Alignment of single-cell trajectories by tuMap enables high-resolution quantitative comparison of cancer samples

Ayelet Alpert, Ornit Nahman, Elina Starosvetsky, Michal Hayun, Tyler J. Curiel, Yishai Ofran, Shai S. Shen-Orr

Research output: Contribution to journalArticlepeer-review

Abstract

Single-cell technologies allow characterization of cancer samples as continuous developmental trajectories. Yet, the obtained temporal resolution cannot be leveraged for a comparative analysis due to the large phenotypic heterogeneity existing between patients. Here, we present the tuMap algorithm that exploits high-dimensional single-cell data of cancer samples exhibiting an underlying developmental structure to align them with the healthy development, yielding the tuMap pseudotime axis that allows their systematic, meaningful comparison. We applied tuMap on single-cell mass cytometry data of acute lymphoblastic and myeloid leukemia to reveal associations between the tuMap pseudotime axis and clinics that outperform cellular assignment into developmental populations. Application of the tuMap algorithm on single-cell RNA sequencing data further identified gene signatures of stem cells residing at the very-early parts of the cancer trajectories. The quantitative framework provided by tuMap allows generation of metrics for cancer patients evaluation.

Original languageEnglish (US)
Pages (from-to)71-82.e8
JournalCell Systems
Volume13
Issue number1
DOIs
StatePublished - Jan 19 2022

Keywords

  • cancer metric
  • quantitative comparison of cancer samples
  • single-cell trajectories
  • tailored alignment
  • trajectory alignment

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

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