Zebrafish pigment cells develop directly from persistent highly multipotent progenitors.

Neural crest cells are highly multipotent stem cells, but it remains unclear how their fate restriction to specific fates occurs. The direct fate restriction model hypothesises that migrating cells maintain full multipotency, whilst progressive fate restriction envisages fully multipotent cells transitioning to partially-restricted intermediates before committing to individual fates. Using zebrafish pigment cell development as a model, we show applying NanoString hybridization single cell transcriptional profiling and RNAscope in situ hybridization that neural crest cells retain broad multipotency throughout migration and even in post-migratory cells in vivo, with no evidence for partially-restricted intermediates. We find that leukocyte tyrosine kinase early expression marks a multipotent stage, with signalling driving iridophore differentiation through repression of fate-specific transcription factors for other fates. We reconcile the direct and progressive fate restriction models by proposing that pigment cell development occurs directly, but dynamically, from a highly multipotent state, consistent with our recently-proposed Cyclical Fate Restriction model.

Novel ChIP-seq simulating program with superior versatility: isChIP.

Chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) is recognized as an extremely powerful tool to study the interaction of numerous transcription factors and other chromatin-associated proteins with DNA. The core problem in the optimization of ChIP-seq protocol and the following computational data analysis is that a 'true' pattern of binding events for a given protein factor is unknown. Computer simulation of the ChIP-seq process based on 'a-priory known binding template' can contribute to a drastically reduce the number of wet lab experiments and finally help achieve radical optimization of the entire processing pipeline. We present a newly developed ChIP-sequencing simulation algorithm implemented in the novel software, in silico ChIP-seq (isChIP). We demonstrate that isChIP closely approximates real ChIP-seq protocols and is able to model data similar to those obtained from experimental sequencing. We validated isChIP using publicly available datasets generated for well-characterized transcription factors Oct4 and Sox2. Although the novel software is compatible with the Illumina protocols by default, it can also successfully perform simulations with a number of alternative sequencing platforms such as Roche454, Ion Torrent and SOLiD as well as model ChIP -Exo. The versatility of isChIP was demonstrated through modelling a wide range of binding events, including those of transcription factors and chromatin modifiers. We also performed a comparative analysis against a few existing ChIP-seq simulators and showed the fundamental superiority of our model. Due to its ability to utilize known binding templates, isChIP can potentially be employed to help investigators choose the most appropriate analytical software through benchmarking of available ChIP-seq programs and optimize the experimental parameters of ChIP-seq protocol. isChIP software is freely available at https://github.com/fnaumenko/isChIP.

Fluorescence-Activated Cell Sorting and NanoString Profiling of Single Neural Crest Cells and Pigment Cells.

Neural crest cells are an important class of multipotent stem cells, generating highly diverse derivatives. Understanding the gene regulatory networks underlying this process is of great interest, but the highly migratory and thus widely dispersed nature of the differentiating cells makes isolation of cells difficult. Fluorescence-activated cell sorting (FACS) of transgenically labelled neural crest-derived cells after disaggregation of embryos is well-suited to purifying these cells. However, their diverse differentiation means that transcriptional analysis at single cell resolution is necessary to dissect the gene regulatory networks at play. NanoString technology provides a method for highly sensitive, quantitative transcriptional profiling for a pre-defined set of genes of interest. Here we provide a detailed protocol for FACS purification of neural crest-derived cells, sorted as single cells into a multi-well plate, and their subsequent NanoString profiling, using a predetermined gene set focused on pigment cells.

A systems biology approach uncovers the core gene regulatory network governing iridophore fate choice from the neural crest.

Multipotent neural crest (NC) progenitors generate an astonishing array of derivatives, including neuronal, skeletal components and pigment cells (chromatophores), but the molecular mechanisms allowing balanced selection of each fate remain unknown. In zebrafish, melanocytes, iridophores and xanthophores, the three chromatophore lineages, are thought to share progenitors and so lend themselves to investigating the complex gene regulatory networks (GRNs) underlying fate segregation of NC progenitors. Although the core GRN governing melanocyte specification has been previously established, those guiding iridophore and xanthophore development remain elusive. Here we focus on the iridophore GRN, where mutant phenotypes identify the transcription factors Sox10, Tfec and Mitfa and the receptor tyrosine kinase, Ltk, as key players. Here we present expression data, as well as loss and gain of function results, guiding the derivation of an initial iridophore specification GRN. Moreover, we use an iterative process of mathematical modelling, supplemented with a Monte Carlo screening algorithm suited to the qualitative nature of the experimental data, to allow for rigorous predictive exploration of the GRN dynamics. Predictions were experimentally evaluated and testable hypotheses were derived to construct an improved version of the GRN, which we showed produced outputs consistent with experimentally observed gene expression dynamics. Our study reveals multiple important regulatory features, notably a sox10-dependent positive feedback loop between tfec and ltk driving iridophore specification; the molecular basis of sox10 maintenance throughout iridophore development; and the cooperation between sox10 and tfec in driving expression of pnp4a, a key differentiation gene. We also assess a candidate repressor of mitfa, a melanocyte-specific target of sox10. Surprisingly, our data challenge the reported role of Foxd3, an established mitfa repressor, in iridophore regulation. Our study builds upon our previous systems biology approach, by incorporating physiologically-relevant parameter values and rigorous evaluation of parameter values within a qualitative data framework, to establish for the first time the core GRN guiding specification of the iridophore lineage.

Hhex and Cer1 mediate the Sox17 pathway for cardiac mesoderm formation in embryonic stem cells.

Cardiac muscle differentiation in vivo is guided by sequential growth factor signals, including endoderm-derived diffusible factors, impinging on cardiogenic genes in the developing mesoderm. Previously, by RNA interference in AB2.2 mouse embryonic stem cells (mESCs), we identified the endodermal transcription factor Sox17 as essential for Mesp1 induction in primitive mesoderm and subsequent cardiac muscle differentiation. However, downstream effectors of Sox17 remained to be proven functionally. In this study, we used genome-wide profiling of Sox17-dependent genes in AB2.2 cells, RNA interference, chromatin immunoprecipitation, and luciferase reporter genes to dissect this pathway. Sox17 was required not only for Hhex (a second endodermal transcription factor) but also for Cer1, a growth factor inhibitor from endoderm that, like Hhex, controls mesoderm patterning in Xenopus toward a cardiac fate. Suppressing Hhex or Cer1 blocked cardiac myogenesis, although at a later stage than induction of Mesp1/2. Hhex was required but not sufficient for Cer1 expression. Over-expression of Sox17 induced endogenous Cer1 and sequence-specific transcription of a Cer1 reporter gene. Forced expression of Cer1 was sufficient to rescue cardiac differentiation in Hhex-deficient cells. Thus, Hhex and Cer1 are indispensable components of the Sox17 pathway for cardiopoiesis in mESCs, acting at a stage downstream from Mesp1/2.

Grouping and classifying electrophysiologically-defined classes of neocortical neurons by single cell, whole-genome expression profiling.

The diversity of neuronal cell types and how to classify them are perennial questions in neuroscience. The advent of global gene expression analysis raised the possibility that comprehensive transcription profiling will resolve neuronal cell types into groups that reflect some or all aspects of their phenotype. This approach has been successfully used to compare gene expression between groups of neurons defined by a common property. Here we extend this approach to ask whether single neuron gene expression profiling can prospectively resolve neuronal subtypes into groups, independent of any phenotypic information, and whether those groups reflect meaningful biological properties of those neurons. We applied methods we have developed to compare gene expression among single neural stem cells to study global gene expression in 18 randomly picked neurons from layer II/III of the early postnatal mouse neocortex. Cells were selected by morphology and by firing characteristics and electrical properties, enabling the definition of each cell as either fast- or regular-spiking, corresponding to a class of inhibitory interneurons or excitatory pyramidal cells. Unsupervised clustering of young neurons by global gene expression resolved the cells into two groups and those broadly corresponded with the two groups of fast- and regular-spiking neurons. Clustering of the entire, diverse group of 18 neurons of different developmental stages also successfully grouped neurons in accordance with the electrophysiological phenotypes, but with more cells misassigned among groups. Genes specifically enriched in regular spiking neurons were identified from the young neuron expression dataset. These results provide a proof of principle that single-cell gene expression profiling may be used to group and classify neurons in a manner reflecting their known biological properties and may be used to identify cell-specific transcripts.

Prolonged illumination up-regulates arrestin and two guanylate cyclase activating proteins: a novel mechanism for light adaptation.

Light adaptation in vertebrate photoreceptors is mediated by multiple mechanisms, one of which could involve nuclear feedback and changes in gene expression. Therefore, we have investigated light adaptation-associated changes in gene expression using microarrays and real-time PCR in isolated photoreceptors, in cultured isolated retinas and in acutely isolated retinas. In all three preparations after 2 h of an exposure to a bright light, we observed an up-regulation of almost 100% of three genes, Sag, Guca1a and Guca1b, coding for proteins known to play a major role in phototransduction: arrestin, GCAP1 and GCAP2. No detectable up-regulation occurred for light exposures of less than 1 h. Functional in vivo electroretinographic tests show that a partial recovery of the dark current occurred 1-2 h after prolonged illumination with a steady light that initially caused a substantial suppression of the photoresponse. These observations demonstrate that prolonged illumination results in the up-regulation of genes coding for proteins involved in the phototransduction signalling cascade, possibly underlying a novel component of light adaptation occurring 1-2 h after the onset of a steady bright light.

Modelling and measuring single cell RNA expression levels find considerable transcriptional differences among phenotypically identical cells.

BACKGROUND: Phenotypically identical cells demonstrate predictable, robust behaviours. However, there is uncertainty as to whether phenotypically identical cells are equally similar at the underlying transcriptional level or if cellular systems are inherently noisy. To answer this question, it is essential to distinguish between technical noise and true variation in transcript levels. A critical issue is the contribution of sampling effects, introduced by the requirement to globally amplify the single cell mRNA population, to observed measurements of relative transcript abundance. RESULTS: We used single cell microarray data to develop simple mathematical models, ran Monte Carlo simulations of the impact of technical and sampling effects on single cell expression data, and compared these with experimental microarray data generated from single embryonic neural stem cells in vivo. We show that the actual distribution of measured gene expression ratios for pairs of neural stem cells is much broader than that predicted from our sampling effect model. CONCLUSION: Our results confirm that significant differences in gene expression levels exist between phenotypically identical cells in vivo, and that these differences exceed any noise contribution from global mRNA amplification.

Electrophysiological and gene expression profiling of neuronal cell types in mammalian neocortex.

It is a challenging question to understand how different neuronal types are organized into a complex architecture in the cortex, an architecture which is also adapted in different regions to subserve very different functions. Recent developments in genetic and molecular techniques have opened up the possibility of using gene expression profiling for neuronal cell typing, with the aim of uncovering novel cell types and the underlying mechanisms which generate and maintain neuronal heterogeneity in the cortex. This review introduces some current ideas about neuronal cell types in the cortex and describes recent approaches to expression profiling for defining cortical neuronal cell types.

Comparative evaluation of linear and exponential amplification techniques for expression profiling at the single-cell level.

BACKGROUND: Single-cell microarray expression profiling requires 108-109-fold amplification of the picogram amounts of total RNA typically found in eukaryotic cells. Several methods for RNA amplification are in general use, but little consideration has been given to the comparative analysis of those methods in terms of the overall validity of the data generated when amplifying from single-cell amounts of RNA, rather than their empirical performance in single studies. RESULTS: We tested the performance of three methods for amplifying single-cell amounts of RNA under ideal conditions: T7-based in vitro transcription; switching mechanism at 5' end of RNA template (SMART) PCR amplification; and global PCR amplification. All methods introduced amplification-dependent noise when mRNA was amplified 108-fold, compared with data from unamplified cDNA. PCR-amplified cDNA demonstrated the smallest number of differences between two parallel replicate samples and the best correlation between independent amplifications from the same cell type, with SMART outperforming global PCR amplification. SMART had the highest true-positive rate and the lowest false-positive rate when comparing expression between two different cell types, but had the lowest absolute discovery rate of all three methods. Direct comparison of the performance of SMART and global PCR amplification on single-cell amounts of total RNA and on single neural stem cells confirmed these findings. CONCLUSION: Under the conditions tested, PCR amplification was more reliable than linear amplification for detecting true expression differences between samples. SMART amplification had a higher true-positive rate than global amplification, but at the expense of a considerably lower absolute discovery rate and a systematic compression of observed expression ratios.

A 1 Mb minimal amplicon at 8p11-12 in breast cancer identifies new candidate oncogenes.

Amplification of 8p11-12 is a well-known alteration in human breast cancers but the driving oncogene has not been identified. We have developed a high-resolution comparative genomic hybridization array covering 8p11-12 and analysed 33 primary breast tumors, 20 primary ovarian tumors and 27 breast cancer cell lines. Expression analysis of the genes in the region was carried out by using real-time quantitative PCR and/or oligo-microarray profiling. In all, 24% (8/33) of the breast tumors, 5% (1/20) of the ovary tumors and 15% (4/27) of the cell lines showed 8p11-12 amplification. We identified a 1 Mb segment of common amplification that excludes previously proposed candidate genes. Some of the amplified genes did not show overexpression, whereas for others, overexpression was not specifically attributable to amplification. The genes FLJ14299, C8orf2, BRF2 and RAB11FIP, map within the 8p11-12 minimal amplicon, two have a putative function consistent with an oncogenic role, these four genes showed a strong correlation between amplification and overexpression and are therefore the best candidate driver oncogenes at 8p12.