|= Emerging. More than 5 years before clinical availability.|
|= Expected to be clinically available in 1 to 4 years.|
|= Clinically available now.|
Authors: Petr Vanhara (1,2), Lukas Moran (1), Lukas Kucera (2), Josef Havel (1,3), Ales Hampl (1,2)
Human embryonic stem cells (hESCs) emerged as promising tool in cell therapy, bio-industry or drug development. However, long-term cultured hESCs develop hidden phenotypic changes, preventing clinically relevant use. Quality control based on karyotyping, (epi)genetic traits, or monitoring of adverse phenotypic changes often suffers of low sensitivity. Therefore, there is ongoing need for methods revealing abnormalities in cell phenotype. We modeled shifts in hESCs using different strategies and addressed changes in cell status by intact cell mass spectrometry. Analysis of spectral fingerprints by chemometrics and machine-learning methods distinguished otherwise identical hESCs and provided efficient tool for quality control of hESCs cultures.
Human embryonic stem cells (hESCs) are able to self-renew, and in parallel to differentiate into all cell types of human body. As such, they are a promising tool for cell therapy, bio-industry or drug development. However, long-term cultured hESCs finally develop hidden phenotypic changes, cumulatively acquire various alterations on both the genetic and non-genetic levels and despite advanced culture techniques, the culture-adapted with unwanted properties clones are inevitably selected. However, these changes could remain unnoticed until they alter the genome, karyotype or cell phenotype, even in case of the high expression of stemness-associated transcription factors, e.g. c-Myc, Sox-2, Klf4, Nanog, or Oct3/4, or their differentiation capacity or a typical morphology. Furthermore, molecular, genetic, and/or light-microscopy analyses can fail in the case of the genetically or karyotypically silent changes that are evoked in cultured cells. Thus, recent quality control approaches often suffers of low sensitivity or may produce biased output. Therefore, there is an ongoing need for sensitive, robust, feasible and affordable methods revealing abnormalities in cell phenotype.
We modeled shifts in CCTL-14 hESC line using different cell culture strategies and addressed changes in cell status by intact cell mass spectrometry. To avoid experimental bias and to make the experimenting feasible and as close to routine as possible, we collected the intact hESCs, washed them in phosphate-buffered saline and counted. Finally, the cells were resuspended in 150 mM ammonium bicarbonate buffer to the final concentration of 10 × 10^6 cells per ml. The cell suspension was then mixed with an acidified matrix containing sinapinic acid and 2,2,2-trifluoroacetic acid in a 2:1 sample to matrix ratio and applied to a target plate. The mass spectra were recorded in linear positive ion mode using an AXIMA-CFR mass spectrometer from Shimadzu Biotech (Kratos, UK) equipped with a nitrogen laser (337 nm). The mass spectra were normalized and analyzed by principal component analysis (PCA) and artificial neural networks (ANN).
hESCs were cultured under identical laboratory conditions, harvested 48 hours after seeding, and processed immediately for mass spectrometry. Then we compared mass spectra of hESCs cultured for varying time that developed distinct karyotypic or molecular traits. The final normalized spectral dataset was subjected to statistical analyses. Using PCA, the correctly clustered populations corresponding to short and long time of culture were clearly identified. Next, we were curious if we are able to determine shifts in the hESC phenotypes after induction of differentiation. We stimulated hESCs shortly to induce neural differentiation and performed MS analysis. We found that the mass spectra from stimulated and control cells reflecting the metabolomic profile were highly similar and lacked obvious specific peaks that would allow a direct search for biomarkers. Also the PCA only partially discriminated the untreated and committed hESCs. However, using the artificial intelligence tools, the correctly trained ANN correctly assigned the unknown spectrum to the appropriate sample group.
Conclusions & Discussion
The stability of stem cell cultures is an essential precondition for applications in which the cell product variability, culture cross-contamination, batch consistency, or phenotype stability are critical. In our previous work (1), we show, that ANN-coupled mass spectrometry is capable to distinguish cell types in binary mixtures both qualitatively and quantitatively, or reflect molecular changes in cultured cells. Here, we present that intact cell MS (MALDI-TOF MS) and ANN analysis can reveal the hidden variability in hESCs. This also suggests that non-linear, statistical and artificial intelligence approaches, can handle complex biological data reflecting minute changes in metabolic states. In summary, we introduce a feasible tool for MS-based monitoring of clinical-grade routine cultures of pluripotent stem cells (2).
References & Acknowledgements:
(1) Valletta E, Kučera L, Prokeš L, Amato F, Pivetta T, Hampl A, Havel J, Vaňhara P. Multivariate Calibration Approach for Quantitative Determination of Cell-Line Cross Contamination by Intact Cell Mass Spectrometry and Artificial Neural Networks. PLoS One. 2016. 11(1):e0147414. doi: 10.1371/journal.pone.0147414.
(2) Vaňhara P, Kučera L, Prokeš L, Jurečková L, Peña-Méndez EM, Havel J, Hampl A. Intact Cell Mass Spectrometry as a Quality Control Tool for Revealing Minute Phenotypic Changes of Cultured Human Embryonic Stem Cells. Stem Cells Transl Med. 2018. 7(1):109-114. doi: 10.1002/sctm.17-0107.
This work was supported by Czech Health Research Council (project no. NV18-08-00299), the National Program of Sustainability II (project no. LQ1605, MEYS CR), and by Masaryk University (MUNI/A/1298/2017).
IP Royalty: no
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