Dataset Information

Profiling stem cell states in three-dimensional biomaterial niches using high content image informatics.

ABSTRACT: 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 72h. Using this framework, the augmentation of osteogenesis by scaffold design exerted by porogen leached scaffolds was also profiled within 72h with ∼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 3days 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.

SUBMITTER: Dhaliwal A

PROVIDER: S-EPMC5262522 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Publications

Profiling stem cell states in three-dimensional biomaterial niches using high content image informatics.

Dhaliwal Anandika A Brenner Matthew M Wolujewicz Paul P Zhang Zheng Z Mao Yong Y Batish Mona M Kohn Joachim J Moghe Prabhas V PV

Acta biomaterialia 20160831

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). T ...[more]

PMID: 27590870

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Project description:Tuning cell shape by altering the biophysical properties of biomaterial substrates on which cells operate would provide a potential shape-driven pathway to control cell phenotype. However, there is an unexplored dimensional scale window of three-dimensional (3D) substrates with precisely tunable porous microarchitectures and geometrical feature sizes at the cell's operating length scales (10-100 μm). This paper demonstrates the fabrication of such high-fidelity fibrous substrates using a melt electrowriting (MEW) technique. This advanced manufacturing approach is biologically qualified with a metrology framework that models and classifies cell confinement states under various substrate dimensionalities and architectures. Using fibroblasts as a model cell system, the mechanosensing response of adherent cells is investigated as a function of variable substrate dimensionality (2D vs. 3D) and porous microarchitecture (randomly oriented, "non-woven" vs. precision-stacked, "woven"). Single-cell confinement states are modeled using confocal fluorescence microscopy in conjunction with an automated single-cell bioimage data analysis workflow that extracts quantitative metrics of the whole cell and sub-cellular focal adhesion protein features measured. The extracted multidimensional dataset is employed to train a machine learning algorithm to classify cell shape phenotypes. The results show that cells assume distinct confinement states that are enforced by the prescribed substrate dimensionalities and porous microarchitectures with the woven MEW substrates promoting the highest cell shape homogeneity compared to non-woven fibrous substrates. The technology platform established here constitutes a significant step towards the development of integrated additive manufacturing-metrology platforms for a wide range of applications including fundamental mechanobiology studies and 3D bioprinting of tissue constructs to yield specific biological designs qualified at the single-cell level.

Project description:The current study introduces two novel, smart polymer three-dimensional (3D)-printable interpenetrating polymer network (IPN) hydrogel biomaterials with favorable chemical, mechanical, and morphological properties for potential applications in traumatic brain injury (TBI) such as potentially assisting in the restoration of neurological function through closure of the wound deficit and neural tissue regeneration. Additionally, removal of injury matter to allow for the appropriate scaffold grafting may assist in providing a TBI treatment. Furthermore, due to the 3D printability of the IPN biomaterials, complex structures can be designed and fabricated to mimic the native shape and structure of the injury sight, which can potentially assist with neural tissue regeneration after TBI. In this study, a peptide-only approach was employed, wherein collagen and elastin in a blend with gelatin methacryloyl were prepared and crosslinked using either Irgacure or Irgacure and Genipin to form either a semi or full IPN hydrogel 3D-printable neuromimicking platform system, respectively. The scaffolds displayed favorable thermal stability and were amorphous in nature with high full width at half-maximum values. Furthermore, no alteration to the peptide secondary structure was noted using Fourier transform infrared spectroscopy. The IPN biomaterials have a stiffness of around 600 Pa and are suitable for softer tissue engineering applications-that is, the brain. Scanning electron micrographs indicated that the IPN biomaterials had a morphological structure with a significant resemblance to the native rat cortex. Both biomaterial scaffolds were shown to support the growth of PC12 cells over a 72 h period. Furthermore, the increased nuclear eccentricity and nuclear area were shown to support the postulation that the IPN biomaterials maintain the cells in a healthy state encouraging cellular mitosis and proliferation. The Genipin component of the full IPN was further shown to exhibit antimicrobial properties and this suggests that Genipin can prevent the growth of pathogens associated with postsurgical brain infections. In addition to these findings, the study presents an anomaly, wherein the full IPN is found to be more brittle than the semi IPN, a finding that is in contradiction with the literature. This research, therefore, contributes to the collection of potential biomaterials for TBI applications coupled with 3D printing and can assist in the progression of neural treatments toward patient-specific scaffolds through the development of custom scaffolds.

Dataset Information

Profiling stem cell states in three-dimensional biomaterial niches using high content image informatics.

Statement of significance

Publications

Profiling stem cell states in three-dimensional biomaterial niches using high content image informatics.

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