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"Cell State"
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Hematopoietic stem cell state and fate in trained immunity
Trained immunity serves as a
de facto
memory for innate immune responses, resulting in long-term functional reprogramming of innate immune cells. It enhances resistance to pathogens and augments immunosurveillance under physiological conditions. Given that innate immune cells typically have a short lifespan and do not divide, persistent innate immune memory may be mediated by epigenetic and metabolic changes in long-lived hematopoietic stem cells (HSCs) in the bone marrow. HSCs fine-tune their state and fate in various training conditions, thereby generating functionally adapted progeny cells that orchestrate innate immune plasticity. Notably, both beneficial and maladaptive trained immunity processes can comprehensively influence HSC state and fate, leading to divergent hematopoiesis and immune outcomes. However, the underlying mechanisms are still not fully understood. In this review, we summarize recent advances regarding HSC state and fate in the context of trained immunity. By elucidating the stem cell-intrinsic and extrinsic regulatory network, we aim to refine current models of innate immune memory and provide actionable insights for developing targeted therapies against infectious diseases and chronic inflammation. Furthermore, we propose a conceptual framework for engineering precision-trained immunity through HSC-targeted interventions.
Journal Article
Using Machine Learning to Discover Parsimonious and Physically‐Interpretable Representations of Catchment‐Scale Rainfall‐Runoff Dynamics
Due largely to challenges associated with physical interpretability of machine learning (ML) methods, and because model interpretability is key to credibility in management applications, many scientists and practitioners are hesitant to discard traditional physical‐conceptual modeling approaches despite their poorer predictive performance. Here, we examine how to develop parsimonious minimally‐optimal representations that can facilitate better insight regarding system functioning. The term “minimally‐optimal” indicates that the desired outcome can be achieved with the smallest possible effort and resources, while “parsimony” is widely held to support understanding. Accordingly, we suggest that ML‐based modeling should use computational units that are inherently physically‐interpretable, and explore how generic network architectures comprised of Mass‐Conserving‐Perceptron can be used to model dynamical systems in a physically‐interpretable manner. In the context of spatially‐lumped catchment‐scale modeling, we find that both physical interpretability and good predictive performance can be achieved using a “distributed‐state” network with context‐dependent gating and “information‐sharing” across nodes. The distributed‐state mechanism ensures a sufficient number of temporally‐evolving properties of system storage while information‐sharing ensures proper synchronization of such properties. The results indicate that MCP‐based ML models with only a few layers (up to two) and relativity few physical flow pathways (up to three) can play a significant role in ML‐based streamflow modeling.
Journal Article
Sequential drug treatment targeting cell cycle and cell fate regulatory programs blocks non-genetic cancer evolution in acute lymphoblastic leukemia
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.
Journal Article
CellTrans: An R Package to Quantify Stochastic Cell State Transitions
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).
Journal Article
Challenges and emerging directions in single-cell analysis
Single-cell analysis is a rapidly evolving approach to characterize genome-scale molecular information at the individual cell level. Development of single-cell technologies and computational methods has enabled systematic investigation of cellular heterogeneity in a wide range of tissues and cell populations, yielding fresh insights into the composition, dynamics, and regulatory mechanisms of cell states in development and disease. Despite substantial advances, significant challenges remain in the analysis, integration, and interpretation of single-cell omics data. Here, we discuss the state of the field and recent advances and look to future opportunities.
Journal Article
Wet Chemical Method ZnF2 Interlayer for High Critical Current Density Lithium Metal Batteries Utilizing Ba and Ta–Doped Li7La3Zr2O12 Garnet Solid Electrolyte
Li metal batteries with garnet‐type solid electrolytes have the potential to increase specific energy and power densities of current Li‐ion batteries. Li metal batteries have been hampered by the poor wettability of solid electrolyte with elemental lithium. Here, to resolve the solid garnet electrolyte/Li interface issue, a scalable, cost‐effective, and efficient surfactant‐assisted wet‐chemical strategy is developed. A ZnF2 interlayer coating is applied on Ba and Ta ‐co‐doped Li7La2.75Ba0.25Zr1.75Ta0.25O12 that formed LiF and Li‐Zn alloy upon contact with molten Li. Conformal contact applying a homogenous surfactant‐assisted ZnF2 coating reduced the interfacial resistance from 87 to 15.5 Ω cm2 which enhanced critical current density to a record high value of 5 mA cm−2 at room temperature. Dense and Li2CO3 free garnet solid electrolyte assisted in achieving long‐term stability for 1000 cycles at 1 mA cm−2. Interface stabilized Li/ZnF2‐ solid electrolyte/liquid electrolyte/LiFePO4 cell displayed a 90% capacity retention over 800 cycles at 0.2 C, with Coulombic efficiency of 99% as well as excellent cycle stability at 1 C, with ≈91% of capacity retention for 500 cycles. Using a new design principle for Li anode interfaces, next‐generation power‐intensive and stable solid‐state Li metal batteries can be developed. A surfactant‐based multifunctional interlayer approach is implemented to improve the interfacial interactions between Li metal anode and Ba and Ta‐doped Li7La3Zr2O12 garnet solid electrolyte. The conformal contact achieved by applying homogenous surfactant‐assisted ZnF2 coating technique reduced interfacial resistance to 15.5 Ω cm2, and critical current density to 5 mA cm−2 at 25 °C.
Journal Article
From Cell States to Cell Fates: How Cell Proliferation and Neuronal Differentiation Are Coordinated During Embryonic Development
The central nervous system (CNS) exhibits an extraordinary diversity of neurons, with the right cell types and proportions at the appropriate sites. Thus, to produce brains with specific size and cell composition, the rates of proliferation and differentiation must be tightly coordinated and balanced during development. Early on, proliferation dominates; later on, the growth rate almost ceases as more cells differentiate and exit the cell cycle. Generation of cell diversity and morphogenesis takes place concomitantly. In the vertebrate brain, this results in dramatic changes in the position of progenitor cells and their neuronal derivatives, whereas in the spinal cord morphogenetic changes are not so important because the structure mainly grows by increasing its volume. Morphogenesis is under control of specific genetic programs that coordinately unfold over time; however, little is known about how they operate and impact in the pools of progenitor cells in the CNS. Thus, the spatiotemporal coordination of these processes is fundamental for generating functional neuronal networks. Some key aims in developmental neurobiology are to determine how cell diversity arises from pluripotent progenitor cells, and how the progenitor potential changes upon time. In this review, we will share our view on how the advance of new technologies provides novel data that challenge some of the current hypothesis. We will cover some of the latest studies on cell lineage tracing and clonal analyses addressing the role of distinct progenitor cell division modes in balancing the rate of proliferation and differentiation during brain morphogenesis. We will discuss different hypothesis proposed to explain how progenitor cell diversity is generated and how they challenged prevailing concepts and raised new questions.
Journal Article
Single-cell analysis resolves the cell state transition and signaling dynamics associated with melanoma drug-induced resistance
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.
Journal Article
Modeling glioblastoma heterogeneity as a dynamic network of cell states
Tumor cell heterogeneity is a crucial characteristic of malignant brain tumors and underpins phenomena such as therapy resistance and tumor recurrence. Advances in single‐cell analysis have enabled the delineation of distinct cellular states of brain tumor cells, but the time‐dependent changes in such states remain poorly understood. Here, we construct quantitative models of the time‐dependent transcriptional variation of patient‐derived glioblastoma (GBM) cells. We build the models by sampling and profiling barcoded GBM cells and their progeny over the course of 3 weeks and by fitting a mathematical model to estimate changes in GBM cell states and their growth rates. Our model suggests a hierarchical yet plastic organization of GBM, where the rates and patterns of cell state switching are partly patient‐specific. Therapeutic interventions produce complex dynamic effects, including inhibition of specific states and altered differentiation. Our method provides a general strategy to uncover time‐dependent changes in cancer cells and offers a way to evaluate and predict how therapy affects cell state composition.
Synopsis
A single cell‐based strategy that tracks and models time‐dependent changes in brain tumor cells indicates that patient‐derived glioblastoma cells follow a near‐hierarchical organisation that can be altered by therapeutic agents.
A general method is developed for
de novo
construction of quantitative network models of cancer cell State Transitions and Growth (STAG) from single‐cell measurements.
Patient‐derived glioblastoma cells transit between transcriptional states, recapitulating normal neural cell types, in a hierarchical fashion.
The STAG model can identify patient differences in cell state dynamics and define how therapeutic agents can alter the transition network.
The long‐term cell population growth and cell state composition can be predicted by a mathematical eigendecomposition of the STAG network.
Graphical Abstract
A single cell‐based strategy that tracks and models time‐dependent changes in brain tumor cells indicates that patient‐derived glioblastoma cells follow a near‐hierarchical organisation that can be altered by therapeutic agents.
Journal Article