iLoF: An intelligent Lab on Fiber Approach for Human Cancer Single-Cell Type Identification

Autor: Pedro A. S. Jorge, Chunsheng Jin, Diana Campos, Meritxell Balmaña, Niclas G. Karlsson, João Paulo Cunha, R. S. Rodrigues Ribeiro, Celso A. Reis, Stefan Mereiter, Joana S. Paiva, Paula Sampaio
Přispěvatelé: Instituto de Investigação e Inovação em Saúde
Jazyk: angličtina
Rok vydání: 2020
Předmět:
0301 basic medicine
Cell type
Glycan
Glycosylation
Optical Tweezers
Computer science
Cell
lcsh:Medicine
Computational biology
Article
Tumour biomarkers
03 medical and health sciences
chemistry.chemical_compound
Computational biophysics
Prognostic markers
0302 clinical medicine
Neoplasms / diagnosis
Artificial Intelligence
Cell Line
Tumor
Cancer screening
Image Processing
Computer-Assisted
medicine
Humans
lcsh:Science
Optical Fibers
Probability
Neoplasms / pathology
Multidisciplinary
biology
business.industry
lcsh:R
Cancer
Signal Processing
Computer-Assisted
medicine.disease
Tumor Cell Biology
3. Good health
030104 developmental biology
medicine.anatomical_structure
chemistry
030220 oncology & carcinogenesis
Cancer cell
biology.protein
Cancer biomarkers
lcsh:Q
Personalized medicine
Single-Cell Analysis
business
Applied optics
Zdroj: Scientific Reports, Vol 10, Iss 1, Pp 1-16 (2020)
Scientific Reports
ISSN: 2045-2322
Popis: With the advent of personalized medicine, there is a movement to develop “smaller” and “smarter” microdevices that are able to distinguish similar cancer subtypes. Tumor cells display major differences when compared to their natural counterparts, due to alterations in fundamental cellular processes such as glycosylation. Glycans are involved in tumor cell biology and they have been considered to be suitable cancer biomarkers. Thus, more selective cancer screening assays can be developed through the detection of specific altered glycans on the surface of circulating cancer cells. Currently, this is only possible through time-consuming assays. In this work, we propose the “intelligent” Lab on Fiber (iLoF) device, that has a high-resolution, and which is a fast and portable method for tumor single-cell type identification and isolation. We apply an Artificial Intelligence approach to the back-scattered signal arising from a trapped cell by a micro-lensed optical fiber. As a proof of concept, we show that iLoF is able to discriminate two human cancer cell models sharing the same genetic background but displaying a different surface glycosylation profile with an accuracy above 90% and a speed rate of 2.3 seconds. We envision the incorporation of the iLoF in an easy-to-operate microchip for cancer identification, which would allow further biological characterization of the captured circulating live cells. This work was partially funded by the projects NanoSTIMA and NORTE-01-0145-FEDER-000029, both supported by the North Portugal Regional Operational Program (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF); and by the Portuguese Foundation for Science and Technology, within the scope of the PhD grant PD/BD/135023/2017 and the projects: PTDC/BBB-EBI/0567/2014 (to CAR) and UID/BIM/04293/2013. It was also funded by FEDER funds through the Operational Programme for Competitiveness Factors-COMPETE (POCI-01-0145-FEDER-016585; POCI-01-0145-FEDER-007274; PPBI-POCI-01-0145-FEDER-022122). MB acknowledges the Marie Sklodowska-Curie grant agreement No. 748880.
Databáze: OpenAIRE
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