Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.
Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada.
Bioinformatics. 2019 Sep 15;35(18):3468-3475. doi: 10.1093/bioinformatics/btz113.
Network analysis and unsupervised machine learning processing of single-molecule localization microscopy of caveolin-1 (Cav1) antibody labeling of prostate cancer cells identified biosignatures and structures for caveolae and three distinct non-caveolar scaffolds (S1A, S1B and S2). To obtain further insight into low-level molecular interactions within these different structural domains, we now introduce graphlet decomposition over a range of proximity thresholds and show that frequency of different subgraph (k = 4 nodes) patterns for machine learning approaches (classification, identification, automatic labeling, etc.) effectively distinguishes caveolae and scaffold blobs.
Caveolae formation requires both Cav1 and the adaptor protein CAVIN1 (also called PTRF). As a supervised learning approach, we applied a wide-field CAVIN1/PTRF mask to CAVIN1/PTRF-transfected PC3 prostate cancer cells and used the random forest classifier to classify blobs based on graphlet frequency distribution (GFD). GFD of CAVIN1/PTRF-positive (PTRF+) and -negative Cav1 clusters showed poor classification accuracy that was significantly improved by stratifying the PTRF+ clusters by either number of localizations or volume. Low classification accuracy (<50%) of large PTRF+ clusters and caveolae blobs identified by unsupervised learning suggests that their GFD is specific to caveolae. High classification accuracy for small PTRF+ clusters and caveolae blobs argues that CAVIN1/PTRF associates not only with caveolae but also non-caveolar scaffolds. At low proximity thresholds (50-100 nm), the caveolae groups showed reduced frequency of highly connected graphlets and increased frequency of completely disconnected graphlets. GFD analysis of single-molecule localization microscopy Cav1 clusters defines changes in structural organization in caveolae and scaffolds independent of association with CAVIN1/PTRF.
Supplementary data are available at Bioinformatics online.
通过对前列腺癌细胞中 Cav1 抗体标记的单分子定位显微镜的网络分析和无监督机器学习处理，确定了质膜微囊（caveolae）和三种不同的非质膜微囊支架（S1A、S1B 和 S2）的生物标志物和结构。为了进一步了解这些不同结构域内的低水平分子相互作用，我们现在在一系列接近阈值上引入了图元分解，并表明不同子图（k=4 个节点）模式的频率对于机器学习方法（分类、识别、自动标记等）有效地区分质膜微囊和支架团块。
质膜微囊的形成需要 Cav1 和衔接蛋白 CAVIN1（也称为 PTRF）。作为一种有监督的学习方法，我们将一个宽场 CAVIN1/PTRF 掩模应用于 CAVIN1/PTRF 转染的 PC3 前列腺癌细胞，并使用随机森林分类器根据图元频率分布（GFD）对团块进行分类。CAVIN1/PTRF 阳性（PTRF+）和 Cav1 阴性簇的 GFD 显示出较差的分类准确性，通过按局部位点数或体积对 PTRF+簇进行分层，可显著提高分类准确性。无监督学习中对大 PTRF+簇和质膜微囊的低分类准确性（<50%）表明其 GFD 是质膜微囊特有的。对小 PTRF+簇和质膜微囊的高分类准确性表明 CAVIN1/PTRF 不仅与质膜微囊而且与非质膜微囊支架相关。在低接近阈值（50-100nm）下，质膜微囊组显示出高度连接的图元频率降低和完全不连接的图元频率增加。质膜微囊和支架中 Cav1 团簇的单分子定位显微镜 GFD 分析定义了结构组织的变化，与 CAVIN1/PTRF 无关。
补充数据可在 Bioinformatics 在线获得。
