Label-free imaging flow cytometry for analysis and sorting of enzymatically dissociated tissues

Area of Science:

• Biotechnology
• Microfluidics
• Cell Biology

Background:

• Biomedical research requires precise cell identification and isolation, often using labeling methods that can alter cell properties.
• Label-free cell sorting based on physical characteristics offers a promising alternative, particularly for clinical applications.
• Existing techniques like soRT-DC excel with suspended blood cells but face challenges with tissue-derived cells.

Purpose of the Study:

• To develop robust methods for label-free single-cell analysis and sorting from challenging biological samples, specifically nervous tissue.
• To enhance deep neural network (DNN) capabilities for distinguishing visually similar cells.
• To apply image-based sorting for enriching specific cell populations, such as photoreceptor cells, for therapeutic purposes.

Main Methods:

• Utilized sorting real-time deformability cytometry (soRT-DC), a microfluidic technique for label-free cell analysis.
• Developed and employed deep neural networks (DNNs) for analyzing bright-field cell images and making sorting decisions.
• Implemented methods to improve the robustness of cell analysis and sorting from dissociated nervous tissue, addressing issues like morphological changes and aggregates.

Main Results:

• Successfully adapted soRT-DC and DNNs for robust analysis and sorting of single cells from dissociated nervous tissue.
• Demonstrated DNNs capable of distinguishing between visually similar cell types within complex tissue samples.
• Achieved enrichment of photoreceptor cells from dissociated retina using DNN-driven image-based sorting.

Conclusions:

• Label-free cell sorting with soRT-DC and DNNs is a viable and robust method for isolating cells from solid tissues.
• This technology holds significant potential for applications in regenerative medicine, such as preparing cells for transplantation.
• The developed DNNs offer improved cell discrimination, advancing the field of label-free cell sorting.