Biology subjects

Shiga, M.

Publications and source records attributed to Shiga, M..

4 recordsLinked to original sources

bioRxiv2021

A Simple Fluorescence Assay for Cystine Uptake via the xCT in Cells using Selenocystine and a Fluorescent Probe

The cystine/glutamate antiporter (xCT) is a crucial transporter that maintains cellular redox balance by regulating intracellular glutathione synthesis via cystine uptake. However, no robust and simple method to determine the cystine uptake activity of xCT is currently available. We have developed a method to measure the xCT activity via the reaction of selenocysteine and fluorescein O,O'-diacrylate (FOdA). Selenocystine, a cystine analog, is transported into cells through xCT on the cell membrane. The amount of the transported selenocystine was then determined by a reaction using tris(2-carboxyethyl)phosphine (TCEP) and FOdA in a weak acidic buffer at pH 6. Using this method, the cystine uptake activity of xCT in various cells, and the inhibitory efficiency of xCT inhibitors, were evaluated.

cell biology↗

bioRxiv2020

SC-JNMF: Single-cell clustering integrating multiple quantification methods based on joint non-negative matrix factorization

AO_SCPLOWBSTRACTC_SCPLOWUnsupervised 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.

bioinformatics↗

bioRxiv2020

Following embryonic stem cells, their differentiated progeny, and cell-state changes during iPS reprogramming by Raman spectroscopy

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.

systems biology↗

bioRxiv2018

A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection

Respiratory viruses are highly infectious; however, the variation of individuals physiologic responses to viral exposure is poorly understood. Most studies examining molecular predictors of response focus on late stage predictors, typically near the time of peak symptoms. To determine whether pre- or early post-exposure factors could predict response, we conducted a community-based analysis to identify predictors of resilience or susceptibility to several respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV) using peripheral blood gene expression profiles collected from healthy subjects prior to viral exposure, as well as up to 24 hours following exposure. This analysis revealed that it is possible to construct models predictive of symptoms using profiles even prior to viral exposure. Analysis of predictive gene features revealed little overlap among models; however, in aggregate, these genes were enriched for common pathways. Heme Metabolism, the most significantly enriched pathway, was associated with higher risk of developing symptoms following viral exposure.

immunology↗