New discoveries in the field of metabolism by applying single-cell and spatial omics

Abstract

Single-cell multi-Omics (SCM-Omics) and spatial multi-Omics (SM-Omics) technologies provide state-of-the-art methods for exploring the composition and function of cell types in tissues/organs. Since its emergence in 2009, single-cell RNA sequencing (scRNA-seq) has yielded many groundbreaking new discoveries. The combination of this method with the emergence and development of SM-Omics techniques has been a pioneering strategy in neuroscience, developmental biology, and cancer research, especially for assessing tumor heterogeneity and T-cell infiltration. In recent years, the application of these methods in the study of metabolic diseases has also increased. The emerging SCM-Omics and SM-Omics approaches allow the molecular and spatial analysis of cells to explore regulatory states and determine cell fate, and thus provide promising tools for unraveling heterogeneous metabolic processes and making them amenable to intervention. Here, we review the evolution of SCM-Omics and SM-Omics technologies, and describe the progress in the application of SCM-Omics and SM-Omics in metabolism-related diseases, including obesity, diabetes, nonalcoholic fatty liver disease (NAFLD) and cardiovascular disease (CVD). We also conclude that the application of SCM-Omics and SM-Omics approaches can help resolve the molecular mechanisms underlying the pathogenesis of metabolic diseases in the body and facilitate therapeutic measures for metabolism-related diseases. This review concludes with an overview of the current status of this emerging field and the outlook for its future.

Keywords: CVD; Diabetes; NAFLD; Obesity; SCM-Omics; SM-Omics.

Graphical abstract

Fig. 1

Single-cell multi-Omics (SCM-Omics) and spatial multi-Omics (SM-Omics) techniques. (A) Brief schematic diagram of the SCM-Omics techniques process. (B) Brief schematic diagram of the SM-Omics techniques process.

Fig. 2

Timeline of single-cell multi-Omics (SCM-Omics) and spatial multi-Omics (SM-Omics) method milestones. (A) Timeline of publications on single-cell omics approaches. (B) Timeline of publications on spatial omics approaches. STRT-seq: single-cell tagged reverse transcription sequencing; SNS: single-nucleus sequencing; SMART-seq: full-transcriptome mRNA-seq; CEL-seq: cell expression by linear amplification and sequencing; scHiC-seq: single-cell Hi-C; Fluidigm C1: the C1 system isolates single cells into individual reaction chambers in the exclusive Fluidigm integrated fluidic circuit; MARS-seq: massively parallel RNA single-cell sequencing framework; scRRBS: single-cell reduced-representation bisulfite sequencing; scBS-seq: single-cell bisulfite sequencing; G&T-seq: genome & transcriptome sequencing; sciATAC-seq: single-cell combinatorial indexing ATAC-seq; scATAC-seq: single-cell ATAC-seq; scChIP-seq: single-cell ChIP-seq; sci-RNA-seq: single cell combinatorial indexing RNA sequencing; CITE-seq: cellular indexing of transcriptomes and epitopes; SCI-seq: single-cell combinatorial indexed sequencing; scNOME-seq: single-cell nucleosome occupancy and methylome sequencing; snmC-seq: single nucleus methylcytosine sequencing; snRNA-seq: single nucleus RNA sequencing; REAP-seq: RNA expression and protein sequencing assay; scM&T-seq: a method for parallel single-cell genome-wide methylome and transcriptome sequencing; COOL-seq: single-cell chromatin overall omic-scale landscape sequencing; LINNAEUS: lineage tracing by nuclease-activated editing of ubiquitous sequences; sci-CAR: a combinatorial indexing-based co-assay that jointly profiles chromatin accessibility and mRNA in each of thousands of single cells; sci-MET: single-cell combinatorial indexing for methylation analysis; scTHS-seq: single cell transposome hypersensitive sites sequencing; scGESTALT: a method that combines cumulative editing of a lineage barcode array by CRISPR–Cas9 with large-scale transcriptional profiling using droplet-based single-cell RNA sequencing; SPLiT-seq: split-pool ligation-based transcriptome sequencing; ChIA-Drop: a strategy for multiplex chromatin-interaction analysis via droplet-based and barcode-linked sequencing; CORTAD-seq: a method for concurrent sequencing of the transcriptome and targeted genomic regions; dscATAC-seq: droplet single-cell assay for transposase-accessible chromatin using sequencing; scNT-seq: single-cell metabolically labeled new RNA tagging sequencing; sci-Hi-C: single-cell combinatorial indexed Hi-C; inCITE-seq: intranuclear CITE-seq; CoTECH: combined assay of transcriptome and enriched chromatin binding; scChaRM-seq: single-cell chromatin accessibility, RNA barcoding, and DNA methylation sequencing; SCITO-seq: single-cell combinatorial indexed cytometry sequencing; SCoPE2: single cell proteomics; mDrop-seq: single cell RNA-seq of microbes; NEAT-seq: simultaneous profiling of intranuclear proteins, chromatin accessibility and gene expression in single cells; Fixed RNA: the single cell Fixed RNA Profiling (FRP) workflow measures RNA levels in samples (single cells or nuclei) fixed with formaldehyde, using probes targeting the whole transcriptome; ISSAAC-seq: in situ sequencing hetero RNA–DNA-hybrid after assay for transposase-accessible chromatin-sequencing; Live-seq: a technology that keeps the cell alive after transcriptome profiling by using a cytoplasmic biopsy; scNanoATAC-seq: single-cell nanowell-assisted assay for transposase-accessible chromatin using sequencing. smFISH: amplification-based single molecule fluorescence in situ hybridization; RNAscope: a commercially available in situ hybridization assay for the detection of RNA in formalin-fixed paraffin-embedded tissue; LCM: laser capture microdissection; ISS: in situ sequencing; TIVA: transcriptome in vivo analysis; FISSEQ: fluorescent in situ sequencing; sequential fluorescence in situ hybridization; seqFISH: sequential fluorescence in situ hybridization; tomo-seq: RNA-seq tomography; MERFISH: multiplexed error-robust fluorescence in situ hybridization; ST: spatial transcriptomics; smHCR: single-molecule hybridization chain reaction; GEO-seq: geographical position sequencing; NICHE-seq: an optimization of a targeted, padlock probe-based technique for in situ barcode sequencing compatible with Illumina sequencing chemistry; BaristaSeq: an optimization of a targeted, padlock probe-based technique for in situ barcode sequencing compatible with Illumina sequencing chemistry; STARmap: spatially-resolved transcript amplicon readout mapping; osmFISH: ouroboros smFISH; seqFISH+: evolution of sequential fluorescence in situ hybridization; Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution; APEX-seq: a method for RNA sequencing based on direct proximity labeling of RNA using the peroxidase enzyme APEX2; HDST: high-definition spatial transcriptomics; 10X Visium: Visium Spatial Gene Expression; HybISS: hybridization-based in situ sequencing; Split-FISH: a multiplexed fluorescence in situ hybridization method; ZipSeq: a method that uses patterned illumination and photocaged oligonucleotides to serially print barcodes (‘zipcodes’) onto live cells in intact tissues, in real time and with an on-the-fly selection of patterns; DBiT-seq: deterministic barcoding in tissue for spatial omics sequencing; XYZeq: a workflow that encodes spatial metadata into scRNA-seq libraries; sci-Space: a method that retains single cell resolution while resolving spatial heterogeneity at larger scales; SEAM: spatial single nuclear metabolomics; SPACECAT: spatially photo activatable color encoded cell address tags; Slide-DNA-seq: a method for capturing spatially resolved DNA sequences from intact tissue sections; SM-Omics: spatial multi-omics; Seq-Scope: a spatial barcoding technology with a resolution comparable to an optical microscope; DVP: deep visual proteomics; Spatial-ATAC-seq: spatially resolved assay for transposase-accessible chromatin using sequencing; Stereo-seq: spatial enhanced resolution omics sequencing.

Fig. 3

Application of single-cell multi-Omics (SCM-Omics) and spatial multi-Omics (SM-Omics) technologies in obesity research. The application of SCM-Omics and SM-Omics technologies in obesity-relevant primary tissues in humans and mice, namely brown adipose tissue (BAT), visceral white adipose tissue (vWAT), inguinal white adipose tissue (iWAT), and epididymal white adipose tissue (eWAT), is described. The timeline shows the number of publications on SCM-Omics and SM-Omics techniques in the PubMed database over the past six years (2017–2022). Representative applications in obesity research in humans and mice are described through publications related to SCM-Omics and SM-Omics technologies.

Fig. 4

Application of single-cell multi-Omics (SCM-Omics) and spatial multi-Omics (SM-Omics) technologies in diabetes research. The application of SCM-Omics and SM-Omics technologies in primary tissues related to diabetes in humans and mice, namely the pancreas and visceral white adipose tissue (vWAT), is described. The timeline shows the number of publications on SCM-Omics and SM-Omics technologies in the field of diabetes in the PubMed database over the past seven years (2016–2022) through statistics. Representative applications in the field of diabetes research in humans and mice are described through publications related to SCM-Omics and SM-Omics technologies.

Fig. 5

Application of single-cell multi-Omics (SCM-Omics) and spatial multi-Omics (SM-Omics) technologies in non-alcoholic fatty liver disease (NAFLD) research. The application of SCM-Omics and SM-Omics technologies in NAFLD-associated primary tissue, namely the liver, in humans, mice, and pigs. The timeline shows the number of publications on SCM-Omics and SM-Omics techniques in the field of NAFLD in the PubMed database over the past six years (2017–2022). Representative applications in the field of NAFLD research in humans, mice, and pigs are described through publications related to the SCM-Omics and SM-Omics technologies.

Fig. 6

Application of single-cell multi-Omics (SCM-Omics) and spatial multi-Omics (SM-Omics) technologies in cardiovascular disease (CVD) research. The application of SCM-Omics and SM-Omics technologies to CVD-relevant primary tissues, namely the heart and aorta, in humans, mice, and pigs. The timeline shows the number of publications on SCM-Omics and SM-Omics techniques in the field of CVD research in the PubMed database over the past seven years (2016–2022). Representative applications in the field of CVD research in humans, mice, and pigs are described through publications related to the SCM-Omics and SM-Omics technologies.