Automated Image Analysis for Characterization of Circulating Tumor Cells and Clusters Sorted by Magnetic Levitation.

Autor: Ogut MG; Canary Center for Cancer Early Detection, Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, 94304, USA.; School of Engineering, Stanford University, Stanford, CA, 94305, USA., Ma P; Canary Center for Cancer Early Detection, Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, 94304, USA., Gupta R; Canary Center for Cancer Early Detection, Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, 94304, USA., Hoerner CR; Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, 94305, USA., Fan AC; Canary Center for Cancer Early Detection, Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, 94304, USA.; Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, 94305, USA., El-Kaffas AN; Molecular Imaging Program at Stanford (MIPS), Department of Radiology, Stanford University, Palo Alto, CA, 94305, USA., Durmus NG; Molecular Imaging Program at Stanford (MIPS), Department of Radiology, Stanford University, Palo Alto, CA, 94305, USA.
Jazyk: angličtina
Zdroj: Advanced biology [Adv Biol (Weinh)] 2023 Oct; Vol. 7 (10), pp. e2300109. Date of Electronic Publication: 2023 Jul 18.
DOI: 10.1002/adbi.202300109
Abstrakt: Magnetic levitation-based sorting technologies have revolutionized the detection and isolation of rare cells, including circulating tumor cells (CTCs) and circulating tumor cell clusters (CTCCs). Manual counting and quantification of these cells are prone to time-consuming processes, human error, and inter-observer variability, particularly challenging when heterogeneous cell types in 3D clusters are present. To overcome these challenges, we developed "Fastcount," an in-house MATLAB-based algorithm for precise, automated quantification and phenotypic characterization of CTCs and CTCCs, in both 2D and 3D. Fastcount is 120 times faster than manual counting and produces reliable results with a ±7.3% deviation compared to a trained laboratory technician. By analyzing 400 GB of fluorescence imaging data, we showed that Fastcount outperforms manual counting and commercial software when cells are aggregated in 3D or staining artifacts are present, delivering more accurate results. We further employed Fastcount for automated analysis of 3D image stacks obtained from CTCCs isolated from colorectal adenocarcinoma and renal cell carcinoma blood samples. Interestingly, we observed a highly heterogeneous spatial cellular composition within CTCCs, even among clusters from the same patient. Overall, Fastcount can be employed for various applications with lab-chip devices, such as CTC detection, CTCC analysis in 3D and cell detection in biosensors.
(© 2023 Wiley-VCH GmbH.)
Databáze: MEDLINE
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