Purely organic materials with room-temperature phosphorescence (RTP) are currently under intense investigation because of their potential applications in sensing, imaging, and displaying. Inspired by certain organometallic systems, where ligand-localized phosphorescence ((3) π-π*) is mediated by ligand-to-metal or metal-to-ligand charge transfer (CT) states, we now show that donor-to-acceptor CT states from the same organic molecule can also mediate π-localized RTP. In the model system of N-substituted naphthalimides (NNIs), the relatively large energy gap between the NNI-localized (1) π-π* and (3) π-π* states of the aromatic ring can be bridged by intramolecular CT states when the NNI is chemically modified with an electron donor. These NNI-based RTP materials can be easily conjugated to both synthetic and natural macromolecules, which can be used for RTP microscopy.

Non-invasively probing metabolites within single live cells is highly desired but challenging. Here we utilize Raman spectro-microscopy for spatial mapping of metabolites within single cells, with the specific goal of identifying druggable metabolic susceptibilities from a series of patient-derived melanoma cell lines. Each cell line represents a different characteristic level of cancer cell de-differentiation. First, with Raman spectroscopy, followed by stimulated Raman scattering (SRS) microscopy and transcriptomics analysis, we identify the fatty acid synthesis pathway as a druggable susceptibility for differentiated melanocytic cells. We then utilize hyperspectral-SRS imaging of intracellular lipid droplets to identify a previously unknown susceptibility of lipid mono-unsaturation within de-differentiated mesenchymal cells with innate resistance to BRAF inhibition. Drugging this target leads to cellular apoptosis accompanied by the formation of phase-separated intracellular membrane domains. The integration of subcellular Raman spectro-microscopy with lipidomics and transcriptomics suggests possible lipid regulatory mechanisms underlying this pharmacological treatment. Our method should provide a general approach in spatially-resolved single cell metabolomics studies.

Previous studies suggested that diabetes mellitus (DM) was associated with risk and mortality of cancer, but studies investigating the correlation between DM and lung cancer prognosis remain controversial. Herein, a meta-analysis was performed to derive a more precise estimate of the prognostic role of DM in lung cancer.Medline and Embase were searched for eligible articles from inception to October 25, 2015. The pooled hazard ratio (HR) with its 95% confidence interval (95% CI) was calculated to evaluate the correlation between DM and lung cancer prognosis. Subgroup meta-analysis was performed based on the histology and the treatment methods.A total of 20 cohort studies from 12 articles were included in the meta-analysis. Also, 16 studies investigated the overall survival (OS) and 4 studies investigated the progression-free survival (PFS). DM was significantly associated with the inferior OS of lung cancer with the pooled HR 1.28 (95% CI: 1.10–1.49, P = .001). The association was prominent in the nonsmall cell lung cancer (NSCLC) subgroup (HR 1.35, 95%CI: 1.14–1.60, P = .002), whereas the association was not significant in the small cell lung cancer (SCLC) subgroup (HR 1.33, 95% CI: 0.87–2.03, P = .18). When NSCLC patients were further stratified by treatment methods, DM had more influence on the surgically treated subgroup than the nonsurgically treated subgroup. There was no obvious evidence for publication bias by Begg's and Egger's test.The results of this meta-analysis exhibit an association of DM with inferior prognosis amongst lung cancer patients, especially the surgically treated NSCLC patients. Given the small number of studies included in this meta-analysis, the present conclusion should be consolidated with more high-quality prospective cohort studies or randomized controlled trials.

Innovations in high-resolution optical imaging have allowed visualization of nanoscale biological structures and connections. However, super-resolution fluorescence techniques, including both optics-oriented and sample-expansion based, are limited in quantification and throughput especially in tissues from photobleaching or quenching of the fluorophores, and low-efficiency or non-uniform delivery of the probes. Here, we report a general sample-expansion vibrational imaging strategy, termed VISTA, for scalable label-free high-resolution interrogations of protein-rich biological structures with resolution down to 78 nm. VISTA achieves decent three-dimensional image quality through optimal retention of endogenous proteins, isotropic sample expansion, and deprivation of scattering lipids. Free from probe-labeling associated issues, VISTA offers unbiased and high-throughput tissue investigations. With correlative VISTA and immunofluorescence, we further validated the imaging specificity of VISTA and trained an image-segmentation model for label-free multi-component and volumetric prediction of nucleus, blood vessels, neuronal cells and dendrites in complex mouse brain tissues. VISTA could hence open new avenues for versatile biomedical studies.

A new strategy to use conjugated polymers to conduct fluorescent enhancement sensing has been developed.