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. 2011 Oct;32(30):7524-31.
doi: 10.1016/j.biomaterials.2011.06.050. Epub 2011 Jul 22.

Controlling multipotent stromal cell migration by integrating "course-graining" materials and "fine-tuning" small molecules via decision tree signal-response modeling

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Controlling multipotent stromal cell migration by integrating "course-graining" materials and "fine-tuning" small molecules via decision tree signal-response modeling

Shan Wu et al. Biomaterials. 2011 Oct.

Abstract

Biomimetic scaffolds have been proposed as a means to facilitate tissue regeneration by multi-potent stromal cells (MSCs). Effective scaffold colonization requires a control of multiple MSC responses including survival, proliferation, differentiation, and migration. As MSC migration is relatively unstudied in this context, we present here a multi-level approach to its understanding and control, integratively tuning cell speed and directional persistence to achieve maximal mean free path (MFP) of migration. This approach employs data-driven computational modeling to ascertain small molecule drug treatments that can enhance MFP on a given materials substratum. Using poly(methyl methacrylate)-graft-poly(ethylene oxide) polymer surfaces tethered with epidermal growth factor (tEGF) and systematically adsorbed with fibronectin, vitronectin, or collagen-I to present hTERT-immortalized human MSCs with growth factor and extracellular matrix cues, we measured cell motility properties along with signaling activities of EGFR, ERK, Akt, and FAK on 19 different substrate conditions. Speed was consistent on collagen/tEGF substrates, but low associated directional persistence limited MFP. Decision tree modeling successfully predicted that ERK inhibition should enhance MFP on collagen/tEGF substrates by increasing persistence. Thus, we demonstrated a two-tiered approach to control MSC migration: materials-based "coarse-graining" complemented by small molecule "fine-tuning".

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Figures

Figure 1
Figure 1
Illustration of PMMA-g-PEO polymer materials modification to provide growth factor and ECM cues influencing MSC migration. Solvent-dissolved polymer was spincoated onto glass coverslips, and the PEO sidechains reacted to tether EGF (tEGF). Varying concentrations of fibronectin, vitronectin, or collagen were adsorbed onto the polymer surface.
Figure 2
Figure 2
MSC migration speed and persistence are affected by tEGF PMMA-g-PEO surfaces with varying levels of ECM protein coating; bar color from lightest to darkest correspond to protein levels from lowest to highest. Sparsely seeded hTERT-MSCs on biomaterials surfaces were tracked via time-lapse image capture for 7 hours. (A) Cell speed on fibronectin-adsorbed polymer surfaces. (B) Cell speeds on tEGF polymer surfaces adsorbed with fibronectin, vitronectin, and collagen. (C) Migration persistence as fitted using the Persistent Random Walk model with overlapping intervals. (D) Mean free path (speed x persistence).* denotes statistical significance between conditions at p < 0.01; error bars show +/−SEM.
Figure 3
Figure 3
MSC signals a cross conditions. Lysates from cells on biomaterials surfaces were collected at 0, 5, 15, 30, 60, and 180 minutes after treatment and levels of EGFR, ERK, Akt, and FAK measured. (A) Representative signaling time-course of EGFR activity of cells on Fn-adsorbed substrates. (B) Heat map of normalized signals for 19substrate conditions. Raw signal measurements within each ECM group (Fn, Vn, Cn) were normalized to 0.3 ug/ml control and then integrated for each condition. All 19 integrated signals for each phosphoprotein were then normalized to that of Fn0.3 control, time 0. (C) Lack of univariate correlation of migration responses of Speed, Persistence, and MFP versus time-integrated EGFR.
Figure 4
Figure 4
MSC signaling and migration response discretized to low, medium, and high for 19 substrate conditions to minimize model-overfitting. For each column of signal or response, the range of values between the column minimum and column maximum was evenly divided into three bins so that each bin contains an equal-sized range.
Figure 5
Figure 5
Decision tree ‘signal-response’ models where signal nodes classify response leaves. Classification trees were generated via Mat lab using the discretized data from Figure 3C for: (A) cell mean free path; and (B) migration persistence.
Figure 6
Figure 6
Successful test of decision tree model prediction that reducing ERK signal on Cn/tEGF substrates will enhance MSC migration persistence and mean free path.(A) mean free path, (B) persistence time, and (C) speed under partial and total ERK inhibition with MEK inhibitor U0126. * and # denote statistical significance with p < 0.01 and p < 0.05 respectively. Error bars show +/− SEM.

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