CellBender is a computational tool designed to eliminate technical artifacts from high-throughput single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) data. Its primary goal is to improve the accuracy and reliability of gene expression measurements by removing systematic biases and background noise, leading to clearer insights from complex single-cell experiments.
The Problem of Ambient RNA in Single-Cell Data
Single-cell RNA sequencing is a powerful technique that allows scientists to analyze gene expression at the resolution of individual cells, providing a detailed view of cellular diversity and function. During scRNA-seq experiments, cells are typically isolated and encapsulated in tiny droplets, each containing reagents and a unique genetic barcode to identify the cell’s RNA. However, the process of preparing cells for sequencing can lead to the presence of “ambient RNA.”
Ambient RNA refers to extracellular RNA molecules that are not contained within an intact cell but are present in the cell suspension. This background RNA can originate from various sources, such as cells that have lysed or died during sample preparation, or from general leakage of RNA into the solution. High levels of debris in samples, particularly in single-nucleus sequencing protocols, can also contribute significantly to ambient RNA.
Ambient RNA contaminates the true gene expression profiles of individual cells. These extraneous transcripts are captured alongside legitimate cellular RNA, inflating counts for genes not actually expressed in a cell. This contamination obscures genuine gene expression patterns, making it difficult to accurately identify distinct cell types and potentially masking rare cell populations. It can also introduce systematic biases or batch effects in downstream analyses, potentially leading to incorrect biological interpretations.
CellBender’s Approach to Data Correction
CellBender addresses ambient RNA by employing computational models to distinguish between authentic cellular RNA and contaminating ambient RNA. Its core methodology involves a deep generative model that simultaneously learns the characteristics of background noise and the true biological signal. This model reflects how background noise is generated in droplet-based single-cell assays.
The tool utilizes a neural network to learn the distribution of gene expression across all droplets in an experiment. This learned distribution serves as a “prior” that helps to estimate cell-endogenous counts and share statistical information among similar cells, which is particularly useful given the sparse nature of single-cell data. CellBender then computationally “subtracts” or “cleans” this estimated ambient RNA profile from the raw single-cell data.
The `remove-background` module within CellBender filters out ambient RNA counts from raw gene-by-cell count matrices. This process also accounts for random barcode swapping, another source of technical noise. By modeling the ambient RNA profile and identifying cell-containing versus empty droplets, CellBender produces improved estimates of gene expression, yielding a cleaner and more accurate count matrix for subsequent analysis.
Enhancing Single-Cell Research
CellBender enhances the quality and reliability of single-cell data, leading to more robust biological discoveries. By removing spurious background noise, the tool ensures observed gene expression patterns reflect the actual cellular state. This improved data quality translates into higher accuracy in identifying distinct cell types, as true gene expression signatures are no longer obscured by contamination.
Cleaner data allows for a better understanding of gene expression within individual cells, facilitating the discovery of subtle changes that might otherwise be missed. CellBender improves the detection of marker genes, which are specific genes used to identify cell types. The tool also aids in discovering rare or previously unannotated cell populations, often masked by high levels of ambient RNA. Ultimately, CellBender accelerates research in various fields, including immunology, neuroscience, and developmental biology, by providing more precise and dependable insights into cellular heterogeneity and function.