Macrophages play an essential role in wound healing due to their dynamic nature and functional plasticity, exhibiting highly heterogeneous morpho-kinetic behaviors depending on their activation states. However, quantitative analysis of macrophage behavior in in vivo settings remains limited, largely due to the complexity of their diverse morphologies and motility patterns over time. In this study, we present an analytic workflow to investigate macrophage dynamics in zebrafish. By computing a comprehensive set of morpho-kinetic features, we reveal the clear distinctions between M1 (pro-inflammatory) and M2 (anti-inflammatory) macrophages in terms of shape elongation, directional movement, and random-like motion. Based on these features, we classify macrophages in the transition period into M1-like and M2-like groups. We compare and analyze their behaviors, which allows us to estimate the timing of the phenotypic switch. In addition, we analyze the behavior of macrophages that do not express Tumor Necrosis Factor (TNF) and are not stimulated by wound signaling. In summary, this study provides a quantitative analysis of macrophage behavior during wound healing and suggests distinct behavioral landscapes across different macrophage activation states.

Topological Data Analysis (TDA) has emerged as a powerful framework for uncovering meaningful structure in high-dimensional, complex datasets. In this talk, we present two applications of TDA in analyzing patterns, one in the tumor microenvironment (TME) and the other in high-resolution chemical profiling. In the first case, we develop a TDA-based framework to quantify malignant-immune cell interactions in Diffuse Large B Cell Lymphoma using multiplex immunofluorescence imaging. By introducing Topological Malignant Clusters (TopMC) and leveraging persistence diagrams, we capture both global infiltration patterns and local density-based features. This robust approach enables consistent prognostic assessment regardless of tumor region heterogeneity and reveals correlations with patient survival. In the second application, we utilize the Ball Mapper algorithm to simplify and visualize high-dimensional data obtained from 2D Chromatography with high-resolution mass spectrometry. This enables interpretable chemical profiling of complex mixtures and supports tasks such as sample authentication and environmental analysis. Together, these studies demonstrate the versatility and interpretability of TDA for extracting biologically and chemically meaningful information.

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https://scholar.google.com/citations?user=4w2vNhcAAAAJ&hl=en