Dataset Information

Visualizing Intrapopulation Hematopoietic Cell Heterogeneity with Self-Organizing Maps of SIMS Data.

ABSTRACT: Characterization of the heterogeneity within stem cell populations, which affects their differentiation potential, is necessary for the design of artificial cultures for stem cell expansion. In this study, we assessed whether self-organizing maps (SOMs) of single-cell time-of-flight secondary ion mass spectrometry (TOF-SIMS) data provide insight into the spectral, and thus the related functional heterogeneity between and within three hematopoietic cell populations. SOMs were created of TOF-SIMS data from individual hematopoietic stem and progenitor cells (HSPCs), lineage-committed common lymphoid progenitors (CLPs), and fully differentiated B cells that had been isolated from murine bone marrow via conventional flow cytometry. The positions of these cells on the SOMs and the spectral variation between adjacent map units, shown on the corresponding unified distance matrix (U-matrix), indicated the CLPs exhibited the highest intrapopulation spectral variation, regardless of the age of the donor mice. SOMs of HSPCs, CLPs, and B cells isolated from young and old mice using the same surface antigen profiles revealed the HSPCs exhibited the most age-related spectral variation, whereas B cells exhibited the least. These results demonstrate that SOMs of single-cell spectra enable characterizing the heterogeneity between and within cell populations that lie along distinct differentiation pathways.

SUBMITTER: Mirshafiee V

PROVIDER: S-EPMC5998825 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Visualizing Intrapopulation Hematopoietic Cell Heterogeneity with Self-Organizing Maps of SIMS Data.

Mirshafiee Vahid V Harley Brendan A C BAC Kraft Mary L ML

Tissue engineering. Part C, Methods 20180507 6

Characterization of the heterogeneity within stem cell populations, which affects their differentiation potential, is necessary for the design of artificial cultures for stem cell expansion. In this study, we assessed whether self-organizing maps (SOMs) of single-cell time-of-flight secondary ion mass spectrometry (TOF-SIMS) data provide insight into the spectral, and thus the related functional heterogeneity between and within three hematopoietic cell populations. SOMs were created of TOF-SIMS ...[more]

PMID: 29652627

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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

Visualizing Intrapopulation Hematopoietic Cell Heterogeneity with Self-Organizing Maps of SIMS Data.

Publications

Visualizing Intrapopulation Hematopoietic Cell Heterogeneity with Self-Organizing Maps of SIMS Data.

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