3-Step flow focusing enables multidirectional imaging of bioparticles for imaging flow cytometry

Area of Science:

• Biotechnology
• Analytical Chemistry
• Microscopy

Background:

• Conventional imaging flow cytometry (IFC) lacks 3D geometrical feature measurement capabilities.
• Automated bio-particle classification often faces challenges with accuracy and false positives.

Purpose of the Study:

• To introduce and validate multidirectional imaging flow cytometry (mIFC) for 3D particle analysis.
• To enhance automated bio-particle classification accuracy using 3D morphological data.
• To reduce false positives in particle classification.

Main Methods:

• Development of a flow rotation unit enabling incremental rotation of sample lamellas.
• Guiding particles in a controlled shear flow within a capillary slit detection chamber.
• Acquiring multiple images of each particle from different angles for 3D reconstruction.

Main Results:

• Successful classification of 15 pollen species using 3D morphological information.
• Demonstrated improvement in automated bio-particle classification accuracy through multidirectional imaging and machine learning.
• Significant reduction in false positives for foreign particle classification.

Conclusions:

• Multidirectional imaging flow cytometry (mIFC) provides a robust platform for 3D particle analysis.
• The integration of mIFC with machine learning substantially improves automated classification of bio-particles.
• mIFC technology offers enhanced precision and reliability in particle identification and analysis.