Machine learning for cell classification and neighborhood analysis in glioma tissue

作者：Leslie Solorzano, Lina Wik, Thomas Olsson Bontell, Yuyu Wang, Anna H. Klemm, Johan Öfverstedt, Asgeir Store Jakola, Arne Östman, Carolina Wählby · 发表于：Cytometry Part A · 年份：2021 · DOI：10.1002/cyto.a.24467 · 被引用次数：10 · 研究领域：Single-cell and spatial transcriptomics、Cell Image Analysis Techniques、Neuroinflammation and Neurodegeneration Mechanisms

Multiplexed and spatially resolved single-cell analyses that intend to study tissue heterogeneity and cell organization invariably face as a first step the challenge of cell classification. Accuracy and reproducibility are important for the downstream process of counting cells, quantifying cell-cell interactions, and extracting information on disease-specific localized cell niches. Novel staining techniques make it possible to visualize and quantify large numbers of cell-specific molecular markers in parallel. However, due to variations in sample handling and artifacts from staining and scanning, cells of the same type may present different marker profiles both within and across samples. We address multiplexed immunofluorescence data from tissue microarrays of low-grade gliomas and present a methodology using two different machine learning architectures and features insensitive to illumination to perform cell classification. The fully automated cell classification provides a measure of confidence for the decision and requires a comparably small annotated data set for training, which can be created using freely available tools. Using the proposed method, we reached an accuracy of 83.1% on cell classification without the need for standardization of samples. Using our confidence measure, cells with low-confidence classifications could be excluded, pushing the classification accuracy to 94.5%. Next, we used the cell classification results to search for cell niches with an unsuper...