Evaluation of nanobodies engineered with extra-cysteines for site-specific functionalization.

Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is an inflammatory demyelinating disorder that overlaps clinically with multiple sclerosis (MS) but immunopathologically distinct. Although often considered an acute inflammatory disease, recurrent attacks in MOGAD can lead to demyelination, axonal injury, and secondary neurodegeneration. Reliable biomarkers associated with relapse risk and disease subphenotypes, including optic neuritis, remain limited. Here, we aimed to define molecular and cellular signatures that distinguish MOGAD from MS as a prototypical neuroinflammatory disease and from Alzheimer disease (AD) as a proxy of neurodegeneration and to identify candidate immune-proteomic features associated with relapse frequency and clinical phenotype in MOGAD. CSF, serum, and whole-blood samples from patients with MOGAD (n = 67), MS (n = 49), and AD (n = 36) were profiled using NULISAseq™ CSF proteomics, Olink Explore 3072 CSF and serum proteomics, and high-dimensional mass cytometry for immune cell characterization. In MOGAD, longitudinal clinical data, including total attack counts from the earliest documented attack through follow-up, were integrated with immune and proteomic profiles to assess associations with disease course and clinical phenotype. CSF and blood proteomic profiling revealed distinct inflammatory and cardiometabolic proteomic profiles in MOGAD, differentiating it from both MS and AD. Compared with MS, MOGAD showed relative reductions in lymphocyte populations with regulatory phenotypes. Within MOGAD, relapsing disease was associated with reduced frequencies of CD8+CCR7+CD31+CTLA4+ T cells and concurrent expansion of double-negative γδ T-cell subsets. IL-13 correlated positively with relapse frequency and inversely with circulating regulatory T cells, whereas IL-32 and CASP4 showed opposite associations, correlating negatively with relapse count and positively with Treg frequency. IL-13 was also inversely associated with CD31-expressing CD8+ T cells. Phenotype-stratified analyses suggested that these immune-proteomic relationships differed according to clinical presentation, including optic neuritis vs nonoptic neuritis phenotypes. This integrative immune-proteomic analysis identifies cellular and molecular features associated with relapsing vs monophasic MOGAD, suggesting a model of impaired peripheral immune regulation in relapsing disease. While exploratory, these findings generate a concrete hypothesis for future longitudinal and functional studies aimed at refining biomarker-based monitoring and informing individualized therapeutic strategies in MOGAD.

Single-cell metabolomic analysis is pivotal for deciphering the dynamic and heterogeneous metabolic processes of individual cells. However, the limited metabolite coverage has constrained its broader applicability, primarily due to the low intracellular abundance and wide physicochemical diversity of metabolites. Here, we developed a coaxial hybrid ionization-mass cytometry platform (Hybrid-CytoMS) by integrating a dielectric barrier discharge ionization (DBDI) source with an electrospray ionization (ESI)-based organic mass cytometry system. This integration significantly enhances ionization efficiency through synergistic complementarity of the two ionization mechanisms. The platform features a coaxial architecture that minimizes ion transmission losses while maintaining high-throughput analysis at 30 cells per minute. Hybrid-CytoMS significantly enhances the signal intensities for polar and moderately polar compounds and enables the detection of nonpolar compounds that are invisible in the ESI-only mode. In single-cell metabolomic analysis using Hybrid-CytoMS, >2500 features, >500 single-cell characteristic peaks, and 369 annotated metabolites were detected in positive ion mode. Compared to the results of conventional ESI-based mass cytometry, increasing of >200 characteristic peaks and >100 additional annotated metabolites was achieved. The Hybrid-CytoMS was further applied in the study of benzo[a]pyrene (BaP) - a prototypical polycyclic aromatic hydrocarbon (PAH) - induced metabolic perturbations at single-cell resolution in HepG2 cells. The uptake and clearance curve of intracellular BaP as well as the dynamic metabolic trajectory across exposure time were successfully resolved, initially characterizing the coupling relationship between exogenous exposure and endogenous metabolic status.

A novel JAK1 mutation identified in a patient with severe atopic dermatitis phenotype: Mechanistic insights and IL-4Rα targeted therapy.

Imaging Mass Cytometry (IMC) enables highly multiplexed, spatially resolved single-cell proteomics, providing simultaneous measurement of dozens of protein markers while preserving tissue architecture. Despite its analytical power, IMC data analysis remains fragmented across multiple software environments, requiring researchers to combine independent tools for visualization, preprocessing, segmentation, feature extraction, phenotyping, batch correction, and spatial analysis. This fragmentation increases technical barriers, complicates reproducibility, and limits accessibility for non-computational users. We developed OpenIMC, an open-source platform that integrates the major stages of IMC analysis within a unified graphical and command-line framework. OpenIMC supports image visualization, quality control, preprocessing, segmentation, feature extraction, dimensionality reduction, batch effect correction, clustering, phenotyping, and spatial analysis while maintaining interoperability with established community tools. The platform incorporates automated provenance tracking, records analytical parameters and software versions, and enables export and sharing of complete analytical sessions. Benchmarking demonstrated deterministic behavior across repeated runs, complete concordance between graphical and command-line workflows, and strong agreement with established IMC analysis pipelines. OpenIMC additionally provides support for high-resolution IMC workflows, including signal attenuation modeling and image deconvolution. We apply OpenIMC to two datasets of circulating cells and breast tissue to demonstrate the platform's ability to support integrated single-cell and spatial proteomics analysis. OpenIMC reduces the complexity of IMC data analysis by providing a unified, reproducible, and extensible framework for common IMC workflows. By combining interactive visualization with scalable computational analysis, OpenIMC lowers technical barriers and facilitates reproducible single-cell and spatial proteomics research.

Many cancer patients treated with immune checkpoint blockade (ICB) do not have durable treatment responses. Circulating biomarkers have the potential to identify patients with primary resistance or early progression on therapy to alter treatment course and potentially avoid unnecessary toxicity. Unbiased multimodal proteomic profiling in blood has been underexplored due to the previously limited scalability of multiplexing technologies or cohorts lacking time-series sampling. To address this, we performed plasma proteomic profiling of >2900 proteins and high-dimensional mass cytometry of peripheral blood lymphocytes across serial time points in 250 metastatic melanoma patients on ICB treatment. We further obtained 92 patient-matched tumor samples, which were processed for single-cell and/or bulk RNA sequencing. Proteins upregulated post-ICB were associated with inflammatory pathways involving the activation of effector immune functions. Expression of genes corresponding to these proteins was higher in immune cells involved in recruitment and tumor reactivity. Expression of genes corresponding to plasma proteins more abundant in non-responders was highest in suppressive myeloid subsets and malignant cells. We further posit the involvement of these non-responder genes in immunosuppressive and pro-tumor interactions, which we confirm using publicly available spatial transcriptomic data. We also find that epithelial-specific proteins in the circulation of responders post-ICB associate with patient toxicity and likely originate from degradation of healthy tissues. Together, these data represent extensive potential peripheral biomarker characterization using paired blood and tumor samples in melanoma patients treated with ICB, and begin to elucidate the complex interplay between tumors and the systemic immune response within the host.

Brain metastases (BrM) remain a major cause of mortality in breast cancer (BC), yet the spatial organization and molecular circuitry of the metastatic immune microenvironment are poorly defined. To address this gap, we integrated high-plex imaging mass cytometry (IMC) performed on human primary breast tumors (n=20 regions of interests (ROIs)) and human brain-metastasis tissues (n=40 ROIs) with publicly available datasets, including single-cell RNA sequencing (scRNA-seq) from BC (n=10) and BrM (n=12) and spatial transcriptomic (ST) data from BC (n=7) and BrM (n=1), enabling single-cell resolution of tissue architecture, functional states, and intercellular signaling. IMC resolved nine major cell classes and diverse epithelial, myeloid, and T-cell subtypes, and revealed a striking shift in macrophage polarization: CD163-CD11b- macrophages were markedly depleted in brain metastases, whereas CD163+ subsets persisted. Spatial analysis demonstrated the loss of the immune-permissive CN1 neighborhood in brain metastases, which is enriched in memory T cells, B cells, and dendritic cells, and the expansion of the immunosuppressive CN9 niche in brain metastases, containing CD163+ macrophages, invasion-like epithelial cells, and exhausted T cells. ScRNA-seq integration corroborated these findings by refining the annotation of major immune and stromal lineages and confirming CD163 expression patterns across macrophage subsets. ST deconvolution further reproduced these CN1-like and CN9-like domains in situ, validating their anatomical organization across BC and BrM. Ligand-receptor inference highlighted specific inhibitory pathways-including programmed cell death protein 1/programmed death-ligand 1, growth arrest-specific 6-Tyro3, Axl, and Mer receptor tyrosine kinase family, nectin cell adhesion molecule 2 (NECTIN2)-T-cell immunoreceptor with Ig and ITIM domains, and prostaglandin E2 (PGE2)-prostaglandin E2 receptor 4-that are associated with immune evasion and metastatic growth. Functional validation in an in vivo brain metastasis model further supported the therapeutic relevance of targeting these immunoregulatory pathways. Together, these findings show a shift from permissive to suppressive immune niches, accompanied by pronounced macrophage reprogramming, as central features of BC adaptation to the brain. This spatially resolved framework provides mechanistic insight into the poor responsiveness of brain metastases to current immunotherapies and identifies defined inhibitory ligand-receptor axes as actionable targets for combination immunotherapy.

Lipids are essential chemical components of living cells, and performing in-depth lipidomics at the single-cell level provides profound insights into the cellular structure and function. Here, we couple ozone-induced dissociation (OzID) with mass cytometry to develop a high-throughput in-depth single-cell lipidomics platform. By precisely controlling the OzID reaction conditions to cleave approximately half of the C═C bonds in unsaturated lipids, appropriate abundances of diagnostic products are generated while retaining a comparable proportion of unreacted lipids. Subsequently, high-throughput single-cell mass cytometry analysis is performed to obtain in-depth lipidomic information comprising intact lipids and C═C structural information. The in-depth lipidomic platform achieves a throughput of 166 cells/min, enabling clear discrimination among clinically relevant cell types with highly similar intact lipid profiles, e.g., normal lung epithelial cells (Beas-2B) versus non-small-cell lung cancer cells (A549 and H1299). Our platform demonstrates outstanding potential for discriminating cell types and discovering refined lipid biomarkers.

Galectin-1 Fuels Monocyte Hyperinflammation and Represents a Novel Therapeutic Target in Myeloproliferative Neoplasms.

BackgroundThis study investigates the functional role and mechanisms of Guilu Erxian Glue (GEG) in regulating immune homeostasis to treat bone marrow (BM) failure in aplastic anemia (AA).MethodsDifferentially expressed miRNAs of AA patients were identified through bioinformatics analysis of the Gene Expression Omnibus (GEO) database, followed by enrichment analysis of the identified miRNAs and their predicted target genes. The chemical profile of GEG was characterized by UPLC-MS/MS. An AA mouse model was established via 60Co γ-ray irradiation combined with the infusion of an allogeneic lymphocyte suspension for immune induction. GEG + Immunosuppressive therapy (IST) was administered as the therapeutic intervention, with IST + Eltrombopag (EPAG) serving as the positive control. Bone marrow hematopoietic function was evaluated via hematological parameters, BM histopathology, and flow cytometry. CyTOF-2 mass cytometry was utilized to analyze the differentiation profile of CD4+ T-cell subsets, and flow cytometry was further used to detect the proliferation and differentiation of naïve T cells, Tregs, and follicular helper T cells (Tfh). The expression of genes and proteins involved in the miRNA-17/IKZF4, miRNA-10a/BCL6, and Fas pathways was quantified by RT-qPCR and Western blot, with serum cytokines measured by enzyme-linked immunosorbent assay (ELISA).ResultsBioinformatic analysis of the GSE82095 dataset identified significantly dysregulated miRNAs in T cells from AA patients, specifically miRNA-17-5p and miRNA-10a-5p. Preliminary enrichment analysis indicated that these miRNAs are primarily involved in hypoxic stress, T-cell differentiation, and apoptosis. Subsequently, the downstream target genes of these miRNAs were predicted, and further functional enrichment analysis corroborated that these target genes are critically associated with T-cell differentiation. HPLC-MS/MS identified 22 compounds from the aqueous extract of GEG. In vivo, GEG treatment significantly improved BM histopathology and restored peripheral blood counts. It also reduced the percentages of Tfh, Th1, Th17, and effector memory T cells (Tem) (P < 0.01), while increasing the percentages of Tregs and central memory T cells (Tcm) (P < 0.01). Molecular analysis revealed that GEG upregulated miRNA-10a and downregulated miRNA-17 in Tregs (P < 0.01), leading to increased expression of the Foxp3 cofactor IKZF4 and decreased BCL6 expression (P < 0.01). GEG elevated Foxp3 protein levels and inhibited cleaved caspase-3 expression in Tregs, and reduced the levels of pro-inflammatory cytokines (IFN-γ, TNF-α, and IL-6) (P < 0.01),while enhancing the secretion of anti-inflammatory cytokines (IL-10 and IL-35) (P < 0.01).ConclusionOur findings suggest that GEG contributes to Treg lineage stability and survival through the potential modulation of the miRNA-17/IKZF4, miRNA-10a/BCL6, and Fas/FasL signaling axes. These synergistic effects appear to support the remodeling of T-cell homeostasis and restoration of cytokine balance, offering a promising, miRNA-targeted strategy for enhancing AA therapy that warrants further clinical investigation.

ABSTRACT Dogs are a critical non‐rodent species used in preclinical safety studies, particularly, in the pharmaceutical field, due to their physiological, metabolic, and immunological similarities to humans. As such, immunophenotyping of canine peripheral blood mononuclear cells (PBMCs) plays a crucial role in translational research, immune monitoring, and safety evaluations in drug development. However, the limited availability of canine‐specific antibodies restricts detailed and accurate immune profiling, which is essential for advancing safety evaluations in drug development. To address this challenge, we developed a 15‐marker panel for comprehensive mass cytometry‐based immunophenotyping of cryopreserved canine PBMCs. This panel encompasses major leukocyte subsets, including B cells, CD4+ T helper cells, regulatory T cells, CD8+ cytotoxic T cells, memory T cell subsets, natural killer T cells, natural killer cells, dendritic cells, CD4+ monocytes, classical monocytes, and neutrophils. We utilized both extracellular and intracellular markers to facilitate in‐depth immune profiling, despite the limited availability of canine‐specific antibodies. The panel was thoroughly optimized in terms of marker selection, antibody clone validation, and metal isotope pairing. Additionally, by the use of mass cytometry, several channels remain unoccupied, providing flexibility for future panel expansion.

A myeloid immunosuppressive phenotype defines primary refractoriness to atezolizumab Plus bevacizumab in hepatocellular caarcinoma.

Cell cycle (CC) dynamics are reflected in diverse T cell processes such as TCR activation, expansion, contraction, differentiation, senescence, anergy, and exhaustion; linking CC behaviors to functional and dysfunctional T cell states in development and disease. Progression through CC checkpoints is also tightly linked to cell fate decisions across development. Yet, much remains unknown about the connection between CC sensing and T cell differentiation programs. To disentangle the relationship across T cell state, time-since-activation, receptor signaling, division, and CC, we leverage high-throughput single-cell mass cytometry for parallel measurement of these diverse biological states. By modulating CC progression and receptor signaling with inhibitors as well as tonic signaling Chimeric Antigen Receptor (CAR) models of T cell exhaustion, we reveal that earlier G1/S CC programs crosstalk with receptor signaling to control T cell fate, and that exhaustion programs are downstream to aberrant, S-G2 phase CC arrest signatures in tonic CAR signalingin vitro,in situ, and in vivoacross human cancers in association with CD8 T-lymphocyte dysfunction.

RationaleSepsis is a state of life-threatening organ dysfunction in the setting of infection. It is biologically heterogeneous, as evidenced by whole blood transcriptomic analyses that reveal distinct molecular subtypes based on gene expression. At the cellular level, sepsis is primarily mediated by neutrophils, a leukocyte population increasingly recognized as heterogeneous across several domains. We sought to further characterize neutrophil heterogeneity by identifying neutrophil subsets in critically ill patients with severe sepsis based on multidimensional mass cytometry analysis.MethodsWe generated time series mass cytometry (CyTOF) data from whole blood samples taken from 17 patients with sepsis admitted to an intensive care unit who were enrolled in a randomized controlled trial of high-dose vitamin C. We analyzed these data using unsupervised machine learning techniques to identify distinct neutrophil subtypes. We characterized the resulting subtypes and described their changes over time.ResultsWe analyzed approximately 1.5 million cytometry events gated as neutrophils. We identified five clusters that reveal complex heterogeneity across multiple neutrophil markers of maturation and activation including olfactomedin-4 (OLFM4), CD177, glucose transporter 1 (GLUT1), CD16, and lipocalin-2. The two dominant clusters differed primarily in the abundance of OLFM4, a neutrophil granule protein. Between Day 1 and Day 7, there was an increased proportion of neutrophils in the dominant OLFM4-expressing cluster, a difference primarily driven by increased abundance of OLFM4 in the placebo group, but not the vitamin C group.ConclusionsOur findings point to complex multidimensional heterogeneity among neutrophils in sepsis, thereby extending the current concept of neutrophil heterogeneity with new data from critically ill patients with sepsis derived from mass cytometry. Variation in the temporal differences in cluster proportions between treatment arms may suggest opportunities for precision sepsis treatment.Supplementary InformationThe online version contains supplementary material available at 10.1186/s40635-026-00924-2.

Breast cancer remains a leading cause of death worldwide. Although chemotherapy reduces primary and metastatic tumour burden, persisting drug-tolerant tumour cell populations, known as minimal residual disease (MRD), pose a significant risk of recurrence and therapy resistance. In this study, we describe the spatiotemporal organisation of therapy response and MRD in BRCA1;p53-deficient mouse mammary tumours and human clinical samples. By integrating single-cell RNA sequencing, spatial transcriptomics, and imaging mass cytometry across multiple treatment timepoints, we characterise dynamic interactions between tumour cell subpopulations and their surrounding microenvironment. Our multiomic analysis uncovers a distinct, chemotherapy-tolerant epithelial-mesenchymal transition (EMT) cancer cell population that displays a conserved expression programme in human BRCA1-deficient tumours, significantly correlates with adverse clinical outcomes, and can be pharmacologically targeted in preclinical models. We reveal the spatial distribution of residual EMT-like tumour cells within discrete anatomical niches, providing a framework for understanding the persistence of MRD and potential therapeutic vulnerabilities.

The introduction of immune checkpoint inhibitors (ICIs) into the treatment of melanoma has significantly reduced mortality over the past decade. However, therapeutic benefit is not observed in all patients, and treatment may be associated with severe adverse events. Therefore, identifying patients who are most likely to benefit from immunotherapy remains of critical importance. Currently used biomarkers, such as programmed death-ligand 1 (PD-L1) expression and manual assessment of tumour-infiltrating lymphocytes (TILs), have limited predictive value. This narrative review provides a critical appraisal of studies employing digital pathology tools, multiplex and spatial techniques (including multiplex immunofluorescence, imaging mass cytometry, and digital spatial profiling), as well as machine learning algorithms for predicting response to ICIs in patients with melanoma. Available evidence suggests that the highest predictive value may be achieved by approaches integrating quantitative assessment of immune infiltration with information on its spatial distribution, functional state, and interactions within the tumour microenvironment. Particular relevance may be attributed to features associated with the “immune-inflamed”, “immune-excluded”, and “immune-desert” phenotypes, the presence of tertiary lymphoid structures, and the organisation of local immune niches. In addition, this review highlights key limitations in the interpretation of current data, including lack of methodological standardisation, data heterogeneity, and insufficient validation. Directions for future research necessary for the implementation of these approaches into routine clinical practice are also discussed.

Quantitative characterization of nanoparticle (NP)-cell interactions by aquatic organisms remains analytically challenging due to the rapid, heterogeneous, and dynamic nature of NP-cell processes. In this study, we presented a label-free, real-time analytical methodology that facilitated high-temporal-resolution analysis of NP-cell interactions in Tetrahymena thermophila by mass cytometry (CyTOF). By utilizing 88Sr as a cellular fingerprint marker of T. thermophila, we achieved subsecond detection resolution of NP-cell interaction signals under continuous monitoring conditions. The interaction kinetics of NPs, evaluated using I0/T1/2 were significantly dependent on temporal resolution, with values obtained at high temporal resolution being approximately 2-fold higher than those derived from low-resolution sampling. This strategy reduced information loss by 39% compared to long-interval sampling. Furthermore, a distinct subpopulation exhibiting high NPs-associated signals was identified, with its proportion increasing from 1.4 ± 0.3 to 9.3 ± 0.5% over 10 h, which provided a quantitative basis for targeted characterization of functionally distinct cellular states at the single-cell level. Overall, this method achieved continuous, high-resolution, and high-throughput quantification of NP-cell interaction dynamics, providing an analytical framework for investigating time-dependent NP behavior at the single-cell level.

Label-free flow cytometry is a powerful technique for cellular property characterization. So far, there has been no cytometry reported to obtain both physical and chemical properties at the single-cell level. Herein, we propose a label-free dual-mode flow cytometry that integrates impedance cytometry and mass cytometry. Single cells flowing through the impedance cytometry are individually characterized with electrical (cytoplasm conductivity, and specific membrane capacitance) and mechanical (Young's modulus, and fluidity) parameters. They are subsequently ionized by downstream on-chip electrospray and characterized with chemical components (42 types of metabolites and lipids) via mass spectrometry analysis. We conducted a pilot test of three different human leukemia cell lines (THP-1, Jurkat, and HL-60 cells) to obtain their physicochemical properties. We found that using multimodal properties for cell typing achieved higher accuracy (∼11% increase) than single-mode properties. More interestingly, property correlation analysis indicates that lipid PC(P-36:0) may be a common component contributing to regulating the electrical properties (i.e., specific membrane capacitance) for all three cell lines, but the major component for each cell line may be different (e.g., PE(P-36:4) for HL-60 cytoplasm conductivity). For some exceptional cells (5% outliers), cell conductivity exhibits a very high correlation coefficient (-0.83) with PC(38:2). These observations, though preliminary, only became available by our proposed dual-mode flow cytometry, and open a new perspective for investigating the molecular mechanisms underpinning cellular biophysics at the single-cell level. We envision that this work would foster a lot of research opportunities in multimodal cell characterization and provide new clues for molecular cell biology.

A pancreatic intraepithelial neoplasia-Oramic view of early pancreatic cancer in 3D.

Autologous cell therapy is a patient-embraced approach to inflammatory disease. Murine models demonstrate that alternatively activated (or regulatory) macrophages can reduce the severity of disease, and human macrophages (M0) educated with interleukin-4 (M(IL4)) limit murine colitis. M0 and M(IL4) from healthy donors and individuals with Crohn's disease (active or remission) were assessed by qPCR, characterized by a time-of-flight mass cytometry (CyTOF) comparison of 16 myeloid marker molecules expression, and subtypes of M(IL4)s tested in the murine dinitrobenzene sulfonic acid (DNBS) model of colitis and in vitro co-culture with T cells. Most macrophage preparations responded to IL-4 by increasing CD206, CCL18 and RAMP1 mRNA expression and produced mediators that promoted epithelial repair in a wound assay with the human CaCo2 epithelial line. Single-cell clustering by FlowSOM defined 8 distinct subpopulations (meta-clusters) of macrophages, the proportions of which were unaffected by biological sex, age or cryopreservation: meta-cluster 4 (CD206highPD-L1highHLA-DRhigh) represented 20 to 40% of M0s, with a higher proportion in controls compared with active Crohn's disease. IL-4 treatment significantly expanded meta-cluster 4 in all M0s. Transfer of M(IL4)-CD206high (ie, predominantly meta-cluster 4) into rag1-/- mice significantly reduced DNBS-induced colitis, and to a greater extent than M(IL4)-CD206low from the same individual. IL-10 production was increased in M(IL4)-CD206high-T-cell co-cultures. Having confirmed the prohealing effect of the human M(IL4), the predominant meta-cluster M(IL4)-CD206high was found to have superior anticolitic effect. Reduced numbers of M0-CD206high and M(IL4)-CD206high cells in some individuals with Crohn's disease suggest that their absence may contribute to more severe inflammation or reduced healing capacity.