Synergistic Material-Topography Combinations to Achieve Immunomodulatory Osteogenic Biomaterials

Human mesenchymal stem cells (hMSCs) are widely represented in ongoing regenerative medicine clinical trials due to their ease of autologous implantation. In bone regeneration, crosstalk between macrophages and hMSCs is critical with macrophages playing a key role in the recruitment and differentiation of hMSCs. However, engineered biomaterials able to both direct hMSC fate and modulate macrophage phenotype have not yet been identified. A novel combinatorial chemistry-microtopography screening platform, the ChemoTopoChip, is used to identify materials suitable for bone regeneration by screening with human immortalized mesenchymal stem cells (hiMSCs) and human macrophages. The osteoinduction achieved in hiMSCs cultured on the “hit” materials in basal media is comparable to that seen when cells are cultured in osteogenic media, illustrating that these materials offer a materials-induced alternative in bone-regenerative applications. These also exhibit immunomodulatory effects, concurrently polarizing macrophages towards a pro-healing phenotype. Control of cell response is achieved when both chemistry and topography are recruited to instruct the required cell phenotype, combining synergistically. The large library of materials reveals that the relative roles of microtopography and material chemistry are similar, and machine learning identifies key material and topographical features for cell-instruction.

their osteogenicity, osteoinductivity, osteoconduction and osteointegration characteristics. [2], [3] Synthetic bone substitutes, such as calcium phosphate (CaP) ceramics, have proven safe and biocompatible but often lack the osteogenicity needed to support bone healing. [4] There is increased interest in the use of hMSCs in combination with synthetic biomaterials to provide a potential way of overcoming these challenges in autologous bone grafting. [5], [6] The inherent multipotency of hMSC cells has allowed in vitro cultured models to be used, in combination with synthetic biomaterials, to differentiate cells into osteoblasts without osteogenic supplements. They have also been shown to form bone in vivo, a process driven by topography, protein adsorption to surface chemistry and phosphorus delivery to the cells. [5], [6], [7], [8], [9] Furthermore, there is growing evidence demonstrating a reciprocal functional role of macrophage polarization in hMSC osteoblast differentiation, with prohealing M2 macrophages enhancing hMSC osteoblastic differentiation. [10] Hence, the crosstalk between macrophages and MSCs plays a key role in normal bone repair. [11], [12], [13] The ability to tune or modulate the foreign-body response to a biomaterial is an ongoing challenge in the field of regenerative medicine. [14] This is further complicated when designing materials for tasks such as induction of osteogenic differentiation of hMSCs at the site of implantation. Surface chemistry [15], [16] and surface topography [17], [18], [19] have both been shown to enhance differentiation and proliferation of stem cells yet, to our knowledge, no biomaterials have been reported capable of simultaneously directing differentiation of hMSCs and polarizing macrophages towards an M2 state.
To enable a wide range of chemistries and topographies to be screened, a combinatorial highthroughput screening tool, the ChemoTopoChip, was developed. This platform contains 980 microtopography and materials chemistry combinations that can simultaneously probe the combined effects on cellular response. The screening platform was used to identify materials that can direct hiMSC differentiation towards an osteoblastic lineage and that polarize human macrophages towards a pro-healing M2 phenotype, two cell phenotypes that have regenerative medicine applications. The design comprised 36 Topo units of size 500 × 500 µm, including one flat control (Figure 1 b-c), arranged in 3 × 3 mm ChemoTopo units. These are repeated 28 times, each with a different chemical functionalization (Figure 1 a). The microtopographies used were chosen from previous TopoChip screens to maximize the morphological differences of MSCs (see Figure S1 for high-resolution images). [20] The chemistries were chosen from libraries of (meth)acrylate and (meth)acylamide monomers to provide maximum chemical diversity (see Figure S2 for structures). The monomers are used to functionalize the surface of topographically molded chips, which minimizes differences in material compliance between chemistries sensed by the attached cells (see Table S1 for AFM modulus measurements).
A silicon mold was fabricated from the ChemoTopoChip design using photolithography and etching to produce the negative master of the topographies. The desired features were produced from this master by injecting a 1:2 mixture of trimethylolpropane tri(3mercaptopropionate):tetra(ethylene glycol) diacrylate (1:2 TMPMP:TEGDA) monomers containing the photoinitiator 2,2-dimethoxy-2-phenylacetophenone (DMPA) between a methacrylate-functionalized glass slide and the silicon master. UV curing and solvent washing then provided the molded ChemoTopoChip substrate, chosen because similar photopolymerized thiol-ene systems have been reported as tough shape memory, flexible materials offering low shrinkage stress that are sufficiently transparent to allow transmission optical imaging. [21] Functionalization of the ChemoTopo units was carried out by deposition of 50% w/v or 75% v/v monomer solutions in N,N-dimethylformamide (DMF) containing 0.05% w/v DMPA onto each ChemoTopo unit; further UV curing and washing steps delivered the final ChemoTopoChip (Figure 1 a-c, Figure S3, Figure S4).
To first investigate the osteoinductive potential of the materials, hiMSCs were seeded on 3 replicate chips in 3 independent experiments (N = 3 , n = 3) and cultured in basal culture media (a non-selective, undefined culture medium containing all elements required for cell growth but not containing osteo-inductive supplements to induce stem cell differentiation).
After 5 days, samples were fixed and stained for both α -tubulin (cytoskeletal marker) and alkaline phosphatase (ALP, an early osteogenic marker), and analyzed using an automated high-throughput fluorescence microscope. ALP expression is a widely used osteogenesis marker as it is known to be involved in bone formation, plays an essential role in matrix mineralization and is induced by a range of osteogenic molecules. [22] Images were processed using CellProfiler software [23] to quantify cell number and ALP staining intensity on each individual topography-material combination. The ALP staining intensity and cell number were normalized to that of the flat TMPMP-co-TEGDA Topo unit within each ChemoTopoChip sample.
A diverse range of hiMSC cell morphologies and attached cell numbers could be seen across the ChemoTopoChip (Figure 1 e-g, Figure S4 and S5), with cells displaying an elongated shape and alignment to the topographies on some ChemoTopo units (e.g. Figure 1 f, Figure   S5 and S6 E2, E3) in contrast to more uniform cell spreading and random alignment on others (eg. Figure 1 e, Figure S5 and S6 B3, C4). Previous TopoChip screens using hMSCs revealed a similar range of cell morphological responses, where more elongated cells were linked to ALP upregulation. [14] To explore the range and identify trends it is useful to plot all the results as both heatmaps and to rank order all results to see the range for all ChemoTopo units (see Figure S7 and Figure S8 a-b). For an exploratory method we indicate the combinations which were determined to have p-value < 0.05 from a two independent sample equal variance t-test, although we note that the sample sizes are small here and this does not account for the multiple comparisons problem. Analysis of the mean integrated ALP expression per cell for each topography-material combination showed that 113 exhibited the most significant upregulation of this osteogenic marker (p < 0.05) compared to the flat base Topo unit (see Table S2 for full list), with all of these displaying a higher ALP intensity than the flat base material region used as a control comparator. Visual inspection reveals trends across various chemistries, e.g. monomers 12 (mono-2-(methacryloyloxy)ethyl succinate, mMAOES) and 20 (N-tert-octylacrylamide, tOcAm), suggesting that a group of chemistries induce upregulation of ALP intensity relative to the mean; equivalent topographical trends were less evident indicating that micro-topographical stimuli did not dominate across the range of chemistries used. A total of 103 combinations were found to have higher normalized cell number than the flat base region (p < 0.05, total area taken into account), but none lower (  Figure S9). These materials therefore induce a similar osteogenic state of the cells, as measured by ALP upregulation, to that of osteo-inductive supplements typically used to differentiate hMSCs to osteoblasts.
Immunomodulation was next screened by seeding primary human monocytes onto ChemoTopoChips for 6 days followed by testing for differentiation into macrophages and polarization to the M1 or M2 phenotype. Monocytes were isolated from peripheral blood of two independent donors, with 3 replicates carried out for each. To determine the polarization status of the cells, samples were fixed and stained for intracellular expression of the pro-and anti-inflammatory cytokines tumor necrosis factor α (TNFα, M1 polarization indicator) and interleukin-10 (IL-10, M2 polarization indicator) respectively, and analyzed using highthroughput fluorescence microscopy. Images were processed using CellProfiler software [19] with an image analysis pipeline designed to quantify cell attachment using DAPI nuclear all topography-material combinations, including those containing flat areas. As was seen with hiMSC attachment, the range of normalized cell number for combinations containing microtopographies was greater than those for flat areas; topography is therefore also important for macrophage attachment. Visual inspection identified some topography-material combinations that were hits for both data sets, with BzHPEA (chemistry 7) in combination with topography 22 appearing strongest for both ALP upregulation in hiMSCs and M2 polarization in macrophages (Figure 1 k-l).
For analysis of the ChemoTopoChip data, unfunctionalized TMPMP-co-TEGDA molded topographies and flat area chemistries were used as control chemistries and topographies. Hit topography-material combinations were compared to these controls to assess their synergy ratios (SR) (see Supporting Information for method). For the hiMSC data set, 15 of the 103 hit combinations that showed greater cell numbers than the flat TPMP-co-TEGDA control were determined to be synergistic (Figure 2 a, see Table S2 for full analysis of all synergistic combinations); additionally, 2 of the 113 hit combinations directing osteogenic differentiation were determined to be synergistic (Figure 2 b, see Table S2 for full analysis of all synergistic combinations). For the hiMSC cell number, 2 combinations appeared to be antagonistic (Figure 2 a). For the human macrophage cell number, 2 topography-material combinations exhibited a synergistic effect (Figure 2 c ,see Table S3 for full analysis). Of the top 22 human macrophage polarization combinations, 4 were determined to be synergistic (Figure 2 d ,see Table S3 for full analysis). A total of 3 combinations were found to promote upregulation of ALP in hiMSCs and polarize human macrophages towards an M2 phenotype (Figure 2 b, d-f). The top hit, BzHPEA in combination with Topo 22, was found to be synergistic for both datasets (Figure 2 b, d).
To investigate the feasibility of extracting rules from ChemoTopoChip screening data to inform future materials development, we used machine learning (ML) methods to generate quantitaive structure-activity relationships. A combination of chemistry descriptors, "1-hot" binary variables indicating the presence or absence of a chemistry in any given combination, and topographical shape descriptors generated from CellProfiler [19] was used to model both data sets using the Random Forest algorithm [24] (Figure 3). particles. The correlation with shape may also imply that features of particles that are closer to being circular (which is highly unlikely in nature) trigger more M2 cytokines, whereas higher eccentricity (as observed with most microorganisms and foreign particles) trigger M1 response in line with the body's natural defenses. These analyses illustrate the potential of the chemistry, and cell response that offer opportunities for bespoke cell phenotype control using materials design alone.
The hiMSC ALP intensity Random Forest model produced a relatively low correlation between predicted and observed ALP induction (R 2 = 0.46). Difficulties in modelling stem cell responses in polymeric biomaterials has been noted previously, [13] in that case due to a relatively small number of polymers with diverse chemotypes driving desirable cell responses.
There were therefore insufficient examples of each chemical feature for the ML models to generate rules from. Topographical descriptors identified as being important in the hiMSC ALP model included the size of the topographical features (see Table S4 for list of feature descriptions), with features ≤ 3.5 µm radius increasing ALP expression. However, this trend was not as strong as that observed for macrophage polarization (see Figure S12 for ALP upregulation vs. descriptor correlations). Orientation of topographical features also contributed to the model, with those having a small number (< 10%) of features rotated > 25° relative to the x-axis of the Topo unit walls driving an increase in ALP expression.
In previous modelling studies of biological responses to polymer libraries, signature and other fragment-based molecular descriptors and traditional Dragon molecular descriptors have been shown to represent surface chemistries well. These descriptors generated robust, predictive models for diverse biological responses. [25] Paradoxically, in the current study, these types of chemical descriptors were unable to generate ML models for the ChemoTopoChip data that were as accurate as the models using simple 1-hot descriptors to encode the identities of the polymer chemistries. We propose that this is due to the 28 chemistries on the ChemoTopoChip being designed to have very diverse chemistries, in order to cover chemical space as widely as possible (albeit sparsely). The key chemical fragments and resultant descriptors are also therefore very sparse. ML models cannot learn features that are not sufficiently represented in the data set, hence the combination of high chemical diversity and low number of samples resulted in inadequate information on which to train the ML models, resulting in lower prediction accuracies.
In summary, we have employed the novel ChemoTopoChip screening platform for shown to be very amenable to machine learning methods, facilitating the development of models that aid design and discovery of bio-instructive materials. ChemoTopoChips are then irradiated with UV light (368 nm, 2 × 15 W bulbs, 10 cm from source) for 15 min, before being removed from the argon glove box and sonicated in isopropanol for 10 min. Due to the lower bond dissociation energy of the acrylate π -bond [1] compared with that of the thiol σ -bond, [2] it is expected that these monomers will polymerize to the thiol moieties on the base TMPMP-co-TEGDA substrate after photoinitiation commences. The samples are then placed under vacuum (0.3 mbar) for 7 days before use.

Time-of-Flight Secondary-Ion Mass Spectrometry (ToF-SIMS) Analysis
ToF-SIMS analysis was carried out using a ToF-SIMS IV (IONTOF GmbH) instrument operated using a 25 kV Bi 3 + primary ion source exhibiting a pulsed target current of

X-Ray Photoelectron Spectroscopy (XPS) Analysis
XPS characterization was carried out using a Kratos AXIS ULTRA DLD. Data was processed using CasaXPS version 2.3.20 rev1.2G

Mesenchymal Stem Cell Culture
Human immortalized mesenchymal stem cells (hiMSCs) were generated in-house by lentiviral transfection of E6/E7 and hTERT genes as previously described. [

Monocyte Isolation and Culture
Buffy coats were obtained from the National Blood Service after obtaining written informed consent and approval from the ethics committee. Monocytes were isolated from peripheral blood mononuclear cells (PBMCs). A MACS magnetic cell separation system (CD14 MicroBeads positive selection with LS columns, Miltenyi Biotec) was used for the isolation as previously described. Isolated monocytes were prepared in RPMI-1640 medium containing 10% foetal bovine serum (FBS), 100 µg/ml streptomycin, 2mM L-glutamine and 100 U/ml penicillin (Sigma-Aldrich). For assessment of cell attachment and phenotype characterization, cells were re-suspended in the appropriate volume of media and seeded on the ChemoTopoChips at 2 × 10 6 monocytes/chip and incubated at 37˚C, 5% CO 2 in a humidified incubator for 9 days.

Macrophage Immunofluorescent Staining
On day 9 all adherent cells cultured on ChemoTopoChips were fixed in 4% paraformaldehyde (BioRad) in PBS, then blocked with 3% BSA (Sigma-Aldrich) and 1% Glycine (Fisher Scientific) in PBS. Subsequently, another blocking step was carried out using 5% goat serum

ChemoTopoChip Imaging
Imaging of all fixed and stained ChemoTopoChip samples was carried out using a widefield deconvolution-TIRF3 system (Zeiss, custom setup). Imaging was carried out in wide field mode using a 20×/0.5 NA air objective in the bright field and fluorescence channels with the excitation at 358 nm, 488nm and 561 nm. The software used to capture was Zeiss Zen Blue, by using the "Sample Carrier Designer" wizard/module to manually create and calibrate the position list which was used to scan all the positions in the chip setup.

CellProfiler Analysis
A custom CellProfiler pipeline was created to correct for uneven background illumination in each image, then each image cropped to within the Topo unit 30 µm wall. Nuclei were detected using an adaptive per-object algorithm in the blue channel images, followed by propagation from these primary detected objects to detect cell cytoskeleton and ALP staining (hiMSCs) or TNFα and IL-10 (human macrophages) in the green and red channel images.
Intensity of detected objects was measured and exported, and images containing overlaid outlines of detected objects also saved to ensure correct operation of the pipeline.

Statistical Analysis
Statistical anlysis and graphical plots were carried out in R version 3.6.1 using RStudio were produced using the complete linkage method [3] with Euclidean distance measure.
Ranked scatter plots and box plots were carried out using base functions in R and the ggplot2 package version 3.2.1.

Synergy Ratio Determination
Assessment of the interactions between binary factors (chemistry and topography) is readily performed using a synergy ratio (SR). Taking the response of factor x 1 alone (y 1 ), the response of factor x 2 alone (y 2 ) and the response of the factors combined x 12 (y 12 ), SR can be calculated as shown in Equation S1 : For a synergistic combination, SR > 1 as the ratio is then greater than the sum of the theoretical maximums of the individual response factor comparators; for a counteractive combination, SR < 0.5 as the ratio is then less than the theoretical maximum of one individual response factor alone (i.e. 0 contribution from the second individual response factor). In analysis of the ChemoTopoChip data, unfunctionalized TMPMP-co-TEGDA molded topographies and flat area chemistries were used as the individual factors x 1 and x 2 to compare with the hit topography-material combinations x 12 .

Random Forest Machine Learning
The raw dataset consisted of three technical repeats for each surface variable (topography, chemistry) within a chip, which were further replicated across multiple batches (biological repeats). Data set from repeats in a chip have been normalized against their correspondent flat values. Subsequently, replicate average values were calculated. The average between batches was then determined as the dependent variable for the predictive models. Macrophage polarization and ALP intensity predictive models were generated.
The various topographies were encoded using descriptors generated by CellProfiler [4] that relate directly to particular primitives in the topographical units. For chemistries, 1-hot descriptors (binary variables indicating the presence or absence of a chemistry in any given combination) were used.
The SHapley Additive exPlanation (SHAP) method was used for feature selection to eliminate uninformative and less informative descriptors and less relevant chemistries. SHAP was implemented using the SHAP package in Python 3.7. Regression models were generated using the random forest approach with the scikit-learn package in Python 3.7. The default parameters from version 0.22 were adopted for the random forest models. That is, 100 estimators were considered using gini as the function to measure the quality of the data instances split. And no limit for the maximum depth of the trees was defined. 70% of the data instances were employed for model training and 30% for testing. The performance of the predictive models and the topographical descriptors that contributed most strongly to the attachment and polarization are shown in Figure 3.    The eccentricity of the ellipse that has the same second-moments as the region.
The eccentricity is the ratio of the distance between the foci of the ellipse and its major axis length. The value is between 0 and 1. (0 and 1 are degenerate cases; an ellipse whose eccentricity is 0 is actually a circle, while an ellipse whose eccentricity is 1 is a line segment.) Pattern_Eccentricity_percenti le_0.1 Eccentricity of the most circular structures (lowest 10% eccentricity) on the surface

Area
The actual number of pixels in the region Pattern_area_mean Mean area of pillars on the surfaces

Compactness
The mean squared distance of the object's pixels from the centroid divided by the area. A filled circle will have a compactness of 1, with irregular objects or objects with holes having a value greater than 1. The angle (in degrees ranging from -90 to 90 degrees) between the x-axis and the major axis of the ellipse that has the same second-moments as the region.

Inscribed Circle Radius
A number of circles that can be fitted in gap between pillars. The algorithm is looped until a circle diameter smaller than 6 µ m is found.

Inscr_Circl_Radius_0.75_per centile
The radius of the top 25% of inscribed circles