Parse Biosciences and Codebreaker Labs Partner to Apply Whole Transcriptome Single Cell Profiling and Causal Genomics at Scale

Collaboration pairs robust synthetic biology platform with massive scale single cell sequencing to overcome long-standing challenges in variant mapping

Parse Biosciences, the leading provider of scalable and accessible single cell sequencing solutions, today announced a collaboration with Codebreaker Labs to develop and validate a breakthrough platform capable of testing thousands of genetic variants in parallel and measuring their effects at single cell resolution. By combining Codebreaker’s synthetic biology platform and variant engineering capabilities with the scale and accessibility of Parse’s Evercode™ technology, the collaboration aims to generate the causal data increasingly sought by AI developers, drug discovery teams, and clinical researchers.

Today’s genomic studies rely heavily on observational data, or variants that appear in large populations. But rare and private variants, often only seen in one individual or family, are nearly impossible to study this way because too few carriers of the variant exist to draw statistically meaningful conclusions. As a result, even the most advanced models trained on observational datasets develop blindspots for these rare variants.

The new platform addresses this limitation by engineering variants at scale and measuring their impact in human cells, generating causal labels that observational datasets can’t create. Pairing this approach with a high-throughput single cell assay ensures that both variant generation and phenotypic readout operate at the same level of scalability, allowing the technologies together to reach further into rare-variant space.

This collaboration has broad implications for biopharma and precision medicine. Researchers will be able to generate causal data, clinicians will be able to access new functional maps for interpreting genomes, and AI teams will be able to use high-dimensional datasets to build more accurate models.

“We are excited to work with Parse on this groundbreaking platform,” says Ryan Gill, PhD, CEO and Co-Founder of Codebreaker Labs. “Together, we are pioneering a new class of genomics data with the potential to impact whole-genome interpretation, target discovery and validation, and even precision clinical trial design.”

“We are pleased to see our technology used for such an important endeavor,” said Charlie Roco, PhD, Chief Technology Officer and Co-founder of Parse Biosciences. “By combining our scalable platform for single cell with Codebreaker’s engineered variant libraries, we can create a unified platform that experimentally determines how thousands of variants influence disease state and progression, and how these rare variants might be more effectively treated in a clinical setting.”

About Parse Biosciences

Parse Biosciences is a global life sciences company whose mission is to accelerate progress in human health and scientific research. Empowering researchers to perform single cell sequencing with unprecedented scale and ease, its pioneering approach has enabled groundbreaking discoveries in cancer treatment, tissue repair, stem cell therapy, kidney and liver disease, brain development, and the immune system. For more information, visit parsebiosciences.com.

About Codebreaker Labs

Codebreaker Labs is pioneering a new class of data for genomics by empirically mapping the functional effects of variants at scale, moving beyond association to causation in human biology. Codebreakers platform for programming biology at scale along with single-cell phenotyping produces experimental ground-truth data of variant effects to accelerate biological insight as well as the production of Data-Centric genomics AI. To learn more and inquire about working with us, please visit codebreakerlabs.io or email at partners@codebreakerlab.io.

“Together, we are pioneering a new class of genomics data with the potential to impact whole-genome interpretation, target discovery and validation, and even precision clinical trial design.”

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