Flow cytometry expression pattern of CD44 and CD18 markers on feline leukocytes.
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ABSTRACT:
SUBMITTER: Martini V
PROVIDER: S-EPMC7488972 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Tumorigenic breast cancer cells characterized by CD44 expression and low or undetectable CD24 levels (CD44+/CD24-/low) may be resistant to chemotherapy and therefore responsible for cancer relapse. Paired breast cancer core biopsies before and after neoadjuvant chemotherapy or lapatinib were obtained and as single cell suspensions stained using antibodies against CD24, CD44, and lineage markers, and then analyzed by flow cytometry. Mammosphere (MS) formation in culture was compared before and after treatment. Global gene expression differences between cancer cells bearing CD44+/CD24-/low cells and all other sorted cells, and between cancer MS and the primary bulk invasive cancers were analyzed. We report that CD44+/CD24-/low tumorigenic breast cancer cells were intrinsically chemoresistant ─ chemotherapy led to increased CD44+/CD24-/low cells, increased self-renewal capacity on MS assays, and enhanced tumorigeneicity in immunocompromised SCID/Beige mice. Conversely, in patients with HER2 overexpressing tumors, the EGFR/HER2 tyrosine kinase inhibitor, lapatinib decreased CD44+/CD24-/low cells, with the majority of these patients after conventional therapy achieving pathologic complete response, a validated surrogate marker for long-term survival. Gene transcription pathways that underlie chemoresistant, MS-forming CD44+/CD24-/low cells involve genes belonging to stem cell self-renewal, Wnt signaling, and early development pathways. Keywords: two group comparison
2009-08-15 | GSE7513 | GEO
Project description:Tumorigenic breast cancer cells characterized by CD44 expression and low or undetectable CD24 levels (CD44+/CD24-/low) may be resistant to chemotherapy and therefore responsible for cancer relapse. Paired breast cancer core biopsies before and after neoadjuvant chemotherapy or lapatinib were obtained and as single cell suspensions stained using antibodies against CD24, CD44, and lineage markers, and then analyzed by flow cytometry. Mammosphere (MS) formation in culture was compared before and after treatment. Global gene expression differences between cancer cells bearing CD44+/CD24-/low cells and all other sorted cells, and between cancer MS and the primary bulk invasive cancers were analyzed. We report that CD44+/CD24-/low tumorigenic breast cancer cells were intrinsically chemoresistant â chemotherapy led to increased CD44+/CD24-/low cells, increased self-renewal capacity on MS assays, and enhanced tumorigeneicity in immunocompromised SCID/Beige mice. Conversely, in patients with HER2 overexpressing tumors, the EGFR/HER2 tyrosine kinase inhibitor, lapatinib decreased CD44+/CD24-/low cells, with the majority of these patients after conventional therapy achieving pathologic complete response, a validated surrogate marker for long-term survival. Gene transcription pathways that underlie chemoresistant, MS-forming CD44+/CD24-/low cells involve genes belonging to stem cell self-renewal, Wnt signaling, and early development pathways. Experiment Overall Design: Human breast tumor samples were sorted using flow cytometry to select for cells that were CD44+ and CD24-. Gene expression profiles of these cells were compared with profiles of the other sorted cells (CD24+ and CD44-/CD24-). Experiment Overall Design: Core biopsies of primary breast tumors were taken and placed immediately in cold RPMI-1640 supplemented with 10% heat-inactivated newborn calf serum (HINCS, Invitrogen, Carlsbad, CA). Within an hour, the samples were minced and then digested in 10-15 mL of MEGM with 250-300 units/mL collagenase at 370C. The samples were filtered, washed, and then subjected to hypotonic shock to lyse red blood cells. About 106 single cells were re-suspended, incubated for 15 min at 40C with anti-CD44 (APC), anti-CD24 (FITC), and anti-lineage cocktail antibodies (PE-conjugated anti-CD2, CD3, CD10, CD16, CD18, CD31 and CD 140B) (Pharmingen, San Diego, CA) using the manufacturerâs suggested concentrations. The cells were then washed twice, re-suspended with the viability dye propidium iodide, and analyzed using Dako MoFlo flow cytometry. Side- and forward- scatter were used to eliminate debris and cell doublets, and the Lin- cells were further analyzed by CD44 and CD24 markers.
2009-08-22 | E-GEOD-7513 | biostudies-arrayexpress
Project description:Tumorigenic breast cancer cells characterized by CD44 expression and low or undetectable CD24 levels (CD44+/CD24-/low) may be resistant to chemotherapy and therefore responsible for cancer relapse. Paired breast cancer core biopsies before and after neoadjuvant chemotherapy or lapatinib were obtained and as single cell suspensions stained using antibodies against CD24, CD44, and lineage markers, and then analyzed by flow cytometry. Mammosphere (MS) formation in culture was compared before and after treatment. Global gene expression differences between cancer cells bearing CD44+/CD24-/low cells and all other sorted cells, and between cancer MS and the primary bulk invasive cancers were analyzed. We report that CD44+/CD24-/low tumorigenic breast cancer cells were intrinsically chemoresistant ─ chemotherapy led to increased CD44+/CD24-/low cells, increased self-renewal capacity on MS assays, and enhanced tumorigeneicity in immunocompromised SCID/Beige mice. Conversely, in patients with HER2 overexpressing tumors, the EGFR/HER2 tyrosine kinase inhibitor, lapatinib decreased CD44+/CD24-/low cells, with the majority of these patients after conventional therapy achieving pathologic complete response, a validated surrogate marker for long-term survival. Gene transcription pathways that underlie chemoresistant, MS-forming CD44+/CD24-/low cells involve genes belonging to stem cell self-renewal, Wnt signaling, and early development pathways. Experiment Overall Design: Human breast tumor samples were sorted using flow cytometry to select for cells that were CD44+ and CD24-. Gene expression profiles of these cells were compared with profiles of the other sorted cells (CD24+ and CD44-/CD24-). Experiment Overall Design: Core biopsies of primary breast tumors were taken and placed immediately in cold RPMI-1640 supplemented with 10% heat-inactivated newborn calf serum (HINCS, Invitrogen, Carlsbad, CA). Within an hour, the samples were minced and then digested in 10-15 mL of MEGM with 250-300 units/mL collagenase at 370C. The samples were filtered, washed, and then subjected to hypotonic shock to lyse red blood cells. About 106 single cells were re-suspended, incubated for 15 min at 40C with anti-CD44 (APC), anti-CD24 (FITC), and anti-lineage cocktail antibodies (PE-conjugated anti-CD2, CD3, CD10, CD16, CD18, CD31 and CD 140B) (Pharmingen, San Diego, CA) using the manufacturer’s suggested concentrations. The cells were then washed twice, re-suspended with the viability dye propidium iodide, and analyzed using Dako MoFlo flow cytometry. Side- and forward- scatter were used to eliminate debris and cell doublets, and the Lin- cells were further analyzed by CD44 and CD24 markers.
| S-ECPF-GEOD-7513 | biostudies-other
Project description:Glioblastoma is the most dangerous brain cancer. One reason for glioblastoma's aggressiveness are glioblastoma stem-like cells. To target them, a number of markers have been proposed (CD133, CD44, CD15, A2B5, CD36, CXCR4, IL6R, L1CAM, and ITGA6). A comprehensive study of co-expression patterns of them has, however, not been performed so far. Here, we mapped the multidimensional co-expression profile of these stemness-associated molecules. Gliomaspheres - an established model of glioblastoma stem-like cells - were used. Seven different gliomasphere systems were subjected to multicolor flow cytometry measuring the nine markers CD133, CD44, CD15, A2B5, CD36, CXCR4, IL6R, L1CAM, and ITGA6 all simultaneously based on a novel 9-marker multicolor panel developed for this study. The viSNE dimensionality reduction algorithm was applied for analysis. All gliomaspheres were found to express at least five different glioblastoma stem-like cell markers. Multi-dimensional analysis showed that all studied gliomaspheres consistently harbored a cell population positive for the molecular signature CD44+/CD133+/ITGA6+/CD36+. Glioblastoma patients with an enrichment of this combination had a significantly worse survival outcome when analyzing the two largest available The Cancer Genome Atlas datasets (MIT/Harvard Affymetrix: P = 0.0015, University of North Carolina Agilent: P = 0.0322). In sum, we detected a previously unknown marker combination - demonstrating feasibility, usefulness, and importance of high-dimensional gliomasphere marker combinatorics.
| S-EPMC6307809 | biostudies-literature
Project description:BackgroundAssays of the abundance of immune cell populations in the tumor microenvironment promise to inform immune oncology research and the choice of immunotherapy for individual patients. We propose to measure the intratumoral abundance of various immune cell populations with gene expression. In contrast to IHC and flow cytometry, gene expression assays yield high information content from a clinically practical workflow. Previous studies of gene expression in purified immune cells have reported hundreds of genes showing enrichment in a single cell type, but the utility of these genes in tumor samples is unknown. We use co-expression patterns in large tumor gene expression datasets to evaluate previously reported candidate cell type marker genes lists, eliminate numerous false positives and identify a subset of high confidence marker genes.MethodsUsing a novel statistical tool, we use co-expression patterns in 9986 samples from The Cancer Genome Atlas (TCGA) to evaluate previously reported cell type marker genes. We compare immune cell scores derived from these genes to measurements from flow cytometry and immunohistochemistry. We characterize the reproducibility of our cell scores in replicate runs of RNA extracted from FFPE tumor tissue.ResultsWe identify a list of 60 marker genes whose expression levels measure 14 immune cell populations. Cell type scores calculated from these genes are concordant with flow cytometry and IHC readings, show high reproducibility in replicate RNA samples from FFPE tissue and enable detailed analyses of the anti-tumor immune response in TCGA. In an immunotherapy dataset, they separate responders and non-responders early on therapy and provide an intricate picture of the effects of checkpoint inhibition. Most genes previously reported to be enriched in a single cell type have co-expression patterns inconsistent with cell type specificity.ConclusionsDue to their concise gene set, computational simplicity and utility in tumor samples, these cell type gene signatures may be useful in future discovery research and clinical trials to understand how tumors and therapeutic intervention shape the immune response.
| S-EPMC5319024 | biostudies-literature
Project description:As checkpoint inhibitor immunotherapies gain traction among cancer researchers and clinicians, the need grows for assays that can definitively phenotype patient immune cells. Herein, we present an 8-color flow cytometry panel for lineage and immune checkpoint markers and validate it using healthy human donor peripheral blood mononuclear cells (PBMCs). Flow cytometry data was generated on a BD LSR Fortessa and supported by Luminex multiplex soluble immunoassay. Our data showed significant variation between donors at both baseline and different stages of activation, as well as a trend in increasing expression of checkpoint markers on stimulated CD4+ and CD8+ T-cells with time. Soluble immune checkpoint quantification assays revealed that LAG-3, TIM-3, CTLA-4, and PD-1 soluble isoforms are upregulated after stimulation. This 8-color flow cytometry panel, supported here by soluble immunoassay, can be used to identify and evaluate immune checkpoints on T-lymphocytes in cryopreserved human PBMC samples. This panel is ideal for characterizing checkpoint expression in clinical samples for which cryopreservation is necessary.
| S-EPMC6706095 | biostudies-literature
Project description:A biochemical system and method have been developed to enable the quantitative measurement of cytoplasmic versus nuclear localization within cells in whole blood. Compared with the analyses of nuclear localization by western blot or fluorescence microscopy, this system saves a lot of time and resources by eliminating the necessity of purification and culturing steps, and generates data that are free from the errors and artifacts associated with using tumor cell lines or calculating nuclear signals from 2D images. This user-friendly system enables the analysis of cell signaling within peripheral blood cells in their endogenous environment, including measuring the kinetics of nuclear translocation for transcription factors without requiring protein modifications. We first demonstrated the efficiency and specificity of this system for targeting nuclear epitopes, and verified the results by fluorescence microscopy. Next, the power of the technique to analyze LPS-induced signaling in peripheral blood monocytes was demonstrated. Finally, both FoxP3 localization and IL-2-induced STAT5 signaling in regulatory T cells were analyzed. We conclude that this system can be a useful tool for enabling multidimensional molecular-biological analyses of cell signaling within endogenous peripheral blood cells by conventional flow cytometry. © 2017 International Society for Advancement of Cytometry.
| S-EPMC5516235 | biostudies-literature
Project description:BackgroundBasophils are circulating cells involved in hypersensitivity reactions and allergy but many aspects of their activation, including the sensitivity to external triggering factors and the molecular aspects of cell responses, are still to be focused. In this context, polychromatic flow cytometry (PFC) is a proper tool to investigate basophil function, as it allows to distinguish the expression of several membrane markers upon activation in multiple experimental conditions.MethodsCell suspensions were prepared from leukocyte buffy coat of K2-EDTA anticoagulated blood specimens; about 1500-2500 cellular events for each tested sample, gated in the lymphocyte CD45dim area and then electronically purified as HLADRnon expressing/CD123bright, were identified as basophilic cells. Basophil activation with fMLP, anti-IgE and calcium ionophore A23187 was evaluated by studying up-regulation of the indicated membrane markers with a two-laser six-color PFC protocol.ResultsFollowing stimulation, CD63, CD13, CD45 and the ectoenzyme CD203c up-regulated their membrane expression, while CD69 did not; CD63 expression occurred immediately (within 60 sec) but only in a minority of basophils, even at optimal agonist doses (in 33% and 14% of basophils, following fMLP and anti-IgE stimulation respectively). CD203c up-regulation occurred in the whole basophil population, even in CD63non expressing cells. Dose-dependence curves revealed CD203c as a more sensitive marker than CD63, in response to fMLP but not in response to anti-IgE and to calcium ionophore.ConclusionUse of polychromatic flow cytometry allowed efficient basophil electronic purification and identification of different behaviors of the major activation markers. The simultaneous use of two markers of activation and careful choice of activator are essential steps for reliable assessment of human basophil functions.
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Project description:Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.
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