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Eurasian Journal of
Medicine and Oncology Single-cell sequencing for lung cancer

Figure 1. Overview of single-cell sequencing technologies. Figure created using Biorender.com
Abbreviations: G&T-seq: Genome and transcriptome sequencing; LIANTI: Linear amplification via transposon insertion; SCI-seq: Single-cell
combinatorial indexing RNA sequencing; scWGS: Single-cell whole-genome sequencing; TARGET-seq: Targeted genomics and transcriptomics
sequencing; scRNA-seq: Single-cell RNA sequencing; CROP-seq: CRISPR droplet sequencing; Drop-seq: Droplet sequencing; SCRB-seq: Single-cell RNA
barcoding and sequencing; CEL-seq: Cell expression by linear amplification and sequencing; SMART-seq: Switching mechanism at the 5’ end of RNA
template sequencing; SPLit-seq: Split-pool ligation-based transcriptome sequencing; scATAC-seq: Single-cell assay for transposase-accessible chromatin
sequencing; scCOOL-seq: Single-cell chromatin overall omic-scale landscape sequencing; SNuBar: Single-nucleus barcode-based; scChIP-seq: Single-cell
chromatin immunoprecipitation sequencing; CoBATCH: Combinatorial barcoding and targeted chromatin release; scTHS-seq: Single-cell transposome
hypersensitive site sequencing; DroNc-seq: Droplet-based nuclear single-cell RNA sequencing; REAP-seq: RNA expression and protein sequencing;
ECCITE-seq: Expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing; SNARE-seq: Single-nucleus chromatin
accessibility and mRNA expression sequencing; CITE-seq: Cellular indexing or transcriptomes and epitopes by sequencing; Ins-seq: Intracellular staining
and sequencing; MALBAC: Multiple annealing and looping-based amplification cycles; MARS-seq: Massively parallel RNA single-cell sequencing; STRT-
seq: Single-cell tagged reverse transcription sequencing

DNA-based single-cell sequencing methods include Single-cell transcriptome sequencing is the most widely
whole-genome sequencing (WGS) and whole-exome used approach in single-cell sequencing. scRNA-seq
sequencing (WES). 16,17 WGS entails fragmenting the quantifies gene expression at the single-cell level without
entire genome and sequencing it without targeting targeting specific transcripts. It captures RNA, converts it
specific DNA regions. Single-cell WGS can distinguish the into cDNA, and employs high-throughput sequencing to
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unique genetic characteristics in different cell clones and generate transcriptomic profiles for individual cells. RNA-
allows researchers to track genomic changes over time. In based single-cell sequencing includes full-length transcript
addition, it facilitates the quantification of copy number sequencing approaches (e.g., scNanoHi-C, Quartz-Seq,
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variations (CNVs) and the characterization of structural Smart-Seq, and Smart-Seq2 ), 3’/5’-end transcript
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variations (SVs) at the single-cell level. However, WGS sequencing technologies (e.g., CEL-Seq, MARS-Seq,
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remains expensive due to its high resource and technology Drop-Seq, and InDrop ), and spatial RNA sequencing
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demands, and it struggles to detect low-frequency variants, derived from single-cell RNA sequencing (scRNA-seq).
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which are rare mutations present in a small portion of the Among available platforms, droplet-based 10× Genomics
sample. In contrast, WES targets protein-coding regions, Chromium and plate-based SMART-Seq are widely used.
which harbor most disease-related variants. While The 10× Genomics Chromium platform, which utilizes
WES is more cost-effective than WGS, it suffers from microfluidic droplet technology, is popular due to its high
uneven coverage, often leaving certain genomic areas throughput and commercial availability. It encapsulates
underrepresented. individual cells in Gel Bead-in-Emulsions, each containing
Volume 9 Issue 2 (2025) 5 doi: 10.36922/ejmo.6883