Dlotko, P. | bioRxiv Science

bioRxiv2025

Spatial Analysis of Malignant-Immune Cell Interactions in the Tumor Microenvironment Using Topological Data Analysis

The spatial interactions between malignant and immune cells in the tumor microenvironment (TME) play a crucial role in cancer biology and treatment response. Understanding these interactions is critical for predicting prognosis and assessing immunotherapy effectiveness. Conventional methods, which focus on local spatial features, often struggle to achieve robust analysis due to the complex and heterogeneous cellular distributions. We propose a Topological Data Analysis (TDA)-based framework using both global and local spatial features between malignant and immune cells. For the global aspects, we introduce Topological Malignant Clusters (TopMC), a method utilizing persistence diagrams in TDA to capture the global spatial shapes of malignant cell distributions. It quantifies tumor-immune cell infiltration at a global scale by computing distances from the boundaries of the TopMC. Local interactions are evaluated through the density of malignant cells. Using high-resolution multiplex immunofluorescence (mIF) imaging in Diffuse Large B Cell Lymphoma, we integrate distance and density measures into a distance-density space to analyze patterns of malignant-immune cell interactions in TME. This study shows the robustness of the proposed approach to variations in cell distribution, enabling consistent analysis irrespective of whether images are acquired from malignant-enriched or border regions of the tumors. Furthermore, we elucidate the correlation between spatial patterns of immune phenotypes and patient survival probability.

bioinformatics↗

bioRxiv2021

Topological Sholl Descriptors For Neuronal Clustering and Classification

MotivationGiven that neuronal morphology can widely vary among cell classes, brain regions, and animal species, accurate quantitative descriptions allowing classification of large sets of neurons is essential for their structural and functional characterization. However, robust and unbiased computational methods currently used to characterize groups of neurons are scarce. ResultsIn this work, we introduce a novel and powerful technique to study neuronal morphologies. We develop mathematical descriptors that quantitatively characterize structural differences among neuronal cell types and thus allow for their accurate classification. Each Sholl descriptor that is assigned to a neuron is a function of a distance from the soma with values in real numbers or more general metric spaces. To illustrate the use of Sholl descriptors, six datasets were retrieved from the large public repository http://neuromorpho.org/ comprising neuronal reconstructions from different species and brain regions. Sholl descriptors were subsequently computed, and standard clustering methods enhanced with detection and metric learning algorithms were then used to objectively cluster and classify each dataset. Importantly, our descriptors outperformed conventional techniques and thus provide a practical and effective approach to the classification of diverse neuronal cell types, with the potential for discovery of subclasses of neurons.

neuroscience↗