Gong Wuming, Granados Alejandro A, Hu Jingyuan, Jones Matthew G, Raz Ofir, Salvador-Martínez Irepan, Zhang Hanrui, Chow Ke-Huan K, Kwak Il-Youp, Retkute Renata, Prusokiene Alisa, Prusokas Augustinas, Khodaverdian Alex, Zhang Richard, Rao Suhas, Wang Robert, Rennert Phil, Saipradeep Vangala G, Sivadasan Naveen, Rao Aditya, Joseph Thomas, Srinivasan Rajgopal, Peng Jiajie, Han Lu, Shang Xuequn, Garry Daniel J, Yu Thomas, Chung Verena, Mason Michael, Liu Zhandong, Guan Yuanfang, Yosef Nir, Shendure Jay, Telford Maximilian J, Shapiro Ehud, Elowitz Michael B, Meyer Pablo
Lillehei Heart Institute, University of Minnesota, 2231 6th St S.E, 4-165 CCRB, Minneapolis, MN 55114, USA.
California Institute of Technology, Pasadena, CA 91125, USA.
Cell Syst. 2021 Aug 18;12(8):810-826.e4. doi: 10.1016/j.cels.2021.05.008. Epub 2021 Jun 18.
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.
CRISPR和其他分子工具的近期出现，使得基于诱导DNA突变的细胞谱系重建成为可能，并有望解决更复杂生物体的细胞谱系重建问题。到目前为止，尚未对谱系重建算法在不同数据集类型和细胞数量下的性能和稳健性进行严格检验。为了对这些方法进行基准测试，我们决定组织一场DREAM挑战赛，使用体外实验性intMEMOIR记录以及关于约1000个细胞的秀丽隐杆线虫谱系树和10000个细胞的小家鼠谱系树的计算机模拟数据。提交的22种方法中有些表现出色，但谱系树的结构特征阻碍了最优重建。事实证明，使用较小的子树作为训练集是调整算法以重建更大谱系树的好方法。此处生成的模拟和重建方法描绘了一条解决更大细胞谱系树（如小鼠中的谱系树）的潜在前进道路。
