To begin, launch the data analysis software on a desktop computer. Click on file, followed by browser and choose select folder to open the folder containing the acquired imaging data. Select multiple rows related to the chosen images, including the non-infected controls and excluding the supersaturated images.
Write down the image ID in a lab notebook along with the information registered during a second imaging session. Click on load as group to assemble the selected images. Now click on the save icon to save the new sequence image in a new folder for re-analysis of raw data if needed.
Next, click on options followed by display and select binning factor to identify the binning used for each image. The acquired binning factor will be displayed at the top of each image in the sequence. To correct all binning factors to the same value, first, double click on the image to be adjusted.
On the tool pal window, click on the image adjust option, then choose the appropriate binning factor. Double click on the non-infected mice image, then select the option counts in the unit field. Adjust the color scale to a minimum value of 600.
Choose the radiance options in the units field to view the radiance of the resulting image. With the sequence window active, choose the option radiance in the units field. Disable the individual box in the color scale limits area to adjust the color scale and color table in the tool palette.
Mark the logarithmic scale and manual boxes, and then set the maximum scale numbers as per the non-infected control and the area with the highest signal as the maximum. After setting the same scale for all images, double click on each image, maximize the window, and use the export graphics option to export the image view in an image format. Name the files by treatment, followed by the mice ID and time point.
Double click on in image from the sequence. On the tool palette window, click on the ROI tool section, then press the square icon. Draw a rectangle over the whole mouse.
Click on the border of the created ROI and copy and paste the same ROI for each mouse. Next, name the ROIs to be recorded. Then click on the save icon in the ROI tool section.
Select the box, apply to sequence. Then apply the saved ROIs for all images by clicking on load. Adjust the position of the ROI for each mouse to better fit them in the measurement area.
When all mice have been labeled, click on the measure ROIs option. The ROI measurements table should appear. Set the measurement types to radiance, the image attributes to all possible value and the ROI dimensions to centimeters.
Then click on the select all option followed by copy. Paste the data directly into the table analysis software. Organize the data columns to sort the groups properly.
Next, color code the data by groups in the spreadsheet. Arrange the total flux values to match the same mouse's ventral and dorsal total flux values. Then sum them to obtain each mouse's whole body bioluminescence.
Calculate the mean and standard deviation of the summed values for each group. Plot the data into a statistical software to generate graphs. Finally, create a panel with bioluminescent images.
Place each mouse in columns and the time points in rows to build a drug efficacy matrix. When the bioluminescence method is applied to evaluate antiparasitic agents, a drug efficacy matrix is built to compare test compounds, exemplified here by posaconazole against the standard drug treatment used for Chagas disease, benznidazole at 100 milligrams per kilogram administered once daily. In this longitudinal study, the infection time course is illustrated by comparing a non-treated group with treated ones.
The results demonstrate either a reduction in parasite burden as indicated by reduced bioluminescence, or a relapse shown by increased parasite load and consequent light detection, suggesting an ineffective compound or treatment regimen. The quantified light is represented by the sum of the ventral and dorsal total flux in photons per second as a function of infection time.
