LifeTime and improving European healthcare through cell-based interceptive medicine

Abstract

Here we describe the LifeTime Initiative, which aims to track, understand and target human cells during the onset and progression of complex diseases, and to analyse their response to therapy at single-cell resolution. This mission will be implemented through the development, integration and application of single-cell multi-omics and imaging, artificial intelligence and patient-derived experimental disease models during the progression from health to disease. The analysis of large molecular and clinical datasets will identify molecular mechanisms, create predictive computational models of disease progression, and reveal new drug targets and therapies. The timely detection and interception of disease embedded in an ethical and patient-centred vision will be achieved through interactions across academia, hospitals, patient associations, health data management systems and industry. The application of this strategy to key medical challenges in cancer, neurological and neuropsychiatric disorders, and infectious, chronic inflammatory and cardiovascular diseases at the single-cell level will usher in cell-based interceptive medicine in Europe over the next decade.

Fig. 1. Early disease detection and interception by understanding and targeting cellular trajectories through time.

a, Cells are programmed to develop and differentiate along many different specific lineage trajectories (blue trajectories) to reach their functional state. When these normal lineage processes go awry, it can cause a cell to deviate from a healthy state and move towards a complex disease space (coloured manifolds defined by multi-dimensional molecular space—including gene expression, protein modifications and metabolism), as shown by red trajectories. b, Many diseases are detected only at a relatively late stage with the onset of symptoms (red trajectory) and when pathophysiological changes can be at an advanced stage (red cells). At this point, cells, tissues and organs have undergone extensive and often irreversible molecular and physiological changes since the initial events that caused them to deviate from a healthy state. Hence, the choice of interventions may be limited and often involves harsh or invasive procedures. c, Understanding the early molecular mechanisms that cause cells to deviate from a healthy to a disease trajectory will provide biomarkers for the early detection of disease, and new drug targets and innovative therapies to intercept diseases before the onset of pathophysiology and the manifestation of symptoms.

Fig. 2. Hallmarks of the LifeTime approach to disease interception and treatment.

The schematic represents the development and integration of key technologies for investigating human diseases, as envisioned by the LifeTime Initiative. Single-cell multi-omics and imaging technologies will be developed for high-throughput applications. Different modalities will be combined to provide insight into underlying mechanisms, based on coordinated changes between different regulatory molecular layers. Insight into cellular genealogies and cellular dynamics will require the integration of lineage tracing tools. Technologies will also need to be scaled for clinical deployment. The integration and analysis of large, longitudinal multi-omics and imaging datasets will require the development of new pipelines and machine learning tools. These include the development of causal inference and interpretative machine learning approaches to create molecular networks for predictive and multiscale disease models. Patient-derived disease models such as organoids will be further developed to improve tissue architecture and the incorporation of physiological processes such as vasculature, nerve innervation and the immune system, to provide models that more faithfully recapitulate disease processes. Improved knowledge of disease mechanisms will require the application of large-scale perturbation tools to organoids. Tissue–tissue and organ–organ interactions will be recreated using microfluidics and organ-on-a-chip technologies to study key systemic interactions in diseases.

Fig. 3. Exploiting the LifeTime dimension to empower disease targeting.

Single-cell multi-omics analysis of patient-derived samples (such as blood or tissue) or personalized disease models (for example, organoids and experimental disease models) will be profiled longitudinally to cover the different disease stages. Large-scale multidimensional datasets will provide quantitative, digitalized information that will provide information about the decision-making processes of cells. These will be analysed using AI and machine learning to arrive at predictive models for disease trajectories, providing cellular and molecular mechanisms of disease onset and progression. Models will be validated using large-scale perturbation analysis and targeted functional studies in disease models, which will be used in an iterative process to improve both computational and disease models.

Fig. 4. Blueprint of the LifeTime Initiative.

LifeTime proposes a large-scale research initiative to coordinate national efforts, and to foster collaboration and knowledge exchange between the public and private sectors. LifeTime recommends the implementation of several programmes. (1) A network of Cell Centres to support the European Community. The interdisciplinary centres would complement each other’s strengths and expertise in the three LifeTime technology areas and operate in tight association with hospitals, integrating technology development with clinical practice. The connected but geographically distributed nodes would serve as both innovation hubs with strong links to industry and open education and training centres. Community coordination would avoid duplication of effort and increase effectiveness; this model requires funding instruments for a central coordination body. (2) The LifeTime research and technology integration programme includes both technology development and integration and the discovery of disease mechanisms and clinical applications. (3) Medical and biological data management platform. (4) Programmes fostering industry and innovation. (5) Education and training. (6) Ethics and societal engagement.