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Image Cytometry[edit]

Introduction[edit]

Image cytometry is a companion technology to flow cytometry and microscopy.[1][2][3] The technique uses instruments that capture and analyse images of cells within a test chamber; the simplest instruments return data about the number and percentage viability of the cells, whereas more complex instruments are capable of performing assays such as cell cycle and various types of apoptosic assays. Image cytometers have been in development since the 1930’s and within the last 20 years have grown in capability to rival commercial flow cytometers, thanks to advances in imaging, data handling and analysis technology.[1][2]
Image cytometers use brightfield, darkfield and/or fluorescence, coupled with an image capture device, to generate images of the cells. These images are subsequently analysed by a computer embedded in or attached to the instrument, running specialised analysis software. Image cytometry has application in molecular biology, cancer research and stem cell research, as well as in the biotech and pharmaceutical industries.

History[edit]

Image cytometry is an evolution of optical and fluorescent microscopy. The precise point at which the technologies began to diverge is difficult to pinpoint, but it is probably fair to say that it began in the 1930s in Stockholm, when Tobjorn Caspersson began to study nucleic acids and cell growth at the Karolinska Institute in Stockholm.[3][4] During the 1950s, high-resolution image cytometry was restricted to analysis of a single cell at a time. [3][5]Advances in automation and computing during the ‘50s and ‘60s allowed the development of more sophisticated cytometers, principally for use in cancer and haematology research.[3][6]Over the next few decades, flow cytometry spearheaded progress in cytometry, integrating advances in laser, computing and fluorescence technology. Static image cytometers continued to exist alongside flow cytometers over the next few decades, and more recent advances in image processing and storage capabilities mean that current image cytometers have equivalent performance to many flow cytometers.
Modern image cytometers use a combination of bright field, dark field and/or fluorescent microscopy, typically using lasers or LEDs for illumination, along with a charge-coupled device (CCD) for image capture. Image data is then processed using a computer and sophisticated image analysis algorithms.

Priciples of operation[edit]

In contrast to a flow cytometer, an image cytometer analyses cells within a flat image plane, rather than in a flow of liquid that passes by a detector. Image cytometers, then, are similar to microscopes, in that they all consist of six main components:

  • A method of illumination – either bright field, dark field, fluorescence, or a combination of these can be used to illuminate the sample. Lamps, lasers and/or LEDs are used to provide the light.
  • Lenses – to focus the beam of light.
  • A stage, on which the sample rests.
  • Filters – these determine which wavelengths of light illuminate the sample and which reach the camera.
  • A detector – photomultiplier tubes, photodiodes, CCDs and cameras can all be used to process light from the sample ready for data acquisition.
  • A computer and analysis software – an image analysis algorithm, typically proprietary, is used to translate the image data.

Modern image cytometers typically fall into two groups, those that use bright field microscopy to count cells using the trypan blue method and those that use fluorescence (sometimes in combination with bright field imaging) to not only count cells, but to perform more complex assays, such as cell cycle and apoptosis assays.
Image cytometers that use fluorescence can have multiple channels.[7] The number of channels is determined by the number of combinations of light sources and filters in the instrument. The current record is five channels in a single instrument. The technology is developing at a rapid rate, though, so it is expected that within a few years benchtop image cytometers will have as many channels available as equivalent flow cytometers.

Image processing and analysis[edit]

After an image has been captured, the pixel data must be processed before gating and statistical analysis can take place. The image cytometry standard governs the way that image data and other pertinent information is stored.[8]

Main article: Image analysis

Spectral overlap and compensation[edit]

Automated image cytometers that use fluorescence must account for spillover, or spectral overlap, using a process known as compensation.Fluorophores emit light at a range of wavelengths and when the excitation and emission spectra of two fluorophores overlap, the intensity of the signal in a given frequency band will be the sum of the emission from the two fluorophores. All commercially available image cytometers use software algorithms that compensate for spillover automatically.

Data analysis[edit]

Data analysis for image cytometry is similar to that for flow cytometry, except that data collection is by image capture rather than accumulating data from one cell at a time. This is also a key difference between image cytometers and conventional microscopes, in that automated image cytometers are used for high content analysis. Data analysis is typically conducted using the analysis software included with the instrument. However, some instruments allow data to be exported in formats that enable it to be analysed using other popular flow cytometry analysis software, e.g. FCS Express or Flowjo.

Gating[edit]

The data generated from the processed image can be plotted as a histogram, scatter plot or other diagram types. In some systems it is possible to subsequently separate different subsets of cells by creating a series of subset extractions, in a manner similar to that used in flow cytometry data analysis.

Applications[edit]

High content analysis (HCA)[edit]

Also known as high content screening, or visual screening, HCA allows information about the functional characteristics of many individual cells.[9]According to Keefer and Zock, ‘‘High content screening can be defined as an automated imaging approach to understanding compound activities in cellular assays where, in each well of a microplate, you can measure spatial distribution of targets in cells, individual cell and organelle morphology, and complex phenotypes. It provides the flexibility to measure cell subpopulations and to combine multiple measurements per cell, while simultaneously eliminating unwanted cells and artifacts.’’[10][11] This approach has an advantage over traditional plate reporter assays, which display only an average measurement for the group of cells.[9] HCA plays a major role in the discovery of novel drugs, but also has application in fields such as RNAi and proteomics. [12][13][14]

Cell sorting[edit]

Cell sorting, as the name implies, is a process whereby subpopulations of cells are physically separated based on factors such as morphometric data, nucleic acid stains, or the presence of fluorescent labels. [15][16][17]
Image cytometers typically are not able to sort cells. However, an instrument described by Schindler et al. [17] incorporated a laser that could destroy cells using a beam of high intensity light. Another way of sorting cells using an image cytometer involves growing the cells on a film within a petri dish. Once the cells to be kept are identified, a laser cuts around the cells of interest, fusing the film to the petri dish. The rest of the film, on which are the cells to be discarded remain, is then removed. [1]

Primary cells[edit]

Because primary cells have greater variability in size and morphology than immortal cells, special assay techniques must sometimes be developed to enable an image cytometer to analyse them. Examples of the types of primary cells used commonly in research include stem cells of adipose or cardiac origin, hepatocytes, sputum samples, hematopoetic progenitor cells and bone marrow samples.[18][19][20][21] Image cytometers that use fluorescence (dyes or monoclonal antibodies conjugated to fluorescent molecules) are often the instruments of choice in these applications, as they identify cells using nucleic acid-binding dyes or cell surface markers. This means that they do not rely on the ability to distinguish the cell membrane, which in primary cells is often irregular in size and shape.

List of assays[edit]

A variety of assays can be performed using image cytometry. Some cytometers are used simply for cell counting (links to competitors’ instruments), whereas others can be used to perform more complex assays related to cell growth, cell death, phenotype and response to stimuli.

References[edit]

  1. ^ a b c Metezeau, P., Image and flow cytometry: companion techniques for adherent and non-adherent cell analysis and sorting. Biol Cell, 1993. 78(1-2): p. 129-34.
  2. ^ a b Tarnok, A., Innovations in image cytometry. Cytometry A, 2012. 81(3): p. 183-4.
  3. ^ a b c d Shapiro, H.M., The evolution of cytometers. Cytometry A, 2004. 58(1): p. 13-20.
  4. ^ Caspersson, T.O., Cell Growth and Cell Function: A Cytochemical Study, 1950: W. W. Norton & Company.
  5. ^ Mellors, R.C., Analytical cytology: methods for studying cellular form and function, 1955: Blakiston Division, McGraw-Hill.
  6. ^ Tolles, W.E., The cytoanalyzer - an example of physics in medical research. Trans N Y Acad Sci, 1955. 17(3): p. 250-6.
  7. ^ Varga, V.S., et al., Automated multichannel fluorescent whole slide imaging and its application for cytometry. Cytometry A, 2009. 75(12): p. 1020-30.
  8. ^ Dean, P., et al., Proposed standard for image cytometry data files. Cytometry, 1990. 11(5): p. 561-9.
  9. ^ a b Zanella, F., J.B. Lorens, and W. Link, High content screening: seeing is believing. Trends in Biotechnology, 2010. 28(5): p. 237 - 245.
  10. ^ Keefer, S. and J. Zock, Approaching high content screening and analysis: Practical advice for users, in High Content Screening: Science, Techniques and Applications, Chapter 1, S.A. Haney, Editor, 2008, Wiley: Hoboken. p. 4.
  11. ^ Tarnok, A., A focus on high content cytometry. Cytometry A, 2008. 73A: p. 381 - 383.
  12. ^ Kiss, E., et al., Cytometry of raft and caveola membrane microdomains: From flow and imaging techniques to high throughput screening assays. Cytometry A, 2008. 73A: p. 599 - 614.
  13. ^ Lee, T.Y., et al., RNA interference-mediated simultaneous silencing of four genes using cross-shaped RNA. Mol Cells, 2013. 35(4): p. 320-6.
  14. ^ Sajeesh, S., et al., Long dsRNA-mediated RNA Interference and Immunostimulation: A Targeted Delivery Approach using Polyethyleneimine based Nano-Carriers. Molecular Pharmaceutics, 2014.
  15. ^ Jahanmehr, S.A., K. Hyde, and C.G. Geary, Simple technique for fluorescence staining of blood cells with acridine orange. Journal of Clinical Pathology, 1987. 40: p. 926 - 929.
  16. ^ Mascotti, K., J. McCullough, and S.R. Burger, HPC viability measurement: trypan blue versus acridine orange and propidium iodide. Transfusion, 2000. 40(6): p. 693-6.
  17. ^ a b Schindler, M., et al., Automated analysis and survival selection of anchorage-dependent cells under normal growth conditions. Cytometry, 1985. 6(4): p. 368-74.
  18. ^ Hervouet, E., et al., Kinetics of DNA methylation inheritance by the Dnmt1-including complexes during the cell cycle. Cell Div, 2012. 7: p. 5.
  19. ^ Scerpa, M.C., et al., A new system for quality control in hematopoietic progenitor cell units before reinfusion in autologous transplant. Transfusion, 2013.
  20. ^ van Wilgenburg, B., et al., Efficient, long term production of monocyte-derived macrophages from human pluripotent stem cells under partly-defined and fully-defined conditions. PLoS One, 2013. 8(8): p. e71098.
  21. ^ Pannem, R.R., et al., CYLD controls c-MYC expression through the JNK-dependent signaling pathway in hepatocellular carcinoma. Carcinogenesis, 2014. 35(2): p. 461-8.
  22. ^ Skindersoe, M.E., M. Rohde, and S. Kjaerulff, A novel and rapid apoptosis assay based on thiol redox status. Cytometry A, 2012. 81(5): p. 430-6.
  23. ^ Cornelius, N., et al., Secondary coenzyme Q10 deficiency and oxidative stress in cultured fibroblasts from patients with riboflavin responsive multiple Acyl-CoA dehydrogenation deficiency. Hum Mol Genet, 2013. 22(19): p. 3819-27.
  24. ^ Dua, P., et al., Alkaline phosphatase ALPPL-2 is a novel pancreatic carcinoma-associated protein. Cancer Res, 2013. 73(6): p. 1934-45.
  25. ^ Ellegaard, A.M., et al., Sunitinib and SU11652 inhibit acid sphingomyelinase, destabilize lysosomes, and inhibit multidrug resistance. Mol Cancer Ther, 2013. 12(10): p. 2018-30.
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