The Development of an Automated Cell Identifier

Authors: Adrianna Calangelo, Theresa Curtis, Dwayne Jones, Julia Uhteg

SUNY Campus: SUNY Cortland

Presentation Type: Poster

Location: UU 111

Presentation #: 76

Timeslot: Session D 3:00-4:00 PM

Abstract: With risks of cross-contamination and misidentification in cell culture, it is important to have a reliable way to differentiate between cell types. There are established genetic tests to identify cells, however, these methods are not practical as they are expensive and can be time consuming. Our study aimed to be a solution to these issues by using Electric Cell-substrate Impedance Sensing (ECIS) to monitor growth patterns and traits amongst various cell lines in hopes that it can statistically quantify the differences. This new method can provide researches with an inexpensive way to assure that the cell line is the same as the one they were initially researching.