The authors acknowledge the financial support of the School of Dental Medicine at the University of Pittsburgh, the National Institutes of Health award K01 AR062598, and grant P30 DE030740. The authors also thank Dr. Jin Zhang for the ICUE1 plasmid, Wayne Rasband for developing ImageJ and Michael Cammer for the ImageJ macro.

The authors have no conflicts to declare.

This work demonstrates how FRET imaging can be used to analyze cellular signaling in 3D hydrogels. While prior studies have demonstrated FRET imaging of cells seeded on hydrogel substrates35-37, of extracellular interactions with hydrogels38, and of molecules trapped in hydrogels39-41, this is the first publication to describe FRET imaging based intracellular analysis of cells embedded within 3D hydrogels. The work solves several implementation issues when translating FRET imaging from 2D to 3D. First, high numerical aperture objectives are needed for FRET imaging to gather sufficient emission signal, but they limit the depth of field. The cells here are allowed to slightly settle via centrifuging to increase the number of cells in a focal plane near the glass to compensate for this. Second, 3D hydrogel scaffolds may drift across the field of view during imaging which complicates analysis. The hydrogels here are bonded to the coverslip so that they remain fixed throughout the experiment. Third, selection of appropriate hydrogel compositions for 3D FRET is essential because hydrogels may hinder visualization of the cells. Here transparent hydrogels of PC-gel and TR-gel are used. However, gathering the cells with centrifugation to a focal plane near the coverslip can be used to image cells in hydrogels with higher diffraction.The choice of hydrogel depends on the microenvironment conditions that must be simulated, which can be found in a review on hydrogels42. Fourth, an alternate calculation is employed here for the FRET ratio of the single chain ICUE1 probe (i.e., EQE&#160;= QE/cAem) to minimize the number of image channels required for analysis and thereby minimize photobleaching. The average FRET ratio per cell is calculated as the average of the ratios per pixel within a cell to minimize underestimation of the actual FRET ratio. Finally, a linear ratiometric analysis and means to calculate the activated fraction of single chain binary probes is described.
There are several critical aspects of carrying out successful FRET experiments using widefield microscopy. With respect to hardware, the camera must be sensitive enough to resolve small signal changes, leaving newer scientific CMOS cameras and electron multiplying CCDs better suited than conventional CCDs. To best analyze the spatial distribution of the FRET ratio, the camera must at minimum possess twice the spatial resolution of the point spread function of the objective (Nyquist criterion). This makes dual-view systems less suited to the task. More crucial is to select an appropriate exposure and image acquisition rate for the given FRET pair so as to minimize photobleaching of the fluorophores. A decreased acquisition rate is recommended as experiment duration increases. The use of rapid filter wheels increases acquisition rate and the number of FOVs for analysis while avoiding the image registration issues with dual-view and turret filter cubes. The inhomogeneous illumination field complicates correction for background fluorescence that arises from sample autofluorescence and camera system noise. Some hydrogels, particularly those containing collagenous proteins are highly autofluorescent43,44. The FRET ratios are underestimated without background correction. Here we employ a simple background correction method that relies on the relatively flat illumination field which can often be configured with epi-fluorescence illumination over the center of the image (Figure 3B, Step 7.2). This method therefore restricts cells of interest to those within this illumination field. The background correction described here does not account for cellular autofluorescence in the wavelengths read for the binary probe. In such a case, unlabeled cells (no probe transfection) should be imaged under the same conditions and background subtracted. A mix of labeled and unlabeled cells may be used and the unlabeled cells selected for the background ROI. Alternate methods which provide for cell analysis over the entire image field include use of a shading mask, multiple background ROIs, or identical samples without cells and a protocol is available upon request.
Specific to 3D FRET imaging, probe response is highly sensitive to the hydrogel permeability to the analyte (Figure 6). Therefore the same hydrogel regions are compared across experimental treatments, preferably at a point of geometry symmetry such as near the scaffold center as used here. Alternatively, microfluidic chambers/bioreactors may be employed to rapidly and uniformly perfuse the hydrogel with analyte solution45. A FRET signal may not be detected (signal to noise ratio is too low) when the hydrogel&#39;s autofluorescence is too high; a thinner hydrogel or different probe (different fluorophores) should be used. With respect to 3D FRET imaging in photocrosslinked hydrogels, use of minimum irradiation exposure and wavelengths outside the excitation spectrums of the probe fluorophores is recommended. LAP may be used with a visible light source at 405 nm to further minimize potential cellular damage31,46. However, using LAP with 365 nm irradiation is recommended because this minimizes probe bleaching. 365 nm lies at the tail end of the CFP donor excitation spectrum, whereas 405 nm lies at the peak excitation. Alternatively, the probe expression may be allowed to recover for one day. Use of binary probes minimizes issues of sensitivity to differences in expression levels and in molecular diffusion of the donor and acceptor fluorophores. Non-binary probes may be used, but with SE instead of QE imaging and additional correction for spectral bleed-through of excitation and emission spectra (see below). Furthermore, the low commercial availability and the complexity in designing FRET probes may be a hindrance. The most critical factor for successful experiments remains proper correction and analysis of the fluorescence signals.
An alternate calculation of the FRET ratio is utilized for several additional reasons besides minimization of photobleaching. For binary single-chain probes, the commonly reported ratio is acceptor emission under donor excitation (sensitized emission, SE) to donor emission under donor excitation (quenched emission, QE), i.e., FRET ratio = SE/QE25,47,48. SE/QE is non-linear relative to changes in FRET efficiency21,22,47. Namely, the ratio has an exponentially non-linear relationship to inherent FRET efficiency of the probe (Ei)(Donius, A. E.,Taboas, J. M. unpublished work. (2015)), with the ratio most linear for Ei less than approximately 15% . SE/QE will also underestimate the fraction of activated probes (FAP, i.e., fraction of probes binding analyte). Therefore, analysis of SE/QE requires a cumbersome equilibrium dose response study and curve fit. Fortunately, the ratios of SE or QE to the acceptor emission under acceptor excitation (Aem) are linear with respect to Ei and the FAP, i.e., the FRET ratios SE/Aem and QE/Aem. SE/Aem is often used for binary probes and only requires end-point calibration (at no probe activity and full probe activity), but basal cellular signaling makes this difficult. To eliminate the need for endpoint calibration, SE can be corrected (cSE) for spectral overlap of donor and acceptor excitation and emission spectrums (spectral bleed-through) and for the quantum yield and spectral sensitivity (QS) of the combined probe fluorophores and microscope imaging system. The corrected SE FRET ratio based on cSE defines the observed FRET efficiency of the probes within each pixel of an image, i.e., ESE&#160;= cSE/Aem. Commercial and free software is available to correct SE for these effects and the reader is referred to the work of Chen and Periasamy 2006 for a detailed explanation of the methods50. However, calculating ESE requires capture of three image channels per time-point (SE, QE, Aem). Therefore, in this work we also employ an alternate FRET ratio EQE&#160;= 1 - QE/cAem, which requires capture of only two image channels (QE and Aem). Calculating EQE from these channels only requires correcting Aem for QS (cAem = Aem x&#160;QS). QS = Dem/Aem is easily estimated using two images from the one calibration sample, where Dem is the donor emission under donor excitation with acceptor bleached. The FAP at any instant can be calculated by comparing ESE or EQE to the Ei of the probe.
Ultimately, FRET ratio selection (ESE&#160;= cSE/Aem&#160;vs. EQE&#160;= QE/cAem) should be based on consideration of the probe type and the channels with best signal. Both approaches include background noise correction of images per frame as described above to account for changes in autofluorescence over time. They assume no overlap of Aem excitation light into the Dem excitation spectrum, which is often the case due to probe design and microscope filter configuration. Single-chain probes can use either and selection is based on best probe signal (brightness) to camera noise and to background signal on SE and QE channels. For the ICUE1 probe and experimental conditions used here (cell and hydrogel types), SE has a stronger signal than QE. Dual-chain probes require use of ESE due to non-equimolar distribution of donor and acceptor fluorophores. For probes that decrease in FRET upon binding analyte such as ICUE1, FRET efficiency can be &#34;inverted&#34; to present a positive signaling change upon binding the analyte as we do here, with ESE&#160;= 1 - cSE/Aem or EQE = QE/(Aem x QS).
Analysis of the FAP is sensitive to proper correction of the FRET ratios and to the estimate of Ei. It can be estimated with the same calibration sample used for QS and a conventional endpoint FRET efficiency assay as Ei = 1-(QE/Dem) (QE image followed by acceptor photobleaching and a Dem image)28, but care must be taken to minimize accidental bleaching of the donor. We describe a modified version where the estimate of Dem based on Aem adjusted for QS is substituted, i.e., Ei = 1 - (QE/(Aem x QS)). Alternately, Ei = cSE/Aem may be used. Estimation of Ei in living cells requires that all probes be driven to full FRET. This is difficult to achieve in practice for probes,&#160;such as ICUE1 that decrease in FRET upon binding the analyte. An in vitro based calculation of Ei using cell lysates and purified analyte is best, but outside the scope of this work. Here we recommend using 2&#8242;,5&#8242;-Dideoxyadenosine to decrease cAMP in the cells. However, a relative FAP may be calculated using an Ei estimate derived from the baseline level of signaling. For these reasons, studies most often report the FRET ratio (as done here) or a relative FAP based on endpoint calibration, where the signaling baseline before adding agonist is the lower bound and the signal after adding a second agonist that saturates signaling is the upper bound. Forskolin is often used as a control agonist to saturate cAMP signal. Alternately, a cAMP analog may be used such as 8-Bromoadenosine 3&#39;,5&#39;-cyclic monophosphate. Positive controls used at the completion of studies to identify potential changes in the signaling pathway among experimental treatments are recommended.
Calculation of the average signaling response per cell requires consideration of several factors. First, the average FRET ratio should be calculated as the average of the ratios at each pixel within a cell, i.e., (&#8721;(QE&#8260;cAem))/(number of pixels), instead of the ratio of the average QE and Aem in a cell, i.e., (&#8721;QE)&#8260;(&#8721;cAem). Though the latter does not require careful masking as background noise is low, it severely underestimates the true average FRET ratio. However, pixel ratios require floating-point calculation and careful binary masking of the cell area to define the ROIs and avoid inclusion of spurious ratios generated from background noise outside the cell. The entire cell area must be quantified to avoid biasing results on cell shape. The masking may need to be redefined over time depending on changes in cell shape and migration. Alternately, ROIs larger than the cell may be used if exclusion criteria are defined to remove pixel ratios from QE and Aem signals that fall well below cellular levels and are near background levels. The described analysis methods detail the basic steps used in this work for FRET analysis without specialized software/plugins. Microscope manufacturers and other vendors sell add-on modules for their microscope software, which support automated ratiometric and FRET analysis. Likewise, the public domain ImageJ software has several plugins available for particle and FRET analysis, such as RiFRET. Second, the pixel-based average FRET ratio underestimates the true average ratio of the 3D cellular structure because a 2D image is used. The 2D signals represent and average along the z-axis. Calculation of the 3D spatial distribution of FRET ratios in live cells is not possible without high speed 3D imaging (e.g., using spinning disk confocal microscopy). This is a problem for both 2D and 3D cultures, but has a larger effect in 3D as more of the cellular structure exists out of plane. This is of great concern for probes that localize to cellular structures, such as the plasma membrane EPAC1 probe PM-ICUE. See Spiering et al. 2013 for suggestions to correct for cell thickness effects on ratio calculations24. Analysis of the distribution of Aem can be used to detect if probe accumulates in regions of the cell and imaging plane adjusted. This will also help to interpret the spatial distribution of FRET ratios and to eliminate spurious results, e.g., identifying probe saturation (i.e., a low Aem region will have low probe concentration and may exhibit probe saturation and high FRET ratio). Third, different FRET baseline levels may indicate a difference in transfection efficiency, probe expression, or basal signaling between experimental groups. &#34;Relative Analysis&#34; (Step 10.1.1) helps remove drift in the FRET ratio over time if present. It facilitates comparison of the relative magnitude of responses between samples, but impedes comparison to the time-course of response for the reference sample (control plot is a flat line). &#34;Absolute Analysis&#34; (Step 10.1.2) facilitates comparison of the rate of response across samples, but hampers comparison of the magnitude of response because each line is scaled by a dissimilar constant. Studies often use a linear regression on the baseline to correct for drift in the FRET ratio over time.
The described 3D FRET method is a useful and practical way to fabricate and perform time-lapse imaging of cell laden 3D hydrogels, which can be applied to other probes and imaging modalities. Other FRET system besides single chain binary probes will require more complex correction of intensity based signals, as discussed above. Alternately, fluorescence lifetime imaging of FRET (FLIM-FRET) may be used to calculate FRET efficiency via a change in emission lifetime of the probe donor. Unlike intensity based FRET, FLIM-FRET is insensitive to background noise, spectral bleed-through, quantum efficiency, and detector spectral sensitivity2. However, FLIM systems are expensive, complex, and uncommon, and work best with fluorophores with single exponent decay and no FLIM run-off 49. The described method may also be used with more advanced microscope platforms (e.g., FRET-TIRF, fluorescence anisotropy, and spectral correlation FRET). Applying this method to 3D imaging with high speed confocal and multiphoton microscopy will facilitate analysis of the sub-cellular distribution of signaling response and increase the accuracy of signaling analysis. This 3D FRET method will enable advanced cell biology studies in simulated 3D cellular microenvironments. As such, it can be readily applied to pharmacology and regenerative medicine needs, including studying intercellular signaling and screening drug response in engineered hydrogel-based microtissues.

Cellular signaling is both complex and consequential for numerous fields, including pharmacology, tissue engineering, and regenerative medicine. Useful investigational tools are needed to further our understanding of cell biology and to develop optimal materials for tissue regeneration. F&#246;rster resonance energy transfer (FRET) imaging is an essential tool enabling analysis of receptor activation, molecular conformation, and supramolecular interactions (e.g., protein/DNA complexation) in live cells. FRET is a type of non-radiative energy transfer between fluorophores1. It has an efficiency that is inversely proportional to the sixth power of the distance between a donor fluorophore and acceptor fluorophore. Thus, it can provide information on the spatial distance between two different molecules or across regions of one molecule at the nanometer scale (typically 1 - 10 nm)2. The donor and acceptor fluorophores may be different molecule types (hetero-FRET), in which the donor has the higher energy emission (shorter wavelength), or of the same type (homo-FRET). FRET imaging can be performed on fixed or live cells, but in general fixed cells are used for imaging of molecular interactions at high spatial resolution when high speed confocal systems are unavailable. Live cells are used to study interactions and processes that are inhibited or altered by fixation, such as the real-time intracellular signaling response3.
Live cell studies that employ FRET imaging use two-dimensional (2D) cellular cultures in part because of simplicity and lower background (no emission from out of plane cells). However, 2D cultures also alter numerous cellular processes compared to the physiologic microenvironment and three-dimensional (3D) culture4,5. For example, native cell to cell and cell to extracellular matrix interactions are drastically altered in 2D culture leading to the easily observed changes in cell morphology and polarity6. Membrane microdomains (e.g., caveolae and lipid rafts) and receptor signaling are strongly regulated by the culture environment, in part because they bind cytoskeletal components7,8 and the cell shape and cytoskeletal structure are strongly regulated by the spatial culture environment9. Mechanotransduction is not only influenced by the 3D matrix, but by the more complex secondary loading conditions engendered in 3D environments compared to 2D cultures10. Finally, the permeability of 3D matrices is less than the culture medium, in general with decreased diffusion and increased binding of cell signaling molecules compared to 2D cultures. With 3D cultures one is able to create and observe a microenvironment more similar to the physiologic with respect to adhesive, chemical, and mechanical cues. Cell to cell interactions can be promoted and the adhesive properties can be controlled with the structure, composition, and architecture (patterning) of the 3D material11-13. The mechanical properties of the material can likewise be tailored by the composition and structure14,15. Culturing cells in hydrogels therefore permits FRET studies on primary cells that would otherwise de-differentiate or change in phenotype using conventional techniques, e.g., cells from articular cartilage. Thus, a method is needed to enable FRET assays in live 3D cultures.
Hydrogel scaffolds are ideal for 3D FRET based studies because they can be made optically transparent and tailored to control the microenvironment and to provide cellular cues. Hydrogels made from natural polymers have been used in cell culture for decades, including gelatin and fibrin16. Greater control over the cellular microenvironment can be achieved with chemical modification of these polymers and with the use of synthetic polymers17,18. Hydrogel mechanical stiffness and permeability can be controlled by changing their polymeric composition and mesh size (polymer and crosslink density)19,20. Further, hydrogels can be made bioactive through the incorporation of growth factors and cellular ligands. Because of these possibilities, the development of hydrogels for biosensing, drug delivery, and tissue engineering applications is a very active area of research21. 3D printing of hydrogels is also undergoing development for fabrication of micro-tissues and -organs15. Thus, FRET imaging of cells in hydrogels is not only useful as a means to approximate physiologic cellular behavior, but also as a tool to study and optimize engineered tissue growth.
Binary ratiometric FRET probes are very useful in studying cellular signaling and can be utilized in hydrogel cultures. For a partial list of published fluorescent and FRET probes, see the Cell Migration Consortium website22. The most common probe type contains two different fluorophores that are associated or linked by a recognition element(s) (analyte sensitive region). These regions undergo a conformational change, association, or disassociation upon detection of the analyte (e.g., binding of an ion or molecule, enzymatic cleavage of the region) that alters the distance between fluorophores and therefore the FRET magnitude (either higher or lower)23. A less common yet useful probe type relies on an analyte sensitive change in the spectral overlap of the two fluorophores, which alters FRET magnitude. &#34;Single-chain&#34; ratiometric probes utilize a fluorophore pair linked on a single molecule. &#34;Dual-chain&#34; probes utilize fluorophore pairs on separate molecules, but their expression can be coupled under the same expression cassette24. Unlike studies with single fluorophore expression (e.g., fluorescent fusion proteins), studies with ratiometric FRET probes do not require dual transfection to control for transfection efficiency or cell viability. Ratiometric FRET probes are relatively insensitive to transfection efficiency when expressed above a minimum threshold (concentration insensitive)23,25. They are insensitive to excitation source fluctuations and other intracellular environment factors (e.g., pH, ions) so long as both fluorophores are similarly responsive. In addition, their response is not subject to transcriptional delay, unlike fluorescent and bioluminescent promoter-reporter constructs26,27.
Ratiometric FRET probes are easily and frequently employed in conventional widefield epifluorescence microscope systems. Widefield microscopes are preferred over line scanning confocal systems for live cell imaging because of concerns over system cost, fluorophore bleaching, and capture of signal changes with sufficient temporal resolution. In addition, single cell analysis over a large population of cells is possible with a motorized stage and image capture over multiple fields of view. Widefield microscopy requires intensity based analysis of the hetero-FRET probe signals. Though intensity analysis does not require complex hardware like other FRET methods, more post-acquisition work is needed to correct for the effects of fluorophore behaviors and microscope/imaging system configurations28. The captured emission intensity of a fluorophore is a function of the excitation energy, fluorophore quantum yield, and the combined filter and camera/detector spectral sensitivity and linearity2. These may be dissimilar for the donor and acceptor and will require calibration for quantitative analysis. In addition, imaging of the acceptor emission is influenced by the spectral overlap of the fluorophores (excitation of acceptor by donor excitation light and bleed-through of donor emission into the acceptor emission spectra)2. &#34;Single-chain&#34; probes are internally normalized because their fluorophores are equimolar at the nanoscale, while the fluorophores of &#34;dual-chain&#34; probes may exist at different stoichiometries throughout a cell25,28. If the fluorophores of &#34;single-chain&#34; probes are of similar brightness (function of extinction coefficient and quantum yield), the effects of non-linear detector sensitivity are small and only correction for the background fluorescence and the coupled quantum yield/detector sensitivity is needed23. Thus &#34;single-chain&#34; ratiometric FRET probes are most easily implemented in widefield microscopy24.
This paper describes a straightforward technique to perform FRET imaging of live cells in 3D hydrogel cultures using a conventional widefield epifluorescent microscope. It can be easily adopted by laboratories already carrying out FRET experiments in 2D, as well laboratories interested in intercellular signaling in microtissues. Here we demonstrate the method with FRET imaging based measurement of cyclic adenosine monophosphate (cAMP) levels in live chondrocytes within photocrosslinking (PC-gel, polyethylene glycol dimethacrylate) and thermoresponsive (TR-gel, Matrigel) hydrogels. A ratiometric FRET probe is utilized based on EPAC1 (ICUE1) to detect cAMP levels in response to forskolin, a chemical agonist for adenylyl cyclase which catalyzes the conversion of ATP to cAMP. cAMP is one of the principal second messengers mediating G protein-coupled receptor signaling and it activates various factors, including downstream exchange proteins directly activated by cAMP (EPACs). The fluorophores move apart upon cAMP binding to the EPAC domain resulting in a decrease in FRET. Described here is the preparation of the hydrogel materials, cell transfection with the ICUE1 probe, and embedding of the cells in 3D hydrogels. The 3D FRET experiment procedure and the image analysis necessary to evaluate probe activity per cell are explained. We also discuss the inherent limitations of FRET analysis in 2D and 3D using widefield microscopy. The technique presented here is an improvement over the existing 2D methods since it enables analysis in a more physiological context and requires less image correction and calibration. Great utility lies in the fact that many transparent materials can be used while cell and transfection preferences can be maintained.

<table><tbody><tr><td>Polydimethylsiloxane (PDMS) [Sylgard 184 Elastomer Kit]</td><td>Fisher Scientific (Ellsworth Adhesives)</td><td>NC0162601</td><td>Reagents</td></tr><tr><td>Polyethylene glycol dimethacrylate (PEGDM)</td><td>Prepared according to Lin-Gibson with PEG from Sigma Aldrich</td><td>95904</td><td>Reagents</td></tr><tr><td>3-(Trimethoxysilyl)propyl methacrylate</td><td>Sigma Aldrich</td><td>M6514</td><td>Reagents</td></tr><tr><td>3-glycidoxypropyltrimethoxysilane</td><td>Sigma Aldrich</td><td>440167</td><td>Reagents</td></tr><tr><td>Dimethyl Phenylphosphonite</td><td>Sigma Aldrich</td><td>149470-5g</td><td>Reagents</td></tr><tr><td>2,4,6-trimethylbenzoyl Chloride</td><td>Fisher Scientific</td><td>AC244280100</td><td>Reagents</td></tr><tr><td>2-Butanone</td><td>Fisher Scientific</td><td>AC149670010</td><td>Reagents</td></tr><tr><td>Lithium Bromide</td><td>Fisher Scientific</td><td>AC199871000</td><td>Reagents</td></tr><tr><td>Isopropanol</td><td>Sigma Aldrich</td><td>190764</td><td>Reagents</td></tr><tr><td>Sigmacote (siliconizing reagent)</td><td>Sigma Aldrich</td><td>SL2</td><td>Reagents</td></tr><tr><td>Toluene&amp;nbsp;</td><td>Fisher Scientific</td><td>AC36441-0010</td><td>Reagents</td></tr><tr><td>Phosphate Buffered Saline (PBS)</td><td>Hyclone</td><td>SH3025601</td><td>Reagents</td></tr><tr><td>Opti-MEM Reduced Serum Medium, no phenol red (phenol- and serum-free medium)</td><td>Life Technologies</td><td>11058-021</td><td>Reagents</td></tr><tr><td>FuGENE HD Transfection Reagent (transfection reagent)</td><td>Promega</td><td>E2311</td><td>Reagents</td></tr><tr><td>Cellstripper (cell dissociation solution)</td><td>Corning</td><td>25-056-CI</td><td>Reagents</td></tr><tr><td>Growth Factor Reduced Matrigel, Phenol Free</td><td>Corning</td><td>356231</td><td>Reagents</td></tr><tr><td>Forskolin</td><td>Sigma Aldrich</td><td>F6886</td><td>Reagents</td></tr><tr><td>Pipette Tips (10,200,1,000 &amp;mu;l)&amp;nbsp;</td><td>Fisher Scientific</td><td>02-707-474, 02-707-478, 02-707-480</td><td>Disposable lab equipment</td></tr><tr><td>Powder Free Examination Gloves</td><td>Fisher Scientific</td><td>19-149-863B</td><td>Disposable lab equipment</td></tr><tr><td>Aluminum foil</td><td>Fisher Scientific</td><td>01-213-100</td><td>Disposable lab equipment</td></tr><tr><td>Biopsy punch (3 mm)</td><td>Miltex</td><td>3332</td><td>Disposable lab equipment</td></tr><tr><td>Glass coverslips</td><td>Fisher Scientific</td><td>12-544C</td><td>Disposable lab equipment</td></tr><tr><td>Precoated glass coverslips (epoxysilane)</td><td>Arrayit</td><td>CCSL</td><td>Disposable lab equipment</td></tr><tr><td>Glass slide</td><td>Fisher Scientific</td><td>12550D</td><td>Disposable lab equipment</td></tr><tr><td>Sterile vials (15 ml, 50 ml conical)</td><td>Fisher Scientific</td><td>14-959-70C, 14-959-49A</td><td>Disposable lab equipment</td></tr><tr><td>Cell culture dish (6-well)</td><td>Falcon</td><td>351146</td><td>Disposable lab equipment</td></tr><tr><td>Epifluorescent Microscope with motorized stage</td><td>Nikon</td><td>MEA53100 (Eclipse TiE Inverted)</td><td>Large/non-disposable lab equipment</td></tr><tr><td>Adjustable Specimen Holder for XY Stage</td><td>Nikon</td><td>77011393</td><td>Non-disposable lab equipment</td></tr><tr><td>60X (1.3 NA) objective (CFI Plan Apochromat 60XH Lamda)</td><td>Nikon</td><td>MRD01605</td><td>Non-disposable lab equipment</td></tr><tr><td>CFP/YFP light source and excitation / emission filter holders (Lambda light source &amp;amp; wheel, Lambda emission filter system)</td><td>Nikon (Sutter Instrument reseller)</td><td>77016231, 77016088</td><td>Non-disposable lab equipment</td></tr><tr><td>CYF/YFP filter set</td><td>Nikon (Chroma Technology Corp)</td><td>77014928</td><td>Non-disposable lab equipment</td></tr><tr><td>Camera (ORCA Flash 4.0 v1)</td><td>Nikon (Hamamatsu reseller)</td><td>77054076</td><td>Non-disposable lab-equipment</td></tr><tr><td>NIS-Elements 40.20.02 microscope software)</td><td>Nikon</td><td>MQS31100&amp;nbsp;</td><td>Non-disposable lab equipment</td></tr><tr><td>Prism (spreadsheet and graphing software)</td><td>GraphPad Software, Inc</td><td>Version 6</td><td>Non-disposable lab equipment</td></tr><tr><td>OmniCure S1000 UV Spot Curing Lamp System with 365nm filter or other light source (&amp;lambda; = 365 or 405)</td><td>OmniCure</td><td>S1000</td><td>Large/non-disposable lab equipment</td></tr><tr><td>Pipette Aid&amp;nbsp;</td><td>Drummond Scientific Co.&amp;nbsp;</td><td>DP-100</td><td>Non-disposable lab equipment</td></tr><tr><td>Hemacytometer</td><td>Hausser Scientific</td><td>Reichert Bright-Line 1475</td><td>Non-disposable lab equipment</td></tr><tr><td>Vacuum desiccator chamber/degasser&amp;nbsp;</td><td>Any</td><td></td><td>Large/non-disposable lab equipment</td></tr><tr><td>Tissue Culture Hood&amp;nbsp;</td><td>Any</td><td></td><td>Large/non-disposable lab equipment</td></tr><tr><td>Chemical Fume Hood&amp;nbsp;</td><td>Any</td><td></td><td>Large/non-disposable lab equipment</td></tr><tr><td>Oven</td><td>Any</td><td></td><td>Large/non-disposable lab equipment</td></tr><tr><td>Spatula</td><td>Any</td><td></td><td>Non-disposable lab equipment</td></tr><tr><td>Beaker</td><td>Any</td><td></td><td>Non-disposable lab equipment</td></tr><tr><td>Incubator&amp;nbsp;</td><td>Any</td><td></td><td>Large/non-disposable lab equipment</td></tr></tbody></table>

fret imaging in three-dimensional hydrogels

Imaging of F&#246;rster resonance energy transfer (FRET)&#160;is a powerful tool for examining cell biology in real-time. Studies utilizing FRET commonly employ two-dimensional (2D) culture, which does not mimic the three-dimensional (3D) cellular microenvironment. A method to perform quenched emission FRET imaging using conventional widefield epifluorescence microscopy of cells within a 3D hydrogel environment is presented. Here an analysis method for ratiometric FRET probes that yields linear ratios over the probe activation range is described. Measurement of intracellular cyclic adenosine monophosphate (cAMP) levels is demonstrated in chondrocytes under forskolin stimulation using a probe for EPAC1 activation (ICUE1) and the ability to detect differences in cAMP signaling dependent on hydrogel material type, herein a photocrosslinking hydrogel (PC-gel, polyethylene glycol dimethacrylate) and a thermoresponsive hydrogel (TR-gel). Compared with 2D FRET methods, this method requires little additional work. Laboratories already utilizing FRET imaging in 2D can easily adopt this method to perform cellular studies in a 3D microenvironment. It can further be applied to high throughput drug screening in engineered 3D microtissues. Additionally, it is compatible with other forms of FRET imaging, such as anisotropy measurement and fluorescence lifetime imaging (FLIM), and with advanced microscopy platforms using confocal, pulsed, or modulated illumination.

All hydrogel precursors, cells, and casting molds were prepared as described above. The cell laden precursor solutions were cast in PDMS molds and then centrifuged before gelation/photocrosslinking to immobilize cells near the coverslip and facilitate FRET imaging analysis (Figure 1). The hydrogels with attached coverslips were placed within prepared PDMS imaging wells for microscopic imaging (Figure 2). The optical configurations and image acquisition parameters were then set as shown in Figure 3A. Capture of all four FRET related image channels for CFP-YFP fluorophore pairs is demonstrated (see under &#34;Optical Conf.&#34; in Figure 3), as is needed for non-binary probes. However in this work only two images are required for the single chain binary ICUE1 probe, QE = &#34;CFP CFP&#34; and Aem = &#34;YFP YFP&#34; (excitation emission). Multiple FOVs (xy positions) were selected in which several cells resided within the homogenous illumination area (Figure 3B). After time lapse acquisition, the signal data for the probe was exported. ROIs for background correction of channel signals and for cell analysis were designated using thresholded Aem images from the start of the experiments (Figure 4). Cells were selected from among the cell pool as those expressing sufficient (not too dim), but not too high (excessively bright) probe (Figure 4B). The QE and SE FRET ratios per pixel at each acquisition were calculated using floating-point division of the background subtracted image channels. The average FRET ratio per cell (i.e., per ROI) was calculated, normalized to the baseline signal prior to stimulation (i.e., a regression on the baseline subtracted from all time-points), and plotted with error bars as the standard error of the mean (SEM). Both the QE and cSE based FRET ratios detect the increased cAMP activity in chondrocytes under forskolin stimulation (100 nM, Figure 5). The QE FRET ratio was more sensitive to background correction than the cSE FRET ratio because the QE signal was lower due to the low basal level of cAMP activity in chondrocytes embedded within the hydrogels. Both hydrogel types (TR-gel and PC-gel) showed an increase in FRET ratio over time (Figure 6), indicating an increased cAMP signaling response to the addition of Forskolin. As indicated by the QE FRET ratio, chondrocytes in PC-gel hydrogels show a greater change in cAMP (shown) and a higher basal level of cAMP (EQE = 0.19 in PC-gel vs. EQE = 0.09 in TR-gel, not shown in baseline subtracted figure).
<img alt="Figure 1" src="/files/ftp_upload/54135/54135fig1.jpg" /> 
Figure 1: Cell Distribution in Photocrosslinked Hydrogel (PC-gel) 
A: The cell laden hydrogel precursor was centrifuged to maximize the number of cells at a focal plane near the coverslip. B: This increases the number of cells in the FOV and minimizes background signal from out of plane cells. (Scale bar = 200 &#181;m) <a href="https://www.jove.com/files/ftp_upload/54135/54135fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/54135/54135fig2.jpg" /> 
Figure 2: Hydrogel in PDMS Dish 
The hydrogel was placed inside a tall PDMS well (10x volume of hydrogel) with a coverslip base for microscopic imaging and analyte stimulation. The PC-gel hydrogels are transparent and bonded to the coverslip so that they remain in place throughout the assay. The PDMS well can be made larger to contain 10x more medium than the hydrogel volume using a wider biopsy punch in addition to a thicker PDMS sheet. (Scale bar = 2 mm) <a href="https://www.jove.com/files/ftp_upload/54135/54135fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/54135/54135fig3.jpg" /> 
Figure 3: Acquisition Configuration for FRET Imaging 
A: The acquisition was configured for image capture every 7 sec at multiple locations. For QE FRET ratio calculation, only two image channels are needed for the ICUE1 probe used here (binary single chain probe), QE = &#34;CFP CFP&#34; and Aem = &#34;YFP YFP&#34; (excitation emission) under the &#34;Optical Conf.&#34; column in the&#160;&#955; tab of the microscope software. B: FOVs (&#34;XY&#34; positions) were selected in which several cells resided within the homogenous illumination area. The plot below the image shows the illumination intensity along the blue arrowed line that traverses through the center of the FOV. <a href="https://www.jove.com/files/ftp_upload/54135/54135fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/54135/54135fig4.jpg" /> 
Figure 4: Selection of ROIs for FRET Imaging Analysis 
A: A region of interest (ROI) free of cells was selected to correct for background signal on each channel. An image of a cell-free hydrogel may instead be used for background subtraction if cell density or migration is high, or if a shading mask is not used when cells do not lie within a homogeneous illumination field. B: The cell ROIs were selected using image thresholding and binary masking of the Aem channel. Selecting a ROI larger than the cells will adversely affect calculation of the average FRET ratio per cell due to inclusion of extracellular ratios generated from background noise. Calculation of the cellular FRET ratio using the average of each channel per cell is not recommended as this underestimates the ratio. (Scale bar = 50 &#181;m) <a href="https://www.jove.com/files/ftp_upload/54135/54135fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/54135/54135fig5.jpg" /> 
Figure 5: Comparison of FRET Ratios in Thermoresponsive Hydrogel (TR-gel) 
Both QE and SE based FRET ratios detect the increased cAMP activity in chondrocytes under forskolin stimulation. Forskolin is an adenyl cyclase agonist which induces cAMP production. FRET decreases when the ICUE1 probe binds cAMP, and thus the QE FRET ratio (EQE&#160;= sQE/(sAem x QS)) increases. Conversely, the SE FRET ratio decreases and is thus plotted as ESE&#160;= 1 - cSE/sAem. In hydrogel embedded chondrocytes, the QE FRET ratio is more sensitive to background correction than the SE FRET ratio because the QE signal is low due to the low basal cAMP activity. Error bars = SEM, n = 25. <a href="https://www.jove.com/files/ftp_upload/54135/54135fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/54135/54135fig6.jpg" /> 
Figure 6: Comparison of FRET Ratio in Different Hydrogels 
The chondrocytes in both hydrogel types responded to forskolin stimulation with an increase in cAMP activity as demonstrated by the increased QE FRET ratio over time. The hydrogel type impacts the cellular response through several effects, including differences in permeability to forskolin and in basal cellular cAMP activity (EQE = 0.19 in PC-gel vs. EQE = 0.09 in TR-gel, not shown). Error bars = SEM, n for TR-gel = 25, n for PC-gel = 15. <a href="https://www.jove.com/files/ftp_upload/54135/54135fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about FRET Imaging in Three-dimensional Hydrogels at JoVE.com

FRET Imaging in Three-dimensional Hydrogels

F&#246;rster resonance energy transfer (FRET) imaging is a powerful tool for real-time cell biology studies. Here a method for FRET imaging cells in physiologic three-dimensional (3D) hydrogel microenvironments using conventional epifluorescence microscopy is presented. An analysis for ratiometric FRET probes that yields linear ratios over the activation range is described.

Imaging of F&#246;rster resonance energy transfer (FRET)&#160;is a powerful tool for examining cell biology in real-time. Studies utilizing FRET commonly ...

fret-imaging-in-three-dimensional-hydrogels

Research

JoVE Journal

Biology

13.0K Views.  University of Pittsburgh.  Förster resonance energy transfer (FRET) beeldvorming is een krachtig hulpmiddel voor real-time celbiologie studies. Hier een werkwijze voor FRET beeldvorming cellen in fysiologische driedimensionale (3D) hydrogel microenvironments middels conventionele epifluorescentie microscopie wordt gepresenteerd. Een analyse voor ratiometrische FRET probes die lineaire verhoudingen over de activering bereik oplevert wordt beschreven. 

Video: FRET Imaging in Three-dimensional Hydrogels

Fabrication of Micropatterned Hydrogels for Neural Culture Systems using Dynamic Mask Projection Photolithography

Increasingly, patterned cell culture environments are becoming a relevant technique to study cellular characteristics, and many researchers believe in the need for 3D environments to represent in vitro experiments which better mimic in vivo qualities 1-3. Studies in fields such as cancer research 4, neural engineering 5, cardiac physiology 6, and cell-matrix interaction7,8have shown cell behavior differs substantially between traditional monolayer cultures and 3D constructs.
Hydrogels are used as 3D environments because of their variety, versatility and ability to tailor molecular composition through functionalization 9-12. Numerous techniques exist for creation of constructs as cell-supportive matrices, including electrospinning13, elastomer stamps14, inkjet printing15, additive photopatterning16, static photomask projection-lithography17, and dynamic mask microstereolithography18. Unfortunately, these methods involve multiple production steps and/or equipment not readily adaptable to conventional cell and tissue culture methods. The technique employed in this protocol adapts the latter two methods, using a digital micromirror device (DMD) to create dynamic photomasks for crosslinking geometrically specific poly-(ethylene glycol) (PEG) hydrogels, induced through UV initiated free radical polymerization. The resulting "2.5D" structures provide a constrained 3D environment for neural growth. We employ a dual-hydrogel approach, where PEG serves as a cell-restrictive region supplying structure to an otherwise shapeless but cell-permissive self-assembling gel made from either Puramatrix or agarose. The process is a quick simple one step fabrication which is highly reproducible and easily adapted for use with conventional cell culture methods and substrates.
Whole tissue explants, such as embryonic dorsal root ganglia (DRG), can be incorporated into the dual hydrogel constructs for experimental assays such as neurite outgrowth. Additionally, dissociated cells can be encapsulated in the photocrosslinkable or self polymerizing hydrogel, or selectively adhered to the permeable support membrane using cell-restrictive photopatterning. Using the DMD, we created hydrogel constructs up to ~1mm thick, but thin film (&lt;200 &mu;m) PEG structures were limited by oxygen quenching of the free radical polymerization reaction. We subsequently developed a technique utilizing a layer of oil above the polymerization liquid which allowed thin PEG structure polymerization.
In this protocol, we describe the expeditious creation of 3D hydrogel systems for production of microfabricated neural cell and tissue cultures. The dual hydrogel constructs demonstrated herein represent versatile in vitro models that may prove useful for studies in neuroscience involving cell survival, migration, and/or neurite growth and guidance. Moreover, as the protocol can work for many types of hydrogels and cells, the potential applications are both varied and vast.

Simple techniques are described for the rapid production of microfabricated neural culture systems using a digital micromirror device for dynamic mask projection lithography on regular cell culture substrates. These culture systems may be more representative of natural biological architecture, and the techniques described could be adapted for numerous applications.

Increasingly, patterned cell culture environments are becoming a relevant technique to study cellular characteristics, and many researchers believe in the ...

Bioengineering

Observing and Quantifying Fibroblast-mediated Fibrin Gel Compaction

Cells embedded in collagen and fibrin gels attach and exert traction forces on the fibers of the gel. These forces can lead to local and global reorganization and realignment of the gel microstructure. This process proceeds in a complex manner that is dependent in part on the interplay between the location of the cells, the geometry of the gel, and the mechanical constraints on the gel. To better understand how these variables produce global fiber alignment patterns, we use time-lapse differential interference contrast (DIC) microscopy coupled with an environmentally controlled bioreactor to observe the compaction process between geometrically spaced explants (clusters of fibroblasts). The images are then analyzed with a custom image processing algorithm to obtain maps of the strain. The information obtained from this technique can be used to probe the mechanobiology of various cell-matrix interactions, which has important implications for understanding processes in wound healing, disease development, and tissue engineering applications.

Time-lapse microscopy and image processing techniques were used to observe and analyze fibroblast-mediated gel compaction and fibrin fiber realignment in an environmentally controlled bioreactor over a 48 hr&#160;period.

Cells embedded in collagen and fibrin gels attach and exert traction forces on the fibers of the gel. These forces can lead to local and global ...

An Optimized O9-1/Hydrogel System for Studying Mechanical Signals in Neural Crest Cells

Neural crest cells (NCCs) are vertebrate embryonic multipotent cells that can migrate and differentiate into a wide array of cell types that give rise to various organs and tissues. Tissue stiffness produces mechanical force, a physical cue that plays a critical role in NCC differentiation; however, the mechanism remains unclear. The method described here provides detailed information for the optimized generation of polyacrylamide hydrogels of varying stiffness, the accurate measurement of such stiffness, and the evaluation of the impact of mechanical signals in O9-1 cells, a NCC line that mimics in vivo NCCs.
Hydrogel stiffness was measured using atomic force microscopy (AFM) and indicated different stiffness levels accordingly. O9-1 NCCs cultured on hydrogels of varying stiffness showed different cell morphology and gene expression of stress fibers, which indicated varying biological effects caused by mechanical signal changes. Moreover, this established that varying the hydrogel stiffness resulted in an efficient in vitro system to manipulate mechanical signaling by altering gel stiffness and analyzing the molecular and genetic regulation in NCCs. O9-1 NCCs can differentiate into a wide range of cell types under the influence of the corresponding differentiation media, and it is convenient to manipulate chemical signals in vitro. Therefore, this in vitro system is a powerful tool to study the role of mechanical signaling in NCCs and its interaction with chemical signals, which will help researchers better understand the molecular and genetic mechanisms of neural crest development and diseases.

Detailed step-by-step protocols are described here for studying mechanical signals in vitro using multipotent O9-1 neural crest cells and polyacrylamide hydrogels of varying stiffness.

Neural crest cells (NCCs) are vertebrate embryonic multipotent cells that can migrate and differentiate into a wide array of cell types that give rise to ...

Developmental Biology

Measuring Global Cellular Matrix Metalloproteinase and Metabolic Activity in 3D Hydrogels

Three-dimensional (3D) cell culture systems often more closely recapitulate in vivo cellular responses and functions than traditional two-dimensional (2D) culture systems. However, measurement of cell function in 3D culture is often more challenging. Many biological assays require retrieval of cellular material which can be difficult in 3D cultures. One way to address this challenge is to develop new materials that enable measurement of cell function within the material. Here, a method is presented for measurement of cellular matrix metalloproteinase (MMP) activity in 3D hydrogels in a 96-well format. In this system, a poly(ethylene glycol) (PEG) hydrogel is functionalized with a fluorogenic MMP cleavable sensor. Cellular MMP activity is proportional to fluorescence intensity and can be measured with a standard microplate reader. Miniaturization of this assay to a 96-well format reduced the time required for experimental set up by 50% and reagent usage by 80% per condition as compared to the previous 24-well version of the assay. This assay is also compatible with other measurements of cellular function. For example, a metabolic activity assay is demonstrated here, which can be conducted simultaneously with MMP activity measurements within the same hydrogel. The assay is demonstrated with human melanoma cells encapsulated across a range of cell seeding densities to determine the appropriate encapsulation density for the working range of the assay. After 24 h of cell encapsulation, MMP and metabolic activity readouts were proportional to cell seeding density. While the assay is demonstrated here with one fluorogenic degradable substrate, the assay and methodology could be adapted for a wide variety of hydrogel systems and other fluorescent sensors. Such an assay provides a practical, efficient and easily accessible 3D culturing platform for a wide variety of applications.

Here, a protocol is presented for encapsulating and culturing cells in poly(ethylene glycol) (PEG) hydrogels functionalized with a fluorogenic matrix metalloproteinase (MMP)-degradable peptide. Cellular MMP and metabolic activity are measured directly from the hydrogel cultures using a standard microplate reader.

Three-dimensional (3D) cell culture systems often more closely recapitulate in vivo cellular responses and functions than traditional two-dimensional (2D) ...

A Multi-well Format Polyacrylamide-based Assay for Studying the Effect of Extracellular Matrix Stiffness on the Bacterial Infection of Adherent Cells

Extracellular matrix stiffness comprises one of the multiple environmental mechanical stimuli that are well known to influence cellular behavior, function, and fate in general. Although increasingly more adherent cell types&#39; responses to matrix stiffness have been characterized, how adherent cells&#39; susceptibility to bacterial infection depends on matrix stiffness is largely unknown, as is the effect of bacterial infection on the biomechanics of host cells. We hypothesize that the susceptibility of host endothelial cells to a bacterial infection depends on the stiffness of the matrix on which these cells reside, and that the infection of the host cells with bacteria will change their biomechanics. To test these two hypotheses, endothelial cells were used as model hosts and Listeria monocytogenes as a model pathogen. By developing a novel multi-well format assay, we show that the effect of matrix stiffness on infection of endothelial cells by L. monocytogenes can be quantitatively assessed through flow cytometry and immunostaining followed by microscopy. In addition, using traction force microscopy, the effect of L. monocytogenes infection on host endothelial cell biomechanics can be studied. The proposed method allows for the analysis of the effect of tissue-relevant mechanics on bacterial infection of adherent cells, which is a critical step towards understanding the biomechanical interactions between cells, their extracellular matrix, and pathogenic bacteria. This method is also applicable to a wide variety of other types of studies on cell biomechanics and response to substrate stiffness where it is important to be able to perform many replicates in parallel in each experiment.

We have developed a multi-well format polyacrylamide-based assay for probing the effect of extracellular matrix stiffness on bacterial infection of adherent cells. This assay is compatible with flow cytometry, immunostaining, and traction force microscopy, allowing for quantitative measurements of the biomechanical interactions between cells, their extracellular matrix, and pathogenic bacteria.

Extracellular matrix stiffness comprises one of the multiple environmental mechanical stimuli that are well known to influence cellular behavior, ...

Immunology and Infection

Fabrication and Implementation of a Reference-Free Traction Force Microscopy Platform

Quantifying cell-induced material deformation provides useful information concerning how cells sense and respond to the physical properties of their microenvironment. While many approaches exist for measuring cell-induced material strain, here we provide a methodology for monitoring strain with sub-micron resolution in a reference-free manner. Using a two-photon activated photolithographic patterning process, we demonstrate how to generate mechanically and bio-actively tunable synthetic substrates containing embedded arrays of fluorescent fiducial markers to easily measure three-dimensional (3D) material deformation profiles in response to surface tractions. Using these substrates, cell tension profiles can be mapped using a single 3D image stack of a cell of interest. Our goal with this methodology is to make traction force microscopy a more accessible and easier to implement tool for researchers studying cellular mechanotransduction processes, especially newcomers to the field.

This protocol provides instructions for implementing multiphoton lithography to fabricate three-dimensional arrays of fluorescent fiducial markers embedded in poly(ethylene glycol)-based hydrogels for use as reference-free, traction force microscopy platforms. Using these instructions, measurement of 3D material strain and calculation of cellular tractions is simplified to promote high-throughput traction force measurements.

Quantifying cell-induced material deformation provides useful information concerning how cells sense and respond to the physical properties of their ...

Controlled Strain of 3D Hydrogels under Live Microscopy Imaging

External forces are an important factor in tissue formation, development, and maintenance. The effects of these forces are often studied using specialized in vitro stretching methods. Various available systems use 2D substrate-based stretchers, while the accessibility of 3D techniques to strain soft hydrogels, is more restricted. Here, we describe a method that allows external stretching of soft hydrogels from their circumference, using an elastic silicone strip as the sample carrier. The stretching system utilized in this protocol is constructed from 3D-printed parts and low-cost electronics, making it simple and easy to replicate in other labs. The experimental process begins with polymerizing thick (&#62;100 &#956;m) soft fibrin hydrogels (Elastic Modulus of ~100 Pa) in a cut-out at the center of a silicone strip. Silicone-gel constructs are then attached to the printed-stretching device and placed on the confocal microscope stage. Under live microscopy the stretching device is activated, and the gels are imaged at various stretch magnitudes. Image processing is then used to quantify the resulting gel deformations, demonstrating relatively homogenous strains and fiber alignment throughout the gel&#8217;s 3D thickness (Z-axis). Advantages of this method include the ability to strain extremely soft hydrogels in 3D while executing in situ microscopy, and the freedom to manipulate the geometry and size of the sample according to the user&#8217;s needs. Additionally, with proper adaptation, this method can be used to stretch other types of hydrogels (e.g., collagen, polyacrylamide or polyethylene glycol) and can allow for analysis of cells and tissue response to external forces under more biomimetic 3D conditions.

The presented method involves uniaxial stretching of 3D soft hydrogels embedded in silicone rubber while allowing live confocal microscopy. Characterization of the external and internal hydrogel strains as well as fiber alignment are demonstrated. The device and protocol developed can assess the response of cells to various strain regimes.

External forces are an important factor in tissue formation, development, and maintenance. The effects of these forces are often studied using specialized ...

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Traction force microscopy (TFM) is the main method used in mechanobiology to measure cell forces. Commonly this is being used for cells adhering to flat soft substrates that deform under cell traction (2D-TFM). TFM relies on the use of linear elastic materials, such as polydimethylsiloxane (PDMS) or polyacrylamide (PA). For 2D-TFM on PA, the difficulty in achieving high throughput results mainly from the large variability of cell shapes and tractions, calling for standardization. We present a protocol to rapidly and efficiently fabricate micropatterned PA hydrogels for 2D-TFM studies. The micropatterns are first created by maskless photolithography using near-UV light where extracellular matrix proteins bind only to the micropatterned regions, while the rest of the surface remains non-adhesive for cells. The micropatterning of extracellular matrix proteins is due to the presence of active aldehyde groups, resulting in adhesive regions of different shapes to accommodate either single cells or groups of cells. For TFM measurements, we use PA hydrogels of different elasticity by varying the amounts of acrylamide and bis-acrylamide and tracking the displacement of embedded fluorescent beads to reconstruct cell traction fields with regularized Fourier Transform Traction Cytometry (FTTC).
To further achieve precise recording of cell forces, we describe the use of a controlled dose of patterned light to release cell tractions in defined regions for single cells or groups of cells. We call this method local UV illumination traction force microscopy (LUVI-TFM). With enzymatic treatment, all cells are detached from the sample simultaneously, whereas with LUVI-TFM traction forces of cells in different regions of the sample can be recorded in sequence. We demonstrate the applicability of this protocol (i) to study cell traction forces as a function of controlled adhesion to the substrate, and (ii) to achieve a greater number of experimental observations from the same sample.

Near-UV lithography and traction force microscopy are combined to measure cellular forces on micropatterned hydrogels. The targeted light-induced release of single cells enables a high number of measurements on the same sample.

Traction force microscopy (TFM) is the main method used in mechanobiology to measure cell forces. Commonly this is being used for cells adhering to flat ...

Pattern Generation for Micropattern Traction Microscopy

Micropattern traction microscopy allows control of the shape of single cells and cell clusters. Furthermore, the ability to pattern at the micrometer length scale allows the use of these patterned contact zones for the measurement of traction forces, as each micropatterned dot allows for the formation of a single focal adhesion that then deforms the soft, underlying hydrogel. This approach has been used for a wide range of cell types, including endothelial cells, smooth muscle cells, fibroblasts, platelets, and epithelial cells. 
This review describes the evolution of techniques that allow the printing of extracellular matrix proteins onto polyacrylamide hydrogels in a regular array of dots of prespecified size and spacing. As micrometer-scale patterns are difficult to directly print onto soft substrates, patterns are first generated on rigid glass coverslips that are then used to transfer the pattern to the hydrogel during gelation. First, the original microcontact printing approach to generate arrays of small dots on the coverslip is described. A second step that removes most of the pattern to leave islands of small dots is required to control the shapes of cells and cell clusters on such arrays of patterned dots. 
Next, an evolution of this approach that allows for the generation of islands of dots using a single subtractive patterning step is described. This approach is greatly simplified for the user but has the disadvantage of a decreased lifetime for the master mold needed to make the patterns. Finally, the computational approaches that have been developed for the analysis of images of displaced dots and subsequent cell-generated traction fields are described, and updated versions of these analysis packages are provided.

We describe improvements to a standard method for measuring cellular traction forces, based on microcontact printing with a single subtractive patterning step of dot arrays of extracellular matrix proteins on soft hydrogels. This method allows for simpler and more consistent fabrication of island patterns, essential for controlling cell cluster shape.

Micropattern traction microscopy allows control of the shape of single cells and cell clusters. Furthermore, the ability to pattern at the micrometer ...

Hydrogel Arrays Enable Increased Throughput for Screening Effects of Matrix Components and Therapeutics in 3D Tumor Models

Cell-matrix interactions mediate complex physiological processes through biochemical, mechanical, and geometrical cues, influencing pathological changes and therapeutic responses. Accounting for matrix effects earlier in the drug development pipeline is expected to increase the likelihood of clinical success of novel therapeutics. Biomaterial-based strategies recapitulating specific tissue microenvironments in 3D cell culture exist but integrating these with the 2D culture methods primarily used for drug screening has been challenging. Thus, the protocol presented here details the development of methods for 3D culture within miniaturized biomaterial matrices in a multi-well plate format to facilitate integration with existing drug screening pipelines and conventional assays for cell viability. Since the matrix features critical for preserving clinically relevant phenotypes in cultured cells are expected to be highly tissue- and disease-specific, combinatorial screening of matrix parameters will be necessary to identify appropriate conditions for specific applications. The methods described here use a miniaturized culture format to assess cancer cell responses to orthogonal variation of matrix mechanics and ligand presentation. Specifically, this study demonstrates the use of this platform to investigate the effects of matrix parameters on the responses of patient-derived glioblastoma (GBM) cells to chemotherapy.

The present protocol describes an experimental platform to assess the effects of mechanical and biochemical cues on chemotherapeutic responses of patient-derived glioblastoma cells in 3D matrix-mimetic cultures using a custom-made UV illumination device facilitating high-throughput photocrosslinking of hydrogels with tunable mechanical features.

Cell-matrix interactions mediate complex physiological processes through biochemical, mechanical, and geometrical cues, influencing pathological changes ...

FRET Imaging in Three-dimensional Hydrogels

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