To monitor cell state transition in pluripotent cells is invaluable for application and basic research. In this study, we demonstrate the pertinence of use non-invasive, label-free Raman spectroscopy to monitor and characterize the cell state transition of mouse stem cells undergoing reprogramming. Using an isogenic cell line of mouse stem cells, reprogramming from neuronal cells was performed, and we showcase a comparative analysis of single cell spectral data of the original stem cells, their neuronal progenitors, and reprogrammed cells. Neural network, regression models, and ratiometric analysis were used to discriminate the cell states and extract several important biomarkers specific to differentiation or reprogramming. Our results indicated that the Raman spectrum allowed to build a low dimensional space allowing to monitor and characterize the dynamics of cell state transition at a single cell level, scattered in heterogeneous populations. Ability of monitoring pluripotency by Raman spectroscopy, and distinguish differences between ES and reprogrammed cells is also discussed.
words)To monitor cell state transition in pluripotent cells is invaluable for application and basic research. In this study, we demonstrate the pertinence of use non-invasive, label-free Raman spectroscopy to monitor and characterize the cell state transition of mouse stem cells undergoing reprogramming. Using an isogenic cell line of mouse stem cells, reprogramming from neuronal cells was performed, and we showcase a comparative analysis of single cell spectral data of the original stem cells, their neuronal progenitors, and reprogrammed cells. Neural network, regression models, and ratiometric analysis were used to discriminate the cell states and extract several important biomarkers specific to differentiation or reprogramming. Our results indicated that the Raman spectrum allowed to build a low dimensional space allowing to monitor and characterize the dynamics of cell state transition at a single cell level, scattered in heterogeneous populations. Ability of monitoring pluripotency by Raman spectroscopy, and distinguish differences between ES and reprogrammed cells is also discussed.
Single-cell RNA-sequencing is a rapidly evolving technology that enables us to understand biological processes at unprecedented resolution. Single-cell expression analysis requires a complex data processing pipeline, and the pipeline is divided into two main parts: The quantification part, which converts the sequence information into gene-cell matrix data; the analysis part, which analyzes the matrix data using statistics and/or machine learning techniques. In the analysis part, unsupervised cell clustering plays an important role in identifying cell types and discovering cell diversity and subpopulations. Identified cell clusters are also used for subsequent analysis, such as finding differentially expressed genes and inferring cell trajectories. However, single-cell clustering using gene expression profiles shows different results depending on the quantification methods. Clustering results are greatly affected by the quantification method used in the upstream process. In other words, even if the original RNA-sequence data is the same, gene expression profiles processed by different quantification methods will produce different clusters. In this article, we propose a robust and highly accurate clustering method based on joint non-negative matrix factorization (joint-NMF) by utilizing the information from multiple gene expression profiles quantified using different methods from the same RNA-sequence data. Our joint-NMF can extract common factors among multiple gene expression profiles by applying each NMF under the constraint that one of the factorized matrices is shared among multiple NMFs. The joint-NMF determines more robust and accurate cell clustering results by leveraging multiple quantification methods compared to conventional clustering methods, which use only a single gene expression profile. Additionally, we showed the usefulness of discovering marker genes with the extracted features using our method.
Unsupervised cell clustering is important in discovering cell diversity and subpopulations. Single-cell clustering using gene expression profiles is known to show different results depending on the method of expression quantification; nevertheless, most single-cell clustering methods do not consider the method. In this article, we propose a robust and highly accurate clustering method using joint non-negative matrix factorization (joint NMF) based on multiple gene expression profiles quantified using different methods. Matrix factorization is an excellent method for dimension reduction and feature extraction of data. In particular, NMF approximates the data matrix as the product of two matrices in which all factors are non-negative. Our joint NMF can extract common factors among multiple gene expression profiles by applying each NMF to them under the constraint that one of the factorized matrices is shared among the multiple NMFs. The joint NMF determines more robust and accurate cell clustering results by leveraging multiple quantification methods compared to the conventional clustering methods, which uses only a single quantification method. In conclusion, our study showed that our clustering method using multiple gene expression profiles is more accurate than other popular methods.
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