Why Single-Cell RNA Sequencing (scRNA-seq) Alone Can't Always Define Cell Identity or Predict Cell Fate

Single-cell RNA sequencing (scRNA-seq) is a powerful tool that reveals which genes are active in individual cells, helping us understand cell function. However, it has some limitations when it comes to defining cell identity and predicting future cell behavior. Here's why:

1. Missing Layers of Information: scRNA-seq measures mRNA, but it misses other critical layers like proteins, DNA modifications, and environmental cues that also shape cell identity. Besides these, important cell traits can be influenced by additional factors we can’t capture with RNA data alone.? Let's think of these unknown or undetected influences as “Factor X”—a biomolecular signature that isn’t captured by current RNA sequencing techniques. Factor X might be missing because our current technology isn’t yet sensitive enough to detect it, or because we don’t yet know exactly what constitutes Factor X, making it impossible to measure with existing methods.
2. Limited to a Snapshot: scRNA-seq gives us a moment-in-time view, but cells change over time (they are dynamic). To understand where a cell might end up (its "fate"), we often need to observe it over time or look at other features, like its DNA accessibility or protein modifications.
3. Challenges in Fate Prediction: Cells that look transcriptionally identical by scRNA-seq can still be “biased” toward different fates in the future. Fate bias is like a hidden “preference” for a future path that the cell will take, which might not be obvious from the current gene expression alone. For more accurate predictions, we need to combine scRNA-seq with other techniques, like tracking cell lineage over time or analyzing additional TBD molecular layers. Let's not forget: directly linking cells (based on their transcriptional information alone) to their future fate is challenging and error prone because cells are killed/destroyed during single cell measurements in traditional single cell methods, so the best we can do is impute.

To truly capture what makes each cell unique and where it might be headed, we need to use integrated approaches that look beyond mRNA alone. Combining multiple techniques paints a fuller picture, helping us understand complex tissues and processes better.