Imaging flow cytometry: a primer.

Area of Science:

• Biomedical Engineering
• Cell Biology
• Data Science

Background:

• Imaging flow cytometry (IFC) integrates high-throughput flow cytometry with microscopy's single-cell imaging.
• IFC generates rich datasets from numerous image features, enabling diverse biomedical applications.
• Advancements in computational analysis are crucial for leveraging IFC data.

Purpose of the Study:

• To provide a primer on imaging flow cytometry instrumentation, data acquisition, and analysis.
• To review the progression of analysis methodologies for IFC data.
• To discuss current limitations and future innovations in IFC.

Main Methods:

• Discussion of typical IFC instrumentation and data formats.
• Overview of analysis tools, including single image features, gating, custom feature design, and machine/deep learning.
• Illustrative examples from literature showcasing analysis method progression and applications.

Main Results:

• Analysis methods have evolved from basic gating to sophisticated machine learning approaches.
• Different methods are suitable for various IFC datasets and research questions.
• IFC has demonstrated a wide range of novel biomedical applications.

Conclusions:

• IFC is a powerful technique for detailed single-cell analysis in biomedical research.
• The evolution of analysis methods, particularly machine learning, enhances IFC's capabilities.
• Ongoing innovations are addressing current limitations, promising broader future applications.