Spatial mapping of single cells in the Drosophila embryo from transcriptomic data based on topological consistency.

Maryam Zand,Jianhua Ruan

doi:10.12688/f1000research.24163.2

Abstract

The advancement in single-cell RNA sequencing technologies allow us to obtain transcriptome at single cell resolution. However, the original spatial context of cells, a crucial knowledge for understanding cellular and tissue-level functions, is often lost during sequencing. To address this issue, the DREAM Single Cell Transcriptomics Challenge launched a community-wide effort to seek computational solutions for spatial mapping of single cells in tissues using single-cell RNAseq (scRNA-seq) data and a reference atlas obtained from in situ hybridization data. As a top-performing team in this competition, we approach this problem in three steps. The first step involves identifying a set of most informative genes based on the consistency between gene expression similarity and cell proximity. For this step, we propose two different approaches, i.e., an unsupervised approach that does not utilize the gold standard location of the cells provided by the challenge organizers, and a supervised approach that relies on the gold standard locations. In the second step, a Particle Swarm Optimization algorithm is used to optimize the weights of different genes in order to maximize matches between the predicted locations and the gold standard locations. Finally, the information embedded in the cell topology is used to improve the predicted cell-location scores by weighted averaging of scores from neighboring locations. Evaluation results based on DREAM scores show that our method accurately predicts the location of single cells, and the predictions lead to successful recovery of the spatial expression patterns for most of landmark genes. In addition, investigating the selected genes demonstrates that most predictive genes are cluster specific, and stable across our supervised and unsupervised gene selection frameworks. Overall, the promising results obtained by our methods in DREAM challenge demonstrated that topological consistency is a useful concept in identifying marker genes and constructing predictive models for spatial mapping of single cells.

Highlights

Our analysis indicated that using raw data instead of the binarized data can potentially increase the consistency between gene expression pattern similarity and cell proximity in this challenge, we are limited by the fact that the true locations of the cells to be predicted are unknown, and prediction accuracy is at least partially defined by comparing to the “gold standard” location obtained from binarized data
In this work, we proposed a method to identify gene markers for RNAseq-based reconstruction of cell spatial information that were lost during single-cell transcriptomics sequencing of Drosophila embryo
The main hypothesis of this study is that the topology of the marker gene expression based cellcell similarity graph should be consistent with the topology of the cell-cell geometric location map