This work was supported by the NINDS Intramural Research Program.

1. NSC culture
NOTE: Manipulation of a human NSC line will be described below but any cell line can be used for this protocol. All cell culture work is performed in a biological safety cabinet.
<ol>
	<li>Coat a 96-well plate with basement membrane/extracellular matrix (ECM).
	<ol>
		<li>Thaw aliquot of ECM (Table of Materials), which will facilitate attachment of NSC, on ice. Dilute ECM to the appropriate concentration (generally 1:100) in 10 mL base medium (Table of Materials) and add 50 &#181;L per well to each of 60 interior wells of a 96-well plate (Figure 1). Use only the interior 60 wells to avoid artifacts that may result from the edge effect16.</li>
		<li>Let the plate sit at room temperature or in a cell culture incubator (37 &#176;C, 5% CO2) for at least 30 min.</li>
	</ol>
	</li>
	<li>Dissociate and plate neural stem cells. 
	NOTE: Cells for use in this method should be grown to at least 80% confluence in a T75 flask.
	<ol>
		<li>Culture cells in a T75 flask in a cell culture incubator at 37 &#176;C and 5% CO2 in NSC medium that is composed of base medium, B27, non-essential amino acids, 2 mM glutamine, and 10 ng/mL basic fibroblast growth factor (FGFb or FGF2).</li>
		<li>Remove cells from the incubator once they reach 80% confluence and aspirate off NSC medium. Add an appropriate amount of cell dissociation reagent (3 mL for a T75 flask; Table of Materials) and incubate for 5 min in the incubator.</li>
		<li>After incubation, add 7 mL of NSC medium in the T75 flask and pipette vigorously to ensure all cells become detached. Transfer the dissociated cell solution to a 15 mL tube and centrifuge at 200 x g for 5 min.</li>
		<li>After centrifugation, remove supernatant from the tube and resuspend cells in 10 mL of NSC medium and count cells.</li>
		<li>Readjust concentration of cells to 200,000 cells/mL with NSC medium. Ensure cells are fully resuspended for homogeneous plating into wells.</li>
		<li>Plate 100 &#181;L of the cell mixture (20,000 cells) in the 60 interior wells of three 96-well plates that have been coated as described in section 1.1. Use six of the eight slots of an 8-channel multichannel pipettor to plate cells column-by-column.</li>
		<li>Add 100 &#181;L of base medium or NSC medium to all wells without cells to minimize potential evaporation from outermost wells.</li>
		<li>Under a cell culture microscope, visually inspect at least 10 wells on each of the three 96-well plates to confirm that the cells are seeded at the expected density. Do not proceed with the assay if cells are plated at a density too sparse or dense.</li>
	</ol>
	</li>
</ol>
2. Treating cells with compounds
NOTE: The home-made library tested in this demonstration contains compounds that modulate wingless/integrated (Wnt), retinoic acid, transforming growth factor-beta (TGF-&#946;), and sonic hedgehog signaling pathways as well as a variety of tyrosine kinases.
<ol>
	<li>Exploratory primary screen for toxicity/cell number
	<ol>
		<li>Aliquot 50&#8722;100 &#956;L of up to 57 test compounds (Supplemental Table 1) at a concentration of 10 mM in 100% dimethyl sulfoxide (DMSO) into the interior 60 wells of a U-bottomed, V-bottomed or round-bottomed 96-well plate with three DMSO wells as a control (see Figure 1 for a plate map). This will serve as the master compound plate with 25 &#181;L of compound that can be frozen and thawed several times. 
		NOTE: Flat bottomed plates should not be used as it will be more difficult to aspirate small volumes of compounds from them with a bench top pipettor.</li>
		<li>Remove cell culture plates from incubator 16-24 h after splitting as described in section 1 and aspirate off NSC medium column-by-column with an 8-channel multi-well pipettor using only six of the eight multi-well slots. Add 95 &#181;L of fresh NSC medium to cells in each of the three replicate plates and place plates back in incubator until step 2.1.4 below is completed.</li>
		<li>Add 49 &#181;L of NSC medium to each of the interior 60 wells of an empty U-bottomed, V-bottomed or round-bottomed 96-well plate with an 8-channel multi-well pipettor. Unseal the master compound plate and use a bench top pipettor or equivalent instrument to pipette 1 &#181;L of compound from the master plate into the 49 &#181;L of NSC medium in each of the interior 60 wells.</li>
		<li>Mix the diluted compound 3x with the bench top pipettor.</li>
		<li>Remove the three 96-well plates of NSCs from the incubator, pipette 15 &#181;L of each diluted compound with the bench top pipettor and dispense a 5 &#181;L aliquot of compound into each of the three plates. 
		NOTE: This 1:20 dilution of compound into the cells in combination with the initial 1:50 dilution in step 2.1.3 yields a 1:1000 dilution such that the final concentration of the compounds on the NSCs will be 10 &#181;M with a DMSO concentration of 0.1% and the final concentration for the DMSO controls will be 0.1%.</li>
		<li>Incubate cells with compound for 72 h and proceed with cytotoxicity assays. Shorter intervals can be used but a 72-hour incubation period should maximize the potential cytotoxic effects of tested compounds.</li>
	</ol>
	</li>
	<li>Dose response assay 
	NOTE: The set-up for the 96-well used for the dose-response is displayed in Figure 2.
	<ol>
		<li>Use column 2 for six DMSO control replicates and test triplicates of up to three different compounds at two-fold serial dilutions at six doses starting with a high dose of 10 &#181;M.</li>
		<li>Dilute 4 &#181;L of DMSO or test compound in DMSO into 196 &#181;L of NSC medium in a 1.5 mL microcentrifuge tube. Add 25 &#181;L of DMSO to the column of wells from B2-G2 and 50 &#181;L of test compounds to the row from B3-B11 with the three tested compounds in 10 mM triplicates in rows B3-B5, B6-B8, and B9-B11.</li>
		<li>Pipette 25 &#181;L of NSC medium to the remaining empty columns in the interior portion of the 96-well plate. Remove 25 &#181;L of compound from wells B3-B11 with a multichannel pipettor, add to wells C3-C11, and mix at least five times. Repeat the process for the remaining rows to generate triplicates at two-fold dilutions for a total of six doses for each of the compounds.</li>
		<li>Generate NSCs for the dose response exactly as described for the primary screen in section 2.1. The compounds for the dose response are added to and incubated on the cells exactly as described in steps 2.1.5 and 2.1.6. 
		NOTE: Three biological replicates of the dose response assay are performed by repeating the assay on the NSCs at different passages on separate days.</li>
	</ol>
	</li>
</ol>
3. Imaging cells on a plate reader
<ol>
	<li>After cells have been incubated with compounds for the allotted time, image cells on a plate reader to determine the pre-treatment cell number per well. 
	NOTE: Instructions for imaging cells are reader-specific but generally follow a similar strategy. The directions below apply to the reader used in this demonstration (Table of Materials).</li>
	<li>Remove the plate from the incubator and place it inside the plate reader. Open the imager software to set up protocol and experiment files for the study. Go to Imager Manual Mode on Task Manager and click Capture now&#8230;.</li>
	<li>Choose 96-well plate as the vessel type, select 10x for the magnification, and red fluorescent protein (RFP) 531 and 593 for imaging tdTomato. Pick a well, then click Autofocus to focus image, and Auto Expose for proper exposure time. Manually adjust focus and exposure if needed.</li>
	<li>Once proper focus and exposure have been obtained, click the camera icon to capture the picture. Then click PROCESS/ANALZYE above image to continue building the protocol and select the ANALYSIS tab.</li>
	<li>Click Cellular Analysis in ADD ANALYSIS STEP to the right of the image and click START. Image will show highlighted cells to indicate each individual cell. The Options selection may be clicked to alter parameters to better select cells based upon the fluorescence threshold or cell size. If the imager is properly counting the cells, then click ADD STEP at the bottom of the screen.</li>
	<li>Click the icon at the top of the screen to Create experiment from image set, which will open a window with the experiment. Once open, click Procedure under the Protocol tab and in the new window that opens select Read, then in the new window click full plate to select only the 60 wells that contain the cells (B2...G11). Click OK to save changes, then click OK in the Procedure window.</li>
	<li>The plate can now be imaged by this protocol and the experimental file can be saved. Click the play icon to run the plate. Once the first plate has been imaged, image the other two plates. Upon completion of the imaging, download the cell count data to a spreadsheet for analysis. Take all images at 10x magnification.</li>
</ol>
4. Terminal MTT cytotoxicity assay
NOTE: Begin the MTT assay within two hours of completing tdTomato imaging.
<ol>
	<li>Make a 5 mg/mL MTT stock solution by weighing out 25 mg of MTT and resuspending it in 5 mL of NSC medium. Vortex the solution until there are no visible precipitates of MTT, which could take several minutes.</li>
	<li>Remove cell culture plates from the incubator and aspirate off cell culture medium. Dilute MTT 1:10 in cell culture medium and add 100 &#181;L of MTT to each well of cells.</li>
	<li>Incubate cells at 37 &#176;C for 2 h. A purplish precipitate should be visible roughly in proportion to the number of cells in the well. Either aspirate the MTT solution off plates or invert the plate quickly to flick solution out of the plate.</li>
	<li>Add 50 &#181;L of 100% DMSO to each well and shake plates at room temperature for 10 min at 400 rpm. Read the absorbance of each well at 595 nm in a plate reader and export data to a spreadsheet for analysis.</li>
</ol>
5. Data analysis
<ol>
	<li>Perform an analysis of tdTomato cell counts and absorbances with appropriate software (commercial spreadsheet, R). Calculate averages for absorbance or cell count of the three DMSO replicates on each plate for normalization purposes, then divide the value for the cell counts or absorbances for each well on the plate by this average and convert to a percentage. This yields the normalized cell count or absorbance relative to DMSO control for each plate.</li>
	<li>Calculate the mean normalized count or absorbance and standard deviation for replicate wells on the three plates. 
	NOTE: At this point there should be four different sets of normalized values: one for each of the plates and one mean for the three replicate plates.</li>
	<li>Be conservative and use a normalized value at or below 25% for the average across the three replicate plates to classify a compound as toxic. Also, because only a single treatment per compound is performed on each plate, only label compounds that fall below this threshold on all three replicate plates as toxic. Examine fluorescent images of all compounds that this analysis filters as toxic to visually confirm toxicity. 
	NOTE: The identification of compounds with a growth inhibitory or growth enhancing effect is more difficult to assess in an exploratory assay of this type due to the lack of replicates on each plate. However, the following is a quick way to identify compounds that may either slow down or enhance cellular growth.</li>
	<li>Calculate the standard deviation for the three replicate DMSO controls on each plate and then filter for any compounds that have average values at least two standard deviations above or below the DMSO control. Compounds that fall out of this filter on each of the three plates may warrant further investigation.</li>
	<li>Use the same analysis strategy for the dose response as the primary toxicity screen. Calculate the averages for the DMSO controls for each biological replicate and use these values to normalize the percent live cells or percent absorbances for each compound/dose combination. Calculate the means and standard error of the means for all compound/dose combinations for the three biological replicates.</li>
	<li>Transform the concentration to its log value, generate a dose response curve for the log of concentration versus normalized viability, and fit the curve with a non-linear regression analysis (analysis can be performed in R or various commercial statistical packages). Calculate the lethal dose 50 (or technically in this case the viable dose 50) or concentration of compound that results in 50% toxicity from the equation of this curve. Many software packages will automatically calculate this figure.</li>
</ol>

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The authors have nothing to disclose.

The primary goal of this article was to describe a strategy that could efficiently and inexpensively identify compounds affecting cell growth in a low- to moderate-throughput screening. Two orthogonal techniques were utilized to assess cell number to increase confidence in the conclusions and offer additional insights that would not be available if only a single assay was used. One of the assays used a fluorescent cell imager to directly count tdTomato-positive cells and the second was dependent on the well-characterized...

One of the most important characteristics that needs to be determined for a chemical compound that has therapeutic potential is its toxicity to animal cells. This characteristic will determine whether a drug is a good candidate for more extensive study. In most instances, compounds with minimal toxicity are sought but there are situations in which a compound with the capacity to kill specific cell types is of interest, e.g., anti-tumorigenic drugs. Although whole animals are the best model systems to determine systemic toxicity, the cost and labor involved is prohibitive when more than a few compounds need to be tested. As such mammalian cell culture is generally used as the most efficient alternative1,2. Small to medium throughput drug screens are an important modality through which toxicity can be assessed in cell culture. These screens can be used to interrogate annotated libraries targeting individual signaling pathways. The general format of such a screen is to initially test all the compounds in the library at a single dose (generally 10 &#181;M) in an exploratory primary toxicity screen, and then perform an in-depth secondary dose response screen to fully characterize the toxicity profile of hits from the primary screen. The methods to implement this strategy will be described here and provide a quick, efficient, and inexpensive way to identify and characterize toxic compounds.
Multiple methods have been developed to assess cytotoxicity of small compounds and nanomaterial in mammalian cells3,4. It should be noted that certain materials can interact with the assay providing misleading results, and such interactions should be tested when characterizing hits from toxicity screens4. Cytotoxicity assays include trypan blue exclusion5, lactate dehydrogenase (LDH) release assay6, Alamar blue assay7, calcien acetoxymethyl ester (AM)8, and the ATP assay9. All these assays measure various aspects of cell metabolism which can serve as a proxy for cell number. While all offer benefits, tetrazolium salt-based assays such as 3-(4,5-dimethylthizol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), 2,3-bis(2-methoxy-4-nitro-5-sulfopheny)-2H-tetrazolium-5-carboxyanilide inner salt (XTT)-1, and 4-(3-[4-Iodophenyl]-2-[4-nitrophenyl]-2H-5-tetrazolio)-1,3-benzene disulfonate (WST-1)10,11 provide good accuracy and ease of use at low cost. MTT, which will be used in this demonstration, is reduced to an insoluble formazan by a mitochondrial reductase and the rate of this conversion correlates strongly with cell number. This assay has been routinely utilized at both a small scale and for screening libraries with up to 2,000 compounds12. Direct counting of cells by a labeled marker offers another method to assess the cellular number, and unlike the MTT assay it can provide additional information about the dynamics of cellular growth. Several publicly available algorithms are available to perform automated cell count analyses and there are also proprietary algorithms that are part of software packages for imaging readers13,14. In this method description, a human neural stem cell (NSC) line that has been genetically edited to constitutively express tdTomato15 will serve as a test line to compare cellular viability results between an MTT assay and an automated cell counting assay in a screen assessing toxicity of 57 test compounds. Although the primary goal of this strategy was to identify and characterize toxic compounds, it has the additional benefit of potentially identifying growth inhibitory and growth enhancing compounds and thus provides an effective method for identifying drugs that can modulate cellular growth.

<table><tbody><tr><td>B-27 (50X)</td><td>ThermoFisher Scientific</td><td>17504001</td><td>Neural stem cell medium component.&amp;nbsp;&amp;nbsp;</td></tr><tr><td>BenchTop pipettor</td><td>Sorenson Bioscience</td><td>73990</td><td>Provides ability to pipette compound library into a 96-well plate in one shot.</td></tr><tr><td>BioLite 96 well multidish</td><td>Thermo Scientific</td><td>130188</td><td>Any 96 well cell culture plate will work.&amp;nbsp; We use these in our work.</td></tr><tr><td>Cell culture microscope</td><td>Nikon</td><td>Eclipse TS100</td><td>Visual inspection of cells to ensure proper density.</td></tr><tr><td>Cytation 5/ Imaging reader</td><td>BioTek</td><td>CYT3MFV</td><td>Used for cell imaging and absorbance readings.</td></tr><tr><td>DMSO</td><td>Fisher Scientific</td><td>610420010</td><td>Solvent for compounds used in screen. Dissolves MTT precipitates to facilitate absorbance measurements.</td></tr><tr><td>FGF-basic</td><td>Peprotech</td><td>100-18B</td><td>Neural stem cell medium component.&amp;nbsp;&amp;nbsp;</td></tr><tr><td>GelTrex</td><td>ThermoFisher Scientific</td><td>A1413202</td><td>Neural stem cell basement membrane matrix.&amp;nbsp; Allows cells to attach to cell culture plates.</td></tr><tr><td>Gen5 3.04</td><td>BioTek</td><td></td><td>Analysis software to determine cell counts for tdTomato expressing cells.</td></tr><tr><td>Glutamine</td><td>ThermoFisher Scientific</td><td>25030081</td><td>Neural stem cell medium component.&amp;nbsp;&amp;nbsp;</td></tr><tr><td>Microtest U-Bottom</td><td>Becton Dickinson</td><td>3077</td><td>Storage of compound libraries.</td></tr><tr><td>MTT</td><td>ThermoFisher Scientific</td><td>M6494</td><td>Active assay reagent to determine cellular viability.</td></tr><tr><td>Multichannel pippette</td><td>Rainin</td><td>E8-1200</td><td>Column-by-column addition of cell culture medium, MTT, or DMSO.</td></tr><tr><td>Neurobasal medium</td><td>ThermoFisher Scientific</td><td>21103049</td><td>Neural stem cell base medium.</td></tr><tr><td>RFP filter cube</td><td>BioTek</td><td>1225103</td><td>Filter in Cytation 5 used to image tdTomato expressing cells.</td></tr><tr><td>TrypLE</td><td>ThermoFisher Scientific</td><td>12605036</td><td>Cell dissociation reagent.</td></tr></tbody></table>

a strategy to identify compounds that affect cell growth and survival in cultured mammalian cells at low-to-moderate throughput

Cytotoxicity is a critical parameter that needs to be quantified when studying drugs that may have therapeutic benefits. Because of this, many drug screening assays utilize cytotoxicity as one of the critical characteristics to be profiled for individual compounds. Cells in culture are a useful model to assess cytotoxicity before proceeding to follow up on promising lead compounds in more costly and labor-intensive animal models. We describe a strategy to identify compounds that affect cell growth in a tdTomato expressing human neural stem cells (NSC) line. The strategy uses two complementary assays to assess cell number. One assay works via the reduction of 3-(4,5-dimethylthizol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) to formazan as a proxy for cell number and the other directly counts the tdTomato expressing NSCs. The two assays can be performed simultaneously in a single experiment and are not labor intensive, rapid, and inexpensive. The strategy described in this demonstration tested 57 compounds in an exploratory primary screen for toxicity in a 96-well plate format. Three of the hits were characterized further in a six-point dose response using the same assay set-up as the primary screen. In addition to providing excellent corroboration for toxicity, comparison of results from the two assays may be effective in identifying compounds affecting other aspects of cell growth.

The automated cell count data identified eleven compounds with less than 25% viability when normalized to the DMSO control while the MTT data identified these same compounds plus two additional ones (Table 1 and Table 2, shaded red). The two compounds found to be toxic only in the MTT assay (wells F3 and G10) had 31% and 39%, respectively, the number of tdTomato-positive cells as the control and by rank order were the next two most toxic compounds in this library after those deemed to be...

Watch this Scientific Journal Video about A Strategy to Identify Compounds that Affect Cell Growth and Survival in Cultured Mammalian Cells at Low-to-Moderate Throughput at JoVE.com

A Strategy to Identify Compounds that Affect Cell Growth and Survival in Cultured Mammalian Cells at Low-to-Moderate Throughput

It is often necessary to assess the potential cytotoxicity of a set of compounds on cultured cells. Here, we describe a strategy to reliably screen for toxic compounds in a 96-well format.

Cytotoxicity is a critical parameter that needs to be quantified when studying drugs that may have therapeutic benefits. Because of this, many drug ...

a-strategy-to-identify-compounds-that-affect-cell-growth-survival

Research

JoVE Journal

Biochemistry

8.4K Views.  National Institutes of Health (NIH). It is often necessary to assess the potential cytotoxicity of a set of compounds on cultured cells. Here, we describe a strategy to reliably screen for toxic compounds in a 96-well format.

Video: A Strategy to Identify Compounds that Affect Cell Growth and Survival in Cultured Mammalian Cells at Low-to-Moderate Throughput

Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures

The identification of functional driver events in cancer is central to furthering our understanding of cancer biology and indispensable for the discovery of the next generation of novel drug targets. It is becoming apparent that more complex models of cancer are required to fully appreciate the contributing factors that drive tumorigenesis in vivo and increase the efficacy of novel therapies that make the transition from pre-clinical models to clinical trials.
Here we present a methodology for generating uniform and reproducible tumor spheroids that can be subjected to siRNA functional screening. These spheroids display many characteristics that are found in solid tumors that are not present in traditional two-dimension culture. We show that several commonly used breast cancer cell lines are amenable to this protocol. Furthermore, we provide proof-of-principle data utilizing the breast cancer cell line BT474, confirming their dependency on amplification of the epidermal growth factor receptor HER2 and mutation of phosphatidylinositol-4,5-biphosphate 3-kinase (PIK3CA) when grown as tumor spheroids. Finally, we are able to further investigate and confirm the spatial impact of these dependencies using immunohistochemistry.

Identifying novel drug targets that transition from pre-clinical testing to human trials is a scientific priority. To that end, here we describe a functional genomics approach for examining the impact of gene depletion on cancer cell line spheroids, which more appropriately model human cancers in vivo.

The identification of functional driver events in cancer is central to furthering our understanding of cancer biology and indispensable for the discovery ...

Cancer Research

Generation of High-Throughput Three-Dimensional Tumor Spheroids for Drug Screening

Cancer cells have routinely been cultured in two dimensions (2D) on a plastic surface. This technique, however, lacks the true environment a tumor mass is exposed to in vivo. Solid tumors grow not as a sheet attached to plastic, but instead as a collection of clonal cells in a three-dimensional (3D) space interacting with their neighbors, and with distinct spatial properties such as the disruption of normal cellular polarity. These interactions cause 3D-cultured cells to acquire morphological and cellular characteristics which are more relevant to in vivo tumors. Additionally, a tumor mass is in direct contact with other cell types such as stromal and immune cells, as well as the extracellular matrix from all other cell types. The matrix deposited is comprised of macromolecules such as collagen and fibronectin.
In an attempt to increase the translation of research findings in oncology from bench to bedside, many groups have started to investigate the use of 3D model systems in their drug development strategies. These systems are thought to be more physiologically relevant because they attempt to recapitulate the complex and heterogeneous environment of a tumor. These systems, however, can be quite complex, and, although amenable to growth in 96-well formats, and some now even in 384, they offer few choices for large-scale growth and screening. This observed gap has led to the development of the methods described here in detail to culture tumor spheroids in a high-throughput capacity in 1536-well plates. These methods represent a compromise to the highly complex matrix-based systems, which are difficult to screen, and conventional 2D assays. A variety of cancer cell lines harboring different genetic mutations are successfully screened, examining compound efficacy by using a curated library of compounds targeting the Mitogen-Activated Protein Kinase or MAPK pathway. The spheroid culture responses are then compared to the response of cells grown in 2D, and differential activities are reported. These methods provide a unique protocol for testing compound activity in a high-throughput 3D setting.

Across a wide variety of disease indications, more physiologically relevant models are being developed and implemented into drug discovery programs. The new model system described here demonstrates how three-dimensional tumor spheroids can be cultured and screened in a high-throughput 1536-well plate-based system to search for new oncology drugs.

Cancer cells have routinely been cultured in two dimensions (2D) on a plastic surface. This technique, however, lacks the true environment a tumor mass is ...

Pharmacological Targeting of Ion Channels to Treat Solid Tumors

Screening Ion Channels in Cancer Cells

Ion channels are critical for cell development and maintaining cell homeostasis. The perturbation of ion channel function contributes to the development of a broad range of disorders or channelopathies. Cancer cells utilize ion channels to drive their own development, as well as to improve as a tumor and to assimilate in a microenvironment that includes various non-cancerous cells. Furthermore, increases in levels of growth factors and hormones within the tumor microenvironment can result in enhanced ion channel expression, which contributes to cancer cell proliferation and survival. Thus, the pharmacological targeting of ion channels is potentially a promising approach to treating solid malignancies, including primary and metastatic brain cancers. Herein, protocols to characterize the function of ion channels in cancerous cells and approaches to analyze modulators of ion channels to determine their impact on cancer viability are described. These include staining a cell(s) for an ion channel(s), testing the polarized state of mitochondria, establishing ion channel function using electrophysiology, and performing viability assays to assess drug potency.

Author Spotlight: Exploring the Role of Ion Channels in Cancer: Characterization and Potential Treatment Approaches 

The pharmacological targeting of ion channels is a promising approach to treating solid tumors. Detailed protocols are provided for characterizing ion channel function in cancer cells and assaying the effects of ion channel modulators on cancer viability.

Ion channels are critical for cell development and maintaining cell homeostasis. The perturbation of ion channel function contributes to the development ...

Genome-Scale Screening to Identify and Compare Therapeutic Targets in Tumors

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are derived from Schwann cells or their precursors. In patients with the tumor susceptibility syndrome neurofibromatosis type 1 (NF1), MPNSTs are the most common malignancy and the leading cause of death. These rare and aggressive soft-tissue sarcomas offer a stark future, with 5-year disease-free survival rates of 34-60%. Treatment options for individuals with MPNSTs are disappointingly limited, with disfiguring surgery being the foremost treatment option. Many once-promising therapies such as tipifarnib, an inhibitor of Ras signaling, have failed clinically. Likewise, phase II clinical trials with erlotinib, which targets the epidermal growth factor (EFGR), and sorafenib, which targets the vascular endothelial growth factor receptor (VEGF), platelet-derived growth factor receptor (PDGF), and Raf, in combination with standard chemotherapy, have also failed to produce a response in patients. 
In recent years, functional genomic screening methods combined with genetic profiling of cancer cell lines have proven useful for identifying essential cytoplasmic signaling pathways and the development of target-specific therapies. In the case of rare tumor types, a variation of this approach known as cross-species comparative oncogenomics is increasingly being used to identify novel therapeutic targets. In cross-species comparative oncogenomics, genetic profiling and functional genomics are performed in genetically engineered mouse (GEM) models and the results are then validated in the rare human specimens and cell lines that are available. 
This paper describes how to identify candidate driver gene mutations in human and mouse MPNST cells using whole exome sequencing (WES). We then describe how to perform genome-scale shRNA screens to identify and compare critical signaling pathways in mouse and human MPNST cells and identify druggable targets in these pathways. These methodologies provide an effective approach to identifying new therapeutic targets in a variety of human cancer types.

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens 

We have developed a cross-species comparative oncogenomics approach utilizing genomic analyses and functional genomic screens to identify and compare therapeutic targets in tumors arising in genetically engineered mouse models and the corresponding human tumor type.

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are derived from Schwann cells or their precursors. In patients with the tumor susceptibility syndrome ...

Establishment of a High-throughput Setup for Screening Small Molecules That Modulate c-di-GMP Signaling in Pseudomonas aeruginosa

Bacterial resistance to traditional antibiotics has driven research attempts to identify new drug targets in recently discovered regulatory pathways. Regulatory systems that utilize intracellular cyclic di-GMP (c-di-GMP) as a second messenger are one such class of target. c-di-GMP is a signaling molecule found in almost all bacteria that acts to regulate an extensive range of processes including antibiotic resistance, biofilm formation and virulence. The understanding of how c-di-GMP signaling controls aspects of antibiotic resistant biofilm development has suggested approaches whereby alteration of the cellular concentrations of the nucleotide or disruption of these signaling pathways may lead to reduced biofilm formation or increased susceptibility of the biofilms to antibiotics. We describe a simple high-throughput bioreporter protocol, based on green fluorescent protein (GFP), whose expression is under the control of the c-di-GMP responsive promoter cdrA, to rapidly screen for small molecules with the potential to modulate c-di-GMP cellular levels in Pseudomonas aeruginosa (P. aeruginosa). This simple protocol can screen upwards of 3,500 compounds within 48 hours and has the ability to be adapted to multiple microorganisms.

Establishment of a High-throughput Setup for Screening Small Molecules That Modulate c-di-GMP Signaling in Pseudomonas aeruginosa

This article describes the high-throughput assay that has been successfully established to screen large libraries of small molecules for their potential ability to manipulate cellular levels of cyclic di-GMP in Pseudomonas aeruginosa, providing a new powerful tool for antibacterial drug discovery and compound testing.

Bacterial resistance to traditional antibiotics has driven research attempts to identify new drug targets in recently discovered regulatory pathways. ...

Immunology and Infection

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Although targeted therapies are initially effective, resistance inevitably emerges. Several methods, such as genetic analysis of resistant clinical specimens, have been applied to uncover these resistance mechanisms to facilitate follow-up care. Although these approaches have led to clinically relevant discoveries, difficulties in attaining the relevant patient material or in deconvoluting the genomic data collected from these specimens have severely hampered the path towards a cure. To this end, we here describe a tool for expeditious discovery that may guide improvement in first-line therapies and alternative clinical management strategies. By coupling preclinical in vitro or in vivo drug selection with next-generation sequencing, it is possible to identify genomic structural variations and/or gene expression alterations that may serve as functional drivers of resistance. This approach facilitates the spontaneous emergence of alterations, enhancing the probability that these mechanisms may be observed in the patients. In this protocol we provide guidelines to maximize the potential for uncovering single nucleotide variants that drive resistance using adherent lines.

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Drug resistance to targeted therapeutics is widespread and the need to identify mechanisms of resistance--prior to or following clinical onset--is critical for guiding alternative clinical management strategies. Here, we present a protocol to couple derivation of drug-resistant lines in vitro with sequencing to expedite discovery of these mechanisms.

Although targeted therapies are initially effective, resistance inevitably emerges. Several methods, such as genetic analysis of resistant clinical ...

Medicine

Competitive Genomic Screens of Barcoded Yeast Libraries

By virtue of advances in next generation sequencing technologies, we have access to new genome sequences almost daily. The tempo of these advances is accelerating, promising greater depth and breadth. In light of these extraordinary advances, the need for fast, parallel methods to define gene function becomes ever more important. Collections of genome-wide deletion mutants in yeasts and E. coli have served as workhorses for functional characterization of gene function, but this approach is not scalable, current gene-deletion approaches require each of the thousands of genes that comprise a genome to be deleted and verified. Only after this work is complete can we pursue high-throughput phenotyping. Over the past decade, our laboratory has refined a portfolio of competitive, miniaturized, high-throughput genome-wide assays that can be performed in parallel. This parallelization is possible because of the inclusion of DNA 'tags', or 'barcodes,' into each mutant, with the barcode serving as a proxy for the mutation and one can measure the barcode abundance to assess mutant fitness. In this study, we seek to fill the gap between DNA sequence and barcoded mutant collections. To accomplish this we introduce a combined transposon disruption-barcoding approach that opens up parallel barcode assays to newly sequenced, but poorly characterized microbes. To illustrate this approach we present a new Candida albicans barcoded disruption collection and describe how both microarray-based and next generation sequencing-based platforms can be used to collect 10,000 - 1,000,000 gene-gene and drug-gene interactions in a single experiment.

We have developed comprehensive, unbiased genome-wide screens to understand gene-drug and gene-environment interactions. Methods for screening these mutant collections are presented.

By virtue of advances in next generation sequencing technologies, we have access to new genome sequences almost daily.  The tempo of these advances is ...

Biology

High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes

Both transcriptional and post-transcriptional regulation have a profound impact on genes expression. However, commonly adopted cell-based screening assays focus on transcriptional regulation, being essentially aimed at the identification of promoter-targeting molecules. As a result, post-transcriptional mechanisms are largely uncovered by gene expression targeted drug development. Here we describe a cell-based assay aimed at investigating the role of the 3&#39; untranslated region (3&#8217; UTR) in the modulation of the fate of its mRNA, and at identifying compounds able to modify it. The assay is based on the use of a luciferase reporter construct containing the 3&#8217; UTR of a gene of interest stably integrated into a disease-relevant cell line. The protocol is divided into two parts, with the initial focus on the primary screening aimed at the identification of molecules affecting luciferase activity after 24 hr of treatment. The second part of the protocol describes the counter-screening necessary to discriminate compounds modulating luciferase activity specifically through the 3&#8217; UTR. In addition to the detailed protocol and representative results, we provide important considerations about the assay development and the validation of the hit(s) on the endogenous target. The described cell-based reporter gene assay will allow scientists to identify molecules modulating protein levels via post-transcriptional mechanisms dependent on a 3&#8217; UTR.

Here we describe a cell-based reporter gene assay as a valuable tool to screen chemical libraries for compounds modulating post-transcriptional control mechanisms exerted through 3&#8217; UTR.

Both transcriptional and post-transcriptional regulation have a profound impact on genes expression. However, commonly adopted cell-based screening assays ...

SA-&#946;-Galactosidase-Based Screening Assay for the Identification of Senotherapeutic Drugs

Cell senescence is one of the hallmarks of aging known to negatively influence a healthy lifespan. Drugs able to kill senescent cells specifically in cell culture, termed senolytics, can reduce the senescent cell burden in vivo and extend healthspan. Multiple classes of senolytics have been identified to date including HSP90 inhibitors, Bcl-2 family inhibitors, piperlongumine, a FOXO4 inhibitory peptide and the combination of Dasatinib/Quercetin. Detection of SA-&#946;-Gal at an increased lysosomal pH is one of the best characterized markers for the detection of senescent cells. Live cell measurements of senescence-associated &#946;-galactosidase (SA-&#946;-Gal) activity using the fluorescent substrate C12FDG in combination with the determination of the total cell number using a DNA intercalating Hoechst dye opens the possibility to screen for senotherapeutic drugs that either reduce overall SA-&#946;-Gal activity by killing of senescent cells (senolytics) or by suppressing SA-&#946;-Gal and other phenotypes of senescent cells (senomorphics). Use of a high content fluorescent image acquisition and analysis platform allows for the rapid, high throughput screening of drug libraries for effects on SA-&#946;-Gal, cell morphology and cell number.

Cellular senescence is the key factor in the development of chronic age-related pathologies. Identification of therapeutics that target senescent cells show promise for extending healthy aging. Here, we present a novel assay to screen for the identification of senotherapeutics based on measurement of senescence associated &#946;-Galactosidase activity in single cells.

Cell senescence is one of the hallmarks of aging known to negatively influence a healthy lifespan. Drugs able to kill senescent cells specifically in cell ...

High-Throughput Screening Analysis for 4B Cysteine Peptidase Autophagy Inhibitor

Cell-Based Drug Screening for Inhibitors of Autophagy Related 4B Cysteine Peptidase

Growing evidence has shown that high autophagic flux is related to tumor progression and cancer therapy resistance. Assaying individual autophagy proteins is a prerequisite for therapeutic strategies targeting this pathway. Inhibition of the autophagy protease ATG4B has been shown to increase overall survival, suggesting that ATG4B could be a potential drug target for cancer therapy. Our laboratory has developed a selective luciferase-based assay for monitoring ATG4B activity in cells. For this assay, the substrate of ATG4B, LC3B, is tagged at the C-terminus with a secretable luciferase from the marine copepod Gaussia princeps (GLUC). This reporter is linked to the actin cytoskeleton, thus keeping it in the cytoplasm of cells when uncleaved. ATG4B-mediated cleavage results in the release of GLUC by non-conventional secretion, which then can be monitored by harvesting supernatants from cell culture as a correlate of cellular ATG4B activity. This paper presents the adaptation of this luciferase-based assay to automated high-throughput screening. We describe the workflow and optimization for exemplary high-throughput analysis of cellular ATG4B activity.

Author Spotlight: A Selective Luciferase-Based Assay for Monitoring ATG4B 27 Activity in Cells 

Here, we describe a detailed protocol for the use of a luciferase-based reporter assay in a semi-automated, high-throughput screening format.

Growing evidence has shown that high autophagic flux is related to tumor progression and cancer therapy resistance. Assaying individual autophagy proteins ...

A Strategy to Identify Compounds that Affect Cell Growth and Survival in Cultured Mammalian Cells at Low-to-Moderate Throughput

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Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures

Generation of High-Throughput Three-Dimensional Tumor Spheroids for Drug Screening

Screening Ion Channels in Cancer Cells

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

Establishment of a High-throughput Setup for Screening Small Molecules That Modulate c-di-GMP Signaling in Pseudomonas aeruginosa

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Competitive Genomic Screens of Barcoded Yeast Libraries

High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes

SA-β-Galactosidase-Based Screening Assay for the Identification of Senotherapeutic Drugs

Cell-Based Drug Screening for Inhibitors of Autophagy Related 4B Cysteine Peptidase