ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) analysis reveals heterogeneity and dynamic cell transitions. However, conventional gene-based analyses require intensive manual curation to interpret biological implications of computational results. Hence, a theory for efficiently annotating individual cells remains warranted. RESULTS: We present ASURAT, a computational tool for simultaneously performing unsupervised clustering and functional annotation of disease, cell type, biological process and signaling pathway activity for single-cell transcriptomic data, using a correlation graph decomposition for genes in database-derived functional terms. We validated the usability and clustering performance of ASURAT using scRNA-seq datasets for human peripheral blood mononuclear cells, which required fewer manual curations than existing methods. Moreover, we applied ASURAT to scRNA-seq and spatial transcriptome datasets for human small cell lung cancer and pancreatic ductal adenocarcinoma, respectively, identifying previously overlooked subpopulations and differentially expressed genes. ASURAT is a powerful tool for dissecting cell subpopulations and improving biological interpretability of complex and noisy transcriptomic data. AVAILABILITY AND IMPLEMENTATION: ASURAT is published on Bioconductor (https://doi.org/10.18129/B9.bioc.ASURAT). The codes for analyzing data in this article are available at Github (https://github.com/keita-iida/ASURATBI) and figshare (https://doi.org/10.6084/m9.figshare.19200254.v4). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quantitative Imaging Analysis of NF-κB for Mathematical Modeling Applications.

Mathematical models can integrate different types of experimental datasets, reconstitute biological systems in silico, and identify previously unknown molecular mechanisms. Over the past decade, mathematical models have been developed based on quantitative observations, such as live-cell imaging and biochemical assays. However, it is difficult to directly integrate next-generation sequencing (NGS) data. Although highly dimensional, NGS data mostly only provides a "snapshot" of cellular states. Nevertheless, the development of various methods for NGS analysis has led to much more accurate predictions of transcription factor activity and has revealed various concepts regarding transcriptional regulation. Therefore, fluorescence live-cell imaging of transcription factors can help alleviate the limitations in NGS data by supplementing temporal information, linking NGS to mathematical modeling. This chapter introduces an analytical method for quantifying dynamics of nuclear factor kappaB (NF-κB) which forms aggregates in the nucleus. The method may also be applicable to other transcription factors regulated in a similar fashion.

Encoding and decoding NF-κB nuclear dynamics.

The NF-κB signaling pathway is crucial for cellular responses to environmental factors. Several studies have tried to decipher the mechanism of cells utilizing this pathway for information transfer and accurately encoding extracellular information that is translated into unique transcriptional programs. This fine-tuned encoding is possible owing to the complex regulatory mechanisms in the NF-κB pathway and is relayed through the nuclear dynamics of the NF-κB transcription factor. The "message" is then decoded through transcriptional and post-transcriptional regulation leading to stimulus-dependent phenotypes. Here, we reviewed recent advances on how different environmental stimuli are encoded and decoded by cells to produce distinct transcriptional responses and the important analytical techniques used in NF-κB research.