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Studies of cell-biomaterial interactions and stem cell dynamics using confocal/multi-photon fluorescence microscopy and high content imaging based modeling.

机译：使用共聚焦/多光子荧光显微镜和基于高含量成像的建模研究细胞-生物材料相互作用和干细胞动力学。

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摘要

A strategy in regenerative medicine involves the restitution of functional tissues using biomaterials pre-populated with transplanted cells such as human mesenchymal stem cells (hMSC). However, current design and optimization of extracellular environments to controllably promote tissue-specific regeneration are guided by empiricism, and there is a lack of structure-activity relations underlying cell-biomaterial interactions. This dissertation is focused on using high resolution confocal/multiphoton fluorescence imaging for developing quantifiable descriptors of cell-biomaterial interactions under complex microenvironments, including three-dimensional scaffolds, textural gradients of polymer substrates, and soluble biochemical factors that stimulate differentiation or cancerous transformation.;In the first project (Chapter 2), we demonstrated the feasibility of using multiphoton imaging to quantitatively characterize microstructure of 3D biomaterial scaffolds and pseudo-3D cell morphology. This approach was further expanded, in the second project (Chapter 3), to a multidimensional space of cellular and subcellular features (termed cell descriptors) derived from: morphology, reporter protein expression, localization and spatial organization of protein reporters. Using spatially graded polymer blend substrates of both continuous roughness and discrete chemical compositions, we combined high throughput screening with high content analysis to identify both "global" and "high-content" structure-property relationships between cell adhesion and biomaterial properties such as polymer chemistry and topography.;In the next project (Chapter 4), we developed a novel molecular screening tool based on the high content descriptors of a nuclear reporter, nuclear mitotic apparatus (NuMA). Using high content imaging, data dimension reduction and machine learning techniques we mapped the nuclear features to different stem cell phenomena, specifically, stem cell lineage commitment to osteogenic versus adipogenic lineages. We reported that NuMA based nuclear descriptors captured the early lineage commitment of hMSC vs self-renewal. Moreover, a combined cytoskeletal and nuclear based "composite" profiling was demonstrated to be a robust tool to parse out not only lineage commitment vs self-renewal but within different lineages (e.g. osteogenic vs adipogenic). In the final project (Chapter 5), nuclear feature based modeling was used to discern early subcellular changes during oncogenic transformation. The utility of this approach was demonstrated by parsing a library of synthetic polymer substrates based on their differential potential to modulate carcinogen-induced transformation of stem cells.
机译：再生医学的策略涉及使用预先植入有移植细胞（例如人间充质干细胞（hMSC））的生物材料来恢复功能组织。然而，当前的设计和细胞外环境的优化以可控地促进组织特异性再生受到经验主义的指导，并且缺乏细胞-生物材料相互作用背后的结构-活性关系。本论文的重点是利用高分辨率共聚焦/多光子荧光成像技术开发复杂微环境下细胞-生物材料相互作用的可量化描述子，包括三维支架，聚合物底物的结构梯度以及刺激分化或癌变的可溶性生物化学因子。在第一个项目（第2章）中，我们证明了使用多光子成像定量表征3D生物材料支架的微观结构和伪3D细胞形态的可行性。在第二个项目（第3章）中，此方法进一步扩展到细胞和亚细胞特征（称为细胞描述符）的多维空间，这些空间衍生自：形态，报道蛋白表达，蛋白报道分子的定位和空间组织。使用具有连续粗糙度和离散化学成分的空间梯度聚合物共混物基底，我们将高通量筛选与高含量分析相结合，以识别细胞粘附力与生物材料特性（例如聚合物化学）之间的“整体”和“高含量”结构-特性关系在下一个项目（第4章）中，我们基于核报告子，核有丝分裂仪（NuMA）的高含量描述符开发了一种新颖的分子筛选工具。使用高内涵成像，数据降维和机器学习技术，我们将核特征映射到了不同的干细胞现象，特别是干细胞谱系对成骨谱系和成脂谱系的承诺。我们报告说，基于NuMA的核描述符捕获了hMSC与自我更新的早期谱系承诺。此外，基于细胞骨架和核的组合“复合”分析被证明是不仅分析谱系承诺与自我更新，而且分析不同谱系（例如成骨与成脂）的强大工具。在最后的项目（第5章）中，基于核特征的建模用于识别致癌转化过程中早期的亚细胞变化。通过分析合成聚合物底物的库来分析这种方法的实用性，这些库基于其潜在的潜力来调节致癌物诱导的干细胞转化。

著录项

  • 作者

    Liu, Er.;

  • 作者单位

    Rutgers The State University of New Jersey - New Brunswick and University of Medicine and Dentistry of New Jersey.;

  • 授予单位 Rutgers The State University of New Jersey - New Brunswick and University of Medicine and Dentistry of New Jersey.;
  • 学科 Engineering Biomedical.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 240 p.
  • 总页数 240
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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