Measurement of macrophage growth and differentiation

Autologous precision-cut lung slice co-culture models for studying macrophage-driven fibrosis

Precision-cut lung slices (PCLS) are commonly used as an ex vivo model to study lung fibrosis; however, traditional models lack immune cell infiltration, including the recruitment of monocytes and macrophages, which are critical for inflammation and fibrosis. To address this limitation, we developed novel autologous PCLS-immune co-culture models that better replicate the processes of inflammation, repair, and immune cell recruitment associated with fibrosis. Fibrotic responses to nicotine, cigarette smoke extract (CSE), and a fibrosis-inducing cocktail (FC) were first evaluated in PCLS containing only tissue-resident macrophages, with upregulation of α-SMA-expressing fibroblasts confirmed by immunofluorescence and Western blotting, and collagen deposition quantified using Sirius Red staining. To study macrophage recruitment, we employed an indirect co-culture model using transwells to approximate blood vessel function. Chemotactic studies revealed increased migration of autologous bone marrow-derived macrophages (BMDMs) toward and infiltration into CSE-injured PCLS. In a direct co-culture model simulating the repair phase of fibrosis, PCLS exposed to CSE and FC showed further increased collagen deposition in the presence of autologous BMDMs, but not heterologous ones. These findings suggest that our novel PCLS-immune co-culture models provide a platform for studying macrophage involvement in fibrosis and offer potential for developing macrophage-targeted therapeutic strategies in pulmonary fibrosis.

Oxygen, angiogenesis, cancer and immune interplay in breast tumour microenvironment: a computational investigation

Breast cancer is a challenging global health problem among women. This study investigates the intricate breast tumour microenvironment (TME) dynamics utilizing data from mammary-specific polyomavirus middle T antigen overexpression mouse models (MMTV-PyMT). It incorporates endothelial cells (ECs), oxygen and vascular endothelial growth factors (VEGF) to examine the interplay of angiogenesis, hypoxia, VEGF and immune cells in cancer progression. We introduce an approach to impute immune cell fractions within the TME using single-cell RNA-sequencing (scRNA-seq) data from MMTV-PyMT mice. We quantify our analysis by estimating cell counts using cell size data and laboratory findings from existing literature. We perform parameter estimation via a Hybrid Genetic Algorithm (HGA). Our simulations reveal various TME behaviours, emphasizing the critical role of adipocytes, angiogenesis, hypoxia and oxygen transport in driving immune responses and cancer progression. Global sensitivity analyses highlight potential therapeutic intervention points, such as VEGFs' role in EC growth and oxygen transportation and severe hypoxia's effect on cancer and the total number of cells. The VEGF-mediated production rate of ECs shows an essential time-dependent impact, highlighting the importance of early intervention in slowing cancer progression. These findings align with clinical observations demonstrating the VEGF inhibitors' efficacy and suggest a timely intervention for better outcomes.

Including glutamine in a resource allocation model of energy metabolism in cancer and yeast cells

Energy metabolism is crucial for all living cells, especially during fast growth or stress scenarios. Many cancer and activated immune cells (Warburg effect) or yeasts (Crabtree effect) mostly rely on aerobic glucose fermentation leading to lactate or ethanol, respectively, to generate ATP. In recent years, several mathematical models have been proposed to explain the Warburg effect on theoretical grounds. Besides glucose, glutamine is a very important substrate for eukaryotic cells-not only for biosynthesis, but also for energy metabolism. Here, we present a minimal constraint-based stoichiometric model for explaining both the classical Warburg effect and the experimentally observed respirofermentation of glutamine (WarburQ effect). We consider glucose and glutamine respiration as well as the respective fermentation pathways. Our resource allocation model calculates the ATP production rate, taking into account enzyme masses and, therefore, pathway costs. While our calculation predicts glucose fermentation to be a superior energy-generating pathway in human cells, different enzyme characteristics in yeasts reduce this advantage, in some cases to such an extent that glucose respiration is preferred. The latter is observed for the fungal pathogen Candida albicans, which is a known Crabtree-negative yeast. Further, optimization results show that glutamine is a valuable energy source and important substrate under glucose limitation, in addition to its role as a carbon and nitrogen source of biomass in eukaryotic cells. In conclusion, our model provides insights that glutamine is an underestimated fuel for eukaryotic cells during fast growth and infection scenarios and explains well the observed parallel respirofermentation of glucose and glutamine in several cell types.

Finding a Direct Method for a Dynamic Process: The DD (Direct and Dynamic) Cell-Tox Method

The main focus of in vitro toxicity assessment methods is to assess the viability of the cells, which is usually based on metabolism changes. Yet, when exposed to toxic substances, the cell triggers multiple signals in response. With this in mind, we have developed a promising cell-based toxicity method that observes various cell responses when exposed to toxic substances (either death, division, or remain viable). Based on the collective cell response, we observed and predicted the dynamics of the cell population to determine the toxicity of the toxicant. The method was tested with two different conformations: In the first conformation, we exposed a monoculture model of blood macrophages to UV light, hydrogen peroxide, nutrient deprivation, tetrabromobisphenol A, fatty acids, and 5-fluorouracil. In the second, we exposed a coculture liver model consisting of hepatocytes, hepatic stellate cells, Kupffer cells, and liver sinusoidal endothelial cells to rifampicin, ibuprofen, and 5-fluorouracil. The method showed good accuracy compared to established toxicity assessment methods. In addition, this approach provided more representative information on the toxic effects of the compounds, as it considers the different cellular responses induced by toxic agents.

COMMBINI: an experimentally-informed COmputational Model of Macrophage dynamics in the Bone INjury Immunoresponse

Bone fracture healing is a well-orchestrated but complex process that involves numerous regulations at different scales. This complexity becomes particularly evident during the inflammatory stage, as immune cells invade the healing region and trigger a cascade of signals to promote a favorable regenerative environment. Thus, the emergence of criticalities during this stage might hinder the rest of the process. Therefore, the investigation of the many interactions that regulate the inflammation has a primary importance on the exploration of the overall healing progression. In this context, an in silico model named COMMBINI (COmputational Model of Macrophage dynamics in the Bone INjury Immunoresponse) has been developed to investigate the mechano-biological interactions during the early inflammatory stage at the tissue, cellular and molecular levels. An agent-based model is employed to simulate the behavior of immune cells, inflammatory cytokines and fracture debris as well as their reciprocal multiscale biological interactions during the development of the early inflammation (up to 5 days post-injury). The strength of the computational approach is the capacity of the in silico model to simulate the overall healing process by taking into account the numerous hidden events that contribute to its success. To calibrate the model, we present an in silico immunofluorescence method that enables a direct comparison at the cellular level between the model output and experimental immunofluorescent images. The combination of sensitivity analysis and a Genetic Algorithm allows dynamic cooperation between these techniques, enabling faster identification of the most accurate parameter values, reducing the disparity between computer simulation and histological data. The sensitivity analysis showed a higher sensibility of the computer model to the macrophage recruitment ratio during the early inflammation and to proliferation in the late stage. Furthermore, the Genetic Algorithm highlighted an underestimation of macrophage proliferation by in vitro experiments. Further experiments were conducted using another externally fixated murine model, providing an independent validation dataset. The validated COMMBINI platform serves as a novel tool to deepen the understanding of the intricacies of the early bone regeneration phases. COMMBINI aims to contribute to designing novel treatment strategies in both the biological and mechanical domains.

A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection

While the development of different vaccines slowed the dissemination of SARS-CoV-2, the occurrence of breakthrough infections has continued to fuel the COVID-19 pandemic. To secure at least partial protection in the majority of the population through 1 dose of a COVID-19 vaccine, delayed administration of boosters has been implemented in many countries. However, waning immunity and emergence of new variants of SARS-CoV-2 suggest that such measures may induce breakthrough infections due to intermittent lapses in protection. Optimizing vaccine dosing schedules to ensure prolonged continuity in protection could thus help control the pandemic. We developed a mechanistic model of immune response to vaccines as an in silico tool for dosing schedule optimization. The model was calibrated with clinical data sets of acquired immunity to COVID-19 mRNA vaccines in healthy and immunocompromised participants and showed robust validation by accurately predicting neutralizing antibody kinetics in response to multiple doses of COVID-19 mRNA vaccines. Importantly, by estimating population vulnerability to breakthrough infections, we predicted tailored vaccination dosing schedules to minimize breakthrough infections, especially for immunocompromised individuals. We identified that the optimal vaccination schedules vary from CDC-recommended dosing, suggesting that the model is a valuable tool to optimize vaccine efficacy outcomes during future outbreaks.

Active Hydrophilic Graphene Oxide Nanocomposites Delivery Mediated by Adipose-Derived Stem Cell for Elevated Photothermal Therapy of Breast Cancer

Purpose

Graphene oxide (GO) and its derivatives have recently been identified as promising candidates for early disease diagnosis and therapy. However, the physiological stability and precise launch requirements present limitations on further clinical practices. Adipose-derived stem cells (ADSCs) were employed as an unobstructed biological vehicle to address the validate this ADSC-based tumor-targeting system for highly efficient GO delivery combined with two-stage NIR radiation for superior tumor ablation.

Methods

GO was modified with poly-ethylene glycol (PEG) and folic acid (FA). Afterward, the GO nanocomposite was internalized into ADSCs. The GO-PEG-FA-laden ADSCs were injected into the tail veins of the tumor-bearing mice. Subsequently, first-stage NIR radiation was utilized to disrupt the ADSCs for GO-PEG-FA release. After this, the heat generated by secondary-stage NIR radiation destroy the malignant cells and shrink the tumor, and the cascade process could be recycled until complete tumor ablation if necessary.

Results

The GO-PEG-FA nanocomposite exhibited negligible cytotoxicity and could be internalized into ADSCs to target specific tumor sites after 32 days of intravenous injection. The nanocomposite was released from the ADSCs and taken up into cancer cells again with the assistance of FA after the first dose of near-infrared radiation. Then, the second radiation dose could directly strike the cancer cell for cancer ablation.

Conclusion

In summary, we reported a stem cell-based anticancer system that used GO-PEG-FA-laden ADSCs for breast cancer therapy through NIR treatment in mice potentially opens a new avenue not only to address precise drug targeting in tumor therapy, but also future clinical practice in diverse areas.

A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection

While the development of different vaccines has slowed the dissemination of SARS-CoV-2, the occurrence of breakthrough infections continues to fuel the pandemic. As a strategy to secure at least partial protection, with a single dose of a given COVID-19 vaccine to maximum possible fraction of the population, delayed administration of subsequent doses (or boosters) has been implemented in many countries. However, waning immunity and emergence of new variants of SARS-CoV-2 suggest that such measures may jeopardize the attainment of herd immunity due to intermittent lapses in protection. Optimizing vaccine dosing schedules could thus make the difference between periodic occurrence of breakthrough infections or effective control of the pandemic. To this end, we have developed a mechanistic mathematical model of adaptive immune response to vaccines and demonstrated its applicability to COVID-19 mRNA vaccines as a proof-of-concept for future outbreaks. The model was thoroughly calibrated against multiple clinical datasets involving immune response to SARS-CoV-2 infection and mRNA vaccines in healthy and immunocompromised subjects (cancer patients undergoing therapy); the model showed robust clinical validation by accurately predicting neutralizing antibody kinetics, a correlate of vaccine-induced protection, in response to multiple doses of mRNA vaccines. Importantly, we estimated population vulnerability to breakthrough infections and predicted tailored vaccination dosing schedules to maximize protection and thus minimize breakthrough infections, based on the immune status of a sub-population. We have identified a critical waiting window for cancer patients (or, immunocompromised subjects) to allow recovery of the immune system (particularly CD4+ T-cells) for effective differentiation of B-cells to produce neutralizing antibodies and thus achieve optimal vaccine efficacy against variants of concern, especially between the first and second doses. Also, we have obtained optimized dosing schedules for subsequent doses in healthy and immunocompromised subjects, which vary from the CDC-recommended schedules, to minimize breakthrough infections. The developed modeling tool is based on generalized adaptive immune response to antigens and can thus be leveraged to guide vaccine dosing schedules during future outbreaks.

Mathematical investigation into the role of macrophage heterogeneity on the temporal and spatio-temporal dynamics of non-small cell lung cancers

Non Small Cell Lung Cancer (NSCLC) is the most common type of lung cancer, and represents the leading cause of cancer-related deaths worldwide. Experimental studies have shown that these solid cancers are heavily infiltrated with macrophages: anti-tumour M1 macrophages, pro-tumour M2 macrophages, and macrophage subtypes sharing both M1 and M2 properties. In this study we aim to investigate qualitatively the role of macrophages with different functional phenotypes (especially those with mixed phenotypes) on cancer dynamics and the success of different immunotherapies for cancer. To this end, we start with two time-evolving mathematical models for cancer-immune interactions that consider: (i) the effect of the two extreme phenotypes, M1 and M2 cells; (ii) the effect of M1 and M2 cells, as well as a macrophage sub-population with a mixed phenotype (throughout this theoretical study we call these cells "M12 cells"). We compare the dynamics of the two models using computational approaches, paying particular attention to the effect of different anti-cancer immunotherapies that focus on macrophages. Since data available for NSCLC and macrophage interactions are incomplete, we perform a global sensitivity analysis to see the influence of input parameters on model outcomes. Finally, we consider extensions of the previous two models to include also the spatial movement of cells, and investigate the role of macrophages with extreme phenotypes and with mixed phenotypes, on the invasion of cancer cells into the surrounding extracellular matrix (ECM). We use numerical simulations to investigate the macrophages phenotypes at the tumour center versus the invasive margin. Again, we examine the impact of immunotherapies for cancer on the spatial dynamics of cancers and immune cells, and observe a shift in the phenotype of macrophages distributed at the tumour center and invasive margin.

TMT-based quantitative proteomic profiling of human monocyte-derived macrophages and foam cells

BACKGROUND

Cardiovascular diseases remain the leading cause of morbidity and mortality worldwide, most of which are caused by atherosclerosis. Discerning processes that participate in macrophage-to-foam cell formation are critical for understanding the basic mechanisms underlying atherosclerosis. To explore the molecular mechanisms of foam cell formation, differentially expressed proteins were identified.

METHODS

Human peripheral blood mononuclear cells were stimulated with macrophage colony-stimulating factor, and obtained macrophages were transformed into foam cells by oxidized low-density lipoprotein. Tandem mass tag (TMT) labeling combined with mass spectrometry was performed to find associations between foam cell transformation and proteome profiles.

RESULTS

Totally, 5146 quantifiable proteins were identified, among which 1515 and 182 differentially expressed proteins (DEPs) were found in macrophage/monocyte and foam cell/macrophage, respectively. Subcellular localization analysis revealed that downregulated DEPs of macrophages/monocytes were mostly located in the nucleus, whereas upregulated DEPs of foam cells/macrophages were mostly extracellular or located in the plasma membrane. Functional analysis of DEPs demonstrated that cholesterol metabolism-related proteins were upregulated in foam cells, whereas immune response-related proteins were downregulated in foam cells. The protein interaction network showed that the DEPs with the highest interaction scores between macrophages and foam cells were mainly concentrated in lysosomes and the endoplasmic reticulum.

CONCLUSIONS

Proteomics analysis suggested that cholesterol metabolism was upregulated, while the immune response was suppressed in foam cells. KEGG enrichment analysis and protein-protein interaction analysis indicated that DEPs located in the endoplasmic reticulum and lysosomes might be key drivers of foam cell formation. These data provide a basis for identifying the potential proteins associated with the molecular mechanism underlying macrophage transformation to foam cells.

Directionality of Macrophages Movement in Tumour Invasion: A Multiscale Moving-Boundary Approach

Invasion of the surrounding tissue is one of the recognised hallmarks of cancer (Hanahan and Weinberg in Cell 100: 57–70, 2000. 10.1016/S0092-8674(00)81683-9), which is accomplished through a complex heterotypic multiscale dynamics involving tissue-scale random and directed movement of the population of both cancer cells and other accompanying cells (including here, the family of tumour-associated macrophages) as well as the emerging cell-scale activity of both the matrix-degrading enzymes and the rearrangement of the cell-scale constituents of the extracellular matrix (ECM) fibres. The involved processes include not only the presence of cell proliferation and cell adhesion (to other cells and to the extracellular matrix), but also the secretion of matrix-degrading enzymes. This is as a result of cancer cells as well as macrophages, which are one of the most abundant types of immune cells in the tumour micro-environment. In large tumours, these tumour-associated macrophages (TAMs) have a tumour-promoting phenotype, contributing to tumour proliferation and spread. In this paper, we extend a previous multiscale moving-boundary mathematical model for cancer invasion, by considering also the multiscale effects of TAMs, with special focus on the influence that their directional movement exerts on the overall tumour progression. Numerical investigation of this new model shows the importance of the interactions between pro-tumour TAMs and the fibrous ECM, highlighting the impact of the fibres on the spatial structure of solid tumour.

Macrophage-based cell therapies: The long and winding road

In the quest for better medicines, attention is increasingly turning to cell-based therapies. The rationale is that infused cells can provide a targeted therapy to precisely correct a complex disease phenotype. Between 1987 and 2010, autologous macrophages (MΦs) were used in clinical trials to treat a variety of human tumors; this approach provided a modest therapeutic benefit in some patients but no lasting remissions. These trials were initiated prior to an understanding of: the complexity of MΦ phenotypes, their ability to alter their phenotype in response to various cytokines and/or the environment, and the extent of survival of the re-infused MΦs. It is now known that while inflammatory MΦs can kill tumor cells, the tumor environment is able to reprogram MΦs into a tumorigenic phenotype; inducing blood vessel formation and contributing to a cancer cell growth-promoting milieu. We review how new information enables the development of large numbers of ex vivo generated MΦs, and how conditioning and gene engineering strategies are used to restrict the MΦ to an appropriate phenotype or to enable production of therapeutic proteins. We survey applications in which the MΦ is loaded with nanomedicines, such as liposomes ex vivo, so when the drug-loaded MΦs are infused into an animal, the drug is released at the disease site. Finally, we also review the current status of MΦ biodistribution and survival after transplantation into an animal. The combination of these recent advances opens the way for improved MΦ cell therapies.

Colony stimulating factor-1 receptor signaling networks inhibit mouse macrophage inflammatory responses by induction of microRNA-21

Macrophage polarization between the M2 (repair, protumorigenic) and M1 (inflammatory) phenotypes is seen as a continuum of states. The detailed transcriptional events and signals downstream of colony-stimulating factor 1 receptor (CSF-1R) that contributes to amplification of the M2 phenotype and suppression of the M1 phenotype are largely unknown. Macrophage CSF-1R pTyr-721 signaling promotes cell motility and enhancement of tumor cell invasion in vitro. Combining analysis of cellular systems for CSF-1R gain of function and loss of function with bioinformatic analysis of the macrophage CSF-1R pTyr-721-regulated transcriptome, we uncovered microRNA-21 (miR-21) as a downstream molecular switch controlling macrophage activation and identified extracellular signal-regulated kinase1/2 and nuclear factor-κB as CSF-1R pTyr-721-regulated signaling nodes. We show that CSF-1R pTyr-721 signaling suppresses the inflammatory phenotype, predominantly by induction of miR-21. Profiling of the miR-21-regulated messenger RNAs revealed that 80% of the CSF-1-regulated canonical miR-21 targets are proinflammatory molecules. Additionally, miR-21 positively regulates M2 marker expression. Moreover, miR-21 feeds back to positively regulate its own expression and to limit CSF-1R-mediated activation of extracellular signal-regulated kinase1/2 and nuclear factor-κB. Consistent with an anti-inflammatory role of miRNA-21, intraperitoneal injection of mice with a miRNA-21 inhibitor increases the recruitment of inflammatory monocytes and enhances the peritoneal monocyte/macrophage response to lipopolysaccharide. These results identify the CSF-1R-regulated miR-21 network that modulates macrophage polarization.