Gene regulatory network analysis reveals differences in site-specific cell fate determination in mammalian brain

NXN Gene Epigenetic Changes in an Adult Neurogenesis Model of Alzheimer's Disease

In view of the proven link between adult hippocampal neurogenesis (AHN) and learning and memory impairment, we generated a straightforward adult neurogenesis in vitro model to recapitulate DNA methylation marks in the context of Alzheimer’s disease (AD). Neural progenitor cells (NPCs) were differentiated for 29 days and Aβ peptide 1–42 was added. mRNA expression of Neuronal Differentiation 1 (NEUROD1), Neural Cell Adhesion Molecule 1 (NCAM1), Tubulin Beta 3 Class III (TUBB3), RNA Binding Fox-1 Homolog 3 (RBFOX3), Calbindin 1 (CALB1), and Glial Fibrillary Acidic Protein (GFAP) was determined by RT-qPCR to characterize the culture and framed within the multistep process of AHN. Hippocampal DNA methylation marks previously identified in Contactin-Associated Protein 1 (CNTNAP1), SEPT5-GP1BB Readthrough (SEPT5-GP1BB), T-Box Transcription Factor 5 (TBX5), and Nucleoredoxin (NXN) genes were profiled by bisulfite pyrosequencing or bisulfite cloning sequencing; mRNA expression was also measured. NXN outlined a peak of DNA methylation overlapping type 3 neuroblasts. Aβ-treated NPCs showed transient decreases of mRNA expression for SEPT5-GP1BB and NXN on day 9 or 19 and an increase in DNA methylation on day 29 for NXN. NXN and SEPT5-GP1BB may reflect alterations detected in the brain of AD human patients, broadening our understanding of this disease.

Integrative Analysis of Hippocampus Gene Expression Profiles Identifies Network Alterations in Aging and Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder contributing to rapid decline in cognitive function and ultimately dementia. Most cases of AD occur in elderly and later years. There is a growing need for understanding the relationship between aging and AD to identify shared and unique hallmarks associated with the disease in a region and cell-type specific manner. Although genomic studies on AD have been performed extensively, the molecular mechanism of disease progression is still not clear. The major objective of our study is to obtain a higher-order network-level understanding of aging and AD, and their relationship using the hippocampal gene expression profiles of young (20-50 years), aging (70-99 years), and AD (70-99 years). The hippocampus is vulnerable to damage at early stages of AD and altered neurogenesis in the hippocampus is linked to the onset of AD. We combined the weighted gene co-expression network and weighted protein-protein interaction network-level approaches to study the transition from young to aging to AD. The network analysis revealed the organization of co-expression network into functional modules that are cell-type specific in aging and AD. We found that modules associated with astrocytes, endothelial cells and microglial cells are upregulated and significantly correlate with both aging and AD. The modules associated with neurons, mitochondria and endoplasmic reticulum are downregulated and significantly correlate with AD than aging. The oligodendrocytes module does not show significant correlation with neither aging nor disease. Further, we identified aging- and AD-specific interactions/subnetworks by integrating the gene expression with a human protein-protein interaction network. We found dysregulation of genes encoding protein kinases (FYN, SYK, SRC, PKC, MAPK1, ephrin receptors) and transcription factors (FOS, STAT3, CEBPB, MYC, NFKβ, and EGR1) in AD. Further, we found genes that encode proteins with neuroprotective function (14-3-3 proteins, PIN1, ATXN1, BDNF, VEGFA) to be part of the downregulated AD subnetwork. Our study highlights that simultaneously analyzing aging and AD will help to understand the pre-clinical and clinical phase of AD and aid in developing the treatment strategies.

Whole-exome sequence analysis highlights the role of unmasked recessive mutations in copy number variants with incomplete penetrance

Several hypotheses have been proposed to explain the phenotypic variability between parent and offspring carrying the same genomic imbalance, including unmasking of a recessive variant by a chromosomal deletion. Here, 19 patients with neurodevelopmental disorders harboring a rare deletion inherited from a healthy parent were investigated by whole-exome sequencing to search for SNV on the contralateral segment. This strategy allowed us to identify a candidate variant in two patients in the NUP214 and NCOR1 genes. This result demonstrates that the analysis of the genes included in non-deleted contralateral allele is a key point in the etiological investigation of patients harboring a deletion inherited from a parent. Finally, this strategy is also an interesting approach to identify new recessive intellectual disability genes.

CD60b: Enriching Neural Stem/Progenitor Cells from Rat Development into Adulthood

CD60b antigens are highly expressed during development in the rat nervous system, while in the adult their expression is restricted to a few regions, including the subventricular zone (SVZ) around the lateral ventricles—a neurogenic niche in the adult brain. For this reason, we investigated whether the expression of C60b is associated with neural stem/progenitor cells in the SVZ, from development into adulthood. We performed in vitro and in vivo analyses of CD60b expression at different stages and identified the presence of these antigens in neural stem/progenitor cells. We also observed that CD60b could be used to purify and enrich a population of neurosphere-forming cells from the developing and adult brain. We showed that CD60b antigens (mainly corresponding to ganglioside 9-O-acetyl GD3, a well-known molecule expressed during central nervous system development and mainly associated with neuronal migration) are also present in less mature cells and could be used to identify and isolate neural stem/progenitor cells during development and in the adult brain. A better understanding of molecules associated with neurogenesis may contribute not only to improve the knowledge about the physiology of the mammalian central nervous system, but also to find new treatments for regenerating tissue after disease or brain injury.

Estimating real cell size distribution from cross-section microscopy imaging

MOTIVATION

Microscopy imaging is an essential tool for medical diagnosis and molecular biology. It is particularly useful for extracting information about disease states, tissue heterogeneity and cell specific parameters such as cell type or cell size from biological specimens. However, the information obtained from the images is likely to be subjected to sampling and observational bias with respect to the underlying cell size/type distributions.

RESULTS

We present an algorithm, Estimate Tissue Cell Size/Type Distribution (EstiTiCS), for the adjustment of the underestimation of the number of small cells and the size of measured cells while accounting for the section thickness independent of the tissue type. We introduce the sources of bias under different tissue distributions and their effect on the measured values with simulation experiments. Furthermore, we demonstrate our method on histological sections of paraffin-embedded adipose tissue sample images from 57 people from a dietary intervention study. This data consists of measured cell size and its distribution over the dietary intervention period at four time points. Adjusting for the bias with EstiTiCS results in a closer fit to the true/expected adipocyte size distribution with earlier studies. Therefore, we conclude that our method is suitable as the final step in estimating the tissue wide cell type/size distribution from microscopy imaging pipeline.

AVAILABILITY AND IMPLEMENTATION

Source code and its documentation are available at https://github.com/michaelLenz/EstiTiCS The whole pipeline of our method is implemented in R and makes use of the 'nloptr' package. Adipose tissue data used for this study are available on request.

CONTACT

Michael.Lenz@Maastrichtuniversity.nl, Gokhan.Ertaylan@Maastrichtuniversity.nl.

Transcriptional and Genomic Targets of Neural Stem Cells for Functional Recovery after Hemorrhagic Stroke

Hemorrhagic stroke is a life-threatening disease characterized by a sudden rupture of cerebral blood vessels, and it is widely believed that neural cell death occurs after exposure to blood metabolites or subsequently damaged cells. Neural stem cells (NSCs), which maintain neurogenesis and are found in subgranular zone and subventricular zone, are thought to be an endogenous neuroprotective mechanism for these brain injuries. However, due to the complexity of NSCs and their microenvironment, current strategies cannot satisfactorily enhance functional recovery after hemorrhagic stroke. It is well known that transcriptional and genomic pathways play important roles in ensuring the normal functions of NSCs, including proliferation, migration, differentiation, and neural reconnection. Recently, emerging evidence from the use of new technologies such as next-generation sequencing and transcriptome profiling has provided insight into our understanding of genomic function and regulation of NSCs. In the present article, we summarize and present the current data on the control of NSCs at both the transcriptional and genomic levels. Using bioinformatics methods, we sought to predict novel therapeutic targets of endogenous neurogenesis and exogenous NSC transplantation for functional recovery after hemorrhagic stroke, which could also advance our understanding of its pathophysiology.

Prox1 Is Required for Oligodendrocyte Cell Identity in Adult Neural Stem Cells of the Subventricular Zone

Adult neural stem cells with the ability to generate neurons and glia cells are active throughout life in both the dentate gyrus (DG) and the subventricular zone (SVZ). Differentiation of adult neural stem cells is induced by cell fate determinants like the transcription factor Prox1. Evidence has been provided for a function of Prox1 as an inducer of neuronal differentiation within the DG. We now show that within the SVZ Prox1 induces differentiation into oligodendrocytes. Moreover, we find that loss of Prox1 expression in vivo reduces cell migration into the corpus callosum, where the few Prox1 deficient SVZ-derived remaining cells fail to differentiate into oligodendrocytes. Thus, our work uncovers a novel function of Prox1 as a fate determinant for oligodendrocytes in the adult mammalian brain. These data indicate that the neurogenic and oligodendrogliogenic lineages in the two adult neurogenic niches exhibit a distinct requirement for Prox1, being important for neurogenesis in the DG but being indispensable for oligodendrogliogenesis in the SVZ. Stem Cells 2016;34:2115-2129.

A differential network analysis approach for lineage specifier prediction in stem cell subpopulations

Background:

Stem cell differentiation is a complex biological process. Cellular heterogeneity, such as the co-existence of different cell subpopulations within a population, partly hampers our understanding of this process. The modern single-cell gene expression technologies, such as single-cell RT-PCR and RNA-seq, have enabled us to elucidate such heterogeneous cell subpopulations. However, the identification of a transcriptional regulatory network (TRN) for each cell subpopulation within a population and genes determining specific cell fates (lineage specifiers) remains a challenge due to the slower development of appropriate computational and experimental workflows. Here, we propose a computational differential network analysis approach for predicting lineage specifiers in binary-fate differentiation events.

Methods:

The proposed method is based on a model that considers each stem cell subpopulation being in a stable state maintained by its specific TRN stability core, and cell differentiation involves changes in these stability cores between parental and daughter cell subpopulations. The method first reconstructs topologically different cell-subpopulation specific TRNs from single-cell gene expression data, literature knowledge and transcription factor (TF)–DNA binding-site prediction. Then, it systematically predicts lineage specifiers by identifying genes in the TRN stability cores in both parental and daughter cell subpopulations.

Results:

Application of this method to different stem cell differentiation systems was able to predict known and putative novel lineage specifiers. These examples include the differentiation of inner cell mass into either primitive endoderm or epiblast, different progenitor cells in the hematopoietic system, and the lung alveolar bipotential progenitor into either alveolar type 1 or alveolar type 2.

Conclusions:

The method is generally applicable to any binary-fate differentiation system, for which single-cell gene expression data are available. Therefore, it should aid in understanding stem cell lineage specification, and in the development of experimental strategies for regenerative medicine.

A computational strategy for predicting lineage specifiers in stem cell subpopulations

Stem cell differentiation is a complex biological event. Our understanding of this process is partly hampered by the co-existence of different cell subpopulations within a given population, which are characterized by different gene expression states driven by different underlying transcriptional regulatory networks (TRNs). Such cellular heterogeneity has been recently explored with the modern single-cell gene expression profiling technologies, such as single-cell RT-PCR and RNA-seq. However, the identification of cell subpopulation-specific TRNs and genes determining specific lineage commitment (i.e., lineage specifiers) remains a challenge due to the slower development of appropriate computational and experimental workflows. Here, we propose a computational method for predicting lineage specifiers for different cell subpopulations in binary-fate differentiation events. Our method first reconstructs subpopulation-specific TRNs, which is more realistic than reconstructing a single TRN representing multiple cell subpopulations. Then, it predicts lineage specifiers based on a model that assumes that each parental stem cell subpopulation is in a stable state maintained by its specific TRN stability core. In addition, this stable state is maintained in the parental cell subpopulation by the balanced gene expression pattern of pairs of opposing lineage specifiers for mutually exclusive different daughter cell subpopulations. To this end, we devised a statistical metric for identifying opposing lineage specifier pairs that show a significant ratio change upon differentiation. Application of this computational method to three different stem cell systems predicted known and putative novel lineage specifiers, which could be experimentally tested. Our method does not require pre-selection of putative candidate genes, and can be applied to any binary-fate differentiation system for which single-cell gene expression data are available. Furthermore, this method is compatible with both single-cell RT-PCR and single-cell RNA-seq data. Given the increasing importance of single-cell gene expression data in stem cell biology and regenerative medicine, approaches like ours would be useful for the identification of lineage specifiers and their associated TRN stability cores.

Neural stem cells respond to stress hormones: distinguishing beneficial from detrimental stress

Neural stem cells (NSCs), the progenitors of the nervous system, control distinct, position-specific functions and are critically involved in the maintenance of homeostasis in the brain. The responses of these cells to various stressful stimuli are shaped by genetic, epigenetic, and environmental factors via mechanisms that are age and developmental stage-dependent and still remain, to a great extent, elusive. Increasing evidence advocates for the beneficial impact of the stress response in various settings, complementing the extensive number of studies on the detrimental effects of stress, particularly in the developing brain. In this review, we discuss suggested mechanisms mediating both the beneficial and detrimental effects of stressors on NSC activity across the lifespan. We focus on the specific effects of secreted factors and we propose NSCs as a “sensor,” capable of distinguishing among the different stressors and adapting its functions accordingly. All the above suggest the intriguing hypothesis that NSCs are an important part of the adaptive response to stressors via direct and indirect, specific mechanisms.