In the dynamic world of cellular biology, cells are rarely static entities. They grow, differentiate, and transition through various states. Trajectory analysis and pseudotime inference are powerful tools that allow us to capture these cellular journeys, offering a glimpse into the narrative of cellular development and differentiation. Let’s embark on an exploration of these fascinating concepts and their applications.
Concepts of Cellular Trajectories
Imagine you’re watching a time-lapse video of a tree growing from a seed. At any single snapshot, you see trees at different stages of growth. Cellular trajectories work similarly – we capture cells at various stages of a biological process and reconstruct their developmental path.
The concept of cellular trajectories assumes that cells transition smoothly between states, rather than jumping abruptly from one state to another. This continuity allows us to order cells along a trajectory, even when we’re looking at a snapshot of a population.
Restricted content
You must be logged in and have a valid subscription to see this content. Please visit our subscription page for more info. If you are already a VIP member, be sure you are logged in with the same email address you made your purchase.
When I delved into trajectory analysis for my paper on PD-L1 (Beyrend et al., "PD-L1 blockade engages tumor-infiltrating lymphocytes to co-express targetable activating and inhibitory receptors"), I felt like a cellular cartographer, mapping the uncharted territories of T cell differentiation. My colorful plots were so pretty, they could've been framed and hung in the Louvre of Immunology. But here's the rub: pretty plots don't always equal biological truth. Because in the end, while in silico analysis might make you feel like a data wizard, Mother Nature always has the final say. And trust me, she's not easily impressed by fancy graphics alone
