TIMING Applied to Identify a Cancer Cell Signature Associated with Extracellular Vesicle Secretion and Cancer Progression

Findings directly demonstrate single-cell EV secretion and identify a tumor gene signature predictive of disease severity

PUBLICATION ALERT

HOUSTON, Texas — May 3, 2023 — iScience has published a paper titled “Identifying signatures of EV secretion in metastatic breast cancer through functional single-cell profiling” that presents an integrated methodology to identify single cancer cells with differences in extracellular vesicle (EV) secretion and further link EV secretion to their transcriptome. The authors elucidated a four gene signature that is associated with EV secretion and correlated with severity of cancer progression. In the publication, the researchers applied a version of Time-lapse Imaging In Nanowell Grids™ (TIMING™) to profile single-cell EV secretion and retrieve individual cancer cells for further molecular and functional analysis.

Cellular secretion of extracellular vesicles is generally accepted as a mechanism to transport molecules outside of cells. Although EVs are known to enhance cancer progression, technical limitations have hampered efforts to map heterogeneity of EV secretion by individual cells and probe the relation to cellular gene signatures using single cell RNAseq. To tackle this problem, the authors previously developed a technique to quantify EV secretion from an individual cell, retrieve the cells, establish monoclonal cell lines, and assess the functional differences between EV-secreting and EV-non-secreting cancer cells in vitro and in vivo. In this follow-up work, the researchers performed single-cell RNA-sequencing (scRNA-seq) on the monoclonal cell lines to identify a gene signature that links the secretion of EVs to cancer metastasis and tumor stages.

This version of the TIMING platform integrated with a conventional immunoassay allowed researchers to rapidly monitor EV secretion from individual cells and to establish monoclonal cell lines at the end of the TIMING assay. The authors applied this methodology to establish monoclonal cell lines with lower and higher EV secretion from a starting heterogeneous population of metastatic breast cancer cells (MDA-MB-231). Later, by profiling whole transcriptomes using scRNA-seq, they identified a gene set consisting of HSP90AA1, HSPH1, EIF5, and DIAPH3 that is highly enriched in the EV-secretor clonal cell line compared to the non-secretor clonal cell line. Retrospective analysis of two independent large-scale gene expression databases of breast tumors with known patient outcomes confirmed that expression of these genes is significantly elevated in tumors from patients with more advanced cancer and is associated with poor survival in patients with metastatic cancers.

The study is significant in identifying a simple gene signature associated with cancer invasiveness and patient outcome that can be exploited to 1) help identify those patients at greater risk for metastatic disease and 2) drive future mechanistic studies to expose new vulnerabilities of EV-secreting cancers to specific therapies. TIMING technology uniquely enabled identification and retrieval of the living tumor cells with differential EV secretion by virtue of spatially limited, dynamic, and non-destructive methodology.

 

Figure 1. Establishment of monoclonal cell lines with different rates of EV secretion. (A) The workflow for the identification and isolation of single cells with differences in EV secretion capacity. (B) Representative images of MDAMB231-S (secretor) and MDAMB231-NS (non-secretor) cells in wells (left), at high resolution (middle), and as contour maps representing intensity of CD63 staining. CD63 protein is enriched on extracellular vesicles. (C) Images of (top to bottom) cells in wells, high resolution of CD63, and CD63 intensity contour maps of representative MDAMB231-NS (left) and MDAMB231-S (middle) cells at 2, 4, and 6 h. Right: Violin plots of medians and quantiles of CD63 intensities (∗∗∗∗p < 0.00001; t-test).

 

The authors on the publication are:

  • University of Houston: Mohsen Fathi, Melisa Martinez-Paniagua, Ali Rezvan, Melisa J. Montalvo, Navin Varadarajan

  • MD Anderson Cancer Center: Vakul Mohanty, Ken Chen

  • MD Anderson Cancer Center/Brown University: Sendurai A. Mani

About CellChorus

CellChorus is the leader in applying artificial intelligence to visually evaluate how thousands of individual immune cells, such as T cells and NK cells, perform over time. The company applies Time-lapse Imaging Microscopy in Nanowell Grids™ (TIMING™) with neural network-based artificial intelligence (AI) to identify cells and evaluate their activity, including how they move, activate, kill and survive. The patent-protected CellChorus platform can link TIMING data and insights with information from other analysis modalities such as single-cell RNA sequencing and flow cytometry to provide a comprehensive understanding of cellular function, state and phenotype for the life sciences industry. Please visit cellchorus.com for more information.

Company Contact:
Daniel Meyer
Chief Executive Officer
CellChorus Inc.
TIMING@cellchorus.com

SOURCE CellChorus Inc.