Dataset Information

Quantifying Heterogeneity of Individual Organelles in Mixed Populations via Mass Cytometry.

ABSTRACT: Macroautophagy is a complex degradative intracellular process by which long-lived proteins and damaged organelles are cleared. Common methods for the analysis of autophagy are bulk measurements which mask organelle heterogeneity and complicate the analysis of interorganelle association and trafficking. Thus, methods for individual organelle quantification are needed to address these deficiencies. Current techniques for quantifying individual autophagy organelles are either low through-put or are dimensionally limited. We make use of the multiparametric capability of mass cytometry to investigate phenotypic heterogeneity in autophagy-related organelle types that have been isolated from murine brain, liver, and skeletal muscle. Detection and phenotypic classification of individual organelles were accomplished through the use of a lanthanide-chelating membrane stain and organelle-specific antibodies. Posthoc sample matrix background correction and nonspecific antibody binding corrections provide measures of interorganelle associations and heterogeneity. This is the first demonstration of multiparametric individual organelle analysis via mass cytometry. The method described here illustrates the potential for further investigation of the inherently complex interorganelle associations, trafficking, and heterogeneity present in most eukaryotic biological systems.

SUBMITTER: Brown HMG

PROVIDER: S-EPMC6453816 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Quantifying Heterogeneity of Individual Organelles in Mixed Populations via Mass Cytometry.

Brown Heather M G HMG Arriaga Edgar A EA

Analytical chemistry 20181105 22

Macroautophagy is a complex degradative intracellular process by which long-lived proteins and damaged organelles are cleared. Common methods for the analysis of autophagy are bulk measurements which mask organelle heterogeneity and complicate the analysis of interorganelle association and trafficking. Thus, methods for individual organelle quantification are needed to address these deficiencies. Current techniques for quantifying individual autophagy organelles are either low through-put or are ...[more]

PMID: 30350631

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Project description:Mass cytometry by time-of-flight (CyTOF) is a valuable technology for high-dimensional analysis at the single cell level. Identification of different cell populations is an important task during the data analysis. Many clustering tools can perform this task, which is essential to identify "new" cell populations in explorative experiments. However, relying on clustering is laborious since it often involves manual annotation, which significantly limits the reproducibility of identifying cell-populations across different samples. The latter is particularly important in studies comparing different conditions, for example in cohort studies. Learning cell populations from an annotated set of cells solves these problems. However, currently available methods for automatic cell population identification are either complex, dependent on prior biological knowledge about the populations during the learning process, or can only identify canonical cell populations. We propose to use a linear discriminant analysis (LDA) classifier to automatically identify cell populations in CyTOF data. LDA outperforms two state-of-the-art algorithms on four benchmark datasets. Compared to more complex classifiers, LDA has substantial advantages with respect to the interpretable performance, reproducibility, and scalability to larger datasets with deeper annotations. We apply LDA to a dataset of ~3.5 million cells representing 57 cell populations in the Human Mucosal Immune System. LDA has high performance on abundant cell populations as well as the majority of rare cell populations, and provides accurate estimates of cell population frequencies. Further incorporating a rejection option, based on the estimated posterior probabilities, allows LDA to identify previously unknown (new) cell populations that were not encountered during training. Altogether, reproducible prediction of cell population compositions using LDA opens up possibilities to analyze large cohort studies based on CyTOF data. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Dataset Information

Quantifying Heterogeneity of Individual Organelles in Mixed Populations via Mass Cytometry.

Publications

Quantifying Heterogeneity of Individual Organelles in Mixed Populations via Mass Cytometry.

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