Hematopoietic aging promotes cancer by fueling IL-1⍺-driven emergency myelopoiesis

Age is a major risk factor for cancer, but how aging impacts tumor control remains unclear. Here, we establish that aging of the immune system, regardless of the age of the stroma and tumor, impacts lung cancer progression. Hematopoietic aging enhances emergency myelopoiesis, resulting in the local accumulation of myeloid progenitor-like cells in lung tumors. These cells are a major source of IL-1⍺ that drives the enhanced myeloid response. The age-associated decline of DNMT3A enhances IL-1⍺ production, and disrupting IL-1R1 signaling early during tumor development normalized myelopoiesis and slowed the growth of lung, colonic, and pancreatic tumors. In human tumors, we identified an enrichment for IL-1⍺-expressing monocyte-derived macrophages linked to age, poorer survival, and recurrence, unraveling how aging impacts cancer. Sample preparation: Single-cell suspensions from lung tissues were obtained, as described above. Samples were broadly enriched for myeloid and lymphoid cells by fluorescence-activated cell sorting, and these cells were suspended in PBS supplemented with 0.5% BSA. Samples were loaded onto the 10x Genomics Next GEM 5’ assay, as per the manufacturer’s instructions, for a target cell recovery of 10,000 cells per lane. Libraries were constructed, according to manufacturer’s instructions. All libraries were quantified via Agilent 2100 hsDNA Bioanalyzer and KAPA library quantification kit (Roche, Cat. #0797014001). Libraries were sequenced at a targeted depth of 25,000 reads per cell; all libraries were sequenced using the Illumina NovaSeq S2 100 cycle kit. scRNAseq analysis: Gene expression reads were aligned to the mm10 reference transcriptome and count matrices were generated using the default CellRanger 2.1 workflow, using the ‘raw’ matrix output. Following alignment, barcodes matching cells that contained > 500 unique molecular identifiers (UMIs) were extracted. From these cells, those with transcripts > 25% mitochondrial genes were filtered from downstream analyses. Matrix scaling, logarithmic normalization, and batch correction via data alignment through canonical correlation analysis, and unsupervised clustering using a K-nn graph partitioning approach were performed as previously described. Differentially expressed genes were identified using the FindMarkers function (Seurat). Mean UMI were imputed to determine logarithmic fold changes in expression between cell states to further the analysis of markers of interest.

衰老是癌症发生的核心风险因素之一，但衰老如何调控肿瘤应答的具体机制仍未阐明。本研究证实，无论肿瘤微环境基质与肿瘤本身的年龄状态如何，免疫系统衰老均可影响肺癌进展。造血系统衰老可促进紧急髓系生成，进而促使肺肿瘤局部聚集髓系祖细胞样细胞。这类细胞是白细胞介素-1α（IL-1α，interleukin-1α）的主要来源，可驱动过度活化的髓系应答。年龄相关的DNA甲基转移酶3A（DNMT3A，DNA methyltransferase 3A）表达下调可促进IL-1α的产生；在肿瘤发生早期阻断白细胞介素-1受体1（IL-1R1，interleukin-1 receptor 1）信号通路，可恢复髓系生成稳态，并延缓肺、结肠及胰腺肿瘤的生长。在人类肿瘤样本中，本研究发现表达IL-1α的单核细胞来源巨噬细胞的富集与年龄增长、不良预后及肿瘤复发显著相关，由此阐明了衰老调控癌症进程的分子机制。

样本制备：按照前述实验方法制备肺组织单细胞悬液。通过荧光激活细胞分选术（FACS）初步富集髓系与淋巴系细胞，随后将细胞重悬于含0.5%牛血清白蛋白（BSA）的磷酸盐缓冲液（PBS）中。依照10x Genomics公司Next GEM 5’试剂盒的官方操作流程进行样本加载，设定每通道目标回收细胞数为10000个。依照试剂盒说明书完成测序文库构建。所有文库均通过Agilent 2100高灵敏度DNA生物分析仪及KAPA文库定量试剂盒（罗氏，货号：0797014001）完成定量检测。设定每细胞目标测序深度为25000条reads，采用Illumina NovaSeq S2 100循环测序试剂盒完成文库测序。

单细胞RNA测序（scRNAseq）分析：将基因表达reads比对至mm10参考转录组，采用CellRanger 2.1默认流程（以‘raw’矩阵作为输出）生成计数矩阵。比对完成后，提取包含超过500个唯一分子标识符（UMIs）的细胞对应的条形码。从中进一步过滤线粒体基因占比超过25%的细胞，以排除后续分析中的低质量样本。依照既往报道的方法，进行矩阵标准化、对数归一化，并通过典型相关分析进行数据对齐以校正批次效应，随后采用K近邻（K-nn）图分区法完成无监督聚类。采用Seurat工具的FindMarkers函数鉴定差异表达基因。对平均UMI进行估算，以计算不同细胞状态间的表达对数倍变化，进而深入分析目标细胞标志物。