The technological landscape and applications of single-cell multi-omics

Abstract

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.

Fig. 1. From single omics to multi-omics and their broad applications.

Single-omics technologies consist of many modalities, each pertaining to specific parts of the central dogma of DNA–RNA–protein. Single-cell single-modality methods utilize technologies for a variety of applications including but not limited to quantification of genomic, exomic and epigenomic (DNA methylation, chromatin accessibility and histone modifications) information from DNA; transcriptomic and epitranscriptomic information from RNA; and proteomic, phosphoproteomic and metabolomic data from proteins. Integration of single-omics modalities, aided by computational analysis methods, contributes to a broad array of fundamental and clinical research applications ranging from generation of cell and tissue atlases to interrogation of complex disease biology. CNV, copy number variation; FC, fold change; lncRNA, long non-coding RNA; SNP, single-nucleotide polymorphism.

Fig. 2. The landscape of multi-omics sequencing.

Many multi-omics methods function with transcriptome profiling as their ‘anchor’ to facilitate single-cell multi-omics interrogation. Genome and transcriptome sequencing (G&T-seq) is a representative multi-omics technology for profiling both the genome and the transcriptome in single cells using biotinylated oligo(dT) capture primers and streptavidin-coated magnetic beads to target the poly(A) tail of mRNA, thereby separating the mRNA from the genomic material of the cell. Single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq) profiles the transcriptome together with the epigenome by simultaneously tagging and fragmenting DNA sequences in open chromatin regions using a DNA transposase (Tn5). This method has paved the way for other technologies that profile both the epigenome and transcriptome in single nuclei, such as Sci-CAR-seq, single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq) and 10x Multiome. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) labels single cells with antibody conjugates bound to biotinylated DNA barcodes. By using droplet-based microfluidics, mRNA and cell-surface protein barcodes are converted to cDNA, which can be split downstream into two respective libraries using size selection. V(D)J sequencing can be used in tandem with transcriptome sequencing to analyse in the same cell full-length V(D)J sequences of B cell and T cell receptors using gene 5′-end sequencing. In contrast to conventional 3′-end sequencing that is traditionally used in transcriptome sequencing, barcodes are not adjacent to the poly d(T) primer, but instead are adjacent to a sequence in the 5′-end of the transcript. V(D)J sequencing and transcriptome sequencing can be performed together in the same cell and in tandem with other modalities such as ATAC-seq, CITE-seq and other methods. These representative technologies are just some of the many examples of multi-modality technologies used in single-cell multi-omics research. ADT, antibody-derived tag; RT, reverse transcription; Sci-CAR-seq, combinatorial indexing-based co-assay that jointly profiles chromatin accessibility and mRNA.

Fig. 3. Towards achieving higher throughput: multi-omics methods that increase throughput in single experiments.

a, In cell hashing, ubiquitously expressed cell-surface proteins (or nucleus-surface proteins, in the case of nuclear hashing) are bound by antibodies that are conjugated to an oligonucleotide or a specific sequence acting as a specific ‘barcode’, thereby enabling the profiling of multiple cells in a single experiment. b, Cell tagging is a combinatorial cell-indexing and high-throughput cell tracking method, in which sequential rounds of cell labelling with unique nucleic acid sequences deliver heritable barcode combinations to single cells. Microfluidic methods, such as used in deterministic barcoding in tissue for spatial omics sequencing (DBiT-seq), deliver barcodes (A1…A50, B1…B50) to cells on a tissue slide through perpendicular microfluidic channels to form a spatial grid (A1,B1…A50,B50) that is sequenced at high throughput to construct spatial omics maps. c, Combinatorial barcoding of single cells involves splitting and pooling either cells or nuclei into wells where barcodes are introduced in situ. Following multiple rounds of barcoding, molecules within the same cell are labelled with a unique barcode combination (BC1–BC4). GFP, green fluorescent protein; P5 and P7, sequencing index primers; pA, poly(A) tail; R1 and R2, read-one and read-two primers; RT, reverse transcription primer.

Fig. 4. Towards spatially resolved multi-omics.

a, Examples of spatial technologies used for multi-omics analysis. Imaging-based technologies such as fluorescence in situ hybridization (FISH) and sequential FISH (seqFISH+) use sequential imaging of different fluorescent probes to characterize spatial organization within cells. Microarray-based and microfluidics technologies use barcodes to label the structural organization of single cells in tissues. Methods such as spatial transcriptomics profile tissue by placing it on a glass slide that is coated with an array of spots containing oligonucleotides designed to extract spatial information from a tissue section, whereas methods such as deterministic barcoding in tissue for spatial omics sequencing (DBiT-seq) use microfluidics channels to deliver reagents and barcodes to a tissue on a glass slide, thereby creating a barcoded grid in which each grid space contains a single cell. Marker genes with known function and physical location are used as references. ‘Genes/pixel’ indicates the number of genes detected in each spatially barcoded pixel. Laser capture microdissection (LCM)-based technologies allow full profiling of a gene or protein within a single cell by cutting a tissue section with a laser. b, Spatial-omics microfluidics-based tissue barcoding modalities have expanded in the past decade, enabling integration of complex datasets with spatial context. DBiT-seq has the integrative capacity to profile many modalities including spatial assay for transposase-accessible chromatin with sequencing (ATAC-seq), cleavage under targets and tagmentation (CUT&Tag), cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and other modalities. c, Spatial multi-omics data analysis methods generally partition data into clusters to find functional cell subtypes within different microenvironments. These high-dimensional data are typically visualized using dimensionality reduction techniques such as uniform manifold approximation and projection (UMAP) to view multimodal single-cell measurements. Various computational clustering algorithms can be applied to downstream multimodal analysis to automatically estimate cell types and compare clustering attributes of different modalities. Within and between cell clusters, heterogeneous archetypes of signalling pathways inferred from cell–cell interaction analyses can be used to characterize local cellular microenvironments. Geo-seq, geographical position sequencing; PDMS, polydimethylsiloxane; SMI, spatial molecular imager; UMI, unique molecular identifier. Part a adapted with permission from ref. , Elsevier.