![\"\u8367\u5149\u62a5\u544a\u57fa\u56e0\u6210\u50cf\"](\"https:\/\/opentrons.com.cn\/wp-content\/uploads\/2024\/07\/image-952x1024.png\")
To demonstrate the platform, we first demonstrated how it can be used in large-scale fluorescence imaging experiments (Nasu et al., 2021). Typical experimental protocols involving fluorescent reporters require imaging of cells for a period of time before and after the application of agonists, growth factors, or other active compounds. To demonstrate the usability of the developed experimental platform for this experimental paradigm, we performed calcium imaging using a synthesized fluorescent indicator of calcium concentration. Cells were automatically labeled with the fluorescent calcium dye Fluo4-AM (Fig. 1C ) and subjected to calcium imaging in response to 100 \u03bc m ATP. Cells were then automatically segmented using the Cellpose algorithm (Stringer et al., 2021) (Figure 1C, right), and the fluorescence dynamics of individual cells were extracted (Figure 1D). The same experimental procedure and analysis were then automatically repeated in the other 30 wells (Figure 1E,F and Supplementary Video 3). These experiments demonstrate that the developed platform enables robust and automated high-throughput imaging of fluorescent reporters.<\/p>\n\n\n\n
Another important advantage of the developed system is that it allows large-scale generation of images in a large number of wells. To demonstrate this usability, we generated a Python protocol class (Supplementary Protocol 1, see Software section for details) that moves the platform over all 96 wells, focuses on each cell, and captures bright field or fluorescence image. Examples of such experiments are shown in Figures 2A,B. Importantly, this system allows scanning a single well and generating multiple images of the same well (Figure 2C), which is useful for finding rare cells (e.g., cells undergoing mitosis\/apoptosis or positive cells with inefficient transfection). it works.<\/p>\n\n\n\n To demonstrate the usability of the developed experimental platform in labeling experiments, we used fluorescent wheat germ agglutinin (WGA), which highlights cell membranes. Five rows of wells (48 wells in total) were automatically washed with PBS and then a solution containing 5 mg\/ml fluorescent WGA was added. After standing at room temperature for 10 minutes, the WGA was washed with PBS and then placed under a fluorescence microscope for observation. The resulting fluorescence image is shown in Figure 2D. Automated labeling produced clear images of cells with well-defined plasma membranes, demonstrating that routine labeling procedures can be automated using the developed platform.<\/p>\n\n\n\n Finally, we also tested whether the developed platform could be used for immunofluorescence staining with anti-HER2 antibody (Herceptin) as primary antibody and Alexa 488-conjugated secondary antibody. Wells containing SKBR3 cells (high expression of HER2) or HeLa cells (low expression of HER2) were automatically perfused with PBS and Herceptin. After incubation for 40 min at room temperature, cells were perfused with PBS followed by secondary antibodies. After an additional 30 min of incubation at room temperature, the wells were perfused with imaging solution and fluorescence imaging was performed (Figure 2E and Supplementary Figure 1). The resulting images show clear labeling of the cell membranes of SKBR3 cells with high HER2 levels but unclear labeling of HeLa cells with low receptor levels, demonstrating the robustness of the automated labeling procedure.<\/p>\n\n\n\n These examples demonstrate the broad usability of the developed experimental platform in automating different types of cell biology experiments. Below, we describe the platform in detail and demonstrate its performance in a series of tests.<\/p>\n\n\n\n Hardware Design The overall structure is constructed from MakerbeamXL 15 mm \u00d7 15 mm extrusions, connected via L- and T-shaped aluminum brackets or 3D printed parts. We found that using an aluminum extrusion frame instead of a fully 3D printed part (both models available at https:\/\/github.com\/frescolabs\/FrescoM) made the entire system more stable and reduced vibration (data not shown). The frame consists of eight side extrusions (four vertical 300 mm and three horizontal 200 mm, two at the top and one at the bottom, Figure 3A). The sides are attached to two 400mm extrusions that hold the MGH12 rails that drive the x-axis. The y-axis (Figure 3B) rests on a square frame constructed of four 200 mm extrusions and is connected to the x-axis via two 3D printed brackets. Two MGH12 rails are located on the frame to accommodate a Nema 17 motor and a 96-well plate holder (Figure 3B, y_plate_holder.stl file).<\/p>\n","protected":false},"excerpt":{"rendered":" Modern data analysis methods, such as optimization algo […]<\/p>\n","protected":false},"author":5,"featured_media":3110,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[],"tags":[],"class_list":["post-3108","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry"],"yoast_head":"\n![\"\u5c55\u793a\u4e86\u6240\u5f00\u53d1\u5e73\u53f0\u7684\u9002\u7528\u6027\"](\"https:\/\/opentrons.com.cn\/wp-content\/uploads\/2024\/07\/image-1-1024x583.png\")