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Technology for Single Cell Protein Analysis in Immunology and Cancer Prognostics.

机译：免疫学和癌症预测中的单细胞蛋白质分析技术。

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摘要

The first chapter of this thesis deals with automating data gathering for single cell microfluidic tests. The programs developed saved significant amounts of time with no loss in accuracy. The technology from this chapter was applied to experiments in both Chapters 4 and 5.;The second chapter describes the use of statistical learning to prognose if an anti-angiogenic drug (Bevacizumab) would successfully treat a glioblastoma multiforme tumor. This was conducted by first measuring protein levels from 92 blood samples using the DNA-encoded antibody library platform. This allowed the measure of 35 different proteins per sample, with comparable sensitivity to ELISA. Two statistical learning models were developed in order to predict whether the treatment would succeed. The first, logistic regression, predicted with 85% accuracy and an AUC of 0.901 using a five protein panel. These five proteins were statistically significant predictors and gave insight into the mechanism behind anti-angiogenic success/failure. The second model, an ensemble model of logistic regression, kNN, and random forest, predicted with a slightly higher accuracy of 87%.;The third chapter details the development of a photocleavable conjugate that multiplexed cell surface detection in microfluidic devices. The method successfully detected streptavidin on coated beads with 92% positive predictive rate. Furthermore, chambers with 0, 1, 2, and 3+ beads were statistically distinguishable. The method was then used to detect CD3 on Jurkat T cells, yielding a positive predictive rate of 49% and false positive rate of 0%.;The fourth chapter talks about the use of measuring T cell polyfunctionality in order to predict whether a patient will succeed an adoptive T cells transfer therapy. In 15 patients, we measured 10 proteins from individual T cells ( ∼300 cells per patient). The polyfunctional strength index was calculated, which was then correlated with the patient's progress free survival (PFS) time. 52 other parameters measured in the single cell test were correlated with the PFS. No statistical correlator has been determined, however, and more data is necessary to reach a conclusion.;Finally, the fifth chapter talks about the interactions between T cells and how that affects their protein secretion. It was observed that T cells in direct contact selectively enhance their protein secretion, in some cases by over 5 fold. This occurred for Granzyme B, Perforin, CCL4, TNFa, and IFNg. IL-10 was shown to decrease slightly upon contact. This phenomenon held true for T cells from all patients tested (n=8). Using single cell data, the theoretical protein secretion frequency was calculated for two cells and then compared to the observed rate of secretion for both two cells not in contact, and two cells in contact. In over 90% of cases, the theoretical protein secretion rate matched that of two cells not in contact.
机译：本文的第一章论述了针对单细胞微流体测试的自动化数据收集。开发的程序节省了大量时间，而准确性没有损失。本章中的技术已应用于第4章和第5章中的实验；第二章介绍了使用统计学习来预测抗血管生成药物（贝伐单抗）能否成功治疗多形性胶质母细胞瘤。这是通过首先使用DNA编码的抗体库平台测量92个血液样本中的蛋白质水平来进行的。这样就可以测量每个样品35种不同的蛋白质，与ELISA的灵敏度相当。为了预测治疗是否成功，开发了两个统计学习模型。第一次逻辑回归分析使用五种蛋白质组预测的准确度为85％，AUC为0.901。这五种蛋白质是统计学上重要的预测因子，可深入了解抗血管生成成功/失败的机制。第二个模型是Logistic回归，kNN和随机森林的集成模型，预测的准确率稍高一些，为87％。;第三章详细介绍了光可裂解结合物的开发，该结合物可在微流控设备中对细胞表面进行检测。该方法在包被的珠子上成功检测到链霉亲和素，阳性预测率为92％。此外，具有0、1、2和3+个磁珠的腔室在统计上是可区分的。然后将该方法用于检测Jurkat T细胞上的CD3，产生49％的阳性预测率和0％的假阳性率。第四章讨论了使用测量T细胞多功能性来预测患者是否会成功进行过继性T细胞转移治疗。在15位患者中，我们测量了单个T细胞的10种蛋白质（每位患者约300个细胞）。计算多功能强度指数，然后将其与患者的无进展生存时间（PFS）相关联。单细胞测试中测得的其他52个参数与PFS相关。然而，尚未确定统计相关因子，还需要更多数据才能得出结论。最后，第五章讨论了T细胞之间的相互作用及其如何影响其蛋白质分泌。观察到直接接触的T细胞选择性地提高其蛋白质分泌，在某些情况下提高5倍以上。对于粒酶B，穿孔素，CCL4，TNFa和IFNg发生这种情况。接触后IL-10显示略有降低。这种现象对所有接受测试的患者的T细胞均成立（n = 8）。使用单细胞数据，计算两个细胞的理论蛋白质分泌频率，然后与两个未接触的细胞和两个接触的细胞的观察到的分泌速率进行比较。在超过90％的情况下，理论蛋白质分泌率与未接触的两个细胞相符。

著录项

  • 作者

    Sutherland, Alexander Muir.;

  • 作者单位

    California Institute of Technology.;

  • 授予单位 California Institute of Technology.;
  • 学科 Biomedical engineering.;Immunology.;Statistics.;Oncology.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 116 p.
  • 总页数 116
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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