ESTIMATION OF CELL LINEAGE TREES BY MAXIMUM-LIKELIHOOD PHYLOGENETICS.

Jean Feng,William S Dewitt Iii,Aaron Mckenna,Amy D Willis,Noah Simon,Frederick A Matsen Iv

doi:10.1214/20-aoas1400

Abstract

CRISPR technology has enabled cell lineage tracing for complex multicellular organisms through insertion-deletion mutations of synthetic genomic barcodes during organismal development. To reconstruct the cell lineage tree from the mutated barcodes, current approaches apply general-purpose computational tools that are agnostic to the mutation process and are unable to take full advantage of the data's structure. We propose a statistical model for the CRISPR mutation process and develop a procedure to estimate the resulting tree topology, branch lengths, and mutation parameters by iteratively applying penalized maximum likelihood estimation. By assuming the barcode evolves according to a molecular clock, our method infers relative ordering across parallel lineages, whereas existing techniques only infer ordering for nodes along the same lineage. When analyzing transgenic zebrafish data from McKenna, Findlay and Gagnon et al. (2016), we find that our method recapitulates known aspects of zebrafish development and the results are consistent across samples.

Highlights

IntroductionRecent advancements in genome editing with CRISPR (clustered regularly interspaced short palindromic repeats) have renewed interest in the construction of large-scale cell lineage trees for complex organisms [McKenna et al, 2016, Woodworth et al, 2017, Spanjaard et al, 2018, Schmidt et al, 2017]
Recent advancements in genome editing with CRISPR have renewed interest in the construction of large-scale cell lineage trees for complex organisms [McKenna et al, 2016, Woodworth et al, 2017, Spanjaard et al, 2018, Schmidt et al, 2017]
We introduce GAPML (GESTALT analysis using penalized Maximum Likelihood), a statistical model for GESTALT and tree-estimation method by an iterative procedure based on maximum likelihood estimation

Summary

Introduction

Recent advancements in genome editing with CRISPR (clustered regularly interspaced short palindromic repeats) have renewed interest in the construction of large-scale cell lineage trees for complex organisms [McKenna et al, 2016, Woodworth et al, 2017, Spanjaard et al, 2018, Schmidt et al, 2017]. These lineage-tracing technologies, such as the GESTALT method [McKenna et al., 2016] that we focus on here , inject Cas and single-guide RNA (sgRNA) into the embryo of a transgenic organism harboring an array of CRISPR/Cas targets separated by short linker sequences (barcodes). Because these barcodes have great diversity, GESTALT provides researchers with rich data with the potential to reveal organism and disease development in high resolution

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