Self-Renewing Blood Progenitors Could Expand the Reach of Cancer Cell Therapy

A team of researchers at the University of Southern California has developed a method to expand a key population of blood-forming progenitor cells in the laboratory while preserving their identity and function, overcoming a longstanding barrier in hematology and opening new possibilities for cancer immunotherapy.

The study, published in Cell, describes how investigators generated large numbers of granulocyte-monocyte progenitors (GMPs)—immune precursor cells that give rise to macrophages, monocytes, and neutrophils—using a culture system that enables these cells to self-renew in vitro. The work not only challenges conventional assumptions about hematopoietic progenitor biology but also provides a potentially scalable platform for engineering immune cells designed to attack cancer.

“This is the first time we can pick single progenitor cells and expand them in large quantities without differentiation,” said senior author Qi-Long Ying, PhD, professor of stem cell biology at USC. “They retain the original identity.”

The achievement addresses a problem that has frustrated researchers for decades. Although hematopoietic stem cells and their descendants have been extensively studied, scientists have struggled to maintain specific blood-forming progenitor populations in culture over long periods without the cells differentiating into mature immune cells.

Ying said the project grew out of his laboratory’s experience working with embryonic stem cells, which can be maintained indefinitely in culture. He reasoned that if embryonic stem cells could be expanded long term, similar approaches might eventually be developed for stem and progenitor cells found in bone marrow.

After years of experimentation, the researchers established culture conditions that selectively support GMPs, a progenitor population responsible for generating several innate immune cell types involved in recognizing and destroying abnormal cells.

Challenging a longstanding paradigm

According to co-author Daniel McKim, PhD, one of the most surprising findings was not simply the ability to expand GMPs but the demonstration that these progenitor cells could undergo extensive self-renewal in vitro.

“The prevailing theory has been that hematopoietic progenitors are short-lived intermediate cells that are incapable of self-renewal,” McKim said. “One of the distinctions between hematopoietic stem cells and progenitors is the belief that these cells are not able to self-renew. What we found is that under the right conditions, they can.”

The researchers emphasize that the self-renewal phenomenon occurs in culture. Once transplanted back into animals, the GMPs behave like normal progenitor cells, producing downstream immune populations before eventually becoming depleted.

Still, the ability to generate vast numbers of GMPs in vitro represents a significant technical advance. The investigators report expansion levels approaching eight orders of magnitude while maintaining the cells’ progenitor characteristics.

Building better cell therapies

Beyond the basic biology, the researchers see major implications for cancer immunotherapy.

Current cellular immunotherapies are dominated by CAR T-cell approaches, which have transformed treatment for several blood cancers but have shown more limited success against solid tumors. Investigators have long been interested in developing therapies based on macrophages and other innate immune cells because those cells naturally infiltrate tumors and can reshape the tumor microenvironment.

However, translating those concepts into viable therapies has proven difficult. Mature macrophages and monocytes are challenging to genetically engineer, difficult to manufacture at scale, and often fail to persist after infusion.

The newly expanded GMPs may provide a solution. Because the progenitor cells can be generated in large numbers and genetically modified before transplantation, they offer a renewable source of tumor-fighting immune cells.

“In our body these cells are very rare,” Ying said. “The mature cells cannot grow, and it is very challenging to genetically modify them. Now we have progenitor cells that can be expanded long-term in large quantities, and we can easily genetically modify them. That makes everything possible.”

The team engineered both mouse and human GMPs with chimeric antigen receptors (CARs) and evaluated them in mouse models. Unlike mature macrophages, which often become trapped in organs such as the lungs and liver after infusion, the progenitor cells distributed broadly throughout the body and engrafted within the bone marrow.

Once established, the cells generated populations of macrophages and monocytes capable of infiltrating tumors.

McKim noted that this approach may overcome several limitations that have hindered macrophage-based immunotherapies. “One of the big issues has been that it’s hard to engineer these cells, and when you put them back into the body they don’t get where they need to go,” he said. “The progenitors solve both problems. They’re easy to engineer, and they expand after transplantation.”

Implications for solid tumors

The researchers believe progenitor-derived innate immune therapies may offer advantages in solid tumors, where CAR T-cell approaches have struggled.

Tumors often create highly suppressive microenvironments that limit T-cell activity. Macrophages and related innate immune cells, by contrast, naturally migrate into tumors and can help stimulate broader immune responses.

“Monocytes and macrophages love going into tumors,” McKim said. “They can kill tumor cells themselves, but they can also help generate a natural antitumor immune response by the host.” That capability could prove particularly important in cancers that evade treatment by losing specific target antigens, a common mechanism of resistance to CAR T-cell therapy.

Although the work remains preclinical, the investigators believe the platform could eventually support a wide range of immune-engineering applications beyond cancer.

 

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Predictive Modeling of Enterovirus Hospital Burden Using Machine Learning and Age-Specific Surveillance Data: Operational Forecasting in Taiwan During the Postpandemic Era

Background: Enterovirus infections cause substantial pediatric morbidity worldwide, with severe cases requiring hospitalization. Accurate forecasting of hospitalization burden supports proactive resource allocation and clinical preparedness. During the postpandemic period (2023‐2024), Taiwan experienced a resurgence of enterovirus activity following COVID-19–related suppression, although at levels below prepandemic baselines, creating unique operational forecasting challenges. Objective: This study aimed to develop and validate random forest models for 1-week-ahead enterovirus hospitalization forecasting using postpandemic surveillance data and to evaluate the impact of epidemiological regime alignment on predictive performance. Methods: We analyzed weekly enterovirus surveillance data from Taiwan’s Centers for Disease Control covering 2023 to 2024, including outpatient, emergency department, and hospitalization counts stratified by five age groups (0‐2, 3‐4, 5‐9, 10‐14, and ≥15 y). Random forest models were trained on data from 2023 week 1 to 2024 week 40 (n=91 wk after lag preprocessing) and validated on a temporally independent test set covering 2024 weeks 41 to 52 (n=11 wk). Feature engineering incorporated age-specific indicators, 1‐ to 4-week temporal lags, seasonal variables, and derived epidemiological ratios. Results: The random forest model achieved strong 1-week-ahead forecasting performance on the test set (²=0.216, root mean square error 23.5 hospitalizations per week, mean absolute percentage error 17.27%). Age-specific outpatient visits among children aged 0 to 2 and 3 to 4 years were the most influential predictors (feature importance=0.0839 and 0.0908, respectively), followed by seasonal week-of-year effects (feature importance=0.0803). The mean absolute error was 17.6 hospitalizations per week, demonstrating practical utility for hospital capacity planning. Test-period hospitalizations averaged 126.5 cases per week, representing a 3.4-fold increase from pandemic suppression levels (28.4 cases per week during 2020‐2022) while remaining 24% below prepandemic baselines (165 cases per week during 2008‐2019). Conclusions: Machine learning models trained on recent postpandemic surveillance data provide useful short-term forecasts of enterovirus hospitalization burden in Taiwan. A mean absolute percentage error of 17.27% represents reasonable accuracy for 1-week-ahead hospital resource planning. Age-specific pediatric outpatient surveillance offers valuable early signals for hospitalization forecasting, supporting the integration of such models into routine public health practice during postpandemic recovery.
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Medical AI Model Privacy Risks

Research led by the Technical University of Munich shows that data from some individuals used to train medical artificial intelligence (AI) models could be at much higher risk of exposure due to cyberattack than others.

Writing in Nature, the researchers explain that underrepresented groups, such as people with a rare disease or a minority ethnicity, are at particularly high risk of having their data exposed.

A type of cyberattack called a “membership inference attack” can be used to uncover sensitive information about individuals or learn about the training data behind an AI system, without seeing the original database. In the wrong hands, this kind of information can be used for discrimination, blackmail, or even to assess who might be vulnerable to exploitative marketing.

“The extent to which this constitutes a privacy violation is nuanced and depends on factors such as the underlying training population and the deployment context of the model. Although inferring membership for a model trained on a general population may be benign, doing so for a model trained on a narrow, disease- or center-specific cohort acts as a direct proxy for sensitive medical information,” explained lead author Moritz Knolle, a doctoral researcher at the Technical University of Munich, and colleagues.

In this study, the team studied seven large, real‑world clinical datasets including medical images, electrocardiograms, and electronic health records. They trained around 200 versions of an AI model for each dataset, then quantified, for every single record and patient, how accurately an attack would be at guessing if a patient was part of the training set.

They showed that membership inference attacks can be almost perfectly successful for some individual patients, such as those with an unusual disease or presentation, even though the average attack performance across the whole training set looked close to random guessing.

As the AI model capacity increased, the number of highly vulnerable patients rose substantially. Underrepresented groups in the training group, for example, by disease, ethnicity, insurance, sex, or imaging protocol, were among the most vulnerable records to this kind of attack.

Current practice tends to check the privacy vulnerability of AI models by taking an average from the whole dataset. “Together, our findings show that aggregate privacy metrics can severely underestimate individual privacy risk,” warned Knolle and colleagues.

“Given this vulnerability, medical AI models and their deployment contexts should be assessed for the sensitive information that attackers could obtain by successfully inferring training dataset membership. To prevent privacy harm, we recommend that vulnerable models be protected by verifiable risk mitigation strategies and/or strict access controls.”

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Recoded E. coli Promises More Scalable Weight Loss Drug Production

The manufacturing of weight loss drugs at large scale could get cheaper and more sustainable thanks to an engineered strain of Escherichia coli (E. coli) bacteria.

The fully recoded E. coli, designed to use only 61 codons to synthesize proteins, is now being rolled out as a new method for manufacturing peptides with non-natural chemistries.

That’s according to Constructive Bio, the company that recoded the E.coli and now hopes this synthetic strain will transform the production of some high-volume hard-to-manufacture protein/peptide therapeutics.

“Our key message is that we’re able to produce long peptides containing non-canonical amino acids to deliver therapeutic proteins at scale by biomanufacturing,” explains Rob Salmon, PhD, head of bioprocess at Constructive Bio.

“And our key differentiator is there’s currently a market in, for example, weight loss drugs.”

According to Salmon, glucagon-like peptide-1 (GLP-1) agonists for weight loss are currently produced using chemical synthesis approaches such as solid phase peptide synthesis, which is hard to scale and generates high volumes of toxic waste.

By contrast, the synthetic E. coli strain can potentially produce these peptides using fermentation via standardized industrial processes, he says.

“We want to fit into standardized industrial unit operations and, through that, scale to thousands of liters of product that we can sell to the market,” he explains.

The strain was developed as part of research into reducing the number of codons needed to synthesize proteins in an organism from 64 to 61, allowing slots for three new non-canonical amino acids, according to the company.

A schematic demonstrating how non-canonical amino acids are incorporated into a protein or peptide chain using the ribosome in Constructive Bio’s Syn61 strain of E. coli. [Constructive Bio]

Constructive Bio was founded in 2022 to take the strain forward into industrial applications, including optimizing for applications such as antibody fragments or the long peptides used for GLP-1 agonist therapies.

Since then, the optimized strain has been taken through some industrial fermentations and demonstrated promising titers, he explains, adding that he will present results at the upcoming Bioprocessing Summit in Boston.

“We’re challenging some of the assumptions from chemists that biology can’t be used to do this,” he says.

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Medra Launches Reasoning Layer for Drug Discovery Robotics

As AI infrastructure for drug discovery continues to proliferate with reasoning workflows capable of generating hypotheses, candidate molecules, and experimental plans, Medra CEO Michelle Lee, PhD, argues that physical AI is the solution to addressing the next bottleneck: experimental validation at scale. 

“Building foundation models in biology that can predict and cure disease will take thousands of years of data generation,” Lee explained in an interview with GEN Edge. “The more I looked at the field, the more I realized that this data problem is actually a robotics problem.”   

In a new collaboration with the Defense Advanced Research Projects Agency (DARPA), Medra has launched AI Experimentalist, the scientific reasoning layer of its robotics platform. The system translates high-level research goals expressed in natural language into executable workflows that span the entire experimental cycle, from literature review, wet-lab execution, data analysis, and protocol refinement. 

In a blog post, Medra presents an example where scientists prompt to “build an Epidermal Growth Factor Receptor (EGFR) blocking antibody assay cascade.” AI Experimentalist can propose small optimizations in execution, including testing linear DNA templates in parallel, optimizing expression conditions, and feeding results immediately into the next run, for compounding time savings from days to hours. 

Partners can access AI Experimentalist through physical AI labs deployed on site at customer facilities or operated remotely through Medra’s flagship science laboratory, Medra Lab 001 (ML001), which unveiled in April and touts running experiments 24/7. Medra describes the 38,000 square foot facility as the largest autonomous lab in the United States. 

Artisanal nature 

In contrast to industrial automation, which has been powerful for repeatable tasks, such as combinatorial chemistry and screening, physical AI equips the same hardware with sensors to enable intelligent decision-making. 

While many robotics players in biology are focused on the manufacturing step, Medra has the ambitious goal of accelerating end-to-end drug discovery campaigns. 

“The artisanal nature of science is actually what makes certain experiments work and others fail,” said Lee. She noted that seemingly subtle variables, such as the angle of a pipette or the precise timing of mixing reagents, can have an outsized impact on experimental outcomes.  

Medra is currently working with partners across academia, biopharma, and government to run and develop assays across a wide array of applications, including antibody discovery, protein engineering, gene editing, and cell biology. 

Looking ahead, Lee says the bottleneck is not robotic capability, but integration and deployment. AI Experimentalist addresses this challenge through a multi-agent architecture and model-agnostic harness that allows Medra to incorporate new biological AI models and scientific agents. Among them are NVIDIA Nemotron models for protocol editing and optimization and the newly launched NVIDIA BioNeMo Agent Toolkit. 

“The flexibility of physical AI will be incredibly key in making scientific discovery truly autonomous,” asserts Lee. 

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Spotlight on RNA Therapeutics



Image of Drew Weissman, MD, PhD

Drew Weissman, MD, PhD

Professor in Vaccine Research
Penn Medicine

Panelist

Image of Drew Weissman, MD, PhD

Drew Weissman, MD, PhD

Drew Weissman, MD, PhD, is a world-renowned physician and Roberts Family Professor in Vaccine Research at Penn Medicine. He is best known for his contributions to RNA biology and the development of COVID-19 RNA vaccines. Weissman and Katalin Karikó, PhD, were jointly awarded the 2023 Nobel Prize in Medicine for their discoveries that enabled the modified mRNA technology used in Pfizer-BioNTech and Moderna’s vaccines to prevent COVID-19. More than 15 years ago, Weissman and Karikó found a way to modify mRNA and developed a delivery technique to package the mRNA in lipid nanoparticles. The COVID-19 RNA vaccine received FDA approval in August 2021.

Weissman is one of the academic leaders of the NSF AIRFoundry, an effort to leverage AI to improve, accelerate, and scale the design, manufacture, and delivery of RNA, which officially opened in April 2026. Weissman’s lab is currently working on a pan-coronavirus vaccine, a universal flu vaccine, and a vaccine to prevent herpes. They are working with Penn colleagues to develop cancer therapeutics with mRNA technology. And they are developing a SARS-CoV-2 mRNA vaccine with Chulalongkorn University in Thailand to help residents of Thailand and other Asian countries access lifesaving vaccines.

Before joining Penn in 1997, Weissman was a fellow at the National Institutes of Health studying HIV in the lab of Anthony Fauci, MD. Weissman received his bachelor’s degree and master’s degree from Brandeis University. He earned his MD and PhD from Boston University and completed his residency at Beth Israel Hospital.



Image of Zachary Ives, PhD

Zachary Ives, PhD

Professor of Computer and Information Science
University of Pennsylvania

Panelist

Image of Zachary Ives, PhD

Zachary Ives, PhD

Zachary Ives, PhD, is the department chair and Adani President’s Distinguished Professor of Computer and Information Science at the University of Pennsylvania. Zack’s research interests include data integration and sharing, data provenance and trustworthiness, and machine learning systems. He is a recipient of the National Science Foundation (NSF) CAREER award, and an alumnus of the DARPA Computer Science Study Panel and Information Science and Technology advisory panel. He has also been awarded the Christian R. and Mary F. Lindback Foundation Award for Distinguished Teaching and an IEEE Technical Committee on Data Engineering Education Award, and he is a fellow of the ACM.

 

Zack is one of the academic leaders of the U.S. NSF Artificial Intelligence-driven RNA BioFoundry (NSF AIRFoundry), an $18-million effort to leverage AI to improve, accelerate, and scale the design, manufacture, and delivery of RNA. The center officially opened in April 2026.

Zack studied computer science at Sonoma State University and holds a PhD in computer science from the University of Washington. He joined the faculty of Penn in 2003. He is a co-author of the textbook Principles of Data Integration. He has been an associate editor for the Proceedings of the VLDB Endowment and The VLDB Journal.



Image of Silvi Rouskin, PhD

Silvi Rouskin, PhD

Asst. Professor of Microbiologyy
Harvard Medical School

Panelist

Image of Silvi Rouskin, PhD

Silvi Rouskin, PhD

Born in Bulgaria, Silvi Rouskin, PhD, is an assistant professor of microbiology at Harvard Medical School. She is the winner of the 2021 Vilcek Prize for Creative Promise in Biomedical Science. Following a six-year spell at the Whitehead Institute, where she was the Andria and Paul Heafy Whitehead Fellow, Silvi joined the faculty of Harvard Medical School in 2021.

Silvi’s Harvard lab studies alternative RNA structures and the myriad roles they have in both viral and human biology. In particular, the lab studies how RNA folding informs alternative splicing and how misfolding can lead to disease. The lab developed DMS-MaPseq (dimethyl sulfate mutational profiling with sequencing) and DREEM (Detection-of-RNA-folding-Ensembles-using-Expectation-Maximization) algorithm to distinguish multiple RNA conformations formed by the same underlying sequence in vivo at single nucleotide resolution.

Silvi immigrated to the United States as a teenager to pursue a career in science. She holds a degree in physics from Florida Institute of Technology and a PhD in biochemistry and molecular biology from the University of California, San Francisco. Her interest in RNA began while working as a staff research associate in the lab of Joseph DeRisi, PhD, at UCSF, where she began developing techniques for the detection of viruses associated with human disease.



Broadcast Date: 

  • Time: 

In anticipation of RNA Day (on August 1), GEN invites you to join our exciting Spotlight virtual event on RNA Therapeutics on Wednesday, July 29.

We are living in a “post-genomic” world where RNA is no longer just a messenger but a programmable drug and molecular therapeutic. From the global impact of mRNA vaccines to advances in RNA editing and the potential of circular RNA, the field of RNA therapeutics is truly taking off. RNA is rapidly becoming a universal software for precision medicine.

Over 2.5 hours, this GENSpotlight on RNA Therapeutics brings you three interlinked sessions that feature outstanding researchers exploring various aspects of RNA biology and therapeutics, including:

  • A keynote panel including two founding members of the AIRFoundry (Artificial Intelligence-driven RNA BioFoundry) at the University of Pennsylvania—Zachary Ives, PhD, and Nobel laureate Drew Weissman, MD, PhD
  • A talk from Silvi Rouskin, PhD, a leading microbiologist at Harvard Medical School, presenting new research on alternative RNA structures and their relevance in health and disease
  • Presentations from our two sponsors, 4basebio and Aldevron
  • Registration to our Spotlight on RNA Therapeutics is entirely free. We look forward to celebrating RNA Day with you (a few days early).

Produced with support from:

4basebio logo

Aldevron Logo

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Hedgehog signaling enhances the Schwann-like and nerve repair-supportive properties of ectomesenchymal stem cells

IntroductionPeripheral nerve injury (PNI) is characterized by limited regenerative capacity and incomplete functional recovery. Schwann cells (SCs) are essential for nerve repair, but their clinical application is constrained by limited availability. Ectomesenchymal stem cells (EMSCs), derived from neural crest lineage, represent a promising alternative; however, their inefficient differentiation into SC-like cells remains a key limitation. This study investigated whether activation of Hedgehog signaling via Sonic hedgehog (Shh) could enhance SC-like differentiation and improve nerve regeneration.MethodsEMSCs were isolated from rat nasal mucosa and transduced with adenoviral vectors to overexpress Shh. SC-like differentiation was assessed using RT-qPCR, Western blot, immunofluorescence, and ELISA. Transcriptomic analysis compared EMSCs with primary SCs. A short-gap rat sciatic nerve defect model was established as an initial proof-of-concept in vivo model, and animals received vehicle, EMSCs, Shh-EMSCs, or autograft treatment. Functional recovery, electrophysiology, histology, and ultrastructural analyses were performed.ResultsTranscriptomic analysis revealed that EMSCs possess a partial SC-related transcriptional profile but lack sufficient Hedgehog activation. Shh overexpression activated canonical Hedgehog signaling, evidenced by increased Gli1/2 expression and nuclear translocation. Shh-EMSCs showed enhanced expression of SCs markers (P75, GFAP, MBP, S100β), increased secretion of neurotrophic factors (BDNF, NT-3), and reduced inflammatory cytokines. In vivo, Shh-EMSCs significantly improved functional recovery, nerve conduction velocity, and gait performance compared with EMSCs alone. Histological and ultrastructural analyses demonstrated increased axonal regeneration, improved organization, and enhanced myelination compared with unmodified EMSCs, although autograft repair remained superior or more complete in several outcome measures.ConclusionHedgehog signaling contributes to SC-like differentiation of EMSCs. Shh-mediated activation promotes a pro-regenerative phenotype and enhances nerve repair-related outcomes in a short-gap sciatic nerve defect model, suggesting that Shh-EMSCs may serve as a potential cell-based strategy for peripheral nerve repair.

Enhancing hematoma expansion prediction in hypertensive intracerebral hemorrhage based on habitat and perihematomal edema radiomics from non-contrast CT: a dual-center study

ObjectivesCharacterizing the microenvironmental habitats within the hematoma may yield crucial imaging biomarkers and improve the early prediction of hematoma expansion (HE) in patients with hypertensive intracerebral hemorrhage (HICH). Our objective was to construct and validate a combined model that integrates clinical data with whole-hematoma radiomics, habitat radiomics of the hematoma, and perihematomal edema (PHE) radiomics features extracted from non-contrast computed tomography (NCCT) images for preoperative HE prediction.MethodsThis retrospective dual-center cohort of 353 HICH patients. Based on baseline NCCT images, radiomics features were extracted from the whole hematoma, three distinct habitats within the hematoma, and the PHE region. Five models were constructed: a clinical model, a whole-hematoma radiomics model, a habitat-based radiomics model, a PHE radiomics model, and a combined model. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis.ResultsThe combined model integrated with smoking history, island sign, maximum distance of the PHE, and the whole-hematoma, habitat, and PHE radiomics models, achieved the best predictive performance. In the training, testing, and validation sets, the combined model predicted the area under the curve for HE as 0.951 (95% CI: 0.915–0.986), 0.937 (95% CI: 0.883–0.991), and 0.939 (95% CI: 0.888–0.989), respectively.ConclusionThe NCCT-based combined model integrating clinical data, whole-hematoma radiomics, habitat radiomics, and PHE radiomics improves HE prediction in patients with HICH, providing a noninvasive tool with potential for guiding treatment strategies.

Transcutaneous auricular vagus nerve stimulation: mechanisms, applications, and research progress

This review systematically examines the mechanisms of action, optimization of stimulation parameters and targets, and the research progress in the application of taVNS for neurological disorders and systemic conditions. Rather than merely cataloging existing findings, this review critically synthesizes recent advances with a focused emphasis on two core aspects: (1) the mechanistic convergence and divergence among neuroimaging, autonomic, and molecular pathways underlying taVNS effects; and (2) the methodological challenges and biomarker-driven strategies for optimizing stimulation parameters and personalizing treatment. By prioritizing these key directions over exhaustive enumeration of clinical applications, this review aims to provide a conceptually structured framework that distinguishes genuine progress from descriptive accumulation. Moreover, compared with previously published reviews on similar topics, the present work offers a distinctive contribution by integrating multi-modal mechanistic evidence into a testable model of taVNS action and critically evaluating the translational gap between parametric optimization in research settings and standardized clinical implementation. Furthermore, it provides an outlook on future research directions and technological developments, aiming to offer a comprehensive theoretical framework to inform both clinical translation and foundational research in this rapidly evolving field.

The metabolic layer of cognition: integrating metabolomics, breathomics, and systems neuroscience

Cognitive neuroscience has made substantial progress in mapping neural activity underlying perception, memory, and decision-making. However, widely used methods such as functional magnetic resonance imaging and electrophysiology primarily measure indirect physiological correlates of neuronal activity and provide limited access to the biochemical processes that support neural signaling. In this review, we propose that metabolism might constitutes a critical intermediate layer linking neural activity and behavior. Drawing on advances in metabolomics and breathomics, we examine how mass spectrometry-based analytical techniques enable sensitive detection of metabolites, neurotransmitters, lipids, and volatile organic compounds that could reflect metabolic processes associated with neuronal signaling and cognitive states. We synthesize emerging research at the intersection of neuroenergetics, systems neuroscience, and metabolic profiling, highlighting how these approaches can complement established neuroimaging and electrophysiological methods. In particular, we discuss the potential of volatile organic compounds in exhaled breath as non-invasive indicators of systemic metabolic responses accompanying cognitive processes. At the same time, we address key conceptual and methodological challenges in interpreting peripheral metabolic signals in relation to brain activity, including the influence of systemic physiology, microbiome metabolism, and environmental factors. Finally, we outline future directions for integrating metabolomic and breathomic measurements with neural and behavioral data in multimodal experimental frameworks. Incorporating metabolic dynamics into systems-level models may provide a new perspective on how cognition emerges from interactions between brain activity and whole-body physiology.