SpaGE: Spatial Gene Enhancement using scRNA-seq

Nucleic Acids Research, vol. 48, pp. e107

Abstract

Single-cell technologies are emerging fast due to their ability to unravel the heterogeneity of biological systems. While scRNA-seq is a powerful tool that measures whole-transcriptome expression of single cells, it lacks their spatial localization. Novel spatial transcriptomics methods do retain cells spatial information but some methods can only measure tens to hundreds of transcripts. To resolve this discrepancy, we developed SpaGE, a method that integrates spatial and scRNA-seq datasets to predict whole-transcriptome expressions in their spatial configuration. Using five dataset-pairs, SpaGE outperformed previously published methods and showed scalability to large datasets. Moreover, SpaGE predicted new spatial gene patterns that are confirmed independently using in situ hybridization data from the Allen Mouse Brain Atlas.

Authors 4

1. Tamim Abdelaal Aachen Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology, Delft 2628XE, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, Leiden 2333ZC, The Netherlands

2. Soufiane M. C. Mourragui

   The Netherlands Cancer Institute · Oncode Institute · Delft University of Technology

   Affiliation as printed

   Computational Cancer Biology, Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, Amsterdam 1066 CX, The Netherlands

   Delft Bioinformatics Lab, Delft University of Technology, Delft 2628XE, The Netherlands

3. Ahmed Essam Mahfouz Aachen Department of Human Genetics Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology, Delft 2628XE, The Netherlands

   Department of Human Genetics, Leiden University Medical Center, Leiden 2333ZC, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, Leiden 2333ZC, The Netherlands

4. Marcel J. T. Reinders corresponding Aachen Department of Human Genetics Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology, Delft 2628XE, The Netherlands

   Department of Human Genetics, Leiden University Medical Center, Leiden 2333ZC, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, Leiden 2333ZC, The Netherlands