Evidence for close molecular proximity between reverting and undifferentiated cells

RNA-Seq Profiling in Chicken Spleen and Thymus Infected with Newcastle Disease Virus of Varying Virulence

Newcastle disease virus (NDV), known as avian paramyxovirus-1, poses a significant threat to poultry production worldwide. Vaccination currently stands as the most effective strategy for Newcastle disease control. However, the mesogenic vaccine strain Mukteswar has been observed to evolve into a velogenic variant JS/7/05/Ch during poultry immunization. Here, we aimed to explore the mechanisms underlying virulence enhancement of the two viruses. Pathogenically, JS/7/05/Ch mediated stronger virulence and pathogenicity in vivo compared to Mukteswar. Comparative transcriptome analysis revealed 834 differentially expressed genes (DEGs), comprising 339 up-regulated and 495 down-regulated genes, in the spleen, and 716 DEGs, with 313 up-regulated and 403 down-regulated genes, in the thymus. Gene Ontology (GO) analysis indicated that these candidate targets primarily participated in cell and biological development, extracellular part and membrane composition, as well as receptor and binding activity. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis unveiled a substantial portion of candidate genes predominantly involved in cellular processes, environmental information processing, metabolism, and organismal systems. Additionally, five DEGs (TRAT1, JUP, LPAR4, CYB561A3, and CXCR5) were randomly identified through RNA-seq analysis and subsequently confirmed via quantitative real-time polymerase chain reaction (qRT-PCR). The findings revealed a marked up-regulation in the expression levels of these DEGs induced by JS/7/05/Ch compared to Mukteswar, with CYB561A3 and CXCR5 exhibiting significant increases. The findings corroborated the sequencing accuracy, offering promising research directions. Taken together, we comprehensively evaluated transcriptomic alterations in chicken immune organs infected by NDV strains of diverse virulence. This study establishes a basis and direction for NDV virulence research.

TopoDoE: a design of experiment strategy for selection and refinement in ensembles of executable gene regulatory networks

BACKGROUND

Inference of Gene Regulatory Networks (GRNs) is a difficult and long-standing question in Systems Biology. Numerous approaches have been proposed with the latest methods exploring the richness of single-cell data. One of the current difficulties lies in the fact that many methods of GRN inference do not result in one proposed GRN but in a collection of plausible networks that need to be further refined. In this work, we present a Design of Experiment strategy to use as a second stage after the inference process. It is specifically fitted for identifying the next most informative experiment to perform for deciding between multiple network topologies, in the case where proposed GRNs are executable models. This strategy first performs a topological analysis to reduce the number of perturbations that need to be tested, then predicts the outcome of the retained perturbations by simulation of the GRNs and finally compares predictions with novel experimental data.

RESULTS

We apply this method to the results of our divide-and-conquer algorithm called WASABI, adapt its gene expression model to produce perturbations and compare our predictions with experimental results. We show that our networks were able to produce in silico predictions on the outcome of a gene knock-out, which were qualitatively validated for 48 out of 49 genes. Finally, we eliminate as many as two thirds of the candidate networks for which we could identify an incorrect topology, thus greatly improving the accuracy of our predictions.

CONCLUSION

These results both confirm the inference accuracy of WASABI and show how executable gene expression models can be leveraged to further refine the topology of inferred GRNs. We hope this strategy will help systems biologists further explore their data and encourage the development of more executable GRN models.

Kernel-based testing for single-cell differential analysis

Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods.

Differentiation is accompanied by a progressive loss in transcriptional memory

BACKGROUND

Cell differentiation requires the integration of two opposite processes, a stabilizing cellular memory, especially at the transcriptional scale, and a burst of gene expression variability which follows the differentiation induction. Therefore, the actual capacity of a cell to undergo phenotypic change during a differentiation process relies upon a modification in this balance which favors change-inducing gene expression variability. However, there are no experimental data providing insight on how fast the transcriptomes of identical cells would diverge on the scale of the very first two cell divisions during the differentiation process.

RESULTS

In order to quantitatively address this question, we developed different experimental methods to recover the transcriptomes of related cells, after one and two divisions, while preserving the information about their lineage at the scale of a single cell division. We analyzed the transcriptomes of related cells from two differentiation biological systems (human CD34+ cells and T2EC chicken primary erythrocytic progenitors) using two different single-cell transcriptomics technologies (scRT-qPCR and scRNA-seq).

CONCLUSIONS

We identified that the gene transcription profiles of differentiating sister cells are more similar to each other than to those of non-related cells of the same type, sharing the same environment and undergoing similar biological processes. More importantly, we observed greater discrepancies between differentiating sister cells than between self-renewing sister cells. Furthermore, a progressive increase in this divergence from first generation to second generation was observed when comparing differentiating cousin cells to self renewing cousin cells. Our results are in favor of a gradual erasure of transcriptional memory during the differentiation process.