Dataset Information

Identification and isolation of splenic tissue-resident macrophage sub-populations by flow cytometry.

ABSTRACT: Tissue-resident macrophages in the spleen, including red pulp and white pulp macrophages, marginal zone macrophages (MZMs) and marginal zone metallophilic macrophages (MMMs), are highly heterogeneous as a consequence of adaptation to tissue-specific environments. Each macrophage sub-population in the spleen is usually identified based on the localization, morphology and membrane antigen expression by immunohistochemistry. However, their phenotypical and functional characteristics remain incompletely understood due to the difficulty of identification and isolation by flow cytometry. We used a cocktail of three enzymes (Collagenase D, Dispase I and DNase I), rather than traditional mechanical grinding, for isolation of each sub-population, which resulted in significant improvement of isolation of these macrophage sub-populations, particularly MZMs and MMMs, as determined by CD11bhiF4/80medTim4hi and CD11bhiF4/80medTim4med, respectively. This method should be helpful for molecular and functional characterization of each splenic resident macrophage sub-population.

SUBMITTER: Fujiyama S

PROVIDER: S-EPMC6364618 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Identification and isolation of splenic tissue-resident macrophage sub-populations by flow cytometry.

Fujiyama Satoshi S Nakahashi-Oda Chigusa C Abe Fumie F Wang Yaqiu Y Sato Kazuki K Shibuya Akira A

International immunology 20190201 1

Tissue-resident macrophages in the spleen, including red pulp and white pulp macrophages, marginal zone macrophages (MZMs) and marginal zone metallophilic macrophages (MMMs), are highly heterogeneous as a consequence of adaptation to tissue-specific environments. Each macrophage sub-population in the spleen is usually identified based on the localization, morphology and membrane antigen expression by immunohistochemistry. However, their phenotypical and functional characteristics remain incomple ...[more]

PMID: 30256964

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Project description:BackgroundFlow cytometry is a widespread single-cell measurement technology with a multitude of clinical and research applications. Interpretation of flow cytometry data is hard; the instrumentation is delicate and can not render absolute measurements, hence samples can only be interpreted in relation to each other while at the same time comparisons are confounded by inter-sample variation. Despite this, most automated flow cytometry data analysis methods either treat samples individually or ignore the variation by for example pooling the data. A key requirement for models that include multiple samples is the ability to visualize and assess inferred variation, since what could be technical variation in one setting would be different phenotypes in another.ResultsWe introduce BayesFlow, a pipeline for latent modeling of flow cytometry cell populations built upon a Bayesian hierarchical model. The model systematizes variation in location as well as shape. Expert knowledge can be incorporated through informative priors and the results can be supervised through compact and comprehensive visualizations. BayesFlow is applied to two synthetic and two real flow cytometry data sets. For the first real data set, taken from the FlowCAP I challenge, BayesFlow does not only give a gating which would place it among the top performers in FlowCAP I for this dataset, it also gives a more consistent treatment of different samples than either manual gating or other automated gating methods. The second real data set contains replicated flow cytometry measurements of samples from healthy individuals. BayesFlow gives here cell populations with clear expression patterns and small technical intra-donor variation as compared to biological inter-donor variation.ConclusionsModeling latent relations between samples through BayesFlow enables a systematic analysis of inter-sample variation. As opposed to other joint gating methods, effort is put at ensuring that the obtained partition of the data corresponds to actual cell populations, and the result is therefore directly biologically interpretable. BayesFlow is freely available at GitHub.

Dataset Information

Identification and isolation of splenic tissue-resident macrophage sub-populations by flow cytometry.

Publications

Identification and isolation of splenic tissue-resident macrophage sub-populations by flow cytometry.

Similar Datasets

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