MSCs have been proposed in tissue engineering to regenerate bone. However, early attempts have been stymied by both low numbers of MSCs and the death of these cells when implanted in vivo. Recently we reported that MSCs can be expanded ex vivo by EGF, and that EGF, when presented in a tethered manner, protects these cells from death7,8
. This led to the finding that culturing MSCs on tethered EGF enhances the osteoinductive properties of osteogenic stimuli via sustained EGFR phosphorylation10
, offering a new biomaterials-oriented approach to enhancing osteogenic differentiation. However, it was not evident whether this arose from greater activity per cell or augmented number of surviving MSCs, or both. Thus we sought to enhance this approach by means of additional growth factors, extracellular matrix adhesion ligands, and/or small molecule pathway modulators. However, rational usage of multiple cues requires increased understanding of their signaling pathway activation to regulate phenotypic behavior52, 53
. For regulation of embryonic stem cell self-renewal and differentiation54
as well as a spectrum of other cell fate decisions55–59
, individual signaling pathways are not univariately predictive of responses to extracellular stimuli nor are input cues simply additive or even synergistic; instead, rational prediction of cell fate outcomes across diverse treatment conditions requires determination of how multiple pathways are quantitatively combined into a ‘network state’ that integratively governs response. We apply this approach here to gain predictive understanding of MSC osteogenic differentiation in response to different biomaterials culture conditions, including tethered EGF in presence or absence of collagen-I.
For the foundational study of MSCs cultured on tEGF versus control substrata (and +/− an EGFR inhibitor), shows the time-courses of measured signal phosphosites: p-EGFR, p-ERK1/2, p-Akt, p-p38, p-HSP27, p-c-Jun, p-STAT3, and p-GSK3α/β. illustrates that none of these signals are strongly correlated with cell proliferation or differentiation outcomes across all culture conditions. In contrast, shows that a computational model using partial least-squares regression ascertains quantitatively weighted combinations of these signal phosphosites capable of predicting osteogenic differentiation behavior across these culture conditions, and locates the qualitative and quantitative contributions of the various signals to the model: p-EGFR, p-Akt, and p-HSP27 are found strongly positively associated with mineralization activity while p-ERK is found strongly negatively associated; p-p38 contributes mildly in positive manner whereas p-c-jun contributes mildly in negative manner.
We then proceeded to test this predictive model capability for its utility in understanding the effects of culture on type I collagen, a major structural protein of bone and produced during osteogenic differentiation. Bone progenitor cells lay down collagen prior to matrix mineralization60, 61
and most likely do so on our polymer surfaces whether EGF is tethered or not. The ultimate goal is to use this material for clinical bone grafts to be seeded with a patient’s own bone marrow progenitor cells, and coating these surfaces with matrix proteins preferred by the cells is an attractive method for enhancing desired MSC behavior. This was not the case with collagen I, however. It did increase MSC osteogenic differentiation with no tEGF () as has been reported62–64
, but MSC engagement of EGFR with tEGF and integrin binding to collagen I actually mitigated the differentiation response induced by tEGF alone (). Reports have shown that soluble EGF decreases collagen I synthesis30
but the converse has not been shown. shows the integrated effects of the growth factor and extracellular matrix ligands on the kinase signaling network activities and that our PLSR model comprehends all these effects. It successfully predicted, in a completely a priori
manner, osteogenic differentiation under a new, independent set of culture conditions involving substrate coated with collagen-I in presence or absence of tEGF. The effects of this materials-based outcome could not be obviously anticipated, for as shown in the influence of collagen-I on the various kinase signals is quite diverse rendering prediction of consequent phenotypic effects difficult.
The kinase signals chosen for this study are generally appreciated to serve as major integrators of disparate canonical pathways. Kinase phosphorylation is transient due to the dynamic interplay between kinases and phosphatases. It may at first glance seem somewhat surprising that transient tools used by cells to transduce extracellular information would be able to predict phenotypic behavior a week or more later, as is demonstrated in . However, upon reflection it is realized (as illustrated in ) that signaling network activities at a given point in time lead to alterations in gene expression (as well as metabolic and cytoskeletal processes) that ultimately execute observable phenotypic behavior, or metaphorically, resets the keyboard upon which the subsequent song is played. Along the way, the signaling network is modulated further by feedback from alterations in gene expression that operate both intracellularly and extracellularly. An intriguing question associated with this understanding is what the time-frame containing the most informative network signaling measurements might be. illustrates this conceptually for the day-21 differentiation outcome studied in this present work. Network activity at an intermediate time-point might be most informative due to a temporal balance between fate decision and effector actions; the earliest time-points may be less informative because more uncertainty exists concerning the evolution of the gene expression-related feedback loops, and the latest time-points may be less informative because the cell fate decision is largely determined before all the ensuing processes are completely played out. shows the actual result of our combined experimental/computational study, with day-7 network activity being most informative for prediction of day-21 phenotype. Nonetheless, it is not the case that day 7 is a “lucky guess” or “magic window” because also shows that day-4 and day-14 signals are also at least reasonably effective in the day-21 outcome prediction. Interestingly, a previous study of a different cell phenotypic fate decision – apoptotic cell death – similarly found that maximal predictive capability of signaling network information was obtained within an intermediate time-frame65
Minimal kinase signaling measurements at day 7 predict, a priori, the synergistic effects of collagen and tEGF on 21-day matrix mineralization
As MSCs differentiate into osteoprogenitors and osteoblasts, timed activation and inactivation of histone acetylases and deacetylases, transcription factors, extracellular matrix production, and osteoblast-specific enzymatic activity are regulated by signals intra- and extra-cellularly as well as intra- and extra-nuclearly; Runx2, TGF-β, Wnt, and osteopontin along with collagen I being among the most intensely studied. Reviews of these interactions and feedback/feed-forward loops have highlighted important findings identifying contributions of various components and pathways during periods of MSC proliferation, maturation, and matrix mineralization60, 66–70
. We note here the timing of peaks of maximal mRNA expression of collagen I and alkaline phosphatase (early and mid) compared to osteocalcin and collagenase (late)66
represents transcriptional processes preparing for the next phase of differentiation, supporting our schematic that potentially explains our finding that day 7 kinase signals are more predictive than day 14 signals for day 21 mineralization. Absent intervening perturbations, our results are consistent with gene expression literature on chronological programming of differentiation from extracellular cues to signaling activities to gene expression, with feedback loops at both extracellular and intracellular aspects of the regulatory process.
This study highlights more than the particular pattern of signal-response relationships for this focused cell culture study. It is quite plausible that assessment of a comparable though alternatively specified set of signaling network nodes could produce a similarly predictive model for longer-term osteogenesis. Such a model enabling prediction of cell fate outcomes from a dynamic network signature could perhaps be utilized for more deeply informative testing combinations of cues (materials, growth factors, extracellular matrix, and/or small molecules), permitting increased effectiveness in translation between ex vivo
and in vivo outcomes by means of the multi-variate signal-response model relationships74