Detail of 3mix-timelapse-plate3_29



Project
SSBD:Repository
Title
Bright-field time-lapse images of live murine immune-related cell lines defined by scRNA-seq
Description
Bright-field time-lapse images of live murine immune-related cell lines defined by scRNA-seq
Release, Updated
2023-02-16
License
CC BY
Kind
Image data
File Formats
uncompressed TIFF
Data size
593.5 KB

Organism
Mus musculus ( NCBITaxon:10090 )
Strain(s)
-
Cell Line
hematopoietic progenitor cells (HPCs)

Datatype
-
Molecular Function (MF)
Biological Process (BP)
Cellular Component (CC)
Biological Imaging Method
time lapse microscopy ( Fbbi:00000249 )
X scale
0.32 micrometer/pixel
Y scale
0.32 micrometer/pixel
Z scale
-
T scale
1 minute of time interval

Image Acquisition
Experiment type
-
Microscope type
-
Acquisition mode
-
Contrast method
-
Microscope model
-
Detector model
-
Objective model
-
Filter set
-

Summary of Methods
See details in Jin J, et. al. (2023) Proc Natl Acad Sci U S A. 120(1):e2210283120.
Related paper(s)

Jianshi Jin, Taisaku Ogawa, Nozomi Hojo, Kirill Kryukov, Kenji Shimizu, Tomokatsu Ikawa, Tadashi Imanishi, Taku Okazaki, Katsuyuki Shiroguchi (2023) Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes., Proceedings of the National Academy of Sciences of the United States of America, Volume 120, Number 1, pp. e2210283120

Published in 2023 Jan 3 (Electronic publication in Dec. 28, 2022, midnight )

(Abstract) Single-cell whole-transcriptome analysis is the gold standard approach to identifying molecularly defined cell phenotypes. However, this approach cannot be used for dynamics measurements such as live-cell imaging. Here, we developed a multifunctional robot, the automated live imaging and cell picking system (ALPS) and used it to perform single-cell RNA sequencing for microscopically observed cells with multiple imaging modes. Using robotically obtained data that linked cell images and the whole transcriptome, we successfully predicted transcriptome-defined cell phenotypes in a noninvasive manner using cell image-based deep learning. This noninvasive approach opens a window to determine the live-cell whole transcriptome in real time. Moreover, this work, which is based on a data-driven approach, is a proof of concept for determining the transcriptome-defined phenotypes (i.e., not relying on specific genes) of any cell from cell images using a model trained on linked datasets.
(MeSH Terms)

Contact
Katsuyuki Shiroguchi , RIKEN , Center for Biosystems Dynamics Research , Laboratory for Prediction of Cell Systems Dynamics
Contributors

OMERO Dataset
OMERO Project
Source