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Decoding cell division history and lineage-resolved phenotypic patterns from single-cell barcode and transcriptomic data

Identification

Public canonical key
radar:r-18ec2e5199fd3970757047fc28fba434
Authors
Xiaochen Yu · Zihao Wang · Zhenquan Zhang · Yuxin Wang · Yuting Lin · L. Elliot Hong · Xiaoshu Chen · Xionglei He · Jian‐Rong Yang · Jiajun Zhang
Reported year
2026
Reported language
en
Source type
preprint
First detected
2026-11-14T02:13:03.394657+00:00
Last metadata observation
2026-11-14T02:13:03.394657+00:00
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Not checked

Institutions reported by the source

Sun Yat-sen University · Guangdong University of Technology · Guangdong Province Key Laboratory of Computational Science

Reported affiliation countries

CN

Documentary abstract — original language

The cell division tree encodes how proliferation and differentiation generate cellular diversity during multicellular development. Advances in single-cell RNA sequencing and CRISPR-based lineage tracing have enabled retrospective reconstruction of these histories at single-cell resolution. Here, we present FateScape, a statistical framework that integrates paired lineage barcodes and transcriptomic profiles to infer cell division topology and characterize depth-resolved phenotypic patterns. FateScape combines barcode consistency with transcriptome-derived state continuity through overlapping state-lineage decomposition and barcode-guided subtree integration. Here, to analyze phenotypic patterns on the inferred tree, we introduce entropy paths, which measure how cells of each state are distributed across depth-defined subtrees, and Moran's I –derived statistics for same-state autocorrelation and cross-state association. In simulations, FateScape shows robust reconstruction performance across varying mutation rates, dropout levels, sample sizes, and barcode target-site numbers. In the Caenorhabditis elegans data analysis, FateScape accurately recovers lineage topology and identifies depth-resolved state patterns, including concentrated intestinal cells and broadly distributed neuronal and glial states. In mouse embryos, FateScape quantifies distinct germ-layer patterns, including early endodermal concentration, broad ectodermal distribution across shallow-to-intermediate depths, and intermediate mesodermal distribution followed by deeper concentration. Together, FateScape provides a framework for reconstructing cell division histories and quantifying state dispersion, concentration, and tree-based association across lineage depth.

Identifiers

OPENALEX
https://openalex.org/W7213871437
DOI
10.1101/gr.281734.125
PMID
https://pubmed.ncbi.nlm.nih.gov/42767804

Documentary context

Documentary family
preprint
Source-reported types
preprint
Publishers
Cold Spring Harbor Laboratory Press

Entity mentions are not evidence of legal recognition. Jurisdiction studied is separate from author affiliation country.

Sources

OpenAlex · Record 1

Cite this reference

Xiaochen Yu; Zihao Wang; Zhenquan Zhang; Yuxin Wang; Yuting Lin; L. Elliot Hong; Xiaoshu Chen; Xionglei He; Jian‐Rong Yang; Jiajun Zhang. Decoding cell division history and lineage-resolved phenotypic patterns from single-cell barcode and transcriptomic data. 2026. https://doi.org/10.1101/gr.281734.125

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APA
Xiaochen Yu; Zihao Wang; Zhenquan Zhang; Yuxin Wang; Yuting Lin; L. Elliot Hong; Xiaoshu Chen; Xionglei He; Jian‐Rong Yang; Jiajun Zhang. (2026). Decoding cell division history and lineage-resolved phenotypic patterns from single-cell barcode and transcriptomic data. https://doi.org/10.1101/gr.281734.125
Chicago
Xiaochen Yu; Zihao Wang; Zhenquan Zhang; Yuxin Wang; Yuting Lin; L. Elliot Hong; Xiaoshu Chen; Xionglei He; Jian‐Rong Yang; Jiajun Zhang. Decoding cell division history and lineage-resolved phenotypic patterns from single-cell barcode and transcriptomic data. 2026. https://doi.org/10.1101/gr.281734.125
ISO 690
Xiaochen Yu; Zihao Wang; Zhenquan Zhang; Yuxin Wang; Yuting Lin; L. Elliot Hong; Xiaoshu Chen; Xionglei He; Jian‐Rong Yang; Jiajun Zhang. Decoding cell division history and lineage-resolved phenotypic patterns from single-cell barcode and transcriptomic data. 2026. https://doi.org/10.1101/gr.281734.125

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Bibliographic / original publication source · https://doi.org/10.1101/gr.281734.125

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