Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy.

Traction force microscopy (TFM) is the main method used in mechanobiology to measure cell forces. Commonly this is being used for cells adhering to flat soft substrates that deform under cell traction (2D-TFM). TFM relies on the use of linear elastic materials, such as polydimethylsiloxane (PDMS) or polyacrylamide (PA). For 2D-TFM on PA, the difficulty in achieving high throughput results mainly from the large variability of cell shapes and tractions, calling for standardization. We present a protocol to rapidly and efficiently fabricate micropatterned PA hydrogels for 2D-TFM studies. The micropatterns are first created by maskless photolithography using near-UV light where extracellular matrix proteins bind only to the micropatterned regions, while the rest of the surface remains non-adhesive for cells. The micropatterning of extracellular matrix proteins is due to the presence of active aldehyde groups, resulting in adhesive regions of different shapes to accommodate either single cells or groups of cells. For TFM measurements, we use PA hydrogels of different elasticity by varying the amounts of acrylamide and bis-acrylamide and tracking the displacement of embedded fluorescent beads to reconstruct cell traction fields with regularized Fourier Transform Traction Cytometry (FTTC). To further achieve precise recording of cell forces, we describe the use of a controlled dose of patterned light to release cell tractions in defined regions for single cells or groups of cells. We call this method local UV illumination traction force microscopy (LUVI-TFM). With enzymatic treatment, all cells are detached from the sample simultaneously, whereas with LUVI-TFM traction forces of cells in different regions of the sample can be recorded in sequence. We demonstrate the applicability of this protocol (i) to study cell traction forces as a function of controlled adhesion to the substrate, and (ii) to achieve a greater number of experimental observations from the same sample.