Virtual lineages: in silico lineage tracing from single-cell and spatial transcriptomes
- 1Peking University
Abstract
Experimental lineage tracing reveals histories and fates of individual cells but is costly and often impractical, particularly in primary human tissues. Gene expression differences that persist across cell divisions provide a basis for inferring ancestry from more accessible single cell transcriptomes, yet accurate lineage reconstruction remains challenging. Here, we introduce isCORAL, an in silico lineage-tracing framework that reconstructs clonal relationships and maps cell-fate dynamics from transcriptome snapshots alone. By identifying memory genes with heritable expression differences, isCORAL infers probabilistic clonal relationships and uses them to construct clone-oriented embeddings and dynamic cell-fate maps. Across diverse lineage-tracing datasets, isCORAL markedly outperforms tested alternatives in both memory-gene identification and lineage reconstruction. We use isCORAL to resolve early fate biases among transcriptionally similar hematopoietic stem cells and predict distinct developmental routes to shared outcomes. We extend isCORAL to spatial transcriptomics, validating inferred clonal relationships against an experimentally recorded lineage tree in mouse tumors and inferring early epiblast and amnion dynamics in a human embryo. Together, isCORAL enables lineage-resolved studies of development and disease from standard single cell and spatial transcriptomes, and provides an explicit representation of lineage memory for future virtual cell models.
Related
- Spatial mitochondrial lineage tracing uncovers a premetastatic niche and microenvironment programmed fate switching in osteosarcoma
- Genome-wide analyses resolve three clonal lineages and lineage-specific ploidy in Phytophthora cinnamomi
- A Cre-responsive reporter quail for flexible genetic lineage tracing
- scRIPT: a rapid, high-coverage single-cell lineage-tracing method applied to the hematoendothelial differentiation of embryonic stem cells
- Lineage tracing reveals asynchronous fate restriction in mouse embryos and stem cell-derived embryo models
BibTeX
@misc{lin2026virtual,
title = {{Virtual lineages: in silico lineage tracing from single-cell and spatial transcriptomes}},
author = {Y. Lin and Y. Lin},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.19.760045},
url = {https://www.biorxiv.org/content/10.64898/2026.10.19.760045v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Virtual lineages: in silico lineage tracing from single-cell and spatial transcriptomes
AU - Y. Lin
AU - Y. Lin
PY - 2026
DA - 2026/10/19
PB - bioRxiv
DO - 10.64898/2026.10.19.760045
UR - https://www.biorxiv.org/content/10.64898/2026.10.19.760045v1
AB - Experimental lineage tracing reveals histories and fates of individual cells but is costly and often impractical, particularly in primary human tissues. Gene expression differences that persist across cell divisions provide a basis for inferring ancestry from more accessible single cell transcriptomes, yet accurate lineage reconstruction remains challenging. Here, we introduce isCORAL, an in silico lineage-tracing framework that reconstructs clonal relationships and maps cell-fate dynamics from transcriptome snapshots alone. By identifying memory genes with heritable expression differences, isCORAL infers probabilistic clonal relationships and uses them to construct clone-oriented embeddings and dynamic cell-fate maps. Across diverse lineage-tracing datasets, isCORAL markedly outperforms tested alternatives in both memory-gene identification and lineage reconstruction. We use isCORAL to resolve early fate biases among transcriptionally similar hematopoietic stem cells and predict distinct developmental routes to shared outcomes. We extend isCORAL to spatial transcriptomics, validating inferred clonal relationships against an experimentally recorded lineage tree in mouse tumors and inferring early epiblast and amnion dynamics in a human embryo. Together, isCORAL enables lineage-resolved studies of development and disease from standard single cell and spatial transcriptomes, and provides an explicit representation of lineage memory for future virtual cell models.
ER -