Application of a deep learning-based image analysis and live-cell imaging system for quantifying adipogenic differentiation kinetics of adipose-derived stem/stromal cells

Emerging technologies in adipose tissue research

Technologies are transforming the understanding of adipose tissue as a complex and dynamic tissue that plays a critical role in energy homoeostasis and metabolic health. This mini-review provides a brief overview of the potential impact of novel technologies in biomedical research and aims to identify areas where these technologies can make the most significant contribution to adipose tissue research. It discusses the impact of cutting-edge technologies such as single-cell sequencing, multi-omics analyses, spatial transcriptomics, live imaging, 3D tissue engineering, microbiome analysis, in vivo imaging, and artificial intelligence/machine learning. As these technologies continue to evolve, we can expect them to play an increasingly important role in advancing our understanding of adipose tissue and improving the treatment of related diseases.

Deep learning models for cancer stem cell detection: a brief review

Cancer stem cells (CSCs), also known as tumor-initiating cells (TICs), are a subset of tumor cells that persist within tumors as a distinct population. They drive tumor initiation, relapse, and metastasis through self-renewal and differentiation into multiple cell types, similar to typical stem cell processes. Despite their importance, the morphological features of CSCs have been poorly understood. Recent advances in artificial intelligence (AI) technology have provided automated recognition of biological images of various stem cells, including CSCs, leading to a surge in deep learning research in this field. This mini-review explores the emerging trend of deep learning research in the field of CSCs. It introduces diverse convolutional neural network (CNN)-based deep learning models for stem cell research and discusses the application of deep learning for CSC research. Finally, it provides perspectives and limitations in the field of deep learning-based stem cell research.

urPTMdb/TeaProt: Upstream and Downstream Proteomics Analysis

We have developed the underrepresented post-translational modification (PTM) database (urPTMdb), a PTM gene set database to accelerate the discovery of enriched protein modifications in experimental data. urPTMdb provides curated lists of proteins reported to be substrates of underrepresented modifications. Their enrichment in proteomics datasets can reveal unexpected PTM regulations. urPTMdb can be implemented in existing workflows, or used in TeaProt, an online Shiny tool that integrates upstream transcription factor enrichment analysis with downstream pathway analysis through an easy-to-use interactive interface. TeaProt annotates user-uploaded data with drug-gene interactions, subcellular localizations, phenotypic functions, gene-disease associations, and enzyme-gene interactions. TeaProt enables gene set enrichment analysis (GSEA) to discover enrichments in gene sets from various resources, including MSigDB, CHEA, and urPTMdb. We demonstrate the utility of urPTMdb and TeaProt through the analysis of a previously published Western diet-induced remodeling of the tongue proteome, which revealed altered cellular processes associated with energy metabolism, interferon alpha/gamma response, adipogenesis, HMGylation substrate enrichment, and transcription regulation through PPARG and CEBPA. Additionally, we analyzed the interactome of ADP-ribose glycohydrolase TARG1, a key enzyme that removes mono-ADP-ribosylation. This analysis identified an enrichment of ADP-ribosylation, ribosomal proteins, and proteins localized in the nucleoli and endoplasmic reticulum. TeaProt and urPTMdb are accessible at https://tea.coffeeprot.com/.