Natural structural color materials, especially those that can undergo reversible changes, are attracting increasing interest in a wide variety of research fields. Inspired by the natural creatures, many elaborately nanostructured photonic materials with variable structural colors were developed. These materials have found important applications in switches, display devices, sensors, and so on. In this critical review, we will provide up-to-date research concerning the natural and bio-inspired photonic materials with variable structural colors. After introducing the variable structural colors in natural creatures, we will focus on the studies of artificial variable structural color photonic materials, including their bio-inspired designs, fabrications and applications. The prospects for the future development of these fantastic variable structural color materials will also be presented. We believe this review will promote the communications among biology, bionics, chemistry, optical physics, and material science (196 references).

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types^1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits.