Single-cell RNA sequencing allows researchers to examine gene activity in individual cells, providing valuable insights into complex tissues, cancer, and development. However, generating high quality data can be difficult because cells often experience stress or damage during sample preparation, which can distort gene expression measurements.

Researchers from the Seventh Medical Center of the Chinese PLA General Hospital in Beijing developed a new quality control framework called scQCenrich to improve the identification of high quality cells in single-cell RNA sequencing datasets.

Many existing quality control methods rely on fixed thresholds, such as the percentage of mitochondrial RNA or the number of detected genes, to determine whether a cell should be included in downstream analyses. While these approaches are simple to apply, they can mistakenly remove healthy cells or retain poor quality ones.

ScQCenrich takes a more comprehensive approach by combining traditional quality metrics with additional indicators of cell health. These include intronic RNA content, MALAT1 enrichment, features associated with dissociation stress, and optional information about RNA splicing. Together, these measurements provide a more complete picture of sample quality.

The researchers evaluated scQCenrich using datasets from mouse brain, mouse heart, lung cancer, and human peripheral blood mononuclear cells. Compared with conventional methods, the framework reduced unnecessary filtering while preserving biologically important cell populations, including neurons, cardiomyocytes, erythroid cells, and malignant cells.

An important feature of scQCenrich is its transparency. Rather than simply classifying cells as acceptable or poor quality, the software generates automated reports that explain quality control decisions and connects them with cluster level metrics, marker genes, and functional enrichment analyses.

As single-cell RNA sequencing datasets continue to grow in size and complexity, robust quality control becomes increasingly important. By integrating multiple complementary measurements instead of relying on fixed cutoffs, scQCenrich may help researchers generate more reliable datasets while reducing the loss of valuable biological information.