Cell Interaction Analysis by Imaging Flow Cytometry

Many processes such as cell adhesion, tissue development, cellular communication, inflammation, tumor metastasis, and microbial infection require direct interactions between cells. Some cell-cell interactions are transient, as is the case of the contacts between cells ofthe immune system, the interactions of white blood cells to malignant cells or to sites oftissue inflammation. These events often entail structural alterations in the point of contact ofthe cells involved, and may involve the fusion, transfer or exchange of material between thecells; which occur in a scale that is suited for optical microscopy analysis. However, due toits low throughput nature, microscopy often suffers from acquisition bias and limitedstatistical power. Moreover, because the data is typically analyzed in a qualitative manner, itis difficult to obtain standardized results. Strong scientific conclusions demand objectivecollection of large amounts of relevant information that can be analyzed in a quantitative,standardized, and statistically robust manner. Flow cytometry overcomes these problemsbut reduces the rich information available via optical microscopy to a set of intensitymeasurements. By combining high speed automated image acquisition with quantitativeimage analysis, Multispectral Imaging Flow Cytometry (MIFC) provides all the elementsrequired for discriminating cells based on intensity and appearance in a standardized andstatistical manner. In recent years, the application of this technology for the analysis of cell-cell interaction has multiplied, in particular in the field of immunology, allowing theobservation and quantification of events in a way that could not be achieved before.Fil: Payés, Cristian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay; ArgentinaFil: Rodríguez, José A.. No especifíca;Fil: Friend, Sherree. No especifíca;Fil: Helguera, Gustavo Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay; Argentin

Analyzing Cellular Internalization of Nanoparticles and Bacteria by Multi-spectral Imaging Flow Cytometry

Nanoparticulate systems have emerged as valuable tools in vaccine delivery through their ability to efficiently deliver cargo, including proteins, to antigen presenting cells1-5. Internalization of nanoparticles (NP) by antigen presenting cells is a critical step in generating an effective immune response to the encapsulated antigen. To determine how changes in nanoparticle formulation impact function, we sought to develop a high throughput, quantitative experimental protocol that was compatible with detecting internalized nanoparticles as well as bacteria. To date, two independent techniques, microscopy and flow cytometry, have been the methods used to study the phagocytosis of nanoparticles. The high throughput nature of flow cytometry generates robust statistical data. However, due to low resolution, it fails to accurately quantify internalized versus cell bound nanoparticles. Microscopy generates images with high spatial resolution; however, it is time consuming and involves small sample sizes6-8. Multi-spectral imaging flow cytometry (MIFC) is a new technology that incorporates aspects of both microscopy and flow cytometry that performs multi-color spectral fluorescence and bright field imaging simultaneously through a laminar core. This capability provides an accurate analysis of fluorescent signal intensities and spatial relationships between different structures and cellular features at high speed. Herein, we describe a method utilizing MIFC to characterize the cell populations that have internalized polyanhydride nanoparticles or Salmonella enterica serovar Typhimurium. We also describe the preparation of nanoparticle suspensions, cell labeling, acquisition on an ImageStreamX system and analysis of the data using the IDEAS application. We also demonstrate the application of a technique that can be used to differentiate the internalization pathways for nanoparticles and bacteria by using cytochalasin-D as an inhibitor of actin-mediated phagocytosis

Analyzing Cellular Internalization of Nanoparticles and Bacteria by Multi-spectral Imaging Flow Cytometry

Nanoparticulate systems have emerged as valuable tools in vaccine delivery through their ability to efficiently deliver cargo, including proteins, to antigen presenting cells1-5. Internalization of nanoparticles (NP) by antigen presenting cells is a critical step in generating an effective immune response to the encapsulated antigen. To determine how changes in nanoparticle formulation impact function, we sought to develop a high throughput, quantitative experimental protocol that was compatible with detecting internalized nanoparticles as well as bacteria. To date, two independent techniques, microscopy and flow cytometry, have been the methods used to study the phagocytosis of nanoparticles. The high throughput nature of flow cytometry generates robust statistical data. However, due to low resolution, it fails to accurately quantify internalized versus cell bound nanoparticles. Microscopy generates images with high spatial resolution; however, it is time consuming and involves small sample sizes6-8. Multi-spectral imaging flow cytometry (MIFC) is a new technology that incorporates aspects of both microscopy and flow cytometry that performs multi-color spectral fluorescence and bright field imaging simultaneously through a laminar core. This capability provides an accurate analysis of fluorescent signal intensities and spatial relationships between different structures and cellular features at high speed. Herein, we describe a method utilizing MIFC to characterize the cell populations that have internalized polyanhydride nanoparticles or Salmonella enterica serovar Typhimurium. We also describe the preparation of nanoparticle suspensions, cell labeling, acquisition on an ImageStreamX system and analysis of the data using the IDEAS application. We also demonstrate the application of a technique that can be used to differentiate the internalization pathways for nanoparticles and bacteria by using cytochalasin-D as an inhibitor of actin-mediated phagocytosis

A point mutation in the murine Hem1 gene reveals an essential role for Hematopoietic Protein 1 in lymphopoiesis and innate immunity

Hem1 (Hematopoietic protein 1) is a hematopoietic cell-specific member of the Hem family of cytoplasmic adaptor proteins. Orthologues of Hem1 in Dictyostelium discoideum, Drosophila melanogaster, and Caenorhabditis elegans are essential for cytoskeletal reorganization, embryonic cell migration, and morphogenesis. However, the in vivo functions of mammalian Hem1 are not known. Using a chemical mutagenesis strategy in mice to identify novel genes involved in immune cell functions, we positionally cloned a nonsense mutation in the Hem1 gene. Hem1 deficiency results in defective F-actin polymerization and actin capping in lymphocytes and neutrophils caused by loss of the Rac-controlled actin-regulatory WAVE protein complex. T cell development is disrupted in Hem1-deficient mice at the CD4−CD8− (double negative) to CD4+CD8+ (double positive) cell stages, whereas T cell activation and adhesion are impaired. Hem1-deficient neutrophils fail to migrate in response to chemotactic agents and are deficient in their ability to phagocytose bacteria. Remarkably, some Rac-dependent functions, such as Th1 differentiation and nuclear factor κB (NF-κB)–dependent transcription of proinflammatory cytokines proceed normally in Hem1-deficient mice, whereas the production of Th17 cells are enhanced. These results demonstrate that Hem1 is essential for hematopoietic cell development, function, and homeostasis by controlling a distinct pathway leading to cytoskeletal reorganization, whereas NF-κB–dependent transcription proceeds independently of Hem1 and F-actin polymerization

Hight Throughput, Parallel Imaging and Biomarker Quantification of Human Spermatozoa by ImageStream Flow Cytometry

8 páginas, 2 figuras, 1 tabla.Spermatid specific thioredoxin-3 protein (SPTRX-3) accumulates in the superfluous cytoplasm of defective human spermatozoa. Novel ImageStream technology combining flow cytometry with cell imaging was used for parallel quantification and visualization of SPTRX-3 protein in defective spermatozoa of five men from infertile couples. The majority of the SPTRX-3 containing cells were overwhelmingly spermatozoa with a variety of morphological defects, detectable in the ImageStream recorded images. Quantitative parameters of relative SPTRX-3 induced fluorescence measured by ImageStream correlated closely with conventional flow cytometric measurements of the same sample set and reflected the results of clinical semen evaluation. Image Stream quantification of SPTRX-3 combines and surpasses the informative value of both conventional flow cytometry and light microscopic semen evaluation. The observed patterns of the retention of SPTRX-3 in the sperm samples from infertility patients support the view that SPTRX3 is a biomarker of male infertility.Peer reviewe

Detection of CD169⁺ IL-7Rα^hiCCR6⁺ lymphocytes in digested lymph node tissue.

<p>(A) Flow cytometric detection of IL-7Rα expression on CD169⁺CD11c^lo cells from digested lymph nodes. (B) Immunofluorescence microscopy of a lymph node section stained with anti-CD169 and anti-IL-7Rα monoclonal antibodies. Enlargements (bottom, far right) show examples of IL-7Rα⁺ cells (white arrows) closely associated with CD169⁺ SSMs. FO, follicle; T, T zone. (C) Flow cytometric analysis showing CD169⁺CD11c^lo cells contain a population of IL-7Rα^hiCCR6⁺ cells. All data in A–C are representative of at least three independent experiments. (D) Flow cytometric analysis of digested lymph node cells from a control and TCRβδ-deficient mouse. IL-7Rα^hiCCR6⁺ cells gated from total cells express CD169 on a fraction of both CD3e⁺ and CD3e⁻ cells. Data are representative of at least three independent experiments (control mice) and one experiment in which a TCRβδ-deficient mouse was analyzed.</p

IL-7Rα^hiCCR6⁺ lymphocytes acquire CD169⁺ SSM-derived membrane blebs.

<p>(A) Flow cytometric detection of CD169 on digested lymph node cells stained with two anti-CD169 monoclonal antibodies, Ser4 and 3D6. The top panel is pre-gated on IL-7Rα^hiCCR6⁺ cells; the bottom panel shows total cells. Data are representative of three independent experiments. (B) Expression of CD169 on total cells from digested lymph nodes from CD169-DTR mice treated with saline or DT 3 or 4 days prior to analysis. Data are representative of two independent experiments. (C) <i>Siglec1</i> mRNA quantification by RT-PCR in sorted CD169⁺ and CD169^– IL-7Rα^hiCCR6⁺ cells, CD169⁺CD11c^loF4/80^– cells, and CD169⁺CD11c^loF4/80⁺ cells. Data are plotted relative to HPRT. (D) Immunofluorescence microscopy of lymph nodes stained with anti-CD169 and anti-CD11b monoclonal antibodies from a <i>Siglec1^–/–</i> bone marrow chimeric mouse (top panel) or control non-chimeric mouse (bottom). Scale bar = 50 µm. Data are representative of one experiment. (E) Flow cytometric detection of CD169 staining on CD45.1⁺<i>Siglec1^+/+</i> radiation-resistant (blue) compared to donor bone-marrow-derived CD45.2⁺<i>Siglec1^–/–</i> (red) IL-7Rα^hiCCR6⁺ cells. CD169⁺ staining on IL-7Rα^hiCCR6⁺ cells from a non-chimeric control animal are represented in black. Data are representative of two experiments. (F) Flow cytometric detection of CD169 and CD11b on IL-7Rα^hiCCR6⁺ cells from digested lymph nodes. Data are representative of two experiments. (G) CD169⁺IL-7Rα^hiCCR6⁺B220⁻ cells from digested lymph nodes were sorted and fixed to a slide for immunofluorescence microscopy. Data are representative of one experiment.</p

IL-7Rα^hiCCR6⁺ lymphocytes are IL-17 producing cells.

<p>(A) CD44, CD62L and CXCR6 expression on digested lymph node cells from control or <i>Cxcr6^GFP/+</i> mice, gated on IL-7Rα^hiCCR6⁺ cells. (B) IL-17A staining of digested lymph node cells, stimulated with PMA/I for 2 h, gated on gated on IL-7Rα^hiCCR6⁺ cells. (C) αβT and γδT staining on digested lymph node cells, gated on IL-7Rα^hiCCR6⁺ cells; CD169 staining of αβT⁺, γδT⁺ and TCR⁻ IL-7Rα^hiCCR6⁺ cells gated as indicated in the left panel. (D) Flow cytometric detection of IL-7Rα^hiCCR6⁺ cells that bind CD1d-tetramers; far right panel shows CD169 staining on CD1d-tetramer⁺ IL-7Rα^hiCCR6⁺ cells. (E) CD169, CD4, and CD8 staining on digested lymph node cells, gated on IL-7Rα^hiCCR6⁺ cells. (F) Immunofluorescence microscopy of a lymph node from a wild-type or a CD4-deficient mouse, stained with anti-CD169 and anti-CD4 monoclonal antibodies. FO, follicle; T, T zone. Scale bar = 50 µm. All data are representative of at least two independent experiments.</p

Analysis of CD169 distribution on IL-7Rα^hiCCR6⁺ lymphocytes and CD11c^hi dendritic cells using ImageStreamX imaging flow cytometry.

<p>(A) Gating scheme to identify CD169⁺IL-7Rα^hiCCR6⁺ cells analyzed on an ImageStreamX imaging flow cytometer (Amnis Corp). The area of CD169 staining on images of the gated cells was quantified using a Threshold mask on the upper 60% of the pixel intensities (right panel). (B) Representative images of cells with small, medium, and large areas of CD169 area based on the histogram in (A). Channels for CD169 (green), IL-17Rα (red), and brightfield are shown. Graph on right shows the frequency of cells falling in each gate (n = 2 mice, lines indicate means). (C) Gating scheme to identify CD169⁺CD11c⁺ cells analyzed on an ImageStreamX imaging flow cytometer. The area of CD169 staining on images of the gated cells was quantified as described in (A). (D) Representative images of cells with small, medium, and large areas of CD169 area based on the histogram in (C). Channels for CD169 (green) IL-17Rα (red), and brightfield are shown. Graph on right shows the frequency of cells falling in each gate (n = 2 mice, lines indicate means). Data are representative of one experiment with two mice. In a second experiment with two mice, CD169⁺ blebs were visualized on CD169⁺CCR6⁺TCRγδ⁺ cells.</p