Single-cell RNA sequencing has become an important tool for studying how cells change over time. Instead of measuring an average signal from millions of cells, it examines individual cells, allowing researchers to observe biological processes such as development, immune responses, and disease progression in much greater detail.

One of the biggest challenges is understanding how cells move from one biological state to another. Scientists often use a computational technique called trajectory inference to arrange cells along a path known as pseudotime. Rather than measuring actual time, pseudotime estimates where each cell falls along a biological process, such as cell differentiation or activation.

Researchers from Waseda University, Japan, have developed a new computational framework called scLS that improves the analysis of gene activity along these trajectories.

After cells have been arranged along a trajectory, researchers often want to identify which genes change as the biological process unfolds. Detecting these differentially expressed genes can reveal the molecular pathways controlling cell behavior. However, many existing methods rely on statistical models that may struggle to detect complex or short-lived patterns of gene expression.

The new scLS method takes a different approach. Instead of analyzing gene activity directly over pseudotime, it converts the data into the frequency domain using a mathematical technique called the Lomb-Scargle periodogram. This allows the software to identify both gradual and transient changes in gene expression without requiring researchers to define a specific regression model in advance.

The framework performs two complementary analyses. One identifies genes whose expression changes as cells progress through pseudotime. The other detects differences in these expression patterns when cells from different experimental conditions are compared, such as healthy and diseased tissues or treated and untreated samples.

An additional advantage is that scLS can analyze complex branching trajectories. In many biological systems, cells divide into multiple developmental paths that eventually produce different cell types. Existing methods often require researchers to manually assign cells to individual branches before analysis. The new framework can work directly with these tree-like trajectories, simplifying the analysis while preserving important biological information.

The researchers evaluated scLS using both simulated datasets and real single-cell RNA sequencing data. The method performed competitively with existing approaches while showing particular sensitivity for identifying transient and complex patterns of gene activity that other methods may overlook.

Although scLS is intended as an initial screening tool rather than a complete analysis pipeline, it provides researchers with a computationally efficient way to identify genes that deserve closer investigation. These candidate genes can then be examined using more specialized lineage-aware methods to better understand the biological processes involved.

As single-cell RNA sequencing datasets continue to grow larger and more complex, improved computational tools will become increasingly important. Methods such as scLS can help researchers extract more meaningful biological information from these datasets, leading to a better understanding of cell development, disease progression, and the molecular mechanisms that drive changes in cell behavior.

Availability – The scLS R package is available on GitHub at https://github.com/hiuchi/scLS.