<p>An AFM-Based Nanomechanical Study of Ovarian Tissues with Pathological Conditions</p>

Abstract

Background: Different diseases affect both mechanical and chemical features of the involved tissue, enhancing the symptoms. Methods: In this study, using atomic force microscopy, we mechanically characterized human ovarian tissues with four distinct pathological conditions: mucinous, serous, and mature teratoma tumors, and non-tumorous endometriosis. Mechanical elasticity profiles were quantified and the resultant data were categorized using K-means clustering method, as well as fuzzy C-means, to evaluate elastic moduli of cellular and non-cellular parts of diseased tissues and compare them among four disease conditions. Samples were stained by hematoxylin-eosin staining to further study the content of different locations of tissues. Results: Pathological state vastly influenced the mechanical properties of the ovarian tissues. Significant alterations among elastic moduli of both cellular and non-cellular parts were observed. Mature teratoma tumors commonly composed of multiple cell types and heterogeneous ECM structure showed the widest range of elasticity profile and the stiffest average elastic modulus of 14 kPa. Samples of serous tumors were the softest tissues with elastic modulus of only 400 Pa for the cellular part and 5 kPa for the ECM. Tissues of other two diseases were closer in mechanical properties as mucinous tumors were insignificantly stiffer than endometriosis in cellular part, 1300 Pa compared to 1000 Pa, with the ECM average elastic modulus of 8 kPa for both. Conclusion: The higher incidence of carcinoma out of teratoma and serous tumors may be related to the intense alteration of mechanical features of the cellular and the ECM, serving as a potential risk factor which necessitates further investigation.

Stain-free identification of tissue pathology using a generative adversarial network to infer nanomechanical signatures

“…In this work we eliminate the need for staining and ad hoc AFM measurements entirely: localisation of measured areas has been fine-tuned to obtain precisely registered pairs of unstained tissue images and EM maps, and these pairs were used to train a style transfer deep learning architecture to infer the sample-wide EM of tissue sections from low-resolution grayscale microscopy images. The resulting EM distributions contain the nanomechanical signatures of the underlying tissue pathology identified previously, 27,[33][34][35] and unsupervised clustering of distribution parameters provides an accurate prediction of tissue pathology, validated through pathologist assessment of post hoc stained sections. The use of a well-optimized prediction algorithm allows for much more rapid diagnoses compared to time-intensive diagnostic methods such as IFS analysis or ad hoc AFM measurements.…”

“…Previous studies that identified the nanomechanical signatures of disease did so through a large number of "blind" AFM measurements of bulk (≥1 mm thick) tissue sections, statistically correlating the findings with post hoc histopathology. 27,[33][34][35] Fig. 1 shows the general procedure for measuring tissue samples using an AFM; the cantilever assembly occludes much of the camera's field-of-view (FOV), reducing the contrast of the resulting image.…”

Scanning Probe Microscopies: Imaging and Biomechanics in Reproductive Medicine Research

“…Furthermore, AFM was used to mechanically characterize four human ovarian tissues distinct pathological conditions: mucinous and serous cystadenoma, mature teratoma, and endometriosis [ 85 ]. As expected, the authors found considerable heterogeneity within the tissue, but they could observe some differences in elastic modulus values of the cellular part and extracellular matrix among the four ovarian samples.…”

“…These findings support that mechanical measurements represent a valuable tool to delineate the mechanical phenotypes of cells and tissue in ovary tumors. Based on the biomechanical properties, a screening approach could be employed to complement standard biopsy procedures, offering a novel and quantitative diagnostic approach to help cancer grading/classification, with an unbiased evaluation of the sample [ 85 , 86 ].…”

<p>Nanomechanics and Histopathology as Diagnostic Tools to Characterize Freshly Removed Human Brain Tumors</p>

“…23 Also, both rheological and MRE measurements provide only average bulk mechanical properties from the entire volume of cells and ECM in the sample. To attain nanoscale and single-cell resolution, atomic force microscopy (AFM) must be used, and has been reported previously 7,[24][25][26] for different tissue types. Reports have clearly indicated that AFM might be successfully used in research on brain tumor tissue to understand the mechanopathology of this deadly disease.…”