Abstract
Single-cell RNA-seq studies profile thousands of cells in developmental processes. Current databases for human single-cell expression atlas only provide search and visualize functions for a selected gene in specific cell types or subpopulations. These databases are limited to technical properties or visualization of single-cell RNA-seq data without considering the biological relations of their collected cell groups. Here, we developed a database to investigate single-cell gene expression profiling during different developmental pathways (SCDevDB). In this database, we collected 10 human single-cell RNA-seq datasets, split these datasets into 176 developmental cell groups, and constructed 24 different developmental pathways. SCDevDB allows users to search the expression profiles of the interested genes across different developmental pathways. It also provides lists of differentially expressed genes during each developmental pathway, T-distributed stochastic neighbor embedding maps showing the relationships between developmental stages based on these differentially expressed genes, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analysis results of these differentially expressed genes. This database is freely available at https://scdevdb.deepomics.org
Highlights
In developmental biology, gene expression changes during the developmental process is an important feature to understand developmental questions such as cell growth, cell differentiation, cell fate decisions, etc. (Ko, 2001; Merks and Glazier, 2005; Gittes, 2009)
We only focused on the normal human developmental processes; we abnegated experiments using tumor and other samples treated with chemical reagents
Every mammalian individual is developed from the totipotent zygote
Summary
Gene expression changes during the developmental process is an important feature to understand developmental questions such as cell growth, cell differentiation, cell fate decisions, etc. (Ko, 2001; Merks and Glazier, 2005; Gittes, 2009). Gene expression changes during the developmental process is an important feature to understand developmental questions such as cell growth, cell differentiation, cell fate decisions, etc. High-throughput RNA sequencing technique has been widely used to study gene expression in developmental processes (Spitz and Furlong, 2006). Bulk RNA sequencing typically uses hundreds to millions of cells and reveals only the average expression level for each gene across a large population of cell populations (Wang and Bodovitz, 2010; Sanchez and Golding, 2013). Single-cell RNA-seq measures the distribution of expression levels for each gene across a population of cells and provides a more accurate representation of cell-to-cell variations instead of the stochastic average (Saliba et al, 2014). High-resolution single-cell transcriptome analysis has been performed during many developmental processes including preimplantation development from oocyte to morula (Xue et al, 2013; Yan et al, 2013), early forebrain and mid/hindbrain cell differentiation from human embryonic stem
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