The Molecular Atlas Project

Abstract

The promise of super-resolution microscopy is a technology to discover new biological mechanisms that occur at smaller length scales then previously observable. Indeed, the core enabling methods were recently awarded a Nobel Prize in chemistry, and such discoveries are reported with increasing frequency in high-impact journals. However, almost every month, another op-ed appears in those very same journals declaring that a major challenge for modern biology is understanding heterogeneity, in all its forms. This realization that “context matters” has led to innovations in patient-specific treatments, individualized gene screening and next-generation therapeutics. While these shifting views are leading to progress in otherwise stagnant fields of science and medicine, the drive to visualize smaller scale detail and the need to understand larger context are diametrically opposed forces. One pushes for smaller scale information at the expense of larger spatial context, while the other strives for a broader picture at the expense of nuanced details. These technological and scientific frontiers, at the moment, are mutually exclusive.The Molecular Atlas Project (MAP) directly asks how these competing interests between super-resolution imaging and broader spatially contextualized information can be reconciled. MAP enables us to acquire, visualize, explore, and annotate proteomic image data representing 7 orders of magnitude in length ranging from molecular (nm) to tissue (cm) scales. This multi-scale understanding is made possible by combining multiplexed DNA-PAINT, a DNA nanotechnology approach to super-resolution imaging, with “big-data” strategies for information management and image visualization. With these innovations combined, MAP enables us to explore cell-specific heterogeneity in ductal carcinoma for every cell in a cm-sized tissue section, analyze organoid growth for advances in high-throughput tissue-on-a-chip technology, and examine individual synapses for connectome mapping over extremely wide areas. Ultimately, MAP is a fundamentally new way to interact with biologically and medically relevant proteome data.