scholarly journals Multiplexed Profiling of Single-cell Extracellular Vesicles Secretion

2018 ◽  
Author(s):  
Yahui Ji ◽  
Dongyuan Qi ◽  
Linmei Li ◽  
Haoran Su ◽  
Xiaojie Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Lines ◽  
Extracellular Vesicles ◽  
Single Cells ◽  
Cell Communication ◽  
Deep Understanding ◽  
Surface Markers ◽  
Cell Heterogeneity ◽  
Health And Disease ◽  
Cytokines Secretion

AbstractExtracellular vesicles (EVs) are important intercellular mediators regulating health and disease. Conventional EVs surface marker profiling, which was based on population measurements, masked the cell-to-cell heterogeneity in the quantity and phenotypes of EVs secretion. Herein, by using spatially patterned antibodies barcode, we realized multiplexed profiling of single-cell EVs secretion from more than 1000 single cells simultaneously. Applying this platform to profile human oral squamous cell carcinoma (OSCC) cell lines led to deep understanding of previously undifferentiated single cell heterogeneity underlying EVs secretion. Notably, we observed the decrement of certain EV phenotypes (e.g. CD63+EVs) were associated with the invasive feature of both OSCC cell lines and primary OSCC cells. We also realized multiplexed detection of EVs secretion and cytokines secretion simultaneously from the same single cells to investigate multidimensional spectrum of intercellular communications, from which we resolved three functional subgroups with distinct secretion profiles by visualized clustering. In particular, we found EVs secretion and cytokines secretion were generally dominated by different cell subgroups. The technology introduced here enables comprehensive evaluation of EVs secretion heterogeneity at single cell level, which may become an indispensable tool to complement current single cell analysis and EV research.SignificanceExtracellular vesicles (EVs) are cell derived nano-sized particles medicating cell-cell communication and transferring biology information molecules like nucleic acids to regulate human health and disease. Conventional methods for EV surface markers profiling can’t tell the differences in the quantity and phenotypes of EVs secretion between cells. To address this need, we developed a platform for profiling an array of surface markers on EVs from large numbers of single cells, enabling more comprehensive monitoring of cellular communications. Single cell EVs secretion assay led to previously unobserved cell heterogeneity underlying EVs secretion, which might open up new avenues for studying cell communication and cell microenvironment in both basic and clinical research.

scholarly journals Multiplexed profiling of single-cell extracellular vesicles secretion

2019 ◽  
Vol 116 (13) ◽  
pp. 5979-5984 ◽  
Author(s):  
Yahui Ji ◽  
Dongyuan Qi ◽  
Linmei Li ◽  
Haoran Su ◽  
Xiaojie Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Lines ◽  
Extracellular Vesicles ◽  
Single Cell Analysis ◽  
Comprehensive Evaluation ◽  
Single Cells ◽  
Deep Understanding ◽  
Principal Component ◽  
Cell Heterogeneity ◽  
Indispensable Tool

Extracellular vesicles (EVs) are important intercellular mediators regulating health and diseases. Conventional methods for EV surface marker profiling, which was based on population measurements, masked the cell-to-cell heterogeneity in the quantity and phenotypes of EV secretion. Herein, by using spatially patterned antibody barcodes, we realized multiplexed profiling of single-cell EV secretion from more than 1,000 single cells simultaneously. Applying this platform to profile human oral squamous cell carcinoma (OSCC) cell lines led to a deep understanding of previously undifferentiated single-cell heterogeneity underlying EV secretion. Notably, we observed that the decrement of certain EV phenotypes (e.g.,CD63+EV) was associated with the invasive feature of both OSCC cell lines and primary OSCC cells. We also realized multiplexed detection of EV secretion and cytokines secretion simultaneously from the same single cells to investigate the multidimensional spectrum of cellular communications, from which we resolved tiered functional subgroups with distinct secretion profiles by visualized clustering and principal component analysis. In particular, we found that different cell subgroups dominated EV secretion and cytokine secretion. The technology introduced here enables a comprehensive evaluation of EV secretion heterogeneity at single-cell level, which may become an indispensable tool to complement current single-cell analysis and EV research.

scholarly journals Cell-to-Cell Communication by Host-Released Extracellular Vesicles in the Gut: Implications in Health and Disease

2021 ◽  
Vol 22 (4) ◽  
pp. 2213
Author(s):  
Natalia Diaz-Garrido ◽  
Cecilia Cordero ◽  
Yenifer Olivo-Martinez ◽  
Josefa Badia ◽  
Laura Baldomà
Keyword(s):  
Extracellular Vesicles ◽  
Intestinal Mucosa ◽  
Current Knowledge ◽  
Cell Communication ◽  
Bioactive Molecules ◽  
Target Cells ◽  
Immune Functions ◽  
Intestinal Homeostasis ◽  
General Terms ◽  
Health And Disease

Communication between cells is crucial to preserve body homeostasis and health. Tightly controlled intercellular dialog is particularly relevant in the gut, where cells of the intestinal mucosa are constantly exposed to millions of microbes that have great impact on intestinal homeostasis by controlling barrier and immune functions. Recent knowledge involves extracellular vesicles (EVs) as mediators of such communication by transferring messenger bioactive molecules including proteins, lipids, and miRNAs between cells and tissues. The specific functions of EVs principally depend on the internal cargo, which upon delivery to target cells trigger signal events that modulate cellular functions. The vesicular cargo is greatly influenced by genetic, pathological, and environmental factors. This finding provides the basis for investigating potential clinical applications of EVs as therapeutic targets or diagnostic biomarkers. Here, we review current knowledge on the biogenesis and cargo composition of EVs in general terms. We then focus the attention to EVs released by cells of the intestinal mucosa and their impact on intestinal homeostasis in health and disease. We specifically highlight their role on epithelial barrier integrity, wound healing of epithelial cells, immunity, and microbiota shaping. Microbiota-derived EVs are not reviewed here.

scholarly journals High Throughput Immunophenotyping and Expression Profiling at Single Cell Level Reveal BCR-ABL1 Dependent Surface Markers of Chronic Myeloid Leukemia Stem Cells

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2920-2920
Author(s):  
Marianna Romzova ◽  
Dagmar Smitalova ◽  
Peter Taus ◽  
Jiri Mayer ◽  
Martin Culen
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Expression Profiling ◽  
Myeloid Leukemia ◽  
Single Cells ◽  
Transcript Level ◽  
Surface Expression ◽  
Surface Markers ◽  
Cell Level ◽  
Tki Treatment

BACKGROUND: Bcr-abl1 oncogene targeted treatment with tyrosine kinase inhibitors (TKI) showed an impressive efficacy against proliferating chronic myeloid leukemia (CML) cells. However, rapid relapses in more than half of CML patients after discontinuation of the treatment suggest a presence of quiescent leukemic stem cells inherently resistant to BCR-ABL1 inhibition. Understanding the heterogeneity of CML stem cell compartment is crucial for preventing the treatment failure. Specificity of already established leukemic stem cell (LSC) markers has been tested mainly in bulk CD34+CD38- populations at diagnosis. Phenotypes and molecular signatures of therapy resistant BCR ABL1 positive stem cells is however yet to be established. AIMS: Identification of BCR-ABL1 dependent LSC markers at single cell level by direct comparison their surface and transcript expression with the levels and the presence of BCR-ABL1 transcript at diagnosis and after administration of TKI treatment. METHODS: Total number of 375 cells were obtained from bone marrow and peripheral blood of 4 chronic phase CML patients. Cells were collected prior any treatment and three months after TKI treatment initiation. Normal bone marrow cells and BCR-ABL1 positive K562 cell line were used as controls. Indexed immuno-phenotyping and sorting of CD34+CD38- single cells was performed using a panel of 11 specific surface markers. Collected single cells were lysed and cDNA was enriched for 11 targets using 22 cycle pre-amplification. Expression profiling was carried on SmartChip real-time PCR system (Takara Bio) detecting following genes: BCR-ABL1, CD26, CD25, IL1-Rap, CD56, CD90, CD93, CD69, KI67, and control genes GUS and HPRT. Unsupervised clustering was performed using principal component analysis (PCA). Correlations were measured by Spearman rank method. RESULTS: At diagnosis, majority of BCR-ABL1+ C34+CD38- stem cells co-express IL1-Rap, CD26, and CD69 on their surface (88%, 82%, 78% overlap). Only 56% of BCR-ABL1+ cells positive for aforementioned markers co-express CD25, 28% CD93 and 16% CD56. The expression of these markers could also be detected in 4-11% of BCR-ABL1- cell, although this could be technical inaccuracy caused by the single cell profiling. CD90 marker did not show any correlation with BCR-ABL1 expression. At transcript level the expression of IL-1Rap, CD26, CD25 and CD56 was observed in 62%, 52% 45% and 16% BCR-ABL1+ cells, and up to 7% of BCR-ABL1- cells. CD69 expression was observed in 90% of BCR-ABL+ cells at transcript level, but also in 71% BCR-ABL- cells. BCR-ABL1 independent expression was observed for cKIT. (60% vs. 76 % in positive vs negative). Finally proliferation marker KI67 was expressed only in 6% of the BCR-ABL1+ cells. PCA analysis divided cells into several distinct clusters with BCR-ABL1 as the main contributor, and cKIT, CD69 and CD26, IL-1RAP as other significant factors. Interestingly BCR-ABL1+ cells collected during TKI treatment showed persistent surface expression of IL-1Rap and CD26, while CD56, CD69 and CD93 were only on part of the BCR-ABL1+ cells. CD25 was significantly deregulated during TKI treatment. CONCLUSION: At diagnosis up to 80% of LSC co-express 3 specific surface markers - IL-1RAP, CD26 and CD69. Variable portion of LSC co-express additional markers such are CD25, CD56 and CD93. During TKI treatment the surface expression of majority of markers is decreased, where the best correlated LSC marker is IL-1Rap, followed by CD26 and CD69. CD56 marker seems to persist in the same proportion of cells while CD25 disappears. cKIT is highly expressed in normal BM and HSC from CML patients, but also in some LSC. CD34+CD38- cells show non-proliferating phenotype. Disclosures Mayer: AOP Orphan Pharmaceuticals AG: Research Funding.

scholarly journals Joint single cell DNA-seq and RNA-seq of gastric cancer cell lines reveals rules of in vitro evolution

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Noemi Andor ◽  
Billy T Lau ◽  
Claudia Catalanotti ◽  
Anuja Sathe ◽  
Matthew Kubit ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Gastric Cancer Cell ◽  
Phenotypic Diversity ◽  
Single Cells ◽  
Cancer Cell Lines ◽  
Gastric Cancer Cell Lines

Abstract Cancer cell lines are not homogeneous nor are they static in their genetic state and biological properties. Genetic, transcriptional and phenotypic diversity within cell lines contributes to the lack of experimental reproducibility frequently observed in tissue-culture-based studies. While cancer cell line heterogeneity has been generally recognized, there are no studies which quantify the number of clones that coexist within cell lines and their distinguishing characteristics. We used a single-cell DNA sequencing approach to characterize the cellular diversity within nine gastric cancer cell lines and integrated this information with single-cell RNA sequencing. Overall, we sequenced the genomes of 8824 cells, identifying between 2 and 12 clones per cell line. Using the transcriptomes of more than 28 000 single cells from the same cell lines, we independently corroborated 88% of the clonal structure determined from single cell DNA analysis. For one of these cell lines, we identified cell surface markers that distinguished two subpopulations and used flow cytometry to sort these two clones. We identified substantial proportions of replicating cells in each cell line, assigned these cells to subclones detected among the G0/G1 population and used the proportion of replicating cells per subclone as a surrogate of each subclone's growth rate.

scholarly journals Mapping multicellular programs from single-cell profiles

2020 ◽  
Author(s):  
Livnat Jerby-Arnon ◽  
Aviv Regev
Keyword(s):  
Single Cell ◽  
Spatial Data ◽  
Spatial Information ◽  
Single Cells ◽  
Cell Types ◽  
Risk Genes ◽  
Health And Disease ◽  
Different Cell Types ◽  
Cellular Components ◽  
Acting In Concert

ABSTRACTTissue homeostasis relies on orchestrated multicellular circuits, where interactions between different cell types dynamically balance tissue function. While single-cell genomics identifies tissues’ cellular components, deciphering their coordinated action remains a major challenge. Here, we tackle this problem through a new framework of multicellular programs: combinations of distinct cellular programs in different cell types that are coordinated together in the tissue, thus forming a higher order functional unit at the tissue, rather than only cell, level. We develop the open-access DIALOGUE algorithm to systematically uncover such multi-cellular programs not only from spatial data, but even from tissue dissociated and profiled as single cells, e.g., by single-cell RNA-Seq. Tested on spatial transcriptomes from the mouse hypothalamus, DIALOGUE recovered spatial information, predicted the properties of a cell’s environment only based on its transcriptome, and identified multicellular programs that mark animal behavior. Applied to brain samples and colon biopsies profiled by scRNA-Seq, DIALOGUE identified multicellular configurations that mark Alzheimer’s disease and ulcerative colitis (UC), including a program spanning five cell types that is predictive of response to anti-TNF therapy in UC patients and enriched for UC risk genes from GWAS, each acting in different cell types, but all cells acting in concert. Taken together, our study provides a novel conceptual and methodological framework to unravel multicellular regulation in health and disease.

scholarly journals Myeloid heterogeneity in kidney disease as revealed through single cell RNA sequencing

Kidney360 ◽  
2021 ◽  
pp. 10.34067/KID.0003682021
Author(s):  
Rachel M B Bell ◽  
Laura Denby
Keyword(s):  
Kidney Disease ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Myeloid Cells ◽  
Rna Seq ◽  
Cellular Compartment ◽  
Single Cell Rna Sequencing ◽  
Health And Disease ◽  
Diseased Kidney

Kidney disease represents a global health burden of increasing prevalence and is an independent risk factor for cardiovascular disease. Myeloid cells are a major cellular compartment of the immune system; they are found in the healthy kidney and in increased numbers in the damaged and/or diseased kidney, where they act as key players in the progression of injury, inflammation and fibrosis. They possess enormous plasticity and heterogeneity, adopting different phenotypic and functional characteristics in response to stimuli in the local milieu. Though this inherent complexity remains to be fully understood in the kidney, advances in single-cell genomics promises to change this. Specifically, single-cell RNA sequencing (scRNA-seq) has had a transformative effect on kidney research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput, and subsequent generation of cell atlases. Moving forward, combining scRNA- and single-nuclear RNA-seq with greater resolution spatial transcriptomics will allow spatial mapping of kidney disease of varying aetiology to further reveal the patterning of immune cells and non-immune renal cells. This review summarises the roles of myeloid cells in kidney health and disease, the experimental workflow in currently available scRNA-seq technologies and published findings using scRNA-seq in the context of myeloid cells and the kidney.

scholarly journals Analysis of the Single-Cell Heterogeneity of Adenocarcinoma Cell Lines and the Investigation of Intratumor Heterogeneity Reveals the Expression of Transmembrane Protein 45A (TMEM45A) in Lung Adenocarcinoma Cancer Patients

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 144
Author(s):  
Patrícia Neuperger ◽  
József Á. Balog ◽  
László Tiszlavicz ◽  
József Furák ◽  
Nikolett Gémes ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Single Cell ◽  
Cell Line ◽  
Cancer Patients ◽  
Cell Lines ◽  
Lung Tumor ◽  
Expression Patterns ◽  
Cell Mass ◽  
Cell Heterogeneity ◽  
Primary Lung Adenocarcinoma

Intratumoral heterogeneity (ITH) is responsible for the majority of difficulties encountered in the treatment of lung-cancer patients. Therefore, the heterogeneity of NSCLC cell lines and primary lung adenocarcinoma was investigated by single-cell mass cytometry (CyTOF). First, we studied the single-cell heterogeneity of frequent NSCLC adenocarcinoma models, such as A549, H1975, and H1650. The intra- and inter-cell-line single-cell heterogeneity is represented in the expression patterns of 13 markers—namely GLUT1, MCT4, CA9, TMEM45A, CD66, CD274 (PD-L1), CD24, CD326 (EpCAM), pan-keratin, TRA-1-60, galectin-3, galectin-1, and EGFR. The qRT-PCR and CyTOF analyses revealed that a hypoxic microenvironment and altered metabolism may influence cell-line heterogeneity. Additionally, human primary lung adenocarcinoma and non-involved healthy lung tissue biopsies were homogenized to prepare a single-cell suspension for CyTOF analysis. The CyTOF showed the ITH of human primary lung adenocarcinoma for 14 markers; particularly, the higher expressions of GLUT1, MCT4, CA9, TMEM45A, and CD66 were associated with the lung-tumor tissue. Our single-cell results are the first to demonstrate TMEM45A expression in human lung adenocarcinoma, which was verified by immunohistochemistry.

scholarly journals Scattome: A Single-Cell Analysis of Targeted Transcriptome Program to Predict Drug Sensitivity of Single Cells within Human Myeloma Tumors

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4249-4249
Author(s):  
Amit Kumar Mitra ◽  
Ujjal Mukherjee ◽  
Taylor Harding ◽  
Holly Stessman ◽  
Ying Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Lines ◽  
Research Funding ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Pcr Analysis ◽  
Cell Analysis

Abstract Multiple myeloma (MM) is characterized by significant genetic diversity at subclonal levels that likely plays a defining role in the heterogeneity of tumor progression, clinical aggressiveness and drug sensitivity. Such heterogeneity is a driving factor in the evolution of MM, from founder clones through outgrowth of subclonal fractions. DNA Sequencing studies on MM samples have indeed demonstrated such heterogeneity in subclonal architecture at diagnosis based on recurrent mutations in pathologically relevant genes that may ultimately to lead to relapse. However, no study so far has reported a predictive gene expression signature that can identify, distinguish and quantify drug sensitive and drug-resistant subpopulations within a bulk population of myeloma cells. In recent years, our laboratory has successfully developed a gene expression profile (GEP)-based signature that could not only distinguish drug response of MM cell lines, but also was effective in stratifying patient outcomes when applied to GEP profiles from MM clinical trials using proteasome inhibitors (PI) as chemotherapeutic agents. Further, we noted myeloma cell lines that responded to the drug often contained residual sub-population of cells that did not respond, and likely were selectively propagated during drug treatment in vitro, and in patients. In this study, we performed targeted qRT-PCR analysis of single cells using a gene panel that included PI sensitivity genes and gene signatures that could discriminate between low and high-risk myeloma followed by intensive bioinformatics and statistical analysis for the classification and prediction of PI response in individual cells within bulk multiple myeloma tumors. Fluidigm's C1 Single-Cell Auto Prep System was used to perform automated single-cell capture, processing and cDNA synthesis on 576 pre-treatment cells from 12 cell lines representing a wide range of PI-sensitivity and 370 cells from 7 patient samples undergoing PI treatment followed by targeted gene expression profiling of single cells using automated, high-throughput on-chip qRT-PCR analysis using 96.96 Dynamic Array IFCs on the BioMark HD System. Probability of resistance for each individual cell was predicted using a pipeline that employed the machine learning methods Random Forest, Support Vector Machine (radial and sigmoidal), LASSO and kNN (k Nearest Neighbor) for making single-cell GEP data-driven predictions/ decisions. The weighted probabilities from each of the algorithms were used to quantify resistance of each individual cell and plotted using Ensemble forecasting algorithm. Using our drug response GEP signature at the single cell level, we could successfully identify distinct subpopulations of tumor cells that were predicted to be sensitive or resistant to PIs. Subsequently, we developed a R Statistical analysis package (http://cran.r-project.org), SCATTome (Single Cell Analysis of Targeted Transcriptome), that can restructure data obtained from Fluidigm qPCR analysis run, filter missing data, perform scaling of filtered data, build classification models and successfully predict drug response of individual cells and classify each cell's probability of response based on the targeted transcriptome. We will present the program output as graphical displays of single cell response probabilities. This package provides a novel classification method that has the potential to predict subclonal response to a variety of therapeutic agents. Disclosures Kumar: Skyline: Consultancy, Honoraria; BMS: Consultancy; Onyx: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Novartis: Research Funding; Takeda: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.

scholarly journals Phenotypic convergence in the brain: distinct transcription factors regulate common terminal neuronal characters

2018 ◽  
Author(s):  
Nikos Konstantinides ◽  
Katarina Kapuralin ◽  
Chaimaa Fadil ◽  
Luendreo Barboza ◽  
Rahul Satija ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Large Scale ◽  
Single Cells ◽  
Deep Understanding ◽  
Cell Types ◽  
Marker Genes ◽  
Cell Type ◽  
Functional Specification ◽  
Phenotypic Convergence

SummaryTranscription factors regulate the molecular, morphological, and physiological characters of neurons and generate their impressive cell type diversity. To gain insight into general principles that govern how transcription factors regulate cell type diversity, we used large-scale single-cell mRNA sequencing to characterize the extensive cellular diversity in the Drosophila optic lobes. We sequenced 55,000 single optic lobe neurons and glia and assigned them to 52 clusters of transcriptionally distinct single cells. We validated the clustering and annotated many of the clusters using RNA sequencing of characterized FACS-sorted single cell types, as well as marker genes specific to given clusters. To identify transcription factors responsible for inducing specific terminal differentiation features, we used machine-learning to generate a ‘random forest’ model. The predictive power of the model was confirmed by showing that two transcription factors expressed specifically in cholinergic (apterous) and glutamatergic (traffic-jam) neurons are necessary for the expression of ChAT and VGlut in many, but not all, cholinergic or glutamatergic neurons, respectively. We used a transcriptome-wide approach to show that the same terminal characters, including but not restricted to neurotransmitter identity, can be regulated by different transcription factors in different cell types, arguing for extensive phenotypic convergence. Our data provide a deep understanding of the developmental and functional specification of a complex brain structure.

scholarly journals Colorectal Cancer Cell-Derived Small Extracellular Vesicles Educate Human Fibroblasts to Stimulate Migratory Capacity

2021 ◽  
Vol 9 ◽  
Author(s):  
Stefano Piatto Clerici ◽  
Maikel Peppelenbosch ◽  
Gwenny Fuhler ◽  
Sílvio Roberto Consonni ◽  
Carmen Veríssima Ferreira-Halder
Keyword(s):  
Colorectal Cancer ◽  
Cell Lines ◽  
Extracellular Vesicles ◽  
Cancer Progression ◽  
Tyrosine Phosphatase ◽  
Human Fibroblasts ◽  
Cell Communication ◽  
Ht29 Cells ◽  
Weight Protein ◽  
Normal Human

Colorectal cancer (CRC) is in the top 10 cancers most prevalent worldwide, affecting equally men and women. Current research on tumor-derived extracellular vesicles (EVs) suggests that these small extracellular vesicles (sEVs) play an important role in mediating cell-to-cell communication and thus potentially affecting cancer progression via multiple pathways. In the present study, we hypothesized that sEVs derived from different CRC cell lines differ in their ability to reprogram normal human fibroblasts through a process called tumor education. The sEVs derived from CRC cell lines (HT29 and HCT116) were isolated by a combination of ultrafiltration and polymeric precipitation, followed by characterization based on morphology, size, and the presence or absence of EV and non-EV markers. It was observed that the HT29 cells displayed a higher concentration of sEVs compared with HCT116 cells. For the first time, we demonstrated that HT29-derived sEVs were positive for low-molecular-weight protein tyrosine phosphatase (Lmwptp). CRC cell-derived sEVs were uptake by human fibroblasts, stimulating migratory ability via Rho-Fak signaling in co-incubated human fibroblasts. Another important finding showed that HT29 cell-derived sEVs are much more efficient in activating human fibroblasts to cancer-associated fibroblasts (CAFs). Indeed, the sEVs produced by the HT29 cells that are less responsive to a cytotoxic agent display higher efficiency in educating normal human fibroblasts by providing them advantages such as activation and migratory ability. In other words, these sEVs have an influence on the CRC microenvironment, in part, due to fibroblasts reprogramming.

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