Single-cell metabolic fingerprints discover a cluster of circulating tumor cells with distinct metastatic potential

Circulating tumor cells (CTCs) are recognized as direct seeds of metastasis, but CTC counts alone neglect cellular heterogeneity. This study develops a molecular typing system to predict colorectal cancer metastasis based on single-CTC metabolic fingerprints. Metabotypes associated with metastasis were first identified by mass spectrometry-based untargeted metabolomics of colorectal cancer cell lines with differing metastatic potential. A home-built single-cell quantitative mass spectrometry platform enabled large-scale metabolic analysis of individual CTCs, and a machine learning workflow combining non-negative matrix factorization and logistic regression stratified CTCs into two subgroups (C1 and C2) by a metabolite fingerprint. In vitro and in vivo assays show that the number of C2 CTCs correlates closely with metastasis incidence, revealing a specific CTC population with distinct metastatic potential at the single-cell metabolite level.

Wenjun Zhang, Feifei Xu, Jiang Yao, Changfei Mao, Mingchen Zhu, Motiq Qian, Jun Hu, Huilin Zhong, Junsheng Zhou, Xiaoyu Shi, Yun Chen