Investigate heterogeneous neutrophils in NSCLC

靶向肿瘤浸润性中性粒细胞亚群（SiglecF+中性粒细胞）， 在NSCLC的GEM模型中，我们决定建立一种在NSCLC中缺乏SiglecF表达的小鼠模型。 中性粒细胞不幸的是，尽管多次尝试，鼠标模式应该只输 中性粒细胞中的SiglecF表达有一个不可预见的缺陷：它不能在任何细胞中表达SiglecF。 细胞类型，可能是由于未知的表观遗传效应。因此，我们决定寻找上游 调节肿瘤内中性粒细胞发育的调节因子，并将RUNX 1鉴定为潜在的 基于我们的单细胞RNA测序与单细胞ATAC（测定转录因子， 转座酶可降解染色质）测序数据。我们建立了一个缺乏RUNX 1的小鼠模型， 仅在成熟的中性粒细胞（Ly 6 G-Cre; RUNX 1-flox）中， 肿瘤浸润性中性粒细胞中的SiglecF+中性粒细胞。这是第一个转录因子 可以调节肿瘤浸润性中性粒细胞的发育，为 了解异质性肿瘤浸润中性粒细胞。为了进一步研究其对 为了在GEM模型中观察肿瘤进展，我们建立了骨髓嵌合体GEM模型， 成熟中性粒细胞中缺乏RUNX 1的免疫系统。为了更准确和公正地 为了测量NSCLC GEM模型中的肿瘤负荷，我们与CCR人工 情报资源。我们开发了一种人工智能算法， 量化我们的手稿，“基于人工智能的小鼠肿瘤分割 肺腺癌模型》发表在《病理学杂志 信息学.

To target a subgroup of tumor-infiltrating neutrophils (SiglecF+ neutrophils) in the GEM model of NSCLC, we decided to create a mouse model lacking SiglecF expression in neutrophils. Unfortunately, despite multiple attempts, the mouse mode supposed only to lose SiglecF expression in neutrophils has an unforeseen defect: it can't express SiglecF in any cell type, likely due to unknown epigenetic effects. Therefore, we decided to find upstream regulators modulating intratumor neutrophil development and identified RUNX1 as a potential transcription factor based on our single-cell RNA sequencing with single-cell ATAC (Assay for Transposase-Accessible Chromatin) sequencing data. We generated a mouse model lacking RUNX1 only in matured neutrophils (Ly6G-Cre; RUNX1-flox) and discovered a significant increase of SiglecF+ neutrophils in tumor-infiltrating neutrophils. This is the first transcription factor that can regulate tumor-infiltrating neutrophil development, laying the groundwork for understanding the heterogeneous tumor-infiltrating neutrophils. To further study its impact on tumor progression in a GEM model, we have created a bone marrow chimera GEM model with an immune system lacking RUNX1 in matured neutrophils. To have a more precise and unbiased measurement of tumor burden in the GEM model of NSCLC, we collaborated with CCR Artificial Intelligence Resource. We developed an artificial intelligence algorithm for automatic tumor quantification. Our manuscript, "Artificial Intelligence-based Tumor Segmentation in Mouse Models of Lung Adenocarcinoma," was published in the Journal of Pathology Informatics.