Development of a one-plasmid system to replace the endogenous protein with point mutation for post-translational modification studies

Molecular Biology Reports - Post-translational modification (PTM) is one of the major regulatory mechanism for protein activities. To understand the function of PTMs, mutants that prevent or mimic...     

Joint wireless power transfer and task offloading in mobile edge computing: a survey

Cluster Computing - The promising technique of Wireless Power Transfer (WPT) to end devices and sensors has gained the attention of researchers recently. Mobile edge computing (MEC) is also...     

Clinicopathological and immunohistochemical study of 29 cases of solid-pseudopapillary neoplasms of the pancreas in patients under 20 years of age along with detailed review of literature

Background

Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. The classification of each cell type requires detection of uniquely colored chromogens localized to cells expressing biomarkers of interest. The most comprehensive and reproducible method to evaluate such slides is to employ digital pathology and image analysis pipelines to whole-slide images (WSIs). Our suite of deep learning tools quantitatively evaluates the expression of six biomarkers in mIHC WSIs. These methods address the current lack of readily available methods to evaluate more than four biomarkers and circumvent the need for specialized instrumentation to spectrally separate different colors. The use case application for our methods is a study that investigates tumor immune interactions in pancreatic ductal adenocarcinoma (PDAC) with a customized mIHC panel.

Methods

Six different colored chromogens were utilized to label T-cells (CD3, CD4, CD8), B-cells (CD20), macrophages (CD16), and tumor cells (K17) in formalin-fixed paraffin-embedded (FFPE) PDAC tissue sections. We leveraged pathologist annotations to develop complementary deep learning-based methods: (1) ColorAE is a deep autoencoder which segments stained objects based on color; (2) U-Net is a convolutional neural network (CNN) trained to segment cells based on color, texture and shape; and (3) ensemble methods that employ both ColorAE and U-Net, collectively referred to as ColorAE:U-Net. We assessed the performance of our methods using: structural similarity and DICE score to evaluate segmentation results of ColorAE against traditional color deconvolution; F1 score, sensitivity, positive predictive value, and DICE score to evaluate the predictions from ColorAE, U-Net, and ColorAE:U-Net ensemble methods against pathologist-generated ground truth. We then used prediction results for spatial analysis (nearest neighbor).

Results

We observed that (1) the performance of ColorAE is comparable to traditional color deconvolution for single-stain IHC images (note: traditional color deconvolution cannot be used for mIHC); (2) ColorAE and U-Net are complementary methods that detect six different classes of cells with comparable performance; (3) combinations of ColorAE and U-Net in ensemble methods outperform ColorAE and U-Net alone; and (4) ColorAE:U-Net ensemble methods can be employed for detailed analysis of the tumor microenvironment (TME).

Summary

We developed a suite of scalable deep learning methods to analyze 6 distinctly labeled cell populations in mIHC WSIs. We evaluated our methods and found that they reliably detected and classified cells in the PDAC tumor microenvironment. We also utilized the ColorAE:U-Net ensemble method to analyze 3 mIHC WSIs with nearest neighbor spatial analysis. We demonstrate a proof of concept that these methods can be employed to quantitatively describe the spatial distribution of immune cells within the tumor microenvironment. These complementary deep learning methods are readily deployable for use in clinical research studies.

     

RAMTaB: robust alignment of multi-tag bioimages

Background

In recent years, new microscopic imaging techniques have evolved to allow us to visualize several different proteins (or other biomolecules) in a visual field. Analysis of protein co-localization becomes viable because molecules can interact only when they are located close to each other. We present a novel approach to align images in a multi-tag fluorescence image stack. The proposed approach is applicable to multi-tag bioimaging systems which (a) acquire fluorescence images by sequential staining and (b) simultaneously capture a phase contrast image corresponding to each of the fluorescence images. To the best of our knowledge, there is no existing method in the literature, which addresses simultaneous registration of multi-tag bioimages and selection of the reference image in order to maximize the overall overlap between the images.

Methodology/Principal Findings

We employ a block-based method for registration, which yields a confidence measure to indicate the accuracy of our registration results. We derive a shift metric in order to select the Reference Image with Maximal Overlap (RIMO), in turn minimizing the total amount of non-overlapping signal for a given number of tags. Experimental results show that the Robust Alignment of Multi-Tag Bioimages (RAMTaB) framework is robust to variations in contrast and illumination, yields sub-pixel accuracy, and successfully selects the reference image resulting in maximum overlap. The registration results are also shown to significantly improve any follow-up protein co-localization studies.

Conclusions

For the discovery of protein complexes and of functional protein networks within a cell, alignment of the tag images in a multi-tag fluorescence image stack is a key pre-processing step. The proposed framework is shown to produce accurate alignment results on both real and synthetic data. Our future work will use the aligned multi-channel fluorescence image data for normal and diseased tissue specimens to analyze molecular co-expression patterns and functional protein networks.     

Spatiotemporal maps of CaMKII in dendritic spines

The calcium calmodulin dependent kinase (CaMKII) is important for long-term potentiation at dendritic spines. Photo-activatable GFP (PaGFP) - CaMKII fusions were used to map CaMKII movements between and within spines in dissociated hippocampal neurons. Photo-activated PaGFP (GFP*) generated in the shaft spread uniformly, but was retained for about 1?s in spines. The differential localization of GFP*-CaMKII isoforms was visualized with hundred nanometer precision frame to frame using de-noising algorithms. GFP*-CaMKIIα localized to the tips of mushroom spines. The spatiotemporal profiles of native and kinase defective GFP*-CaMKIIβ, differed markedly from GFP*-CaMKIIα and mutant GFP*-CaMKIIβ lacking the association domain. CaMKIIβ bound to cortical actin in the dendrite and the stable actin network in spine bodies. Glutamate produced a transiently localized GFP*-CaMKIIα fraction and a soluble GFP*-CaMKIIβ fraction in spine bodies. Single molecule simulations of the interplay between diffusion and biochemistry of GFP* species were guided by the spatiotemporal maps and set limits on binding parameters. They highlighted the role of spine morphology in modulating bound CaMKII lifetimes. The long residence times of GFP*-CaMKIIβ relative to GFP*-CaMKIIα followed as consequence of more binding sites on the actin cytoskeleton than the post-synaptic density. These factors combined to retain CaMKII for tens of seconds, sufficient to outlast the calcium transients triggered by glutamate, without invoking complex biochemistry.     

Ascorbic acid improves the tolerance of wheat plants to lead toxicity

Among the heavy metals (HMs), lead (Pb) is considered as a toxic HM which adversely affects growth and development of crop plants. The present experiment was aimed to investigate the potential role of ascorbic acid (ASC) in the reversal of Pb-inhibited nitrogen and sulfur assimilation enzymes activity and activity of photosynthesis enzyme ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) and growth response in wheat plants. Wheat seedlings were subjected to 0 mM (control) and 0.2 mM and 0.6 mM of ASC with and without 2 mM of Pb. Plants treated with Pb exhibited the following reduced growth characteristics (root length, shoot length, root fresh weight (FW), shoot FW, root dry weight (DW) and shoot DW). A decrease was also observed in the activity of Rubisco and ATP sulfurylase (ATP-S), relative water content (RWC), accumulation of total chlorophyll (Total Chl) and content of nutrients [nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg)] in Pb-treated plants. However, an increase in Chl degradation and in the activity of O-acetylserine(thiol)lyase (OAS-TL) and accumulation of cysteine (Cys), malondialdehyde (MDA) and hydrogen peroxide (H₂O₂) was observed in plants under Pb stress. On the contrary, exogenous application of ASC mitigated the Pb-toxicity-induced oxidative damage by enhancing the activities of antioxidant enzymes, such as superoxide dismutase, catalase and glutathione reductase. Improved activity of antioxidant enzymes suppressed the formation of MDA and H₂O₂, which was reflected in the form of improved growth characteristics. Moreover, ASC induced improvement in plants defense systems by reduced Chl degradation and improved the content of essential nutrients (N, P, K, Ca and Mg) and Cys, RWC and the activity of Rubisco, ATP-S, NR and OAS-TL.