Decoding Human Biology and Disease Using Single-cell Omics Technologies

Abstract

Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.

Keywords: Cancer research; Cellular heterogeneity; Computational method; Disease; Single-cell omics.

Figure 1

Cellular heterogeneity at different molecular layers The cell-to-cell heterogeneity is reflected at several distinct molecular layers. Representative methods for profiling each of the individual molecular levels are noted. Single-cell multimodal omics sequencing technologies have been developed to simultaneously profile multiple layers in the same cell. SCoPE, single-cell proteomics; CyTOF, cytometry by time of flight; MALBAC, multiple annealing and looping-based amplification cycles; LIANTI, linear amplification via transposon insertion; SMOOTH-seq, single-molecule real-time sequencing of long fragments amplified through transposon insertion; scATAC-seq, single-cell assay for transposase-accessible chromatin using sequencing; scDNase-seq, single-cell DNase sequencing; SMAC-seq, single-molecule long-read accessible chromatin mapping sequencing; scMNase-seq, single-cell micrococcal nuclease sequencing; scNOMe-seq, single-cell nucleosome occupancy and methylome sequencing; MERFISH, multiplexed error robust fluorescence in situ hybridization; CODEX, co-detection by indexing; scHi-C, single-cell high-throughput chromosome conformation capture; mRNA, messenger RNA; Smart-seq, switching mechanism at 5′ end of the RNA transcript; Dip-C, diploid chromatin conformation capture; scSPRITE, single-cell split-pool recognition of interactions by tag extension; scRRBS, single-cell reduced-representation bisulfite sequencing; sci-MET, single-cell combinatorial indexing for methylation analysis; scXRBS, single-cell extended-representation bisulfite sequencing; itChIP-seq, simultaneous indexing and tagmentation-based chromatin immunoprecipitation with massively parallel DNA sequencing; CoBATCH, combinatorial barcoding and targeted chromatin release; scCUT&Tag, single-cell cleavage under targets and tagmentation.

Figure 2

Basic analysis in the scRNA-seq dataanalysis workflow scRNA-seq data produced by sequencers undergo pre-processing steps including quality control, normalization, HVG selection, optional imputation, and integration. Dimensionality-reduced data are then visualized and clustered, ready to be assigned cell types with manual or automatic approaches. scRNA-seq, single-cell RNA sequencing; pANN, proportion of artificial nearest neighbors; HVG, highly variable gene; NK, natural killer; DC, dendritic cell.

Figure 3

Advanced downstream analysis in the scRNA-seq dataanalysis workflow Downstream analysis of scRNA-seq data includes transcriptional and compositional comparative analysis, trajectory inference, GRN reconstruction, cell–cell interaction exploration, and multimodal integration. UMAP, uniform manifold approximation and projection; GRN, gene regulatory network; TF, transcription factor, MCP, multicellular program.

Figure 4

Representative applications of SCO sequencing A. A single-cell cross-tissue molecular map of the human. B. SCO sequencing identifies potent neutralizing antibodies in COVID-19. C. Dissection of TMEs using SCO sequencing. SCO, single-cell omics; COVID-19, coronavirus disease 2019; PBMC, peripheral blood mononuclear cell; scBCR-seq, single-cell B cell receptor sequencing; TME, tumor microenvironment; Treg, regulatory T cell; cDC, conventional DC; LN, lymph node; TAM, tumor-associated macrophage; TAN, tumor-associated neutrophil; CAF, cancer-associated fibroblast.

Figure 5

Perspectives of SCO Perspectives of SCO consist of technological advances, human fundamental research, and clinical prospects.