Analyze 2D fluorescence microscopy images to detect nuclei and measure their counts, intensity, and area. Run repeatable CellProfiler batches, inspect nucleus outlines, and review measurement tables with recorded pipeline settings and quality checks.
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Runs reproducible CellProfiler microscopy pipelines for nuclear segmentation, cell counts, and per-object fluorescence measurements. Supports image/channel manifests, headless batch execution, segmentation overlays, and measurement QC for 2D fluorescence assays.
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Use this skill when a user needs a repeatable CellProfiler .cppipe, nuclear counts, nuclear
fluorescence, or batch microscopy measurements. The bundled assay accepts one 2D grayscale
TIFF nuclear channel per field, with black-is-zero (MINISBLACK) pixels and bright nuclei on a
dark background. Palette and white-is-zero TIFFs need an explicit conversion. For volumetric
segmentation, multichannel cell painting, or tissue-specific models, design a separate pipeline
and validate those assumptions rather than silently projecting or splitting the images.
The official application and manual remain 4.2.8. PyPI publishes 4.2.8.1; its seven modules used here and embedded Threshold module match the 4.2.8 source, but this review did not execute that native distribution. Keep the helper environment separate from CellProfiler's older dependency stack; see the runtime reference for the verification boundary.
image_path is absolute or relative to the manifest; sample IDs use letters, digits, dots, dashes, or underscores; sample IDs
and plate/well/site combinations are unique. Use a nonnumeric sample ID such as sample_001:
LoadData infers column types and can otherwise turn 001 into 1. Avoid surrounding
whitespace in identifiers. TIFFs must be uint8 or uint16, single plane/series/resolution, and
nonconstant. The helper rejects RGB, z-stacks, and float images rather than guessing channels..cppipe, and pass --pipeline to preserve
it. Do not choose settings separately for each treatment to make their counts agree.From this skill directory, create images.csv:
sample_id,image_path,plate,well,site
control_A01_1,images/control_A01_1_DAPI.tif,Plate1,A01,1
python scripts/nuclei_assay.py prepare images.csv load_data.csv
python scripts/nuclei_assay.py run images.csv results --executable cellprofiler
python scripts/nuclei_assay.py summarize results
run requires a fresh/empty output directory and executes CellProfiler with -c -r, a saved
pipeline copy, --data-file, output folder, and --done-file. Success requires exit code zero,
a Complete marker, and valid measurement tables. It records the command, pipeline checksum,
input image checksums, and sample QC in assay_qc.json before execution, retaining failed
status and the error if execution or output validation fails. CellProfiler output goes to
cellprofiler.log. Rerun in a new output folder. summarize checks CSV contents independently;
it does not prove an engine run completed.
Custom pipelines must preserve DNA, Nuclei, Metadata_Sample, integer-dtype scaling, and
the unprefixed single-object Image.csv/Nuclei.csv export contract. Keep the required
intensity/area measurements. A renamed object set or different intensity scale needs a
corresponding helper adaptation, not an unchecked --pipeline substitution.
The executable can also be the CellProfiler application launcher or a local container launcher;
see references/runtime-and-qc.md for the container target,
filesystem mapping, and verification evidence. prepare and summarize work without CellProfiler.
Image.csv: one image/field row, including Count_Nuclei and acquisition metadata.Nuclei.csv: one accepted object per row, with mean/integrated DNA intensity, area, and shape.*_nuclei.png: green nuclear boundaries over the input image for visual QC.pipeline.cppipe and cellprofiler.done: the exact pipeline copy and engine completion marker.assay_qc.json: run status, unique image/object keys, exact counts, finite mean/integrated
intensity and positive area checks, field mean area in pixels, and storage saturation flags.LoadData ignores camera metadata for scaling in this asset and divides by the integer storage maximum: uint8 → 255, uint16 → 65535. A 12-bit camera stored in uint16 therefore has a maximum near 0.0625. Do not compare intensities across different bit depths, exposures, gains, or staining batches without an explicit calibration. A saturated image can pass segmentation while its intensity measurement is unusable. Illumination correction and background subtraction are assay-specific additions; this starter does neither.
The saturation fraction only counts pixels at the storage maximum. A 12-bit detector may saturate at 4095 while the uint16 storage maximum is 65535; inspect the known acquisition ceiling separately. Integrated intensity sums pixel values and may exceed 1; only per-pixel mean intensity is constrained to 0–1. The field's mean nuclear intensity weights each nucleus equally, rather than weighting each pixel equally.
A count check cannot prove correct segmentation. Inspect overlays and independently annotated fields; report boundary exclusions and segmentation errors alongside the biological result. The optional synthetic engine test targets a known three-nucleus example, not assay performance on unseen cell types. It was skipped in the current review because no engine was configured.
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Estimate intracellular reaction rates from steady-state carbon-13 tracing measurements. Validate carbon atom mappings and measurement inputs, simulate labeling, fit constrained fluxes, and diagnose which pathway rates the data can meaningfully constrain.
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Plan protein characterization experiments with Adaptyv's cloud laboratory, estimate costs, submit sequences, track progress, and retrieve measured results. Supports binding, thermal stability, expression, fluorescence, and enzyme activity workflows through the API and Python SDK.
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Analyze time series with aeon, a Python toolkit for data that changes over time. Supports classification, regression, clustering, forecasting, anomaly detection, and pattern search, with guidance on preprocessing, algorithm selection, and evaluation.
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Investigate how DNA variants may affect gene activity using precomputed AlphaGenome Atlas results and model predictions. Rank candidate variants, examine tissue-specific effects, and inspect the features behind scores to develop research hypotheses about biological mechanisms.
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Plan and document studies that assess whether an analytical procedure suits its intended use. Select the governing framework, evaluate calibration, accuracy, precision, and detection limits, and compare methods using acceptance criteria defined before data collection.
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Organize single-cell measurements with cell and gene metadata, read and write h5ad or Zarr files, and prepare subsets or combined datasets. Includes guidance on handling large annotated matrices without loading all measurement values into memory.
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