Table 1 Technologies for high-throughput single-cell sequencing
Category | Technology | Method | Description | Advantages | Disadvantages | Commercial supplier | Reference |
|---|---|---|---|---|---|---|---|
Single- omics | Single-cell transcriptomics | 10X Chromium | 10X Chromium single cell gene expression | High throughput, high reproducibility,low technical noise, time saving | High cost, high sample requirement | 10X Genomics | [46] |
Drop-seq | Droplet-based single cell RNA sequencing | High throughput, low cost, fast | Low cell capture efficiency, low sensitivity | Dolomite Bio | [47] | ||
inDrop | Droplet-based single cell RNA sequencing | High throughput, low cost | Low mRNA capture efficiency, high error rate | 1CellBio | [48] | ||
Seq-Well | Single-cell RNA-seq with microwells | High throughput, low cost | Low cell capture efficiency | - | [49] | ||
CytoSeq | Cytometry-based sequencing | High throughput, low cost | Cell size limited (smaller than 20 μm) | BD Rhapsody | [50] | ||
Smart-seq2 | Switching mechanism at the end of the 5′-end of the RNA transcript sequencing | Full-length coverage across transcripts, high sensitivity | High cost, low throughput | Fluidigm C1 | [51] | ||
CEL-Seq2 | Single-cell RNA-seq by multiplexed linear amplification | High sensitivity, low cost | Strong 3' preference, low throughput | - | [52] | ||
sci-RNA-seq | Single-cell combinatorial indexing RNA sequencing | High throughput, minimize perturbation to RNA integrity | Some cell types cannot be defined | - | [53] | ||
MARS-seq | Massively parallel single-cell RNA sequencing | High throughput | Low sensitivity, high dropout rate | - | [54] | ||
Single-cell spatial transcriptomics | Spatial indexing | Spatial indexing methods perform hybridization of RNAs to barcoded capture arrays, followed by fragment pooling and NGS | Unbiased, greater coverage, greater field of view, more accessible (typically sequenced using standard NGS machine) | Limited to capture spot resolution, lower depth (per transcript) | 10X Visuim, BGI STOmics (Stereo-Seg), AtlasXomics (DBiT-seq) | ||
Imaging-based | Imaging-based approaches use fluorescent tagging of mRNA molecules in situ and high-resolution fluorescence microscopy to detect mRNA transcripts | Single-cell resolution, greater depth (per transcript), better suited to capture subtype change due to spatial influence | Biased, lower coverage, smaller field of view, more read-out noise, requires more specialized equipment | Vizgen (MERFISH), Spatial Genomics (segFISH), NanoString Technologies (CosMx), 10X Genomics Xenium | |||
Single-cell genomics | DOP-PCR | degenerate oligonucleotide primed PCR | High throughput | Uneven amplification, low coverage, amplification errors, allele dropout | GenomePlex, Mission Bio Tapestri | ||
MDA | multiple displacement amplification | Simplicity, high fidelity, low false positive rate | Amplification bias, allele dropout | Qiagen, REPLI-g, 10X Genomics chromium CNV | [64] | ||
MALBAC | multiple annealing and looping-based amplification cycles | High uniformity, low amplification bias | Allele dropout | Yikon genomics | [65] | ||
Single-cell epigenomics | RRBS | reduced representation bisulfite sequencing, detecting DNA modification | Relatively low cost, high coverage of the promoters | Low throughput, low coverage of the genome-wide CpG dinucleotides | - | [66] | |
WGBS | whole genome bisulfite sequencing, detecting DNA modification | Low amplification bias, correct assignment of paired-end fragments | Low library complexity | - | [67] | ||
CGI-seq | genome-wide CpG island methylation sequencing, detecting DNA modification | High efficiency, simplified procedure, good coverage of the CpG islands and no DNA damage | Inconsistent and/or low coverage, Low throughput | - | [68] | ||
ATAC-seq | assay for transposase accessible chromatin sequencing, detecting chromatin accessibility | High coverage, high sensitivity, high-throughput | Low recovery of DNA fragments | 10X Chromium Single Cell ATAC, Bio-Rad SureCell ATAC-Seq | |||
ChIP-seq | chromatin immunoprecipitation sequencing, detecting histone modification | High resolution, high throughput | Highly dependent on the quality of antibody | Mobidrop | [72] | ||
scCUT&Tag | Cleavage Under Targets and Tagmentation | Low cell inputs, low cost, profiling protein–DNA interactions, fast | Native conditions are not always suitable, not very well suitable for the analysis of regions of the genome that are silenced or contain heterochromatin | - | [73] | ||
Drop-ChIP | Droplet-based single-cell chromatin immune-precipitation sequencing, detecting histone modification | High throughput, high specificity | Low coverage | - | [72] | ||
Single-cell proteomics | Mass spectrometry-based | Mass spectrometry-based single-cell proteomics | Quantify more proteins per cell (1,000 to 1,500), label free analysis is permitted, mature data analytics | Limited throughput to ~ 10 cells per hour per instrument, low sensitivity, destructive method | Standard BioTools | [74] | |
Antibody-based | Antibody-based single-cell proteomics | Standard high-throughput, suitable for analysis of cell-surface, cytoplasmic and secreted proteins, cells can be either live or fixed, suitable for sorting live cells | Antibody cross-reactivity, quantify fewer protein per cell | BD Rhapsody | [75] | ||
Multi-omics | Genome + transcriptome | G&T-seq | Genome and transcriptome sequencing | Powerful capability to characterize cellular diversity, high accuracy | Low throughput | - | [76] |
DR-seq | gDNA–mRNA sequencing | minimize the risk of losing (deoxy)ribonucleic acids | Low throughput | - | [77] | ||
TARGET-seq | Targeted mutation detection and parallel transcriptome characterization | minimize the risk of losing (deoxy)ribonucleic acids | Low throughput | - | [78] | ||
Transcriptome-DNA methylome | scM&T-seq | Single-cell genome-wide methylome and transcriptome sequencing | Amplified DNA and RNA separately and independently | Does not distinguish between 5mC and 5hmC | - | [79] | |
Genome + transcriptome + DNA methylome | scTrio-seq | single-cell triple omics sequencing | Simultaneous analyses of genome, epigenome, and transcriptome in the same single cell | Low throughput | - | [80] | |
Transcriptome + chromatin accessibility | sci-CAR | single-cell chromatin accessibility and mRNA-seq | Joint profiling of chromatin accessibility and gene expression | Low cell capture efficiency, limited throughput | - | [81] | |
Paired-seq | Single-cell RNA and chromatin accessibility sequencing | Powerful capability to characterize cellular diversity, high accuracy | lower library complexity than stand-alone single-cell and snATAC-seq and RNA-seq | - | [53] | ||
Transcriptome + proteome | CITE-seq | Cellular indexing of transcriptomes and epitopes by sequencing | Providing additional phenotypic information, high compatibility | Only cell surface protein can be characterized | 10X Genomics | [82] | |
REAP-seq | RNA expression and protein sequencing assay | Can be used to characterize unknown cellular populations, minimizing steric hindrance | Limited to cell surface proteins | Fluidigm C1 | [83] | ||
RAID | single-cell RNA and immuno-detection | Allow combined analysis of the transcriptome and intracellular (phospho-)proteins from fixed single cells | Not suitable for cell surface proteins | - | [84] | ||
Transcriptome + DNA–protein interactome | scDam&T-seq | single-cell DNA adenine methyltransferase identification and transcriptome sequencing | Allow combined analysis of the transcriptome and DNA–protein interactions | Limits throughput | - | [85] |