A comprehensive understanding of dental pulp cellular compositions and their molecular responses to infection are crucial for the advancement of regenerative dentistry. Here, we presented a pilot study of single-cell transcriptomic profiles of 6,810 pulpal cells isolated from a sound human maxillary third molar and three carious teeth with enamel and deep dental caries. We observed altered immune cell compositions of the dental pulp in deep, but not enamel ones. Differential expression analysis revealed up-regulation of several pro-inflammatory, anti-inflammatory, and mineralization-related genes in the immune and stromal cells of the deep dental caries. Making use of an algorithm for predicting cell-to-cell interactions from single-cell transcriptomic profiles, we showed an increase in cell-cell interactions between B cells, plasma cells and macrophages, and other cell types in deep dental caries, including those between TIMP1 (odontoblasts)—CD63 (myeloid cells), and CCL2 (macrophages)—ACKR1 (endothelial cells). Collectively, our work highlighted the single-cell level gene regulations and intercellular interactions in the dental pulps in health and disease.

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Predicting phenotypes and complex traits from genomic variations has always been a big challenge in molecular biology, at least in part because the task is often complicated by the influences of external stimuli and the environment on regulation of gene expression. With today's abundance of omic data and advances in high‐throughput computing and machine learning (ML), we now have an unprecedented opportunity to uncover the missing links and molecular mechanisms that control gene expression and phenotypes. To empower molecular biologists and researchers in related fields to start using ML for in‐depth analyses of their large‐scale data, here we provide a summary of fundamental concepts of machine learning, and describe a wide range of research questions and scenarios in molecular biology where ML has been implemented. Due to the abundance of data, reproducibility, and genome‐wide coverage, we focus on transcriptomics, and two ML tasks involving it: (a) predicting of transcriptomic profiles or transcription levels from genomic variations in DNA, and (b) predicting phenotypes of interest from transcriptomic profiles or transcription levels. Similar approaches can also be applied to more complex data such as those in multi‐omic studies. We envisage that the concepts and examples described here will raise awareness and promote the application of ML among molecular biologists, and eventually help improve a framework for systematic design and predictions of gene expression and phenotypes for synthetic biology applications.