Protein microarrays represent an emerging class of proteomic tools to investigate multiple protein-protein interactions in parallel. A sufficient proportion of immobilized proteins must maintain an active conformation and an orientation that allows for the sensitive and specific detection of antibody and ligand binding. In order to establish protein array technology for the characterization of the weak interactions between leukocyte membrane proteins, we selected the human leukocyte membrane protein CD200 (OX2) and its cell surface receptor (hCD200R) as a model system. As antibody-antigen reactions are generally of higher affinity than receptor-ligand binding, we first analyzed the reactivity of monoclonal antibodies (mAb) to normal and mutant forms of immobilized CD200R.-1 corresponding to a limit of sensitivity of 0.01–0.05 femtomol per spot. Orientating hCD200R using capture antibodies, showed that DX147 reacts with an epitope spatially distinct from the more closely related DX136 and OX108 epitopes. A panel of soluble recombinant proteins with mutations in hCD200R domain 1 produced by transiently transfected cells, was arrayed directly without purification and screened for binding to the three mAb. Several showed decreased binding to the blocking mAb DX136 and OX108, suggesting close proximity of these epitopes to the CD200 binding site. Binding of hCD200 to directly immobilized rat, mouse, and hCD200R was achieved with multimeric ligands, in the form of biotinylated-hCD200 coupled to FITC-labelled avidin coated beads.Fluorescently labelled mAb DX147, DX136 and OX108 were specifically reactive with immobilized recombinant hCD200R extracellular region, over a range of 0.1–40 μg mlWe have achieved sensitive, specific and reproducible detection of immobilized CD200R with different antibodies and mapped antigenic epitopes for two mAb in the vicinity of the ligand binding site using protein microarrays. We also detected CD200 binding to its receptor, a low affinity interaction, using beads presenting multivalent ligands. Our results demonstrate the quantitative aspects of protein arrays and their potential use in detecting simultaneously multiple protein-protein interactions and in particular the weak interactions found between leukocyte membrane proteins. Given the complexity and the dynamic range of the proteome, estimated at 10aneously . The devaneously ,4 who praneously  than theaneously -8) due taneously ,10 or thaneously ,12 have D in the range 1–200 μM [D = 200 μM) [The study of leukocyte membrane protein interactions provides a particular need because of the large number of interactions yet to be defined ,14 and a1–200 μM ,16. Alth 200 μM) . The pro 200 μM) . Such do 200 μM) ,19. Whenin vivo ameliorates autoimmunity in disease models [In this study we chose a well characterized interaction between CD200  and its receptor CD200R (reviewed in ) as a moe models ,26.Protein arrays can be divided into two major classes: 'forward phase' if the analytes are captured from solution; or 'reverse phase' if the analytes are bound directly to the solid phase . In forwWe have used the forward phase approach in the mapping of antigenic epitopes of hCD200R where different antibodies were immobilized on epoxy coated glass slides, incubated with the hCD200R analyte and detected with fluorescently labelled anti-CD200R antibodies. We have applied reverse phase arrays to three different purposes: -to test the reactivity of the fluorescently labelled mAb with directly immobilized hCD200R protein, -to map epitopes located near the ligand binding site using arrayed mutant hCD200R recombinant proteins and detection with fluorescently labelled mAb that block ligand-receptor interactions and -to detect the low affinity binding of immobilized CD200R to the multivalent CD200 ligand presented on fluorescently labelled beads. Our study extends the use of protein microarrays to the detection of transient cell surface protein interactions, which are of lower affinity than the reported cytokine arrays ,30.-1 mAb, reaching the upper limits of detection under the optimized voltage settings  at 40 μg/ml  for DX136. A three-fold signal to noise ratio was achieved for DX147 at that concentration suggesting that sensitivity of detection was closer to 0.05 μg ml-1. For OX108, the limit of sensitivity was estimated at 0.3 μg ml-1. Thus DX147, DX136 and OX108 were able to detect 0.5 pg, 0.8 pg and 3.0 pg of hCD200R per spot respectively estimating a spot volume of 10 nl. The limit of sensitivity achieved was therefore between 8 and 50 attomol, assuming a molecular weight of 60,000 for hCD200R-CD4d3+4 protein. The amounts of human and mouse CD200R-CD4d3+4 and rCD4d3+4 protein on the microarray spots were similar as visualized by the red fluorescence of OX68 mAb recognising the CD4 tag present in each of the recombinant proteins   was dire/ml Fig. . Bindingins Fig.  and 1D. -1. Each capture mAb binds to a different epitope on the hCD200R-rCD4d3+4 recombinant protein, thus orientating the protein on the array in a conformation that permits or restricts access to the same panel of mAb, used as detection reagents , but not via OX108 or via itself. This is an indication that the binding epitopes for DX136 and DX147 are dissimilar. DX136 in turn, binds well to hCD200R-CD4d3+4 immobilized on OX68  and to a lesser degree to hCD200R-CD4d3+4 immobilized on DX147 , while not at all to hCD200R captured by OX108 (496 units). These data indicate that the DX136 epitope is spatially distinct from the DX147 epitope, but in close proximity to the OX108 epitope. This conclusion is substantiated by the lack of binding of OX108 mAb to hCD200R-CD4d3+4 captured on DX136. Orientating hCD200R via mAb OX68 allows for specific binding of all three anti-human CD200R mAb, in a linear fashion with limits of sensitivity of about 5 μg ml-1 of immobilized mAb. The maximum amount of signal was obtained by capturing hCD200R with 80 μg ml-1 of mAb OX68 and was equivalent to that observed by directly immobilizing approximately 10 μg ml-1 hCD200R. OX68 is therefore the best mAb for capturing the chimaeric hCD200R-CD4 protein for optimal detection of hCD200R epitopes.We used forward phase protein microarrays to define the epitopes of hCD200R recognized by the three mAb introduced in the previous section. Serial dilutions of DX147, DX136 and OX108 mAb and the control CD4 mAb OX68 were directly immobilized on epoxy-coated glass slides as shown Fig. . The mAbnts Fig. . Visual nts Fig.  and quannts Fig.  showed tnts Fig. . DX147 bBoth CD200R and its ligand, CD200 contain two extracellular IgSF domains. The ligand-receptor interaction is therefore likely to occur in an end-to-end topology, requiring opposing cell surfaces to come into close proximity . Previou-1. Lack of binding by a specific mutant or group of mutants is suggestive of the corresponding residues defining the location of the antigenic epitopes. None of the hCD200R mutants tested had lost binding to DX147, confirming unpublished data that this epitope lies, not in the N-terminal domain of CD200R but in the membrane proximal domain. Some of the mutants showed increased binding of mAb over the wild type despite these being normalized with OX68 . This presumably reflects variations in epitope availability due to direct immobilization of the mutant proteins on the array, which are less likely to occur in BIAcore studies where mutants were immobilized via OX68 mAb [The same panel of hCD200R-CD4d3+4 mutant proteins Table  was analOX68 mAb . Thus ouOX68 mAb . These mOX68 mAb . The finD of ~0.5 μM at 37°C and t1/2 of 7 s [E. coli BirA enzyme [-1 corresponding to 1–8 femtomol per spot on the microarray. The non-specific binding of hCD200-beads to immobilized rCD4 protein was negligible  DX147 (rat IgG1), and DX136 (rat IgG2a) to human CD200R were generously given by DNAX Research Institute . The mAb OX108 (mouse IgG1) to human CD200R  and OX68XbaI/SalI digestion to the pEF-BOS-CD4d3+4bio-XB vector [The soluble biotinylated forms of human, mouse and rat CD200R were produced as described ,31,36. BB vector .-1 BSA so after concentration the final protein concentration is around 10 mg/ml.Mutants of human CD200R (hCD200R) were prepared as described . Briefly-6 M. The degree of conjugation was estimated at 2–4 moles of dye per mole protein. Labelled antibodies were stable for up to 2 months at 4°C.Purified antibodies were dialysed against PBS prior to labelling with Alexa Fluor 555 or Alexa Fluor 647 fluorescent amine-reactive dyes using the Molecular Probes Monoclonal Antibody Labelling Kits (Cat. No. A-20186 and A-20187) and according to the manufacturer's instructions . Labelling reactions were carried out using 100 μg of IgG and yielded labelled proteins ranging in concentration from 1 to 4 × 103. A QArrayMini microarray printer (Genetix) was used to apply the protein solutions onto epoxy-coated microscope slides using 300 μm solid tipped tungsten microarraying pins (Genetix). Preliminary experiments established the printing conditions with fluorescently labeled OX68 mAb. Of several types of slides tested, the epoxy-coated ones were the best in terms of spot morphology, cost and reproducibility and were used in all subsequent experiments.Protein solutions to be arrayed were prepared in 96 well plates and 12 μl aliquots were transferred to single wells of Genetix 7020, 384-well plates . Concentrations tested ranged from 0–80 μg/ml and all dilutions were performed in Protein Array Spotting Solution (Genetix) with the addition of 0.5 mg/ml BSA and 0.02% NaN2O to remove excess salt and dried using an air brush, and stored at 4°C, with desiccant in a sealed slide box. Preliminary experiments were done by forward phase arrays to establish optimal conditions. OX68 mAb (100 μg ml-1 to 20 μg ml-1) was immobilized, incubated with rat CD4 at 5 μg ml-1 0.1 mg ml-1 BSA, washed and incubated with labeled W3/25 mAb (5 μg/ml) and linearity of detection demonstrated. Specificity was also shown by the fact that OX68 was not reactive with CD4 immobilised on OX68 and vice versa.Most array designs were performed using 8 pins to obtain spots with a 440 μm diameter and centre-to-centre spot spacing of 700 μm in both directions. Source plates were kept at 8°C, and a 65% average internal humidity was maintained. After printing, the slides were left in the arraying chamber for 30–60 minutes under the same conditions. The slides were then washed using the Protein Array Processing Kit  by inversion for 1 min in Clean Up Buffer (Genetix) to remove unbound proteins and incubation for 30 min in Blocking Buffer. Slides were washed 3 times in PBS, once in H-1) were added to measure the amount of specific antibody bound and Alexa-647-CD4 mAb (OX68 5–10 μg ml-1) was included to assess the amount of hybrid protein present. In experiments where capture antibodies were arrayed  binding to immobilized CD200R  scanner using 532 nm and 635 nm lasers using the GenePix Pro 5.0 (Axon Laboratories) software. The PMT values were 720 and 1000 (532 nm and 635 nm respectively) for Figure Slides were placed in hybridization chambers  and the humidification wells filled. LifterSlips,  were placed gently over the marked boundaries of the arrays and the binding reagent 25–70 μl) was introduced with a micropipette. In experiments where CD200R-CD4 hybrid proteins (including the mutant studies) were arrayed, Alexa-555 anti-CD200R antibodies  and ScanArray Express (Perkin Elmer). The background, calculated as the median of pixel intensities from the local area around each spot, was subtracted from the mean pixel intensity within each spot. To graphically represent the data, the values of the background-subtracted signal intensities were plotted against the known concentration of the protein spotted in the array. Sensitivity of detection for each spot was defined as a signal to noise ratio (S/N) of two-fold above background. S/N was calculated as: S/N =  / .In the case of the mutant hCD200R proteins generated from culture supernatants of transient transfections, where protein concentration is unknown, the background-subtracted values for both 532 and 635 nm-signal intensities were corrected for internal protein signal by subtracting the corresponding value of a "mock transfectant" spot. The corrected values for the red channel (representing the amount of protein assessed from the CD4 content) were normalised to 100% with respect to the wild-type hCD200R transient transfection sample. All hCD200R mutants with red channel values below 50% were assumed to contain insufficient amount of protein and were excluded from the analysis. The green channel background-subtracted, "mock"- transfectant corrected values (G) were divided by the corresponding red channel ones (R) to correct for variations in the amount of expressed protein contained in each individual spot (G/R ratio). Finally, the G/R ratio was normalized to 100% with respect to the hCD200R-CD4d3+4 wild-type protein, before graphical representation.IgSF, immunoglobulin superfamily; hCD200, human CD200; CD200-CD4d3+4, chimaeric recombinant CD200 protein with rat CD4 domains 3+4; hCD200R, human CD200 receptor; mCD200R, mouse CD200 receptor; rCD200R, rat CD200 receptor; SPR, surface plasmon resonance; WT, wild type.ML set up and developed the protein arrays with DV specialising in the experiments with the multimeric beads and data analysis. Both wrote the paper and DV prepared the figures. DH produced the mutant CD200R proteins and advised on their analysis. MF produced the mAb OX108 and the human, mouse and rat CD200R proteins. NS provided expertise in setting up the system for protein microarrays and their basic analysis. NB devised the initial project, advised throughout and helped with the manuscript writing.


Hand-carried ultrasound (HCU) devices have been demonstrated to improve the diagnosis of cardiac diseases over physical examination, and have the potential to broaden the versatility in ultrasound application. The role of these devices in the assessment of hospitalized patients is not completely established. In this study we sought to perform a direct comparison between bedside evaluation using HCU and comprehensive echocardiography (CE), in cardiology inpatient setting.We studied 44 consecutive patients  who underwent bedside echocardiography using HCU and CE. HCU was performed by a cardiologist with level-2 training in the performance and interpretation of echocardiography, using two-dimensional imaging, color Doppler, and simple calliper measurements. CE was performed by an experienced echocardiographer (level-3 training) and considered as the gold standard.There were no significant differences in cardiac chamber dimensions and left ventricular ejection fraction determined by the two techniques. The agreement between HCU and CE for the detection of segmental wall motion abnormalities was 83% (Kappa = 0.58). There was good agreement for detecting significant mitral valve regurgitation (Kappa = 0.85), aortic regurgitation (kappa = 0.89), and tricuspid regurgitation (Kappa = 0.74). A complete evaluation of patients with stenotic and prosthetic dysfunctional valves, as well as pulmonary hypertension, was not possible using HCU due to its technical limitations in determining hemodynamic parameters.Bedside evaluation using HCU is helpful for assessing cardiac chamber dimensions, left ventricular global and segmental function, and significant valvular regurgitation. However, it has limitations regarding hemodynamic assessment, an important issue in the cardiology inpatient setting. Bedside echocardiography can bring important anatomical and hemodynamic information for the management of critically ill patients, and is often required in hospitalized patients for the assessment of left ventricular function. Standard echocardiographic equipments, while optimal, have large size and sometimes are difficult to maneuver in the emergency room or intensive care units. Recently, hand-carried ultrasound (HCU) devices have been demonstrated to broaden the versatility in ultrasound application. Due to their portability and low cost, HCU acts like a stethoscope, providing information beyond physical examination at the point-of-care ,2. AlthoWe studied 44 consecutive hospitalized patients with cardiovascular disorders. Patients were included in the study when their referring physicians asked for a bedside evaluation with conventional echocardiography. In all patients, we performed the echocardiography with both HCU and CE within a maximal interval of 24 hours. The clinical characteristics of patient population are shown in Table All patients underwent two echocardiographic evaluations. First, HCU was performed with the portable device OptiGo  and, consecutively, by a commercially available system  equipped with a 4-2 MHz transducer and second-harmonic imaging. HCU was performed by one same cardiologist with level 2-training in the performance and interpretation of echocardiography according to the specifications of the American Society of Echocardiography [OptiGo is equipped with a 2.5 MHz phased-array transducer and operates on a rechargeable Lithium ion battery, which facilitates its use at bedside. HCU was performed using two-dimensional imaging, color Doppler flow mapping, and simple caliper measurements. Images were frozen and scrolled for review and the measurements were performed on-line.CE evaluation included two-dimensional with second-harmonic imaging, M-mode, and both spectral and color Doppler flow mapping. Images were recorded on videotape or digitalized. The aorta, left atrium, left ventricular end-diastolic and end-systolic diameters, as well as interventricular septal and posterior wall thickness were measured according to the recommendations of the American Society of Echocardiography . The lefValve structure and function were analyzed. Dysfunctions were classified into mild, moderate, or severe degree according to the qualitative evaluation by HCU, and using both qualitative and quantitative parameters by CE . A signiThe intraobserver variability of CE findings was assessed in 15 randomly assigned patients, with analysis made at least 4 weeks apart. The interobserver agreement between the experienced echocardiographer and the level 2-trained cardiologist was assessed by the analysis of 15 CE recorded examinations.t test. Chi-square and Fisher Exact tests were used for categorical variables. Agreement between HCU and CE results were assessed by the Kappa statistics. Interobserver and interobserver variability was determined by intra-class correlation and linear regression. A two-tailed p value <0.05 was considered significant.Continuous data are expressed as mean ± one standard deviation (SD) and categorical data as proportions. Comparisons between groups for continuous variables were made using Student Imaging analysis was feasible with HCU and CE in all patients. The percentage of patients with good, regular and bad image quality using CE were 80%, 18% and 2%, respectively. HCU had a lower percentage of patients with good image quality (59%), and a higher percentage of patients with regular (27%) and bad (14%) image quality (p < 0.05 versus CE), as shown in Figure There were no significant differences in cardiac chamber dimensions obtained by HCU and CE, except for the posterior wall thickness, which was lower when assessed by HCU Table . Among tThe analysis of segmental wall motion was not possible by HCU in two patients, due to poor endocardial border delineation, and was deemed feasible in all patients using CE. In the remaining 42 patients, HCU correctly identified eight of the 11 patients with WMA by CE, and failed to identify three of them. These three false-negative results occurred in patients with bad image quality by HCU. On the other hand, among the 31 patients without WMA by CE, HCU correctly identified 27 patients, and had four false-positive results. Among these four cases, three patients had global left ventricular dysfunction and one had asynchronic movement of the interventricular septum. The agreement between HCU and CE for the detection of WMA was 83% .CE identified nine patients with pericardial effusion, one patient with a moderate effusion and the remaining eight patients with mild effusions. HCU correctly detected and classified pericardial effusion in seven patients, and failed to diagnose two patients with mild effusions, both localized posterior to the left ventricle.Significant mitral valve regurgitation was diagnosed by CE in 16 patients, and by HCU in 13 of them. The agreement between HCU and CE for detection of this abnormality was 93% . Significant aortic valve regurgitation was detected in three patients by CE and in two by HCU, and the agreement between the two techniques was 98% . Significant tricuspid regurgitation was detected by CE in 16 patients and by HCU in 12 of them. However, there was one additional case misdiagnosed by HCU as of significant degree. The agreement between HCU and CE for the detection of significant tricuspid regurgitation was 88% .Four patients (9%) had aortic valve stenosis, two cases of moderate, one of mild, and the other one of severe degree. Qualitative analysis of valve structure by HCU was capable to identify aortic stenosis in two of these patients, but the severity of these lesions was not determined. There were six (14%) patients with prosthetic valves and eight (18%) patients with pulmonary hypertension, in which estimation of transvalvar gradients or valvular areas were performed only by CE. A complete evaluation of these patients was not possible using HCU due to its technical limitations in determining hemodynamic parameters.The intraobserver agreement for detection of pericardial effusion, WMA, and significant valvular dysfunction was 100%, and the interobserver agreement was 91%. There was an excellent correlation between LVEF estimated in the first and second evaluation by the same observer . The correlation between LVEF estimated by the experienced echocardiographer and the cardiologist with level 2-training in echocardiography was r = 0.88 (p < 0.05).The present study describes the value of bedside evaluation of cardiac patients using HCU in comparison to CE. Our results demonstrate that HCU may be used for the assessment of cardiac chamber dimensions, estimation of left ventricular function, and detection of WMA. Moreover, we found a good agreement between HCU and CE for detecting significant valvular regurgitation. However, HCU presents important limitations regarding the assessment of prosthetic and stenotic valves, as well as for the evaluation of patients with pulmonary hypertension, due to the lack of spectral Doppler.Bedside echocardiography is a frequently used diagnostic tool in the cardiology inpatient setting and can affect the patient management, direct further diagnostic work-up, and modify therapeutic decisions. The recent development of portable ultrasound devices has the potential to allow quick and easy-to-use echocardiography at the point-of-care, although its value in hospitalized patients was not completely defined. In patients with cardiovascular disorders, HCU has been shown to increase the diagnostic accuracy over physical examination when performed by cardiologists with level-2 training in echocardiography . In the However, when considering the full spectrum of abnormalities in hospitalized patients, previous reports demonstrated that hand-carried bedside echocardiography failed to quantify valvular regurgitation and also missed findings relevant to clinical questions in a significant number of patients. These studies concluded that HCU falls far short of standard echocardiography in evaluation of critically ill patients ,12. We dIn the present study, we did not evaluate the effect of HCU on patient management. Agreement between CE and HCU for detection of WMA was analyzed in 42 patients, since two patients were initially excluded because of inadequate acoustic window. The specific issue of agreement between HCU and CE according to image quality was not addressed in the present study because of the limited number of patients in each group.We concluded that HCU is useful for bedside assessment of left ventricular global and segmental left ventricular function as well as for evaluation of significant valvular regurgitation. However, it has limitations regarding hemodynamic assessment, which is an important issue in the cardiology inpatient setting.Therefore, we emphasize that bedside echocardiography using HCU should be performed by cardiologist with at least level 2-training in echocardiography, and always complemented with CE when hemodynamic evaluation is necessary.The authors declare that they have no competing interests.JMT and WMJ designed the study, did the selection and recruitment of the patients, and wrote the text. RRM and JMC performed the echocardiograms and participated in the subsequent analysis of data. JLA and JFR analyzed the statistics and revised this manuscript.


These methods differ in the choice of probe measure (quantification of probe hybridization), summarizing multiple probe intensities into a gene expression value, and analysis of differential gene expression. Research papers that describe these methods focus on performance, and how their approaches differ from others. To better understand the common features and differences between various methods, and to evaluate their impact on the results of gene expression analysis, we describe a class of models, referred to as generalized probe models (GPMs), which encompass various currently available methods.Various analytical methods exist that first quantify gene expression and then analyze differentially expressed genes from Affymetrix GeneChipon a genome-wide scale , different methods yield similar results if the same probe measures are chosen.Using an empirical dataset, we compared different formulations of GPMs, and GPMs with three other commonly used methods, i.e. MAS 5.0, dChip, and RMA. The comparison shows that, In this paper we present a general framework, i.e. GPMs, which encompasses various methods. GPMs permit the use of a wide range of probe measures and facilitate appropriate comparison between commonly used methods. We demonstrate that the dissimilar results stem primarily from different choice of probe measures, rather than other factors. Recognizing that non-specific hybridization could significantly alter the accurate quantification of transcript abundance, Affymetrix designs the array to contain two types of probes. Probes that are perfectly complementary to the target sequence, called Perfect Matches (PM), are intended to measure mainly specific hybridization. A second set of probes identical to PM except for a single nucleotide in the center of the probe sequence (the 13th nucleotide), called Mismatches (MM), are intended to quantify non-specific hybridization [Microarray experiments are routinely conducted to assess associations of experimental factors (or disease outcomes) with gene expression profiles. The Affymetrix GeneChip® expression analysis array data using different probe measures and summary schemes. Among them, Microarray Suite 5.0 (MAS 5.0) [A number of methods have been developed to quantify gene expression abundance from GeneChipMAS 5.0) , dChip [MAS 5.0)  and robuMAS 5.0)  are the Prior to MAS 5.0, the probe measure used in MAS 4.0 was PM-MM . Then, aLi and Wong modelled probe level data to generate model based expression index (MBEI) and implemented it in the dChip software . Noting rma) [rma.Another approach, RMA, available from Bioconductor , summarirma) . RMA est® gene expression data are complicated considering these three factors. Generalizing the various algorithms into one framework would facilitate comparisons.The gene expression obtained from either MAS 5.0 or dChip or RMA can then be used to associate the gene expression values with experimental factors using an algorithm of the users' choice. Three main factors affect the analytical results of differential gene expression analysis: the probe measure chosen, the algorithm used to summarize probe level data into gene expression , and the model used to associate gene expression with the experimental factors . Direct comparisons of the various approaches proposed for analyzing GeneChip® gene expression analysis array data as special cases. Using an empirical dataset, we assess the impact of different processes on the analytical results by comparing different formulations of GPM as well as GPMs with three other methods, MAS 5.0, dChip, and RMA.In this paper we propose a class of generalized probe models (GPMs) that includes various analytical approaches for GeneChip®. The dataset consists of ten samples from the ATRA treatment group and ten samples from the control group (ETOH treated) in four medulloblastoma cell lines. We are interested in identifying genes that are differentially expressed between the two treatment groups.We applied GPM to the analyses of data obtained from a study investigating gene response to ATRA  or drug diluent . Briefly, at twenty-four hours after treatment, total RNA was extracted from cells, processed and hybridized to the HuGeneFL GeneChiprma, and compared the performance of different methods using standardized coefficients, defined as the estimated coefficient divided by its standard error. The reason for using this index is that the standardized coefficients, usually known as Z-score test statistics, are independent of scale, and may be used to make statistical inference.We used three different probe measures: PM-IM, PM only and PMGPM-1 (2), GPM-2 (3) and GPM-3 (4) can be derived from the full GPM model (1) by making different statistical modeling assumptions. GPM-1 takes summarized gene expressions and associates them with experimental factors; GPM-2 and GPM-3 directly associate probe level data with experimental factors without first summarizing gene expressions (see Methods).rma in RMA. We found that all the methods were similar when the same probe measure was used, and the dissimilarity between the MAS 5.0 and other PM based approaches most likely stems from the different probe measure used. We first computed the gene expression using the software available for MAS 5.0, dChip (using the PM only option) and RMA, and then estimated the standardized coefficients for each gene with the association model GPM-1. We refer to these analytical options as MAS 5.0 PM-IM and hereafter , dChip PM and RMA PMrma, respectively. Figure rma. For each pair-wise comparison, we plotted the standardized coefficients for each pair in a XY plot. To assess the similarity between two methods, we computed the correlation coefficients (R) between the standardized coefficients generated from the two methods. In addition, we computed the mean squared error (MSE) between the two standardized coefficients, i.e. , where N is total number of genes, Zj1 and Zj2 are the standardized coefficients for j th gene, for two methods, respectively. When two methods are similar, the XY plot of their standardized coefficients will lie closely along the diagonal line. Correspondingly, the correlation coefficient will be closer to one and the MSE will be closer to zero. In Figure rma compared to dChip PM versus RMA PMrma.We compared GPMs with three commonly used methods, i.e. MAS 5.0, dChip, and RMA. Each of these methods dictates its own specific probe measure, i.e. PM-IM in MAS 5.0, PM-MM, or PM only in dChip and PMNext, using probe measures of PM only and PM-IM, we directly (in a single step) estimated standardized coefficients with GPM-2  and GPM-3, respectively. Figure rma. The six plots in Figure rma) probe measures are used in the analyses.We also compared the results from GPM-1 (2), GPM-2 3) and GPM-3 (4), to evaluate their differences, and found similar results when using the same probe measure. We selected the top eight candidate genes from the results of MAS 5.0 PM-IM and used them to compare the performance of GPM-2 PM-IM and GPM-3 PM-IM. In Table  and GPM-In summary, we conclude that the GPMs are similar to MAS 5.0, dChip and RMA on a genome-wide level when using the same probe measures and that the choice of probe measure may be more important than the summary algorithms to obtain the gene expression or models used to compute the coefficients.In this paper, we have described a general framework that can be used to compare various methods and evaluate their similarities and differences. We found that various methods tend to generate similar results, on a genome-wide scale, when the same probe measure is chosen, and probe measure seems to have greater impact on the analytical results than other factors.rma were similar to each other, but different from those obtained from MAS 5.0 PM-IM. Although dChip PM and RMA PMrma use different summary algorithms, their analytical results are similar due to the PM based probe measures used in both analyses.In Figure In Figure For the three variants of GPMs, we compared the standardized coefficients from GPM-2 or GPM-3 with those from GPM-1 using the gene expression values computed in MAS 5.0, dChip and RMA. From the high R values  in the six plots shown in Figure For seven of the eight candidate genes selected by GPM-1 using gene expression values generated by MAS 5.0, the gene-specific regression coefficients were similar among the MAS 5.0 PM-IM, GPM-2 PM-IM and GPM-3 PM-IM. This indicates that for these seven genes it makes little difference between using summary measures or modeling directly at the probe level data in GMP-2 or GPM-3, when the same probe measure is used.In addition to the three factors we mentioned  that have an impact on analytical results, data pre-processing/normalization could also affect the analytical results. Some researchers combine the probe measure and pre-processing normalization together. Normalization matters the most when the arrays in an experiment are not comparable to each other. In such cases, normalization process could significantly impact on the result. In our case, we normalized the data in the GPMs using a regression-based approach , either An important feature of the framework presented in this paper is that it accommodates various probe measures , the ith probe pair , PM (l = 1) or MM (l = 0), and the kth sample . Table yjilk , quantifying the abundance of the RNA hybridized on a probe, is treated as a random variable, influenced by the effects of probe-specific hybridization, gene-specific hybridization, non-specific hybridization and random noise. In this paper we use Zjik to denote the quantification of the signal of the ith probe, in the jth gene from the kth sample. Zjik could be based on any probe measure, such as PM only or PM-IM ' with covariates xk via the distribution function f ( | xk ). In essence, Zjik are treated as vectors of multivariate correlated outcome variables, and used to identify the probes/genes that are differentially expressed. Recognizing the high dimensionality of multiple probes and multiple genes, we propose to apply a marginal model that uses marginal means to describe relationship of probes/genes with the covariates without the necessity of specifying the full distribution f ( | xk ). Our framework directly associates experimental factors with probe intensities and is referred to as the generalized probe model or GPM. We propose the following (1) to describe relationship between  and xk ,In a typical experiment as described above, it is frequently of interest to discover genes that are significantly associated with one or more experimental covariates δk , λk ) are chip-specific heterogeneity factors for k th chip [τji are gene- and probe-specific parameters quantifying the mean intensity value for the ith probe of the jth gene, βji quantifies the gene- and probe-specific parameters quantifying the difference between treated and control groups, and vjik quantifies expression values for individual probe pairs. Lastly,  represents a vector of gene- and probe-specific random variations across K independent samples. Since probe pairs are selected to target the jth gene and are spatially arranged by a pre-selected design to eliminate common artifacts, they may be correlated because of cross-hybridizations or spatial dependencies. From the biological perspective, specifying a joint distribution for  would be difficult, if not impossible. It is thus preferable to leave it unspecified.where  simplifies to the following model:The above GPM (1) includes a range of more simplified models based on specific assumptions. First, under the assumption that all probe-specific parameters are the same, i.e., and is equivalent to using a summarized gene expression to associate with the experimental factors . For simβji = βj , with probe-specific values  may be written asIf one postulates that all probe-specific parameters are not the same, but follow an additive probe model, then general model (1) under modeling assumption that βj is of primary interest. This variation of the general model is referred to as GPM-2.in which estimating τji ≈ φji τj and βji ≈ φji βj , and may be written asOn the other hand, the probe parameters may follow a multiplicative model (in the spirit of Li and Wong's model), then the third model, referred to as GPM-3, is derived under the assumption that φji denote the multiplicative probe-specific effects and can be uniquely determined by constraining the mean to be one.where  for the estimated parameters  in the model. Both estimates can be used to construct test statistics, such as the ratio of  over , known as Wald-statistic. Under the null hypothesis, each statistic has an asymptotic normal distribution when the sample size is sufficiently large, and can therefore be used for making statistical inferences. When the sample size is small, this quantity is treated as a standardized regression coefficient.Our estimation procedures do not require any assumptions with respect to the error distribution, since any distributional assumptions, which may be appropriate for some genes, are likely to be violated for other genes. To ensure the robustness of statistical inference, we propose to use generalized estimating equation theory, which has been fully described in a seminal paper by . In the ATRA: All Trans Retinoic AcidETOH: Ethyl AlcoholGPM: Generalized Probe ModelIM: Ideal MismatchMAS: Microarray SuiteMM: MismatchORF: Open Reading FramePM: Perfect MatchRMA: Robust Multiple Array averageWF carried out analyses and prepared the manuscript. JIP conducted microarray experiments. JMO conceived the study. NK: prepared the manuscript. LPZ: conceived the study, developed the GPM algorithms and prepared the manuscript.


Walking along disease-associated protein sequences in the search for specific segments able to induce cellular immune response may direct clinical research towards effective peptide-based vaccines. To this aim, we are studying the targets of the immune response in autoimmune diseases by applying the principle of non-self-discrimination as a driving concept in the identification of the autoimmunogenic peptide sequences.Computer-assisted proteomic analysis of the autoantigen protein sequence and dot-blot/NMR immunoassays are applied to the prediction and subsequent validation of the epitopic sequences.Pemphigus vulgaris/desmoglein 3, we have identified the antigenic linear determinant recognized by MAb 5H10, a monoclonal antibody raised against the extracellular domain of human desmoglein-3. The computer-assisted search for the Dsg3 epitope was conducted by analyzing the similarity level to the mouse proteome of the human desmoglein protein sequence. Dot-blot immunoassay analyses mapped the epitope within the sequence Dsg349–60 REWVKFAKPCRE, which shows low similarity to the mouse proteome. NMR spectroscopy analyses confirmed the specificity of MAb 5H10 for the predicted epitope.Using the experimental model This report promotes the concept that low level of sequence similarity to the host's proteome may modulate peptide epitopicity. In the last decades, several computer-driven algorithms have been devised to take advantage of the linear representation of protein sequence information to search for epitopic motifs -5. Thesein silico technology platforms to identify epitopic peptide sequence(s) from disease-associated-antigens by following the hypothesis that peptide epitopicity might be regulated by the peptide similarity level to the host's proteome, in addition to other factors such as MHC binding potential [We are using otential -10. As aotential ,9,12.Pemphigus vulgaris (PV). PV is an autoimmune bullous skin disease characterized by autoantibodies to a desmosomal adhesion molecule, the cadherin desmoglein-3 (Dsg3) [In this context we have tested here the possibility that endogenous, normal, housekeeping self-proteins harbouring sequences with little or no similarity to the collective host proteome might be epitopic targets in autoimmune responses. Self-reactivity and autoimmunity are processes related to the breakage of self-tolerance that can be distinguished by their different clinical outcome. The transition from self-reactivity to the autoimmune pathology appears to be mediated by a complex network of overlapping phenomena that comprehend epitope spreading, uncovering of cryptic epitopes, natural autoantibodies production, cross-reactivity, microchimerism, altered B lymphocyte function, inflammation, etc. . In our 3 (Dsg3) . Dsg3 re3 (Dsg3) -18 and t3 (Dsg3) -23, it s3 (Dsg3) . Given t[The amino acid sequence corresponding to accession number P32926, SWISS-PROT, was used in the similarity analysis of the extracellular domain of human Dsg3  to the mouse proteome. The Dsg3 sequence under analysis was dissected into pentamer motifs, that were probed for sequence similarity to mouse proteome by using PIR non-redundant reference protein database and peptide match program . The seaAnti-Dsg3 MAb 5H10 and 5G11, raised in BALB/c mice, were a generous gift of Dr. Margaret Wheelock, University of Toledo, Ohio. Both MAbs recognize a linear determinant and have been described in detail . Briefly36–45EEMTMQQAKR, Dsg349–60REWVKFAKPCRE, and Dsg3173–187 NSLVMILNATDADEP peptides were obtained from PeptidoGenic Research & Co., Livermore, CA., and used in dot-blot immunoassay experiments. Human recombinant Dsg3 protein was used as further control [15N-labelled synthetic peptide Dsg349–60REWVKFAKPCRE (where 15N-labelled amino acid residues are underlined) was from Primm srl, Milan, Italy. Peptides were dissolved in 0.9% NaCl, aliquoted and stored at -20°C.Peptides were synthesized using standard Fmoc (N-(9-fluorenyl)methoxycarbonyl) solid phase peptide synthesis. Peptide purity (>90%) was controlled by analytical HPLC, and the molecular mass of purified peptides confirmed by fast atomic bombardment mass spectrometry. The EC1/EC2 Dsg3 control . The 15NNitrocellulose membrane  was pretreated for 10 min with 1.0% glutaraldehyde. Synthetic peptides were spotted on the activated membranes, left to dry at RT and exposed to UV rays for 10 min. Membranes were incubated for 1 h in phosphate-buffered saline/0.05% (v/v) Tween 20 (PBST) containing 4% bovine serum albumin (BSA), and then with the primary MAb (1:800) for further 2 h. Membranes were washed for 10 min with PBST (x3) and incubated in PBST/4% BSA with HRP-conjugated affinity-purified goat anti-mouse IgG (1:1000) for 1 h. Membranes were washed in PBST for 5 min (x3), in PBS for 5 min (x3) and immunoblots were developed using the enhanced chemiluminescence detection assay  following supplier's instructions.15N-labelled peptide Dsg349–60REWVKFAKPCRE (where 15N-labelled amino acid residues are underlined) and MAb 5H10 (raised against EC1/EC2 from Dsg3) or 5G11 (raised against EC4/EC5 from Dsg3) were recorded at 298°K on a Bruker Avance DRX500WB spectrometer. The spectra were acquired by heteronuclear single quantum correlation (HSQC) experiments that correlate the chemical shift of proton with the chemical shift of the directly bonded nitrogen. Specifically, the two-dimensional 1H-15N inverse detected correlation spectra were acquired by the gradient pulse sensitivity improved Bruker automation program INVIETGPSI [1H acquisition dimension was collected with a spectral width of 5 ppm, centered at 7.6 ppm, using 4096 datapoints for each of the total 32 scans/expt. Spectral width in the indirect detected 15N dimension was 200 ppm, centered at 90 ppm, obtained with a total of 1024 points. The spectra were processed by XwinNMR program, version 2.6, running on an INDY R5000 Silicon Graphics Workstation. Chemical shift values were referenced to sodium tetrasilyl propionate (TSP)  and external neat nitromethane  standards. To avoid interferences of TSP standard with peptide samples, the instrument scale was preliminarily calibrated in parallel experiments with samples containing the peptide to be analysed plus 0.2 mg TSP. We used chemical shift statistics from the full BioMagResBank database, where the calculated statistics are derived from a total of 559392 chemical shifts (website: ). Sequence-specific correction factor tabulations were applied to backbone 1H and 15N resonances [REWVKFAKPCRE peptide, or 20 mg MAb 5H10 complexed with 0.1 mg REWVKFAKPCRE peptide, or 20 mg MAb 5G11 complexed with 0.1 mg REWVKFAKPCRE peptide, in 0.5 ml aqueous solution H2O/D2O .NMR spectra of the reaction between the synthetic VIETGPSI . The 1H 49–60REWVKFAKPCRE sequence was the longest (12 amino acid residues long) with the lowest number of matches to the murine proteome (number of matches to murine proteome = 9). The Dsg349–60REWVKFAKPCRE sequence and a second low-similarity fragment, Dsg336–45EEMTMQQAKR (number of matches to murine proteome = 12), were selected to test our hypothesis.The EC1/EC2 domain (aa1-212) of human Dsg3 was dissected into 5-mer sequences and analyzed for similarity to mouse proteins. The pentamers used for epitope scanning were offset by one residue, i.e. overlapped by 4 residues: MMGLF, MGLFP, GLFPR, LFPRT, etc. The 5-mer sequences were probed in computer-assisted similarity analyses against the complete mouse genome sequences. Fig. 173–187NSLVMILNATDADEP peptide (matches to murine proteome = 109) was available and served as a high similarity peptide control. The three synthetic peptides are described in Fig. 49–60REWVKFAKPCRE sequence having a lowest level of similarity to the mouse proteome  defining structure and conformation; 2) defining structure-activity relationships; 3) monitoring chemical reactions. Informations are mainly derived by measuring NMR chemical shifts. The chemical shift of a nucleus is the difference between the resonance frequency of the nucleus and a standard. This quantity is reported in parts per million (ppm). The NMR standards are molecules as tetrasilyl propionate, the signal of which is set at 0 ppm by having shielded protons. The NMR chemical shift allows for distinguishing magnetically inequivalent nuclei in a molecule, i.e. chemical shifts are a measure of the motional freedom.As a further step, we carried out a parallel experimental confirmation because of the doubts of false negative/positive data intrinsic to immunoassay. To this aim, the binding of the predicted epitopic Dsg315N-labelled REWVKFAKPCRE peptide(where 15N-labelled amino acid residues are underlined) was synthesized. Theoretical chemical shift values of the 15N-labelled residues in the Dsg349–60 REWVKFAKPCRE peptide were calculated as described under Methods, and then were compared to the experimental values. Table 15N-labelled residues in the Dsg349–60 REWVKFAKPCRE peptide, and their values following addition of MAb 5H10 or MAb 5G11.We utilized NMR spectroscopy in order to determine whether the predicted peptide specifically binds to the MAb 5H10. To this aim, a 15N-labelled Dsg349–60 REWVKFAKPCRE peptide are illustrated in Fig. 15N one-bond coupling of the labelled amino acid residues in the peptide. In Fig. 1H-15N correlation signals of the free peptide in aqueous solution. As already illustrated in Table ) following sequence-dependent correction of random coil NMR chemical shifts. [ residues. The lower panel in fig. 15N-Arg-11 whereas the 15N-Arg-1 was undetectable because of the terminal position in the peptide. Fig. 15N-labelled residue spectral resonance signals thus indicating a complete loss of Dsg349–60REWVKFAKPCRE peptide mobility. In contrast to the total signal deletion provoked by MAb 5H10, the chemical shift values of the Dsg349–60 REWVKFAKPCRE peptide remained unaltered following the addition of the anti-Dsg3 MAb 5G11 relevant epitopes within potential autoantigens with clinical application to wide ranging human diseases where the precise targets of self-directed attack are unknown [In a wider context, the epitope mapping approach here described might be helpful in defining peptide antigenicity in a wide spectrum of human diseases including cancer pathologies as well as a range of diverse autoimmune disorders such as insulin-dependent diabetes mellitus, multiple sclerosis, rheumatoid arthritis, and  unknown .Pemphigus vulgaris autoantigen; PIR, Protein Information Resource; HSQC, heteronuclear single quantum correlation.Dsg3, desmoglein 3; EC, extracellular; PVA,


Current protein clustering methods rely on either sequence or functional similarities between proteins, thereby limiting inferences to one of these areas.Here we report a new approach, named CLAN, which clusters proteins according to both annotation and sequence similarity. This approach is extremely fast, clustering the complete SwissProt database within minutes. It is also accurate, recovering consistent protein families agreeing on average in more than 97% with sequence-based protein families from Pfam. Discrepancies between sequence- and annotation-based clusters were scrutinized and the reasons reported. We demonstrate examples for each of these cases, and thoroughly discuss an example of a propagated error in SwissProt: a vacuolar ATPase subunit M9.2 erroneously annotated as vacuolar ATP synthase subunit H. CLAN algorithm is available from the authors and the CLAN database is accessible at CLAN creates refined function-and-sequence specific protein families that can be used for identification and annotation of unknown family members. It also allows easy identification of erroneous annotations by spotting inconsistencies between similarities on annotation and sequence levels. To achieve high quality of annotation, curators are using direct evidence from additional experiments or infer functional roles by sequence similarity to experimentally characterized genes or proteins. However, erroneous annotations generated at early stages may propagate to new homologous sequences, ultimately leading to erroneous annotation of entire families. The number of database errors is known to grow with time, as the number of entries and errors tend to accumulate. A recent analysis suggested that as databases grow annotation errors may propagate at an exponential rate .One of the most accurate and consistently annotated databases is SwissProt  – a manuThis impressive collection of textual information provides excellent opportunities for natural language processing in computational biology. Text mining of the free text in biomedical literature is well established  function, according to the functional descriptions of the corresponding database entries. To group pairwise hits between these proteins, clustering of connected components was used, and sequence similarity within the clusters was considered. Using CLAN, an exemplar error was identified in the annotation of the M9.2 (M9.7) subunit of a vacuolar H+ ATPase (V-ATPase).-2 and 10-6. Depending on the threshold, the output contained between 2,070,221 (for e ≤ 10-6) and 3,980,763 (e ≤ 10-2) pairwise annotation similarities. The output of CLAN was subjected to clustering of connected components, resulting in 43,416 (e ≤ 10-2) up to 47,344 (e ≤ 10-4) clusters, including singletons. When a lower score threshold was used, the number of clusters in the output decreased, as more proteins were excluded from any clusters (see below).We used CLAN [see Methods] to cluster SwissProt entries based on annotation strings. We experimented with score cut-offs [see Figure A sample of few hundred of the annotation clusters was examined manually to assess whether they contained proteins annotated as functionally unrelated. At the low score cut-offs, we failed to find any such examples, thus suggesting a high degree of specificity. Also, hypothetical proteins were correctly not grouped together, which reflects their singularity in the protein function space. However, at these cut-off values, proteins annotated with a single word (i.e. Plastocyanin) failed to pass the threshold of meaningful annotation. This effect is due to the fact that no single term has a sufficiently low frequency to provide an score below the threshold. In total, 29,845 entries were not even defined as singletons  as their annotation strings are non-descriptive, resulting in high scores above the threshold.-2, the biggest cluster contains proteins annotated as "Hypothetical X kDa protein in Y intergenic region", where X is the calculated protein molecular weight and Y is the genomic location. These proteins are most probably unrelated, perform different functions or may not be expressed at all, and thus should not be clustered together. In fact, this cluster is eliminated at score cut-off of 10-3.On the other hand, at the highest score threshold the method succeeds in clustering together many single-word annotations. However, this increase in sensitivity is counter-balanced by a decrease in specificity. At the score of 10e ≤ 10-2) to the Pfam-A database [To estimate how well annotation clusters correspond to the sequence-based protein families, we compared the CLAN clusters  family from Pfam is divided by CLAN to distinct annotation families, thus providing a finer distinction between family members that can be used for more accurate annotation of functional specificity [As many of the CLAN clusters which correspond to several Pfam families contain multi-subunit complexes or multidomain proteins, a rigorous domain detection procedure is expected to drastically improve this result. For the results of the combination of CLAN annotation clusters with sequence-based clustering, see the section describing the CLAN database.Though SwissProt annotation is not independent from Pfam assignments and often depend on them, a few clusters were found that did not correspond to Pfam families. Thus, CLAN system might facilitate identification and rapid incorporation of new families to curated protein family databases such as Pfam.We aimed to determine whether similarity of the annotation corresponds to the sequence similarity of the proteins. For this, we compared protein sequences within annotation clusters using BlastP program . OverallAnalysis of sequence similarity within annotation-based families identified the following exceptions to the consistent similarity patterns between all family members.1. Fragments of proteins. The fragments often fail to produce significant Blast scores, thus appearing as singletons on the similarity graph.2. Taxa – specific forms of enzymes that form separate protein families, such as Bacterial and Archaeal adenylate kinases, forming distinctive sequence-based families.3. Non-orthologous gene displacements, such as malate dehydrogenases.4. Protein complexes. Annotation – derived families often included multiple chains/subunits of protein complexes, such as Glutamyl-tRNA(Gln) amidotransferase subunits A, C, D and E.5. False positives of CLAN. The only case found so far involves the 'hypothetical protein' cluster, appearing at the permissive score cut-off 0.01 (see previous section).6. SwissProt annotation errors.Vibrio species documented in SwissProt (LUXE_VIBHA) contains 378 residues. There are at least 71 entries annotated as "fragment", whose sequences are less then 10 residues long. As their sequence often contains a subset from full-length close homologs, the information from these fragments is often redundant. Also, these short fragments fail to produce significant scores in any sequence similarity search procedure, and thus are usually irrelevant for sequence search engines. Moreover, fragments often create noise in sequence alignments and HMM analyses creating pseudo-conserved domains, and pseudo-gaps. In summary, we question the value of the presence of short protein fragments in a manually curated database, aiming for maximum precision rather then maximum coverage.SwissProt contains 7,232 entries annotated as "fragment". These are normally short fragments of larger protein molecules. An extreme example is LUXE_VIBFI (3 residues only), a fragment of Long-chain-fatty-acid-luciferin-component ligase. The complete sequence from a related In some taxa, proteins performing identical functions might diverge to a point where they are defined as separate sequence-based families, sharing marginal similarity. One such example is the bacterial and archaeal adenylate kinases. Though similar at the 3-dimensional level , the seqEnzymes belonging to different structural families may perform identical cellular function. In this case, annotation does not provide enough information about the type of the family a particular enzyme belongs to. One such case is represented by glucokinases, which belong to different families but perform identical function. For example, GNTK_BACSU and GNTK_ECOLI are both glucokinases and are annotated as such, were thus clustered together by CLAN but are unrelated to each other .Subunits in protein complexes often have almost identical annotation. For example, Glutamyl-tRNA(Gln) amidotransferase subunits A, C, D, and E differ only in the chain identifier. The annotation of the subunits is somewhat misleading, as subunits A, B and C form one form of a rather ubiquitous complex , and subFinally, CLAN can serve as a powerful tool to identify annotation errors. Overall, we found SwissProt annotations very consistent and robust. Most of the annotation-based sets resulted in tight sequence-based similarity clusters. A few cases where no similarity was found between a protein and the rest of the cluster were normally due to the limits in sensitivity by BlastP, and the homology was clear when multiple alignments, profile- or HMM-based search methods were used. Nevertheless, there are cases where annotation is problematic. Below, we discuss in detail an example of inconsistency found in the annotation of a cluster containing vacuolar H-ATPase.-6, we found an interesting example of a propagated error. The protein VAOH_CAEEL (Q20591) from Caenorhabditis elegans has been annotated as "Probable vacuolar ATP synthase subunit H (EC 3.6.3.14) (V-ATPase H subunit) (Vacuolar proton pump H subunit)". This protein is clustered by CLAN with other vacuolar ATP synthase subunits. However, BlastP alignment failed to produce evidence of similarity between this protein and other members of the cluster annotated as vacuolar ATP synthase subunit H. Also, this protein is much shorter then other members of the annotation cluster, comprising only of 96 residues, while other members of the cluster have more then 400 residues. In fact, C. elegans was reported to contain another two V-ATPase H subunits, with length of 451 and 470 residues that share substantial sequence similarity with other members of the family. Multiple alignments of the family suggested that VA0H_CAEEL is a distant protein not related to the rest of the family. The SwissProt entry does not contain a link to a published work. A PSI-Blast search against the NCBI NR database allowed to identify other members of the family. The family was first described in the literature as "M9.2 V-ATPase subunit" in bovine [-6, which was unified with other members of "vacuolar ATP synthase subunit" cluster at lower score cut-offs.When analyzing the output of CLAN with score threshold 10n bovine . InteresC. elegans [Mesembryanthemum crystallinum (gi 26986112) [Arabidopsis thaliana, Anopheles gambiae and a few paralogs in Drosophila melanogaster. The multiple alignment of these proteins is provided as supplementary data.The M9.2 V-ATPase subunits were identified in mammals ,12, inse elegans . The alt elegans ,15 and A6986112) . Using PM. sexta protein indicates that the C-terminus is exposed to the extracellular surface, whereas the C-terminus of Vma21p appears to be localized on the cytosolic side of the membrane [The M9.2 protein was suggested to be a homolog  of yeastmembrane . To idenmembrane , and promembrane  search mThe error in the annotation of M9.2 subunit of V-ATPase was reported to the SwissProt developers, and is likely to be corrected at the time of publication of this work.Another form of annotation error results in a false negative case for CLAN. It occurs when a protein clearly belongs to a certain family but is not annotated as such. This event can be detected when several CLAN families map to a single sequence similarity-based family. One example is SUCD_BACSU, annotated as " Succinyl-CoA synthetase alpha chain (EC 6.2.1.5) ". This protein belongs to the SUCD family of proteins, and should cluster with its other members. However, its annotation contains synonyms " (Vegetative protein 239) (VEG239)", which makes this protein virtually unique in annotation space. Another well-known example is the initial characterization of some general stress proteins in bacteria, later shown to be ribosomal proteins   – the in-2. The CLAN database contains two levels of clustering. The first level contains clusters based on comparison of protein annotations, as described in the Methods section. To ensure completeness and reliability of the results, any protein annotated as 'fragment' was excluded from further processing. The second level in the database is generated by applying sequence-similarity-based grouping to each of the clusters obtained in the first stage. All members within annotation clusters are aligned using BlastP. The output of BlastP is subjected to various clustering procedures. In order to distinguish between the annotation-based clusters of the first database level, and the sequence-based sub-clusters of the second level, we call these 'clusters' and 'sub-clusters' accordingly. When referring to both these categories simultaneously, we use the notation (sub)clusters.Based on the results described above, we designed a database using the CLAN output with score threshold of 10To estimate the contribution from the second level of clustering, we repeated the comparison to Pfam database, using sub-clusters instead of clusters. We found that on average 97% of CLAN sub-clusters members belong to a single Pfam family, (compared to the 91% for clusters). The gain in selectivity was accompanied by gain in sensitivity, and 66% of Pfam family members corresponded to a single sub-cluster (compared to 51% for clusters). Compared to annotation-based clustering alone, the dramatic increase in the sensitivity reflects the fact that in the second level of clustering, non-homologous sequences are removed and form separate sub-clusters.. To facilitate database navigation, we introduced (sub)cluster names, computed as a minimal consensus string for all (sub)cluster members. The MySQL-based storage of data allows fast searches based on sequence or cluster name, SwissProt accession or cluster identifier. The sequence similarity within clusters can be examined visually using the BioLayout software [The CLAN database has a web-based interactive interface, located at software . To facisoftware , and theWe have presented CLAN, a rapid and powerful method to build consistent annotation-based families, and assessed the consistency of annotation in protein databases. We have also presented the CLAN database, as a collection of protein families grouped by function. We believe that these tools can be used to improve the quality of database curation and assist annotation of newly sequenced proteins.The double-step clustering presented here has resulted in the creation of a reliable database of protein families, with include proteins similar on both annotation and sequence levels. One of the potential uses of this database is an accurate assignment of function to newly sequenced proteins by protein – to – profile alignments or HMMs. Our method has the potential to distinguish between subtle changes that might reflect diversification of protein function. For example, homologous Malate and Lactate dehydrogenases are found in different (sub)clusters, allowing their characterization as separate protein families. As most protein family databases group proteins by homology, as opposed to function, to the best of our knowledge, our approach is unique in its ability to build protein sequence families distinguishing between functions. In the future, other such comparisons could include other structural classifications such as CATH  or TRIBEWe used SwissProt release of Feb 13, 2003 that contained 121,744 protein sequences. We aimed to find the similarity between the database entries solely based on the annotation and without any reference to sequence similarity. The annotation corpus considered here consisted of description line (protein name) and synonyms from the SwissProt database. We interchangeably use the term 'function' or 'annotation' to denote the contents of this corpus, implying the use of the corresponding annotation strings from the database.φ of each word as the number of database entries containing the word N divided by the total number of entries in the database D (Equation 1). The word frequencies served as weights, because the words describing specific functions (such as "plastocyanin") occur less frequently than non-specific words (such as "protein").To obtain a distance measure, we first calculated the frequency p between protein functions may be computed as the product of frequencies of n terms shared between the two annotations (Equation 2).We aimed to devise a scoring scheme that would produce highly significant scores for proteins with related function, while being able to distinguish between proteins with different functions. The score for the common terms However, sharing only part of the total number of significant words is insufficient: proteins may use similar substrates for different reactions, or perform similar chemical reactions on different substrates. There may be three proteins with annotations AB, BC, and CD respectively, and clustering by similarity only could lead to clustering together AB and CD. For example, "lactate dehydrogenase" should be differentiated from "lactate permease" or "alcohol dehydrogenase".q is the product of frequencies of m terms unique to any of the two annotations (Equation 3).To achieve this distinction, we computed the difference between the two annotations, as above, for the case of the similarity measure. The score for the unique terms e between the two annotations is defined as the fraction between the score for common terms p and the score for unique terms q for the two annotation strings (Equation 4). We refer to this scoring function as 'score' throughout the manuscript.Finally, the distance An example of calculation of the score is shown on Figure The procedure calculating pairwise distances between protein annotations described above was implemented in a perl program. Its sole input consists of SwissProt identifiers and annotation lines, and the output contains pairs of proteins with the score of annotation similarity lower than a given threshold. Compared to sequence-similarity clustering, this procedure is very rapid: an all-against-all comparison of SwissProt database containing 121,744 entries took about 26 minutes on a single processor of a Sun-Fire-480R server with 2 GB of RAM.et al., submitted). To evaluate the ability of CLAN to detect genuine protein families on the basis of annotation alone, the output (e ≤ 10-2) was compared to the Pfam-A database release 9.0 [The result of the pairwise comparisons between protein annotations were then subjected to clustering of connected components. The protein sequences of the clusters obtained were subsequently analyzed with the BlastP program  and the ease 9.0 . For eac


In this paper, we present and validate a way to measure automatically the extent of cell migration based on automated examination of a series of digital photographs. It was designed specifically to identify the impact of Second Hand Smoke (SHS) on endothelial cell migration but has broader applications. The analysis has two stages: (1) preprocessing of image texture, and (2) migration analysis.The output is a graphic overlay that indicates the front lines of cell migration superimposed on each original image, with automated reporting of the distance traversed vs. time. Expert preference compares to manual placement of leading edge shows complete equivalence of automated vs. manual leading edge definition for cell migration measurement.Our method is indistinguishable from careful manual determinations of cell front lines, with the advantages of full automation, objectivity, and speed. The images we deal with are images of endothelial cells. Endothelial cells migrate to sites of injury in the body. They are involved in forming new blood vessels to help repair damaged areas ,2.In particular, we are interested in the effects of SHS on endothelial migration. By comparing automated migration analysis with varied exposure to SHS, for cells with and without specific genes, we can examine why exposure to SHS impairs endothelial cell migration and explore possible cures ,4.Cell migration is a basic biologic function that can be modified by changes in genetic code and in response to chemical and other stimuli. Upon 24 hours serum starvation, the cells were artificially wounded using P20 pipette tip across the plate, then cultured respectively in regular DMEM or DMEM containing Second Hand Smoke (SHS) (unpublished). The subsequent gaps were imaged at 0 and 6 hours post SHS exposure as previously described to determine the rate of migration of the front lines to close in the gap -8,23. FiThe automated borders are compared blindly by a team of domain experts to manual borders created by a technician to assess accuracy. Results are also evaluated blind to biologic significance to determine concordance and power to demonstrated biological effects.Biologists deal with this by making multiple manual measurements, to report an average. Observers have difficulty deciding where and how many times to measure the width. Besides, there are many pairs of images to be processed. Therefore, automatic measurement is desired.The primary difference between cell-populated areas and the clear lane is texture. The cell-populated areas are speckled with cells, the clear lane is not.In order to capture the cellularity characteristic of the source images, we sought to compute a texture index that would emphasize the cellular attribute of the region of interest and also minimize the influence of non-cellular signal variations -14. BecaFor each point in the original imageSearch for darker point in this line verticallyIf findSet the distance between start and darker point as the gray value of the corresponding point in the texture mapSearch for continual darker points and set the distance as the value of them Scale the value to 0–255:where pv is the new pixel intensity value, cv is the distance value of the corresponding point and max and min are the minimum and maximum distance value.Panel b of figure Based on the texture map, the region we are interested in appears as a white vertical band. Thus the second stage of analysis must determine the position and width of this lane. As the information in each vertical column is equivalent to repeated measures, we can combine the data to marginal projection. From the histogram of this we can compute a classifier for lane vs. cells and determine the half-height width. The locations are then mapped to a graphic overlay on the original image to demarcate the front lines of cell migration. The change in distance between the front lines reports the amount of (bi-front) cell migration.n] where n is the width of the image. The value of each P [i] is the intensity sum of all the pixels in i column.1. Project the texture values to a marginal profile "cellularity index profile" Figure  which isi].2. Compute the discriminant classifier (DC) which is average value of P [i] < DC and P [i+1] > P [i], i is the leading edge. Conversely, if P [i] > DC and P [i+1] < DC, i is the trailing edge. Then a few pairs of leading and trailing edges could be obtained. The target pair is identified based on the width, P [i] values between leading and trailing edges and "clear lane" location in the time neighboring image.3. Locate the leading and trailing edges based on classifier crossing. If P [4. Record locations and generate graphic overlay for original image.Since the manual assessment is the research gold standard for image processing -18, a teAfter analysis, results like the ones shown in the Figure The results of domain expert preference by quality for automated vs. manual assignation of migration front lines, evaluated blinded to method, randomized, and subsequently decoded. Overall, there is complete equivalence of automated vs. manual with respect to expert preference for quality. The values ranged 2–4. In no cases was manual strongly preferred over automatic. Preference testing of analysis methods showed near equivalence, favoring preference for the automated borders (3.02 ± 0.11). Agreement between observers in preference was examined for two domain experts, revealing good agreement . Agreement in preferences by a technologist without domain expertise was lower  but supported the same conclusion: the automated analysis is at least as good as manual selection by domain experts.Application of this method to determine the effect of SHS on endothelial cell migration demonstrates that SHS can reduce the cell migration rate, which is statistically significant Figure .Our migration analysis is based on the texture index of the images. This index should reflect the attribute of the images. Since no global thresholding technique could be used in our images, the segmentation of regions and boundaries (edges) have to consider the local property . BecauseOur results show a robust automatic method with no detected errors. This study is a pilot study demonstrating feasibility and biologic significance in real application. Further collective experience in multi-center applications are needed to establish the full utility of the method.In addition, the program runs on the software platform, ImageJ  and the We describe a novel method of cell migration analysis based on texture pre-processing and discriminant analysis. Domain expert preference testing demonstrates that this automated method compares favorably to the much more painstaking manual method.The further study is to apply this to evaluation of the impact of SHS on endothelial cell migration. For that purpose, we have constructed a SHS capture system in which we bubble the SHS through tissue culture medium to assess its impact on cell migration. Our results indicate that this analysis system is very sensitive to biological effects, documenting that SHS impairs cell migration -22.Project name: Cell migration measurement projectProject home page: Operating system(s): Platform independentProgramming language: JavaOther requirements: Java 1.3.1 or higher, ImageJLicense: NullAny restrictions to use by non-academics: Licence neededJDP proposed and designed the method to evaluate cell migration objectively and automatically. JQ implemented the method as a plugin of ImageJ and validated the method. TWC performed the cell experiments and captured the images. LG performed manual edge definition and subsequently performed statistic analysis of expert preferences. All authors read and approved the final manuscript.


Supervisors are often involved in the assessment of projects they have supervised themselves. Previous research suggests that detailed marking sheets may alleviate leniency and halo effects. We set out to determine if, despite using such a marking schedule, leniency and halo effects were evident in the supervisors' marking of undergraduate short research projects ).th year medical students who had participated in an SSM during two full academic years (n = 399). Paired t-tests were used to compare mean marks, Pearson correlation to look at agreement between marks and multiple linear regression to test the prediction of one mark from several others adjusted for one another.Review of grades awarded by supervisors, second markers and control markers to the written reports of 4There was a highly significant difference of approximately half a grade between supervisors and second markers with supervisors marking higher. . There was a high correlation between the two marks awarded for performance of the project and the written report by the supervisor (r = 0.75), but a low-modest correlation between supervisor and second marker (r = 0.28). Linear regression analysis of the influence of the supervisors' mark for performance on their mark for the report gave a non-significant result. This suggests a leniency effect but no halo effect.This study shows that with the use of structured marking sheet for assessment of undergraduate medical students, supervisors marks are not associated with a halo effect, but leniency does occur. As supervisor assessment is becoming more common in both under graduate and postgraduate teaching new ways to improve objectivity in marking and to address the leniency of supervisors should be sought. There is compelling evidence from the literature that supervisors may be unreliable when asked to assess the performance of their own students. Effects such as the so-called 'halo' effect  in whichSSMs were introduced into the fourth year of the 5-year undergraduate medical curriculum in 1995. This was in response to the recommendations from the General Medical Council's document Tomorrow's Doctors . EdinburFor assessment an identical structured form was used by all assessors. Supervisors were asked to assess students on both their performance during the 8-week SSM and on their written report. Each component was awarded a separate grade by the supervisor and a combined grade for both was calculated by taking the mean grade. This mean grade contributed 50% to the final SSM mark. A second marker, usually another SSM supervisor working in a related area of research, with no prior knowledge of the student or the project would also assess the written report and this mark contributed 50% of the final mark. It was intended that this would permit the supervisors to be able to compare their own students' projects with others and would ensure greater consistency in the marking. Where there was a discrepancy of more than one full alphabetic grade category (e.g. A and C) between the supervisor and the second marker or where a fail grade was awarded, the report was assessed without prior knowledge of previous marks by at least one other experienced member of the Board of Examiners (control marker). The mark schemes for these assessments are described in Figure On reviewing the marks we noticed that there appeared to be a high correlation between the supervisor's marks for any one student's performance during the attachment and marks for their written report but a low correlation between the supervisor's and second marker's marks for the student's written report. This observation led us to investigate the hypothesis that the supervisors' knowledge of the students influenced their mark for the written report.We reviewed the grades of all the students from two full academic years (n = 399) who had participated in an SSM between 1999–2001 to answer the following questions: What is the correlation between the supervisor's marks for performance and report, and if this is high is there a causal relationship? Is there a real difference in the marks awarded for the report between the supervisor and the second marker, and if so what is the cause of the difference? In cases of discrepant marks where the reports were further marked by control markers; what is the correlation between the control markers with the supervisors' and second markers?The grades awarded for Performance and Reports were translated to a numerical scale thus: A+ = 1, A = 2, A- = 3, B+ = 4, through to E = 14. No grades below E  were awarded.We used paired t-tests to compare mean marks, Pearson correlation for looking at agreement between markers, and multiple linear regression to test the prediction of one mark from several others adjusted for one another.Table Using paired t-tests to compare mean marks for the written report between supervisors and second markers revealed a highly significant difference , with the supervisor scoring higher than the second marker . Correlation between the two marks was modest, r = 0.28. Control markers tended to mark the lower scoring students. While there was a numerical difference (lower) between control marks for the written report and the supervisor this failed to reach significance . Despite there being no significant difference between control markers and second markers, correlation was low (r = 0.11).There was considerably higher correlation between the two marks awarded by each supervisor i.e. for the students' performance and written report r = 0.75 but again there was a highly significant difference in the mean marks t = 5.69, P < 0.001 Analysis of the influence of the supervisor's mark for performance on his/her mark for the report was done by linear regression. This gave a non-significant result for the performance mark adjusted for the written mark. Table Analysis of the grades awarded demonstrated that there is a significant difference in the mean marks awarded by the supervisors and second markers, with the supervisors marking nearly half a grade higher than the second markers. The correlation was also modest between these markers' assessments of the reports suggesting that the two groups of markers were not using the same criteria to reach their decision, despite being provided with descriptors and a mark scheme. It is important to note that most supervisors were also second markers. At the same time they were assessing their own students' project, and so had a direct and simultaneous comparison. Therefore, the same individual appeared to use different criteria depending on whether they marked their supervised student's report or others. The lack of significant difference between the mean marks awarded by the second marker and the control marker suggests that they were awarding the same range of grades overall but the modest correlations indicate that in the case of individual students there was again significant inter-marker variability. Control markers, unlike supervisors and second markers (who may only supervise one project a year) have experience of reviewing large numbers of SSM reports. There was also a significant difference in the mean marks awarded by supervisors for performance and for written reports but in this analysis there was a much higher correlation between the marks. However, further analysis of this finding by linear regression failed to demonstrate an undue influence of the performance mark on that of the report.Although we have been unable to provide evidence that the supervisor's mark for performance has an undue influence on the mark for the written report , we have demonstrated that the supervisors mark significantly higher than second markers, suggesting a leniency effect. This indicates that the supervisor's mark is influenced by having known and worked with the student. Such effects have been demonstrated before in many forms of education -8. Some Increasingly in medical education supervisors are expected to summatively assess their students ,10. AsseWe are aware that the marking scheme in Figure These findings raise the ethical question as to whether or not we should continue to utilise supervisors in this assessment process. We are planning to continue to use supervisors as markers because of the expertise they bring to the specific field of study and their realistic expectation of the difficulties encountered by the student during the course of the project. Also the supervisor is sometimes the only person capable of marking the student's performance, which we consider a very valuable assessment of the students personal and professional abilities. We do realise that this is a difficult responsibility for supervisors. Better staff development of supervisors as markers and a more detailed marking schedule may help ensure appropriate marks for performance. Furthermore, we will also consider introducing 360 degree assessment to include all members of staff who have interacted with the student, particularly to improve formative feedback to students.In this paper we have demonstrated the problem of inter-marker variability between the supervisor of undergraduate projects and the second marker even when using a mark scheme. This emphasises the difficulty in creating mark schemes and providing adequate staff training which ensures that markers apply the criteria in the same way in very varied reports. On average, supervisors awarded higher marks for their students' reports than the second markers but the influence of the performance mark on this was not significant. We would suggest that this difference is due to leniency in the supervisor resulting from the student being part of the supervisor's team, but these influences need further exploration.SR, HC and BMcK are all involved in undergraduate teaching at the University of EdinburghBMcK, HC and SR contributed equally to the design of the research and the writing of the project. RE analysed the data.The pre-publication history for this paper can be accessed here:


This last effect is potentially mutagenic in vivo. Biochemical studies performed in parallel with in vivo genetic analyses, would represent an extremely powerful approach to investigate further, both DNA replication and repair in eukaryotes.We and others have shown four distinct and presumably related effects of mammalian proliferating cell nuclear antigen (PCNA) on DNA synthesis catalyzed by mammalian DNA polymerase δ(pol δ). In the presence of homologous PCNA, pol δ exhibits 1) increased absolute activity; 2) increased processivity of DNA synthesis; 3) stable binding of synthetic oligonucleotide template-primers , can only substitute poorly for either calf thymus or human PCNA (~10% as well) in affecting calf thymus pol δ. However, by mutating one or only a few amino acids in the region of Drosophila PCNA thought to interact with pol δ, all four effects can be enhanced dramatically.Drosophila offers the potential for immediate in vivo genetic analyses. Although it has proven difficult to obtain sufficient amounts of homologous pol δ for parallel in vitro biochemical studies, by altering Drosophila PCNA using site-directed mutagenesis as suggested by our results, in vitro biochemical studies may now be performed using human and/or calf thymus pol δ preparations.Our results therefore suggest that all four above effects depend at least in part on the PCNA-pol δ interaction. Moreover unlike mammals,  Drosophila melanogaster homologs of the proteins required for both DNA replication and repair have been identified and in several cases purified to apparent homogeneity. These include DNA polymerase α holoenzyme ) and respective proteins were expressed. Finally bacteria were lysed and his-tagged proteins were purified using various procedures including Ni2+-IDA Sepharose chromatography. The purity of each was determined by SDS-PAGE and is shown as indicated  and replacement of the entire fly interdomain connector loop with corresponding human amino acids (dr119-133h dPCNA) had demonstrable effects. Of note, at relatively high concentrations, Drosophila PCNA but with the entire fly interdomain connector loop replaced by corresponding human amino acids (dr119-133h dPCNA) was similarly effective to wild-type human PCNA at stimulating the activity of calf thymus pol δ; however, it was considerably less effective at lower concentrations  was purCNA Fig. . Mutatio acid Q12V dPCNA aDrosophila PCNA had relatively much less effect on the processivity of calf thymus pol δ  was readily detectable but neither gave results as robust as those seen with wild-type human PCNA . PCNA-dependent TLS by pol δ was first reported by O'Day et al.  and subssic site ) used prsic site ). The momer Fig.  lane 3. l δ Fig.  lane 4 aded Fig.  lane 5. ded Fig.  lane 6. Drosophila PCNA are more than 70% identical at the level of primary amino acid sequence, wild-type Drosophila PCNA is only a very poor substitute for human PCNA in cell-free reactions with calf thymus pol δ. This is documented both in this report and previously ATP and [α-32P]dTTP were from Amersham Corp. E. coli DNA polymerase I Klenow fragment without 3'-5' exonuclease activity (exo-), was expressed and purified according to standard protocols ATP. Afterward, labeled primer was annealed to an unlabeled template. The standard reaction mixture for pol δ contained 40 mM Bis-Tris, pH 6.7, 6 mM MgCl2, 1 mM dithiothreitol, 10% glycerol and 40 μg/ml bovine serum albumin. Additional details are provided in the figure legends. Incubations were terminated by addition of standard stop solution and aliquots were subjected to 12% PAGE in the presence of 7 M urea and 15% formamide. After electrophoresis, gels were subjected to autoradiography and/or Molecular Dynamics 445 SI PhosphorImager analyses.Assays of pol δ on synthetic oligonucleotide template-primers were performed essentially as previously described . Primers~500 annealed to (dT)12–18 (both from Pharmacia) in a final volume of 5 μl containing 6 nmol poly(dA) (nucleotide), 0.2 nmol (dT)12–18 (nucleotide), 10 μM dTTP, 100 μCi [α-32P]dTTP, 40 mM Bis-Tris, pH 6.7, 6 mM MgCl, 1 mM dithiothreitol, 10% glycerol, 40 μg/ml bovine serum albumin, 10 ng of highly purified pol δ and various quantities of different PCNA samples as indicated. Assays were for 5 min at room temperature and were stopped by addition of standard PAGE stop solution and PAGE in the presence of 7 M urea. After electrophoresis, gels were subjected to autoradiography and/or Molecular Dynamics 445 SI PhosphorImager analyses.Processivity was evaluated qualitatively using (dA)2 and otherwise as detailed in the figure legend. EDTA was included in each incubation and in the gel electrophoresis buffer at a final concentration of 3 mM.Nondenaturing PAGE band mobility shift assays were performed essentially as previously described  but withDJuM performed all enzymologic and mobility shift assays with DNA polymerase δ in combination with both wild-type and various mutant PCNA molecules. He also designed, engineered and characterized all recombinant PCNA molecules. DJuM expressed several recombinant proteins in bacteria and purified them. Finally, he participated in DNA polymerase purification and drafted the original manuscript. MM expressed some recombinant proteins in bacteria and purified them. She also purified and characterized most DNA polymerase substrates. HM participated in DNA polymerase purification and manuscript preparation. PAF advised DJuM on execution and interpretation of experiments and assisted both in figure design and all other aspects of manuscript preparation. All authors read and approved the final manuscript.


We have developed a new method that enables agar microstructures to be used to cultivate cardiac myocyte cells in a manner that allows their connection patterns to be controlled. Non-contact three-dimensional photo-thermal etching with a 1064-nm infrared focused laser beam was used to form the shapes of agar microstructures. This wavelength was selected as it is not absorbed by water or agar. Identical rat cardiac myocytes were cultured in adjacent microstructures connected by microchannels and the interactions of asynchronous beating cardiac myocyte cells observed. Two isolated and independently beating cardiac myocytes were shown to form contacts through the narrow microchannels and by 90 minutes had synchronized their oscillations. This occurred by one of the two cells stopping their oscillation and following the pattern of the other cell. In contrast, when two sets of synchronized beating cells came into contact, those two sets synchronized without any observable interruptions to their rhythms. The results indicate that the synchronization process of cardiac myocytes may be dependent on the community size and network pattern of these cells. Single-cell based analysis methods have become more and more important for understanding the cell-group effects such as how information is controlled and recorded in a cell group or a network shape. Early tissue culture studies of cardiac myocyte cells demonstrated that a single beating cell can influence the rate of a neighbouring cell in close contact and that a group of heart cells in a culture, beating synchronously with a rapid rhythm, can act as pacemaker for a contiguous cell sheet . Though This paper reports the practical use of the agar chamber for screening the community size effect of the synchronization process of adjacent cardiac myocyte cells having independent oscillation.Figure 2 HPO4, 1.5 mM KH2PO4, pH 7.4) containing 0.9 mM CaCl2 and 0.5 mM MgCl2. The ventricles were minced in PBS without CaCl2 or MgCl2 and then incubated in PBS containing 0.25% collagenase  for 30 minutes at 37°C to digest the ventricular tissue. This procedure was repeated twice more and the cell suspension was then transferred to cell culture medium  at 4°C. The cells were filtered through a 40-μm nylon mesh and centrifuged at 180 g for 5 minutes at room temperature. The cell pellet was re-suspended in a HEPES buffer . Cardiac myocytes present in the suspension were separated from other cells  by the density centrifugation method. The cell suspension was then layered onto 40.5% Percoll  diluted in the HEPES buffer, which had previously been layered onto 58.5% Percoll diluted in the same buffer. The cell suspension was then centrifuged at 2200 g for 30 minutes at room temperature. Cardiac myocytes were retrieved from the interface of the 40.5% and 58.5% Percoll layers. Retrieved cells were then re-suspended in the cell culture medium. An aliquot (5- μl) of the suspension was diluted to achieve a final concentration of 3.0 × 105 cells/ml then plated into the chip. Individual cardiac myocytes were picked up by a micropipette and manually introduced into each chip microchamber and incubated on a cell-cultivation microscope system at 37°C in the presence of a humidified atmosphere of 95% air /5% CO2. It should be noted that because the microchamber sidewalls were made of agar, then the cells could not easily pass over the chambers.Neonatal rat cardiac myocytes were isolated and purified as follows. First, the hearts of 1- to 3-day-old Wistar rats  were excised under ether anaesthesia. The ventricles were separated from the atria and then washed with phosphate buffered saline  of cardiac myocytes, which changed considerably with contraction, were also analyzed and recorded every 1/30 s by a personal computer with a video capture board.Figure The same method was also used to make more complicated network patterns of cardiac myocytes. Figure In conclusion, we present a 1064-nm photo-thermal etching technology with which to create agarose microchambers for growing networks of cardiac myocyte cells. Using the system, we first observed the differences of the synchronization process of cardiac myocyte cells and their dependence on community size. This system has great potential for use in the biological/medical fields for cultivating the next stage of single-cell based networks and measuring their properties in laboratories.KK and TK carried out the microchamber design, cell preparation, single cell cultivation and observation, image analysis. Both authors contributed equally to this article. KY conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.two cells. synchronized oscillation of the two cellsClick here for filetwo sets of cells. synchronized oscillation of the two sets of cellsClick here for file