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Measuring Confluence Using High Contrast Brightfield下載
Related Products: Cytation 5 自動化影像系統暨多功能光學檢測儀, Gen5 影像系統軟體特色, Lionheart FX自動化活細胞影像系統
September 05, 2017
Measuring confluence is a direct and straightforward approach to quantify a population of cells. However, manual methods for measuring confluence are subjective, while most automated approaches require fluorescent stains or dyes. Here we describe an automated label-free technique for measuring confluence using high contrast brightfield images and Gen5 image analysis. This robust and convenient method enables accurate quantification of cell proliferation over time and normalization of data across multiple experiments and time points.
Kinetic Proliferation Cell Handling
Confluence values over time are determined from high contrast brightfield images collected during a kinetic proliferation experiment. When seeding microplates, initial cell densities should take into account cell doubling time and duration of experiment. A four day experiment with 2000 cells seeded per well in a 96-well microplate is a good starting point for cell lines with a doubling time of around 24 hours. Allow cells ample time to adhere to the microplate before starting assay.
Gen5™ Workflow Overview
Gen5 Imaging Procedure
Images are acquired using a Cytation™ or Lionheart™ FX equipped with high contrast brightfield.
- Select 4x PL FL (4x PLN) objective.
- Select Bright Field for channel 1.
- Click channel 1 to view imaging settings.
- Click to bring cells into focus and determine exposure settings.
Note: Cells should appear in focus and darker than background (Figure 1). Auto-focus and auto-exposure settings in Gen5 work well for acquiring high contrast brightfield images for most cell types, although some applications may benefit from adjusting these settings.
- If using laser autofocus, capture a reference scan.
Gen5 Cellular Analysis Settings
Gen5 Cellular Analysis features enable accurate identification of cell outlines from high contrast brightfield images. The following settings have been verified on HT-1080, NIH3T3, HeLa, and HCT116, and are recommended starting values for analyzing other cell types (Table 1). Preprocessing of high contrast bright field images is not required for confluence measurements.
- Open the Data Reduction window and add a new Cellular Analysis step.
- Set Primary Mask around cells using the values from Table 1.
- In the Calculated Metrics tab, click to remove default well-level results, then click Select or create object-level metrics of interest…
- In the Manage Object-Level Metrics, click
- Create Confluence Metric (Figure 2).
Figure 2. Custom Object Metric Window for creating Confluence metric. Confluence is calculated from total (Sum) masked area (Area) divided by the total image area (2874661 mm2), and multiplied by 100 to yield percent.
Test Confluence Protocol
Test image acquisition and analysis settings by running the protocol on a few selected wells. Percent confluence is now available within the Plate matrix Data field, as well as displayed for individual wells in the Plate window (Figure 3A) and Analyze Tool (Figure 3B). Confirm that masks are accurately placed around all cells.
Figure 3. Screen shots demonstrating percent confluence metric displayed in the (A) Plate window and (B) Analyze Tool.
Adjusting Confluence Protocol Settings
If modifications to the Cellular Analysis settings are required, adjustments to the threshold value should be sufficient to address most conditional variations. Smaller cell types may require decreasing the Min object size, however adjustments to this setting should take into account the possibility of including debris into the analysis. All settings should be determined empirically by visually confirming accurate placement of outlines around cells (Figure 4). Once you are satisfied with the Cellular Analysis settings, the protocol is now ready to measure confluence for end point applications or kinetic proliferation experiments, and should be saved for future use with the same or similar cell types.
Figure 4. Visual confirmation of placement of cell outlines. Setting the threshold value too high will result in cells being excluded from the analysis. Conversely, a threshold value that is too low will include background in the analysis. Optimal settings will result in accurate placement of outlines around all cells while excluding background.