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Abstract

Quantitative fluorescence microscopy is central to modern cell biology, yet extracting reproducible measurements from images remains a bottleneck for biologists without programming experience. Here we present cellquant, a single-script command line pipeline for multi-channel fluorescence images that performs cell segmentation, puncta quantification, colocalization analysis, and spatial proximity measurements. Because the interface is entirely text based, the exact command used to generate any result can be recorded and re-executed. We validate cellquant on two biological systems. In human HCT116 cells, the pipeline quantified arsenite-induced stress granule formation. In budding yeast, simultaneous measurement of nucleolar morphology, colocalization, and spatial proximity across a temperature gradient revealed a coordinated sequence of nucleolar reorganization. Applying PCA and UMAP to the multi-parameter output of cellquant resolved a continuous cell state transition across the temperature gradient, with condensate redistribution and nucleolar morphology defining orthogonal axes. The pipeline produces publication-ready quantification with visual quality control and statistically rigorous replicate analysis. All code, documentation, and example datasets are freely available.

In-depth summary

Claude Opus 5.5 · xhigh effort · 8 Nov 2026 · from the full text of version 2 (3 Sept 2026) · CC BY 4.0

One Python script, configured through LLM-assisted command-line calls, segments and quantifies 2D/3D multi-channel fluorescence images with provenance records; in a yeast temperature series it exposed a non-responding subpopulation.

Many experimental biologists do not routinely quantify their fluorescence images, and existing tools (CellProfiler, Fiji/ImageJ, napari) still require learning new software, while LLM add-ons such as Omega and fiji-llm generate fresh analysis code on each run. cellquant instead keeps vetted, versioned analysis code fixed and uses the LLM only to help compose a command-line call, with each run recording its exact command and provenance.

Key results

  1. ShownIn 3D analysis of yeast grown at 25 to 40°C, cell volume and Nsr1 intensity rose with temperature while nucleolar volume and Tif6 intensity did not track it, Sis1 intensity rose at the two highest temperatures, and Nsr1-Tif6 colocalization fell at 36°C and 40°C.

    8,309 cells from 30 biological replicates (6/5/5/6/8 per temperature), replicate-median rank tests vs 25°C, Bonferroni-corrected: cell volume median 37 µm^3 at 25°C vs 65 µm^3 at 40°C (padj = 0.003); Nsr1 intensity padj = 0.003 at 40°C; Sis1 intensity up at 36°C and 40°C (padj = 0.017, 0.035); Nsr1-Tif6 Pearson R down (padj = 0.009, 0.003). · Multi-parameter analysis of temperature…; Statistical analysis; Figure legends

  2. SuggestedPrincipal component analysis (PCA) of 45 per-cell features placed temperature on PC2, while PC1 split cells into two modes: one shifted with heat and the other stayed put, and the shifting ('responsive') share dropped at 40°C.

    PC2 vs temperature ρ = +0.61 (PC1 ρ = +0.20); two-component Gaussian fit on PC1, Ashman's D = 2.62, antimode at −3.29; from 25 to 40°C the high mode moved +4.2 PC1 units (padj = 0.003), the low mode −1.0 (padj = 0.57); high-mode share fell from 84% to 64% at 40°C (padj = 0.003), on 7,569 of 8,309 cells after a 10 µm^3 volume filter. · Dimensionality reduction of single cell data…; Statistical analysis; Figure legends

  3. ShownA single CLI script handles segmentation, puncta, colocalization, morphometry and proximity in 2D or 3D. By default it refuses common shortcuts: it blocks colocalization on projections and aborts 3D runs with missing, implausible or metadata-inconsistent voxel sizes, unless the user passes an explicit override flag.

    3D colocalization uses Pearson and Manders on all voxels with per-channel Costes thresholds; projection values need --allow-2d-colocalization and are flagged in provenance. 3D runs abort if XY voxel <0.01 or >10 µm, Z <0.05 or >50 µm, or values differ from metadata by >1%. provenance.json stores the CLI, SHA-256 of inputs, model checkpoints and dependency versions. · A single-script pipeline for multi-channel…; Three-dimensional segmentation and…; Metadata handling and provenance

  4. SuggestedIn arsenite-treated HCT116 cells, the share of G3BP1 and PABPC1 signal in puncta rose substantially and puncta size modestly, but the experiment was too small to establish significance.

    500 µM sodium arsenite for 1 h; 79 cells from 3 control and 4 arsenite biological replicates; two-sided Mann-Whitney on replicate medians, where the smallest attainable p is 0.057, so effect sizes (fold change, Cliff's δ in Table S1) carry the result. · Stress granule quantification in human cells; Statistical analysis; Figure legends; Tables

  5. ClaimedThe intended workflow is for users to describe the analysis to an LLM and iterate on QC overlays. The pipeline itself was assembled with Claude Code from existing open-source packages, without new analytical algorithms.

    Development example: 3× downsampling before Cellpose helped mammalian segmentation but destroyed spatial detail in the smaller yeast cells. The authors report the yeast analysis needed eleven lines of experiment-specific code. No evaluation with users lacking coding experience is reported. · Development of cellquant through vibecoding; Installation, tutorial, documentation; Discussion

Methods, data and reproducibility

Open-source tools (Cellpose/Cellpose-SAM segmentation, Laplacian-of-Gaussian puncta detection, Costes-thresholded colocalization) were linked with Claude Code into one CLI script that users configure with LLM help and check via QC overlays. It was applied to confocal z-stacks of arsenite-treated HCT116 cells and of yeast at five temperatures, with replicate-median statistics and PCA/UMAP.

  • HCT116 cells expressing G3BP1-RFP, G3BP2-RFP, PABPC1-GFP, DAPI-stained, with or without 500 µM sodium arsenite (1 h); confocal z-stacks of 10 slices at 0.50 µm · human (HCT116 cell line) · n = 3 control and 4 arsenite biological replicates; 79 cells · 10.5281/zenodo.21843280
  • Budding yeast (W303) with endogenous Sis1-mVenus, Tif6-HaloTag (JF646) and Nsr1-mScarlet-I, 6 h at 25, 30, 32, 36 or 40°C; confocal z-stacks of 71 slices at 0.23 µm · Saccharomyces cerevisiae (budding yeast) · n = 8,309 cells from 30 biological replicates (6/5/5/6/8 per temperature) · 10.5281/zenodo.21843280
Methods & tools
cellquant, Cellpose, Cellpose-SAM (cpsam), Claude Code (Anthropic), scikit-image, SciPy (mannwhitneyu), scikit-learn PCA, umap-learn, bioio, OpenCV, Nikon SoRa spinning disk confocal microscope, Janelia Fluor 646 HaloTag ligand
Software & environment
environment.yml: Python 3.11 to 3.12, Cellpose ≥4.0, scikit-image ≥0.24, numpy ≥1.24 and <2.0, opencv-python-headless <4.10, bioio readers; a PEP 723 header lets uv run cellquant.py install dependencies; v1.1.0 archived at Zenodo (DOI: 10.5281/zenodo.21830047).
Assay conditions & compute
2D analyses on a MacBook Pro (Apple M-series) with CPU-mode Cellpose (MPS GPU incompatible with cpsam); 3D yeast analysis on the University of Chicago Midway3 cluster, also in CPU mode.
Reproducibility gaps
The full imaging datasets are on Zenodo, cited by DOI only (10.5281/zenodo.21843280; see methods.data). The GitHub repository holds example data only. Run times and memory use are not reported, and there is no batch parallelization.
Limitations (6)
  • No time-lapse support and no batch parallelization; images run one after another, which may be impractical for large screening datasets. · stated by the authors
  • Puncta detection presumes roughly spherical or circular objects and may fail on elongated or irregular shapes; global Costes thresholding for colocalization may be suboptimal in heterogeneous cell populations. · stated by the authors
  • Results hinge on Cellpose segmentation quality, which varies by cell type and imaging conditions, and on the AI assistant, whose updates can change suggested parameters; the user's reading of QC overlays is the main safeguard. · stated by the authors
  • The HCT116 comparison (3 vs 4 replicates) cannot reach conventional significance because the smallest attainable two-sided p is 0.057, so the authors rely on effect sizes (fold change, Cliff's δ) rather than p-values. · stated by the authors
  • Ease of use for people without coding experience is asserted but not tested: no user study and no comparison of time or accuracy against CellProfiler or Fiji is reported. · noted in this summary
  • Segmentation and puncta detection are checked only by visual QC overlays and expected biological trends, not against annotated ground truth, and the yeast temperature series is a single experiment. · noted in this summary

Read this if you run fluorescence microscopy without a coding background and want a fixed, provenance-tracked segmentation and colocalization pipeline that an LLM helps you configure, or if you study yeast nucleolar heat responses.

Citations
1

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