Cells are sorted by their behaviour over time, not just by a single snapshot of their contents. Current cell-sorting technology, such as Fluorescence Activated Cell Sorting (FACS), captures only a static moment—a cell’s state at one instant. This misses a crucial dimension: how cells change over time in response to stimuli, which is central to processes like development, hormone secretion, and neuronal activity. This project builds a new platform that traps single cells in microdroplets, films their internal activity (such as calcium signals) over minutes, and uses machine learning to sort them based on their dynamic response profile. The system is designed to be high-throughput and compatible with existing FACS probes, allowing researchers to isolate rare cell states that static methods cannot detect. If successful, this technology will let biologists link a cell’s real-time behaviour to its molecular and functional identity. The immediate application is fundamental science: the team will test it on pituitary cells that control the stress response, connecting calcium dynamics to gene expression and hormone output. Longer term, the ability to select cells by functional history could improve cell therapies—choosing the right sub-population for transplantation—and reveal new targets for diseases where cellular timing goes wrong.
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Recent technological advances in imaging and genomics have highlighted cellular heterogeneity as a fundamental characteristic of biological systems, playing a critical functional role in processes ranging from development and cell differentiation to neuronal activity and hormone secretion. While technologies like Fluorescence Activated Cell Sorting (FACS) have facilitated the isolation and analysis of cell populations based on a static reading of a fluorescent signal, a significant gap remains: the ability to link dynamic, time-dependent cellular responses to changes in molecular, metabolic, and integrative functions at the single-cell level. This gap limits our ability to understand the impact of cellular dynamics on specific biological outcomes. The proposed project addresses this limitation by developing a transformative technology that enables high-throughput sorting of cells based on the time-course of intracellular events. By leveraging state-of-the-art microfluidic systems, we will encapsulate single cells in microdroplets, allowing us to measure and analyze their time-dependent changes in intracellular pathways (e.g. a response to a stimulus) in real-time. Our platform will integrate advanced image analysis and machine learning to classify and sort these cells depending on these dynamic changes, capturing dynamic functional states that cannot be detected with current static methods. The significance of this innovation lies in its ability to isolate cells depending on the specific temporal profile of a response, a characteristic that is crucial to generate different biological outcomes. This will be delivered in a way that is scalable for high-throughput sorting of large numbers of cells of interest, also allowing to capture rare cell states, and compatible with existing FACS probes. We will use this system to investigate functional states in pituitary corticotrophs, key modulators of the hormonal stress response, as an example of a highly plastic cell population that needs to respond to unpredictable, time-varying external stimuli, linking their intracellular calcium dynamics to their dynamic transcriptional states and hormonal output. In summary, this transformative technology will allow the selection of specific functional cell states, allowing further downstream single-cell analyses to link dynamic intracellular pathways to specific biological outcomes. The potential impact is vast, from providing new insights into fundamental cellular functions and behaviours, to understanding developmental processes, selecting specific cell sub-populations for cell therapy, and informing novel treatments for diseases.
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