A predictive framework for the evolution of stem cell biology in 3-D is currently lacking. In this study we propose deep image informatics of the nuclear biology of stem cells to elucidate how 3-D biomaterials steer stem cell lineage phenotypes. The approach is based on high content imaging informatics to capture minute variations in the 3-D spatial organization of splicing factor SC-35 in the nucleoplasm as a marker to classify emergent cell phenotypes of human mesenchymal stem cells (hMSCs). The cells were cultured in varied 3-D culture systems including hydrogels, electrospun mats and salt leached scaffolds. The approach encompasses high resolution 3-D imaging of SC-35 domains and high content image analysis (HCIA) to compute quantitative 3-D nuclear metrics for SC-35 organization in single cells in concert with machine learning approaches to construct a predictive cell-state classification model. Our findings indicate that hMSCs cultured in collagen hydrogels and induced to differentiate into osteogenic or adipogenic lineages could be classified into the three lineages (stem, adipogenic, osteogenic) with >= 80% precision and sensitivity, within 72 h. Using this framework, the augmentation of osteogenesis by scaffold design exerted by porogen leached scaffolds was also profiled within 72 h with similar to 80% high sensitivity. Furthermore, by employing 3-D SC-35 organizational metrics, differential osteogenesis induced by novel electrospun fibrous polymer mats incorporating decellularized matrix could also be elucidated and predictably modeled at just 3 days with high precision. We demonstrate that 3-D SC-35 organizational metrics can be applied to model the stem cell state in 3-D scaffolds. We propose that this methodology can robustly discern minute changes in stem cell states within complex 3-D architectures and map single cell biological readouts that are critical to assessing population level cell heterogeneity. Statement of Significance The sustained development and validation of bioactive materials relies on technologies that can sensitively discern cell response dynamics to biomaterials, while capturing cell-to-cell heterogeneity and preserving cellular native phenotypes. In this study, we illustrate the application of a novel high content image informatics platform to classify emergent human mesenchymal stem cell (hMSC) phenotypes in a diverse range of 3-D biomaterial scaffolds with high sensitivity and precision, and track cell responses to varied external stimuli. A major in silico innovation is the proposed image profiling technology based on unique three dimensional textural signatures of a mechanoreporter protein within the nuclei of stem cells cultured in 3-D scaffolds. This technology will accelerate the pace of high-fidelity biomaterial screening. (C) 2016 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.