Authors

Jovan Tanevski, Heidelberg University, Jožef Stefan Institute
Thin Nguyen, Deakin University
Buu Truong, University of South Australia
Nikos Karaiskos, Max Delbrück Center for Molecular Medicine in the Helmholtz Association
Mehmet Eren Ahsen, Icahn School of Medicine at Mount Sinai, University of Illinois, Urbana-Champaign
Xinyu Zhang, Yale School of Medicine, Columbia University Irving Medical Center
Chang Shu, Yale School of Medicine
Ke Xu, Yale School of Medicine
Xiaoyu Liang, Yale School of Medicine
Ying Hu, National Cancer Institute
Hoang Vv Pham, University of South Australia
Li Xiaomei, University of South Australia
Thuc D Le, University of South Australia
Adi L Tarca, Wayne State University
Gaurav Bhatti, National Institute of Child Health and Human Development (NICHD)/National Insitutes of Health (NIH)/ Department of Health & Human Services (DHHS)
Roberto Romero, National Institute of Child Health and Human Development (NICHD)/National Insitutes of Health (NIH)/ Department of Health & Human Services (DHHS)
Nestoras Karathanasis, Thomas Jefferson UniversityFollow
Phillipe Loher, Thomas Jefferson UniversityFollow
Yang Chen, The Jackson Laboratory for Genomic Medicine
Zhengqing Ouyang, University of Massachusets
Disheng Mao, University of Connecticut
Yuping Zhang, University of Connecticut
Maryam Zand, University of Texas at San Antonio
Jianhua Ruan, University of Texas at San Antonio
Christoph Hafemeister, New York Genome Center
Peng Qiu, Georgia Institute of Technology, Emory University
Duc Tran, University of Nevada
Tin Nguyen, University of Nevada
Attila Gabor, Heidelberg University
Thomas Yu, Sage Bionetworks
Justin Guinney, Sage Bionetworks
Enrico Glaab, University of Luxembourg
Roland Krause, University of Luxembourg
Peter Banda, University of Luxembourg
Gustavo Stolovitzky, International Buisness Machines (IBM) T.J. Watson Research Center
Nikolaus Rajewsky, Max Delbrück Center for Molecular Medicine in the Helmholtz Association
Julio Saez-Rodriguez, Heidelberg University, RWTH Aachen University, Aachen
Pablo Meyer, International Buisness Machines (IBM) T.J. Watson Research Center

Document Type

Article

Publication Date

11-1-2020

Comments

This is the final published version of the article from Life Science Alliance, 2020 Sep 24;3(11):e202000867.

The final published version can be found at http://doi.org/10.26508/lsa.202000867.

Copyright. The Authors.

Abstract

Single-cell RNA-sequencing (scRNAseq) technologies are rapidly evolving. Although very informative, in standard scRNAseq experiments, the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to maintain cell localization have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To fill this gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as silver standard, genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize clusters of cells. Selection of predictor genes was essential for this task. Predictor genes showed a relatively high expression entropy, high spatial clustering and included prominent developmental genes such as gap and pair-rule genes and tissue markers. Application of the top 10 methods to a zebra fish embryo dataset yielded similar performance and statistical properties of the selected genes than in the Drosophila data. This suggests that methods developed in this challenge are able to extract generalizable properties of genes that are useful to accurately reconstruct the spatial arrangement of cells in tissues.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

PubMed ID

32972997

Language

English

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