Recent Advances of Manganese-Based Hybrid Nanomaterials for Cancer Precision Medicine

Cancer precision medicine (CPM) could tailor the best treatment for individual cancer patients, while imaging techniques play important roles in its application. With the characteristics of noninvasion, nonionized, radiation-free, multidimensional imaging function, and real-time monitoring, magnetic resonance imaging (MRI) is an effective way for early tumor detection, and it has become a tower of strength in CPM imaging techniques. Due to linkage with nephrogenic systemic fibrosis (NSF), gadolinium (Gd)-based contrast agent (CA), which was long used in MRI, has been restricted by the Food and Drug Administration (FDA). In this review, we would like to introduce the manganese (Mn)-based CAs that could significantly increase the safety of MRI CAs by realizing more superior performance and functions simultaneously in the diagnosis and treatment of tumors. Also, recent advances in Mn-based hybrid nanomaterials for CPM are summarized and discussed.

Data science in cell imaging

ABSTRACTCell imaging has entered the ‘Big Data’ era. New technologies in light microscopy and molecular biology have led to an explosion in high-content, dynamic and multidimensional imaging data. Similar to the ‘omics’ fields two decades ago, our current ability to process, visualize, integrate and mine this new generation of cell imaging data is becoming a critical bottleneck in advancing cell biology. Computation, traditionally used to quantitatively test specific hypotheses, must now also enable iterative hypothesis generation and testing by deciphering hidden biologically meaningful patterns in complex, dynamic or high-dimensional cell image data. Data science is uniquely positioned to aid in this process. In this Perspective, we survey the rapidly expanding new field of data science in cell imaging. Specifically, we highlight how data science tools are used within current image analysis pipelines, propose a computation-first approach to derive new hypotheses from cell image data, identify challenges and describe the next frontiers where we believe data science will make an impact. We also outline steps to ensure broad access to these powerful tools – democratizing infrastructure availability, developing sensitive, robust and usable tools, and promoting interdisciplinary training to both familiarize biologists with data science and expose data scientists to cell imaging.

Multiscale imaging of basal cell dynamics in the functionally mature mammary gland

The mammary epithelium is indispensable for the continued survival of more than 5,000 mammalian species. For some, the volume of milk ejected in a single day exceeds their entire blood volume. Here, we unveil the spatiotemporal properties of physiological signals that orchestrate the ejection of milk from alveolar units and its passage along the mammary ductal network. Using quantitative, multidimensional imaging of mammary cell ensembles from GCaMP6 transgenic mice, we reveal how stimulus evoked Ca2+ oscillations couple to contractions in basal epithelial cells. Moreover, we show that Ca2+-dependent contractions generate the requisite force to physically deform the innermost layer of luminal cells, compelling them to discharge the fluid that they produced and housed. Through the collective action of thousands of these biological positive-displacement pumps, each linked to a contractile ductal network, milk begins its passage toward the dependent neonate, seconds after the command.