Dataset Information

MAPS-seq: magnetic bead-assisted parallel single-cell gene expression profiling.

ABSTRACT: Recently developed single-cell RNA sequencing methods allow the simultaneous profiling of the transcriptomes of thousands of individual cells. However, current methods still require advanced equipment or entail substantial waste of reagents. Here, we introduce magnetic bead-assisted parallel single-cell gene expression sequencing (MAPS-seq), a microwell-based method that pools samples before the reverse transcription step, increasing the ease of sample preparation and reducing reagent waste. Moreover, because this method uses universal reagents and standard molecular biology lab instruments, it is easy to implement, even in labs that have not previously conducted single-cell RNA sequencing. We validated our method by demonstrating that it can generate gene expression data at the single-cell level. We then applied the MAPS-seq method to analyze 237 human myelogenous leukemia cells treated with one of three different drugs or dimethyl sulfoxide. We observed transcriptional changes and identified marker genes that indicate a drug response. Furthermore, the MAPS-seq method produced data of comparable quality to those of existing single-cell RNA sequencing methods. Consequently, we expect that our method will provide researchers with a more accessible, less wasteful, and less burdensome method for investigating the transcriptomes of individual cells.

SUBMITTER: Park M

PROVIDER: S-EPMC7272464 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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MAPS-seq: magnetic bead-assisted parallel single-cell gene expression profiling.

Park Munsu M Lee Dongin D Bang Duhee D Lee Ji Hyun JH

Experimental & molecular medicine 20200513 5

Recently developed single-cell RNA sequencing methods allow the simultaneous profiling of the transcriptomes of thousands of individual cells. However, current methods still require advanced equipment or entail substantial waste of reagents. Here, we introduce magnetic bead-assisted parallel single-cell gene expression sequencing (MAPS-seq), a microwell-based method that pools samples before the reverse transcription step, increasing the ease of sample preparation and reducing reagent waste. Mor ...[more]

PMID: 32404928

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Project description:BackgroundThe advancement of the next-generation sequencing technology enables mapping gene expression at the single-cell level, capable of tracking cell heterogeneity and determination of cell subpopulations using single-cell RNA sequencing (scRNA-seq). Unlike the objectives of conventional RNA-seq where differential expression analysis is the integral component, the most important goal of scRNA-seq is to identify highly variable genes across a population of cells, to account for the discrete nature of single-cell gene expression and uniqueness of sequencing library preparation protocol for single-cell sequencing. However, there is lack of generic expression variation model for different scRNA-seq data sets. Hence, the objective of this study is to develop a gene expression variation model (GEVM), utilizing the relationship between coefficient of variation (CV) and average expression level to address the over-dispersion of single-cell data, and its corresponding statistical significance to quantify the variably expressed genes (VEGs).ResultsWe have built a simulation framework that generated scRNA-seq data with different number of cells, model parameters, and variation levels. We implemented our GEVM and demonstrated the robustness by using a set of simulated scRNA-seq data under different conditions. We evaluated the regression robustness using root-mean-square error (RMSE) and assessed the parameter estimation process by varying initial model parameters that deviated from homogeneous cell population. We also applied the GEVM on real scRNA-seq data to test the performance under distinct cases.ConclusionsIn this paper, we proposed a gene expression variation model that can be used to determine significant variably expressed genes. Applying the model to the simulated single-cell data, we observed robust parameter estimation under different conditions with minimal root mean square errors. We also examined the model on two distinct scRNA-seq data sets using different single-cell protocols and determined the VEGs. Obtaining VEGs allowed us to observe possible subpopulations, providing further evidences of cell heterogeneity. With the GEVM, we can easily find out significant variably expressed genes in different scRNA-seq data sets.

Dataset Information

MAPS-seq: magnetic bead-assisted parallel single-cell gene expression profiling.

Publications

MAPS-seq: magnetic bead-assisted parallel single-cell gene expression profiling.

Similar Datasets

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