Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Abstract

The appearance and the movements of immune cells are driven by their environment. As a reaction to a pathogen invasion, the immune cells are recruited to the site of inflammation and are activated to prevent a further spreading of the invasion. This is also reflected by changes in the behavior and the morphological appearance of the immune cells. In cancerous tissue, similar morphokinetic changes have been observed in the behavior of microglial cells: intra-tumoral microglia have less complex 3-dimensional shapes, having less-branched cellular processes, and move more rapidly than those in healthy tissue. The examination of such morphokinetic properties requires complex 3D microscopy techniques, which can be extremely challenging when executed longitudinally. Therefore, the recording of a static 3D shape of a cell is much simpler, because this does not require intravital measurements and can be performed on excised tissue as well. However, it is essential to possess analysis tools that allow the fast and precise description of the 3D shapes and allows the diagnostic classification of healthy and pathogenic tissue samples based solely on static, shape-related information. Here, we present a toolkit that analyzes the discrete Fourier components of the outline of a set of 2D projections of the 3D cell surfaces via Self-Organizing Maps. The application of artificial intelligence methods allows our framework to learn about various cell shapes as it is applied to more and more tissue samples, whilst the workflow remains simple.

Photomanipulation of minimal synthetic cells: area increase, softening and interleaflet coupling of membrane models doped with azobenzene-lipid photoswitches

“…The abnormal changes in the membrane morphology (e.g. excess membrane area with respect to enclosed volume) or the incompetency of biological cells to modulate their membrane mechanics can dramatically affect the cellular homeostasis [21] and lead to pathological developments [22] , cancer progression [23] or cell apoptosis [24] . A fast-responding external trigger such as light for controlling membrane area and mechanics can facilitate transmembrane transport and exchange of substances across the cell membrane thus reducing the above-mentioned harmful effects stemming from the malfunction of cellular processes.…”

Enhanced segmentation of label‐free cells for automated migration and interaction tracking

“…Once the whole tracks of migrating cells are reconstructed by AMIT, various possibilities for further quantitative analyses exist [30], such as computing a range of measures to distinguish [31][32][33][34][35][36] and model [34,[37][38][39][40] migration behavior. Moreover, the enhanced segmentation by AMIT-v3 enables a more accurate analysis of cell shape dynamics, for example, based on Fourier transformation of the cellular segmentation outline in 2D [41,42] or spherical harmonics transformation of the cell surface in 3D [43].…”

Self-Organizing Maps for Cellular In Silico Staining and Cell Substate Classification

“…The Seg-SOM workflow consists of three primary steps: (1) nuclear segmentation, (2) nuclei grouping based on their shape and size performed by the self-organizing map algorithm combined with hierarchical clustering of SOM nodes, and (3) in silico cell type staining. SOMs have been previously used in digital pathology for red blood cell classification (26), megakaryocyte subtypes clustering (27), and analyzing 3D cell surface information (28). This work is the first to present the combination of SOMs and NMF as a general tool for dimensionality reduction of nuclear morphology, the grouping of nuclei in complex tissues, discovering nuclear subtypes, nuclear in silico labeling, and extracting machine learning features as potential spatial biomarkers.…”