Franz-Josef Müller

Neural stem cells improve cognition via BDNF in a transgenic model of Alzheimer disease

Neural stem cell (NSC) transplantation represents an unexplored approach for treating neurodegenerative disorders associated with cognitive decline such as Alzheimer disease (AD). Here, we used aged triple transgenic mice (3xTg-AD) that express pathogenic forms of amyloid precursor protein, presenilin, and tau to investigate the effect of neural stem cell transplantation on AD-related neuropathology and cognitive dysfunction. Interestingly, despite widespread and established Aß plaque and neurofibrillary tangle pathology, hippocampal neural stem cell transplantation rescues the spatial learning and memory deficits in aged 3xTg-AD mice. Remarkably, cognitive function is improved without altering Aß or tau pathology. Instead, the mechanism underlying the improved cognition involves a robust enhancement of hippocampal synaptic density, mediated by brain-derived neurotrophic factor (BDNF). Gain-of-function studies show that recombinant BDNF mimics the beneficial effects of NSC transplantation. Furthermore, loss-of-function studies show that depletion of NSC-derived BDNF fails to improve cognition or restore hippocampal synaptic density. Taken together, our findings demonstrate that neural stem cells can ameliorate complex behavioral deficits associated with widespread Alzheimer disease pathology via BDNF.

Regulatory networks define phenotypic classes of human stem cell lines

Stem cells are defined as self-renewing cell populations that can differentiate into multiple distinct cell types. However, hundreds of different human cell lines from embryonic, fetal and adult sources have been called stem cells, even though they range from pluripotent cells—typified by embryonic stem cells, which are capable of virtually unlimited proliferation and differentiation—to adult stem cell lines, which can generate a far more limited repertoire of differentiated cell types. The rapid increase in reports of new sources of stem cells and their anticipated value to regenerative medicine has highlighted the need for a general, reproducible method for classification of these cells. We report here the creation and analysis of a database of global gene expression profiles (which we call the ‘stem cell matrix’) that enables the classification of cultured human stem cells in the context of a wide variety of pluripotent, multipotent and differentiated cell types. Using an unsupervised clustering method to categorize a collection of ∼150 cell samples, we discovered that pluripotent stem cell lines group together, whereas other cell types, including brain-derived neural stem cell lines, are very diverse. Using further bioinformatic analysis we uncovered a protein–protein network (PluriNet) that is shared by the pluripotent cells (embryonic stem cells, embryonal carcinomas and induced pluripotent cells). Analysis of published data showed that the PluriNet seems to be a common characteristic of pluripotent cells, including mouse embryonic stem and induced pluripotent cells and human oocytes. Our results offer a new strategy for classifying stem cells and support the idea that pluripotency and self-renewal are under tight control by specific molecular networks.

Gene therapy: can neural stem cells deliver?

Neural stem cells are a self-renewing population that generates the neurons and glia of the developing brain. They can be isolated, proliferated, genetically manipulated and differentiated in vitro and reintroduced into a developing, adult or pathologically altered CNS. Neural stem cells have been considered for use in cell replacement therapies in various neurodegenerative diseases, and an unexpected and potentially valuable characteristic of these cells has recently been revealed — they are highly migratory and seem to be attracted to areas of brain pathology such as ischaemic and neoplastic lesions. Here, we speculate on the ways in which neural stem cells might be exploited as delivery vehicles for gene therapy in the CNS.

Nanog-dependent feedback loops regulate murine embryonic stem cell heterogeneity

A number of key regulators of mouse embryonic stem (ES) cell identity, including the transcription factor Nanog, show strong expression fluctuations at the single-cell level. The molecular basis for these fluctuations is unknown. Here we used a genetic complementation strategy to investigate expression changes during transient periods of Nanog downregulation. Employing an integrated approach that includes high-throughput single-cell transcriptional profiling and mathematical modelling, we found that early molecular changes subsequent to Nanog loss are stochastic and reversible. However, analysis also revealed that Nanog loss severely compromises the self-sustaining feedback structure of the ES cell regulatory network. Consequently, these nascent changes soon become consolidated to committed fate decisions in the prolonged absence of Nanog. Consistent with this, we found that exogenous regulation of Nanog-dependent feedback control mechanisms produced a more homogeneous ES cell population. Taken together our results indicate that Nanog-dependent feedback loops have a role in controlling both ES cell fate decisions and population variability.

Few inputs can reprogram biological networks

Arising from Y. Liu, J. Slotine & A. Barabási 473, 167–173 (2011)10.1038/nature10011; Liu et al. replyLiu, Slotine and Barabasi identify subsets U of nodes in complex networks, which are required to exert full control of these networks. Control in this context means that for each possible state S of the network there exist inputs for all nodes in U, which are sufficient to force the network to state S. Application of the methodology to gene regulatory networks suggests that roughly 80% of all nodes must be controlled to drive such a network. This seems to contradict recent empirical findings in the cellular reprogramming field.

Neural stem cells genetically-modified to express neprilysin reduce pathology in Alzheimer transgenic models

IntroductionShort-term neural stem cell (NSC) transplantation improves cognition in Alzheimer’s disease (AD) transgenic mice by enhancing endogenous synaptic connectivity. However, this approach has no effect on the underlying beta-amyloid (Aβ) and neurofibrillary tangle pathology. Long term efficacy of cell based approaches may therefore require combinatorial approaches.MethodsTo begin to examine this question we genetically-modified NSCs to stably express and secrete the Aβ-degrading enzyme, neprilysin (sNEP). Next, we studied the effects of sNEP expression in vitro by quantifying Aβ-degrading activity, NSC multipotency markers, and Aβ-induced toxicity. To determine whether sNEP-expressing NSCs can also modulate AD-pathogenesis in vivo, control-modified and sNEP-NSCs were transplanted unilaterally into the hippocampus of two independent and well characterized transgenic models of AD: 3xTg-AD and Thy1-APP mice. After three months, stem cell engraftment, neprilysin expression, and AD pathology were examined.ResultsOur findings reveal that stem cell-mediated delivery of NEP provides marked and significant reductions in Aβ pathology and increases synaptic density in both 3xTg-AD and Thy1-APP transgenic mice. Remarkably, Aβ plaque loads are reduced not only in the hippocampus and subiculum adjacent to engrafted NSCs, but also within the amygdala and medial septum, areas that receive afferent projections from the engrafted region.ConclusionsTaken together, our data suggest that genetically-modified NSCs could provide a powerful combinatorial approach to not only enhance synaptic plasticity but to also target and modify underlying Alzheimer’s disease pathology.

iPSCORE: A Resource of 222 iPSC Lines Enabling Functional Characterization of Genetic Variation across a Variety of Cell Types.

Summary Large-scale collections of induced pluripotent stem cells (iPSCs) could serve as powerful model systems for examining how genetic variation affects biology and disease. Here we describe the iPSCORE resource: a collection of systematically derived and characterized iPSC lines from 222 ethnically diverse individuals that allows for both familial and association-based genetic studies. iPSCORE lines are pluripotent with high genomic integrity (no or low numbers of somatic copy-number variants) as determined using high-throughput RNA-sequencing and genotyping arrays, respectively. Using iPSCs from a family of individuals, we show that iPSC-derived cardiomyocytes demonstrate gene expression patterns that cluster by genetic background, and can be used to examine variants associated with physiological and disease phenotypes. The iPSCORE collection contains representative individuals for risk and non-risk alleles for 95% of SNPs associated with human phenotypes through genome-wide association studies. Our study demonstrates the utility of iPSCORE for examining how genetic variants influence molecular and physiological traits in iPSCs and derived cell lines.

A guide to stem cell identification: progress and challenges in system-wide predictive testing with complex biomarkers.

We have developed a first generation tool for the unbiased identification and characterization of human pluripotent stem cells, termed PluriTest. This assay utilizes all the information contained on a microarray and abandons the conventional stem cell marker concept. Stem cells are defined by the ability to replenish themselves and to differentiate into more mature cell types. As differentiation potential is a property that cannot be directly proven in the stem cell state, biologists have to rely on correlative measurements in stem cells associated with differentiation potential. Unfortunately, most, if not all, of those markers are only valid within narrow limits of specific experimental systems. Microarray technologies and recently next‐generation sequencing have revolutionized how cellular phenotypes can be characterized on a systems‐wide level. Here we discuss the challenges PluriTest and similar global assays need to address to fulfill their enormous potential for industrial, diagnostic and therapeutic applications.

A 3-dimensional extracellular matrix as a delivery system for the transplantation of glioma-targeting neural stem/progenitor cells

Neural stem/progenitor cells (NSPCs) display inherent pathotropic properties that can be exploited for targeted delivery of therapeutic genes to invasive malignancies in the central nervous system. Optimizing transplantation efficiency will be essential for developing relevant NSPC-based brain tumor therapies. To date, the real-world issue of handling and affixing NSPCs in the context of the neurosurgical resection cavity has not been addressed. Stem cell transplantation using biocompatible devices is a promising approach to counteract poor NSPC graft survival and integration in various types of neurological disorders. Here, we report the development of a 3-dimensional substrate that is based on extracellular matrix purified from tissue-engineered skin cultures (3DECM). 3DECM enables the expansion of embedded NSPCs in vitro while retaining their uncommitted differentiation status. When implanted in intracerebral glioma models, NSPCs were able to migrate out of the 3DECM to targeted glioma growing in the contralateral hemisphere, and this was more efficient than the delivery of NSPC by intracerebral injection of cell suspensions. Direct application of a 3DECM implant into a tumor resection cavity led to a marked NSPC infiltration of recurrent glioma. The semisolid consistency of the 3DECM implants allowed simple handling during the surgical procedure of intracerebral and intracavitary application and ensured continuous contact with the surrounding brain parenchyma. Here, we demonstrate proof-of-concept of a matrix-supported transplantation of tumor-targeting NSPC. The semisolid 3DECM as a delivery system for NSPC has the potential to increase transplantation efficiency by reducing metabolic stress and providing mechanical support, especially when administered to the surgical resection cavity after brain tumor removal.

PhysioSpace: Relating Gene Expression Experiments from Heterogeneous Sources Using Shared Physiological Processes

Relating expression signatures from different sources such as cell lines, in vitro cultures from primary cells and biopsy material is an important task in drug development and translational medicine as well as for tracking of cell fate and disease progression. Especially the comparison of large scale gene expression changes to tissue or cell type specific signatures is of high interest for the tracking of cell fate in (trans-) differentiation experiments and for cancer research, which increasingly focuses on shared processes and the involvement of the microenvironment. These signature relation approaches require robust statistical methods to account for the high biological heterogeneity in clinical data and must cope with small sample sizes in lab experiments and common patterns of co-expression in ubiquitous cellular processes. We describe a novel method, called PhysioSpace, to position dynamics of time series data derived from cellular differentiation and disease progression in a genome-wide expression space. The PhysioSpace is defined by a compendium of publicly available gene expression signatures representing a large set of biological phenotypes. The mapping of gene expression changes onto the PhysioSpace leads to a robust ranking of physiologically relevant signatures, as rigorously evaluated via sample-label permutations. A spherical transformation of the data improves the performance, leading to stable results even in case of small sample sizes. Using PhysioSpace with clinical cancer datasets reveals that such data exhibits large heterogeneity in the number of significant signature associations. This behavior was closely associated with the classification endpoint and cancer type under consideration, indicating shared biological functionalities in disease associated processes. Even though the time series data of cell line differentiation exhibited responses in larger clusters covering several biologically related patterns, top scoring patterns were highly consistent with a priory known biological information and separated from the rest of response patterns.