A double-switch pHLIP system enables selective enrichment of circulating tumor microenvironment-derived extracellular vesicles

Extracellular Vesicle Preparation and Analysis: A State-of-the-Art Review

In recent decades, research on Extracellular Vesicles (EVs) has gained prominence in the life sciences due to their critical roles in both health and disease states, offering promising applications in disease diagnosis, drug delivery, and therapy. However, their inherent heterogeneity and complex origins pose significant challenges to their preparation, analysis, and subsequent clinical application. This review is structured to provide an overview of the biogenesis, composition, and various sources of EVs, thereby laying the groundwork for a detailed discussion of contemporary techniques for their preparation and analysis. Particular focus is given to state-of-the-art technologies that employ both microfluidic and non-microfluidic platforms for EV processing. Furthermore, this discourse extends into innovative approaches that incorporate artificial intelligence and cutting-edge electrochemical sensors, with a particular emphasis on single EV analysis. This review proposes current challenges and outlines prospective avenues for future research. The objective is to motivate researchers to innovate and expand methods for the preparation and analysis of EVs, fully unlocking their biomedical potential.

Choline Phosphate-Grafted Nanozymes as Universal Extracellular Vesicle Probes for Bladder Cancer Detection

Urinary extracellular vesicles (uEVs) are regarded as highly promising liquid-biopsy biomarkers for the early diagnosis and prognosis of bladder cancer (BC). However, detection of uEVs remains technically challenging owing to their huge heterogeneity and ultralow abundance in real samples. We herein present a choline phosphate-grafted platinum nanozyme (Pt@CP) that acts as a universal EV probe for the construction of a high-throughput and high-sensitivity immunoassay, which allowed multiplex profiling of uEV protein markers for BC detection. With the Pt@CP-based immunoassays, three uEV protein markers (MUC-1, CCDC25, and GLUT1) were identified for BC, by which the BC cases (n = 48), cystitis patients (n = 27), and healthy donors (n = 24) were discriminated with high clinical sensitivity and specificity (area under curve = 98.3%). For the BC cases (n = 9) after surgery, the Pt@CP-based immunoassay could report the postoperative residual tumor that cannot be observed by cystoscopy, which is clinically significant for assessing BC recurrence. This work provides generally high sensitivity for EV detection, facilitating the discovery and clinical use of EV-based biomarkers.

Peptide-based capture-and-release purification of extracellular vesicles and statistical algorithm enabled quality assessment

Circulating extracellular vesicles (EVs) have gained significant attention for discovering tumor biomarkers. However, isolating EVs with well-defined homogeneous populations from complex biological samples is challenging. Different isolation methods have been found to derive different EV populations carrying different molecular contents, which confounds current investigations and hinders subsequent clinical translation. Therefore, standardizing and building a rigorous assessment of isolated EV quality associated with downstream molecular analysis is essential. To address this need, we introduce a statistical algorithm (ExoQuality Index, EQI) by integrating multiple EV characterizations (size, particle concentration, zeta potential, total protein, and RNA), enabling direct EV quality assessment and comparisons between different isolation methods. We also introduced a novel capture-release isolation approach using a pH-responsive peptide conjugated with NanoPom magnetic beads (ExCy) for simple, fast, and homogeneous EV isolation from various biological fluids. Bioinformatic analysis of next-generation sequencing (NGS) data of EV total RNAs from pancreatic cancer patient plasma samples using our novel EV isolation approach and quality index strategy illuminates how this approach improves the identification of tumor associated molecular markers. Results showed higher human mRNA coverage compared to existing isolation approaches in terms of both pancreatic cancer pathways and EV cellular component pathways using gProfiler pathway analysis. This study provides a valuable resource for researchers, establishing a workflow to prepare and analyze EV samples carefully and contributing to the advancement of reliable and rigorous EV quality assessment and clinical translation.

Biosynthesized tumor acidity and MMP dual-responsive plant toxin gelonin for robust cancer therapy

Among all kinds of anticancer agents, small molecule drugs produce an unsatisfactory therapeutic effect due to the lack of selectivity, notorious drug resistance and side effects. Therefore, researchers have begun to pay extensive attention to macromolecular drugs with high efficacy and specificity. As a plant toxin, gelonin exerts potent antitumor activity via inhibiting intracellular protein synthesis. However, gelonin lacks a translocation domain, and thus its poor cellular uptake leads to low outcomes of antitumor response. Here, tumor acidity and matrix metalloproteinase (MMP) dual-responsive functional gelonin (Trx-PVGLIG-pHLIP-gelonin, TPpG), composed of a thioredoxin (Trx) tag, a pH low insertion peptide (pHLIP), an MMP-responsive motif PVGLIG hexapeptide and gelonin, was innovatively proposed and biologically synthesized by a gene recombination technique. TPpG exhibited good thermal and serum stability, showed MMP responsiveness and could enter tumor cells under weakly acidic conditions, especially for MMP2-overexpressing HT1080 cells. Compared to low MMP2-expressing MCF-7 cells, TPpG displayed enhanced in vitro antitumor efficacy to HT1080 cells at pH 6.5 as determined by different methods. Likewise, TPpG was much more effective in triggering cell apoptosis and inhibiting protein synthesis in HT1080 cells than in MCF-7 cells. Intriguingly, with enhanced stability and pH/MMP dual responsiveness, TPpG notably inhibited subcutaneous HT1080 xenograft growth in mice and no noticeable off-target side effect was observed. This ingeniously designed strategy aims at providing new perspectives for the development of a smart platform that can intelligently respond to a tumor microenvironment for efficient protein delivery.

Isolation of PD-L1 Extracellular Vesicle Subpopulations Using DNA Computation Mediated Microfluidic Tandem Separation

Accurate isolation of targeted extracellular vesicle (EV) is challenging due to the antigenic heterogeneity of EV subpopulations which are from different cell origins. Most EV subpopulations lack a single marker whose expression cleanly distinguishes them from mixed populations of closely related EVs. Here, a modular platform capable of taking multiple binding events as input, performing logic computations, and producing two independent outputs for tandem microchips for EV subpopulation isolation, is developed. Taking advantages of the excellent selectivity of dual-aptamer recognition and the sensitivity of tandem microchips, this method achieves, for the first time, sequential isolation of tumor PD-L1 EVs and non-tumor PD-L1 EVs. As a result, the developed platform can not only effectively distinguish cancer patients from healthy donors but also provides new clues for assessing immune heterogeneity. Moreover, the captured EVs can be released through a DNA hydrolysis reaction with high efficiency, which is compatible with downstream mass spectrometry for EV proteome profiling. Overall, this strategy is expected to isolate different EV subpopulations, translate EVs into reliable clinical biomarkers, and accurately investigate the biological functions of different EV subsets.

Reliable Detection of Extracellular PD-L1 by DNA Computation-Mediated Microfluidics

Extracellular vesicle PD-L1 (programmed death-1 ligand 1) is of greater value in tumor diagnosis, prognosis, and efficacy monitoring of anti-PD-1/PD-L1 immunotherapy. However, soluble PD-L1 interferes with the accurate detection of extracellular vesicle (EV) PD-L1. Here, we developed a microfluidic differentiation method for the detection of extracellular PD-L1, without the interference of soluble, by DNA computation with lipid probes and PD-L1 aptamer as inputs (DECLA). For the developed DECLA method, a cholesterol-DNA probe was designed that efficiently embeds into the EV membrane, and an aptamer-based PD-L1 probe was used for PD-L1 recognition. Due to the stable secondary structure of the designed connector, only cobinding of cholesterol-DNA and PD-L1 affinity probe induced biotin-labeled connector activation, while soluble PD-L1 cannot hybridize. As a result, PD-L1 EVs can be efficiently captured by streptavidin-functioned herringbone chip and quantified by anti-CD63-induced fluorescence signal. The high specificity of dual-input DNA computation allied to the high sensitivity of microfluidic-based detection was suitable for distinguishing lung cancer patients from healthy donors, highlighting its potential translation to clinical diagnosis and therapy monitoring.

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.