PCprophet: a framework for protein complex prediction and differential analysis using proteomic data.

Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein-protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography-sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein-protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography-MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics.

Identification of Activating Mutations in the Transmembrane and Extracellular Domains of EGFR.

The epidermal growth factor receptor (EGFR) is frequently mutated in human cancer, most notably non-small-cell lung cancer and glioblastoma. While many frequently occurring EGFR mutations are known to confer constitutive EGFR activation, the situation is less clear for rarely detected variants. In fact, more than 1000 distinct EGFR mutations are listed in the Catalogue of Somatic Mutations in Cancer (COSMIC), but for most of them, the functional consequence is unknown. To identify additional, previously unknown activating mutations in EGFR, we screened a randomly mutated EGFR library for constitutive EGFR phosphorylation using a recently developed high-throughput approach termed PhosphoFlowSeq. Enrichment of the well-known activating mutations S768I, T790M, and L858R validated the experimental approach. Importantly, we also identified the activating mutations S442I and L658Q located in the extracellular and transmembrane domains of EGFR, respectively. To the best of our knowledge, neither S442I nor L658Q has been associated with an activating phenotype before. However, both have been detected in cancer samples. Interestingly, molecular dynamics (MD) simulations suggest that the L658Q mutation located in the hydrophobic transmembrane region forms intermolecular hydrogen bonds, thereby promoting EGFR dimerization and activation. Based on these findings, we screened the COSMIC database for additional hydrophilic mutations in the EGFR transmembrane region and indeed detected moderate constitutive activation of EGFR-G652R. Together, this study demonstrates that unbiased screening for activating mutations in EGFR not only yields well-established substitutions located in the kinase domain but also activating mutations in other regions of EGFR, including the extracellular and transmembrane domains.

Root traits of European Vicia faba cultivars-Using machine learning to explore adaptations to agroclimatic conditions.

Faba bean (Vicia faba L.) is an important source of protein, but breeding for increased yield stability and stress tolerance is hampered by the scarcity of phenotyping information. Because comparisons of cultivars adapted to different agroclimatic zones improve our understanding of stress tolerance mechanisms, the root architecture and morphology of 16 European faba bean cultivars were studied at maturity. Different machine learning (ML) approaches were tested in their usefulness to analyse trait variations between cultivars. A supervised, that is, hypothesis-driven, ML approach revealed that cultivars from Portugal feature greater and coarser but less frequent lateral roots at the top of the taproot, potentially enhancing water uptake from deeper soil horizons. Unsupervised clustering revealed that trait differences between northern and southern cultivars are not predominant but that two cultivar groups, independently from major and minor types, differ largely in overall root system size. Methodological guidelines on how to use powerful ML methods such as random forest models for enhancing the phenotypical exploration of plants are given.

Bacterial protein signals are associated with Crohn's disease.

OBJECTIVE: No Crohn's disease (CD) molecular maker has advanced to clinical use, and independent lines of evidence support a central role of the gut microbial community in CD. Here we explore the feasibility of extracting bacterial protein signals relevant to CD, by interrogating myriads of intestinal bacterial proteomes from a small number of patients and healthy controls. DESIGN: We first developed and validated a workflow-including extraction of microbial communities, two-dimensional difference gel electrophoresis (2D-DIGE), and LC-MS/MS-to discover protein signals from CD-associated gut microbial communities. Then we used selected reaction monitoring (SRM) to confirm a set of candidates. In parallel, we used 16S rRNA gene sequencing for an integrated analysis of gut ecosystem structure and functions. RESULTS: Our 2D-DIGE-based discovery approach revealed an imbalance of intestinal bacterial functions in CD. Many proteins, largely derived from Bacteroides species, were over-represented, while under-represented proteins were mostly from Firmicutes and some Prevotella members. Most overabundant proteins could be confirmed using SRM. They correspond to functions allowing opportunistic pathogens to colonise the mucus layers, breach the host barriers and invade the mucosae, which could still be aggravated by decreased host-derived pancreatic zymogen granule membrane protein GP2 in CD patients. Moreover, although the abundance of most protein groups reflected that of related bacterial populations, we found a specific independent regulation of bacteria-derived cell envelope proteins. CONCLUSIONS: This study provides the first evidence that quantifiable bacterial protein signals are associated with CD, which can have a profound impact on future molecular diagnosis.

On the Validation of Protein Force Fields Based on Structural Criteria.

Molecular dynamics simulations depend critically on the quality of the force field used to describe the interatomic interactions and the extent to which it has been validated for use in a specific application. Using a curated test set of 52 high-resolution structures, 39 derived from X-ray diffraction and 13 solved using NMR, we consider the extent to which different parameter sets of the GROMOS protein force field can be distinguished based on comparing a range of structural criteria, including the number of backbone hydrogen bonds, the number of native hydrogen bonds, polar and nonpolar solvent-accessible surface area, radius of gyration, the prevalence of secondary structure elements, J-coupling constants, nuclear Overhauser effect (NOE) intensities, positional root-mean-square deviations (RMSD), and the distribution of backbone ϕ and ψ dihedral angles. It is shown that while statistically significant differences between the average values of individual metrics could be detected, these were in general small. Furthermore, improvements in agreement in one metric were often offset by loss of agreement in another. The work establishes a framework and test set against which protein force fields can be validated. It also highlights the danger of inferring the relative quality of a given force field based on a small range of structural properties or small number of proteins.

Analysis of extracellular vesicle microRNA profiles reveals distinct blood and lymphatic endothelial cell origins.

Extracellular vesicles (EVs) are crucial mediators of cell-to-cell communication in physiological and pathological conditions. Specifically, EVs released from the vasculature into blood were found to be quantitatively and qualitatively different in diseases compared to healthy states. However, our understanding of EVs derived from the lymphatic system is still scarce. In this study, we compared the mRNA and microRNA (miRNA) expression in blood vascular (BEC) and lymphatic (LEC) endothelial cells. After characterization of the EVs by fluorescence-triggered flow cytometry, nanoparticle tracking analysis and cryo-transmission electron microscopy (cryo-TEM) we utilized small RNA-sequencing to characterize miRNA signatures in the EVs and identify cell-type specific miRNAs in BEC and LEC. We found miRNAs specifically enriched in BEC and LEC on the cellular as well as the extracellular vesicle level. Our data provide a solid basis for further functional in vitro and in vivo studies addressing the role of EVs in the blood and lymphatic vasculature.

An analysis of single amino acid repeats as use case for application specific background models.

BACKGROUND: Sequence analysis aims to identify biologically relevant signals against a backdrop of functionally meaningless variation. Increasingly, it is recognized that the quality of the background model directly affects the performance of analyses. State-of-the-art approaches rely on classical sequence models that are adapted to the studied dataset. Although performing well in the analysis of globular protein domains, these models break down in regions of stronger compositional bias or low complexity. While these regions are typically filtered, there is increasing anecdotal evidence of functional roles. This motivates an exploration of more complex sequence models and application-specific approaches for the investigation of biased regions. RESULTS: Traditional Markov-chains and application-specific regression models are compared using the example of predicting runs of single amino acids, a particularly simple class of biased regions. Cross-fold validation experiments reveal that the alternative regression models capture the multi-variate trends well, despite their low dimensionality and in contrast even to higher-order Markov-predictors. We show how the significance of unusual observations can be computed for such empirical models. The power of a dedicated model in the detection of biologically interesting signals is then demonstrated in an analysis identifying the unexpected enrichment of contiguous leucine-repeats in signal-peptides. Considering different reference sets, we show how the question examined actually defines what constitutes the 'background'. Results can thus be highly sensitive to the choice of appropriate model training sets. Conversely, the choice of reference data determines the questions that can be investigated in an analysis. CONCLUSIONS: Using a specific case of studying biased regions as an example, we have demonstrated that the construction of application-specific background models is both necessary and feasible in a challenging sequence analysis situation.

The impact of quantitative optimization of hybridization conditions on gene expression analysis.

BACKGROUND: With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studies. While probe-design is a major research area, relatively little work has been reported on the optimization of microarray protocols. RESULTS: As shown in this study, suboptimal conditions can have considerable impact on biologically relevant observations. For example, deviation from the optimal temperature by one degree Celsius lead to a loss of up to 44% of differentially expressed genes identified. While genes from thousands of Gene Ontology categories were affected, transcription factors and other low-copy-number regulators were disproportionately lost. Calibrated protocols are thus required in order to take full advantage of the large dynamic range of microarrays.For an objective optimization of protocols we introduce an approach that maximizes the amount of information obtained per experiment. A comparison of two typical samples is sufficient for this calibration. We can ensure, however, that optimization results are independent of the samples and the specific measures used for calibration. Both simulations and spike-in experiments confirmed an unbiased determination of generally optimal experimental conditions. CONCLUSIONS: Well calibrated hybridization conditions are thus easily achieved and necessary for the efficient detection of differential expression. They are essential for the sensitive pro filing of low-copy-number molecules. This is particularly critical for studies of transcription factor expression, or the inference and study of regulatory networks.

Model-based probe set optimization for high-performance microarrays.

A major challenge in microarray design is the selection of highly specific oligonucleotide probes for all targeted genes of interest, while maintaining thermodynamic uniformity at the hybridization temperature. We introduce a novel microarray design framework (Thermodynamic Model-based Oligo Design Optimizer, TherMODO) that for the first time incorporates a number of advanced modelling features: (i) A model of position-dependent labelling effects that is quantitatively derived from experiment. (ii) Multi-state thermodynamic hybridization models of probe binding behaviour, including potential cross-hybridization reactions. (iii) A fast calibrated sequence-similarity-based heuristic for cross-hybridization prediction supporting large-scale designs. (iv) A novel compound score formulation for the integrated assessment of multiple probe design objectives. In contrast to a greedy search for probes meeting parameter thresholds, this approach permits an optimization at the probe set level and facilitates the selection of highly specific probe candidates while maintaining probe set uniformity. (v) Lastly, a flexible target grouping structure allows easy adaptation of the pipeline to a variety of microarray application scenarios. The algorithm and features are discussed and demonstrated on actual design runs. Source code is available on request.

Investigating Explanatory Factors of Machine Learning Models for Plant Classification.

Recent progress in machine learning and deep learning has enabled the implementation of plant and crop detection using systematic inspection of the leaf shapes and other morphological characters for identification systems for precision farming. However, the models used for this approach tend to become black-box models, in the sense that it is difficult to trace characters that are the base for the classification. The interpretability is therefore limited and the explanatory factors may not be based on reasonable visible characters. We investigate the explanatory factors of recent machine learning and deep learning models for plant classification tasks. Based on a Daucus carota and a Beta vulgaris image data set, we implement plant classification models and compare those models by their predictive performance as well as explainability. For comparison we implemented a feed forward convolutional neuronal network as a default model. To evaluate the performance, we trained an unsupervised Bayesian Gaussian process latent variable model as well as a convolutional autoencoder for feature extraction and rely on a support vector machine for classification. The explanatory factors of all models were extracted and analyzed. The experiments show, that feed forward convolutional neuronal networks (98.24% and 96.10% mean accuracy) outperforms the Bayesian Gaussian process latent variable pipeline (92.08% and 94.31% mean accuracy) as well as the convolutional autoenceoder pipeline (92.38% and 93.28% mean accuracy) based approaches in terms of classification accuracy, even though not significant for Beta vulgaris images. Additionally, we found that the neuronal network used biological uninterpretable image regions for the plant classification task. In contrast to that, the unsupervised learning models rely on explainable visual characters. We conclude that supervised convolutional neuronal networks must be used carefully to ensure biological interpretability. We recommend unsupervised machine learning, careful feature investigation, and statistical feature analysis for biological applications.

Predictive Monitoring of Shake Flask Cultures with Online Estimated Growth Models.

Simplicity renders shake flasks ideal for strain selection and substrate optimization in biotechnology. Uncertainty during initial experiments may, however, cause adverse growth conditions and mislead conclusions. Using growth models for online predictions of future biomass (BM) and the arrival of critical events like low dissolved oxygen (DO) levels or when to harvest is hence important to optimize protocols. Established knowledge that unfavorable metabolites of growing microorganisms interfere with the substrate suggests that growth dynamics and, as a consequence, the growth model parameters may vary in the course of an experiment. Predictive monitoring of shake flask cultures will therefore benefit from estimating growth model parameters in an online and adaptive manner. This paper evaluates a newly developed particle filter (PF) which is specifically tailored to the requirements of biotechnological shake flask experiments. By combining stationary accuracy with fast adaptation to change the proposed PF estimates time-varying growth model parameters from iteratively measured BM and DO sensor signals in an optimal manner. Such proposition of inferring time varying parameters of Gompertz and Logistic growth models is to our best knowledge novel and here for the first time assessed for predictive monitoring of Escherichia coli (E. coli) shake flask experiments. Assessments that mimic real-time predictions of BM and DO levels under previously untested growth conditions demonstrate the efficacy of the approach. After allowing for an initialization phase where the PF learns appropriate model parameters, we obtain accurate predictions of future BM and DO levels and important temporal characteristics like when to harvest. Statically parameterized growth models that represent the dynamics of a specific setting will in general provide poor characterizations of the dynamics when we change strain or substrate. The proposed approach is thus an important innovation for scientists working on strain characterization and substrate optimization as providing accurate forecasts will improve reproducibility and efficiency in early-stage bioprocess development.

PhosphoFlowSeq - A High-throughput Kinase Activity Assay for Screening Drug Resistance Mutations in EGFR.

Drug resistance poses a major challenge for targeted cancer therapy. To be able to functionally screen large randomly mutated target gene libraries for drug resistance mutations, we developed a biochemically defined high-throughput assay termed PhosphoFlowSeq. Instead of selecting for proliferation or resistance to apoptosis, PhosphoFlowSeq directly analyzes the enzymatic activities of randomly mutated kinases, thereby reducing the dependency on the signaling network in the host cell. Moreover, simultaneous analysis of expression levels enables compensation for expression-based biases on a single cell level. Using EGFR and its kinase inhibitor erlotinib as a model system, we demonstrate that the clinically most relevant resistance mutation T790M is reproducibly detected at high frequencies after four independent PhosphoFlowSeq selection experiments. Moreover, upon decreasing the selection pressure, also mutations which only confer weak resistance were identified, including T854A and L792H. We expect that PhosphoFlowSeq will be a valuable tool for the prediction and functional screening of drug resistance mutations in kinases.

Identifying geographically differentiated features of Ethopian Nile tilapia (Oreochromis niloticus) morphology with machine learning.

Visual characteristics are among the most important features for characterizing the phenotype of biological organisms. Color and geometric properties define population phenotype and allow assessing diversity and adaptation to environmental conditions. To analyze geometric properties classical morphometrics relies on biologically relevant landmarks which are manually assigned to digital images. Assigning landmarks is tedious and error prone. Predefined landmarks may in addition miss out on information which is not obvious to the human eye. The machine learning (ML) community has recently proposed new data analysis methods which by uncovering subtle features in images obtain excellent predictive accuracy. Scientific credibility demands however that results are interpretable and hence to mitigate the black-box nature of ML methods. To overcome the black-box nature of ML we apply complementary methods and investigate internal representations with saliency maps to reliably identify location specific characteristics in images of Nile tilapia populations. Analyzing fish images which were sampled from six Ethiopian lakes reveals that deep learning improves on a conventional morphometric analysis in predictive performance. A critical assessment of established saliency maps with a novel significance test reveals however that the improvement is aided by artifacts which have no biological interpretation. More interpretable results are obtained by a Bayesian approach which allows us to identify genuine Nile tilapia body features which differ in dependence of the animals habitat. We find that automatically inferred Nile tilapia body features corroborate and expand the results of a landmark based analysis that the anterior dorsum, the fish belly, the posterior dorsal region and the caudal fin show signs of adaptation to the fish habitat. We may thus conclude that Nile tilapia show habitat specific morphotypes and that a ML analysis allows inferring novel biological knowledge in a reproducible manner.

The transcriptome of syncytia induced by the cyst nematode Heterodera schachtii in Arabidopsis roots.

Arabidopsis thaliana is a host for the sugar beet cyst nematode Heterodera schachtii. Juvenile nematodes invade the roots and induce the development of a syncytium, which functions as a feeding site for the nematode. Here, we report on the transcriptome of syncytia induced in the roots of Arabidopsis. Microaspiration was employed to harvest pure syncytium material, which was then used to prepare RNA for hybridization to Affymetrix GeneChips. Initial data analysis showed that the gene expression in syncytia at 5 and 15 days post-infection did not differ greatly, and so both time points were compared together with control roots. Out of a total of 21 138 genes, 18.4% (3893) had a higher expression level and 15.8% (3338) had a lower expression level in syncytia, as compared with control roots, using a multiple-testing corrected false discovery rate of below 5%. A gene ontology (GO) analysis of up- and downregulated genes showed that categories related to high metabolic activity were preferentially upregulated. A principal component analysis was applied to compare the transcriptome of syncytia with the transcriptome of different Arabidopsis organs (obtained by the AtGenExpress project), and with specific root tissues. This analysis revealed that syncytia are transcriptionally clearly different from roots (and all other organs), as well as from other root tissues.

RNA interference in ageing research--a mini-review.

BACKGROUND: The search for genetic mechanisms affecting life-span and ageing represents an important part of ageing research, especially since the discovery of single-gene mutations with dramatic effects on these traits. Due to its relative ease of use and its power to specifically target arbitrary genes, RNA interference (RNAi) has rapidly been adopted as a technique for silencing gene expression. The feasibility of genome-wide RNAi screens potentially much simplifies the identification of novel ageing-related genes. OBJECTIVE: In a review of applications of RNAi in ageing research with a focus on the model organisms Caenorhabditis elegans and Drosophila melanogaster and discussing recent technical developments, we aim to highlight the current and future impact of this technology in the field. METHOD: We show how RNAi has successfully been used to complement classic mutant studies. Moreover, we discuss the novel opportunities and challenges of an application of RNAi in genome-wide screens in D. melanogaster, which has become possible with the recent availability of a comprehensive transgenic RNAi library for the fly. We highlight, in particular, how the flexible control of RNAi induction can support the study of dynamic processes like ageing through specific experiments and the development of matching computational methods. In an overview of complementary approaches we discuss the challenge of extracting insight from the high-dimensional measurement datasets that are required for the study of dynamic effects and interaction dependencies. CONCLUSION: RNAi has emerged as a powerful tool for the study of ageing, allowing the further characterization of the roles of specific genes in the ageing process as well as the efficient identification of new genes implicated. RNAi has contributed to our understanding of age-related diseases especially by making genes amenable to manipulation for which mutants were not easily available. Recent developments enable genome-wide screens with unprecedented temporal and spatial control of RNAi induction. Specific RNAi time-course experiments provide an opportunity for the analysis of high-resolution gene expression profiles capturing the dynamics of ageing-relevant processes and gene interactions. Research exploiting new avenues opened by the growing RNAi toolbox will considerably contribute to the next steps in researching the genetics of ageing and age-related diseases.

Cognitive tasks for driving a brain-computer interfacing system: a pilot study.

Different cognitive tasks were investigated for use with a brain-computer interface (BCI). The main aim was to evaluate which two of several candidate tasks lead to patterns of electroencephalographic (EEG) activity that could be differentiated most reliably and, therefore, produce the highest communication rate. An optimal signal processing method was also sought to enhance differentiation of EEG profiles across tasks. In ten normal subjects (five male), aged 29-54 years, EEG activity was recorded from four channels during cognitive tasks grouped in pairs, and performed alternately. Four imagery tasks were: spatial navigation around a familiar environment; auditory imagery of a familiar tune; and right and left motor imagery of opening and closing the hand. Signal processing methodology included autoregressive (AR) modeling and classification based on logistic regression and a nonlinear generative classifier. The highest communication rate was found using the navigation and auditory imagery tasks. In terms of classification performance and, hence, possible communication rate, these results were significantly better (p < 0.05) than those obtained with the classical pairing of motor tasks involving imaginary movements of the left and right hands. In terms of EEG data analysis, a nonlinear classification model provided more robust results than a linear model (p << 0.01), and a lower AR model order than those used in previous work was found to be effective. These findings have implications for establishing appropriate methods to operate BCI systems, particularly for disabled people who may experience difficulty with motor tasks, even motor imagery.

Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation.

This paper proposes the use of variational Kalman filtering as an inference technique for adaptive classification in a brain computer interface (BCI). The proposed algorithm translates electroencephalogram segments adaptively into probabilities of cognitive states. It, thus, allows for nonstationarities in the joint process over cognitive state and generated EEG which may occur during a consecutive number of trials. Nonstationarities may have technical reasons (e.g., changes in impedance between scalp and electrodes) or be caused by learning effects in subjects. We compare the performance of the proposed method against an equivalent static classifier by estimating the generalization accuracy and the bit rate of the BCI. Using data from two studies with healthy subjects, we conclude that adaptive classification significantly improves BCI performance. Averaging over all subjects that participated in the respective study, we obtain, depending on the cognitive task pairing, an increase both in generalization accuracy and bit rate of up to 8%. We may, thus, conclude that adaptive inference can play a significant contribution in the quest of increasing bit rates and robustness of current BCI technology. This is especially true since the proposed algorithm can be applied in real time.

Detecting and correcting systematic variation in large-scale RNA sequencing data.

High-throughput RNA sequencing (RNA-seq) enables comprehensive scans of entire transcriptomes, but best practices for analyzing RNA-seq data have not been fully defined, particularly for data collected with multiple sequencing platforms or at multiple sites. Here we used standardized RNA samples with built-in controls to examine sources of error in large-scale RNA-seq studies and their impact on the detection of differentially expressed genes (DEGs). Analysis of variations in guanine-cytosine content, gene coverage, sequencing error rate and insert size allowed identification of decreased reproducibility across sites. Moreover, commonly used methods for normalization (cqn, EDASeq, RUV2, sva, PEER) varied in their ability to remove these systematic biases, depending on sample complexity and initial data quality. Normalization methods that combine data from genes across sites are strongly recommended to identify and remove site-specific effects and can substantially improve RNA-seq studies.

A dual role for autophagy in a murine model of lung cancer.

Autophagy is a mechanism by which starving cells can control their energy requirements and metabolic states, thus facilitating the survival of cells in stressful environments, in particular in the pathogenesis of cancer. Here we report that tissue-specific inactivation of Atg5, essential for the formation of autophagosomes, markedly impairs the progression of KRas(G12D)-driven lung cancer, resulting in a significant survival advantage of tumour-bearing mice. Autophagy-defective lung cancers exhibit impaired mitochondrial energy homoeostasis, oxidative stress and a constitutively active DNA damage response. Genetic deletion of the tumour suppressor p53 reinstates cancer progression of autophagy-deficient tumours. Although there is improved survival, the onset of Atg5-mutant KRas(G12D)-driven lung tumours is markedly accelerated. Mechanistically, increased oncogenesis maps to regulatory T cells. These results demonstrate that, in KRas(G12D)-driven lung cancer, Atg5-regulated autophagy accelerates tumour progression; however, autophagy also represses early oncogenesis, suggesting a link between deregulated autophagy and regulatory T cell controlled anticancer immunity.

Gene expression changes in long term expanded human neural progenitor cells passaged by chopping lead to loss of neurogenic potential in vivo.

Numerous cell culture protocols have been described for the proliferation of multipotent human neural progenitor cells (HNPCs). The mitogen combinations used to expand HNPCs vary, and it is not clear to what extent this may affect the subsequent differentiation of these cells. In this study human foetal cortical tissue was cultured in the presence of either EGF, or FGF-2, or a combination of both using a unique chopping method in which cell to cell contact is maintained. The differentiation potential of neurospheres following mitogen withdrawal was assessed at early (8 weeks) and late (20 weeks) times of expansion, both in vitro and in vivo. In addition, changes in gene expression with time were analysed by microarray experiments. Results show that the presence of FGF-2 was highly predictive of neuronal differentiation after short term culture both in vitro and in vivo. In addition, time in culture had a significant effect on transplant size and neural constituents suggesting that cells have a limited life span and restricted lineage potential. Array analysis confirms that following extensive time in culture cells are entering growth arrest with fundamental expression changes in genes associated with cell cycle regulation, apoptosis and immune functions.