Reprogramming Hypoxic Tumor-Associated Macrophages by Nanoglycoclusters for Boosted Cancer Immunotherapy.

The tumor-associated macrophages (TAMs) in intratumoral hypoxic regions are key drivers of immune escape. Reprogramming the hypoxic TAMs to antitumor phenotype holds great therapeutic benefits but remains challenging for current drugs. Here, an in situ activated nanoglycocluster is reported to realize effective tumor penetration and potent repolarization of hypoxic TAMs. Triggered by the hypoxia-upregulated matrix metalloproteinase-2 (MMP-2), the nanoglycocluster is self-assembled from the administered mannose-containing precursor glycopeptides and presents densely-arrayed mannoses to multivalently engage with mannose receptors on M2-like TAMs for efficient phenotype switch. By virtue of the high diffusivity of precursor glycopeptides due to their low molecular mass and weak affinity with TAMs in perivascular regions, the nanoglycoclusters are capable of substantially accumulating in hypoxic areas to strongly interact with local TAMs. This enables the efficient repolarization of overall TAMs with a higher rate than the small-molecule drug R848 and CD40 antibody, and beneficial therapeutic effects in mouse tumor models especially when combining with PD-1 antibody. This on-demand activated immunoagent is endowed with tumor-penetrating properties and inspires the design of diverse intelligent nanomedicines for hypoxia-related cancer immunotherapy.

Increased CO2 and light intensity regulate growth and leaf gas exchange in tomato.

Carbon dioxide concentration (CO2 ) and light intensity are known to play important roles in plant growth and carbon assimilation. Nevertheless, the underlying physiological mechanisms have not yet been fully explored. Tomato seedlings (Solanum lycopersicum Mill. cv. Jingpeng No. 1) were exposed to two levels of CO2 and three levels of light intensity and the effects on growth, leaf gas exchange and water use efficiency were investigated. Elevated CO2 and increased light intensity promoted growth, dry matter accumulation and pigment concentration and together the seedling health index. Elevated CO2 had no significant effect on leaf nitrogen content but did significantly upregulate Calvin cycle enzyme activity. Increased CO2 and light intensity promoted photosynthesis, both on a leaf-area basis and on a chlorophyll basis. Increased CO2 also increased light-saturated maximum photosynthetic rate, apparent quantum efficiency and carboxylation efficiency and, together with increased light intensity, it raised photosynthetic capacity. However, increased CO2 reduced transpiration and water consumption across different levels of light intensity, thus significantly increasing both leaf-level and plant-level water use efficiency. Among the range of treatments imposed, the combination of increased CO2 (800 µmol CO2 mol-1 ) and high light intensity (400 µmol m-2 s-1 ) resulted in optimal growth and carbon assimilation. We conclude that the combination of increased CO2 and increased light intensity worked synergistically to promote growth, photosynthetic capacity and water use efficiency by upregulation of pigment concentration, Calvin cycle enzyme activity, light energy use and CO2 fixation. Increased CO2 also lowered transpiration and hence water usage.

Automated Stoichiometry Analysis of Single-Molecule Fluorescence Imaging Traces via Deep Learning.

The stoichiometry of protein complexes is precisely regulated in cells and is fundamental to protein function. Singe-molecule fluorescence imaging based photobleaching event counting is a new approach for protein stoichiometry determination under physiological conditions. Due to the interference of the high noise level and photoblinking events, accurately extracting real bleaching steps from single-molecule fluorescence traces is still a challenging task. Here, we develop a novel method of using convolutional and long-short-term memory deep learning neural network (CLDNN) for photobleaching event counting. We design the  convolutional layers to accurately extract features of steplike photobleaching drops and long-short-term memory (LSTM) recurrent layers to distinguish between photobleaching and photoblinking events. Compared with traditional algorithms, CLDNN shows higher accuracy with at least 2 orders of magnitude improvement of efficiency, and it does not require user-specified parameters. We have verified our CLDNN method using experimental data from imaging of single dye-labeled molecules in vitro and epidermal growth factor receptors (EGFR) on cells. Our CLDNN method is expected to provide a new strategy to stoichiometry study and time series analysis in chemistry.