Neurodegenerative diseases are typically defined by the presence of characteristic clinical phenotypes. However, it is increasingly recognized that early intervention could give people the best chance for meaningful positive effects. As a result, early detection and prevention are becoming major priorities.
Months to several years before amyotrophic lateral sclerosis (ALS) symptoms arise, levels of certain blood proteins may dramatically shift. By anticipating the arrival of symptoms, investigators could intervene with preventative therapies before the irreversible motor neuron damage that is characteristic of ALS sets in.
In this study, researchers analyzed data from the long-running, National Institutes of Health (NIH)-funded Pre-symptomatic Familial ALS (Pre-fALS) study, to identify a lineup of key proteins that may predict the emergence of clinically manifest ALS.
“If someone carrying an ALS-associated genetic variant had asked me in the past when they would become symptomatic, I would have struggled to provide a reasonable estimate,” said Michael Benatar, MD, PhD, professor of neurology and public health sciences at the University of Miami. “These biomarkers give us a far better idea of the timing, allowing us to estimate the time to symptom onset with an average error of about 18 months. That’s something we can work with.”
For nearly 20 years, the Pre-fALS study has collected data and biological samples from people who are at significantly elevated genetic risk for ALS but have not yet progressed, or phenoconverted, to the disease. While this cohort is unique, permitting the examination of presymptomatic ALS, recent studies suggest that findings from Pre-fALS are likely relevant to the broader population.
In 2017, an analysis of ten Pre-fALS participants who had developed symptoms showed that neurofilament light chain (NfL), a structural protein in neurons, spiked in their blood in the months preceding ALS phenoconversion. As more study participants have begun showing symptoms or signs of disease, new opportunities to search for other pre-symptomatic ALS biomarkers have emerged.
Now, using Olink Explore, investigators report a high-throughput, proteomic study on 516 serially collected plasma samples from 137 study participants; 33 phenoconverters, 35 patients with ALS, 10 pre-symptomatic pathogenic variant carriers and 59 controls.
The team identified 92 whose levels differed in people before they eventually showed symptoms. Using machine-learning techniques, the authors tested how various combinations of proteins could predict future risk of phenoconversion. Characterizing the longitudinal trajectory of these proteins, they identified a core panel of 19 proteins (including NfL) which, collectively, the authors note, predicted phenoconversion over the 0.5-year to 5-year time horizons and yielded estimates of time to phenoconversion with a mean absolute error of 1.6 years.
They also produced similar results using data from the UK Biobank, which, despite some limitations, is more representative of the general population than the genetically predisposed cohort of Pre-fALS.
“With preventative gene-targeting treatments now becoming available, there is a particularly urgent need for reliable biofluid-based signatures that indicate near-term onset in individuals that carry ALS risk genes,” said Amy Bany Adams, PhD, acting director of NIH’s National Institute of Neurological Disorders and Stroke (NINDS).
Tofersen, a drug approved for symptomatic ALS, is currently being evaluated as a preventative therapeutic in pre-symptomatic ALS through ATLAS, a clinical trial designed by Benatar in partnership with the company Biogen. ATLAS will test whether starting treatment shortly before symptoms appear could avert or delay the onset of ALS.
“This is all possible because of the members of the carrier community who believe in our mission of preventing ALS and have supported and participated in our research. It has been one of my life’s greatest privileges to give something back,” Benatar said.
Longitudinal proteomic analysis of plasma from unaffected carriers of ALS-associated pathogenic variants revealed early protein changes before ALS symptoms and enabled mapping of their trajectories as well as prediction of the timing of phenoconversion
Researchers at EMBL Hamburg and collaborators at the Leibniz Research Institute for Molecular Pharmacology (FMP) have mapped in unprecedented detail how the influenza A virus (AIV) rewires infected human cells. The researchers developed a customized experimental workflow that used in-cell cross-linking mass spectrometry (XL-MS), combined with AlphaFold-based structural modeling and functional assays, to directly map protein-protein interactions (PPIs) in IAV-infected human cells.
They claim that the study marks the first time that scientists have mapped direct virus-host protein contacts at scale inside intact influenza-infected cells, with enough structural detail to model how the proteins fit together. “Our work provides a new way to study flu-host interactions in their native context and with structural insight,” said Jan Kosinski, PhD, group leader at EMBL Hamburg and the Centre for Structural Systems Biology (CSSB). “The current results are a snapshot of a moment during infection, and it opens the door to studying flu-host interactions across the entire infection cycle.”
Every year, seasonal influenza kills up to 650,000 people globally and causes serious illness for 3–5 million individuals. When IAV infects cells, it releases RNA that contains the blueprints for a handful of proteins that spread throughout the host cell and repurpose its molecular machinery to make more viruses. “Its replication relies on protein–protein interactions (PPIs) between up to 14 viral proteins and host factors, often confined to cellular compartments and organelles,” the team stated.
Scientists want to understand this process in detail, as it would help in designing better drug therapies and vaccines against the flu virus. “Understanding these host–IAV PPIs in context is essential for elucidating viral strategies and therapeutic targets,” they added.
Studying protein-protein interactions in action during infection is challenging. Most previous studies relied on biochemical methods that required the cell to be broken open before the interactions could be measured. Once the cell’s compartments were gone, proteins that were never in contact inside the cell could meet in the test tube, and fragile or location-specific contacts could be lost. It was then hard to know which interactions actually happened inside an infected cell.
“This is when we learned that our collaborators—Boris Bogdanow and Fan Liu—at FMP Berlin had developed a specialized version of cross-linking mass spectrometry (XL-MS), a long-established technique for mapping protein contacts, tailored specifically to virus-infected cells,” said Kosinski. This was the critical breakthrough. It allowed researchers to do what previous methods couldn’t, including capturing short-lived and location-specific interactions.
“XL-MS allows us to capture protein-protein interactions directly in infected intact cells, while also providing structural information about how these interactions are happening,” explained Bogdanow, who is now a junior research group leader at the Institute of Virology, Charité—Universitätsmedizin Berlin. “This gives us insight into the interface between the virus and the human cell and may, through structural modelling, help identify actionable targets for future pharmaceutical interventions.”
By combining the results obtained through XL-MS with computational structural modeling, the researchers could identify which viral and human proteins interact and also predict how they physically fit together. For this, they used a modified version of the protein structure prediction algorithm AlphaFold.
“The key advantage of the modified AlphaFold approach is that it allowed us to feed our experimental cross-linking data directly into the structural modeling,” explained Kosinski. “This tells the model which parts of the viral and host proteins are close to each other inside infected cells. This was especially useful for virus-host complexes, which are often difficult to predict reliably.”
The study findings revealed two important ways in which the virus hijacks the cell. One involves hemagglutinin, a protein on the virus’s surface that it uses to bind and enter host cells. Tracing how hemagglutinin moves through the cell’s internal transport and processing system revealed how host proteins, some with previously unknown functions, helped the virus correctly fold and modify hemagglutinin during infection.
The other involves paraspeckles, small droplet-like compartments in the nucleus. The researchers found that infection by the influenza A virus causes these organelles to dissolve, releasing the RNA-binding proteins bound within them, which the virus can then use to replicate. “We identified host factors linked to the maturation of distinct glycoforms of the viral surface glycoprotein haemagglutinin through the membrane-bound endoplasmic reticulum–Golgi system,” the scientists wrote in summary. “In the nucleus, we observed the progressive disassembly of paraspeckles (phase-separated membraneless compartments) across multiple cell lines.”
First author Iuliia Kotova, PhD, former predoctoral fellow at the Kosinski group at EMBL Hamburg, and currently at ETH, said, “What surprised us most was the paraspeckles. Watching these tiny organelles in the nucleus dissolve, consistently across every cell line and every flu strain we tested, told us this isn’t a side effect of infection—it might be a strategy.”
Kosinski added, “There may also be a second benefit for the virus: some evidence suggests paraspeckles contribute to cellular stress responses and antiviral gene regulation, so disrupting them could also weaken parts of the cell’s defense response.”
The researchers believe that their “mapping in context” approach can be used to understand the mechanism of action of other viruses that act similarly. “While the exact host factors and mechanisms often differ from virus to virus, we think our overall approach—combining in-cell cross-linking, structural modeling, and targeted cell-biology follow-up to map native virus-host interactions at specific stages of infection—remains broadly applicable,” Kosinski said.
Bogdanow further commented, “Although this study has focused on a lab-adapted strain, this study lays the groundwork to apply the methodology to viruses of potential pandemic relevance, such as H5N1, and for uncovering the interaction networks that support their multiplication in human cells.”
Diagnosing dementia is rarely straightforward, particularly early in the disease course. Alzheimer’s disease, dementia with Lewy bodies, and frontotemporal dementia can overlap clinically, while mixed pathology is common in older patients. This creates a major barrier for precision medicine: treatment selection, trial enrolment, prognosis, and patient counselling increasingly require a more biologically grounded diagnosis.
Blood biomarkers have already begun to change Alzheimer’s disease diagnostics, especially for detecting amyloid and tau pathology. But the field still lacks robust plasma tools for distinguishing Alzheimer’s disease from dementia with Lewy bodies (DLB) or frontotemporal dementia (FTD).
Proteomics across dementia cohorts
A new study, published in Nature Aging, used proximity extension assay proteomics to profile plasma proteins across international dementia cohorts. In the discovery phase, researchers analyzed more than 1,300 plasma samples, including controls and individuals across preclinical, mild cognitive impairment, and dementia stages of Alzheimer’s disease, dementia with Lewy bodies, and frontotemporal dementia.
The analysis identified more than 200 dysregulated proteins across disease groups. For Alzheimer’s disease, glial fibrillary acidic protein, or GFAP, showed the strongest increase across the disease continuum. Neurofilament light chain also rose with clinical stage, while several other proteins declined as Alzheimer’s disease progressed. However, the authors emphasize that these proteomic markers did not outperform established plasma markers of amyloid and tau pathology for detecting Alzheimer’s disease.
The more clinically novel findings came from the non-Alzheimer’s dementias. In dementia with Lewy bodies, integrin alpha-V and integrin alpha-M were consistently reduced, including in analyses stratified by amyloid status and in autopsy-confirmed Lewy body disease. The same integrin-related signal was also seen in Parkinson’s disease data from the PPMI cohort, supporting a broader link to Lewy body pathology.
For frontotemporal dementia, neurofilament light chain remained one of the strongest markers, consistent with its role as a general marker of neuroaxonal injury. Lower GFAP helped distinguish frontotemporal dementia from Alzheimer’s disease, while proteins such as OSM appeared more relevant in earlier frontotemporal degeneration.
A 21-protein dementia panel
The researchers then refined these signals into a custom 21-protein plasma panel and tested it in an independent multicenter cohort. The panel showed its strongest value in differential diagnosis, helping separate dementia with Lewy bodies and frontotemporal dementia from both controls and Alzheimer’s disease dementia. Its performance was moderate to good across these comparisons, suggesting that plasma proteomics may be most useful as an added layer of biological stratification when clinical symptoms overlap.
These values are not sufficient to replace specialist clinical assessment, CSF testing, imaging, or established Alzheimer’s blood biomarkers. But they suggest that plasma proteomics could provide clinically useful support where diagnostic uncertainty remains high.
Toward biomarker-based dementia stratification
The immediate relevance is not simply another biomarker list. The study addresses a practical gap in dementia medicine: identifying scalable blood-based tools that help separate biologically different diseases with overlapping symptoms. That could improve referral pathways, enrich clinical trials with the right patient populations, and support future disease-modifying therapies beyond Alzheimer’s disease.
Important limitations remain. Many DLB and FTD diagnoses were clinical rather than autopsy-confirmed, prodromal groups were relatively small, and biomarker performance may depend on assay platform and cohort calibration. Mixed pathology, especially coexisting Alzheimer’s and Lewy body disease, remains a major challenge.
Even so, the study provides a strong proof of concept. Plasma proteomics may help move dementia diagnostics from broad syndromic categories toward molecular stratification, an essential step if precision neurology is to match the progress already seen in Alzheimer’s biomarker development.
Circio Holding and Avenue Biosciences agreed to a research collaboration designed to combine their synergistic technologies to improve long-term expression of secreted proteins, relevant for treatment of a broad range of diseases.
Secreting proteins from cells into surrounding tissue, or into circulation, opens new treatment opportunities beyond classical gene therapy for monogenic disease, say scientists from both companies. Circio maintains that its circVec platform drives higher and more durable protein expression, and a spokesperson from Avenue Biosciences explains that its protein engineering technology improves protein secretion from cells by screening thousands of signal peptide-protein combinations.
Circio and Avenue will jointly explore whether the two technologies in combination can act synergistically to improve the production and secretion of proteins, including antibodies, for the treatment of genetic and chronic diseases.
“Many gene therapies are limited by insufficient protein expression, driving high doses, manufacturing complexity, and cost. The secretory pathway—the cellular machinery that produces and exports proteins—is a largely underutilized engineering opportunity. By combining Circio’s durable circular RNA expression with our technology, we aim to increase protein output per dose and ultimately help more patients benefit from life-changing genetic medicines,” says Avenue Biosciences CEO Tero-Pekka Alastalo, MD, PhD.
In the collaboration, Avenue Biosciences will deploy its protein engineering platform to identify signal peptides that enable improved secretion of therapeutic proteins expressed by circVec. The initial screening will be performed by Avenue Biosciences, followed by further in vitro and in vivo testing by Circio.
“A significant proportion of therapeutically relevant payloads for circVec are secreted proteins,” adds Victor Levitsky, PhD, CSO of Circio. “With the Avenue platform, we will test how signal peptide optimization can enhance secretion of proteins and thereby open novel opportunities for the circVec platform in genetic and chronic disease. This collaboration is an important addition to our pre-clinical development strategy of testing circVec in multiple settings through R&D partnerships to broadly explore the range of therapeutic options available for our unique circular RNA expression technology.”
At the 74th American Society for Mass Spectrometry (ASMS) Conference in San Diego, the obvious story was hardware. Vendors showcased faster acquisition, higher sensitivity, alternative fragmentation, spatial workflows, and software ecosystems. New or highlighted platforms and workflows came from Waters, Thermo Fisher Scientific, Sciex, Bruker, Biognosys, and Evosep.
But after several days of talks, posters, hallway conversations, and interviews with senior figures in mass spectrometry (MS)-based proteomics, the deeper story was not simply that instruments are getting better. The field is beginning to look past the instrument. The mass spectrometer is still central, but the question is shifting: what has to happen around it for proteomics to become clinically useful, scalable, trusted, and routine?
Beyond the instrument
Jennifer Van Eyk, PhD, professor of cardiology, biomedical sciences, pathology, and laboratory medicine, and director of the Advanced Clinical Biosystems Research Institute at Cedars-Sinai Health Science University, put it most directly: “I think mass spec is no longer the limitation. We have the sensitivity, the throughput, and the accuracy at discovery and targeted levels.”
Jennifer Van Eyk, PhD [Gustav Ceder]
That is a remarkable statement in a field long defined by instrument performance. Van Eyk was not saying that MS innovation is finished. She pointed to continuing gains in quantitation, protein structure, conformational analysis, post-translational modifications (PTMs), top-down proteomics, and protein dynamics. But for clinical impact, she argued, the next bottlenecks are increasingly sample preparation, data analysis, standardization, harmonization, and quality control.
Joshua Coon, PhD, professor of biomolecular chemistry at the University of Wisconsin-Madison and the Pyle Chair at the Morgridge Institute for Research, saw instrument speed as the force opening new applications. Faster scanning mass analyzers are allowing deeper proteome coverage, more post-translational modification (PTM) measurements, and shorter runs. Ryan Kelly, PhD, professor of chemistry and biochemistry at Brigham Young University, framed the same shift as a throughput problem. “Now the mass spec is so fast that we need to figure out how to feed it faster,” he said. In plasma proteomics, Coon said, faster instruments, nanoparticle-based enrichment, and improved chromatography are moving the field from hundreds
Joshua Coon, PhD [Gustav Ceder]
toward thousands of detectable proteins in blood.
John R. Yates III, PhD, the John Lytton Young Endowed Chair in the department of integrative structural and computational biology at Scripps Research, highlighted electron activation dissociation methods and the possibility that high-throughput workflows could push MS deeper into plasma and population-level studies. He described targeted affinity platforms as powerful for “known knowns” because they measure targets defined in advance. “But with mass spectrometry,” he added, “you can look for unknown unknowns, which is where the gold lies.”
John R. Yates III, PhD [Gustav Ceder]
The point cuts to the heart of where the field now stands, and a recurring ASMS tension. The future of proteomics is not a choice between platforms. It is a division of labor. Targeted affinity technologies have become central to large-scale plasma proteomics and population studies. MS remains uniquely powerful for unbiased discovery, tissue proteomics, complex sample matrices, protein modifications, structural diversity, and biology that is not yet named.
From depth to trust
If the first era of modern proteomics was about seeing more, the next may be about measuring better. Devin Schweppe, PhD, assistant professor in the Department of Genome Sciences at the University of Washington, described the current moment as a “duality.” Instruments can now deliver deep coverage, and computational tools are making interpretation faster. Together, he said, they are creating “a comfort level with trusting the data.”
Devin Schweppe, PhD [Gustav Ceder]
Trust came up repeatedly. For discovery biology, a strong signal can be enough to generate a hypothesis. For clinical practice, it is not. Van Eyk said clinical-grade assays are “way harder than people think they are.” A research study can iterate. A clinical assay has to deliver the same measurement today, in five weeks, in six months, and years later. Once a test is locked, “you can’t go, ‘Oh no, we should have had this extra protein in there,’” she said. “It’s done.”
This distinction matters across assay types. Targeted MS methods such as multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) can provide absolute quantification, but only for preselected proteins. Data-independent acquisition (DIA), meanwhile, has moved discovery proteomics closer to translation by improving reproducibility and scalability. DIA is still often used for relative quantification, but its ability to capture patterns across tens or hundreds of proteins may become important as clinical decision-making moves beyond single biomarkers and reference intervals.
The field is responding to these demands. David Kotol, PhD, R&D manager at ProteomEdge, discussed an independently validated nine-protein plasma panel designed to improve emergency department triage and imaging decisions for patients with suspected venous thromboembolism, compared with D-dimer alone.
David Kotol, PhD [Gustac Ceder]
Kotol described a shift “from relative protein measurements toward robust, multiplexed absolute quantification.” He emphasized stable isotope-labeled protein standards added early in sample preparation to monitor digestion efficiency, downstream analytical variation, and multi-peptide quantification. These standards cannot remove variation introduced during sample collection, handling, or storage. But they can make the analytical workflow more transparent and transferable.
The clinical gap
Mathieu Lavallée-Adam, PhD, associate professor in the department of biochemistry, microbiology and immunology and director of the specialization in bioinformatics at the University of Ottawa, gave the least glamorous answer to what still blocks clinical translation. “My answer is going to be boring,” he said. “It’s going to be education.”
Mathieu Lavallée-Adam, PhD [Gustav Ceder]
Lavallée-Adam argued that many clinicians and biomedical researchers still do not fully understand what modern MS can do. Too often, the outside view is still: give me a list of differentially expressed proteins. But MS-based proteomics has moved beyond lists, into proteoforms, structural information, PTMs, protein dynamics, and flexible acquisition. “We’re past that now,” he said. “The main barrier is our inability to communicate the possibilities that we offer.”
Sasha Singh, PhD, assistant professor of medicine at Harvard Medical School, associate scientist at Brigham and Women’s Hospital, and director of proteomics research at the Center for Interdisciplinary Cardiovascular Sciences (CICS), described this translation role from inside a hospital environment. “That’s actually my role at the hospital,” Singh said. “I am a liaison between the technology and the application scientist.”
Sasha Singh, PhD [Gustav Ceder]
The translation is becoming harder because proteomics is diversifying. End users often need to distinguish among discovery MS, which can provide broad relative quantification; targeted MS, which can provide absolute concentrations for selected proteins; and targeted affinity proteomics, which can scale well for plasma cohorts but is limited by predefined assays and available binding reagents. Singh added that different technologies may produce profiles that do not fully overlap. Rather than treating that as a failure, she suggested it reveals something real: the circulation contains many subproteomes, and different technologies enrich different views.
AI with guardrails
No 2026 conference escapes artificial intelligence (AI), and ASMS was no exception. But the mood among the researchers was cautious rather than breathless.
Lavallée-Adam said agent-based AI was dominating conversations in his part of the field. The dream is seductive: put a sample on an instrument, ask an AI agent to maximize protein identifications or optimize a method, and let it select the best protocol. But he drew a clear line between potential and reality. “Are such agents really driving change? It’s unclear at this point,” he said. “I think it’s unproven.”
Still, AI-assisted acquisition strategies are entering workflows. Lavallée-Adam’s group works on real-time MS data acquisition, where software analyzes data as it is acquired and adapts the run to the biological question. Instead of measuring the same abundant proteins repeatedly, the system can decide it has seen enough and move on to new targets. In that sense, AI becomes less a magical oracle than an instrument assistant.
Faster instruments are generating more data, and faster analysis is needed to keep up. Schweppe also argued that open-source tools remain essential because they let laboratories build on one another’s work rather than rebuild it.
More than abundance
Much of the clinical proteomics effort is focused on plasma because it is minimally invasive and suitable for screening, longitudinal sampling, and routine monitoring. But even in blood, researchers are learning that plasma is only part of the story.
Roman Fischer, PhD, associate professor and head of the Discovery Proteomics Facility at the Target Discovery Institute, University of Oxford, pushed the conversation back toward biology. Plasma alone does not capture the full circulating system, he noted. Peripheral blood mononuclear cells, extracellular vesicles, microvesicles, and other compartments may contain disease-relevant information that conventional workflows miss. “We have to be more sophisticated in addressing the compartments of the blood,” Fischer said.
Roman Fischer, PhD [Gustav Ceder]
He also pointed to the proteoform problem. A single gene can give rise to many transcripts, isoforms, modified proteins, and glycosylated forms. These differences may affect activity, localization, disease pathways, and therapy response. Capturing that diversity is not possible with targeted affinity assays alone. It requires deeper characterization of the proteome, not only quantification.
Yates offered a clinical example. His group has been developing protein-footprinting approaches that can detect conformational changes in proteins in blood. In one transthyretin amyloid cardiomyopathy project, he said, abundance alone was not the answer. The important signal was how the protein folded or misfolded. That kind of assay moves proteomics beyond proteins going up or down, into structural disease biology.
Van Eyk’s work on remote sampling devices pointed to another future: patient-collected blood samples that make longitudinal cardiovascular studies easier, more inclusive, and better matched to real clinical questions.
In the background was a broader translational arc: discovery, verification, clinical validation, health economics, and access. Plasma proteomics highlights included Lekha Sleno, PhD, professor at Université du Québec à Montréal, who is combining nanoparticle enrichment with isotope-enabled targeted proteomics, and a CinderBio breakfast seminar featuring Fredrik Edfors, PhD, assistant professor at KTH Royal Institute of Technology and SciLifeLab, and Simion Kreimer, PhD, senior research project advisor in the Proteomics and Metabolomics Core at Cedars-Sinai Health Science University.
The seminar focused on accelerated plasma proteomics, rapid digestion workflows, stable isotope standards, Human Protein Atlas resources, and faster enzyme workflows that can reduce lead times. The common message was that sample preparation, quantification, and validation may become as decisive as instrument resolution.
The next bottleneck
ASMS 2026 was not short on technical spectacle. High-resolution instruments, electron-based fragmentation, narrow-window DIA, rapid acquisition, MS imaging, top-down workflows, and AI-enabled software all had their moment. But the most interesting conversations were less about spectacle than maturity.
Proteomics is no longer trying only to prove that it can see more. It is trying to prove that it can measure consistently, explain biology more deeply, support drug development, fit into clinical laboratories, and eventually improve patient decisions.
That means the next bottleneck is distributed across the ecosystem: sample preparation, standards, software, education, reimbursement, clinical menus, regulatory validation, open tools, and the ability to translate technical power into something a clinician can use.
Longer term, integrated proteomics, other omics, imaging, clinical data, and AI may support not only single biomarkers, but interpretable molecular patterns, longitudinal trajectories, and digital-twin-like models of patient biology.
The field spent decades making proteins visible. The next challenge is making proteomic measurements dependable enough to act on.
IntroductionCannabis use during pregnancy and the postpartum period has increased in recent years, raising clinical concerns regarding maternal and infant health, particularly during lactation. However, evidence regarding Δ9-THC concentrations in breast milk during the early postpartum period and their relationship with other biological matrices remains limited.ObjectiveThis study aimed to assess Δ9-THC concentrations in breast milk and saliva, and 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THC-COOH) concentrations in urine, among postpartum women with cannabis use at the time of delivery. A secondary objective was to explore correlations between these biological matrices during early lactation.MethodsA longitudinal observational study was conducted at Vall d’Hebron University Hospital (Barcelona, Spain) between April 2022 and December 2023. Thirteen postpartum women aged over 18 years with a positive urine test for cannabis at delivery and intent to breastfeed were included. Saliva, urine, and breast milk samples were collected at 24 hours, 48 hours, and one week after birth. Δ9-THC concentrations in breast milk and saliva and THC-COOH concentrations in urine were analyzed using liquid chromatography–tandem mass spectrometry (LC-MS/MS).ResultsAmong participants who remained abstinent during the first postpartum week, urinary THC-COOH concentrations progressively decreased but remained quantifiable across all study stages. In contrast, Δ9-THC concentrations in breast milk decreased over time and were below the limit of quantification (LOQ) one week postpartum. Salivary Δ9-THC concentrations were generally low and frequently below the LOQ. Breast milk Δ9-THC concentrations at the first sampling stage were significantly correlated with salivary Δ9-THC and urinary THC-COOH concentrations, whereas no significant correlations were observed at later stages.ConclusionsThis preliminary study suggests that Δ9-THC concentrations in breast milk may decline rapidly after postpartum cannabis cessation, becoming non-quantifiable within the first postpartum week among participants who discontinued use after delivery. In contrast, urinary THC-COOH remained quantifiable for a longer period. Salivary Δ9-THC showed limited concordance with breast milk Δ9-THC and should therefore be interpreted cautiously as a potential surrogate marker. Larger prospective studies are needed to confirm these findings and to support evidence-based breastfeeding counseling for women with recent cannabis use.
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.
Myeloid cells show marked heterogeneity in Alzheimer’s disease. This study introduces CODEX-CNS, a single-cell spatial proteomics pipeline, and identifies a human microglial subpopulation enriched in Alzheimer’s disease brains that associates with dense amyloid-β plaques.
Ovarian cancer remains one of the deadliest gynaecological malignancies, with high-grade serous ovarian cancer (HGSOC) accounting for the majority of deaths.1,2 Despite advances in surgery and chemotherapy, most patients are diagnosed at an advanced stage, and the 5-year survival rate has stubbornly remained below 50% for decades.3 A major obstacle is that the traditional FIGO staging system, while useful for prognosis, does not explain why patients within the same stage often follow dramatically different clinical trajectories.
