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NIH Offers $500K Prizes for Single Cell Technology Challenge

NEW YORK (GenomeWeb) – The National Institutes of Health Common Fund has issued a challenge to spur scientists to develop novel single cell analysis technologies, and is offering two $500,000 top prizes for winning teams, NIH said today.

Through the new "Follow that Cell" challenge, the NIH Common Fund's Single Cell Analysis Program aims to inspire researchers to come up with methods for analyzing individual cells that could be used to predict alterations in cell behavior over time. The hope is that these tools will enable time-dependent measurements at the single-cell level in complex tissue environments. Such technologies could provide information about the health status of a given cell, guide treatments related to specific disease states, and enable researchers to identify rare cells in a mixed population, such as cells that are potentially cancerous, becoming drug resistant, or infected with a pathogenic virus, NIH said.

The challenge will be split into two phases, with as many as six phase-one winning teams receiving $100,000 each, and up to two prizes of $400,000 for teams that win the second phase. Finalists also will be invited to the Third Annual Single Cell Analysis Investigators Meeting near NIH next spring.

The solutions the challenge teams develop should include measurements or assays that are not destructive and may include temporal data at the individual cell level, address at least one "impactful biological or clinical question," and be reproducible. Teams should also develop tools that offer much better sensitivity and selectivity for the spatiotemoporal resolution of molecules, structures, and activities within cells, such as high-resolution imaging of molecular interactions within single cells or molecular probes that are at least an order of magnitude smaller than existing versions. These also may include automated and scalable assays to detect functional changes in single cells that can be done faster and cheaper than current methods, or new combinations of tools and approaches to maximize data generation for proteins, lipids, metabolites, and other tools that substantially advance upon the current state of the art.

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