Podcast recap: Molly He on multi-omics and sequencing innovation

In the latest episode of The Genetics Podcast, we spoke with Dr. Molly He, CEO and co-founder of Element Biosciences. With over a hundred patents and leadership roles at Illumina and Pacific Biosciences, Molly has been a force behind some of the most transformative technologies in genomics. At Element, she’s now leading a revolution in sequencing and multi-omics that could reshape how we understand biology, develop therapeutics, and personalize healthcare.

The unmet promise of precision medicine

Despite decades of investment and enthusiasm, true precision medicine remains just out of reach for most patients. Molly identifies two key barriers: our limited ability to predict biological behavior at an individual level, and the high cost and complexity of developing new therapies. While genetic information provides foundational insights, it doesn’t fully explain how someone will respond to treatment or environmental exposures. The unpredictable nature of biology limits our ability to tailor interventions.

On top of that, drug development continues to be prohibitively expensive. Developing a single therapy from target discovery to FDA approval can cost upwards of $1–2 billion. Customizing treatments for individual patients, while scientifically appealing, remains economically unfeasible. Bridging this gap between aspiration and reality requires new tools that generate deeper insights and accelerate discovery.

A new era of integrated multi-omics

Element Biosciences was built to address these challenges head-on. Its AVITI platform offers a truly integrated approach to multi-omics by enabling genomic, transcriptomic, proteomic, and morphological analysis from the same single cell, using the same workflow and instrument. Unlike current multi-omics workflows that require different assays, instruments, and samples, Element’s platform eliminates the need to piece together fragmented data.

This ability to capture rich, multi-layered biological information from a single cell in its native state unlocks more accurate and actionable insights. It also removes much of the noise introduced by traditional methods, where cross-sample variability can obscure meaningful patterns. For researchers and clinicians alike, this opens the door to a more complete and holistic understanding of disease mechanisms and drug responses.

The technology: Sequencing by avidity

At the heart of Element’s innovation is a proprietary approach known as sequencing by avidity. Unlike traditional sequencing by synthesis, which combines base incorporation and detection into one step, avidity sequencing separates these processes. This two-step approach increases signal clarity and allows for longer read lengths with fewer errors.

The chemistry uses specially designed molecules (avidites) that bind tightly to DNA and carry multiple fluorescent labels. This multivalent binding improves detection accuracy, reduces the need for high reagent volumes, and lowers sequencing costs. It also preserves the integrity of DNA strands, enabling multiple rounds of sequencing without degradation.

Compared to existing platforms and technologies, this is a complete reimagining of the sequencing process that is both flexible and scalable.

Applying the platform: Insights into drug resistance

To demonstrate the real-world utility of this platform, Molly and her team applied it to investigate drug resistance in non-small cell lung cancer treated with tyrosine kinase inhibitors (TKIs). Many patients develop resistance to TKIs over time, and the underlying biological mechanisms remain poorly understood.

Using a custom multi-omic panel of 350 RNAs, 50 proteins (including phosphorylation states), and cell morphology markers, the team treated lung cancer cell lines with both first- and third-generation TKIs across multiple doses and time points. Within 24 hours and without any library prep, they were able to visualize how specific pathways were activated or suppressed in response to treatment.

They discovered that Cyclin D1 phosphorylation enabled cells to bypass apoptosis in response to first-generation TKIs. This pathway contributed to reduced efficacy and higher resistance. This finding pointed to a new target for combination therapy, which Element’s team validated through follow-up experiments. Their findings were in line with results from a clinical trial published just months later, reinforcing the platform’s ability to uncover clinically relevant mechanisms quickly and cost-effectively.

Rethinking drug discovery from the ground up

Element’s platform offers the ability to analyze up to 2 million cells per run and link genotypic and phenotypic data directly from individual cells. This enables a completely new paradigm for screening, target validation, and mechanistic studies.

The team has already used the platform for CRISPR perturbation assays, combining functional genomics with phenotypic profiling in a single run. By sequencing guide RNAs and phenotypic markers from the same cells, researchers can now directly connect gene perturbations to functional outcomes without relying on inference or multi-step workflows.

This kind of tight feedback loop where a perturbation and its consequence are measured in real-time from the same biological context could dramatically shorten the timeline of early-stage drug discovery. It also allows for better prediction of which candidate therapies are likely to succeed in clinical trials, potentially reducing the 90% failure rate seen in current drug pipelines.

From discovery to the clinic: A tool for personalized medicine

While AVITI is currently a research platform, its potential for clinical application is enormous. By profiling patient-derived cells with different drugs and doses, clinicians could one day predict which treatments will be most effective on an individual basis. In this way, Element’s technology could bring us closer to personalized medicine by predicting biological responses up front.

This could also help address the financial challenges of precision medicine. Instead of developing new and individualized drugs, clinicians could test approved drugs or combinations in a patient-specific context, selecting the best option based on empirical data from that person’s own cells. This kind of surrogate assay model could dramatically lower costs while improving outcomes.

Unlocking the potential of AI in biology

Like many in the life sciences, Molly sees AI as a powerful enabler of discovery. However, she emphasizes that AI is only as useful as the quality, clarity, and context of the data it consumes. While large language models are trained on the vast and relatively clean dataset of the internet, biology is messier, more fragmented, and less standardized.

This is where Element’s platform shines. By generating high-fidelity, multi-omic data from native biological samples, AVITI offers a rich foundation for building AI models that can predict disease states, drug responses, and biological pathways. In Molly’s view, the key to impactful AI in biology is not more data, but better data.

Looking ahead: Predicting biology at scale

Molly’s long-term vision is ambitious but clear: to make biology mathematically predictable. While she acknowledges the complexity of this goal given the vast number of variables at play, she believes that focused, high-quality data streams could allow for predictive models within constrained systems.

This vision is still in its early stages, but platforms like AVITI bring it closer to reality. By providing deep, reliable insights into how cells respond to different perturbations, Molly and her team are laying the groundwork for a new era of systems biology where researchers can anticipate outcomes.

Listen to the full episode below.