Elastin is a versatile elastic protein that dominates flexible tissues capable of recoil, and facilitates commensurate cell interactions in these tissues in all higher vertebrates. Elastin's persistence and insolubility hampered early efforts to construct versatile biomaterials. Subsequently the field has progressed substantially through the adapted use of solubilized elastin, elastin-based peptides and the increasing availability of recombinant forms of the natural soluble elastin precursor, tropoelastin. These interactions allow for the formation of a sophisticated range of biomaterial constructs and composites that benefit from elastin's physical properties of innate assembly and elasticity, and cell interactive properties as discussed in this tutorial review.
Surprisingly little is known about the effects of the physical microenvironment on hemopoietic stem and progenitor cells. To explore the physical effects of matrix elasticity on well-characterized primitive hemopoietic cells, we made use of a uniquely elastic biomaterial, tropoelastin. Culturing mouse or human hemopoietic cells on a tropoelastin substrate led to a two- to threefold expansion of undifferentiated cells, including progenitors and mouse stem cells. Treatment with cytokines in the presence of tropoelastin had an additive effect on this expansion. These biological effects required substrate elasticity, as neither truncated nor cross-linked tropoelastin reproduced the phenomenon, and inhibition of mechanotransduction abrogated the effects. Our data suggest that substrate elasticity and tensegrity are important mechanisms influencing hemopoietic stem and progenitor cell subsets and could be exploited to facilitate cell culture.
PurposeTo characterize macular ganglion cell layer (GCL) changes with age and provide a framework to assess changes in ocular disease. This study used data clustering to analyze macular GCL patterns from optical coherence tomography (OCT) in a large cohort of subjects without ocular disease.MethodsSingle eyes of 201 patients evaluated at the Centre for Eye Health (Sydney, Australia) were retrospectively enrolled (age range, 20–85); 8 × 8 grid locations obtained from Spectralis OCT macular scans were analyzed with unsupervised classification into statistically separable classes sharing common GCL thickness and change with age. The resulting classes and gridwise data were fitted with linear and segmented linear regression curves. Additionally, normalized data were analyzed to determine regression as a percentage. Accuracy of each model was examined through comparison of predicted 50-year-old equivalent macular GCL thickness for the entire cohort to a true 50-year-old reference cohort.ResultsPattern recognition clustered GCL thickness across the macula into five to eight spatially concentric classes. F-test demonstrated segmented linear regression to be the most appropriate model for macular GCL change. The pattern recognition–derived and normalized model revealed less difference between the predicted macular GCL thickness and the reference cohort (average ± SD 0.19 ± 0.92 and −0.30 ± 0.61 μm) than a gridwise model (average ± SD 0.62 ± 1.43 μm).ConclusionsPattern recognition successfully identified statistically separable macular areas that undergo a segmented linear reduction with age. This regression model better predicted macular GCL thickness. The various unique spatial patterns revealed by pattern recognition combined with core GCL thickness data provide a framework to analyze GCL loss in ocular disease.
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