Development and Validation of a Highly Integrated and Scalable, Full Factorial Microbioreactor Array Platform for hPSCs

The microbioreactor array generates all combinations of 3 concentrations each of 3 soluble factors (a full factorial array; 3³ = 27 distinct conditions in total), from only 6 fluidic inputs. Our resistive flow design for the array simultaneously allowed aliquoting, mixing, and full factorial multiplexing of exogenous factors to be encoded solely by the geometry of the microfluidic channel network (Figure 1A,B,D), eliminating the need for integrated valves and thereby greatly reducing the amount of peripheral equipment required for operation while still achieving complex flow control outcomes. Importantly, this flexible architecture is readily scalable to a parallel and/or serial replicate chambers, b concentration levels, and c exogenous factors, giving a total of ab^c experimental points while requiring only 2c (or minimally c+1 if all buffers are the same) external connections, and 2 physical device layers [13], presenting an important scalability advantage over valved microfluidic systems. After multiplexing, the array supplies exogenous factors to a grid of 270 culture chambers comprising 27 columns of 10 serial chambers (Figure 1A). Each column constitutes a distinct composition of exogenous factors, whereas under continuous fluid flow, secreted paracrine factors are able to accumulate along the dimension of the serial chambers (rows 1 through 10), allowing for subsequent visualisation of differential effects on cell phenotype.

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Figure 1. Microbioreactor array design and validation.

A Microbioreactor array photomask design, with key features marked. B Perspective schematic view showing assembly of glass substrate and PDMS structural layers 1 and 2. Via holes join microchannel structures between layers. C Computational fluid dynamic model of velocity field through centreline planes at nominal operating conditions. Chambers are 1.63 mm in diameter and 250 µm high. D Design normalised concentrations of factors in each column, corresponding to panels E and F. Stock factor and buffer solutions are provided at normalised concentrations of 3 and 0, respectively, to allow for subsequent dilution. E Photograph of microbioreactor array filled with red, yellow and blue food dyes (representing factors A1, B1 & C1, respectively), and mixed with PBS (buffers A0, B0, & C0). F Fluorimetric quantification of soluble factor levels in each column. Stock solution of 40 kDa FITC-dextran was provided at 100 µM, therefore the design concentration levels are 0, 16.7 and 33.3 µM. Bars represent mean ± SD of 2 independently fabricated devices. G Heatmap of number of nuclei in each chamber, with individual numbers marked. Cell numbers do not represent densities used in microbioreactor experiments. H Phase contrast images of hESCs imaged 0 and 2 h after seeding into microbioreactor arrays. Chambers are ∼1.63 mm in diameter.

https://doi.org/10.1371/journal.pone.0052405.g001

Arrays can be coated with various attachment substrata and sustain hESCs under medium perfusion for at least 7 days. Under the nominal flow conditions which we validated previously for hESCs [14], cells are exposed to creeping laminar flow and low shear stresses, while 250 µm wide interconnects spatially discretise serial chambers (computational fluid dynamic modelling, Figure 1C). Table S1 details the physical parameters and flow conditions in the microbioreactor array. Different flow requirements for preliminary and endpoint operations such as surface coating, cell seeding, and immunofluorescence labelling were realised through alternative fluidic inlets/outlets that were plugged or opened to switch between operation modes. Microbioreactor arrays had a culture area of 2.08 mm² per chamber, with 2.25 mm horizontal and vertical pitch between adjacent chambers, reflecting the layout of a 1536-well microtiter plate.

To evaluate the microfluidic network performance and for visualisation and qualitative validation of the spectrum of culture conditions formed by the array, we performed dye-loading experiments and fluorimetric quantification of soluble factor levels. Colorimetric validation confirmed the successful generation of an array of discrete microenvironments within the device (Figure 1D,E), with the expected partitioning of dyes and uniform appearance along serial chambers in a column. Fluorimetric quantification of 40 kDa FITC-dextran concentrations in the array revealed clear generation of the full spectrum of designed microenvironmental compositions as well as sufficient diffusive mixing capacity (Figure 1F). Detection of low-level fluorescence in zero-concentration conditions in Channels B and C was due to residual adsorbed dye. The accurate generation of all concentration levels also implied the flow distribution across the device was in agreement with design flowrates and therefore equal between columns. Cell seeding uniformity was also evaluated using fixed, Hoechst-labelled cells and image analysis to count nuclei, which showed acceptably uniform cell distribution throughout the array (coefficient of variation 10.7%, Figure 1G). Cell attachment was also assessed by seeding live hESCs into arrays, which revealed that hESCs were attached 2 h after seeding and were uniformly distributed within a chamber (Figure 1H).

Microbioreactor Array Screening of BMP, Activin and Wnt Signals in hESC Mesendodermal Differentiation Uncovers Complex “Paracrine Signatures”

To exemplify the microbioreactor array’s ability to screen and dissect extracellular patterning signals, we differentiated hESCs towards a primitive streak-like (mesendodermal) phenotype, which is marked by the transcription factor MIXL1 and known to be induced by BMP, Activin and Wnt signals [7], [15]. As a readout of MIXL1 expression status, we utilised HES3(MIXL1^GFP/w) hESCs [15] (karyotyping and in vivo teratoma formation data, Figure S1). This reporter line contains an eGFP expression cassette inserted at the MIXL1 locus. HES3(MIXL1^GFP/w) hESCs were seeded into microbioreactor arrays and exposed to a full-factorial array of BMP-4, Activin A, and the GSK-3β inhibitor/canonical Wnt activator 6-bromoindirubin-3′-oxime (BIO) [16] in RPMI B27 medium for 2.5 d under continuous flow (Figure 2A).

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Figure 2. Microbioreactor screening of HES3(MIXL1^GFP/w) cells undergoing mesendodermal differentiation demonstrates paracrine dependencies.

A Screening panel showing array conditions in each column. Numbers indicate concentrations of BIO (µM), BMP-4 (ng/mL) and Activin A (ng/mL). B Confocal images of HES3(MIXL1^GFP/w) hESCs expressing GFP and counterstained with Hoechst at experiment endpoint. Medium flow direction was from top to bottom. C Heat maps of total fluorescence intensities in the array (arbitrary units). D Higher-magnification confocal images of individual wells within the microbioreactor array highlighting position-dependent phenotypes, scale bar: 500 µm. E Main effect magnitudes of exogenous factors (BIO, BMP-4, Activin A) and positional dependency (Position) on expression index I_MIXL1-GFP. Units represent global standard deviations relative to global mean. F Interaction effect magnitudes of combinations of 2 factors on expression index I_MIXL1-GFP. Full dataset is shown in Figures S2, S3. G Traces of expression index I_MIXL1-GFP and DNA-normalised expression index I_{MIXL1-GFP/DNA} versus position coordinate for 0 (Column 6) and 1 µM BIO (Column 15) showing extended expression under BIO treatment.

https://doi.org/10.1371/journal.pone.0052405.g002

MIXL1-GFP expression was activated under tightly delineated factor conditions, appearing at only selective positions in the array (Figure 2B,C). The highest expression was in columns 6, 15 and 24, which each contained 10 ng/mL BMP-4 and 6 ng/mL Activin A combined with 0, 1 or 2 µM BIO, respectively. Of note, high MIXL1-GFP-expressing chambers, which contain cells characterised by dim Hoechst DNA staining, were in each case immediately preceded by a “DNA-bright” chamber, characterised by clustered layers of cells and bright DNA staining (Figure 2D). These phenotypic observations recurred periodically throughout the array and suggest patterning of intermediate populations dependent on combinations of exogenous and paracrine factors. Factorial analysis (Figures S2, S3), which shows the average effect magnitude across the array for a single or combination of two factors, showed a linear, positive dependence for I_MIXL1-GFP on Activin A, peak effects for BIO and BMP-4 at 1 µM and 10 ng/mL, respectively (Figure 2E), and highlighted the synergistic action of BMP-4 and Activin A at the best performing concentrations of 10 ng/mL and 6 ng/mL, respectively (Figure 2F).

Most notably, the dependence of MIXL1-GFP expression on Position coordinate (the row number within a series of 10 chambers) showed a peak at row 7 which was preceded by an increasing exponential trend and immediately followed by a decreasing exponential trend in MIXL1-GFP levels (Figure 2E). Across the whole array, appreciable MIXL1-GFP expression was only detected below the 4th row of serial chambers, suggesting that combinations of only BMP, Activin and canonical Wnt stimulation were not sufficient to directly activate robust MIXL1 expression at concentrations tested. Rather, this data implies that progressive accumulation of surplus, diffusible paracrine factors is required for MIXL1 activation.

Subsequent to MIXL1-GFP activation, expression intensity was attenuated rapidly in downstream chambers, suggesting presence of a negative feedback mechanism to the MIXL1-GFP⁺ population, perhaps mediated by soluble factors (Figure 2D,G). However, in column 15, which contained 1 µM BIO, bright MIXL1-GFP expression extended over 4–5 rows, rather than the single peak of fluorescence seen at row 7 in column 6, which comprised identical conditions but lacked BIO (Figure 2B,C,G). Tracing of MIXL1-GFP expression through these columns highlighted extended expression under the condition with 1 µM BIO, both in terms of absolute and DNA-normalised MIXL1-GFP expression (Figure 2G). This suggested that the negative paracrine feedback effect could potentially be overcome by enforcing canonical Wnt signalling using a GSK-3β inhibitor. Although column 24, which contained 2 µM BIO, did not have noticeably extended MIXL1-GFP expression, BIO is known to be a non-specific GSK-3β inhibitor, and is also cytotoxic at higher concentrations and lower cell densities. We therefore switched to a more specific and less cytotoxic GSK-3β inhibitor, CHIR99021 [17], to reconfirm the effect in subsequent experiments.

Such position-dependent patterns of MIXL1-GFP expression under continuous flow conditions could therefore be interpreted as representing a “paracrine signature” present under the various exogenous factor conditions, encapsulating information about the requirements for factor accumulation or presence of negative feedback loops.

Replicate arrays had Pearson’s correlation coefficients of 0.66 and 0.69 for I_DNA and I_MIXL1-GFP, respectively, based on paired fluorescence values of corresponding chambers (Figure S4), comparing favourably to ECM protein array experiments, which had correlation coefficients of 0.35–0.65 for averaged responses taken from a subset of spots [18]. To ensure that the array results were not an artefact of transient MIXL1-GFP induction, we also analysed MIXL1-GFP expression in an array run for 3.5 d, and found that this had a similar GFP distribution to arrays run for 2.5 days, demonstrating that absence of MIXL-GFP expression in the initial rows was not simply due to insufficient induction time or transient MIXL1 expression (Figure S5).

Provision of Conditioned Medium and Modulation of Wnt Signalling Affect Paracrine Signatures in MIXL1-GFP Induction

Based on the emergent position-dependent expression patterns from the initial screen, which could be interpreted as “paracrine signatures”, we next hypothesised that modulation of such signatures resulting from various exogenous factor treatments would be an effective strategy to confirm the presence of putative paracrine effects and provide clues as to the identity of factors involved. In this way, direct provision of putative factors, inhibition of their associated signalling pathways, or depletion of putative factors from the medium could be applied whilst looking for changes to the expression pattern of phenotype induction.

To evaluate this in the case of MIXL1-GFP induction, we collected induction-conditioned medium (re-supplemented with 50% of nominal levels of BMP-4 and Activin A, referred to as CM) from static 2D cultures of HES3(MIXL1^GFP/w) hESCs induced to differentiate towards mesendoderm, assuming that this would contain paracrine factors secreted by the cells during differentiation and thus should counteract the need for accumulation of further factors under continuous medium flow. We then constructed another screen where the whole of the array was provided with a background of the best-performing concentrations of BMP-4 and Activin A from the initial screen (10 ng/mL and 6 ng/mL respectively; Figure 3A), conditions which clearly displayed the paracrine-dependent expression pattern. CM was then directly provided as a factor in this array at 0, 17 and 33% v/v, again for 2.5 d under continuous medium flow. Indeed, in strong support of the existence of transferable paracrine factors necessary for MIXL1-GFP induction in this format, supply of CM shifted MIXL1-GFP expression towards the initial rows of the array in a dose-dependent manner (Figure 3B-D; complete factorial analysis dataset, Figures S6, S7). This time, arrays were also immuno-stained in situ for the early mesodermal marker NCAM [19] (also know as CD56; Figure 3B,C). NCAM also co-localised with MIXL1 expression across the array, providing further confirmation independently of the gene reporter of PS induction and a shift resulting from CM treatment. Although Column 1 did not present a clear peak at Row 7 as in the initial screen, this may be due to slight variations in cell density, paracrine output, or changes in maximum MIXL1-GFP intensity (and therefore resultant imaging parameters) in the array when CHIR was included as a factor. Nevertheless, positional shifts resulting from various factor treatments were clearly discernible.

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Figure 3. Confirmation and screening of paracrine factors involved in hESC mesendodermal differentiation. A

Screening panel showing array conditions, corresponding to B-C. Numbers indicate concentrations of induction-conditioned and factor re-supplemented medium (CM, final % v/v), IWP-4 (µM), and CHIR99021 (CHIR, µM). B Confocal images of HES3(MIXL1^GFP/w) hESCs expressing GFP and co-immunostained in situ for NCAM (CD56) at experiment endpoint. Flow direction was from top to bottom. C Heat maps of total fluorescence intensities in the array (arbitrary units). D Selected interaction effect plot showing average effect magnitudes for combinations of CM and Position on expression index I_MIXL1-GFP and DNA-normalised expression index I_{MIXL1-GFP/DNA}, highlighting the shift towards earlier rows with CM treatment (legend represents CM concentration in % v/v). Full dataset, Figures S6, S7. E Traces of expression index I_MIXL1-GFP and DNA-normalised expression index I_{MIXL1-GFP/DNA} versus position coordinate for 0 (Column 19), 2.5 (Column 20), and 5 µM CHIR (Column 21) showing extended expression under CHIR treatment. F Blocking of MIXL1-GFP induction in static cultures with IWP-4, and improvement and rescue of induction with CHIR. Bars represent mean ± SD of 4 biological replicates over 2 independent experiments. * indicates p<0.05 relative to +None condition.

https://doi.org/10.1371/journal.pone.0052405.g003

To test the additional hypothesis arising from the initial screen – that GSK-3β inhibitors increased the extent of MIXL1-GFP expression – HES3(MIXL1^GFP/w) hESCs were subjected to treatment with a distinct, highly specific GSK-3β inhibitor, CHIR99021 [17]. Similar to BIO, CHIR increased the length of chambers over which MIXL1-GFP expression persisted (Figure 3B,C,E; complete factorial analysis dataset, Figures S6, S7). We then tested whether the outcomes of modulating paracrine regulators in the array could be translated to improve directed differentiation protocols in conventional static cultures. Indeed, we found that addition of CHIR resulted in improved outcomes not only in the microbioreactor array, but also in static controls. Relative to standard conditions of RPMI B27+10 ng/mL BMP-4+6 ng/mL Activin A, addition of CHIR increased MIXL1-GFP induction from 51% to 86% of cells (Figure 3F), demonstrating applicability of array outcomes when translated to static cultures.

Regulation of the primitive streak-like phenotype by Wnt is a mechanism that has been highlighted in the literature, with several studies reporting that active Wnt signalling is required for induction of primitive streak [7], [20]. Since Wnt proteins may be involved in the effect of CM, we took a chemical stimulation and inhibition approach to evaluate Wnt pathway involvement. To assess whether Wnt is a paracrine factor required for MIXL1-GFP induction we used the small molecule inhibitor of Wnt production IWP-4 [21]. IWP-4 completely blocked MIXL1-GFP induction in static controls (Figure 3F), and did not show appreciable induction when applied over an inductive BMP/Activin background in the microbioreactor array (Figure 3B,C, Columns 4 & 7), however this could be overcome by addition of CM or CHIR (Figure 3B,C, Columns 16 & 9, respectively). Since it is expected that conditioned medium would contain Wnt proteins, and IWP-4 blocks only endogenous secretion and not exogenous factor activity, Wnt proteins present in CM or CHIR treatment could override IWP-4. Furthermore, in the absence of CM, increasing IWP-4 concentration shifted the peak of MIXL-GFP expression downstream in the array (Figure 3B,C, Columns 4–9). This indicated, in agreement with the previous reports, that Wnt stimulation is also required, and likely as a result of endogenous Wnt production. Further support for this assertion was provided by the observation that addition of CHIR could rescue the inhibitory effect of IWP-4 in static cultures (Figure 3F).

To confirm the expression of these putative inductive and inhibitory paracrine factors in the respective populations present, we sorted GFP^- and induced GFP⁺ cells from induced static cultures and compared their gene expression profiles with uninduced cells (Figure S8). We detected upregulation of transcripts for the canonical Wnts WNT3A and WNT8A following induction in static cultures and likewise detected upregulation of DKK1, a secreted Wnt antagonist and known canonical Wnt target gene [22], (Figure S8).

FGF Stimulation Promotes the Paracrine Factor-Dependent Emergence of MIXL1-GFP⁺ Cells

While Wnt was identified as a critical component of the paracrine inducing factors, induction of MIXL1-GFP did not proceed from the top rows of the array when treated with CHIR, suggesting that other types of factors may also be required. Based on FGF’s proposed role in mesodermal induction [23], and to demonstrate the array’s capability for screening specific paracrine signals, we investigated stimulation of FGF signalling by screening FGF-2 as a potential paracrine factor, as well as an inhibitor of the MEK signalling cascade downstream of FGF. HES3(MIXL1^GFP/w) hESCs were grown under the same background of RPMI B27/BMP-4/Activin A and subjected to stimulation under continuous flow for 2.5 d with FGF-2, the MEK cascade inhibitor PD0325901, and CHIR (Figure 4A). The array identified FGF-2 and CHIR as having a positive effect on induction, whereas PD0325901 strongly inhibited MIXL1-GFP expression (Figure 4B,C,E). FGF-2 acted similarly to conditioned medium in shifting MIXL1-GFP expression towards initial rows of the array in a dose-dependent manner (Figure 4B,C,F), whereas the MEK cascade inhibitor PD0325901 completely blocked MIXL1 induction in the array. FGF-2 in combination with BMP, Activin, and Wnt signals was efficient in inducing MIXL1-GFP⁺ and NCAM⁺ cells in the highest-expressing chamber (Figure 4D). PD0325901 as well as the FGF receptor tyrosine kinase inhibitor PD173074 strongly inhibited MIXL1-GFP induction in static controls (Figure 4G), suggesting FGFs are endogenously accumulated paracrine components involved in MIXL1 activation. In static controls, addition of FGF-2 in combination with CHIR further increased the percentage of positive cells (Figure 4H) and median fluorescence intensity (Figure 4I), without significantly increasing total cell number (Figure 4J), suggesting the effect did not solely arise from mitogenic effects. This again highlighted the transferability of array results to improving outcomes of static culture protocols.

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Figure 4. Interrogation of FGF stimulation and MEK inhibition on hESC mesendodermal differentiation in HES3(MIXL1^GFP/w) hESCs.

A Screening panel showing array conditions. Numbers indicate concentrations of FGF-2 (ng/mL), PD0325901 (PD; µM), and CHIR99021 (CHIR; µM). B Confocal tile scan image of HES3(MIXL1^GFP/w) hESCs expressing GFP and and co-immunostained in situ for NCAM (CD56) and Hoechst at 2.5 d experiment endpoint. Flow direction was from top to bottom. C Heatmaps of total fluorescence intensities in the array (arbitrary units). D Higher-magnification images of cells in highest MIXL1-expressing conditions – Column 21, Row 7– demonstrating efficient induction of MIXL1-GFP and NCAM. Scale bar: 500 µm. E Main effect magnitudes of factors on expression index I_MIXL1-GFP. Units represent global standard deviations relative to global mean. F Selected interaction effect plots showing average effect magnitudes for combinations of FGF-2 and Position on expression index I_MIXL1-GFP and DNA-normalised expression index I_{MIXL1-GFP/DNA}, highlighting the shift towards earlier rows with FGF-2 treatment (legend represents FGF-2 concentration in ng/mL). G Chemical inhibition of MIXL1-GFP induction with MEK cascade inhibitor PD0325901 and FGF receptor tyrosine kinase inhibitor PD173074. Bars represent mean ± s.d., n = 2−3 from one representative experiment. H-J Addition of FGF-2 or FGF-2+ CHIR99021 improved induction efficiency (H) and median fluorescence intensity (I) in static controls, but without significantly increasing total cell count (J). Bars represent mean ± s.d., n = 4 from 2 independent experiments. * p<0.05 compared to +None condition, one-way ANOVA.

https://doi.org/10.1371/journal.pone.0052405.g004

Conclusions

This paper applied a microbioreactor array platform multiplexing exogenous and paracrine factors to hESC differentiation to a primitive streak-like population. Paracrine effects are extremely difficult to assess in static cultures, and we demonstrate here that these microbioreactor arrays are very useful in the study of their effects in controlling cell fate. By revealing paracrine signatures that result from various exogenous factor stimulation regimes, the device provided an assay to identify agents that modulate paracrine-dependent outcomes, and thereby allowed decoding of the hierarchy of direct-acting inducing factors as well as inhibitory, negative feedback factors. Importantly, these results were then translated to improve directed differentiation protocols in static cultures. Using the microbioreactor array platform we demonstrated that mapping of combinations of both exogenous and paracrine stimulation is possible and therefore use of this technology is generally applicable to studies examining the effect of external stimuli on cell behaviour and phenotype.

Microbioreactor Design, Fabrication and Validation

Microbioreactor arrays were designed using scalable, hierarchically-nested, resistive flow-based dilution networks, as described previously [13]. Array designs were fabricated by SU-8 2100 photolithography and poly(dimethylsiloxane) (PDMS) soft lithography [32], and were assembled as described previously [13]. For visualisation and qualitative validation of conditions, red, yellow and blue food dyes and PBS buffers were perfused through the device at 60 µL/h total flowrate. Solution concentrations in each column were quantitatively validated using a 40 kDa (average MW) FITC-dextran solution (100 µM in PBS). FITC-dextran solution was perfused through each of the factor inlets (A1, B1, C1) and quantified independently and serially, with all other inlets containing PBS. Fluorescence levels were quantified by fluorescence microscopy and compared against known standards prepared by micropipetting and imaged in similar microbioreactor chambers. Solution concentration was related to fluorescence intensity by a linear function with R²>0.99 in the concentration range of interest.

Cell Culture and Static Controls

Human embryonic stem cells were dealt with under approval of the UQ Medical Research Ethics Committee, and were maintained by and obtained from StemCore, the Australian Stem Cell Centre’s core hESC laboratory. HES3(MIXL1^GFP/w) hESCs [15] were used between passages 79 and 86, and were cultured in mTeSR-1/Matrigel cultures for 1–3 passages before array experiments. Cells were verified for a normal karyotype (passage 86) and teratoma formation capacity (passage 77), as shown in Figure S1. Static culture controls in 24-well plates were coated at solution concentrations adjusted to supply equivalent total amounts of protein per surface area, and were also seeded with equivalent surface densities of cells. HES3(MIXL1^GFP/w) hESCs maintained in mTeSR-1/Matrigel cultures were detached with TrypLE Express, seeded at 7.5×10⁴ cells/cm² into 24-well plates coated with a limiting dilution of hESC-qualified Matrigel and allowed to attach overnight, after which cultures were typically ∼70% confluent. MIXL1-GFP expression was then induced by differentiating in RPMI B27 medium with 10 ng/ml BMP-4 and 6 ng/ml Activin A (both R&D Systems) for 2.5 d, unless indicated otherwise. Additional small molecules or factors were optionally included at the start of induction at concentrations indicated: IWP-4, an inhibitor of Wnt production and signalling [21]; CHIR99021, a specific GSK-3β inhibitor/canonical Wnt activator; PD0325901, a MEK cascade inhibitor; PD173074, an FGF receptor tyrosine kinase inhibitor (all Stemgent); or FGF-2 (Millipore). 0.1% v/v DMSO was used as a vehicle control.

Microbioreactor Array Screening

Microbioreactor arrays were autoclaved, filled with sterile PBS by the channel-outgas technique [33], then surface-coated (2–4 h, room temperature) with a limiting dilution of hESC-qualified Matrigel (BD Biosciences). hESCs maintained in tissue culture flasks were single-cell dissociated with TrypLE Express (Gibco), then washed with and resuspended at 3.0×10⁶ cells/mL and seeded into microbioreactor arrays (7.5×10⁴ cells/cm²). Cells were allowed to attach for 8–10 h in an incubator (37°C, 5% CO₂ in air), then subjected to continuous fluid flow under the specified factor conditions at 60 µL/h total flowrate. Positive-displacement-driven flow was provided by a syringe pump (NE-1800, New Era Pump Systems), via 3 or 1 mL syringes (Terumo), and polyethylene tubing (PE50, 0.58 mm ID, BD Biosciences), with stainless steel, 22 gauge blunt-nose needle tips as connectors.

Stock factor solutions are provided at 3× the highest concentration required in the array, to allow for subsequent dilution through diffusive mixing in the array. For HES3(MIXL1^GFP/w) experiments, the stock factor/buffer pairs were: RPMI B27+6 µM BIO (Stemgent)/RPMI B27; RPMI B27+60 ng/mL BMP-4 (R&D Systems)/RPMI B27; and RPMI B27+18 ng/mL Activin A (R&D Systems)/RPMI B27. RPMI B27 medium consisted of RPMI 1640 basal medium (Gibco) +2% v/v B27 Supplement (Invitrogen). For subsequent experiments, the factor/buffer pairs were: Conditioned Medium/RPMI B27 10B6A; RPMI B27 10B6A +15 µM IWP-4 (Stemgent)/RPMI B27 10B6A; and RPMI B27 10B6A +15 µM CHIR99021 (Stemgent)/RPMI B27 10B6A, where RPMI B27 10B6A consisted of RPMI B27+10 ng/mL BMP-4, +6 ng/mL Activin A. Conditioned medium (CM) was recovered from static controls differentiated with 10 ng/mL BMP-4 and 6 ng/mL Activin A in RPMI B27 medium. Cells were removed by centrifugation and supernatant medium stored at 4°C. Media from days 1 and 2 were combined and re-supplemented with 5 ng/mL BMP-4 and 3 ng/mL Activin A (i.e. 50% of nominal levels) before use in arrays, to account for depletion and degradation. This CM was then titrated in as a factor up to 33% v/v, against base medium of RPMI B27 10B6A. For FGF-2 screening, CM and IWP-4 factors were replaced with 60 ng/mL FGF-2 (Millipore) and 3 µM PD0325901 (Stemgent), respectively. These stock factor/buffer pairs respectively resulted in the generation of conditions predicted in Figures 2A, 3A and 4A. All media were supplemented with 1% v/v penicillin/streptomycin solution (Gibco).

In situ Immunofluorescence Staining and Confocal Imaging

Arrays were terminated 2.5 d after the start of fluid flow for in situ immunostaining. Factor/buffer inlets were plugged closed and the common seeding/coating inlet/outlet left open, with serial exchange of staining and washing solutions driven by a syringe pump. The immunodetection phase did not noticeably affect cell numbers or GFP fluorescence in the array. Arrays were washed with PBS and fixed with 2% paraformaldehyde/PBS solution (30 min, RT), followed by blocking with 3% bovine serum albumin (BSA)/PBS solution with 0.2% sodium azide (30–45 min, RT), then stained with 10 µg/mL Hoechst 33342 (Molecular Probes) in 0.3% BSA/PBS (1 h, RT). HES3(MIXL1^GFP/w) arrays were stained only with Hoechst, or optionally blocked with 0.3% BSA/PBS with 0.02% sodium azide and stained with NCAM-PE (1∶10, BioLegend) along with Hoechst. Arrays were finally washed/mounted in 0.3% BSA/PBS for imaging. 16-bit, multi-colour montage images of entire arrays were acquired with a Zeiss LSM 710 laser scanning confocal microscope system and Zen 2008 acquisition software (Carl Zeiss). To adjust for intensity variations in the z-direction, 3 optical sections were acquired and then processed into a maximum intensity projection for image analysis. Images were linearly adjusted for publication.

Flow Cytometry

Static control samples from 24-well plates were analysed by flow cytometry. Cells were washed with PBS, detached with TrypLE Express, neutralised with complete medium and aspirated into eppendorf tubes. Samples were centrifuged and fixed in 2% paraformaldehyde/PBS solution (30 min, RT). Cells were resuspended in wash solution and GFP expression was detected with an Accuri C6 flow cytometer. Uninduced cells were used as a negative control with the GFP expression cutoff set to give a false positive rate <1% of cells.

Cell Sorting and Gene Expression Profiling

HES3(MIXL1^GFP/w) hESCs maintained in mTeSR-1/Matrigel cultures were differentiated in RPMI B27 medium with 10 ng/ml BMP-4 and 6 ng/ml Activin A. After 2 d, cells were detached with TrypLE express, washed with mTeSR-1, then resuspended in DMEM/F12+2 µg/mL propidium iodide (Molecular Probes), and placed on ice. Viable, single cells were identified on the basis of FSC and SSC parameters and PI exclusion, then sorted for GFP⁺ and GFP^- fractions (top 10% and bottom 20% of target population in terms of GFP-expression, respectively) with a Cytopeia Influx instrument (BD Biosciences). Total RNA was extracted from sorted fractions and uninduced controls with RNeasy kits combined with on-column DNase treatment (Qiagen). RNA was quantified with a Nanodrop 1000 spectrophotometer (Thermo Scientific) and ∼350ng RNA used per reaction to synthesize cDNA, which was stored at −20°C until RT-qPCR was performed with Platinum SYBR Green qPCR SuperMix-UDG or Ssofast EvaGreen qPCR supermix (172–5200, Biorad). Thermocycling and analysis were performed using an ABI 7500 Fast Real-Time PCR system (Applied Biosystems) or a CFX96 Real-Time/C1000 thermal cycler (Biorad Systems) with fast cycling parameters of 50°C for 2 min (UDG incubation), 95°C for 2 min (denaturation) and then 95°C for 3 s and 60°C for 30 s for a total of 40 cycles. Results were analyzed using the 2^−ΔCt method relating gene expression to GAPDH and were further normalised to the control (uninduced) sample. Melt curve analysis was used to ensure product specificity, along with reverse transcriptase-deficient and water controls. Primer sequences (Table S2) were validated using RNA collected from experiments.

Data Processing and Statistical Methods

Total fluorescence intensities (T_MIXL1-GFP, for example) were extracted using AGScan software (www.sigenae.org). Spot intensities were linearly transformed about the mean and standard deviation for all spots in that channel in an individual array, by I_MIXL1-GFP = (T_MIXL1-GFP - µ_MIXL1-GFP)/σ_MIXL1-GFP, where I_MIXL1-GFP is termed the expression index of MIXL1-GFP, and µ_MIXL1-GFP is the mean and σ_MIXL1-GFP the standard deviation of all spot intensities (T_MIXL1-GFP) for a particular array.

Factorial analyses were performed on expression index data with MINITAB 15 software (Minitab Inc.), using exogenous factor levels and row coordinate (Position) as input variables. Effect magnitudes were calculated by MINITAB as described elsewhere [34]. Pearson product-moment correlation coefficients (r_X,Y) and coefficients of determination for linear regression (R²) were calculated with Microsoft Excel. For pair wise comparisons, one-way ANOVA with post-hoc Tukey or Games-Howell tests were performed with SPSS Statistics 17.0. Differences with p<0.05 were considered significant. Kolmogorov-Smirnov tests were used for data normality, and Levene’s tests for homogeneity of variance.