Explore the vast range of titles available.

Continuous BRAF inhibition of BRAF mutant melanomas triggers a series of cell state changes that lead to therapy resistance and escape from immune control before establishing acquired resistance genetically. We used genome-wide transcriptomics and single-cell phenotyping to explore the response kinetics to BRAF inhibition for a panel of patient-derived BRAFV600 -mutant melanoma cell lines. A subset of plastic cell lines, which followed a trajectory covering multiple known cell state transitions, provided models for more detailed biophysical investigations. Markov modeling revealed that the cell state transitions were reversible and mediated by both Lamarckian induction and nongenetic Darwinian selection of drug-tolerant states. Single-cell functional proteomics revealed activation of certain signaling networks shortly after BRAF inhibition, and before the appearance of drug-resistant phenotypes. Drug targeting those networks, in combination with BRAF inhibition, halted the adaptive transition and led to prolonged growth inhibition in multiple patient-derived cell lines.

Extracting dynamical information from single‐cell transcriptomics is a novel task with the promise to advance our understanding of cell state transition and interactions between genes. Yet, theory‐oriented, bottom‐up approaches that consider differences among cell states are largely lacking. Here, we present spliceJAC, a method to quantify the multivariate mRNA splicing from single‐cell RNA sequencing (scRNA‐seq). spliceJAC utilizes the unspliced and spliced mRNA count matrices to constructs cell state‐specific gene–gene regulatory interactions and applies stability analysis to predict putative driver genes critical to the transitions between cell states. By applying spliceJAC to biological systems including pancreas endothelium development and epithelial–mesenchymal transition (EMT) in A549 lung cancer cells, we predict genes that serve specific signaling roles in different cell states, recover important differentially expressed genes in agreement with pre‐existing analysis, and predict new transition genes that are either exclusive or shared between different cell state transitions. Synopsis spliceJAC builds a multivariate mRNA splicing model from single‐cell transcriptome data to infer the context‐specific gene regulation and the key driver genes that guide the transition between cell states. spliceJAC constructs cell state‐specific gene regulatory networks and quantifies changes in signaling roles between cell states. spliceJAC employs stability analysis to identify driver genes that guide transitions between cell states. Context‐specific gene regulation and transition genes are identified using spliceJAC during pancreas endothelium development and epithelial–mesenchymal transition (EMT) in A549 lung cancer cells. Graphical Abstract spliceJAC builds a multivariate mRNA splicing model from single‐cell transcriptome data to infer the context‐specific gene regulation and the key driver genes that guide the transition between cell states.

Background Targeted therapies exploiting vulnerabilities of cancer cells hold promise for improving patient outcome and reducing side-effects of chemotherapy. However, efficacy of precision therapies is limited in part because of tumor cell heterogeneity. A better mechanistic understanding of how drug effect is linked to cancer cell state diversity is crucial for identifying effective combination therapies that can prevent disease recurrence. Results Here, we characterize the effect of G2/M checkpoint inhibition in acute lymphoblastic leukemia (ALL) and demonstrate that WEE1 targeted therapy impinges on cell fate decision regulatory circuits. We find the highest inhibition of recovery of proliferation in ALL cells with KMT2A-rearrangements. Single-cell RNA-seq and ATAC-seq of RS4;11 cells harboring KMT2A::AFF1, treated with the WEE1 inhibitor AZD1775, reveal diversification of cell states, with a fraction of cells exhibiting strong activation of p53-driven processes linked to apoptosis and senescence, and disruption of a core KMT2A-RUNX1-MYC regulatory network. In this cell state diversification induced by WEE1 inhibition, a subpopulation transitions to a drug tolerant cell state characterized by activation of transcription factors regulating pre-B cell fate, lipid metabolism, and pre-BCR signaling in a reversible manner. Sequential treatment with BCR-signaling inhibitors dasatinib, ibrutinib, or perturbing metabolism by fatostatin or AZD2014 effectively counteracts drug tolerance by inducing cell death and repressing stemness markers. Conclusions Collectively, our findings provide new insights into the tight connectivity of gene regulatory programs associated with cell cycle and cell fate regulation, and a rationale for sequential administration of WEE1 inhibitors with low toxicity inhibitors of pre-BCR signaling or metabolism.

Background Inductive signaling interactions between different cell types are a major mechanism for the further diversification of embryonic cell fates. Most blastomeres in the model chordate Ciona robusta become restricted to a single predominant fate between the 64-cell and mid-gastrula stages. The deeply stereotyped and well-characterized Ciona embryonic cell lineages allow the transcriptomic analysis of newly established cell types very early in their divergence from sibling cell states without the pseudotime inference needed in the analysis of less synchronized cell populations. This is the first ascidian study to use droplet scRNAseq with large numbers of analyzed cells as early as the 64-cell stage when major lineages such as primary notochord first become fate restricted. Results and conclusions We identify 59 distinct cell states, including new subregions of the b-line neural lineage and the early induction of the tail tip epidermis. We find that 34 of these cell states are directly or indirectly dependent on MAPK-mediated signaling critical to early Ciona patterning. Most of the MAPK-dependent bifurcations are canalized with the signal-induced cell fate lost upon MAPK inhibition, but the posterior endoderm is unique in being transformed into a novel state expressing some but not all markers of both endoderm and muscle. Divergent gene expression between newly bifurcated sibling cell types is dominated by upregulation in the induced cell type. The Ets family transcription factor Elk1/3/4 is uniquely upregulated in nearly all the putatively direct inductions. Elk1/3/4 upregulation together with Ets transcription factor binding site enrichment analysis enables inferences about which bifurcations are directly versus indirectly controlled by MAPK signaling. We examine notochord induction in detail and find that the transition between a Zic/Ets-mediated regulatory state and a Brachyury/FoxA-mediated regulatory state is unexpectedly late. This supports a “broad-hourglass” model of cell fate specification in which many early tissue-specific genes are induced in parallel to key tissue-specific transcriptional regulators via the same set of transcriptional inputs.

Cancer metabolism is a hallmark of cancer. Metabolic plasticity defines the ability of cancer cells to reprogram a plethora of metabolic pathways to meet unique energetic needs during the various steps of disease progression. Cell state transitions are phenotypic adaptations which confer distinct advantages that help cancer cells overcome progression hurdles, that include tumor initiation, expansive growth, resistance to therapy, metastasis, colonization, and relapse. It is increasingly appreciated that cancer cells need to appropriately reprogram their cellular metabolism in a timely manner to support the changes associated with new phenotypic cell states. We discuss metabolic alterations that may be adopted by cancer cells in relation to the maintenance of cancer stemness, activation of the epithelial–mesenchymal transition program for facilitating metastasis, and the acquisition of drug resistance. While such metabolic plasticity is harnessed by cancer cells for survival, their dependence and addiction towards certain metabolic pathways also present therapeutic opportunities that may be exploited.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide, and heterogeneity of HCC is the major barrier in improving patient outcome. To stratify HCC patients with different degrees of malignancy and provide precise treatment strategies, we reconstructed the tumor evolution trajectory with the help of scRNA-seq data and established a 30-gene prognostic model to identify the malignant state in HCC. Patients were divided into high-risk and low-risk groups. C-index and receiver operating characteristic (ROC) curve confirmed the excellent predictive value of this model. Downstream analysis revealed the underlying molecular and functional characteristics of this model, including significantly higher genomic instability and stronger proliferation/progression potential in the high-risk group. In summary, we established a novel prognostic model to overcome the barriers caused by HCC heterogeneity and provide the possibility of better clinical management for HCC patients to improve their survival outcomes.

The intricate multi-scale phenomenon of entropy generation, resulting from the inhomogeneous and anisotropic rearrangement of cells during their collective migration, is examined across three distinct regimes: (i) convective, (ii) conductive (diffusion), and (iii) sub-diffusion. The collective movement of epithelial monolayers on substrate matrices induces the accumulation of mechanical stress within the cells, which subsequently influences cell packing density, velocity, and alignment. Variations in these physical parameters affect cell-cell interactions, which play a crucial role in the storage and dissipation of energy within multicellular systems. The internal dynamics of entropy generation, as a consequence of energy dissipation, are characterized in each regime using viscoelastic constitutive models and the surface properties at the cell-matrix biointerface. The focus of this theoretical review is to clarify how cells can modulate their rate of energy dissipation by altering cell-cell and cell-matrix adhesion interactions, undergoing changes in shape, and re-establishing polarity due to the contact inhibition of locomotion. We approach these questions by discussing the physical aspects of these complex phenomena.

Inducing apoptosis in cancer cells is a primary goal in anti-cancer therapy, but curing cancer with a single drug is unattainable due to drug resistance. The complex molecular network in cancer cells causes heterogeneous responses to single-target drugs, thereby inducing an adaptive drug response. Here, we showed that targeted drug perturbations can trigger state conflicts between multi-stable motifs within a molecular regulatory network, resulting in heterogeneous drug responses. However, we revealed that properly regulating an interconnecting molecule between these motifs can synergistically minimize the heterogeneous responses and overcome drug resistance. We extracted the essential cellular response dynamics of the Boolean network driven by the target node perturbation and developed an algorithm to identify a synergistic combinatorial target that can reduce heterogeneous drug responses. We validated the proposed approach using exemplary network models and a gastric cancer model from a previous study by showing that the targets identified with our algorithm can better drive the networks to desired states than those with other control theories. Of note, our approach suggests a new synergistic pair of control targets that can increase cancer drug efficacy to overcome adaptive drug resistance.

Inertial effects caused by perturbations of dynamical equilibrium during the flow of soft matter constitute a hallmark of turbulence. Such perturbations are attributable to an imbalance between energy storage and energy dissipation. During the flow of Newtonian fluids, kinetic energy can be both stored and dissipated, while the flow of viscoelastic soft matter systems, such as polymer fluids, induces the accumulation of both kinetic and elastic energies. The accumulation of elastic energy causes local stiffening of stretched polymer chains, which can destabilise the flow. Migrating multicellular systems are hugely complex and are capable of self-regulating their viscoelasticity and mechanical stress generation, as well as controlling their energy storage and energy dissipation. Since the flow perturbation of viscoelastic systems is caused by the inhomogeneous accumulation of elastic energy, rather than of kinetic energy, turbulence can occur at low Reynolds numbers. This theoretical review is focused on clarifying the role of viscoelasticity in the appearance of low-Reynolds turbulence. Three types of system are considered and compared: (1) high-Reynolds turbulent flow of Newtonian fluids, (2) low and moderate-Reynolds flow of polymer solutions, and (3) migration of epithelial collectives, discussed in terms of two model systems. The models considered involve the fusion of two epithelial aggregates, and the free expansion of epithelial monolayers on a substrate matrix.

Many normal and cancerous cell lines exhibit a stable composition of cells in distinct states which can, e.g., be defined on the basis of cell surface markers. There is evidence that such an equilibrium is associated with stochastic transitions between distinct states. Quantifying these transitions has the potential to better understand cell lineage compositions. We introduce CellTrans, an R package to quantify stochastic cell state transitions from cell state proportion data from fluorescence-activated cell sorting and flow cytometry experiments. The R package is based on a mathematical model in which cell state alterations occur due to stochastic transitions between distinct cell states whose rates only depend on the current state of a cell. CellTrans is an automated tool for estimating the underlying transition probabilities from appropriately prepared data. We point out potential analytical challenges in the quantification of these cell transitions and explain how CellTrans handles them. The applicability of CellTrans is demonstrated on publicly available data on the evolution of cell state compositions in cancer cell lines. We show that CellTrans can be used to (1) infer the transition probabilities between different cell states, (2) predict cell line compositions at a certain time, (3) predict equilibrium cell state compositions, and (4) estimate the time needed to reach this equilibrium. We provide an implementation of CellTrans in R, freely available via GitHub (https://github.com/tbuder/CellTrans).