Towards Closed Loop Control of Cell Production: Analysis of Volatile Organic Profiles in Stem Cells By Comprehensive Two-Dimensional Gas Chromatography with Time-of-Flight Mass Spectrometry

Abstract

Personalized cell therapies have demonstrated clinical benefits and have shown great promise in solving monogenetic diseases. Despite these documented benefits, commercialization has been limited due to challenges including manufacturing, characterization, quality control, and stability. Previous work has demonstrated the potential of gas chromatography coupled with mass spectrometry to evaluate the volatile organic profiles of cell cultures. However, due to the complexity of the medium and the intensity of the identified peaks, the data has been limited. This work reports on the development of a method to investigate the volatile organic compound (VOC) profiles of stem cells using the increased resolution of comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GCxGC/TOF MS) for the purpose of stem cell characterization, growth status analysis, and identification of critical quality attributes. Mesenchymal stem cells (MSCs), from a variety of sources, were cultured and analyzed throughout their growth process. Samples of spent cell culture media were collected during cell passaging and incubated prior to analyzing the headspace above the media. Solid phase microectraction (SPME) was used as to concentrate the VOCs present in the sample headspace. The use of SPME, in conjunction with GCxGC/TOF MS allowed for an expanded understanding of the MSC volatilome and the development of relevant VOC profiles. The effect of growth media, cell source, growth status, and confluency on the chemicals identified in the VOC profiles was studied. Non-targeted analysis revealed more than 100 compounds of potential interest identified through spectral deconvolution and the NIST library of electron impact spectra. The identities of the compounds of interest was validated through the use of analytical standards. Principal component analysis (PCA) was used to reduce dimensionally and visualize variance in the data. Hierarchical clustering analysis was employed to assess relatedness between samples based on their volatile organic profiles. The distinct variations between the volatilomes is able to distinguish different cell sources and growth statuses. While this work is in its preliminary stage, it lays the foundation for closed loop control of cell manufacturing. The identification of critical quality attributes and their associated VOC profiles, using powerful laboratory equipment is the first step in closing the manufacturing loop. With the newfound knowledge, miniaturized in-line sensors can be developed to allow for complete control of the cell manufacturing process, assuring not only a high quality product, but an efficient manufacturing process as well. Figure 1