Automated multi-target super-resolution microscopy with trust regions

Abstract

We describe a dedicated microscope for automated sequential localization microscopy which we term Sequential Super-resolution Microscope (SeqSRM). This microscope automates precise stage stabilization on the order of 5-10 nanometers and data acquisition of all user-selected cells on a coverslip, limiting user interaction to only cell selection and buffer exchanges during sequential relabeling. We additionally demonstrate that nanometer-scale changes to cell morphology affect the fidelity of the resulting multi-target super-resolution overlay reconstructions generated by sequential super-resolution microscopy, and that regions affected by these shifts can be reliably detected and masked out using brightfield images collected periodically throughout the experiment. The SeqSRM enables automated multi-target imaging on multiple user-selected cells without the need for multiple distinct fluorophores and emission channels, while ensuring that the resulting multi-target localization data accurately reflect the relative organization of the underlying targets.

Fig. 1.

SeqSRM Optical Diagram. A 647 nm fiber laser ("647 Laser") passes through an electronically controllable shutter (Shutter) and a neutral density filter on an electronically controllable flip mount (ND Filter). A 405 nm diode laser (405 Laser) is combined with the 647 nm laser before both are passed through an active vibrating membrane diffuser (Diffuser) before being coupled into a multimode fiber (MM). The tip of the multimode fiber is then imaged to the sample plane. Fluorescence emission light is collected by a 100X objective (100X) and is passed through a dichroic mirror (DM) and an emission filter (EF) before being imaged onto the sensor (sCMOS). The mirror indicated by "M*" is placed in a plane conjugated to the back focal plane of the objective lens to allow for placement of point-spread function engineering components (e.g., a deformable mirror). All lens focal lengths are given in units of millimeters (e.g., "f=150" indicates a lens with focal length of 150 mm.))

Fig. 2.

Trust region generation flowchart. Step-by-step flowchart illustrating the procedure used to generate trust regions for two-target data. In Step 1, we collect and save a brightfield image immediately before each sequence of super-resolution data is collected for each label. In Step 2, we divide the $N$ brightfield images for each of the labels into 32x32 pixel sub-ROIs and compute the shifts between corresponding sub-ROIs in each sequence of each label. In Step 3, we compute the median of the $N$ resulting shifts within each sub-ROI and generate a mask by comparing the median shifts to a user-defined threshold. Finally, in Step 4 we use the mask to identify regions of the super-resolved multi-target overlay that we deem trustworthy.

Fig. 3.

Local brightfield shifts versus super-resolution shifts. (a) Representative super-resolution overlay result from imaging $\alpha$ -tubulin twice sequentially (first round in magenta, second round in green, with overlapping localizations appearing white due to the pixel sum of the magenta and green colors in reconstruction images) on three separate cells as described in Section 2.8. Yellow arrows represent the brightfield shifts computed within each sub-ROI and cyan arrows represent the corresponding super-resolution shifts, with the shift magnitudes scaled by a factor of 25 to improve visibility. The super-resolution image was also rescaled as described in Section 2.7 to improve visual contrast. (b) Local brightfield shifts within 32x32 pixel sub-ROIs versus the corresponding super-resolution shifts for the 3 cells imaged as described in Section 2.8, where a representative example is shown in (a). The solid red line indicates a weighted least-squares fit for the shown data, with the weighting being the inverse variance of the brightfield shifts. The data shown includes approximately 81% of the total sub-ROI shifts (i.e., shifts within 50 nm shown in the plot), with the outliers excluded from the plot attributed to sub-ROIs falling outside of the cell. Data points indicated by hollow black circles represent points with brightfield shift variances greater than the 513 ${\mathrm{nm}}^2$ scale of the colorbar. (c) Cumulative distribution of brightfield shift variances color-coding points in (b), with the black dotted line indicating the visual cutoff of the colorbar. The white scale bar in (a) is 3 $\mu$ m wide.

Fig. 4.

$\beta$ - and $\alpha$ -tubulin dSTORM. (a) Selected results from sequential imaging of (green) $\beta$ - and (magenta) $\alpha$ -tubulin on 40 cells/ROIs on a single coverslip. Pixels corresponding to both $\beta$ - and $\alpha$ -tubulin localizations appear white due to the sum of the color channels in the reconstruction image. Super-resolution localizations are represented by Gaussians with standard deviations equal to the estimated localization precision for each localization. Sub-ROIs were masked out in areas with shifts greater than 25 nm apparent from collected brightfield images. The white scale bar is 3 $\mu$ m wide. The full set of 40 cells is provided in the Supplement 1. (b) Zoom in of selected sub-ROIs (indicated by yellow boxes) of the images in (a). (c) Image sub-ROIs that were masked out in (a). (d) Zoom in of selected sub-ROIs (indicated by yellow boxes) of the images in (c). The white scale bar is 3 $\mu$ m wide. The yellow sub-ROIs are 1.1 $\mu$ m × 1.1 $\mu$ m. All images were rescaled as described in Section 2.7 to improve visual contrast.