FishFeats: streamlined quantification of multimodal labeling at the single-cell level in 3D tissues

Area of Science:

• * Computational Biology
• * Microscopy Image Analysis
• * Bioinformatics

Background:

• * Analyzing biological marker distribution at the single-cell level in 3D tissues is complex.
• * Existing software often requires integration of multiple independent tools.
• * A comprehensive pipeline for 3D image analysis remains a challenge.

Purpose of the Study:

• * To develop an integrated, user-friendly pipeline for single-cell quantification from 3D microscopy.
• * To streamline the extraction of quantitative data from multimodal 3D fluorescent images.
• * To facilitate manual correction for improved accuracy in image analysis.

Main Methods:

• * Development of FishFeats, an open-source napari plugin in Python.
• * Integration of diverse image analysis steps: cell/nuclei segmentation, RNA detection, and cell mapping.
• * Creation of a unified cell-by-cell table for downstream analysis without coding.

Main Results:

• * FishFeats successfully integrates multiple image analysis steps within a single framework.
• * Enables extraction of quantitative data (smFISH, IHC, morphology, classification) from 3D images.
• * Provides a user-friendly interface with options for manual correction.

Conclusions:

• * FishFeats simplifies the complex process of single-cell quantification from 3D microscopy.
• * The plugin offers a powerful, accessible tool for researchers without computational expertise.
• * Facilitates accurate analysis of biological marker expression and cell characteristics.