In a recent study, researchers combined single-cell transcriptomics with spatial antibody-based profiling of proteins to create high resolution maps of single cells and human tissues.
Single cell analysis
Advancements in molecular profiling have opened the possibility of mapping gene expression in cells, tissues, and organs in the human body at unprecedented accuracy. Improvements in massive parallel sequencing coupled with single-cell sample preparations and data deconvolution have allowed single cell-RNA sequencing (scRNA-seq) to become a powerful approach in characterising gene expression profiles in single cells.
The primary aim of this study was to create one publicly available Single Cell Type Atlas, combining the Human Cell Atlas genome-wide expression data from scRNA-seq experiments with the Human Protein Atlas spatial antibody-based bioimaging data.
Human Cell Atlas (HCA)
The objective of this collaborative effort was to study distinctive gene expression profiles on an RNA level across diverse cell and tissue types. This recently developed platform now allows connections of cellular and gene expression data with summarised descriptions of their location, morphology, and other key features.
Human Protein Atlas (HPA)
In parallel, the development of millions of available antibodies for human proteins has allowed single-cell analysis of corresponding proteins in tissues and organs. The HPA utilises fluorescent-based bioimaging to map the expression of protein-coding genes across all major human cells, tissues, and organs. This data is also openly accessible along with annotations from certified pathologists.
Results and implications
In this recent paper, published in Science Advances, researchers performed expression specificity classifications in 192 individual cell type clusters, which determined the number of genes and their degree of expression in each different cell type. This allowed comparisons to be made with the bulk transcriptomics data, distinguishing gene expression clusters in relation to cell types and sharing the same or similar functions both within the same organ and between different organs.
Surveying scRNA-seq data from healthy human tissues and organs helped determine correlation expression profiles across the 192 different cell types. Publicly available data from human tissues was used with more than 4,000 cells analysed and over 20 million read counts by the sequencing of each tissue. The cell-type expression landscape was summarised in network plots, illustrating number of cell type-enriched and group-enriched genes and their relationships. Altogether, the 192 single-cell type clusters analysed were grouped into 51 main cell types and 122 further functional groups of cells.
The Single Cell Type Atlas has been successfully created based on this new data for protein-coding genes. It now offers over 250,000 interactive UMAP plots presenting primary data for all analysed cells, with body-wide maps of gene expression at both RNA and protein levels. Moreover, this has demonstrated momentous success in the creation of a comprehensive yet efficient alternative for public accessibility of genome-wide knowledge. These efforts have helped build and improve public knowledge of protein-coding genes in single cell types and across tissues and organs in the human body. This will no doubt aid upcoming efforts in cell-, tissue- and organ-wide mapping of proteins in human biology and disease.
Image credit: Arek Socha on Pixabay.
