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19. February 2024
Key Applications of TMA Samples in Tissue-Based Research
In research and clinical diagnosis, tissue microarrays (TMA) have become a standard technique. One advantage of TMAs is that they allow the high-throughput analysis of 10th to hundreds of patient samples in a single block/on a single slide.1 This makes TMAs an excellent, efficient way of performing diagnostic or screening measurements and helps eliminate inconsistencies in staining.
A TMA is a single paraffin block often constructed from a series of different paraffin blocks of tissue cores embedded together to make a single microarray that will then be stained and imaged on a single slide.
A number of in-situ molecular techniques can be used on TMAs. Another key advantage of TMA analysis is using paraffin blocks and analysis methods. TMAs are compatible with the types of archival material that must be kept in hospitals.2 DNA microarray and proteomics methods are generally a poor choice for anything other than fresh tissue samples.
Automation of tissue cytometry analysis of TMAs is necessary to process multiple tissue cores in a TMA sample. To get maximum throughput and extract the most information possible from the TMA, the automation needs to be on both the hardware side for the acquisition of the data and the software side for the analysis of the acquired images.
TissueGnostics complete analysis package for multichannel tissue cytometry, StrataQuest, is ideal for such applications.
TissueFAXS and StrataQuest
Histology/tissue cytometry is commonly combined with TMAs, as the method allows for single-slide imaging, and microscopy methods are highly effective for recovering information on cell morphology for pathology and research applications.3 Combining a single slide with automation of the tissue cytometry workflow makes it straightforward to perform whole slide imaging, where the entire slide (with a large tissue section) is scanned to produce a virtual slide.
TissueGnostics offers comprehensive solutions for the scanning and image analysis of TMA samples. The whole workflow is designed to manage, visualize as well as analyse high amount of TMA cores in order to increase speed of the process and quality of the results.
Tissue Classification
An example of a powerful automated workflow is shown in a case study, in which StrataQuest is used to for machine learning-based tissue classification for the epithelium, stroma and lymphoid cluster in a TMA sample of inflamed bladder tissue. As next, nuclei where identified and marker positive cells where detected.
Once regions are identified in the TMA images, different colors can be used to improve visualization. Additional analysis, such as sectioning the sample into proximity ranges, can also be carried out.
Given the power of TMA for cancer diagnostics, particularly when combined with machine learning algorithms4, StartaQuest can help enable straightforward and rapid image analysis. Types of analysis include compartment segmentation, single cell detection, phenotyping, stained area detection and more complex procedures.
Since its development, TMA has rapidly become one of the tools of choice for histology and therapeutic screening. Combining this with highly efficient automation methods opens up a wealth of possibilities for cell/tissue analysis. TissueGnostics offers automation options for both the acquisition of images and their analysis.
Contact us today to learn how automating TMA analysis could improve your throughput and give new insight into your tissue analysis.
References
1. M. Jawhar. (2009). Tissue microarray: A rapidly evolving diagnostic and research tool. Ann Saudi Med, 29(2), 123–127 https://doi.org/10.4103/0256-4947.51806
2. M. Kim, C. T. (2008). Tissue Microarrays in Clinical Oncology. Semin Radiat Oncol, 18(2), 89–97. https://doi.org/10.1016/j.semradonc.2007.10.006
3. Hewitt, S. M. (2012). Tissue microarrays as a tool in the discovery and validation of predictive biomarkers. Methods in Molecular Biology, 823(16), 201–214. https://doi.org/10.1007/978-1-60327-216-2_13
4. Nguyen, H. G., Blank, A., Dawson, H. E., Lugli, A., & Zlobec, I. (2021). Classification of colorectal tissue images from high throughput tissue microarrays by ensemble deep learning methods. Scientific Reports, 11(1), 1–11. https://doi.org/10.1038/s41598-021-81352-y
