Shots:
- Sapient’s new partnership with Alamar Biosciences supercharges its multi-omics engine, bringing the ultra-sensitive NULISA™ platform in-house to capture hard-to-measure cytokines and chemokines, unlocking deeper biological insights beyond the reach of mass spectrometry alone
- The collaboration with Rancho Biosciences is redefining large-scale data intelligence, enabling Sapient’s DynamiQ™ platform to seamlessly integrate, structure, and analyze massive multi-omics and real-world datasets, accelerating biomarker discovery and precision insights across disease areas
- PharmaShots is thrilled to host Mo Jain for this powerful dialogue, diving into the science, strategy, and collaborative vision driving Sapient’s mission to transform multi-omics research through innovation and world-class partnerships
Saurabh: How does Sapient’s collaboration with Alamar Biosciences improve Sapient’s multi-omics offerings, and what is the main strategic goal of the companies?
Mo: Sapient’s underlying mission is to generate the highest-quality multi-omics data that enables discovery of new biology and novel mechanisms of action for therapeutics, as well as to support drug development by understanding target engagement and patient responders.
Now, achieving this mission is not a case of having a single hammer for every nail, but rather having the right tools to answer the biological questions for our clients. Sapient’s deep expertise lies in mass spectrometry, and we have state-of-the-art capabilities that allow us to broadly interrogate the proteome, metabolome, and lipidome, including post-translational modifications and protein isoforms – covering thousands of proteins, metabolites, and lipids per sample.
That said, there are certain proteins and molecules in human samples that are not well captured by mass spectrometry. One particularly important subset includes inflammatory cytokines and chemokines, as well as neuroinflammatory markers. These signaling molecules are central to understanding and modulating immune responses, but are often present in very low concentrations in blood and can be masked by higher-abundance proteins also present. They are therefore difficult to measure by mass spectrometry, even using enrichment techniques.
Now, Alamar Biosciences’ NULISA™ platform is designed specifically for the ultra-sensitive detection of hard-to-measure cytokines, chemokines, and neuroinflammatory markers in biofluids – capturing attomolar-level changes linked to disease and drug response.
By bringing Alamar’s NULISA™ technology in-house, we can broadly interrogate these important molecular features and integrate that information with Sapient’s mass spectrometry-based proteomic and metabolomic measurements. Together, this enables us to provide our clients with the most comprehensive and high-quality multi-omics datasets possible.
Saurabh: Could you describe how NULISA™ technology solves the problem of identifying proteins with low abundance, like cytokines and chemokines, and why this is important for studies on human health?
Mo: When we think about human health, there are diseases characterized by high levels of inflammation, such as sepsis or acute rheumatologic disorders. However, there are also many other conditions that involve much subtler, chronic immune activation – heart disease, liver disease, obesity, cancer, and other cardiometabolic disorders – where studying low-level inflammation is critical to deciphering underlying disease mechanisms.
The NULISA™ technology transforms our ability to profile these low-abundance inflammatory markers with exceptional signal-to-noise. Unlike conventional proximity ligation assays, the system uses dual pairs of orthogonal monoclonal antibodies – meaning they bind to different sites on the target molecule. Each antibody carries a DNA tag and once bound to their target, the overlapping DNA sequences form a highly specific signal that is amplified for detection. Through multiple washes and a proprietary second capture step, the assay significantly reduces background noise, improving the signal-to-noise ratio, and enabling detection to attomolar concentrations.
This level of sensitivity is essential for accurately measuring cytokines and chemokines, making NULISA™ a powerful tool for studying these low-abundance inflammatory markers across a broad range of human diseases.
Saurabh: Can you share more about Sapient’s partnership with Rancho Biosciences? Are you exploring additional opportunities to innovate or expand together in the market?
Mo: Sapient is an industry leader in generating multi-omics data at scale. This capability not only allows us to serve our clients but also to collect and analyze our own biological specimens through our various collaborations. In doing so, we are able to produce large-scale, comprehensive multi-omics datasets across very large sample sets.
All of this information is integrated into a growing data lakehouse we call DynamiQ™. This database continues to expand daily as we bring in plasma, tissue, and tumor samples across a wide range of disease areas. The samples are matched with real-world data on individuals’ demographics, diseases or conditions, treatments, and clinical outcomes. Within DynamiQ™ we perform multi-omics analyses – including proteomics, metabolomics, lipidomics, RNA sequencing, and cytokine and chemokine profiling – and link those data to real-world clinical information within a relational framework.
Our partnership with Rancho Biosciences is central to structuring, curating, and analyzing these massive datasets. As subject matter experts in technology-enabled life science data science, Rancho has helped us construct a robust, AI-enabled infrastructure to accelerate the ingestion of new multi-omics data as we generate it, including integration of matched real-world data, and to rapidly homogenize it with our existing datasets – significantly scaling the breadth and depth of analyses we can perform for biomarker discovery.
Together, we’re enabling more powerful insights for target identification and validation and helping bridge the translational gap between early discovery and clinical medicine. As these datasets continue to grow, we see significant opportunities to expand this collaboration further to accelerate therapeutic innovation across multiple disease areas.
Saurabh: With the launch of the next-generation data insights engine, DynamiQ™, how do you foresee this changing the market landscape and advancing multi-omics and real-world data integration?
Mo: As with many applications across industries today, having access to data at scale is absolutely critical. Large-scale datasets enable advanced AI modeling and the creation of foundational model systems that allow us to understand disease processes with much greater precision.
That’s exactly why we built the DynamiQ™ database. First, it enables our clients to identify and prioritize new therapeutic targets. Second, it helps bridge a long-standing gap in drug development: the divide between early discovery scientists, who focus on target identification, validation, and chemistry, and the clinical study teams responsible for implementing trials.
DynamiQ™ allows us to connect these two ends of the spectrum by de-risking early biomarkers and targets through translational insights that can be validated in large, independent cohorts. This integration of multi-omics and real-world data creates a clearer path from discovery to clinical application, ultimately accelerating drug development and improving the likelihood of success in clinical studies.
Saurabh: How do the AI and machine learning tools integrated within DynamiQ™ enhance biomarker discovery and patient stratification?
Mo: DynamiQ™ is a truly unique database because it is longitudinal, meaning patients are followed over extended periods with serial blood sampling and clinical information, creating a rich, time-based dataset. Data is collected at multiple timepoints, both from biological specimens such as tissue, tumor, and blood samples, together with longitudinal clinical and drug response information.
Within these biological samples, we generate comprehensive multi-omics data, including proteomics, metabolomics, lipidomics, DNA and RNA sequencing, as well as cytokine and chemokine profiling. This creates an exceptionally rich data foundation for applying AI and machine learning approaches.
By leveraging these tools, we can classify patients more effectively, identify subpopulations such as responders versus non-responders, discover and validate ideal therapeutic targets, and better understand patient stratification – essentially determining which individuals are most likely to benefit from a particular therapy.
The integration of advanced AI with DynamiQ’s multi-omics framework enables us to capture the biological diversity within disease populations and apply it in a way that advances precision medicine and accelerates drug development.
Saurabh: What are the most significant findings from the large-scale analysis of over 26,000 plasma samples in the DynamiQ™ database, particularly regarding the metabolic aging clock and disease subtypes?
Mo: The analysis you are referring to was recently published and actually represents one of the largest discovery metabolomics studies conducted to date. The dataset includes over 26,000 plasma samples from our DynamiQ™ platform, analyzed through our rapid LC-MS (rLC-MS) systems for broad metabolomics profiling – capturing tens of thousands of circulating molecular markers per biosample. From this large-scale dataset, we can begin to decipher the chemical landscape in human populations and derive insights that relate directly to clinical outcomes.
There are several key findings across a range of diseases. We are identifying early biomarkers in blood for cancer, cardiometabolic disease, and other chronic conditions that can appear years before diagnosis. This opens the door to earlier detection and preventive interventions.
When it comes to disease subtypes, we have uncovered multiple “metabotypes”: distinct metabolic subgroups, within common diseases like hypercholesterolemia, hypertension, renal disease, and liver disease. These subtypes often have very different outcomes that are not apparent through traditional clinical phenotyping. Understanding these metabotypes helps us pinpoint the biological pathways altered in each disorder and improves patient selection and stratification in clinical studies.
Finally, using these rich datasets, we can model complex, multi-system processes such as human aging. For this, we used a subset of the rLC-MS data to train a machine learning model to analyze underlying metabolic networks in both healthy and disease populations and estimate an individual’s biological age relative to their chronological age. As we all know, some 50-year-olds look and act like healthy 30-year-olds, while others appear much older than their biological age. Our metabolic aging clock helps to quantify that difference through a simple blood test.
This approach not only provides insights into biological aging but also guides optimization of therapeutics, whether through lifestyle interventions, diet, exercise, or emerging treatments like GLP-1 therapeutics – to improve and potentially reverse aspects of metabolic aging. In fact, when we applied the metabolic aging clock model to patients with chronic kidney disease that underwent kidney transplantation, we saw their predicted biological age drop by more than 9 years!
Saurabh: Is Sapient currently looking for new partnerships to further expand its multi-omics and proteomics capabilities, or are you fully focused on your existing collaborations such as with Alamar Biosciences and Rancho Biosciences?
Mo: Human disease and biology are extremely complex – truly a vast ocean – and by no means do we yet have all the tools needed to fully navigate it. Sapient is always exploring new collaborations, clients, and approaches to data, from collection and quality control to integrative analysis.
Over the past several years, we have made significant advances in our mass spectrometry workflows that now enable us to broadly capture the proteome and measure over 10,000 proteins in complex biospecimens, including FFPE tissue and frozen samples, alongside metabolites, lipids, and cytokines. We also perform integrative analyses combining RNA sequencing with protein measurements, among other approaches.
However, the field is far from fully charted. As technologies advance, data handling becomes more sophisticated, and AI algorithms continue to improve, we expect to expand both our existing collaborations and forge new ones, driving technical innovation and leading the charge to advance multi-omics research.
About the Author
Mo Jain
Founder and CSO, Sapient
Dr. Jain is a physician-scientist with more than 20 years of expertise in physiology, biomedicine, engineering, computational biology, and mass spectrometry. Prior to founding Sapient, he formed and was director of Jain Laboratory at the University of California San Diego (UCSD). There he led a multi-disciplinary research team to develop next-generation rapid liquid chromatography-mass spectrometry (rLC-MS) systems to probe dynamic biomarkers of health, disease, and drug response across population-scale human studies. Dr. Jain founded Sapient in 2021 as a spinout of Jain Laboratory to expand upon this mission, providing bespoke services multi-omics biomarker discovery.
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