Cell shape characterization and classification with discrete Fourier transforms and self-organizing maps

Technical Note: On the development of an outcome-driven frequency filter for improving Radiomics-based modeling of Human Papilloma Virus (HPV) in patients with oropharyngeal squamous cell carcinomas

PURPOSE

To implement an outcome-driven frequency filter for improving radiomics-based modeling of human papilloma virus (HPV) for patients with oropharyngeal squamous cell carcinoma (OPSCC).

METHODS AND MATERIALS

One hundred twenty-eight OPSCC patients with known HPV status (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16 protein testing) were retrospectively studied. A 3D Discrete Fourier Transform was applied on contrast-enhanced CT images of patient gross tumor volumes (GTV's) to transform intensity distributions to the frequency domain and estimate frequency power spectrums of HPV- and HPV+ patient cohorts. Statistical analyses were performed to rank frequency bands contributing towards prediction of HPV status. An outcome-driven frequency filter was designed accordingly and applied to GTV frequency information. 3D Inverse-Discrete-Fourier-Transform was applied to reconstruct HPV-related frequency-filtered images. Radiomics features (11 feature-categories) were extracted from pre- and post- frequency filtered images using our previously published 'ROdiomiX' software. Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) combined with a Generalized-Linear-Model (Lasso-GLM) was developed to identify and rank feature subsets with optimal information for prediction of HPV+/-. Radiomics-based Lasso-GLM classifiers (pre- and post-frequency filtered) were constructed and validated using a random permutation sampling and nested cross-validation techniques. Average Area Under Receiver Operating Characteristic (AUC), and Positive and Negative Predictive values (PPV, NPV) were computed to estimate generalization error and prediction performance.

RESULTS

Among 192 radiomic features, 15 features were found to be statistically significant discriminators between HPV+/- cohorts on post-frequency filtered CE-CT images; 14 such radiomic features were observed on pre-frequency filtered datasets. Discriminant features included tumor morphology and intensity contrast. Performances for prediction of HPV for the pre- and post-frequency filtered Lasso-GLM classifiers were: AUC/PPV/NPV = 0.789/0.755/0.805 and 0.850/0.808/0.877 respectively. Nested-CV performances for prediction of HPV for the pre- and post-frequency filtered Lasso-GLM classifiers were: AUC/PPV/NPV = 0.814/0.725/0.877 and 0.890/0.820/0.911 respectively.

CONCLUSION

Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results on the importance of frequency analysis prior to radiomic feature extraction toward enhancement of model performance for characterizing HPV in patients with OPSCC. This article is protected by copyright. All rights reserved.

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

In biomedical research, the migration behavior of cells and interactions between various cell types are frequently studied subjects. An automated and quantitative analysis of time-lapse microscopy data is an essential component of these studies, especially when characteristic migration patterns need to be identified. Plenty of software tools have been developed to serve this need. However, the majority of algorithms is designed for fluorescently labeled cells, even though it is well-known that fluorescent labels can substantially interfere with the physiological behavior of interacting cells. We here present a fully revised version of our algorithm for migration and interaction tracking (AMIT), which includes a novel segmentation approach. This approach allows segmenting label-free cells with high accuracy and also enables detecting almost all cells within the field of view. With regard to cell tracking, we designed and implemented a new method for cluster detection and splitting. This method does not rely on any geometrical characteristics of individual objects inside a cluster but relies on monitoring the events of cell-cell fusion from and cluster fission into single cells forward and backward in time. We demonstrate that focusing on these events provides accurate splitting of transient clusters. Furthermore, the substantially improved quantitative analysis of cell migration by the revised version of AMIT is more than two orders of magnitude faster than the previous implementation, which makes it feasible to process video data at higher spatial and temporal resolutions.

OrgDyn: feature- and model-based characterization of spatial and temporal organoid dynamics

SUMMARY

Organoid model systems recapitulate key features of mammalian tissues and enable high throughput experiments. However, the impact of these experiments may be limited by manual, non-standardized, static or qualitative phenotypic analysis. OrgDyn is an open-source and modular pipeline to quantify organoid shape dynamics using a combination of feature- and model-based approaches on time series of 2D organoid contour images. Our pipeline consists of (i) geometrical and signal processing feature extraction, (ii) dimensionality reduction to differentiate dynamical paths, (iii) time series clustering to identify coherent groups of organoids and (iv) dynamical modeling using point distribution models to explain temporal shape variation. OrgDyn can characterize, cluster and model differences among unique dynamical paths that define diverse final shapes, thus enabling quantitative analysis of the molecular basis of tissue development and disease.

AVAILABILITY AND IMPLEMENTATION

https://github.com/zakih/organoidDynamics (BSD 3-Clause License).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Dynamic spherical harmonics approach for shape classification of migrating cells

Cell migration involves dynamic changes in cell shape. Intricate patterns of cell shape can be analyzed and classified using advanced shape descriptors, including spherical harmonics (SPHARM). Though SPHARM have been used to analyze and classify migrating cells, such classification did not exploit SPHARM spectra in their dynamics. Here, we examine whether additional information from dynamic SPHARM improves classification of cell migration patterns. We combine the static and dynamic SPHARM approach with a support-vector-machine classifier and compare their classification accuracies. We demonstrate that the dynamic SPHARM analysis classifies cell migration patterns more accurately than the static one for both synthetic and experimental data. Furthermore, by comparing the computed accuracies with that of a naive classifier, we can identify the experimental conditions and model parameters that significantly affect cell shape. This capability should – in the future – help to pinpoint factors that play an essential role in cell migration.

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

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.

NAD(P)H Oxidase Activity in the Small Intestine Is Predominantly Found in Enterocytes, Not Professional Phagocytes

The balance between various cellular subsets of the innate and adaptive immune system and microbiota in the gastrointestinal tract is carefully regulated to maintain tolerance to the normal flora and dietary antigens, while protecting against pathogens. The intestinal epithelial cells and the network of dendritic cells and macrophages in the lamina propria are crucial lines of defense that regulate this balance. The complex relationship between the myeloid compartment (dendritic cells and macrophages) and lymphocyte compartment (T cells and innate lymphoid cells), as well as the impact of the epithelial cell layer have been studied in depth in recent years, revealing that the regulatory and effector functions of both innate and adaptive immune compartments exhibit more plasticity than had been previously appreciated. However, little is known about the metabolic activity of these cellular compartments, which is the basic function underlying all other additional tasks the cells perform. Here we perform intravital NAD(P)H fluorescence lifetime imaging in the small intestine of fluorescent reporter mice to monitor the NAD(P)H-dependent metabolism of epithelial and myeloid cells. The majority of myeloid cells which comprise the surveilling network in the lamina propria have a low metabolic activity and remain resting even upon stimulation. Only a few myeloid cells, typically localized at the tip of the villi, are metabolically active and are able to activate NADPH oxidases upon stimulation, leading to an oxidative burst. In contrast, the epithelial cells are metabolically highly active and, although not considered professional phagocytes, are also able to activate NADPH oxidases, leading to massive production of reactive oxygen species. Whereas the oxidative burst in myeloid cells is mainly catalyzed by the NOX2 isotype, in epithelial cells other isotypes of the NADPH oxidases family are involved, especially NOX4. They are constitutively expressed by the epithelial cells, but activated only on demand to ensure rapid defense against pathogens. This minimizes the potential for inadvertent damage from resting NOX activation, while maintaining the capacity to respond quickly if needed.