Project description:Tumor microenvironment (TME) consists of different cell populations, whose interactions contribute to tumor heterogeneity and therapy response. Spatial transcriptomics (ST) offers valuable insights into spatial complexity and heterogeneity of TME. We established a Python package, Geographic Information System (GIS)-augmented In-Silico Reconstruction of Tumor Architecture (GIS-ROTA), based on application of GIS methods to ST data to examine/explore spatial heterogeneity of co-regulated gene sets, such as pathways and cell types within the TME. In our Visium dataset of primary and metastatic estrogen receptor positive breast tumor samples, GIS-ROTA revealed extensive co-localization of estrogen response with metabolic pathway gene sets and mutual exclusivity with metastasis-related and specific immune-related pathway gene sets. Our findings demonstrate the robustness of GIS-ROTA in quantitating tumor heterogeneity and identifying spatially significant regions while minimizing the subjectivity involved in interpreting the clusters from conventional statistical methods. Thus, GIS-ROTA enables the development of therapeutic strategies that target multiple cell populations.
Project description:We report the application of single-molecule-based sequencing technology for high-throughput profiling of transcription start sites for Escherichia coli under different conditions. By obtaining sequence from 5' RACE (rapid amplification of cDNA ends) followed by deep sequencing, we generated genome-wide TSS (transcription start site) maps for E. coli. This TSS-map was integrated with ChIP-chip data generated for 6 sigma factors in E. coli, resulting in reconstruction of sigma factor network in E. coli. Examination of transcription start sites by biological duplicates from E. coli for 3 different conditions