Cell damage evaluation by intelligent imaging flow cytometry.

Area of Science:

• Biomedical Engineering
• Cell Biology
• Flow Cytometry

Background:

• Essential thrombocythemia (ET) involves excessive platelet production, increasing risks of thrombosis.
• Acoustofluidic methods offer efficient platelet removal but require cell damage assessment.
• Current cell damage evaluation methods are often time-consuming and labor-intensive due to staining requirements.

Purpose of the Study:

• To develop and validate a high-throughput, label-free method for evaluating cell damage post-acoustofluidic sorting.
• To assess the impact of acoustofluidic sorting parameters on erythrocyte and leukocyte integrity.
• To establish a novel approach for real-time cell damage assessment in research and clinical settings.

Main Methods:

• Utilized optical time-stretch (OTS) imaging flow cytometry for high-throughput, label-free imaging of erythrocytes and leukocytes.
• Employed acoustofluidic sorting chips with varying acoustic wave powers and flow speeds (up to 1 m/s).
• Applied machine learning algorithms for extracting biophysical phenotypic features and classifying cellular images.

Main Results:

• Biophysical feature errors and abnormal cell proportions were below 10% for undamaged cell groups.
• Significantly higher errors (>10%) were observed in damaged cell groups, distinguishing between intact and compromised cells.
• Results indicate minimal cell damage from acoustofluidic sorting within appropriate acoustic power settings, correlating with clinical findings.

Conclusions:

• OTS imaging flow cytometry provides a rapid, label-free method for assessing cell damage after acoustofluidic sorting.
• The study validates the safety of acoustofluidic methods for ET treatment under optimized conditions.
• This technique offers a valuable tool for optimizing cell sorting processes and ensuring cell viability in biomedical applications.