Understanding how cells function and change over time is a cornerstone of biology. However, capturing dynamic processes in single cells can be challenging, especially when experiments provide only “snapshots” of gene expression across a diverse population of cells at one moment in time. Single-cell transcriptomics, which measures gene activity in individual cells, has been used to arrange these snapshots into inferred timelines called “pseudotime.” While useful, pseudotime lacks a clear connection to the actual physical or biological timing of events.

A New Perspective: Process Time

Researchers at the California Institute of Technology have developed a new approach called Chronocell to address this limitation. Instead of pseudotime, Chronocell introduces “process time,” a concept rooted in the biophysics of how cells change states. Process time provides a more meaningful and measurable way to track how cells progress through biological processes.

The input of Chronocell comprises three components: 1) the trajectory structure, which outlines the states each lineage traverses as paths on a directed graph. 2) The sampling assumption, which defines the prior distribution of latent variables, namely lineages and process time, with a default uniform distribution over both; and 3) the scRNA-seq data, consisting of unspliced and spliced count matrices. The Chronocell model consists of a expression model with piecewise-constant transcription rates, and a Bernoulli measurement model. Each state s is associated with a transcription rate αs for each gene, as well as an exit time τk denoting the switching time to the next state, where k is the index for the time segment. The EM algorithm is used for inference, with each iteration alternating between E-steps and M-steps. The results of Chronocell primarily include the estimated parameters and posterior distributions over latent variables for each cell.

Here’s what makes Chronocell stand out:

1. Biophysical Foundations: Unlike pseudotime, which is more descriptive, Chronocell models cellular changes based on biophysical principles, focusing on how cells transition between states.
2. Flexibility: Chronocell can handle both continuous processes, where cells gradually change state, and discrete processes, where cells exist in distinct clusters.
3. Parameter Inference: The model can infer meaningful biological parameters, such as rates of molecular degradation, providing insights that align with experimental data.

How Chronocell Works

Chronocell uses advanced mathematical modeling to reconstruct the sequence of cellular events. By analyzing single-cell data, it can:

• Identify whether cells follow a continuous trajectory or form discrete clusters.
• Estimate the timing of key cellular changes.
• Reveal the biological processes underlying these transitions, like gene expression changes during cell differentiation.

For example, the researchers applied Chronocell to various datasets and found that it could reveal distinct cellular distributions along process time that matched biological expectations. They also compared Chronocell’s estimates of molecular degradation rates to results from experimental techniques like metabolic labeling, demonstrating its accuracy and utility.

Challenges and Considerations

While Chronocell shows promise, it has some challenges:

• Data Quality Matters: Poor-quality datasets can make it difficult to infer accurate process times.
• Model Assessment Is Key: Careful evaluation of the model’s performance is essential to ensure reliable results.

Why Chronocell is Important

Chronocell represents an exciting step forward in single-cell biology. By providing a biophysically grounded way to track cellular changes over time, it bridges the gap between descriptive models and the actual biology of cellular processes. This approach could lead to deeper insights into how cells develop, respond to their environment, and contribute to health and disease.

Availability – https://github.com/pachterlab/FGP_2024