Emerging technologies of single-cell multi-omics

Abstract

The heterogeneity of the hematopoietic system was largely veiled by traditional bulk sequencing methods, which measure the averaged signals from mixed cellular populations. In contrast, single-cell sequencing has enabled the direct measurement of individual signals from each cell, significantly enhancing our ability to unveil such heterogeneity. Building on these advances, numerous single-cell multi-omics techniques have been developed into high-throughput, routinely accessible platforms, delineating the precise relationships among different layers of the central dogma in molecular biology. These technologies have uncovered the intricate landscape of genetic clonality and transcriptional heterogeneity in both normal and malignant hematopoietic systems, highlighting their roles in differentiation, disease progression, and therapy resistance. This review aims to provide a brief overview of the principles of single-cell technologies, their historical development, and a subset of ever-expanding multi-omics tools, emphasizing the specific research questions that inspired their creation. Amidst the evolving landscape of single-cell multi-omics technologies, our main objective is to guide investigators in selecting the most suitable platforms for their research needs.

Figure 1.

The milestones of single-cell sequencing platforms in the past decade. Color coding is as follows: green for single-cell RNA sequencing, orange for single-cell DNA sequencing, purple for single-cell epigenomics sequencing, red for single-cell multi-omics sequencing, pink for single-cell isolation and library preparation techniques, and turquoise for whole-genome amplification methods. DOP-PCR: degenerate oligonucleotide-primed polymerase chain reaction; MDA: multiple displacement amplification; scRNA-seq: single-cell RNA sequencing; FACS: fluorescence-activated cell sorting; scDNA-seq: single-cell DNA sequencing; MALBAC: multiple annealing and looping-based amplification cycles: scATAC-seq: single-cell assay for transposase-accessible chromatin sequencing; scTrio-seq: single-cell triple-omics sequencing; scRRBS: single-cell reduced-representation bisulfite sequencing; UMI: unique molecular identifier; CITE-seq: cellular indexing of transcriptomes and epitopes by sequencing; GoT: genotyping of transcriptomes; SPARC: single-cell protein and RNA co-profiling; PTA: primary template-directed amplification; MAESTER: mitochondrial alteration enrichment from single-cell transcriptomes to establish relatedness; GoT-ChA: genotyping of targeted loci with single-cell chromatin accessibility; ReDeeM: regulatory multimaps with deep mitochondrial mutation profiling.

Figure 2.

The tradeoff between coverage and throughput due to the high cost of single-cell sequencing. (A) The tradeoff between cellular throughput and transcript coverage in single-cell RNA sequencing requires a choice between high-throughput sequencing with partial transcript coverage on thousands of cells and low-throughput sequencing with full transcript coverage on tens or hundreds of cells. (B) The tradeoff between cellular throughput and genome coverage in single-cell DNA sequencing requires a choice between narrow, targeted sequencing on thousands of cells and broad, genome-wide sequencing on tens or hundreds of cells. scRNA-seq: single-cell RNA sequencing; scDNA-seq: single-cell DNA sequencing.

Figure 3.

Summary of capabilities across multi-modal single-cell platforms. GoT: genotyping of transcriptomes; GoT-ChA: genotyping of targeted loci with single-cell chromatin accessibility; scTrio-seq: single-cell triple-omics sequencing; Smart-RRBS: Smart-seq2 and reduced representation bisulfite sequencing; DAb-seq: DNA-antibody sequencing; CITE-seq: cellular indexing of transcriptomes and epitopes by sequencing; SPARC: single-cell protein and RNA co-profiling; MAESTER: mitochondrial alteration enrichment from single-cell transcriptomes to establish relatedness; ReDeeM: regulatory multimaps with deep mitochondrial mutation profiling.