Automated Image Analysis for Characterization of Circulating Tumor Cells and Clusters Sorted by Magnetic Levitation
Mehmet Giray Ogut1,2, Peng Ma1, Rakhi Gupta1
1Canary Center for Cancer Early Detection, Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, 94304, USA.
Advanced Biology
|July 18, 2023
Summary
Automated quantification of rare cells like circulating tumor cells (CTCs) and clusters (CTCCs) is now faster and more accurate with Fastcount, an algorithm designed for 2D and 3D imaging analysis.
Area of Science:
- Biomedical Engineering
- Cell Biology
- Computational Biology
Background:
- Manual quantification of rare cells (CTCs, CTCCs) is time-consuming and prone to errors, especially with complex 3D structures.
- Existing methods struggle with heterogeneous cell types within clusters and staining artifacts.
- Magnetic levitation-based sorting offers advanced rare cell isolation but requires robust analysis tools.
Purpose of the Study:
- To develop and validate "Fastcount," an automated MATLAB-based algorithm for precise quantification and phenotypic characterization of CTCs and CTCCs.
- To overcome limitations of manual counting and commercial software in analyzing 2D and 3D rare cell imaging data.
- To assess the accuracy and efficiency of Fastcount compared to manual methods and commercial software.
Main Methods:
- Development of an in-house MATLAB-based algorithm named Fastcount.
- Automated quantification and phenotypic characterization of CTCs and CTCCs in 2D and 3D.
- Analysis of 400 GB of fluorescence imaging data, including challenging samples with 3D aggregation and staining artifacts.
- Validation against manual counting by a trained technician and comparison with commercial software.
Main Results:
- Fastcount demonstrated a 120-fold increase in speed compared to manual counting.
- Results showed a deviation of ±7.3% compared to trained laboratory technicians, indicating high reliability.
- Fastcount outperformed manual counting and commercial software in accuracy, particularly for 3D aggregated cells and samples with staining artifacts.
- Analysis of colorectal adenocarcinoma and renal cell carcinoma CTCCs revealed significant heterogeneity in spatial cellular composition within clusters.
Conclusions:
- Fastcount provides a precise, automated, and efficient solution for rare cell quantification and characterization.
- The algorithm is robust in handling complex 3D cell structures and imaging challenges.
- Fastcount has broad applicability in lab-chip devices for CTC detection, CTCC analysis, and biosensor applications.
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