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        <datestamp>2026-09-12T10:48:38Z</datestamp>
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          <dc:title>Benchmarking three simple DNA staining-based image metrics for live-cell tracking of chromatin organization</dc:title>
          <dc:creator>Minwoo Kang (14304098)</dc:creator>
          <dc:creator>Aidan Tomas Cabral (24830220)</dc:creator>
          <dc:creator>Manasi Sawant (13776849)</dc:creator>
          <dc:creator>Hawa Racine Thiam (10866687)</dc:creator>
          <dc:subject>Biochemistry</dc:subject>
          <dc:subject>Genetics</dc:subject>
          <dc:subject>Molecular Biology</dc:subject>
          <dc:subject>Chemical Sciences not elsewhere classified</dc:subject>
          <dc:subject>Biological Sciences not elsewhere classified</dc:subject>
          <dc:subject>Information Systems not elsewhere classified</dc:subject>
          <dc:subject>Cancer</dc:subject>
          <dc:subject>Chromatin</dc:subject>
          <dc:subject>chromatin reorganization</dc:subject>
          <dc:subject>DNA staining</dc:subject>
          <dc:subject>image analysis</dc:subject>
          <dc:subject>mitosis</dc:subject>
          <dc:subject>NETosis</dc:subject>
          <dc:subject>nucleus</dc:subject>
          <dc:description>&lt;p&gt;Quantifying chromatin-state dynamics in living cells remains challenging, in part because most methods require fixation or cell lysis. Here, we introduce two simple DNA image-derived metrics - Diffuse Signal Index (DSI)and 1-Gini - and benchmark them against the coefficient of variation (CV) as fixation-free readouts of chromatin reorganization. Leveraging the pronounced compact-to-decompact chromatin transition of NETosis, we show that all three metrics track progressive chromatin reorganization, with DSI providing the strongest trajectory-level discrimination between NETing and non-NETing cells. All three metrics also correlate with ATAC-see-based chromatin accessibility measurement in fixed cells, supporting their biological interpretability. In dividing cells, the metrics capture mitotic chromatin compaction and post-mitotic decompaction, demonstrating applicability in diverse biological processes. Together, these results provide a practical framework for extracting readouts of chromatin reorganization from routine live-cell DNA staining. We also provide NucMetrics, an open-source ImageJ/Fiji macro toolset for easily computing CV, DSI and 1-Gini.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-12T10:48:38Z</dc:date>
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          <dc:identifier>10.6084/m9.figshare.33661623.v1</dc:identifier>
          <dc:relation>https://figshare.com/articles/dataset/Benchmarking_three_simple_DNA_staining-based_image_metrics_for_live-cell_tracking_of_chromatin_organization/33661623</dc:relation>
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