Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects

Abstract

Single-cell sequencing is a biotechnology to sequence one layer of genomic information for individual cells in a tissue sample. For example, single-cell DNA sequencing is to sequence the DNA from every single cell. Increasing in complexity, single-cell multi-omics sequencing, or single-cell multimodal omics sequencing, is to profile in parallel multiple layers of omics information from a single cell. In practice, single-cell multi-omics sequencing actually detects multiple traits such as DNA, RNA, methylation information and/or protein profiles from the same cell for many individuals in a tissue sample. Multi-omics sequencing has been widely applied to systematically unravel interplay mechanisms of key components and pathways in cell. This survey overviews recent developments in single-cell multi-omics sequencing, and their applications to understand complex diseases in particular the COVID-19 pandemic. We also summarize machine learning and bioinformatics techniques used in the analysis of the intercorrelated multilayer heterogeneous data. We observed that variational inference and graph-based learning are popular approaches, and Seurat V3 is a commonly used tool to transfer the missing variables and labels. We also discussed two intensively studied issues relating to data consistency and diversity and commented on currently cared issues surrounding the error correction of data pairs and data imputation methods. The survey is concluded with some open questions and opportunities for this extraordinary field.

Single-cell RNA-sequencing of zebrafish hair cells reveals novel genes potentially involved in hearing loss

“…After high-throughput sequencing based on 10×Genomics system, the gene expression data was obtained. Based on Seurat [20] analysis, the cells were divided into 21 clusters. By analyzing the gene expression pattern of the top genes in each cluster, the four clusters of cells, from the cluster 0, 5, 7, 12, were annotated as hair cells, and the cells from the cluster 2, 3 and 10 were identi ed as retinal ganglion cells (RGCs).…”

“…The basic procedure for single cell sequencing analysis were carried out as previously described [36] . Brie y, Seurat V4.0.1 [20] was used for the integrated analysis of the single cell sequencing data, including data ltering, data normalization, cell clustering and cluster-level marker gene identi cation. For data ltering, only the genes expressed in at least ve cells were considered, the maximal percentage of reads from mitochondrial genes allowed was up to 10%, and the number of genes detected for each cell was between the range of 200 to 5000.…”

Multiomics integration-based molecular characterizations of COVID-19

“…Through integrations of single-cell- and bulk sample data, their analysis identified a few hallmarks of severe COVID-19, such as megakaryocytes, erythroid cells and plasmablasts [ 49 ]. A more in-depth analysis on the subject on single-cell analyses in COVID-19 is provided in a recent review by Huo et al.’s review [ 77 ]. The integration of bulk- and single-cell multiomics also pose a new set of challenge for bioinformatics algorithms.…”

Computational solutions for spatial transcriptomics

“…Careful experimental design and data analysis planning can streamline the production of high-quality findings. The recent reviews on spatial transcriptomics and multiomics [1] , [27] , [28] , [29] , [30] , [31] , [32] cover the history, technology, and advances in the analysis in more detail; this review focuses on covering ways to help in choosing the suitable platform and analysis framework for spatial transcriptomics studies. We focus on the currently widely (and commercially) available platforms, their limitations, the available data analysis tools, and their suitability for the collection of different types of data.…”