Pseudotime vs. actual time for genes

As well as actual, canonical time marked by the ticking of a clock, time can be marked by events. Pseudotime and canonical expression time are concepts used in the study of gene expression dynamics, particularly in the context of developmental biology and single-cell RNA sequencing (scRNA-seq).

Single cell sequencing, allows cell-specific expression data to be generated from large numbers of cells. In a complex sample containing many cells taken in a moment of time, each cell will have a gene expression pattern that can be compared with the expression patterns of other cells. As an individual gene’s expression ramps up and dials back down in a pattern that is dependent on the expression of other genes, a pseudotime of related events can be built up.

Using pseudotime has provided great insights into developmental biology and disease progression. These insights are being used, for example, understand the development of tissues and diseases such as cancer, and to enable the design of regenerative medicine and therapeutic interventions.

Canonical expression time

Canonical expression time refers to the actual chronological time during which gene expression changes occur in a biological process. It is the real-time measurement of gene expression changes as a cell or organism progresses through different stages of development or responds to stimuli.

It is measured in units of real time (minutes, hours, days) and is often determined through time-course experiments where samples are collected at specific time points. Understanding canonical expression time is crucial for mapping the temporal dynamics of gene expression and for correlating these changes with specific biological events or stages. Expression of genes in canonical time can be achieved, for example, by taking multiple samples from a group of synchronised cells, or a tissue in development, over time

Pseudotime

Pseudotime is a computational construct used to order cells based on their gene expression profiles, representing a progression through a biological process, such as differentiation or development, without relying on actual chronological time. It is particularly useful in single-cell RNA sequencing studies where cells are captured at a single time point but are at different stages of a process.

Instead of minutes, hours and days, Pseudotime is inferred using algorithms that is able to order cells along a trajectory based on similarities in their gene expression profiles. Algorithms to produce pseudotime representations from single cell sequence data are provided by Monocle, Slingshot, and similar programmes. Pseudotime allows researchers to reconstruct the temporal order of gene expression changes and infer developmental trajectories from single-cell data. These genes are often key regulators of the process being studied. This is especially valuable when it is difficult or impossible to collect samples at multiple time points.

As well as developmental biology, pseudotime enables modelling of disease progression, especially in cancer, where cells can be at different stages of malignancy. Pseudotime can help identify early and late-stage markers of disease. Pseudotime can also guide the design of experiments by identifying critical time points or stages in a process that warrant further investigation.

Pseudotime is also used to study the dynamics of immune cell activation and differentiation. For example, pseudotime can reveal the sequence of gene expression changes as T cells become activated and differentiate into effector cells.

Pseudotime is used to study the differentiation of stem cells into specific cell types for regenerative therapies. Pseudotime can identify key stages and regulatory genes involved in the differentiation process. Pseudotime data can be combined with canonical expression time data to provide a framework for designing time-course experiments to optimize differentiation protocols and validate the sequence of events inferred from pseudotime.

IMAGE Canonical vs pseudotime gene expression CREDIT Cell Guidance Systems

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