Characterisation and correction of signal fluctuations in successive acquisitions of microarray images

<p>Abstract</p> <p>Background</p> <p>There are many sources of variation in dual labelled microarray experiments, including data acquisition and image processing. The final interpretation of experiments strongly relies on the accuracy of the measurement of the signal intensity. For low intensity spots in particular, accurately estimating gene expression variations remains a challenge as signal measurement is, in this case, highly subject to fluctuations.</p> <p>Results</p> <p>To evaluate the fluctuations in the fluorescence intensities of spots, we used series of successive scans, at the same settings, of whole genome arrays. We measured the decrease in fluorescence and we evaluated the influence of different parameters (PMT gain, resolution and chemistry of the slide) on the signal variability, at the level of the array as a whole and by intensity interval. Moreover, we assessed the effect of averaging scans on the fluctuations. We found that the extent of photo-bleaching was low and we established that 1) the fluorescence fluctuation is linked to the resolution e.g. it depends on the number of pixels in the spot 2) the fluorescence fluctuation increases as the scanner voltage increases and, moreover, is higher for the red as opposed to the green fluorescence which can introduce bias in the analysis 3) the signal variability is linked to the intensity level, it is higher for low intensities 4) the heterogeneity of the spots and the variability of the signal and the intensity ratios decrease when two or three scans are averaged.</p> <p>Conclusion</p> <p>Protocols consisting of two scans, one at low and one at high PMT gains, or multiple scans (ten scans) can introduce bias or be difficult to implement. We found that averaging two, or at most three, acquisitions of microarrays scanned at moderate photomultiplier settings (PMT gain) is sufficient to significantly improve the accuracy (quality) of the data and particularly those for spots having low intensities and we propose this as a general approach. For averaging and precise image alignment at sub-pixel levels we have made a program freely available on our web-site <url>http://bioinfome.cgm.cnrs-gif.fr</url> to facilitate implementation of this approach.</p

Segmentation and intensity estimation for microarray images with saturated pixels

<p>Abstract</p> <p>Background</p> <p>Microarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (2¹⁶- 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation.</p> <p>Results</p> <p>We developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study.</p> <p>Conclusions</p> <p>The presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.</p

Identification of a human neonatal immune-metabolic network associated with bacterial infection

Understanding how human neonates respond to infection remains incomplete. Here, a system-level investigation of neonatal systemic responses to infection shows a surprisingly strong but unbalanced homeostatic immune response; developing an elevated set-point of myeloid regulatory signalling and sugar-lipid metabolism with concomitant inhibition of lymphoid responses. Innate immune-negative feedback opposes innate immune activation while suppression of T-cell co-stimulation is coincident with selective upregulation of CD85 co-inhibitory pathways. By deriving modules of co-expressed RNAs, we identify a limited set of networks associated with bacterial infection that exhibit high levels of inter-patient variability. Whereas, by integrating immune and metabolic pathways, we infer a patient-invariant 52-gene-classifier that predicts bacterial infection with high accuracy using a new independent patient population. This is further shown to have predictive value in identifying infection in suspected cases with blood culture-negative tests. Our results lay the foundation for future translation of host pathways in advancing diagnostic, prognostic and therapeutic strategies for neonatal sepsis

Longitudinal Antibody Dynamics Against Structural Proteins of SARS-CoV-2 in Three COVID-19 Patients Shows Concurrent Development of IgA, IgM, and IgG

Mohd Raeed Jamiruddin,1 Md Ahsanul Haq,2 Kazuhito Tomizawa,3 Eiry Kobatake,4 Masayasu Mie,4 Sohel Ahmed,5 Shahad Saif Khandker,2 Tamanna Ali,2 Nowshin Jahan,2 Mumtarin Jannat Oishee,2 Mohib Ullah Khondoker,2 Bijon Kumar Sil,2 Mainul Haque,6 Nihad Adnan7 1Department of Pharmacy, BRAC University, Dhaka, 1212, Bangladesh; 2Gonoshasthaya-RNA Molecular Diagnostic &amp; Research Center, Dhaka, 1205, Bangladesh; 3Department of Molecular Physiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, 860-0811, Japan; 4School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, Kanagawa, 226-8502, Japan; 5Department of Biochemistry and Molecular Biology, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh; 6The Unit of Pharmacology, Faculty of Medicine and Defence Health, Universiti Pertahanan Nasional Malaysia (National Defence University of Malaysia), Kuala Lumpur, 57000, Malaysia; 7Department of Microbiology, Jahangirnagar University, Savar, Dhaka, 1342, BangladeshCorrespondence: Nihad AdnanDepartment of Microbiology, Jahangirnagar University, Savar, Dhaka, 1342, BangladeshTel +8801705709910Email [email protected] HaqueThe Unit of Pharmacology, Faculty of Medicine and Defence Health, Universiti Pertahanan Nasional Malaysia (National Defence University of Malaysia), Kem Perdana Sungai Besi, Kuala Lumpur, 57000, MalaysiaTel +60109265543Email [email protected]: Dynamics and persistence of neutralizing and non-neutralizing antibodies can give us the knowledge required for serodiagnosis, disease management, and successful vaccine design and development. The disappearance of antibodies, absence of humoral immunity activation, and sporadic reinfection cases emphasize the importance of longitudinal antibody dynamics against variable structural antigens.Methods: In this study, twenty-five healthy subjects working in a SARS-COV-2 serodiagnostic assay development project were enrolled, and their sign and symptoms were followed up to six months. Three subjects showed COVID-19-like symptoms, and three subjects&rsquo; antibody dynamics were followed over 120 days by analyzing 516 samples. We have developed 12 different types of in-house ELISAs to observe the kinetics of IgG, IgM, and IgA against four SARS-CoV-2 proteins, namely nucleocapsid, RBD, S1, and whole spike (S1+S2). For the development of these assays, 30&ndash; 104 pre-pandemic samples were taken as negative controls and 83 RT-qPCR positive samples as positive ones.Results: All three subjects presented COVID-19-like symptoms twice, with mild symptoms in the first episode were severe in the second, and RT-qPCR confirmed the latter. The initial episode did not culminate with any significant antibody development, while a multifold increase in IgG antibodies characterized the second episode. Interestingly, IgG antibody development concurrent with IgM and IgA and persisted, whereas the latter two weans off rather quickly if appeared.Conclusion: Antibody kinetics observed in this study can provide a pathway to the successful development of sero-diagnostics and epidemiologists to predict the fate of vaccination currently in place.Keywords: antibody dynamics, COVID-19, SARS-CoV-2, reinfection, vaccinatio