Dataset Information

Unsupervised High-Dimensional Analysis Aligns Dendritic Cells across Tissues and Species.

ABSTRACT: Dendritic cells (DCs) are professional antigen-presenting cells that hold great therapeutic potential. Multiple DC subsets have been described, and it remains challenging to align them across tissues and species to analyze their function in the absence of macrophage contamination. Here, we provide and validate a universal toolbox for the automated identification of DCs through unsupervised analysis of conventional flow cytometry and mass cytometry data obtained from multiple mouse, macaque, and human tissues. The use of a minimal set of lineage-imprinted markers was sufficient to subdivide DCs into conventional type 1 (cDC1s), conventional type 2 (cDC2s), and plasmacytoid DCs (pDCs) across tissues and species. This way, a large number of additional markers can still be used to further characterize the heterogeneity of DCs across tissues and during inflammation. This framework represents the way forward to a universal, high-throughput, and standardized analysis of DC populations from mutant mice and human patients.

SUBMITTER: Guilliams M

PROVIDER: S-EPMC5040826 | biostudies-other | 2016 Sep

REPOSITORIES: biostudies-other

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Publications

Unsupervised High-Dimensional Analysis Aligns Dendritic Cells across Tissues and Species.

Guilliams Martin M Dutertre Charles-Antoine CA Scott Charlotte L CL McGovern Naomi N Sichien Dorine D Chakarov Svetoslav S Van Gassen Sofie S Chen Jinmiao J Poidinger Michael M De Prijck Sofie S Tavernier Simon J SJ Low Ivy I Irac Sergio Erdal SE Mattar Citra Nurfarah CN Sumatoh Hermi Rizal HR Low Gillian Hui Ling GHL Chung Tam John Kit TJK Chan Dedrick Kok Hong DKH Tan Ker Kan KK Hon Tony Lim Kiat TLK Fossum Even E Bogen Bjarne B Choolani Mahesh M Chan Jerry Kok Yen JKY Larbi Anis A Luche Hervé H Henri Sandrine S Saeys Yvan Y Newell Evan William EW Lambrecht Bart N BN Malissen Bernard B Ginhoux Florent F

Immunity 20160913 3

Dendritic cells (DCs) are professional antigen-presenting cells that hold great therapeutic potential. Multiple DC subsets have been described, and it remains challenging to align them across tissues and species to analyze their function in the absence of macrophage contamination. Here, we provide and validate a universal toolbox for the automated identification of DCs through unsupervised analysis of conventional flow cytometry and mass cytometry data obtained from multiple mouse, macaque, and ...[more]

PMID: 27637149

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Project description:Neural coding and memory formation depend on temporal spiking sequences that span high-dimensional neural ensembles. The unsupervised discovery and characterization of these spiking sequences requires a suitable dissimilarity measure to spiking patterns, which can then be used for clustering and decoding. Here, we present a new dissimilarity measure based on optimal transport theory called SpikeShip, which compares multi-neuron spiking patterns based on all the relative spike-timing relationships among neurons. SpikeShip computes the optimal transport cost to make all the relative spike-timing relationships (across neurons) identical between two spiking patterns. We show that this transport cost can be decomposed into a temporal rigid translation term, which captures global latency shifts, and a vector of neuron-specific transport flows, which reflect inter-neuronal spike timing differences. SpikeShip can be effectively computed for high-dimensional neuronal ensembles, has a low (linear) computational cost that has the same order as the spike count, and is sensitive to higher-order correlations. Furthermore, SpikeShip is binless, can handle any form of spike time distributions, is not affected by firing rate fluctuations, can detect patterns with a low signal-to-noise ratio, and can be effectively combined with a sliding window approach. We compare the advantages and differences between SpikeShip and other measures like SPIKE and Victor-Purpura distance. We applied SpikeShip to large-scale Neuropixel recordings during spontaneous activity and visual encoding. We show that high-dimensional spiking sequences detected via SpikeShip reliably distinguish between different natural images and different behavioral states. These spiking sequences carried complementary information to conventional firing rate codes. SpikeShip opens new avenues for studying neural coding and memory consolidation by rapid and unsupervised detection of temporal spiking patterns in high-dimensional neural ensembles.

Dataset Information

Unsupervised High-Dimensional Analysis Aligns Dendritic Cells across Tissues and Species.

Publications

Unsupervised High-Dimensional Analysis Aligns Dendritic Cells across Tissues and Species.

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