CAR-T(CAR-T)细胞疗法在非肿瘤性疾病中的应用
Chimeric antigen receptor T (CAR-T) cell therapy in non-oncological diseases.
CAR-T(CAR-T)细胞在血液系统恶性肿瘤中的应用推动了这种免疫治疗形式的显著进展。
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Machine Learning-Assisted Rapid Optical Imaging for Label-Free CAR T-Cell Detection in Whole Blood.
Machine Learning-Assisted Rapid Optical Imaging for Label-Free CAR T-Cell Detection in Whole Blood.
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嵌合抗原受体(CAR)T细胞疗法是治疗血液系统恶性肿瘤的有效方法。然而,其应用受到高昂成本、细胞因子释放综合征和神经毒性等严重毒性风险,以及患者应答异质性的限制。目前治疗监测主要依赖主观症状评估、常规实验室检查和基本生命体征,临床实践中缺乏对CAR-T 细胞扩增或活化的实时定量评估。免疫监测不及时妨碍个体化照护,并增加治疗成本。为满足这一需求,我们提出一种概念验证方案:采用无标记快速光学成像(ROI)生物传感器,结合自动化机器学习分析,直接定量全血中的CAR-T 细胞。该微流控平台在单芯片上整合红细胞去除、CAR-T 细胞捕获和基于成像的定量,无需离心、染色或依赖操作人员判读。验证时,将表达CD19 CAR的Jurkat细胞加入50 μL全血样本,通过凝集和微滤去除红细胞。随后将剩余血液成分置于重组CD19蛋白功能化的传感芯片上孵育。通过明场显微镜对捕获的CAR-T 细胞成像,并使用基于荧光验证细胞训练的机器学习算法自动计数。通过流式细胞术和荧光成像验证了CD19细胞捕获性能。训练后的机器学习模型经验证,灵敏度为88%,特异度为96%。在具有临床相关性的浓度范围(1–1000个细胞/μL)内,分别对缓冲液和全血建立了三重复校准曲线。
结果显示,加入样本的浓度与检测到的CAR-T 细胞数高度相关(R²分别为0.975和0.990)。对于加标缓冲液,95%置信水平下的检测限(LOD)和定量限(LOQ)分别为0.6和1.1个细胞/μL;对于加标全血,分别为14和67个细胞/μL。
Chimeric antigen receptor (CAR) T-cell therapy is an effective treatment for hematologic malignancies.
However, it is limited by high costs, risk of severe toxicities such as cytokine release syndrome and neurotoxicity, and heterogeneous patient responses. The current therapy monitoring depends largely on subjective symptom assessment, routine laboratory tests, and basic vital signs, without real-time, quantitative evaluation of CAR T-cell expansion or activation in clinical practice. This lack of timely immune monitoring hampers individualized care and contributes to increased treatment costs. To address this need, we present a proof-of-concept, label-free rapid optical imaging (ROI) biosensor with automated machine learning analysis for direct quantification of CAR T-cells from whole blood. This microfluidic platform integrates red blood cell (RBC) removal, CAR T-cell capture, and imaging-based quantification on a single chip, eliminating the need for centrifugation, staining, and operator-dependent interpretation.
For validation, 50 L whole blood samples spiked with Jurkat cells expressing CD19 CARs underwent RBC depletion by agglutination and microfiltration. The remaining blood components were then incubated on a sensor chip functionalized with recombinant CD19 protein. Captured CAR T-cells were imaged by brightfield microscopy and automatically enumerated using a machine learning algorithm trained on fluorescence-validated cells. The CD-19 cells' capture performance was validated by flow cytometry and fluorescence imaging.
The trained machine learning model validated at 88% sensitivity and 96% specificity. Buffer and whole blood calibration curves were established across clinically relevant concentrations (1-1000 cells/ L) with triple replicates. The results showed high correlation (0. 975 and 0. 990 R 2 ) between the spiked concentration and the detected CAR T-cells, with a 95% certainty limit of detection (LOD) and quantification (LOQ) of 0. 6 and 1. 1 cells/ L for spiked buffer, and 14 and 67 cells/ L for spiked whole-blood, respectively.
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