Understanding the structural organization of organs and organisms at the cellular level is a fundamental challenge in biology. This task has been approached by reconstructing three-dimensional structure from images taken from serially sectioned tissues, which is not only labor-intensive and time-consuming but also error-prone. Recent advances in tissue clearing techniques allow visualization of cellular structures and neural networks inside of unsectioned whole tissues or the entire body. However, currently available protocols require long process times. Here, we present the rapid and highly reproducible ACT-PRESTO (active clarity technique-pressure related efficient and stable transfer of macromolecules into organs) method that clears tissues or the whole body within 1 day while preserving tissue architecture and protein-based signals derived from endogenous fluorescent proteins. Moreover, ACT-PRESTO is compatible with conventional immunolabeling methods and expedites antibody penetration into thick specimens by applying pressure. The speed and consistency of this method will allow high-content mapping and analysis of normal and pathological features in intact organs and bodies.

Tissue-clearing techniques have received great attention for volume imaging and for the potential to be applied in optical diagnosis. In principle, tissue clearing is achieved by reducing light scattering through a combination of lipid removal, size change, and matching of the refractive index (RI) between the imaging solution and the tissue. However, the contributions of these major factors in tissue clearing have not been systematically evaluated yet. In this study, we experimentally measured and mathematically calculated the contribution of these factors to the clearing of four organs (brain, liver, kidney, and lung). We found that these factors differentially influence the maximal clearing efficacy of tissues and the diffusivity of materials inside the tissue. We propose that these physical properties of organs can be utilized for the quality control (Q/C) process during tissue clearing, as well as for the monitoring of the pathological changes of tissues.

Background Both genetic and lifestyle factors play an etiologic role in colorectal cancer (CRC). Objectives We evaluated potential gene–environment interactions in CRC risk. Methods We used data from 346,297 participants in the UK Biobank cohort. Healthy lifestyle scores (HLSs) were constructed using 8 lifestyle factors, primarily according to the American Cancer Society guidelines, and were categorized into unhealthy, intermediate, and healthy groups. A polygenic risk score (PRS) was created using 95 genetic risk variants identified by genome-wide association studies of CRC and was categorized by tertile. Cox models were used to estimate the HRs and 95% CIs of CRC risk associated with the HLS and PRS. Results During a median follow-up of 5.8 y, 2066 incident cases of CRC were identified. Healthier HLSs were associated with reduced risk of CRC in a dose–response manner. The risk reduction was more apparent among those with high PRS (HRhealthy vs. unhealthy HLS1: 0.58; 95% CI: 0.43, 0.79 for men and 0.71; 0.58, 0.85 for men and women combined) than those with low PRS. Although no multiplicative interactions were identified, the HLS1 and PRS showed a significant additive interaction (P = 0.02 for all participants combined, 0.04 for men). In analyses including all participants, the adjusted CRC cumulative risk from age 40 to 75 y was 6.40% for those with high PRS/unhealthy HLS1, with a relative excess risk due to interaction of 0.58 (95% CI: 0.06, 1.10), compared with 2.09% among those with low PRS/healthy HLS1. This pattern was more apparent among those who reported not having received any bowel screening before baseline. Conclusions Although the observational nature of the study precludes proof of causality, our findings suggest that individuals with a high genetic susceptibility could benefit more substantially than those with a low genetic risk from lifestyle modification in reducing CRC risk.

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