Dataset Information

Molecular cartography of the human skin surface in 3D.

ABSTRACT: The human skin is an organ with a surface area of 1.5-2 m(2) that provides our interface with the environment. The molecular composition of this organ is derived from host cells, microbiota, and external molecules. The chemical makeup of the skin surface is largely undefined. Here we advance the technologies needed to explore the topographical distribution of skin molecules, using 3D mapping of mass spectrometry data and microbial 16S rRNA amplicon sequences. Our 3D maps reveal that the molecular composition of skin has diverse distributions and that the composition is defined not only by skin cells and microbes but also by our daily routines, including the application of hygiene products. The technological development of these maps lays a foundation for studying the spatial relationships of human skin with hygiene, the microbiota, and environment, with potential for developing predictive models of skin phenotypes tailored to individual health.

SUBMITTER: Bouslimani A

PROVIDER: S-EPMC4418856 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Publications

Molecular cartography of the human skin surface in 3D.

Bouslimani Amina A Porto Carla C Rath Christopher M CM Wang Mingxun M Guo Yurong Y Gonzalez Antonio A Berg-Lyon Donna D Ackermann Gail G Moeller Christensen Gitte Julie GJ Nakatsuji Teruaki T Zhang Lingjuan L Borkowski Andrew W AW Meehan Michael J MJ Dorrestein Kathleen K Gallo Richard L RL Bandeira Nuno N Knight Rob R Alexandrov Theodore T Dorrestein Pieter C PC

Proceedings of the National Academy of Sciences of the United States of America 20150330 17

The human skin is an organ with a surface area of 1.5-2 m(2) that provides our interface with the environment. The molecular composition of this organ is derived from host cells, microbiota, and external molecules. The chemical makeup of the skin surface is largely undefined. Here we advance the technologies needed to explore the topographical distribution of skin molecules, using 3D mapping of mass spectrometry data and microbial 16S rRNA amplicon sequences. Our 3D maps reveal that the molecula ...[more]

PMID: 25825778

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Project description:BackgroundParallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs). SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand.ResultsThe method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories (adipose, endocrine, homeostasis, digestion, exocrine, epithelium, sexual reproduction, muscle, immune system and nervous tissues). SOM mapping reduces the dimension of expression data from ten of thousands of genes to a few thousand metagenes, each representing a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of genes related to specific molecular processes in the respective tissue. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering are better represented and provide better signal-to-noise ratios if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues broadly into three clusters containing nervous, immune system and the remaining tissues.ConclusionsThe SOM technique provides a more intuitive and informative global view of the behavior of a few well-defined modules of correlated and differentially expressed genes than the separate discovery of the expression levels of hundreds or thousands of individual genes. The program is available as R-package 'oposSOM'.

Dataset Information

Molecular cartography of the human skin surface in 3D.

Publications

Molecular cartography of the human skin surface in 3D.

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