Earliest discovery of cells, considered the fundamental building blocks of life, dates back over three hundred years to the late 1600s. While our understanding of biology has expanded over the intervening years, many critical questions in cell biology remain unanswered. At the root of this is the fundamental constraint of today’s most widely used platforms: they rely on destructive, static assays that capture a single molecular snapshot of each cell. This means we can often tell what genes are expressed at a specific moment, but not how the cell got there, what it is doing, or what happens next.
Remarkable advances in single-cell technologies have transformed our ability to deconstruct cellular heterogeneity and build high-resolution atlases of tissues, development, and disease. These innovations have catalyzed projects like the Human Cell Atlas, defined new immune cell states, and accelerated discoveries in cancer and neurobiology. Despite these achievements, there appeared to be a fundamental gap when we evaluated current biological datasets. While current platforms provide rich snapshots of cellular identity, they cannot observe how cells behave or evolve over time.
What we don’t know – the who, when and how
In immune responses, which T cells actually kill tumor cells? In stem cell differentiation, what intermediate states drive lineage commitment? In cancer, how does resistance emerge in only a subset of cells?
Once a cell is sequenced, it’s gone—precluding any understanding of processes such as activation, response, differentiation, or interaction. This lack of temporal and functional context leaves us blind to important biological dynamics and unable to answer some of the most biologically meaningful questions. Traditional platforms cannot answer these questions because they analyze dead cells in isolation, disconnected from time and function. Additionally, fragile or adherent cell types like neurons and epithelial cells are often incompatible with droplet-based workflows, further limiting the range of biology we can study.
To fill these gaps, researchers have recently begun integrating multi-omics approaches, combining sequencing and spatial technologies. However, spatial transcriptomics, while offering location-based insights, is limited to static histological sections and cannot capture dynamic interactions or live responses to perturbation. Though single-cell biology has exploded in complexity and depth, there remains a gap in our inability to connect live-cell function with molecular identity at scale and over time. Current tools cannot fully answer questions about how cells behave, change, and interact. This is the unmet need that we believe Cellanome is uniquely positioned to address.
What does a day in the life of a cell look like?
What do cells do? How do they move? How do they interact with their environment and other cells? How do they respond to perturbation and stimuli?
Most current platforms sacrifice one or more dimensions to capture cellular level information—e.g., killing cells for molecular profiling, or performing functional assays without transcriptomic follow-up. The Cellanome platform is designed to overcome these limitations by offering a fundamentally new way to study cells – alive, dynamically, and comprehensively. Its core innovation lies in enabling live-cell functional assays combined with high-throughput multiomic profiling. This means researchers can observe what cells do, then immediately capture and profile the same cells at the molecular level, enabling them to generate richer and more causally informative datasets than ever before.
At the heart of the platform is a hydrogel-based photopolymerization system, capable of encapsulating and isolating live cells into separate compartments. These isolated microenvironments allow for controlled perturbation, live imaging, and functional readouts over time without compromising cell viability. Thus, Cellanome makes it possible to perform longitudinal, iterative experiments, watching cells change over time under different conditions. Researchers can, for example, expose cells to stimuli such as drugs, cytokines, or immune challenges, then monitor how they behave in real time.
Another key differentiator is the platform’s compatibility with diverse and fragile cell types. Traditional droplet-based systems often exclude larger or adherent cells like neurons, epithelial cells, or cardiomyocytes, which are essential for studying diseases like Alzheimer’s, cystic fibrosis, or heart failure. Cellanome’s gentle microenvironment and modular assay design enable these cell types to be studied in their functional, live state, vastly expanding the range of biological systems that can be interrogated at single-cell resolution.
Importantly, the platform links phenotypic function with transcriptomic and proteomic identity from the same cell. This bridges the longstanding gap between genotype and phenotype in cell biology. Rather than guessing a cell’s behavior from gene expression data, researchers can now observe behavior directly and then understand the underlying molecular mechanisms. This enables “function-first” discovery workflows that have been out of reach for most labs.
What if? New possibilities in R&D and experimental design
What if we could monitor real-time responses of thousands of cells to a panel of candidate compounds, isolate those that survive, and determine why? What if we could identify and retrieve the cells that exhibit resistance or hypersensitivity, and sequence them to uncover the molecular correlates of the response? What if we could isolate the most interesting cells from a high throughput screen and only sequence these cells?
Cellanome offers a paradigm shift, from isolated, static, and destructive measurements to integrated, live, functional, and multi-dimensional single-cell analysis. Cellanome does not just make traditional experiments better, it enables an entirely new generation of R&D workflows that go far beyond what existing single-cell platforms can achieve. It empowers researchers to see cells as dynamic decision-makers, not just molecular snapshots, and gives them the tools to track, interrogate, and understand those decisions in unprecedented detail.
This opens new horizons in research and development, enabling a richer class of experiments that were previously impossible or impractical. It eliminates the guesswork and population-level averaging inherent in bulk assays and allows for a functionally driven approach to lead optimization and mechanism-of-action elucidation. Cellanome is also ideally suited for phenotype-first screening, where instead of beginning with gene expression or genetic perturbation, experiments start with observable cell behaviors. This not only enhances data quality but also greatly reduces unnecessary sequencing costs and noise.
The implications for R&D are profound. Drug development can move from guessing at mechanisms to observing them directly. Cell therapies can be optimized by selecting the most effective responders in real time. Complex disease models can be interrogated with a richness of data that captures not just what happens, but how and why. And importantly, a broader diversity of cell types – fragile, adherent, co-cultured – can now be studied with equal rigor.
Conclusion: A new frontier in cell biology
When does a stem cell become a neuron? How does a T cell kill a tumor? Why did one cancer cell survive therapy while its neighbor died?
Biology is a story of transitions and to understand those stories, we need tools that operate in time, function, and context. The Cellanome platform represents a fundamental shift in how we approach the study of living cells. For decades, cell biology has been shaped by the constraints of our tools—static, destructive, and limited in their scope. Much of what we "know" about cells has been based on inference – piecing together how a cell behaves, or changes based on what it looked like at a single moment in time. Cellanome allows researchers to observe live cells in action, measure their functional responses, and then molecularly dissect their identities, all within a scalable and automated system, bringing a new dimensionality to single-cell analysis.
The incredible team at Cellanome, led by its CEO Omead Ostadan and co-founder Mostafa Ronaghi is reimagining what’s possible in cell biology. Their platform closes critical knowledge gaps, improves the fidelity of biological data, and sets the stage for a future where every cell is not just a datapoint, but a story waiting to be told. We at Premji Invest are extremely excited to partner with Cellanome and its team in this telling!