Extended depth of field imaging for high speed object analysis

A high speed, high-resolution flow imaging system is modified to achieve extended depth of field imaging. An optical distortion element is introduced into the flow imaging system. Light from an object, such as a cell, is distorted by the distortion element, such that a point spread function (PSF) of the imaging system is invariant across an extended depth of field. The distorted light is spectrally dispersed, and the dispersed light is used to simultaneously generate a plurality of images. The images are detected, and image processing is used to enhance the detected images by compensating for the distortion, to achieve extended depth of field images of the object. The post image processing preferably involves de-convolution, and requires knowledge of the PSF of the imaging system, as modified by the optical distortion element

An open-source solution for advanced imaging flow cytometry data analysis using machine learning

Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary .cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using “user-friendly” platforms such as CellProfiler Analyst. Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data set. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye that include subtle measured differences in label free detection channels such as bright-field and dark-field imagery

Abstract 3074: Detection and enumeration of circulating tumor cells using imaging flow cytometery

Abstract Circulating tumor cells (CTCs), released into the bloodstream from primary and metastatic cancers, are valuable tools for understanding tumor biology. However, since they are rare, their detection is hampered by low efficiency and a lack of standardization in current technologies. To overcome these problems, we used Imaging Flow Cytometery along with fluorescent RNA detection probes to collect imagery from large number of cells to assess the number of CTCs. In this study, we spiked peripheral blood mononucleated cells (PBMCs) with SKBR-3 human breast cancer cells and added probes for relevant RNA targets such as EPCAM and Her-2. Taking advantage of the probes’ ability to detect RNA in live cells, combined with the capacity to acquire multi-spectral images of large numbers of cells, we demonstrate image based parameters that can be used to assess the frequency of CTCs in an objective and statistically significant manner. Citation Format: Shobana Vaidyanathan, Don Weldon, David Basiji, Philip Morrissey. Detection and enumeration of circulating tumor cells using imaging flow cytometery. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3074. doi:10.1158/1538-7445.AM2014-3074</jats:p

An innovative method for assessing autophagy using the FlowSight imaging flow cytometer (P3253)

Abstract Autophagy is a process in which normal cellular components that accumulate during growth and differentiation are degraded via the lysosome; it is a survival mechanism that reallocates nutrients from unnecessary processes to more vital processes in the cell. Basal levels of autophagy are usually low but can be up-regulated by numerous stimuli including starvation, physiological stress, pharmacological agents and infections. In addition, suppression of autophagy has been associated with cancer, neurodegenerative disorders, infectious diseases and inflammation. During autophagy, cytoplasmic LC3 is processed and recruited to the autophagosomal membranes; therefore, cells undergoing autophagy can be identified by visualizing LC3 puncta using immunofluorescence microscopy. While manual microscopy allows visual identification of autophagy on a per-cell basis, an objective and statistically rigorous assessment is difficult to obtain. To overcome these problems, we used the FlowSight imaging cytometry platform collect imagery of large numbers of cells to assess autophagy in an objective, quantitative, and statistically robust manner. In this study, we demonstrate a method for determining the best image-based parameter for assess the LC3 puncta in starved and non-starved U2OS RFP-LC3 human osteosarcoma reporter cells.</jats:p

Cell Classification in Human Peripheral Blood Using the Amnis ImageStream® System.

Abstract Amnis Corporation’s ImageStream® system combines the sample handling and quantitative power of flow cytometry with high-resolution brightfield, darkfield, and fluorescence cellular imagery. The system simultaneously generates up to six images of each cell in flow and can acquire data sets consisting of tens of thousands of cells in just a few minutes, while offering fluorescence sensitivity equal to or better than flow cytometry. The image data are analyzed using Amnis’ IDEAS® software, which automatically calculates over 200 morphometric and photometric features and associated statistics for each cell, identifying unique cell groups based not only on their fluorescence intensity signature but also on their morphological characteristics. The software offers the ability to view the imagery associated with any cell in a scatter plot, perform “virtual cell sorts” of user-specified sub-populations, and generate custom features of biological significance (e.g. N/C ratio). The ImageStream platform’s ability to quantitate morphologic and immunofluorescent differences between very large numbers of cells in suspension make it particularly well suited for hematology. In the present study, human peripheral blood mononuclear cells were stained with a fluorescent DNA binding dye to reveal nuclear morphology, as well as fluorescently labeled mAb to various CD markers. Five images of each cell were acquired: brightfield (transmitted light), darkfield (laser side scatter), and three fluorescent colors for nuclear imagery and quantitation of the CD marker abundance. The object of the study was to identify morphometric parameters in the brightfield, darkfield, and nuclear imagery that would prove useful in hematologic cell type classification. The mAb to CD antigens provided a positive control for use in the evaluation of the of the various morphometric parameters. Parameters with discriminating power included cellular size and texture, darkfield intensity and granularity, and nuclear fluorescence intensity, texture, and shape. Cell types that could be automatically discriminated using these parameters in lieu of immunofluorescent markers included neutrophils, eosinophils, monocytes, and lymphocytes (including putative activated lymphocytes). In addition to forming the basis for an advanced ImageStream hematology platform, it is envisioned that the automated morphometric classification of blood cells will act as the foundation for a wide range of image-based cellular assays performed in peripheral blood (e.g. NF-kB translocation, apoptosis, mAb compartmentalization), allowing the differential quantitation of assay results in various cell types for the purposes of basic research, drug discovery, and clinical diagnostics.</jats:p

Measuring T cell activation in T cell-APC conjugates using the ImageStream imaging flow cytometer (P5043)

Abstract Adaptive immune responses require antigen presentation to antigen-specific effector cells. Upon interaction between the antigen presenting cell (APC), specific antigen and T cell there is a reorganization of the cytoskeleton and recruitment of adhesive and signaling molecules to the site of intercellular contact forming an immunological synapse (IS). Upon formation of a functional IS, signaling molecules join the clusters and contribute to the signaling cascade leading to translocation of nuclear factors from the cytoplasm to the nucleus culminating in gene activation. Recruitment of these molecules is measured by imaging cell conjugates directly. To analyze a statistically significant number of specific conjugates we used the ImageStream imaging flow cytometer with a 60X objective, which objectively and rapidly collects large numbers of images and provides quantitative image analysis tools to evaluate subcellular localization of molecules. In this study we evaluate the specific location of the adhesion and signaling molecules LFA-1 and Lck within the IS complex in T cells when presented with SEB pulsed APC. In addition we measured the translocation of NFkB in the T cell from the cytoplasm to the nucleus showing the activation of the T cells when conjugated to APC.</jats:p

Measuring immunological synapse and actin organization using the FlowSight imaging flow cytometer (P5018)

Abstract Interaction between antigen-specific T cells and antigen presenting cells (APC) cognate ligand involve reorganization of the cytoskeleton and recruitment of adhesive and signaling molecules to the site of intercellular contact. Sustained adhesion of T cells to APCs and formation of the immunological synapse (IS) after T cell receptor stimulation are required for the antigen-specific response. One way to measure an IS is by fluorescently labeling the molecules that have been recruited to the synapse and imaging via microscopy. However, IS are often rare and therefore difficult to analyze objectively and statistically by traditional microscopy methods. To overcome these problems, we employed the Amnis FlowSight imaging flow cytometry platform to objectively collect imagery of large numbers of cells to assess the percentage of T cells involved in an organized IS. In this study, Raji B cells were loaded with Staphylococcal enterotoxin B (SEB) to make APCs. The SEB-loaded APCs were incubated with human T cells to create T cell-APC conjugates. In addition, we investigated the inhibition of actin by cytochalasin D and how that affects the T cell-APC conjugates. The T cells, APCs and conjugates were fluorescently labeled. Using the FlowSight imaging flow cytometry platform we demonstrate image-based parameters that were used to assess the frequency of conjugates with an organized IS in an objective and statistically significant manner.</jats:p