Human tissues are sophisticated ensembles of various cell types embedded in the complex but defined structures of the extracellular matrix (ECM). ECM is configured in a hierarchical structure from nano- to microscale, with many biological molecules forming large scale configurations and textures with feature sizes up to macroscopic scale (several hundred microns). The physicochemical, biological and mechanostructural properties of native ECM play a critical role in constructing a microenvironment for cells and tissues. In conjunction with the rapid evolution of material science and its fabrication techniques, studies of the topography and elasticity of ECM and other materials have allowed advanced interrogation of cellular mechanotransduction and cellular responses to mechanostructural cues. By learning from and mimicking the highly organized ECM structures found in vivo, topography-guided approaches to regulate cell function and fate have been widely investigated in the last several decades. Here, we review recent efforts in mimicking the micro- and nanotopography of the native ECM in vitro for the regulation of cellular behaviors. We also discuss how these biomimetic topographical surfaces have been applied to fundamental cell mechanobiology studies into cell adhesions, migrations, and differentiation as well as toward efforts in tissue engineering.

Live cells can sense the mechanical characteristics of the microenvironment and translate the mechanical cues to intracellular biochemical signals in physiology and disease. To investigate intracellular signaling transduction during mechanosensing, nanotechnologies, and FRET live-cell imaging technologies have been developed to visualize the output signals in real time, such as intracellular molecular activity. Meanwhile, micropatterned technologies have been applied to modulate the physical and mechanical environment surrounding the cell to fine-tune the input signals in cellular mechanosensing. These advanced technologies can join forces and shed new light into the molecular networks that control mechanotransduction in normal conditions and disease.

Live cells can sense the mechanical characteristics of the microenvironment and translate the mechanical cues to intracellular biochemical signals in physiology and disease. To investigate intracellular signaling transduction during mechanosensing, nanotechnologies, and FRET live-cell imaging technologies have been developed to visualize the output signals in real time, such as intracellular molecular activity. Meanwhile, micropatterned technologies have been applied to modulate the physical and mechanical environment surrounding the cell to fine-tune the input signals in cellular mechanosensing. These advanced technologies can join forces and shed new light into the molecular networks that control mechanotransduction in normal conditions and disease.

This chapter discusses recent progress and future directions regarding mechanobiology as applied to neuronal function. Along with the generation and transduction of mechanical forces by neuronal elements, the influence of mechanical forces on the neuronal membrane, actin, and ion channels is highlighted. Further topics such as cortical folding and traumatic brain injury expand discussion of the role of mechanical forces into a more macroscopic scale. As the mechanical properties of the nervous tissue environment and other mechanical cues influence neural development and contribute to the regulation of endogenous brain function, there is great utility in investigating the mechanical properties of the central nervous system. Through discussion of the role of mechanical forces in neural elements, and early biophysical formulations to understand neural systems that incorporate mechanical analysis, this chapter hopes to encourage expansion of studies and methods investigating mechanobiology applied to the nervous system.

Light microscopy techniques are essential tools for visualizing the mechanobiology of cells. Computational image analysis transforms light microscopy techniques beyond tools of visualization by making it possible to extract from collected images quantitative measurements of cellular mechanical processes and to understand their behavior and mechanisms. The main goal of this chapter is to provide an up-to-date and selective review of computational image analysis techniques for cell mechanobiology applications. We aim to provide practical information to cell mechanobiology practitioners looking for image analysis techniques as well as to image analysis practitioners looking for cell mechanobiology applications. The focus of the chapter is exclusively on computational analysis techniques for dynamic fluorescence microscopy images. We first classify the images into two different categories: singe particle images and continuous region images. We then review computational analysis techniques for each category, respectively. For single particle images, we review related particle detection and particle tracking techniques and their cell mechanobiology applications. Similarly, for continuous region images, we review related region detection and region tracking techniques and their cell mechanobiology applications. We conclude with an outlook on future development of computational image analysis techniques for cell mechanobiology.