Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line

Purinergic Ca2+ Signaling as a Novel Mechanism of Drug Tolerance in BRAF-Mutant Melanoma

Drug tolerance is a major cause of relapse after cancer treatment. Despite intensive efforts, its molecular basis remains poorly understood, hampering actionable intervention. We report a previously unrecognized signaling mechanism supporting drug tolerance in BRAF-mutant melanoma treated with BRAF inhibitors that could be of general relevance to other cancers. Its key features are cell-intrinsic intracellular Ca2+ signaling initiated by P2X7 receptors (purinergic ligand-gated cation channels) and an enhanced ability for these Ca2+ signals to reactivate ERK1/2 in the drug-tolerant state. Extracellular ATP, virtually ubiquitous in living systems, is the ligand that can initiate Ca2+ spikes via P2X7 channels. ATP is abundant in the tumor microenvironment and is released by dying cells, ironically implicating treatment-initiated cancer cell death as a source of trophic stimuli that leads to ERK reactivation and drug tolerance. Such a mechanism immediately offers an explanation of the inevitable relapse after BRAFi treatment in BRAF-mutant melanoma and points to actionable strategies to overcome it.

scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.

Leveraging Cancer Phenotypic Plasticity for Novel Treatment Strategies

Cancer cells, like all other organisms, are adept at switching their phenotype to adjust to the changes in their environment. Thus, phenotypic plasticity is a quantitative trait that confers a fitness advantage to the cancer cell by altering its phenotype to suit environmental circumstances. Until recently, new traits, especially in cancer, were thought to arise due to genetic factors; however, it is now amply evident that such traits could also emerge non-genetically due to phenotypic plasticity. Furthermore, phenotypic plasticity of cancer cells contributes to phenotypic heterogeneity in the population, which is a major impediment in treating the disease. Finally, plasticity also impacts the group behavior of cancer cells, since competition and cooperation among multiple clonal groups within the population and the interactions they have with the tumor microenvironment also contribute to the evolution of drug resistance. Thus, understanding the mechanisms that cancer cells exploit to tailor their phenotypes at a systems level can aid the development of novel cancer therapeutics and treatment strategies. Here, we present our perspective on a team medicine-based approach to gain a deeper understanding of the phenomenon to develop new therapeutic strategies.

Stem-cell states converge in multistage cutaneous squamous cell carcinoma development

Stem cells play a critical role in cancer development by contributing to cell heterogeneity, lineage plasticity, and drug resistance. We created gene expression networks from hundreds of mouse tissue samples (both normal and tumor) and integrated these with lineage tracing and single-cell RNA-seq, to identify convergence of cell states in premalignant tumor cells expressing markers of lineage plasticity and drug resistance. Two of these cell states representing multilineage plasticity or proliferation were inversely correlated, suggesting a mutually exclusive relationship. Treatment of carcinomas in vivo with chemotherapy repressed the proliferative state and activated multilineage plasticity whereas inhibition of differentiation repressed plasticity and potentiated responses to cell cycle inhibitors. Manipulation of this cell state transition point may provide a source of potential combinatorial targets for cancer therapy.

Purinergic Ca2+ signaling as a novel mechanism of drug tolerance in BRAF mutant melanoma

Drug tolerance is a major cause of relapse after cancer treatment. In spite of intensive efforts1-9, its molecular basis remains poorly understood, hampering actionable intervention. We report a previously unrecognized signaling mechanism supporting drug tolerance in BRAF-mutant melanoma treated with BRAF inhibitors that could be of general relevance to other cancers. Its key features are cell-intrinsic intracellular Ca2+ signaling initiated by P2X7 receptors (purinergic ligand-gated cation channels), and an enhanced ability for these Ca2+ signals to reactivate ERK1/2 in the drug-tolerant state. Extracellular ATP, virtually ubiquitous in living systems, is the ligand that can initiate Ca2+ spikes via P2X7 channels. ATP is abundant in the tumor microenvironment and is released by dying cells, ironically implicating treatment-initiated cancer cell death as a source of trophic stimuli that leads to ERK reactivation and drug tolerance. Such a mechanism immediately offers an explanation of the inevitable relapse after BRAFi treatment in BRAF-mutant melanoma, and points to actionable strategies to overcome it.

The MITF/mir-579-3p regulatory axis dictates BRAF-mutated melanoma cell fate in response to MAPK inhibitors

Therapy of melanoma has improved dramatically over the last years thanks to the development of targeted therapies (MAPKi) and immunotherapies. However, drug resistance continues to limit the efficacy of these therapies. Our research group has provided robust evidence as to the involvement of a set of microRNAs in the development of resistance to target therapy in BRAF-mutated melanomas. Among them, a pivotal role is played by the oncosuppressor miR-579-3p. Here we show that miR-579-3p and the microphthalmia-associated transcription factor (MITF) influence reciprocally their expression through positive feedback regulatory loops. In particular we show that miR-579-3p is specifically deregulated in BRAF-mutant melanomas and that its expression levels mirror those of MITF. Luciferase and ChIP studies show that MITF is a positive regulator of miR-579-3p, which is located in the intron 11 of the human gene ZFR (Zink-finger recombinase) and is co-transcribed with its host gene. Moreover, miR-579-3p, by targeting BRAF, is able to stabilize MITF protein thus inducing its own transcription. From biological points of view, early exposure to MAPKi or, alternatively miR-579-3p transfection, induce block of proliferation and trigger senescence programs in BRAF-mutant melanoma cells. Finally, the long-term development of resistance to MAPKi is able to select cells characterized by the loss of both miR-579-3p and MITF and the same down-regulation is also present in patients relapsing after treatments. Altogether these findings suggest that miR-579-3p/MITF interplay potentially governs the balance between proliferation, senescence and resistance to therapies in BRAF-mutant melanomas.

Ex vivo mapping of enhancer networks that define the transcriptional program driving melanoma metastasis

Mortality from vmelanoma is associated with metastatic disease, but the mechanisms leading to spreading of the cancer cells remain obscure. Spatial profiling revealed that melanoma is characterized by a high degree of heterogeneity, which is established by the ability of melanoma cells to switch between different phenotypical stages. This plasticity, likely a heritage from embryonic pathways, accounts for a relevant part of the metastatic potential of these lesions, and requires the rapid and efficient reorganization of the transcriptional landscape of melanoma cells. A large part of the non-coding genome cooperates to control gene expression, specifically through the activity of enhancers (ENHs). In this study, we aimed to identify ex vivo the network of active ENHs and to outline their cooperative interactions in supporting transcriptional adaptation during melanoma metastatic progression. We conducted a genome-wide analysis to map active ENHs distribution in a retrospective cohort of 39 melanoma patients, comparing the profiles obtained in primary (N = 19) and metastatic (N = 20) melanoma lesions. Unsupervised clustering showed that the profile for acetylated histone H3 at lysine 27 (H3K27ac) efficiently segregates lesions into three different clusters corresponding to progressive stages of the disease. We reconstructed the map of super-ENHs (SEs) and cooperative ENHs that associate with metastatic progression in melanoma, which showed that cooperation among regulatory elements is a mandatory requirement for transcriptional plasticity. We also showed that these elements carry out specialized and non-redundant functions, and indicated the existence of a hierarchical organization, with SEs on top as masterminds of the entire transcriptional program and classical ENHs as executors. By providing an innovative vision of how the chromatin landscape of melanoma works during metastatic spreading, our data also point out the need to integrate functional profiling in the analysis of cancer lesions to increase definition and improve interpretation of tumor heterogeneity.

Non-Mass Spectrometric Targeted Single-Cell Metabolomics

Metabolic assays serve as pivotal tools in biomedical research, offering keen insights into cellular physiological and pathological states. While mass spectrometry (MS)-based metabolomics remains the gold standard for comprehensive, multiplexed analyses of cellular metabolites, innovative technologies are now emerging for the targeted, quantitative scrutiny of metabolites and metabolic pathways at the single-cell level. In this review, we elucidate an array of these advanced methodologies, spanning synthetic and surface chemistry techniques, imaging-based methods, and electrochemical approaches. We summarize the rationale, design principles, and practical applications for each method, and underscore the synergistic benefits of integrating single-cell metabolomics (scMet) with other single-cell omics technologies. Concluding, we identify prevailing challenges in the targeted scMet arena and offer a forward-looking commentary on future avenues and opportunities in this rapidly evolving field.

Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity

Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.

Cell state transition analysis identifies interventions that improve control of Mycobacterium tuberculosis infection by susceptible macrophages

Understanding cell state transitions and purposefully controlling them to improve therapies is a longstanding challenge in biological research and medicine. Here, we identify a transcriptional signature that distinguishes activated macrophages from the tuberculosis (TB) susceptible and resistant mice. We then apply the cSTAR (cell state transition assessment and regulation) approach to data from screening-by-RNA sequencing to identify chemical perturbations that shift the transcriptional state of tumor necrosis factor (TNF)-activated TB-susceptible macrophages toward that of TB-resistant cells, i.e., prevents their aberrant activation without suppressing beneficial TNF responses. Last, we demonstrate that the compounds identified with this approach enhance the resistance of the TB-susceptible mouse macrophages to virulent Mycobacterium tuberculosis.

Single-cell metabolomics by mass spectrometry: ready for primetime?

Single-cell metabolomics (SCMs) is a powerful tool for studying cellular heterogeneity by providing insight into the differences between individual cells. With the development of a set of promising SCMs pipelines, this maturing technology is expected to be widely used in biomedical research. However, before SCMs is ready for primetime, there are some challenges to overcome. In this review, we summarize the trends and challenges in the development of SCMs. We also highlight the latest methodologies, applications, and sketch the perspective for integration with other omics and imaging approaches.

Understanding tumour endothelial cell heterogeneity and function from single-cell omics

Anti-angiogenic therapies (AATs) are used to treat different types of cancers. However, their success is limited owing to insufficient efficacy and resistance. Recently, single-cell omics studies of tumour endothelial cells (TECs) have provided new mechanistic insight. Here, we overview the heterogeneity of human TECs of all tumour types studied to date, at the single-cell level. Notably, most human tumour types contain varying numbers but only a small population of angiogenic TECs, the presumed targets of AATs, possibly contributing to the limited efficacy of and resistance to AATs. In general, TECs are heterogeneous within and across all tumour types, but comparing TEC phenotypes across tumours is currently challenging, owing to the lack of a uniform nomenclature for endothelial cells and consistent single-cell analysis protocols, urgently raising the need for a more consistent approach. Nonetheless, across most tumour types, universal TEC markers (ACKR1, PLVAP and IGFBP3) can be identified. Besides angiogenesis, biological processes such as immunomodulation and extracellular matrix organization are among the most commonly predicted enriched signatures of TECs across different tumour types. Although angiogenesis and extracellular matrix targets have been considered for AAT (without the hoped success), the immunomodulatory properties of TECs have not been fully considered as a novel anticancer therapeutic approach. Therefore, we also discuss progress, limitations, solutions and novel targets for AAT development.

Continuous microfluidic flow-through protocol for selective and image-activated electroporation of single cells

Electroporation of cells is a widely-used tool to transport molecules such as proteins or nucleic acids into cells or to extract cellular material. However, bulk methods for electroporation do not offer the possibility to selectively porate subpopulations or single cells in heterogeneous cell samples. To achieve this, either presorting or complex single-cell technologies are required currently. In this work, we present a microfluidic flow protocol for selective electroporation of predefined target cells identified in real-time by high-quality microscopic image analysis of fluorescence and transmitted light. While traveling through the microchannel, the cells are focused by dielectrophoretic forces into the microscopic detection area, where they are classified based on image analysis techniques. Finally, the cells are forwarded to a poration electrode and only the target cells are pulsed. By processing a heterogenically stained cell sample, we were able to selectively porate only target cells (green-fluorescent) while non-target cells (blue-fluorescent) remained unaffected. We achieved highly selective poration with >90% specificity at average poration rates of >50% and throughputs of up to 7200 cells per hour.

Gene networks reveal stem-cell state convergence during preneoplasia and progression to malignancy in multistage skin carcinogenesis

Adult mammalian stem cells play critical roles in normal tissue homeostasis, as well as in tumor development, by contributing to cell heterogeneity, plasticity, and development of drug resistance. The relationship between different types of normal and cancer stem cells is highly controversial and poorly understood. Here, we carried out gene expression network analysis of normal and tumor samples from genetically heterogeneous mice to create network metagenes for visualization of stem-cell networks, rather than individual stem-cell markers, at the single-cell level during multistage carcinogenesis. We combined this approach with lineage tracing and single-cell RNASeq of stem cells and their progeny, identifying a previously unrecognized hierarchy in which Lgr6+ stem cells from tumors generate progeny that express a range of other stem-cell markers including Sox2, Pitx1, Foxa1, Klf5, and Cd44. Our data identify a convergence of multiple stem-cell and tumor-suppressor pathways in benign tumor cells expressing markers of lineage plasticity and oxidative stress. This same single-cell population expresses network metagenes corresponding to markers of cancer drug resistance in human tumors of the skin, lung and prostate. Treatment of mouse squamous carcinomas in vivo with the chemotherapeutic cis-platin resulted in elevated expression of the genes that mark this cell population. Our data have allowed us to create a simplified model of multistage carcinogenesis that identifies distinct stem-cell states at different stages of tumor progression, thereby identifying networks involved in lineage plasticity, drug resistance, and immune surveillance, providing a rich source of potential targets for cancer therapy.

Single-Cell Analysis in Immuno-Oncology

The complexity of the cellular and non-cellular milieu surrounding human tumors plays a decisive role in the course and outcome of disease. The high variability in the distribution of the immune and non-immune compartments within the tumor microenvironments (TME) among different patients governs the mode of their response or resistance to current immunotherapeutic approaches. Through deciphering this diversity, one can tailor patients' management to meet an individual's needs. Single-cell (sc) omics technologies have given a great boost towards this direction. This review gathers recent data about how multi-omics profiling, including the utilization of single-cell RNA sequencing (scRNA-seq), assay for transposase-accessible chromatin with sequencing (scATAC-seq), T-cell receptor sequencing (scTCR-seq), mass, tissue-based, or microfluidics cytometry, and related bioinformatics tools, contributes to the high-throughput assessment of a large number of analytes at single-cell resolution. Unravelling the exact TCR clonotype of the infiltrating T cells or pinpointing the classical or novel immune checkpoints across various cell subsets of the TME provide a boost to our comprehension of adaptive immune responses, their antigen specificity and dynamics, and grant suggestions for possible therapeutic targets. Future steps are expected to merge high-dimensional data with tissue localization data, which can serve the investigation of novel multi-modal biomarkers for the selection and/or monitoring of the optimal treatment from the current anti-cancer immunotherapeutic armamentarium.

Dynamics of Epithelial-Mesenchymal Plasticity: What Have Single-Cell Investigations Elucidated So Far?

Epithelial-mesenchymal plasticity (EMP) is a key driver of cancer metastasis and therapeutic resistance, through which cancer cells can reversibly and dynamically alter their molecular and functional traits along the epithelial-mesenchymal spectrum. While cells in the epithelial phenotype are usually tightly adherent, less metastatic, and drug-sensitive, those in the hybrid epithelial/mesenchymal and/or mesenchymal state are more invasive, migratory, drug-resistant, and immune-evasive. Single-cell studies have emerged as a powerful tool in gaining new insights into the dynamics of EMP across various cancer types. Here, we review many recent studies that employ single-cell analysis techniques to better understand the dynamics of EMP in cancer both in vitro and in vivo. These single-cell studies have underlined the plurality of trajectories cells can traverse during EMP and the consequent heterogeneity of hybrid epithelial/mesenchymal phenotypes seen at both preclinical and clinical levels. They also demonstrate how diverse EMP trajectories may exhibit hysteretic behavior and how the rate of such cell-state transitions depends on the genetic/epigenetic background of recipient cells, as well as the dose and/or duration of EMP-inducing growth factors. Finally, we discuss the relationship between EMP and patient survival across many cancer types. We also present a next set of questions related to EMP that could benefit much from single-cell observations and pave the way to better tackle phenotypic switching and heterogeneity in clinic.

Recent advances in microfluidics for single-cell functional proteomics

Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic view of biological details underlying cellular processes, disease onset and progression, as well as facilitates unique biomarker identification from individual cells. Microfluidic-based strategies have become methods of choice for single-cell analysis because they allow facile assay integrations, such as cell sorting, manipulation, and content analysis. Notably, they have been serving as an enabling technology to improve the sensitivity, robustness, and reproducibility of recently developed SCP methods. Critical roles of microfluidics technologies are expected to further expand rapidly in advancing the next phase of SCP analysis to reveal more biological and clinical insights. In this review, we will capture the excitement of the recent achievements of microfluidics methods for both targeted and global SCP, including efforts to enhance the proteomic coverage, minimize sample loss, and increase multiplexity and throughput. Furthermore, we will discuss the advantages, challenges, applications, and future prospects of SCP.

Crosstalk between COVID-19 Infection and Kidney Diseases: A Review on the Metabolomic Approaches

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19, a respiratory disorder. Various organ injuries have been reported in response to this virus, including kidney injury and, in particular, kidney tubular injury. It has been discovered that infection with the virus does not only cause new kidney disease but also increases treatment difficulty and mortality rates in people with kidney diseases. In individuals hospitalized with COVID-19, urinary metabolites from several metabolic pathways are used to distinguish between patients with acute kidney injury (AKI) and those without. This review summarizes the pathogenesis, pathophysiology, treatment strategies, and role of metabolomics in relation to AKI in COVID-19 patients. Metabolomics is likely to play a greater role in predicting outcomes for patients with kidney disease and COVID-19 with varying levels of severity in the near future as data on metabolic profiles expand rapidly. Here, we also discuss the correlation between COVID-19 and kidney diseases and the available metabolomics approaches.

Cell state transition analysis identifies interventions that improve control of M. tuberculosis infection by susceptible macrophages

Understanding cell state transitions and purposefully controlling them to improve therapies is a longstanding challenge in biological research and medicine. Here, we identify a transcriptional signature that distinguishes activated macrophages from TB-susceptible and TB-resistant mice. We then apply the cSTAR (cell State Transition Assessment and Regulation) approach to data from screening-by-RNA sequencing to identify chemical perturbations that shift the. transcriptional state of the TB-susceptible macrophages towards that of TB-resistant cells. Finally, we demonstrate that the compounds identified with this approach enhance resistance of the TB-susceptible mouse macrophages to virulent M. tuberculosis .

A Network of MicroRNAs and mRNAs Involved in Melanosome Maturation and Trafficking Defines the Lower Response of Pigmentable Melanoma Cells to Targeted Therapy

BACKGROUND

The ability to increase their degree of pigmentation is an adaptive response that confers pigmentable melanoma cells higher resistance to BRAF inhibitors (BRAFi) compared to non-pigmentable melanoma cells.

METHODS

Here, we compared the miRNome and the transcriptome profile of pigmentable 501Mel and SK-Mel-5 melanoma cells vs. non-pigmentable A375 melanoma cells, following treatment with the BRAFi vemurafenib (vem). In depth bioinformatic analyses (clusterProfiler, WGCNA and SWIMmeR) allowed us to identify the miRNAs, mRNAs and biological processes (BPs) that specifically characterize the response of pigmentable melanoma cells to the drug. Such BPs were studied using appropriate assays in vitro and in vivo (xenograft in zebrafish embryos).

RESULTS

Upon vem treatment, miR-192-5p, miR-211-5p, miR-374a-5p, miR-486-5p, miR-582-5p, miR-1260a and miR-7977, as well as GPR143, OCA2, RAB27A, RAB32 and TYRP1 mRNAs, are differentially expressed only in pigmentable cells. These miRNAs and mRNAs belong to BPs related to pigmentation, specifically melanosome maturation and trafficking. In fact, an increase in the number of intracellular melanosomes-due to increased maturation and/or trafficking-confers resistance to vem.

CONCLUSION

We demonstrated that the ability of pigmentable cells to increase the number of intracellular melanosomes fully accounts for their higher resistance to vem compared to non-pigmentable cells. In addition, we identified a network of miRNAs and mRNAs that are involved in melanosome maturation and/or trafficking. Finally, we provide the rationale for testing BRAFi in combination with inhibitors of these biological processes, so that pigmentable melanoma cells can be turned into more sensitive non-pigmentable cells.

Pterostilbene-Mediated Inhibition of Cell Proliferation and Cell Death Induction in Amelanotic and Melanotic Melanoma

Melanoma is one of the fastest-growing cancers worldwide. Treatment of advanced melanoma is very difficult; therefore, there is growing interest in the identification of new therapeutic agents. Pterostilbene is a natural stilbene that has been found to have several pharmacological activities. The aim of this study was to evaluate the influence of pterostilbene on the proliferation and apoptosis of human melanoma cells. Proliferation of pterostilbene-treated amelanotic (C32) and melanotic (A2058) melanoma cells was determined by BRDU assay. Flow cytometric analyses were used to determine cell cycle progression, and further molecular investigations were performed using real-time RT-qPCR. The expression of the p21 protein and the DNA fragmentation assay were determined by the ELISA method. The results revealed that pterostilbene reduced the proliferation of both amelanotic and melanotic melanoma cells. Pterostilbene induced apoptosis in amelanotic C32 melanoma cells, and this effect was mediated by an increase in the expression of the BAX, CASP9, and CASP9 genes; induction of caspase 3 activity; and DNA degradation. Pterostilbene did not affect the activation of apoptosis in the A2058 cell line. It may be concluded that pterostilbene has anticancer potential against human melanoma cells; however, more studies are still needed to fully elucidate the effects of pterostilbene on amelanotic and melanotic melanoma cells.

Suppression of exosomal hsa_circ_0001005 eliminates the Vemurafenib resistance of melanoma

OBJECTIVES

More and more evidences show that circular RNAs (circRNAs) can be used as miRNA sponge to regulate the drug resistance of malignancies, including melanoma. However, how exosomal circRNAs participate in the therapeutic resistance of melanoma remains ambiguous.

METHODS

Vemurafenib-resistant A375 cells were cultured and then the circRNA profile of exosomes from the parental A375 and A375-resistant cells were sequenced. Transmission electron microscopy (TEM), exogenous nanoparticle tracking analysis (NTA) and Western Blot assays were leveraged to confirm the successful collection of exosomes from A375 and A375R cells. Another five published RNA-seq data and microRNA-seq data, and seven miRNA databases were collected to construct a competing endogenous RNA (ceRNA) network. Comprehensive bioinformatic analysis was adopted to identify key molecules related to the drug resistance, including multiscale embedded gene co-expression network analysis (MEGENA). Then, qRT-PCR, cell viability and colony formation were used to estimate the function of hub circRNAs. The role of has_circ_0001005 in vivo was verified via xenograft assay. The Tumor online Prognostic analyses Platform (ToPP) was leveraged to develop the has_circ_0001005-related prognostic models for melanoma patients based on TCGA data.

RESULTS

Compared with parental cells, hsa_circ_0001005 expression was significantly increased in resistant cells and their exosomes. The elevated level of hsa_circ_0001005 was related to the poor clinical prognosis of melanoma patients. Hsa_circ_0001005 found in melanoma was mainly secreted by drug-resistant cells as exosomes. Exosomal hsa_circ_0001005 activated multiple canonical pathways related to drug resistance through sponging four miRNAs, thus suppressing the drug sensitivity of melanoma. Knocking down hsa_circ_0001005 in vitro, we found that the inhibition of hsa_circ_0001005 could hinder the clone formation of melanoma. Further in vivo animal experiments suggested that suppression of hsa_circ_0001005 can increase the sensitivity to Vemurafenib of melanoma cells. Finally, we also constructed the functional regulatory ceRNA network and prognostic risk models for hsa_circ_0001005, and further survival analysis reveals that the regulatory network and prognostic risk models obviously affected the prognosis of melanoma patients.

CONCLUSIONS

This study confirmed that the level of hsa_circ_0001005 in exosomes is the key factor affecting drug resistance of melanoma, which provides a new potential therapeutic target for melanoma patients.

Epigenetic adaptations in drug-tolerant tumor cells

Traditional chemotherapy against cancer is often severely hampered by acquired resistance to the drug. Epigenetic alterations and other mechanisms like drug efflux, drug metabolism, and engagement of survival pathways are crucial in evading drug pressure. Herein, growing evidence suggests that a subpopulation of tumor cells can often tolerate drug onslaught by entering a "persister" state with minimal proliferation. The molecular features of these persister cells are gradually unraveling. Notably, the "persisters" act as a cache of cells that can eventually re-populate the tumor post-withdrawal drug pressure and contribute to acquiring stable drug-resistant features. This underlines the clinical significance of the tolerant cells. Accumulating evidence highlights the importance of modulation of the epigenome as a critical adaptive strategy for evading drug pressure. Chromatin remodeling, altered DNA methylation, and de-regulation of non-coding RNA expression and function contribute significantly to this persister state. No wonder targeting adaptive epigenetic modifications is increasingly recognized as an appropriate therapeutic strategy to sensitize them and restore drug sensitivity. Furthermore, manipulating the tumor microenvironment and "drug holiday" is also explored to maneuver the epigenome. However, heterogeneity in adaptive strategies and lack of targeted therapies have significantly hindered the translation of epigenetic therapy to the clinics. In this review, we comprehensively analyze the epigenetic alterations adapted by the drug-tolerant cells, the therapeutic strategies employed to date, and their limitations and future prospects.

Into the multi-omics era: Progress of T cells profiling in the context of solid organ transplantation

T cells are the common type of lymphocyte to mediate allograft rejection, remaining long-term allograft survival impeditive. However, the heterogeneity of T cells, in terms of differentiation and activation status, the effector function, and highly diverse T cell receptors (TCRs) have thus precluded us from tracking these T cells and thereby comprehending their fate in recipients due to the limitations of traditional detection approaches. Recently, with the widespread development of single-cell techniques, the identification and characterization of T cells have been performed at single-cell resolution, which has contributed to a deeper comprehension of T cell heterogeneity by relevant detections in a single cell - such as gene expression, DNA methylation, chromatin accessibility, surface proteins, and TCR. Although these approaches can provide valuable insights into an individual cell independently, a comprehensive understanding can be obtained when applied joint analysis. Multi-omics techniques have been implemented in characterizing T cells in health and disease, including transplantation. This review focuses on the thesis, challenges, and advances in these technologies and highlights their application to the study of alloreactive T cells to improve the understanding of T cell heterogeneity in solid organ transplantation.

PI3K/AKT signaling allows for MAPK/ERK pathway independency mediating dedifferentiation-driven treatment resistance in melanoma

BACKGROUND

Current therapeutic management of advanced melanoma patients largely depends on their BRAF mutation status. However, the vast heterogeneity of the tumors hampers the success of therapies targeting the MAPK/ERK pathway alone. Dissecting this heterogeneity will contribute to identifying key players in the oncogenic progression to tailor more effective therapies.

METHODS

We performed a comprehensive molecular and phenotypic characterization of a panel of patient-derived BRAF^V600E-positive melanoma cell lines. Transcriptional profiling was used to identify groups of coregulated genes whose expression relates to an increased migratory potential and a higher resistance.

RESULTS

A decrease in sensitivity to MAPK/ERK pathway inhibition with vemurafenib or trametinib corresponded with an increasing quiescence and migratory properties of the cells. This was accompanied by the loss of transcriptional signatures of melanocytic differentiation, and the gain of stem cell features that conferred highly-resistant/mesenchymal-like cells with increased xenobiotic efflux capacity. Nevertheless, targeting of the implicated ABC transporters did not improve the response to vemurafenib, indicating that incomplete BRAF inhibition due to reduced drug uptake is not a main driver of resistance. Rather, indifference to MAPK/ERK pathway inhibition arose from the activation of compensatory signaling cascades. The PI3K/AKT pathway in particular showed a higher activity in mesenchymal-like cells, conferring a lower dependency on MAPK/ERK signaling and supporting stem-like properties that could be reverted by dual PI3K/mTOR inhibition with dactolisib.

CONCLUSIONS

In case of MAPK/ERK independency, therapeutic focus may be shifted to the PI3K/AKT pathway to overcome late-stage resistance in melanoma tumors that have acquired a mesenchymal phenotype. Video Abstract.

Unravelling the landscape of skin cancer through single-cell transcriptomics

The human skin is a complex organ that forms the first line of defense against pathogens and external injury. It is composed of a wide variety of cells that work together to maintain homeostasis and prevent disease, such as skin cancer. The exponentially rising incidence of skin malignancies poses a growing public health challenge, particularly when the disease course is complicated by metastasis and therapeutic resistance. Recent advances in single-cell transcriptomics have provided a high-resolution view of gene expression heterogeneity that can be applied to skin cancers to define cell types and states, understand disease evolution, and develop new therapeutic concepts. This approach has been particularly valuable in characterizing the contribution of immune cells in skin cancer, an area of great clinical importance given the increasing use of immunotherapy in this setting. In this review, we highlight recent skin cancer studies utilizing bulk RNA sequencing, introduce various single-cell transcriptomics approaches, and summarize key findings obtained by applying single-cell transcriptomics to skin cancer.

Mapping phenotypic heterogeneity in melanoma onto the epithelial-hybrid-mesenchymal axis

Epithelial to mesenchymal transition (EMT) is a well-studied hallmark of epithelial-like cancers that is characterized by loss of epithelial markers and gain of mesenchymal markers. Melanoma, which is derived from melanocytes of the skin, also undergo phenotypic plasticity toward mesenchymal-like phenotypes under the influence of various micro-environmental cues. Our study connects EMT to the phenomenon of de-differentiation (i.e., transition from proliferative to more invasive phenotypes) observed in melanoma cells during drug treatment. By analyzing 78 publicly available transcriptomic melanoma datasets, we found that de-differentiation in melanoma is accompanied by upregulation of mesenchymal genes, but not necessarily a concomitant loss of an epithelial program, suggesting a more “one-dimensional” EMT that leads to a hybrid epithelial/mesenchymal phenotype. Samples lying in the hybrid epithelial/mesenchymal phenotype also correspond to the intermediate phenotypes in melanoma along the proliferative-invasive axis - neural crest and transitory ones. As melanoma cells progress along the invasive axis, the mesenchymal signature does not increase monotonically. Instead, we observe a peak in mesenchymal scores followed by a decline, as cells further de-differentiate. This biphasic response recapitulates the dynamics of melanocyte development, suggesting close interactions among genes controlling differentiation and mesenchymal programs in melanocytes. Similar trends were noted for metabolic changes often associated with EMT in carcinomas in which progression along mesenchymal axis correlates with the downregulation of oxidative phosphorylation, while largely maintaining glycolytic capacity. Overall, these results provide an explanation for how EMT and de-differentiation axes overlap with respect to their transcriptional and metabolic programs in melanoma.

Into the multiverse: advances in single-cell multiomic profiling

Single-cell transcriptomic approaches have revolutionised the study of complex biological systems, with the routine measurement of gene expression in thousands of cells enabling construction of whole-organism cell atlases. However, the transcriptome is just one layer amongst many that coordinate to define cell type and state and, ultimately, function. In parallel with the widespread uptake of single-cell RNA-seq (scRNA-seq), there has been a rapid emergence of methods that enable multiomic profiling of individual cells, enabling parallel measurement of intercellular heterogeneity in the genome, epigenome, transcriptome, and proteomes. Linking measurements from each of these layers has the potential to reveal regulatory and functional mechanisms underlying cell behaviour in healthy development and disease.

Emerging Role of Deubiquitinating Enzymes (DUBs) in Melanoma Pathogenesis

Metastatic melanoma is the leading cause of death from skin cancer. Therapies targeting the BRAF oncogenic pathway and immunotherapies show remarkable clinical efficacy. However, these treatments are limited to subgroups of patients and relapse is common. Overall, the majority of patients require additional treatments, justifying the development of new therapeutic strategies. Non-genetic and genetic alterations are considered to be important drivers of cellular adaptation mechanisms to current therapies and disease relapse. Importantly, modification of the overall proteome in response to non-genetic and genetic events supports major cellular changes that are required for the survival, proliferation, and migration of melanoma cells. However, the mechanisms underlying these adaptive responses remain to be investigated. The major contributor to proteome remodeling involves the ubiquitin pathway, ubiquitinating enzymes, and ubiquitin-specific proteases also known as DeUBiquitinases (DUBs). In this review, we summarize the current knowledge regarding the nature and roles of the DUBs recently identified in melanoma progression and therapeutic resistance and discuss their potential as novel sources of vulnerability for melanoma therapy.

Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer

The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.

Heterogeneity in Melanoma

There is growing evidence that tumour heterogeneity has an imperative role in cancer development, evolution and resistance to therapy. Continuing advancements in biomedical research enable tumour heterogeneity to be observed and studied more critically. As one of the most heterogeneous human cancers, melanoma displays a high level of biological complexity during disease progression. However, much is still unknown regarding melanoma tumour heterogeneity, as well as the role it plays in disease progression and treatment response. This review aims to provide a concise summary of the importance of tumour heterogeneity in melanoma.

LuminoCell: a versatile and affordable platform for real-time monitoring of luciferase-based reporters

Luciferase reporter assays represent a simple and sensitive experimental system in cell and molecular biology to study multiple biological processes. However, the application of these assays is often limited by the costs of conventional luminometer instruments and the versatility of their use in different experimental conditions. Therefore, we aimed to develop a small, affordable luminometer allowing continuous measurement of luciferase activity, designed for inclusion into various kinds of tissue culture incubators. Here, we introduce LuminoCell-an open-source platform for the construction of an affordable, sensitive, and portable luminometer capable of real-time monitoring in-cell luciferase activity. The LuminoCell costs $40, requires less than 1 h to assemble, and it is capable of performing real-time sensitive detection of both magnitude and duration of the activity of major signalling pathways in cell cultures, including receptor tyrosine kinases (EGF and FGF), WNT/β-catenin, and NF-κB. In addition, we show that the LuminoCell is suitable to be used in cytotoxicity assays as well as for monitoring periodic circadian gene expression.

Transformation of Health and Social Care Systems-An Interdisciplinary Approach Toward a Foundational Architecture

Objective

For realizing pervasive and ubiquitous health and social care services in a safe and high quality as well as efficient and effective way, health and social care systems have to meet new organizational, methodological, and technological paradigms. The resulting ecosystems are highly complex, highly distributed, and highly dynamic, following inter-organizational and even international approaches. Even though based on international, but domain-specific models and standards, achieving interoperability between such systems integrating multiple domains managed by multiple disciplines and their individually skilled actors is cumbersome.

Methods

Using the abstract presentation of any system by the universal type theory as well as universal logics and combining the resulting Barendregt Cube with parameters and the engineering approach of cognitive theories, systems theory, and good modeling best practices, this study argues for a generic reference architecture model moderating between the different perspectives and disciplines involved provide on that system. To represent architectural elements consistently, an aligned system of ontologies is used.

Results

The system-oriented, architecture-centric, and ontology-based generic reference model allows for re-engineering the existing and emerging knowledge representations, models, and standards, also considering the real-world business processes and the related development process of supporting IT systems for the sake of comprehensive systems integration and interoperability. The solution enables the analysis, design, and implementation of dynamic, interoperable multi-domain systems without requesting continuous revision of existing specifications.

The Intriguing Landscape of Single-Cell Protein Analysis

Profiling protein expression at single-cell resolution is essential for fundamental biological research (such as cell differentiation and tumor microenvironmental examination) and clinical precision medicine where only a limited number of primary cells are permitted. With the recent advances in engineering, chemistry, and biology, single-cell protein analysis methods are developed rapidly, which enable high-throughput and multiplexed protein measurements in thousands of individual cells. In combination with single cell RNA sequencing and mass spectrometry, single-cell multi-omics analysis can simultaneously measure multiple modalities including mRNAs, proteins, and metabolites in single cells, and obtain a more comprehensive exploration of cellular signaling processes, such as DNA modifications, chromatin accessibility, protein abundance, and gene perturbation. Here, the recent progress and applications of single-cell protein analysis technologies in the last decade are summarized. Current limitations, challenges, and possible future directions in this field are also discussed.

Rise of the SARS-CoV-2 Variants: can proteomics be the silver bullet?

INTRODUCTION

The challenges posed by emergent strains of SARS-CoV-2 need to be tackled by contemporary scientific approaches, with proteomics playing a significant role.

AREAS COVERED

In this review, we provide a brief synthesis of the impact of proteomics technologies in elucidating disease pathogenesis and classifiers for the prognosis of COVID-19 and propose proteomics methodologies that could play a crucial role in understanding emerging variants and their altered disease pathology. From aiding the design of novel drug candidates to facilitating the identification of T cell vaccine targets, we have discussed the impact of proteomics methods in COVID-19 research. Techniques varied as mass spectrometry, single-cell proteomics, multiplexed ELISA arrays, high-density proteome arrays, surface plasmon resonance, immunopeptidomics, and in silico docking studies that have helped augment the fight against existing diseases were useful in preparing us to tackle SARS-CoV-2 variants. We also propose an action plan for a pipeline to combat emerging pandemics using proteomics technology by adopting uniform standard operating procedures and unified data analysis paradigms.

EXPERT OPINION

The knowledge about the use of diverse proteomics approaches for COVID-19 investigation will provide a framework for future basic research, better infectious disease prevention strategies, improved diagnostics, and targeted therapeutics.

Evidence that the transcriptional repressor ICER is regulated via the N-end rule for ubiquitination

ICER is a transcriptional repressor that is mono- or poly-ubiquitinated. This either causes ICER to be translocated from the nucleus, or degraded via the proteasome, respectively. In order to further studies the proteins involved in ICER regulation mass spectrometry analysis was performed to identify potential candidates. We identified twenty eight ICER-interacting proteins in human melanoma cells, Sk-Mel-24. In this study we focus on two proteins with potential roles in ICER proteasomal degradation in response to the N-end rule for ubiquitination: the N-alpha-acetyltransferase 15 (NAA15) and the E3 ubiquitin-protein ligase UBR4. Using an HA-tag on the N- or C-terminus of ICER (_NHAICER or ICER_CHA) it was found that the N-terminus of ICER is important for its interaction to UBR4, whereas NARG1 interaction is independent of HA-tag position. Silencing RNA experiments show that both NAA15 and UBR4 up-regulates ICER levels and that ICER's N-terminus is important for this regulation. The N-terminus of ICER was found to have dire consequences on its regulation by ubiquitination and cellular functions. The half-life of _NHAICER was found to be about twice as long as ICER_CHA. Polyubiquitination of ICER was found to be dependent on its N-terminus and mediated by UBR4. This data strongly suggests that ICER is ubiquitinated as a response to the N-end rule that governs protein degradation rate through recognition of the N-terminal residue of proteins. Furthermore, we found that _NHAICER inhibits transcription two times more efficiently than ICER_CHA, and causes apoptosis 5 times more efficiently than ICER_CHA. As forced expression of ICER has been shown before to block cells in mitosis, our data represent a potentially novel mechanism for apoptosis of cells in mitotic arrest.

The Development of Single-Cell Metabolism and Its Role in Studying Cancer Emergent Properties

Metabolic reprogramming is one of the hallmarks of malignant tumors, which provides energy and material basis for tumor rapid proliferation, immune escape, as well as extensive invasion and metastasis. Blocking the energy and material supply of tumor cells is one of the strategies to treat tumor, however tumor cell metabolic heterogeneity prevents metabolic-based anti-cancer treatment. Therefore, searching for the key metabolic factors that regulate cell cancerous change and tumor recurrence has become a major challenge. Emerging technology––single-cell metabolomics is different from the traditional metabolomics that obtains average information of a group of cells. Single-cell metabolomics identifies the metabolites of single cells in different states by mass spectrometry, and captures the molecular biological information of the energy and substances synthesized in single cells, which provides more detailed information for tumor treatment metabolic target screening. This review will combine the current research status of tumor cell metabolism with the advantages of single-cell metabolomics technology, and explore the role of single-cell sequencing technology in searching key factors regulating tumor metabolism. The addition of single-cell technology will accelerate the development of metabolism-based anti-cancer strategies, which may greatly improve the prognostic survival rate of cancer patients.

Roadmap on plasticity and epigenetics in cancer

The role of plasticity and epigenetics in shaping cancer evolution and response to therapy has taken center stage with recent technological advances including single cell sequencing. This roadmap article is focused on state-of-the-art mathematical and experimental approaches to interrogate plasticity in cancer, and addresses the following themes and questions: is there a formal overarching framework that encompasses both non-genetic plasticity and mutation-driven somatic evolution? How do we measure and model the role of the microenvironment in influencing/controlling non-genetic plasticity? How can we experimentally study non-genetic plasticity? Which mathematical techniques are required or best suited? What are the clinical and practical applications and implications of these concepts?

Integrated analysis of plasma and single immune cells uncovers metabolic changes in individuals with COVID-19

A better understanding of the metabolic alterations in immune cells during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may elucidate the wide diversity of clinical symptoms experienced by individuals with coronavirus disease 2019 (COVID-19). Here, we report the metabolic changes associated with the peripheral immune response of 198 individuals with COVID-19 through an integrated analysis of plasma metabolite and protein levels as well as single-cell multiomics analyses from serial blood draws collected during the first week after clinical diagnosis. We document the emergence of rare but metabolically dominant T cell subpopulations and find that increasing disease severity correlates with a bifurcation of monocytes into two metabolically distinct subsets. This integrated analysis reveals a robust interplay between plasma metabolites and cell-type-specific metabolic reprogramming networks that is associated with disease severity and could predict survival.

Toward modeling metabolic state from single-cell transcriptomics

Background

Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge.

Scope of Review

We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals.

Major Conclusions

Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models.

•

Single-cell RNA sequencing and prior metabolic knowledge enable metabolic predictions.

•

When compared to bulk, single-cell modeling is linked to unique insights and challenges.

•

Computational modelling approaches differ in applicability and newly provided insights.

•

The use of prior metabolic knowledge paves the way for mechanistic machine-learning.

RNF43 inhibits WNT5A-driven signaling and suppresses melanoma invasion and resistance to the targeted therapy

RNF43 is an E3 ubiquitin ligase and known negative regulator of WNT/β-catenin signaling. We demonstrate that RNF43 is also a regulator of noncanonical WNT5A-induced signaling in human cells. Analysis of the RNF43 interactome using BioID and immunoprecipitation showed that RNF43 can interact with the core receptor complex components dedicated to the noncanonical Wnt pathway such as ROR1, ROR2, VANGL1, and VANGL2. RNF43 triggers VANGL2 ubiquitination and proteasomal degradation and clathrin-dependent internalization of ROR1 receptor and inhibits ROR2 activation. These activities of RNF43 are physiologically relevant and block pro-metastatic WNT5A signaling in melanoma. RNF43 inhibits responses to WNT5A, which results in the suppression of invasive properties of melanoma cells. Furthermore, RNF43 prevented WNT5A-assisted development of resistance to BRAF V600E and MEK inhibitors. Next, RNF43 acted as melanoma suppressor and improved response to targeted therapies in vivo. In line with these findings, RNF43 expression decreases during melanoma progression and RNF43-low patients have a worse prognosis. We conclude that RNF43 is a newly discovered negative regulator of WNT5A-mediated biological responses that desensitizes cells to WNT5A.

Single-Cell Multiomics Analysis for Drug Discovery

Given the heterogeneity seen in cell populations within biological systems, analysis of single cells is necessary for studying mechanisms that cannot be identified on a bulk population level. There are significant variations in the biological and physiological function of cell populations due to the functional differences within, as well as between, single species as a result of the specific proteome, transcriptome, and metabolome that are unique to each individual cell. Single-cell analysis proves crucial in providing a comprehensive understanding of the biological and physiological properties underlying human health and disease. Omics technologies can help to examine proteins (proteomics), RNA molecules (transcriptomics), and the chemical processes involving metabolites (metabolomics) in cells, in addition to genomes. In this review, we discuss the value of multiomics in drug discovery and the importance of single-cell multiomics measurements. We will provide examples of the benefits of applying single-cell omics technologies in drug discovery and development. Moreover, we intend to show how multiomics offers the opportunity to understand the detailed events which produce or prevent disease, and ways in which the separate omics disciplines complement each other to build a broader, deeper knowledge base.

Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma

Phenotypic (i.e. non-genetic) heterogeneity in melanoma drives dedifferentiation, recalcitrance to targeted therapy and immunotherapy, and consequent tumor relapse and metastasis. Various markers or regulators associated with distinct phenotypes in melanoma have been identified, but, how does a network of interactions among these regulators give rise to multiple "attractor" states and phenotypic switching remains elusive. Here, we inferred a network of transcription factors (TFs) that act as master regulators for gene signatures of diverse cell-states in melanoma. Dynamical simulations of this network predicted how this network can settle into different "attractors" (TF expression patterns), suggesting that TF network dynamics drives the emergence of phenotypic heterogeneity. These simulations can recapitulate major phenotypes observed in melanoma and explain de-differentiation trajectory observed upon BRAF inhibition. Our systems-level modeling framework offers a platform to understand trajectories of phenotypic transitions in the landscape of a regulatory TF network and identify novel therapeutic strategies targeting melanoma plasticity.

High-Dimensional Single-Cell Transcriptomics in Melanoma and Cancer Immunotherapy

Recent advances in single-cell transcriptomics have greatly improved knowledge of complex transcriptional programs, rapidly expanding our knowledge of cellular phenotypes and functions within the tumour microenvironment and immune system. Several new single-cell technologies have been developed over recent years that have enabled expanded understanding of the mechanistic cells and biological pathways targeted by immunotherapies such as immune checkpoint inhibitors, which are now routinely used in patient management with high-risk early-stage or advanced melanoma. These technologies have method-specific strengths, weaknesses and capabilities which need to be considered when utilising them to answer translational research questions. Here, we provide guidance for the implementation of single-cell transcriptomic analysis platforms by reviewing the currently available experimental and analysis workflows. We then highlight the use of these technologies to dissect the tumour microenvironment in the context of cancer patients treated with immunotherapy. The strategic use of single-cell analytics in clinical settings are discussed and potential future opportunities are explored with a focus on their use to rationalise the design of novel immunotherapeutic drug therapies that will ultimately lead to improved cancer patient outcomes.

Coordinated pyruvate kinase activity is crucial for metabolic adaptation and cell survival during mitochondrial dysfunction

Deoxyguanosine kinase (DGUOK) deficiency causes mtDNA depletion and mitochondrial dysfunction. We reported long survival of DGUOK knockout (Dguok-/-) mice despite low (<5%) mtDNA content in liver tissue. However, the molecular mechanisms enabling the extended survival remain unknown. Using transcriptomics, proteomics and metabolomics followed by in vitro assays, we aimed to identify the molecular pathways involved in the extended survival of the Dguok-/- mice. At the early stage, the serine synthesis and folate cycle were activated but declined later. Increased activity of the mitochondrial citric acid cycle (TCA cycle) and the urea cycle and degradation of branched chain amino acids were hallmarks of the extended lifespan in DGUOK deficiency. Furthermore, the increased synthesis of TCA cycle intermediates was supported by coordination of two pyruvate kinase genes, PKLR and PKM, indicating a central coordinating role of pyruvate kinases to support the long-term survival in mitochondrial dysfunction.

Single-cell trajectories of melanoma cell resistance to targeted treatment

OBJECTIVE

Cellular heterogeneity is regarded as a major factor affecting treatment response and resistance in malignant melanoma. Recent developments in single-cell sequencing technology have provided deeper insights into these mechanisms.

METHODS

Here, we analyzed a BRAFV600E-mutant melanoma cell line by single-cell RNA-seq under various conditions: cells sensitive to BRAF inhibition with BRAF inhibitor vemurafenib and cells resistant to BRAF inhibition with vemurafenib alone or vemurafenib in combination with the MEK1/2 inhibitors cobimetinib or trametinib. Dimensionality reduction by t-distributed stochastic neighbor embedding and self-organizing maps identified distinct trajectories of resistance development clearly separating the 4 treatment conditions in cell and gene state space.

RESULTS

Trajectories associated with resistance to single-agent treatment involved cell cycle, extracellular matrix, and de-differentiation programs. In contrast, shifts detected in double-resistant cells primarily affected translation and mitogen-activated protein kinase pathway reactivation, with a small subpopulation showing markers of pluripotency. These findings were validated in pseudotime analyses and RNA velocity measurements.

CONCLUSIONS

The single-cell transcriptomic analyses reported here employed a spectrum of bioinformatics methods to identify mechanisms of melanoma resistance to single- and double-agent treatments. This study deepens our understanding of treatment-induced cellular reprogramming and plasticity in melanoma cells and identifies targets of potential relevance to the management of treatment resistance.

The Identification of the Metabolism Subtypes of Skin Cutaneous Melanoma Associated With the Tumor Microenvironment and the Immunotherapy

Skin cutaneous melanoma (SKCM) is a highly aggressive and resistant cancer with immense metabolic heterogeneity. Here, we performed a comprehensive examination of the diverse metabolic signatures of SKCM based on non-negative matrix factorization (NMF) categorization, clustering SKCM into three distinct metabolic subtypes (C1, C2, and C3). Next, we evaluated the metadata sets of the metabolic signatures, prognostic values, transcriptomic features, tumor microenvironment signatures, immune infiltration, clinical features, drug sensitivity, and immunotherapy response of the subtypes and compared them with those of prior publications for classification. Subtype C1 was associated with high metabolic activity, low immune scores, and poor prognosis. Subtype C2 displayed low metabolic activity, high immune infiltration, high stromal score, and high expression of immune checkpoints, demonstrating the drug sensitivity to PD-1 inhibitors. The C3 subtype manifested moderate metabolic activity, high enrichment in carcinogenesis-relevant pathways, high levels of CpG island methylator phenotype (CIMP), and poor prognosis. Eventually, a 90-gene classifier was produced to implement the SKCM taxonomy and execute a consistency test in different cohorts to validate its reliability. Preliminary validation was performed to ascertain the role of SLC7A4 in SKCM. These results indicated that the 90-gene signature can be replicated to stably identify the metabolic classification of SKCM. In this study, a novel SKCM classification approach based on metabolic gene expression profiles was established to further understand the metabolic diversity of SKCM and provide guidance on precisely targeted therapy to patients with the disease.

Overcoming resistance to BRAFV600E inhibition in melanoma by deciphering and targeting personalized protein network alterations

BRAFV600E melanoma patients, despite initially responding to the clinically prescribed anti-BRAFV600E therapy, often relapse, and their tumors develop drug resistance. While it is widely accepted that these tumors are originally driven by the BRAFV600E mutation, they often eventually diverge and become supported by various signaling networks. Therefore, patient-specific altered signaling signatures should be deciphered and treated individually. In this study, we design individualized melanoma combination treatments based on personalized network alterations. Using an information-theoretic approach, we compute high-resolution patient-specific altered signaling signatures. These altered signaling signatures each consist of several co-expressed subnetworks, which should all be targeted to optimally inhibit the entire altered signaling flux. Based on these data, we design smart, personalized drug combinations, often consisting of FDA-approved drugs. We validate our approach in vitro and in vivo showing that individualized drug combinations that are rationally based on patient-specific altered signaling signatures are more efficient than the clinically used anti-BRAFV600E or BRAFV600E/MEK targeted therapy. Furthermore, these drug combinations are highly selective, as a drug combination efficient for one BRAFV600E tumor is significantly less efficient for another, and vice versa. The approach presented herein can be broadly applicable to aid clinicians to rationally design patient-specific anti-melanoma drug combinations.

Applications of Single-Cell Sequencing in Dermatology

Single-cell sequencing (SCS) is a promising new technique used to assess the genomics, transcriptomics, epigenetics, and other multi-omics at the single-cell level. In addition to elucidating the immune microenvironment and revealing the pathomechanisms of disease and drug resistance, SCS can profile the actual state of an individual cell and identify a novel cell type and differentiation trajectories, which cannot be achieved by bulk tissue sequencing technique. SCS technique serves as powerful tools to explore more meaningful biomarkers of diagnosis, prognosis, and new therapeutic targets in clinical practice. The SCS technique has been widely applied in the field of dermatology. In this review, we summarize the advances of SCS in dermatology.