- [Resource Hub](/) - [Publication](/?resource-type=publication#library) - Circulating tumor cell investigation in breast cancer patient-derived xenograft models by automated immunofluorescence staining, image acquisition, and single cell retrieval and analysis # Circulating tumor cell investigation in breast cancer patient-derived xenograft models by automated immunofluorescence staining, image acquisition, and single cell retrieval and analysis Ramirez AB, Bhat R, Sahay D, De Angelis C, Thangavel H, Hedayatpour S, et al. BMC Cancer . 2019;19(1):220. DOI [10.1186/s12885-019-5382-1](https://doi.org/10.1186/s12885-019-5382-1). PMID 30871481. PMCID PMC6419430. How to cite ### AMA Ramirez AB, Bhat R, Sahay D, De Angelis C, Thangavel H, Hedayatpour S, et al. Circulating tumor cell investigation in breast cancer patient-derived xenograft models by automated immunofluorescence staining, image acquisition, and single cell retrieval and analysis. BMC Cancer . 2019;19(1):220. doi:10.1186/s12885-019-5382-1 ### APA Ramirez, A B., Bhat, R., Sahay, D., De Angelis, C., Thangavel, H., Hedayatpour, S., et al. (2019). Circulating tumor cell investigation in breast cancer patient-derived xenograft models by automated immunofluorescence staining, image acquisition, and single cell retrieval and analysis. BMC Cancer , 19(1), 220. https://doi.org/10.1186/s12885-019-5382-1 ### BibTeX @article{ramirez2019circulating, title = {Circulating tumor cell investigation in breast cancer patient-derived xenograft models by automated immunofluorescence staining, image acquisition, and single cell retrieval and analysis}, author = {Ramirez, Arturo B and Bhat, Raksha and Sahay, Debashish and De Angelis, Carmine and Thangavel, Hariprasad and Hedayatpour, Sina and others}, journal = {BMC Cancer}, volume = {19}, number = {1}, pages = {220}, year = {2019}, doi = {10.1186/s12885-019-5382-1} } Circulating tumor cells are cancer cells that break away from a tumor and travel in the blood, where they can seed new sites of disease. Studying them in laboratory mouse models has usually meant a slow, manual method — cutting thin tissue sections and identifying stained cells by eye — that is hard to repeat at any scale. The researchers adapted an automated blood-to-slide platform to work with the very small blood volumes available from mouse models of human breast cancer. From about 500 microliters of blood the system isolated nucleated cells, stained and imaged them to pick out the tumor cells, and lifted individual cells off the slide for genetic analysis. It recovered tumor cells reliably, matched the older manual method, found cells that surface-marker capture would have missed, and let the team confirm a tumor mutation in single retrieved cells. [Read publication at BMC Cancer](https://pmc.ncbi.nlm.nih.gov/articles/PMC6419430/) ## Key findings - The workflow recovered spiked tumor cells accurately and matched the laboratory’s established manual method. Human breast cancer cells spiked into mouse blood were recovered at 83 ± 12% (N = 3), and circulating tumor cell counts in tumor-bearing mice were not significantly different from the prior immunohistochemistry method (N = 4; P ≥ 0.05). - Because it identifies cells by cytokeratin instead of by surface-marker capture, the workflow caught tumor cells an immunocapture method would miss. In one model 9 of 13 circulating tumor cells (69%) stained for the EpCAM/EGFR/HER2 surface-marker cocktail, so nearly a third of the cells would have been lost to a marker-based method yet were still detected here. - Single tumor cells retrieved from the slides were sequenced to confirm a tumor-specific mutation. The PIK3CA T1035A mutation carried by the BCM-4888 model was recovered in 3 of 13 single circulating tumor cells, along with matched primary tumor and lung-metastasis cells, after whole-genome amplification. ## The AccuCyte–CyteFinder workflow in the methods “Cells on the slides processed with AccuCyte were labeled by multicolor immunofluorescence (IF) using the Ventana Discovery automated slide stainer. Slides were stained with DAPI (to mark nuclei), anti-human CK (AE1/AE3, eBioscience; C11, Biolegend), anti-mouse CD45 (30F11, Biolegend) cells, and a cocktail of antibodies against human cell surface markers [EpCAM (9C4, Biolegend), EGFR (EP38Y, Abcam), and HER2 (24D2, Biolegend)]. Stained slides were imaged by the CyteFinder® multi-channel scanning fluorescence microscope [ 13 ]. CyteMapper® software analyzed the scans and identified candidate cells that were presented to the reviewer for confirmation of CTC identity. Two independent reviewers identified CTCs from the scans and inconsistencies were resolved by consensus. CTCs were identified as DAPI+, human pan-CK+ and mouse CD45- cells. Individual CTCs were retrieved from slide by the CytePicker® module, which is integrated into CyteFinder as described previously, and placed into PCR tubes [ 13 ].” — Ramirez et al., BMC Cancer (2019), Methods, “CTC detection and retrieval” Disclosure: RareCyte is listed as an author affiliation on the publication cited above. Disclosure: RareCyte is named in the competing-interests statement of the publication cited above. ## Why it matters for The AccuCyte–CyteFinder workflow users If your work depends on finding rare tumor cells in very small blood samples, this study shows the AccuCyte–CyteFinder workflow doing exactly that in a preclinical breast cancer model. Blood was mixed with transfer solution and spread onto slides by density rather than sorted first by an epithelial marker, so every nucleated cell stayed on the slide and available for review. Because identification rests on cytokeratin rather than surface capture, the workflow found circulating tumor cells that a marker-based method would have left behind — here, nearly a third of the cells carried too little surface marker to be caught that way. Just as important, single cells were retrieved from the slide and carried through whole-genome amplification and sequencing, so a counted cell became a cell you can interrogate. For a laboratory running longitudinal mouse studies, that means one workflow can both enumerate rare cells and return intact single cells for downstream genomics, without an antigen-capture step deciding in advance which cells you get to study. 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