High-Throughput and Combinatorial Technologiesfor Tissue Engineering Applications

Anthony Peters, Darren M. Brey, M.Eng., and Jason A. Burdick, Ph.D.

As the field of tissue engineering progresses, new technology is essential to accelerate the identification of po-tentially translatable approaches for the repair of tissues damaged due to disease or trauma. The development ofhigh-throughput and combinatorial technologies is helping to speed up research that is applicable to all aspects ofthe tissue engineering paradigm. This diverse technology can be used for both the rapid synthesis of polymers andtheir characterization with respect to local and bulk properties in a high-throughput fashion. The interactions ofcells with many diverse materials in both two- and three-dimensions can be assessed rapidly through the useof microarrays and rapid outcome measures and with microfluidic devices for investigation of soluble factor andmaterial combinations. Finally, small molecule screening of large libraries is being used to identify molecules thatexhibit control over cell behavior, including stem cell differentiation. All of these approaches are aimed to movebeyond traditional iterative methods to identify unique materials and molecules that would not have beenidentified otherwise. Although much of this work is only applicable for in vitro studies, future methods maytranslate into rapid screening of these approaches in vivo.

Introduction

Over the past few decades, scientists and engineershave sought to develop combinations of cells, scaffolds,

and signaling molecules as the ideal microenvironments forapplication in tissue engineering.1–3 The important environ-mental factors vary with the tissue of interest, as well as thetype of cells recruited or delivered in the approach. Throughthis research, numerous scaffolds have been developed from awide range of materials and incorporating numerous mole-cules (e.g., growth factors) to enhance matrix production andaccumulation.2 This has advanced our understanding of theimportance of many cues toward optimal tissue engineeringapproaches, yet the field of tissue engineering has proceededin a relatively iterative fashion with the use of tedious low-throughput studies. Due to the nature of the field, the inputparameters to a tissue engineering approach are nearly end-less and include the material selected, material properties, cellchoice, culture environments, and soluble cues.

With this in mind, the complexity of cell signaling with theenvironment due to the multitude of factors involved mayhave slowed down tissue engineering development, due tothe difficulty in isolating effects to one parameter. For exam-ple, it is necessary to examine not only the bulk materialproperties such as mechanics and degradation, but also howscaffolds interact with cells through toxicity studies, as well astheir ability to regulate cellular behavior such as adhesion,

proliferation, or even stem cell differentiation.3,4 Adding tothe complexity, proliferation and differentiation are largelycontrolled by cell–cell and cell–extracellular matrix (ECM)interactions, soluble factor stimuli, and the physical structureof the microenvironment itself.3,4 Additionally, intrinsic cel-lular factors such as small molecules and RNA also play animportant role in cell signaling and differentiation.3

Traditional methods of assessing these various factorshave involved iterative approaches that require a tremendousamount of time and effort. Recently, the development and useof high-throughput and combinatorial techniques in tissueengineering have significantly enhanced this process throughefficient and productive platforms. For example, polymersmay be synthesized through combinatorial processes, whichaccelerate the often tedious synthesis and purification schemesthat may be necessary for development of nontoxic and bio-degradable polymers.5 Upon synthesis, the identification ofoptimal properties from large numbers of materials may alsobe performed through newly developed rapid assessmenttechniques.5 This continues with the assessment of optimalcell–material interactions in a rapid fashion, often throughthe development of microdevices or use of printing tech-nology.6 For example, radically polymerized polymers withvarious functionalities can be synthesized in nanoliter vol-umes and plated in combinations onto a microarray to analyzethe influence of the biomaterial on cell growth and differ-entiation.1 These techniques also include platforms such as

Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania.

TISSUE ENGINEERING: Part BVolume 15, Number 00, 2009ª Mary Ann Liebert, Inc.DOI: 10.1089=ten.teb.2009.0049

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microfluidics, which can analyze combinations of biomate-rial gradient streams in a dynamic and temporally changingthree-dimensional microenvironment, and high-throughputscreens to characterize the role of small molecules on cellularbehavior through the screening of large libraries of solublefactors.2,3 Notably, all of these areas may lead to the morerapid identification of optimal tissue engineering componentsand their unique combinations.

While these techniques also have applications in genetherapy and drug candidacy analysis, this review will focusspecifically on applications in tissue engineering for injuryand degenerative disease treatments.4 Regenerative medi-cine techniques are being developed for a wide range oftreatments for conditions such as cardiac ischemia, liver dis-ease, and spinal cord injury, and combinatorial and high-throughput approaches are accelerating the process for manytissue types.6 As this field progresses, existing and new high-throughput techniques will be crucial in efficiently and rap-idly developing ideal synergies between microenvironmentsand signaling factors for engineered tissues to be successfulin clinical applications. This review will present the cur-rent landscape of high-throughput and combinatorial tech-niques as they apply to tissue engineering applications, theexperimental and clinical benefits of these techniques, andthe future directions of this rapidly growing field. Specifically,the focus will be on (1) combinatorial polymer synthesis,(2) tools for the rapid assessment of material properties, (3)high-throughput assessment of cell–material interactions, (4)microfluidic devices for the rapid development of engineeredenvironments, and (5) high-throughput screening (HTS) foridentification of small molecules that influence cellular be-havior.

Combinatorial Polymer Synthesis

In the pharmaceutical industry, researchers have usedhigh-throughput, combinatorial techniques including auto-mation, miniaturization, and parallel synthesis to transformtheir approach to developing new small molecules and dis-covering drug candidates.7 These approaches are now beingutilized in biomaterial development and in the field of tissueengineering due to the numerous advantages to acceleratingmaterial design and development. This includes the synthe-sis of polymers with a wide range of chemical componentsand bulk properties. Typically, polymer development hasinvolved the use of tedious synthetic and purification steps,along with iterative design for material development since itis often difficult to predict the resulting polymer propertiesbased on the polymer chemistry and structure. With advancedapproaches where syntheses are performed more rapidly,many polymers can be developed quickly and then screenedfor the desired properties.

For over two decades, the pharmaceutical industry hasdeveloped and utilized methods in combinatorial synthesisand high-throughput analysis in its drug discovery efforts(originally reviewed by Gordon et al.8 in 1994). The goal wasto create large, diverse combinatorial libraries and charac-terize their pharmaceutical effects through a range of tech-niques. The synthesis and screening of combinatorial librariesprimarily used either tagged methods (e.g., phage technol-ogy, peptides-on-plasmids, and encoded combinatorial librar-ies) or untagged synthesis=analysis techniques (e.g., mixture

synthesis and portioning-mixing).9 These initial approacheshave been expanded, and there has been extensive researchand development in combinatorial synthesis and character-ization in identifying lead compounds and therapeuticallyrelevant combinations for pharmaceutical applications.10,11

Beyond drug development, similar techniques have beenapplied to gene therapy efforts over the years to identify un-ique polymers that would be most effective in nonviral genetherapy approaches on a range of length scales.12–15 One ap-proach has been the combinatorial synthesis of degradablecationic polymers [e.g., poly(b-amino ester)s (PBAEs)] and theanalysis of structure=function relationships, something thatwould not be possible on this scale without combinatorialsynthesis procedures.12–14 While there are numerous appli-cations that would benefit from this technology, this sectionwill focus on polymer development applicable to the field oftissue engineering.

Some of the early combinatorial polymer libraries werecreated for subsequent high-throughput analysis of mate-rial properties specific to an application of interest (e.g.,engineered bone and artificial medical implants). One suchlibrary, developed by Kohn and coworkers,16 combinedtyrosine-derived diphenols and diacids in permutationallydesigned monomer systems to produce alternating A-B-typecopolymers (A—contains reactive group for pendent chainattachments; B—allows for systematic variations in polymerbackbone structure). Combining these copolymers, the sys-tem allowed for the production and analysis of a library of 112polyarylates with small structural differences and some sim-ilar properties, but also with slight differences in polymer freevolume, bulkiness, flexibility, hydrophobicity, and cellularresponse that affected their potential utility in medical im-plant applications.16 Another study investigated more tradi-tional polymer combinations of poly(D,L-lactide) and poly(e-caprolactone) and their resulting influence on osteoblastactivity.17 This study employed composition spread andtemperature gradients (and specifically heat-induced phaseseparation) to synthesize and screen hundreds of combina-torial polymers with varying characteristics and therebydemonstrated differences in osteoblast response.

Since these early models, researchers have developed manyadditional methods of parallel, automated, and combinato-rial polymer synthesis, including polycondensation, radicalpolymerization (free-radical polymerization and controlledradical polymerization), ring-opening polymerization, poly-olefins (mostly for applications in the chemical industry), andsupramolecular polymerization.5 For example, Gravert et al.18

utilized free-radical polymerizations to construct block co-polymers, which were subsequently twice split and used asmacroinitiators to polymerize other sets of monomers. Thefinal polymers displayed variable solubility profiles andfunctionality and demonstrated that this process may be usedfor a variety of high-throughput applications. Numerousother synthesis methods in controlled radical polymeriza-tion and ring-opening polymerization have been effective aswell.5,19–21 Additionally, the development of synthesis meth-ods for supramolecular polymers, which include both cova-lent and noncovalent bonds (as opposed to only covalentbonds), has introduced another variety of polymers withpotential biomedical applications.5 This research included thefirst method for fully automated production of supramolec-ular coordination polymers developed by Schmatloch et al.,22

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which helped realize the potential for developing many newsupramolecular polymers.5

In 2006, the first combinatorial library of degradable pho-tocrosslinked biomaterials was synthesized by Andersonet al.,23 who built on a previously developed library of potentialgene carriers.13 Specifically, a combinatorial approach wasused to synthesize a library of 120 acrylate-terminated PBAEmacromers that could be formed into networks using a pho-toinitiated polymerization. The synthesis did not involve anypurification steps and used commercially available reagents,which accelerated the synthesis process. This rapid processproduced polymers that varied greatly in their property char-acteristics, including degradation behavior (e.g., mass loss) andmechanical properties (e.g., elastic modulus).23 The generalsynthesis procedure and an example of the diverse mass lossbetween polymers are shown in Figure 1. Brey et al.24,25 alsofound that properties such as molecular weight and macromerbranching significantly influence the resulting PBAE networkand bulk properties. With the appropriate screening process,photocrosslinked polymer scaffolds could be selected to meetdesign criteria, including optimal degradation profiles andmechanical properties for specific engineered tissue functions.

Combinatorial syntheses can also be performed on thenanoliter-scale by mixing reactive macromers and spot-ting onto a surface.1,26,27 This was performed with 24 poly-

mers in various combinations and ratios to produce 3456individual spots on a single array with the goal of analyzingcell–biomaterial interactions.27 This platform relied on the useof a photoinitiated radical polymerization compatible withthe technology. A robotic handling system was modifiedto handle the unique challenges of polymerizing diversemonomers, including the viscous nature of some acrylatemonomers and the inhibition of radical polymerization byoxygen.1,27 Therefore, array synthesis was performed in anatmosphere of humid argon with oxygen present at <0.1%and used a long-wave UV lamp to quickly polymerize themonomers.1,27 One of the benefits of this technology is that itis relatively universal to radically polymerized materials, agroup of materials that is gaining interest in tissue engineer-ing.28 For example, natural polymers such as hyaluronic acid(HA) can be modified to be photopolymerizable and utilized intissue engineering applications.28,29–33 Schmidt and cowork-ers32 constructed a variety of photopolymerized, crosslinkedglycidyl methacrylate-HA hydrogels with a correspondingrange of degradation rates, swelling, mesh size, and otherproperties and demonstrated their potential use in wound-healing applications by implanting them subcutaneously inrats. Subsequently, in 2005, Burdick et al.34 analyzed the use ofphotopolymerizable HA as a scaffold for both 3T3-fibroblastsand auricular swine chondrocytes for macromers with a rangeof molecular weights and formed into gels at different con-centrations. With the range of polymers available, theseapproaches may help in identification of unique materials aswell as their combinations (i.e., copolymers) that would be te-dious to characterize individually. Overall, the development ofa large variety of advanced polymer libraries, both syntheticand natural, has laid the framework for the identificationof highly effective microenvironments for tissue engineeringapplications.

These synthetic techniques can also be used with a varietyof preparation techniques to further their combinatorial pos-sibilities. These preparation methods include flow-coatingdevices that produce thin films with gradients in thicknessand temperature-gradient thin film platforms that providecomposition=thickness, composition=temperature, or thickness=temperature two-dimensional libraries, and methods usingphotopolymerization to create a gradient polymer film.5,35–37

A more recent setup employed the use of a microextruderwith two feeder heads designed by Potyrailo et al.38 that wascapable of producing polymers with a variety of step or gra-dient polymer combinations for subsequent analysis. Gra-dients provide the ultimate combinatorial synthesis methodbecause polymer development extends beyond discrete for-mulations, providing even more compositions, as long ascharacterization techniques are available for assessment. Allof these synthesis and preparation methods have contributedto the development of combinatorial polymer libraries withunique properties and functionalities, which can then beanalyzed in high throughput to identify ideal polymericcombinations for applications such as tissue engineering.Polymer synthesis in three-dimensional environments isalso of interest for tissue engineering applications. For ex-ample, scaffolds were constructed from varying composi-tions of two biodegradable tyrosine-derived polycarbonates,resulting in three-dimensional combinatorial libraries on 96-well plates.39 This three-dimensional design should provide avariety of microenvironments that are more similar to in vivo

FIG. 1. Combinatorial polymer synthesis. (a) Schematic ofsynthesis scheme for fabrication of poly(b-amino ester)s(PBAEs) from primary amines and diacrylates, where theversatility is found in the selection of reagents for synthesis(i.e., R1 and R2). (b) Representative mass loss for polymersformed from a group of 120 PBAE macromers after 32 days.The PBAE library exhibits a range of degradation and me-chanical properties for diverse applicability in tissue engi-neering applications. Color images available online atwww.liebertonline.com=ten.

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conditions and should thereby enhance the development ofideal polymer scaffolds for tissue engineering applications.

As identified by Kohn40 in 2004 and further described byKohn et al.41 in 2007, one important issue to consider ascombinatorial molecule synthesis moves from drug discoveryapplications to material science and tissue engineering ap-plications is the need for reproducibility in polymer synthesis(e.g., molecular weight and polydispersities). With smallmolecule development, the chemical structure is the essentialdriving factor; however, there are a variety of factors that aresignificant when considering polymer design, including thepolymer molecular weight, the molecular weight distributionof polymers within the sample, the presence of trace impuri-ties, and the mechanism of fabrication, which all play a rolein resulting material properties.40,41 Maintaining uniformityin polymer design and accelerating characterization of poly-mers both on the molecular and bulk levels may be some ofthe limitations with this approach. One solution to this issueis the use of the parallel synthesis of polymers (i.e., usingindividual reaction vessels to synthesize many individualpolymers simultaneously and separately).40,41 The next steprequires the characterization of the synthesized polymerswith respect to their formed properties, including surfaceprotein adsorption, rate of degradation, cytotoxicity, level ofbiocompatibility in vivo, and cellular response.40,41

Tools for the Rapid Assessment of Material Properties

The previous section indicates that the synthesis of a largenumber of polymers rapidly is possible; however, withoutample techniques to characterize the properties of these ma-terials, their rapid development may not be advantageous.One of the earliest methods of rapidly assessing materialproperties was demonstrated by Kohn and coworkers42 in1998. This was aimed at assessing the combinatorial polymerlibrary consisting of 112 polyarylates that was described in theprevious section. In addition to creating many distinct poly-mers, this work enabled the researchers to determine rela-tionships between polymer structure and glass transitiontemperature (Tg), hydrophobicity, and mechanical properties.These properties were determined by systematic analysisusing gel permeation chromatography (for molecular weight),differential scanning calorimetry (for Tg), thermogravimetricanalysis (for decomposition temperature), and goniometryand sessile drop method (for air–water contact angle). Someof their conclusions included that oxygen substitution inthe backbone is effective in increasing polymer Tg and indecreasing hydrophobicity (for substitution in the backboneand=or pendent chain) and that additional methylene groupsin the backbone and=or pendent chain can significantly dimin-ish polymer strength and stiffness. Thus, these approachescan also be used to develop polymer structure=property cor-relations for future material development.

In early 2004, Meier et al.43 provided a comprehensiveoverview of the current development in HTS and assessmentof material properties, which built on a previous reviewpublished in 2003.5 For the high-speed characterization ofstandard polymer molecular weights, recent high-throughputtechniques have focused on using methods such as gel per-meation chromatography in combination with high-speedcolumns, parallelization, and=or flow-injection analysis.43,44

Parallelization utilizes more machines to run test samples, but

is obviously limited by lab space and costs. New, higher speedcolumns allow for rapid size exclusion separations to occur in2–6 min rather than 30 min to 3 h.44 Additionally, researchershave utilized matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) techniques forabsolute determinations of molecular weight, molecular-weight distribution, and end group analysis of macromole-cules (which can be optimized using multiple-layer spottingsample preparation techniques, online monitoring of reac-tions, and=or ink-jet printing).45–47 Next, optical screeningmethods have been increasingly improved for high-throughputproperty and chemical composition assessment purposes.43

These methods have been enhanced by combining them withimaging and commercial reader technologies for absorbance,fluorescence, and infrared (and near infrared) spectroscopy.43

Infrared and near-infrared spectroscopy–based techniques,including attenuated total reflection Fourier-transform in-frared spectroscopy (ATR-FTIR), are effective tools formeasuring monomer=polymer compositions and polymeri-zations,48–50 while fluorescence spectroscopy approaches havebeen used to determine properties such as molecular weight,amount of branching, and catalyst selectivity.51 Lastly, nu-merous methods have been developed to address the area ofscreening for morphology and physical properties, particu-larly for polymer film analysis.52–57 These methods includeusing relationships between various physical properties andthe preceding optical data, as well as the evaluation of thinfilms to determine adhesion, crystallization, or dewettingproperty characteristics.52,53,55–57 Fast differential scanningcalorimetry analyses have been considerably accelerated withautomated large sample array differential scanning calo-rimetry. This field is still developing and should continue toimprove the speed of property characterization while main-taining equal or even better accuracy of data compared totraditional methods.43

In combination with the above-mentioned syntheticnanoliter-spotting techniques, Tweedie et al.26 synthesized alibrary of over 1700 photopolymerizable acrylate-based bio-materials using automated array synthesis and characterizedthe resulting mechanical properties with nanoindentation.Nanoindentation is a method of measuring nanometer-scaledisplacement with respect to load while depressing the ma-terial’s surface with a rigid indenter. These methods werefound to be quick and accurate in determining numerousmechanical response characteristics and could potentially beapplied to other sets of combinatorial=crosslinked poly-mers.26 This example presents a situation where a technologywas modified to accelerate material property characterizationwith a new, rapid synthesis process. An additional methodfor measuring mechanical properties of thin films was strain-induced elastomer buckling instability for mechanical mea-surements.58 This method utilizes the light scattering producedby the buckling of two mismatched polymers, the glassy thinfilm of interest and a softer silicone sheet.

The rapid characterization of polymer properties and cel-lular responses will be vital for the continuing developmentof tissue engineering research. As Kohn et al.41 described, thechemical composition of polymeric scaffolds significantlyaffects its mechanical=cell interaction properties and, there-fore, will often determine the applicability of a potentialengineered tissue scaffold. Kohn et al.41 cite recent studiesthat demonstrate how slight differences in polymer compo-

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sition and structure [poly(L-lactic acid) vs. poly(DTE carbon-ate) and poly(DTE carbonate) vs. poly(DTB carbonate), re-spectively] can significantly affect cell response and long-term tissue growth, including differences in bone resorption,inflammatory response, degradation, tissue in-growth, andforeign body response.59 The considerable effects of minorpolymeric differences support the need to develop and uti-lize high-throughput methods that rapidly and systemicallycharacterize many polymer compositions. In the future, thismay increasingly depend on computational programming aswe will discuss in the subsequent section on assessing cell–material interactions.41 Additionally, x–y spatially resolvedlibraries, in which polymer composition varies across the xand y coordinates, respectively, have also been utilized toassess a wide range of polymer mixtures using some of theanalytical methods previously described (i.e., optical mi-croscopy).36,41,60 This type of method should also play animportant role in the future development of rapid materialcharacterization.

As important as the different methods for measuringoutcomes is the use of statistical methods to minimize thenumber of repeats,61,62 design experiments to isolate criticalvariables,63–68 and mine large amounts of data for optimalconditions. This is important for all topics covered in thisreview. For example, by using high-quality assays withsufficient separation between positive and negative controls,the calculation of the Z-factor greater than 0.562 allows che-mical hits to be determined with high confidence withoutusing replicates. Likewise, a formalized ‘‘Design of Experi-ments,’’ using full and fractional factorial designs, allows forthe variation of several parameters at once to determineoptimal conditions.66,68 Traditional methods of single param-eter variation for optimization rapidly increases the amountof experimentation as more variables are introduced, whilefractional factorial designs can greatly reduce the studiesneeded to determine critical inputs. Methods for using in-formatics to mine the large amounts of data produced usingcombinatorial and HTS are crucial to gain the most insightinto processing capabilities. While biochemical assays oftenare looking for the discovery of hits, for combinatorial libraryscreening it is important to try and tease out relations be-tween properties with the chemical structures and proces-sing parameters. For these types of studies, cluster analysiscan be used to link patterns of material properties with theirassociated processing parameters, whereas principal com-ponent analysis reduces data into descriptive vectors to morerapidly describe structure property relationships.69

The availability and quality of high-throughput assays formaterial characterization are commercially relatively limited,leading to inefficient iterative approaches for analysis. Re-cently, there has been significant progress in characterizingvarious biomaterial assays and developing reliable and ad-vanced high-throughput assay systems. Additionally, theuse of computational systems to artificially determine cell–biomaterial interactions based on known properties hasgained traction, but remains limited due to the complexity ofthese interactions.40 These algorithmic systems have the po-tential to exponentially increase efficiency in developing andanalyzing combinatorial virtual libraries by bypassing theactual, physical production of thousands of polymeric com-binations. It is clear that there is still room for the developmentof new techniques to rapidly characterize material properties

from large libraries of polymers. Beyond bulk properties andtoward tissue engineering applications, it is also necessary torapidly characterize how cells interact with a range of mate-rials.

High-Throughput Assessment of Cell–MaterialInteractions

Using the vast array of combinatorial polymer libraries andmany of the tools for material property analysis, the next stepin developing the ideal microenvironment for engineeredtissue is the high-throughput assessment of cell–material in-teractions. This ideal microenvironment will often seek tomimic the natural environment of particular cell types or evenimprove on this environment by cultivating growth andproliferation.4 Particularly in the field of tissue engineering,the extracellular environment including surface propertiessuch as roughness and hydrophobicity and specific cell–surface interactions maintains a large influence on cellularattachment, spreading, overall growth, and differentiation inthe case of stem cells.1 The use of high-throughput systemsto improve the speed and breadth of characterizing andunderstanding these effects on cellular responses would sig-nificantly accelerate the process of developing effective en-gineered tissue constructs that could be utilized in medicalapplications.

The development of systems for analyzing cell–materialinteractions in bulk began in the late 1990s, shortly after theproduction of the first large combinatorial polymer libraries.In addition to developing a combinatorial polymer libraryand demonstrating rapid polymer property characterization,Kohn and coworkers42 also performed one of the earliesthigh-throughput analyses of cell–material interactions in1998. This analysis studied the interaction between 42 poly-arylate polymers (a subset of the 112 polymer library) andfibroblast cells, focusing on proliferation using an MTS(3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) assay. In general, these in-vestigators found a linear correlation between decreasedproliferation and increased surface hydrophobicity of ma-terials derived from non-oxygen–containing diacids. Theanalysis also showed that all polymers derived from oxygen-containing diacids acted as good substrates for fibroblastproliferation. Along with these findings and others, this studyillustrated the ability to determine important material inter-actions with only small amounts of polymer material.

Since these early pioneering analyses, high-throughputassessment has seen significant growth in the volume of an-alyses, the breadth of cells and polymers studied, and thevariety of cell responses that have been characterized. In onestudy, over 1700 combinatorial nanoliter-scale polymer spotsof various (meth)acrylates were introduced to a single slide,thereby providing a high-throughout platform for analyz-ing cell attachment, cell spreading, and cell-type-specificgrowth.1,7 This work examined the effects of these polymerson human embryonic stem cells (hESCs) and C2C12 cells (amouse myoblast progenitor cell line), including cell growth,adhesion, and proliferation.1 Additionally, positive matchesfor cell attachment and spreading were also tested for dif-ferentiation into cytokeratin-positive cells. A representativepolymer array with cells attached is shown in Figure 2. Theprocess of successfully identifying cell–material interactions

HIGH-THROUGHPUT TECHNOLOGY FOR TISSUE ENGINEERING 5

and the ease of producing and characterizing cytokeratin-positive cells in particular could be utilized in developingengineered epithelia tissue. C2C12 cells showed attachmentand growth on almost all material combinations. This differ-entiation between the two cell types could also be effectivelyused in advanced scaffolding for engineered tissue applica-tions. Lastly, it was found that cell growth and proliferation is

dependent on the presence of retinoic acid for some specificpolymer combinations, which could potentially be employedto control cell behavior in tissue engineering. This study reliedon fluorescent techniques that could be quantified with imageanalyses. The importance of this work is that it assessed manyformulations, including material=soluble factor combina-tions, rapidly and that it went beyond identifying correlations

FIG. 2. Polymer arrays to assess cell–materialinteractions. (a–c) Examples of fluorescent im-ages of human embryonic stem cells (hESCs)cultured on polymer arrays in the presence ofretinoic acid for 6 days and then stained forcytokeratin 7 (green) and vimentin (red) (a–c, e)or nuclei (green) in (d). (f) Quantification of cellcoverage as a function of polymer compositionafter 6 days. These techniques are useful toidentify unexpected relationships between ma-terial chemistry=properties and cellular interac-tions, including stem cell differentiation.Reprinted with permission from Anderson et al.1

Color images available online at www.liebertonline.com=ten.

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and focused on the identification of random combinations byutilizing the benefits of high-throughput studies.

This technology was further expanded to encompassarrays consisting of 3456 individual combinations of biode-gradable polymers, and the interactions of human mesen-chymal stem cells (hMSCs) were assessed with regard toproperties such as attachment and spreading.27 This platformmaintained the added benefit of being able to synthesize andcharacterize polymeric materials using conventional methodsinstead of producing nanoliter-scale materials on the array.Many of the combinations were found to foster attachmentand growth, including poly(lactic-co-glycolic acid) (PLGA)combined with L-lactide. These characterizations display thepotential of designing effective biomaterials to enhance stemcell development, including neural stem cell growth andperhaps differentiation through engineered, combinatorialmicroenvironments. Looking forward, Anderson et al.27

identified the screening of biomaterial interactions with othermicroenvironment components such as proteins as a potentialapplication for this specific high-throughput microarrayplatform.

As researchers have continued to study cell–biomaterialinteractions, these analyses have become increasingly com-plex and multifunctional. For instance, Neuss et al.70 developeda grid-based system for the screening of various combinationsof 7 different stem cell lines (including pluripotent embryonicand multipotent adult stem cells) and 19 polymeric materials.Through this research, five factors (morphology, vitality, cy-totoxicity, apoptosis, and proliferation) were characterizedusing multiplex assays for each of 140 combinations. Fromthis standardized, parallel analysis, they were able to identifycombinations of stem cells and polymer scaffolds, includinghuman dental pulp stem cells on poly-DL-lactic acid andhuman preadipocytes on texin, that support cell adhesion andproliferation and prevent apoptosis and necrosis. This type ofdiverse combinatorial assessment of numerous cell=materialparameters should be extremely useful in quickly character-izing cell and scaffolding combinations for specific tissue en-gineering functions.

As the field has progressed, high-throughput methodshave increasingly taken advantage of fluorescence-basedanalysis of cellular responses. For instance, investigators haverecently developed a high-throughput method for testing thegrowth, adhesion, and morphology responses of mouse con-nective tissue fibroblast cells (L929) on a set of 214 polyure-thane-based polymers.71 In this study, a probe composed ofnonfluorescent 5-chloromethylfluorescein diacetate was uti-lized to enter the cell and then display fluorescence whenit was cleaved by intracellular esterases. Combining imagecapture and screening analysis with the appearance of fluo-rescence enabled the team to rapidly quantify the growth,adhesion, and morphology of the fibroblasts. In addition toidentifying clear trends relating cellular adhesion and poly-mer structure, the researchers also emphasized that this meth-odology could be scaled up to 1024 individual experimentsper slide, allowing very high-volume and quick analyses withonly small quantities of materials.71 Numerous other studieshave also utilized fluorescence-based analyses of cell behav-ior27,64,72,73 or protein–material interactions,74 as these tech-niques are easily applied and effective. Modern transfectiontechniques have allowed researchers to transfect cell lineswith genes for fluorescent proteins that are activated when the

reporters of genes of interest are activated.3,75 This allowsfor passive observation of genetic activation of cell differen-tiation.

Another trend is the progression toward complete micro-environment analyses, including both the ECM componentsand the soluble growth factors and molecules. This holisticapproach should better represent the actual in vivo condi-tions and enhance the assessment of cellular response to theseconditions. Flaim et al.75 developed one of the first models forthis type of analysis, which consisted of a platform for 1200simultaneous experiments studying the effects of 240 differ-ent microenvironments on stem cell behavior. These micro-environments were composed of mixtures of both ECM(fibronectin, laminin, collagen I, collagen III, and collagen IV)and soluble factors (wnt3a, activin A, bone morphogeneticprotein-4 [BMP-4], and fibroblast growth factor-4). Flaimet al.75 employed an overall microarray design but also uti-lized a multiwell platform, in which each well containedseveral spots where a single ECM arrangement was main-tained and soluble factor composition was varied. Growthlevels were recorded by measuring the amount of nuclearDNA, while differentiation was again measured using afluorescence-based reporter gene. This approach could po-tentially be utilized with any set of environmental factors andcell lines, thereby creating a more natural environment forengineered tissue development.

As mentioned previously, one of the recent advances inengineered tissue development was the design and produc-tion of the first large-pore polymer biomaterials that couldbe analyzed in three-dimensional, high-throughput envi-ronments for their effects on cell behavior (developed byYang et al.39). Previous studies, including one performedby Levenberg et al.,76 had completed similar work includ-ing the analysis of hESC growth and differentiation in athree-dimensional 50% PLGA=50% PLLA environment withgrowth factors. While successful growth and differentiationwas found with different growth factors in specific pore en-vironments (250–500 mm), this study did not vary the poly-mer composition or analyze polymer variations in highthroughput.76 In their advanced, high-throughput analysis,Yang et al.39 found a relationship between increasing poly(desaminotyrosyl-tyrosine ethyl ester carbonate) content andincreasing osteoblast adhesion. It was also determined thatprotein adsorption affects cell responses, including osteoblastadhesion for these polymeric scaffolds. Overall, this anal-ysis demonstrates a tremendous improvement over two-dimensional films or surfaces that have traditionally beenused in studying cell–material interactions. This is due largelyto the similarity of three-dimensional environments to in vivoconditions, as well as the varying cell response to the envi-ronment structure and material topography. Even so, in thiscase, Yang et al.39 found that the cell adhesion trends in three-dimensional scaffolds fit with the trends previously deter-mined in two-dimensional screening, which indicates thattwo-dimensional screening may be used to effectively predictcell response in three-dimensional environments. Additionally,one drawback of this system is that wells of only 0.2 mL ofmedium may not be enough to support mature, high-densitytissue growth greater than 14 days. The small scale also lim-its cell access to the scaffold and thereby restricts nutrient=waste exchange and deep tissue formation. Even so, thesetypes of platforms can still provide a great environment

HIGH-THROUGHPUT TECHNOLOGY FOR TISSUE ENGINEERING 7

for testing early stage cell properties such as adhesion andproliferation in a variety of scaffold dynamics.39 Alongwith high-throughput platforms, the capacity to use three-dimensional screening for cell–material interactions shouldalso considerably enhance the development of applicablepolymeric scaffolding and engineered tissues.

Beyond experimental assessment, computational systemanalysis of combinatorial polymer virtual libraries has beenidentified as an area of great potential in analyzing materialproperties for tissue engineering applications. One recent de-velopment in computational methods was designed by Kohnand coworkers77 to successfully predict the cell growth re-sponse to a biomaterial based on its chemical composition andphysical properties such as Tg and contact angle (y). Themodel, called the logical analysis of data, was developed using62 known polymers and was then able to distinguish betweenhigh- and low-growth polymers for 50 uncharacterized mate-rials and even correctly characterize several materials thatcultivated especially high cell growth. This particular modelwas used to predict the response of rat lung fibroblasts andforeskin fibroblasts to the set of polymers, but could potentiallybe applied to other cell types and other biomaterials. While thisstudy demonstrates the potential of computational systems toenhance efficiency in combinatorial analysis by bypassing thephysical construction of polymer libraries, this type of mod-eling software still requires further development, including theaddition of more input parameters and output measurements,to become widely applicable across various analyses.77

Quantitative structure activity relationship (QSAR) ana-lyses have been previously used in the pharmaceuticalindustry, but are recently being applied to the study of cell–biomaterial interactions.78 Recent models have utilized thisquantitative analysis to predict cellular response and proteinadsorption to biodegradable polymeric biomaterials.78,79 Themodel predicted the biological activity for six polyarylateswith an average percent error of only 15.8% compared to themeasured values using a quantitative system based on fiveparameters—the number of tertiary carbons, the number ofbranches in the pendent chain, the molar refractivity, thepolar surface area, and the logarithm of the octanol–waterpartition coefficient.78 A subsequent model again utilizedfive input parameters chosen through QSAR analyses andput into an artificial neural network (ANN) platform topredict the protein adsorption and biological response for77% and 71% of the polymeric test materials, respectively.79

Kohn and his coworkers80 also utilized an ANN model topredict fibrinogen absorption and cell growth for the previ-ously presented 112 polyarylate library.

More recently, larger scale attempts at computationallypredicting cell–material interactions have been pursued. Astudy performed by Kholodovych et al.81 analyzed the cel-lular response to a combinatorial library of 2000 poly-methacrylates using a polynomial neural network (PNN) asopposed to the traditionally used ANN algorithms. Thebenefits of the PNN tool include its ability to assemble thearchitecture (i.e., the precise number of neurons in the input,output, and hidden layers) in response to the characteristicsof the data and to handle very small or very large data sets.81

These qualities will likely make PNN algorithms, in con-junction with QSAR analyses, extremely useful in the futurefor computationally screening many diverse cell–biomaterialinteractions. Even so, despite this progress, the computa-

tional prediction field still needs further development tobecome fully applicable across the wide range of polymericmaterials and the complex variety of cell lines. Additionally,at this point in development, prediction results from com-putational systems must be rechecked in actual, full-scalephysical experiments to confirm the cell–material interaction.However, computational systems will certainly help to rap-idly assess and identify optimal material formulations, be-yond traditional experimental techniques.

Overall, the field of high-throughput assessment of cell–material interactions has experienced a tremendous amountof progress as a variety of platforms and analysis techniqueshave been designed to better identify successful microenvi-ronments. Even so, this field is still considered to be in itsearly stage.4 There are still significant shortcomings to thesesystems; namely, the systems are in vitro and do not per-fectly imitate in vivo conditions, as well as the limitationsin drawing conclusions from high-throughput, very low-volume conditions (i.e., cell–material interactions must beretested in higher volume conditions to demonstrate accu-racy). In moving forward, researchers will need to continueto develop platforms and microenvironments that emulateactual in vivo conditions (i.e., scaffolding combined withsoluble factors and fluidic environments), as well as movefrom the micro-scale to the macro-scale to fully approachtissue engineering applications.4

Microfluidic Devices for the Rapid Developmentof Engineered Environments

One effective method to assess cell–material interactions isthe use of microfluidic systems. Microfluidic systems can beused for a variety of applications and simulations, but fortissue engineering, these devices are primarily used for HTS,characterization of cell–material interactions, and the opti-mization of the conditions that precisely regulate cell be-havior and fate.2,82 Like many other microscale techniques,microfluidics are inexpensive and able to operate with fewercells and reagents.2 In addition to these advantages, micro-fluidic devices provide a fluid, dynamic environment that ismuch more similar to in vivo conditions compared to tradi-tional static platforms, such as those described in the previ-ous section.

One microfluidic platform that has gained use involves thegeneration of gradients of soluble or bound factors to assessthe factor’s influence on interacting cells.83 For instance, in2005, Chung et al.84 produced a microfluidic system that de-livered a gradient of growth factors, including epidermalgrowth factor, fibroblast growth factor-2, and platelet-derivedgrowth factor to human neural stem cells. In response, thesecells proliferated and differentiated into astrocytes pro-portionally to the concentration of growth factors. Prolifera-tion was directly proportional to growth factor concentrationand conversely, differentiation occurred in an inversely pro-portional relationship with growth factor concentration. Forthis experiment, the actual microfluidic device was producedusing the techniques of rapid prototyping and soft lithogra-phy. The gradient flow of multiple factors allows for the rapidoptimization of culture conditions while using low concen-trations of relatively expensive growth factors and cells.84

These benefits of gradient-generating microfluidic platformsshould considerably enhance the process of optimizing

8 PETERS ET AL.

culture and scaffolding conditions for a variety of tissue en-gineering applications. Also, microfluidics allow precise con-trol over the timing of multiple soluble factors.

Microfluidics have also helped identify ideal cell microen-vironments through the development of control over solublechemical distribution within a scaffold. This strategy wasexplored in contrast to previous research, which has largelyfocused on adjusting the chemical and mechanical charac-teristics of the scaffold.85 In 2007, Choi et al.85 described amethod for managing this distribution using microfluidicchannels constructed within the cell=biomaterial combinationto complete convective mass transfer of the solutes. The soluteexchange is completed in two steps involving interfacialconvective mass transfer between the flowing solutions andthe walls of the microchannels and molecular diffusion be-tween these walls and the bulk of the scaffold.85 This modelincluded a calcium alginate hydrogel and chondrocyte celllines and utilized lithography to develop the microfluidicstructure. This precise control of soluble chemical distributioncould help prevent necrosis from developing at the center ofthick engineered tissues as well as guide cell growth towardregions within engineered tissue.85 Even so, these models stillmust overcome numerous challenges, including the organi-zation of multiple cell types into conditions similar to thosefound in vivo and the inclusion of multiple stimuli types, in-cluding mechanical forces.85

Khademhosseini and coworkers86 have also developedmicrofluidic channels in cell-seeded agarose hydrogels usingsoft lithographic techniques. This work showed that cell vi-ability in large engineered tissue constructs can depend lar-gely on the distribution of nutrients and oxygen throughvasculature-like channels and proved that microfluidics canhelp overcome the difficulty in transport and exchange ofmaterials, namely, nutrients, that typically causes cell ne-

crosis in large three-dimensional engineered tissues. Whilefurther development is necessary, this method will certainlyenhance the progress toward engineered three-dimensionalscaffolds with artificial vasculature that allow for the ex-change of nutrients, waste, and signaling molecules. Thetechnology could also be used for assessment and screeningof multiple environments in single constructs. This work hasbeen expanded to produce three-dimensional cardiac orga-noids on a patterned HA platform.87 In this model, seededcardiomyocytes grew parallel to the pattern direction andsubsequently detached from the scaffold to develop intocontractile cardiac organoids. For confirmation, contractileproperties were assessed and quantified using imaging tech-nology. This method displays the double advantage of bothgrowing and aligning the myocytes on a biocompatible scaf-fold and allowing for natural detachment of the contractilecells without requiring enzymes. In addition to a variety ofother studies, these methods, namely the microfluidic pat-terning, could potentially be used in biodegradable scaffoldsto organize cell growth for the purposes of tissue engineeringand full tissue replacement.87

Other researchers have also used soft lithography tech-niques to develop hydrogel-based microfluidic platforms. Forinstance, in 2007, Figallo et al.88 used soft lithography meth-ods to design a micro-bioreactor array (MBA), which consistsof layers of poly(dimethylsiloxane) and glass and maintainsmicrofluidic channels throughout the system. The microflu-idic channels are able to supply the perfusion of a controlledmedium and enhance the growth of cells in high density, in-cluding the advantage of purging residual nonpolymerizedreactants. A schematic of the device, as well as representativecellular outcomes are shown in Figure 3. The miniaturizationof the bioreactor allows for better use of expensive mediafactors while still maintaining this advantage of a fluid,

FIG. 3. Micro-bioreactor arrays (MBAs) for identification of ideal cellular environments. Microfluidic device (a) and wellschematics (b) for configurations that include a bottom inlet=outlet (BIO) where the flow is directly over the cells and amiddle inlet=outlet (MIO) configuration where the cells are in a well with fluid flow over top. hESCs were cultured andstained for smooth muscle actin (red) and DAPI (blue) in three different flow configurations (c): static (left column: a, d, andg), perfused MIO (middle column: b, e, and h), and perfused BIO (right column: c, f, and i) at three different cell densities:60� 6 (top row: a, b, and c), 160� 4 (middle row: d, e, and f), and 314� 15 (bottom row: g, h, i). This technology is useful toassess microenvironments for tissue engineering, particularly under a range of flow conditions. Reprinted with permissionfrom Figallo et al.88 Color images available online at www.liebertonline.com=ten.

HIGH-THROUGHPUT TECHNOLOGY FOR TISSUE ENGINEERING 9

dynamic system. In different perfusion setups, the MBA sys-tem of 12 micro-bioreactors was utilized to compare the ex-pression of smooth muscle actin and cell density (for variouscells including hESCs). Additionally, the group was able toanalyze cell differentiation by quantifying the expression ofcell differentiation markers using an in situ and automatedimage analysis system. As noted by Figallo et al.,88 this com-plete system should prove to be quite useful for the study ofcell behavior, especially the complex development of hESCs,in fluidic environments.

Other microfluidic platforms have been developed for thepurposes of complex cell culture and experimentation inconditions very similar to an in vivo environment. In 2007,Lee et al.89 developed such a model consisting of a cell cul-ture niche, an artificial, microfluidic endothelial (perfusion)layer, and a nutrient transport vessel. This system utilizedmicrofluidics to direct mass transport on the microscale andto help localize cells for increased density and viability incertain areas of the microenvironment. More recently, chip-based microfluidic devices have been developed and uti-lized to produce microscale scaffolds. For instance, Lee andcoworkers90 have constructed tissue engineering scaffolds byintegrating PLGA microfibers into a microfluidic chip. In thispreparation, the microfibers were generated by precipitat-ing streams of PLGA in dimethyl sulfoxide out of a water=glycerol mixture and varying the flow rate of PLGA toproduce fibers of different widths.90 Neural progenitor cellswere then grown on the three-dimensional scaffolds to de-termine its potential for use in neural tissue engineering.90

It is clear that complex environments are crucial to trulyassess the potential of approaches in recapitulating the com-plex microenvironments found in vivo. This includes systemsthat utilize fluid flow, three-dimensional structures, and bothbound and soluble cues. Thus, microfluidics provide platformtechnology for rapidly assessing these environments, whetherit is with gradients of cues or through the interface of fluidflow and arrays of polymers. However, much of this previouswork has focused on the assessment of known soluble cues(i.e., growth factors) for tissue engineering applications.

Screening Molecules for Stem Cell Differentiation

The identification of molecules that control cellular be-havior is essential to expand the breadth of cues that may beincorporated into tissue engineering approaches. One emerg-ing field is the use of HTS of molecule libraries for the controlof stem cell differentiation. Extensive studies have assessedpotential drugs and growth factors looking for soluble cuesto direct the fate of stem cells toward a desired phenotype.These attempts have been limited to smaller studies of alimited number of factors due to the techniques in culture andcost of reagents and proteins. Additionally, efforts to reca-pitulate growth factor cocktails and precise timing of theirdelivery, as seen during development, have been similarlylimited in scope and complexity. With the advent of HTStechniques, smaller numbers of cells can be used to screen awider range of potential drugs and cytokine delivery schemesto optimize stem cell differentiation. Although many cell typescould be investigated, this section will focus specifically on theuse of HTS technology toward stem cell differentiation.

To date, few studies have specifically investigated the useof HTS of small molecules for stem cell differentiation. One of

the earliest examples involved a study from the Schultz labthat screened a heterocycle combinatorial library.91 This li-brary was built using the template of known kinase inhibitorscaffolds, such as purine and pyrimidines, to include nearly50,000 different small molecules of unknown potential. Ori-ginally, this library was thought to be a possible source fornew ligands to further investigate the function of new pro-teins by inhibiting or stimulating their signaling; however,they soon began to investigate their differentiation potentialon stem cells. For example, the library was screened withmouse embryonic mesoderm fibroblast C3H10T1=2 cells forosteogenic differentiation.92 The initial screen used increasedalkaline phosphatase (ALP) expression, which is important tobone mineralization, as a marker for differentiation. Fifteencompounds were identified that stimulated ALP activity, butthe molecule they named purmorphamine was the mostpotent of these. This compound was shown to be more potentthan BMP-4 with optimized concentrations, and the combinedeffect of BMP-4 and purmorphamine were synergistic and notsurrogate. The osteogenic nature of purmorphamine wasconfirmed with increases in the induction of the transcriptionfactor Cbfa1 and histological staining for ALP on several othermurine mesenchymal cell lines. Further investigation93 foundthat purmorphamine stimulated osteogenic differentiationalong the hedgehog signaling pathway.

Similarly, HTS was used to identify molecules from thislibrary that drove neuronal94 and cardiomyogenic95 differ-entiation of murine embryonic stem cells. Hits were initiallyscreened using transfection of reporter gene activated lucif-erase plasmids. The use of the library to drive dedifferentia-tion96 also led to the discovery of reversine, which allowedmurine myoblasts to dedifferentiate into stem-like cells andthen redifferentiate along osteogenic or adipogenic cell lines.This could be important in discovering new autologoussources for cell therapies. These discoveries were largely usedto investigate the processes and signaling used during dif-ferentiation, not for actual therapy.

More recent studies have screened libraries for suppres-sors of differentiation rather than looking for new promot-ers.93,97 These can be useful for treating certain diseaseprogressions where it would be therapeutically advanta-geous to prevent differentiation or in understanding thenature of diseases where normal function is inhibited, but thecauses are not understood. The study by Yui et al.93 screenedover 7500 commercially available compounds by investi-gating their effects on the normal embryonic development ofthe dorsal structures in zebrafish. This required the obser-vation and measurement of several individual embryosmanually, so it lost some of the rapidity of the soluble factorassays previously discussed, but includes the complexity ofthe in vivo system that two-dimensional culture lacks. Thismethod was able to identify dorsomorphin as a BMP type Ireceptor inhibitor, which prevents osteogenic differentiation.

A high-throughput siRNA library was used to screenhMSCs for osteogenic suppressors by Zhao and Ding.97 MSCswere transfected by a library of 10,000 unique sequences, twoper gene, and the ALP activity was then imaged. An increasein ALP activity indicated that the inhibited gene was normallyan osteogenic suppressor. This initial screen identified 55 hitsfor osteogenic prevention, which included genes for a widevariety of kinases, ECM proteins, protein receptors, ionchannels, among others. From this initial screen, 12 were

10 PETERS ET AL.

selected to investigate further for prevention of osteogenicdifferentiation. Interestingly, the different suppressors fell intotwo types the authors designated ‘‘fate specific’’ or ‘‘fate non-specific.’’ The fate-specific genes were suppressors of osteo-genic differentiation because they preferred an adipogeniclineage. The fate-nonspecific type prevented differentiationalong any line. These would be important in maintaining themultipotency and proliferation of stem cells, and may beuseful for understanding and prolonging a stem cell linein vitro or possibly in the dedifferentiation of committed celllines.

One recent study by Mauck and coworkers98 used HTSof hMSC pellets to find molecules that stimulate or sup-press chondrogenesis. Since MSCs typically undergo chon-drogenesis in pellets, it was necessary to scale down pelletcultures for rapid assessment and culture in round-bottom384-well plates. Chondrogenesis was assayed by measuringthe glycosaminoglycan (GAG) content of the pellets after aweek in culture. The setup was first run to evaluate thecombinatorial effects of four different growth factors at dif-ferent concentrations. This allowed for the confirmation ofBMP-2 and transforming growth factor-b as being synergisticin chondrogenic differentiation, but also demonstrated howfibroblast growth factor-2 increased cell proliferation andchondrogenic potential of hMSCs. The small volumes of the384-well plates allow for extensive screening of differentgrowth factor cocktails that are often limited in large scale ex-periments by the cost of cytokines. Secondly, the National In-stitute of Neurological Disorders and Stroke (NINDS) librarywas screened for both chondrogenic inducers and inhibitors.Compounds that led to a 40% reduction in DNA content wereremoved from the screen to eliminate compounds that reducedGAG content due to their cytotoxicity. This method identified24 potential inhibitors, including many antimitotics, antibiot-ics, and antineoplastics, among others. Outcomes that includehits for molecules that either induce or inhibit stem cell chon-drogenesis are shown in Figure 4. Dose response investigationmay be useful in these cases to determine if changes in the dosewould lead to further cell death or increased effectiveness.

HTS techniques are beneficial due to their ability toquickly screen large libraries of small molecules for desiredoutcomes. Often, though, the hits are just the beginning,whereby the mechanisms of their action and the optimiza-

tion of the dosage must be further investigated. Thankfully,the same HTS setups can aid in the optimization of dosageor the synergies of multiple factors. As always, in vitro cul-ture is often limited in capturing effects in an artificial en-vironment that may not translate in vivo. The zebrafishexperiments93 are interesting in their ability to add a level ofcomplexity, but made it impossible to isolate the effects onspecific cells.

Rubin99 lamented that often more information could begleaned from these experiments if the researchers had morediverse sets of compounds and the expertise in chemistryand drug design to further develop initial hits. As such, oftenresearchers are limited by smaller more established sets, suchas the NINDS library, that is already characterized for otherapplications and may already be in use for other therapeu-tics. This has the benefit of a possibly faster implementationof these therapies if they are discovered, but that the largerresearch misses out on the true promise of these screens toinvestigate more diverse and uncharted libraries.

From a tissue engineering standpoint, these small moleculediscoveries can be useful in the design of materials, the dif-ferentiation of implanted cells, or to discover new cell sources.Scaffolds can incorporate the release of newly discovered smallmolecules into the environment around these implants to ei-ther drive specific differentiation of incorporated cell therapiesor to recruit native local stem cells and drive their differen-tiation for desired outcomes. The molecules, as shown previ-ously,96 can be used to dedifferentiate autologous cells towarda stem cell line that can then be expanded to increase cellsnumbers to therapeutic levels. Then the cells can be differen-tiated along the desired lineage in vitro before implantation.

Summary and Future Directions

Through this review, we have discussed many of thestages involved in research and development for tissue en-gineering applications and have specifically focused onhigh-throughput and combinatorial techniques in polymersynthesis, material characterization, cell–material interactionsincluding microfluidic platforms, and small molecule screen-ing to direct cell behavior. While combinatorial synthesismethods have been studied for years and for various appli-cations, these techniques are still developing as a crucial tool

FIG. 4. High-throughputscreening of small moleculesthat influence stem cell differ-entiation. Screening of a smallmolecule library (NINDS) toidentify inducers (CM� hits,gray area) and inhibitors(CMþ hits, gray area) of MSCchondrogenesis, measured byglycosaminoglycan (GAG)levels. This technology is ap-plicable to a range of cell typesuseful in tissue engineeringapplications. Reprinted withpermission from Huang et al.98

HIGH-THROUGHPUT TECHNOLOGY FOR TISSUE ENGINEERING 11

in rapidly producing a wide variety of polymeric materialsas potential scaffolds. In the future, miniaturization of poly-mer libraries to the nanoscale level, the design of three-dimensional combinatorial libraries, and parallel synthesismethods should continue to influence the ease and utility ofusing libraries to develop polymer scaffolds. In assessingthe characteristics of these large libraries, a variety of tech-niques, from parallelization to fluorescence spectroscopy,have been developed and employed, yet there is still roomfrom improvement in the commercialization and standardi-zation of these methods. Moving forward, computationalsystems should play an increasingly important role in de-termining polymer properties and cell–material interactionsas they become applicable to a wider variety of cell and=ormaterial combinations.

The high-throughput analysis of cell–material interactions isperhaps the most important step in developing a successfulengineered tissue. This field has also seen significant progressas researchers have produced more complex and HTS plat-forms as well as designing three-dimensional screening envi-ronments and including additional factors such as solublegrowth factors. Microfluidic systems have also proved to bequite useful in replicating an in vivo environment and should befurther utilized in studying a range of polymer arrays andsoluble factors in high-throughput and under fluid conditions.Ideally, microfluidic platforms will provide structural support;a three-dimensional, dynamic environment for analysis of cellbehavior; and pathways to control solute and factor distribu-tion=flow. New imaging techniques will be developed that willhelp accelerate the identification of optimal environmentsduring screening. Finally, although it is a young field, the ap-plication of high-throughput techniques to the screening ofmolecules for stem cell response has increased the speed ofcharacterizing large libraries of small molecules. These tech-niques can help a number of engineered tissue factors, butshould be most useful in controlling cell differentiation. To thisend, future research should maintain a partial focus on thestandardization of techniques for measuring differentiation(e.g., the previously discussed fluorescence and transfectiontools). Additionally, the further development of analysismethods for the tremendous amount of data produced by thesehigh-throughput systems will be necessary for the field toprogress appropriately in the coming years. Overall, all of theseplatforms and analyses will continue to contribute to the de-velopment of the ideal microenvironment for engineered tis-sue, including the optimal synergy between cells, scaffolding,and extracellular factors. In pursuing this goal, the use of high-throughput and combinatorial techniques should continue toenhance our progress toward in vivo testing and clinical tissueengineering applications.

Disclosure Statement

No competing financial interests exist.

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Address reprint requests to:Jason A. Burdick, Ph.D.

Department of BioengineeringUniversity of Pennsylvania

240 Skirkanich Hall210 S. 33rd St.

Philadelphia, PA 19104

E-mail: burdick2@seas.upenn.edu

Received: January 21, 2009Accepted: March 16, 2009

Online Publication Date: April 29, 2009

HIGH-THROUGHPUT TECHNOLOGY FOR TISSUE ENGINEERING 15