Project description:External stimulations of cells by hormones, growth factors or cytokines activate signal transduction pathways that subsequently induce a rearrangement of cellular gene expression. The representation and analysis of changes in the gene response is complicated, and essentially consists of multiple layered temporal responses. In such situations, matrix factorization techniques may provide efficient tools for the detailed temporal analysis. Related methods applied in bioinformatics intentionally do not take prior knowledge into account. In signal processing, factorization techniques incorporating data properties like second-order spatial and temporal structures have shown a robust performance. However, large-scale biological data rarely imply a natural order that allows the definition of an autocorrelation function. We therefore develop the concept of graph-autocorrelation. We encode prior knowledge like transcriptional regulation, protein interactions or metabolic pathways as a weighted directed graph. By linking features along this underlying graph, we introduce a partial ordering of the samples to define an autocorrelation function. Using this framework as constraint to the matrix factorization task allows us to set up the fast and robust graph decorrelation (GraDe) algorithm. To analyze the alterations in the gene response in IL-6 stimulated primary mouse hepatocytes by GraDe, a time-course microarray experiment was performed. Extracted gene expression profiles show that IL-6 activates genes involved in cell cycle progression and cell division in a time-resolved manner. On the contrary, genes linked to metabolic and apoptotic processes are down-regulated indicating that IL-6 mediated priming rendered hepatocytes more responsive towards cell proliferation and reduces expenses for the energy household. Primary mouse hepatocytes were used in the analysis comprising stimulations with 1 nM IL-6 for 1 h, 2 h, 4 h and an unstimulated control (0 h), each performed in triplicate.
Project description:External stimulations of cells by hormones, growth factors or cytokines activate signal transduction pathways that subsequently induce a rearrangement of cellular gene expression. The representation and analysis of changes in the gene response is complicated, and essentially consists of multiple layered temporal responses. In such situations, matrix factorization techniques may provide efficient tools for the detailed temporal analysis. Related methods applied in bioinformatics intentionally do not take prior knowledge into account. In signal processing, factorization techniques incorporating data properties like second-order spatial and temporal structures have shown a robust performance. However, large-scale biological data rarely imply a natural order that allows the definition of an autocorrelation function. We therefore develop the concept of graph-autocorrelation. We encode prior knowledge like transcriptional regulation, protein interactions or metabolic pathways as a weighted directed graph. By linking features along this underlying graph, we introduce a partial ordering of the samples to define an autocorrelation function. Using this framework as constraint to the matrix factorization task allows us to set up the fast and robust graph decorrelation (GraDe) algorithm. To analyze the alterations in the gene response in IL-6 stimulated primary mouse hepatocytes by GraDe, a time-course microarray experiment was performed. Extracted gene expression profiles show that IL-6 activates genes involved in cell cycle progression and cell division in a time-resolved manner. On the contrary, genes linked to metabolic and apoptotic processes are down-regulated indicating that IL-6 mediated priming rendered hepatocytes more responsive towards cell proliferation and reduces expenses for the energy household.
Project description:Spatial transcriptomics (ST) is an innovative technology that holds tremendous potential for transforming the field of tissue biology research. By simultaneously capturing multiple types of spatial data, including gene expression values, spatial distance information, and tissue morphology, ST enables a comprehensive understanding of biological samples. However, the effective integration of these diverse data types remains a challenge. In this study, we present stLearn, a collection of three computational-statistical algorithms specifically designed to exploit the combined power of gene expression, spatial distance, and tissue morphology data. Our aim is to unlock novel insights into tissue maintenance, development, and disease. The first algorithm, known as pseudo-time-space (PSTS), employs a spatial-graph-based approach to uncover the spatial relationships between cells' transcriptional states in dynamic tissue contexts. To demonstrate the effectiveness of stLearn, we utilize traumatic brain injury datasets to investigate the spatio-temporal dynamics of microglia activation. By applying the PSTS algorithm to a well-established mouse model of acquired brain injury, we successfully reconstruct the spatial trajectory of microglia activation following insult, thereby validating this key component of stLearn.
Project description:Although global analyses of transcription factor binding provide one view of potential transcriptional regulatory networks, regulation also occurs at levels distinct from transcription factor binding. Here, we use a genetic approach to identify targets of transcription factors in yeast and reconstruct a functional regulatory network. First, we profiled transcriptional responses in S. cerevisiae strains with individual deletions of 263 transcription factors. Then we used directed-weighted graph modeling and regulatory epistasis analysis to identify indirect regulatory relationships between these transcription factors, and from this we reconstructed a functional transcriptional regulatory network. The enrichment of promoter motifs and Gene Ontology annotations provide insight into the biological functions of the transcription factors. Data values are X scores and corresponding p-values derived using an error model. We profiled transcriptional responses upon the individual deletion of more than 260 TFs. We used a directed-weighted graph modelling approach and regulatory epistasis analysis to identify indirect regulatory relationships, and thus constructed a refined, functional transcriptional regulatory network.