extracellular-vesicle-uptake-assay-via-confocal-microscope-imaging

Extracellular Vesicle Uptake Assay <em>via</em> Confocal Microscope Imaging Analysis

There is a need for practical assays to visualize and quantify the cells' extracellular vesicle (EV) uptake. EV uptake plays a role in intercellular ...

Johns Hopkins University School of Medicine

To investigate the circulating tumour cells (CTCs) capture abilities of two technologies that are not dependent on cell-surface marker expression: a selection-free platform [AccuCyte -CyteFinder system (Rarecyte)] and a size-based platform [fluid-assisted separation technology (FAST)]. In addition, the combination of the two systems to more completely assess CTCs was investigated.

The combination of size-based separation and selection-free technology provides higher circulating tumour cells detection sensitivity than either method alone in patients with metastatic prostate cancer.

Circulating tumor cells (CTCs) are a promising biomarker for cancer liquid biopsy. To evaluate the CTC capture bias and detection capability of the slit filter-based CTC isolation platform (CTC-FIND), we prospectively compared it head to head to a selection-free platform (AccuCyte-CyteFinder system). We used the two methods to determine the CTC counts, CTC positive rates, CTC size distributions, and CTC phenotypes in 36 patients with metastatic cancer. Between the two methods, the median CTC counts were not significantly different and the total counts were correlated ( = 0.63,  < 0.0001). The CTC positive rate by CTC-FIND was significantly higher than that by AccuCyte-CyteFinder system (91.7% vs. 66.7%,  < 0.05). The median diameter of CTCs collected by CTC-FIND was significantly larger (13.0 μm, range 5.2-52.0 vs. 10.4 μm, range 5.2-44.2,  < 0.0001). The distributions of CTC phenotypes (CK+EpCAM+, CK+EpCAM- or CK-EpCAM+) detected by both methods were similar. These results suggested that CTC-FIND can detect more CTC-positive cases but with a bias toward large size of CTCs.

Analysis of the Circulating Tumor Cell Capture Ability of a Slit Filter-Based Method in Comparison to a Selection-Free Method in Multiple Cancer Types.

One of the challenges that restricts the evolving extracellular vesicle (EV) research field is the lack of a consensus method for EV separation. This may also explain the diversity of the experimental results, as co-separated soluble proteins and lipoproteins may impede the interpretation of experimental findings. In this study, we comprehensively evaluated the EV yields and sample purities of three most popular EV separation methods, ultracentrifugation, precipitation and size exclusion chromatography combined with ultrafiltration, along with a microfluidic tangential flow filtration device, Exodisc, in three commonly used biological samples, cell culture medium, human urine and plasma. Single EV phenotyping and density-gradient ultracentrifugation were used to understand the proportion of true EVs in particle separations. Our findings suggest Exodisc has the best EV yield though it may co-separate contaminants when the non-EV particle levels are high in input materials. We found no 100% pure EV preparations due to the overlap of their size and density with many non-EV particles in biofluids. Precipitation has the lowest sample purity, regardless of sample type. The purities of the other techniques may vary in different sample types and are largely dependent on their working principles and the intrinsic composition of the input sample. Researchers should choose the proper separation method according to the sample type, downstream analysis and their working scenarios.

Comprehensive evaluation of methods for small extracellular vesicles separation from human plasma, urine and cell culture medium.

Cancer is responsible for the deaths of millions of people worldwide each year. Once metastasized, the disease is incurable and shows resistance to all anti-cancer therapies. The already-elevated level of reactive oxygen species (ROS) in cancer cells is further increased by therapies. The oxidative stress activates the DNA damage response (DDR) and the stressed cancer cell moves towards cell cycle arrest. Once arrested, the majority of cancer cells will undergo programmed cell death in the form of apoptosis. If the cancer cell is able to exit the cell cycle prior to cell division and enter a protected G0 state, it is able to withstand and survive therapy as a polyaneuploid cancer cell (PACC) and eventually seed resistant tumor growth.

ROS-induced cell cycle arrest as a mechanism of resistance in polyaneuploid cancer cells (PACCs).

Renal cell carcinoma (RCC) accounts for over 400,000 new cases and 175,000 deaths annually. Diagnostic RCC biomarkers may prevent overtreatment in patients with early disease. Extracellular vesicles (EVs) are a promising source of RCC biomarkers because EVs carry proteins and messenger RNA (mRNA) among other biomolecules. We aimed to identify biomarkers and assess biological functions of EV cargo from clear cell RCC (ccRCC), papillary RCC (pRCC), and benign kidney cell lines. EVs were enriched from conditioned cell media by size exclusion chromatography. The EV proteome was assessed using Tandem Mass Tag mass spectrometry (TMT-MS) and NanoString nCounter technology was used to profile 770 cancer-related mRNA present in EVs. The heterogeneity of protein and mRNA abundance and identification highlighted the heterogeneity of EV cargo, even between cell lines of a similar pathological group (e.g., ccRCC or pRCC). Overall, 1726 proteins were quantified across all EV samples, including 181 proteins that were detected in all samples. In the targeted profiling of mRNA by NanoString, 461 mRNAs were detected in EVs from at least one cell line, including 159 that were present in EVs from all cell lines. In addition to a shared EV cargo signature, pRCC, ccRCC, and/or benign renal cell lines also showed unique signatures. Using this multi-omics approach, we identified 34 protein candidate pRCC EV biomarkers and 20 protein and 8 mRNA candidate ccRCC EV biomarkers for clinical validation.

Defining candidate mRNA and protein EV biomarkers to discriminate ccRCC and pRCC from non-malignant renal cells in vitro.

To propose EV-derived mRNA as a potential diagnostic biomarker detecting the presence of clear cell renal cell carcinoma (ccRCC). There is currently no kidney cancer specific screening or diagnostic technology. Therefore, one-third of kidney cancer diagnoses occur after the cancer has metastasized and is past curative measures MATERIALS AND METHODS: Urine, plasma, normal tumor adjacent tissue, and tumor tissue was collected from a limited population of ccRCC patients. Extracellular vesicle (EV) isolation was performed on each sample, followed by mRNA extraction from isolated EVs. NanoString nCounter technology was utilized to count the mRNA transcripts present in matched plasma, urine, tumor tissue, and normal tumor adjacent tissue samples.

Article	Year
The combination of size-based separation and selection-free technology provides higher circulating tumour cells detection sensitivity than either method alone in patients with metastatic pro... BJU international\| PubMed ID: 32115854	2020
Analysis of the Circulating Tumor Cell Capture Ability of a Slit Filter-Based Method in Comparison to a Selection-Free Method in Multiple Cancer Types. International journal of molecular sciences\| PubMed ID: 33261132	2020
Comprehensive evaluation of methods for small extracellular vesicles separation from human plasma, urine and cell culture medium. Journal of extracellular vesicles\| PubMed ID: 33489012	2020
ROS-induced cell cycle arrest as a mechanism of resistance in polyaneuploid cancer cells (PACCs). Progress in biophysics and molecular biology\| PubMed ID: 33991583	2021
Defining candidate mRNA and protein EV biomarkers to discriminate ccRCC and pRCC from non-malignant renal cells in vitro. Medical oncology (Northwood, London, England)\| PubMed ID: 34331598	2021
Advancements in the identification of EV derived mRNA biomarkers for liquid biopsy of clear cell renal cell carcinomas. Urology\| PubMed ID: 34793840	2021

Morgan D. Kuczler

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