Figure S4.
CLEMSite-EM interface and Run Checker details. (a) Screenshot of the CLEMSite-EM interface to outline the details of the software User Interface (UI). In the top left panel, a map depicts targets (green) and landmarks (blue if SEM stage coordinates are matched with light microscopy stage coordinates, red if no match is present). Bottom left: A messaging console is used to display the communications with the server and which instructions are sent to the microscope. The right panel displays the list of all the targets to be acquired. The list presents which targets are already acquired (purple) and which ones are intact (green). Targets can be selected or deselected by ticking the “To Do” checkbox in the first column. For each target, it is possible to decide on a rectangular field of view of the cross-section in x and y and assign it here to each phenotype according to its expected size (ROI, red outline). In the last modifiable column, the ZEISS Atlas 5 recipes for the actual acquisition, which includes the size of the section imaged from the total 3D volume milled by the FIB-SEM (Setup, blue outline). The last two columns show the individual folder where the acquisition is saved and the percentage of progression during the acquisition. (b) Flowchart of the logic applied by the Run Checker module. This module becomes active once a run starts and triggers a script each time a newly acquired image is stored in the folder. During the progression of the acquisition, the FOV carries a translational shift that has to be tracked and corrected continuously. In this module, a routine calculates the translation between two consecutive frames, and given the incremental shift, it decides to move the imaging ROI if the sample has drifted with respect to the image acquired at the beginning of the acquisition (1). The reference used to track is the upper coating, which cannot be drifted more than a tolerance (one-fourth of the image height). If that happens, the FOV is moved up or down respectively. The same principle is applied to the position of the autotune box (small window where the AFAS is applied, magenta and blue squares) which is moved into a new position before a new AFAS is executed (2). In this case, the image is analyzed to find optimal positions for the autotune box, first executing the same algorithm as used in Fig. 3 c, but now with the hard constraint that the position must be in the upper part of the image (half of the image height) and below the upper coating. The image coordinates are translated to FOV coordinates and the autotune box is repositioned.

CLEMSite-EM interface and Run Checker details. (a) Screenshot of the CLEMSite-EM interface to outline the details of the software User Interface (UI). In the top left panel, a map depicts targets (green) and landmarks (blue if SEM stage coordinates are matched with light microscopy stage coordinates, red if no match is present). Bottom left: A messaging console is used to display the communications with the server and which instructions are sent to the microscope. The right panel displays the list of all the targets to be acquired. The list presents which targets are already acquired (purple) and which ones are intact (green). Targets can be selected or deselected by ticking the “To Do” checkbox in the first column. For each target, it is possible to decide on a rectangular field of view of the cross-section in x and y and assign it here to each phenotype according to its expected size (ROI, red outline). In the last modifiable column, the ZEISS Atlas 5 recipes for the actual acquisition, which includes the size of the section imaged from the total 3D volume milled by the FIB-SEM (Setup, blue outline). The last two columns show the individual folder where the acquisition is saved and the percentage of progression during the acquisition. (b) Flowchart of the logic applied by the Run Checker module. This module becomes active once a run starts and triggers a script each time a newly acquired image is stored in the folder. During the progression of the acquisition, the FOV carries a translational shift that has to be tracked and corrected continuously. In this module, a routine calculates the translation between two consecutive frames, and given the incremental shift, it decides to move the imaging ROI if the sample has drifted with respect to the image acquired at the beginning of the acquisition (1). The reference used to track is the upper coating, which cannot be drifted more than a tolerance (one-fourth of the image height). If that happens, the FOV is moved up or down respectively. The same principle is applied to the position of the autotune box (small window where the AFAS is applied, magenta and blue squares) which is moved into a new position before a new AFAS is executed (2). In this case, the image is analyzed to find optimal positions for the autotune box, first executing the same algorithm as used in Fig. 3 c, but now with the hard constraint that the position must be in the upper part of the image (half of the image height) and below the upper coating. The image coordinates are translated to FOV coordinates and the autotune box is repositioned.

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