21. April 2022
The Diagnostic Potential of Image Cytometry
Image cytometry is a method that uses imaging as well as image analysis to extract quantitative information from stained cells/tissues. Image cytometry is frequently used alongside flow cytometry and provides deep insight of cell/tissue morphology and cell-cell interactions whilst preserving the original cellular milieu as well as the native tissue environment. This powerful technique has extended the frontiers of biology, showing particular promise in the field of disease diagnostics.
Image Cytometry in Modern Oncology
Reliable and accurate diagnostic methods for cancer detection and patient stratification are a cornerstone of modern oncology. In clinical practice, an array of techniques can be used to obtain cancer specimens. The less invasive of these techniques, such as brushing and touch preps, can create a significant challenge for image cytometry as they often yield scant cellular specimens. Breakthroughs in engineering and advanced AI have driven a new class of image cytometers with the potential of performing with minimal biological material. This advancement opens new opportunities for image cytometry in diagnosing cancers.
Image cytometry has been among other applications successfully applied to cell lineages associated with head and neck cancers. By modifying an existing algorithm designed for the detection of hematoxylin and eosin-stained images, Tsujikawa et al. were able to identify immune cells characteristic of specific oropharyngeal squamous cell carcinomas. Similarly, using ultra-fast optical imaging, Goda et al. demonstrated high-throughput image-based screening of rare breast cancer cells. With a false-positive rate of one in a million, refinement of this process could allow for routine non-invasive and inexpensive detection of cancer. The use of image cytometry for cancer diagnosis is also supported by Agarwal et al. who used optical projection tomographic microscopy (OPTM), a technique combining attributes from flow- and image cytometry, to quantitatively measure DNA content in 3D. This approach can be used to detect various biomarkers for diseases including cancer.
Image Cytometry in Autoimmune Disorders
Amid the vast collection of autoimmune disorders, image cytometry has had success when applied to diabetes and multiple sclerosis. In a landmark study by Damond et al. a 35-parameter panel was developed to investigate the correlates of beta cell loss; the cells that synthesize insulin. They found that, on the outset of type 1 diabetes, beta cell numbers are comparable between healthy and disease patients. However, expression of beta-cell markers varied widely across islets of the same donor. This revealed that beta cells population sizes may not be different at early stages of the disease, but do appear differently when observed using image cytometry.
Multiple sclerosis (MS), a disease characterized by the loss of myelin surrounding nerve cells and an accompanying range of motor and mental symptoms, was the subject of a study by Park et al. Using a panel of 13 biomarkers, image cytometry was used to characterize the condition in detail: identifying six subtypes of myeloid cells within MS lesions and revealing significant immune interactions. These results develop our knowledge of MS pathology and could later be used to re-address our diagnostic approach.
By generating in-depth immune profiling, image cytometry represents an important clinical tool for immunophenotyping and analyzing tumor micro-environment, which can aid in the diagnosis of cancers. Not limited to cancer, image cytometry has demonstrated promising applications in autoimmune disease diagnosis, and is expected to be applied to a broad scope of diseases.
• Agarwal, N., et al. 2014. Three-dimensional DNA image cytometry by optical projection tomographic microscopy for early cancer diagnosis. J. of Medical Imaging
• Goda, K., et al. 2013. Ultrafast automated image cytometry for cancer detection. IEEE 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Osaka (2013.7.3-2013.7.7)
• Tsujikawa, T., et al. Robust Cell Detection and Segmentation for Image Cytometry Reveal Th17 Cell Heterogeneity. Cytometry A.
• Weissleder, R., et al. 2020. Automated molecular-image cytometry and analysis in modern oncology. Nat Rev Mater.
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