| 著者 |
Fukatsu N, Tanisumi Y, Cheung DL, Saito Y, Hashimoto A, Takahashi N, Takeda I, Inoue M, Moorhouse AJ, Wake H.
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| Abstract |
Two-photon imaging enables minimally invasive, deep-tissue chronic observation of cellular dynamics, including neural activity, generating massive datasets that require scalable analysis. While platforms like Suite2p automate region of interest (ROI) extraction, downstream validation and cross-session ROI tracking remain bottlenecked by manual workflows. To address this, we present OPTIC (optimized toolbox for image-based cellular analysis), an open-source, Python-based platform featuring three intuitive graphical user interface (GUI) applications: OpticROICuration for basic ROI classification and integration of external curation data, OpticROITracking for automated cross-session ROI matching, and OpticRawTracking for general cell tracking, validated here on microglia. By pairing robust image registration and optimal transport-based ROI matching, OPTIC automates cross-session tracking with higher accuracy than popular alternatives like CellReg and TrackMate. Validated on in vivo datasets of calcium imaging (neurons, astrocytes, and axonal boutons) and complex cell migration (microglia), OPTIC provides a fully customizable solution requiring no programming expertise to streamline high-throughput neuroscience pipelines.
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