1. Place a power meter sensor (e.g., Thorlabs S170C) on the microscope stage.

2. Center the sensor with the objective to ensure proper alignment.

3. Zero the sensor to ensure accurate readings.

4. Using your power meter controller (e.g., Thorlabs PM400) or compatible software, select the wavelength of the light source you want to monitor.

5. Turn on the light source and immediately record the power output over time until it stabilizes.

   I personally record every 10 seconds for 24 hours and stops when it has been stable for 1 h.

6. Repeat steps 3 to 5 for each light source you want to monitor

   Keep the light source turned on at all times. Depending on your hardware, the light source may remain continuously on or be automatically shut down by the software when not in use.

Use the provided spreadsheet template, Illumination Warmup Kinetic_Template.xlsx, and fill in the orange cells to visualize your results. For each light source, plot the measured power output (in mW) against time to analyze the data.

Calculate the relative power using the formula: Relative Power = (Power / Max Power). Then, plot the Relative Power (%) against time to visualize the data.

We observe some variability in the power output for the 385 nm light source.

To assess the stability:

1. Define a Stability Duration Window:
   Select a time period (e.g., 10 minutes) during which the power output should remain stable.

2. Specify a Maximum Coefficient of Variation (CV) Threshold:
   Determine an acceptable variability limit for the selected window (e.g., 0.01%).

3. Calculate the Coefficient of Variation (CV):
   Use the formula: CV = (Standard Deviation / Mean)
   Compute the CV for the specified stability duration window.

4. Visualize Stability:
   Plot the calculated CV over time to analyze the stability of the power output.

We observe that the light sources stabilize quickly, within less than 10 minutes, while the 385 nm light source takes approximately 41 minutes to reach stability.

Report the results in a table

 Selected Stability Duration Window (min): 10 min and Maximum Coefficient of Variation: 0.01%

The illumination warm-up time for this instrument is approximately 40 minutes. This duration is essential for ensuring accurate quantitative measurements, as the Coefficient of Variation (CV) threshold is strict, with a maximum allowable variation of 0.01% within a 10-minute window.

1. Warm up the light sources for the required duration (see previous section).
2. Place the power meter sensor (e.g., Thorlabs S170C) on the microscope stage.
3. Center the sensor with the objective to ensure proper alignment.
4. Zero the sensor to ensure accurate readings.
5. Using your power meter controller (e.g., Thorlabs PM400) or software, select the wavelength of the light source you wish to monitor.
6. Turn the light source on to 100% power.
7. Record the average power output over 10 seconds.
   I personally re-use the data collected during the warm-up kinetic experiment (when the power is stable) for this purpose.
8. Repeat steps 5 to 7 for each light source and wavelength you wish to monitor.

Fill in the orange cells in the Maximum Illumination Power Output_Template.xlsx spreadsheet to visualize your results. For each light source, plot the measured maximum power output (in mW).

Plot the measured maximum power output (in mW) and compare it to the manufacturer’s specifications. Calculate the Relative Power using the formula: Relative Power = (Measured Power / Manufacturer Specifications) and plot the relative power for each light source

Report the results in a table

This instrument provides 80% of the power specified by the manufacturer. These results are consistent, as the manufacturer’s specifications are based on a different objective, and likely different filters and mirrors, which can affect the measured power output.

1. Warm up the light sources for the required duration (see previous section).
2. Place the power meter sensor (e.g., Thorlabs S170C) on the microscope stage.
3. Center the sensor with the objective to ensure proper alignment.
4. Zero the sensor to ensure accurate readings.
5. Using your power meter controller (e.g., Thorlabs PM400) or software, select the wavelength of the light source you wish to monitor.
6. Turn the light source on to 100% power.
7. Record the power output every 100 ms for 1 minute. For some microscope dedicated to a fast imaging it might be required to record stability at a faster rate. The THorlab S170C sensor can record at 1 kHz
   

   I personally acquire this data immediately after the warm-up kinetic experiment, without turning off the light source.

8. Repeat steps 5 to 7 for each light source and wavelength you wish to monitor.

Fill in the orange cells in the Illumination Stability_Template.xlsx spreadsheet to visualize your results. For each light source, plot the measured power output (in mW) over time.

Calculate the relative power using the formula: Relative Power = (Power / Max Power). Then, plot the Relative Power (%) over time.

Calculate the Stability Factor (S) using the formula: S (%) = 100 × (1 - (Pmax - Pmin) / (Pmax + Pmin)). Also, calculate the Coefficient of Variation (CV) using the formula: CV = Standard Deviation / Mean. Reports the results in a table.

From the Stability Factor results, we observe that the difference between the maximum and minimum power is less than 0.03%. Additionally, the Coefficient of Variation indicates that the standard deviation is less than 0.004% of the mean value, demonstrating excellent power stability.

The light sources exhibit a very high stability, >99.9% during a 1-minute period.

1. Warm up the light sources for the required duration (see previous section).
2. Place the power meter sensor (e.g., Thorlabs S170C) on the microscope stage.
3. Ensure the sensor is properly centered with the objective for accurate measurements.
4. Zero the sensor to ensure precise readings.
5. Using your power meter controller (e.g., Thorlabs PM400) or software, select the desired wavelength of the light source.
6. Turn on the light source to 100% intensity.
7. Record the power output every 10 seconds for 15 minutes.

   I personally re-use the data collected during the warm-up kinetic experiment (when the power is stable) for this purpose.

8. Repeat steps 5 to 7 for each light source you wish to monitor.

Fill in the orange cells in the Illumination Stability_Template.xlsx spreadsheet to visualize your results. For each light source, plot the measured power output (in mW) over time.

Calculate the relative power using the formula: Relative Power = Power / Max Power. Then, plot the Relative Power (%) over time.

Calculate the Stability Factor (S) using the formula: S (%) = 100 × (1 - (Pmax - Pmin) / (Pmax + Pmin)). Also, calculate the Coefficient of Variation (CV) using the formula: CV = Standard Deviation / Mean. Reports the results in a table.

From the Stability Factor results, we observe that the difference between the maximum and minimum power is less than 0.01%. Additionally, the Coefficient of Variation indicates that the standard deviation is less than 0.004% of the mean value, demonstrating excellent power stability.

The light sources exhibit high stability, maintaining >99.9% stability during a 15-minute period.

1. Warm up the light sources for the required duration (see previous section).
2. Place the power meter sensor (e.g., Thorlabs S170C) on the microscope stage.
3. Ensure the sensor is properly centered with the objective for accurate measurements.
4. Zero the sensor to ensure precise readings.
5. Using your power meter controller (e.g., Thorlabs PM400) or software, select the desired wavelength of the light source.
6. Turn on the light source to 100% intensity.
7. Record the power output every 10 seconds for 1 hour.

   I personally re-use the data collected during the warmup kinetic experiment.

8. Repeat steps 5 to 7 for each light source you wish to monitor

Fill in the orange cells in the Illumination Stability_Template.xlsx spreadsheet to visualize your results. For each light source, plot the measured power output (in mW) over time to assess stability.

Calculate the relative power using the formula: Relative Power = Power / Max Power. Then, plot the Relative Power (%) over time.

Calculate the Stability Factor (S) using the formula: S (%) = 100 × (1 - (Pmax - Pmin) / (Pmax + Pmin)). Also, calculate the Coefficient of Variation (CV) using the formula: CV = Standard Deviation / Mean. Reports the results in a table.

From the Stability Factor results, we observe that the difference between the maximum and minimum power is less than 0.03%. Additionally, the Coefficient of Variation indicates that the standard deviation is less than 0.02% of the mean value, demonstrating excellent power stability.

The light sources exhibit exceptional stability, maintaining a performance of >99.9% during a 1-hour period.

1. Warm up the light sources for the required duration (see previous section).
2. Place the power meter sensor (e.g., Thorlabs S170C) on the microscope stage.
3. Center the sensor with the objective to ensure proper alignment.
4. Zero the sensor to ensure accurate readings.
5. Using your power meter controller (e.g., Thorlabs PM400) or software, select the wavelength of the light source you wish to monitor.
6. Turn the light source on to 100% power.
7. Record the average power output over 10 seconds.
   I personally re-use the data collected for the maximal power output section and plot it over time.
8. Repeat steps 5 to 7 for each light source and wavelength you wish to monitor.

Fill in the orange cells in the Illumination Stability_Template.xlsx spreadsheet to visualize your results. For each light source, plot the measured power output (in mW) over time to assess the stability of the illumination.

Calculate the relative power using the formula: Relative Power = Power / Max Power. Then, plot the Relative Power (%) over time.

Calculate the Relative Power_Spec by comparing the measured power to the manufacturer’s specifications using the following formula: Relative Power_Spec = Power / Power_Spec. Then, plot the Relative Power_Spec (% Spec) over time.

We expect a gradual decrease in power output over time due to the aging of hardware. Light sources should be replaced when the Relative Power_Spec falls below 50%.

Reports the results in a table.

The light sources are somehow stable over the last 2 years but a decrease in the maximum power output is seen.

1. Warm up the light sources for the required duration (refer to the previous section).
2. Place the power meter sensor (e.g., Thorlabs S170C) on the microscope stage.
3. Ensure the sensor is properly centered with the objective for accurate measurements.
4. Zero the sensor to ensure precise readings.
5. Using your power meter controller (e.g., Thorlabs PM400) or software, select the wavelength of the light source you wish to monitor.
6. Turn on the light source and adjust its intensity to 0%, then incrementally increase to 10%, 20%, 30%, and so on, up to 100%.
   I typically collect this data immediately after the warm-up kinetic phase and once the real-time power stability data has been recorded.
7. Record the power output corresponding to each input level.
8. Repeat steps 5 to 7 for each light source you wish to monitor

Fill in the orange cells in the llumination Power Linearity_Template.xlsx spreadsheet to visualize your results. For each light source, plot the measured power output (in mW) as a function of the input (%).

Calculate the Relative Power using the formula: Relative Power = Power / MaxPower. Then, plot the Relative Power (%) as a function of the input (%).

Determine the equation for each curve, which is typically a linear relationship of the form: Output = K × Input. Report the slope (K) and the coefficient of determination (R²) for each curve in a table.

The slopes demonstrate a nearly perfect linear relationship between the input and the measured output power, with values very close to 1. The coefficient of determination (R²) indicates a perfect linear fit, showing no deviation from the expected relationship.

The light sources are highly linear.

1. Warm up the light sources for the required duration (refer to the previous section).
2. Place the power meter sensor (e.g., Thorlabs S170C) on the microscope stage.
3. Ensure the sensor is properly centered with the objective.
4. Zero the sensor to ensure accurate readings.
5. Using your power meter controller (e.g., Thorlabs PM400) or software, select the wavelength of the light source you wish to monitor.
6. Turn on the light source to 100% intensity.
7. Record the power output for each objective, as well as without the objective in place.

   I typically collect this data after completing the warm-up kinetic phase, followed by the real-time power stability measurements, and immediately after recording the power output linearity.

8. Repeat steps 5 to 7 for each light source and wavelength

Fill in the orange cells in the Objective and cube transmittance_Template.xlsx spreadsheet to visualize your results. For each objective, plot the measured power output (in mW) as a function of the wavelength (in nm).

Calculate the Relative Transmittance using the formula: Relative Transmittance = Power / PowerNoObjective. Then, plot the Relative Transmittance (%) as a function of the wavelength (in nm).

Calculate the average transmittance for each objective and report the results in a table. Compare the average transmittance to the specifications provided by the manufacturer to assess performance.

The measurements are generally close to the specifications, with the exception of the 63x-1.4 objective. This deviation is expected, as the 63x objective has a smaller back aperture, which reduces the amount of light it can receive. Additionally, you can compare the shape of the transmittance curves to further assess performance.

The objectives are transmitting light properly.

1. Warm up the light sources for the required duration (refer to the previous section).
2. Place the power meter sensor (e.g., Thorlabs S170C) on the microscope stage.
3. Ensure the sensor is properly centered with the objective.
4. Zero the sensor to ensure accurate readings.
5. Using your power meter controller (e.g., Thorlabs PM400) or software, select the wavelength of the light source you wish to monitor.
6. Turn on the light source to 100% intensity.
7. Record the power output for each filter cube.

   I typically collect this data after completing the warm-up kinetic phase, followed by the real-time power stability measurements, and immediately after recording the power output linearity.

8. Repeat steps 5 to 7 for each light source and wavelength

Fill in the orange cells in the Objective and cube transmittance_Template.xlsx spreadsheet to visualize your results. For each filter cube, plot the measured power output (in mW) as a function of the wavelength (in nm).

Calculate the Relative Transmittance using the formula: Relative Transmittance = Power / PowerMaxFilter. Then, plot the Relative Transmittance (%) as a function of the wavelength (in nm).

Calculate the average transmittance for each filter at the appropriate wavelengths and report the results in a table.

• The DAPI cube transmits only 14% of the excitation light compared to the Quad Band Pass DAPI/GFP/Cy3/Cy5. While it is still usable, it will provide a low signal. This is likely because the excitation filter within the cube does not match the light source properly. Since an excitation filter is already included in the light source, the filter in this cube could be removed.

• The GFP and DsRed cubes transmit 47% of the excitation light compared to the Quad Band Pass DAPI/GFP/Cy3/Cy5, and they are functioning properly.

• The DHE cube does not transmit any light from the Colibri. This cube may need to be removed and stored.

• The Cy5 cube transmits 84% of the excitation light compared to the Quad Band Pass DAPI/GFP/Cy3/Cy5, and it is working properly.

Actions to be Taken:

• Remove the excitation filter from the DAPI cube, as it does not properly match the light source and is redundant with the excitation filter already included in the light source.
• Remove and store the DHE cube, as it does not transmit any light from the Colibri and is no longer functional.

1. Place a fluorescent plastic slide (Thorlabs FSK5) onto the stage.
2. Center the slide and the objective.
3. Adjust the focus to align with the surface of the slide.
4. Adjust the focus to the surface of the slide
   

   I typically use a red fluorescent slide and focus on a scratch mark on its surface for alignment.

5. Slightly adjust the focus deeper into the slide to minimize the visibility of dust, dirt, and scratches.

6. Modify the acquisition parameters to ensure the image is properly exposed.

   I typically use the auto-exposure feature, aiming for a targeted intensity of 30%.

7. Capture a multi-channel image.
8. Repeat the process for each objective and filter combination.

You should have acquired several multi-channel images that now need processing to yield meaningful results. To process them, use the Field Illumination analysis feature of the MetroloJ_QC plugin for FIJI.

1. Open FIJI.
2. Load your image by dragging it into the FIJI bar.
3. Launch the MetroloJ_QC plugin by navigating to Plugins > MetroloJ QC.
4. Click on Field Illumination Report.
5. Enter a title for your report.
6. Type in your name.
7. Click Filter Parameters and input the filter's names, excitation, and emission wavelengths.
8. Check Remove Noise using Gaussian Blur.
9. Enable Apply Tolerance to the Report and reject uniformity and accuracy values below 80%.
10. Click File Save Options.
11. Select Save Results as PDF Reports
12. Select Save Results as spreadsheets.
13. Click OK.
14. Repeat steps 4 through 13 for each image you have acquired.

This will generate detailed results, which will be stored in a folder named Processed. The Processed folder will be located in the same directory as the original images, with each report and result saved in its own subfolder.

• Use the following R script to merge all _results.xls files into a single file.

• This script will load all _results.xls files located in the "Input" folder on your desktop or prompt you to select an input folder.

• It will compile and merge data generated by the MetroloJ_QC Field Illumination analysis, including metrics such as Uniformity and Centering Accuracy for each channel from every file.

• Additionally, it will split the filenames using the _ character and create columns named Variable-001, Variable-002, etc. This will help in organizing the data if the filenames are formatted as Objective_VariableA_VariableB, for example.

• The combined data will be saved as Combine_output.csv in the Output folder on your desktop.

# Clear the workspace
rm(list = ls())

# Set default input and output directories
default_input_dir <- file.path(Sys.getenv("USERPROFILE"), "Desktop", "Input")  # Default to "Input" on Desktop
InputFolder <- default_input_dir  # Use default folder

# Prompt user to select a folder (optional, currently commented)
InputFolder <- choose.dir(default = default_input_dir, caption = "Select an Input Folder") # You may comment this line if you want to directly use the InputFolder on your desktop

# If no folder is selected, fall back to the default
if (is.na(InputFolder)) {
  InputFolder <- default_input_dir
}

# Specify the Output folder (this is a fixed folder on the Desktop)
OutputFolder <- file.path(Sys.getenv("USERPROFILE"), "Desktop", "Output")
if (!dir.exists(OutputFolder)) dir.create(OutputFolder, recursive = TRUE)

# List all XLS files ending with "_results.xls" in the folder and its subfolders
xls_files <- list.files(path = InputFolder, pattern = "_results.xls$", full.names = TRUE, recursive = TRUE)
if (length(xls_files) == 0) stop("No _results.xls files found in the selected directory.")

# Define the header pattern to search for
header_pattern <- "Filters combination/set.*Uniformity.*Field Uniformity.*Centering Accuracy.*Image.*Coef. of Variation.*Mean c fit value"

# Initialize a list to store tables
all_tables <- list()

# Loop over all XLS files
for (file in xls_files) {
  # Read the file as raw lines (since it's tab-separated)
  lines <- readLines(file)
  
  # Find the line that matches the header pattern
  header_line_index <- grep(header_pattern, lines)
  
  if (length(header_line_index) == 0) {
    cat("No matching header found in file:", file, "\n")
    next  # Skip this file if the header isn't found
  }
  
  # Extract the table starting from the header row
  start_row <- header_line_index[1]
  
  # The table might end at an empty line, so let's find the next empty line after the header
  end_row <- min(grep("^\\s*$", lines[start_row:length(lines)]), na.rm = TRUE) + start_row - 1
  if (is.na(end_row)) end_row <- length(lines)  # If no empty line found, use the last line
  
  # Extract the table rows (lines between start_row and end_row)
  table_lines <- lines[start_row:end_row]
  
  # Convert the table lines into a data frame
  table_data <- read.table(text = table_lines, sep = "\t", header = TRUE, stringsAsFactors = FALSE)
  
  # Add the filename as the first column (without extension)
  filename_no_extension <- tools::file_path_sans_ext(basename(file))
  table_data$Filename <- filename_no_extension
  
  # Split the filename by underscores and add as new columns
  filename_parts <- unlist(strsplit(filename_no_extension, "_"))
  for (i in 1:length(filename_parts)) {
    table_data[[paste0("Variable-", sprintf("%03d", i))]] <- filename_parts[i]
  }
  
  # Rename columns based on the exact names found in the data
  colnames(table_data) <- gsub("Filters.combination.set", "Channel", colnames(table_data))
  colnames(table_data) <- gsub("Uniformity....", "Uniformity (%)", colnames(table_data))
  colnames(table_data) <- gsub("Field.Uniformity....", "Field Uniformity (%)", colnames(table_data))
  colnames(table_data) <- gsub("Centering.Accuracy....", "Centering Accuracy (%)", colnames(table_data))
  colnames(table_data) <- gsub("Coef..of.Variation", "CV", colnames(table_data))
  
  # Remove the "Mean.c.fit.value" column if present
  colnames(table_data) <- gsub("Mean.c.fit.value", "", colnames(table_data))
  
  # Drop any columns that were renamed to an empty string
  table_data <- table_data[, !grepl("^$", colnames(table_data))]
  
  # Divide the relevant columns by 100 to convert percentages into decimal values
  table_data$`Uniformity (%)` <- table_data$`Uniformity (%)` / 100
  table_data$`Field Uniformity (%)` <- table_data$`Field Uniformity (%)` / 100
  table_data$`Centering Accuracy (%)` <- table_data$`Centering Accuracy (%)` / 100
  
  # Store the table in the list with the file name as the name
  all_tables[[basename(file)]] <- table_data
}

# Check if any tables were extracted
if (length(all_tables) == 0) {
  stop("No tables were extracted from the files.")
}

# Optionally, combine all tables into a single data frame (if the structures are consistent)
combined_data <- do.call(rbind, all_tables)

# View the combined data
#head(combined_data)

# Optionally, save the combined data to a CSV file
output_file <- file.path(OutputFolder, "combined_output.csv")
write.csv(combined_data, output_file, row.names = FALSE)
cat("Combined data saved to:", output_file, "\n")

The following spreadsheet provides a dataset that can be manipulated with a pivot table to generate informative graphs and statistics Illumination_Uniformity_Template.xlsx. Plot the uniformity and centering accuracy for each objective.

 Plot the uniformity and centering accuracy for each filter set. 

This specific instrument has a quad-band filter as well as individual filter cubes. We can plot the uniformity and centering accuracy per filter types. 

 Finally store the original field illumination images to be able to perform shading corrections.

The uniformity and centering accuracy are excellent across all objectives and filters, consistently exceeding 90%. However, the single-band filter cubes exhibit slightly lower uniformity and centering accuracy compared to the quad-band filter cube.

1. Place 4 µm diameter fluorescent beads (TetraSpec Fluorescent Microspheres Size Kit, mounted on a slide) on the stage

2. Center the sample under a high-NA dry objective

3. Select an imaging channel (e.g., Cy5)

4. Acquire a large Z-stack every minute for 24 hours

   It is crucial to account for potential drift in the Z-axis by acquiring a Z-stack that is significantly larger than the visible bead size (e.g., 40 µm)

1. Use the TrackMate plugin for FIJI to detect and track spots over time
2. Apply Difference of Gaussians (DoG) spot detection with a detection size of 4 µm
3. Set a quality threshold greater than 20 and enable sub-pixel localization for increased accuracy
4. Export the detected spot coordinates as a CSV file for further analysis

Fill in the orange cells in the following spreadsheet template XYZ Drift Kinetic_Template.xlsx. to visualize your results. Just copy paste XYZT and Frame columns from trackmate spots CSV file to the orange column in the XLSX file. Fill in the NA and Emission wavelength used.

Calculate the relative displacement in X, Y and Z: Relative Displacement = Position - Position_Initial and plot the relative displacement over time.

We observe an initial drift that stabilizes over time in X (+2 um), Y (+1.3 um) and Z (-10.5 um).

Calculate the displacement 3D Displacement = Sqrt( (X₂-X₁)² + (Y₂-Y₁)²) + (Z₂-Z₁)² ) and plot the displacement over time. Calculate the resolution of your imaging configuration, Lateral Resolution = Lambda_Emission / 2*NA and plot the resolution over time (constant).

Identify visually the time when the displacement is lower than the resolution of the system. On this instrument it takes 120 min to reach its stability. 

Calculate the velocity, Velocity = (Displacement₂-Displacement₁)/T₂-T₁) and plot the velocity over time.

Calculate the average velocity before and after stabilisation and report the results in a table

The warmup time for this specific instrument is about 2 hours. The average displacement velocity after warmup is 14 nm/min which is acceptable.