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Results for “systems biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2024

Integrated systems biology identifies disruptions in mitochondrial function and metabolism as key contributors to heart failure with preserved ejection fraction (HFpEF)

BackgroundHeart failure with preserved ejection fraction (HFpEF) accounts for [~]50% of HF cases, with no effective treatments. The ZSF1-obese rat model recapitulates numerous clinical features of HFpEF including hypertension, obesity, metabolic syndrome, exercise intolerance, and LV diastolic dysfunction. Here, we utilized a systems-biology approach to define the early metabolic and transcriptional signatures to gain mechanistic insight into the pathways contributing to HFpEF development. MethodsMale ZSF1-obese, ZSF1-lean hypertensive controls, and WKY (wild-type) controls were compared at 14w of age for extensive physiological phenotyping and LV tissue harvesting for unbiased metabolomics, RNA-sequencing, and assessment of mitochondrial morphology and function. Utilizing ZSF1-lean and WKY controls enabled a distinction between hypertension-driven molecular changes contributing to HFpEF pathology, versus hypertension + metabolic syndrome. ResultsZSF1-obese rats displayed numerous clinical features of HFpEF. Comparison of ZSF1-lean vs WKY (i.e., hypertension-exclusive effects) revealed metabolic remodeling suggestive of increased aerobic glycolysis, decreased {beta}-oxidation, and dysregulated purine and pyrimidine metabolism with few transcriptional changes. ZSF1-obese rats displayed worsened metabolic remodeling and robust transcriptional remodeling highlighted by the upregulation of inflammatory genes and downregulation of the mitochondrial structure/function and cellular metabolic processes. Integrated network analysis of metabolomic and RNAseq datasets revealed downregulation of nearly all catabolic pathways contributing to energy production, manifesting in a marked decrease in the energetic state (i.e., reduced ATP/ADP, PCr/ATP). Cardiomyocyte ultrastructure analysis revealed decreased mitochondrial area, size, and cristae density, as well as increased lipid droplet content in HFpEF hearts. Mitochondrial function was also impaired as demonstrated by decreased substrate-mediated respiration and dysregulated calcium handling. ConclusionsCollectively, the integrated omics approach applied here provides a framework to uncover novel genes, metabolites, and pathways underlying HFpEF, with an emphasis on mitochondrial energy metabolism as a potential target for intervention.

molecular biology↗

bioRxiv2024

ORCO: Ollivier-Ricci Curvature-Omics: an unsupervised method for analyzing robustness in biological systems

Although recent advanced sequencing technologies have improved the resolution of genomic and proteomic data to better characterize molecular phenotypes, efficient computational tools to analyze and interpret the large-scale omic data are still needed. To address this, we have developed a network-based bioinformatic tool called Ollivier-Ricci curvature-omics (ORCO). ORCO incorporates gene interaction information with omic data into a biological network, and computes Ollivier-Ricci curvature (ORC) values for individual interactions. ORC, an edge-based measure, indicates network robustness and captures global gene signaling changes in functional cooperation using a consistent information passing measure, thereby helping identify therapeutic targets and regulatory modules in biological systems. This tool can be applicable to any data that can be represented as a network. ORCO is an open-source Python package and publicly available on GitHub at https://github.com/aksimhal/ORC-Omics.

bioinformatics↗

bioRxiv2024

Exploration of the link between COVID-19 and gastric cancer from the perspective of bioinformatics and systems biology

BackgroundCoronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has caused a global pandemic. Gastric cancer (GC) poses a great threat to peoples health, which is a high- risk factor for COVID-19. Previous studies have found some associations between GC and COVID-19, whereas the underlying molecular mechanisms are not well understood. MethodsWe used a bioinformatics and systems biology approach to investigate the relationship between GC and COVID-19. The gene expression profiles of COVID-19 (GSE196822) and GC (GSE179252) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the shared differentially expressed genes (DEGs) for GC and COVID-19, functional annotation, protein-protein interaction (PPI) network, hub genes, transcriptional regulatory networks and candidate drugs were analyzed. ResultsA total of 209 shared DEGs were identified to explore the linkages between COVID-19 and GC. Functional analyses showed that Immune-related pathway collectively participated in the development and progression of COVID-19 and GC. In addition, there are selected 10 hub genes including CDK1, KIF20A, TPX2, UBE2C, HJURP, CENPA, PLK1, MKI67, IFI6, and IFIT2. The transcription factor/gene and miRNA/gene interaction networks identified 38 transcription factors (TFs) and 234 miRNAs. More importantly, we identified ten potential therapeutic agents, including ciclopirox, resveratrol, etoposide, methotrexate, trifluridine, enterolactone, troglitazone, calcitriol, dasatinib and deferoxamine, some of which have been reported to improve and treat GC and COVID-19. This study also provides insight into the diseases most associated with mutual DEGs, which may provide new ideas for research on the treatment of COVID-19. ConclusionsThis research has the possibility to be contributed to effective therapeutic in COVID-19 and GC.

bioinformatics↗

bioRxiv2023

Inclusive pedagogy strategies to introduce high schoolers to systems biology

Women and non-white racial and ethnic groups remain underrepresented in science, technology, engineering, and math (STEM). To achieve a more diverse and equitable STEM workforce, the recruitment and retention of these historically marginalized communities in postsecondary education will also need to increase. Recently, the lens has turned to pedagogy and how creating a more inclusive classroom environment can foster STEM identity in previously marginalized communities. This work focuses on developing and piloting systems biology education modules designed to promote an inclusive learning environment in a summer outreach program for high school students by (1) utilizing a hybrid of unplugged activities with coded simulation and (2) a female-oriented problem statement. Based on initial findings in our pilot program, we anticipate that these techniques will enable students with limited prior computational experience to feel more comfortable and able to complete tasks, thereby increasing their self-efficacy and STEM identity. These modules could be a valuable tool for practitioners teaching high school or early college-level computational courses who instruct students with varied coding experience. Additionally, our analysis of the use of a female-oriented problem statement on female-identifying students self-efficacy provides potential evidence of the usefulness of representative problem statements in engaging underrepresented student populations. We believe these techniques could be adapted to address a variety of contexts across STEM disciplines and other fields to foster inclusive learning environments.

scientific communication and education↗

bioRxiv2019

Maximization of non-nitrogenous metabolite production in E. coli using population systems biology

The E. coli metabolome is an interconnected set of enzymes that has measurable kinetic parameters ascribed for the production of most of its metabolites. Flux Balance Analysis (FBA) or Ordinary Differential Equation (ODE) models are used to increase product yield using defined media. However, they either give a range (FBA) or exact amount (ODE) of metabolite yield which isnt true as the transcriptome diversity of individual cells isnt considered. We formulate the metabolic-behaviour of individual cells by using a POpulation SYstems-Biology ALgorithm (POSYBAL) which predicts multiple-gene knockouts for increasing industrially relevant metabolites. We validate this prediction for producing isobutanol (Heterogenous metabolite) and shikimate (Homogenous metabolite) where, the product-yield was increased by 40 times (~2000 ppm) and 42 times (~3000 ppm) respectively. Also, we introduce a nitrogen-swap in standard media to its low-nitrogen counterpart during post-growth phase to redistribute flux towards non-nitrogenous pathways for increasing overall product-yield. Further, our model shows growth-phase diversity in bacterial population even under normal glucose-uptake, portraying a real-world scenario of diverse and robust environment thus, making it evolutionarily favourable to threats such as anti-bacterial attack.

bioengineering↗

bioRxiv2021

Evolutionary systems biology reveals patterns of rice adaptation to drought-prone agro-ecosystems

Rice was domesticated around 10,000 years ago and has developed into a staple for half of humanity. The crop evolved and is currently grown in stably wet and intermittently dry agro-ecosystems, but patterns of adaptation to differences in water availability remain poorly understood. While previous field studies have evaluated plant developmental adaptations to water deficit, adaptive variation in functional and hydraulic components, particularly in relation to gene expression, has received less attention. Here, we take an evolutionary systems biology approach to characterize adaptive drought resistance traits across roots and shoots. We find that rice harbors heritable variation in molecular, physiological, and morphological traits that is linked to higher fitness under drought. We identify modules of co-expressed genes that are associated with adaptive drought avoidance and tolerance mechanisms. These expression modules showed evidence of polygenic adaptation in rice subgroups harboring accessions that evolved in drought-prone agro-ecosystems. Fitness-linked expression patterns had predictive value and allowed us to identify the drought-adaptive nature of optimizing photosynthesis and interactions with arbuscular mycorrhizal fungi. Taken together, our study provides an unprecedented, integrative view of rice adaptation to water-limited field conditions.

plant biology↗

bioRxiv2023

Spatial cancer systems biology resolves heterotypic interactions and identifies disruption of spatial hierarchy as a pathological driver event

Spatially annotated single-cell datasets provide unprecedented opportunities to dissect cell-cell communication in development and disease. Heterotypic signaling includes interactions between different cell types and is well established in tissue development and spatial organization. Epithelial organization requires several different programs that are tightly regulated. Planar cell polarity (PCP) is the organization of epithelial cells along the planar axis, orthogonal to the apical-basal axis. Here, we investigate PCP factors and explore the implications of developmental regulators as malignant drivers. Utilizing cancer systems biology analysis, we derive a gene expression network for WNT-ligands (WNT) and their cognate frizzled (FZD) receptors in skin cutaneous melanoma. The profiles supported by unsupervised clustering of multiple-sequence alignments identify ligand-independent signaling and implications for metastatic progression based on the underpinning developmental spatial program. Omics studies and spatial biology connect developmental programs with oncological events and explain key spatial features of metastatic aggressiveness. Dysregulation of prominent PCP factors such as specific representatives of the WNT and FZD families in malignant melanoma recapitulates the development program of normal melanocytes but in an uncontrolled and disorganized fashion.

cancer biology↗

bioRxiv2023

Stress Knowledge Map: A knowledge graph resource for systems biology analysis of plant stress responses

Stress Knowledge Map (SKM, https://skm.nib.si) is a publicly available resource containing two complementary knowledge graphs describing current knowledge of biochemical, signalling, and regulatory molecular interactions in plants: a highly curated model of plant stress signalling (PSS, 543 reactions) and a large comprehensive knowledge network (CKN, 488,390 interactions). Both were constructed by domain experts through systematic curation of diverse literature and database resources. SKM provides a single entrypoint for plant stress response investigations and the related growth tradeoffs. SKM provides interactive exploration of current knowledge. PSS is also formulated as qualitative and quantitative models for systems biology, and thus represents a starting point of a plant digital twin. Here, we describe the features of SKM and show, through two case studies, how it can be used for complex analyses, including systematic hypothesis generation, design of validation experiments, or to gain new insights into experimental observations in plant biology.

plant biology↗

bioRxiv2021

A clinically validated human saliva metatranscriptomic test for global systems biology studies

We report here the development of a high throughput, automated, inexpensive, and clinically validated saliva metatranscriptome test that requires less than 100 microliters of saliva. The RNA in the samples is preserved at the time of collection. Samples can then be stored and transported at ambient temperatures for up to 28 days without significantly affecting the test results. Another important feature of the sample preservative is that it inactivates all classes of pathogens, enabling safe transportation globally. Given the unique set of convenience, low cost, safety, and technical performance, this saliva metatranscriptomic test can be integrated into longitudinal, global scale, systems biology studies that will lead to an accelerated development of precision medicine diagnostic and therapeutic tools. Method SummaryThis report describes an important improvement to saliva transcriptome analysis. While current methods are complicated and expensive, the method reported here includes at-home sample collection, global shipping at ambient temperatures and pathogen inactivation at the point of collection; it uses fully automated, clinically validated and licensed laboratory and bioinformatic analyses.

microbiology↗

bioRxiv2019

GPSRdocker: A Docker-based Resource for Genomics, Proteomics and Systems biology

BackgroundIn past number of web-based resources has been developed in the field of Bioinformatics. These resources are heavily used by scientific community to provide solution for challenges faced by experimental researchers particularly in the field of biomedical sciences. There are number of challenges in utilizing full potential of these services that includes internet speed, limits on computing power, and security of data. In order to enhance utilities of these web-based assets, we developed a docker-based container that integrates large number resources available in literature.\n\nResultsThis paper describes GPSRdocker a docker-based container developed for providing wide-range of computational tools in the field of bioinformatics particularly in genomics, proteomics and system biology. Majority of tools integrated in GPSRdocker are based on web services developed at Raghavas group in last two decades. Broadly, these tools can be categorized in three categories; i) general scripts, ii) supporting software and iii) major standalone software. In order to facilitate students or developers working in the field of bioinformatics, we developed general scripts in Perl and Python. These general-purpose scripts serve as building block for any bioinformatics tools like computing features/descriptors of a protein. Supporting software packages includes SCIKIT, WEKA, SVMlight, and PSI-BLAST; these software packages allow one to develop/implement bioinformatics software. Major Standalone software is core of this container which allows predicting function/class of biomolecules. These tools can be classified broadly in following categories; protein annotation, epitope-based vaccines, prediction of interaction and drug discovery.\n\nConclusionA docker-based container has been developed which can be easily run on any operating system as well as it can be directly ported on cloud. Scripts can be run to build pipelines for addressing problems at system level like prediction of vaccine candidate for a pathogen. GPSRdocker including manual is available free for academic use from https://webs.iiitd.edu.in/gpsrdocker.

bioinformatics↗

bioRxiv2020

A computational systems biology approach identifies SLUG as a mediator of partial Epithelial-Mesenchymal Transition (EMT)

Epithelial-mesenchymal plasticity comprises of reversible transitions among epithelial, hybrid epithelial/mesenchymal (E/M) and mesenchymal phenotypes, and underlies various aspects of aggressive tumor progression such as metastasis, therapy resistance and immune evasion. The process of cells attaining one or more hybrid E/M phenotypes is termed as partial EMT. Cells in hybrid E/M phenotype(s) can be more aggressive than those in either fully epithelial or mesenchymal state. Thus, identifying regulators of hybrid E/M phenotypes is essential to decipher the rheostats of phenotypic plasticity and consequent accelerators of metastasis. Here, using a computational systems biology approach, we demonstrate that SLUG (SNAIL2) - an EMT-inducing transcription factor - can inhibit cells from undergoing a complete EMT and thus stabilizing them in hybrid E/M phenotype(s). It expands the parametric range enabling the existence of a hybrid E/M phenotype, thereby behaving as a phenotypic stability factor (PSF). Our simulations suggest that this specific property of SLUG emerges from the topology of the regulatory network it forms with other key regulators of epithelial-mesenchymal plasticity. Clinical data suggests that SLUG associates with worse patient prognosis across multiple carcinomas. Together, our results indicate that SLUG can stabilize hybrid E/M phenotype(s).

cancer biology↗

bioRxiv2021

Systems Biology Analysis of Human Genomes Points to Key Pathways Conferring Spina Bifida Risk

Spina bifida (SB) is a debilitating birth defect caused by multiple gene and environment interactions. Though SB shows non-Mendelian inheritance, genetic factors contribute to an estimated 70% of cases. Nevertheless, identifying human mutations conferring SB risk is challenging due to its relative rarity, genetic heterogeneity, incomplete penetrance and environmental influences that hamper GWAS approaches to untargeted discovery. Thus, SB genetic studies may suffer from population substructure and/or selection bias introduced by typical candidate gene searches. We report a population based, ancestry-matched whole-genome sequence analysis of SB genetic predisposition using a systems biology strategy to interrogate 298 case-control subject genomes (149 pairs). Genes that were enriched in likely gene disrupting (LGD), rare protein-coding variants were subjected to machine learning analysis to identify genes in which LGD variants occur with a different frequency in cases vs. controls and so discriminate between these groups. Those genes with high discriminatory potential for SB significantly enriched pathways pertaining to carbon metabolism, inflammation, innate immunity, cytoskeletal regulation and essential transcriptional regulation, indicating their impact on the pathogenesis of human SB. Additionally, interrogation of conserved non-coding sequences identified robust variant enrichment in regulatory regions of several transcription factors critical to embryonic development. This genome-wide perspective offers an effective approach to interrogation of coding and non-coding sequence variant contributions to rare complex genetic disorders.

genomics↗

bioRxiv2019

Targetable Tetrazine-Based Dynamic Nuclear Polarization Agents for Biological Systems

Dynamic nuclear polarization (DNP) has shown great promise as a tool to enhance the nuclear magnetic resonance (NMR) signals of proteins in the cellular environment. As the sensitivity increases, the ability to select and efficiently polarize a specific macromolecule over the cellular background has become desirable. Here, we address this need and present a tetrazine-based DNP polarization agent that can be targeted selectively to proteins containing the unnatural amino acid (UAA) norbornene-lysine. The UAA can be introduced efficiently by genetic means in the cellular milieu. Our approach is bio-orthogonal and easily adaptable to any protein of interest. We illustrate the scope of our methodology and investigate the DNP polarization transfer mechanisms in several biological systems. Our results present the first molecular view of the complex polarization transfer pathways in targeted DNP and ultimately pave the way to selective DNP-enhanced NMR spectroscopy in both bacterial and mammalian cells.

biophysics↗

bioRxiv2021

Systems biology analysis identifies TCF7L1 as a key regulator of metastasis in Ewing sarcoma

Identification of cancer stemness genes is crucial to understanding the underlying biology of therapy resistance, relapse, and metastasis. Ewing sarcoma (EwS) is the second most common bone tumor in children and adolescents. It is a highly aggressive cancer associated with a dismal survival rate (<30%) for patients with metastatic disease at diagnosis ([~]25% of cases). Hence, deciphering the underlying mechanisms of metastasis is imperative. EwS tumors are characterized by a remarkably silent genome with a single driver mutation generating an oncogenic fusion transcription factor (EWSR1-ETS). Thus, EwS constitutes an ideal model to study how perturbation of a transcriptional network by a dominant oncogene can mediate metastasis, even though canonical metastasis-associated genes are not mutated. Here, through the implementation of an integrative systems biology approach, we identified transcription factor 7 like 1 (TCF7L1, alias TCF3) as a prognostically-relevant and EWSR1-ETS suppressed determinant of metastasis in EwS. We demonstrated that conditional TCF7L1 re-expression significantly reduces EwS single-cell migration, invasion and anchorage-independent growth in 3D assays in vitro, and tumorigenesis in vivo mediated by its DNA binding domain. In primary EwS tumors as well as in functional orthotopic in vivo models, low TCF7L1 expression was associated with pro-metastatic gene signatures and a much higher migratory and metastatic capacity of EwS cells, which correlated with poor outcome of EwS patients. Collectively, our findings establish TCF7L1 as a major regulator of metastasis in EwS, which may be utilized as a prognostic biomarker and open inroads to future therapeutic intervention.

cancer biology↗

bioRxiv2014

Bridging the Genotype and the Phenotype: Towards An Epigenetic Landscape Approach to Evolutionary Systems Biology

Understanding the mapping of genotypes into phenotypes is a central challenge of current biological research. Such mapping, conceptually represents a developmental mechanism through which phenotypic variation can be generated. Given the nongenetic character of developmental dynamics, phenotypic variation to a great extent has been neglected in the study of evolution. What is the relevance of considering this generative process in the study of evolution? How can we study its evolutionary consequences? Despite an historical systematic bias towards linear causation schemes in biology; in the post-genomic era, a systems-view to biology based on nonlinear (network) thinking is increasingly being adopted. Within this view, evolutionary dynamics can be studied using simple dynamical models of gene regulatory networks (GRNs). Through the study of GRN dynamics, genotypes and phenotypes can be unambiguously defined. The orchestrating role of GRNs constitutes an operational non-linear genotype-phenotype map. Further extension of these GRN models in order to explore and characterize an associated Epigenetic Landscape enables the study of the evolutionary consequences of both genetic and non-genetic sources of phenotypic variation within the same coherent theoretical framework. The merging of conceptually clear theories, computational/mathematical tools, and molecular/genomic data into coherent frameworks could be the basis for a transformation of biological research from mainly a descriptive exercise into a truly mechanistic, explanatory endeavor.

Systems Biology↗

bioRxiv2020

Rethinking the role of lipids in lager yeast cell during beer fermentation from a transcriptome and systems biology perspective

Brewing lager yeast (Saccharomyces pastorianus) is exposed to stressful conditions during beer fermentation, including ethanol toxicity. In response to ethanol toxicity, various biological mechanisms are modulated, including lipid biosynthesis. It is well known that during beer fermentation, the composition of yeast membranes changes in response to ethanol toxicity, making it less fluid and permeable. Additionally, neutral lipids and lipid droplets (LDs) are produced in response to ethanol toxicity. LDs are membranous organelles that transport lipids and proteins, acting as hubs for inter-organellar communication and modulating the activity of mechanisms necessary for ethanol tolerance, such as proteostasis and autophagy. Unfortunately, little is known about the interplay between autophagy, lipid metabolism, and proteostasis (ALP) in lager cells during beer fermentation. Therefore, transcriptome analyses using publicly available DNA microarray data obtained from lager yeast cells were used to identify all the ALP-associated genes that were upregulated during beer fermentation compared to yeast biomass propagation. Thereafter, a top-down systems biology analysis was applied, involving constructing an ALP-associated shortest-pathway protein-protein interaction network (ALP network), identifying important nodes and communities within the ALP network, and identifying the overrepresented biological processes and cellular components using a Gene Ontology (GO) analysis. The transcriptome analyses indicated the upregulation of 204 non-redundant ALP-associated genes during beer fermentation, whose respective proteins interact in the shortest-pathway ALP network. Thirteen communities were selected from the ALP network, and they were associated with multiple overrepresented GO biological processes and cellular components, such as mitophagy, cytoplasm-to-vacuole transport, piecemeal microautophagy of the nucleus, endoplasmic reticulum (ER) stress, ergosterol and lipid biosynthesis, LDs, ER membrane, and phagophore assembly. These results indicate that ethanol tolerance in lager yeasts could be due to the modulation of proteostasis and various forms of autophagy by lipid biosynthesis and LDs, thus highlighting the importance of lipids for beer fermentation.

microbiology↗

bioRxiv2019

Systems biology of cold adaptation in the polyextremophilic red alga Galdieria sulphuraria

Rapid fluctuation of environmental conditions can impose severe stress upon living organisms. Surviving such episodes of stress requires a rapid acclimation response, e.g., by transcriptional and post-transcriptional mechanisms. Persistent change of the environmental context, however, requires longer-term adaptation at the genetic level. Fast-growing unicellular aquatic eukaryotes enable analysis of adaptive responses at the genetic level in a laboratory setting. In this study, we applied continuous cold stress (28{degrees}C) to the thermoacidophile red alga G. sulphuraria, which is 14{degrees}C below its optimal growth temperature of 42{degrees}C. Cold stress was applied for more than 100 generations to identify components that are critical for conferring thermal adaptation. After cold exposure for more than 100 generations, the cold-adapted samples grew ~30% faster than the starting population. Whole-genome sequencing revealed 757 variants located on 429 genes (6.1% of the transcriptome) encoding molecular functions involved in cell cycle regulation, gene regulation, signaling, morphogenesis, microtubule nucleation, and transmembrane transport. CpG islands located in the intergenic region accumulated a significant number of variants, which is likely a sign of epigenetic remodeling. We present 20 candidate genes and three putative cis-regulatory elements with various functions most affected by temperature. Our work shows that natural selection towards temperature tolerance is a complex systems biology problem that involves gradual reprogramming of an intricate gene network and deeply nested regulators.

evolutionary biology↗

bioRxiv2024

Single cell autofluorescence imaging reveals immediate metabolic shifts of neutrophils with activation across biological systems

Neutrophils, the most abundant leukocytes in human peripheral circulation, are crucial for the innate immune response. They are typically quiescent but rapidly activate in response to infection and inflammation, performing diverse functions such as oxidative burst, phagocytosis, and NETosis, which require significant metabolic adaptation. Deeper insights into such metabolic changes will help identify regulation of neutrophil functions in health and diseases. Due to their short lifespan and associated technical challenges, the metabolic processes of neutrophils are not completely understood. This study uses optical metabolic imaging (OMI), which entails optical redox ratio and fluorescence lifetime imaging microscopy of intrinsic metabolic coenzymes NAD(P)H and FAD to assess the metabolic state of single neutrophils. Primary human neutrophils were imaged in vitro under a variety of activation conditions and metabolic pathway inhibitors, while metabolic and functional changes were confirmed with mass spectrometry, oxidative burst, and NETosis measurements. Our findings show that neutrophils undergo rapid metabolic remodeling to a reduced redox state indicated by changes in NAD(P)H lifetime and optical redox ratio, with a shift to an oxidized redox state during activation. Additionally, single cell OMI analysis reveals a heterogeneous metabolic response across neutrophils and human donors to live pathogen infection (Pseudomonas aeruginosa and Toxoplasma gondii). Finally, consistent OMI changes with activation were confirmed between in vitro human and in vivo zebrafish larvae neutrophils. This study demonstrates the potential of OMI as a versatile tool for studying neutrophil metabolism and underscores its use across different biological systems, offering insights into neutrophil metabolic activity and function at a single cell level.

immunology↗