Red blood cells (RBCs) are abundant , yet relatively simple, as they lack nuclei and organelles, including mitochondria. Since the earliest days of biochemistry, the accessibility of blood and RBCs made them an ideal matrix for the characterization of metabolism. Because of this, investigations into RBC metabolism are of extreme relevance for research and diagnostic purposes in scientific and clinical endeavors. The relative simplicity of RBCs has made them an eligible model for the development of reconstruction maps of eukaryotic cell metabolism since the early days of systems biology. Computational models hold the potential to deepen knowledge of RBC metabolism, but also and foremost to predict in silico RBC metabolic behaviors in response to environmental stimuli. Here, we review now classic concepts on RBC metabolism, prior work in systems biology of unicellular organisms, and how this work paved the way for the development of reconstruction models of RBC metabolism. Translationally, we discuss how the fields of metabolomics and systems biology have generated evidence to advance our understanding of the RBC storage lesion, a process of decline in storage quality that impacts over a hundred million blood units transfused every year. Approximately 25 out of 30 trillion cells in the human body are red blood cells (RBCs), which make up for >99% of the corpuscular blood fraction . RBCs caWhile the primary purpose of an RBC is to carry oxygen, RBCs contain more than 77 active transporters that can carry metabolites to and from tissues, making them a vehicle for studying diverse physiological processes ,6. As suL. lactis) [Despite the apparent simplicity, proteomics studies of RBCs have determined that the residual <10% of the RBC dry weight beyond hemoglobin is made up of up to 3000 proteins  , with ma lactis) . These oUnderstanding RBC metabolism has also significant applications in transfusion medicine. Over 110 million units of packed RBCs are transfused each year into patients worldwide, making blood transfusion the most common iatrogenic intervention after vaccination . During This review will proceed by discussing relevant RBC metabolic processes, prior work in systems biology in unicellular organisms and RBCs, prior work in deep learning on RBC morphology and metabolic network reconstruction, personalized medicine and systems biology, and potential directions for future work around the area of systems biology of RBCs and personalized transfusion medicine .Ten main metabolic pathways work in concert within a RBC . A colleThe Rapoport–Luebering (RL) shunt is a pathway branching off glycolysis at the triose phosphate step to generate 2,3-diphosphoglycerate (DPG) . By stabThe pentose phosphate pathway  producesMost importantly, NADPH is critical for the conversion of oxidized glutathione to its reduced form  values in each system. In turn, these parameters help dictate the physiological behavior of a system. However, the concept of “one-model-fits-all” is now being refined in light of the results emerging from high-throughput omics data, pushing systems biology into the arena of personalized medicine. Personalized medicine aims to tailor treatments based on individual patients’ biology. By accounting for interpatient variability, personalized medicine aims to improve the outcomes in complex clinical situations [Systems biology combines mathematical models of biological systems executed with computational techniques to create models of biological systems executed in silico . Models tuations . ThroughSystems biology approaches have been previously leveraged to map and analyze metabolic networks in both unicellular organisms and RBCs, demonstrating the versatility of systems biology techniques used to study relatively simple systems ,68,69,70L. lactis produces lactic acid from glucose or lactose via the Embden–Meyerhof glycolytic pathway. One finds glucose, phosphoenolpyruvate (PEP), glucose 6-phosphate (G6P), pyruvate, fructose 1,6-bisphosphate (FBP), 3-phosphoglycerate (3-PGA), and lactate on this pathway [L. lactis produces lactic acid during periods of glucose abundance and, crucially, can survive periods of glucose absence before restarting lactate production. Voit et al. asked how L. lactis does this. They approached this question from two angles. First, using in vivo nuclear magnetic resonance (NMR) spectroscopy of non-growing cells, they monitored the accumulations and depletions of glucose, lactic acid, and intermediates at 30 s intervals after adding a pulse of carbon-13-labeled glucose. Second, they simulated the system with a generalized mass action (GMA) representation of all metabolites along the kinetic pathway. L. lactis uses PEP to phosphorylate glucose to G6P. Hence, they reasoned that if the PEP pool was wholly depleted, lactic acid production could not restart when glucose became available again. Consistent with this reasoning, the NMR observations and simulations showed a pool of PEP built up after glucose depletion. G6P could be produced when glucose was re-introduced to the cells. They performed two more tests (one each with the model and NMR) to test whether this PEP pool found in the experiment and simulation would restart glycolysis. First, they added a second pulse of carbon-13 glucose to the NMR experiment 23 min after the first. Second, they added a second pulse of carbon-13 glucose in the simulation. In both cases, they observed that lactate would accumulate again after the re-introduction of glucose to the system. Taken together, the experiments and simulations pointed to a regulatory system managing the pool of PEP. pathway . L. lactTo explain the accumulation of PEP during periods of glucose starvation, they hypothesized that pyruvate kinase (PK), an enzyme on the pathway, would no longer be activated by FBP and that phosphate accumulation would inhibit PK also. Therefore, PK could no longer turnover PEP to pyruvate. Thus, they reasoned that PEP would accumulate in the pathway. To validate this reasoning, they simulated another model incorporating regulatory feedback  of glucose to pyruvate. The advantage of the simpler model is that it was optimized to study regulation in metabolic networks. When they executed this simulation, they found that PEP would accumulate when the supply of glucose was interrupted. Thus, by using simulation alongside experiments, they tested and validated their hypothesis.L. lactis example above is an example of a simple model of a simple biochemical network in a prokaryotic organism, models can be expanded in terms of scale of metabolic reconstruction and the complexity of the organisms they model. In terms of model complexity, commonly used genome-scale metabolic model models (GEMs) are based on knowledge bases incorporating information about genes, reactions, and proteins within organisms, provide a platform for analysis of omics data [E. coli, yeast, and humans [S. cerevisae, which has recently seen the reconstruction of the GEM Yeast8 [While the ics data , and enhics data . GEMs ard humans . In termM Yeast8 .S. cerevisae have many applications, ranging from uses in bioengineering (where yeasts are used as a cell factory) to uses as a model organism in biomedical research [Studies of research . The recSignificant work has been carried out by many teams to make open-source genome-scale metabolic models (GEMs) of metabolism in humans and other organisms. GEMs provide a framework to study the genotype–phenotype relationship and place the analysis of high-throughput metabolomics data into a larger context . Many ofAs an example of what GEMs include and can accomplish, we will consider the Recon series of models: Recon 1 (published in 2007) , Recon 2Classic studies in the field have originally proposed a three-step approach to systems biology in RBCs. First, they suggested untargeted data collection on RBCs using integrated omics techniques. Next, they suggested using computational models from systems biology to generate hypotheses. They suggested testing these hypotheses with targeted omics approaches as the third step. Finally, these steps could be repeated, and the hypotheses successively refined in subsequent iterations .This iterative approach of combining untargeted data collection, computational modeling and analysis, and experimental verification is essential to ensure that computational models produce the correct output and model the system as intended. Downs and others use the simile of a “three-legged stool” to advance knowledge of biochemical pathways with computational models of metabolism. In their model, the stool consists of standard experimental techniques with mass data collection of data with omics techniques and computational analysis. Taken together, all three approaches lead to robust scientific understanding. However, when any one piece is taken out, they argue that the quality of the science will suffer . SimilarRecognizing the need to provide a framework for the analysis of massive datasets from high-throughput omics studies, Bordbar and colleagues developed iAB-RBC-283, a knowledge base of the metabolic network of the RBC . SignifiIn the iAB-RBC-283 study, the team found that their knowledge base predicted that erythrocytes contained at least 142 known targets of approved small-molecule medications . Later, Nemkov and colleagues demonstrated that a xenometabolite identified in that knowledge base, ranitidine affects RBC resting in blood storage units. In this case, Nemkov et al. posited and observed that, if RBCs were supplemented with ranitidine during storage, the quality of the RBCs would be improved through the up-regulation of glycolysis .Yurkovich and colleagues have proposed using omics data generated by investigations of RBC storage lesions as a platform for systems biology modeling and study in a way that contributes to the body of knowledge in transfusion medicine . In an iTaken together, these studies show how initial observations from machine learning and data analysis can be used to create a computational model which later can inform further targeted experimental work. However, such models do not capture the heterogeneity of RBC metabolism as a function of genetics and non-genetics factors, an aspect that the introduction of high-throughput omics strategies has shed light on over the last five years.According to Redekop and Mldasi, there are three major definitions of personalized medicine . The fir0, Km, and kcat), thereby circumventing parameterization challenges typically associated with traditional kinetic parameters. Km and kcat depend on the exact protein-coding exon sequence among individuals. Hence individualized changes to the coding sequence potentially change the PERC associated with those values for that protein. Indeed, the Bordbar team found a significant correlation between PERCs and minor allele frequencies (MAFs). In further support of their model’s biological relevance, they found timescales of the dynamics on the order of milliseconds to seconds, which are relevant to fast reactions near equilibrium and key parts of RBC pathways. They found that most inter-individual variation was within dynamics on the circulation time scale. Finally, they studied the effects of the antiviral drug ribavirin in RBC metabolic pathways by adding ribavirin kinetic expressions to the individualized models. Using classification trees based on PERCs as predictors, they separated the individuals into RBV responder and non-responder groups. The classification revealed that PERCs for phosphoglycerate kinase and adenine transport could potentially be important mechanisms in RBV-induced hemolytic anemia.As an example of personalized systems biology, Bordbar et al. created personalized kinetic models of RBCs based on metabolomic data from 24 individuals . Their mWhile this review generally focuses on high throughput omics techniques that generate data for large values of metabolites, another interesting direction is studying a small number of metabolites to predict the future trend of values for a larger number of metabolites. There are two advantages of using a smaller number of metabolites as predictors. First, targeted measurements of a small number of metabolites can yield more detailed insights about the system those metabolites are found in. Second, targeting fewer metabolites makes cost-effective measurement techniques more accessible in a wider variety of settings . In anotRBC as a model of all cells can potentially move systems biology into personalized medicine. As a simplified eukaryotic cell, RBCs can serve as a tractable system of study for creating systems biology models. As a cell that travels throughout the body, RBCs can serve as windows onto many systems of clinical relevance .0, Km, and kcat, vary among individuals because of genetic variation that affects the enzymes in question. They suggested a follow-up question necessitating conducting a GWAS study: how does it affect the PERCs [L. lactis with a generalized mass action (GMA) model [Building on the prior work mentioned here, future research paths can potentially involve (1) connecting donor genotypes to the phenotype of markers of storage lesion and (2) modeling the kinetic response of RBC reactions to hypoxic stress. First, as mentioned above, Bordbar et al. found that pseudo-elementary rate constants (PERCs), which encapsulate Ehe PERCs ? BuildinA) model . An analA) model . Multipl


Bacillus subtilis is an effective workhorse for the production of many industrial products. The high interest aroused by B. subtilis has guided a large metabolic modeling effort of this species. Genome-scale metabolic models (GEMs) are powerful tools for predicting the metabolic capabilities of a given organism. However, high-quality GEMs are required in order to provide accurate predictions. In this work, we construct a high-quality, mostly manually curated genome-scale model for B. subtilis (iBB1018). The model was validated by means of growth performance and carbon flux distribution and provided significantly more accurate predictions than previous models. iBB1018 was able to predict carbon source utilization with great accuracy while identifying up to 28 metabolites as potential novel carbon sources. The constructed model was further used as a tool for the construction of the panphenome of B. subtilis as a species, by means of multistrain genome-scale reconstruction. The panphenome space was defined in the context of 183 GEMs representative of 183 B. subtilis strains and the array of carbon sources sustaining growth. Our analysis highlights the large metabolic versatility of the species and the important role of the accessory metabolism as a driver of the panphenome, at a species level. Bacillus subtilis is a biotechnological workhorse for the production of many industrial products, such as enzymes, vitamins, and antibiotics [B. subtilis strains, producing high added value industrial products by means of metabolic engineering [Aside from being the model organism of Firmicutes clade, ibiotics . In receineering ,3. Howevineering . In thisineering ,6. Genomineering . Their iB. subtilis has been subject to significant metabolic modeling efforts, and various genome-scale metabolic network models of B. subtilis are available: iYO844 [iBsu1103 [iBsu1103v2 [iBsu1147 [iBsu1144 [iYO844, was constructed including phenotyping and gene essentiality data. More recently, this pioneer model has been updated by integrating 17 enzyme kinetics for the central carbon and related pathway reactions, significantly improving model predictions regarding growth rate and flux distribution [B. subtilis was constructed using the semi-automatic workflow developed by the SEED Project [iBsu1103 , has been updated several times in the last decade [iBsu1103v2, analyzed and fixed the incorrect gene essentiality predictions of its initial version. Finally, the latest model updates (iBsu1147 and iBsu1144) were aiming to optimize the production of relevant added-value compounds including riboflavin, Egl-237, isobutanol, butanodiol, and serine protease. Despite the efforts in the reconstruction of B. subtilis metabolism, it has been recently shown that the current models are far from accurately predicting well-known metabolic traits of this strain [To date, iBsu1103 , iBsu110ribution . Another Project ,13,14,15s strain . OverallOn the other hand, the increasing number of genomes being sequenced is allowing researchers to address the variability across strains within the same species. As more genome sequences become available, substantial differences in genomic content and functions across strains can be identified . TherefoB. subtilis (iBB1018) based on the annotations of the SEED project [iBB1018 was built based on iBsu1103v2 but subject to a thoroughly manual curation that included the available biochemical and physiological knowledge. The quality of iBB1018 was further validated by means of growth performance and carbon flux distribution providing significantly more accurate predictions than previous models. Overall, iBB1018 predicts the carbon sources tested with high precision (84%), while it identifies up to 28 metabolites as potential novel carbon sources, thus significantly expanding the known metabolic versatility of B. subtilis. Furthermore, this updated B. subtilis GEM provides an essential tool to study pan-metabolic capabilities, thus providing clues about the metabolic range of Bacillus subtilis as a species. Subsequently, we provide here a multistrain metabolic model of B. subtilis.In this work, we develop a new and high-quality genome-scale model for  project . iBB1018iBB1018 (iBsu1103v2 following a procedure that includes three iterative steps (iBsu1103v2 model. SEED nomenclature was then converted to BiGG annotations using the platform MetaNetX as an intermediate while genes ID were updated according to NCBI (BSU) nomenclature.The genome-scale metabolic model iBB1018  containsve steps . FirstlySecondly, we carried out an exhaustive revision and manual curation of all the reactions based on the analysis of each Gene–Protein Reaction (GPR) association included in the draft. Therefore, each individual GPR was evaluated based on physiological evidence and information allocated on several databases, such as KEGG , MetaCycFinally, in a third step, we applied a manual gap-filling analysis in order to complete several biosynthetic pathways required to synthesize elements from the biomass reaction. This process was profusely guided by the information available in biological databases and the scientific literature. For instance, it was necessary to add the transporter reactions for D-arabinose, galactitol, and sucrose due to a lack of genes associated with these reactions in the initial draft model.iBB1018 in comparison with the highly curated model iYO844 was highlighted by means of a Clusters of Orthologous Genes (COG) analysis , as previous iBsu1103v2 and iYO844 models did  can predict experimentally reported metabolic fluxes with more accuracy . The iBsu1103v2 model shows, as expected, the lowest correlation values  to 7044  (B. subtilis BEST7613 was identified as a chimera genome harboring an important fragment of the genome of the cyanobacterium Synechocystis strain PCC6803 [The pangenome of 184 EST7613) . Interes PCC6803 ; thus, iB. subtilis GEMs constructed. Subsequently, we used the estimated pangenome to compute the panphenome of B. subtilis. For this, we filtered metabolic genes using the iBB1018 and BiGG database as a source of metabolic functions (Methods) and, subsequently, the list of metabolic genes for each strain was reduced to those with any known metabolic function. Interestingly, we found a more homogeneous number of genes in each strain (between 900 and 1200 genes), which strongly suggests that metabolism is highly conserved in contrast to other functionalities. The computed panphenome was finally used as basic metabolic information to construct strain-specific GEMs for each strain analyzed. The GEMs were automatically gap-filled followed by semi-manual curation to include metabolic information other than that included in the core panphenome. A total of 183 strain-specific GEMs were constructed, covering 2324 of the 13,398 gene families present in the calculated pangenome (17.3%). Most of the sequences not included into the GEMs were not annotated or failed to show any known function (35%).Pangenomes make reference to the entire set of genes from all the strains within a clade. Therefore, we defined the core pangenome as that including those genes present in a high percentage of strains (>95%), while the accessory pangenome was defined as that representing unique or shared functions by a reduced group of strains . While tB. subtilis, strain-specific GEMs were used to compute those carbon sources supporting growth in the analyzed strains.  High Metabolically Versatile (HMV) strains and (ii) Low Metabolically Versatile (LMV) strains (B. subtilis strain PS38 (CP016789) and PG10 (CP016788), were subject to synthetic genome reduction in the context of minimal genome identification [To validate this multistrain modeling and gain further insights in the phenotypic features of strains. . This an strains . HMV strfication , therefoB. subtilis strains. Interestingly, such carbon sources provided the highest growth rate computed, strongly suggesting optimal carbon sources for B. subtilis irrespective of the particular strain being analyzed. A second group of carbon sources that included additional mono and polysaccharides, amino acids, nucleotides, and organic acids were used for a large number of B. subtilis strains, constituting a second core carbon source for this species. Finally, we identified an important variety of carbon sources providing growth to only a low number of strains (less than 50); therefore, we consider these compounds as strain-specific carbon sources.Regarding carbon sources, we identified a group of selected monosaccharides, oligosaccharides, and polysaccharides, such as xylose, fructose, and starch, as universal carbon sources (>90% strains with growth) that were able to sustain the growth of most of the Bacillus subtilis is an important microorganism used for biotechnological processes which have triggered a large effort of metabolic modeling in order to construct accurate and predictive computational platforms towards biotechnology endeavors. However, available in silico GEMs of B. subtilis are far away, in term of completeness and predictive potential, from equivalent models of other well-known and widely used microbial cell factories such as Escherichia coli [Pseudomonas putida [Saccharomyces cerevisiae [B. subtilis (iBB1018). iBB1018 presents higher metabolic completeness and predictive capabilities than previously published B. subtilis GEMs, and scores as the highest-quality GEM when analyzed with the MEMOTE tool [B. subtilis metabolism exhibiting a high grade of accuracy in terms of carbon source utilization, growth rate, and flux distribution predictions under various nutritional scenarios.hia coli , Pseudoms putida , and Sacrevisiae . In thisOTE tool . The finE. coli was the first species subject to multistrain modeling following its high number of GEMs. An initial study by Monk and colleagues addressed the modeling of 55 fully sequenced E. coli strains identifying strain-specific auxotrophies and substrate preferences among the set of strains [E. coli strains and demonstrated a large variability both in gene content and function within the species. The computed panphenome was further used to construct a robust classification tree for determination between extra-intestinal and intra-intestinal pathogens [E. coli example, multistrain modeling has been applied to compute the panphenome of other bacteria having high-quality GEMs available. For instance, 450 Salmonella strains were modeled from various serovars to show that metabolic capabilities can be used to discriminate these serovars [P. putida were subject to multistrain modeling, showing that the large metabolic versatility described for many P. putida strains is a common feature of the whole species [Once a high-quality GEM exists, its contents  can be straightforwardly mapped onto a closely related strain in a species. Following this, a multistrain approach can be integrated with genome-scale modeling in order to gain novel insights underlying the variability of phenotypes. This approach enables panphenome analysis, thus empowering species-wide comparative systems biology that has been applied to well-known species in a variety of studies, mainly involving the assessment of metabolic potential.  strains . Extendiathogens . Followiserovars . Additio species .B. subtilis, its multistrain modeling has remained elusive dues to the lack of high-quality GEMs to be used as guide. Here, by using iBB1018 as a high-quality template, we have addressed the modeling of 184 B. subtilis strains whose genomes were available at the time this study started. Overall, we were able to construct a total of 165 functional GEMs which define a large panphenome space in terms of a carbon source supporting growth  and the metabolic content from model iBsu1103v2 based on SEED annotations. The draft model was converted to BiGG annotations through MetaNetX [fillGaps to ensure that the model could grow in silico. Moreover, a manual gap-filling step was performed in order to connect the network as much as possible and remove potential inconsistencies or close death-end metabolites. Finally, it was confirmed that all reactions were balanced, and all metabolites were annotated with their charge and location .truction ,49. An iMetaNetX . The finMetaNetX , MetaCycMetaNetX , BRENDA MetaNetX , and BiGMetaNetX . TranspoMetaNetX . To imprA robust correlation method, biweight midcorrelation  implemenTo compare the performance between the three models, MEMOTE  was execiBB1018 and iYO844 models were linked to COGs by using the NCBI COG database version 2020   . The gen−1) predicted under the minimal M9 media, as previously described [−1 h−1 to avoid limitations. Regarding carbon sources, growth rate was further normalized considering the number of carbons of each compound in both oxic and anoxic conditions. The COBRA ToolBox v.3.0 [Flux Balance Analysis (FBA) was used to evaluate the biomass production and to predict flux distributions once the biomass reaction was fixed as the objective function . The foundations and applications of FBA have been reviewed elsewhere . The groescribed . All theox v.3.0  running B. subtilis were downloaded from the NCBI database (488 downloaded assemblies). Only the complete assemblies (NCBI completeness class) with gene annotation and proteome availability were used for this analysis. To reduce redundancy, only non-redundant genomes at the proteome level were selected, with 184 strains in total. The final genome dataset contained 184 genomes of B. subtilis. To perform multistrain models, the model developed for this work was used as a template and, following the protocol designed by Norsigian [All assemblies of diverse strains of orsigian , the modB. subtilis 168 was 70%, but this threshold was relaxed when the number of genes of strains with a percentage of homology over 40% was present in more than 75% of strains. In this case, the threshold decreased to 40% of homology.Multi-gap filling was run over the single-strain models without growth in any carbon source to identify essential genes from the metabolic information, with the results combined with the homology results. For this analysis, the minimum percentage of homology used to mark a gene as an ortholog of The genes without homology against the template were compared to the total BiGG gene datasets to identify possible homologs in other species and models. The involved reactions were included in the final models.B. subtilis strains GEMs were driven by the FBA implementation of COBRApy v0.20. The uptakes were normalized based on the number of carbons. The code and the multistrain GEMs can be accessed in the following GitHub repository (https://github.com/SBGlab/Bacillus_Subtilis_multistrain_GEM).The carbon source analysis over the


Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux  to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux’s capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types. Metabolic reprogramming is a common indicator of the tumour microenvironment. Here the authors develop the METAflux framework to predict metabolic fluxes from single cell RNA-seq data. Clinical studies have demonstrated that metabolism is associated with patient outcomes and that specific metabolic phenotypes could present vulnerabilities to cancer treatment4. For example, upregulation of fatty acid oxidation (FAO) has been shown to fuel acute myeloid leukemia (AML) venetoclax with azacytidine (ven/aza) resistance and inhibition of FAO may restore the efficacy of ven/aza treatment5. A recent study shows that dysregulated propionate metabolism increases the metastatic potential for breast and lung cancer6. Therefore, understanding how metabolic dysregulation promotes cancer is key to treating it.Metabolism is essential for proper cellular function. Cancer cells harboring aberrant genetic alterations such as amino acid substitutions and copy number alterations, often exhibit distinct metabolic programs from normal cells7. Recent advancement in MALDI-MS has also enabled metabolomics profiling with around 100 molecules detected at 5–10 um resolution9. However, the process of metabolite identification for metabolomics is still low throughput, requiring considerable time and effort to analyze the datasets10. Moreover, reproducing results is challenging due to complexity of the experiments and a lack of methodology standardization11. In addition, metabolomics only provides static snapshots, missing dynamic profiles of metabolite trafficking, velocities of metabolic reactions (a.k.a. fluxes), etc., which are critical to understanding mechanisms of metabolic regulation.Over the past two decades, technological innovations have allowed for detailed characterization of metabolic alterations. These established techniques, such as metabolomics, stable isotope tracing, and XF Extracellular Flux Analyzer, have facilitated discoveries in metabolism from different perspectives. LC/MS (liquid chromatography/mass spectrometry) based metabolomics is a powerful tool for measuring concentration of metabolites and is often the first choice of metabolic profiling13C metabolic flux analysis (13C-MFA) and Seahorse Extracellular Flux (XF) analyzer, have become widely used in metabolic research10. 13C-MFA is the current gold standard in measuring intracellular fluxes of central carbon metabolism, while the Seahorse Extracellular Flux (XF) analyzer is the benchmark for assessing cells’ extracellular bioenergetic state. Although 13C-MFA offers valuable insights, it has limited usage in deriving large metabolic networks12. Seahorse Extracellular Flux (XF) analyzer can measure OCR  and ECAR (extracellular acidification rate: an indicator of glycolysis) of living cells simultaneously in real-time13. Even though the Seahorse platform provides valuable insight into the functional status of cells, fluxes of other metabolites are not measured. For in vivo metabolic assessment, approaches such as positron emission tomography (PET) are used. These include techniques like 18F-fluorodeoxyglucose (FDG) PET and 18F-Glutamine14. In addition, in vivo stable isotope tracing has emerged as a novel approach, utilizing stable isotope-labeled nutrients to investigate metabolic activity within intact tumors15. Notably, these methods are limited to probing specific subsets of metabolic reactions14.Consequently, metabolic flux techniques, for instance, 16, consisting of a dynamic mixture of malignant and non-malignant cells. As a result, traditional cell culture nutrient milieu does not accurately resemble human physiological nutrient environment18.Beyond limited scales, substantial experimental challenges exist in studying cancer metabolism in culture. It is difficult to accurately mimic a complex metabolic environment20. Given the challenges of metabolic profiling, transcriptomic analyses have been utilized as a surrogate to reveal metabolic reprogramming and vulnerabilities in tumors24. Those studies often employ statistical methods that score mRNA expression levels of genes in a predefined  metabolic pathway. Several scoring methods exist, for example, ssGSEA25, Seurat AddModuleScore26, AUCell27, singscore28, Z-score, etc. However, these methods cannot be applied at reaction level because they become unstable for metabolic reactions involving a few  genes29. Moreover, they cannot be applied to non-enzymatic reactions. For example, previtamin D3 forms vitamin D3 via spontaneous reaction30. More importantly, these methods examine each pathway disjointly, ignoring the fact that metabolic networks are highly connected and dynamic31.The advent of RNA-seq and scRNA-seq provided an opportunity to systemically interrogate the transcriptomic profiles of biospecimens at tissue and cellular resolution32, have been utilized. One of the most common analytic methods used in GEMs is flux balance analysis (FBA), a well-established constrained optimization method that estimate flow of metabolites in a complex bio-system33. It maximizes or minimizes the flux of a particular reaction or a linear combination of reactions, under steady-state assumptions and flux bounds constraints. Versions of FBA have been successfully applied in various settings37.To interrogate the entire metabolic circuits, genome-scale metabolic models (GEMs), which encapsulate an organism’s stoichiometrically balanced metabolic reactions via gene-protein-reactions (GPR) association49. A few varieties exist, one of which is to define an objective function that includes gene expression levels. For example, Lee et al. characterize the biological objective function based on correlation between fluxes and gene expression levels43. Similarly, iMAT uses gene expression levels to dichotomize highly expressed and lowly expressed reactions and finds the flux distribution that best explains gene expression patterns. It maximizes the number of reactions classified as highly expressed and minimizes the number of reactions classified as lowly expressed38. Promising efforts have been made to predict COVID-19 metabolic targets and changes using iMAT on scRNA-seq data51. Another way to incorporate gene expression data into FBA is to define flux bounds in FBA using expression levels. For example, E-Flux uses transformed gene expression levels as flux upper and lower bounds45. Also, Damiani et al. estimate single-cell fluxomics using modified transformed expression values as constraints52. Although studies have demonstrated the utility of combining gene expression and FBA in assessing metabolic states, many do not directly produce unique flux distributions, nor include biologically meaningful constraints that account for nutrient exchange or competition among cell types in the TME. Moreover, systematic validation of in silico estimation is largely lacking.To apply FBA on gene expression data, gene expression levels need to be systematically interpreted in the context of metabolic networks. Previous studies have shown such exercises could lead to rational estimation of metabolic fluxeshttps://github.com/KChen-lab/METAFlux.In this work, we introduce METAFlux, a computational tool that predicts cancer metabolic fluxes from bulk RNA-seq and scRNA-seq data to address these analytic gaps. METAFlux is capable of characterizing the entire metabolic circuits and output non-degenerative fluxes using cancer gene expression data in a nutrient-aware manner. For scRNA-seq data, METAFlux additionally examines metabolic heterogeneity and interactions amongst cell types in TME. We evaluate METAFlux prediction accuracy using the matched flux data generated on the NCI-60 cell lines and find a substantial improvement over existing approaches. We further examine METAFlux on scRNA-seq data obtained from an in vivo Raji-NK cell co-culturing model and observe high consistency between the predicted and experimental  flux measurements. METAFlux only requires gene expression data as input and is customized to fit binary experimental conditions (nutrient presence vs absence). METAFlux can predict 13,082 reaction flux scores for each bulk sample, and for each single cell data, it can predict  reaction flux scores. Because of the wide availability of RNA-seq and scRNA-seq data, METAFlux can dramatically improve our understanding of metabolism in disease samples at both tissue and cell-type levels. Results produced by METAFlux can serve as a resource to identify metabolic targets for precision medicine. The open-source implementation is available at 53. It contains 13,082 reactions and 8378 metabolites53. We choose Human1 because it shows a considerable improvement over other GEMs in terms of stoichiometric consistency, percentages of mass, and charge-balanced reactions53.METAFlux utilizes Human1, a genome-scale metabolic model (GEM) that encodes the mechanistic relationships between genes, metabolites, and reactions in a human cell. Human1 integrates the Recon, iHSA, and HMR models53. We next apply convex quadratic programming (QP) that simultaneously optimizes the biomass objective and minimizes the sum of fluxes’ squares, similar to a previous approach54 (Methods). We also propose a workflow for single-cell data, where the whole TME is modeled as one community to account for metabolic interaction between the groups with the whole community biomass optimized, since we believe cell groups strongly influence one another in TMEs  for each reaction, which describes the reaction activity as a function of the associated gene expression levels Fig. , MethodsMEs Fig. , Methods56. We selected 11 cell lines for evaluation, as other cell lines had nutrient depletion that could affect reliability of flux profiling58. Each cell line had 26 experimentally measured metabolite fluxes and one biomass flux.We benchmarked METAFlux using the NCI-60 RNA-seq data and matched metabolite flux datacell line culture medium composition . In all the 11 cell lines, METAFlux performed better Fig. . Across 61. These TNBC samples had matched gene expression and Standardized Uptake Value (SUV) data, with the SUV representing a measure of glucose uptake derived from the radioactivity concentration in tissue and the injected dose of radioactivity per kilogram of the patient’s weight. We analyzed these samples under a human blood profile medium  between the predicted glucose uptake and the measured SUV  using FDG-PET data62 (N = 8) using a human blood profile . Zero P-values were obtained for both the overall Spearman correlation and the directionality accuracy, indicating that the results were unlikely to have been obtained by chance . We examined glucose uptake flux (GUF) predicted by METAFlux from the LUSC and the LUAD RNA-seq data, respectively. The results indicated that LUSC tumors had a higher GUF than did LUAD tumors, which is consistent with the 18F-FDG PET-CT (18F-fluorodeoxyglucose positron emission tomography) scan results  with proliferation signature scores obtained in an independent study64  and TCGA LUSC (lung squamous cell carcinoma) data using P values < 0.0001), implying that LUSC-like LUAD tumors are metabolically more active  than ‘LUSC-like’ LUADs  , which is consistent with an earlier study, indicating an association between bile acid metabolism upregulation and worse prognosis in LUAD66.Clustering METAFlux metabolic flux profiles revealed two clusters of LUAD and LUSC samples Fig. . The LUAive Fig. . SurvivaNotably, such a result cannot be obtained from the metabolic gene expression data, without applying METAFlux assessment , while glucose uptake was positively associated with hypoxia score . Lactate secretion was also positively associated with hypoxia score . These results align with hypoxia-mediated metabolic reprogramming and demonstrate METAFlux’s capacity to accurately capture hypoxia-associated metabolic adaptations.Subsequently, we regressed METAFlux features against the hypoxia scores: entclass1pt{minima70, flow-sorted into immune cells (CD45+ EPCAM−), endothelial cells (CD31+ CD45− EPCAM−), tumor cells (EPCAM+ CD45− CD31−), and fibroblasts (CD10+ EPCAM− CD45− CD31−). After data processing (Supplementary Methods), we had 15 lung adenocarcinoma (LUAD) and nine lung squamous cell carcinoma (LUSC) samples with complete RNA-seq profiles for all four cell types. We used the cell type proportions calculated using CIBERSORT in the original study70 as input parameters to run METAFlux.Different cell-types in a TME can have distinct metabolic programs. Such heterogeneity can be revealed by applying METAFlux on sorted RNA-seq data. We applied METAFlux in a community-based setting (Methods) on data acquired from primary lung cancer patientsP-value = 0.058, 95% CI of effect size , two-sample t-test)  . However, a significant difference emerged in immune cells , indicating that immune cells may not be deprived of glucose in the TMEs of the LUSCs compared with those in the LUADs. Alternatively, it can also suggest that a specific subtype of immune cells may drive glucose uptake in a population of immune cells. Moreover, the metabolic profiles of immune cells, which typically elevate upon activation, could be significantly influenced by variations in their activation states.Comparing the resulting predicted total glucose uptake between the LUSCs and the LUADs, trends consistent with the bulk data were found: LUSCs had higher overall glucose uptake than the LUADs (st) Fig. , while t71. While tumor cells appeared to dominate across the TMEs, immune cells also contributed large fractions in citrate and 2-hydroxybutyrate, especially in LUSC. Citrate metabolic pathways, connecting with lipid and glucose metabolism, are known to play crucial roles in regulating immune cell functions72.To gain insights at cell type level, we multiplied the per cell nutrient uptake flux by corresponding cell type abundance calculated using CIBERSORT   Fig. . Taken t73. We excluded cell types with an average proportion of less than 5%. A total of 25,536 cells, including B cells, epithelial cells, myeloid cells, and T cells, were used to construct a single-cell metabolism map using METAFlux community setting  on average. Myeloid cells had the highest level of glucose uptake, followed by B cells, T cells, and epithelial cells .To further deconvolute the TME and assess METAFlux, we examined a single-cell LUAD dataset containing seven patients with lymph node metastasis samples Fig. 73. We exing Fig. . Except ing Fig. . This fiity Fig. . OverallOur study also revealed a complex metabolic interplay with cooperation and competition relationships in metastatic LUAD Fig. . Serine,P value < 0.0001, Cuzick trend test), while Seurat AddModuleScore results revealed a trend of Myeloid cells > Epithelial cells > T cells > B cells . Correlating glycolysis pathway scores with METAFlux predicted glucose uptake level for seven patients, we found that only Seurat AddModulescores had good consistency with METAFlux for epithelial cells  gene expression levels. We observed that GLUT1 (SLC2A1) is expressed mainly in epithelial cells and GLUT3 (SLC2A3) expressed mostly in immune cells, particularly myeloid cells  with glucose uptake in epithelial cells, and GLUT3 expression correlated with (Spearman correlation ρ = 0.39) glucose uptake in myeloid cells  with glucose uptake in epithelial cells, which implies that it could potentially mediate glucose metabolism in epithelial cells. Overall, METAFlux analysis revealed metabolic heterogeneity among various cell types in LUAD patient cohorts and nominated metabolic genes or reactions that could be further examined in functional studies.We further investigated if the predicted glucose uptake could be substantiated by glucose transporter  cells at four time points: day 7, day 14, day 21, and day 28.To further examine METAFlux’s applicability in studying cancer immunotherapy, we analyzed scRNA-seq data generated from an in vivo experiment of engineered CAR-NK cells with Raji, a non-curative CD19+ lymphoma cell-line in immunocompromised NSG mice78 two hours after coculturing NK and Raji cells (N = 3). More details regarding the Seahorse assay protocol can be found in our previous study76. We found a highly consistent trend between METAFlux prediction and Seahorse results  using cell type proportions estimated from the scRNA-seq data. The METAFlux prediction at Day 7 indicated that the three NK cell products interacted with tumor cells via competition, cooperation, or joint release of a variety of metabolites Fig. . Among tlts Fig. . For baslts Fig. . Intereslts Fig. .Fig. 6CA76.We further examined the metabolic changes over time under the CAR19/IL15 product. We found that the PCCS for oxygen and glucose decreased from day 7 to day 14 but ramped up after day 14 and peaked on day 28 Fig. . Similar79. Overall, our findings indicated that metabolic competition in TME likely contributed to tumor resistance and relapse and that METAFlux provided mechanistic insights on specific metabolites and cell-types that cannot be readily measured via experimental means.We also observed that tumor cells increased lactate production in the TME over time, which peaked on day 21 and day 28 Fig. . SimilarDespite advances in new technologies, performing comprehensive metabolic profiling is still limited by coverage, robustness, and cost. Although computational methods have been developed to characterize metabolic traits from RNA expression data, the reliability of these methods is unclear, particularly when applying to single-cell settings. In this work, our method METAFlux appears as a viable solution that enables a broad spectrum of metabolic flux characterization from bulk, flow-sorted and single-cell RNA expression data, in a medium and TME community-aware manner, which has led to deeper insights on metabolic subtypes of cancer patients, metabolic heterogeneity in TME, and mechanisms of tumor resistance in immunotherapeutic settings. Of particular interest, METAFlux enables characterization of metabolism architypes and heterogeneity at cell-type resolution in various cancer datasets under a community-based model, which has not been possible before using bulk data and existing modeling methods.16. However, an avenue toward developing metabolism-based drugs is very challenging, partly because of the shared metabolic pathways among cancer cells, immune cells, and normal cells. Targeting specific metabolic pathways can often inhibit tumor growth but also derail immune cell function. In this regard, METAFlux holds great promise in exploring clinically differential metabolic targets, because it can account for interaction dynamics  among multiple cell types residing in TME, aiding the process of discovering differential or coordinated metabolic responses.We expect that METAFlux will enhance hypothesis generation and validation from perspective of metabolomics and facilitate translation of new therapies into clinical trials. The recent pioneering discovery of JHU083, a glutamine-antagonizing drug that can suppress tumor growth and promote anti-tumor T cell function, showcases the great importance of disentangling the metabolism of cancer cells from that of immune cellsAlthough the strengths of METAFlux have been successfully demonstrated, some limitations remain. First, the predicted glucose uptake does not correlate as consistently as other metabolites in NCI-60 panel. This discrepancy potentially results from the limitations of the cell-line models which do not accurately encapsulate the multifaceted nature of glucose metabolism. Alternatively, it suggests that the methodology implemented by METAFlux need improvement to fully capture the intricacies of metabolic processes under certain scenarios. Second, METAFlux does not distinguish different levels of nutrient concentrations and only utilized binary values (presence vs. absence) in specifying medium constraints. Although this feature can be advantageous when detailed experimental parameters are not available, there can be situations where concentrations or fluxes of certain nutrients vary in wide ranges and can significantly affect accuracy of the results. Thus, improving the approach so that it can further leverage the granular medium conditions may be required to further improve the prediction accuracy.biomass_human from Human1), which encompasses various cellular components for cellular proliferation including, for example, proteins, lipids, small metabolites, etc. This formulation has been proven to be a fairly accurate characterization of generic human cell composition and demand53. However, it may be more accurate to employ a cancer-specific biomass formulation, assuming tumor cells are the ones driving biomass growth in a TME.Third, current objective function of METAFlux maximizes the flux of generic biomass reaction , the elapsed time was 17 minutes, and the maximum memory usage was 12 GB. If widely used, METAFlux can enable systemic, accurate quantification of cancer metabolism in numerous studies and generate insights on metabolic heterogeneity and mechanisms across patients and cell types, potentially driving discoveries for cancer therapy.53. Reactions are across nine compartments .Human1, available publicly, contains 13,082 reactions and 8378 metabolites81, to map the relationship between gene products and then summarize a total of 3625 metabolic related gene expressions into Metabolic Reaction Activity Scores (MRAS) given the predefined relationships. We do not consider reaction kinetic constants and binding affinity of proteins, since it is difficult to robustly estimate all these parameters in genome-scale modeling82. Our approach is adopted from what has been proposed earlier to infer activity of metabolic reaction from gene expression data83.We use GPR rules, which decode Boolean logic relationship between genes in a reaction84. For a reaction catalyzed by an enzyme complex, all the subunits need to be expressed to catalyze a reaction, and the lowest expressed unit will be the rate-limiting step for this complex. Therefore, the metabolic activity of such an enzyme complex will be the lowest expression value among all genes associated with this enzyme complex. For a reaction catalyzed by isoenzymes, all the isozymes contribute additively to this reaction83. Thus, metabolic activity will be the sum of all expressions of isoenzyme genes. Some genes are involved in multiple reactions , and we hypothesize that there may be enzyme resource competition may exist between reactions. We adjust for the enzyme promiscuity by dividing the expression value of a gene by the number of reactions the gene has participated in. A similar approach has been shown in85. The steps of deriving In GPR, the AND operator joins the genes encoding for different subunits of the same enzyme, and the OR operator joins the genes encoding for isoenzymesk participates in for the jth reaction and i, thenLet 45, where the expression levels of the genes associated with a metabolic reaction serve as the maximum possible flux that the reaction can carry. The rationale is that, although enzyme activities do not have a high correlation with RNA levels, given a specific level of translational efficiency and assuming there is a limited accumulation of enzymes over a certain time window45, RNA expression levels can be used as the maximum amount of protein products available, which can then serve as the maximum reaction fluxes.To connect transcriptome and fluxes, a possible solution is to use the MRAS calculated before to define the flux constraints. We use an approach similar to E-fluxWe set normalized MRAS as the flux upper bound to their corresponding metabolic reactions. The lower bound of reaction flux is set zero, if the reaction is non-reversible and The constraints of flux are as follows:S. Here, Stoichiometric matrix is an M by N sparse matrix, where M equals 8378, the number of metabolites in different compartments, and N equals 13,082, the total number of metabolic reactions. The negative coefficient in S matrix refers to the moles of metabolites consumed in a particular reaction. The positive coefficient means how many moles of the metabolites are produced in a specific reaction. At the same time, zero implies that this metabolite does not participate in a specific reaction.A chemical reaction is a basic unit in metabolic pathways, and stoichiometry can represent the quantitative relationship between products and reactants in a reaction. The stoichiometric coefficients of the reactions populate the stoichiometric matrix the cell line culture medium (Supplementary Data File S53. These 44 metabolites include major components from Hams F-12 medium (amino acids and vitamins) and other essential nutrients and ions from serum supplements (Supplementary Methods). At this point, we only offered a binary list of significant metabolites present in the environment. The reason we need to define the metabolite availability is to guide the optimization search in a biologically relevant sub-space. The uptake or secretion rates of these 44 metabolites are not limited. For the remaining metabolites in the model, we do not allow cells to uptake them from the medium but instead, allow cells to secrete them into the medium. For tissue samples from patients, it is necessary to define a more physiologically relevant environment, as the traditional synthetic medium does not mimic human blood. Cantor et al. developed a human plasma-like medium [HPLM] to better capture the composition of human blood, and we derived a list of 64 metabolites in human blood based on their profiling  to overcome degeneracy.Traditional FBA by linear programming (LP) gives a unique optimal objective value. However, the solution to FBA from LP is most likely degenerate, meaning the solutions are not unique, and different solvers will likely return different vectors. Usually, the flux variability analysis (FVA) is used afterward to calculate the range of fluxes that achieves the optimal objectivev with a length of 13,082. The dot product of matrix S and a vector of unknown fluxes v approximation to 0 represents the steady-state assumption, where the metabolite concentrations are essentially held constant x stands for the concentration of all the metabolites.We define single-sample unsolved metabolic fluxes by vector 87 and formulated as the following optimization problem:C is a vector of zeros with a one at the position of our designated biomass reaction, and constraint (9) are the growth medium constraints. a is set to 10,000, the same order of magnitude with respect to fluxes used in Fit methods54. j stands for the G and all exchange reactions E and The framework is implemented using OSQP solverGiven a scRNA-seq dataset, we assign a group (cluster or cell type) label to each cell. We first perform stratified bootstrapping, which means we sample with replacement with respect to each group.Step 1: Bootstrap sample generation. Let group be Step 2: MRAS calculation on bootstrap samples. For each bootstrap sample Step 3: Define group fraction parameter 88. However, most datasets do not have such information. As a result, directly observed cluster (group) fractions in single-cell data could be used, but further studies are warranted to evaluate the findings since those proportions may deviate from the truth due to bias introduced during tissue dissociation90.We require group fractions as our input. Group fractions indicate the proportions of groups of interest with respect to the whole sample. Ideally, these proportions should be retrieved from experiments or calculated from matched bulk data using CIBERSORTxN × 8378 + 1648) × , where N is the number for groups we defined. A specific construct of the merged stoichiometric matrix for three groups is shown below:Step 4: Merging metabolic networks. Let Our model aims to maximize the entire community’s biomass while minimizing the sum square of overall fluxes.87. For each bootstrap sample mean expression vector, it is formulated as follows:Step 5: Flux calculation. This step is implemented using OSQP solverC is a vector of zeros with ones at each cell group’s designated biomass reaction position. a is set to 10,000. j stands for 91. When one or more cell types benefit from the metabolites produced by other cell types, we define the interaction as a metabolic Cooperation program. When all cell types compete for limited resources in TME, we define it as a competition metabolic program. When all cell types secrete certain nutrients, we define it as a Release program.TME is a highly complex mixture, and TME components either form metabolic antagonism or symbiosis when uptaking nutrientsTo estimate the medium effect on model performance, we compare the results obtained from our biologically meaningful medium with same-sized random mediums. The assumed medium contains 44 metabolites, so we randomly select 44 metabolites from the total 1648 exchange metabolites. We then allow our model to uptake or secrete those 44 metabolites without rate restriction, while allowing the rest of 1604 metabolites to only secrete with no rate restriction. We repeat this process The sample size for our study was not predetermined through a statistical method. The sample sizes were primarily decided based on the availability of public data. The number of bootstraps performed was mainly driven by the computational time frame and resources. Detailed protocols for analyzing flux data and gene expression data are described in the Supplementary Methods.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


Additionally, scandium is a group-3 transition metal (like 177Lu) and exhibits affinity for DOTA-based chelators, which have been studied in depth, making the barrier to implementation lower for 43/47Sc than for other proposed true theranostics. Before 43/47Sc can see widespread pre-clinical evaluation, however, an accessible production methodology must be established and each isotope’s radiolabeling and animal imaging capabilities studied with a widely utilized tracer. As such, a simple means of converting an 18 MeV biomedical cyclotron to support solid targets and produce 43Sc via the 42Ca43Sc reaction has been devised, exhibiting reasonable yields. The NatTi47Sc reaction is also investigated along with the successful implementation of chemical separation and purification methods for 43/47Sc. The conjugation of 43/47Sc with PSMA-617 at specific activities of up to 8.94 MBq/nmol and the subsequent imaging of LNCaP-ENZaR tumor xenografts in mouse models with both 43/47Sc-PSMA-617 are also presented.In the field of nuclear medicine, the β In nuclear medicine, the field of theranostics is predicated upon the use of theranostic pairs with a combination of complementary diagnostic and therapeutic capabilities that can be used in tandem. Within a theranostic pair, the diagnostic radiopharmaceutical allows for imaging and an accurate assessment of the state of the disease under study, while the therapeutic radiopharmaceutical delivers the desirable radiation to treat the disease effectively. When conjugated to a tumor-targeting ligand, these radionuclide pairs have demonstrated high efficacy in identifying, staging, and treating patients with high-risk and advanced tumors such as in metastatic castration-resistant prostate cancer (mCRPC) ,2,3,4. T68Ga (t1/2 = 67.71(9) min) and 177Lu (t1/2 = 6.647(4) days) are clinically the most commonly used theranostic pair [131I/123I and 99mTc/90Y [Presently, tic pair ,6,7,8. O2 = 67.71 min) and43Sc (t1/2 = 3.891(12) h) decays by β+ emission with an 88.1% branching ratio, and the remaining decays occur through electron capture. Conversely, 47Sc (t1/2 = 3.3492(6) days) decays entirely through β− emissions. Together, the complementary β+ and β− emissions of 43Sc and 47Sc enable them to serve as a “true” theranostic pair for cancer therapy. The scandium true theranostic pair has several distinct advantages over traditionally used theranostic pairs, such as 68Ga and 177Lu: (1) As both radioisotopes are of the same element, they benefit from identical radiolabeling and chemical processes, making any production methods developed applicable to both nuclides; (2) radiopharmaceutical agents with the same element have identical binding characteristics to the target and in vivo pharmacokinetics, whereas tumor-targeting compounds labeled with nuclides of two different elements may have differential biological, chemical, and metabolic properties. As such, 43Sc positron emission tomography (PET) imaging provides improved fidelity of the magnitude and location of dosage, reflecting more accurately its radiotherapeutic counterpart, 47Sc, while PET images from one positron emitter, such as 68Ga, may not be completely representative of the localization of a β− emitter of a different element such as 177Lu; (3) through the use of longer-lived isotopes, the applications of nuclear medicine can be further broadened through the expansion of the PET imaging window [43Sc’s half-life allows for extended imaging of the physiological and biological effects of this theranostic pair as well as higher-quality images at later time points relative to 68Ga [47Sc emits a 159 keV γ-ray that is near-optimal for single photon emission computed tomography (SPECT) imaging [44Sc has been more commonly investigated and shown promise in the context of Sc-based theranostics, it has a distinct disadvantage in the emission of a high-energy γ-ray  [43Sc provides a better quality of PET imaging and reduced toxicity to adjacent healthy tissue without high energy γ-ray emissions [g window ,11. 43Sc2 = 3.891 h) decay imaging ,14,15. WV, 100%) ,17,18. Tmissions ,20.47Sc using their Low-Energy Accelerator Facility (LEAF) through the 48Ti47Sc reaction using natTiO2 targets, achieving typical yields between 111 and 185 MBq and > 1110 GBq/mg [42CaCO3 targets by the IBA Cyclone 18/9 system’s 9-MeV deuteron beam produced 43Sc through the 42Ca43Sc reaction, providing the foundation for future routine production of 43Sc at other medical cyclotron facilities.The Argonne National Laboratory has previously conducted preliminary investigations into the production of 0 GBq/mg . At the 68Ga and 177Lu, has shown great promise in the diagnosis and treatment of mCRPC with PSMA overexpression, making labeled 43/47Sc an ideal candidate for preclinical studies. These studies will lay the foundation for further studies into potential radioscandium conjugation with other TRT ligands [Small-molecule prostate-specific membrane antigen (PSMA) inhibitor, PSMA-617, in conjunction with various radionuclides, namely  ligands ,6,7,8,1743Sc and 47Sc, label it with PSMA-617 as 43Sc/47-Sc-PSMA-617, and investigate its utilization in a preclinical PSMA-positive prostate cancer model via PET and SPECT imaging studies. The results of this proof-of-concept study, a precursor to the full-fledged development of scandium theranostics, are herein described.This work aims to establish a method to produce 43Sc can be accomplished through various production routes, with the 42Ca43Sc reaction being favorable due to its high yields at beam energies less than 10 MeV and the relative affordability of 42Ca compared to the 43Ca utilized in the 43Ca43Sc reaction [The synthesis of reaction ,22,23,24In this study, an IBA Cyclone 18/9 cyclotron with deuteron capabilities was utilized. While the cyclotron lacks a solid target station, it is outfitted with two beam stops: water-cooled, aluminum, conically shaped stops capable of withstanding the maximum current of the accelerator. To produce the nuclide, one of the beam stops was converted into a solid target irradiation station through the design and fabrication of a solid target holder insert, consisting of a backing aluminum disk (5 mm thickness) with a thin aluminum ring (1.5 mm thickness) held against the disk with screws. The insert had a diameter of 16 mm and was designed to fit snugly into the beam stop to ensure proper cooling see .42Ca powder was pressed and sandwiched between two graphite foils, which were held against the aluminum backing disk with the ring before insertion into the beam stop. A small amount of diffusion pump oil is also applied to the sides and outer edge of the target holder to increase the heat transfer from the target-holder to the water-cooled beam stop. To form the target, 42CaO was prepared through the calcination of 94.37–96.30% isotopically enriched 42CaCO3 at 900 °C. The isotopic composition of the calcium material, along with potential impurities due to irradiation, is provided in 42CaO material was pressed into a cylindrical pellet with a diameter of approximately 7 mm. Target masses were adjusted based upon the expected amount of activity necessary for subsequent experiments and ranged from 4.6–22.4 mg. Targets were stored in a container wrapped in parafilm to minimize CO2 reabsorption.42Ca pellet was carefully slid onto the graphite foil and a second foil was placed over it. Finally, the retaining aluminum ring was set on top of the outermost foil and the M3 screws were threaded into the backing disk to hold the entire assembly together. The excess graphite was trimmed off with a razor blade, the diffusion pump oil was applied to the target body, and the target was inserted into the beam-stop, as shown in Once the pellet had been prepared, the backing aluminum disk of the target holder was placed onto a jig that holds all pieces in place. On top of the disk, a first graphite foil was placed. Then, the The target was irradiated with a 9 MeV deuteron beam for a duration of approximately 8 h. Over several iterations, a variety of beam currents were used, ranging from 1–8 µA. After bombardment, the beam-stop/target-assembly was allowed to cool for one hour before the beam-stop was removed.The target holder was manually removed from the beam-stop. The dose rates of the beam-stop were between 1–2 R/h (10–20 mGy/h) on contact. Typical body doses were 10–25 mR after removal of the target holder and disassembly. The graphite/calcium/graphite layers of the target are easily and quickly separated from the target holder and placed in a PTFE beaker. A summary of target masses and resulting yields are shown in 43Sc activity obtained from the irradiation process, a Raymon10 cadmium zinc telluride (CZT) detector was used to obtain a gamma spectrum of a 962.0 MBq-irradiated target at 12 inches shortly after being removed from the target holder 43Sc reaction were calculated through the numerical solving of Equation (1), where AN is Avogadro’s number, I is the incident particle flux, TA is the atomic weight, λ is the decay constant, t is the beam time, E is particle energy, E0 is the incident particle energy, eE is the exit particle energy, σ(E) is the cross-section, and S(E) is the mass stopping power [Theoretical end-of-bombardment (EOB) yields of ng power .(1)Yiel42Ca43Sc reaction is consistent with the current literature, which shows TENDL to significantly overestimate the reaction’s cross-section in the presently measured deuteron energy range (~3–7 MeV) [Cross-sections were obtained from TENDL simulations in the EXFOR database, as experimentally determined cross-sections are not presently defined for this energy range . Mass st3–7 MeV) .42Ca43Sc cross-section and the interactions of deuterons in calcium oxide. The discrepancy between experimental and theoretical yields may also be the result of the culmination of a multitude of unevaluated uncertainties, such as well-counter accuracy and activity loss originating from the makeshift target holder. The current workflow requirements imposed by the daily routine cyclotron operation did not allow for more detailed measurements of these potential activity losses associated with the non-traditional use of the beam-stop space to accommodate the target holder.Deviation of the experimental yield from the theoretical yield was also seen to generally increase with target mass, as shown in All reagents used in the separation, purification, and radiolabeling processes were trace analysis grade.3. The resultant mixture was placed on a hotplate at 70 °C and with a magnetic stirrer at 180 RPM. While stirring, 4 mL of trace analysis grade water was added slowly over 30 min, followed by an additional 5 min of stirring.Once removed from the target holder and placed in a PFA beaker, dissolution began with the addition of 2 mL of 15.7 M HNO3 and 3 FCV of 1 M HCl. 95 °C 0.1 M HCl was used to elute trapped 43Sc from the DGA cartridge into ten 500 µL fractions as 43Sc-Cl3. Aliquots were assayed, and those of the highest concentration were used for radiolabeling.The dissolved target solution was transferred to a 15 mL Falcon tube and centrifuged at 4000 RPM for 5 min. After centrifugation, the resulting supernatant was extracted with a syringe and loaded onto a preconditioned DGA cartridge (2 mL) for separation ,14,21. T3, 49% of theoretical density) with a hydraulic press by applying 1.5 ton of pressure for 5 min at ambient temperature in a 12.7 mm diameter die. Three pellets with mass of ~1 g were produced.Titanium metal targets  were preThe three Ti targets were stacked in a water-cooled aluminum target carrier and irradiated at the Low Energy Accelerator Facility (LEAF) at the Argonne National Laboratory. NatTi47Sc reaction. The target carrier was placed behind the convertor so that the target was 20 mm from the last converter plate. The targets were allowed to cool for 10–16 h post irradiation and were then transported to a hood for processing. A 40 MeV electron beam at a current of 12.5 µA for 10–12 h irradiated six tantalum plates to produce Bremsstrahlung photons for the 46Sc, 47Sc, and 48Sc were observed in the products. The ratios observed were typically in the range of 0.7–1% 46Sc, 89–91% 47Sc, and 7–9% 48Sc decay corrected to EOB. These impurities are observed in the product’s gamma spectrum in 47Sc maximized by using enriched 48Ti targets: for a 100%-pure 48Ti target, the only radionuclidic impurity would be from 46Sc produced via 48Ti46Sc.Since natural Ti was used as the target material, 43Sc separation, the separation of radioscandiums from the Ti target was completed using DGA resin. A DGA resin was prepared by soaking it overnight in 2.5 M HNO3 and decanting the fine particles. The resin was loaded into a plastic column, compressed with a sintered glass frit, and washed with 10 mL of water, followed by 3 M HCl, then 6 M HCl.The irradiated Ti targets were dissolved in 60 mL of concentrated HCl under reflux. After complete dissolution, indicated by the solution turning a deep purple color, the solution was allowed to cool for 30 min, then transferred to a graduated flask and diluted to 100 mL using washings from the Erlenmeyer flask. Similar to 3 and 3M HCl. A final wash of 1 M HCl was used to lower the pH of the solution and enhance the elution of the product. Over 90% of radioscandiums were eluted with 15 mL of warm 0.1 M HCl. An oversized DGA column bed (0.75 g) was chosen based on the significant mass of Ti (3 g) from which Sc was to be separated. Smaller resin beds in similar separations have eluted the Sc product in smaller fractions [The target solution was gravity-fed to the DGA resin and washed with 6M HNOractions . Optimiz47Sc with DOTA. These values are consistent with high-purity radioscandium products observed in the literature [The product solution was evaporated to dryness on a hot plate. The purity and molar activity were analyzed by inductively coupled plasma–mass spectroscopy and by complexation with DOTA. The product was typically void of common impurities  below the method detection limit (MDL) : typicalterature ,14,27.43Sc to the DOTA chelator on PSMA-617 and minimize colloid formation, purified selected fractions of 43ScCl3 were adjusted to a pH between 4.2 and 5.0 through the addition of an equal volume of 0.5 M NH4OAc (pH 4.5) buffer. Once buffered, DOTA-PSMA-617 was added to the 43ScCl3/43Sc(OAc)3 solution and placed in a thermomixer at 95 °C and 500 RPM for 40 min. The radiolabeling reaction was monitored by spotting the reaction on an iTLC-SG paper developed on a 50:50 mixture of 1 M ammonium acetate and methanol and scanning with a Lablogic radio-TLC scanner , pH 7.4. The final molar activity of each 43Sc-PSMA-617 dose was calculated using a standard calibration curve generated from the UV trace of the reference, cold Sc-PSMA-617, and found to be 7.14 MBq/nmol, although successful labeling was performed up to 8.77 MBq/nmol, with gradual improvement seen each experiment. A representative HPLC chromatogram of the radiolabeled compound and cold trace can be seen in Once successful radiolabeling of >90% was confirmed, labeled activity was loaded onto a C-18 cartridge preconditioned with 6 mL of EtOH and 10 mL of water. The C-18 cartridge was then washed with 10 mL of water, and 47ScCl3 residue, received from the Argonne National Laboratory, was reconstituted in 0.2 mL of 0.01 M HCl  in a 4 mL glass v-vial. The remaining radiolabeling process proceeded as described in 2.3.1 for 43Sc radiolabeling and yielded a specific activity of 8.07 MBq/nmol. A representative TLC and HPLC for 47SC are shown below in The dry 43Sc-PSMA-617 and 47Sc-PSMA-617 prepared in PBS were stability-checked over 24 and 48 h at ambient temperature, respectively, and were found to be stable over that period  of the tumor and muscle tissue corresponding to the contralateral side of the tumor  was found to be 129.16. These findings show that 43Sc-PSMA-617 has a high specificity for PSMA expression and exhibits tumor uptake characteristic of other PSMA-617-based radiopharmaceuticals [43Sc-PSMA-617 is viable in the pre-clinical imaging of prostate cancer.The imaging revealed a significant uptake of euticals ,29. This47Sc-PSMA-617 through a tail vein injection. Two hours post-injection, animals were imaged with a 90-min SPECT scan followed by a CT scan for anatomical information; this is shown in 47Sc-PSMA-617 in the xenografted tumor was observed, with the remaining uptake being non-specific uptake in the renal system [47Sc-PSMA-617 has a high degree of selectivity in PSMA-positive tumors, which is congruent with other PMSA-617-based radiopharmaceuticals [47Sc-PSMA-617 is feasible, and the biodistribution of radiotracer observed shows promise for future therapeutic evaluation.An SCID mouse exhibiting a PSMA-expressing LNCaP-EnzaR-Luc tumor xenograft on the right flank was given 53.7 MBq of l system ,30,31. Teuticals ,29,32. A42CaO pellets for 43Sc production with a deuteron beam. The implementation of the solid target holder involved the precision machining of two aluminum disks, each having a diameter of 16 mm. The first disk was designed to incorporate a shallow well with a depth of 0.05 mm and diameter of 7.1 mm, providing sufficient space for the target material. The second disk was machined into a ring and possessed an outer diameter of 16 mm and an inner diameter of 7.1 mm. This disk had the primary function of securely attaching a thin carbon cover foil to the target holder. Four radially spaced, threaded M3 holes were machined into this backing disk with corresponding clear holes being machined into the ring. Two of these holes were employed for the purpose of joining the bracket and target holder, while the remaining two facilitated the manual retrieval of the target with a long M3 screw.The beam stop of a Cyclone 18/9 cyclotron  was converted into a simple solid target holder to accommodate the use of 42CaO purchased from Isoflex USA had a 42Ca enrichment of 96.30% and was utilized for target synthesis in the first 10 productions indicated in 42CaO containing an enrichment of 94.37% was purchased from Oak Ridge National Laboratory and was utilized in the remaining 43Sc productions. Targets were prepared by transferring the material into a pellet press  and manually pressing each target. After the target was synthesized, two pieces of graphite foil  were prepared by pre-coring two small holes through which the M3 screws passed when joining the target to the holder. The wooden handle (3.175 mm) of a simple swab was used to make the holes. The target was then placed between these two graphite foils on the backing plate of the target holder and then held in place by the retaining ring, which was secured by two M3 screws. Before the beam stop was inserted into the cyclotron port for irradiation, the carbon foil was trimmed, and diffusion pump oil was applied to the target holder backing to improve thermal transfer. After mounting the beam stop with the target holder into the IBA Cyclone 18/9 medical cyclotron and bringing the system to vacuum, targets were irradiated with a 9 MeV deuteron beam at 1–8 µA for approximately 8 h. Targets were allowed to cool for 1 h prior to retrieval to minimize the dose from short-lived nuclides to personnel involved in target retrieval. Once the short-lived nuclides had died out, the target gate valve was closed, and the target was vented. Immediately after venting, the beam stop was removed and two long M3 screws were inserted into the empty screw holes of the target holder for manual retrieval of the target.Argonne houses a variable-energy with variable-power L-band (1300 Hz) electron linear accelerator with a maximum energy of 53 MeV with an average beam power of up to 25 KW at 30 MeV. It can produce steady-state pulses that are continuously variable from 200 ns to 5.5 µs. The maximum repetition rate is 210 Hz. The peak current for the microsecond pulse can achieve 2 amps. The effective beam energy range is from 20–45 MeV. Bremsstrahlung photons are generated by bombarding a high “Z” converter with the incident electron beam. A Ta converter was employed for these experiments. Briefly, six Ta plates (each 0.5 mm thick) spaced ~1.0 mm apart were enclosed in an aluminum carrier with cooling channels. The convertor was cooled with 0.16 L/s flow of water directly over the plates.In a representative case, three pressed metal powder pellets were prepared by pressing natural Ti metal powder into 1 g 12.7 mm diameter pellets with a hydraulic pellet press (1 ton for 5 min). The three 1 g pellets were stacked and loaded into a water-cooled aluminum target holder. The target holder was placed behind and proximal to a converter and this system was irradiated for ~10–12 h with beam energies of 40 MeV and ~2.5–3 kW beam power using a beam spot of 6 × 6 mm. The temperature of the target holder was monitored and kept below 200 °C throughout the irradiations.47Sc stock solution was sampled for gamma-ray analysis and inductively coupled plasma mass spectroscopy utilizing an HPGe detector  and NexION 2000 ICP Mass Spectrometer , respectively. Gamma peak analysis was performed using GammaVision version 7.02.01 software. ICP-MS analysis was performed with Syngistix Version 2.5.The targets were allowed to decay for ~12 h prior to retrieval. The Ti pellets (~3 g) were dissolved in concentrated HCl (60 mL). The solution was refluxed with a condenser for 1 h, whereupon the solution became dark purple/blue. The solution was allowed to cool to room temperature, then transferred to a 100 mL graduated flask, and diluted to the mark with water. This 47Sc was isolated from the Ti target material using a gravity-fed DGA resin column (0.75 g). The DGA resin  was primed with 6 M HCl and then loaded with the 47Sc stock solution. The column was washed twice with HCl , once with HNO3 , and a final HCl  wash. The 47Sc was eluted with warm HCl . The product fractions were combined and evaporated to dryness. The dry residue, containing 47Sc, 46Sc, and 48Sc, was packaged and shipped to the University of Chicago for testing.The 47Sc was determined by complexation with varying amounts of DOTA. Five vessels were labeled as D0, D1, D1, D3, and D4. Each tube was loaded with ammonium acetate buffer . DOTA was added to each tube from a standard DOTA stock solution to provide 16.3 µg, 1.63 µg, 0.163 µg, 0.0489 µg, and 0.00163 µg of DOTA to the D0, D1, D2, D3, and D4 tubes, respectively. These solutions were spiked with 47Sc (3.7–7.4 MBq) and then incubated at 90 °C for 30 min. Each tube was spotted on two separate silica get TLC plates, which were developed using an EDTA (50 mM) or NH4OH (10% w/v)/methanol solution (1:1).The apparent molar activity (AMA) of the isolated 43Sc/47ScCl3 was reconstituted in 0.01 M HCl and pH was adjusted to 4.0 by 1 M ammonium acetate buffer to a final buffer concentration of 0.5 M. DOTA-PSMA-617 was added and the reaction was mixed for 45 min at 95 °C on a thermomixer at 450 rpm. The resulting 43Sc/47Sc labeled ligands were then trapped on a preconditioned C18 SPE cartridge . The column was washed with water. The purified product was eluted with ethanol, dried and reconstituted in PBS for cellular and animal studies.Characterization of the synthesized probes was done by iTLC-SG  and HPLC  equipped with a Flow-Ram radioHPLC detector . Typically, a 50% MeOH solution in 1 M ammonium formate is used to develop the iTLC papers and then evaluated with a Scan-Ram radioTLC scanner .2. In addition, the maintenance medium for the LNCaP-EnzaR cells contains 10 µM enzalutamide  and 300 µg/mL hygromycin B .PSMA-expressing parental LNCaP or the luciferase-expressing enzalutamide-resistant LNCaP-EnzaR-Luc cells were used . All hum6 luciferase-expressing LNCaP-EnzaR were resuspended in 35 µL Hanks Balanced Salt Solution (HBSS)  and mixed with 115 µL Matrigel  and inoculated subcutaneously into an immunocompromised 8–10 week-old SCID or athymic nude mouse  [43Sc/47Sc-PSMA-617 imaging.Animal experiments were performed in the context of an IACUC-approved animal protocol (P.I. Szmulewitz). As previously described, 2 × 10IN, USA) . Animals43Sc-PSMA-617 in 100 µL of PBS. After a 2 h awake uptake period, micro CT and microPET scans were acquired on the X-Cube and β-Cube microCT and microPET systems, respectively . A general purpose protocol was used for microCT and a 30 min acquisition time for microPET scans. MicroPET images were reconstructed using an OSEM reconstruction algorithm with an isotropic voxel size of 400 μm. CT images were reconstructed with a 200 μm isotropic voxel size and used for anatomic co-registration and scatter correction. The animal was maintained under 1–2% isoflurane anesthesia in 100% oxygen during imaging. Respiratory rate and body temperature were constantly monitored and maintained using Molecubes onboard monitoring and a Small Animal Instruments  setup. Images were coregistered and post-processed using VivoQuant software . Utilizing the CT scans, regions of interest were drawn around tumors and the muscle tissue corresponding to the contralateral side of the tumor. Data are shown in standard uptake volume.The animal model was injected with approximately 5.66 MBq of 47Sc-PSMA617 in 100 µL of isotonic saline solution. After 2 h incubation, animals were anesthetized with 1–2% isoflurane, placed in the γ-Cube microSPECT instrument  and scanned for 90 min, followed by microCT scan for anatomic reference.An animal model was injected with 53.9 MBq of 43Sc via the 42Ca43Sc reaction was found to be feasible using a deuteron-capable medical cyclotron. Radioactivity was readily produced at a level capable of supporting pre-clinical studies with the addition of an easily fabricated beam stop modification. Similarly, 47Sc was shown to be readily producible with the NatTi47Sc reaction at a linear accelerator with off-site production proving to be no obstacle for a pre-clinical study. 43Sc and 47Sc produced through these methods were successfully used for labeling PSMA-617 at specific activities appropriate for preliminary in vivo studies. Clear detection of LNCaP-r tumors was possible with both 43Sc-PSMA-617 PET and 47Sc-PSMA-617 SPECT imaging.The production of 43Sc/47Sc as a promising theranostic pair. Through the experimentally demonstrated pipeline defined by this publication, 43Sc/47Sc is easily adaptable to the pre-clinical space. As such, this theranostic pipeline will be utilized to vastly expand upon the dataset presented in this publication through the full-scale investigation of 43Sc/47Sc-PSMA-617 with subsequent expansion to the utilization of scandium theranostics with other DOTA based tracers.Together, these preliminary findings exhibit This paper also illustrates the initial progress of the UChicago/Argonne Joint Radioisotope Initiative’s (JRI) efforts in building a complete pipeline for rapid development of novel theranostics.