Parse Biosciences and bit.bio map transcription factor-driven cell identity

Parse Biosciences and bit.bio have announced a new alliance aimed at creating a comprehensive map of transcription factor-driven cell identity in a move designed to accelerate predictive drug discovery and therapeutic development.

The collaboration will combine advanced single cell sequencing technologies with large-scale causal transcriptomics to better understand how genetic inputs influence cell behaviour, fate and state.

The companies said the resulting dataset could help create highly accurate human-relevant biological models capable of predicting how cells respond to drugs and disease, potentially transforming the development of new therapies.

The project brings together technologies from both firms, including bit.bio’s opti-ox™ cell programming platform and Discovery platform, The Cell Foundry™, alongside Parse Biosciences’ Evercode™ single cell sequencing technology.

Together, the companies plan to generate what they describe as an unprecedented dataset showing how specific transcription factors drive distinct biological outcomes.

Building a blueprint for predictive biology

The alliance will use massively parallel causal transcriptomics, a technique that allows scientists to test thousands of genetic variables simultaneously in order to understand what controls cell identity and behaviour.

Researchers believe the data generated could serve as a foundational blueprint for developing scalable human cell models that more accurately mimic biological responses observed in living systems.

By reproducing in vivo biology more precisely, scientists hope the models will improve the prediction of drug efficacy and safety before therapies reach clinical testing.

The companies also expect the data to contribute to the development of artificial intelligence systems capable of forecasting cellular responses to treatments and disease processes.

By reproducing in vivo biology more precisely, scientists hope the models will improve the prediction of drug efficacy and safety before therapies reach clinical testing

“Cells operate on code, and by mapping how specific transcription factors dictate cell fate, we are unlocking that operating system,” said Przemek Obloj, CEO of bit.bio. ”This collaboration doesn’t just generate data; it provides a foundational map for bit.bio to scale human-relevant models and feed predictive AI systems, moving the entire field closer to reliably replicating and therefore predicting human biology.”

bit.bio said the information generated through the project could eventually guide not only its own research but also broader industry efforts in therapy design and the large-scale manufacturing of human cells.

Single cell sequencing at scale

Parse Biosciences, which specialises in scalable single cell sequencing solutions, said the collaboration reflects growing demand for datasets that clearly connect genetic changes with measurable biological outcomes.

Single cell sequencing technologies allow researchers to analyse the genetic activity of individual cells rather than studying large mixed populations, providing more precise insight into disease mechanisms and therapeutic responses.

“Researchers need insights that they can translate into impact,” said Dr Charlie Roco, Co-founder and Chief Technology Officer at Parse Biosciences. ”Our close alliance with bit.bio will create foundational datasets that establish clear causal links between genetic changes and biological outcomes, the kind of information that predictive medicine needs but has rarely had.”

Researchers need insights that they can translate into impact

The companies believe the project could have significant implications for drug development, regenerative medicine and the use of AI in biomedical research.

Further details about the initiative and its applications in predictive biology modelling and manufacturing will be discussed during a Parse webinar scheduled for 17 June at 7:00am Pacific Time.