Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis

Abstract

Rapid cost drops and advancements in next-generation sequencing have made profiling of cells at individual level a conventional practice in scientific laboratories worldwide. Single-cell transcriptomics [single-cell RNA sequencing (SC-RNA-seq)] has an immense potential of uncovering the novel basis of human life. The well-known heterogeneity of cells at the individual level can be better studied by single-cell transcriptomics. Proper downstream analysis of this data will provide new insights into the scientific communities. However, due to low starting materials, the SC-RNA-seq data face various computational challenges: normalization, differential gene expression analysis, dimensionality reduction, etc. Additionally, new methods like 10× Chromium can profile millions of cells in parallel, which creates a considerable amount of data. Thus, single-cell data handling is another big challenge. This paper reviews the single-cell sequencing methods, library preparation, and data generation. We highlight some of the main computational challenges that require to be addressed by introducing new bioinformatics algorithms and tools for analysis. We also show single-cell transcriptomics data as a big data problem.

Keywords: Sc-RNA-seq; big data; downstream analysis; normalization; single-cell analysis; single-cell big data; single-cell transcriptomics.

FIGURE 1

Single-cell analysis in disease and health. Starting from the dissociation of target cells from the target tissue/organ, their isolation based on fluorescence-activated cell sorting (FACS) or other microfluidic techniques to RNA extraction. The RNA extraction is followed by cDNA synthesis by reverse transcriptase, followed by amplification and sequencing. From the sequencing, the reads are aligned and subjected to quantification that results in a quantification matrix or Gene Expression Matrix.

FIGURE 2

(A) There is a steep rise every year for the publications of studies addressing the big data and SC-RNA-seq. For big data papers on PubMed, we used the query “[big data (All Fields) AND MapReduce (All Fields) AND Hadoop (All fields)].” For SC-RNA-seq and big data papers on PubMed, we used “[(scRNA-seq OR Big Data) OR (Single-cell AND big data)].” (B,C) Numbers were collected from the Human Cell Atlas Data portal of some exemplary projects.