Paper Info
Title
Image-based mapping of cells onto a cell cycle continuum for integration of single cell sequencing and live cell imaging
Abstract
Incorporation of prior information in the form of pathway activity profiles was key in the success of the algorithm that won the DREAM challenge to predict in vitro cell fitness from transcriptomic and other multi-omic datasets [1]. We hypothesize that leveraging additional prior information on the spatial distribution of transcriptome activity inside the cell will yield better predictions of cell fitness, which span longer timeframes. A prerequisite to achieve this is the ability to integrate transcriptomic and phenotypic measurements at high cellular resolution. One way to achieve this is to leverage the fact that cells sampled for both assay are in various stages of the cell cycle. ScRNA-seq provides a high temporal resolution on the cell cycle progression of sequenced cells. We show that 3D imaging of subcellular compartments can provide a comparable resolution on the cell cycle state of imaged cells. We use a method developed for scRNA-seq on size and shape statistics of subcellular compartments to assign a so-called “pseudotime” to each imaged cell. Preliminary validation with live-cell imaging supports the hypothesis that the inferred pseudotime follows the cells’ progress through the cell cycle.
Year
DOI
Venue
2022
10.1109/BigDataService55688.2022.00026
2022 IEEE Eighth International Conference on Big Data Computing Service and Applications (BigDataService)
Keywords
DocType
ISBN
Data integration,Single cell RNA sequencing,Live-cell imaging,convolutional neural networks,cell cycle
Conference
978-1-6654-5891-7
Citations 
PageRank 
References 
0
0.34
1
Authors
8
Name
Order
Citations
PageRank
Andrew Schultz100.34
Saeed Alahmari200.34
Joeseph Johnson300.34
Thomas Veith400.34
Zaid Siddiqui500.34
Issam El-Naqa652836.31
Laura Heiser700.34
Noemi Andor800.34