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Review
. 2023 Dec 31;13(1):91.
doi: 10.3390/cells13010091.

Insect Insights at the Single-Cell Level: Technologies and Applications

Affiliations
Review

Insect Insights at the Single-Cell Level: Technologies and Applications

Chao Sun et al. Cells. .

Abstract

Single-cell techniques are a promising way to unravel the complexity and heterogeneity of transcripts at the cellular level and to reveal the composition of different cell types and functions in a tissue or organ. In recent years, advances in single-cell RNA sequencing (scRNA-seq) have further changed our view of biological systems. The application of scRNA-seq in insects enables the comprehensive characterization of both common and rare cell types and cell states, the discovery of new cell types, and revealing how cell types relate to each other. The recent application of scRNA-seq techniques to insect tissues has led to a number of exciting discoveries. Here we provide an overview of scRNA-seq and its application in insect research, focusing on biological applications, current challenges, and future opportunities to make new discoveries with scRNA-seq in insects.

Keywords: insect; scRNA-seq; single cell technology.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic workflow of scRNA-seq in insects. Generally, it includes five main steps: Tissue dissection, single-cell suspension, single-cell capture, library construction, sequencing and data analysis. This figure was created using BioRender (www.biorender.com; accessed on 1 August 2023).
Figure 2
Figure 2
Overview of various insects and their organs studied using single-cell RNA sequencing (scRNA-seq). This figure illustrates Panel: (A), Different insects on which scRNA-seq technology has been applied; Panel: (B), organs or tissues of insects on which scRNA-seq technology has been applied for comprehensive gene expression profiling (see Table 1 for relevant references and description). This figure was created using BioRender (www.biorender.com; accessed on 12 August 2023).
Figure 3
Figure 3
The color code represents the proportion of articles related to each tissue category within the dataset.

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