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Obtaining Cancer Stem Cell Spheres from Gynecological and Breast Cancer Tumors
Published on: March 1, 2020
AQSA-Algorithm for Automatic Quantification of Spheres Derived from Cancer Cells in Microfluidic Devices
Ana Belén Peñaherrera-Pazmiño1,2, Ramiro Fernando Isa-Jara2,3, Elsa Hincapié-Arias4,5
1Centro de Investigación Biomédica (CENBIO), Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito 170527, Ecuador.
Abstract:
Sphere formation assay is an accepted cancer stem cell (CSC) enrichment method. CSCs play a crucial role in chemoresistance and cancer recurrence. Therefore, CSC growth is studied in plates and microdevices to develop prediction chemotherapy assays in cancer. As counting spheres cultured in devices is laborious, time-consuming, and operator-dependent, a computational program called the Automatic Quantification of Spheres Algorithm (ASQA) that detects, identifies, counts, and measures spheres automatically was developed. The algorithm and manual counts were compared, and there was no statistically significant difference (p = 0.167). The performance of the AQSA is better when the input image has a uniform background, whereas, with a nonuniform background, artifacts can be interpreted as spheres according to image characteristics. The areas of spheres derived from LN229 cells and CSCs from primary cultures were measured. For images with one sphere, area measurements obtained with the AQSA and SpheroidJ were compared, and there was no statistically significant difference between them (p = 0.173). Notably, the AQSA detects more than one sphere, compared to other approaches available in the literature, and computes the sphere area automatically, which enables the observation of treatment response in the sphere derived from the human glioblastoma LN229 cell line. In addition, the algorithm identifies spheres with numbers to identify each one over time. The AQSA analyzes many images in 0.3 s per image with a low computational cost, enabling laboratories from developing countries to perform sphere counts and area measurements without needing a powerful computer. Consequently, it can be a useful tool for automated CSC quantification from cancer cell lines, and it can be adjusted to quantify CSCs from primary culture cells. CSC-derived sphere detection is highly relevant as it avoids expensive treatments and unnecessary toxicity.
Insights
A new algorithm, ASQA, automates cancer stem cell (CSC) sphere counting and measurement, improving chemoresistance prediction. This tool offers accurate, efficient, and accessible quantification for cancer research, potentially reducing treatment costs and toxicity.
Area of Science:
- Oncology
- Bioinformatics
- Cancer Research
Background:
- Cancer stem cells (CSCs) are crucial in chemoresistance and recurrence.
- Sphere formation assays enrich CSCs but manual counting is laborious and subjective.
- Automated quantification is needed for reliable prediction chemotherapy assays.
Purpose of the Study:
- To develop and validate an automated computational program, the Automatic Quantification of Spheres Algorithm (ASQA), for detecting, counting, and measuring CSC spheres.
- To compare ASQA's performance against manual counts and existing software (SpheroidJ).
- To assess ASQA's utility in observing treatment response and its applicability in resource-limited settings.
Main Methods:
- Development of the Automatic Quantification of Spheres Algorithm (ASQA).
- Comparison of ASQA counts with manual counts (p = 0.167).
- Comparison of ASQA area measurements with SpheroidJ for single spheres (p = 0.173).
- Evaluation of ASQA performance with uniform and nonuniform backgrounds.
- Testing ASQA on LN229 cell line and primary culture CSCs.
Main Results:
- ASQA showed no statistically significant difference compared to manual counts.
- ASQA demonstrated high accuracy in area measurements, comparable to SpheroidJ.
- The algorithm efficiently detects and quantifies multiple spheres, enabling treatment response observation.
- ASQA functions effectively with uniform backgrounds; non-uniformity may introduce artifacts.
- The algorithm processes images rapidly (0.3s/image) with low computational cost.
Conclusions:
- ASQA provides an automated, accurate, and efficient method for CSC sphere quantification.
- The algorithm is adaptable for both cell lines and primary cultures.
- ASQA's accessibility and speed can benefit cancer research globally, aiding in developing better chemotherapy strategies.
- Automated CSC detection can help avoid ineffective treatments and reduce patient toxicity.
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