ASHLAR

ASHLAR aligns and stitches multiplexed immunofluorescence image tiles to produce precise whole-slide mosaics for single-cell–accurate spatial analysis.

Key Features:

• Robust stitching and registration: Coordinates stitching and registration of numerous multiplexed images to generate accurate mosaics that preserve cellular neighborhoods and tissue architecture.
• Large-scale mosaic support: Handles reconstruction of whole-slide mosaics from 103 or more individual image tiles with single-cell accuracy.
• Compatibility with microscope image formats: Supports image formats from commercial microscopes and slide scanners for integration with diverse acquisition systems.
• Standardized output: Exports mosaics in OME-TIFF format for downstream analysis.
• Implemented in Python: Provided as a Python implementation for computational integration into analysis pipelines.

Scientific Applications:

• Multiplexed immunofluorescence (t-CyCIF) on FFPE tissues: Applied to t-CyCIF datasets from formaldehyde-fixed, paraffin-embedded (FFPE) human tonsil and lung cancer tissues to reconstruct high-plex images from sequential lower-plex rounds.
• Single-cell spatial analysis and immune dynamics: Enables extraction of single-cell features while preserving spatial context to support studies of immune system organization and disease-associated changes.

Methodology:

Image acquisition using tissue cyclic immunofluorescence (t-CyCIF); artifact correction with BaSiC; stitching and registration performed by ASHLAR to produce mosaics; segmentation using ilastik and MATLAB; feature extraction with HistoCAT; visualization using image browsers and high-dimensional single-cell analysis methods.