We need a new platform – a ‘tissue time machine’ – that can profile tissue states and predict transitions between states (‘Delta Tissue’ or ‘ΔT’). The platform would provide quantitative, multi-scale, multi-modal information sufficient to build integrated prediction models of key cell and tissue states and transitions. If we are successful, we’ll be able to intervene in diseases earlier and with approaches that are targeted to the individual. We’ll also have an improved understanding of the mechanisms that drive disease, which, in turn, will provide more opportunities for intervention. If we succeed, we’ll begin to eradicate the stubbornly challenging diseases that cause so much suffering around the world.
Such a platform is now possible if we combine the latest cell and tissue profiling technologies with recent advances in machine learning and other computational methods. With this foundation, we can now imagine the tissue time machine, which assembles a rational set of profiling modalities, integrates their outputs and builds predictive models of tissue states and transitions.
Background and URL for more information:
To build this new platform, we will need to overcome key limitations and achieve three main goals:
1. Develop and optimize method(s) to select modalities that accurately profile tissue in a given state.
1a. The method(s) should quantitatively assess the value of a set of integrated modalities for predicting different tissue and disease states.
1b. The method should be demonstrably better than expert human judgement with respect to time, cost, resource requirements, and predictive value.
2. Develop new or improve existing individual molecular and structural profiling capabilities, with respect to spatial and/or temporal resolution, number of markers, volume of tissue and/or other assay properties, so as to reveal the states and transitions for the exemplar diseases described in the Platform Demonstration Areas (see full program announcement).
2a. New or enhanced profiling methods should improve one or all of the following in the context of a sample volume of at least 1 mm3:
i. Number of molecular markers or features routinely detected by 10-100x;
ii. Spatial resolution by 5-10x over what is achievable by conventional light microscopy;
iii. Sample processing time by 5-10x.
2b. A key goal is the expansion and linkage of markers and structures, e.g., establishing the relative value and linkage of molecular markers and features derived from an organelle or cell/tissue structure.
3. Develop a platform that integrates multi-scale, multi-modal data from different states and builds models that predict states and transitions. Inclusion of explainable models in the platform is of interest. Performers working in this goal will:
3a. Identify and implement methods to integrate models or knowledge of state gained in Goal 1, ultimately improving the prediction of profiling methods.
3b. Test the platform against the Platform Demonstration Areas at least annually.
3c. Construct an open data resource to share models and datasets, providing a route to integrate contributions from others and/or commercialize advances, as appropriate.
Platform Demonstration Areas. To demonstrate and validate the ‘tissue time machine’, we have chosen to develop, test, and validate our platform in biomedical contexts that are as broad as possible: an infectious disease, tuberculosis (TB), and two different cancers, triple-negative breast cancer (TNBC) and glioblastoma multiforme (GBM). Each represents a current, unmet biomedical challenge and features a complex, dynamic set of cell and tissue states and transitions. See the full program announcement for more information. Advances across models and measures should inform each other to improve and validate predictive markers, environmental influences and optimize the key ingredients necessary for promoting healthy network development. It is not necessary to form a large consortium or team to do this. Synergies and integrated system demonstrations will be facilitated by Wellcome Leap on an annual basis as we make progress together towards the program goals.
Call for abstracts and proposals.
We are soliciting abstracts and proposals for work over 3 years (with a potential additional one-year option). Proposers should clearly relate their work to one or more Platform Goals and indicate which of the Program Demonstration Areas (PDAs) they will participate in. Additional PDAs can be proposed, but all performers must validate their work against at least one of the specified PDAs.
Wellcome Leap accepts project proposals from any legal entity, based in any legal jurisdiction, including academic, non-profit and for-profit organizations. Applicants are encouraged to contact Wellcome Leap about joining its Health Breakthrough Network by executing its MARFA (or CORFA for commercial entities) agreement. Full execution of the Wellcome Leap MARFA is not required for application submission but is required for any award.
Jason Swedlow, PhD has expertise in mechanisms and regulation of chromosome segregation during mitotic cell division and the development of software tools for accessing, processing, sharing and publishing large scientific image datasets. He is co-founder of the Open Microscopy Environment (OME), a community-led open source software project that develops specifications and tools for biological imaging. He earned his PhD in Biophysics from the University of California San Francisco. In 2012, he was named Fellow of the Royal Society of Edinburgh.
Submission deadline: 17 May 2021
Abstract feedback sent: 1 June 2021
Submission deadline: 1 July 2021
Proposal decision sent: 30 July 2021
Associate Director of Foundation Relations
University of Virginia
Filed Under: Funding Opportunities