Meryem Beyza Avci, Fatma Kurul, Mehmet Turkan, Arif E Cetin
{"title":"Automated smartphone based cell analysis platform.","authors":"Meryem Beyza Avci, Fatma Kurul, Mehmet Turkan, Arif E Cetin","doi":"10.1038/s44303-025-00093-z","DOIUrl":null,"url":null,"abstract":"<p><p>Cell analysis technologies play a critical role in biomedical research, enabling precise evaluation of essential parameters such as cell viability, density, and confluency. In this article, we introduce Quantella, a smartphone-based platform designed to perform comprehensive cell analysis encompassing these key metrics. Addressing limitations of conventional systems, such as high cost, hardware complexity, and limited adaptability, Quantella integrates low-cost optics, a rinsable flow cell, bluetooth-enabled hardware control, and a cloud-connected mobile application. Its adaptive image-processing pipeline employs multi-exposure fusion, thresholding, and morphological filtering for accurate, morphology-independent segmentation without requiring deep learning or user-defined parameters. System validation studies across diverse cell types showed deviations under 5% from flow cytometry. With the capacity to analyze over 10,000 cells per test, Quantella delivers high-throughput, reproducible results. Its accessible, scalable design makes it a promising tool for biomedical research, diagnostics, and education, particularly in resource-limited settings.</p>","PeriodicalId":501709,"journal":{"name":"npj Imaging","volume":"3 1","pages":"53"},"PeriodicalIF":0.0000,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12546929/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Imaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1038/s44303-025-00093-z","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Cell analysis technologies play a critical role in biomedical research, enabling precise evaluation of essential parameters such as cell viability, density, and confluency. In this article, we introduce Quantella, a smartphone-based platform designed to perform comprehensive cell analysis encompassing these key metrics. Addressing limitations of conventional systems, such as high cost, hardware complexity, and limited adaptability, Quantella integrates low-cost optics, a rinsable flow cell, bluetooth-enabled hardware control, and a cloud-connected mobile application. Its adaptive image-processing pipeline employs multi-exposure fusion, thresholding, and morphological filtering for accurate, morphology-independent segmentation without requiring deep learning or user-defined parameters. System validation studies across diverse cell types showed deviations under 5% from flow cytometry. With the capacity to analyze over 10,000 cells per test, Quantella delivers high-throughput, reproducible results. Its accessible, scalable design makes it a promising tool for biomedical research, diagnostics, and education, particularly in resource-limited settings.