Limitations and optimizations of cellular lineages tracking

Tracking cellular lineages using genetic barcodes provides insights across biology and has become an important tool. However, barcoding strategies remain ad hoc. We show that elevating barcode insertion probability and thus increasing the average number of barcodes within the cells, adds to the number of traceable lineages but may decrease the accuracy of lineages inference due to reading errors. We establish the trade-off between accuracy in tracing lineages and the total number of traceable lineages, and find optimal experimental parameters under limited resources concerning the populations size of tracked cells and barcode pool complexity.

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Publication Details

Journal
PLoS Computational Biology
Published
2025-04-14
DOI
https://doi.org/10.1371/journal.pcbi.1012880
Primary Topic
Single-cell and spatial transcriptomics
Type
article
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article

Limitations and optimizations of cellular lineages tracking

Sidhartha Goyal, Nava Leibovich
PLoS Computational Biology
Single-cell and spatial transcriptomics
article

Limitations and optimizations of cellular lineages tracking

Sidhartha Goyal, Nava Leibovich
article en

Abstract

Tracking cellular lineages using genetic barcodes provides insights across biology and has become an important tool. However, barcoding strategies remain ad hoc. We show that elevating barcode insertion probability and thus increasing the average number of barcodes within the cells, adds to the number of traceable lineages but may decrease the accuracy of lineages inference due to reading errors. We establish the trade-off between accuracy in tracing lineages and the total number of traceable lineages, and find optimal experimental parameters under limited resources concerning the populations size of tracked cells and barcode pool complexity.

PLoS Computational BiologyVol. 21(4)
University of Toronto (CA), National Research Council Canada (CA), Fields Institute for Research in Mathematical Sciences (CA)
Quality Education
Openalex Percentile: Top 93%
Single-cell and spatial transcriptomics
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