Machine learning based lineage tree reconstruction improved with knowledge of higher level relationships between cells and genomic barcodes - Research paper summary

Aug 6, 2026

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Tracking cells as they divide and differentiate is essential for understanding organism development and disease progression. This study develops a machine learning method to reconstruct lineage trees from experimental mutating synthetic genomic barcodes. The approach refines prior work by embedding higher-level relationships between cells together with single-cell barcode values into a feature space. Tests on shallow (≤100 cells) and deep (≤10,000 cells) trees show improved accuracy over maximum parsimony reconstruction, at the cost of increased computation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Abstract

Introduction

Background on single-cell lineage tracing and barcode systems

Computational approaches and limitations

Published online 21 August 2023 NAR Genomics and Bioinformatics, 2023, Vol. 5, No. 3 1 [https://doi.org/10.1093/nargab/lqad077](https://doi.org/10.1093/nargab/lqad077) Machine learning based lineage tree reconstruction improved with knowledge of higher level relationships between cells and genomic barcodes Alisa Prusokiene1 , Augustinas Prusokas2 and Renata Retkute 3 ,* 1 School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK, 2 Independent researcher, London, SW7 2BX, UK and 3 Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK Received January 24, 2023; Revised June 26, 2023; Editorial Decision August 08, 2023; Accepted August 11, 2023 ABSTRACT Tracking cells as they divide and progress through differentiation is a fundamental step in understanding many biological processes, such as the development of organisms and progression of diseases. In this study, we investigate a machine learning approach to reconstruct lineage trees in experimental systems based on mutating synthetic genomic barcodes. We reﬁne previously proposed methodology by embedding information of higher level relationships between cells and single-cell barcode values into a feature space. We test performance of the algorithm on shallow trees (up to 100 cells) and deep trees (up to 10 000 cells). Our proposed algorithm can improve tree reconstruction accuracy in comparison to reconstructions based on a maximum parsimony method, but this comes at a higher computational time requirement. INTRODUCTION Single-cell lineage tracing, reconstructing the relationship between individual dividing cells in a tissue or organism, has the potential to improve our understanding ofmany biological processes, including the major transitions in evolution (1), development from the founding zygote to a complex organism (2), stem cell properties in tissue (3), cancer cell differentiation (4) or metastasis (5), and pathways of tumour evolution (6) . The first full development lineage tree was traced for the embryonic cells of the nematode Caenorhabditis elegans by sketching the events of cell division and development histories observed directly through light microscopy (7) . Recently, platforms based on cell barcoding were proposed for tracking the lineage of individual cells at a high resolution. An integrase-based synthetic barcode system, intMEMOIR, uses the serine integrase Bxb1 to perform ir- reversible random modification of DNA recording arrays that can be read out using fluorescence in situ hybridization imaging (8) . Fluorescent reporter assays allow the rapid characterization of these recording units (9) . This experimental system is coupled with a time-lapse movie of the cells as they divide to provide a ground truth lineage tree (8) . Another technique, substitution mutation-aided lineage tracing (SMALT) system, used a 3-kb readout sequence with 16 iSceI binding motifs to map single-cell resolution cell phylogenies during organ development (10) . Additional strategies to achieve lineage tracing at single-cell resolution have been developed in the past few years: integration barcodes (designed as short DNA fragments placed in an expressed locus) and polylox barcodes (comprising a DNA cassette with multiple loxP sites in alternating orientations) (11) . These, and future, technical advancements have to be matched by progress in relevant computational methods (12) . Various statistical methods for reconstructing gene trees and species trees (13, 14) have been developed over the last few decades, but these methods have not been widely used on time and individual-cell resolved datasets. Mutations induced by cell barcoding methods are irreversible, which is different from the somatic mutations accumulated during mitotic cell division (8) . Previously, we have developed a machine learning (ML) approach for cell lineage reconstruction, with results submitted to the Allen

FAQs

What problem does the study address in cell biology?

It addresses reconstruction of cell lineage trees, i.e., determining relationships between dividing cells during differentiation and development, using barcode-based experimental data.

How does the proposed machine learning approach improve lineage reconstruction?

It embeds information about higher-level relationships between cells together with single-cell barcode values into a feature space and trains an ML model to predict these relationships.

How does the method perform compared with maximum parsimony, and what is the trade-off?

It improves reconstruction accuracy over maximum parsimony, but requires higher computational time.

Aug 6, 2026

Upload by Himbo