Staining-free cell viability measurement technique using lens-free shadow imaging platform

Abstract

Tests for cell viability, i.e., an assay quantifying the ratio of viable cells or tissues over the total cells or tissues within an index between 0 and 1 (or 0 and 100%), play an important role in cell or tissue culturing procedures. The viability test result, varying with several biological factors such as mechanical activity, motility, contraction, or mitotic activity of cells or tissues, is a crucial indicator in cell related research protocols including toxicity and anabolic activity assays. There are several well-established methods for evaluating cell viability, such as trypan blue assay, propidium iodide assay, 7-aminoactinomycin D assay and resazurin and formazan (MTT/XTT) assay. However, most of these methods determine viability using stained cell samples, which intern affect the cells morphology eventually making it unable to keep culturing the specimen. To address this issue, we have developed a novel shadow imaging technique to capture the diffraction patterns ( shadow patterns) of micro objects without the use of any staining reagent. In this paper, we introduce a shadow imaging platform that can determine cell viability of more than 3000 human cancer cells immediately with a single digital image. Our custom-built lens-free shadow imaging platform consists of a compact, cost-effective light source, i.e., a light-emitting diode, and an optoelectronic image recording device, i.e., a complementary metal-oxide semiconductor image sensor. Three types of human cancer cell lines (Caco-2, HepG2, and MCF7) were incubated in 24-well plates, and H202 was added to track and compare the cell viability at each concentration tested. We obtained high correlation indices, with a minimum of 0.94, between the MU assay and the shadow imaging platform. All these characterizations were done by custom developed automated detection algorithm. This algorithm analyzes the various elements of the diffraction pattern (shadow image), such as pixel intensity and connected pixel numbers, and counts the viable cells automatically, allowing the cell viability to be determined easily and immediately in a staining-free manner. (C) 2015 Elsevier B.V. All rights reserved.