Development of liquid biopsies for non-invasive tumor characterization techniques shows a great promise for real-time monitoring of tumor recurrence, progression, and treatment response. To better assess the various tumor evolution, cellular heterogeneity, consequent drug-resistance mechanisms, it is critical to screen specific proteins and nucleic acids from biofluid-derived extracellular vesicles (EVs). We are developing a rapid integrated ultra-sensitive digital bead-based sensor enabling evaluation of molecular profiling of EVs in glioblastoma (GBM). The tumor relevant nucleic acid information in EVs is enriched by antibody-specific magnetic beads and amplified by reverse transcription recombinase polymerase amplification using the CRISPR/Cas13a system with around twenty-five thousand droplets. Detection results are analyzed by a miniaturized portable imaging device. The niches of our developing technique include: (a) the streamlined sample preparation process and miniaturized detection system enable the sensitive detection of important mRNAs, EGFR, EGFRvIII, IDH1wt, IDH1 R132H and GAPDH, in EVs within 1.5 hours; (b) the fluorescent signal-to-noise ratio (SNR) in each micrometer sized droplet is significantly enhanced via using a simple Cas13a assay and triggering greater than 104 turnovers of fluorescent reporters on a high-throughput microfluidic chip; (c) real-time image data are transferred to a cloud-based server and classified using several trained YOLOv5 models; (d) the RNAomics results are displayed on the Raspberry Pi touchscreen. This approach improves the speed of GBM diagnosis and treatment decision-making in using liquid biopsy.
Glioblastoma (GBM) is the most aggressive malignant primary brain tumor[1]. Effective treatments on the early-stage glioblastoma may improve its poor survival rate and poor prognosis[2]. In this pilot study, a low copy number of nucleic acid detection system using a high-throughput microfluidic droplet imaging device was developed to achieve the specific nucleic acid detection of extracellular vesicles (EVs). Our aim is to realize an inexpensive liquid biopsy tool for the early detection of GBM using human plasma and cerebrospinal fluid (CSF). Our results demonstrate that: (a) the fabricated microfluidic chip (10 mm × 25 mm) can produce more than 25,000 droplets within 3 minutes. The average volume of generated droplets is 0.043 nanoliter (nL); (b) the entire droplet image can be acquired within 15 seconds without using the conventional stitching technique; (c) additionally, we automated the droplet image processing to determine the absolute copy number and concentration of nucleic acid samples. This portable IoT-integrated digital droplet nucleic acid sensing system provide an inexpensive and affordable optical biopsy screening tool for the early diagnosis and treatment surveillance of brain tumors. Our work can be applied into other diseases, such as colorectal cancer, pancreatic cancer and breast cancer.
5-ALA (5-aminolevulinic acid) administration for positioning tumor tissues in craniotomy is growing since the FDA approval has already passed in June 2017 [1]. This may also open a path to the convenient single extracellular vesicle (EV) analysis in liquid biopsy for tracking and analyzing the tumor-derived EVs with PPIX expression, the middle byproduct upon the oral intake of 5-ALA. 5-ALA-based fluorescence is due to preferential accumulation of the fluorophore protoporphyrin-IX (PpIX) in the malignant glioma tissue. Single EV analysis has become technically feasible and promising in studies of early cancer diagnosis and clinical translational medicine applications [2]. It can minimize the heterogenous issue between EVs once capturing a group of low percentage biomarker-positive EVs. There is a need to scale up and implement an easy-to-use single EV analysis platform to be organized into the routine practices of current research and clinical laboratories to enable early cancer diagnostics. We are combining the 5-ALA dyed EV samples with single EV detection method to establish a more efficient and precise approach for patient’s early cancer diagnosis.
Digital droplet analysis divides a liquid biopsy sample into twenty-five thousand of nanoliter beads. Specific molecular recognition and amplification reactions are spaced into each droplet. To achieve sensitive molecular detection, we need to group droplets by different fluorescent colors and count the number of sorted beads. To expedite the entire molecular omic data analysis, we use Roboflow to label positive/negative droplets, and train the labeled images through several YOLOv5 models. We are developing and integrating an efficient droplet identification method into a point-of-use Raspberry Pi bead imaging device for counting the copy number of specific gene expression in biofluids. We particularly focus on the specific gene expression on extracellular vesicles of a malignant brain tumor.
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