The biology and function of extracellular vesicles in immune response and immunity.

Extracellular vesicles (EVs), such as ectosomes and exosomes, contain DNA, RNA, proteins and are encased in a phospholipid bilayer. EVs provide intralumenal cargo for delivery into the cytoplasm of recipient cells with an impact on the function of immune cells, in part because their biogenesis can also intersect with antigen processing and presentation. Motile EVs from activated immune cells may increase the frequency of immune synapses on recipient cells in a proximity-independent manner for local and long-distance modulation of systemic immunity in inflammation, autoimmunity, organ fibrosis, cancer, and infections. Natural and engineered EVs exhibit the ability to impact innate and adaptive immunity and are entering clinical trials. EVs are likely a component of an optimally functioning immune system, with the potential to serve as immunotherapeutics. Considering the evolving evidence, it is possible that EVs could be the original primordial organic units that preceded the creation of the first cell.

The collagen landscape in cancer: profiling collagens in tumors and in circulation reveals novel markers of cancer-associated fibroblast subtypes.

Cancer-associated fibroblasts (CAFs) deposit and remodel collagens in the tumor stroma, impacting cancer progression and efficacy of interventions. CAFs are the focus of new therapeutics with the aim of normalizing the tumor microenvironment. To do this, a better understanding of CAF heterogeneity and collagen composition in cancer is needed. In this study, we sought to profile the expression of collagens at multiple levels with the goal of identifying cancer biomarkers. We investigated the collagen expression pattern in various cell types and CAF subtypes in a publicly available single-cell RNA sequencing (RNA-seq) dataset of pancreatic ductal adenocarcinoma. Next, we investigated the collagen expression profile in tumor samples across cancer types from The Cancer Genome Atlas (TCGA) database and evaluated if specific patterns of collagen expression were associated with prognosis. Finally, we profiled circulating collagen peptides using a panel of immunoassays to measure collagen fragments in the serum of cancer patients. We found that pancreatic stellate cells and fibroblasts were the primary producers of collagens in the pancreas. COL1A1, COL3A1, COL5A1, COL6A1 were expressed in all CAF subtypes, whereas COL8A1, COL10A1, COL11A1, COL12A1 were specific to myofibroblast CAFs (myCAF) and COL14A1 specific to inflammatory CAFs (iCAF). In TCGA database, myCAF collagens COL10A1 and COL11A1 were elevated across solid tumor types, and multiple associations between high expression and worse survival were found. Finally, circulating collagen biomarkers were elevated in the serum of patients with cancer relative to healthy controls with COL11A1 (myCAF) having the best diagnostic accuracy of the markers measured. In conclusion, CAFs express a noncanonical collagen profile with specific collagen subtypes associated with iCAFs and myCAFs in PDAC. These collagens are deregulated at the cellular, tumor, and systemic levels across different solid tumors and associate with survival. These findings could lead to new discoveries such as novel biomarkers and therapeutic targets. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes.

Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.

Mouse Models to Evaluate the Functional Role of the Tumor Microenvironment in Cancer Progression and Therapy Responses.

The tumor microenvironment (TME) is a complex ecosystem of both cellular and noncellular components that functions to impact the evolution of cancer. Various aspects of the TME have been targeted for the control of cancer; however, TME composition is dynamic, with the overall abundance of immune cells, endothelial cells (ECs), fibroblasts, and extracellular matrix (ECM) as well as subsets of TME components changing at different stages of progression and in response to therapy. To effectively treat cancer, an understanding of the functional role of the TME is needed. Genetically engineered mouse models have enabled comprehensive insight into the complex interactions within the TME ecosystem that regulate disease progression. Here, we review recent advances in mouse models that have been employed to understand how the TME regulates cancer initiation, progression, metastasis, and response to therapy.

Single Extracellular Vesicle Imaging and Computational Analysis Identifies Inherent Architectural Heterogeneity.

Evaluating the heterogeneity of extracellular vesicles (EVs) is crucial for unraveling their complex actions and biodistribution. Here, we identify consistent architectural heterogeneity of EVs using cryogenic transmission electron microscopy (cryo-TEM), which has an inherent ability to image biological samples without harsh labeling methods while preserving their native conformation. Imaging EVs isolated using different methodologies from distinct sources, such as cancer cells, normal cells, immortalized cells, and body fluids, we identify a structural atlas of their dominantly consistent shapes. We identify EV architectural attributes by utilizing a segmentation neural network model. In total, 7,576 individual EVs were imaged and quantified by our computational pipeline. Across all 7,576 independent EVs, the average eccentricity was 0.5366 ± 0.2, and the average equivalent diameter was 132.43 ± 67 nm. The architectural heterogeneity was consistent across all sources of EVs, independent of purification techniques, and compromised of single spherical, rod-like or tubular, and double shapes. This study will serve as a reference foundation for high-resolution images of EVs and offer insights into their potential biological impact.

Type I conventional dendritic cells facilitate immunotherapy in pancreatic cancer.

Inflammation and tissue damage associated with pancreatitis can precede or occur concurrently with pancreatic ductal adenocarcinoma (PDAC). We demonstrate that in PDAC coupled with pancreatitis (ptPDAC), antigen-presenting type I conventional dendritic cells (cDC1s) are specifically activated. Immune checkpoint blockade therapy (iCBT) leads to cytotoxic CD8+ T cell activation and elimination of ptPDAC with restoration of life span even upon PDAC rechallenge. Using PDAC antigen-loaded cDC1s as a vaccine, immunotherapy-resistant PDAC was rendered sensitive to iCBT with elimination of tumors. cDC1 vaccination coupled with iCBT identified specific CDR3 sequences in the tumor-infiltrating CD8+ T cells with potential therapeutic importance. This study identifies a fundamental difference in the immune microenvironment in PDAC concurrent with, or without, pancreatitis and provides a rationale for combining cDC1 vaccination with iCBT as a potential treatment option.

Genetic reprogramming with stem cells regenerates glomerular epithelial podocytes in Alport syndrome.

Glomerular filtration relies on the type IV collagen (ColIV) network of the glomerular basement membrane, namely, in the triple helical molecules containing the α3, α4, and α5 chains of ColIV. Loss of function mutations in the genes encoding these chains (Col4a3, Col4a4, and Col4a5) is associated with the loss of renal function observed in Alport syndrome (AS). Precise understanding of the cellular basis for the patho-mechanism remains unknown and a specific therapy for this disease does not currently exist. Here, we generated a novel allele for the conditional deletion of Col4a3 in different glomerular cell types in mice. We found that podocytes specifically generate α3 chains in the developing glomerular basement membrane, and that its absence is sufficient to impair glomerular filtration as seen in AS. Next, we show that horizontal gene transfer, enhanced by TGFß1 and using allogenic bone marrow-derived mesenchymal stem cells and induced pluripotent stem cells, rescues Col4a3 expression and revive kidney function in Col4a3-deficient AS mice. Our proof-of-concept study supports that horizontal gene transfer such as cell fusion enables cell-based therapy in Alport syndrome.

Development of an engineered extracellular vesicles-based vaccine platform for combined delivery of mRNA and protein to induce functional immunity.

mRNA incorporated in lipid nanoparticles (LNPs) became a new class of vaccine modality for induction of immunity against COVID-19 and ushered in a new era in vaccine development. Here, we report a novel, easy-to-execute, and cost effective engineered extracellular vesicles (EVs)-based combined mRNA and protein vaccine platform (EVX-M+P vaccine) and explore its utility in proof-of-concept immunity studies in the settings of cancer and infectious disease. As a first example, we engineered EVs to contain ovalbumin mRNA and protein (EVOvaM+P) to serve as cancer vaccine against ovalbumin-expressing melanoma tumors. EVOvaM+P administration to mice with established melanoma tumors resulted in tumor regression associated with effective humoral and adaptive immune responses. As a second example, we generated engineered EVs, natural nanoparticle carriers shed by all cells, that contain mRNA and protein Spike (S) protein to serve as a combined mRNA and protein vaccine (EVSpikeM+P vaccine) against SARS-CoV-2 infection. EVSpikeM+P vaccine administration in mice and baboons elicited robust production of neutralizing IgG antibodies against RBD (receptor binding domain) of S protein and S protein specific T cell responses. Our proof-of-concept study describes a new platform with an ability for rapid development of combination mRNA and protein vaccines employing EVs for deployment against cancer and other diseases.

Single extracellular vesicle imaging and computational analysis identifies inherent architectural heterogeneity.

Evaluating the heterogeneity of extracellular vesicles (EVs) is crucial for unraveling their complex actions and biodistribution. Here, we identify consistent architectural heterogeneity of EVs using cryogenic transmission electron microscopy (cryo-TEM) which has an inherent ability to image biological samples without harsh labeling methods and while preserving their native conformation. Imaging EVs isolated using different methodologies from distinct sources such as cancer cells, normal cells, and body fluids, we identify a structural atlas of their dominantly consistent shapes. We identify EV architectural attributes by utilizing a segmentation neural network model. In total, 7,576 individual EVs were imaged and quantified by our computational pipeline. Across all 7,576 independent EVs, the average eccentricity was 0.5366, and the average equivalent diameter was 132.43 nm. The architectural heterogeneity was consistent across all sources of EVs, independent of purification techniques, and compromised of single spherical (S. Spherical), rod-like or tubular, and double shapes. This study will serve as a reference foundation for high-resolution EV images and offer insights into their potential biological impact.