Development of an impedance-based cell assurance monitor to aid in identifying and characterizing cells in tissue culture

There is concern regarding the quality of animal cells grown in tissue culture for scientific studies. It is reported that many laboratories use misidentified cells or cells unwittingly contaminated with mycoplasma microorganisms. In addition, as cell populations are passaged for extended periods, elusive epigenetic changes may arise that can go unnoticed and alter experimental results. Finally, in research involving isolating a specific cell type from primary tissue, small subpopulations of unwanted cells may overgrow and replace the cells of interest. The failure of researchers to detect these problems and changes can lead to erroneous experimental results, wasting valuable time and research funds. The proposed SBIR research aims to develop an innovative instrument to assist researchers in detecting these subtle yet critical issues before they jeopardize research efforts. The Cell Assurance Monitor (CAM) is based on a technology where cells are cultured upon electrodes carrying weak AC signals. By monitoring the complex impedance of these electrode substrates at multiple AC frequencies, several parameters related to cell morphology and behavior can be discerned. In past studies, impedance measurements did not fully leverage their multifaceted nature. However, in feasibility studies with Cornell University, multivariate statistical analysis has been used to show the ability of the impedance data to both identify different cell lines and detect mycoplasma infections. In the SBIR project, impedance data will be recorded at multiple laboratories using groups of cell lines having similar visual morphology. Multivariate statistics, where several impedance variables are reduced to a limited set of features, will be extended to discover the most discriminating features for describing these challenging clusters of cells. Research studies will also continue to investigate the efficacy of the method to detect mycoplasma infection. Other studies will commence to study the succession of different subpopulations in primary tissue isolates and the epigenetic changes occurring with cells grown to high passage numbers. With this statistical information, software algorithms will be developed for automated data acquisition and statistical analysis yielding a visual feature space where each cell variant will have a defined position. Electronics tailored to retrieve the required data and electrode arrays will be designed and fabricated, and the completed instrument beta tested using our existing customer base. The CAM is not intended to replace well-established techniques to determine cell identity or detect mycoplasma contamination. Instead, this new tool would provide a frequent, quantitative, and affordable means to complement routine microscopic cell morphology checks to spot cell changes that could jeopardize research results.