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Quick Start

This guide walks through the basic workflow for analyzing senescence markers in tissue images using SenoQuant.

Tip: There is a help icon in the top-right corner that opens the current tab's User Guide page.

Prerequisites

  • napari installed with SenoQuant.
  • System meets the installation requirements.
  • Multi-channel microscopy image (supported formats: .tif, .czi, .lif, .nd2, .zarr, etc.).
  • Channels containing: Nuclei, IF markers, and/or spots.

Basic workflow

1. Launch napari and load image

If you used the Installer:

  • Launch SenoQuant from the Start Menu or the desktop icon.

Manual installs (conda/uv):

In your terminal, activate the conda environment where SenoQuant is installed and start napari:

conda activate senoquant
napari --with senoquant

Load your image:

  • File → Open File(s)... → Select your image.

    A popup may appear for you to select the appropriate reader plugin. Choose senoquant.

  • Or drag-and-drop the file into the napari window.

Expected result: Each channel appears as a separate layer in the layer list. If you're opening a multi-scene file, select the desired scene(s) from the popup.

2. Open SenoQuant

SenoQuant should launch automatically in Step 1. If not:

Plugins → SenoQuant

The plugin window opens as a docked widget with 8 tabs:

3. Run nuclear segmentation

  1. Switch to the Segmentation tab.
  2. In the Nuclear segmentation box, select Nuclear layer: Choose the DAPI or nuclear stain channel.
  3. Select Model: default_2d (for 2D images) or default_3d (for Z-stacks). cpsam is also available for nuclear+cytoplasmic segmentation.

  4. Adjust model settings if needed (e.g., Object diameter (px)).

    To quickly estimate object diameter, create a napari Shapes layer, draw a line across a representative nucleus, then use Layers → Measure → Toggle shapes dimensions measurement (napari builtins). See https://napari.org/stable/howtos/layers/shapes.html for details.

    The default settings work well for most images.

  5. Click Run.

Output: A new labels layer named <channel>_<model>_nuc_labels appears in the layer list.

4. (Optional) Run cytoplasmic segmentation

If you need to catch cytoplasmic regions for marker quantification:

  1. In the Segmentation tab, go to the Cytoplasmic segmentation box.
  2. Select Model: cpsam, nuclear_dilation or perinuclear_rings.
  3. Select image/labels layers.
  4. Adjust model settings if needed.
  5. Click Run.

Output: A new labels layer named <channel>_<model>_cyto_labels.

5. (Optional) Detect spots

If your image contains punctate spots (e.g., gH2AX, telomeres, FISH spots):

  1. Switch to the Spots tab.
  2. Select Image Layer: Choose the channel with spots.
  3. Select Detector: rmp, ufish.

  4. Adjust detection settings (for example, Threshold).

    The default settings work well for most images.

  5. (Optional) Set Minimum diameter and Maximum diameter to filter detected spots.

  6. Click Run.

Output: A labels layer named <channel>_<detector>_spot_labels.

6. (Optional) Run a prediction model

Use this tab for computer-vision models that predict senescence-associated feature layers.

  1. Switch to the Prediction tab.
  2. Select Select model: demo_model (current placeholder).
  3. In Model interface:
    • Select Image layer.
    • Set Multiplier.
  4. Click Run.

Output: A new image layer named <image layer>_demo_model.

7. Configure quantification features

The quantification tab organizes exports by Features. A feature defines what to quantify and how. In the current version, two feature types are supported: Markers and Spots. This is based on common data types in senescence research:

  • Markers: Measure intensity-based markers (e.g., IF markers) within nuclear/cytoplasmic masks.
  • Spots: Count spots and analyze colocalization within cell masks.

To add a feature:

  1. Switch to the Quantification tab.

  2. Click Add feature → Select feature Type:

    • Markers: For intensity-based IF marker quantification.
    • Spots: For spot counting and colocalization.

  3. Name your feature (e.g., IF markers, IF spots).

Configure a Markers feature

  1. Click Add channel(s).

  2. In the popup:

    • In the top Segmentations box, click Add segmentation → Add nuclear/cytoplasmic labels layer.

      The selected segmentation defines the nuclei/cells for quantification. SenoQuant will export one cell x marker table per segmentation.

    • In the Channels box, click Add channel → Add intensity channel(s) to quantify.

    • For each channel, name the channel (e.g., DAPI, p16), and select the image layer containing the marker. Optionally, click the Set threshold checkbox to define an intensity threshold for positive/negative calls automatically or manually. The threshold sliders are linked to the napari layer contrast limits for easy visualization.
    • Click Save or close the popup when done.

  3. (Optional) Draw ROIs with a shapes layer. Enable ROIs → Name the ROI → Select the shapes layer. Select the ROI Type to be Include or Exclude. Nuclei/cells inside Include ROIs or outside Exclude ROIs will be marked in the output table.

Configure a Spots feature

  1. Click Add channel(s).

  2. In the popup:

    • (Optional) In the top box, click Add segmentation → Add a nuclear/cytoplasmic labels layer if you want per-cell spot summaries.

      With segmentation(s), SenoQuant exports one *_cells + *_spots table pair per segmentation. Without segmentation, it still exports all spots in all_spots.

    • In the Channels box, click Add channel → Add spot channel(s) to quantify.

    • For each channel, name the channel (e.g., gH2AX, Telomere), select the image layer in Channel, and select the corresponding spot-label layer generated in the Spots tab under Spots segmentation.
    • Click Save or close the popup when done.

  3. (Optional) Enable ROIs → Name the ROI → Select the shapes layer. ROIs work the same way as in the Markers feature.

  4. (Optional) Enable Export colocalization to analyze spot colocalization between two or more spot channels. Colocalization will only be computed if two or more spot channels are added to the feature.

8. Run quantification

  1. In the Quantification tab, ensure all features are configured
  2. In the Output box, browse to select an output folder.
  3. Name the quantification run in Save name.
  4. Choose Format: xlsx (Excel) or csv.
  5. Click Process and save.
  6. Wait for quantification to complete.

Outputs:
Excel/CSV files containing:

  • Markers: Marker intensities per cell, morphological features.
  • Spots: Spot counts per cell, spot intensities, colocalization data (if enabled).

Segmentation masks are also saved, plus a .json file with the configuration used for the run.

Batch processing workflow

For high-throughput analysis of multiple images:

1. Open the Batch tab

In the SenoQuant dock widget, select Batch.

2. Configure inputs

  1. Input folder → Choose the directory containing images.
  2. Extensions → List the file types to include (e.g., .tif, .nd2, .czi, .zarr).
  3. (Optional) Include subfolders → Enable if your data are nested.
  4. (Optional) Process all scenes → Enable for multi-scene files.

3. Map channels

Add channel names and indices so they appear in all dropdowns:

  • Click Auto populate channel(s) to load channel names/indices from the first matching input image in the selected folder.
  • Name: DAPI, FITC, Cy3, etc.
  • Index: zero-based channel index

The Auto populate channel(s) button is enabled only after a valid Input folder is selected.

4. Enable processing steps

Configure only the steps you need:

  • Nuclear segmentation → Enable, select model and channel, adjust settings.
  • Cytoplasmic segmentation (optional) → Enable if needed.
  • Spot detection (optional) → Choose detector and channels; set min/max diameter-style filtering if needed.

5. Configure quantification (optional)

If you want batch exports:

  1. Enable Quantification.
  2. Click Add feature and set up Markers or Spots features as in the single-image workflow.

Note: ROI selection and threshold tuning are not available in batch mode.

6. Set outputs and run

  1. Output folder → Choose where results are written.
  2. (Optional) Overwrite → Enable to replace existing outputs.
  3. Click Run batch.

Outputs: Each input image gets its own output folder with (if enabled) quantification tables plus per-feature feature_settings.json metadata files. Masks are also saved. The batch output root includes a senoquant_settings.json file with the batch configuration used for the run.

Before closing napari, save your configuration:

  1. Open the Settings tab.
  2. Click Save settings and store senoquant_settings.json.

To continue later, click Load settings in the same tab. If the JSON contains batch configuration, the Batch tab is populated too.

Next steps

  • Segmentation - Detailed model settings and parameters
  • Spots - Advanced spot detection configuration
  • Prediction - Prediction model workflow and placeholder example
  • SenNet Portal - Browse and download compatible SenNet datasets
  • Quantification - Feature export details and column definitions
  • Visualization - Plot generation from quantification tables
  • Batch - Batch processing and automation
  • Data - Supported file formats and metadata handling