High content screening microscopy

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Describe the methodological approach used in the ‘primary and validation screens’ in the Nature Cell Biology article

The primary screen used Ambion siRNA while the validation screen used both Ambion and Qiagen siRNAs. Researchers arrayed 148 chambered slide cover glasses over a density of around 384 siRNAs spots for each chamber. Each of the primary chambers had an average of 1-4 +ve control siRNAs that were arrayed against (COPBI) with an average of 7-8 non-silencing –ve control siRNAs. Researchers visually assessed cell plating densities of the chambers after each experiment came to an end. Arrays that displayed obvious cell densities were omitted from quantifications (Simpson et al, 2012). Individual cell surface ratio tsOS45G to the total tsOS45G was calculated in order to determine the secretory transport from each cell. Researchers then determined mean values per each sport by converting them into deviation scores and labeling them appropriately. A value of 0.75 to 1.0 reflected a mild cell surface reduction, all values greater than one indicated a strong cell surface reduction tsO45G delivery while all values less than one indicated an enhanced cell surface tsO45G delivery (Simpson et al 2012).  Other values beyond the classification were considered irrelevant or as to have had no effect.

The team subtracted Texperiment from Tcontrol and multiplied the answer by 1/2 s.e.m control where T was the cell surface ratio tsO45G to total tsO45G in order to determine the secretion deviation value for the validation screens. To quantify the data from the screens the analysis software had to recognize not less 15 cells for validation (Simpson et al 2012). Other quality control measures were put in place to ensure the reliability of siRNA data collected.

Describe the methodological approach used in the ‘secondary screen’ in the Nature Cell Biology article

Researchers incubated HeLa Kyoto cells for about 50hours on the siRNA arrays and then fixed them with paraformaldehyde or methanol. The cells were then immunostained using standardized methods. This experiment used same sets of arrays with the validation screens experiment. Primary antibodies were arraigned against the COPB2 subunit of the COPI used at a 1 is to 4 dilution ratio and a 1:5 dilution ratio of protein GM130 and BD. The team used (A-11034, A-11029, A-11036, and A-11034) fluorescently conjugated secondary antibodies at a 1:800 dilution ratio. All from molecular probe technology. The team visually scrutinized images for any phenotypic change to the organelle morphology using a *20 scan^r Olympus microscope. Classifications for the COPI coat was classified as either wild or no effect to disorganized, diffuse and fragmented (Simpson et al, 2012). Tabulated COPI coats had a large juxta-nuclear signal enlargement area while condensed COPI coats lacked punctate structures but were rich in single concentrated regions. The team quantified phenotype strengths through customized imaging software’s which enabled measurement of haralick textures and granularity. The aberrant-shape nuclei cells determined from the 7 shape features were omitted and disregarded through the expectations maximization clustering methods. Mahalanobis distance was calculated using the data achieved from the 24 noncorrelated matrix features generated. This allowed the researchers to measure distances for all experimental cells from the control cells. The mean MD (Mahalanobis distance) for either of the siRNAs was later calculated and later the secondary screens deviations was equated with that of the control cell (Simpson et al, 2012).

Appropriate approaches to better study and quantify the cellular phenotypes observed in the secondary screen in the Nature Cell Biology article

Today, high content screening uses a combination of automated microscopy and quantity analysis to increase the speed at which results are arrived at. New technologies have advanced both hardware and software for automated imagery analysis which allows researchers to screen images without limitation of the cell quantity being screened (Usaj et al, 2016). Back in 2012, analysis usually involved calculating deviations of single parameters but today machine learning and artificial intelligence is used for high-dimensional analysis of data. A combination of automated fluorescence and quantitative imagery enables researchers to acquire unbiased multipara-metric information down at a single cells’ level. Studying cellular phenotypes is very dependent on the dynamics and functionality of imaging tools at an optimum or temporal resolution. Intravital CLEM (intravital correlatives lights and electrons microscopy) uses a combination of multicellular model system imaging and electron microscopy to provide a full ultra-structural detail of transient activities in vivo (Karreman et al, 2016).

Advanced microscopy allows cell imaging at the spatial and temporal levels relevant to cell migration. Advances in 3D technology have enabled scientist to carry out experimental workflows that provide images with enough spatiotemporal resolutions which are helpful in the study of molecular processes which govern migration of cells in a 3D environment. Because it is difficult to visualize and analyze 3D movies, scientist use technologies such as image rendering to generate a more visually analyzable image (Driscoll, 2015). Current computer algorithms like computer vision and computer statistics are used instead of traditional image analysis computational methods for calculating the cell sizes and complexities.

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  1. Driscoll, M.K., and Danuser, G., 2015. Quantifying modes of 3D cell migration. Trends in cell biology, 25(12), pp.749-759.
  2. Karreman, M.A., Hyenne, V., Schwab, Y. and Goetz, J.G., 2016. Intravital Correlative Microscopy: Imaging Life at the Nanoscale. Trends in cell biology, 26(11), pp.848-863.
  3. Simpson, J.C., Joggerst, B., Laketa, V., Verissimo, F., Cetin, C., Erfle, H., Bexiga, M.G., Singan, V.R., Hériché, J.K., Neumann, B. and Mateos, A., 2012. Genome-wide RNAi screening identifies human proteins with a regulatory function in the early secretory pathway. Nature cell biology, 14(7), pp.764-774.
  4. Usaj, M.M., Styles, E.B., Verster, A.J., Friesen, H., Boone, C. and Andrews, B.J., 2016. High-Content Screening for Quantitative Cell Biology. Trends in cell biology.
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