Reviewer #2 (Public review):
Summary:
AutoMorphoTrack provides an end-to-end workflow for organelle-scale analysis of multichannel live-cell fluorescence microscopy image stacks. The pipeline includes organelle detection/segmentation, extraction of morphological descriptors (e.g., area, eccentricity, "circularity," solidity, aspect ratio), tracking and motility summaries (implemented via nearest-neighbor matching using cKDTree), and pixel-level overlap/colocalization metrics between two channels. The manuscript emphasizes a specific application to live imaging in neurons, demonstrated on iPSC-derived dopaminergic neuronal cultures with mitochondria in channel 0 and lysosomes in channel 1, while asserting adaptability to other organelle pairs.
The tool is positioned for cell biologists, including users with limited programming experience, primarily through two implemented modes of use: (i) a step-by-step Jupyter notebook and (ii) a modular Python package for scripted or batch execution, alongside an additional "AI-assisted" mode that is described as enabling analyses through natural-language prompts.
The motivation and general workflow packaging are clear, and the notebook-plus-modules structure is a reasonable engineering choice. However, in its current form, the manuscript reads more like a convenient assembly of standard methods than a validated analytical tool. Key claims about robustness, accuracy, and scope are not supported by quantitative evidence, and the 'AI-assisted' framing is insufficiently defined and attributes to the tool capabilities that are provided by external LLM platforms rather than by AutoMorphoTrack itself. In addition, several figure, metric, and statistical issues-including physically invalid plots and inconsistent metric definitions-directly undermine trust in the quantitative outputs.
Strengths:
(1) Clear motivation: lowering the barrier for organelle-scale quantification for users who do not routinely write custom analysis code.
(2) Multiple entry points: an interactive notebook together with importable modules, emphasizing editable parameters rather than a fully opaque black box.
(3) End-to-end outputs: automated generation of standardized visualizations and tables that, if trustworthy, could help users obtain quantitative summaries without assembling multiple tools.
Weaknesses:
(1) "AI-assisted / natural-language" functionality is overstated.
The manuscript implies an integrated natural-language interface, but no such interface is implemented in the software. Instead, users are encouraged to use external chatbots to help generate or modify Python code or execute notebook steps. This distinction is not made clearly and risks misleading readers.
(2) No quantitative validation against trusted ground truth.
There is no systematic evaluation of segmentation accuracy, tracking fidelity, or interaction/overlap metrics against expert annotations or controlled synthetic data. Without such validation, accuracy, parameter sensitivity, and failure modes cannot be assessed.
(3) Limited benchmarking and positioning relative to existing tools.
The manuscript does not adequately compare AutoMorphoTrack to established platforms that already support segmentation, morphometrics, tracking, and colocalization (e.g., CellProfiler) or to mitochondria-focused toolboxes (e.g., MiNA, MitoGraph, Mitochondria Analyzer). This is particularly problematic given the manuscript's implicit novelty claims.
(4) Core algorithmic components are basic and likely sensitive to imaging conditions.
Heavy reliance on thresholding and morphological operations raises concerns about robustness across varying SNR, background heterogeneity, bleaching, and organelle density; these issues are not explored.
(5) Multiple figure, metric, and statistical issues undermine confidence.
The most concerning include:<br />
(i) "Circularity (4πA/P²)" values far greater than 1 (Figures 2 and 7, and supplementary figures), which is inconsistent with the stated definition and strongly suggests a metric/label mismatch or computational error.
(ii) A displacement distribution extending to negative values (Figure 3B). This is likely a plotting artifact (e.g., KDE boundary bias), but as shown, it is physically invalid and undermines confidence in the motility analysis.
(iii) Colocalization/overlap metrics that are inconsistently defined and named, with axis ranges and terminology that can mislead (e.g., Pearson r reported for binary masks without clarification).
(iv) Figure legends that do not match the displayed panels, and insufficient reporting of Ns, p-values, sampling units, and statistical assumptions.