Exploration into the Unknown, one single-cell at a time

Advancements in whole-genome and -transcriptome sequencing have provided an unbiased approach for discovering more molecular details about a given disease. Advancements in next generation sequencing (NGS) and microfluidics enables mapping of gene expression at the level of the individual cell, allowing for interrogation of individual cell heterogeneity of cell population using single-cell RNA sequencing (scRNA-seq). Where conventional RNA-seq allows for differential expressed gene (DEG) analysis, scRNA-seq permits the identification of highly variable genes within individual cells and across cell populations. Single-cell omics (both single-cell transcriptomics and epigenomics) data provide unprecedented opportunities to overcome challenges identified from bulk sequencing. The key advantage of scRNA-seq is the capability to deconvolve cell heterogeneity, enabling the interrogation of subpopulations that may be critical to the understanding of the mechanisms underlying various biological processes. Given the high dimensionality, enormous noise level, and large size of datasets, scRNA-seq has presented new computational challenges to analyze the single-cell RNA-seq datasets. To overcome these challenges, various machine learning (ML) methods have been developed and continue to develop as the diversity of scRNA-seq datasets expands. The demand for ML approaches and resulting interpretable algorithms that integrate diverse scRNA-seq datasets that can infer underlying regulatory networks is increasing as interdisciplinary research fields leverage scRNA-seq to answer questions of cell heterogeneity.

The overarching goal of this edition is to provide the scientific community with the recent advances in single-cell sequencing (scRNA-seq) and how partnerships with artificial intelligence is providing algorithms that facilitate the interpretation of cell heterogeneity. As interdisciplinary approaches for understanding biology emerge, a robust and transparent application of ML as it applies to scRNA-seq will enable more accurate and comprehensive understanding of the biological process one cell at a time.

Frontiers Cell and Developmental Biology Guest editor Dr. Paola Marignani and co-editor Dr. Jun Ding, invite experts in the field of scRNA-seq and machine learning for scRNA-seq data analysis to submit Original Research Articles, Brief Reports, Method and Technology, and Review Articles from diverse fields of research including human diseases, aquatic biology, bacteriology and virology, and artificial intelligence.