LANCE: a Label-Free Live Apoptotic and Necrotic Cell Explorer Using Convolutional Neural Network Image Analysis.

Area of Science:

• Cell biology
• Biomedical imaging
• Machine learning in life sciences

Background:

• Accurate quantification of cell death is crucial for biological research.
• Existing biomarker-based methods face limitations including specificity, cost, and complex sample preparation.
• Need for efficient, label-free techniques to monitor cell death dynamics.

Purpose of the Study:

• To develop and validate a novel label-free method for categorizing cell death status.
• To leverage machine learning and image processing for cell death analysis.
• To provide a cost-effective and rapid alternative to traditional methods.

Main Methods:

• Development of the Live, Apoptotic, and Necrotic Cell Explorer (LANCE) workflow.
• Utilizing machine learning, specifically convolutional neural networks, for image analysis.
• Training and validation using a large image database of individual cells across various conditions.

Main Results:

• Achieved high accuracy (96.3 ± 0.5%) in categorizing live, apoptotic, and necrotic cells.
• Demonstrated the method's ability to track cell death dynamics non-destructively.
• Established LANCE as a fast, low-cost, and label-free cell status identification tool.

Conclusions:

• LANCE offers a precise and efficient label-free approach for cell death quantification.
• The non-destructive nature of LANCE facilitates deeper understanding of cell death regulation.
• LANCE has broad applicability in cell death research and other biomedical fields.