Visualizing Subcellular Enrichment of Glycogen in Live Cancer Cells by Stimulated Raman Scattering

Glycogen, a branched glucose polymer, helps regulate glucose homeostasis through immediate storage and release of glucose. Reprogramming of glycogen metabolism has recently been suggested to play an emerging role in cancer progression and tumorigenesis. However, regulation of metabolic rewiring for glycogen synthesis and breakdown in cancer cells remains less understood. Despite the availability of various glycogen detection methods, selective visualization of glycogen in living cells with high spatial resolution has proven to be highly challenging. Here, we present an optical imaging strategy to visualize glycogen in live cancer cells with minimal perturbation by combining stimulated Raman scattering microscopy with metabolic incorporation of deuterium-labeled glucose. We revealed the subcellular enrichment of glycogen in live cancer cells and achieved specific glycogen mapping through distinct spectral identification. Using this method, different glycogen metabolic phenotypes were characterized in a series of patient-derived BRAF mutant melanoma cell lines. Our results indicate that cell lines manifesting high glycogen storage level showed increased tolerance to glucose deficiency among the studied melanoma phenotypes. This method opens up the possibility for noninvasive study of complex glycogen metabolism at subcellular resolution and may help reveal new features of glycogen regulation in cancer systems

Visualizing Subcellular Enrichment of Glycogen in Live Cancer Cells by Stimulated Raman Scattering

Glycogen, a branched glucose polymer, helps regulate glucose homeostasis through immediate storage and release of glucose. Reprogramming of glycogen metabolism has recently been suggested to play an emerging role in cancer progression and tumorigenesis. However, regulation of metabolic rewiring for glycogen synthesis and breakdown in cancer cells remains less understood. Despite the availability of various glycogen detection methods, selective visualization of glycogen in living cells with high spatial resolution has proven to be highly challenging. Here, we present an optical imaging strategy to visualize glycogen in live cancer cells with minimal perturbation by combining stimulated Raman scattering microscopy with metabolic incorporation of deuterium-labeled glucose. We revealed the subcellular enrichment of glycogen in live cancer cells and achieved specific glycogen mapping through distinct spectral identification. Using this method, different glycogen metabolic phenotypes were characterized in a series of patient-derived BRAF mutant melanoma cell lines. Our results indicate that cell lines manifesting high glycogen storage level showed increased tolerance to glucose deficiency among the studied melanoma phenotypes. This method opens up the possibility for noninvasive study of complex glycogen metabolism at subcellular resolution and may help reveal new features of glycogen regulation in cancer systems

Toward photoswitchable electronic pre-resonance stimulated Raman probes

Reversibly photoswitchable probes allow for a wide variety of optical imaging applications. In particular, photoswitchable fluorescent probes have significantly facilitated the development of super-resolution microscopy. Recently, stimulated Raman scattering (SRS) imaging, a sensitive and chemical-specific optical microscopy, has proven to be a powerful live-cell imaging strategy. Driven by the advances of newly developed Raman probes, in particular the pre-resonance enhanced narrow-band vibrational probes, electronic pre-resonance SRS (epr-SRS) has achieved super-multiplex imaging with sensitivity down to 250 nM and multiplexity up to 24 colors. However, despite the high demand, photoswitchable Raman probes have yet to be developed. Here, we propose a general strategy for devising photoswitchable epr-SRS probes. Toward this goal, we exploit the molecular electronic and vibrational coupling, in which we switch the electronic states of the molecules to four different states to turn their ground-state epr-SRS signals on and off. First, we showed that inducing transitions to both the electronic excited state and triplet state can effectively diminish the SRS peaks. Second, we revealed that the epr-SRS signals can be effectively switched off in red-absorbing organic molecules through light-facilitated transitions to a reduced state. Third, we identified that photoswitchable proteins with near-infrared photoswitchable absorbance, whose states are modulable with their electronic resonances detunable toward and away from the pump photon energy, can function as the photoswitchable epr-SRS probes with desirable sensitivity (M) and low photofatigue (>40 cycles). These photophysical characterizations and proof-of-concept demonstrations should advance the development of novel photoswitchable Raman probes and open up the unexplored Raman imaging capabilities.Peer reviewe

Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells

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

Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells

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

NICE 2023 Zero-shot Image Captioning Challenge

In this report, we introduce NICE project\footnote{\url{https://nice.lgresearch.ai/}} and share the results and outcomes of NICE challenge 2023. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks.Comment: Tech report, project page https://nice.lgresearch.ai

Factors Contributing to Disaster Evacuation: The Case of South Korea

There has been increasing interest in effective evacuation in response to natural disasters, particularly in understanding human evacuation behavior. It is important to determine the factors affecting evacuation decision making to promote prompt evacuation. This study focuses on the effects of past experiences on evacuation behavior in South Korea, especially the evacuation drill experience. Additionally, the influence of demographic and socio-economic characteristics on evacuation behavior is considered. After collecting data through telephone surveys, t-tests and logit regression models were used to evaluate the data. The results reveal that an evacuation drill experience is positively related to making a decision to evacuate. The results also confirm that certain demographic factors, such as age and household size, as well as socio-economic factors, such as household income and housing type, influence evacuation decisions. Besides these, knowing the location of a shelter is another factor that improves the chances of evacuation. Finally, discussions and suggestions for increasing participation in evacuation drills are provided

An Analysis of the Effects of Development-Restricted Areas on Land Price Using Spatial Analysis

The purpose of this study was to estimate the effects of development-restricted areas (DRAs) on land price. The study area used was Goyang city in South Korea, where DRAs occupy a large proportion of the city’s administrative area. To examine the economic impact of the DRA, this study estimated log-linear regression models and calculated the difference between the land price determined within the DRA and the land price of the developed areas within buffers created by using a geographic information system (GIS). The results showed that a designation of DRA decreased land price, and that there was a large difference in the land price between the inner and the outer DRA, with a difference of USD 871/m2 in the average land price of the study area. These results serve as a reference for policymakers regarding land use in metropolitan areas in the future

Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

As a characteristic of senior drivers aged 65 +, the low-mileage bias has been reported in previous studies. While it is thought to be a well-known phenomenon caused by aging, the characteristics of urban environments create more opportunities for crashes. This calls for investigating the low-mileage bias and scrutinizing whether it has the same impact on other age groups, such as young and middle-aged drivers. We use a crash database from the Ohio Department of Public Safety from 2006 to 2011 and adopt a macro approach using Negative Binomial models and Conditional Autoregressive (CAR) models to deal with a spatial autocorrelation issue. Aside from the low-mileage bias issue, we examine the association between the number of crashes and the built environment and socio-economic and demographic factors. We confirm that the number of crashes is associated with vehicle miles traveled, which suggests that more accumulated driving miles result in a lower likelihood of being involved in a crash. This implies that drivers in the low mileage group are involved in crashes more often, regardless of the driver’s age. The results also confirm that more complex urban environments have a higher number of crashes than rural environments