental biology, or chemotaxis.This new technique, also paves the way for the monitoring of cell lineage that will hopefully bring, interesting insight to study cell cycle. After watching this video, you should have a good understanding of how to conduct time-lapse acquisition by the means of lens-free video microscopy and how to analyze a large population of cells.

lens-free-video-microscopy-for-dynamic-quantitative-analysis-adherent

Research

JoVE Journal

Biology

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing measurement of multiple cellular phenotypes within a single assay. In this study, we utilized HCA to develop a novel assay for neurotoxicity. Neurotoxicity assessment represents an important part of drug safety evaluation, as well as being a significant focus of environmental protection efforts. Additionally, neurotoxicity is also a well-accepted in vitro marker of the development of neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Recently, the application of HCA to neuronal screening has been reported. By labeling neuronal cells with &beta;III-tubulin, HCA assays can provide high-throughput, non-subjective, quantitative measurements of parameters such as neuronal number, neurite count and neurite length, all of which can indicate neurotoxic effects. However, the role of astrocytes remains unexplored in these models. Astrocytes have an integral role in the maintenance of central nervous system (CNS) homeostasis, and are associated with both neuroprotection and neurodegradation when they are activated in response to toxic substances or disease states. GFAP is an intermediate filament protein expressed predominantly in the astrocytes of the CNS. Astrocytic activation (gliosis) leads to the upregulation of GFAP, commonly accompanied by astrocyte proliferation and hypertrophy. This process of reactive gliosis has been proposed as an early marker of damage to the nervous system. The traditional method for GFAP quantitation is by immunoassay. This approach is limited by an inability to provide information on cellular localization, morphology and cell number. We determined that HCA could be used to overcome these limitations and to simultaneously measure multiple features associated with gliosis - changes in GFAP expression, astrocyte hypertrophy, and astrocyte proliferation - within a single assay. In co-culture studies, astrocytes have been shown to protect neurons against several types of toxic insult and to critically influence neuronal survival. Recent studies have suggested that the use of astrocytes in an in vitro neurotoxicity test system may prove more relevant to human CNS structure and function than neuronal cells alone. Accordingly, we have developed an HCA assay for co-culture of neurons and astrocytes, comprised of protocols and validated, target-specific detection reagents for profiling &beta;III-tubulin and glial fibrillary acidic protein (GFAP). This assay enables simultaneous analysis of neurotoxicity, neurite outgrowth, gliosis, neuronal and astrocytic morphology and neuronal and astrocytic development in a wide variety of cellular models, representing a novel, non-subjective, high-throughput assay for neurotoxicity assessment. The assay holds great potential for enhanced detection of neurotoxicity and improved productivity in neuroscience research and drug discovery.

This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing ...

Video Bioinformatics Analysis of Human Embryonic Stem Cell Colony Growth

Because video data are complex and are comprised of many images, mining information from video material is difficult to do without the aid of computer software.  Video bioinformatics is a powerful quantitative approach for extracting spatio-temporal data from video images using computer software to perform dating mining and analysis. In this article, we introduce a video bioinformatics method for quantifying the growth of human embryonic stem cells (hESC) by analyzing time-lapse videos collected in a Nikon BioStation CT incubator equipped with a camera for video imaging. In our experiments, hESC colonies that were attached to Matrigel were filmed for 48 hours in the BioStation CT.  To determine the rate of growth of these colonies, recipes were developed using CL-Quant software which enables users to extract various types of data from video images.  To accurately evaluate colony growth, three recipes were created. The first segmented the image into the colony and background, the second enhanced the image to define colonies throughout the video sequence accurately, and the third measured the number of pixels in the colony over time.  The three recipes were run in sequence on video data collected in a BioStation CT to analyze the rate of growth of individual hESC colonies over 48 hours. To verify the truthfulness of the CL-Quant recipes, the same data were analyzed manually using Adobe Photoshop software. When the data obtained using the CL-Quant recipes and Photoshop were compared, results were virtually identical, indicating the CL-Quant recipes were truthful. The method described here could be applied to any video data to measure growth rates of hESC or other cells that grow in colonies. In addition, other video bioinformatics recipes can be developed in the future for other cell processes such as migration, apoptosis, and cell adhesion.  

Video bioinformatics is the automated processing, analysis, understanding, and data mining of biological spatio-temporal data extracted from microscopic videos. The purpose of this article is to demonstrate a method for measuring human embryonic stem cell colony growth using a video bioinformatics method.

Because video data are complex and are comprised of many images, mining information from video material is difficult to do without the aid of computer ...

Clonogenic Assay: Adherent Cells

The clonogenic (or colony forming) assay has been established for more than 50 years; the original paper describing the technique was published in 19561. Apart from documenting the method, the initial landmark study generated the first radiation-dose response curve for X-ray irradiated mammalian (HeLa) cells in culture1. Basically, the clonogenic assay enables an assessment of the differences in reproductive viability (capacity of cells to produce progeny; i.e. a single cell to form a colony of 50 or more cells) between control untreated cells and cells that have undergone various treatments such as exposure to ionising radiation, various chemical compounds (e.g. cytotoxic agents) or in other cases genetic manipulation. The assay has become the most widely accepted technique in radiation biology and has been widely used for evaluating the radiation sensitivity of different cell lines. Further, the clonogenic assay is commonly used for monitoring the efficacy of radiation modifying compounds and for determining the effects of cytotoxic agents and other anti-cancer therapeutics on colony forming ability, in different cell lines. A typical clonogenic survival experiment using adherent cells lines involves three distinct components, 1) treatment of the cell monolayer in tissue culture flasks, 2) preparation of single cell suspensions and plating an appropriate number of cells in petri dishes and 3) fixing and staining colonies following a relevant incubation period, which could range from 1-3 weeks, depending on the cell line. Here we demonstrate the general procedure for performing the clonogenic assay with adherent cell lines with the use of an immortalized human keratinocyte cell line (FEP-1811)2. Also, our aims are to describe common features of clonogenic assays including calculation of the plating efficiency and survival fractions after exposure of cells to radiation, and to exemplify modification of radiation-response with the use of a natural antioxidant formulation.

The applicability of the clonogenic assay for evaluating reproductive viability has been established for more than 50 years. Here we demonstrate the general procedure for performing the clonogenic assay with adherent cells.

The clonogenic (or colony forming) assay has been established for more than 50 years; the original paper describing the technique was published in 19561. ...

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 ...

Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy

Cell migration is a dynamic process, which is important for embryonic development, tissue repair, immune system function, and tumor invasion 1, 2. During directional migration, cells move rapidly in response to an extracellular chemotactic signal, or in response to intrinsic cues 3 provided by the basic motility machinery. Random migration occurs when a cell possesses low intrinsic directionality, allowing the cells to explore their local environment.
Cell migration is a complex process, in the initial response cell undergoes polarization and extends protrusions in the direction of migration 2. Traditional methods to measure migration such as the Boyden chamber migration assay is an easy method to measure chemotaxis in vitro, which allows measuring migration as an end point result. However, this approach neither allows measurement of individual migration parameters, nor does it allow to visualization of morphological changes that cell undergoes during migration.
Here, we present a method that allows us to monitor migrating cells in real time using video - time lapse microscopy. Since cell migration and invasion are hallmarks of cancer, this method will be applicable in studying cancer cell migration and invasion in vitro. Random migration of platelets has been considered as one of the parameters of platelet function 4, hence this method could also be helpful in studying platelet functions. This assay has the advantage of being rapid, reliable, reproducible, and does not require optimization of cell numbers. In order to maintain physiologically suitable conditions for cells, the microscope is equipped with CO2 supply and temperature thermostat. Cell movement is monitored by taking pictures using a camera fitted to the microscope at regular intervals. Cell migration can be calculated by measuring average speed and average displacement, which is calculated by Slidebook software.

This method allows monitoring of cells in real time and quantitative measurements of different cell migration parameters such as speed, displacement, and velocity. Unlike the traditional methods, this real time approach is not based on endpoint quantitative migration measurements; instead it allows monitoring and calculating different parameters continuously.

Cell migration is a dynamic process, which is important for embryonic development, tissue repair, immune system function, and tumor invasion 1, 2. During ...

A Quantitative Fitness Analysis Workflow

Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel1,2,3,4. QFA can be applied to focused observations of single cultures but is most useful for genome-wide genetic interaction or drug screens investigating up to thousands of independent cultures. The central experimental method is the inoculation of independent, dilute liquid microbial cultures onto solid agar plates which are incubated and regularly photographed. Photographs from each time-point are analyzed, producing quantitative cell density estimates, which are used to construct growth curves, allowing quantitative fitness measures to be derived. Culture fitnesses can be compared to quantify and rank genetic interaction strengths or drug sensitivities. The effect on culture fitness of any treatments added into substrate agar (e.g. small molecules, antibiotics or nutrients) or applied to plates externally (e.g. UV irradiation, temperature) can be quantified by QFA.
The QFA workflow produces growth rate estimates analogous to those obtained by spectrophotometric measurement of parallel liquid cultures in 96-well or 200-well plate readers. Importantly, QFA has significantly higher throughput compared with such methods. QFA cultures grow on a solid agar surface and are therefore well aerated during growth without the need for stirring or shaking.
QFA throughput is not as high as that of some Synthetic Genetic Array (SGA) screening methods5,6. However, since QFA cultures are heavily diluted before being inoculated onto agar, QFA can capture more complete growth curves, including exponential and saturation phases3. For example, growth curve observations allow culture doubling times to be estimated directly with high precision, as discussed previously1.
Here we present a specific QFA protocol applied to thousands of S. cerevisiae cultures which are automatically handled by robots during inoculation, incubation and imaging. Any of these automated steps can be replaced by an equivalent, manual procedure, with an associated reduction in throughput, and we also present a lower throughput manual protocol. The same QFA software tools can be applied to images captured in either workflow.
We have extensive experience applying QFA to cultures of the budding yeast S. cerevisiae but we expect that QFA will prove equally useful for examining cultures of the fission yeast S. pombe and bacterial cultures.

Quantitative Fitness Analysis (QFA) is a complementary series of experimental and computational methods for estimating microbial culture fitnesses. QFA estimates the effect of genetic mutations, drugs or other applied treatments on microbe growth. Experiments scaling from focussed analysis of single cultures to thousands of parallel cultures can be designed.

Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel1,2,3,4. ...

Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy

Prostate cancer is the leading form of malignancies among men in the U.S. While surgery carries a significant risk of impotence and incontinence, traditional chemotherapeutic approaches have been largely unsuccessful. Hormone therapy is effective at early stage, but often fails with the eventual development of hormone-refractory tumors. We have been interested in developing therapeutics targeting specific metabolic deficiency of tumor cells. We recently showed that prostate tumor cells specifically lack an enzyme (argininosuccinate synthase, or ASS) involved in the synthesis of the amino acid arginine1. This condition causes the tumor cells to become dependent on exogenous arginine, and they undergo metabolic stress when free arginine is depleted by arginine deiminase (ADI)1,10. Indeed, we have shown that human prostate cancer cells CWR22Rv1 are effectively killed by ADI with caspase-independent apoptosis and aggressive autophagy (or macroautophagy)1,2,3. Autophagy is an evolutionarily-conserved process that allows cells to metabolize unwanted proteins by lysosomal breakdown during nutritional starvation4,5. Although the essential components of this pathway are well-characterized6,7,8,9, many aspects of the molecular mechanism are still unclear - in particular, what is the role of autophagy in the death-response of prostate cancer cells after ADI treatment? In order to address this question, we required an experimental method to measure the level and extent of autophagic response in cells - and since there are no known molecular markers that can accurately track this process, we chose to develop an imaging-based approach, using quantitative 3D fluorescence microscopy11,12.
Using CWR22Rv1 cells specifically-labeled with fluorescent probes for autophagosomes and lysosomes, we show that 3D image stacks acquired with either widefield deconvolution microscopy (and later, with super-resolution, structured-illumination microscopy) can clearly capture the early stages of autophagy induction. With commercially available digital image analysis applications, we can readily obtain statistical information about autophagosome and lysosome number, size, distribution, and degree of colocalization from any imaged cell. This information allows us to precisely track the progress of autophagy in living cells and enables our continued investigation into the role of autophagy in cancer chemotherapy.

Autophagy is a ubiquitous process that enables cells to degrade and recycle proteins and organelles. We apply advanced fluorescence microscopy to visualize and quantify the small, but essential, physical changes associated with the induction of autophagy, including the formation and distribution of autophagosomes and lysosomes, and their fusion into autolysosomes.

Prostate cancer is the leading form of malignancies among men in the U.S. While surgery carries a significant risk of impotence and incontinence, ...

Intravital Microscopy of the Microcirculation in the Mouse Cremaster Muscle for the Analysis of Peripheral Stem Cell Migration

In the era of intravascular cell application protocols in the context of regenerative cell therapy, the underlying mechanisms of stem cell migration to nonmarrow tissue have not been completely clarified. We describe here the technique of intravital microscopy applied to the mouse cremaster microcirculation for analysis of peripheral bone marrow stem cell migration in vivo. Intravital microscopy of the M. cremaster has been previously introduced in the field of inflammatory research for direct observation of leucocyte interaction with the vascular endothelium. Since sufficient peripheral stem and progenitor cell migration includes similar initial steps of rolling along and firm adhesion at the endothelial lining it is conceivable to apply the M. cremaster model for the observation and quantification of the interaction of intravasculary administered stem cells with the endothelium. As various chemical components can be selectively applied to the target tissue by simple superfusion techniques, it is possible to establish essential microenvironmental preconditions, for initial stem cell recruitment to take place in a living organism outside the bone marrow.

Intravital microscopy of the mouse M. cremaster microcirculation offers a unique and well-standardized in vivo model for the analysis of peripheral bone marrow stem cell migration.

In the era of intravascular cell application protocols in the context of regenerative cell therapy, the underlying mechanisms of stem cell migration to ...

Analysis of the Gap Junction-dependent Transfer of miRNA with 3D-FRAP Microscopy

Small antisense RNAs, like miRNA and siRNA, play an important role in cellular physiology and pathology and, moreover, can be used as therapeutic agents in the treatment of several diseases. The development of new, innovative strategies for miRNA/siRNA therapy is based on an extensive knowledge of the underlying mechanisms. Recent data suggest that small RNAs are exchanged between cells in a gap junction-dependent manner, thereby inducing gene regulatory effects in the recipient cell. Molecular biological techniques and flow cytometric analysis are commonly used to study the intercellular exchange of miRNA. However, these methods do not provide high temporal resolution, which is necessary when studying the gap junctional flux of molecules. Therefore, to investigate the impact of miRNA/siRNA as intercellular signaling molecules, novel tools are needed that will allow for the analysis of these small RNAs at the cellular level. The present protocol describes the application of three-dimensional fluorescence recovery after photobleaching (3D-FRAP) microscopy to elucidating the gap junction-dependent exchange of miRNA molecules between cardiac cells. Importantly, this straightforward and non-invasive live-cell imaging approach allows for the visualization and quantification of the gap junctional shuttling of fluorescently labeled small RNAs in real time, with high spatio-temporal resolution. The data obtained by 3D-FRAP confirm a novel pathway of intercellular gene regulation, where small RNAs act as signaling molecules within the intercellular network.

Here, we describe the application of three-dimensional fluorescence recovery after photobleaching (3D-FRAP) for the analysis of the gap junction-dependent shuttling of miRNA. In contrast to commonly applied methods, 3D-FRAP allows for the quantification of the intercellular transfer of small RNAs in real time, with high spatio-temporal resolution.

Small antisense RNAs, like miRNA and siRNA, play an important role in cellular physiology and pathology and, moreover, can be used as therapeutic agents ...

Oligomerization Dynamics of Cell Surface Receptors in Living Cells by Total Internal Reflection Fluorescence Microscopy Combined with Number and Brightness Analysis

Despite the importance and ubiquity of receptor oligomerization, few methods are applicable for detecting clustering events and measuring the degree of clustering. Here, we describe an imaging approach to determine the average oligomeric state of mEGFP-tagged-receptor homocomplexes in the membrane of living cells. The protocol is based on Total Internal Reflection Fluorescence (TIRF) microscopy combined with Number and Brightness (N&#38;B) analysis. N&#38;B is a method similar to fluorescence-correlation spectroscopy (FCS) and photon counting histogram (PCH), which are based on the statistical analysis of the fluctuations of the fluorescence intensity of fluorophores diffusing in and out of an illumination volume during an observation time. In particular, N&#38;B is a simplification of PCH to obtain information on the average number of proteins in oligomeric mixtures. The intensity fluctuation amplitudes are described by the molecular brightness of the fluorophore and the average number of fluorophores within the illumination volume. Thus, N&#38;B considers only the first and second moments of the amplitude distribution, namely, the mean intensity and the variance. This is, at the same time, the strength and the weakness of the method. Because only two moments are considered, N&#38;B cannot determine the molar fraction of unknown oligomers in a mixture, but it only estimates the average oligomerization state of the mixture. Nevertheless, it can be applied to relatively small time series (compared to other moment methods) of images of live cells on a pixel-by-pixel basis, simply by monitoring the time fluctuations of the fluorescence intensity. It reduces the effective time-per-pixel to a few microseconds, allowing acquisition in the time range of seconds to milliseconds, which is necessary for fast oligomerization kinetics. Finally, large cell areas as well as sub-cellular compartments can be explored.

We describe an imaging approach for the determination of the average oligomeric state of mEGFP-tagged-receptor oligomers induced by ligand binding in the plasma membrane of living cells. The protocol is based on Total Internal Reflection Fluorescence (TIRF) microscopy combined with Number and Brightness (N&#38;B) analysis.

Despite the importance and ubiquity of receptor oligomerization, few methods are applicable for detecting clustering events and measuring the degree of ...