Density Distribution Maps: A Novel Tool for Subcellular Distribution Analysis and Quantitative Biomedical Imaging

The Effectiveness of an Adaptive Method to Analyse the Transition between Tumour and Peritumour for Answering Two Clinical Questions in Cancer Imaging

Based on the well-known role of peritumour characterization in cancer imaging to improve the early diagnosis and timeliness of clinical decisions, this study innovated a state-of-the-art approach for peritumour analysis, mainly relying on extending tumour segmentation by a predefined fixed size. We present a novel, adaptive method to investigate the zone of transition, bestriding tumour and peritumour, thought of as an annular-like shaped area, and detected by analysing gradient variations along tumour edges. For method validation, we applied it on two datasets (hepatocellular carcinoma and locally advanced rectal cancer) imaged by different modalities and exploited the zone of transition regions as well as the peritumour ones derived by adopting the literature approach for building predictive models. To measure the zone of transition's benefits, we compared the predictivity of models relying on both "standard" and novel peritumour regions. The main comparison metrics were informedness, specificity and sensitivity. As regards hepatocellular carcinoma, having circular and regular shape, all models showed similar performance (informedness = 0.69, sensitivity = 84%, specificity = 85%). As regards locally advanced rectal cancer, with jagged contours, the zone of transition led to the best informedness of 0.68 (sensitivity = 89%, specificity = 79%). The zone of transition advantages include detecting the peritumour adaptively, even when not visually noticeable, and minimizing the risk (higher in the literature approach) of including adjacent diverse structures, which was clearly highlighted during image gradient analysis.

NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation

Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing indirect immunofluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is very different from the conventional neural network approaches but has an equivalent quantitative and qualitative performance, and it is also robust against adversative noise. The method is robust, based on formally correct functions, and does not suffer from having to be tuned on specific data sets. Results: This work demonstrates the robustness of the method against variability of parameters, such as image size, mode, and signal-to-noise ratio. We validated the method on three datasets (Neuroblastoma, NucleusSegData, and ISBI 2009 Dataset) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional and structural point of view, guarantees the achievement of optimized and functionally correct results. The excellent performance of our deterministic method (NeuronalAlg) in segmenting cells and nuclei from fluorescence images was measured with quantitative indicators and compared with those achieved by three published ML approaches.

Pro-inflammatory RNA:DNA Hybrids Are p53 Independently Boosted by Hyperbaric Oxygen: a Subcellular Distribution Analysis by Automated Quantitative Imaging

PURPOSE

RNA:DNA hybrids are co-transcriptional products with acknowledged cytoplasmic pro-inflammatory role as activators of the cGAS-STING pathway. We recently proved them also as radiation-induced senescence messages for the abscopal effect mediation, demonstrating the need for a functional p53 for their production and release in A549 and H1299 tumour cells. However, little is known about their role under different stress conditions, especially in cancer cells.

METHODS

In this work, we open the investigation making use of automated quantitative imaging to characterize the hybrid subcellular distribution in HeLa cells grown under different oxygen pressures or exposed to different ionizing radiation doses. After cell imaging by confocal fluorescent microscopy, we apply automated imaging methods developed on purpose to quantify hybrid foci and nuclear cluster intensity, regional and local density and dimension.

RESULTS

We show that alteration of culture oxygenation increases hybrid cytoplasmic presence, especially when caused by an hyperoxic environment, with evident hybrid gathering at the cell membrane. Ionizing radiations always fail to increase hybrids, in accordance with the absence of functional p53 in HeLa cells. However, dose-dependent effects are still evident and suggest a threshold dose of 7.5 Gy for remarkable hybrid reduction.

CONCLUSION

Together with our previous results, these data demonstrate for the first time that different types of stress can increase hybrid production in cancer cells and by at least two different pathways, one p53-dependent triggerable by ionizing radiations and one p53-independent triggerable by oxidative stress. Together, our findings provide a starting point for understanding hybrid role in tumour stress response.

Co-Density Distribution Maps for Advanced Molecule Colocalization and Co-Distribution Analysis

Cellular and subcellular spatial colocalization of structures and molecules in biological specimens is an important indicator of their co-compartmentalization and interaction. Presently, colocalization in biomedical images is addressed with visual inspection and quantified by co-occurrence and correlation coefficients. However, such measures alone cannot capture the complexity of the interactions, which does not limit itself to signal intensity. On top of the previously developed density distribution maps (DDMs), here, we present a method for advancing current colocalization analysis by introducing co-density distribution maps (cDDMs), which, uniquely, provide information about molecules absolute and relative position and local abundance. We exemplify the benefits of our method by developing cDDMs-integrated pipelines for the analysis of molecules pairs co-distribution in three different real-case image datasets. First, cDDMs are shown to be indicators of colocalization and degree, able to increase the reliability of correlation coefficients currently used to detect the presence of colocalization. In addition, they provide a simultaneously visual and quantitative support, which opens for new investigation paths and biomedical considerations. Finally, thanks to the coDDMaker software we developed, cDDMs become an enabling tool for the quasi real time monitoring of experiments and a potential improvement for a large number of biomedical studies.