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Single-Cell RNA Sequencing Technologies

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Bioinformatics in Rice Research

Abstract

All cellular structures are heterogeneous. Thus, investigating true cell heterogeneity is highly required to further understand cellular connectivity and accountability within a disease or normal conditions. Because of its rapidly decreasing costs, the Next-Generation (NGS) sequence is widely used to analyze various biological data. However, these approaches may fail to provide detailed insight into cells’ true heterogeneity. Recently developed single-cell RNA sequencing (scRNA-seq) technology tries to tackle these bulk NGS issues by linking transcriptomic, epigenomic, proteomic, and molecular sequences to a specific cell. Thus, in this chapter, the author addresses the process involved, relative strengths, possible uses, and limitations of scRNA-seq techniques methods. Information obtained revealed that cell isolation methods may be broadly divided into two categories centered on different principles. The first category centered on physical characteristics, while the second category mainly focuses on cell’ features and primarily involves affinity approaches. After obtaining raw data, the general approach for analyzing ScRNA-Seq data are pre-processing, batch effect correction, normalization, dimensionality reduction, feature selection, cell type identification, differential expression analysis, and rebuilding cell hierarchy. scRNA-seq technologies are continuously being employed to unmask various biological processes ranging from epigenetic regulation to biomarker identification. However, scRNA-seq technologies do have difficulties like cumbersome activity and high detection costs that restrict technology promotion. Hence, there is an urgent requirement to develop more robust tools so that, in the near future, the technology for single-cell sequencing will be streamlined and is more efficient.

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Abbreviations

5mC:

5-Methylcytosine

5hmC:

5-Hydroxymethylcytosine

AML:

Acute myeloid leukemia

CCA:

Canonical correlation analysis

cDNAs:

Complementary DNAs

DBC:

Density-based clustering

DC:

Dendritic cells

DE:

Differential expression

ESCs:

Embryonic stem cell

FACS:

Fluorescence-activated cell sorting

GSE:

Gene expression

HVG:

Highly variable genes

KM:

k-means

LCM:

Laser capture microdissection

MACS:

Magnetic activated cell sorting

MCS:

Manual cell selection

MNNs:

Mutually closest neighbors

NGS:

Next-generation sequence

PCA:

Principal component analysis

scRNA-seq:

Single-cell RNA sequencing

t-SNE:

T-distributed stochastic neighbor embedding

UMAP:

Uniform manifold approximation and projection

UMIs:

Unique molecular identifiers

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Figure 25.1 [14], Fig. 25.2 [48] and Fig. 25.3 [120] are reused under the terms of the Creative Commons Attribution License CC BY 4.0.

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Gupta, M.K. et al. (2021). Single-Cell RNA Sequencing Technologies. In: Gupta, M.K., Behera, L. (eds) Bioinformatics in Rice Research. Springer, Singapore. https://doi.org/10.1007/978-981-16-3993-7_25

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