- Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling. - Background: Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. - However, single-cell methods inherently suffer from limitations in the recovery of complete. - Results: Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. - We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures.. - Conclusion: Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.. - Keywords: Single cell, Transcriptomics, Single-cell RNA-seq, High throughput sequencing, Immune-cell profiling. - Although techniques such as FACS and mass cytometry [1] are useful for studying cellular diversity according to well-characterized cell-surface-protein markers, the advent of single-cell RNA sequencing. - These single-cell technologies have enabled immunolo- gists to characterize inflammation [2] and immune re- sponses to cancer [3–7], uncovering previously uncharacterized cellular diversity and cell-type specific transcriptional responses. - As recent advances have in- creased cell throughput and lowered per-cell costs, the number of high-throughput single-cell RNA-. - Several key factors, such as variable capture and amplifi- cation efficiencies during library preparation, impact the ability of single-cell RNA-seq techniques to accurately and comprehensively characterize immune-cell diversity. - The relatively small size and low mRNA content of im- mune cells may impact the performance of single-cell RNA-seq methods differently than was previously de- scribed using larger cells [8–13]. - Previous benchmarking studies using somatic cell lines or peripheral blood mononuclear cells (PBMCs) reported that high-throughput single-cell RNA-seq methods generally enabled broader sampling of diverse populations at a lower per-cell cost. - Next, we assess mRNA detection sensitiv- ity and the correlation of cellular profiles to immune cell signatures from bulk RNA-seq. - This study serves as useful guidelines for the selection of a suitable single-cell RNA- seq method to study immune cells.. - Design of single-cell RNA-seq benchmarking experiments We benchmarked four commercially-available high- throughput single-cell systems: the Chromium [14] (10x Genomics), the ddSEQ (Illumina and Bio-Rad), the. - To mirror typical single-cell sequencing runs and to ensure a comparison independent of sequencing limitations, we normalized the read depth of our librar- ies to ~ 50,000 reads per cell (Fig. - Cells were identified and classified by correlating single-cell expression profiles to bulk RNA-seq.. - One important consideration for single-cell RNA-seq is the capture rate, or the fraction of cells recovered in the data relative to input. - The extent of this issue is influ- enced by the quality of the single-cell suspension, cell health, and cell loading concentration. - Single-cell RNA-seq methods are inherently prone to dropouts due to inefficiencies during library. - 1 Overview of high-throughput single-cell benchmarking experiments. - Experiments were performed using four immune cell lines to benchmark cell recovery, transcript detection sensitivity, concordance to bulk RNA-seq and differentially-expressed gene identification. - Table 1 Summary of average mRNA/gene detection sensitivities and capture rates for each single-cell RNA-seq method. - The dropout rate was mod- eled as a function of the expression level in bulk RNA- seq (FPKM) for each cell type. - mRNA detection affects the fidelity of single-cell and pseudo-bulk transcriptomes. - We next investigated how well single-cell expression re- capitulates immune signatures from bulk RNA-seq. - For this purpose, we correlated expression of a set of marker genes (defined using bulk RNA-seq data. - see Methods) between bulk RNA-seq and single cells. - In general, cells with more genes detected had a better concordance to bulk RNA-seq immune signatures (Supplement Fig. - We observed higher Pearson correlation coefficients for 10x 3′ v3, 5′ v1 and ddSEQ methods against EL4, IVA12 and Jurkat bulk RNA-seq expression signatures (Fig. - Overall, poorer correlation to TALL-104 bulk RNA-seq was in line with fewer transcripts and genes detected for this cell type in the single-cell data.. - We further examined the correlation between pooled single-cell RNA-seq pseudo-bulk transcriptomes and bulk RNA-seq data using all genes. - DE ( r = 0.90 and 3′ DE-UMI methods ( r compared to other methods ( r and correl- ation was generally lower for TALL-104 cells in all methods, suggesting that lower mRNA detection sensi- tivity not only affects data fidelity at a per-cell level but also impacts aggregated single-cell data. - However, it is likely that higher variance in the detection of lowly expressed transcripts drives much of the difference in expression observed in single-cell and bulk RNA-seq, and aggregation across individual cells may not increase the correlation of expression for these lowly-expressed genes. - difference in bulk RNA-seq data as a proxy for ground- truth expression differences, the trend remained the same (Fig. - 4 Correlation to bulk RNA-seq: a Pearson correlation ( r ) of cell identifiers (CIDs) to bulk RNA-seq data using highly-expressed variable genes.. - 1.5-fold difference in expression in bulk RNA-seq (5868 genes) are plotted in cyan. - c Median bulk RNA-seq expression (FPKM) of all significant DE genes (red) or DE genes with >. - the fraction of DE genes in bulk RNA-seq data that was identified as differentially expressed in the single-cell data. - Precision was defined by the fraction of DE genes from single-cell data that were also differentially expressed in bulk RNA-seq data. - In general, we observed that fold changes in single-cell data correlated well with gene expression differences in bulk RNA-seq data, especially for genes with higher ex- pression levels (Supplement Fig. - In contrast, genes with low expression correlated poorly with smaller fold changes observed in the single-cell data, consistent with higher dropout probabilities for lowly-expressed tran- scripts. - Also, the distribution of FPKM values was gener- ally higher for DE genes from single-cell data compared to genes with at least 1.5-fold changes in bulk RNA-seq (Supplement Figs. - Furthermore, we found the low- est median FPKM in bulk RNA-seq for DE genes from the methods with the highest detection sensitivity, 10x 3′ v3 (median = 3.43 FPKM) and 10x 5′ v1 (median = 3.45 FPKM), and the highest median FPKM for the ICELL8 3′ DE-UMI method (median = 4.91 FPKM), which had the lowest transcript detection sensitivity (Fig. - Many immune single-cell experiments profile an un- defined mixture of cell types that potentially vary in mRNA content. - We classified cells by correlating their expression pro- file to gene signatures from bulk RNA-seq. - As it is common in single-cell profiling of mixed populations, we observed differences in read and UMI recovery across cell types in each method (Fig. - In this paper we explored several important quality met- rics of single-cell RNA-seq methods: efficiency of cell. - a Single-cell data generated with the 10x 3 ′ v3 and 10x 5 ′ v1 chemistries were projected onto an annotated PBMC CITE-Seq reference dataset. - Quality of single-cell suspensions are also important factors to these metrics. - Notably, the resulting se- quencing depth is typical for common high-throughput single-cell RNA-seq experiments. - Multiple aspects of single-cell RNA-seq protocols such as efficiencies in mRNA capture, reverse transcription, and cDNA amplification can affect the overall mRNA detection sensitivity. - Additional improvements to mRNA capture such as improving oligonucleotide chem- istry for mRNA capture and cDNA amplification may enhance mRNA detection sensitivity and improve single- cell RNA-seq techniques in the future.. - In gen- eral, our results showed that expression profiles of cells with high mRNA content generated by methods with a high mRNA detection rate correlated well to bulk-RNA- seq data. - Also, the number of DE genes as well as the overall correlation in fold-change differences to bulk RNA-seq improved with higher mRNA detection sensi- tivity. - Here, all 10x Genomics methods, which had the highest mRNA detection sensitivity, exhibited a high correlation to bulk RNA-seq data as well as more DE genes with a lower range of expression levels in bulk data. - Importantly, our data also provides insight into the per- formance of single-cell techniques across heterogenous populations of immune cells. - Furthermore, large cells are filtered from single-cell sus- pensions to avoid clogging narrow microfluidic channels.. - However, most immune cells are smaller than the 40 μm filter size commonly used during single-cell sample preparation and can be captured by a variety of systems.. - It would be inter- esting to further explore the capabilities of various single-cell RNA-seq and sample preparation methods to assay other immune populations that are particularly dif- ficult to survey.. - Our comparison of data from seven high-throughput single-cell methods can help guide method selection for immune profiling experiments. - For normalizing single-cell libraries, we considered the fact that cell types with low mRNA content are more prone to dropouts and thus, may compromise proper normalization based on the mean read count per CID.. - Here, ddSEQ data was processed using SureCell RNA Single-Cell v1.1.0 (with STAR v2.5.2b). - Cells were assigned to one of four input cell classes by their similarity to cell type signatures from bulk RNA- seq data. - 50 in any bulk RNA-seq sample. - Next, gene ex- pression was contrasted between bulk RNA-seq samples from the same species (EL4 vs IVA12 and Jurkat vs TALL-104) and we filtered 184 highly variable genes (93 murine, 91 human) with a ln fold difference >. - Pseudo-bulk analyses analyzing correlation to bulk RNA-seq data and gene detection rates were performed by summing UMI counts across multiple cells. - The aggregated expression matrix was used for analyzing its correlation to bulk RNA-seq and for quantifying the number of detected genes. - Dropout rate of bulk RNA-seq data was modeled by fitting the function f ( x. - Dropout rates were similarly calculated for single-cell RNA-seq data by bin- ning cells by mRNA detection rates. - Downsampling of cells was performed to the smallest number of observed cells from a single cell type ( n = 199). - where TP are significant DE genes as calculated with MAST with a 1.5 fold change in bulk RNA-seq data, FN are not differentially expressed in single-cell data, but had a 1.5 fold change in bulk data, and FP are significant DE genes with less than a 1.5 fold change in bulk data.. - RNA-seq: RNA sequencing. - Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. - Single-cell. - Deep single-cell RNA sequencing data of individual T cells from treatment-naive colorectal cancer patients. - Landscape of infiltrating T cells in liver Cancer revealed by single-cell sequencing. - Single-cell Transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species. - 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Bias, robustness and scalability in single-cell differential expression analysis. - Comprehensive integration of single-cell data. - EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. - Hao Y, Hao S, Andersen-Nissen E, Mauck WM, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zagar M et al : Integrated analysis of multimodal single-cell data. - Kaestner KH: Comparative analysis of commercially available single-cell RNA sequencing platforms for their performance in complex human tissues. - STAR: ultrafast universal RNA-seq aligner.. - ddSeeker: a tool for processing bio-rad ddSEQ single cell RNA-seq data. - Normalization and variance stabilization of single- cell RNA-seq data using regularized negative binomial regression
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