Benchmarked approaches for reconstruction of in vitro cell lineages and in silico models of C. elegans and M. musculus developmental trees
Wuming Gong, Alejandro A. Granados, Jingyuan Hu, Matthew G. Jones, Ofir Raz, Irepan Salvador‐Martínez, Hanrui Zhang, Ke-Huan K. Chow, Il‐Youp Kwak, Renata Retkutė, Alisa Prusokiene, Augustinas Prusokas, Alex Khodaverdian, Richard Zhang, Suhas Rao, Robert Wang, Phil Rennert, Vangala Govindakrishnan Saipradeep, Naveen Sivadasan, Aditya Rao, Thomas Joseph, Rajgopal Srinivasan, Jiajie Peng, Lu Han, Xuequn Shang, Daniel J. Garry, Thomas Yu, Verena Chung, Mike J. Mason, Zhandong Liu, Yuanfang Guan, Nir Yosef, Jay Shendure, Maximilian J. Telford, Ehud Shapiro, Michael B. Elowitz, Pablo Meyer
Abstract
The recent advent of CRISPR and other molecular tools enabled the reconstruction of cell lineages based on induced DNA mutations and promises to solve the ones of more complex organisms. To date, no lineage reconstruction algorithms have been rigorously examined for their performance and robustness across dataset types and number of cells. To benchmark such methods, we decided to organize a DREAM challenge using in vitro experimental intMEMOIR recordings and in silico data for a C. elegans lineage tree of about 1,000 cells and a Mus musculus tree of 10,000 cells. Some of the 22 approaches submitted had excellent performance, but structural features of the trees prevented optimal reconstructions. Using smaller sub-trees as training sets proved to be a good approach for tuning algorithms to reconstruct larger trees. The simulation and reconstruction methods here generated delineate a potential way forward for solving larger cell lineage trees such as in mouse.