Liver metastasis, the leading cause of colorectal cancer mortality, exhibits a highly heterogeneous and suppressive immune microenvironment. Here, we sequenced 97 matched samples by using single-cell RNA sequencing and spatial transcriptomics. Strikingly, the metastatic microenvironment underwent remarkable spatial reprogramming of immunosuppressive cells such as MRC1^(+)\mathrm{MRC1}^{+}CCL18+ M 2 -like macrophages. We further developed scMetabolism, a computational pipeline for quantifying single-cell metabolism, and observed that those macrophages harbored enhanced metabolic activity. Interestingly, neoadjuvant chemotherapy could block this status and restore the antitumor immune balance in responsive patients, whereas the nonresponsive patients deteriorated into a more suppressive one. Our work described the immune evolution of metastasis and uncovered the black box of how tumors respond to neoadjuvant chemotherapy. 肝转移是结直肠癌死亡的主要原因，表现出高度异质性和抑制性的免疫微环境。在这里，我们使用单细胞 RNA 测序和空间转录组学对 97 个匹配的样本进行了测序。引人注目的是，转移性微环境经历了免疫抑制细胞（如 MRC1^(+)\mathrm{MRC1}^{+} CCL18+ M 2 样巨噬细胞）的显着空间重编程。我们进一步开发了 scMetabolism，这是一种用于量化单细胞代谢的计算管道，并观察到这些巨噬细胞具有增强的代谢活性。有趣的是，新辅助化疗可以阻断这种状态并恢复反应患者的抗肿瘤免疫平衡，而无反应的患者恶化为更具抑制性的患者。我们的工作描述了转移的免疫进化，并揭示了肿瘤如何对新辅助化疗做出反应的黑匣子。
SIGNIFICANCE: We present a single-cell and spatial atlas of colorectal liver metastasis and found the highly metabolically activated MRC1+ CCL18+ M2-like macrophages in metastatic sites. Efficient neoadjuvant chemotherapy can slow down such metabolic activation, raising the possibility to target metabolism pathways in metastasis. 意义： 我们提出了结直肠肝转移的单细胞和空间图谱，并在转移部位发现了高度代谢激活的 MRC1 + CCL18 + M2 样巨噬细胞。有效的新辅助化疗可以减慢这种代谢激活，提高转移中靶向代谢途径的可能性。
INTRODUCTION 介绍
Liver metastasis remains a major hurdle to long-lasting survival of patients with colorectal cancer ( 1,2 ), which can be partly explained by the highly dynamic spreading routes of cancer cells (3,4)(3,4). Outside the cancer cells, the tumor microenvironment (TME) of liver metastasis harbors a highly immunosuppressive phenotype (5), induces a systemic loss of antigen-specific T lymphocytes (6), and drives the spread of tumors (7). It still remains largely unknown how the immune cells spatially orchestrate colorectal cancer liver metastasis (CRLM) progression and whether the metastatic cellular 肝转移仍然是结直肠癌患者长期生存的主要障碍 （ 1,2 ），这部分可以由癌细胞 (3,4)(3,4) 的高度动态扩散途径来解释。在癌细胞之外，肝转移的肿瘤微环境 （TME） 具有高度免疫抑制的表型 （5），诱导抗原特异性 T 淋巴细胞的全身性丢失 （6），并驱动肿瘤扩散 （7）。免疫细胞如何在空间上协调结直肠癌肝转移 （CRLM） 进展以及转移细胞是否转移仍然在很大程度上仍然未知
microenvironment differs from the primary ones (8-13). There is hence a pressing need for recording, identifying, and quantifying cellular or even spatial landscape of CRLM to refresh our understanding of metastasis biology. 微环境与原初不同 （8-13）。因此，迫切需要记录、识别和量化 CRLM 的细胞甚至空间景观，以刷新我们对转移生物学的理解。
Neoadjuvant chemotherapy (NAC) refers to chemical drugs that are administered prior to surgical removal of a tumor. Paradoxical results have been seen for the effect of NAC in CRLM. In some reports, NAC was shown to prolong progressionfree survival (14) and overall survival (15). However, especially in the patients with low-risk resectable CRLM, NAC was unexpectedly associated with an unimproved overall survival (16). Another independent group also reported that NAC did not prolong disease-free interval (17). Those seemingly contradictory data raise a critical but poorly understood question of whether NAC could favor antitumor response and improve outcome of patients with CRLM. Thus, dissecting the precise effect of NAC, especially TME alterations, is crucial for understanding the key mechanisms of NAC and designing novel therapeutic strategies. 新辅助化疗 （NAC） 是指在手术切除肿瘤之前施用的化学药物。NAC 在 CRLM 中的作用已经看到了矛盾的结果。在一些报告中，NAC 被证明可以延长无进展生存期 （14） 和总生存期 （15）。然而，特别是在低风险可切除 CRLM 患者中，NAC 出乎意料地与未改善的总生存期相关 （16）。另一个独立小组也报告说，NAC 不会延长无病间期 （17）。这些看似矛盾的数据提出了一个关键但知之甚少的问题，即 NAC 是否有利于抗肿瘤反应并改善 CRLM 患者的预后。因此，剖析 NAC 的确切效果，尤其是 TME 改变，对于理解 NAC 的关键机制和设计新的治疗策略至关重要。
Here, using single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) of 97 samples from 24 patients, we aim to explore the immune atlas of CRLM. We revealed that immune microenvironment showed dynamic cellular and spatial changes from primary to metastatic sites. Following NAC treatment, the immune phenotypes underwent antitumor remodeling in responsive patients but shifted toward more immunosuppression in nonresponsive patients, where the immunosuppressive cells were reprogrammed at metastatic sites. Our findings highlight the favorable effect of efficient NAC treatment in patients with resectable CRLM and allow for the data-driven design of novel therapeutic combinations such as NAC plus immunotherapy in selected patients. 在这里，使用来自 24 名患者的 97 个样本的单细胞 RNA 测序 （scRNA-seq） 和空间转录组学 （ST），我们旨在探索 CRLM 的免疫图谱。我们揭示了免疫微环境表现出从原发部位到转移部位的动态细胞和空间变化。NAC 治疗后，免疫表型在反应性患者中发生抗肿瘤重塑，但在无反应患者中转向更多的免疫抑制，其中免疫抑制细胞在转移部位被重编程。我们的研究结果强调了高效 NAC 治疗对可切除 CRLM 患者的有利效果，并允许对选定的患者进行数据驱动设计新的治疗组合，例如 NAC 加免疫疗法。
RESULTS 结果
Integrated scRNA-seq and ST Precisely Quantify Immune Cell Diversity in Resectable CRLM 集成的 scRNA-seq 和 ST 可精确量化可切除 CRLM 中的免疫细胞多样性
To define the single-cell landscape of CRLM, we applied scRNA-seq and ST to quantify CD^(+)5^(+)\mathrm{CD}^{+} 5^{+}cell dynamics (Fig. 1A; 为了定义 CRLM 的单细胞景观，我们应用 scRNA-seq 和 ST 来量化 CD^(+)5^(+)\mathrm{CD}^{+} 5^{+} 细胞动力学（图 1A;
Figure 1. scRNA-seq and ST revealed the immune landscape of CRLM. A, The experimental scheme for discovering and validating functional immune subpopulations in CRLM. CRC, colorectal cancer; LM, liver metastasis; LN, lymph node. B, The UMAP plot of all main immune cell types. CD4, CD4 T cell; CD8, CD8 T cell; MAIT, mucosal associated invariant T cell; myeloid, myeloid cell; Neu, neutrophil; Treg, regulatory T cell. C, The heat map of selected cell markers. Left, the immune cell in colorectal cancer; right, the immune cell gene-expression pattern in liver metastasis. D. Histogram showing the proportion of main immune cells across tissues. Right, the T-cell proportion; left, other immune cells. E, The UMAP plot showing the subpopulation of myeloid cells and CD8 T cells. F, The unsupervised clustering analysis of ST in colorectal cancer and LM. G, The CD8 T-cell score of ST in colorectal cancer and liver metastasis. 图 1.scRNA-seq 和 ST 揭示了 CRLM 的免疫景观。A，在 CRLM 中发现和验证功能性免疫亚群的实验方案。CRC，结直肠癌;LM，肝转移;LN，淋巴结。B，所有主要免疫细胞类型的 UMAP 图。CD4，CD4 T 细胞;CD8，CD8 T 细胞;MAIT，粘膜相关的不变 T 细胞;髓系，髓系细胞;Neu，中性粒细胞;Treg，调节性 T 细胞。C，所选单元格标记的热图。左图为结直肠癌中的免疫细胞;右图为肝转移中的免疫细胞基因表达模式。D. 直方图显示组织中主要免疫细胞的比例。右图为 T 细胞比例;左图，其他免疫细胞。E，显示髓系细胞和 CD8 T 细胞亚群的 UMAP 图。F， ST 在结直肠癌和 LM 中的无监督聚类分析。G， ST 在结直肠癌和肝转移患者中的 CD8 T 细胞评分。
^(1){ }^{1} Department of Liver Surgery and Transplantation, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China. ^(2){ }^{2} The Center for Microbes, Development and Health, Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai, China. ^(3){ }^{3} School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing, China. ^(4){ }^{4} Affiliated Hospital of Nantong University; School of Medicine, Nantong University, Jiangsu, China. ^(5){ }^{5} Department of Colorectal Surgery and Colorectal Cancer Center, Zhongshan Hospital; Shanghai Engineering Research Center of Colorectal Cancer Minimally Invasive, Fudan University, Shanghai, China. ^(6){ }^{6} Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China. ^(7){ }^{7} Key Laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, China. ^(8){ }^{8} State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, China. ^(1){ }^{1} 复旦大学附属中山医院肝癌研究所肝脏外科与移植科，致癌与癌症侵袭教育部重点实验室，中国上海。 ^(2){ }^{2} 中国科学院上海巴斯德研究所微生物、发展与健康中心、分子病毒学和免疫学重点实验室，中国上海。 ^(3){ }^{3} 北京大学数学科学学院和统计科学中心，中国北京。 ^(4){ }^{4} 南通大学附属医院;南通大学医学院，中国江苏。 ^(5){ }^{5} 中山医院结直肠外科和结直肠癌中心;复旦大学上海市结直肠癌微创工程研究中心，中国 上海。 ^(6){ }^{6} 北京大学生命科学学院生物医学先锋创新中心 （BIOPIC），中国北京。 ^(7){ }^{7} 复旦大学生物医学研究院医学表观遗传学与代谢重点实验室，中国上海。 ^(8){ }^{8} 复旦大学基因工程国家重点实验室，中国上海。
Note: Supplementary data for this article are available at Cancer Discovery Online (http://cancerdiscovery.aacrjournals.org/). 注意：本文的补充数据可在 Cancer Discovery Online （http://cancerdiscovery.aacrjournals.org/） 上获得。
Y. Wu, S. Yang, J. Ma, and Z. Chen contributed equally to this article. Y. Wu、S. Yang、J. 马和 Z. Chen 对本文做出了同样的贡献。
