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Large-scale generation of protein capture reagents remains a technical challenge, but their generation is just the beginning. Validation is a critical, iterative process that yields different results for different uses. Independent, community-based validation offers the possibility of transparent data sharing, with use case–specific results made broadly available. This type of resource, which can grow as new validation data are obtained for an expanding group of reagents, provides a community resource that should accompany future reagent-generating efforts. To address a pressing need for antibodies or other reagents that recognize human proteins, the National Institutes of Health Common Fund launched the Protein Capture Reagents Program in 2010 as a pilot to target human transcription factors. Here, we describe lessons learned from this program concerning generation and validation of research reagents, which we believe are generally applicable for future research endeavors working in a similar space.

The NIH Roadmap Epigenomics Program was launched to deliver reference epigenomic data from human tissues and cells, develop tools and methods for analyzing the epigenome, discover novel epigenetic marks, develop methods to manipulate the epigenome, and determine epigenetic contributions to diverse human diseases. Here, we comment on the outcomes from this program: the scientific contributions made possible by a consortium approach and the challenges, benefits, and lessons learned from this group science effort.

It has become exceedingly important to understand the precise molecular profiles of the nearly 40 trillion cells in an adult human because of their role in determining health, disease, and therapeutic outcome. The National Institutes of Health (NIH) Common Fund-supported Single Cell Analysis Program (SCAP) was designed to address this challenge. In this review, we outline the original program goals and provide a perspective on the impact of the program as a catalyst for exploration of heterogeneity of human tissues at the cellular level. We believe that the technological advances in single-cell RNA sequencing and multiplexed imaging combined with computational methods made by this program will undoubtedly have an impact on broad and robust applications of single-cell analyses in both health and disease research.