Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs

Abstract

Background As core units of organ tissues, cells of various types play their harmonious rhythms to maintain the homeostasis of the human body. It is essential to identify the characteristics of cells in human organs and their regulatory networks for understanding the biological mechanisms related to health and disease. However, a systematic and comprehensive single-cell transcriptional profile across multiple organs of a normal human adult is missing. Results We perform single-cell transcriptomes of 84,363 cells derived from 15 tissue organs of one adult donor and generate an adult human cell atlas. The adult human cell atlas depicts 252 subtypes of cells, including major cell types such as T, B, myeloid, epithelial, and stromal cells, as well as novel COCH+ fibroblasts and FibSmo cells, each of which is distinguished by multiple marker genes and transcriptional profiles. These collectively contribute to the heterogeneity of major human organs. Moreover, T cell and B cell receptor repertoire comparisons and trajectory analyses reveal direct clonal sharing of T and B cells with various developmental states among different tissues. Furthermore, novel cell markers, transcription factors, and ligand-receptor pairs are identified with potential functional regulations in maintaining the homeostasis of human cells among tissues. Conclusions The adult human cell atlas reveals the inter- and intra-organ heterogeneity of cell characteristics and provides a useful resource in uncovering key events during the development of human diseases in the context of the heterogeneity of cells and organs.

The Cellular Composition of Bovine Coccygeal Intervertebral Discs: A Comprehensive Single-Cell RNAseq Analysis

“…Unsupervised cell clustering methods allow the detection of cell types and differentiation states based on the whole transcriptome in a fine-grained manner. These methods have readily and successfully been used [ 57 , 58 , 59 , 60 , 61 ], even if minor imprecisions may occur at defining cross-species identities of specific cell types and functional differences among identified cell types [ 62 , 63 ]. In our single-cell RNAseq analysis, we used the shared nearest neighbor approach for unsupervised cell clustering to identify distinct cell types in the four AFs and four NPs of the bovine IVDs from two animals [ 64 ].…”

Single-Cell Gene Network Analysis and Transcriptional Landscape of MYCN-Amplified Neuroblastoma Cell Lines

“…Recent technological advances have provided the unprecedented opportunity to investigate heterogeneity at single-cell resolution [ 9 ], allowing researchers to quantitatively identify cellular subpopulations and to uncover molecular mechanisms underlying phenotypic diversity among cells [ 10 ]. The majority of the single-cell RNA-Sequencing (scRNA-Seq) studies published so far in cancer biology aim at deciphering tumor tissue samples’ composition complexity and the interplay between cancer cells and the cellular components of the tumor microenvironment [ 11 ].…”

Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space

“…We applied SCALEX integration to two large and complex datasets-the mouse atlas dataset (comprising multiple organs from two studies assayed by 10X, Smart-seq2, and Microwell-seq 6,51 ) (Fig. 4a) and the human atlas dataset (comprising multiple organs from two studies assayed by 10X and Microwell-seq 39,52 ).…”