代谢重编程在癌症进展中发挥着关键作用，导致肿瘤内显着异质性并影响肿瘤行为。然而，在单细胞水平上对多种癌症类型的代谢异质性的系统表征仍然有限。我们整合了涵盖六种常见癌症类型的 296 个肿瘤和正常样本，构建了代谢基因表达谱的单细胞纲要并鉴定了细胞类型 -特定的代谢特性和重编程模式。基于非负矩阵分解（NMF）的计算方法被用来识别显示肿瘤内异质性的代谢元程序（MMP）。进行体外细胞实验以确认 MMP 与化疗耐药性之间的关联，以及关键代谢调节因子的功能。进行生存分析以评估细胞代谢特性的临床相关性。我们的分析揭示了不同细胞类型之间共有的糖酵解上调和柠檬酸循环的不同调节。在恶性细胞中，我们发现了一种结直肠癌特异性 MMP，该 MMP 与铜凋亡诱导剂艾司氯醇 (elesclomol) 的抗性相关，并通过体外细胞实验进行了验证。此外，我们的研究结果使得能够根据特定细胞类型（例如骨髓细胞）的代谢特性将患者分为不同的预后亚型。这项研究提出了对多层代谢异质性的细致入微的理解，为针对肿瘤代谢的潜在个性化治疗提供了宝贵的见解。国家重点研发计划（2021YFA1300601）。国家自然科学基金（重点资助82030081和81874235）。深圳市高水平医院建设基金和深圳市基础研究重点项目（JCYJ20220818102811024）。林松年系统生物医学基金会。版权所有 © 2024 作者。由 Elsevier B.V. 出版。保留所有权利。

Metabolic reprogramming plays a pivotal role in cancer progression, contributing to substantial intratumour heterogeneity and influencing tumour behaviour. However, a systematic characterization of metabolic heterogeneity across multiple cancer types at the single-cell level remains limited.We integrated 296 tumour and normal samples spanning six common cancer types to construct a single-cell compendium of metabolic gene expression profiles and identify cell type-specific metabolic properties and reprogramming patterns. A computational approach based on non-negative matrix factorization (NMF) was utilised to identify metabolic meta-programs (MMPs) showing intratumour heterogeneity. In-vitro cell experiments were conducted to confirm the associations between MMPs and chemotherapy resistance, as well as the function of key metabolic regulators. Survival analyses were performed to assess clinical relevance of cellular metabolic properties.Our analysis revealed shared glycolysis upregulation and divergent regulation of citric acid cycle across different cell types. In malignant cells, we identified a colorectal cancer-specific MMP associated with resistance to the cuproptosis inducer elesclomol, validated through in-vitro cell experiments. Furthermore, our findings enabled the stratification of patients into distinct prognostic subtypes based on metabolic properties of specific cell types, such as myeloid cells.This study presents a nuanced understanding of multilayered metabolic heterogeneity, offering valuable insights into potential personalized therapies targeting tumour metabolism.National Key Research and Development Program of China (2021YFA1300601). National Natural Science Foundation of China (key grants 82030081 and 81874235). The Shenzhen High-level Hospital Construction Fund and Shenzhen Basic Research Key Project (JCYJ20220818102811024). The Lam Chung Nin Foundation for Systems Biomedicine.Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.