Teppei Shimamura

The recent development of single-cell multiomics analysis has enabled simultaneous detection of multiple traits at the single-cell level, providing deeper insights into cellular phenotypes and functions in diverse tissues. However, currently, it is challenging to infer the joint representations and learn relationships among multiple modalities from complex multimodal single-cell data. Here, we present scMM, a novel deep generative model-based framework for the extraction of interpretable joint r

The cellular interactions in the tumor microenvironment of colorectal cancer (CRC) are poorly understood, hindering patient treatment. In the current study, we investigate whether events occurring at the invasion front are of particular importance for CRC treatment strategies. To this end, we analyze CRC tissues by combining spatial transcriptomics from patients with a public single-cell transcriptomic atlas to determine cell-cell interactions at the invasion front. We show that CRC cells are lo

The recursive elastic net is a powerful tool for inferring gene networks from time-course gene expression profiles.

Tolerance to severe tumor microenvironments, including hypoxia and nutrient starvation, is a common feature of aggressive cancer cells and can be targeted. However, metabolic alterations that support cancer cells upon nutrient starvation are not well understood. Here, by comprehensive metabolome analyses, we show that glutamine deprivation leads to phosphoethanolamine (PEtn) accumulation in cancer cells via the downregulation of PEtn cytidylyltransferase (PCYT2), a rate-limiting enzyme of phosph

Patient-specific analysis of molecular networks is a promising strategy for making individual risk predictions and treatment decisions in cancer therapy. Although systems biology allows the gene network of a cell to be reconstructed from clinical gene expression data, traditional methods, such as bayesian networks, only provide an averaged network for all samples. Therefore, these methods cannot reveal patient-specific differences in molecular networks during cancer progression. In this study, w

We propose a statistical method based on graphical Gaussian models for estimating large gene networks from DNA microarray data. In estimating large gene networks, the number of genes is larger than the number of samples, we need to consider some restrictions for model building. We propose weighted lasso estimation for the graphical Gaussian models as a model of large gene networks. In the proposed method, the structural learning for gene networks is equivalent to the selection of the regularizat

Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Science Research; OITA Cancer Research Foundation; AMED under Grant Number; Japan Science and Technology Agency (JST); Takeda Science Foundation; The Princess Takamatsu Cancer Research Fund.