Redefining hyperviscosity in acute leukemia: Potential implications for red cell transfusions in the microvasculature.

Hyperleukocytosis is an emergency of acute leukemia leading to blood hyperviscosity, potentially resulting in life-threatening microvascular obstruction, or leukostasis. Due to the high number of red cells in the circulation, hematocrit/hemoglobin levels (Hct/Hgb) are major drivers of blood viscosity, but how Hct/Hgb mediates hyperviscosity in acute leukemia remains unknown. In vivo hemorheological studies are difficult to conduct and interpret due to issues related to visualizing and manipulating the microvasculature. To that end, a multi-vessel microfluidic device recapitulating the size-scale and geometry of the microvasculature was designed to investigate how Hct/Hgb interacts with acute leukemia to induce "in vitro" leukostasis. Using patient samples and cell lines, the degree of leukostasis was different among leukemia immunophenotypes with respect to white blood cell (WBC) count and Hct/Hgb. Among lymphoid immunophenotypes, severe anemia is protective against in vitro leukostasis and Hct/Hgb thresholds became apparent above which in vitro leukostasis significantly increased, to a greater extent with B-cell acute lymphoblastic leukemia (ALL) versus T-cell ALL. In vitro leukostasis in acute myeloid leukemia was primarily driven by WBC with little interaction with Hct/Hgb. This sets the stage for prospective clinical studies assessing how red cell transfusion may affect leukostasis risk in immunophenotypically different acute leukemia patients.

Synthetic symmetry breaking and programmable multicellular structure formation.

During development, cells undergo symmetry breaking into differentiated subpopulations that self-organize into complex structures.1,2,3,4,5 However, few tools exist to recapitulate these behaviors in a controllable and coupled manner.6,7,8,9 Here, we engineer a stochastic recombinase genetic switch tunable by small molecules to induce programmable symmetry breaking, commitment to downstream cell fates, and morphological self-organization. Inducers determine commitment probabilities, generating tunable subpopulations as a function of inducer dosage. We use this switch to control the cell-cell adhesion properties of cells committed to each fate.10,11 We generate a wide variety of 3D morphologies from a monoclonal population and develop a computational model showing high concordance with experimental results, yielding new quantitative insights into the relationship between cell-cell adhesion strengths and downstream morphologies. We expect that programmable symmetry breaking, generating precise and tunable subpopulation ratios and coupled to structure formation, will serve as an integral component of the toolbox for complex tissue and organoid engineering.

Development of constitutively synergistic nanoformulations to enhance chemosensitivity in T-cell leukemia.

Advances in multiagent chemotherapy have led to recent improvements in survival for patients with acute lymphoblastic leukemia (ALL); however, a significant fraction do not respond to frontline chemotherapy or later relapse with recurrent disease, after which long-term survival rates remain low. To develop new, effective treatment options for these patients, we conducted a series of high-throughput combination drug screens to identify chemotherapies that synergize in a lineage-specific manner with MRX-2843, a small molecule dual MERTK and FLT3 kinase inhibitor currently in clinical testing for treatment of relapsed/refractory leukemias and solid tumors. Using experimental and computational approaches, we found that MRX-2843 synergized strongly-and in a ratio-dependent manner-with vincristine to inhibit both B-ALL and T-ALL cell line expansion. Based on these findings, we developed multiagent lipid nanoparticle formulations of these drugs that not only delivered defined drug ratios intracellularly in T-ALL, but also improved anti-leukemia activity following drug encapsulation. Synergistic and additive interactions were recapitulated in primary T-ALL patient samples treated with MRX-2843 and vincristine nanoparticle formulations, suggesting their clinical relevance. Moreover, the nanoparticle formulations reduced disease burden and prolonged survival in an orthotopic murine xenograft model of early thymic precursor T-ALL (ETP-ALL), with both agents contributing to therapeutic activity in a dose-dependent manner. In contrast, nanoparticles containing MRX-2843 alone were ineffective in this model. Thus, MRX-2843 increased the sensitivity of ETP-ALL cells to vincristine in vivo. In this context, the additive particles, containing a higher dose of MRX-2843, provided more effective disease control than the synergistic particles. In contrast, particles containing an even higher, antagonistic ratio of MRX-2843 and vincristine were less effective. Thus, both the drug dose and the ratio-dependent interaction between MRX-2843 and vincristine significantly impacted therapeutic activity in vivo. Together, these findings present a systematic approach to high-throughput combination drug screening and multiagent drug delivery that maximizes the therapeutic potential of combined MRX-2843 and vincristine in T-ALL and describe a novel translational agent that could be used to enhance therapeutic responses to vincristine in patients with T-ALL. This broadly generalizable approach could also be applied to develop other constitutively synergistic combination products for the treatment of cancer and other diseases.

iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays.

While microscopy-based cellular assays, including microfluidics, have significantly advanced over the last several decades, there has not been concurrent development of widely-accessible techniques to analyze time-dependent microscopy data incorporating phenomena such as fluid flow and dynamic cell adhesion. As such, experimentalists typically rely on error-prone and time-consuming manual analysis, resulting in lost resolution and missed opportunities for innovative metrics. We present a user-adaptable toolkit packaged into the open-source, standalone Interactive Cellular assay Labeled Observation and Tracking Software (iCLOTS). We benchmark cell adhesion, single-cell tracking, velocity profile, and multiscale microfluidic-centric applications with blood samples, the prototypical biofluid specimen. Moreover, machine learning algorithms characterize previously imperceptible data groupings from numerical outputs. Free to download/use, iCLOTS addresses a need for a field stymied by a lack of analytical tools for innovative, physiologically-relevant assays of any design, democratizing use of well-validated algorithms for all end-user biomedical researchers who would benefit from advanced computational methods.

Early dynamic changes in iPSC oxygen consumption rate predict future cardiomyocyte differentiation.

Human induced pluripotent stem cells (iPSCs) hold great promise for reducing the mortality of cardiovascular disease by cellular replacement of infarcted cardiomyocytes (CMs). CM differentiation via iPSCs is a lengthy multiweek process and is highly subject to batch-to-batch variability, presenting challenges in current cell manufacturing contexts. Real-time, label-free control quality attributes (CQAs) are required to ensure efficient iPSC-derived CM manufacturing. In this work, we report that live oxygen consumption rate measurements are highly predictive CQAs of CM differentiation outcome as early as the first 72 h of the differentiation protocol with an accuracy of 93%. Oxygen probes are already incorporated in commercial bioreactors, thus methods presented in this work are easily translatable to the manufacturing setting. Detecting deviations in the CM differentiation trajectory early in the protocol will save time and money for both manufacturers and patients, bringing iPSC-derived CM one step closer to clinical use.

The 'omics of obesity in B-cell acute lymphoblastic leukemia.

The obesity pandemic currently affects more than 70 million Americans and more than 650 million individuals worldwide. In addition to increasing susceptibility to pathogenic infections (eg, SARS-CoV-2), obesity promotes the development of many cancer subtypes and increases mortality rates in most cases. We and others have demonstrated that, in the context of B-cell acute lymphoblastic leukemia (B-ALL), adipocytes promote multidrug chemoresistance. Furthermore, others have demonstrated that B-ALL cells exposed to the adipocyte secretome alter their metabolic states to circumvent chemotherapy-mediated cytotoxicity. To better understand how adipocytes impact the function of human B-ALL cells, we used a multi-omic RNA-sequencing (single-cell and bulk transcriptomic) and mass spectroscopy (metabolomic and proteomic) approaches to define adipocyte-induced changes in normal and malignant B cells. These analyses revealed that the adipocyte secretome directly modulates programs in human B-ALL cells associated with metabolism, protection from oxidative stress, increased survival, B-cell development, and drivers of chemoresistance. Single-cell RNA sequencing analysis of mice on low- and high-fat diets revealed that obesity suppresses an immunologically active B-cell subpopulation and that the loss of this transcriptomic signature in patients with B-ALL is associated with poor survival outcomes. Analyses of sera and plasma samples from healthy donors and those with B-ALL revealed that obesity is associated with higher circulating levels of immunoglobulin-associated proteins, which support observations in obese mice of altered immunological homeostasis. In all, our multi-omics approach increases our understanding of pathways that may promote chemoresistance in human B-ALL and highlight a novel B-cell-specific signature in patients associated with survival outcomes.

Single-Cell Kinetic Modeling of ß-Lapachone Metabolism in Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSCC) cells are highly heterogeneous in their metabolism and typically experience elevated reactive oxygen species (ROS) levels such as superoxide and hydrogen peroxide (H2O2) in the tumor microenvironment. Tumor cells survive under these chronic oxidative conditions by upregulating antioxidant systems. To investigate the heterogeneity of cellular responses to chemotherapeutic H2O2 generation in tumor and healthy tissue, we leveraged single-cell RNA-sequencing (scRNA-seq) data to perform redox systems-level simulations of quinone-cycling ß-lapachone treatment as a source of NQO1-dependent rapid superoxide and hydrogen peroxide (H2O2) production. Transcriptomic data from 10 HNSCC patient tumors was used to populate over 4000 single-cell antioxidant enzymatic network models of drug metabolism. The simulations reflected significant systems-level differences between the redox states of healthy and cancer cells, demonstrating in some patient samples a targetable cancer cell population or in others statistically indistinguishable effects between non-malignant and malignant cells. Subsequent multivariate analyses between healthy and malignant cellular models pointed to distinct contributors of redox responses between these phenotypes. This model framework provides a mechanistic basis for explaining mixed outcomes of NAD(P)H:quinone oxidoreductase 1 (NQO1)-bioactivatable therapeutics despite the tumor specificity of these drugs as defined by NQO1/catalase expression and highlights the role of alternate antioxidant components in dictating drug-induced oxidative stress.

Mass Spectrometry Imaging Reveals Early Metabolic Priming of Cell Lineage in Differentiating Human-Induced Pluripotent Stem Cells.

Induced pluripotent stem cells (iPSCs) hold great promise in regenerative medicine; however, few algorithms of quality control at the earliest stages of differentiation have been established. Despite lipids having known functions in cell signaling, their role in pluripotency maintenance and lineage specification is underexplored. We investigated the changes in iPSC lipid profiles during the initial loss of pluripotency over the course of spontaneous differentiation using the co-registration of confocal microscopy and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging. We identified phosphatidylethanolamine (PE) and phosphatidylinositol (PI) species that are highly informative of the temporal stage of differentiation and can reveal iPS cell lineage bifurcation occurring metabolically. Several PI species emerged from the machine learning analysis of MS data as the early metabolic markers of pluripotency loss, preceding changes in the pluripotency transcription factor Oct4. The manipulation of phospholipids via PI 3-kinase inhibition during differentiation manifested in the spatial reorganization of the iPS cell colony and elevated expression of NCAM-1. In addition, the continuous inhibition of phosphatidylethanolamine N-methyltransferase during differentiation resulted in the enhanced maintenance of pluripotency. Our machine learning analysis highlights the predictive power of lipidomic metrics for evaluating the early lineage specification in the initial stages of spontaneous iPSC differentiation.

Engineering multicellular living systems-a Keystone Symposia report.

The ability to engineer complex multicellular systems has enormous potential to inform our understanding of biological processes and disease and alter the drug development process. Engineering living systems to emulate natural processes or to incorporate new functions relies on a detailed understanding of the biochemical, mechanical, and other cues between cells and between cells and their environment that result in the coordinated action of multicellular systems. On April 3-6, 2022, experts in the field met at the Keystone symposium "Engineering Multicellular Living Systems" to discuss recent advances in understanding how cells cooperate within a multicellular system, as well as recent efforts to engineer systems like organ-on-a-chip models, biological robots, and organoids. Given the similarities and common themes, this meeting was held in conjunction with the symposium "Organoids as Tools for Fundamental Discovery and Translation".

Redox integration of signaling and metabolism in a head and neck cancer model of radiation resistance using COSMRO.

Redox metabolism is increasingly investigated in cancer as driving regulator of tumor progression, response to therapies and long-term patients' quality of life. Well-established cancer therapies, such as radiotherapy, either directly impact redox metabolism or have redox-dependent mechanisms of action defining their clinical efficacy. However, the ability to integrate redox information across signaling and metabolic networks to facilitate discovery and broader investigation of redox-regulated pathways in cancer remains a key unmet need limiting the advancement of new cancer therapies. To overcome this challenge, we developed a new constraint-based computational method (COSMro) and applied it to a Head and Neck Squamous Cell Cancer (HNSCC) model of radiation resistance. This novel integrative approach identified enhanced capacity for H2S production in radiation resistant cells and extracted a key relationship between intracellular redox state and cholesterol metabolism; experimental validation of this relationship highlights the importance of redox state in cellular metabolism and response to radiation.

Systems Biology Approaches to Enzyme Kinetics.

Intracellular drug metabolism involves transport, bioactivation, conjugation, and other biochemical steps. The dynamics of these steps are each dependent on a number of other cellular factors that can ultimately lead to unexpected behavior. In this review, we discuss the confounding processes and coupled reactions within bioactivation networks that require a systems-level perspective in order to fully understand the time-varying behavior. When converting known in vitro characteristics of drug-enzyme interactions into descriptions of cellular systems, features such as substrate availability, cell-to-cell variability, and intracellular redox state, deserve special focus. Two examples are provided. First, a model of hydrogen peroxide clearance during chemotherapy treatment serves as a basis to discuss an example of sensitivity analysis. Second, an example of doxorubicin bioactivation is used for discussing points of consideration when constructing and analyzing network models of drug metabolism.

Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.

Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

Personalized Genome-Scale Metabolic Models Identify Targets of Redox Metabolism in Radiation-Resistant Tumors.

Redox cofactor production is integral toward antioxidant generation, clearance of reactive oxygen species, and overall tumor response to ionizing radiation treatment. To identify systems-level alterations in redox metabolism that confer resistance to radiation therapy, we developed a bioinformatics pipeline for integrating multi-omics data into personalized genome-scale flux balance analysis models of 716 radiation-sensitive and 199 radiation-resistant tumors. These models collectively predicted that radiation-resistant tumors reroute metabolic flux to increase mitochondrial NADPH stores and reactive oxygen species (ROS) scavenging. Simulated genome-wide knockout screens agreed with experimental siRNA gene knockdowns in matched radiation-sensitive and radiation-resistant cancer cell lines, revealing gene targets involved in mitochondrial NADPH production, central carbon metabolism, and folate metabolism that allow for selective inhibition of glutathione production and H2O2 clearance in radiation-resistant cancers. This systems approach represents a significant advancement in developing quantitative genome-scale models of redox metabolism and identifying personalized metabolic targets for improving radiation sensitivity in individual cancer patients.

MTHFD2 Blockade Enhances the Efficacy of ß-Lapachone Chemotherapy With Ionizing Radiation in Head and Neck Squamous Cell Cancer.

Head and Neck Squamous Cell Cancer (HNSCC) presents with multiple treatment challenges limiting overall survival rates and affecting patients' quality of life. Amongst these, resistance to radiation therapy constitutes a major clinical problem in HNSCC patients compounded by origin, location, and tumor grade that limit tumor control. While cisplatin is considered the standard radiosensitizing agent for definitive or adjuvant radiotherapy, in recurrent tumors or for palliative care other chemotherapeutics such as the antifolates methotrexate or pemetrexed are also being utilized as radiosensitizers. These drugs inhibit the enzyme dihydrofolate reductase, which is essential for DNA synthesis and connects the 1-C/folate metabolism to NAD(P)H and NAD(P)+ balance in cells. In previous studies, we identified MTHFD2, a mitochondrial enzyme involved in folate metabolism, as a key contributor to NAD(P)H levels in the radiation-resistant cells and HNSCC tumors. In the study presented here, we investigated the role of MTHFD2 in the response to radiation alone and in combination with ß-lapachone, a NQO1 bioactivatable drug, which generates reactive oxygen species concomitant with NAD(P)H oxidation to NAD(P)+. These studies are performed in a matched HNSCC cell model of response to radiation: the radiation resistant rSCC-61 and radiation sensitive SCC-61 cells reported earlier by our group. Radiation resistant rSCC-61 cells had increased sensitivity to ß-lapachone compared to SCC-61 and knockdown of MTHFD2 in rSCC-61 cells further potentiated the cytotoxicity of ß-lapachone with radiation in a dose and time-dependent manner. rSCC-61 MTHFD2 knockdown cells irradiated and treated with ß-lapachone showed increased PARP1 activation, inhibition of mitochondrial respiration, decreased respiration-linked ATP production, and increased mitochondrial superoxide and protein oxidation as compared to control rSCC-61 scrambled shRNA. Thus, these studies point to MTHFD2 as a potential target for development of radiosensitizing chemotherapeutics and potentiator of ß-lapachone cytotoxicity.

An Integrative Gene Expression and Mathematical Flux Balance Analysis Identifies Targetable Redox Vulnerabilities in Melanoma Cells.

Melanomas harboring BRAF mutations can be treated with BRAF inhibitors (BRAFi), but responses are varied and tumor recurrence is inevitable. Here we used an integrative approach of experimentation and mathematical flux balance analyses in BRAF-mutated melanoma cells to discover that elevated antioxidant capacity is linked to BRAFi sensitivity in melanoma cells. High levels of antioxidant metabolites in cells with reduced BRAFi sensitivity confirmed this conclusion. By extending our analyses to other melanoma subtypes in The Cancer Genome Atlas, we predict that elevated redox capacity is a general feature of melanomas, not previously observed. We propose that redox vulnerabilities could be exploited for therapeutic benefits and identify unsuspected combination targets to enhance the effects of BRAFi in any melanoma, regardless of mutational status. SIGNIFICANCE: An integrative bioinformatics, flux balance analysis, and experimental approach identify targetable redox vulnerabilities and show the potential for modulation of cancer antioxidant defense to augment the benefits of existing therapies in melanoma.

Risk-associated alterations in marrow T cells in pediatric leukemia.

Current management of childhood leukemia is tailored based on disease risk determined by clinical features at presentation. Whether properties of the host immune response impact disease risk and outcome is not known. Here, we combine mass cytometry, single cell genomics, and functional studies to characterize the BM immune environment in children with B cell acute lymphoblastic leukemia and acute myelogenous leukemia at presentation. T cells in leukemia marrow demonstrate evidence of chronic immune activation and exhaustion/dysfunction, with attrition of naive T cells and TCF1+ stem-like memory T cells and accumulation of terminally differentiated effector T cells. Marrow-infiltrating NK cells also exhibit evidence of dysfunction, particularly in myeloid leukemia. Properties of immune cells identified distinct immune phenotype-based clusters correlating with disease risk in acute lymphoblastic leukemia. High-risk immune signatures were associated with expression of stem-like genes on tumor cells. These data provide a comprehensive assessment of the immune landscape of childhood leukemias and identify targets potentially amenable to therapeutic intervention. These studies also suggest that properties of the host response with depletion of naive T cells and accumulation of terminal-effector T cells may contribute to the biologic basis of disease risk. Properties of immune microenvironment identified here may also impact optimal application of immune therapies, including T cell-redirection approaches in childhood leukemia.

A Co-registration Pipeline for Multimodal MALDI and Confocal Imaging Analysis of Stem Cell Colonies.

Multimodal mass spectrometry imaging (MSI) data presents unique big data challenges in handling and analysis. Here, we present a pipeline for co-registering matrix-assisted laser desorption/ionization MSI and confocal immunofluorescence imaging data for extracting single-cell metabolite signatures. We further describe methods and introduce software for the simultaneous analysis of these concatenated data sets, which are designed to establish a connection between cell traits of interest (shape metrics, position within sample) and the cells' own metabolic signatures.

Dynamic Mitochondrial Migratory Features Associated with Calcium Responses during T Cell Antigen Recognition.

A T cell clone is able to distinguish Ags in the form of peptide-MHC complexes with high specificity and sensitivity; however, how subtle differences in peptide-MHC structures translate to distinct T cell effector functions remains unknown. We hypothesized that mitochondrial positioning and associated calcium responses play an important role in T cell Ag recognition. We engineered a microfluidic system to precisely manipulate and synchronize a large number of cell-cell pairing events, which provided simultaneous real-time signaling imaging and organelle tracking with temporal precision. In addition, we developed image-derived metrics to quantify calcium response and mitochondria movement. Using myelin proteolipid altered peptide ligands and a hybridoma T cell line derived from a mouse model of experimental autoimmune encephalomyelitis, we observed that Ag potency modulates calcium response at the single-cell level. We further developed a partial least squares regression model, which highlighted mitochondrial positioning as a strong predictor of calcium response. The model revealed T cell mitochondria sharply alter direction within minutes following exposure to agonist peptide Ag, changing from accumulation at the immunological synapse to retrograde movement toward the distal end of the T cell body. By quantifying mitochondria movement as a highly dynamic process with rapidly changing phases, our result reconciles conflicting prior reports of mitochondria positioning during T cell Ag recognition. We envision applying this pipeline of methodology to study cell interactions between other immune cell types to reveal important signaling phenomenon that is inaccessible because of data-limited experimental design.

Early alterations in stem-like/resident T cells, innate and myeloid cells in the bone marrow in preneoplastic gammopathy.

Preneoplastic lesions carry many of the antigenic targets found in cancer cells but often exhibit prolonged dormancy. Understanding how the host response to premalignancy is maintained and altered during malignant transformation is needed to prevent cancer. In order to understand the immune microenvironment in precursor monoclonal gammopathy of undetermined significance (MGUS) and myeloma, we analyzed bone marrow immune cells from 12 healthy donors and 26 MGUS/myeloma patients by mass cytometry and concurrently profiled transcriptomes of 42,606 single immune cells from these bone marrows. Compared to age-matched healthy donors, memory T cells from both MGUS and myeloma patients exhibit greater terminal-effector differentiation. However, memory T cells in MGUS show greater enrichment of stem-like TCF1/7hi cells. Clusters of T cells with stem-like and tissue-residence genes were also found to be enriched in MGUS by single-cell transcriptome analysis. Early changes in both NK and myeloid cells were also observed in MGUS. Enrichment of stem-like T cells correlated with a distinct genomic profile of myeloid cells and levels of Dickkopf-1 in bone-marrow plasma. These data describe the landscape of changes in both innate and adaptive immunity in premalignancy and suggest that attrition of the bone-marrow-resident T cell compartment due to loss of stem-like cells may underlie loss of immune surveillance in myeloma.

Agent-based modeling of morphogenetic systems: Advantages and challenges.

The complexity of morphogenesis poses a fundamental challenge to understanding the mechanisms governing the formation of biological patterns and structures. Over the past century, numerous processes have been identified as critically contributing to morphogenetic events, but the interplay between the various components and aspects of pattern formation have been much harder to grasp. The combination of traditional biology with mathematical and computational methods has had a profound effect on our current understanding of morphogenesis and led to significant insights and advancements in the field. In particular, the theoretical concepts of reaction-diffusion systems and positional information, proposed by Alan Turing and Lewis Wolpert, respectively, dramatically influenced our general view of morphogenesis, although typically in isolation from one another. In recent years, agent-based modeling has been emerging as a consolidation and implementation of the two theories within a single framework. Agent-based models (ABMs) are unique in their ability to integrate combinations of heterogeneous processes and investigate their respective dynamics, especially in the context of spatial phenomena. In this review, we highlight the benefits and technical challenges associated with ABMs as tools for examining morphogenetic events. These models display unparalleled flexibility for studying various morphogenetic phenomena at multiple levels and have the important advantage of informing future experimental work, including the targeted engineering of tissues and organs.