We thank the Hamilton programmers and specialists for continuing support and Eva Blaas for technical assistance. This work was supported by two NWO Investment grants (911-07-031 and 40-00506-98-10011), The Prinses Beatrix Fonds Wetenschapsprijs 2009 and the Neuroscience Campus Amsterdam; S.J. is supported by Ti-Pharma: T5-207.

1. Automated adherent cell culture process (Figure 1)20
<ol>
	<li>Prepare the automated cell culture system for the input of cell culture plates. Load consumables (e.g. pipette tips, cell culture plates, assay plates) into the system using the graphical user interface (GUI). Ensure there is sufficient cell culture media, phosphate buffered saline (PBS) and trypsin in the robotic system.</li>
	<li>Manually seed two omnitray plates with 2 x 106 cells per plate, of the SH-SY5Y neuroblastoma cell line. Maintain cells in Opti-MEM with 10% fetal bovine serum (FBS). Put the plates into the cell culture robot using the GUI. The cells will be incubated at 37&deg;C and 5% CO2.</li>
	<li>Choose which cell culture protocol needs to be initiated20. One can chose from an adherent cell culture process22, culturing and expansion of embryonic stem (ES) cells on mouse feeder cells23 or culturing of suspension cells22.</li>
	<li>Select the adherent cell culture protocol (Figure 1) and ensure adherent cell line specific parameter files are adjusted so that the confluence threshold (the area of the omnitray plate that contains cells) is set at 70%. Set total trypsinization time to two minutes.</li>
	<li>Instruct the robot to prepare new omnitray plates, with a seeding cell number of 2 x 106 cells per plate.</li>
	<li>Input omnitray plates into the cell culture system using the automated adherent cell culture protocol (Figure 1). This protocol involves the following steps: plates are incubated and imaged until they reach the pre-defined confluence threshold. If cells do not reach the confluence threshold within 5 readings, plates are removed from the system. Upon reaching the user defined confluence threshold, cells are washed, trypsinized and counted. A pre-defined number of cells are added to new omnitray plates and if there are sufficient numbers of cells, a specified number of assay plates are transported to the deck and a defined number of cells dispensed into each well. Assay plates can be directly imaged using the integrated microscope or output from the system for further processing.</li>
	<li>Instruct the system to prepare 4 assay plates per omnitray plate, with a total of 5,000 cells per well.</li>
</ol>
2. shRNA virus production and plating into assay plates (Time required: 6 days)
<ol>
	<li>Grow bacterial glycerol stocks containing the shRNA vectors (Open Biosystems, TRC1) overnight in 2ml of Luria-Bertani medium media containing 100 &mu;g/ml of ampicilin (Sigma-Aldrich).</li>
	<li>Extract plasmids following the manufacturer's protocol (Promega Wizard MagneSil Tfx).</li>
	<li>Produce virus using the RNAi Consortium High-Throughput Lentiviral Production (96 well plate) protocol 24. Working with lentivirus is relatively safe because the virus particles used for transduction are replication-deficient and split-gene packaging strategies are used for their production. However, when working with lentivirus, additional biosafety procedures are necessary to minimize the risk to oneself and others 25. All experiments must be conducted in a MLII or BSL2 safety level laboratory. All plastics (pipettes, plastic dishes, media) that have been in contact with lentivirus particles should be incubated with bleach for 24 hours prior to disposal.</li>
	<li>Calculate the multiplicity of infection (MOI) of the lentivirus by determining the percentage of GFP positive cells using the pLKO.1 GFP plasmid (Sigma-Aldrich).</li>
	<li>Plate lentivirus into assay plates, with an MOI of 3.</li>
</ol>
3. Lentiviral transduction and neuronal differentiation of SH-SY5Y cells (Time required: 6 days)
<ol>
	<li>Cells are added to the assay plates (see step 1.7). Load assay plates containing shRNA lentivirus into the automated cell culture system.</li>
	<li>After 24 hours, the media on the assay plates will be changed to Opti-MEM containing 0.5% FBS and 0.1 &mu;M retinoic acid to begin the differentiation process. Differentiation of SH-SY5Y cells allows visualization of neuritic structures and synchronizes cell division.</li>
	<li>Continue incubation of the assay plates in the differentiation media for 5 days. This ensures maximal knockdown of target gene expression.</li>
	<li>On day 5, add 50 &mu;M H2O2 to half of the assay plates for 24 hours to stimulate translocation of DJ1 to the mitochondria.</li>
	<li>On day 6, add Mitotracker CmxROS (Invitrogen) to the cells, at a final concentration of 200 nM per well and incubate at 37&deg;C for 30 minutes.</li>
	<li>One can instruct the system to image plates directly using the HC imager or plates can be exported from the system for further processing.</li>
</ol>
4. Automated immunostaining of assay plates (Time required: 2 days)
Image quality is paramount for conducting a sensitive and reliable HCS. Damage to the cellular monolayer due to inaccurate pipetting can lead to poor image quality and irreproducible results. In order to minimize cell layer damage, the immunostaining was conducted using a robotic station. The procedure is similar to one that has been previously described26 but has been customized to increase throughput and reduce consumable usage.
<ol>
	<li>Fix cells with 100 &mu;l of 4% paraformaldehyde pre-warmed to 37&deg;C. Incubate for 20 minutes at room temperature.</li>
	<li>Wash cells with 200 &mu;l of PBS for 5 minutes, 3 times.</li>
	<li>Incubate assay plates with 200 &mu;l of PBS containing 0.1 % Triton (PBST) for 10 minutes.</li>
	<li>Wash cells with 200 &mu;l of PBS for 5 minutes, 3 times.</li>
	<li>Incubate assay plates with 200 &mu;l of block buffer (PBST with 5% FBS) for 1 hour at room temperature.</li>
	<li>Wash cells with 200 &mu;l of PBS for 5 minutes, 3 times.</li>
	<li>Incubate with the following primary antibodies overnight at 4&deg;C:
	<ol>
		<li>Goat DJ1 N20 (Santa Cruz, 5 &mu;g/ml)</li>
		<li>Rabbit &beta;-III tubulin (Sigma-Aldrich, 1 &mu;g/ml)</li>
	</ol>
	</li>
	<li>On the following day, wash cells with 200 &mu;l of PBS for 5 minutes, 3 times.</li>
	<li>Incubate assay plates with the following secondary antibodies 1 hour at room temperature:
	<ol>
		<li>AlexaFluor 488 donkey anti-goat (Invitrogen, 2 &mu;g/ml)</li>
		<li>AlexaFluor 647 goat anti-rabbit (Invitrogen, 2 &mu;g/ml</li>
	</ol>
	</li>
	<li>Wash cells with 200 &mu;l of PBS for 5 minutes, 3 times.</li>
	<li>Incubate cells with Hoechst (Invitrogen; 1 &mu;g/ml) for 10 minutes.</li>
	<li>Wash cells with 200 &mu;l PBS for 5 minutes, 3 times.</li>
	<li>Store plates at 4&deg;C until they can be imaged.</li>
</ol>
5. High content image acquisition and image analysis (Time required: 5 days)
<ol>
	<li>Image a total of 30 fields per well using the 20x objective lens. Visualize DJ1 with the FITC filter set, the mitochondria with the TRITC filter set, &beta;-III tubulin with the Cy5 filter set and the nuclei using the UV filter set (Figure 3).</li>
	<li>Analyze the images using the Compartmental Analysis Bioapplication (Cellomics, ThermoFisher) to determine the average intensity of the Mitotracker signal within the mitochondria. (Figure 4B, F).</li>
	<li>To determine the average overlap coefficient between DJ1 and the mitochondria, analyze the images using the Cellomics Colocalisation bioapplication (Cellomics, ThermoFisher). Define regions of interest (ROI) as follows: ROI A - nucleus (Figure 4A, E), ROI B - mitochondria (Figure 4 B, F). Exclude ROI A from ROI B to ensure analysis of only the cytoplasm. Define the mitochondria as target region I and DJ1 as target region II (Figure 4C, G).</li>
	<li>Analyze the images using the Neuronal Profiling bioapplication (Cellomics, Thermofisher) to trace the average lengths of the neurites from the &beta;-III tubulin staining (Figure 4D, H).</li>
	<li>Image plates using the Opera LX automated confocal reader (Perkin-Elmer). Image a total of 30 fields per well using the 60x objective lens with water immersion. Visualize mitochondria with the 561 nM laser and nuclei with UV excitation.</li>
	<li>Analyze the images using the Spot-Edge-Ridge (SER) texture features algorithm. The SER-Ridge filter transmits intensity in pixels forming ridge-like patterns. The more fragmented the mitochondria, the higher the SER-Ridge score (Figure 8).</li>
</ol>
6. Data normalization and analysis
<ol>
	<li>Import the data from the image analysis software into the BioConductor CellHTS2 package for the R software environment (R version 2.11.1, BioConductor version 2.6).</li>
	<li>Logarithm base (2) transform the data prior to per plate median based normalization27, 28. Do not apply the variance adjustment per plate.</li>
	<li>To identify modifiers of a phenotype, use a two way ANOVA between the different treatment groups i.e. Scrambled infected untreated cells vs. scrambled toxin treated cells vs. target gene untreated cells vs. target gene treated cells (Figures 5-7).</li>
</ol>
7. Representative results
Mutations within DJ1 give rise to early onset-recessive parkinsonism21, but it is unclear how loss of DJ1 gives rise to the disease phenotype. It is known that cells deficient of DJ1 are more susceptible to oxidative stress-induced cell death and in response to oxidative stress, DJ1 translocates from the cytoplasm to the mitochondria 29, 30. By constructing HC assays to monitor these phenotypes, we can identify genes that regulate or affect phenotypes associated with DJ1. This approach can help decipher the pathways within which DJ1 functions and that could be involved in disease pathogenesis.
Example of an epistatic interaction with DJ1 (Figure 5): Knockdown of DJ1 in cells exposed to toxin results in a greater loss of cell viability (BAR-B: image-B) compared to cells infected with scrambled lentivirus (BAR-A: image-A). Knockdown of target gene A has a similar effect to that observed in cells with a DJ1 knockdown (BAR-C: image-C). Knockdown of both DJ1 and target gene A results in a significantly greater loss of cell viability than loss of either gene alone (BAR-D: image-D). This suggests an epistatic interaction between DJ1 and target gene A.
Example of a gene regulating DJ1 translocation (Figure 6): When cells are exposed to a toxin, DJ1 translocates from the cytoplasm to the mitochondria, which is quantified by a higher overlap coefficient between DJ1 and the mitochondria (BAR-A: image A versus BAR-C: image C). In cells where target gene B has been silenced, less DJ1 translocates to the mitochondria when cells are exposed to the toxin. This suggests that target gene B is involved in the transport of DJ1 to the mitochondria. (BAR-B: image B and BAR-D: image D)
Example of a gene involved in neuronal outgrowth (Figure 7): Knockdown of target gene C in wild type SH-SY5Y cells results in a significant increase in neurite length (BAR-B: image-B) compared to cells infected with lentivirus expressing scrambled shRNA (BAR-A: image A). This effect is lost in cells incubated with toxin (BAR-C and D).
Example of a gene involved in mitochondrial morphology (Figure 8): Infection of wild type SH-SY5Y cells with shRNA targeting gene D results in a decrease in the mitochondrial SER-Ridge segmentation value (Figure 8, Image C and D) when compared to cells infected with scrambled lentivirus (Figure 8, Image A and B).
<img alt="Figure 1." src="/files/ftp_upload/3452/3452fig1.jpg" /> Figure 1. Outline of automated cell culture protocol. Please <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/3452/3452fig1_lg.jpg">click here</a> to see a large version of this figure.
<img alt="Figure 2." src="/files/ftp_upload/3452/3452fig2.jpg" /> Figure 2. Schematic overview of the screening process, image analysis and statistical methods used during the screening process: i) Cells are cultured until they are confluent and subsequently plated into the assay plates containing the shRNA lentivirus. Cells are differentiated for 5 days and toxin is then added to the plates for 24 hours. Assay plates are output from the system and immunostained. The amount of time taken for each of the processes is indicated in brackets. ii) Data is acquired using a HC imager (cell viability, protein translocation and neuronal outgrowth) and an automated confocal imager (mitochondrial morphology). Data are exported to CellHTS2 package within R, base (2) log transformed and normalized. Two-way ANOVA is used to identify significant interactions between the different variables. Please <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/3452/3452fig2_lg.jpg">click here</a> to see a large version of this figure.
<img alt="Figure 3." src="/files/ftp_upload/3452/3452fig3.jpg" /> Figure 3. Composite images of cells acquired by HC imaging. A) Untreated cells, B) Cells treated with H2O2. DJ1 is labeled in green, the mitochondria in red and the nuclei in blue. Neurite staining is not highlighted.
<img alt="Figure 4." src="/files/ftp_upload/3452/3452fig4.jpg" /> Figure 4. Quantification of several cellular features obtained from a HCS. A-D) Untreated SH-SY5Y cells. E-H) H2O2 treated SH-SY5Y cells. A, E) Nuclei segmentation and definition of ROI A; B, F) Identification and quantification of mitochondria, ROI B; C, G) Identification of DJ1, Target channel II; D, G) Identification and calculation of average neurite length. Cells close to the edge of the image are excluded from the analysis. Inset images are images prior to analysis.
<img alt="Figure 5." src="/files/ftp_upload/3452/3452fig5.jpg" /> Figure 5. Identification of a gene in epistasis with DJ1 (letters on the bars correspond to the lettering on the images). Images A through D are SH-SY5Y cells labeled with Mitotracker CMXRos (red) that was used for quantification of cell health.
<img alt="Figure 6." src="/files/ftp_upload/3452/3452fig6.jpg" /> Figure 6. Identification of a gene regulating DJ1 translocation (letters on the bars correspond to the lettering on the images). Images A through D are SH-SY5Y cells labeled for DJ1 (green), mitochondria (red) and nuclei (blue) that were used for quantification of DJ1 translocation to the mitochondria.
<img alt="Figure 7." src="/files/ftp_upload/3452/3452fig7.jpg" /> Figure 7. Identification of a gene involved in neuronal outgrowth (letters on the bars correspond to the lettering on the images). Images A through D are SH-SY5Y cells labeled for &beta;-III tubulin (green) and nuclei (blue) which were used for quantification of neurite length.
<img alt="Figure 8." src="/files/ftp_upload/3452/3452fig8.jpg" /> Figure 8. Identification of a gene involved in regulating mitochondrial morphology. Image A and C are composite images of SH-SY5Y cells infected with scrambled shRNA or an shRNA targeting gene D respectively. Mitochondria are colored in red while nuclei are colored in blue. Image B and D are visualizations of the SER-Ridge quantification.

<ol>
	<li>Geschwind, D. H., Konopka, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuroscience+in+the+era+of+functional+genomics+and+systems+biology.">Neuroscience in the era of functional genomics and systems biology.</a> Nature. 461, 908-915 (2009).</li><li>Ge, H., Walhout, A. J., Vidal, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrating+'omic'+information:+a+bridge+between+genomics+and+systems+biology.">Integrating 'omic' information: a bridge between genomics and systems biology.</a> Trends. Genet. 19, 551-560 (2003).</li><li>Zhu, H., Snyder, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term='Omic'+approaches+for+unraveling+signaling+networks.">'Omic' approaches for unraveling signaling networks.</a> Curr. Opin. Cell. Biol. 14, 173-179 (2002).</li><li>Jain, S., Heutink, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=From+single+genes+to+gene+networks:+high-throughput-high-content+screening+for+neurological+disease.">From single genes to gene networks: high-throughput-high-content screening for neurological disease.</a> Neuron. 68, 207-217 (2010).</li><li>Manolio, T. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Finding+the+missing+heritability+of+complex+diseases.">Finding the missing heritability of complex diseases.</a> Nature. 461, 747-753 (2009).</li><li>Nalls, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imputation+of+sequence+variants+for+identification+of+genetic+risks+for+Parkinson's+disease:+a+meta-analysis+of+genome-wide+association+studies.">Imputation of sequence variants for identification of genetic risks for Parkinson's disease: a meta-analysis of genome-wide association studies.</a> Lancet. 377, 641-649 (2011).</li><li>Simon-Sanchez, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+association+study+confirms+extant+PD+risk+loci+among+the+Dutch.">Genome-wide association study confirms extant PD risk loci among the Dutch.</a> Eur. J. Hum. Genet. 19 (6), 655-661 (2011).</li><li>Van Regenmortel, M. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reductionism+and+complexity+in+molecular+biology.+Scientists+now+have+the+tools+to+unravel+biological+and+overcome+the+limitations+of+reductionism.">Reductionism and complexity in molecular biology. Scientists now have the tools to unravel biological and overcome the limitations of reductionism.</a> EMBO. Rep. 5, 1016-1020 (2004).</li><li>Aherne, G. W., McDonald, E., Workman, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Finding+the+needle+in+the+haystack:+why+high-throughput+screening+is+good+for+your+health.">Finding the needle in the haystack: why high-throughput screening is good for your health.</a> Breast. Cancer. Res. 4, 148-154 (2002).</li><li>An, W. F., Tolliday, N. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Introduction:+cell-based+assays+for+high-throughput+screening.">Introduction: cell-based assays for high-throughput screening.</a> Methods. Mol. Biol. 486, 1-12 (2009).</li><li>Mayr, L. M., Bojanic, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Novel+trends+in+high-throughput+screening.">Novel trends in high-throughput screening.</a> Curr. Opin. Pharmacol. 9, 580-588 (2009).</li><li>Hertzberg, R. P., Pope, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+screening:+new+technology+for+the+21st+century.">High-throughput screening: new technology for the 21st century.</a> Curr. Opin. Chem. Biol. 4, 445-451 (2000).</li><li>Conrad, C., Gerlich, D. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+microscopy+for+high-content+RNAi+screening.">Automated microscopy for high-content RNAi screening.</a> J. Cell. Biol. 188, 453-461 (2010).</li><li>Thomas, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-content+screening:+a+decade+of+evolution.">High-content screening: a decade of evolution.</a> J. Biomol. Screen. 15, 1-9 (2010).</li><li>Arrasate, M., Finkbeiner, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+microscope+system+for+determining+factors+that+predict+neuronal+fate.">Automated microscope system for determining factors that predict neuronal fate.</a> Proc. Natl. Acad. Sci. U. S. A. 102, 3840-3845 (2005).</li><li>Dragunow, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-content+analysis+in+neuroscience.">High-content analysis in neuroscience.</a> Nat. Rev. Neurosci. 9, 779-788 (2008).</li><li>Varma, H., Lo, D. C., Stockwell, B. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+throughput+screening+for+neurodegeneration+and+complex+disease+phenotypes.">High throughput screening for neurodegeneration and complex disease phenotypes.</a> Comb. Chem. High. Throughput. Screen. 11, 238-248 (2008).</li><li>Durr, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Robust+hit+identification+by+quality+assurance+and+multivariate+data+analysis+of+a+high-content,+cell-based+assay.">Robust hit identification by quality assurance and multivariate data analysis of a high-content, cell-based assay.</a> J. Biomol. Screen. 12, 1042-1049 (2007).</li><li>Miller, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+relationships+between+huntingtin+levels,+polyglutamine+length,+inclusion+body+formation,+and+neuronal+death+provide+novel+insight+into+huntington's+disease+molecular+pathogenesis.">Quantitative relationships between huntingtin levels, polyglutamine length, inclusion body formation, and neuronal death provide novel insight into huntington's disease molecular pathogenesis.</a> J. Neurosci. 30, 10541-10550 (2010).</li><li>Jain, S., Sondervan, D., Rizzu, P., Bochdanovits, Z., Caminada, D., Heutink, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Complete+Automation+of+Cell+Culture.">The Complete Automation of Cell Culture.</a> Journal of Biomolecular Screening. 16 (8), (2011).</li><li>Bonifati, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mutations+in+the+DJ-1+gene+associated+with+autosomal+recessive+early-onset+parkinsonism.">Mutations in the DJ-1 gene associated with autosomal recessive early-onset parkinsonism.</a> Science. 299, 256-259 (2003).</li><li>Ricardo, R., Phelan, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Trypsinizing+and+Subculturing+Mammalian+Cells.">Trypsinizing and Subculturing Mammalian Cells.</a> J. Vis. Exp. (16), e755-e755 (2008).</li><li>Kent, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Culture+and+Maintenance+of+Human+Embryonic+Stem+Cells.">Culture and Maintenance of Human Embryonic Stem Cells.</a> J. Vis. Exp. (34), e1427-e1427 (2009).</li><li>Moffat, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+lentiviral+RNAi+library+for+human+and+mouse+genes+applied+to+an+arrayed+viral+high-content+screen.">A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen.</a> Cell. 124, 1283-1298 (2006).</li><li>Volksgezondheid, M. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrale+versie+van+de+Regeling+genetisch+gemodificeerde+organismen+en+het+Besluit+genetische+gemodificeerde.">Integrale versie van de Regeling genetisch gemodificeerde organismen en het Besluit genetische gemodificeerde.</a> Organismen. , (2004).</li><li>Anderl, J. L., Redpath, S., Ball, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Neuronal+and+Astrocyte+Co-Culture+Assay+for+High+Content+Analysis+of+Neurotoxicity.">A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity.</a> J. Vis. Exp. (27), e1173-e1173 (2009).</li><li>Wiles, A. M., Ravi, D., Bhavani, S., Bishop, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+analysis+of+normalization+methods+for+Drosophila+RNAi+genomic+screens+and+development+of+a+robust+validation+scheme.">An analysis of normalization methods for Drosophila RNAi genomic screens and development of a robust validation scheme.</a> J. Biomol. Screen. 13, 777-784 (2008).</li><li>Canet-Aviles, R. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Parkinson's+disease+protein+DJ-1+is+neuroprotective+due+to+cysteine-sulfinic+acid-driven+mitochondrial+localization.">The Parkinson's disease protein DJ-1 is neuroprotective due to cysteine-sulfinic acid-driven mitochondrial localization.</a> Proc. Natl. Acad. Sci. U. S. A. 101, 9103-9108 (2004).</li><li>Blackinton, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Formation+of+a+stabilized+cysteine+sulfinic+acid+is+critical+for+the+mitochondrial+function+of+the+parkinsonism+protein+DJ-1.">Formation of a stabilized cysteine sulfinic acid is critical for the mitochondrial function of the parkinsonism protein DJ-1.</a> J. Biol. Chem. 284, 6476-6485 (2009).</li><li>Gupta, P. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+selective+inhibitors+of+cancer+stem+cells+by+high-throughput+screening.">Identification of selective inhibitors of cancer stem cells by high-throughput screening.</a> Cell. 138, 645-659 (2009).</li><li>Luo, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+genome-wide+RNAi+screen+identifies+multiple+synthetic+lethal+interactions+with+the+Ras+oncogene.">A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene.</a> Cell. 137, 835-848 (2009).</li><li>Mouchet, E. H., Simpson, P. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-content+assays+in+oncology+drug+discovery:+opportunities+and+challenges.">High-content assays in oncology drug discovery: opportunities and challenges.</a> IDrugs. 11, 422-427 (2008).</li><li>Vogt, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+image-based+phenotypic+analysis+in+zebrafish+embryos.">Automated image-based phenotypic analysis in zebrafish embryos.</a> Dev. Dyn. 238, 656-663 (2009).</li><li>Pardo-Martin, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+in+vivo+vertebrate+screening.">High-throughput in vivo vertebrate screening.</a> Nat. Methods. 7, 634-636 (2010).</li><li>Vogt, A., Codore, H., Day, B. W., Hukriede, N. A., Tsang, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+of+automated+imaging+and+analysis+for+zebrafish+chemical+screens.">Development of automated imaging and analysis for zebrafish chemical screens.</a> J. Vis. Exp. (40), e1900-e1900 (2010).</li><li>Ross, P. J., Ellis, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+complex+neuropsychiatric+disease+with+induced+pluripotent+stem+cells.">Modeling complex neuropsychiatric disease with induced pluripotent stem cells.</a> F1000. Biol. Rep. 2, 84-84 (2010).</li><li>Ebert, A. D., Svendsen, C. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Human+stem+cells+and+drug+screening:+opportunities+and+challenges.">Human stem cells and drug screening: opportunities and challenges.</a> Nat. Rev. Drug. Discov. 9, 367-372 (2010).</li><li>An, W. F., Tolliday, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell-based+assays+for+high-throughput+screening.">Cell-based assays for high-throughput screening.</a> Mol. Biotechnol. 45, 180-186 (2010).</li></ol>

We have nothing to disclose

With the decreasing costs of HT/HC cellular screening systems, combined with the availability of powerful genome-wide tools to modify gene function, HT/HC screens are becoming commonplace in academia. The approach has already been successfully applied to diverse areas of research such as the identification of drug targets in cancer 9, 31-33 and embryonic development 34-36 and even has potential for application in deciphering the pathways involved in neuropsychiatric disorders 37,38. However implementation of such a system requires a significant investment of time and effort with process optimization often taking a minimum of 6 months. All steps, such as trypsinization times, pipetting speeds and seeding densities have to be adjusted, to ensure that cells are healthy and grow consistently. Prevention of bacterial contamination is one of the most difficult challenges facing automated cell culture with weekly cleaning protocols in combination with constant rinsing of all liquid bearing lines with 70% ethanol being necessary for contamination free cultures. It will be also necessary to improve the robotics so that additional instruments, such as confocal microscopes for higher resolution and -80&deg;C freezers for compound storage can be integrated.
There are also limitations that need to be addressed to improve the sensitive, speed and utility of this method to study gene networks and identify genes involved in pathogenic molecular pathways.
To conduct a HT/HC screen and ensure that reliable data is collected, several aspects have to be optimized. First, reliability of the measurement is paramount and is dependent upon the robustness and sensitivity of the assay. For example, the assays described above are suitable for smaller screens, but are difficult to implement on a genome wide scale, due to the number processing steps required before image acquisition. Thus, one would have to construct stable cell lines expressing the reporter gene, which would allow for direct imaging and lead to decreased variation due to the reduced number of processing steps. At present, designing an assay that accurately depicts and reliably quantifies a phenotype of interest is a major bottleneck in the HC screening process.
Many screens are conducted in mammalian cells using different RNAi libraries, all of which suffer from off-target effects, limited gene silencing efficiency and incomplete genome coverage. Thus libraries need to be made which are more specific, potent and have better coverage. Efforts are underway to create such libraries <a href="http://www.sigmaaldrich.com/life-science/functional-genomics-and-rnai/shrna/library-information.html">(http://www.sigmaaldrich.com/life-science/functional-genomics-and-rnai/shrna/library-information.html)</a> and it is hoped such endeavors will improve the reproducibility of HT/HC screen hits.
A limitation of many large scale cell based screens are that they are conducted in neuroblastoma cells because they can be genetically manipulated and cultured to large numbers with relative ease. However, the relevance of 'hits' identified in ex vivo cell culture models to in vivo function is questionable, especially as the brain consists of highly specialized cell types that form a dense and complicated network of synaptic connections to function as a highly integrated unit. As a consequence, it is common that hits identified using the screening approach described above, are validated in secondary screens using additional techniques and in more physiological relevant models 39. To improve the translation of hits identified during HCS, more representative and sophisticated models, such as primary cells and differentiated stem cells or co-culture systems need to be developed and adapted for HT/HC approaches.
With a combination of automated cell culturing and HC imaging one can rapidly gain new insights into how neurons function and determine which pathways are important to disease development. Combining HCS/HTS data with information generated from other 'omics' approaches, it will then be possible to construct a systems biology overview of brain diseases, thus facilitating therapeutic development.

<table border="1">
	<tbody>
 <tr>
 <td>Name of reagent</td>
 <td>Company</td>
 <td>Catalogue Number</td>
 </tr>
 <tr>
 <td>AI.CELLHOST</td>
 <td>HAMILTON</td>
 <td><a href="http://www.hamiltonrobotics.com/en-uk/applications/cellomics/">http://www.hamiltonrobotics.com/en-uk/applications/cellomics/</a></td>
 </tr>
 <tr>
 <td>OPTI-MEM</td>
 <td>INVITROGEN</td>
 <td>31985-054</td>
 </tr>
 <tr>
 <td>RETINOIC ACID</td>
 <td>SIGMA-ALDRICH</td>
 <td>R2625</td>
 </tr>
 <tr>
 <td>OMNITRAY PLATES</td>
 <td>NUNC</td>
 <td>465219</td>
 </tr>
 <tr>
 <td>96 WELL CULTURE PLATES</td>
 <td>GRENIER</td>
 <td>655086</td>
 </tr>
 <tr>
 <td>DJ1 N20 ANTIBODY</td>
 <td>SANTA CRUZ</td>
 <td>SC27004</td>
 </tr>
 <tr>
 <td>BETA-III TUBULIN ANTIBODY</td>
 <td>SIGMA-ALDRICH</td>
 <td>T3952</td>
 </tr>
 <tr>
 <td>MITOTRACKER CMXROS</td>
 <td>INVITROGEN</td>
 <td>M-7512</td>
 </tr>
 <tr>
 <td>HOECHST-33342</td>
 <td>INVITROGEN</td>
 <td>H1399</td>
 </tr>
 <tr>
 <td>HYDROGEN PEROXIDE</td>
 <td>SIGMA-ALDRICH</td>
 <td>216763-100ML</td>
 </tr>
 <tr>
 <td>TRYPSIN</td>
 <td>INVITROGEN</td>
 <td>25050014</td>
 </tr>
 <tr>
 <td>DULBECCO'S PHOSPHATE BUFFERED SALINE</td>
 <td>INVITROGEN</td>
 <td>14190086</td>
 </tr>
 <tr>
 <td>PROMEGA WIZARD MAGNESIL TFX </td>
 <td>PROMEGA</td>
 <td>A2380</td>
 </tr>
 <tr>
 <td>SHRNA CLONES</td>
 <td>OPEN BIOSYSTEMS</td>
 <td><a href="http://www.openbiosystems.com/RNAi/shRNALibraries/TRCLibraryDetails/">http://www.openbiosystems.com/RNAi/shRNALibraries/ TRCLibraryDetails/</a></td>
 </tr>
 <tr>
 <td>CELLOMICS BIOAPPLICATIONS</td>
 <td>THERMO-FISHER</td>
 <td><a href="http://www.thermo.com/hcs">http://www.thermo.com/hcs</a></td>
 </tr>
	 </tbody>
 </table>

high content screening in neurodegenerative diseases

The functional annotation of genomes, construction of molecular networks and novel drug target identification, are important challenges that need to be addressed as a matter of great urgency1-4. Multiple complementary 'omics' approaches have provided clues as to the genetic risk factors and pathogenic mechanisms underlying numerous neurodegenerative diseases, but most findings still require functional validation5. For example, a recent genome wide association study for Parkinson's Disease (PD), identified many new loci as risk factors for the disease, but the underlying causative variant(s) or pathogenic mechanism is not known6, 7. As each associated region can contain several genes, the functional evaluation of each of the genes on phenotypes associated with the disease, using traditional cell biology techniques would take too long. 
	There is also a need to understand the molecular networks that link genetic mutations to the phenotypes they cause. It is expected that disease phenotypes are the result of multiple interactions that have been disrupted. Reconstruction of these networks using traditional molecular methods would be time consuming. Moreover, network predictions from independent studies of individual components, the reductionism approach, will probably underestimate the network complexity8. This underestimation could, in part, explain the low success rate of drug approval due to undesirable or toxic side effects. Gaining a network perspective of disease related pathways using HT/HC cellular screening approaches, and identifying key nodes within these pathways, could lead to the identification of targets that are more suited for therapeutic intervention.
	High-throughput screening (HTS) is an ideal methodology to address these issues9-12. but traditional methods were one dimensional whole-well cell assays, that used simplistic readouts for complex biological processes. They were unable to simultaneously quantify the many phenotypes observed in neurodegenerative diseases such as axonal transport deficits or alterations in morphology properties13, 14. This approach could not be used to investigate the dynamic nature of cellular processes or pathogenic events that occur in a subset of cells. To quantify such features one has to move to multi-dimensional phenotypes termed high-content screening (HCS)4, 15-17. HCS is the cell-based quantification of several processes simultaneously, which provides a more detailed representation of the cellular response to various perturbations compared to HTS. 
	HCS has many advantages over HTS18, 19, but conducting a high-throughput (HT)-high-content (HC) screen in neuronal models is problematic due to high cost, environmental variation and human error. In order to detect cellular responses on a 'phenomics' scale using HC imaging one has to reduce variation and error, while increasing sensitivity and reproducibility. 
	Herein we describe a method to accurately and reliably conduct shRNA screens using automated cell culturing20 and HC imaging in neuronal cellular models. We describe how we have used this methodology to identify modulators for one particular protein, DJ1, which when mutated causes autosomal recessive parkinsonism21. 
	Combining the versatility of HC imaging with HT methods, it is possible to accurately quantify a plethora of phenotypes. This could subsequently be utilized to advance our understanding of the genome, the pathways involved in disease pathogenesis as well as identify potential therapeutic targets.

Watch this Scientific Journal Video about High Content Screening in Neurodegenerative Diseases at JoVE.com

High Content Screening in Neurodegenerative Diseases

We describe a methodology combining automated cell culturing with high-content imaging to visualize and quantify multiple cellular processes and structures, in a high-throughput manner. Such methods can aid in the further functional annotation of genomes as well as identify disease gene networks and potential drug targets.

The functional annotation of genomes, construction of molecular networks and novel drug target identification, are important challenges that need to be ...

high-content-screening-in-neurodegenerative-diseases

Universidad de Cartagena UDEC

Research

JoVE Journal

Medicine

17.4K Views.  VU University Medical Center. We describe a methodology combining automated cell culturing with high-content imaging to visualize and quantify multiple cellular processes and structures, in a high-throughput manner. Such methods can aid in the further functional annotation of genomes as well as identify disease gene networks and potential drug targets.

Video: High Content Screening in Neurodegenerative Diseases

The Use of Primary Human Fibroblasts for Monitoring Mitochondrial Phenotypes in the Field of Parkinson's Disease

Parkinson's disease (PD) is the second most common movement disorder and affects 1% of people over the age of 60 1. Because ageing is the most important risk factor, cases of PD will increase during the next decades 2. Next to pathological protein folding and impaired protein degradation pathways, alterations of mitochondrial function and morphology were pointed out as further hallmark of neurodegeneration in PD 3-11.
After years of research in murine and human cancer cells as in vitro models to dissect molecular pathways of Parkinsonism, the use of human fibroblasts from patients and appropriate controls as ex vivo models has become a valuable research tool, if potential caveats are considered. Other than immortalized, rather artificial cell models, primary fibroblasts from patients carrying disease-associated mutations apparently reflect important pathological features of the human disease.
Here we delineate the procedure of taking skin biopsies, culturing human fibroblasts and using detailed protocols for essential microscopic techniques to define mitochondrial phenotypes. These were used to investigate different features associated with PD that are relevant to mitochondrial function and dynamics. Ex vivo, mitochondria can be analyzed in terms of their function, morphology, colocalization with lysosomes (the organelles degrading dysfunctional mitochondria) and degradation via the lysosomal pathway. These phenotypes are highly relevant for the identification of early signs of PD and may precede clinical motor symptoms in human disease-gene carriers. Hence, the assays presented here can be utilized as valuable tools to identify pathological features of neurodegeneration and help to define new therapeutic strategies in PD.

Fibroblasts from patients carrying mutations in Parkinson's disease-causing genes represent an easily accessible ex vivo model to study disease-associated phenotypes. Live cell imaging gives the opportunity to study morphological and functional parameters in living cells. Here we describe the preparation of human fibroblasts and subsequent monitoring of mitochondrial phenotypes.

Parkinson's disease (PD) is the second most  common movement disorder and affects 1% of people over the age of 60 1. Because ageing is  the most important ...

Screening for Melanoma Modifiers using a Zebrafish Autochthonous Tumor Model

Genomic studies of human cancers have yielded a wealth of information about genes that are altered in tumors1,2,3. A challenge arising from these studies is that many genes are altered, and it can be difficult to distinguish genetic alterations that drove tumorigenesis from that those arose incidentally during transformation. To draw this distinction it is beneficial to have an assay that can quantitatively measure the effect of an altered gene on tumor initiation and other processes that enable tumors to persist and disseminate. Here we present a rapid means to screen large numbers of candidate melanoma modifiers in zebrafish using an autochthonous tumor model4 that encompasses steps required for melanoma initiation and maintenance. A key reagent in this assay is the miniCoopR vector, which couples a wild-type copy of the mitfa melanocyte specification factor to a Gateway recombination cassette into which candidate melanoma genes can be recombined5. The miniCoopR vector has a mitfa rescuing minigene which contains the promoter, open reading frame and 3'-untranslated region of the wild-type mitfa gene. It allows us to make constructs using full-length open reading frames of candidate melanoma modifiers. These individual clones can then be injected into single cell Tg(mitfa:BRAFV600E);p53(lf);mitfa(lf)zebrafish embryos. The miniCoopR vector gets integrated by Tol2-mediated transgenesis6 and rescues melanocytes. Because they are physically coupled to the mitfa rescuing minigene, candidate genes are expressed in rescued melanocytes, some of which will transform and develop into tumors. The effect of a candidate gene on melanoma initiation and melanoma cell properties can be measured using melanoma-free survival curves, invasion assays, antibody staining and transplantation assays.

A rapid way to screen for melanoma modifiers using a zebrafish autochthonous tumor model is presented. It takes advantage of the miniCoopR vector which allows for expression of candidate melanoma genes in melanocytes. A method to obtain melanoma-free survival curves, an invasion assay, a protocol for antibody staining of scale melanocytes and a melanoma transplantation assay are described.

Genomic studies of  human cancers have yielded a wealth of information about genes that are altered  in tumors1,2,3. A challenge arising from these ...

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls. 	
DTI data analysis is performed in a variate fashion, i.e. voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e. differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels.	
In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.

Diffusion tensor imaging (DTI) basically serves as an MRI-based tool to identify in vivo the microstructure of the brain and pathological processes due to neurological disorders within the cerebral white matter. DTI-based analyses allow for application to brain diseases both at the group level and in single subject data.

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present ...

In vivo 19F MRI for Cell Tracking

In vivo 19F MRI allows quantitative cell tracking without the use of ionizing radiation. It is a noninvasive technique that can be applied to humans. Here, we describe a general protocol for cell labeling, imaging, and image processing. The technique is applicable to various cell types and animal models, although here we focus on a typical mouse model for tracking murine immune cells. The most important issues for cell labeling are described, as these are relevant to all models. Similarly, key imaging parameters are listed, although the details will vary depending on the MRI system and the individual setup. Finally, we include an image processing protocol for quantification. Variations for this, and other parts of the protocol, are assessed in the Discussion section. Based on the detailed procedure described here, the user will need to adapt the protocol for each specific cell type, cell label, animal model, and imaging setup. Note that the protocol can also be adapted for human use, as long as clinical restrictions are met.

In vivo 19F MRI for Cell Tracking

We describe a general protocol for in vivo cell tracking using MRI in a mouse model with ex vivo labeled cells. A typical protocol for cell labeling, image acquisition processing and quantification is included.

In vivo 19F MRI allows quantitative cell tracking without the use of ionizing radiation. It is a noninvasive technique that can be applied to humans. ...

Detection of Disease-associated &#945;-synuclein by Enhanced ELISA in the Brain of Transgenic Mice Overexpressing Human A53T Mutated &#945;-synuclein

In addition to established methods like Western blot, new methods are needed to quickly and easily quantify disease-associated &#945;-synuclein (&#945;SD) in experimental models of synucleopathies. A transgenic mouse line (M83) over-expressing the human A53T &#945;S and spontaneously developing a dramatic clinical phenotype between eight and 22 months of age, characterized by symptoms including weight loss, prostration, and severe motor impairment, was used in this study. For molecular analyses of &#945;SD (disease-associated &#945;S) in these mice, an ELISA was designed to specifically quantify &#945;SD in sick mice. Analysis of the central nervous system in this mouse model showed the presence of &#945;SD mainly in the caudal brain regions and the spinal cord. There were no differences in &#945;SD distribution between different experimental conditions leading to clinical disease, i.e., in uninoculated and normally aging transgenic mice and in mice inoculated with brain extracts from sick mice. The specific detection of &#945;SD immunoreactivity using an antibody against Ser129 phosphorylated &#945;S by ELISA essentially correlated with that obtained by Western blot and immunohistochemistry. Unexpectedly, similar results were observed with several other antibodies against the C-terminal part of &#945;S. The propagation of &#945;SD, suggesting the involvement of a &#8220;prion-like&#8221; mechanism, can thus be easily monitored and quantified in this mouse model using an ELISA approach.

An ELISA offering a novel quantitative approach is described. It specifically detects disease-associated &#945;-synuclein (&#945;SD) in a transgenic mouse model (M83) of synucleinopathy using several antibodies against either the Ser129 phosphorylated &#945;S form or the C-terminal part of the protein.

In addition to established methods like Western blot, new methods are needed to quickly and easily quantify disease-associated &#945;-synuclein (&#945;SD) ...

A High-content In Vitro Pancreatic Islet &#946;-cell Replication Discovery Platform

Loss of insulin-producing &#946;-cells is a central feature of diabetes. While a variety of potential replacement therapies are being explored, expansion of endogenous insulin-producing pancreatic islet &#946;-cells remains an attractive strategy. &#946;-cells have limited spontaneous regenerative activity; consequently, a crucial research effort is to develop a precise understanding of the molecular pathways that restrain &#946;-cell growth and to identify drugs capable of overcoming these restraints. Herein an automated high-content image-based primary-cell screening method to identify &#946;-cell replication-promoting small molecules is presented. Several, limitations of prior methodologies are surmounted. First, use of primary islet cells rather than an immortalized cell-line maximizes retention of in vivo growth restraints. Second, use of mixed-composition islet-cell cultures rather than a &#946;-cell-line allows identification of both lineage-restricted and general growth stimulators. Third, the technique makes practical the use of primary islets, a limiting resource, through use of a 384-well format. Fourth, detrimental experimental variability associated with erratic islet culture quality is overcome through optimization of isolation, dispersion, plating and culture parameters. Fifth, the difficulties of accurately and consistently measuring the low basal replication rate of islet endocrine-cells are surmounted with optimized immunostaining parameters, automated data acquisition and data analysis; automation simultaneously enhances throughput and limits experimenter bias. Notable limitations of this assay are the use of dispersed islet cultures which disrupts islet architecture, the use of rodent rather than human islets and the inherent limitations of throughput and cost associated with the use of primary cells. Importantly, the strategy is easily adapted for human islet replication studies. This assay is well suited for investigating the mitogenic effect of substances on &#946;-cells and the molecular mechanisms that regulate &#946;-cell growth.

A High-content In Vitro Pancreatic Islet &#946;-cell Replication Discovery Platform

Critical challenges for the diabetes research field are to understand the molecular mechanisms that regulate islet &#946;-cell replication and to develop methods for stimulating &#946;-cell regeneration. Herein a high-content screening method to identify and assess the &#946;-cell replication-promoting activity of small molecules is presented.

Loss of insulin-producing &#946;-cells is a central feature of diabetes. While a variety of potential replacement therapies are being explored, expansion ...

A High Throughput, Multiplexed and Targeted Proteomic CSF Assay to Quantify Neurodegenerative Biomarkers and Apolipoprotein E Isoforms Status

Many neurodegenerative diseases are&#160;still lacking effective treatments. Reliable biomarkers for identifying and classifying these diseases will be important in the development of future novel therapies. Often&#160;potential new biomarkers do not make it&#160;into the clinic&#160;due to limitations&#160;in their development and&#160;high costs.&#160;However,&#160;targeted proteomics using Multiple Reaction Monitoring Liquid Chromatography-tandem/Mass Spectrometry (MRM LC-MS/MS), specifically using triple quadrupole mass spectrometers, is one method that can be used to rapidly evaluate and validate biomarkers&#160;for clinical translation into diagnostic laboratories. Traditionally, this platform has been used extensively for measurement of small molecules&#160;in clinical laboratories, but it is the potential to analyze proteins, that makes it an attractive alternative to ELISA (Enzyme-Linked Immunosorbent Assay)-based methods. We describe here how targeted proteomics can be used to measure multiplexed markers of dementia, including the detection and quantitation of the known risk factor apolipoprotein E isoform 4 (ApoE4).
In order to make the assay suitable for translation, it is designed to be rapid, simple, highly specific and cost effective. To achieve this, every step in the development of the assay must be optimized for the individual proteins and tissues they are analyzed in. This method describes a typical workflow including various tips and tricks to developing a targeted proteomics MRM LC-MS/MS for translation.
The method development is optimized using custom synthesized versions of tryptic quantotypic peptides, which calibrate&#160;the MS for detection and then spiked into CSF to determine correct identification of the endogenous peptide in the chromatographic separation prior to analysis in the MS. To achieve absolute quantitation, stable isotope-labeled internal standard versions of the peptides with short amino acid sequence tags and containing a trypsin cleavage site, are included in the assay.

We describe a high-throughput, multiplex, and targeted proteomic cerebrospinal fluid (CSF) assay developed with potential for clinical translation. The test can quantitate potential markers and risk factors for neurodegeneration, such as the apolipoprotein E variants (E2, E3 and E4), and measure their allelic expression.

Many neurodegenerative diseases are&#160;still lacking effective treatments. Reliable biomarkers for identifying and classifying these diseases will be ...

Longitudinal In Vivo Imaging of the Cerebrovasculature: Relevance to CNS Diseases

Remodeling of the brain vasculature is a common trait of brain pathologies. In vivo imaging techniques are fundamental to detect cerebrovascular plasticity or damage occurring overtime and in relation to neuronal activity or blood flow. In vivo two-photon microscopy allows the study of the structural and functional plasticity of large cellular units in the living brain. In particular, the thinned-skull window preparation allows the visualization of cortical regions of interest (ROI) without inducing significant brain inflammation. Repetitive imaging sessions of cortical ROI are feasible, providing the characterization of disease hallmarks over time during the progression of numerous CNS diseases. This technique accessing the pial structures within 250 &#956;m of the brain relies on the detection of fluorescent probes encoded by genetic cellular markers and/or vital dyes. The latter (e.g., fluorescent dextrans) are used to map the luminal compartment of cerebrovascular structures. Germane to the protocol described herein is the use of an in vivo marker of amyloid deposits, Methoxy-O4, to assess Alzheimer&#39;s disease (AD) progression. We also describe the post-acquisition image processing used to track vascular changes and amyloid depositions. While focusing presently on a model of AD, the described protocol is relevant to other CNS disorders where pathological cerebrovascular changes occur.

Longitudinal In Vivo Imaging of the Cerebrovasculature: Relevance to CNS Diseases

This manuscript describes a procedure to track the remodeling of the cerebrovasculature during amyloid plaque accumulation in vivo using longitudinal two-photon microscopy. A thinned-skull preparation enables the visualization of fluorescent dyes to assess the progression of cerebrovascular damage in a mouse model of Alzheimer&#39;s disease.

Remodeling of the brain vasculature is a common trait of brain pathologies. In vivo imaging techniques are fundamental to detect cerebrovascular ...

High-throughput Nitrobenzoxadiazole-labeled Cholesterol Efflux Assay

Atherosclerosis leads to cardiovascular disease (CVD). It is still unclear whether cholesterol-HDL (cHDL) concentration plays a causal role in atherosclerosis development. However, an important factor in early stages of atheroma plaque formation is cholesterol efflux capacity to HDL (the ability of HDL particles to accept cholesterol from macrophages) in order to avoid foam cell formation. This is a key step in avoiding the accumulation of cholesterol in the endothelium and a part of reverse cholesterol transport (RCT) to eliminate cholesterol through the liver. Cholesterol efflux capacity to serum or plasma in macrophage cell models is a promising tool that can be used as biomarker for atherosclerosis. Traditionally, [3H]-cholesterol has been used in cholesterol efflux assays. In this study, we aim to develop a safer and faster strategy using fluorescent labelled-cholesterol (NBD-cholesterol) in a cellular assay to trace the cholesterol uptake and efflux process in THP-1-derived macrophages. Finally, we optimize and standardize the NBD-cholesterol efflux method and develop a high-throughput analysis using 96-well plates.

Measurement of in vitro cholesterol efflux capacity to serum or plasma in macrophage cell models is a promising tool as a biomarker for atherosclerosis. In the present study, we optimize and standardize a fluorescent NBD-cholesterol efflux method and develop a high-throughput analysis using 96-well plates.

Atherosclerosis leads to cardiovascular disease (CVD). It is still unclear whether cholesterol-HDL (cHDL) concentration plays a causal role in ...

A High-Throughput Electrochemiluminescence 7-Plex Assay Simultaneously Screening for Type 1 Diabetes and Multiple Autoimmune Diseases

Islet autoantibodies (IAbs) are widely used in type 1 diabetes (T1D) diagnosis and prediction. Four major IAbs to insulin (IAA), glutamate decarboxylase-65 (GADA), insulinoma antigen-2 (IA-2A), and zinc transporter-8 (ZnT8A) are equally important in disease prediction. Presently, up to 40% of patients diagnosed with T1D go on to develop other autoimmune disorders. Unfortunately, current screening methods using a single autoantibody for measurement are laborious and inefficient for large scale screening studies. We recently developed a simple multiplexed electrochemiluminescence (ECL) assay to address these current issues. The assay combines all 7 autoantibody tests into one well. Each well includes three IAbs (IAA, GADA, and IA-2A), autoantibodies to thyroid peroxidase (TPOA) and thyroid globulin (ThGA) to detect autoimmune thyroid disease, autoantibodies to tissue transglutaminase (TGA) for celiac disease, and autoantibodies to interferon alpha (IFN&#945;A) for autoimmune polyglandular syndrome-1 (APS-1); all of which screen for T1D and other relevant autoimmune diseases, simultaneously. The multiplex ECL assay is based on the single ECL assay platform, but instead uses the multiplex plate combining multiple autoantibody assays, up to 10, into a single well. The main difference from the single ECL assay is that each antibody-antigen complex formed in the fluid-phase is restrained onto a specific spot on each well through a linker system on the multiplex plate. The 7-Plex ECL assay, in the present study, is validated against standard radio-binding assays (RBA) and single ECL assays, using a large cohort of newly diagnosed T1D patients and age-matched healthy controls, resulting in excellent assay sensitivity and specificity.

We model a simple multiplexed ECL assay that combines 7 autoantibody assays together. The assay is capable of screening for T1D and multiple other autoimmune diseases, simultaneously, including celiac disease, autoimmune thyroid disease, and autoimmune polyglandular syndrome 1.

High Content Screening in Neurodegenerative Diseases

Summary

Explore More Videos

Chapters in this video

The Use of Primary Human Fibroblasts for Monitoring Mitochondrial Phenotypes in the Field of Parkinson's Disease

Screening for Melanoma Modifiers using a Zebrafish Autochthonous Tumor Model

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

In vivo ¹⁹F MRI for Cell Tracking

Detection of Disease-associated α-synuclein by Enhanced ELISA in the Brain of Transgenic Mice Overexpressing Human A53T Mutated α-synuclein

A High-content In Vitro Pancreatic Islet β-cell Replication Discovery Platform

A High Throughput, Multiplexed and Targeted Proteomic CSF Assay to Quantify Neurodegenerative Biomarkers and Apolipoprotein E Isoforms Status

Longitudinal In Vivo Imaging of the Cerebrovasculature: Relevance to CNS Diseases

High-throughput Nitrobenzoxadiazole-labeled Cholesterol Efflux Assay

A High-Throughput Electrochemiluminescence 7-Plex Assay Simultaneously Screening for Type 1 Diabetes and Multiple Autoimmune Diseases