EPFL researchers uncover how transcription factor dosage reshapes cell identity, showing that even small differences in dose can steer cells toward completely different fates. Their findings reveal a new layer of control in cell reprogramming
Transcription factors are proteins that control gene expression in a cell. They are so powerful that they can be used to reprogram a cell into an entirely new type of cell, or even "rewind" it back into a stem cell. In fact, this capacity has shaped decades of research in regenerative medicine, where researchers hope to produce specific cell types on demand for repairing tissues, studying diseases, or testing drugs.
And yet, putting transcription factors to work in real-world experiments has been unreliable. In many experiments, only a small percentage of cells respond as expected, while the rest either ignore the instructions or veer into unwanted states. Even when using known reprogramming transcription factors, the outcomes are often hard to predict and inconsistent. Why do some cells respond and others don't?
A new method from EPFL brings clarity by zeroing in on a long-overlooked factor: dosage. In a study published in Nature Genetics, a team led by Bart Deplancke at Laboratory of Systems Biology and Genetics at EPFL's School of Life Sciences, in collaboration with colleagues at the Chinese Academy of Sciences, has developed scTF-seq, a high-throughput method that systematically dissects how transcription factors influence cell fate at single-cell resolution. The study reveals that the dose of a transcription factor is as important as the transcription factor itself in determining the outcome.
Not an on-off switch
"We've long known that transcription factors can trigger dramatic changes in cell identity," says Wangjie Liu, PhD candidate and first author of the study. "But our results show that the dose of a transcription factor can completely reshape what that transformation looks like. It's often not a binary on-off switch, it's more like tuning a dial, and the output can change entirely depending on where that dial is set."
For scTF-seq, the team built a library of 384 transcription factors, each tagged with a unique "genetic barcode" and placed under doxycycline-inducible control. They then added them into mouse stromal cells, which are versatile precursor cells that can turn into fat, muscle, or bone cells.
By combining this setup with single-cell RNA sequencing and genetic barcoding, the researchers tracked nearly 40,000 individual cells, each expressing a specific transcription factor at varying doses. This let them build a detailed, dose-resolved map of how each transcription factor influences gene expression and cell fate.
The team found that transcription factors vary widely in their reprogramming power and in how sensitive they are to dose. Some transcription factors triggered strong changes at low levels; others required high doses; some had little to no effect.
A powerful toolkit
Some also showed nonlinear responses, e.g. inducing one fate at low dose and another at high. However, for some transcription factors, the same dose could still trigger distinct outcomes in different cells. This suggests that rother still hidden factors other than dose influence the response.
The study also found that pairs of transcription factors add another layer of complexity. Depending on the ratio, two transcription factors can either cooperate or interfere with each other.
"We often think of transcription factors as keys that unlock specific cell types," says Bart Deplancke. "But what we're showing here is that each key behaves differently depending on how firmly you turn it and whether another key is in the lock at the same time. If we want to engineer cells reliably, we need to understand this dose logic."
The work carries a lot of practical interest. As scientists increasingly seek to engineer cells in a dish, e.g. for tissue repair, disease modeling, or drug screening, understanding how transcription factors behave across a dose range will be essential. The scTF-seq platform provides a powerful toolkit for this purpose, enabling researchers to decode the rules that govern how transcription factors drive cell fate.
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Swiss Institute of Bioinformatics
