Understanding Quality Control in Single-Cell RNA Sequencing: Part IV – Detecting Ambient RNA

In our series on single-cell RNA sequencing (scRNA-seq) quality control (QC), we have covered detecting low UMI cells, empty droplets, and doublets. In this fourth part, we will focus on detecting ambient RNA, a common source of contamination in scRNA-seq data. We will use the 𝚜𝚒𝚗𝚐𝚕𝚎𝙲𝚎𝚕𝚕𝚃𝙺 [Hong 2022] toolkit to illustrate how to identify and mitigate the impact of ambient RNA.

What Is Ambient RNA?

Ambient RNA refers to extracellular RNA molecules that are present in the solution but not associated with any particular cell. During droplet-based scRNA-seq, this RNA can get encapsulated in droplets, leading to contamination. Ambient RNA can distort gene expression profiles, especially in lowly expressed genes, and can create false signals.

Why Detect Ambient RNA?

Detecting and correcting for ambient RNA can be crucial for accurate downstream analyses. If not accounted for, ambient RNA can lead to incorrect cell type identification, biased differential expression results, and inaccurate biological interpretations. Of note, there is currently no accepted standard in the field, and a recent paper explores three different methods, including the two mentioned in this post [Janssen 2023].

Step-by-Step Guide to Detecting Ambient RNA with 𝚜𝚒𝚗𝚐𝚕𝚎𝙲𝚎𝚕𝚕𝚃𝙺

𝚜𝚒𝚗𝚐𝚕𝚎𝙲𝚎𝚕𝚕𝚃𝙺 offers tools to help identify and correct for ambient RNA contamination. Here’s how you can use it:

Step 1: Load the Data

First, load your scRNA-seq data into R. 𝚜𝚒𝚗𝚐𝚕𝚎𝙲𝚎𝚕𝚕𝚃𝙺 supports various data formats, including 𝚂𝚒𝚗𝚐𝚕𝚎𝙲𝚎𝚕𝚕𝙴𝚡𝚙𝚎𝚛𝚒𝚖𝚎𝚗𝚝 objects and Seurat objects.

Step 2: empty droplet detection

To estimate the amount of ambient RNA, empty droplets are used as a background, since they do not contain cells, but might contain ambient RNA. This is used to estimate the ambient RNA content in our data after empty droplet removal. We are familiar with this from part II of our series on single cell QC. Only here we use a the 𝚎𝚖𝚙𝚝𝚢𝙳𝚛𝚘𝚙𝚜 command through the 𝚛𝚞𝚗𝙳𝚛𝚘𝚙𝚕𝚎𝚝𝚀𝙲 function, which is doing the same thing, but allows for other QC steps to be run in a single command.

Step 3: Run Ambient RNA Detection

𝚜𝚒𝚗𝚐𝚕𝚎𝙲𝚎𝚕𝚕𝚃𝙺 allows the uder to choose from different methods for ambient RNA detection, such as methods like 𝚂𝚘𝚞𝚙𝚇 [Young 2020] or 𝙳𝚎𝚌𝚘𝚗𝚝𝚇 [Yang 2020] to model and remove ambient RNA. This involves estimating the ambient RNA profile and adjusting the cell gene expression profiles accordingly. In this example we show ho to run 𝙳𝚎𝚌𝚘𝚗𝚝𝚇.

Step 4: Examine the Results

We can now inspect in a UMAP plot what percentage of cells are flagged with potential ambient RNA content:

Putting it all together

Let’s put it all together, applied to the 𝚙𝚋𝚖𝚌𝟹𝚔 dataset, which contains peripheral blood mononuclear cells (PBMCs).

Conclusion

Detecting and correcting for ambient RNA is should be performed during scRNA-seq quality control, especially when there are concerns around the quality of the data. By using 𝚜𝚒𝚗𝚐𝚕𝚎𝙲𝚎𝚕𝚕𝚃𝙺, ambient RNA contamination can effectively be managed, ensuring more accurate and reliable data for downstream analyses. In the next part of this series, we will explore mitochondrial gene QC, another important aspect of ensuring high-quality scRNA-seq data. Stay tuned!

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By following these steps, you can enhance the quality of your scRNA-seq data, leading to more accurate and insightful biological discoveries.

References

Hong R, Koga Y, Bandyadka S, Leshchyk A, Wang Y, Akavoor V, Cao X, Sarfraz I, Wang Z, Alabdullatif S, Jansen F. Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data. Nature communications. 2022 Mar 30;13(1):1688.

Janssen P, Kliesmete Z, Vieth B, Adiconis X, Simmons S, Marshall J, McCabe C, Heyn H, Levin JZ, Enard W, Hellmann I. The effect of background noise and its removal on the analysis of single-cell expression data. Genome Biology. 2023 Jun 19;24(1):140.

Yang S, Corbett SE, Koga Y, Wang Z, Johnson WE, Yajima M, Campbell JD. Decontamination of ambient RNA in single-cell RNA-seq with DecontX. Genome biology. 2020 Dec;21:1-5.

Young MD, Behjati S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience. 2020 Dec;9(12):giaa151.