Classification of Cells in CTC-Enriched Samples by Advanced Image Analysis
Abstract
:1. Introduction
2. Results
2.1. Enumeration of Nucleated Cells in the EpCAM-Enriched Cells
2.2. Improved Image Analysis
2.3. Assignment of Nucleated Cells to a Cell Lineage
2.4. Increasing Leukocyte Identification by Adding CD16 Immunostaining
2.5. LED as a Light Source to Improve Excitation Efficiency of CD45-APC and CD16-PerCP
2.6. Identification of Unstained Nuclei by Adding Wheat Germ Agglutinin Immunostaining
3. Discussion
- Hematopoietic cells without sufficient expression of CD45 and CD16: This suggests they would need additional CD markers for identification or an improved labeling method that amplifies low signals, which might yield increased detection of very dim stained cells and separate the fluorescent signal of densely packed cells [11,26,27,28]. Early myeloid cells have recently been observed to surround the tumor in high numbers, and as these cells do not yet express CD16, this remains a possibility [29];
- CTC with no or low expression of the CK antigens detected by the C11 and A53.B/A2 clones: Since these clones only recognize a subset of the CK present in a cell, it might be beneficial to include antibody clones that recognize all CK. Previously, we have shown that adding several CK clones to the CellSearch antibody cocktail improved the detection of CTC positive patients by 11% [30]. Also, it might be possible that EpCAM+/CK- CTC are present, remaining undetected because of the downregulation of epithelial markers through epithelial-to-mesenchymal transition [31,32,33]. In order to detect these cells, antibodies specific to this process could be added to the CellSearch immunostaining [34,35,36];
- Cells of other origin: Such as circulating stromal, endothelial, or stem cells [37]. Detection of these cells would also require the addition of other antibodies to the assay.
4. Materials and Methods
4.1. Cancer Patients, Patients with Benign Disease, and Healthy Volunteers
4.2. Cell Lines and Spiking
4.3. Processing Blood with CellSearch
4.4. Image Acquisition
4.5. Image Analysis with ACCEPT
4.6. Statistical Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Analysis Type | Regression Coefficient | 95% Confidence Interval | p-Value |
---|---|---|---|
Crude analysis | 17.0 | 14.1–20.6 | <0.001 |
Corrected analysis for age | 19.2 | 15.3–24.2 | <0.001 |
Corrected analysis for age, and patient variables (sample age, CTC count and treatment) | 12.4 | 9.4–16.4 | <0.001 |
Cell Population | Mean Intensity Value | Overlay Nucleus | Size (µm2) | |||||||
---|---|---|---|---|---|---|---|---|---|---|
DAPI | CD45 | CK | CD16 1 | wga 1 | with CK | with CD45 | with CD16 | CK | ||
CTC | CK+/CD45- | >0 | 0 | ≥50 | 0 | >0 | >0.4 | ND | ND | >9 |
Leukocytes | CD45+/CD16- | >0 | >0 | 0 | 0 | >0 | ND | >0 | ND | ND |
CD45+/CD16+ | >0 | >0 | 0 | >0 | >0 | ND | >0 | >0 | ND | |
CD45-/CD16+ | >0 | 0 | 0 | >0 | >0 | ND | ND | >0 | ND | |
Nucleus | Bare nucleus | >0 | 0 | 0 | 0 | 0 | ND | ND | ND | ND |
Unstained cell | >0 | 0 | 0 | 0 | >0 | ND | ND | ND | ND |
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De Wit, S.; Zeune, L.L.; Hiltermann, T.J.N.; Groen, H.J.M.; Dalum, G.V.; Terstappen, L.W.M.M. Classification of Cells in CTC-Enriched Samples by Advanced Image Analysis. Cancers 2018, 10, 377. https://doi.org/10.3390/cancers10100377
De Wit S, Zeune LL, Hiltermann TJN, Groen HJM, Dalum GV, Terstappen LWMM. Classification of Cells in CTC-Enriched Samples by Advanced Image Analysis. Cancers. 2018; 10(10):377. https://doi.org/10.3390/cancers10100377
Chicago/Turabian StyleDe Wit, Sanne, Leonie L. Zeune, T. Jeroen N. Hiltermann, Harry J. M. Groen, Guus Van Dalum, and Leon W. M. M. Terstappen. 2018. "Classification of Cells in CTC-Enriched Samples by Advanced Image Analysis" Cancers 10, no. 10: 377. https://doi.org/10.3390/cancers10100377
APA StyleDe Wit, S., Zeune, L. L., Hiltermann, T. J. N., Groen, H. J. M., Dalum, G. V., & Terstappen, L. W. M. M. (2018). Classification of Cells in CTC-Enriched Samples by Advanced Image Analysis. Cancers, 10(10), 377. https://doi.org/10.3390/cancers10100377