Abstract
Advances in single-cell (SC) genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells, as determined by phylogenetic analysis of the somatic mutations harbored by each cell. Theoretically, complete and accurate knowledge of the genome of each cell of an individual can produce an extremely accurate cell lineage tree of that individual. However, the reality of SC genomics is that such complete and accurate knowledge would be wanting, in quality and in quantity, for the foreseeable future. In this paper we offer a framework for systematically exploring the feasibility of answering cell lineage questions based on SC somatic mutational analysis, as a function of SC genomics data quality and quantity. We take into consideration the current limitations of SC genomics in terms of mutation data quality, most notably amplification bias and allele dropouts (ADO), as well as cost, which puts practical limits on mutation data quantity obtained from each cell as well as on cell sample density. We do so by generating in silico cell lineage trees using a dedicated formal language, eSTG, and show how the ability to answer correctly a cell lineage question depends on the quality and quantity of the SC mutation data. The presented framework can serve as a baseline for the potential of current SC genomics to unravel cell lineage dynamics, as well as the potential contributions of future advancement, both biochemical and computational, for the task.
Highlights
Recent advances in SC technologies have generated a unique opportunity to delineate the complex behavior of heterogeneous cell populations and uncover their underlying mechanistic dynamics [1]
A human cell lineage tree describes the entire developmental dynamics of a person starting from the zygote and ending with each and every extant cell
Fundamental open problems in biology and medicine are questions about the human cell lineage tree: its structure and its dynamics in development, growth, renewal, aging, and disease
Summary
Recent advances in SC technologies have generated a unique opportunity to delineate the complex behavior of heterogeneous cell populations and uncover their underlying mechanistic dynamics [1]. Published work have shown the great potential of using SC mutational analysis for unraveling cell lineage dynamics, there are still several major limitations, which hamper further generalization of this concept to various biological questions and prevent its use in large scale experiments These limitations include 1) technical issues related to SC genomics, including the need for DNA amplification that introduces technical noise, 2) lack of high throughput SC isolation techniques, especially if one wants to retain the original 3D structure, or analyze rare cell types that are difficult to isolate, 3) associated costs, such as Whole Genome Amplification (WGA) kits, sequencing costs, and other consumable products (e.g., reagents and microfluidic devices), and 4) lack of computational infrastructure and dedicated algorithms designed for the unique challenges of SC genomics
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