This work was supported by grants from the German Science Foundation (DFG) to Collaborative Research Center (SFB) 1032. Support by the German Federal Ministry of Education, Research and Technology (BMBF) under the cooperative project 05K2018-2017-06716 Medisoft as well as a grant from the Bayerische Forschungsstiftung are gratefully acknowledged. Anita Reiser was supported by a DFG Fellowship through the Graduate School of Quantitative Biosciences Munich (QBM).

<ol>
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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=Versatile+method+to+generate+multiple+types+of+micropatterns.">Versatile method to generate multiple types of micropatterns.</a> Biointerphases. 11 (1), 011005 (2016).</li><li>Piel, M., Th&#233;ry, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Micropatterning in cell biology, part A/B/C. , (2014).</li><li>Reiser, A., Zorn, M. L., Murschhauser, A., R&#228;dler, J. O., Ertl, P., Rothbauer, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Cell-Based Microarrays: Methods and Protocols. , 41-54 (2018).</li><li>Picone, R., Baum, B., McKendry, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Methods in cell biology. 119, 73-90 (2014).</li><li>Reiser, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Single-cell time courses of mRNA transport and translation kinetics. , (2019).</li><li>. Softmatter LMU-Raedler Group Available from: <a target="_blank" href="https://github.com/SoftmatterLMU-RaedlerGroup/pyama">https://github.com/SoftmatterLMU-RaedlerGroup/pyama</a> (2020)</li><li>. MIAAB, 29262 Available from: <a target="_blank" href="https://github.com/SoftmatterLMU-RaedlerGroup/pyama">https://github.com/SoftmatterLMU-RaedlerGroup/pyama</a> (2020)</li><li>Reiser, 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=Correlation+of+mRNA+delivery+timing+and+protein+expression+in+lipid-based+transfection.">Correlation of mRNA delivery timing and protein expression in lipid-based transfection.</a> Integrative Biology. 11 (9), 362-371 (2019).</li><li>Reiser, A., Wosch&#233;e, D., Mehrotra, N., Krzyszto&#324;, R. S., Strey, H. H., R&#228;dler, J. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Supplementing+data+and+code+for:+"Correlation+of+mRNA+delivery+timing+and+protein+expression+in+lipid-based+transfection".">Supplementing data and code for: "Correlation of mRNA delivery timing and protein expression in lipid-based transfection".</a> Zenodo. , (2019).</li><li>Ferizi, 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=Stability+analysis+of+chemically+modified+mRNA+using+micropattern-based+single-cell+arrays.">Stability analysis of chemically modified mRNA using micropattern-based single-cell arrays.</a> Lab on a Chip. 15 (17), 3561-3571 (2015).</li><li>Fr&#246;hlich, F., 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=Multi-experiment+nonlinear+mixed+effect+modeling+of+single-cell+translation+kinetics+after+transfection.">Multi-experiment nonlinear mixed effect modeling of single-cell translation kinetics after transfection.</a> NPJ systems biology and applications. 4 (1), 1-12 (2018).</li><li>Krzyszto&#324;, 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=Single-cell+kinetics+of+siRNA-mediated+mRNA+degradation.">Single-cell kinetics of siRNA-mediated mRNA degradation.</a> Nanomedicine: Nanotechnology, Biology and Medicine. 21, 102077 (2019).</li><li>R&#246;ttgermann, P. J., Alberola, A. P., R&#228;dler, J. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cellular+self-organization+on+micro-structured+surfaces.">Cellular self-organization on micro-structured surfaces.</a> Soft Matter. 10 (14), 2397-2404 (2014).</li><li>R&#246;ttgermann, P. J., Dawson, K. A., R&#228;dler, J. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Time-resolved+study+of+nanoparticle+induced+apoptosis+using+microfabricated+single+cell+arrays.">Time-resolved study of nanoparticle induced apoptosis using microfabricated single cell arrays.</a> Microarrays. 5 (2), 8 (2016).</li><li>Murschhauser, 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=A+high-throughput+microscopy+method+for+single-cell+analysis+of+event-time+correlations+in+nanoparticle-induced+cell+death.">A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death.</a> Communications Biology. 2 (1), 1-11 (2019).</li><li>Chatzopoulou, E. 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=Chip-based+platform+for+dynamic+analysis+of+NK+cell+cytolysis+mediated+by+a+triplebody.">Chip-based platform for dynamic analysis of NK cell cytolysis mediated by a triplebody.</a> Analyst. 141 (7), 2284-2295 (2016).</li><li>Sztilkovics, 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=Single-cell+adhesion+force+kinetics+of+cell+populations+from+combined+label-free+optical+biosensor+and+robotic+fluidic+force+microscopy.">Single-cell adhesion force kinetics of cell populations from combined label-free optical biosensor and robotic fluidic force microscopy.</a> Scientific Reports. 10 (1), 1-13 (2020).</li></ol>

The authors declare that they have no competing financial interests.

Here we described LISCA as a versatile technique to follow cellular kinetics of intracellular fluorescent labels at the single-cell level. In order to perform a successful LISCA experiment, each of the described steps of the protocol section must be established individually and then all steps must be combined. Each of the three major aspects of LISCA feature crucial steps.
Single-cell microarray fabrication 
The quality of the microarray is crucial as the cellular alignment on th...

In recent years, the importance of single-cell experiments has become apparent. Data from single cells allow the investigation of cell-to-cell variability, the resolution of intracellular parameter correlations and the detection of cellular kinetics that remain hidden in ensemble measurements1,2,3. In order to investigate cellular kinetics of thousands of single cells in parallel, new approaches are needed that enable monitoring the cells under standardized conditions over a time period of several hours up to several days followed by a quantitative data analysis 4. Here, we present Live-cell Imaging of Single-Cell Arrays (LISCA), which combines the use of microstructured arrays with time-lapse microscopy and automated image analysis.
Several methods for generating microstructured single-cell arrays have been established and published in literature5,6. Here, we briefly describe Microscale Plasma-Initiated Protein Patterning (&#181;PIPP). A detailed protocol of the single-cell array fabrication using &#181;PIPP is also found in reference7. The use of single-cell arrays enables alignment of thousands of cells on standardized adhesion spots presenting defined microenvironments for each cell and thus reduces a source of experimental variability (Figure 1A). Single-cell arrays are used to monitor the time courses of fluorescent markers purposed to indicate a variety of cellular processes. Long-term microscopy in scanning time-lapse mode allows for monitoring a large area of the single-cell arrays and hence sampling single-cell data in high-throughput over an observation time of several hours or even days. This generates time-line stacks of images from each position of the array (Figure 1B). In order to reduce the large amount of image data and to extract the desired single-cell fluorescence time courses in an efficient way, automated image processing is required that takes advantage of the positioning of cells (Figure 1C).
The challenge of LISCA is to adapt the experimental protocols and computational tools to form a high-throughput assay that generates quantitative and reproducible data of cellular kinetics. In this article we provide a step-by-step description of the individual methods and how they are combined in a LISCA assay. As an example, we discuss the time course of enhanced green fluorescent protein (eGFP) expression after artificial mRNA delivery. eGFP expression following mRNA delivery is described by reaction rate equations modeling translation and degradation of mRNA. Fitting the model function for the time course of eGFP concentration to the LISCA readout of the fluorescence intensity for each individual cell over time yields best estimates of model parameters such as the mRNA degradation rate. As a representative result we discuss the mRNA delivery efficiency of two different lipid-based transfection agents and how their parameter distributions differ.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62025/62025fig01.jpg" /> 
Figure 1: Representation of the LISCA workflow combining (A) micro-patterned single-cell arrays (B) scanning time-lapse microscopy and (C) automated image analysis of recorded image series.&#160;The single-cell arrays consist of a two-dimensional pattern of cell-adhesive squares with a cell-repellent interspace leading to an arrangement of the cells on the micropattern, as can be seen in the phase-contrast image as well as the fluorescence image of eGFP expressing cells (A). The entire microstructured area is imaged in a scanning time-lapse mode repeatedly taking images at a sequence of positions (B). Recorded image series are processed to read out the fluorescence intensity per cell over time (C). Scale bars: 500 &#181;m (A), 200 &#181;m (C).&#160;<a href="https://www.jove.com/files/ftp_upload/62025/62025fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Adtech Polymer Engineering PTFE Microtubing&#160;</td>
			<td>Fisher Scientific</td>
			<td>10178071</td>
			<td></td>
		</tr>
		<tr>
			<td>baking oven</td>
			<td>Binder</td>
			<td>9010-0190</td>
			<td></td>
		</tr>
		<tr>
			<td>CFI Plan Fluor DL 10x</td>
			<td>Nikon</td>
			<td>MRH20100</td>
			<td></td>
		</tr>
		<tr>
			<td>Desiccator</td>
			<td>Roth</td>
			<td>NX07.1</td>
			<td></td>
		</tr>
		<tr>
			<td>Eclipse Ti-E</td>
			<td>Nikon</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>eGFP mRNA</td>
			<td>Trilink</td>
			<td>L-7601</td>
			<td></td>
		</tr>
		<tr>
			<td>Female Luer to Tube Connector</td>
			<td>MEDNET</td>
			<td>FTL210-6005</td>
			<td></td>
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		<tr>
			<td>Fetal bovine serum</td>
			<td>Thermo Fisher</td>
			<td>10270106</td>
			<td></td>
		</tr>
		<tr>
			<td>Fibronectin</td>
			<td>Yo Proteins</td>
			<td>663</td>
			<td></td>
		</tr>
		<tr>
			<td>Filter set eGFP</td>
			<td>AHF</td>
			<td>F46-002</td>
			<td></td>
		</tr>
		<tr>
			<td>Fisherbrand&#160;Translucent Platinum-Cured Silicone Tubing</td>
			<td>Fisher Scientific</td>
			<td>11768088</td>
			<td></td>
		</tr>
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			<td>HEPES (1 M)</td>
			<td>Thermo Fisher</td>
			<td>15630080</td>
			<td></td>
		</tr>
		<tr>
			<td>Incubation Box</td>
			<td>Okolab</td>
			<td>OKO-H201</td>
			<td></td>
		</tr>
		<tr>
			<td>incubator</td>
			<td>Binder</td>
			<td>9040-0012</td>
			<td></td>
		</tr>
		<tr>
			<td>L-15 without phenol red</td>
			<td>Thermo Fisher</td>
			<td>21083027</td>
			<td></td>
		</tr>
		<tr>
			<td>Lipofectamine 2000</td>
			<td>Thermo Fisher</td>
			<td>11668027</td>
			<td></td>
		</tr>
		<tr>
			<td>Male Luer</td>
			<td></td>
			<td></td>
			<td>in-house fabricated consisting of teflon</td>
		</tr>
		<tr>
			<td>Male Luer to Tube Connector</td>
			<td>MEDNET</td>
			<td>MTLS210-6005</td>
			<td>alternative to in-house fabricated male luers</td>
		</tr>
		<tr>
			<td>NaCl (5 M)</td>
			<td>Thermo Fisher</td>
			<td>AM9760G</td>
			<td></td>
		</tr>
		<tr>
			<td>Needleless Valve to Male Luer Connector</td>
			<td>MEDNET</td>
			<td>NVFMLLPC</td>
			<td></td>
		</tr>
		<tr>
			<td>NIS Elements</td>
			<td>Nikon</td>
			<td></td>
			<td>Imaging software Version 5.02.00</td>
		</tr>
		<tr>
			<td>NOA81</td>
			<td>Thorlabs</td>
			<td>NOA81</td>
			<td>Fast Curing Optical Adhesive for tube system assembly</td>
		</tr>
		<tr>
			<td>Opti-MEM</td>
			<td>Thermo Fisher</td>
			<td>31985062</td>
			<td></td>
		</tr>
		<tr>
			<td>PCO edge 4.2 M-USB-HQ-PCO</td>
			<td>pco</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Phosphate buffered saline (PBS)</td>
			<td></td>
			<td></td>
			<td>in-house prepared</td>
		</tr>
		<tr>
			<td>Plasma Cleaner</td>
			<td>Diener Femto</td>
			<td>Pico-BRS</td>
			<td></td>
		</tr>
		<tr>
			<td>PLL(20 kDa)-g[3.5]-PEG(2 kDa)</td>
			<td>SuSoS AG</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>silicon wafer mit mircorstructures</td>
			<td></td>
			<td></td>
			<td>in-house fabricated</td>
		</tr>
		<tr>
			<td>Sola Light Engine</td>
			<td>Lumencor</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>sticky slide VI 0.4&#160;</td>
			<td>ibidi</td>
			<td>80608</td>
			<td></td>
		</tr>
		<tr>
			<td>Sylgard 184 Silicone Elastomer Kit</td>
			<td>Dow Corning</td>
			<td>1673921</td>
			<td></td>
		</tr>
		<tr>
			<td>Tango 2</td>
			<td>M&#228;rzh&#228;user</td>
			<td>00-24-626-0000</td>
			<td></td>
		</tr>
		<tr>
			<td>Ultrapure water</td>
			<td></td>
			<td></td>
			<td>in-house prepared</td>
		</tr>
		<tr>
			<td>uncoated coverslips</td>
			<td>ibidi</td>
			<td>10813</td>
			<td></td>
		</tr>
		<tr>
			<td>Injekt-F Solo, 1 mL</td>
			<td>Omilab</td>
			<td>9166017V</td>
			<td>with replacement sporn</td>
		</tr>
	</tbody>
</table>

live-cell imaging of single-cell arrays (lisca) - a versatile technique to quantify cellular kinetics

Live-cell Imaging of Single-Cell Arrays (LISCA) is a versatile method to collect time courses of fluorescence signals from individual cells in high throughput. In general, the acquisition of single-cell time courses from cultured cells is hampered by cell motility and diversity of cell shapes. Adhesive micro-arrays standardize single-cell conditions and facilitate image analysis. LISCA combines single-cell microarrays with scanning time-lapse microscopy and automated image processing. Here, we describe the experimental steps of taking single-cell fluorescence time courses in a LISCA format. We transfect cells adherent to a micropatterned array using mRNA encoding for enhanced green fluorescent protein (eGFP) and monitor the eGFP expression kinetics of hundreds of cells in parallel via scanning time-lapse microscopy. The image data stacks are automatically processed by newly developed software that integrates fluorescence intensity over selected cell contours to generate single-cell fluorescence time courses. We demonstrate that eGFP expression time courses after mRNA transfection are well described by a simple kinetic translation model that reveals expression and degradation rates of mRNA. Further applications of LISCA for event time correlations of multiple markers in the context of signaling apoptosis are discussed.

The LISCA approach enables to efficiently collect fluorescence time courses from single cells. As a representative example we outline how the LISCA method is applied to measure single-cell eGFP expression after transfection. The data of the LISCA experiment is used to assess mRNA delivery kinetics, which is important for the development of efficient mRNA drugs.
In particular we demonstrate the different impact of two lipid-based mRNA delivery systems with respect to the time point of translati...

Watch this Scientific Journal Video about Live-cell Imaging of Single-Cell Arrays LISCA - a Versatile Technique to Quantify Cellular Kinetics at JoVE.com

Live-cell Imaging of Single-Cell Arrays LISCA - a Versatile Technique to Quantify Cellular Kinetics

We present a method for the acquisition of fluorescence reporter time courses from single cells using micropatterned arrays. The protocol describes the preparation of single-cell arrays, the setup and operation of live-cell scanning time-lapse microscopy and an open-source image analysis tool for automated preselection, visual control and tracking of cell-integrated fluorescence time courses per adhesion site.

Live-cell Imaging of Single-Cell Arrays (LISCA) - a Versatile Technique to Quantify Cellular Kinetics

Live-cell Imaging of Single-Cell Arrays (LISCA) is a versatile method to collect time courses of fluorescence signals from individual cells in high ...

live-cell-imaging-single-cell-arrays-lisca-versatile-technique-to

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JoVE Journal

Biology

3.6K Views.  Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, 80539 München, Germany. We present a method for the acquisition of fluorescence reporter time courses from single cells using micropatterned arrays. The protocol describes the preparation of single-cell arrays, the setup and operation of live-cell scanning time-lapse microscopy and an open-source image analysis tool for automated preselection, visual control and tracking of cell-integrated fluorescence time courses per adhesion site.

Video: Live-cell Imaging of Single-Cell Arrays LISCA - a Versatile Technique to Quantify Cellular Kinetics

Imaging Mismatch Repair and Cellular Responses to DNA Damage in Bacillus subtilis

Both prokaryotes and eukaryotes respond to DNA damage through a complex set of physiological changes. Alterations in gene expression, the redistribution of existing proteins, and the assembly of new protein complexes can be stimulated by a variety of DNA lesions and mismatched DNA base pairs. Fluorescence microscopy has been used as a powerful experimental tool for visualizing and quantifying these and other responses to DNA lesions and to monitor DNA replication status within the complex subcellular architecture of a living cell. Translational fusions between fluorescent reporter proteins and components of the DNA replication and repair machinery have been used to determine the cues that target DNA repair proteins to their cognate lesions in vivo and to understand how these proteins are organized within bacterial cells. In addition, transcriptional and translational fusions linked to DNA damage inducible promoters have revealed which cells within a population have activated genotoxic stress responses. In this review, we provide a detailed protocol for using fluorescence microscopy to image the assembly of DNA repair and DNA replication complexes in single bacterial cells. In particular, this work focuses on imaging mismatch repair proteins, homologous recombination, DNA replication and an SOS-inducible protein in Bacillus subtilis. All of the procedures described here are easily amenable for imaging protein complexes in a variety of bacterial species.

Imaging Mismatch Repair and Cellular Responses to DNA Damage in Bacillus subtilis

A detailed protocol is described for imaging the real time formation of DNA repair complexes in Bacillus subtilis cells.

Both prokaryotes and eukaryotes respond to DNA damage through a complex set of physiological changes. Alterations in gene expression, the redistribution ...

Live-cell Imaging and Quantitative Analysis of Embryonic Epithelial Cells in Xenopus laevis

Embryonic epithelial cells serve as an ideal model to study morphogenesis where multi-cellular tissues undergo changes in their geometry, such as changes in cell surface area and cell height, and where cells undergo mitosis and migrate. Furthermore, epithelial cells can also regulate morphogenetic movements in adjacent tissues1. A traditional method to study epithelial cells and tissues involve chemical fixation and histological methods to determine cell morphology or localization of particular proteins of interest. These approaches continue to be useful and provide "snapshots" of cell shapes and tissue architecture, however, much remains to be understood about how cells acquire specific shapes, how various proteins move or localize to specific positions, and what paths cells follow toward their final differentiated fate. High resolution live imaging complements traditional methods and also allows more direct investigation into the dynamic cellular processes involved in the formation, maintenance, and morphogenesis of multicellular epithelial sheets. Here we demonstrate experimental methods from the isolation of animal cap tissues from Xenopus laevis embryos to confocal imaging of epithelial cells and simple measurement approaches that together can augment molecular and cellular studies of epithelial morphogenesis.

Live-cell Imaging and Quantitative Analysis of Embryonic Epithelial Cells in Xenopus laevis

Xenopus embryonic epithelia are an ideal model system to study cell behaviors such as polarity development and shape change during epithelial morphogenesis. Traditional histology of fixed samples is increasingly being complemented by live-cell confocal imaging. Here we demonstrate methods to isolate frog tissues and visualize live epithelial cells and their cytoskeleton using live-cell confocal microscopy.

Embryonic epithelial cells serve as an ideal model to study morphogenesis where multi-cellular tissues undergo changes in their geometry, such as changes ...

Live Cell Imaging of Early Autophagy Events: Omegasomes and Beyond

Autophagy is a cellular response triggered by the lack of nutrients, especially the absence of amino acids. Autophagy is defined by the formation of double membrane structures, called autophagosomes, that sequester cytoplasm, long-lived proteins and protein aggregates, defective organelles, and even viruses or bacteria. Autophagosomes eventually fuse with lysosomes leading to bulk degradation of their content, with the produced nutrients being recycled back to the cytoplasm. Therefore, autophagy is crucial for cell homeostasis, and dysregulation of autophagy can lead to disease, most notably neurodegeneration, ageing and cancer.	
	Autophagosome formation is a very elaborate process, for which cells have allocated a specific group of proteins, called the core autophagy machinery. The core autophagy machinery is functionally complemented by additional proteins involved in diverse cellular processes, e.g. in membrane trafficking, in mitochondrial and lysosomal biology. Coordination of these proteins for the formation and degradation of autophagosomes constitutes the highly dynamic and sophisticated response of autophagy. Live cell imaging allows one to follow the molecular contribution of each autophagy-related protein down to the level of a single autophagosome formation event and in real time, therefore this technique offers a high temporal and spatial resolution.	
	Here we use a cell line stably expressing GFP-DFCP1, to establish a spatial and temporal context for our analysis. DFCP1 marks omegasomes, which are precursor structures leading to autophagosomes formation. A protein of interest (POI) can be marked with either a red or cyan fluorescent tag. Different organelles, like the ER, mitochondria and lysosomes, are all involved in different steps of autophagosome formation, and can be marked using a specific tracker dye. Time-lapse microscopy of autophagy in this experimental set up, allows information to be extracted about the fourth dimension, i.e. time. Hence we can follow the contribution of the POI to autophagy in space and time.

Time-lapse microscopy of fluorescently labeled autophagy markers allows monitoring of the dynamic autophagy response with high temporal resolution. Using specific autophagy and organelle markers in a combination of 3 different colors, we can follow the contribution of a protein to autophagosome formation in a robust spatial and temporal context.

Autophagy is a  cellular response triggered by the lack of nutrients, especially the absence of  amino acids. Autophagy is defined by the formation of ...

Single-cell Microinjection for Cell Communication Analysis

Gap junctions are intercellular channels that allow the communication of neighboring cells. This communication depends on the contribution of a hemichannel by each neighboring cell to form the gap junction. In mammalian cells, the hemichannel is formed by six connexins, monomers with four transmembrane domains and a C and N terminal within the cytoplasm. Gap junctions permit the exchange of ions, second messengers, and small metabolites. In addition, they have important roles in many forms of cellular communication within physiological processes such as synaptic transmission, heart contraction, cell growth and differentiation. We detail how to perform a single-cell microinjection of Lucifer Yellow to visualize cellular communication via gap-junctions in living cells. It is expected that in functional gap junctions, the dye will diffuse from the loaded cell to the connected cells. It is a very useful technique to study gap junctions since you can evaluate the diffusion of the fluorescence in real time. We discuss how to prepare the cells and the micropipette, how to use a micromanipulator and inject a low molecular weight fluorescent dye in an epithelial cell line.

We describe here how to perform a single-cell microinjection of Lucifer Yellow to visualize cellular communication via gap-junctions in living cells, and provide some useful tips. We expect that this paper will help everyone to evaluate the degree of cellular coupling due to functional gap junctions. Everything described here could be, in principle, adapted to other fluorescent dyes with molecular weight below 1,000 Daltons.

Gap junctions are intercellular channels that allow the communication of neighboring cells. This communication depends on the contribution of a ...

Techniques to Induce and Quantify Cellular Senescence

In response to cellular stress or damage, proliferating cells can induce a specific program that initiates a state of long-term cell-cycle arrest, termed cellular senescence. Accumulation of senescent cells occurs with organismal aging and through continual culturing in vitro. Senescent cells influence many biological processes, including embryonic development, tissue repair and regeneration, tumor suppression, and aging. Hallmarks of senescent cells include, but are not limited to, increased senescence-associated &#946;-galactosidase activity (SA-&#946;-gal); p16INK4A, p53, and p21 levels; higher levels of DNA damage, including &#947;-H2AX; the formation of Senescence-associated Heterochromatin Foci (SAHF); and the acquisition of a Senescence-associated Secretory Phenotype (SASP), a phenomenon characterized by the secretion of a number of pro-inflammatory cytokines and signaling molecules. Here, we describe protocols for both replicative and DNA damage-induced senescence in cultured cells. In addition, we highlight techniques to monitor the senescent phenotype using several senescence-associated markers, including SA-&#946;-gal, &#947;-H2AX and SAHF staining, and to quantify protein and mRNA levels of cell cycle regulators and SASP factors. These methods can be applied to the assessment of senescence in various models and tissues.

Cellular senescence, the irreversible state of cell-cycle arrest, can be induced by various cellular stresses. Here, we describe protocols to induce cellular senescence and methods to assess markers of senescence.

In response to cellular stress or damage, proliferating cells can induce a specific program that initiates a state of long-term cell-cycle arrest, termed ...

Single-cell Microfluidic Analysis of Bacillus subtilis

Microfluidic technology overcomes many of the limitations to traditional analytical methods in microbiology. Unlike bulk-culture methods, it offers single-cell resolution and long observation times spanning hundreds of generations; unlike agarose pad-based microscopy, it has uniform growth conditions that can be tightly controlled. Because the continuous flow of growth medium isolates the cells in a microfluidic device from unpredictable variations in the local chemical environment caused by cell growth and metabolism, authentic changes in gene expression and cell growth in response to specific stimuli can be more confidently observed. Bacillus subtilis is used here as a model bacterial species to demonstrate a &#34;mother machine&#34;-type method for cellular analysis. We show how to construct and plumb a microfluidic device, load it with cells, initiate microscopic imaging, and expose cells to a stimulus by switching from one growth medium to another. A stress-responsive reporter is used as an example to reveal the type of data that may be obtained by this method. We also briefly discuss further applications of this method for other types of experiments, such as analysis of bacterial sporulation.

Single-cell Microfluidic Analysis of Bacillus subtilis

We present a method for the microfluidic analysis of individual bacterial cell lineages using Bacillus subtilis as an example. The method overcomes shortcomings of traditional analytical methods in microbiology by allowing observation of hundreds of cell generations under tightly controllable and uniform growth conditions.

Microfluidic technology overcomes many of the limitations to traditional analytical methods in microbiology. Unlike bulk-culture methods, it offers ...

Analysis of Beta-cell Function Using Single-cell Resolution Calcium Imaging in Zebrafish Islets

Pancreatic beta-cells respond to increasing blood glucose concentrations by secreting the hormone insulin. The dysfunction of beta-cells leads to hyperglycemia and severe, life-threatening consequences. Understanding how the beta-cells operate under physiological conditions and what genetic and environmental factors might cause their dysfunction could lead to better treatment options for diabetic patients. The ability to measure calcium levels in beta-cells serves as an important indicator of beta-cell function, as the influx of calcium ions triggers insulin release. Here we describe a protocol for monitoring the glucose-stimulated calcium influx in zebrafish beta-cells by using GCaMP6s, a genetically encoded sensor of calcium. The method allows monitoring the intracellular calcium dynamics with single-cell resolution in ex vivo mounted islets. The glucose-responsiveness of beta-cells within the same islet can be captured simultaneously under different glucose concentrations, which suggests the presence of functional heterogeneity among zebrafish beta-cells. Furthermore, the technique provides high temporal and spatial resolution, which reveals the oscillatory nature of the calcium influx upon glucose stimulation. Our approach opens the doors to use the zebrafish as a model to investigate the contribution of genetic and environmental factors to beta-cell function and dysfunction.

Beta-cell functionality is important for blood-glucose homeostasis, which is evaluated at single-cell resolution using a genetically encoded reporter for calcium influx.

Pancreatic beta-cells respond to increasing blood glucose concentrations by secreting the hormone insulin. The dysfunction of beta-cells leads to ...

LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring

The regulation of LDL cholesterol uptake through LDLR-mediated endocytosis is an important area of study in various major pathologies including metabolic disorder, cardiovascular disease, and kidney disease. Currently, there is no available method to assess LDL uptake while simultaneously monitoring for health of the cells. The current study presents a protocol, using a live cell imaging analysis system, to acquire serial measurements of LDL influx with concurrent monitoring for cell health. This novel technique is tested in three human cell lines (hepatic, renal tubular epithelial, and coronary artery endothelial cells) over a four-hour time course. Moreover, the sensitivity of this technique is validated with well-known LDL uptake inhibitors, Dynasore and recombinant PCSK9 protein, as well as by an LDL uptake promoter, Simvastatin. Taken together, this method provides a medium-to-high throughput platform for simultaneously screening pharmacological activity as well as monitoring of cell morphology, hence cytotoxicity of compounds regulating LDL influx. The analysis can be used with different imaging systems and analytical software.

This protocol provides an efficient approach to measuring LDL cholesterol uptake with real time influx rates using a live cell imaging system in various cell types. This technique provides a platform to screen the pharmacological activity of compounds affecting LDL influx while monitoring for cell morphology and hence potential cytotoxicity.

The regulation of LDL cholesterol uptake through LDLR-mediated endocytosis is an important area of study in various major pathologies including metabolic ...

Live Cell Imaging to Assess the Dynamics of Metaphase Timing and Cell Fate Following Mitotic Spindle Perturbations

Live cell time-lapse imaging is an important tool in cell biology that provides insight into cellular processes that might otherwise be overlooked, misunderstood, or misinterpreted by the fixed-cell analysis. While the fixed cell imaging and analysis is robust and sufficient to observe cellular steady-state, it can be limited in defining a temporal order of events at the cellular level and is ill-equipped to assess the transient nature of dynamic processes including mitotic progression. In contrast, live cell imaging is an eloquent tool that can be used to observe cellular processes at the single-cell level over time and has the capacity to capture the dynamics of processes that would otherwise be poorly represented in fixed cell imaging. Here we describe an approach to generate cells carrying fluorescently labeled markers of chromatin and microtubules and their use in live cell imaging approaches to monitor metaphase chromosome alignment and mitotic exit. We describe imaging-based techniques to assess the dynamics of spindle formation and mitotic progression, including the identification of cells at various stages in mitosis, identification&#160;and tracking of mitotic defects, and analysis of spindle dynamics and mitotic cell fate following the treatment with mitotic inhibitors.

Here we present a protocol to assess the dynamics of spindle formation and mitotic progression. Our application of time-lapse imaging enables the user to identify cells at various stages of mitosis, track and identify mitotic defects, and analyze spindle dynamics and mitotic cell fate upon exposure to anti-mitotic drugs.

Live cell time-lapse imaging is an important tool in cell biology that provides insight into cellular processes that might otherwise be overlooked, ...

Single-Cell Resolution Three-Dimensional Imaging of Intact Organoids

Organoid technology, in vitro 3D culturing of miniature tissue, has opened a new experimental window for cellular processes that govern organ development and function as well as disease. Fluorescence microscopy has played a major role in characterizing their cellular composition in detail and demonstrating their similarity to the tissue they originate from. In this article, we present a comprehensive protocol for high-resolution 3D imaging of whole organoids upon immunofluorescent labeling. This method is widely applicable for imaging of organoids differing in origin, size and shape. Thus far we have applied the method to airway, colon, kidney, and liver organoids derived from healthy human tissue, as well as human breast tumor organoids and mouse mammary gland organoids. We use an optical clearing agent, FUnGI, which enables the acquisition of whole 3D organoids with the opportunity for single-cell quantification of markers. This three-day protocol from organoid harvesting to image analysis is optimized for 3D imaging using confocal microscopy.

The entire 3D structure and cellular content of organoids, as well as their phenotypic resemblance to the original tissue can be captured using the single-cell resolution 3D imaging protocol described here. This protocol can be applied to a wide range of organoids varying in origin, size and shape.

Organoid technology, in vitro 3D culturing of miniature tissue, has opened a new experimental window for cellular processes that govern organ development ...