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Single-cell differences in matrix gene expression do not predict matrix deposition.

ABSTRACT: Mesenchymal stem cells (MSCs) display substantial cell-to-cell heterogeneity, complicating their use in regenerative medicine. However, conventional bulk assays mask this variability. Here we show that both chondrocytes and chondrogenically induced MSCs exhibit substantial mRNA expression heterogeneity. Single-molecule RNA FISH to measure mRNA expression of differentiation markers in single cells reveals that sister cell pairs have high levels of mRNA variability, suggesting that marker expression is not heritable. Surprisingly, this variability does not correlate with cell-to-cell differences in cartilage-like matrix production. Transcriptome-wide analysis suggests that no combination of markers can predict functional potential. De-differentiating chondrocytes also show a disconnect between mRNA expression of the cartilage marker aggrecan and cartilage-like matrix accumulation. Altogether, these quantitative analyses suggest that sorting subpopulations based on these markers would only marginally enrich the progenitor population for 'superior' MSCs. Our results suggest that instantaneous mRNA abundance of canonical markers is tenuously linked to the chondrogenic phenotype at the single-cell level.

SUBMITTER: Cote AJ

PROVIDER: S-EPMC4782061 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Single-cell differences in matrix gene expression do not predict matrix deposition.

Cote Allison J AJ McLeod Claire M CM Farrell Megan J MJ McClanahan Patrick D PD Dunagin Margaret C MC Raj Arjun A Mauck Robert L RL

Nature communications 20160303

Mesenchymal stem cells (MSCs) display substantial cell-to-cell heterogeneity, complicating their use in regenerative medicine. However, conventional bulk assays mask this variability. Here we show that both chondrocytes and chondrogenically induced MSCs exhibit substantial mRNA expression heterogeneity. Single-molecule RNA FISH to measure mRNA expression of differentiation markers in single cells reveals that sister cell pairs have high levels of mRNA variability, suggesting that marker expressi ...[more]

PMID: 26936319

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Project description:BackgroundGenomics provides opportunities to develop precise tests for diagnostics, therapy selection and monitoring. From analyses of our studies and those of published results, 32 candidate genes were identified, whose expression appears related to clinical outcome of breast cancer. Expression of these genes was validated by qPCR and correlated with clinical follow-up to identify a gene subset for development of a prognostic test.MethodsRNA was isolated from 225 frozen invasive ductal carcinomas,and qRT-PCR was performed. Univariate hazard ratios and 95% confidence intervals for breast cancer mortality and recurrence were calculated for each of the 32 candidate genes. A multivariable gene expression model for predicting each outcome was determined using the LASSO, with 1000 splits of the data into training and testing sets to determine predictive accuracy based on the C-index. Models with gene expression data were compared to models with standard clinical covariates and models with both gene expression and clinical covariates.ResultsUnivariate analyses revealed over-expression of RABEP1, PGR, NAT1, PTP4A2, SLC39A6, ESR1, EVL, TBC1D9, FUT8, and SCUBE2 were all associated with reduced time to disease-related mortality (HR between 0.8 and 0.91, adjusted p < 0.05), while RABEP1, PGR, SLC39A6, and FUT8 were also associated with reduced recurrence times. Multivariable analyses using the LASSO revealed PGR, ESR1, NAT1, GABRP, TBC1D9, SLC39A6, and LRBA to be the most important predictors for both disease mortality and recurrence. Median C-indexes on test data sets for the gene expression, clinical, and combined models were 0.65, 0.63, and 0.65 for disease mortality and 0.64, 0.63, and 0.66 for disease recurrence, respectively.ConclusionsMolecular signatures consisting of five genes (PGR, GABRP, TBC1D9, SLC39A6 and LRBA) for disease mortality and of six genes (PGR, ESR1, GABRP, TBC1D9, SLC39A6 and LRBA) for disease recurrence were identified. These signatures were as effective as standard clinical parameters in predicting recurrence/mortality, and when combined, offered some improvement relative to clinical information alone for disease recurrence (median difference in C-values of 0.03, 95% CI of -0.08 to 0.13). Collectively, results suggest that these genes form the basis for a clinical laboratory test to predict clinical outcome of breast cancer.

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Single-cell differences in matrix gene expression do not predict matrix deposition.

Publications

Single-cell differences in matrix gene expression do not predict matrix deposition.

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