The patient-derived explants are a short term method providing immediate drug response data that can accurately predict patient outcomes. The PD platforms allows drug responses to be examined in a 3D context that mirrors the pathological and architectural features of the regional tumors as closely as possible. Explants have the potential to offer novel insights into the metabolism and mechanism of action of a drug and to facilitate the identification of new pharmacodynamic biomarkers.
Before starting the experiment, clean all surgical equipment with 70%industrial methylated spirits solution. Fill a 10 centimeter culture dish with 25 milliliters of fresh medium on ice. Using tweezers, transfer the specimen onto a dental wax surface And use two skin graft blades to slice the tissue into fragments of approximately two to three cubic millimeters.
Transfer the explants into a 10 centimeter dish and place six to nine pieces of the tissue into a one milliliter tube containing 10%non buffered formalin solution for a 24 hour incubation at room temperature. Fill the desired number of wells of a six well plate with 1.5 milliliters of fresh medium per well. And place an organotypic culture insert dish into each well so that it floats on top of the medium.
Place six to nine explants onto each insert disk and store for a 16 hour incubation in a 5%carbon dioxide incubator to allow recovery. The next day, add fresh medium and drug to each well of a new plate. Include a well for the vehicle control.
When all drug treatments have been prepared, use tweezers to transfer one insert to each well of the new six well plate for a 24 to 48 hour incubation in the carbon dioxide incubator. After the drug treatment, transfer the discs into new six well plates containing 10%formalin. And add a few drops of 10%formalin to the top of each explant to ensure complete coverage with the fixative for 24 hours of incubation at room temperature.
The next day, soak an appropriate number of small histology sponges in 70%industrial methylated solution. And place the sponges inside histology cassettes. When all sponges have been placed, transfer all the explants from the same drug treatment condition onto a single sponge with the drug treated side of each explant contacting the inserted disc.
Place another presoaked sponge on top of each explant and close the cassette to secure the explants in the cassette. Then submerge the cassettes in 70%industrial methylated solution before proceeding with the histological processing. After scanning, perform a training analysis and load the scans containing the stamps for training into the appropriate analysis software.
In single mode view, navigate through the images and select the scans to analyze and the scan with intrinsic fluorescence. with the intrinsic fluorescence scan on a single view, click the autofluorescence picker to draw a segment on an area of unstained tissue. Click edit markers and colors and enter the marker names in the box associated with each fluorophore.
Click prepare images to allow the software to subtract the intensity of intrinsic fluorescence from all the uploaded images. Click the segment tissue step on top of the screen to open the tissue segmentation training window. In the tissue categories panel, enter the name of each tissue category to be segmented.
Click draw, to draw regions around groups of cells in the tissue category of interest. Then switch to the next tissue category and draw new regions. When all the training regions have been defined, use train tissue segmenter, and wait for the training accuracy to settle.
Possibly to a value close to 100%Click segment all to segment the tissues. After verifying the quality of the tissue segmentation, click cell segmentation and select the cellular compartments to segment. To configure the nuclear component, use the typical intensity slider to adjust the threshold used to detect the nuclear pixels.
Live feedback is provided by the preview window. To split the clusters of nuclear pixels, in the nuclear components splitting panel, select the button that best describes the staining quality of the nuclei and use the slider bar to adjust the splitting sensitivity. Then click segment all to segment all of the images.
When all of the images have been segmented, in the phenotypes panel, add the list of phenotypes to be detected and enter the name of the phenotypes into the corresponding text box. Click edit phenotypes to select a particular cell and select the corresponding phenotype in the dropdown menu. After completing the selection for training, click the train classifier to evaluate the result of each phenotype.
Proceed with phenotype all, save the project and click export. Select the batch analysis tab and load the project in the batch algorithm or project box. To export the images and tables, select all of the boxes in the export options, click add slides and load all the images containing the stamps for the previously prepared batches.
Then click run to the start batch analysis of one stamp and export the corresponding data when it has been acquired. Multi-spectral imaging of NSCLC explant tissue stained for markers of cell viability, permits the identification and phenotyping of individual cell populations, as well as the identification of tumor and stromal components within the explant tumor micro environment. Tissue and cell segmentation of cell viability marker stains multi-spectral images as demonstrated, can be used to quantify cell death and proliferation in patient derived explants treated with drugs of interest.
For example, in this analysis, in tissue samples from tumors treated with Nivolumab, a higher number of dying cells were identified compared to the control treated tumor samples. In addition, as illustrated, the ability to extract spatial information from stained dissections, allows the calculation of intercell distances. Explants can also be used for extensive spatial profiling or mass cytometry, which allows hundreds of biomarkers to be profiled simultaneously at subsidiary resolution while preserving the 3D structure of the sample.
PDs could predict the efficacy of immunotherapy. For example, it could be possible to distinguish sensitive or resistant cases to immune checkpoint inhibitors and investigate the mechanisms underpinning this distinction.
