Abstract
Characterization of materials with biological applications and assessment of physiological effects of therapeutic interventions are critical for translating research to the clinic and preventing adverse reactions. Analytical techniques typically used to characterize targeted nanomaterials and tissues rely on bulk measurement. Therefore, the resulting data represent an average structure of the sample, masking stochastic (randomly generated) distributions that are commonly present. In this Perspective, we examine almost 20 years of work our group has done in different fields to characterize and control distributions. We discuss the analytical techniques and statistical methods we use and illustrate how we leverage them in tandem with other bulk techniques. We also discuss the challenges and time investment associated with taking such a detailed view of distributions as well as the risks of not fully appreciating the extent of heterogeneity present in many systems. Through three case studies showcasing our research on conjugated polymers for drug delivery, collagen in bone, and endogenous protein nanoparticles, we discuss how identification and characterization of distributions, i.e., a molecular view of the system, was critical for understanding the observed biological effects. In all three cases, data would have been misinterpreted and insights missed if we had only relied upon spatially averaged data. Finally, we discuss how new techniques are starting to bridge the gap between bulk and molecular level analysis, bringing more opportunity and capacity to the research community to address the challenges of distributions and their roles in biology, chemistry, and the translation of science and engineering to societal challenges.