A 3D gene expression atlas of the floral meristem based on spatial reconstruction of single nucleus RNA sequencing data

Identity and functions of plant cells are influenced by their precise cellular location within the plant body. Cellular heterogeneity in growth and differentiation trajectories results in organ patterning. Therefore, assessing this heterogeneity at molecular scale is a major question in developmental biology. Single-cell transcriptomics (scRNA-seq) allows to characterize and quantify gene expression heterogeneity in developing organs at unprecedented resolution. However, the original physical location of the cell is lost during the scRNA-seq procedure. To recover the original location of cells is essential to link gene activity with cellular function and morphology. Here, we reconstruct genome-wide gene expression patterns of individual cells in a floral meristem by combining single-nuclei RNA-seq with 3D spatial reconstruction. By this, gene expression differences among meristematic domains giving rise to different tissue and organ types can be determined. As a proof of principle, the data are used to trace the initiation of vascular identity within the floral meristem. Our work demonstrates the power of spatially reconstructed single cell transcriptome atlases to understand plant morphogenesis. The floral meristem 3D gene expression atlas can be accessed at http://threed-flower-meristem.herokuapp.com

Gene Ontology Analysis of 3D Microarray Gene Expression Data using Hybrid PSO Optimization

At present, triclustering is the well known data mining technique for analysis of 3D gene expression data (GST). Triclustering is a simultaneously clustering of subset of Gene (G), subset of Sample (S), and over a subset of Time point (T). Triclustering approach identifies a coherent pattern in the 3D gene expression data using Mean Correlation Value (MCV). In this chapter, Hybrid PSO based algorithm is developed for triclustering of 3D gene expression data. This algorithm can effectively find the coherent pattern with high volume of a tricluster. The experimental study is conducted on yeast cycle dataset to study the biological significance of the coherent tricluster using gene ontology tool

Quantification of gene expression patterns to reveal the origins of abnormal morphogenesis

The earliest developmental origins of dysmorphologies are poorly understood in many congenital diseases. They often remain elusive because the first signs of genetic misregulation may initiate as subtle changes in gene expression, which can be obscured later in development due to secondary phenotypic effects. We here develop a method to trace back the origins of phenotypic abnormalities by accurately quantifying the 3D spatial distribution of gene expression domains in developing organs. By applying geometric morphometrics to 3D gene expression data obtained by Optical Projection Tomography, our approach is sensitive enough to find regulatory abnormalities never previously detected. We identified subtle but significant differences in gene expression of a downstream target of the Fgfr2 mutation associated with Apert syndrome. Challenging previous reports, we demonstrate that Apert syndrome mouse models can further our understanding of limb defects in the human condition. Our method can be applied to other organ systems and models to investigate the etiology of malformations.

Is Porcine Kobuvirus 1 a Typical Diarrhoeic Pathogen of Piglets?

The objective of this study was to show if porcine kobuvirus 1 (PKV-1) participates in the development of diarrhoea in piglets. The experiments were focused on comparing the occurrence of PKV-1 with the occurrence of rotavirus A (RVA) infection in suckling pigs on Slovak pig farms. A total of 91 rectal swabs of piglets (age < 28 days) were collected from 8 pig farms. RT-PCR was employed to detect PKV-1 through amplification of the 495 bp fragment of the 3D gene using primers KoVF/ KoVR, and RVA was detected through amplification of the 309 bp fragment of the VP6 gene using primers rot3 and rot5. As expected, the detection of RVA in diarrhoeic piglets was 56.8 % (P < 0.01), while only 14.8 % in healthy animals. These results confirm that RVA is one of the main causes of diarrhoea in young piglets. Comparatively, PKV-1 was detected in approximately equal numbers in the same group of both healthy and diarrhoeic pigs, with 74.1 % in healthy animals and 81.1 % in diarrhoeic animals, which was not statistically significant (P < 0.05). The level of co-infection of both viruses was 11.1 % in healthy animals. A portion of 48.6 % (P < 0.01) of diarrhoeic animals were found with RVA and PKV-1 coinfections. The results of this study indicate that while RVA is an enteric virus, PKV-1 cannot confidently be confirmed as an enteric pathogen.