Zellen als lebende Materialien: Kraftspektroskopische Untersuchung der Mechanotransduktion

Mechanotransduction describes a cellular mechanism of sensing and converting mechanical cues into biochemical signals to regulate cell processes, such as adhesion, migration, proliferation and/or apoptosis. Thus, becoming an ever-growing field of research with high potential for medical applications. I present a new strategy towards reliable microindentation measurements, which is essential for investigating mechanotransduction using soft substrates. I show a precise, reproducible determination of Young’s moduli through an automatic analysis of indentation data. The algorithm presented detects Young’s moduli in a region without dependence on indentation depth while minimizing the fitting error. This strategy is a step towards a comprehensive study of soft materials on a spatial scale similar to cell interactions. It has broad applicability ranging from fundamental research to developing innovative implants that match the in vivo situation. Also, I present novel approaches for multifaceted cellular manipulation. I show that layer thickness of a soft material fixed to a stiff underlying substrate can be crucial for cell adhesion. These findings are pioneer for new implant designs and advanced application fields. I present two atomic force microscopy-based manipulation systems that allow applying specific mechanical stimuli to single cells and a subsequent correlation to whole cell detachment and single bond strengths. The unique AFM-based shear system presented combines application of shear stimuli and cell detachment measurements, whereas the AFM-based modulation system combines oscillatory pushing and pulling with cell detachment measurements. Both shear and oscillatory forces are essential in our body. Thus, the strategies presented in this thesis are of significant medical interest allowing an overarching study of mechanotransduction and may pave the way towards smart stimulation devices that allow cell adhesion on demand

Automated analysis of soft hydrogel microindentation: Impact of various indentation parameters on the measurement of Young's modulus.

Measurements of Young's moduli are mostly evaluated using strong assumptions, such as sample homogeneity and isotropy. At the same time, descriptions of measurement parameters often lack detailed specifications. Many of these assumptions are, for soft hydrogels especially, not completely valid and the complexity of hydrogel microindentation demands more sophisticated experimental procedures in order to describe their elastic properties more accurately. We created an algorithm that automates indentation data analysis as a basis for the evaluation of large data sets with consideration of the influence of indentation depth on the measured Young's modulus. The algorithm automatically determines the Young's modulus in indentation regions where it becomes independent of the indentation depth and furthermore minimizes the error from fitting an elastic model to the data. This approach is independent of the chosen elastic fitting model and indentation device. With this, we are able to evaluate large amounts of indentation curves recorded on many different sample positions and can therefore apply statistical methods to overcome deviations due to sample inhomogeneities. To prove the applicability of our algorithm, we carried out a systematic analysis of how the indentation speed, indenter size and sample thickness affect the determination of Young's modulus from atomic force microscope (AFM) indentation curves on polyacrylamide (PAAm) samples. We chose the Hertz model as the elastic fitting model for this proof of principle of our algorithm and found that all of these parameters influence the measured Young's moduli to a certain extent. Hence, it is essential to clearly state the experimental parameters used in microindentation experiments to ensure reproducibility and comparability of data

Raw data for figure 4_5µm/s

The folder contains the force-distance curves used for figure 4 (speed of 5 µm/s)

Data used for the supporting information

The folder contains the data used for the supporting information including the Matlab algorithms. The folder is divided into subfolders for each SI figure

Fig2_beadsize

The folder contains the force-distance curves used for figure 2 divided into subfolders for each beadsize

Raw data for figure 4_40µm/s

The folder contains the force-distance curves used for figure 4 (speed of 40 µm/s)

Raw data for figure 1

The folder contains the force-distance curve used for figure 1

Raw data for figure 4_10µm/s

The folder contains the force-distance curves used for figure 4 (speed of 10 µm/s)

Raw data for figure 4_20µm/s

The folder contains the force-distance curves used for figure 4 (speed of 20 µm/s)

Raw data for figure 4_1µm/s

The folder contains the force-distance curves used for figure 4 (speed of 1 µm/s)