Cell identity revealed by precise cell cycle state mapping links data modalities
Alahmari, S.; Schultz, A.; Albrecht, J.; Tagal, V.; Siddiqui, Z.; Prabhakaran, S.; El Naqa, I.; Anderson, A.; Heiser, L.; Andor, N.
Show abstract
Several methods for cell cycle inference from sequencing data exist and are widely adopted. In contrast, methods for classification of cell cycle state from imaging data are scarce. We have for the first time integrated sequencing and imaging derived cell cycle pseudo-times for assigning 449 imaged cells to 693 sequenced cells at an average resolution of 3.4 and 2.4 cells for sequencing and imaging data respectively. Data integration revealed thousands of pathways and organelle features that are correlated with each other, including several previously known interactions and novel associations. The ability to assign the transcriptome state of a profiled cell to its closest living relative, which is still actively growing and expanding opens the door for genotype-phenotype mapping at single cell resolution forward in time.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning 95%
- Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting 95%
- Deep learning tools and modeling to estimate the temporal expression of cell cycle proteins from 2D still images 95%
Similar papers in this journal
Similar papers in this journal
- DynaMorph: self-supervised learning of morphodynamic states of live cells 93%
- Spatiotemporal analysis of F-actin polymerization with micropillar arrays reveals synchronization between adhesion sites 93%
- A Multiparametric Activity Profiling Platform for Neuron Disease Phenotyping and Drug Screening 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.