Insights gained from single-cell analysis of chimeric antigen receptor T-cell immunotherapy in cancer

Tang, Lu; Huang, Zhong-Pei; Mei, Heng; Hu, Yu

doi:10.1186/s40779-023-00486-4

Military Medical Research

Table 1 Summary of techniques of single cell sequencing

From: Insights gained from single-cell analysis of chimeric antigen receptor T-cell immunotherapy in cancer

Omics	Technique	Application	Limitation
Genome	SNS [105]	Detect DNA mutations including CNVs, SVs, SNVs in cancer cells, contributing to knowing dynamics of tumorigenesis, heterogeneous of tumors and identifying specific cancer clones	Low throughput
	SCI-seq [106]		Currently limited to CNV detection
	SCANPY [107]		Python-based rather than R-based frameworks
	SMOOTH-seq [108]		Low throughput
Epigenome	scATAC-seq [109]	Detect the open status of chromatins; Link regulatory elements to their target genes and discover in vivo correlates of heterogeneity in accessibility within cell types; Explain heterogeneity in the shift of cell states and epigenetic mechanisms for CAR-T exhaustion, differentiation and resistance	Remain challenges in detection of large-scale SVs and haplotype phasing Speculate TF binding from motif or chromatin profiling data indirectly
	sciATAC-seq [110]		Speculate TF binding from motif or chromatin profiling data indirectly
	scNanoATAC-seq [111]		Expensive under current sequencing depth (less than 2.5 dollars per cell) Speculate TF binding from motif or chromatin profiling data indirectly
	scTHS-seq [126]	Improve coverage of highly cell specific distal enhancers compared with scATAC-seq	High cost
	scDrop-CHIP [112]	Learn heterogeneity of tumors from aspects of histone modification and transfer factor binding directly, offer the opportunity to predict sensitivity to therapeutic agents [127]	Non-duplicated reads are low (less than 1000)
	scCUT&Tag [113, 114]		High cost
	Spatial-CUT&Tag [115]		High cost
	scBS-seq [116]	Detect DNA methylation to study cell development and connections between methylation and diseases	Very low cell throughput
	scCGI-seq [117]		Very low cell throughput
	snmC-seq [128] snmC-seq2 [129]		High cost
	sci-MET [130]		The coverage of methylation is not enough
	scRRBS [131]		Very low cell throughput Not full-length genome
	scHi-C-seq [83]	Specific for chromosome conformation	Very low cell throughput
Transcriptome	SMART-seq SMART-seq2 [132] SMART-seq3 [133]	Suitable for samples with small numbers of cells; Find rare fragments in mRNA by its whole mRNA sequencing	Low throughput Long experimental period
	CEL-seq [134] CEL-seq2 [135]	Identify cell types, discover tumor cells and TME, explain treatment outcomes and mechanism of diseases from clonal dynamics and other transcriptional profiles; MARS-seq2.0 enables intuitive approaches for depletion or enrichment of cell populations	Low throughput 3′-end counting, not full-length
	MARS-seq [136]/MARS-seq2.0 [137]		3′-end counting, not full-length
	Drop-seq [102]
	InDrop [138]
	Seq-Well [139]
	MULTI-seq [140]
	Microwell-seq [141] /Microwell-seq2.0 [142]	It’s cost-efficient, allowing for the mapping of cell atlas [141, 142]	Not full-length
	10 × Genomics [143, 144]	Beside applications above, they can acquire sequencing of TCR & BCR beside gene transcripts as well, which are helpful for immune cell phenotype and associated cancers	High cost
	BD Rhapsody [145]		High cost
	SPLiT-seq [146]	It allows super high-throughput sequencing, and support to draw comprehensive atlas of cells or find rare cell clusters	3′-end counting, not full-length
	sci-RNA-seq [147] sci-RNA-seq3 [148]		Limited to detect exon High cost
	sci-Plex [149]	sci-Plex scales to thousands of samples, enables high-throughput sequencing and multiplex chemical transcriptomics at single-cell level. It’s a cost-effective and scalable method, allowing large-scale drug screening	Only affix barcodes to nuclei of cells, which means detecting mRNA in nuclei alone
	scISOr-seq [150]	Using the third-generation sequencing platform to get full-length transcripts	High cost Low throughput High error rate
	ScNaUmi-seq [151]
	R2C2 [152]
Proteome	High-resolution microscopy [153]	Quantify proteins in single cells; Biomarker detection	The number of screened proteins is small High false positive rate
	Flow cytometry [154]
	Immunohistochemistry [155]
	CyTOF [156]	Quantify multiple proteins in single cells; Immune profiling; Biomarker discovery	Limited in the number of parameters cells per sample they can simultaneously assess High cost
	BD Abseq [157] BioLegend TotalSeq [158]		High cost
	CITE-seq, REAP-seq, SCITO-seq ^{[159,160,161,162]}	Single-cell sequencing is performed simultaneously at the transcriptome and proteome levels	High cost

SNS single nuclear sequencing, SCI-seq single-cell combinatorial indexed sequencing, 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, sciATAC-seq single-cell combinatorial indexing assay for transposase-accessible chromatin using sequencing, CNVs copy number variations, SVs structure variations, SNVs single nucleotide variants, TF transcription factors, scNanoATAC-seq single-cell assay for transposase-accessible chromatin on Nanopore sequencing platform, scTHS-seq single-cell transposome hypersensitive site sequencing, scChIP-seq single-cell chromatin immunoprecipitation followed by sequencing, TCR T cell receptor, BCR B cell receptor, scCUT&Tag single-cell Cleavage Under Targets and Tagmentation, scBS-seq single-cell bisulfite sequencing, scCGI-seq genome-wide CpG island methylation sequencing for single cells, snmC-seq single nucleus methylcytosine sequencing, sci-MET single-cell combinatorial indexing for methylation analysis, scRRBS single-cell reduced representation bisulfite sequencing, scHi-C-seq single-cell Hi-C method for chromosome conformation, SMART-seq switching mechanism at 5′ end of the RNA transcript sequencing, CEL-seq cell expression by linear amplification and sequencing, MARS-seq massively parallel RNA single-cell sequencing, MULTI-seq multiplexing using lipid-tagged indices for single-cell and single-nucleus RNA sequencing, SPLiT-seq split-pool ligation-based transcriptome sequencing, sci-RNA-seq single-cell combinatorial indexing RNA sequencing, sci-Plex single-cell combinatorial indexing for multiplex transcriptomics, scISOr-seq single-cell isoform RNA sequencing, ScNaUmi-seq single-cell Nanopore sequencing with UMIs, R2C2 rolling circle amplification to concatemeric consensus, CyTOF mass cytometry by time-of-flight, CITE-seq cellular indexing of transcriptomes and epitopes by sequencing, REAP-seq RNA expression and protein sequencing, SCITO-seq single-cell combinatorial indexed cytometry sequencing

Back to article page

ISSN: 2054-9369

Contact us

Submission enquiries: Access here and click Contact Us
General enquiries: info@biomedcentral.com