are dictated by the type of cell in use. However, thus far, synthetic hydrogel formulations are largely synthesized and discovered manually, one-by-one, using trial-and-error approaches.[5] This empirical method severely limits the parameter space that can be explored in a reasonable timescale, and as such, the potential of hydrogels for var-ious applications remains largely untapped.
We postulated that microfluidic technology could be employed to expedite the discovery of unique hydrogel formulations for applications in the life sciences. In par-ticular, droplet microfluidic technology is widely used to produce micrometer-scale hydrogel beads, termed microgels, for cell biology and tissue engineering.[6–11] Furthermore, previous work has shown that microgels can be produced with variable stiffness[12] or concentra-tion of bioactive ligands,[8,13,14] but microfluidic tech-nology has not yet been used to systematically generate a sizable diversity of hydrogel formulations. Moreover, methods to extract the composition and properties of hydrogels based on read-outs that are compatible with high-throughput experimentation are currently missing. Here, we addressed this technological gap by developing a programmable droplet microfluidics system to rap-idly synthesize, process, and screen thousands of com-positionally distinct microgels. Specifically, we work from a library of reactive molecular hydrogel building blocks that can be combinatorially mixed to generate an unprecedented diversity of microgel formulations via droplet microfluidics (Figure 1). By programming the flow rates of different inlets supplying individual hydrogel
A droplet microfluidics strategy to rapidly synthesize, process, and screen up to hundreds of thousands of compositionally distinct synthetic hydrogels is presented. By programming the flow rates of multiple microfluidic inlet channels supplying individual hydrogel building blocks, microgel compositions and properties are systematically modulated. The use of fluo-rescent labels as proxies for the physical and chemical proper-ties of the microgel permits the rapid screening and discovery of specific formulations through fluorescence microscopy or flow cytometry. This concept should accelerate the discovery of new hydrogel formulations for various novel applications.
Microfluidic Programming of Compositional Hydrogel LandscapesS. Allazetta, A. Negro, M. P. Lutolf*
Dr. S. Allazetta, Dr. A. Negro, Prof. M. P. LutolfLaboratory of Stem Cell BioengineeringInstitute of Bioengineering School of Life Sciences and School of Engineering Ecole Polytechnique Fédérale de Lausanne (EPFL) CH-1015 Lausanne, SwitzerlandE-mail: [email protected]. M. P. LutolfInstitute of Chemical Sciences and Engineering School of Basic Sciences, EPFL CH-1015 Lausanne, Switzerland
1. Introduction
The high water content, tissue-like physicochemical prop-erties, and ability to change their volume in response to external stimuli have made hydrogels the materials of choice for many biomedical applications.[1] The breadth of applications requires an equally diverse collection of hydrogel formulations, specifically tailored for the con-text of use. Importantly, the unique applications prohibit a universal hydrogel makeup, even within a specific dis-cipline. For example, in tissue engineering, a targeted cell type might require a hydrogel substrate that matches the mechanical properties of its tissue of origin,[2,3] as well as the biomolecular composition of its tissue-specific extracel-lular matrix (ECM).[4] Thus within this field, hydrogel stiff-nesses comprising several orders of magnitude, from a few tens of Pa (e.g., neural tissues) up to megapascals (e.g., bone tissue), and a wide biochemical parameter space must be covered. Then the specific hydrogel characteristics required
Macromol. Rapid Commun. 2017, , 1700255
S. Allazetta et al.MacromolecularRapid Communications
components (Figure 1a), we hypothesized that droplet composition can be systematically modulated, resulting in a population of microgels of hugely diverse composi-tion upon crosslinking (Figure 1b). Importantly, in order to determine the microgel properties easily after produc-tion, we aimed to employ fluorescent labels as proxies for targeted physical and chemical hydrogel parameters. This approach should permit the rapid screening and dis-covery of specific hydrogel formulations via conventional fluorescence microscopy or flow cytometry.
2. Results and Discussion
To implement the concept shown in Figure 1, we selected a hydrogel system that could offer well-defined relationships between structure and property. In hydrogel networks composed of end-linked hydrophilic polymers, termed macromers, the gel topology is essentially predetermined by the macromer structure, which often results in excel-lent structure–property relationships and highly predict-able gel characteristics.[15–18] Thus, we chose previously developed, end-linked hydrogels composed of branched poly(ethylene glycol) (PEG) that can be crosslinked by stepwise copolymerization via Michael-type addition
reaction.[18] For the major network building blocks, we selected vinylsulfone (VS)-terminated eight-arm-PEG (8-arm-PEG-VS) and thiol (SH)-terminated four-arm-PEG (4-arm-PEG-SH), respectively (macromer A and macromer B, Figure 1a). Under physiological conditions (pH 7.4, 37 °C) and equal stoichiometric ratio, these two macromers react covalently in a few minutes to yield well-defined and inert PEG networks.[19,20]
We first attempted to create various hydrogel compo-sitions through microfluidic programming in order to synthesize microgels with modular and predictable stiff-ness, a parameter that plays a key role in many hydrogel applications in biology and medicine.[2,4,21] To this end, a microfluidic chip was designed to produce microdroplets by flow focusing (Figure 2a).[6,22] To generate microgels with varying elasticity, the flow focusing system com-prised five channels: three inner channels for an aqueous phase containing the various gel precursors and two outer channels for the immiscible oil phase. The channel geometry allowed the oil phase to shear off microdroplets from the polymer-containing aqueous stream.
PEG-based microgels were generated by mixing 8-arm-PEG-VS and 4-arm-PEG-SH in the two lateral aqueous channels (Figure 2a). The central aqueous channel (depicted in red) contained a solution of thiol-reactive
Macromol. Rapid Commun. 2017, , 1700255
Figure 1. Microfluidic generation of a compositional landscape of microgels. a) Components of a library of reactive gel building blocks that can copolymerize to form a huge diversity of microgel formulations. b) Combinatorial mixing of hydrogel building blocks into micrometer-sized droplets via microfluidic flow focusing. The fine-tuning of flow rates of the different inlets permits the generation of a continuous landscape of microgels with diverse physical and biochemical properties. For instance, at time t1 low flow rates of the macromers A and B (Q1 and Q2, respectively) and, simultaneously, high flow rate of bioactive moiety A (Q4) and low flow rate of bioactive moiety B (Q3) will result in microgel formulations with relatively low crosslinking density (i.e., elasticity) and low bioactivity. On the contrary, at time t2 high flow rate of the macromers A and B, low flow rate of bioactive moiety A, and high flow rate of bioactive moiety B will lead to microgels with high crosslinking density and high bioactivity.
MacromolecularRapid CommunicationsMicrofluidic Programming of Compositional Hydrogel Landscapes
Figure 2. Programmable microfluidic synthesis of gels with variable stiffness. a) Schematic of microfluidic system used for the generation of microgels with variable stiffness. b) Schematic showing the independent generation of four distinct microgel populations with variable PEG content. The flow rate of the fluorescent reporter Mal-Alexa660 (Q3) is decreased and, simultaneously, both PEG macromer flow rates (Q1 and Q2) are increased, resulting in microgels with higher crosslinking density and lower fluorescence intensity. The fluorescent micro-scopy images show decreasing intensity for microgels having increased PEG content. c) Elastic moduli of microgels (determined by AFM) and intensity are linearly dependent on the macromer concentration. d) Linear correlation between the microgel intensity and the elastic modulus. e) Mixed populations of microgels generated by computer-controlled programming of the different flows. Variable fluorescence intensities encode the corresponding microgel elasticity levels. f) Hierarchical clustering showing four microgel populations that are nicely separated by distinct fluorescence and diameter. g) Linear correlation between microgel fluorescence and elastic modulus. h) Correlation between the intensity of four microgel populations synthesized separately (x-axis) and in one experiment on the same chip after clustering of the pool of mixed microgels (y-axis). Each microgel and bulk gel was analyzed by indenting at three different locations. Measurements were performed on 20 beads and three gels per condition. Scale bars: 100 µm.
S. Allazetta et al.MacromolecularRapid Communications
maleimide-Alexa660, termed “Mal-Alexa660,” used to dilute the PEG macromer solution and simultaneously label the macromers with variable amounts of the fluo-rophore, depending on the dilution. When the microgels possessed a 20% molar excess of thiol groups, fluorophore incorporation into the hydrogel network was found to be nearly complete, validating our reporter-based approach to assess hydrogel crosslinking (Figure S1, Supporting Information).
Due to the high efficiency of the Michael-type addition of SH-containing peptides onto VS-containing PEG,[23] we expected that by increasing or decreasing the flow rate of the Mal-Alexa660-containing central channel, we could rationally control microgel stiffness by modulating the PEG concentration; the quantity of covalently tethered Mal-Alexa660 should indicate the density of covalent crosslinks, that is, the microgel stiffness (Figure 2b). To test this, we first produced four “reference” microgel pop-ulations in separate experiments using the micro fluidic programming (Figure 2b, and Table S1, Supporting Infor-mation). Thus, by diluting the Mal-Alexa660 solution (Q3) accordingly, four PEG concentrations (2.5%, 5%, 7.5%, and 10%) were specified and fluorescent microgels with dis-tinct levels of fluorescence intensity were obtained after washing, as shown by fluorescence microscopy images (Figure 2b, inlets). By image analysis, we found that increasing fluorescence resulted from greater dilution of polymer content with the fluorophore solution. Specifi-cally, the fluorescence intensity levels of the four popula-tions were precisely linearly dependent on the PEG con-centration (R2 = 0.99, Figure 2c, red curve).
Next, in order to translate fluorescence intensity levels into the physical properties of the microgels, we per-formed atomic force microscopy (AFM) measurements of individual microgels from the four populations to ascer-tain the stiffness. Applying a Hertz model, Young’s moduli ranging from 9.9 ± 1.2 kPa (2.5%) to 99.4 ± 4.4 kPa (10%) were obtained from the force–distance curves (Figure 2c, black curve). These values are in good agreement with measurements of bulk hydrogel samples obtained by shear rheometry (Figure S2, Supporting Information). Importantly, a nearly ideal linear correlation (R2 = 0.99) was found between the PEG concentration and microgel stiffness, indicating a high level of precision of micro-fluidic mixing in individual droplets enabled by the design. Based on the linear dependency between PEG content and fluorescence intensity, as well as gel stiff-ness (Figure 2c), a direct linear relationship could be established between fluorescence intensity and microgel stiffness (Figure 2d). Therefore, the mechanical properties of the microgel can be readily extracted by fluorescence microscopy (or cytometry), opening up exciting possibili-ties for rapidly testing a large population of mechanically distinct microgels.
After successfully synthesizing four reference popula-tions of microgels separately, we aimed to generate the same formulations (i.e., microgels having a polymer con-tent of 2.5%, 5%, 7.5%, or 10%) in a single event on the same microfluidic chip. To achieve this, the syringes were programmed to serially dilute the PEG streams (Q1, Q2) with the Mal-Alexa660 solution (Q3) (Movie S1, Table S2, Supporting Information). The resulting mixed population of microgels was assessed by fluorescence microscopy (Figure 2e) and image analysis to determine the fluores-cence intensity and diameter of individual microbeads. Using a hierarchical clustering method, the four microgel subpopulations clearly separated based on intensity and diameter (Figure 2f); the latter increased due to an increase in swelling at a higher PEG concentration.[6,18] Using the previously established experimental relation-ship between the elastic moduli and fluorescence inten-sity of the microgels (Figure 2d), we then converted each intensity value of Figure 2f into a specific elastic mod-ulus and found a very good agreement between creating the populations in a single event and separate events (Figure 2g,h). These data demonstrate the programmable microfluidic synthesis of hydrogels with modular, fluo-rescently encoded mechanical properties.
We next sought to utilize our microfluidic approach to synthesize PEG-based microgels with variable and, more-over, predictable biochemical characteristics (Figure 3). We focused on conjugating our otherwise inert gels with bioactive ligands that could promote specific interactions with mammalian cells, a useful application for biology and biotechnology. Specifically, we selected the widely used cell adhesion peptide RGD, a fibronectin-derived motif that mediates cell adhesion of many cell types via binding to a subset of the integrins.[24] We designed a pep-tide sequence that could covalently attach to the microgel network, by bearing a cysteine, and simultaneously have its concentration determined quantitatively through a fluorescent Alexa488 (for simplicity we refer to this pep-tide here as “Alexa488-RGD”).
To generate microgels with variable RGD concentra-tion but equal crosslinking density and stiffness, an addi-tional channel, containing a buffer, was included in the microfluidic design (Figure 3a). As such, an increase in the flow rate of the Alexa488-RGD stream (Q4) could be bal-anced with a simultaneous decrease in the buffer stream (Q3), keeping the total amount of diluting solutions con-stant. Thus, we could generate microgels of varying fluo-rescence but equal stiffness by proportionally modifying the flow rates of the buffer and Alexa488-RGD stream while keeping the PEG precursors (Q1 and Q2) constant (Figure 3b). The amount of fluorophore should therefore be indicative of the amount of peptide coupled to an indi-vidual microgel. To directly test this, we programmed the microfluidic system (Table S2, Supporting Information)
Macromol. Rapid Commun. 2017, , 1700255
MacromolecularRapid CommunicationsMicrofluidic Programming of Compositional Hydrogel Landscapes
Figure 3. Programmable microfluidic synthesis of gels with variable bioactivity. a) Schematic displaying the microfluidic chip used for the generation of microgels with different RGD concentrations. b) Programmed microfluidic synthesis of four distinct microgel populations with variable RGD concentration. The flow rate of both PEG macromers (Q1 and Q2) are kept constant to assure the formation of microgels with the same elasticity (at 5% PEG). Microgels with increasing RGD concentration are formed by increasing the flow rate of Alexa488-RGD (Q4) and simultaneously decreasing the flow rate of the buffer (Q3), resulting in microgels with increasing fluorescent intensity. c) Example of mixed population of microgels bearing variable RGD concentrations and fluorescent intensities. d) Hierarchical clustering revealing four microgel populations synthesized at constant PEG concentration (5%) with distinct fluorescence intensity but similar diameter. e) AFM measurements showing no significant difference in elastic moduli of microgels having variable RGD concentration at 5% PEG content, compared to microgels with constant RGD concentration at the same PEG content. Standard deviation was measured over 20 beads per condition. f) Release assay performed on 5% gels with increasing concentration of Alexa488-RGD. The green bars depict the theoretical mass in µg of RGD within the gel. The blue bars depict the mass in µg of RGD released into the supernatant. The amount of released RGD linearly increased with increasing RGD concentrations within the gel. g) Percentage of RGD released with increasing RGD concentrations. In all conditions the percentage of released RGD is around 2.5%. h) Linear correlation between microgels fluorescence intensity and RGD concentration. Standard deviation was calculated over three independent gels. Statistical t-test (type 2, number of tails 2) was performed on the samples (***p < 0.001; *p < 0.05). Scale bars: 100 µm.
S. Allazetta et al.MacromolecularRapid Communications
to generate fluorescent microgels of constant stiffness at four RGD concentrations (Figure 3b,c). Image analysis of fluorescence microscopy images revealed four dis-tinct populations of RGD-containing microgels that could be readily separated using the clustering approach (Figure 3d). Importantly, the elastic modulus of the microgels was determined by AFM and found to be com-pletely independent of the concentration of tethered RGD (Figure 3e). Furthermore, because the percentage of nonincorporated (i.e., released) Alexa488-RGD was found to be very low (<2.5%) and constant across all conditions (Figure 3f,g), we obtained a nearly perfect (R2 = 0.997) linear correlation between microgel intensity and input Alexa488-RGD concentration (Figure 3h). These data dem-onstrate the programmable microfluidic synthesis of hydrogels with modular, fluorescently encoded biochem-ical composition, independent of the mechanical proper-ties of the gel.
Due to the well-documented selectivity of the Michael-type addition reaction,[25] we reasoned that precise micro-fluidic mixing could synthesize combinatorial microgels with distinct and independently controllable stiffness and RGD content, each encoded by its corresponding fluo-rophore. Therefore, to generate microgels with indepen-dently programmable stiffness and bioactivity, we modi-fied the microfluidic chip architecture by combining the setup for stiffness (Figure 2) and bioactivity modulation (Figure 3). Specifically, modification of PEG precursor con-centration via Mal-Alexa660 was combined with titra-tion of Alexa488-RGD concentration via an extra buffer inlet (Figure 4a). As a proof of concept, we first specified and synthesized four combinatorial microgel formula-tions that differed in stiffness and RGD concentration (low/low, low/high, high/low, and high/high) (Figure 4b, and Table S3, Movie S2, Supporting Information). As indi-cated in the scatter plot of Figure 4c, image analysis and clustering revealed the presence of the four expected microgel subpopulations. Of note, the softer microgels (i.e., those of higher red fluorescence; population 1 and 2 in Figure 4c) showed a wider distribution of signal inten-sity in both fluorescent channels. We attribute this to a greater heterogeneity of incorporation of the reporters at the relatively low concentration of PEG macromer. Indeed, a release assay performed on these combinatorial gels demonstrated a higher percentage of release of both fluorescent moieties at the low PEG concentration (combi-nation 1 and 2), accompanied by a higher standard devia-tion (Figure S3, Supporting Information). However, at equal PEG content, the percentage of released fluorescent reporter was found to be independent of the reporter con-centration, in accordance with data shown in Figure 3g. Therefore, the linear correlation between fluorescence intensity and hydrogel stiffness (Figure 2) as well as bio-chemical content (Figure 3) remains preserved when
generating more complex combinatorial microenviron-ments with orthogonally modular characteristics.
Next, in order to demonstrate the potential of our tech-nology platform in producing an unparalleled diversity of microgels, we programmed the microfluidic system to generate 100 different conditions, by specifying ten dif-ferent levels of stiffness and RGD concentrations, respec-tively (Table S4, Supporting Information). Due to the distribution of properties for each of the formulations, a continuous “landscape” of microgel formulations was obtained (Figure 4d,e).
To render our microfluidic bead platform amenable to high-throughput experimentation, circumventing more laborious microscopy-based analysis, we sought to employ flow cytometry for rapidly testing a large popu-lation of mechanically and biochemically distinct micro-gels. The microfluidic design was scaled down to permit beads about 30 µm in diameter to be generated while still keeping five aqueous inlets and one oil inlet. Further-more, the channel widths upstream of the oil–aqueous junction were widened to reduce back pressure. Using the design, three different concentrations of Mal-Alexa660 and Cys-Alexa488 were used to generate nine sepa-rate populations of microgels. Flow cytometry was then employed to analyze the microgels (Figure S4a,b, Sup-porting Information). The nine distinct populations were able to be discriminated by flow cytometry, showing the ability of the microgel platform to be paired with high-throughput analysis techniques.
Finally, to illustrate the potential of our system for biomedical and biological applications, we generated combinatorial microgels for mammalian cell culture and investigated how biochemical and mechanical microgel properties influence cell phenotype. We specifically focused on assessing cell proliferation and epithelial-to-mesenchymal transition (EMT), a cellular process by which epithelial cells lose their polarity and gain invasive properties.
To perform this analysis, the biological read-out of interest must be detectable by fluorescence. In case of cell proliferation, the number of cells can be estimated by measuring the intensity of the DAPI signal staining the nuclei. Normal murine mammary gland (NMuMG) epithe-lial cells were cultured in suspension on four combinato-rial microgel populations, generated at flow conditions summarized in Table S5 (Supporting Information): 2.5% (w/v) and 10% (w/v), respectively, were chosen as PEG pre-cursor contents and 1.08 and 0.54 mg mL−1, respectively, as RGD peptide concentrations to support adequate cell adhe-sion. Images were acquired in multiple fluorescent chan-nels, upon staining cell nuclei with DAPI on different days. A proliferation trend was detected comparing the DAPI signal at day 1 to day 7, showing a higher intensity at later time points (Figure S5a, Supporting Information).
Macromol. Rapid Commun. 2017, , 1700255
MacromolecularRapid CommunicationsMicrofluidic Programming of Compositional Hydrogel Landscapes
Figure 4. Programmable microfluidic synthesis of combinatorial microgels. a) Schematic displaying the microfluidic chip used for the gen-eration of combinatorial microgels. b) Computer-controlled modulation of the flows for the on-chip formation of combinatorial microgels. c) Scatter plot of intensities of the red and green fluorescent reporter of the four populations. Hierarchical clustering was used to identify the four populations. Histograms next to axis show the Gaussian distribution of intensities for each population. Examples of fluorescent microgels corresponding to the four populations. Scale bars: 100 µm. d) Scatter plot of intensities of the red and green fluorescent reporter of the 100 populations. e) Density plot revealing a continuous landscape of microgel properties represented here as a 3D surface.
S. Allazetta et al.MacromolecularRapid Communications
The microgel system also allows the expression of the desired biological marker of interest to be investigated. To prove this, we looked at TGF-ß-induced EMT.[26,27] Cells were cultured in the presence or absence of TGF-ß on combinatorial microgels with the same mechanical and biochemical properties as before. After 2 d of treatment, cells were stained with DAPI. As expected, a decrease in the DAPI signal was observed due to fewer cells remaining adherent on the microgels after treatment with TGF-ß, which is commonly known to have an antim-itogenic effect.[28] Furthermore, immunostaining was per-formed to detect the expression of an epithelial marker, E-cadherin, and a mesenchymal marker, vimentin. Next, images were acquired in all the four fluorescent channels (Figure S5b, Supporting Information). Normal epithelial morphology could be seen in the absence of TGF-ß and independent of the microgel property. However, when treated with TGF-ß, cells changed their morphology depending on the microgel property. In accordance with the literature,[29–31] cells maintained an epithelial-like round shape on soft microgel (characterized by high red intensity), while being more elongated on stiff microgels (characterized by low red intensity), suggesting a mesen-chymal phenotype. These data demonstrate that the pro-grammable microfluidic synthesis of hydrogels with mod-ular, fluorescently encoded composition can be explored to study the influence of a cell’s microenvironment on phenotype.
3. Conclusion
Synthetic hydrogels have established themselves as crucial materials for many biomedical applications such as cell culture, drug delivery, and tissue engineering. To cover this broad spectrum of applications, hydrogels must be incred-ibly diverse. Therefore, there has been an urgent need to precisely synthesize hydrogels with specific physicochem-ical properties. Unfortunately, discovering new hydrogel formulations remains largely a trial-and-error process in which many conditions must be tested in a combinato-rial fashion. Robotic microarray technologies have been developed to screen hundreds of combinations of hydro-gels.[32–38] However, the cost of using these technologies, as well as the difficulty in manipulating their final products, prevents them from reaching their full potential for high-throughput screening applications. Here, we present a powerful microfluidic platform to generate combinatorial microgels with varying elasticity, biochemical composi-tion, or both. An automated droplet microfluidics approach was employed to precisely mix fluorescently labeled hydrogel building blocks on chip via modulation of flow rates, resulting in the generation of microgels with highly controlled and modular composition.
The microfluidics-based mixing approach is suitable to generate an unprecedented, continuous landscape of microgel properties. Importantly, the nearly ideal network characteristic of the microgels allowed the physico chemical and biochemical properties to be reli-ably encoded by simple fluorescence, opening up exciting avenues for the high-throughput applications of the gels for many applications. For example, we envision that this approach will be useful for the screening of multifacto-rial cell culture microenvironments for stem cell biology or cancer research. Furthermore, the versatility of this approach permits the generation of microgel formula-tions “on-demand,” permitting formulations to be tar-geted during the microbead production and without prior setup. Overall, this technique could provide rapid explo-ration of hydrogel formulations and give rise to novel compositions and applications of hydrogels, whether as microbeads or bulk gels.
4. Experimental Section
Microfluidic Chips: For the generation of microgels with vary ing elasticity (Figure 2) the two lateral microchannels were designed forming 45° with the oil channels, whereas for the gen-eration of microgels with different RGD concentration (Figure 3), as well as combinatorial microgels (Figure 4), the lateral channels were designed forming 30° and 60° angles with the oil channels, respectively. The height of the channels was 100 µm and the width of the upstream channels was 75 µm. For the microgels analyzed by flow cytometry (Figure S4, Supporting Information), the lateral channels were designed forming a 45° angle with the oil channel. The channel height was 30 µm, and the channel width at the oil–aqueous junction was 30 µm. All microfluidic devices were fabricated as described in ref.[6] Briefly, PDMS chips (Sylgard 84, Dow Corning) were fabricated from SU8-patterned silicon masters using conventional soft lithography. Chips were bonded on oxygen plasma-activated hydrophobic substrates and baked at 80 °C overnight.
Microfluidic Synthesis of Combinatorial Microgels: Microfluidic chips were connected to Socochim syringes (Switzerland) using flexible Tygon tubing, previously filled with PEG solutions, Mal-Alexa660 solution, fluorescently labeled peptide solution, triethanolamine buffer (0.3 m, pH 8), and hexadecane (Sigma, USA) containing 2% (w/v) ABIL EM surfactant (Evonik, Germany) as oil phase. 4-Arm-PEG-SH (10 kDa, NOF, Japan) was reconstituted in bidistilled water and 8-arm-PEG-VS (40 kDa, NOF, Japan) in triethanolamine buffer. Depending on the targeted molar excess of functional groups, the syringes were filled with PEG solutions at different concentrations. The concentration of 4-arm-PEG-SH was 20% (w/v) for all conditions, whereas the concentration of 8-arm-PEG-VS was 8.33% (w/v), 10% (w/v), and 12% (w/v) for a 20% excess of SH groups, for no excess of any functional group, and for a 20% excess of VS groups, respectively. Microgel washing was performed as reported in ref.[6]. For the microgels analyzed by flow cytometry, HFE-7500 (3 m) with 1% of an FSH-PEG polymer surfactant was used for the oil phase.
Macromol. Rapid Commun. 2017, , 1700255
MacromolecularRapid CommunicationsMicrofluidic Programming of Compositional Hydrogel Landscapes
Programming of Syringe Pumps: Programmable syringe pumps (neMESYS, Cetoni, Germany) were used to control the flow rates of the different inlets. The “flow profile” mode of the software was used for setting each syringe flow for 5 min at the rates summarized in Tables S1–S3 (Supporting Information). The sum of the flow rates of the three streams was maintained constant at 5 µL min−1, while simultaneously changing them independently. The flow rate of the oil phase was set at 10 µL min−1, allowing the formation of microgels with a diameter of ≈100 µm before swelling.
Release Assays: Mal-Alexa660 (Life Technologies, USA) and RGD-Alexa440 (CRGDLF(Lys)-FITC; GenScript, USA) were used for the release assays. Both molecules were reconstituted in water at desired concentrations immediately before use. 10 µL gels were casted in IBIDI µ-chambers at the same conditions used for the generation of microgels with variable stiffness or RGD concentrations. 50 µL of PBS was added and collected after 24 h for measuring the fluorescence at 660 nm (for Mal-Alexa660) and at 488 nm (for RGD-Alexa440) using a spectrophotometer (Tecan, USA). The total mass of RGD released from the gels was then calculated based on a calibration curve.
Synthesis of RGD-Alexa440: Ac-GRCGRGDSPG-NH2 (GL Biochem, China) and N-hydroxysuccinimide (NHS) ester-Alexa488 fluorophore (Life Technologies, USA) were reconstituted in water, mixed together with 10% molar excess of peptide. The pH of the solution was adjusted to 7.4 by adding PBS (10×). The components were reacted for 1 h at room temperature. The unreacted succinimidyl groups were subsequently cleaved with TRIS buffer at pH 7 for 30 min.
Atomic Force Microscopy: Microgels and bulk gels were immobilized onto glass substrates by following the protocol in ref.[6]. All measurements were performed in PBS using a NanoWizard II atomic force microscope (JPK Instruments, Germany). Conical silicon cantilevers with a Cr/Au backside coating (CSC37/Cr-Au, MikroMasch, USA) and a nominal average spring constant of 0.3 N m−1 were used to indent the samples, applying an indentation force of 100 nN. Force–distance curves were analyzed with JPK Data Processing software, applying the Hertz model to calculate the Young’s modulus (Poisson’s ratio considered 0.5). Each microgel and bulk gel was analyzed by indenting three different locations. Measurements were performed on 20 beads and three gels per condition.
Image Acquisition and Analysis: Images were acquired using an inverted Olympus IX81 CellR microscope equipped with a Hamamatsu camera. All illumination settings were kept constant during the acquisition of all images. Different parameters were chosen to minimize the cross-talk between the two channels. Times of exposure of 200 and 1 ms, 100% and 70% lamp intensity, and binning 1 were used during the acquisition of the far red channel (ex. 650/13) and the green channel (ex. 485/20), respectively. Multiple images were acquired in order to generate a beads population of ≈200 beads. Each image was taken at different wavelengths, bright-field, far red (660 nm) and/or green fluorescence (488 nm), and stored in the same folder, assigning an increasing index and a code for the wavelength in a 16-bit codification. A Matlab (Mathworks, Natick, USA) script was developed for image analysis and subsequent data clustering. After loading of the images, the intensities of the red and green channels were summed up to enhance the contrast between the
beads and the background, and a watershed-based routine was used to create a binary mask, discriminating each bead from the background. Diameter and intensity in both channels were measured for each microgel. All experiments were repeated independently three times. Indicated intensities show the average value of three independent repetitions of a sample of around 200 beads.
Hierarchical Clustering: A Matlab script was developed for the hierarchical clustering of the data and was performed based on the expected number of stiffness and/or protein concentration classes. Linkage function was used to measure the relative distance between the data, and the cluster function was adopted for the classification. Data were represented as a scatter plot where each bead was represented by a dot with a specific value of intensity. Combinatorial microgels were represented as a scatter plot in the stiffness (red intensity)-RGD (green intensity) plane, where different colors encode for distinct beads populations. For each bead population the intensity distribution for both channels was represented by an ellipse in the plane and by a Gaussian projected along each axis.
Flow Cytometry Analysis: Beads were washed then resuspended in DMEM + Glutamax with 10% FBS. Beads were analyzed using a LSRII analyzer (BD Biosciences).
Cell Culture on Combinatorial Microgels: Normal murine mammary (NMuMG) epithelial cells were generously provided by Prof. Christofori, Department of Biomedicine, University of Basel, Switzerland. They were cultured on tissue culture flasks in Dulbecco’s modified Eagle medium (DMEM) with 10% fetal bovine serum (FBS) and pen-strep (10 mg mL+). After dissociation with trypsin (TripLE express, Gibco), cells were cultured on combinatorial microgels in a suspension bioreactor (BioLevitator, Hamilton, Switzerland). During the inoculation phase, the rotation speed of the tubes was 50 rpm with an agitation period of 2 min for an entire duration of 4 h. The agitation pause duration was set at 10 min to minimize microcarrier “bridging.” During cell culture, the rotation speed was increased to 80 rpm for all cell types. Cells were seeded on microcarriers at a density of 14 200 cells cm−2 (corresponding to ≈10 cells per carrier). 6 mL of medium was used during the inoculation phase (to maximize cell–microgel contact) and 20 mL during cell culture.
Immunostaining: Fixation with paraformaldehyde (PFA) (4%, 10 min) was followed by membrane permeabilization with a solution of TritonX 0.2% in PBS for 5 min. Incubation with a goat serum blocking solution for 2 h was followed by overnight incubation with a monoclonal mouse antibody to E-cadherin or vimentin (1:100, Sigma, USA). After extensive washing, primary antibodies were stained by anti-mouse (1:500) Alexa546-labeled secondary antibodies (Life Technologies, USA) for 2 h. DAPI (1:1000) was then added for 15 min.
Supporting Information
Supporting Information is available from the Wiley Online Library or from the author.
Acknowledgements: We thank Michael Snyder for proofreading of the manuscript and for developing small PEG-based microgels
Macromol. Rapid Commun. 2017, , 1700255
S. Allazetta et al.MacromolecularRapid Communications
for flow cytometry applications. We thank Esther Amstad for help with the design of microfluidic chips to manufacture small microgels for flow cytometry and for valuable feedback on the manuscript. This work was funded by the EU framework 7 HEALTH research programme PluriMes (http://www.plurimes.eu/), an ERC grant (StG_311422), and a Swiss National Science Foundation Sinergia grant (CRSII3_147684). M.P.L. and S.A. conceived the concept and designed experiments. S.A. and M.S. performed the experiments and analyzed the data. A.N. helped with data analysis (clustering). M.P.L. and S.A. wrote the paper.
Conflict of Interest: The authors declare no conflict of interest.
Received: April 20, 2017; Revised: May 9, 2017; Published online: June 12, 2017; DOI: 10.1002/marc.201700255
[1] A. D. Celiz, J. G. W. Smith, R. Langer, D. G. Anderson, D. A. Winkler, D. A. Barrett, M. C. Davies, L. E. Young, C. Denning, M. R. Alexander, Nat. Mater. 2014, 13, 570.
[2] A. J. Engler, S. Sen, H. L. Sweeney, D. E. Discher, Cell 2006, 126, 677.
[3] D. E. Discher, P. Janmey, Y. L. Wang, Science 2005, 310, 1139.[4] O. Chaudhuri, S. T. Koshy, C. B. da Cunha, J.-W. Shin,
C. S. Verbeke, K. H. Allison, D. J. Mooney, Nat. Mater. 2014, 13, 970.
[5] S. Ding, Y. Liu, Y. Li, Z. Liu, S. Sohn, F. J. Walker, J. Schroers, Nat. Mater. 2014, 13, 494.
[6] S. Allazetta, T. C. Hausherr, M. P. Lutolf, Biomacromolecules 2013, 14, 1122.
[7] D. M. Headen, G. Aubry, H. Lu, A. J. Garcia, Adv. Mater. 2014, 26, 3003.
[8] E. Tumarkin, L. Tzadu, E. Csaszar, M. Seo, H. Zhang, A. Lee, R. Peerani, K. Purpura, P. W. Zandstra, E. Kumacheva, Integr. Biol. 2011, 3, 653.
[9] T. Rossow, J. A. Heyman, A. J. Ehrlicher, A. Langhoff, D. A. Weitz, R. Haag, S. Seiffert, J. Am. Chem. Soc. 2012, 134, 4983.
[10] D. Velasco, E. Tumarkin, E. Kumacheva, Small 2012, 8, 1633.[11] W. H. Tan, S. Takeuchi, Adv. Mater. 2007, 19, 2696.[12] A. Kumachev, J. Greener, E. Tumarkin, E. Eiser, P. W. Zandstra,
E. Kumacheva, Biomaterials 2011, 32, 1477.[13] K. A. Mosiewicz, L. Kolb, A. J. van der Vlies, M. M. Martino,
P. S. Lienemann, J. A. Hubbell, M. Ehrbar, M. P. Lutolf, Nat. Mater. 2013, 12, 1072.
[14] I. A. Eydelnant, B. B. Li, A. R. Wheeler, Nat. Commun. 2014, 5, 3355.
[15] Y. Gnanou, G. Hild, P. Rempp, Macromolecules 1987, 20, 1662.
[16] S. K. Patel, S. Malone, C. Cohen, J. R. Gillmor, R. H. Colby, Macromolecules 1992, 25, 5241.
[17] K. Sivasailam, C. Cohen, J. Rheol. 2000, 44, 897.[18] M. P. Lutolf, J. A. Hubbell, Biomacromolecules 2003, 4, 713.[19] S. Cosson, S. A. Kobel, M. P. Lutolf, Adv. Funct. Mater. 2009,
19, 3411.[20] S. Gobaa, S. Hoehnel, M. Roccio, A. Negro, S. Kobel,
M. P. Lutolf, Nat. Methods 2011, 8, 949.[21] P. M. Gilbert, K. L. Havenstrite, K. E. Magnusson, A. Sacco,
N. A. Leonardi, P. Kraft, N. K. Nguyen, S. Thrun, M. P. Lutolf, H. M. Blau, Science 2010, 329, 1078.
[22] S. Allazetta, L. Kolb, S. Zerbib, J. Bardy, M. P. Lutolf, Small 2015, 11, 5647.
[23] M. P. Lutolf, N. Tirelli, S. Cerritelli, L. Cavalli, J. A. Hubbell, Bioconjugate Chem. 2001, 12, 1051.
[24] E. Ruoslahti, Annu. Rev. Cell Dev. Biol. 1996, 12, 697.[25] P. van de Wetering, A. T. Metters, R. G. Schoenmakers,
J. A. Hubbell, J. Controlled Release 2005, 102, 619.[26] J. Xu, S. Lamouille, R. Derynck, Cell Res. 2009, 19, 156.[27] J. Zavadil, E. P. Bottinger, Oncogene 2005, 24, 5764.[28] C. H. Heldin, K. Miyazono, P. ten Dijke, Nature 1997,
390, 465.[29] K. Lee, Q. K. Chen, C. Lui, M. A. Cichon, D. C. Radisky,
C. M. Nelson, Mol. Biol. Cell 2012, 23, 4097.[30] C. M. Nelson, D. Khauv, M. J. Bissell, D. C. Radisky, J. Cell. Bio-
chem. 2008, 105, 25.[31] E. W. Gomez, Q. K. Chen, N. Gjorevski, C. M. Nelson, J. Cell.
Biochem. 2010, 110, 44.[32] L. Jongpaiboonkit, W. J. King, W. L. Murphy, Tissue Eng., Part
A 2009, 15, 343.[33] A. Dolatshahi-Pirouz, M. Nikkhah, A. K. Gaharwar,
B. Hashmi, E. Guermani, H. Aliabadi, G. Camci-Unal, T. Ferrante, M. Foss, D. E. Ingber, A. Khademhosseini, Sci. Rep. 2014, 4, 3896.
[34] M. Nii, J. H. Lai, M. Keeney, L.-H. Han, A. Behn, G. Imanbayev, F. Yang, Acta Biomater. 2013, 9, 5475.
[35] A. Ranga, S. Gobaa, Y. Okawa, K. Mosiewicz, A. Negro, M. P. Lutolf, Nat. Commun. 2014, 5, 4324.
[36] R. Zhang, A. Liberski, F. Khan, J. J. Diaz-Mochon, M. Bradley, Chem. Commun. 2008, 1317.
[37] R. Zhang, A. Liberski, R. Sanchez-Martin, M. Bradley, Bioma-terials 2009, 30, 6193.
[38] R. Zhang, H. K. Mjoseng, M. A. Hoeve, N. G. Bauer, S. Pells, R. Besseling, S. Velugotla, G. Tourniaire, R. E. B. Kishen, Y. Tsenkina, C. Armit, C. R. E. Duffy, M. Helfen, F. Edenhofer, P. A. de Sousa, M. Bradley, Nat. Commun. 2013, 4, 1335.
