南京医科大学附属金陵临床医学院使用人CD8+T细胞分选试剂盒发表文章（IF=13）

IPHASE

2025/07/30

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PD-L1 胶质母细胞瘤多形性胶质母细胞瘤胶质瘤 CD8

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近日，南京医科大学附属金陵临床医学院周清清老师，使用IPHASE品牌产品：小鼠CD8+T细胞分选试剂盒在《Journal of Advanced Research》权威期刊上发表文章《Rapid visualization of PD-L1 expression level in glioblastoma immune microenvironment via machine learning cascade-based Raman histopathology》，影响因子13！

本研究致力于解决胶质母细胞瘤（GBM）术中决策中的重要问题，即程序性死亡配体1（PD-L1）的异质性表达和当前评估方法的耗时性。为此，我们提出了一种创新的术中诊断方法，即基于机器学习级联的拉曼组织病理学（MLC-Raman histopathology），旨在实现对胶质瘤术中关键脑区免疫微环境中PD-L1表达水平的可视化，有助于脑外科医师根据胶质瘤术中残余病灶的PD-L1表达水平和关键脑功能区位置，对患者做出最佳治疗决策，同时为术后个体化免疫治疗提供重要参考。

摘要

Introduction: Combination immunotherapy holds promise for improving survival in responsive glioblastoma (GBM) patients. Programmed death-ligand 1 (PD-L1) expression in immune microenvironment (IME) is the most important predictive biomarker for immunotherapy. Due to the heterogeneous distribution of PD-L1, post-operative histopathology fails to accurately capture its expression in residual tumors, making intra-operative diagnosis crucial for GBM treatment strategies. However, the current methods for evaluating the expression of PD-L1 are still time-consuming.

Objective: To overcome the PD-L1 heterogeneity and enable rapid, accurate, and label-free imaging of PD-L1 expression level in GBM IME at the tissue level.

Methods: We proposed a novel intra-operative diagnostic method, Machine Learning Cascade (MLC)-based Raman histopathology, which uses a coordinate localization system (CLS), hierarchical clusteringanalysis (HCA), support vector machine (SVM), and similarity analysis (SA). This method enables visualization of PD-L1 expression in glioma cells, CD8+ T cells, macrophages, and normal cells in addition to the tumor/normal boundary. The study quantified PD-L1 expression levels using the tumor proportion, combined positive, and cellular composition scores (TPS, CPS, and CCS, respectively) based on Raman data. Furthermore, the association between Raman spectral features and biomolecules was examined bio chemically.

Results: The entire process from signal collection to visualization could be completed within 30 min. In an orthotopic glioma mouse model, the MLC-based Raman histopathology demonstrated a high average accuracy (0.990) for identifying different cells and exhibited strong concordance with multiplex immunofluorescence (84.31 %) and traditional pathologists’ scoring (R2 ≥ 0.9). Moreover, the peak intensities at 837 and 874 cm^-1 showed a positive linear correlation with PD-L1 expression level.

Conclusions: This study introduced a new and extendable diagnostic method to achieve rapid and accurate visualization of PD-L1 expression in GBM IMB at the tissular level, leading to great potential in GBM intraoperative diagnosis for guiding surgery and post-operative immunotherapy.