Early time-point cell morphology classifiers successfully predict human bone marrow stromal cell differentiation modulated by fiber density in nanofiber scaffolds

Abstract

Nanofiber scaffolds can induce osteogenic differentiation and cell morphology alterations of human bone marrow stromal cells (hBMSCs) without introduction of chemical cues. In this study, we investigate the predictive power of day 1 cell morphology, quantified by a machine learning based method, as an indicator of osteogenic differentiation modulated by nanofiber density. Nanofiber scaffolds are fabricated via electrospinning. Microscopy, quantitative image processing and clustering analysis are used to systematically quantify scaffold properties as a function of fiber density. hBMSC osteogenic differentiation potential is evaluated after 14 days using osteogenic marker gene expression and after 50 days using calcium mineralization, showing enhanced osteogenic differentiation with an increase in nanofiber density. Cell morphology measurements at day 1 successfully predict differentiation potential when analyzed with the support vector machine (SVM)/supercell tools previously developed and trained on cells from a different donor. A correlation is observed between differentiation potential and cell morphology, demonstrating sensitivity of the morphology measurement to varying degrees of differentiation potential. To further understand how nanofiber density determines hBMSC morphology, both full 3-D morphology measurements as well as other measurements of the 2-D projected morphology are investigated in this study. To achieve predictive power on hBMSC osteogenic differentiation, at least two morphology metrics need to be considered together for each cell, with the majority of metric pairs including one 3-D morphology metric. Analysis of the local nanofiber structure surrounding each cell reveals a correlation with single-cell morphology and indicates that the osteogenic differentiation phenotype may be predictive at the single-cell level.