PO.TB10.07 · 肿瘤生物学
CosMx 6K发现面板揭示WHO 4级IDH野生型胶质母细胞瘤中空间分辨的细胞-细胞结构
Spatially resolved cell-cell architecture in WHO grade 4 IDH- wildtype glioblastoma revealed by CosMx 6K discovery panel
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摘要 Abstract
中文摘要
背景:IDH野生型胶质母细胞瘤以其肿瘤内异质性而闻名,恶性、免疫和基质细胞群共存于共享微环境中,可能塑造超进展和治疗耐药。这些相互作用可能高度局部化,而斑点分辨率的空间检测可能掩盖了潜在的细胞结构。靶向单细胞空间转录组学(如CosMx SMI)如今可提供可信的细胞分割,用于揭示GBM中精细的患者特异性相互作用网络。
方法:使用CosMx 6K人类发现面板对GBM组织进行分析。标准化预处理(包括RNA QC、批次协调和多参考细胞分型)在Seurat v5中完成。细胞经过逐步清理,包括去除AtoMx标记的细胞以及低于检测基因数和总转录本计数第一四分位数的细胞,并进一步排除细胞过滤后保留细胞<50%的视野(FOV)。使用Giotto v4以AtoMx推荐参数对原始计数应用SCTransform、PCA和UMAP。使用基于半径和k近邻标准构建空间网络和邻域。用Ripley's K函数评估共定位。用CellChat v2推断配体-受体(L-R)网络。所有计算和分析均参照AtoMx手册v2.1(MAN-10162-10),在位于HKU CPOS HPCF2的R 4.4.1下进行。
结果:经过严格过滤后,保留了23个TMA芯中413个FOV共297,125个可信细胞用于研究细胞-细胞通讯。0.99的Pearson相关性表明细胞密度未对每芯分析造成偏倚。基于半径的空间网络揭示了患者间和患者内广泛的肿瘤内异质性。值得注意的是,#025号患者表现出显著转变,一个芯以干样/间充质样细胞簇为主,而另一芯以分化样细胞为主,尽管两个芯均取自初诊时切除的同一FFPE蜡块。k近邻建模显示可重复的肿瘤-肿瘤和肿瘤-血管邻接关系,而髓系和淋巴系细胞在各芯间仍呈空间弥散分布,这一点经Ripley's K函数进一步显著证实。接着,在患者间推断出高度异质的L-R配对,每芯平均有546个高度可变的配体-受体配对。一组经典GBM相互作用通路(如NCAM1-、CD99-和JAG1介导的信号)在各芯间保守。除肿瘤和血管内部的同型网络外,还存在广泛的患者特异性L-R,如#011号患者中的CNTN1-NOTCH1网络。
结论:通过解析细胞-细胞相互作用,CosMx揭示了患者间不同的配体-靶点依赖关系,这为设计定制化的新抗原导向疫苗或配体-靶点双特异性抗体策略提供了基础。
查看英文原文 English abstract
Background:
IDH -wildtype glioblastoma is well-known for its intratumoral heterogeneity, with malignant, immune, and stromal populations coexisting in shared microenvironment that may shape hyper-progression and therapeutic resistance. These could be highly localized while spot-resolution spatial assays probably mask the underlying cellular architecture. Targeted single-cell spatial transcriptomics, like CosMx SMI now provide confident cells segmentation for delicate patient-specific interaction networks across GBM.
Methods :
GBM tissues were profiled using the CosMx 6K Human Discovery Panel. Standardized preprocessing, including RNA QC, batch harmonization, and multi-reference cell typing were completed with Seurat v5. Cells underwent stepwise cleanup, which are the removal of AtoMx-flagged cells and those below the first quartile of number of detected genes and total transcript counts and further exclusion of fields of view (FOVs) retaining <50% of cells after cell filter. SCTransform, PCA and UMAP were applied on raw counts using AtoMx-recommended parameters with Giotto v4. Spatial networks and neighborhood were built using radius-based and k-nearest neighbor criteria. Co-localization was evaluated with Ripley's K-function. L-R networks were inferred with CellChat v2. All computation and analysis were with reference to AtoMx manual v2.1 (MAN-10162-10) under R 4.4.1 located at HPCF2, CPOS, HKU.
Results:
After the stringent filtering, 297,125 confident cells across 413 FOVs in 23 TMA cores were retained for studying cell-cell communication. Pearson correlation of 0.99 indicated that cell density did not bias per-core analyses. Radius-based spatial network revealed extensive intratumoral heterogeneity between and within patient. Of notes, Patient #025 showed a marked shift, with a stem-like/mesenchymal-like cluster dominating one core and differentiated-like cells predominating in another, given both cores extracted from the same FFPE block resected at primary diagnosis. k-nn modeling showed reproducible tumor-tumor and tumor-vascular adjacency, while myeloid and lymphoid cells remained spatially diffuse across the cores, which was further confirmed significantly with Ripley's K function. Next, highly heterogeneous L-R pairs were inferred across patients, with a mean of 546 highly variable ligand-receptor pairs per core. A set of canonical GBM interaction pathways, like NCAM1-, CD99-, and JAG1-mediated signaling was conserved across cores. Apart from the homotypic network within tumoral and vasculature, there are extensive patient-specific L-R, like CNTN1-NOTCH1 networking in patient # 011.
Conclusions:
By resolving cell-cell interaction, CosMx exposed distinct ligand-target dependencies across patients, which is a basis for designing bespoke neoantigen-directed vaccines or ligand-target bispecific antibody approaches.
利益披露 Disclosure
D. Lee, None..
K. Xu, None..
P. Li, None..
J. Li, None..
W. Dai, None..
K. Kiang, None..
G. Leung, None..
A. El Helali, None.