scholarly journals Visualizing Cell State Transition Using Raman Spectroscopy

PLoS ONE ◽  
2014 ◽  
Vol 9 (1) ◽  
pp. e84478 ◽  
Author(s):  
Taro Ichimura ◽  
Liang-da Chiu ◽  
Katsumasa Fujita ◽  
Satoshi Kawata ◽  
Tomonobu M. Watanabe ◽  
...  
2020 ◽  
Author(s):  
Arno Germond ◽  
Yulia Panina ◽  
Mikio Shiga ◽  
Hirohiko Niioka ◽  
Tomonobu M. Watanabe

AbstractTo 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.


2017 ◽  
Author(s):  
Meng Amy Li ◽  
Paulo P Amaral ◽  
Priscilla Cheung ◽  
Jan H Bergmann ◽  
Masaki Kinoshita ◽  
...  

2020 ◽  
Vol 92 (22) ◽  
pp. 14915-14923
Author(s):  
Arno Germond ◽  
Yulia Panina ◽  
Mikio Shiga ◽  
Hirohiko Niioka ◽  
Tomonobu M. Watanabe

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Taro Ichimura ◽  
Liang-da Chiu ◽  
Katsumasa Fujita ◽  
Hiroaki Machiyama ◽  
Tomoyuki Yamaguchi ◽  
...  

2011 ◽  
Vol 275 (1) ◽  
pp. 59-69 ◽  
Author(s):  
Peter S. Kim ◽  
Peter P. Lee

2018 ◽  
Author(s):  
Jiajun Zhang ◽  
Qing Nie ◽  
Tianshou Zhou

AbstractCell fate decisions play a pivotal role in development but technologies for dissecting them are limited. We developed a multifunction new method, Topographer to construct a ‘quantitative’ Waddington’s landscape of single-cell transcriptomic data. This method is able to identify complex cell-state transition trajectories and to estimate complex cell-type dynamics characterized by fate and transition probabilities. It also infers both marker gene networks and their dynamic changes as well as dynamic characteristics of transcriptional bursting along the cell-state transition trajectories. Applying this method to single-cell RNA-seq data on the differentiation of primary human myoblasts, we not only identified three known cell types but also estimated both their fate probabilities and transition probabilities among them. We found that the percent of genes expressed in a bursty manner is significantly higher at (or near) the branch point (∼97%) than before or after branch (below 80%), and that both gene-gene and cell-cell correlation degrees are apparently lower near the branch point than away from the branching. Topographer allows revealing of cell fate mechanisms in a coherent way at three scales: cell lineage (macroscopic), gene network (mesoscopic) and gene expression (microscopic).


2021 ◽  
Vol 320 (5) ◽  
pp. C750-C760
Author(s):  
Antara Biswas ◽  
Subhajyoti De

Cancer is a clonal disease, i.e., all tumor cells within a malignant lesion trace their lineage back to a precursor somatic cell that acquired oncogenic mutations during development and aging. And yet, those tumor cells tend to have genetic and nongenetic variations among themselves—which is denoted as intratumor heterogeneity. Although some of these variations are inconsequential, others tend to contribute to cell state transition and phenotypic heterogeneity, providing a substrate for somatic evolution. Tumor cell phenotypes can dynamically change under the influence of genetic mutations, epigenetic modifications, and microenvironmental contexts. Although epigenetic and microenvironmental changes are adaptive, genetic mutations are usually considered permanent. Emerging reports suggest that certain classes of genetic alterations show extensive reversibility in tumors in clinically relevant timescales, contributing as major drivers of dynamic intratumor heterogeneity and phenotypic plasticity. Dynamic heterogeneity and phenotypic plasticity can confer resistance to treatment, promote metastasis, and enhance evolvability in cancer. Here, we first highlight recent efforts to characterize intratumor heterogeneity at genetic, epigenetic, and microenvironmental levels. We then discuss phenotypic plasticity and cell state transition by tumor cells, under the influence of genetic and nongenetic determinants and their clinical significance in classification of tumors and therapeutic decision-making.


1983 ◽  
Vol 131 (3) ◽  
pp. 555-558 ◽  
Author(s):  
Dimitrina ASLANIAN ◽  
Henriette VAINER ◽  
Jean-Pierre GUESDON

Sign in / Sign up

Export Citation Format

Share Document