Aim: Extracellular vesicles (EVs) are emerging as important mediators in myeloproliferative neoplasms (MPNs), but the characteristics of distinct EV subtypes remain unclear. This study aimed to characterize the molecular heterogeneity of MEG-01-derived large EVs (lEVs) and small EVs (sEVs). Methods: lEVs and sEVs were isolated from MEG-01 cell culture medium by differential centrifugation followed by density gradient ultracentrifugation. EVs were characterized by transmission electron microscopy, nanoparticle tracking analysis, immunoblotting, and on-bead flow cytometry. Quantitative proteomic analysis was performed to compare the protein cargo of lEVs and sEVs, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Results: Density gradient purification yielded high purity of lEV and sEV with distinct size, morphologies, and protein compositions. On-bead flow cytometry enabled efficient profiling of EV surface markers. Proteomic analysis revealed a shared core proteome between lEVs and sEVs, together with subtype specific protein signatures. Enrichment analyses indicated distinct molecular characteristics of the two EV populations, with sEVs showing enrichment of proteins associated with RNA-related processes and vesicle-mediated transport, whereas lEVs were enriched in proteins related to cytoskeletal organization and metabolism. Conclusion: MEG-01 derived lEVs and sEVs comprise molecularly distinct subpopulations with shared and subtype specific protein cargo. The workflow established in this study provides a robust platform for high purity EV isolation and characterization, and offers insights into MEG-01 EV heterogeneity relevant to megakaryocyte biology and MPN research.

Supplementary Figure 4: Effects of resiquimod on CD45-and CD45+ cell fractions in MPDOs.

Melanoma is one of the top 5 cancer types, causes most deaths among skin cancers, and can be frequently misdiagnosed. Recent pathology image foundation models remain difficult to make accurate differential diagnosis across over forty melanocytic neoplasm histologic subtypes. Motivated by the diagnostic reasoning process of dermatopathologists, we curated a high-quality image and knowledge corpus database containing 2893 images and 1102 knowledge entries annotated by expert dermatopathologists at the University of Pennsylvania. Leveraging this multi-modal dataset, we present "Melan-Dx", a knowledge-enhanced AI framework that augments frozen pathology vision-language models through retrieval from a curated vision-knowledge database, improving differential diagnosis at both patch and whole-slide levels. Melan-Dx, at its best performance, demonstrates 0.869 accuracy for binary classification, 0.699 Top-1 accuracy among forty-class classification, 0.915 ROC AUC for few-shot WSI tasks, and 0.925 AUPRC for fully supervised WSI tasks. Across all experimental settings, Melan-Dx shows improvements up to 13.8% over linear and fully finetuned methods, 23-70.6% over zero-shot approaches and up to 8.4% improvements in whole slide image classification. These findings suggest that a query database with a knowledge-enhanced AI framework can further improve existing pathology foundation models without fine-tuning the vision backbone. The code is publicly available at https://www.github.com/zhihuanglab/Melan-Dx-code .

Melanoma is a common and aggressive cancer, with rising incidence in most developed countries. Major discoveries in melanoma biology have been rapidly translated, allowing cures for patients in late-stage disease. Despite these advances, many tumors remain refractory, in part due to an incomplete understanding of the genes and pathways gained or lost during melanoma tumorigenesis. To address this gap and provide a broadly useful resource for the scientific community, we established melPDomiX, a multi-omics cohort of melanoma-patient-derived xenografts. By linking mutations with transcriptomic and proteomic features, melPDomiX enables systematic characterization of gain- and loss-of-function alterations in treatment-refractory melanoma. Using multi-omics integration and structural-context representation, we demonstrate how this resource distinguishes gain- from loss-of-function variants and uncovers new candidate melanoma drivers and therapeutic targets. Together, melPDomiX provides a comprehensive, deeply profiled set of tumor models that supports mechanistic discovery and facilitates the development of improved treatments for this devastating heterogeneous malignancy.

Despite advances in immune checkpoint blockade, resistance in metastatic melanoma remains a major challenge. To decode resistance mechanisms, we generate a comprehensive longitudinal, multi-omic, and spatial atlas of 45 tumor samples across 10 patients. Analysis reveals resistant tumors undergo convergent evolution toward a shared, spatially organized immunosuppressive ecosystem. We identify a structural mechanism characterized by spatial partitioning of immune checkpoints, where B7-H3 dominates MITF-high niches while IDO1 characterizes MITF-low zones. Furthermore, integrated single-cell and spatial analysis identifies a specific malignant subclone (c1) and a distinct architectural niche (RCN3), both exhibiting aberrant PI3K-mTOR signaling. Notably, c1 promotes the “ignored tumor” phenotype via FN1-ITGB1 and GDF15 signaling. Validated across independent cohorts, these spatial and molecular signatures predict poor survival and point to actionable targets. Ultimately, our study elucidates the spatial logic of resistance and provides a rationale for translating multi-omic discoveries into actionable, personalized therapeutic strategies.

Melanoma metastasis is driven by extensive intratumoral heterogeneity and phenotypic plasticity, yet how clonal identity relates to transcriptional programs during metastasis remains unclear. Here, we applied MeRLin, a single-cell lineage tracing platform, to dissect the clonal and transcriptional heterogeneity of metastatic melanoma in a patient-derived spontaneous metastasis model. Clonal analyses revealed hierarchical structures during tumor progression, with a subset of lineages from primary tumors consistently enriched across metastatic sites, supporting a model of polyclonal seeding followed by selective expansion of pre-existing highly metastatic subpopulations. Single-cell transcriptomic profiling identified two major metastatic subpopulations of distinct transcriptional programs, characterized by neural crest stem cell-like and lipid metabolism signatures. Both programs were enriched for invasion-associated genes and maintained across organs through distinct regulatory networks. Spatial mapping by barcode RNA-FISH linked these transcriptional states to their tissue context and showed that OLFML3 expression partially co-localized with a dominant subpopulation at the tumor-liver interface, marking the invasive fronts of metastatic growth. Together, these findings establish a framework in which clonal identity, transcriptional state, and spatial organization jointly shape metastatic melanoma progression.

Supplementary Figure 5: Granzyme B and Ki-67 expression in CD8+ T cells in the MPDOs.

Abstract The Toll-like receptor (TLR) 7/8 agonist resiquimod shows promise for treating cutaneous T-cell lymphoma and actinic keratosis, yet its mechanism of action remains unclear. We demonstrated that topical resiquimod significantly inhibited melanoma growth across various genetic and syngeneic mouse models, prolonged survival, and reduced lymph node metastasis in vivo. Resiquimod suppressed B16 melanoma growth, with an effect superior to that of imiquimod. The therapeutic effect was CD8+ T cell–dependent, as evidenced by the loss of efficacy upon CD8+ T-cell depletion or in Rag2−/− mice. Resiquimod increased intratumoral CD45+ inflammatory cells, particularly antigen-experienced PD-1+CD62L−CD8+ effector T cells, and enhanced their Ki-67 and granzyme B expression. Resiquimod significantly expanded Pmel- and Trp2-specific CD8+ T cells in the presence of dendritic cells (DC). Topical treatment of melanoma-bearing mice induced systemic protection in rechallenge experiments. In addition, combining topical resiquimod with anti–PD-1 antibodies led to superior inhibition of tumor growth and metastasis across multiple melanoma models. Proteomic analysis revealed increased granzyme B and CD26 and decreased phosphorylated FOXO3a after treatment. In patient-derived organoids and melanoma slice cultures, resiquimod induced significant tumor killing and CD8+ T-cell activation, further augmented by PD-1 antibodies. Our findings support the conclusion that resiquimod promotes CD8+ T-cell priming via DCs and enhances the therapeutic efficacy of anti–PD-1 checkpoint blockade in melanoma.

The plasma protein corona (PC) critically influences the in vivo fate of nanomedicines, yet its composition and impact on extracellular vesicles (EVs) remain poorly defined. Using a biomimetic circulation system, we characterized PC formation and modulation on two clinically relevant EV types: mesenchymal stromal cell-derived EVs (MSC-EVs) and HEK293F-derived EVs (293F-EVs). Under dynamic flow, both EV types acquired stable coronas, resulting in increased particle size and decreased surface charge. Proteomic profiling revealed a shared corona signature enriched in immunoglobulins, complements, and other plasma components. Functionally, PC formation enhanced macrophage uptake and triggered inflammatory activation, primarily via interactions between corona-bound immunoglobulins or complement C3 and their respective receptors. To disrupt this process, we developed a charge-shielding strategy using positively charged chitosan oligosaccharide (COS) to inhibit PC assembly. COS coating effectively neutralized EV surface charge and reduced opsonin adsorption and non-specific macrophage clearance, thereby reshaping EV biodistribution-limiting hepatic sequestration and enhancing delivery to extrahepatic organs. In a murine sepsis model, COS-modified MSC-EVs further improved renal and pulmonary outcomes and markedly increased survival. Collectively, these findings elucidate the molecular architecture and immunological impact of the EV-associated plasma PC and introduce a promising anti-corona strategy for engineering stealthier and more effective EV-based nanotherapeutics.