Spatial Transcriptome (ST) technologies are emerging as powerful tools for studying tumor biology. However, existing tools for analyzing ST data are limited, as they mainly rely on algorithms developed for single-cell RNA sequencing (scRNAseq) data and do not fully utilize the spatial information. While some algorithms have been developed for ST data, they are often designed for specific tasks, lacking a comprehensive analytical framework for leveraging spatial information.
In this study, we present StereoSiTE, an analytical framework that combines open-source bioinformatics tools with custom algorithms to accurately infer the functional Spatial Cell Interaction Intensity (SCII) within the Cellular Neighborhood (CN) of interest. We applied StereoSiTE to decode ST datasets from xenograft models and found that the CN efficiently distinguished different cellular contexts, while the SCII analysis provided more precise insights into intercellular interactions by incorporating spatial information. By applying StereoSiTE to multiple samples, we successfully identified a CN region dominated by neutrophils, suggesting their potential role in remodeling the immune Tumor MicroEnvironment (iTME) after treatment. Moreover, the SCII analysis within the CN region revealed neutrophil-mediated communication, supported by pathway enrichment, transcription factor regulon activities, and protein-protein interactions.
StereoSiTE represents a promising framework for unraveling the mechanisms underlying treatment response within the iTME by leveraging CN-based tissue domain identification and SCII-inferred spatial intercellular interactions. The software is designed to be scalable, modular, and user-friendly, making it accessible to a wide range of researchers.

The May 2011 outbreak of an E. coli infection in Europe resulted in serious concerns about the potential appearance of a new deadly strain of bacteria, Escherichia coli O104:H4 TY-2482. In response to this situation, and immediately after the reports of deaths, the University Medical Centre Hamburg-Eppendorf and BGI-Shenzhen worked together to sequence the bacterium and assess its human health risk.

The bacteriums genome was first sequenced using Life Technologies; Ion Torrent sequencing platform. According to the results of the draft assembly, the estimated genome size of this new E. coli strain is about 5.2 Mb. Sequence analysis indicated this bacterium is an EHEC serotype O104 E. coli strain. Comparative analysis showed that this bacterium has 93% sequence similarity with the EAEC 55989 E. coli strain, which was isolated in the Central African Republic and known to cause serious diarrhea. This strain of E. coli, however, has also acquired specific sequences that appear to be similar to those involved in the pathogenicity of hemorrhagic colitis and hemolytic-uremic syndrome. The acquisition of these genes may have occurred through horizontal gene transfer.

To maximize its utility to the research community and aid those fighting the epidemic, this genomic data was released into the public domain under a CC0 license.

To the extent possible under law, BGI Shenzhen has waived all copyright and related or neighboring rights to genomic data from the 2011 E. coli outbreak. This work is published from China.