Transfected cell microarrays: an efficient tool for high-throughput ...

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Transfected cell microarrays: an efficient tool for high-throughput functional analysis Vytaute Starkuviene, Rainer Pepperkok and Holger Erfle†

CONTENTS Principle of transfected cell microarrays Cell microarrays & modifications of transfected cell microarrays Applicability of cell microarrays to work with diverse reagents Biological assays with cell microarrays Automated data acquisition & data handling Data analysis Expert commentary Five-year view Financial disclosure Key issues References Affiliations

†

Author for correspondence BIOQUANT Center, University of Heidelberg, RNAi Screening Unit, INF 267, 69120 Heidelberg, Germany Tel.: +49 622 1545 1272 Fax: +49 622 151 483 holger.erfle@bioquant. uni-heidelberg.de; [email protected] KEYWORDS: automated microscopy, cDNA, RNA interference, transfected cell microarray

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Transfected cell microarrays are considered to be a breakthrough methodology for high-throughput and high-content functional genomics. Here, recent advances in the cell microarray field are reviewed, along with its potential to increase the speed of determining gene function. These advances, combined with an increasing number and diversity of gene perturbing systems, such as RNAi and ectopic gene expression, provide tools for expanding our understanding of biology at the systems level. Expert Rev. Proteomics 4(4), 479–489 (2007)

The information of full genome sequences gives the challenging opportunity to analyze encoded genetic information on a large scale. The comprehensive understanding of gene function requires the integration of experimental data from diverse sources, serving as the basis for a relevant modeling of physiological processes. Recent developments in high-throughput biochemical and genetic methodologies, such as quantitative proteomics [1,2] or yeast two-hybrid analysis [3,4], enabled the analysis of the proteome and interactome on a genome-wide scale. However, for cell biology, tools were only recently developed to investigate gene and protein function on a large scale. Only with the discovery of RNAi, its introduction to cultured cells and the development of vector systems expressing green fluorescent protein (GFP)-tagged proteins did high-throughput functional analysis of protein function in living cells become possible [5–9]. However, functional screens in 96- or 384-well plates are quite costly; therefore, the establishment of transfected cell microarrays as a cost-saving alternative is highly welcome. A transfected cell microarray, as defined by Ziauddin and Sabatini in 2001 [10], is a slide with hundreds or even thousands of cell clusters, each transfected with a defined cDNA in a plasmid expression vector, which directs the overexpression of a particular gene product

10.1586/14789450.4.4.479

(FIGURE 1).

The technique has been extended to RNAi using a siRNA and shRNA [11–15]. The phenotypic consequences of perturbing individual genes either by overexpression or downregulation can be probed in a parallel high-throughput fashion by appropriate assays. Different types of cell arrays have been developed over the last years – a comprehensive overview is given by Angres [16]; here we present a summary of transfected cell microarray applications published to date (TABLE 1). By combining miniaturization and multiplexing capabilities of transfected cell microarrays with recently developed methods for cellular investigation, like high-content and highthroughput microscopy and image processing [17,18], a wealth of information about protein function in the natural environment of the cell will be obtained in the future. Here, recent advances in the field of cell microarrays are reviewed and the ongoing challenges associated with high-throughput cell biology, such as automated microscopy and image processing, are addressed. Principle of transfected cell microarrays

The principle of transfected cell microarrays is that GFP-tagged cDNAs, synthetic siRNAs or expression plasmids encoding shRNAs are printed, usually with a contact printer, together with a gelatin solution at defined locations on a glass slide (FIGURE 1). A transfection reagent can

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ISSN 1478-9450

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Erfle, Starkuviene & Pepperkok

data-acquisition speeds. Consequently, quick biological processes can be resolved in time-lapse experiments for an increased number of samples [22]. A transfected cell microarray is a technique characterized by a number of positive features. Importantly, one printing round produces multiple replicates that can serve for many independent biological assays, thus allowing parallelism and multiplexing. Parallelism is also reached at the point of cell seeding and immunofluorescence, as the same solutions are applied to many hundreds to thousands of samples simultaneously. This not only reduces costs, but also increases the comparability of the results. In addition, fewer cells are needed for the experiment with cell arrays, which is especially important for difficult-to-culture cell lines. Furthermore, as siRNA arrays can be stored in a dry atmosphere before applying the cells for more than 15 months without significant loss of function [23], large-scale experiments can be performed with the substrate produced at any given time point after the production of the replicas. Owing to the storage capabilities of printed microarrays, high-throughput experiments can even be performed in laboratories not equipped with expensive robotics. However, there are several points to consider when the use of transfected cell arrays is planned. One is the risk of cross contamination between neighboring spots. For this reason, we use a large spot-to-spot distance of 1125 µm, resulting in only 384 samples per chambered coverglass [22,23]. Finally, a few limitations of transfected cell microarrays are the small number of cells per spot and the diminished possibility for a subsequent analysis of cells after knock-down experiments, for instance by reverse transfection B A (RT)-PCR or fluorescence-activated cell sorting (FACS). Finally, cells not being separated by any physical barriers may affect each other by intercellular signaling chains, such as cytokines, and this may interfere with a spatially separated readout of the experimental results. On the other hand, this feature might be indispensable in investigating a cooperative behavior of cell popuC D lations, which is more relevant to events occurring naturally. For instance, the influence of local viral infections or treatment with defined cytokines on neighboring cells could be quantitatively detected and eventually modeled. In addition, the possibility of printing diverse types of cells in close and well-defined vicinities would enable the assessment of intercellular connections in a Figure. 1. Cell microarrays. (A) Numerous cell arrays can be produced from a single spotting round, quantitative and predictive manner.

be printed together with the nucleic acid/gelatin mix or can be added to the printed array prior to cell seeding. The spot size may vary from 200 to 400 µm, depending on the application, allowing between 50 and 200 cells to reside on one spot. In our laboratory we prefer a spot size of 400 µm. This relatively large size increases the statistical value of the result by allowing more cells to reside on a spot. After drying of the substrate (solidphase transfection), tissue culture cells are plated on the slides resulting in clusters of live cells with a specific gene perturbation in a lawn of untransfected cells. This approach is called ‘reverse transfection’, because the order of addition of siRNA or expression plasmid and cells is reversed compared with conventional transfection. The transfection efficiency and the strength of adherence of the cells to the spots are increased by the addition of components of the extracellular matrix (ECM) to the spotting solution [15,19], with different ECM components being required for different cell types [20]. In a parallel approach, hundreds to thousands of different proteins are downregulated or overexpressed and the cellular response to these perturbations is monitored. The arrays can be made at densities of up to 5600 cell clusters per standard slide [21] and can be screened with any technique that is compatible with cells grown on a surface, including immunofluorescence, autoradiography and in situ hybridization. One of the major advantages of solidphase reverse transfection compared with liquid-phase transfection is that the short distances between the spots allow high

which ensures a high reproducibility of assay results. (B) An example of a LabTek chamber slide (Nalge Nunc) with 384 spots arrayed. (C) A closer look at the geometry of the spots: the distance between Cell microarrays & modifications of the spots is 1125 µm and the average spot size is 400 µm. The spotted volume is approximately 50 nl, transfected cell microarrays containing approximately 4 ng of cDNA–GFP and/or approximately 2 ng of a double-stranded siRNA (to Depending on the application, numerous label the spot position a Cy3-labeled siRNA was used). The image was obtained on a StX10 stereomicroscope [117]. (D) Yellow fluorescent protein (YFP) is overexpressed in 3T3 NIH cells attached on a cell microarray methods have been introspot. Nuclei are labeled with Hoecht 33342 stain. A combined image is taken on a widefield microscope duced [16]. Basically, one distinguishes Zeiss Axiovert 200 (Carl Zeiss, Jena, Germany) with a 20× PlanApo NA 0.8 air-immersion objective.

between substrate-based cell microarrays

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Applications of transfected-cell microarrays

Table 1. Summary of transfected cell microarrays applied for functional assays. Cells

Assay

Molecules tested

Result under given condition Ref.

Apoptosis Apoptosis Levels of intracellular phosphotyrosine Subcellular localization of proteins residing on chromosome 21 Calcium ion flux

1959 cDNAs 382 cDNAs 192 cDNAs 89 cDNAs

10 positive hits 10 positive hits 6 positive hits 52 proteins localized

[59]

900 combinations of 36 cDNAs encoding GPCR proteins and 25 agonists 11 cDNAs

15 positive GPCR–agonist interactions

[31]

80% of cDNAs correctly localized

[30] [66]

cDNA-GFP

All antibodies retained ligand–cDNA binding specificity 50% of cells transfected

Cell proliferation

384 dsRNAs

44 positive hits

[46]

HeLa-H2B GFP

Mitosis

42 positive hits

[22]

HeLa

Transport efficiency of ts-O45-G

7 positive hits

[61]

HeLa

Transport efficiency of ts-O45-G

4 positive hits

[15]

HeLa-d2eGFP hMSC

Downregulation efficiency of GFP Downregulation efficiency of GFP

49 siRNAs targeting regulators of mitosis 92 siRNAs targeting 37 regulators of transport 4 siRNAs targeting regulators of transport siRNAs targeting GFP siRNAs targeting GFP

75% of GFP downregulated 80% of GFP downregulated

[12]

30 positive hits

[14]

>50% of GFP is downregulated

[14]

2 positive hits

[40]

~80% of lamin A/C downregulated 30–40% express GFP

[40]

cDNA HEK293 HEK293 HEK293 HEK293 HEK293

MCF7 HEK293 hMSC

Automated identification of subcellular localization Ligand-binding affinities of antibodies expressed on cell surface Transfection efficiency

4 cDNAs encoding diverse antibodies

[41] [10] [73]

[19]

dsRNA Drosophila melanogaster

siRNA

[19]

shRNA HEK293T

Proteosome-mediated proteolysis

HEK293T

Downregulation efficiency of GFP

30 shRNA-targeting proteosome subunits shRNAs targeting GFP

Lentivirus HeLa HeLa HeLa

Effects on cell nuclei and growth

2 lentiviruses encoding shRNA targeting lamin A/C and mTOR Downregulation efficiency of lamin A/C Lentivirus encoding shRNA targeting lamin A/C Transfection efficiency Lentivirus encoding GFP

[40]

Only tests with a quantitative evaluation of the results were included. cDNA: Complementary DNA; dsRNA: Double stranded RNA; GFP: Green fluorescent protein; GPCR: G-protein-coupled receptor; shRNA: Short hairpin RNA; siRNA: Small interfering RNA.

(cells are overlayed to microarrayed reagents or biomolecules) and genuine cell microarrays (cells themselves are microarrayed). Our review does not focus on the latter, but it is worth mentioning that ‘genuine’ cell microarray methodologies, such as ‘frozen’ cell microarrays [24] for high-throughput characterization of different cell types (e.g., cancer cells) and ‘spotted’ cell microarrays, where living cells are applied directly on the substrate, are alternatives to substrate-based cell microarrays, depending on the application. Two groups use an ink-jet printer for cell printing [25,26], whereas one group uses a Piezodispenser to apply cells to the substrate [27], allowing reverse transfection to occur in these cell clusters. However, it needs to www.future-drugs.com

be mentioned that the microarraying of live cells with a liquid dispenser may be problematic due to cell adhesion to the syringe or nozzle, and shearing or lyses of the cells. With these developments, different and rare, or differently treated cell types might be used for drug screening. Interesting substrate-based cell microarrays are, for example, peptide and small-molecule microarrays for cell-based drug screening [28] and antibody microarrays for the detection of cell surface antigens [29]. We will focus on modifications of the transfected cell microarray technique, which could be considered a substrate-based cell microarray. For instance, our group spotted in chambered 481

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coverglasses [30] allowing high-resolution and time-lapse data acquisition [22]. Another group printed grids of siRNAs or cDNAs in 96-well plates [31]. This format offers several advantages, such as simultaneous, cost-effective and parallel tests of drugs in the background of knock-down or knock-in phenotypes. In general, not only chambered coverglasses or normal microscopy slides, but virtually any cell culture-compatible dishes can be used to produce cell microarrays. Diverse strategies for substrate surface coating have been introduced and a very good overview is given in the review by Hook and colleagues [32]; in addition, a comparison of microscope slide substrates for use in transfected cell microarrays is given by Delehanty and colleagues [33]. In general, surface coatings should enable strong cell attachment, high transfection efficiencies and cell growth only in the spotted regions. Different or modified surfaces and protocols have been used to perform the transfection of rat mesenchymal stem cells [34] and nonadherent cells [35]. A very interesting modification of cell microarrays was recently demonstrated by Peterbauer and colleagues where, by using cell-repellent materials such as polyvinyl alcohol, the whole surface is made cell repellent and, by a subsequent treatment with sodium hypochlorite or exposure of defined positions to UV light, cell-adhesive regions were created [36]. This technique is particularly important when dealing with cells prone to migrate and may even minimize the risk for cross contamination. Another approach to creating cell-repellent surfaces implicated a graft copolymer of poly(L-lysine) and poly(ethylene glycol) (PLL-g-PEG) surface coating [37]. Apart from the most commonly used transfection reagents [38], two very different methods for the transfection of cells have been used. One of them utilizes arrayed electrodes to perform localized electroporation [39]. The other uses lentiviruses for the transfection of transformed and primary human cells [40]. Both techniques might be useful for otherwise hard-to-transfect cell types. Applicability of cell microarrays to work with diverse reagents

The first publication introducing transfected cell microarrays described an efficient uptake of cDNAs in expression vectors spotted on a glass surface by human cells [10]. Applications of cell microarrays with transfected cDNAs demonstrated that they perform in a comparable fashion to conventional cDNA overexpression experiments; and thus, are useful for testing the role of these cDNAs in diverse cellular processes (TABLE 1) [10,41]. Consequently, combining the ever-growing list of identified and cloned human cDNAs [42–44] with the high-throughput capacities of cell arrays would provide the challenging opportunity to analyze biological processes on a large scale. By having cDNAs tagged with GFP [45], numerous applications can be pursued, including automated localization studies of unknown human proteins [30]. By extending this approach to time-lapse microscopy, a comprehensive picture of the dynamic protein subcellular localization patterns and their alterations in response to diverse cellular changes related to diverse diseases can be obtained.

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An alternative approach, namely the analysis of loss-offunction phenotypes in mammals became possible with the discovery of RNAi, only a few years ago [5,6]. Still, it presents a financial challenge due to the costs of the diverse RNAi types (for instance, siRNAs and shRNAs targeting genes of diverse organisms) and, similar to experiments with cDNAs, the costs for their introduction into cells. In order to achieve cost-effective procedures, diverse types of RNAi were probed for the production of transfected cell microarrays [12–15]. At first, screening experiments using cell microarrays with double stranded (dsRNAs) were performed in Drosophila melanogaster, a model organism, which is superior for RNAi experiments in terms of efficiency [46]. Lately, successful proof-of-principle screens using transfected cell microarrays were also performed in mammalian cells [22,46], which eventually opened the avenue for a cost-effective and highthroughput RNAi-based functional analysis of mammalian genomes. Accordingly, individual academic laboratories and largely commercial suppliers are currently working on the production of diverse libraries of RNAi types. For instance, a set of retroviral vectors encoding 23,742 distinct shRNAs, which target 7914 different human genes, was reported by Berns [47]. A second generation of shRNAs with sequence sites of miRNA (shRNA-mir) was produced by Silva and cover a substantial part of the human and mouse genomes [48]. A library of endoribonuclease-prepared siRNAs (esiRNAs) from a sequence-verified complementary DNA collection representing 15,497 human genes was published by Kittler [49]. Another library of 8000 siRNAs was prepared by inserting a synthetic DNA encoding a gene-specific siRNA sequence between two promoters [50]. The ever-growing list of producers offering genome-wide libraries or sublibraries of human, mouse and rat siRNAs and shRNAs has recently been reviewed in [38,51,52]. Prospectively, other types of regulatory nucleic acids, such as small noncoding miRNAs could be successfully implicated for functional assays on cell microarrays [53,54]. Not only reagents that need to enter cells to gain their functional activity but also diverse classes of molecules acting on the cell surface have been successfully tested for the production of cell microarrays. For instance, antibody arrays were made more than two decades ago to identify specific cell surface antigens [55], and currently antibody arrays are implemented in routine diagnostics of some diseases, such as colorectal or blood cancer [56,57]. To investigate cellular differentiation, an ECM microarray was created recently [58]. Slight modifications in the procedure enabled the spotting of five of the most abundant proteins of the ECM in 32 different combinations at concentrations two orders of magnitude lower than needed for experiments in 96-well plates. By immunofluorescence staining of albumin and a reporter assay of a liver-specific gene, the influence of the ECM protein composition on the differentiation status of hepatocytes and the differentiation of stem cells along the hepatic lineage was assessed.

Expert Rev. Proteomics 4(4), (2007)

Applications of transfected-cell microarrays

An interesting type of cell microarray was described by Bailey [28], where small molecules were embedded into a degradable polymer prior to spotting. The polymer enabled slow diffusion of compounds; this consequently affected the neighboring cells. The biological value of such an approach was demonstrated by the downregulation of seven cancer-related genes and measuring whether the activity of 70 cell-neutral compounds had been changed. Ultimately, out of 980 compound–siRNA combinations tested, four were found to alter the sensitivity of cancer cells. However, even though it is compatible with most chemical libraries, this method might have serious limitations due to insufficient spatial specificity. Biological assays with cell microarrays

Thus far, cell microarrays have largely been utilized for ‘proofof-principle’ projects only, and their large-scale biological applications are still to come (TABLE 1). This might be due to a long assay development phase, consisting of both experimental and technological developments; this might involve, for example, the establishment of specific labeling strategies, (e.g., production of antibodies or recombinant cDNA–GFP constructs) in order to achieve a good signal-to-noise ratio and biological assay specificity. The choice or even creation of proper cell lines might be needed and is usually a very timeconsuming part of assay optimization. Also, the upscaling of experimental protocols to high-throughput formats usually requires numerous optimization steps in order to find a fine balance between biological meaning and experimental robustness. Next, proper positive and negative controls need to be chosen for an estimate and measurement of the dynamic range of the assays, and to control the production and processing of the transfected cell microarrays. In addition, image processing and scoring of hits need to be automated. In general, a higher throughput is achieved by working with well-established gene perturbing reagents, such as cDNAs and siRNAs in human cells and dsRNAs in D. melanogaster. Recently developed approaches (e.g., libraries of lentoviruses) are still in the initial phase of testing. The same is true concerning the type of applications: a higher throughput is reported for well-established assays, such as apoptosis (TABLE 1). As these points are not necessarily specific to transfected cell microarray, further developments in the field of automated cellular assays will considerably facilitate the use of transfected cell arrays for large-scale applications. It is already obvious that the versatility and biological compatibility of cell microarrays enables the assessment of virtually any biological phenotype of interest. For instance, by measuring the formation of apoptotic bodies, DNA cleavage and fragmentation of nuclei in fixed HEK293T cells, 382 full-length human cDNAs were tested for having a role in apoptosis [41]. Another study presented almost 2000 full-length human cDNAs tested for apoptosis by the terminal transferase dUTP nick end labeling (TUNEL) assay [59]. A more powerful and precise readout of processes associated with nuclei changes can be obtained by assessing live cells as described by Neumann [22].

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Time-resolved measurements of a GFP-tagged histone in HeLa cells enabled the creation of time-resolved phenotypes (including diverse stages of mitosis and apoptosis) for 49 human genes suppressed by specific siRNAs. The work of Ziauddin and Sabatini in 2001 was not only the first description of transfected cell microarrays as a new high-throughput methodology but also described the functional characterization of 192 diverse cDNA–GFPs for their involvement in signaling events. For this, altered levels of intracellular phosphotyrosine were measured with a phosphotyrosine-specific antibody and, besides five known molecules, one uncharacterized cDNA induced an increase of the phosphotyrosine level. The possibility of monitoring the MAP kinase signaling pathway in vivo was demonstrated by creating a fusion construct of GFP and serum response element (SRE) [60], and visualizing whether the overexpression of several known activators of the MAP kinase pathway acting upstream of SRE leads to the expression of SRE–GFP. Finally, 384 dsRNAs targeting the majority of tyrosine kinases and serine/threonine phosphatases were tested in D. melanogaster cells for their influence on cell proliferation and the phosphorylation state of dAkt/dPKB [46]. The analysis of mammalian secretory membrane trafficking is also currently being successfully addressed by using cell microarrays [15,61]. As a read-out parameter, the transport efficiency of the conventional secretory marker ts-O45-G (a temperature-sensitive mutant of the vesicular stomatitis virus glycoprotein) is automatically measured and compared between treated and nontreated cells [62–64]. The high-throughput quantitative analysis of alternative transport markers is currently under development and will enable the appreciation of the complexity of the secretory pathway (FIGURE 2) [65]. The usage of cell arrays is not only limited to the assessment of phenotypic cellular changes; biochemical processes and constants could also be measured. For instance, Delehanty and colleagues developed cell arrays with recombinant single-chain antibodies (scFvs) expressed on the cell surface [66]. The authors could demonstrate both a dose-dependent expression of native and mutated antibodies in cell array format and their ability to bind ligands with different affinities, comparable with those of their soluble counterparts. As such, this method offers a rapid and quantitative method of characterizing diverse membrane receptor–ligand interactions and, eventually, of screening for novel cell-based therapeutics. Finally, not only well-established cell cultures, but also difficult-to-handle cells, such as primary cells, can be used for cell microarray experiments. This would eventually broaden the repertoire of biological pathways (e.g., neurological or immunological) to be amenable for high-throughput analysis. For instance, an efficient and nontoxic transfection of adult stem cells with cDNAs and siRNAs was recently demonstrated by Okazaki and Yoshikawa [19,67]. Primary human fibroblasts and bone-marrow-derived mouse dendritic cells were efficiently transfected with spotted lentiviruses encoding shRNAs and cDNAs [40].

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Procollagen

Merged

Ascorbate

No ascorbate

Sar-GTPase-YFP

Figure 2. Procollagen secretion assay on cell microarrays. Procollagen is one of the most abundant proteins synthesized in fibroblasts and is localized in the endoplasmic reticulum (ER) in a steady state (upper row). By adding ascorbate to the cell culture, folding of procollagen is induced; consequently, the protein gains a secretion-competent state and is secreted into the extracellular space (lower row) [65]. Exit from the ER is severely hindered in cells expressing the dominant–negative mutant of Sar1a GTPase, H79G [72]; therefore, procollagen is largely retained in these cells. The secretion efficiency of procollagen in the cells growing on a spot and expressing Sar1a-GTPase (indicated by a circle) can be quantitatively compared with the neighbouring nontransfected cells. Combined images are taken on a widefield microscope Zeiss Axiovert 200 (Carl Zeiss, Jena, Germany) with a 20× PlanApo NA 0.8 air-immersion objective. YFP: Yellow fluorescent protein.

Automated data acquisition & data handling

One of the most important components of successful transfected cell microarray experiments is an appropriate imaging system. The ideal imaging system would allow a high-throughput and high-resolution capturing of the behavior of live individual cells [17]. Numerous technical developments concerning functional features of fluorescence microscopes, like automated focusing and sample positioning, and improvements of light excitation sources together with the availability of diverse genetically encoded and chemically synthesized fluorophores resulted in the development of diverse automated fluorescence microscopy platforms (TABLE 2). These are either widefield or confocal systems and are equipped with advanced optics, which enables a broad range of fluorescent probes to be analyzed in parallel. Widefield microscopes usually have lamp-based (mercury or xenon) excitation light sources, which enable a broad range of fluorophores to be used with appropriate filters (e.g., ScanR from Olympus and ArrayScan® from Cellomics). The imaging platforms based on confocal microscopy usually utilize a laser as a source of fluorophore excitation, which provides stronger illumination. In addition, the blockage of out-of-focus light is advantageous in improving subcellular resolution and the possibility of 3D imaging. Confocal microscopes can be divided into two major groups: • Those utilizing line scanning (e.g., IN Cell Analyzer 3000 from GE Healthcare). These offer a better sample illumination but lower throughput;

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• Those operating with a Nipkow-disk (e.g., Opera™, Evotec Technologies), which are able to acquire images rapidly but compromise the strength of illumination. Another important feature of any imaging platform is the way an object of interest is focused. Current imaging systems can be differentiated by two modes of autofocus routines. One of these is image based and rapidly acquires and analyzes images in the zplane, and the plane with the sharpest focus of a cellular structure (e.g., nuclei) is chosen for the acquisition. The hardwarebased autofocus acts by measuring changes of the refractive index between dish and solution by using an infra-red (IR) beam and a detector and focuses on the bottom of the dish. Next, the desired offset to a structure in the cell is chosen for imaging. The first approach enables sharp images to be obtained, even if there are irregularities in the substrate, but is relatively slow. Hardwarebased autofocus is faster and essentially causes no bleaching of the sample. There are a number of additional features characterizing automated imaging systems. For instance, an increasing number of imaging platforms are capable of performing live-cell imaging because of their environmental control chambers. These usually ensure a constant maintenance of temperature, humidity and CO2 level. Most automatic microscopes have been designed to acquire images from multiwell plates rather than cell microarrays; however, they should be suitable for imaging cell microarrays with only slight adaptations, namely more accurate stage positioning. Indeed, some of the devices are already capable of imaging cells

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Applications of transfected-cell microarrays

Table 2. Examples of automated imaging platforms. Manufacturer

Microscope

Confocal

Live cell imaging

Ref.

Applied Precision

CellWoRx

No (Wf)

No

[103]

BD BioScience

BD Pathway 415

No (Wf)

Yes

[104]

BD BioSciences

BD Pathway 435

Yes (Nd)

Yes

[104]

BD BioSciences

BD Pathway 855

Yes (Nd)

Yes

[104]

Beckman Coulter

Cell Lab IC 100

No (Wf)

No

[105]

Cellomics

ArrrayScan live

No (Wf)

Yes

[106]

Cellomics

CellWoRx

No (Wf)

No

[106]

Cellomics

Kinetic Scan

No (Wf)

Yes

[106]

Compucyte

iCyte

No (Lsnc)

No

[107]

Evotec Technologies

Opera

Yes (Nd)

No

[108]

GE Healthcare

In Cell Analyzer 1000

No (Wf)

Yes

[109]

GE Healthcare

In Cell Analyzer 3000

Yes (Lsc)

Yes

[109]

MAIA Scientific

MIAS-2

No (Wf)

Yes

[110]

Molecular Devices

ImageXpress Micro

No (Wf)

Yes

[111]

Molecular Devices

ImageXpress Ultra

Yes (Lsc)

Yes

[111]

Olympus

ScanR

No (Wf)

Yes

[112]

TTP Lab Tech

Acumen Explorer

No (Lsnc)

No

[113]

Lsc: Laser scanning confocal; Lsnc: Laser scanning nonconfocal; Nd: Nipkow-disk confocal; Wf: Widefield.

growing in virtually any type of culture dishes, including cell microarrays (e.g., iCyte™ from CompuCyte and ScanR from Olympus). In many cases, it is necessary for the imaging platform to be connected to appropriate robotics and a liquid-delivery system in order to image metabolic reactions or drug effects. Genome-wide cellular screens generate between several tens of thousands and millions of images in a single screen. This results in data storage requiring hundreds of gigabytes (Gb) to tens of terabytes (Tb); therefore, imaging platforms need to be linked to efficient data storage and compression systems as well as imageprocessing platforms and databases [68]. An open-source program for the storage and manipulation of images is, for example, the Open Microscopy Environment (OME) [101]. Two examples of commercial software platforms are High Content Informatics (HCi) from Cellomics and CellMine HCS from Bioimagene [102]. Data analysis

The strength of automated imaging is seen when image processing is also automated, allowing phenotypes to be scored by a computer in a truly quantitative and unbiased manner. Extracting information by eye would be impossible owing to the large number of images to be analyzed. In addition, visual analysis is only useful for identifying unusual samples rather than recording a quantitative measurement for each sample. The first step in www.future-drugs.com

image processing is to determine your biological object of interest labeled with a fluorescent tag (e.g., whole-cell or subcellular structures like nuclei stained with Hoechst); this step usually involves filtering and ends with a segmentation step, which defines the boundaries of the objects. Simple features, such as cell number, size and shape can then be calculated. Fluorescence intensities in other fluorescence channels are then calculated in this boundary or in a defined dilated region of interest (ROI) around this boundary. True multiplexing of the read out is achieved by measuring numerous parameters for each fluorophore used. The cellby-cell analysis offers many other advantages, such as separation of cell subpopulations and their analysis. When complex phenotypes (e.g., different mitotic stages) need to be distinguished, cells have to be classified into appropriate phenotypic categories. Several developments of this type, for instance machine learning approaches, are becoming more suitable for high-throughput applications [22,30]. A comprehensive overview of technologies for analyzing and reconstructing dynamic structures and processes in living cells is given by Eils and Athale [69]. Increasingly, automated microscope providers are incorporating modules for high-content and single-cell analyses and even ‘ready assays’ into their imaging platforms. All the commercially available imaging platforms listed in TABLE 2 have integrated image-analysis software packages acting off- and/or largely online. 485

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Table 3. Examples of image analysis software for high-content, high-throughput experiments. Manufacturer

Name of software

Ref.

Open Source

Cell Profiler

[114]

Open Source

ImageJ

[115]

Definiens

Cellenger

[116]

Evotec Technologies

Acapella

[108]

GE Healthcare

Developer

[109]

Unfortunately, an enormous effort is still needed to adapt these packages to specific assays. Examples of commercial and opensource image-processing programs are given in TABLE 3. Taking into consideration the increasing popularity of microscopy-based functional analysis, a particular need is emerging for data analysis packages, which would allow biologists without training in computer vision or programming to quantitatively measure complex cellular phenotypes from thousands of images in a high-throughput manner. One of the programs, namely CellProfiler, was developed in the group of Anne Carpenter [18,70]; the software is modular and can therefore be adapted to any cell type and to any visual phenotype. Another example is findSpots, an image-analysis program integrated in OME [71]. Expert commentary

So far, a genome-wide application of transfected cell microarrays has not yet been completed; this might be due to several reasons, one of them being that the majority of high-throughput imaging centers have already established multiwell plate-based assay platforms. A restructuring of these platforms to transfected cell microarrays will cause additional costs. However, as soon as restructuring has occurred, many laboratories can participate in the ‘ready-to-transfect’ replicates performing their specific assays. Consequently, the cost per genome-wide screen is drastically reduced. Solid-phase transfection may give laboratories the possibility of genome-wide screening in their own laboratory without access to any automated liquid handling.

A solution would be to create specialized centers for the production and development of transfected cell microarrays supplying the scientific community with ready-to-transfect substrates and also support with automated image-processing platforms and curated databases. This would increase the scientific value of each assay owing to the more reliable comparison across diverse assays, provided that cell microarrays come from the same source and the results of the different assays are managed in the same database. Five-year view

High-content and high-throughput technologies for quantitative cell biology are still at the developmental stage, though the major and basic directions have been defined. With the ongoing advances in automated sample preparation for cell microarrays, automated microscopy, image processing and data mining, enormous progress towards the understanding of every gene function will be achieved. In 5 years time we expect that evaluated libraries of siRNAs and shRNAs will exist and a fair number of GFPtagged full-length cDNAs will be available for performing systems biology research in living cells in a high-throughput manner. Possibly, the next generation of automated microscopes will be developed to examine more complex biological processes, such as molecular interactions in living cells by fluorescence energy transfer (FRET), molecular dynamics using fluorescence recovery after photobleaching (FRAP) or fluorescence correlation spectroscopy (FCS). Doubtless, these automated platforms will enable the creation, exchange, comparison and evaluation of scientific data on a large scale. Transfected cell microarrays will find their place as a versatile, efficient and cost-reducing technology, which can be used to assess the biological role of diverse classes of compounds influencing the function of individual cells or their communities. Acknowledgements

We thank David Ibberson for carefully reading the manuscript. Financial disclosure

This work has been supported by the Bundesministerium für Bildung und Forschung with grants 01GR0101 and 01KW0013 and by a grant from the Verbundforschung Baden-Wuerttemberg (Grant No. 24–720.431–1-2/2) to Rainer Pepperkok.

Key issues • Microscopy-based large-scale cell biology makes a potent contribution to the understanding of protein function in the natural environment of living cells. • Transfected cell microarrays in combination with libraries of reagents for perturbing gene function by knock-down or knock-in are advanced and specific tools for high-content cell biology. • Transfected cell microarrays are the most cost-efficient large-scale functional analysis methodology currently available. • Automated microscopy and image processing are essential for transforming cell microarrays into a high-throughput technique. • A function of a protein can only be assigned by an appropriate assay design. • The integration of the results of diverse functional assays in open databases shared among the research community will help to advance the comprehensive analysis of different biological systems.

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Rainer Pepperkok Team Leader, European Molecular Biology Laboratory (EMBL), Cell Biology & Cell Biophysics Unit, Meyerhofstrasse 1, 69117 Heidelberg, Germany Tel.: +49 622 1387 8332 Fax: +49 622 1387 8306 [email protected]

•

Holger Erfle Head of the RNAi Screening Unit, BIOQUANT Center, University of Heidelberg, RNAi Screening Unit, INF 267, 69120 Heidelberg, Germany Tel.:+49 622 1545 1272 Fax:+49 622 151 483 [email protected]; [email protected]

Affiliations •

Vytaute Starkuviene Scientist, European Molecular Biology Laboratory (EMBL), Cell Biology & Cell Biophysics Unit, Meyerhofstrasse 1, 69117 Heidelberg; Heidelberg & Junior Group Leader, BIOQUANT, INF 267, Ruprecht-Karls-Universität, 69120 Heidelberg, Germany Tel.: +49 622 1387 8232 Fax: +49 622 1387 8306 [email protected]

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