Single-Cell Transcriptome Atlas of Murine Endothelial Cells.

The heterogeneity of endothelial cells (ECs) across tissues remains incompletely inventoried. We constructed an atlas of >32,000 single-EC transcriptomes from 11 mouse tissues and identified 78 EC subclusters, including Aqp7+ intestinal capillaries and angiogenic ECs in healthy tissues. ECs from brain/testis, liver/spleen, small intestine/colon, and skeletal muscle/heart pairwise expressed partially overlapping marker genes. Arterial, venous, and lymphatic ECs shared more markers in more tissues than did heterogeneous capillary ECs. ECs from different vascular beds (arteries, capillaries, veins, lymphatics) exhibited transcriptome similarity across tissues, but the tissue (rather than the vessel) type contributed to the EC heterogeneity. Metabolic transcriptome analysis revealed a similar tissue-grouping phenomenon of ECs and heterogeneous metabolic gene signatures in ECs between tissues and between vascular beds within a single tissue in a tissue-type-dependent pattern. The EC atlas taxonomy enabled identification of EC subclusters in public scRNA-seq datasets and provides a powerful discovery tool and resource value.

From cancer big data to treatment: Artificial intelligence in cancer research.

In recent years, developing the idea of "cancer big data" has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a significant contribution to cancer big data in biomedicine and disease diagnosis. The increasingly availability of extensive cancer big data has set the stage for the development of multimodal artificial intelligence (AI) frameworks. These frameworks aim to analyze high-dimensional multi-omics data, extracting meaningful information that is challenging to obtain manually. Although interpretability and data quality remain critical challenges, these methods hold great promise for advancing our understanding of cancer biology and improving patient care and clinical outcomes. Here, we provide an overview of cancer big data and explore the applications of both traditional machine learning and deep learning approaches in cancer genomic and proteomic studies. We briefly discuss the challenges and potential of AI techniques in the integrated analysis of omics data, as well as the future direction of personalized treatment options in cancer.

Molecular Landscape of Bladder Cancer: Key Genes, Transcription Factors, and Drug Interactions.

Bladder cancer is among the most prevalent tumors in the urinary system and is known for its high malignancy. Although traditional diagnostic and treatment methods are established, recent research has focused on understanding the molecular mechanisms underlying bladder cancer. The primary objective of this study is to identify novel diagnostic markers and discover more effective targeted therapies for bladder cancer. This study identified differentially expressed genes (DEGs) between bladder cancer tissues and adjacent normal tissues using data from The Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to explore the functional roles of these genes. A protein-protein interaction (PPI) network was also constructed to identify and analyze hub genes within this network. Gene set variation analysis (GSVA) was conducted to investigate the involvement of these genes in various biological processes and pathways. Ten key genes were found to be significantly associated with bladder cancer: IL6, CCNA2, CCNB1, CDK1, PLK1, TOP2A, AURKA, AURKB, FOXM1, and CALML5. GSVA analyses revealed that these genes are involved in a variety of biological processes and signaling pathways, including coagulation, UV-response-down, apoptosis, Notch signaling, and Wnt/beta-catenin signaling. The diagnostic relevance of these genes was validated through ROC curve analysis. Additionally, potential therapeutic drug interactions with these key genes were identified. This study provides valuable insights into key genes and their roles in bladder cancer. The identified genes and their interactions with therapeutic drugs could serve as potential biomarkers, presenting new opportunities for enhancing the diagnosis and prognosis of bladder cancer.

BIOMEX: an interactive workflow for (single cell) omics data interpretation and visualization.

The amount of biological data, generated with (single cell) omics technologies, is rapidly increasing, thereby exacerbating bottlenecks in the data analysis and interpretation of omics experiments. Data mining platforms that facilitate non-bioinformatician experimental scientists to analyze a wide range of experimental designs and data types can alleviate such bottlenecks, aiding in the exploration of (newly generated or publicly available) omics datasets. Here, we present BIOMEX, a browser-based software, designed to facilitate the Biological Interpretation Of Multi-omics EXperiments by bench scientists. BIOMEX integrates state-of-the-art statistical tools and field-tested algorithms into a flexible but well-defined workflow that accommodates metabolomics, transcriptomics, proteomics, mass cytometry and single cell data from different platforms and organisms. The BIOMEX workflow is accompanied by a manual and video tutorials that provide the necessary background to navigate the interface and get acquainted with the employed methods. BIOMEX guides the user through omics-tailored analyses, such as data pretreatment and normalization, dimensionality reduction, differential and enrichment analysis, pathway mapping, clustering, marker analysis, trajectory inference, meta-analysis and others. BIOMEX is fully interactive, allowing users to easily change parameters and generate customized plots exportable as high-quality publication-ready figures. BIOMEX is open source and freely available at https://www.vibcancer.be/software-tools/biomex.

EndoDB: a database of endothelial cell transcriptomics data.

Endothelial cells (ECs) line blood vessels, regulate homeostatic processes (blood flow, immune cell trafficking), but are also involved in many prevalent diseases. The increasing use of high-throughput technologies such as gene expression microarrays and (single cell) RNA sequencing generated a wealth of data on the molecular basis of EC (dys-)function. Extracting biological insight from these datasets is challenging for scientists who are not proficient in bioinformatics. To facilitate the re-use of publicly available EC transcriptomics data, we developed the endothelial database EndoDB, a web-accessible collection of expert curated, quality assured and pre-analyzed data collected from 360 datasets comprising a total of 4741 bulk and 5847 single cell endothelial transcriptomes from six different organisms. Unlike other added-value databases, EndoDB allows to easily retrieve and explore data of specific studies, determine under which conditions genes and pathways of interest are deregulated and assess reprogramming of metabolism via principal component analysis, differential gene expression analysis, gene set enrichment analysis, heatmaps and metabolic and transcription factor analysis, while single cell data are visualized as gene expression color-coded t-SNE plots. Plots and tables in EndoDB are customizable, downloadable and interactive. EndoDB is freely available at https://vibcancer.be/software-tools/endodb, and will be updated to include new studies.

Single-Cell RNA Sequencing Reveals Renal Endothelium Heterogeneity and Metabolic Adaptation to Water Deprivation.

BACKGROUND: Renal endothelial cells from glomerular, cortical, and medullary kidney compartments are exposed to different microenvironmental conditions and support specific kidney processes. However, the heterogeneous phenotypes of these cells remain incompletely inventoried. Osmotic homeostasis is vitally important for regulating cell volume and function, and in mammals, osmotic equilibrium is regulated through the countercurrent system in the renal medulla, where water exchange through endothelium occurs against an osmotic pressure gradient. Dehydration exposes medullary renal endothelial cells to extreme hyperosmolarity, and how these cells adapt to and survive in this hypertonic milieu is unknown. METHODS: We inventoried renal endothelial cell heterogeneity by single-cell RNA sequencing >40,000 mouse renal endothelial cells, and studied transcriptome changes during osmotic adaptation upon water deprivation. We validated our findings by immunostaining and functionally by targeting oxidative phosphorylation in a hyperosmolarity model in vitro and in dehydrated mice in vivo. RESULTS: We identified 24 renal endothelial cell phenotypes (of which eight were novel), highlighting extensive heterogeneity of these cells between and within the cortex, glomeruli, and medulla. In response to dehydration and hypertonicity, medullary renal endothelial cells upregulated the expression of genes involved in the hypoxia response, glycolysis, and-surprisingly-oxidative phosphorylation. Endothelial cells increased oxygen consumption when exposed to hyperosmolarity, whereas blocking oxidative phosphorylation compromised endothelial cell viability during hyperosmotic stress and impaired urine concentration during dehydration. CONCLUSIONS: This study provides a high-resolution atlas of the renal endothelium and highlights extensive renal endothelial cell phenotypic heterogeneity, as well as a previously unrecognized role of oxidative phosphorylation in the metabolic adaptation of medullary renal endothelial cells to water deprivation.

Spatial transcriptomics data and analytical methods: An updated perspective.

Spatial transcriptomics (ST) is a newly emerging field that integrates high-resolution imaging and transcriptomic data to enable the high-throughput analysis of the spatial localization of transcripts in diverse biological systems. The rapid progress in this field necessitates the development of innovative computational methods to effectively tackle the distinct challenges posed by the analysis of ST data. These platforms, integrating AI techniques, offer a promising avenue for understanding disease mechanisms and expediting drug discovery. Despite significant advances in the development of ST data analysis techniques, there is an ongoing need to enhance these models for increased biological relevance. In this review, we briefly discuss the ST-related databases and current deep-learning-based models for spatial transcriptome data analyses and highlight their roles and future perspectives in biomedical applications.

PD-L1 is a biomarker of real-world clinical outcomes for anti-CTLA-4 plus anti-PD-1 or anti-PD-1 monotherapy in metastatic melanoma.

BACKGROUND: Metastatic melanoma (MM) is commonly treated with a combination of nivolumab and ipilimumab, regardless of tumor PD-L1 expression. METHODS: We conducted a population-based study including all patients with MM (except ocular melanoma) treated in Denmark with first-line combination therapy or anti-PD-1 monotherapy since January 2017. Baseline data including known prognostic characteristics were used in multivariable and propensity-matched score (PMS) analyses to assess progression-free survival (PFS), melanoma-specific survival (MSS), and overall survival (OS) according to PD-L1 expression. RESULTS: We identified 1341 eligible patients, with known PD-L1 status for 1081 patients (43% PD-L1 ≥ 1%, 57% PD-L1 < 1%). PD-L1 ≥ 1% was an independent positive prognostic biomarker for survival in the overall cohort (MSS: HR 0.66, CI 0.52-0.83, p < 0.001). In the PMS PD-L1 ≥ 1% cohort, combination therapy showed similar clinical outcomes to monotherapy (PFS: HR 1.41, CI 0.94-2.11, p = 0.101; MSS: HR 1.21, CI 0.70-2.11, p = 0.49; OS: HR 1.17, CI 0.68-2.00, p = 0.567). In contrast, in the PMS PD-L1 < 1% and in the PMS PD-L1 < 1% BRAF WT cohorts, combination therapy improved PFS (respectively with HR 0.70, CI 0.53-0.93, p = 0.013; and HR 0.54, CI 0.37-0.78, p = 0.001), but did not reach statistically significant improvements of MSS (HR 0.72, CI 0.50-1.02, p = 0.065; and HR 0.79, CI 0.51-1.21, p = 0.278) or OS (HR 0.78, CI 0.56-1.08, p = 0.135; and HR 0.81, CI 0.54-1.21, p = 0.305) compared to monotherapy. CONCLUSION: Our findings support previous exploratory analyses of Checkmate-067, highlighting that improved clinical outcomes with combination therapy are not established in unselected patients with high (≥1%) tumor PD-L1 expression.

Dissecting tumor microenvironment heterogeneity in syngeneic mouse models: insights on cancer-associated fibroblast phenotypes shaped by infiltrating T cells.

Murine syngeneic tumor models have been used extensively for cancer research for several decades and have been instrumental in driving the discovery and development of cancer immunotherapies. These tumor models are very simplistic cancer models, but recent reports have, however, indicated that the different inoculated cancer cell lines can lead to the formation of unique tumor microenvironments (TMEs). To gain more knowledge from studies based on syngeneic tumor models, it is essential to obtain an in-depth understanding of the cellular and molecular composition of the TME in the different models. Additionally, other parameters that are important for cancer progression, such as collagen content and mechanical tissue stiffness across syngeneic tumor models have not previously been reported. Here, we compare the TME of tumors derived from six common syngeneic tumor models. Using flow cytometry and transcriptomic analyses, we show that strikingly unique TMEs are formed by the different cancer cell lines. The differences are reflected as changes in abundance and phenotype of myeloid, lymphoid, and stromal cells in the tumors. Gene expression analyses support the different cellular composition of the TMEs and indicate that distinct immunosuppressive mechanisms are employed depending on the tumor model. Cancer-associated fibroblasts (CAFs) also acquire very different phenotypes across the tumor models. These differences include differential expression of genes encoding extracellular matrix (ECM) proteins, matrix metalloproteinases (MMPs), and immunosuppressive factors. The gene expression profiles suggest that CAFs can contribute to the formation of an immunosuppressive TME, and flow cytometry analyses show increased PD-L1 expression by CAFs in the immunogenic tumor models, MC38 and CT26. Comparison with CAF subsets identified in other studies shows that CAFs are skewed towards specific subsets depending on the model. In athymic mice lacking tumor-infiltrating cytotoxic T cells, CAFs express lower levels of PD-L1 and lower levels of fibroblast activation markers. Our data underscores that CAFs can be involved in the formation of an immunosuppressive TME.

Chitooligosaccharides Improve the Efficacy of Checkpoint Inhibitors in a Mouse Model of Lung Cancer.

YKL-40 (also named chitinase 3 like-1 protein [CHI3L1]) is a secreted chitinase-like protein which is upregulated in cancers and suggested to have pro-tumorigenic activity. YKL-40 lacks enzymatic function, but it can bind carbohydrates such as chitin. Chitooligosaccharides (COS) derived from deacetylation and hydrolysis of chitin might be used for the blockade of YKL-40 function. Here, public single-cell RNA sequencing datasets were used to elucidate the cellular source of YKL-40 gene expression in human tumors. Fibroblasts and myeloid cells were the primary sources of YKL-40. Screening of YKL-40 gene expression in syngeneic mouse cancer models showed the highest expression in the Lewis lung carcinoma (LL2) model. LL2 was used to investigate COS monotherapy and combinations with immune checkpoint inhibitors (anti-PD-L1 and anti-CTLA-4) (ICIs) and radiotherapy (8 Gy × 3) (RT). COS tended to reduce plasma YKL-40 levels, but it did not affect tumor growth. LL2 showed minimal responses to ICIs, or to RT alone. Interestingly, ICIs combined with COS led to delayed tumor growth. RT also enhanced the efficacy of ICIs; however, the addition of COS did not further delay the tumor growth. COS may exert their anti-tumorigenic effects through the inhibition of YKL-40, but additional functions of COS should be investigated.

Mitochondrial respiration supports autophagy to provide stress resistance during quiescence.

Mitochondrial oxidative phosphorylation (OXPHOS) generates ATP, but OXPHOS also supports biosynthesis during proliferation. In contrast, the role of OXPHOS during quiescence, beyond ATP production, is not well understood. Using mouse models of inducible OXPHOS deficiency in all cell types or specifically in the vascular endothelium that negligibly relies on OXPHOS-derived ATP, we show that selectively during quiescence OXPHOS provides oxidative stress resistance by supporting macroautophagy/autophagy. Mechanistically, OXPHOS constitutively generates low levels of endogenous ROS that induce autophagy via attenuation of ATG4B activity, which provides protection from ROS insult. Physiologically, the OXPHOS-autophagy system (i) protects healthy tissue from toxicity of ROS-based anticancer therapy, and (ii) provides ROS resistance in the endothelium, ameliorating systemic LPS-induced inflammation as well as inflammatory bowel disease. Hence, cells acquired mitochondria during evolution to profit from oxidative metabolism, but also built in an autophagy-based ROS-induced protective mechanism to guard against oxidative stress associated with OXPHOS function during quiescence.Abbreviations: AMPK: AMP-activated protein kinase; AOX: alternative oxidase; Baf A: bafilomycin A1; CI, respiratory complexes I; DCF-DA: 2',7'-dichlordihydrofluorescein diacetate; DHE: dihydroethidium; DSS: dextran sodium sulfate; ΔΨmi: mitochondrial inner membrane potential; EdU: 5-ethynyl-2'-deoxyuridine; ETC: electron transport chain; FA: formaldehyde; HUVEC; human umbilical cord endothelial cells; IBD: inflammatory bowel disease; LC3B: microtubule associated protein 1 light chain 3 beta; LPS: lipopolysaccharide; MEFs: mouse embryonic fibroblasts; MTORC1: mechanistic target of rapamycin kinase complex 1; mtDNA: mitochondrial DNA; NAC: N-acetyl cysteine; OXPHOS: oxidative phosphorylation; PCs: proliferating cells; PE: phosphatidylethanolamine; PEITC: phenethyl isothiocyanate; QCs: quiescent cells; ROS: reactive oxygen species; PLA2: phospholipase A2, WB: western blot.

Single cell atlas identifies lipid-processing and immunomodulatory endothelial cells in healthy and malignant breast.

Since a detailed inventory of endothelial cell (EC) heterogeneity in breast cancer (BC) is lacking, here we perform single cell RNA-sequencing of 26,515 cells (including 8433 ECs) from 9 BC patients and compare them to published EC taxonomies from lung tumors. Angiogenic ECs are phenotypically similar, while other EC subtypes are different. Predictive interactome analysis reveals known but also previously unreported receptor-ligand interactions between ECs and immune cells, suggesting an involvement of breast EC subtypes in immune responses. We also identify a capillary EC subtype (LIPEC (Lipid Processing EC)), which expresses genes involved in lipid processing that are regulated by PPAR-γ and is more abundant in peri-tumoral breast tissue. Retrospective analysis of 4648 BC patients reveals that treatment with metformin (an indirect PPAR-γ signaling activator) provides long-lasting clinical benefit and is positively associated with LIPEC abundance. Our findings warrant further exploration of this LIPEC/PPAR-γ link for BC treatment.

Effects of the Novel PFKFB3 Inhibitor KAN0438757 on Colorectal Cancer Cells and Its Systemic Toxicity Evaluation In Vivo.

BACKGROUND: Despite substantial progress made in the last decades in colorectal cancer (CRC) research, new treatment approaches are still needed to improve patients' long-term survival. To date, the promising strategy to target tumor angiogenesis metabolically together with a sensitization of CRC to chemo- and/or radiotherapy by PFKFB3 (6-phosphofructo-2-kinase/fructose-2,6-biphosphatase-3) inhibition has never been tested. Therefore, initial evaluation and validation of newly developed compounds such as KAN0438757 and their effects on CRC cells are crucial steps preceding to in vivo preclinical studies, which in turn may consolidate new therapeutic targets. MATERIALS AND METHODS: The efficiency of KAN0438757 to block PFKFB3 expression and translation in human CRC cells was evaluated by immunoblotting and real-time PCR. Functional in vitro assays assessed the effects of KAN0438757 on cell viability, proliferation, survival, adhesion, migration and invasion. Additionally, we evaluated the effects of KAN0438757 on matched patient-derived normal and tumor organoids and its systemic toxicity in vivo in C57BL6/N mice. RESULTS: High PFKFB3 expression is correlated with a worse survival in CRC patients. KAN0438757 reduces PFKFB3 protein expression without affecting its transcriptional regulation. Additionally, a concentration-dependent anti-proliferative effect was observed. The migration and invasion capacity of cancer cells were significantly reduced, independent of the anti-proliferative effect. When treating colonic patient-derived organoids with KAN0438757 an impressive effect on tumor organoids growth was apparent, surprisingly sparing normal colonic organoids. No high-grade toxicity was observed in vivo. CONCLUSION: The PFKFB3 inhibitor KAN0438757 significantly reduced CRC cell migration, invasion and survival. Moreover, on patient-derived cancer organoids KAN0438757 showed significant effects on growth, without being overly toxic in normal colon organoids and healthy mice. Our findings strongly encourage further translational studies to evaluate KAN0438757 in CRC therapy.

Rapid Identification of the Tumor-Specific Reactive TIL Repertoire via Combined Detection of CD137, TNF, and IFNγ, Following Recognition of Autologous Tumor-Antigens.

Detecting the entire repertoire of tumor-specific reactive tumor-infiltrating lymphocytes (TILs) is essential for investigating their immunological functions in the tumor microenvironment. Current in vitro assays identifying tumor-specific functional activation measure the upregulation of surface molecules, de novo production of antitumor cytokines, or mobilization of cytotoxic granules following recognition of tumor-antigens, yet there is no widely adopted standard method. Here we established an enhanced, yet simple, method for identifying simultaneously CD8+ and CD4+ tumor-specific reactive TILs in vitro, using a combination of widely known and available flow cytometry assays. By combining the detection of intracellular CD137 and de novo production of TNF and IFNγ after recognition of naturally-presented tumor antigens, we demonstrate that a larger fraction of tumor-specific and reactive CD8+ TILs can be detected in vitro compared to commonly used assays. This assay revealed multiple polyfunctionality-based clusters of both CD4+ and CD8+ tumor-specific reactive TILs. In situ, the combined detection of TNFRSF9, TNF, and IFNG identified most of the tumor-specific reactive TIL repertoire. In conclusion, we describe a straightforward method for efficient identification of the tumor-specific reactive TIL repertoire in vitro, which can be rapidly adopted in most cancer immunology laboratories.

Loss of Ambra1 promotes melanoma growth and invasion.

Melanoma is the deadliest skin cancer. Despite improvements in the understanding of the molecular mechanisms underlying melanoma biology and in defining new curative strategies, the therapeutic needs for this disease have not yet been fulfilled. Herein, we provide evidence that the Activating Molecule in Beclin-1-Regulated Autophagy (Ambra1) contributes to melanoma development. Indeed, we show that Ambra1 deficiency confers accelerated tumor growth and decreased overall survival in Braf/Pten-mutated mouse models of melanoma. Also, we demonstrate that Ambra1 deletion promotes melanoma aggressiveness and metastasis by increasing cell motility/invasion and activating an EMT-like process. Moreover, we show that Ambra1 deficiency in melanoma impacts extracellular matrix remodeling and induces hyperactivation of the focal adhesion kinase 1 (FAK1) signaling, whose inhibition is able to reduce cell invasion and melanoma growth. Overall, our findings identify a function for AMBRA1 as tumor suppressor in melanoma, proposing FAK1 inhibition as a therapeutic strategy for AMBRA1 low-expressing melanoma.

Tumor vessel co-option probed by single-cell analysis.

Tumor vessel co-option is poorly understood, yet it is a resistance mechanism against anti-angiogenic therapy (AAT). The heterogeneity of co-opted endothelial cells (ECs) and pericytes, co-opting cancer and myeloid cells in tumors growing via vessel co-option, has not been investigated at the single-cell level. Here, we use a murine AAT-resistant lung tumor model, in which VEGF-targeting induces vessel co-option for continued growth. Single-cell RNA sequencing (scRNA-seq) of 31,964 cells reveals, unexpectedly, a largely similar transcriptome of co-opted tumor ECs (TECs) and pericytes as their healthy counterparts. Notably, we identify cell types that might contribute to vessel co-option, i.e., an invasive cancer-cell subtype, possibly assisted by a matrix-remodeling macrophage population, and another M1-like macrophage subtype, possibly involved in keeping or rendering vascular cells quiescent.

Heterogeneous Effects of Calorie Content and Nutritional Components Underlie Dietary Influence on Pancreatic Cancer Susceptibility.

Pancreatic cancer is a rare but fatal form of cancer, the fourth highest in absolute mortality. Known risk factors include obesity, diet, and type 2 diabetes; however, the low incidence rate and interconnection of these factors confound the isolation of individual effects. Here, we use epidemiological analysis of prospective human cohorts and parallel tracking of pancreatic cancer in mice to dissect the effects of obesity, diet, and diabetes on pancreatic cancer. Through longitudinal monitoring and multi-omics analysis in mice, we found distinct effects of protein, sugar, and fat dietary components, with dietary sugars increasing Mad2l1 expression and tumor proliferation. Using epidemiological approaches in humans, we find that dietary sugars give a MAD2L1 genotype-dependent increased susceptibility to pancreatic cancer. The translation of these results to a clinical setting could aid in the identification of the at-risk population for screening and potentially harness dietary modification as a therapeutic measure.

Single-Cell RNA Sequencing Maps Endothelial Metabolic Plasticity in Pathological Angiogenesis.

Endothelial cell (EC) metabolism is an emerging target for anti-angiogenic therapy in tumor angiogenesis and choroidal neovascularization (CNV), but little is known about individual EC metabolic transcriptomes. By single-cell RNA sequencing 28,337 murine choroidal ECs (CECs) and sprouting CNV-ECs, we constructed a taxonomy to characterize their heterogeneity. Comparison with murine lung tumor ECs (TECs) revealed congruent marker gene expression by distinct EC phenotypes across tissues and diseases, suggesting similar angiogenic mechanisms. Trajectory inference predicted that differentiation of venous to angiogenic ECs was accompanied by metabolic transcriptome plasticity. ECs displayed metabolic transcriptome heterogeneity during cell-cycle progression and in quiescence. Hypothesizing that conserved genes are important, we used an integrated analysis, based on congruent transcriptome analysis, CEC-tailored genome-scale metabolic modeling, and gene expression meta-analysis in cross-species datasets, followed by in vitro and in vivo validation, to identify SQLE and ALDH18A1 as previously unknown metabolic angiogenic targets.

An Integrated Gene Expression Landscape Profiling Approach to Identify Lung Tumor Endothelial Cell Heterogeneity and Angiogenic Candidates.

Heterogeneity of lung tumor endothelial cell (TEC) phenotypes across patients, species (human/mouse), and models (in vivo/in vitro) remains poorly inventoried at the single-cell level. We single-cell RNA (scRNA)-sequenced 56,771 endothelial cells from human/mouse (peri)-tumoral lung and cultured human lung TECs, and detected 17 known and 16 previously unrecognized phenotypes, including TECs putatively regulating immune surveillance. We resolved the canonical tip TECs into a known migratory tip and a putative basement-membrane remodeling breach phenotype. Tip TEC signatures correlated with patient survival, and tip/breach TECs were most sensitive to vascular endothelial growth factor blockade. Only tip TECs were congruent across species/models and shared conserved markers. Integrated analysis of the scRNA-sequenced data with orthogonal multi-omics and meta-analysis data across different human tumors, validated by functional analysis, identified collagen modification as a candidate angiogenic pathway.

Biophysical and molecular docking insight into interaction mechanism and thermal stability of human serum albumin isoforms with a semi-synthetic water-soluble camptothecin analog irinotecan hydrochloride.

In the present work, we have examined the binding parameters, thermodynamics, and stability of human serum albumin (HSA) isoforms at pH 7.4 and 9.0, using spectroscopic, calorimetric, and molecular docking methods in the presence of water-soluble camptothecin analog irinotecan hydrochloride (CPT-11). We observed that CPT-11 binds to HSA through a static quenching procedure of ground-state complex formation with N-isoform and B-isoform. Hydrogen bond and hydrophobic interactions are the major governing forces that participating in the formation of protein-drug complex. To determine the binding site of CPT-11 within HSA molecules, we also have performed molecular docking experiments. We explored the CPT-11-mediated stability and modulation of HSA by performing dynamic light scattering (DLS) and differential scanning calorimetry (DSC) experiments. DLS and DSC techniques are used to determine the size and the melting point (Tm) of HSA, which was decreased in the presence of CPT-11. Therefore, CPT-11 plays an important role in HSA stability and protein-ligand interactions. The present study provides valuable information in the field of pharmacokinetics, pharmaco-dynamics, and drug discovery.