Highlights d CHAPS forms smaller micelles allowing full permeabilization of aged human organs d SHANEL enables centimeters deep molecular labeling and clearing of whole human organs d SHANEL renders intact adult human brain and kidney transparent d Deep learning and light-sheet microscopy with SHANEL allows human organ mapping

Highlights d DeepMACT is a deep learning-based pipeline for comprehensive analysis of metastases d DeepMACT identifies micrometastases and single cancer cells in full-body 3D scans d DeepMACT reveals the efficacy of antibody-drug targeting in the entire body d DeepMACT indicates that the tumor microenvironment affects drug targeting efficacy

Whole-body imaging of mice is a key source of information for research. Organ segmentation is a prerequisite for quantitative analysis but is a tedious and error-prone task if done manually. Here, we present a deep learning solution called AIMOS that automatically segments major organs (brain, lungs, heart, liver, kidneys, spleen, bladder, stomach, intestine) and the skeleton in less than a second, orders of magnitude faster than prior algorithms. AIMOS matches or exceeds the segmentation quality of state-of-the-art approaches and of human experts. We exemplify direct applicability for biomedical research for localizing cancer metastases. Furthermore, we show that expert annotations are subject to human error and bias. As a consequence, we show that at least two independently created annotations are needed to assess model performance. Importantly, AIMOS addresses the issue of human bias by identifying the regions where humans are most likely to disagree, and thereby localizes and quantifies this uncertainty for improved downstream analysis. In summary, AIMOS is a powerful open-source tool to increase scalability, reduce bias, and foster reproducibility in many areas of biomedical research.

Many hospitals lack facilities for accurate diagnosis of acute ischemic stroke (AIS). Circular RNA (circRNA) is highly expressed in the brain and is closely associated with stroke. In this study, we examined whether the blood-borne circRNAs could be promising candidates as adjunctive diagnostic biomarkers and their pathophysiological roles after stroke. We profiled the blood circRNA expression in mice subjected to experimental focal cerebral ischemia and validated the selected circRNAs in AIS patients. We demonstrated that 128, 198, and 789 circRNAs were significantly altered at 5 min, 3 h, and 24 h after ischemic stroke, respectively. Our bioinformatics analysis revealed that the circRNA-targeted genes were associated with the Hippo signaling pathway, extracellular matrix-receptor interaction, and fatty acid metabolism at 5 min, 3 h and 24 h after ischemic stroke, respectively. We verified that many of these circRNAs existed in the mouse brain. Furthermore, we found that most of the predicted circRNA-miRNA interactions apparently exhibited functional roles in terms of regulation of their target gene expression in the brain. We also verified that many of these mouse circRNAs were conserved in human. Finally, we found that circBBS2 and circPHKA2 were differentially expressed in the blood of AIS patients. These results demonstrate that blood circRNAs may serve as potential biomarkers for AIS diagnosis and reveal the pathophysiological responses in the brain after ischemic stroke.