GBM CODEX clustered single cell data

We imaged in total 58 glioblastoma explants using CODEX technology with a 55-marker panel. The imaging data was segmented into single cells based on best-focus nuclear staining using DAPI and DRAQ5 as reference. Spatial (X/Y/Z) coordinates can locate each cell within a specific slide. Cell type annotation was thereafter performed using both VORTEX clustering and manual supervision on the slides (refer to software package from the NOLAN lab, Stanford (https://github.com/nolanlab/CODEX).
A previously established highly multiplexed tissue cytometry platform called CO-Detection by indEXing (CODEX) was re-engineered here here to create multidimensional imaging datasets of glioblastoma (GBM) bioreactor explants. In this procedure, DNA-barcoded antibodies bound to antigens present in the tissue were iteratively rendered visible by hybridizing complementary fluorescent DNA oligonucleotides. An algorithmic pipeline was used to process raw imaging data, segment and identify single cells and their localizations within tissues, and quantify their marker expression. Unsupervised clustering, followed by manual curation of clusters based on marker expression, morphology and tissue localization, was used to call out specific cell types. Expression of selected markers per cell were manually gated in CellEngine (https://cellengine.com). Cellular neighborhoods were algorithmically identified. We identified seven conserved, distinct tissue compartments (TCs) –a collection of components characteristic of the GBM iTME. This study provides a framework for interrogating how glioblastoma patients could potentially profit from local neoadjuvant immunotherapies.

Clustering_results.csv table contains cell types annotation, expression profiles, coordinates of all segmented objected identified in the explants analyzed in this study. Further, it contains information on the biopsy location (c=center, p= periphery of the tumor), treatment info of the explants (control, anti-CD47, anti-PD1, combination), and the respective anonymized tumor internal tumor ID.

本研究采用CODEX技术对总共58例胶质母细胞瘤组织块进行了成像，并使用了包含55个标志物的面板。基于DAPI和DRAQ5的最佳聚焦核染色，将成像数据分割为单细胞。通过空间坐标（X/Y/Z）可以定位每个细胞在特定切片中的位置。随后，利用VORTEX聚类和手动监督（参照斯坦福大学NOLAN实验室的软件包，https://github.com/nolanlab/CODEX）对细胞类型进行了标注。（在此过程中，对细胞进行了类型注释，并运用了VORTEX聚类和手动监督技术，参考斯坦福大学NOLAN实验室提供的软件包。）

在此研究中，对先前建立的称为CO-Detection by indEXing（CODEX）的高度多路复用组织细胞光度平台进行了重构，以创建胶质母细胞瘤（GBM）生物反应器组织块的多元成像数据集。在此过程中，通过互补荧光DNA寡核苷酸的原位杂交，迭代地使组织中的抗原结合的DNA条形码抗体可见。使用算法流程处理原始成像数据，分割和识别组织中的单个细胞及其定位，并量化其标志物表达。通过无监督聚类，随后基于标志物表达、形态和组织定位手动校准簇，以识别特定的细胞类型。在CellEngine（https://cellengine.com）中手动设置选定标志物的细胞表达阈值。通过算法识别了细胞邻域。我们识别了七个保守的、独特的组织区室（TCs），这些区室是胶质母细胞瘤间质微环境（iTME）的特征集合。本研究为探究胶质母细胞瘤患者可能从局部新辅助免疫治疗中获益提供了框架。

聚类结果.csv表格包含细胞类型注释、表达谱、本研究分析的标本中所有分割对象的坐标。此外，它还包含活检位置信息（c=中心，p=肿瘤边缘）、组织块的疗法信息（对照组、抗CD47、抗PD1、联合疗法）以及相应的匿名肿瘤内部肿瘤ID。