Phenotypic heterogeneity and evolution of melanoma cells associated with targeted therapy resistance

Continuous, Label-Free Phenotyping of Single Cells Based on Antibody Interaction Profiling in Microfluidic Channels

Flow cytometry commonly utilizes fluorescence labeling and extensive sample preparation to detect specific cell surface markers, making analysis under native cell conditions impractical. In this work, a label-free flow cytometry technique is presented that spatiotemporally resolves cell-surface interactions in antibody-coated microfluidic channels. Using computational imaging, numerous cells are tracked across a large field of view (12 × 3 mm2) and the resulting motion profiles are used for phenotypic cell characterization. As proof-of-principle, experiments targeting T-cell receptor CD8 are performed directly on cell cultures. Individual T-cells are successfully tracked in 98% cases for flow velocities of 1-3 mm·s-1. In 14 μm high channels coated with only nonspecific antibodies, both CD8-positive SUP-T1 and CD8-negative Jurkat cells exhibit mostly constant velocities. In contrast, using channels functionalized with CD8-specific antibodies, numerous CD8-positive cells but not CD8-negative cells show temporary delays in motion linked to surface interaction. Cell classification based on the observed interactions results in a clear contrast ratio of 23.9 ± 11.6 (mean ± standard deviation) between SUP-T1 and Jurkat cells at 1 mm·s-1. The contrast decreases at higher flow velocities as fewer cells interact due to the increased hydrodynamic lift. Our results affirm our method's ability to differentiate cells without prior labeling or sample preparation.

Signaling and transcriptional dynamics underlying early adaptation to oncogenic BRAF inhibition

A major contributor to poor sensitivity to anti-cancer kinase inhibitor therapy is drug-induced cellular adaptation, whereby remodeling of signaling and gene regulatory networks permits a drug-tolerant phenotype. Here, we resolve the scale and kinetics of critical subcellular events following oncogenic kinase inhibition and preceding cell cycle re-entry, using mass spectrometry-based phosphoproteomics and RNA sequencing (RNA-seq) to monitor the dynamics of thousands of growth- and survival-related signals over the first minutes, hours, and days of oncogenic BRAF inhibition in human melanoma cells. We observed sustained inhibition of the BRAF-ERK axis, gradual downregulation of cell cycle signaling, and three distinct, reversible phase transitions toward quiescence. Statistical inference of kinetically defined regulatory modules revealed a dominant compensatory induction of SRC family kinase (SFK) signaling, promoted in part by excess reactive oxygen species, rendering cells sensitive to co-treatment with an SFK inhibitor in vitro and in vivo, underscoring the translational potential for assessing early drug-induced adaptive signaling. A record of this paper's transparent peer review process is included in the supplemental information.

Recent Developments in Single-Cell Metabolomics by Mass Spectrometry─A Perspective

Recent advancements in single-cell (sc) resolution analyses, particularly in sc transcriptomics and sc proteomics, have revolutionized our ability to probe and understand cellular heterogeneity. The study of metabolism through small molecules, metabolomics, provides an additional level of information otherwise unattainable by transcriptomics or proteomics by shedding light on the metabolic pathways that translate gene expression into functional outcomes. Metabolic heterogeneity, critical in health and disease, impacts developmental outcomes, disease progression, and treatment responses. However, dedicated approaches probing the sc metabolome have not reached the maturity of other sc omics technologies. Over the past decade, innovations in sc metabolomics have addressed some of the practical limitations, including cell isolation, signal sensitivity, and throughput. To fully exploit their potential in biological research, however, remaining challenges must be thoroughly addressed. Additionally, integrating sc metabolomics with orthogonal sc techniques will be required to validate relevant results and gain systems-level understanding. This perspective offers a broad-stroke overview of recent mass spectrometry (MS)-based sc metabolomics advancements, focusing on ongoing challenges from a biologist's viewpoint, aimed at addressing pertinent and innovative biological questions. Additionally, we emphasize the use of orthogonal approaches and showcase biological systems that these sophisticated methodologies are apt to explore.

Mannose metabolism reshapes T cell differentiation to enhance anti-tumor immunity

Cellular metabolic status profoundly influences T cell differentiation, persistence, and anti-tumor efficacy. Our single-cell metabolic analyses of T cells reveal that diminished mannose metabolism is a prominent feature of T cell dysfunction. Conversely, experimental augmentation/restoration of mannose metabolism in adoptively transferred T cells via D-mannose supplementation enhances anti-tumor activity and restricts exhaustion differentiation both in vitro and in vivo. Mechanistically, D-mannose treatment induces intracellular metabolic programming and increases the O-GlcNAc transferase (OGT)-mediated O-GlcNAcylation of β-catenin, which preserves Tcf7 expression and epigenetic stemness, thereby promoting stem-like programs in T cells. Furthermore, in vitro expansion with D-mannose supplementation yields T cell products for adoptive therapy with stemness characteristics, even after extensive long-term expansion, that exhibits enhanced anti-tumor efficacy. These findings reveal cell-intrinsic mannose metabolism as a physiological regulator of CD8+ T cell fate, decoupling proliferation/expansion from differentiation, and underscoring the therapeutic potential of mannose modulation in cancer immunotherapy.

Mutually exclusive teams-like patterns of gene regulation characterize phenotypic heterogeneity along the noradrenergic-mesenchymal axis in neuroblastoma

Neuroblastoma is the most frequent extracranial pediatric tumor and leads to 15% of all cancer-related deaths in children. Tumor relapse and therapy resistance in neuroblastoma are driven by phenotypic plasticity and heterogeneity between noradrenergic (NOR) and mesenchymal (MES) cell states. Despite the importance of this phenotypic plasticity, the dynamics and molecular patterns associated with these bidirectional cell-state transitions remain relatively poorly understood. Here, we analyze multiple RNA-seq datasets at both bulk and single-cell resolution, to understand the association between NOR- and MES-specific factors. We observed that NOR-specific and MES-specific expression patterns are largely mutually exclusive, exhibiting a "teams-like" behavior among the genes involved, reminiscent of our earlier observations in lung cancer and melanoma. This antagonism between NOR and MES phenotypes was also associated with metabolic reprogramming and with immunotherapy targets PD-L1 and GD2 as well as with experimental perturbations driving the NOR-MES and/or MES-NOR transition. Further, these "teams-like" patterns were seen only among the NOR- and MES-specific genes, but not in housekeeping genes, possibly highlighting a hallmark of network topology enabling cancer cell plasticity.

Kinetic Trajectories of Glucose Uptake in Single Cancer Cells Reveal a Drug-Induced Cell-State Change Within Hours of Drug Treatment

The development of drug resistance is a nearly universal phenomenon in patients with glioblastoma multiforme (GBM) brain tumors. Upon treatment, GBM cancer cells may initially undergo a drug-induced cell-state change to a drug-tolerant, slow-cycling state. The kinetics of that process are not well understood, in part due to the heterogeneity of GBM tumors and tumor models, which can confound the interpretation of kinetic data. Here, we resolve drug-adaptation kinetics in a patient-derived in vitro GBM tumor model characterized by the epithelial growth factor receptor (EGFR) variant(v)III oncogene treated with an EGFR inhibitor. We use radiolabeled 18F-fluorodeoxyglucose (FDG) to monitor the glucose uptake trajectories of single GBM cancer cells over a 12 h period of drug treatment. Autocorrelation analysis of the single-cell glucose uptake trajectories reveals evidence of a drug-induced cell-state change from a high- to low-glycolytic phenotype after 5-7 h of drug treatment. Information theoretic analysis of a bulk transcriptome kinetic series of the GBM tumor model delineated the underlying molecular mechanisms driving the cellular state change, including a shift from a stem-like mesenchymal state to a more differentiated, slow-cycling astrocyte-like state. Our results demonstrate that complex drug-induced cancer cell-state changes of cancer cells can be captured via measurements of single cell metabolic trajectories and reveal the extremely facile nature of drug adaptation.

Deep learning identifies heterogeneous subpopulations in breast cancer cell lines

Motivation

Cells exhibit a wide array of morphological features, enabling computer vision methods to identify and track relevant parameters. Morphological analysis has long been implemented to identify specific cell types and cell responses. Here we asked whether morphological features might also be used to classify transcriptomic subpopulations within in vitro cancer cell lines. Identifying cell subpopulations furthers our understanding of morphology as a reflection of underlying cell phenotype and could enable a better understanding of how subsets of cells compete and cooperate in disease progression and treatment.

Results

We demonstrate that cell morphology can reflect underlying transcriptomic differences in vitro using convolutional neural networks. First, we find that changes induced by chemotherapy treatment are highly identifiable in a breast cancer cell line. We then show that the intra cell line subpopulations that comprise breast cancer cell lines under standard growth conditions are also identifiable using cell morphology. We find that cell morphology is influenced by neighborhood effects beyond the cell boundary, and that including image information surrounding the cell can improve model discrimination ability.

Signaling and transcriptional dynamics underlying early adaptation to oncogenic BRAF inhibition

A major contributor to poor sensitivity to anti-cancer kinase inhibitor therapy is drug-induced cellular adaptation, whereby remodeling of signaling and gene regulatory networks permits a drug-tolerant phenotype. Here, we resolve the scale and kinetics of critical subcellular events following oncogenic kinase inhibition and preceding cell cycle re-entry, using mass spectrometry-based phosphoproteomics and RNA sequencing to capture molecular snapshots within the first minutes, hours, and days of BRAF kinase inhibitor exposure in a human BRAF -mutant melanoma model of adaptive therapy resistance. By enriching specific phospho-motifs associated with mitogenic kinase activity, we monitored the dynamics of thousands of growth- and survival-related protein phosphorylation events under oncogenic BRAF inhibition and drug removal. We observed early and sustained inhibition of the BRAF-ERK axis, gradual downregulation of canonical cell cycle-dependent signals, and three distinct and reversible phase transitions toward quiescence. Statistical inference of kinetically-defined signaling and transcriptional modules revealed a concerted response to oncogenic BRAF inhibition and a dominant compensatory induction of SRC family kinase (SFK) signaling, which we found to be at least partially driven by accumulation of reactive oxygen species via impaired redox homeostasis. This induction sensitized cells to co-treatment with an SFK inhibitor across a panel of patient-derived melanoma cell lines and in an orthotopic mouse xenograft model, underscoring the translational potential for measuring the early temporal dynamics of signaling and transcriptional networks under therapeutic challenge.

Mechanisms of Melanoma Progression and Treatment Resistance: Role of Cancer Stem-like Cells

Melanoma is the third most common type of skin cancer, characterized by its heterogeneity and propensity to metastasize to distant organs. Melanoma is a heterogeneous tumor, composed of genetically divergent subpopulations, including a small fraction of melanoma-initiating cancer stem-like cells (CSCs) and many non-cancer stem cells (non-CSCs). CSCs are characterized by their unique surface proteins associated with aberrant signaling pathways with a causal or consequential relationship with tumor progression, drug resistance, and recurrence. Melanomas also harbor significant alterations in functional genes (BRAF, CDKN2A, NRAS, TP53, and NF1). Of these, the most common are the BRAF and NRAS oncogenes, with 50% of melanomas demonstrating the BRAF mutation (BRAFV600E). While the successful targeting of BRAFV600E does improve overall survival, the long-term efficacy of available therapeutic options is limited due to adverse side effects and reduced clinical efficacy. Additionally, drug resistance develops rapidly via mechanisms involving fast feedback re-activation of MAPK signaling pathways. This article updates information relevant to the mechanisms of melanoma progression and resistance and particularly the mechanistic role of CSCs in melanoma progression, drug resistance, and recurrence.

Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics

Phenotypic plasticity of cancer cells can lead to complex cell state dynamics during tumor progression and acquired resistance. Highly plastic stem-like states may be inherently drug-resistant. Moreover, cell state dynamics in response to therapy allow a tumor to evade treatment. In both scenarios, quantifying plasticity is essential for identifying high-plasticity states or elucidating transition paths between states. Currently, methods to quantify plasticity tend to focus on 1) quantification of quasi-potential based on the underlying gene regulatory network dynamics of the system; or 2) inference of cell potency based on trajectory inference or lineage tracing in single-cell dynamics. Here, we explore both of these approaches and associated computational tools. We then discuss implications of each approach to plasticity metrics, and relevance to cancer treatment strategies.

Systematic analysis and optimization of early warning signals for critical transitions using distribution data

Abrupt shifts between alternative regimes occur in complex systems, from cell regulation to brain functions to ecosystems. Several model-free early warning signals (EWS) have been proposed to detect impending transitions, but failure or poor performance in some systems have called for better investigation of their generic applicability. Notably, there are still ongoing debates whether such signals can be successfully extracted from data in particular from biological experiments. In this work, we systematically investigate properties and performance of dynamical EWS in different deteriorating conditions, and we propose an optimized combination to trigger warnings as early as possible, eventually verified on experimental data from microbiological populations. Our results explain discrepancies observed in the literature between warning signs extracted from simulated models and from real data, provide guidance for EWS selection based on desired systems and suggest an optimized composite indicator to alert for impending critical transitions using distribution data.

Toward a systems-level probing of tumor clonality

Cancer has been described as a genetic disease that clonally evolves in the face of selective pressures imposed by cell-intrinsic and extrinsic factors. Although classical models based on genetic data predominantly propose Darwinian mechanisms of cancer evolution, recent single-cell profiling of cancers has described unprecedented heterogeneity in tumors providing support for alternative models of branched and neutral evolution through both genetic and non-genetic mechanisms. Emerging evidence points to a complex interplay between genetic, non-genetic, and extrinsic environmental factors in shaping the evolution of tumors. In this perspective, we briefly discuss the role of cell-intrinsic and extrinsic factors that shape clonal behaviors during tumor progression, metastasis, and drug resistance. Taking examples of pre-malignant states associated with hematological malignancies and esophageal cancer, we discuss recent paradigms of tumor evolution and prospective approaches to further enhance our understanding of this spatiotemporally regulated process.

Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools

Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.

Phenotype Switching and the Melanoma Microenvironment; Impact on Immunotherapy and Drug Resistance

Melanoma, a highly heterogeneous tumor, is comprised of a functionally diverse spectrum of cell phenotypes and subpopulations, including stromal cells in the tumor microenvironment (TME). Melanoma has been shown to dynamically shift between different transcriptional states or phenotypes. This is referred to as phenotype switching in melanoma, and it involves switching between quiescent and proliferative cell cycle states, and dramatic shifts in invasiveness, as well as changes in signaling pathways in the melanoma cells, and immune cell composition in the TME. Melanoma cell plasticity is associated with altered gene expression in immune cells and cancer-associated fibroblasts, as well as changes in extracellular matrix, which drive the metastatic cascade and therapeutic resistance. Therefore, resistance to therapy in melanoma is not only dependent on genetic evolution, but it has also been suggested to be driven by gene expression changes and adaptive phenotypic cell plasticity. This review discusses recent findings in melanoma phenotype switching, immunotherapy resistance, and the balancing of the homeostatic TME between the different melanoma cell subpopulations. We also discuss future perspectives of the biology of neural crest-like state(s) in melanoma.

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.

Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer

Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC. A record of this paper's Transparent Peer Review process is included in the supplemental information.

Scales of Cancer Evolution: Selfish Genome or Cooperating Cells?

Simple Summary

Cancer continuously evolves its ability to survive in time-varying microenvironment, which results, regarding the therapeutic context, in its therapeutic resistance. As it is accepted that the development of resistance is the direct consequence of intratumour heterogeneity, its evolutionary etiology is intensively studied. Models of carinogenesis are often assessed accordingly to how well they fit into the evolutionary scenario. In the paper, the relevant observations and concepts in cancer research, such as intratumour heterogeneity, cell plasticity, and Markov cell state dynamics, are reviewed and integrated into an evolutionary model. The possibility that the interaction between cancer cells can be interpreted as cooperation is proposed.

Abstract

The exploitation of the evolutionary modus operandi of cancer to steer its progression towards drug sensitive cancer cells is a challenging research topic. Integrating evolutionary principles into cancer therapy requires properly identified selection level, the relevant timescale, and the respective fitness of the principal selection unit on that timescale. Interpretation of some features of cancer progression, such as increased heterogeneity of isogenic cancer cells, is difficult from the most straightforward evolutionary view with the cancer cell as the principal selection unit. In the paper, the relation between the two levels of intratumour heterogeneity, genetic, due to genetic instability, and non-genetic, due to phenotypic plasticity, is reviewed and the evolutionary role of the latter is outlined. In analogy to the evolutionary optimization in a changing environment, the cell state dynamics in cancer clones are interpreted as the risk diversifying strategy bet hedging, optimizing the balance between the exploitation and exploration of the cell state space.

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.

Histone Deacetylase (HDAC) Inhibitors: A Promising Weapon to Tackle Therapy Resistance in Melanoma

Melanoma is an aggressive malignant tumor, arising more commonly on the skin, while it can also occur on mucosal surfaces and the uveal tract of the eye. In the context of the unresectable and metastatic cases that account for the vast majority of melanoma-related deaths, the currently available therapeutic options are of limited value. The exponentially increasing knowledge in the field of molecular biology has identified epigenetic reprogramming and more specifically histone deacetylation (HDAC), as a crucial regulator of melanoma progression and as a key driver in the emergence of drug resistance. A variety of HDAC inhibitors (HDACi) have been developed and evaluated in multiple solid and hematologic malignancies, showing promising results. In melanoma, various experimental models have elucidated a critical role of histone deacetylases in disease pathogenesis. They could, therefore, represent a promising novel therapeutic approach for advanced disease. A number of clinical trials assessing the efficacy of HDACi have already been completed, while a few more are in progress. Despite some early promising signs, a lot of work is required in the field of clinical studies, and larger patient cohorts are needed in order for more valid conclusions to be extracted, regarding the potential of HDACi as mainstream treatment options for melanoma.

Hematopoietic differentiation is characterized by a transient peak of entropy at a single-cell level

Background

Mature blood cells arise from hematopoietic stem cells in the bone marrow by a process of differentiation along one of several different lineage trajectories. This is often represented as a series of discrete steps of increasing progenitor cell commitment to a given lineage, but as for differentiation in general, whether the process is instructive or stochastic remains controversial. Here, we examine this question by analyzing single-cell transcriptomic data from human bone marrow cells, assessing cell-to-cell variability along the trajectories of hematopoietic differentiation into four different types of mature blood cells. The instructive model predicts that cells will be following the same sequence of instructions and that there will be minimal variability of gene expression between them throughout the process, while the stochastic model predicts a role for cell-to-cell variability when lineage commitments are being made.

Results

Applying Shannon entropy to measure cell-to-cell variability among human hematopoietic bone marrow cells at the same stage of differentiation, we observed a transient peak of gene expression variability occurring at characteristic points in all hematopoietic differentiation pathways. Strikingly, the genes whose cell-to-cell variation of expression fluctuated the most over the course of a given differentiation trajectory are pathway-specific genes, whereas genes which showed the greatest variation of mean expression are common to all pathways. Finally, we showed that the level of cell-to-cell variation is increased in the most immature compartment of hematopoiesis in myelodysplastic syndromes.

Conclusions

These data suggest that human hematopoietic differentiation could be better conceptualized as a dynamical stochastic process with a transient stage of cellular indetermination, and strongly support the stochastic view of differentiation. They also highlight the need to consider the role of stochastic gene expression in complex physiological processes and pathologies such as cancers, paving the way for possible noise-based therapies through epigenetic regulation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01264-9.

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.

Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease

Medicines and agricultural biocides are often discovered using large phenotypic screens across hundreds of compounds, where visible effects of whole organisms are compared to gauge efficacy and possible modes of action. However, such analysis is often limited to human-defined and static features. Here, we introduce a novel framework that can characterize shape changes (morphodynamics) for cell-drug interactions directly from images, and use it to interpret perturbed development of Phakopsora pachyrhizi, the Asian soybean rust crop pathogen. We describe population development over a 2D space of shapes (morphospace) using two models with condition-dependent parameters: a top-down Fokker-Planck model of diffusive development over Waddington-type landscapes, and a bottom-up model of tip growth. We discover a variety of landscapes, describing phenotype transitions during growth, and identify possible perturbations in the tip growth machinery that cause this variation. This demonstrates a widely-applicable integration of unsupervised learning and biophysical modeling.

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.

Phenotypic Switching of B16F10 Melanoma Cells as a Stress Adaptation Response to Fe3O4/Salicylic Acid Nanoparticle Therapy

Melanoma is a melanocyte-derived skin cancer that has a high heterogeneity due to its phenotypic plasticity, a trait that may explain its ability to survive in the case of physical or molecular aggression and to develop resistance to therapy. Therefore, the therapy modulation of phenotypic switching in combination with other treatment modalities could become a common approach in any future therapeutic strategy. In this paper, we used the syngeneic model of B16F10 melanoma implanted in C57BL/6 mice to evaluate the phenotypic changes in melanoma induced by therapy with iron oxide nanoparticles functionalized with salicylic acid (SaIONs). The results of this study showed that the oral administration of the SaIONs aqueous dispersion was followed by phenotypic switching to highly pigmented cells in B16F10 melanoma through a cytotoxicity-induced cell selection mechanism. The hyperpigmentation of melanoma cells by the intra- or extracellular accumulation of melanic pigment deposits was another consequence of the SaIONs therapy. Additional studies are needed to assess the reversibility of SaIONs-induced phenotypic switching and the impact of tumor hyperpigmentation on B16F10 melanoma’s progression and metastasis abilities.

Topological signatures in regulatory network enable phenotypic heterogeneity in small cell lung cancer

Phenotypic (non-genetic) heterogeneity has significant implications for the development and evolution of organs, organisms, and populations. Recent observations in multiple cancers have unraveled the role of phenotypic heterogeneity in driving metastasis and therapy recalcitrance. However, the origins of such phenotypic heterogeneity are poorly understood in most cancers. Here, we investigate a regulatory network underlying phenotypic heterogeneity in small cell lung cancer, a devastating disease with no molecular targeted therapy. Discrete and continuous dynamical simulations of this network reveal its multistable behavior that can explain co-existence of four experimentally observed phenotypes. Analysis of the network topology uncovers that multistability emerges from two teams of players that mutually inhibit each other, but members of a team activate one another, forming a 'toggle switch' between the two teams. Deciphering these topological signatures in cancer-related regulatory networks can unravel their 'latent' design principles and offer a rational approach to characterize phenotypic heterogeneity in a tumor.

Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19

We present an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention.

Mechanisms of Acquired BRAF Inhibitor Resistance in Melanoma: A Systematic Review

This systematic review investigated the literature on acquired v-raf murine sarcoma viral oncogene homolog B1 (BRAF) inhibitor resistance in patients with melanoma. We searched MEDLINE for articles on BRAF inhibitor resistance in patients with melanoma published since January 2010 in the following areas: (1) genetic basis of resistance; (2) epigenetic and transcriptomic mechanisms; (3) influence of the immune system on resistance development; and (4) combination therapy to overcome resistance. Common resistance mutations in melanoma are BRAF splice variants, BRAF amplification, neuroblastoma RAS viral oncogene homolog (NRAS) mutations and mitogen-activated protein kinase kinase 1/2 (MEK1/2) mutations. Genetic and epigenetic changes reactivate previously blocked mitogen-activated protein kinase (MAPK) pathways, activate alternative signaling pathways, and cause epithelial-to-mesenchymal transition. Once BRAF inhibitor resistance develops, the tumor microenvironment reverts to a low immunogenic state secondary to the induction of programmed cell death ligand-1. Combining a BRAF inhibitor with a MEK inhibitor delays resistance development and increases duration of response. Multiple other combinations based on known mechanisms of resistance are being investigated. BRAF inhibitor-resistant cells develop a range of 'escape routes', so multiple different treatment targets will probably be required to overcome resistance. In the future, it may be possible to personalize combination therapy towards the specific resistance pathway in individual patients.

Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells

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

Single-cell metabolomics can offer deep insights into the metabolic reprogramming that accompanies disease states. Here, the authors use Raman spectro-microscopy for non-invasive metabolite analysis and identification of druggable metabolic susceptibilities in single live melanoma cells.

Deep Profiling of Cellular Heterogeneity by Emerging Single-Cell Proteomic Technologies

The ability to comprehensively profile cellular heterogeneity in functional proteome is crucial in advancing the understanding of cell behavior, organism development, and disease mechanisms. Conventional bulk measurement by averaging the biological responses across a population often loses the information of cellular variations. Single-cell proteomic technologies are becoming increasingly important to understand and discern cellular heterogeneity. The well-established methods for single-cell protein analysis based on flow cytometry and fluorescence microscopy are limited by the low multiplexing ability owing to the spectra overlap of fluorophores for labeling antibodies. Recent advances in mass spectrometry (MS), microchip, and reiterative staining-based techniques for single-cell proteomics have enabled the evaluation of cellular heterogeneity with high throughput, increased multiplexity, and improved sensitivity. In this review, the principles, developments, advantages, and limitations of these advanced technologies in analysis of single-cell proteins, along with their biological applications to study cellular heterogeneity, are described. At last, the remaining challenges, possible strategies, and future opportunities that will facilitate the improvement and broad applications of single-cell proteomic technologies in cell biology and medical research are discussed.

Histone Deacetylase Inhibitors to Overcome Resistance to Targeted and Immuno Therapy in Metastatic Melanoma

Therapies that target oncogenes and immune checkpoint molecules constitute a major group of treatments for metastatic melanoma. A mutation in BRAF (BRAF V600E) affects various signaling pathways, including mitogen activated protein kinase (MAPK) and PI3K/AKT/mammalian target of rapamycin (mTOR) in melanoma. Target-specific agents, such as MAPK inhibitors improve progression-free survival. However, BRAF^V600E mutant melanomas treated with BRAF kinase inhibitors develop resistance. Immune checkpoint molecules, such as programmed death-1 (PD-1) and programmed death ligand-1(PD-L1), induce immune evasion of cancer cells. MAPK inhibitor resistance results from the increased expression of PD-L1. Immune checkpoint inhibitors, such as anti-PD-L1 or anti-PD-1, are main players in immune therapies designed to target metastatic melanoma. However, melanoma patients show low response rate and resistance to these inhibitors develops within 6–8 months of treatment. Epigenetic reprogramming, such as DNA methylaion and histone modification, regulates the expression of genes involved in cellular proliferation, immune checkpoints and the response to anti-cancer drugs. Histone deacetylases (HDACs) remove acetyl groups from histone and non-histone proteins and act as transcriptional repressors. HDACs are often dysregulated in melanomas, and regulate MAPK signaling, cancer progression, and responses to various anti-cancer drugs. HDACs have been shown to regulate the expression of PD-1/PD-L1 and genes involved in immune evasion. These reports make HDACs ideal targets for the development of anti-melanoma therapeutics. We review the mechanisms of resistance to anti-melanoma therapies, including MAPK inhibitors and immune checkpoint inhibitors. We address the effects of HDAC inhibitors on the response to MAPK inhibitors and immune checkpoint inhibitors in melanoma. In addition, we discuss current progress in anti-melanoma therapies involving a combination of HDAC inhibitors, immune checkpoint inhibitors, and MAPK inhibitors.

RKIP Regulates Differentiation-Related Features in Melanocytic Cells

Raf Kinase Inhibitor Protein (RKIP) has been extensively reported as an inhibitor of key signaling pathways involved in the aggressive tumor phenotype and shows decreased expression in several types of cancers. However, little is known about RKIP in melanoma or regarding its function in normal cells. We examined the role of RKIP in both primary melanocytes and malignant melanoma cells and evaluated its diagnostic and prognostic value. IHC analysis revealed a significantly higher expression of RKIP in nevi compared with early-stage (stage I–II, AJCC 8th) melanoma biopsies. Proliferation, wound healing, and collagen-coated transwell assays uncovered the implication of RKIP on the motility but not on the proliferative capacity of melanoma cells as RKIP protein levels were inversely correlated with the migration capacity of both primary and metastatic melanoma cells but did not alter other parameters. As shown by RNA sequencing, endogenous RKIP knockdown in primary melanocytes triggered the deregulation of cellular differentiation-related processes, including genes (i.e., ZEB1, THY-1) closely related to the EMT. Interestingly, NANOG was identified as a putative transcriptional regulator of many of the deregulated genes, and RKIP was able to decrease the activation of the NANOG promoter. As a whole, our data support the utility of RKIP as a diagnostic marker for early-stage melanomas. In addition, these findings indicate its participation in the maintenance of a differentiated state of melanocytic cells by modulating genes intimately linked to the cellular motility and explain the progressive decrease of RKIP often described in tumors.

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

The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge for understanding biological changes ranging from cellular differentiation to epigenetic responses of diseased cells upon drugging. We integrate experiments and theory to determine the trajectories that single BRAFV600E mutant melanoma cancer cells take between drug-naive and drug-tolerant states. Although single-cell omics tools can yield snapshots of the cell-state landscape, the determination of individual cell trajectories through that space can be confounded by stochastic cell-state switching. We assayed for a panel of signaling, phenotypic, and metabolic regulators at points across 5 days of drug treatment to uncover a cell-state landscape with two paths connecting drug-naive and drug-tolerant states. The trajectory a given cell takes depends upon the drug-naive level of a lineage-restricted transcription factor. Each trajectory exhibits unique druggable susceptibilities, thus updating the paradigm of adaptive resistance development in an isogenic cell population.