Determining cellular phylogeny and migration at high resolution in spatial genomics

Abstract

Cellular migration and tumor evolution are central to tissue homeostasis and cancer biology. Rapid advances in spatial genomics have raised the possibility of using somatic mutations to track these processes in situ in human tissues. However, frequent data dropouts in spatial genomics measurements make this challenging to implement. Here, we present StarTrak (SpaTial Analysis for lineage ReconsTRuction with somatic vAriant calling and tracKing), a framework for somatic variant calling and lineage reconstruction at single-pixel spatial resolution. StarTrak employs a neural network framework to integrate mutational evidence (from both nuclear and mitochondrial reads), spatial proximity, histological images, and transcriptomic states to identify spatially informative patterns of mutations. It performs robustly across tissue types and spatial-omics platforms (being particularly successful with spatial ATAC-seq). Applying StarTrak to tumor sections, we infer the migration of mutated tumor cells into adjacent non-malignant tissues and reconstruct distinct lineage trees associated with different spatial regions. Notably, we identify tumor regions with nearly identical transcriptomic states but distinct mutational profiles. Moreover, for skin sections, StarTrak identifies sunlight-associated mutations in non-malignant epidermis and evidence suggesting the migration of these mutant surface cells into deeper skin layers. Overall, by integrating mutation calling with spatial context, StarTrak enables the inference of spatially resolved cellular migration and cellular phylogeny.

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Cite

BibTeX RIS

BibTeX

@misc{zhang2026determining,
  title = {{Determining cellular phylogeny and migration at high resolution in spatial genomics}},
  author = {D. Zhang and Y. Jiang and X. Yu and D. Bai and J. Tian and A. Enninful and X. Tian and H. Li and Y. Huo and Y. Chen and N. Yao and J. Li and Z. Chu and Z. Bai and R. Fan and J. Lu and M. Gerstein},
  howpublished = {bioRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.16.759405},
  url = {https://www.biorxiv.org/content/10.64898/2026.10.16.759405v1},
  note = {CC BY-NC-ND},
}

RIS

TY  - UNPB
TI  - Determining cellular phylogeny and migration at high resolution in spatial genomics
AU  - D. Zhang
AU  - Y. Jiang
AU  - X. Yu
AU  - D. Bai
AU  - J. Tian
AU  - A. Enninful
AU  - X. Tian
AU  - H. Li
AU  - Y. Huo
AU  - Y. Chen
AU  - N. Yao
AU  - J. Li
AU  - Z. Chu
AU  - Z. Bai
AU  - R. Fan
AU  - J. Lu
AU  - M. Gerstein
PY  - 2026
DA  - 2026/10/16
PB  - bioRxiv
DO  - 10.64898/2026.10.16.759405
UR  - https://www.biorxiv.org/content/10.64898/2026.10.16.759405v1
AB  - Cellular migration and tumor evolution are central to tissue homeostasis and cancer biology. Rapid advances in spatial genomics have raised the possibility of using somatic mutations to track these processes in situ in human tissues. However, frequent data dropouts in spatial genomics measurements make this challenging to implement. Here, we present StarTrak (SpaTial Analysis for lineage ReconsTRuction with somatic vAriant calling and tracKing), a framework for somatic variant calling and lineage reconstruction at single-pixel spatial resolution. StarTrak employs a neural network framework to integrate mutational evidence (from both nuclear and mitochondrial reads), spatial proximity, histological images, and transcriptomic states to identify spatially informative patterns of mutations. It performs robustly across tissue types and spatial-omics platforms (being particularly successful with spatial ATAC-seq). Applying StarTrak to tumor sections, we infer the migration of mutated tumor cells into adjacent non-malignant tissues and reconstruct distinct lineage trees associated with different spatial regions. Notably, we identify tumor regions with nearly identical transcriptomic states but distinct mutational profiles. Moreover, for skin sections, StarTrak identifies sunlight-associated mutations in non-malignant epidermis and evidence suggesting the migration of these mutant surface cells into deeper skin layers. Overall, by integrating mutation calling with spatial context, StarTrak enables the inference of spatially resolved cellular migration and cellular phylogeny.
ER  -

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