Even and odd data sets were then processed separately as indicated (Average Z projection, LiveSRRF or SACD) and the two output images were used for the FRC analyses. Image decorrelation analysis was performed in ImageJ using the Image decorrelation analysis plugin.54 This analysis requires a single image as input and therefore the full FBSR data sets were used here. Bead Tracking and Local Force Measurements The bead tracking and local force measurements were performed either using MATLAB (Mathworks, version R2019a) or using Fiji.49?51 For the MATLAB-based analyses, the TFM software developed by the Danuser laboratory was used.15 If not indicated otherwise, bead trackings were performed by cross-correlation within the search window. Key parameters used can be found in Table 4. Table 4 Key Parameters for TFM Analyses Using MATLAB-Based Software

Figure?2chigh-resolution subsampling of beads and use subpixel correlation via image interpolation20 and 21 pxFTTC (Fourier transform traction cytometry)Figure?3bhigh-resolution subsampling of beads and use subpixel correlation via image interpolation40 and 41 pxFTTCFigure?3c,dhigh-resolution subsampling of beads and use subpixel correlation via image interpolation80 and 81 pxFTTCFigure?4aPIV80 and 81 pxFTTCFigure?4bhigh-resolution subsampling of beads and use subpixel correlation via image interpolation40 and 41 pxFTTCFigure?4chigh-resolution subsampling of beads and use subpixel correlation via image interpolation80 and 81 pxFTTCFigure?4e?and?fhigh-resolution subsampling of beads and use subpixel correlation via image interpolation20 and 21 pxFTTCFigure?5bhigh-resolution subsampling of beads and use subpixel correlation via image interpolation60 and 61 pxFTTC Open in a separate window To generate the displacement and traction maps in Fiji, the particle image velocity (PIV) plugin and the Fourier transform traction cytometry (FTTC) plugin32 were used. = 26; LiveSRRF widefield, = 26, = 24; SACD widefield, = 26, = 26). (e) Graph showing bead densities (beads per square micrometer) measured from multiple published TFM data sets28?31 and from the TFM gels (improved protocol described here) imaged using either spinning-disk confocal or widefield followed by FBSR processing using LiveSRRF or SACD. To validate that FBSR can improve the detection of 40 nm beads, we performed simulations CKLF with known and increasing bead densities (see Materials and Methods for details; Supplementary Figure 1aCd). These simulations show that, at low bead densities, accurate bead numbers can be recovered from both widefield and FBSR images with FBSR processing clearly improving the quality and resolution of the final images (Supplementary Figure 1a,b). However, at higher bead densities (over 1 bead per square micrometer), FBSR processing allowed a higher recovery of bead numbers compared to the widefield images (Supplementary Figure 1a,b). To assess the improvement in bead trackability enabled by the detection of higher bead density using FBSR processing, a realistic displacement field was applied to our simulated data (see Materials and Methods for details; Supplementary Figure 1c). The bead displacement maps generated using FBSR imaging demonstrated that while the overall displacement field was apparent at low bead densities, fine details could only be retrieved at high bead densities (Supplementary Figure 1c,d). Altogether, our simulations demonstrate that Ilorasertib FBSR processing allows for the detection of higher bead densities, which leads to increased trackability of the beads after image reconstruction and in turn to improved recovery of spatial details in the force map. To optimize TFM gels for FBSR, and inspired by previous work,13,15,24?26 we optimized a simplified gel casting protocol where the 40 nm beads are embedded only on the topmost layer of the gel (Supplementary Figure 2a,b). This was achieved by precoating the top coverslip, used to flatten the gel solution prior to casting, with the beads instead of mixing the Ilorasertib beads within the gel solution itself (Supplementary Figure 2a). Importantly, using the FBSR algorithms LiveSRRF and SACD and our optimized protocol, we were able to improve the detection of 40 nm beads located on top of the TFM gel using both spinning-disk confocal and widefield microscopes (Figure ?Figure11b). To ensure that as few artefacts as possible were introduced during the FBSR reconstruction process, the image quality was assessed using NanoJ SQUIRREL27 and the resolution scaled Pearsons correlation (RSP) and resolution scaled error (RSE) parameters were calculated by the software (Figure ?Figure11c). In addition to these parameters, when choosing the reconstruction settings, the amount of beads detected and the absence of patterning in the final image were also taken into consideration (Supplementary Figure 2c,d). FBSR processing led to a 2C3-fold improvement in the resolution of bead images as measured by Fourier ring correlation and decorrelation analyses (Figure ?Figure11d). Ilorasertib Prior to FBSR, our confocal-based TFM analyses have yielded between 0.2 to 0.5 trackable beads per square micrometer28?31 (Figure ?Figure11e), in agreement with values reported by others.13 Here, by taking advantage of the densely packed 40 nm bead layer gels and by implementing FBSR, and conservative reconstruction parameters, we were able to substantially increase the number of trackable beads to 1 1.2 beads per square micrometer (Figure ?Figure11e). This is a modest improvement over a protocol using structured illumination microscopy14 (1 bead per square micrometer) but remains inferior to another protocol based Ilorasertib on STED imaging within small fields of view (2.2 beads per square micrometer) (Table 1).13 Interestingly, FBSR performed especially well when images were acquired using widefield microscopy as the final SR images were more homogeneous (Figure ?Figure11b). When the images were acquired using spinning-disk confocal, the corners of the field of view were often off focus due to uneven/wrinkled gels resulting in much lower bead density in.