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).

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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 the microarray is not only important for all further experimental steps but also has influence on the data quality. For this reason, the geometry of the pattern and the fabrication method must be adapted with respect to the used cells. The representative results discussed in this article have been generated with the liver carcinoma cell line HuH7 aligned on a (30 &#181;m)2 square pattern. This pattern geometry is also suitable for other cell lines such as A549 or HEK293. For larger cells such as the BEAS-2B, a larger square pattern with 35 &#181;m edge length and an inter-square distance of 80 &#181;m can be used. The described seeding procedure is optimized for the HuH7 cells but it can be adapted to many other adherent cells17. For example, some cell lines need different sizes of the adhesion spots or need larger spacing between the adhesion spots to avoid cell-cell contacts through elongated cells.
The micropattern should be adjusted such that the adhesion site area meets roughly the average area of a cell in standard culture dishes. The geometry can be round or squared and has no measurable effect of cell viability. Cell motions seem to be more restricted on a squared pattern compared to round pattern, where cells are often seen to rotate. Separate viability testing is recommended when new cell lines are used for the first time. Seeding density needs to be adjusted for specific cell lines to reach a high occupancy of the adhesion spots and to minimize double occupancies of adhesion spots at the same time. Typically, the resulting occupancy of number of cells per adhesion site follows Poisson statistics and is either zero, one or two. Hence total occupancy between 60% and 80% should be aimed at to avoid double occupancies17. The seeding protocol may need to be adapted regarding the number of seeded cells and the time duration between seeding and the first washing step. For example, a washing step 30 min after seeding instead of 1 h (see steps 5 and 6 of protocol section 2 Cell seeding) will reduce the number of double-occupied adhesion spots but will also lower the total number of occupied adhesion spots for HuH7 cells.
As the connection of a tubing system to the channel slides is not mandatory, it is easier to establish the use of a reagent or fluorescent marker without using a tubing system to check for the best suitable concentration and incubation time. After a suitable protocol is established for the reagent/marker of interest, the tubing system can be included in the workflow.
Time-lapse microscopy 
Another crucial step are the settings of the time-lapse measurement. For example, the exposure time for the fluorescence marker must be chosen carefully to avoid photobleaching of the fluorophore and phototoxicity effects but still ensure a good fluorescence signal. Another important parameter of the time-lapse setup is the best suitable combination of spatial and temporal resolution, which heavily depends on the observed cellular kinetics. If a high temporal resolution is needed, only a smaller number of positions in the microarray can be scanned between two time points, which reduces the scanned observation area and, thus, also reduces statistics. The observation area is furthermore not only dependent on the number of scanned positions but also on the objective and the size of one field of view of the camera. In the given example, a time resolution of 10 min is sufficient using a 10x objective to observe the translation kinetics. This combination allows to scan 70-100 positions per time point depending on the size of the camera chip, the speed of the microscope stage, and the number of imaging channels (e.g., phase-contrast and eGFP fluorescence).
Image processing 
The analysis of the total fluorescence per cell is facilitated as the cells are positioned in an array. Yet, the quality of the single-cell fluorescence time courses, in particular the signal-to-noise ratio (SNR), depends on the algorithm used for the integration of cell fluorescence intensities from the image series. The image processing steps of the software tool PyAMA are shown in Figure 5. The determination of the integration areas for the single-cell fluorescence readout is particularly important because the cell contour of living cells varies with time. PyAMA integrates the fluorescence intensity over a cell contour, which can be determined by a built-in segmentation algorithm. An alternative would be to integrate over fixed boundaries based on the micropattern geometry12,16. The current version of PyAMA performs image segmentation based on a threshold on the standard deviation of pixel neighborhoods of the phase-contrast image channel. Segmentation results can be imported from external software as well. For future versions, native support for segmentation based on machine learning and for integration over fixed boundaries is planned.
PyAMA offers to filter out outlier cells or anomalous time courses using an interface that allows for visual inspection of both the fluorescence image as well as the fluorescence time courses of selected cells (Figure 1C). Examples of cells that may be excluded from analysis are cells that undergo division or apoptosis or that are located outside of an adhesion site. Aggregations of multiple cells erroneously recognized by the tracking algorithm as a single cell also need to be filtered out to ensure that the time courses originate from single cells. PyAMA performs a pre-selection of cells to reduce the amount of manual interaction required for filtering out anomalous cells. The pre-selection can be inspected and corrected by hand before exporting the time courses of the selected cells to compensate for inaccuracies of the pre-selection. The pre-selection of the current version of PyAMA is based on a threshold for the cell size to deselect cell aggregations. For future versions, an additional machine-learning based pre-selection is planned, which allows to account for further criteria including the examples for anomalous cells described above.
In summary, the LISCA approach employs single-cell arrays to efficiently collect single-cell fluorescence time courses. Confining cells to microfabricated adhesion sites facilitates tracking and image analysis. Furthermore, cells are cultured in standardized local microenvironments and, hence, are presented with uniform surface area when exposed to agents like lipid nanoparticles for transfection. This aspect is particularly beneficial when cellular heterogeneity within a population is investigated. The &#181;PIPP technique described here is one of many microfabrication techniques that result in regular microarrays of protein patterns. The reader is referred to literature reviewing microcontact printing, photolithographic approaches and soft lithography as alternative fabrication processes6. Depending on the cell line, the one or the other patterning technique may be preferable. In our experiments, &#181;PIPP technique showed well-positioned cells after seeding of cells due to cellular self-sorting, which relies on residual cell adhesiveness on the PEGylated area so that cells are able to search in random migration and spread on the protein target arrays with differentially larger adhesiveness17.
LISCA allows for the acquisition of large numbers of single-cell time courses of arbitrary fluorescence markers. Analysis of fluorescence signals at the single-cell level, in contrast to bulk experiments, yields pristine single-cell time courses and reveals cell-to-cell variability. Cellular heterogeneity plays an eminent role in cell fate decisions such as apoptosis18,19,20. In this context, we recently extended the LISCA approach using two fluorescence channels allowing for analysis of temporal correlations of two cellular events at any one time indicating particular stages within the cell-death signaling cascade19. In this field of research, LISCA presents itself as an alternative to flow cytometry measurements. While flow cytometry undeniably provides a faster workflow and typically yields larger statistics, the data are acquired at one particular point in time. Full time dependencies yield estimates of kinetics parameters and reveal temporal correlations between different fluorescence signals, which otherwise are difficult to access. In order to use multiple fluorescence channels, the setup requires automated filter wheels or multiple cameras. In this case also single-cell FRET analysis should be feasible and enable time-resolved studies of proximity between fluorescently labeled molecules. One drawback of image-based analysis like LISCA is the labor linked to visual controls and outlier detection. Here machine learning tools could ease data processing and allow fully automated data analysis. In the future, using automated microscopy platforms, rapid array microfabrication and artificial intelligence, the throughput and applicability of LISCA could be substantially increased. Furthermore, subsequent analysis of cells that exhibit unusual fluorescence responses using microfluidic extraction 21and single cell genomics is a frequent requirement in pharmaceutical industry. The protocol presented in this article suits the demand for studying the kinetics of cellular processes with single-cell resolution and adequate statistics.

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.remotexs.ntu.edu.sg/files/ftp_upload/62025/62025fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table><tbody><tr><td>Adtech Polymer Engineering PTFE Microtubing&amp;nbsp;</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></tr><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&amp;nbsp;Translucent Platinum-Cured Silicone Tubing</td><td>Fisher Scientific</td><td>11768088</td><td></td></tr><tr><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&amp;nbsp;</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&amp;auml;rzh&amp;auml;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 translation onset and the expression rate at the single-cell level. We cultured cells and divided the batch into two populations. One subpopulation was transfected with lipoplexes as described in the protocol section. The other subpopulation was transfected using the same mRNA with the same final mRNA concentration, but with lipid nanoparticles (LNP) as delivery system, which were produced using microfluidic mixing12. Due to a different lipid composition and the different fabrication of the mRNA delivery systems of the lipoplexes and the LNPs we expect an impact on the translation kinetics as the uptake kinetics should be influenced. Using the LISCA method we quantify the time point t0 of translation onset after the transfection and how strong the cells express eGFP, which depends on the product of transfected mRNA molecules m0 and the translation rate kTL. To obtain these two parameters we fit a three-stage translation model as sketched in Figure 6A. After successful release of the mRNA molecules m at time point t0 in the cytosol, the mRNA is translated with rate&#160;kTL into unmaturated eGFP G*, which is non-fluorescent. The unmaturated&#160;G* matures with rate kM to eGFP G, the fluorescence intensity of which is measured during the time-lapse measurement. The mRNA as well as the (unmaturated and maturated) eGFP degrade over time with respective degradation rates&#160;&#948; and &#947;. The model is described by ordinary differential equations and the analytical solution for G is used as a model function for parameter estimation. The model function is fitted to each of the single-cell time courses as shown in Figure 6B with example time courses (grey) and the respective fits (green). In Figure 6C we show the histograms of the time point t0&#160;of translation onset and the expression rate m0kTL of lipoplex transfected cells. As both parameters are estimated for each cell, the correlation of these parameters can be analyzed as shown in the scatterplot (Figure 6D, blue data) and can be compared to cells transfected with LNPs (red). As shown in Figure 6D, cells transfected with LNPs show less cell-to-cell variability compared to cells transfected with lipoplexes and the population average shows a faster onset of translation as well as a higher expression rate (thick dots with black outline).
These two data sets are just one example how LISCA can be used to study translation after mRNA transfection. Further investigations can be made for example with regard to mRNA stability dependent on mRNA sequence modifications 14, varied reporter protein stabilities 15, or siRNA mediated mRNA degradation 16.
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/62025/62025fig06.jpg" /> 
Figure 6: Data analysis of single-cell eGFP translation kinetics. (A) The translation kinetics of the reporter protein eGFP after mRNA delivery can be described by a three-stage reaction rate equation with the respective parameters. (B) The model is fitted (green traces) to each single-cell eGFP expression time course (gray traces) to estimate the model parameters such as the time point (t0) of transfection onset and the expression rate (m0kTL), two parameters to quantify mRNA delivery efficacy. (C) Histograms of the parameter distribution for the transfection onset time and the expression rate. (D) As the parameters are estimated for each cell, a scatter plot of these parameters shows parameter correlation. The small dots represent the parameters of a single cell. The plot shows cells transfected with mRNA LNPs (red) and mRNA lipoplexes (blue). The thick dots with black outline correspond to the respective population average.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62025/62025fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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 ...

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JoVE Journal

Biology

3.6K Views.  Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, 80539 München, Germany. Live-Cell Imaging van Single-Cell Arrays genaamd LISCA zijn ook een geautomatiseerde uitlezing van de fluorescerende tijdscursussen van honderden individuele cellen. Het voordeel van de aanpak is dat individuele celresponsen worden geregistreerd van ruimtelijk geïsoleerde cellen in identieke microomgevingen, waardoor een efficiënte emissieanalyse met geweldige statistieken mogelijk is. Om een eencellige array te fabriceren, gebruikt u een scalpel om een enkel MEESTERWERK van PDMs met zes mi...

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

Live-cell Imaging of Migrating Cells Expressing Fluorescently-tagged Proteins in a Three-dimensional Matrix

Traditionally, cell migration has been studied on two-dimensional, stiff plastic surfaces. However, during important biological processes such as wound healing, tissue regeneration, and cancer metastasis, cells must navigate through complex, three-dimensional extracellular tissue. To better understand the mechanisms behind these biological processes, it is important to examine the roles of the proteins responsible for driving cell migration. Here, we outline a protocol to study the mechanisms of cell migration using the epithelial cell line (MDCK), and a three-dimensional, fibrous, self-polymerizing matrix as a model system. This optically clear extracellular matrix is easily amenable to live-cell imaging studies and better mimics the physiological, soft tissue environment. This report demonstrates a technique for directly visualizing protein localization and dynamics, and deformation of the surrounding three-dimensional matrix. 
Examination of protein localization and dynamics during cellular processes provides key insight into protein functions. Genetically encoded fluorescent tags provide a unique method for observing protein localization and dynamics. Using this technique, we can analyze the subcellular accumulation of key, force-generating cytoskeletal components in real-time as the cell maneuvers through the matrix. In addition, using multiple fluorescent tags with different wavelengths, we can examine the localization of multiple proteins simultaneously, thus allowing us to test, for example, whether different proteins have similar or divergent roles. Furthermore, the dynamics of fluorescently tagged proteins can be quantified using Fluorescent Recovery After Photobleaching (FRAP) analysis. This measurement assays the protein mobility and how stably bound the proteins are to the cytoskeletal network.
By combining live-cell imaging with the treatment of protein function inhibitors, we can examine in real-time the changes in the distribution of proteins and morphology of migrating cells. Furthermore, we also combine live-cell imaging with the use of fluorescent tracer particles embedded within the matrix to visualize the matrix deformation during cell migration. Thus, we can visualize how a migrating cell distributes force-generating proteins, and where the traction forces are exerted to the surrounding matrix. Through these techniques, we can gain valuable insight into the roles of specific proteins and their contributions to the mechanisms of cell migration.

Cellular processes such as cell migration have traditionally been studied on two-dimensional, stiff plastic surfaces. This report describes a technique for directly visualizing protein localization and analyzing protein dynamics in cells migrating in a more physiologically relevant, three-dimensional matrix.

Traditionally, cell migration has been studied on two-dimensional, stiff plastic surfaces. However, during important biological processes such as wound ...

Bioengineering

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

Cells should respond properly to temporally changing environments, which are influenced by various factors from surrounding cells. The Notch signaling pathway is one of such essential molecular machinery for cell-to-cell communications, which plays key roles in normal development of embryos. This pathway involves a cell-to-cell transfer of oscillatory information with ultradian rhythms, but despite the progress in molecular biology techniques, it has been challenging to elucidate the impact of multicellular interactions on oscillatory gene dynamics. Here, we present a protocol that permits optogenetic control and live monitoring of gene expression patterns in a precise temporal manner. This method successfully revealed that intracellular and intercellular periodic inputs of Notch signaling entrain intrinsic oscillations by frequency tuning and phase shifting at the single-cell resolution. This approach is applicable to the analysis of the dynamic features of various signaling pathways, providing a unique platform to test a functional significance of dynamic gene expression programs in multicellular systems.

Here, we present a protocol to analyze cell-to-cell transfer of oscillatory information by optogenetic control and live monitoring of gene expression. This approach provides a unique platform to test a functional significance of dynamic gene expression programs in multicellular systems.

Cells should respond properly to temporally changing environments, which are influenced by various factors from surrounding cells. The Notch signaling ...

Genetics

Long-term Live-cell Imaging to Assess Cell Fate in Response to Paclitaxel

Live-cell imaging is a powerful technique that can be used to directly visualize biological phenomena in single cells over extended periods of time. Over the past decade, new and innovative technologies have greatly enhanced the practicality of live-cell imaging. Cells can now be kept in focus and continuously imaged over several days while maintained under 37 &#176;C and 5% CO2 cell culture conditions. Moreover, multiple fields of view representing different experimental conditions can be acquired simultaneously, thus providing high-throughput experimental data. Live-cell imaging provides a significant advantage over fixed-cell imaging by allowing for the direct visualization and temporal quantitation of dynamic cellular events. Live-cell imaging can also identify variation in the behavior of single cells that would otherwise have been missed using population-based assays. Here, we describe live-cell imaging protocols to assess cell fate decisions following treatment with the anti-mitotic drug paclitaxel. We demonstrate methods to visualize whether mitotically arrested cells die directly from mitosis or slip back into interphase. We also describe how the fluorescent ubiquitination-based cell cycle indicator (FUCCI) system can be used to assess the fraction of interphase cells born from mitotic slippage that are capable of re-entering the cell cycle. Finally, we describe a live-cell imaging method to identify nuclear envelope rupture events.

Live-cell imaging provides a wealth of information on single cells or whole populations that is unattainable by fixed cell imaging alone. Here, live-cell imaging protocols to assess cell fate decisions following treatment with the anti-mitotic drug paclitaxel are described.

Live-cell imaging is a powerful technique that can be used to directly visualize biological phenomena in single cells over extended periods of time. Over ...

Cancer Research

Single-Cell Quantification of Protein Degradation Rates by Time-Lapse Fluorescence Microscopy in Adherent Cell Culture

Proteins are in a dynamic state of synthesis and degradation and their half-lives can be adjusted under various circumstances. However, most commonly used approaches to determine protein half-life are either limited to population averages from lysed cells or require the use of protein synthesis inhibitors. This protocol describes a method to measure protein half-lives in single living adherent cells, using SNAP-tag fusion proteins in combination with fluorescence time-lapse microscopy. Any protein of interest fused to a SNAP-tag can be covalently bound by a fluorescent, cell permeable dye that is coupled to a benzylguanine derivative, and the decay of the labeled protein population can be monitored after washout of the residual dye. Subsequent cell tracking and quantification of the integrated fluorescence intensity over time results in an exponential decay curve for each tracked cell, allowing for determining protein degradation rates in single cells by curve fitting. This method provides an estimate for the heterogeneity of half-lives in a population of cultured cells, which cannot easily be assessed by other methods. The approach presented here is applicable to any type of cultured adherent cells expressing a protein of interest fused to a SNAP-tag. Here we use mouse embryonic stem (ES) cells grown on E-cadherin-coated cell culture plates to illustrate how single cell degradation rates of proteins with a broad range of half-lives can be determined.

This protocol describes a method to determine protein half-lives in single living adherent cells, using pulse labeling and fluorescence time-lapse imaging of SNAP-tag fusion proteins.

Proteins are in a dynamic state of synthesis and degradation and their half-lives can be adjusted under various circumstances. However, most commonly used ...

Developmental Biology

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

Inter-cellular communication is an integral part of a complex system that helps in maintaining basic cellular activities. As a result, the malfunctioning of such signaling can lead to many disorders. To understand cell-to-cell signaling, it is essential to study the spatial and temporal nature of the secreted molecules from the cell without disturbing the local environment. Various assays have been developed to study protein secretion, however, these methods are typically based on fluorescent probes which disrupt the relevant signaling pathways. To overcome this limitation, a label-free technique is required.
In this paper, we describe the fabrication and application of a label-free localized surface plasmon resonance imaging (LSPRi) technology capable of detecting protein secretions from a single cell. The plasmonic nanostructures are lithographically patterned onto a standard glass coverslip and can be excited using visible light on commercially available light microscopes. Only a small fraction of the coverslip is covered by the nanostructures and hence this technique is well suited for combining common techniques such as fluorescence and bright-field imaging.
A multidisciplinary approach is used in this protocol which incorporates sensor nanofabrication and subsequent biofunctionalization, binding kinetics characterization of ligand and analyte, the integration of the chip and live cells, and the analysis of the measured signal. As a whole, this technology enables a general label-free approach towards mapping cellular secretions and correlating them with the responses of nearby cells.

Inter-cellular communication is critical for controlling various physiological activities within and outside the cell. This paper describes a protocol for measuring the spatio-temporal nature of single cell secretions. To achieve this, a multidisciplinary approach is used which integrates label-free nanoplasmonic sensing with live cell imaging.

Inter-cellular communication is an integral part of a complex system that helps in maintaining basic cellular activities. As a result, the malfunctioning ...

Automated Kinetic Imaging for Drug Assessment in Patient-Derived Tumor Organoids

Multiplexed Live-Cell Imaging for Drug Responses in Patient-Derived Organoid Models of Cancer

Patient-derived organoid (PDO) models of cancer are a multifunctional research system that better recapitulates human disease as compared to cancer cell lines. PDO models can be generated by culturing patient tumor cells in extracellular basement membrane extracts (BME) and plating them as three-dimensional domes. However, commercially available reagents that have been optimized for phenotypic assays in monolayer cultures often are not compatible with BME. Herein, we describe a method to plate PDO models and assess drug effects using an automated live-cell imaging system. In addition, we apply fluorescent dyes that are compatible with kinetic measurements to quantify cell health and apoptosis simultaneously. Image capture can be customized to occur at regular time intervals over several days. Users can analyze drug effects in individual Z-plane images or a Z Projection of serial images from multiple focal planes. Using masking, specific parameters of interest are calculated, such as PDO number, area, and fluorescence intensity. We provide proof-of-concept data demonstrating the effect of cytotoxic agents on cell health, apoptosis, and viability. This automated kinetic imaging platform can be expanded to other phenotypic readouts to understand diverse therapeutic effects in PDO models of cancer.

Author Spotlight: Developing Multiplexed Kinetic Assays for Organoid-Based Drug Response Analysis

Patient-derived tumor organoids are a sophisticated model system for basic and translational research. This methods article details the use of multiplexed fluorescent live-cell imaging for simultaneous kinetic assessment of different organoid phenotypes.

Patient-derived organoid (PDO) models of cancer are a multifunctional research system that better recapitulates human disease as compared to cancer cell ...

Through the Looking Glass: Time-lapse Microscopy and Longitudinal Tracking of Single Cells to Study Anti-cancer Therapeutics

The response of single cells to anti-cancer drugs contributes significantly in determining the population response, and therefore is a major contributing factor in the overall outcome. Immunoblotting, flow cytometry and fixed cell experiments are often used to study how cells respond to anti-cancer drugs. These methods are important, but they have several shortcomings. Variability in drug responses between cancer and normal cells, and between cells of different cancer origin, and transient and rare responses are difficult to understand using population averaging assays and without being able to directly track and analyze them longitudinally. The microscope is particularly well suited to image live cells. Advancements in technology enable us to routinely image cells at a resolution that enables not only cell tracking, but also the observation of a variety of cellular responses. We describe an approach in detail that allows for the continuous time-lapse imaging of cells during the drug response for essentially as long as desired, typically up to 96 hr. Using variations of the approach, cells can be monitored for weeks. With the employment of genetically encoded fluorescent biosensors numerous processes, pathways and responses can be followed. We show examples that include tracking and quantification of cell growth and cell cycle progression, chromosome dynamics, DNA damage, and cell death. We also discuss variations of the technique and its flexibility, and highlight some common pitfalls.

Here, we describe a method of long-term time-lapse microscopy to longitudinally track single cells in response to anti-cancer therapeutics.

The response of single cells to anti-cancer drugs contributes significantly in determining the population response, and therefore is a major contributing ...

Medicine

Monitoring Kinase and Phosphatase Activities Through the Cell Cycle by Ratiometric FRET

F&ouml;rster resonance energy transfer (FRET)-based reporters1 allow the assessment of endogenous kinase and phosphatase activities in living cells. Such probes typically consist of variants of CFP and YFP, intervened by a phosphorylatable sequence and a phospho-binding domain. Upon phosphorylation, the probe changes conformation, which results in a change of the distance or orientation between CFP and YFP, leading to a change in FRET efficiency (Fig 1). Several probes have been published during the last decade, monitoring the activity balance of multiple kinases and phosphatases, including reporters of PKA2, PKB3, PKC4, PKD5, ERK6, JNK7, Cdk18, Aurora B9 and Plk19. Given the modular design, additional probes are likely to emerge in the near future10. 
Progression through the cell cycle is affected by stress signaling pathways 11. Notably, the cell cycle is regulated differently during unperturbed growth compared to when cells are recovering from stress12.Time-lapse imaging of cells through the cell cycle therefore requires particular caution. This becomes a problem particularly when employing ratiometric imaging, since two images with a high signal to noise ratio are required to correctly interpret the results. Ratiometric FRET imaging of cell cycle dependent changes in kinase and phosphatase activities has predominately been restricted to sub-sections of the cell cycle8,9,13,14.
Here, we discuss a method to monitor FRET-based probes using ratiometric imaging throughout the human cell cycle. The method relies on equipment that is available to many researchers in life sciences and does not require expert knowledge of microscopy or image processing.

FRET-based reporters are increasingly used to monitor kinase and phosphatase activities in live cells. Here we describe a method on how to use FRET-based reporters to assess cell cycle-dependent changes in target phosphorylation.

F&ouml;rster resonance energy transfer (FRET)-based reporters1 allow the assessment of endogenous kinase and phosphatase activities in living cells. Such ...

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

Understanding the mechanisms that control critical biological events of neural cell populations, such as proliferation, differentiation, or cell fate decisions, will be crucial to design therapeutic strategies for many diseases affecting the nervous system. Current methods to track cell populations rely on their final outcomes in still images and they generally fail to provide sufficient temporal resolution to identify behavioral features in single cells. Moreover, variations in cell death, behavioral heterogeneity within a cell population, dilution, spreading, or the low efficiency of the markers used to analyze cells are all important handicaps that will lead to incomplete or incorrect read-outs of the results. Conversely, performing live imaging and single cell tracking under appropriate conditions represents a powerful tool to monitor each of these events. Here, a time-lapse video-microscopy protocol, followed by post-processing, is described to track neural populations with single cell resolution, employing specific software. The methods described enable researchers to address essential questions regarding the cell biology and lineage progression of distinct neural populations.

A robust protocol to monitor neural populations by time-lapse video-microscopy followed by software-based post-processing is described. This method represents a powerful tool to identify biological events in a selected population during live imaging experiments.

Understanding the mechanisms that control critical biological events of neural cell populations, such as proliferation, differentiation, or cell fate ...

Neuroscience

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.

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 ...

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

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