Detail of Fig1A_Tcell_Raman



Project
Title
Rman images of T cells from a DO11.10 mouse
Description
NA
Release, Updated
2017-10-03
License
CC BY
Kind
Image data based on Experiment
File Formats
Data size
160.0 MB

Organism
M. musculus ( NCBI:txid10090 )
Strain(s)
DO11.10
Cell Line
-

Datatype
cell dynamics
Molecular Function (MF)
Biological Process (BP)
-
Cellular Component (CC)
cell ( GO:0005623 )
Biological Imaging Method
XYZ Scale
-
T scale
-

Image Acquisition
Experiment type
TimeLapse
Microscope type
BrightfieldMicroscope
Acquisition mode
BrightField
Contrast method
Fluorescence
Microscope model
Nikon Ti-E
Detector model
Princeton Instruments PIXIS400BR
Objective model
Nikon CFP Plan Apo IR
Filter set

Summary of Methods
See details in Ichimura et al. (2016) Scientific Reports, 6: 37562.
Related paper(s)

Taro Ichimura, Liang-da Chiu, Katsumasa Fujita, Hiroaki Machiyama, Tomoyuki Yamaguchi, Tomonobu M Watanabe, Hideaki Fujita (2016) Non-label immune cell state prediction using Raman spectroscopy., Scientific reports, Volume 6, pp. 37562

Published in 2016 Nov 23 (Electronic publication in Nov. 23, 2016, midnight )

(Abstract) The acquired immune system, mainly composed of T and B lymphocytes, plays a key role in protecting the host from infection. It is important and technically challenging to identify cell types and their activation status in living and intact immune cells, without staining or killing the cells. Using Raman spectroscopy, we succeeded in discriminating between living T cells and B cells, and visualized the activation status of living T cells without labeling. Although the Raman spectra of T cells and B cells were similar, they could be distinguished by discriminant analysis of the principal components. Raman spectra of activated T cells with anti-CD3 and anti-CD28 antibodies largely differed compared to that of naive T cells, enabling the prediction of T cell activation status at a single cell level. Our analysis revealed that the spectra of individual T cells gradually change from the pattern of naive T cells to that of activated T cells during the first 24 h of activation, indicating that changes in Raman spectra reflect slow changes rather than rapid changes in cell state during activation. Our results indicate that the Raman spectrum enables the detection of dynamic changes in individual cell state scattered in a heterogeneous population.
(MeSH Terms)

Contact
Hideaki Fujita , RIKEN , Quantitative Biology Center , Laboratory for Comprehensive Bioimaging
Contributors
Taro Ichimura, Liang-da Chiu, Katsumasa Fujita, Hiroaki Machiyama, Tomoyuki Yamaguchi, Tomonobu M. Watanabe, Hideaki Fujita

OMERO Dataset
OMERO Project
Source