Oct 29, 2018

Autoencoder and Optimal Transport to Infer Single-Cell Trajectories of Biological Processes

BioRxiv : the Preprint Server for Biology
Karren D. YangCaroline Uhler

Abstract

Although we can increasingly image and measure biological processes at single-cell resolution, most assays can only take snapshots from a population of cells in time. Here we describe ImageAEOT, which combines an AutoEncoder, to map single-cell Images from different cell populations to a common latent space, with the framework of Optimal Transport to infer cellular trajectories. As a proof-of-concept, we apply ImageAEOT to nuclear and chromatin images during the activation of fibroblasts by tumor cells in engineered 3D tissues. We further validate ImageAEOT on chromatin images of various breast cancer cell lines and human tissue samples, thereby linking alterations in chromatin condensation patterns to different stages of tumor progression. Importantly, ImageAEOT can infer the trajectory of a particular cell from one snapshot in time and identify the changing features to provide early biomarkers for developmental and disease progression.

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Mentioned in this Paper

Single-Cell Analysis
Biological Markers
Tumor Cells, Uncertain Whether Benign or Malignant
Patterns
Specimen Type - Fibroblasts
Three-dimensional
Neoplastic Cell
Anatomical Space Structure
Fibroblasts
Chromatin Location

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