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Statistical analysis of multi-dimensional, temporal gene expression of stem cells to elucidate colony size-dependent neural differentiation.

ABSTRACT: High throughput gene expression analysis using qPCR is commonly used to identify molecular markers of complex cellular processes. However, statistical analysis of multi-dimensional, temporal gene expression data is complicated by limited biological replicates and large number of measurements. Moreover, many available statistical tools for analysis of time series data assume that the data sequence is static and does not evolve over time. With this assumption, the parameters used to model the time series are fixed and thus, can be estimated by pooling data together. However, in many cases, dynamic processes of biological systems involve abrupt changes at unknown time points, making the assumption of stationary time series break down. We addressed this problem using a combination of statistical methods including hierarchical clustering, change point detection, and multiple testing. We applied this multi-step method to multi-dimensional, temporal gene expression data that resulted from our study of colony size-dependent neural cell differentiation of stem cells. The gene expression data were time series as the observations were recorded sequentially over time. Hierarchical clustering segregated the genes into three distinct clusters based on their temporal expression profiles; change point detection identified specific time points at which the entire dataset was divided into several homogenous subsets to allow a separate analysis of each subset; and multiple testing procedure identified the differentially expressed genes in each cluster within each subset of data. We established that our multi-step approach pinpoints specific sets of genes that underlie colony size-mediated neural differentiation of stem cells and demonstrated its advantages over conventional parametric and non-parametric tests that do not take into account temporal dynamics of the data. Importantly, our proposed approach is broadly applicable to any multivariate data sets of limited sample size from high throughput and high content screening such as in drug and biomarker discovery studies.

SUBMITTER: Joshi R

PROVIDER: S-EPMC5905708 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Statistical analysis of multi-dimensional, temporal gene expression of stem cells to elucidate colony size-dependent neural differentiation.

Joshi Ramila R Fuller Brendan B Li Jun J Tavana Hossein H

Molecular omics 20180401 2

High throughput gene expression analysis using qPCR is commonly used to identify molecular markers of complex cellular processes. However, statistical analysis of multi-dimensional, temporal gene expression data is complicated by limited biological replicates and large number of measurements. Moreover, many available statistical tools for analysis of time series data assume that the data sequence is static and does not evolve over time. With this assumption, the parameters used to model the time ...[more]

PMID: 29659650

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Project description:IntroductionStem cells have the ability to self-renew or to differentiate into numerous cell types; however, our understanding of how to control and exploit this potential is currently limited. An emerging hypothesis is that microRNAs (miRNAs) play a central role in controlling stem cell-fate determination. Herein, we have characterized the effects of miRNAs in differentiated human neural stem cells (hNSCs) by using a cell line currently being tested in clinical trials for stroke disability (NCT01151124, Clinicaltrials.gov).MethodsHNSCs were differentiated on 2- (2D) and 3-dimensional (3D) cultures for 1 and 3 weeks. Quantification of hNSC differentiation was measured with real-time PCR and axon outgrowth. The miRNA PCR arrays were implemented to investigate differential expression profiles in differentiated hNSCs. Evaluation of miRNA effects on hNSCs was performed by using transfection of miRNA mimics, real-time PCR, Western blot, and immunocytochemistry.ResultsThe 3D substrate promoted enhanced hNSC differentiation coupled with a loss of cell proliferation. Differentiated hNSCs exhibited a similar miRNA profiling. However, in 3D samples, the degree and timing of regulation were significantly different in miRNA members of cluster mi-R17 and miR-96-182, and hsa-miR-302a. Overall, hNSC 3D cultures demonstrated differential regulation of miRNAs involved in hNSC stemness, cell proliferation, and differentiation. The miRNA mimic analysis of hsa-miR-146b-5p and hsa-miR-99a confirmed induction of lineage-committed progenitors. Downregulated miRNAs were more abundant; those most significantly downregulated were selected, and their putative target mRNAs analyzed with the aim of unraveling their functionality. In differentiated hNSCs, downregulated hsa-miR-96 correlated with SOX5 upregulation of gene and protein expression; similar results were obtained for hsa-miR-302a, hsa-miR-182, hsa-miR-7, hsa-miR-20a/b, and hsa-miR-17 and their target NR4A3. Moreover, SOX5 was identified as a direct target gene of hsa-miR-96, and NR43A, a direct target of hsa-miR-7 and hsa-mir-17 by luciferase reporter assays. Therefore, the regulatory role of these miRNAs may occur through targeting NR4A3 and SOX5, both reported as modulators of cell-cycle progression and axon length.ConclusionsThe results provide new insight into the identification of specific miRNAs implicated in hNSC differentiation. These strategies may be exploited to optimize in vitro hNSC differentiation potential for use in preclinical studies and future clinical applications.

Project description:The ability to derive neural progenitors, differentiated neurons and glial cells from human embryonic stem cells (hESCs) with high efficiency holds promise for a number of clinical applications. However, investigating the temporal events is crucial for defining the underlying mechanisms that drive this process of differentiation along different lineages. We carried out quantitative proteomic profiling using a multiplexed approach capable of analyzing eight different samples simultaneously to monitor the temporal dynamics of protein abundance as human embryonic stem cells differentiate into motor neurons or astrocytes. With this approach, a catalog of approximately 1200 proteins along with their relative quantitative expression patterns was generated. The differential expression of the large majority of these proteins has not previously been reported or studied in the context of neural differentiation. As expected, two of the widely used markers of pluripotency, alkaline phosphatase (ALPL) and LIN28, were found to be downregulated during differentiation, while S-100 and tenascin C were upregulated in astrocytes. Neurofilament 3 protein, doublecortin and CAM kinase-like 1 and nestin proteins were upregulated during motor neuron differentiation. We identified a number of proteins whose expression was largely confined to specific cell types, embryonic stem cells, embryoid bodies and differentiating motor neurons. For example, glycogen phosphorylase (PYGL) and fatty acid binding protein 5 (FABP5) were enriched in ESCs, while beta spectrin (SPTBN5) was highly expressed in embryoid bodies. Karyopherin, heat shock 27 kDa protein 1 and cellular retinoic acid binding protein 2 (CRABP2) were upregulated in differentiating motor neurons but were downregulated in mature motor neurons. We validated some of the novel markers of the differentiation process using immunoblotting and immunocytochemical labeling. To our knowledge, this is the first large-scale temporal proteomic profiling of human stem cell differentiation into neural cell types highlighting proteins with limited or undefined roles in neural fate.

Dataset Information

Statistical analysis of multi-dimensional, temporal gene expression of stem cells to elucidate colony size-dependent neural differentiation.

Publications

Statistical analysis of multi-dimensional, temporal gene expression of stem cells to elucidate colony size-dependent neural differentiation.

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