Summary of 53-Ichimura-CellDyn

SSBD:database
SSBD:database URL
Title
-
Description
-
Relase date
2017-10-03
Updated date
-
License
CC BY
Kind
Image data based on Experiment
Number of Datasets
4 ( Image datasets: 4, Quantitative data datasets: 0 )
Size of Datasets
320.4 MB ( Image datasets: 320.4 MB, Quantitative data datasets: 0 bytes )

Organism(s)
M. musculus
Strain(s)
DO11.10
Gene symbol(s)
NA
Protein name(s)
NA

Datatype
cell dynamics
Molecular Function (MF)
Biological Process (BP)
-
Cellular Component (CC)
cell
Biological Imaging Method
-
XYZ Scale
NA, XY: 0.13158 micrometer/pixel, Z: NA
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
Longpass filter: Semrock LP03-532RU-25

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


Dataset List of 53-Ichimura-CellDyn

#
Dataset ID
Kind
Size
4D View
SSBD:OMERO
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# 1348
Dataset Kind Image data
Dataset Size 276.1 KB
4D view
SSBD:OMERO
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# 1349
Datast ID Fig1A_Tcell_Raman
Dataset Kind Image data
Dataset Size 160.0 MB
4D view
SSBD:OMERO
Download BDML
Download Image data

# 1350
Dataset Kind Image data
Dataset Size 265.0 KB
4D view
SSBD:OMERO
Download BDML
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# 1351
Datast ID Fig1C_Bcell_Raman
Dataset Kind Image data
Dataset Size 160.0 MB
4D view
SSBD:OMERO
Download BDML
Download Image data