Dataset Information

Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types.

ABSTRACT: Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for the data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analyzed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry data sets. HSNE constructs a hierarchy of non-linear similarities that can be interactively explored with a stepwise increase in detail up to the single-cell level. We apply HSNE to a study on gastrointestinal disorders and three other available mass cytometry data sets. We find that HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed due to downsampling. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a feature that makes it highly suitable for the analysis of massive high-dimensional data sets.

SUBMITTER: van Unen V

PROVIDER: S-EPMC5700955 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types.

van Unen Vincent V Höllt Thomas T Pezzotti Nicola N Li Na N Reinders Marcel J T MJT Eisemann Elmar E Koning Frits F Vilanova Anna A Lelieveldt Boudewijn P F BPF

Nature communications 20171123 1

Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for the data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analyzed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry data ...[more]

PMID: 29170529

Similar Datasets

Project description:Flow cytometry is the prototypical assay for multi-parameter single cell analysis, and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies (0.1% or less). Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts, a process that is subjective and often difficult to reproduce. An alternative and more objective approach is the use of statistical models to identify cell subsets of interest in an automated fashion. Two specific challenges for automated analysis are to detect extremely low frequency event subsets without biasing the estimate by pre-processing enrichment, and the ability to align cell subsets across multiple data samples for comparative analysis. In this manuscript, we develop hierarchical modeling extensions to the Dirichlet Process Gaussian Mixture Model (DPGMM) approach we have previously described for cell subset identification, and show that the hierarchical DPGMM (HDPGMM) naturally generates an aligned data model that captures both commonalities and variations across multiple samples. HDPGMM also increases the sensitivity to extremely low frequency events by sharing information across multiple samples analyzed simultaneously. We validate the accuracy and reproducibility of HDPGMM estimates of antigen-specific T cells on clinically relevant reference peripheral blood mononuclear cell (PBMC) samples with known frequencies of antigen-specific T cells. These cell samples take advantage of retrovirally TCR-transduced T cells spiked into autologous PBMC samples to give a defined number of antigen-specific T cells detectable by HLA-peptide multimer binding. We provide open source software that can take advantage of both multiple processors and GPU-acceleration to perform the numerically-demanding computations. We show that hierarchical modeling is a useful probabilistic approach that can provide a consistent labeling of cell subsets and increase the sensitivity of rare event detection in the context of quantifying antigen-specific immune responses.

Project description:Understanding the development and function of an organ requires the characterization of all of its cell types. Traditional methods for visualizing and isolating sub-populations of cells are based on mRNA or protein expression of only few known marker genes. The unequivocal identification of a specific marker gene, however, poses a major challenge, particularly if this cell type is rare. Identifying rare cell types, such as stem cells, short-lived progenitors, cancer stem cells, or circulating tumor cells is crucial to acquire a better understanding of normal or diseased tissue biology. To address this challenge we sequenced the transcriptome of hundreds of randomly selected cells from mouse intestinal organoids, cultured self-organizing epithelial structures that contain all cell lineages of the mammalian intestine. Organoid buds, like intestinal crypts, harbor stem cells that continuously differentiate into a variety of cell types, occurring at widely different abundances. Since available computational methods can only resolve more abundant cell types, we developed RaceID, an algorithm for rare cell type identification in complex populations of single cells. We demonstrate that this algorithm can resolve cell types represented by only a single cell in a population of randomly sampled organoid cells. We use this algorithm to identify Reg4 as a novel marker for enteroendocrine cells, a rare population of hormone producing intestinal cells. Next, we use Reg4 expression to enrich for these rare cells and investigate the heterogeneity within this population. Reassuringly, RaceID confirmed the existence of known enteroendocrine lineages, and moreover, discovered novel subtypes, which we subsequently validated in vivo. Having validated RaceID by this proof-of-principle experiment we then apply the algorithm to ex vivo isolated LGR5 positive cells and their direct progeny and demonstrate homogeneity of the stem cell pool. We envision broad applicability of our method for discovering rare cell types and the corresponding marker genes in healthy and diseased organs.

Dataset Information

Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types.

Publications

Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets