Resistance is Futile: Physical Science, Systems Biology and Single-Cell Analysis to Understanding the Plastic and Heterogeneous Nature of Melanoma and Their Role in Non-Genetic Drug Resistance

Melanoma is the most deadly form of skin cancer due to its great metastatic potential. Targeted therapy that inhibits the BRAF-V600E driver mutation has shown impressive initial responses in melanoma patients. However, drug resistance, as the universal phenomenon for any cancer therapy, always limits treatment efficacy and compromises outcomes. As the early-step of resistance development, non-genetic mechanisms enable cancer cells to transition into a drug-resistant state in as early as a few days after drug treatment without alteration of the genome. This early mechanism is, to a large extent, due to the heterogeneous and highly plastic nature of tumor cells. Therefore, it imperative to understand the plastic and heterogeneous nature of the melanoma cells in order to identify combination therapies that can overcome resistance. In this thesis, we investigate these two fundamental natures of non-genetic drug resistance using BRAF inhibition of BRAF-mutant melanomas as the model system. These melanoma cells undergo multi-step, reversible drug-induced cell-state transitions from the original sensitive phenotype to a drug-resistant one. We first conducted bulk analysis to characterize the detailed kinetics of the entire transition from drug-sensitive state towards drug-resistant state, revealing expression changes of thousands of genes and extensive chromatin remodeling. A 3-step computational biology approach greatly simplified the complexity and revealed that the whole cell-state transition was controlled by a gene module activated within just the first three days of drug treatment, with the RelA transcription factor driving chromatin remodeling to establish an epigenetic program encoding long-term phenotype changes towards resistance. From there, a detailed mechanism connecting tumor epigenetic plasticity with non-genetic drug resistance was resolved through in-depth molecular biology experiments. The mechanism was validated in clinical patient samples. We further investigated heterogeneity by moving from bulk cellular studies to single-cell analysis. The single-cell view further revealed that two driving forces from both cell-state interconversions and phenotype-specific drug selection control the cell-state transition dynamics. The single-cell studies also pinpointed the signaling network hub, RelA, as the driver molecule of the initiation of the adaptive transition. These two competing driving forces were further quantitatively modeled via a thermodynamic-inspired surprisal analysis and a modified Fokker-Planck-type kinetic model. Finally, using integrated single-cell proteomic and metabolic technology I developed to characterize the early-stage signaling and metabolic changes upon initial drug responses, we further identified two distinct paths connecting drug-sensitive and drug-tolerant states. Melanoma cells exclusively traverse one of the two paths depending on the level of MITF in the drug-naïve cells. The two trajectories are associated with distinct signaling and metabolic susceptibilities and are independently druggable. In total, this thesis combines and synergizes various physical science and systems biology approaches together with several unique single-cell technologies and analysis to obtain a deep and comprehensive understanding of non-genetic drug resistance in cancer. The findings from this thesis provide several novel insights into the rational design of effective combination therapy for overcoming the development of resistance in response to cancer treatments.</p

Single Cell Proteomics in Biomedicine: High-dimensional Data Acquisition, Visualization and Analysis

New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features, and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions

Separating Invisible Sounds Toward Universal Audiovisual Scene-Aware Sound Separation

The audio-visual sound separation field assumes visible sources in videos, but this excludes invisible sounds beyond the camera's view. Current methods struggle with such sounds lacking visible cues. This paper introduces a novel "Audio-Visual Scene-Aware Separation" (AVSA-Sep) framework. It includes a semantic parser for visible and invisible sounds and a separator for scene-informed separation. AVSA-Sep successfully separates both sound types, with joint training and cross-modal alignment enhancing effectiveness.Comment: Accepted at ICCV 2023 - AV4D, 4 figures, 3 table

Single Cell Proteomics in Biomedicine: High-dimensional Data Acquisition, Visualization and Analysis

New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features, and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions

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

Chemical Methods for the Simultaneous Quantitation of Metabolites and Proteins from Single Cells

We describe chemical approaches for integrated metabolic and proteomic assays from single cells. Quantitative assays for intracellular metabolites, including glucose uptake and three other species, are designed as surface-competitive binding assays with fluorescence readouts. This enables integration into a microarray format with functional protein immunoassays, all of which are incorporated into the microchambers of a single-cell barcode chip (SCBC). By using the SCBC, we interrogate the response of human-derived glioblastoma cancer cells to epidermal growth factor receptor inhibition. We report, for the first time, on both the intercellular metabolic heterogeneity as well as the baseline and drug-induced changes in the metabolite–phosphoprotein correlation network

Characteristics and causes analysis of Nandianzi landslide in Lingtai County, Gansu Province

On October 3, 2021, a large landslide occurred at the loess-mudstone interface in Nandianzi, Lingtai, its successful early warning measures preventing casualties. In order to investigate the occurrence mechanism behind the Nandianzi landslide, a basic investigation was conducted, covering the topography, lithology, hydrogeological conditions, and human engineering activities related to the landslide. Based on the characteristics and differences of the crack development of the landslide, the landslide mass was divided into five blocks. The characteristics of each block were thoroughly analyzed through qualitative and quantitative analysis. The specific sliding situation of different parts of the landslide was analyzed, and further evidence was provided for the objective rationality of landslide classification and zoning, as well as the analysis of landslide mechanisms. Ultimately, it is concluded that the main causes of landslide disasters are as follows: (1) Large-scale excavation and earthwork activities at the lower and middle parts of the slope and the toe, leading to slope steepening and reduced resistance to sliding; (2) Formation of slope depressions, causing inadequate drainage and softening of the rock layer contact surface, thereby diminishing slope stability; and (3) Prolonged heavy rainfall that leads to instability and causes significant loss. While the Nandianzi landslide in Lintai county represents a successfully averted disaster, it serves as a noteworthy case study and a cautionary example for scientifically and standardizedly approaching urban construction and rural revitalization in China. This study holds significance value for monitoring, early warning,risk assessmen, and engineering treatment in comparable regions