The overall goal of this procedure is to quantify basic physical features of cellular specimens, including mass and volume, with a standard optical microscope and image processing. This is accomplished by first mounting cellular specimens grown on glass cover slips on microscope slides. Next through focus brightfield and differential interference contrast images are obtained.
Then each Z stack of images is input into separate MATLAB image processing programs that extract the physical data. Ultimately, the basic physical properties of cellular specimens are obtained using through focus intensity measurements, under brightfield contrast and differential interference contrast microscopy. The main advantage of this technique over existing methods like fluorescence microscopy, is that cellular specimens need not be fixed, permeated, or stained.
This method can help answer key questions in the cell biology field, such as how is subcellular density organized during the cell cycle or among different cell populations contributing to a disease state Using a microscope with both DIC and brightfield capabilities and additional specifications according to the text protocol begin by opening slide book software and creating a new slide for image collection. Next, open the focus window and under the filter set section select DIC On the scope tab under the condenser section, adjust the aperture slide bar to the furthest right position. This provides high numerical aperture illumination and enhances optical sectioning of the specimen.
After capturing a DIC stack of the sample, open the focus window and select open. Under the filter set section, adjust the aperture slide bar to the furthest left position closed all the way to provide low NA illumination. Following adjustment of the signal intensity, open the image capture window.
The 3D capture settings from the DICZ stack acquisition will be displayed in the filter set section of the image capture window. Check the open box and specify exposure time. In the image information section, name the image and select start to initiate Zack image acquisition.
Then export the Z stacks according to the text protocol to carry out volume measurements once in the H-T-D-I-C MATLAB program entitled JoVE HT DCO V one M Under section zero, update the dependencies directory variable by copying and pasting the directory containing the hilbert. Transform DM and sobel edge Detect M files from explorer. In between the single quotes following dependencies directory equals execute section zero of the joco HT DCO V one M program.
In section one, update the images directory by copying and pasting the directory containing the through focus images in dot TIF form. In between the single quotes only run this section once to align and rotate images. For hilbert transform run section two of the code A dialogue box titled Define HT DIC parameters will appear.
Then enter the focal plane number where the DIC image of the sample is in focus, the lateral resolution, the axial resolution, the rotation angle of the DIC image needed to perform the hilbert transform, and lastly, the region of interest size. Then click Okay. An image of the DIC focal plane specified by the focal plane number will appear with a blue box.
Position the box over the feature of interest such as a cell. Once the box has been positioned over the desired region, double click inside the B.The contrast of the image should be such that dark features appear on the left while bright features appear on the right. Drag the blue box over the region of interest and reshape as necessary.
Next, under section three, to generate a rectangular mask, un comment line 1 6 7 and comment line 1 7 0 execute section three of program by clicking in the image and dragging the mouse to begin defining the rectangular mask. Then double click on the box to accept it. To generate a hand drawn mask, comment line 1 6 7 and un comment line 1 7 0 before executing section three By clicking and drawing the desired mask with the mouse, double click on the mask to accept it after running Section four, to construct hilbert transformed image stacks according to the text protocol runs section five.
To optimize the image segmentation of the xz cross-sectional images of the region of interest. Figure 500 produced by the program appears displaying three different types of contrast. The success of the algorithm to find the borders of the cell is reliant on a combination of the mask used and the value of threshold at line 2, 2, 9 of the program begin with a value of 0.5 and adjust the value of the threshold and rerun this section of the program until the proper outlining is achieved in one of the columns.
If the outlining was best in column one, in DIC segmentation, use section six to determine the volume. If column two gave optimal results, run section seven to determine cell volume from hilbert, transform DIC imagery IF column three gave the optimal results, use forer filtered hilbert transform DIC imagery while running section eight to determine volume for taking mass measurements in the NIQ PM MATLAB program and titled JO VCO NI qpm V1 M Under section zero. Update the location of the three directories dependencies, BRIGHTFIELD and DIC directory after running section one, run section two in the dialogue box titled define N-I-Q-P-M parameters enter the focal plane number where the bright field image of the sample is in focus, the lateral resolution, the axial resolution, and the region of interest size.
Then click Okay. An image of the brightfield focal plane specified by the focal plane number will appear with a blue box. After adjusting the focus if necessary by rerunning section two, drag the box around the image and select the nodes of the box and drag it to resize it before double clicking inside the box to accept it.
Once a stack of brightfield images have been constructed by running section three, run section four to generate the phase map, pseudo DIC images and comparisons to brightfield imagery and the true DIC image when the pseudo DIC and true DIC images are as similar as possible, run section five A or five B that allows the user to outline the cell to generate the mass density map, the total mass and the histogram of the cell density correct. Sample illumination during through focus image acquisition is critical to the successful implementation of the N-I-Q-P-M and H-T-D-I-C algorithm. This figure illustrates low and high NA illumination under both DIC and brightfield contrast for a polystyrene sphere and the human colorectal adenocarcinoma cell line SW six 20.
These panels demonstrate optimal imaging for N-I-Q-P-M and these display optimal imaging for HT DIC. These images demonstrate the parameter dependence of the NIQ PM algorithm highlighting both successful and unsuccessful implementations. Here we explore the phase profile of a 4.8 micrometer diameter polystyrene sphere.
The expected profile is known theoretically and can thus be compared directly to the NIQ PM reconstruction. These panels present the best reconstruction obtainable for the sphere diffraction effects at both the boundaries of the sphere and inside prevent stable reconstruction at all points on the sphere. The central region of the sphere can be captured with N-I-Q-P-M with a percent error of one to 5%as seen in Panel L cellular specimens whose phase properties are not known.
A priori can be reconstructed using NIQ PMM in conjunction with a procedure to compare a pseudo DIC image to an actual DIC image. N-I-Q-P-M has one free parameter. The plane in the right field image stack at which the calculation is centered.
This central focal plane should be adjusted until the pseudo DIC and true DIC images look as similar as possible. Presented here are an out of focus pseudo DIC image, an optimal pseudo DIC image and the corresponding DIC image of the cell taken with an illumination NA of 0.9. Interestingly, the best phase map and corresponding pseudo DIC image do not necessarily correspond to an in-focus bright field image as shown in these panels.
Lastly, shown here are the steps involved in the H-T-D-I-C image processing algorithm from the DIC through focus imagery under NA equals 0.9 illumination, the hilbert transform is performed to remove the base relief of the DIC images. This comes with some blurring along the optical axis that can be removed from high pass forer filtering. These final images are easily segmented to determine the area in each cross-sectional plane of the specimen to infer the total cellular volume.
While attempting this procedure, it's important to remember the correct illumination for brightfield ore differential contrast imaging following this procedure. Other methods like fluorescence microscopy can be performed in order to answer additional questions using colocalization of floor force with density maps of the specimen.
