Large-scale analysis by SAGE reveals new mechanisms of v-erbA oncogene action

<p>Abstract</p> <p>Background:</p> <p>The <it>v-erbA </it>oncogene, carried by the Avian Erythroblastosis Virus, derives from the <it>c-erbAα </it>proto-oncogene that encodes the nuclear receptor for triiodothyronine (T3R). v-ErbA transforms erythroid progenitors <it>in vitro </it>by blocking their differentiation, supposedly by interference with T3R and RAR (Retinoic Acid Receptor). However, v-ErbA target genes involved in its transforming activity still remain to be identified.</p> <p>Results:</p> <p>By using Serial Analysis of Gene Expression (SAGE), we identified 110 genes deregulated by v-ErbA and potentially implicated in the transformation process. Bioinformatic analysis of promoter sequence and transcriptional assays point out a potential role of c-Myb in the v-ErbA effect. Furthermore, grouping of newly identified target genes by function revealed both expected (chromatin/transcription) and unexpected (protein metabolism) functions potentially deregulated by v-ErbA. We then focused our study on 15 of the new v-ErbA target genes and demonstrated by real time PCR that in majority their expression was activated neither by T3, nor RA, nor during differentiation. This was unexpected based upon the previously known role of v-ErbA.</p> <p>Conclusion:</p> <p>This paper suggests the involvement of a wealth of new unanticipated mechanisms of v-ErbA action.</p

Automated cell cycle and cell size measurements for single-cell gene expression studies

Abstract Objectives Recent rise of single-cell studies revealed the importance of understanding the role of cell-to-cell variability, especially at the transcriptomic level. One of the numerous sources of cell-to-cell variation in gene expression is the heterogeneity in cell proliferation state. In order to identify how cell cycle and cell size influences gene expression variability at the single-cell level, we provide an universal and automatic toxic-free label method, compatible with single-cell high-throughput RT-qPCR. The method consists of isolating cells after a double-stained, analyzing their morphological parameters and performing a transcriptomic analysis on the same identified cells. Results This led to an unbiased gene expression analysis and could be also used for improving single-cell tracking and imaging when combined with cell isolation. As an application for this technique, we showed that cell-to-cell variability in chicken erythroid progenitors was negligibly influenced by cell size nor cell cycle

Drugs modulating stochastic gene expression affect the erythroid differentiation process

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Erythroid differentiation displays a peak of energy consumption concomitant with glycolytic metabolism rearrangements

International audienceOur previous single-cell based gene expression analysis pointed out significant variations of LDHA level during erythroid differentiation. Deeper investigations highlighted that a metabolic switch occurred along differentiation of erythroid cells. More precisely we showed that self-renewing progenitors relied mostly upon lactate-productive glycolysis, and required LDHA activity, whereas differentiating cells, mainly involved mitochondrial oxidative phosphorylation (OXPHOS). These metabolic rearrangements were coming along with a particular temporary event, occurring within the first 24h of erythroid differentiation. The activity of glycolytic metabolism and OXPHOS rose jointly with oxgene consumption dedicated to ATP production at 12-24h of the differentiation process before lactate-productive glycolysis sharply fall down and energy needs decline. Finally, we demonstrated that the metabolic switch mediated through LDHA drop and OXPHOS upkeep might be necessary for erythroid differentiation. We also discuss the possibility that metabolism, gene expression and epigenetics could act together in a circular manner as a driving force for differentiation

MOESM1 of Automated cell cycle and cell size measurements for single-cell gene expression studies

Additional file 1: Table S1. Statistical analysis of gene expression according to the stained or the unstained condition. Statistical tests (Wilcoxon) were performed for each gene between their expression in stained and unstained condition. A Bonferroni correction was applied in p-values for multiple tests

WASABI: a dynamic iterative framework for gene regulatory network inference

International audienceBackgroundInference of gene regulatory networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene regulatory network inference, with both successes and limitations.ResultsIn the present work we propose an iterative algorithm called WASABI, dedicated to inferring a causal dynamical network from time-stamped single-cell data, which tackles some of the limitations associated with current approaches. We first introduce the concept of waves, which posits that the information provided by an external stimulus will affect genes one-by-one through a cascade, like waves spreading through a network. This concept allows us to infer the network one gene at a time, after genes have been ordered regarding their time of regulation. We then demonstrate the ability of WASABI to correctly infer small networks, which have been simulated in silico using a mechanistic model consisting of coupled piecewise-deterministic Markov processes for the proper description of gene expression at the single-cell level. We finally apply WASABI on in vitro generated data on an avian model of erythroid differentiation. The structure of the resulting gene regulatory network sheds a new light on the molecular mechanisms controlling this process. In particular, we find no evidence for hub genes and a much more distributed network structure than expected. Interestingly, we find that a majority of genes are under the direct control of the differentiation-inducing stimulus.ConclusionsTogether, these results demonstrate WASABI versatility and ability to tackle some general gene regulatory networks inference issues. It is our hope that WASABI will prove useful in helping biologists to fully exploit the power of time-stamped single-cell data

An image-guided microfluidic system for single-cell lineage tracking

Cell lineage tracking is a long-standing and unresolved problem in biology. Microfluidic technologies have the potential to address this problem, by virtue of their ability to manipulate and process single-cells in a rapid, controllable and efficient manner. Indeed, when coupled with traditional imaging approaches, microfluidic systems allow the experimentalist to follow single-cell divisions over time. Herein, we present a valve-based microfluidic system able to probe the decision-making processes of single-cells, by tracking their lineage over multiple generations. The system operates by trapping single-cells within growth chambers, allowing the trapped cells to grow and divide, isolating sister cells after a user-defined number of divisions and finally extracting them for downstream transcriptome analysis. The platform incorporates multiple cell manipulation operations, image processing-based automation for cell loading and growth monitoring, reagent addition and device washing. To demonstrate the efficacy of the microfluidic workflow, 6C2 (chicken erythroleukemia) and T2EC (primary chicken erythrocytic progenitors) cells are tracked inside the microfluidic device over two generations, with a cell viability rate in excess of 90%. Sister cells are successfully isolated after division and extracted within a 500 nL volume, which was demonstrated to be compatible with downstream single-cell RNA sequencing analysis

New mechanisms of v-ErbA oncogene action revealed by SAGE analysis

National audienceThe v-erbA oncogene, carried by the Avian Erythroblastosis Virus, derives from the c-erbAalpha proto-oncogene which encodes the nuclear receptor for the thyroid hormone triiodothyronine (T3). In vitro, v-ErbA transforms erythroid progenitors by blocking their differentiation. It has been proposed that v-ErbA acts as a transcriptional repressor for genes normally activated by T3 and retinoic acid (RA), upregulated during the differentiation process. However, v-ErbA target genes responsible for transformation have yet to be identified. We used Serial Analysis of Gene Expression (SAGE) to analyze the transcriptome of avian erythroid progenitors (T2ECs), the natural target cells of v-erbA, expressing either an oncogenic form or a non-transforming form of verbA. The comparison of these two libraries revealed 83 genes differentially expressed between these two conditions. So far, the differential expression for 16 of them has been confirmed by real-time PCR on multiple independent repetitions. We observed that, among these v-ErbA target genes, some are activated by T3 and RA. This confirms that activation by T3 and RA receptors is indeed inhibited by v-ErbA. However, the expression of a vast majority of v-ErbA target genes did not vary in response to T3 and RA. These results suggest that v-ErbA must also act by T3- and RA-independent mechanisms in the transformation process. Furthermore, most v-erbA target genes do not vary during the differentiation process, in contrast to the expected role of v-erbA. In order to determine which major functions are deregulated by v-ErbA, we clustered the target genes identified according to the cellular function encoded by their corresponding proteins. We found that many of them are involved in the protein translation process. In order to understand the molecular mechanisms responsible for the coordinated variation of the v-ErbA target gene, we analyzed their promoter sequences and found the presence of c-myb binding sites as a signature motif of v-erbA target genes. This suggests a role for c-myb in the v-erbA-induced transformation process. Altogether, these studies demonstrate the involvement of new mechanisms pointing toward an unanticipated complexity of v-erbA oncogene action

Identification of human, rat and chicken ribosomal proteins by a combination of two-dimensional polyacrylamide gel electrophoresis and mass spectrometry

To identify the exact spot position of human, rat and chicken ribosomal proteins (RP) separated by two-dimensional polyacrylamide gel electrophoresis (2-DE), a 2-DE system was designed to separate RP with a pI>8.6 according to their charge in the first dimension and to their molecular mass in the second dimension. Individual proteins were excised from the gels and identified by mass spectrometry after digestion by trypsin. In addition, a mixture of purified RP from these three species was also analyzed by tandem mass tag spectrometry. By combining those two methods 74 RP from human, 76 from rat and 67 from chicken were identified according to the nomenclature initially defined for rat liver RP and by using the Swiss-Prot/trEMBL databases. Whereas human and rat RP were well described, most of RP from chicken were not characterized in databases, since 35 out of 67 chicken RP identified in this study were not listed yet. We propose here the first comprehensive description of chicken RP and their comparison to those from human and rat