Axonal Injury in the Lateral Geniculate Body: Radiological Diagnosis

<p>Damage to the lateral geniculate body by diffuse axonal injury in brain trauma is uncommon. The authors present the clinical case and <i>in vivo</i> fibre tractography using diffusion tensor magnetic resonance imaging of this lesion in a patient presenting with homonymous sectoranopia after a traumatic head injury.</p

Image_1_Distribution of blue and sei whale vocalizations, and temperature - salinity characteristics from glider surveys in the Northern Chilean Patagonia mega-estuarine system.jpg

Northern Chilean Patagonia is a mega-estuarine system where oceanic waters mix with freshwater inputs in the coastal fjords, channels and gulfs. The aim of this study was to examine the distribution of blue and sei whales with respect to oceanographic conditions of the study area from the estuarine inner sea to the outer ocean. Ocean gliders were used, mounted with a hydrophone to determine acoustic presence of whales (Southeast Pacific and Antarctic blue whale song calls, and blue whales D-calls; sei whale downsweeps and upsweeps), and a temperature and salinity instrument. Four glider deployments were carried out in April 2018 and April-June 2019 navigating a total of 2817 kilometers during 2110 hours. To examine interannual variation, the average percentage of day with presence of calls was compared between years using the adjusted p-values for one-way ANOVA and descriptive statistics. To examine spatial variation between the hourly acoustic presence of blue whales and sei whales and temperature and salinity conditions, Generalized Linear Models (GLMs) were used. Salinities were higher in 2019 compared to 2018. Southeast Pacific blue whales produced song calls throughout the study area in both years, across estuarine and oceanic areas, but percentage of day with presence was higher in 2019 vs 2018. Percentage of day with presence of D-calls was similar between years, but higher in oceanic areas during both study periods. In contrast, the spatial pattern of sei whale acoustic presence was ambiguous and interannual variability was high, suggesting that sei whales preferred estuarine areas in 2018 and oceanic areas in 2019. We discuss possible explanations for observed acoustic presence in relation to foraging behavior and prey distribution.</p

Image_2_Distribution of blue and sei whale vocalizations, and temperature - salinity characteristics from glider surveys in the Northern Chilean Patagonia mega-estuarine system.jpeg

Northern Chilean Patagonia is a mega-estuarine system where oceanic waters mix with freshwater inputs in the coastal fjords, channels and gulfs. The aim of this study was to examine the distribution of blue and sei whales with respect to oceanographic conditions of the study area from the estuarine inner sea to the outer ocean. Ocean gliders were used, mounted with a hydrophone to determine acoustic presence of whales (Southeast Pacific and Antarctic blue whale song calls, and blue whales D-calls; sei whale downsweeps and upsweeps), and a temperature and salinity instrument. Four glider deployments were carried out in April 2018 and April-June 2019 navigating a total of 2817 kilometers during 2110 hours. To examine interannual variation, the average percentage of day with presence of calls was compared between years using the adjusted p-values for one-way ANOVA and descriptive statistics. To examine spatial variation between the hourly acoustic presence of blue whales and sei whales and temperature and salinity conditions, Generalized Linear Models (GLMs) were used. Salinities were higher in 2019 compared to 2018. Southeast Pacific blue whales produced song calls throughout the study area in both years, across estuarine and oceanic areas, but percentage of day with presence was higher in 2019 vs 2018. Percentage of day with presence of D-calls was similar between years, but higher in oceanic areas during both study periods. In contrast, the spatial pattern of sei whale acoustic presence was ambiguous and interannual variability was high, suggesting that sei whales preferred estuarine areas in 2018 and oceanic areas in 2019. We discuss possible explanations for observed acoustic presence in relation to foraging behavior and prey distribution.</p

Analysis of GFP (γ-globin) expression during development.

<p>(<b>A</b>) Histogram overlay of embryonic blood from transgenic and WT embryos in the GFP axis. The percentages of positive GFP cells and primitive cells for each developmental stage are included in the table underneath (SSC: side scatter). Differences in the MFI of WT cells amongst the three histograms shown are due to the developmental stage and the gated cells plotted (i.e. SSC^high in the third histogram). (<b>B</b>) Dot plot depicting embryonic blood of representative γGPA-GFP/βDsRed transgenic mice at 14.5<i>dpc</i> in which the GFP⁺ population is depicted as green dots against the Forward (FSC) and Side Scatter (SSC). GFP⁺ cells (8.62±2.68%) are SSC^high, <i>i.e.</i> primitive cells. (<b>C</b>) qPCR analysis of GFP expression in WT and transgenic fetal liver cells at 11.5<i>dpc</i> and 14.5<i>dpc</i>. RFE is relative fold enrichment. Average and standard deviation derived from three mice per group is depicted. T-test was performed to calculate the <i>p</i> values.</p

Knockdown assays in transgenic dual reporter fetal liver cell lines.

<p>(<b>A</b>) Flow cytometry analysis of the knockdown of cMyb, Bcl11a, Hdac3 and Fop in the γGFP/βDsRed cell line. The same vector with a non-specific shRNA sequence was used as a control. Percentages of cells positive for GFP (upper panel) and DsRed (lower panel) are shown. Contour plots show gated live cells. (<b>B</b>) Western blots of the knockdown experiments in protein extracts of transduced cells. Equal numbers of cells are loaded on each lane. <b>φ</b>, empty vector control extracts; KD, knockdown extracts.</p

Characterization of the transgenic dual reporter fetal liver cell lines.

<p>(<b>A</b>) Flow cytometry analysis of transgenic fetal liver cell lines before and after differentiation. Histograms against forward scatter and erythroid surface markers CD117 (cKit) and CD71 (transferrin receptor) are depicted. (<b>B</b>) Flow cytometry analysis of transgenic fetal liver cell lines before and after differentiation. Histograms against DsRed and GFP are depicted. (<b>C</b>) Representative pictures taken during erythroid differentiation of transgenic fetal liver cell lines. Arrows indicate spontaneously differentiating cells expressing DsRed protein (left) and differentiated cells with much smaller size that are not as bright as the bigger ones, as a consequence of the continuous production of endogenous hemoglobin and subsequent quenching of cytoplasmic DsRed fluorescent signal (right).</p

<i>In vivo</i> treatment of transgenic mice with 5-Azacytidine.

<p>(<b>A</b>) Graph representing absolute numbers of GFP⁺ cells/10⁶ events measured gated in blood of γGPA-GFP/βDsRed mice treated (PHZ+AZA, red dots) or mock treated (PBS, white dots). (<b>B</b>) Graph representing absolute numbers of GFP⁺ cells/10⁵ events measured after 2 days of bone marrow hanging drop culture (BM HD D2) derived from γGPA-GFP/βDsRed mice treated (PHZ+AZA, orange dots) or mock treated (PBS, white dots). A logarithmic scale is used to better visualize the distribution of values found (each dot represents a mouse). The average per group is depicted as a black line. <i>P</i> values were calculated from the Log transformed data with a T-test.</p

Analysis of DsRed (β-globin) expression during development.

<p>(<b>A</b>) Flow cytometry analysis of fetal liver of γGPA-GFP/βDsRed transgenic mice during development. Arrowhead at 11.5<i>dpc</i> and 12.5<i>dpc</i> indicates the DsRed positive population. Representative data are depicted. (<b>B</b>) qPCR analysis of DsRed expression in WT and transgenic fetal liver cells at 11.5<i>dpc</i> and 14.5<i>dpc</i>. RFE is relative fold enrichment. Average and standard deviation derived from three mice per group is depicted. T-test was performed to calculate the <i>p</i> values. (<b>C</b>) Histogram overlays of DsRed and GFP expression in 14.5<i>dpc</i> fetal liver cells cultured for 2 days in hanging drops. DsRed expression is detected in transgenic cells differentiated <i>in vitro</i> when compared to WT while GFP is not. Representative data are depicted.</p

Modification of the human β-globin locus and generation of transgenic dual reporter mouse lines.

<p>(<b>A</b>) The human β-globin locus (<i>SceI</i> flanked) PAC used for the modifications made in the γ- and β-globin genes. GFP and DsRed were introduced in the ATG (+1) position of the transcripts followed by a stop codon (*). (<b>B</b>) Schematic representation of the GPA-GFP construct. The grey numbered stretches of the cartoon (1–114, 834–1234 bp) represent the glycophorin-A cDNA and the green stretch represents the GFP cDNA (114–834). The bilayer represents the transmembrane part of the protein, thus the GFP is expressed in the extracellular part of the fusion GPA-GFP protein. (<b>C</b>) Representative picture of K562 cells transfected with the γGFP/βDsRed modified human β-globin locus to check expression of γ-globin and flow cytometry analysis of GFP expression in 12.5<i>dpc</i> embryonic blood of γGFP/βDsRed transgenic embryos (left). Representative picture of fetal liver cells transduced with the γGPA-GFP construct to check expression of GFP protein in the plasma membrane and flow cytometry analysis of GFP expression in 12.5<i>dpc</i> embryonic blood of γGPA-GFP/βDsRed transgenic embryos (right). Mean fluorescence intensity (MFI) ratio is indicated in both graphs. (<b>D</b>) Southern blot of both mouse transgenic lines (γGFP/βDsRed and γGPA-GFP/βDsRed). Tail genomic DNA was digested with <i>SacI</i> restriction enzyme and hybridized with cosLCRε (left) and cosγγδβ (right), as previously described <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051272#pone.0051272-Strouboulis1" target="_blank">[2]</a>. Lane 1: γGPA-GFP/βDsRed tail DNA, Lanes 2, 3: mouse line PAC8 carrying the human β-globin locus and Lane 4: γGFP/βDsRed tail DNA. Symbol ▹ indicates end fragments, ▸ HGG1 3.6 Kb <i>SacI</i> fragment, ▸▸ HGG1-GFP 4.3 Kb <i>SacI</i> fragment, ▸▸▸ HGG1-GPA-GFP 4.9 Kb <i>SacI</i> fragment, ⧫ β-DsRed modification (16.4 to 17 Kb fragment). (<b>E</b>) S1 nuclease protection analysis of mouse globin expression of WT and transgenic mice at different developmental stages as indicated.</p

A comprehensive proteomics study on platelet concentrates: Platelet proteome, storage time and Mirasol pathogen reduction technology

<p>Platelet concentrates (PCs) represent a blood transfusion product with a major concern for safety as their storage temperature (20–24°C) allows bacterial growth, and their maximum storage time period (less than a week) precludes complete microbiological testing. Pathogen inactivation technologies (PITs) provide an additional layer of safety to the blood transfusion products from known and unknown pathogens such as bacteria, viruses, and parasites. In this context, PITs, such as Mirasol Pathogen Reduction Technology (PRT), have been developed and are implemented in many countries. However, several studies have shown <i>in vitro</i> that Mirasol PRT induces a certain level of platelet shape change, hyperactivation, basal degranulation, and increased oxidative damage during storage. It has been suggested that Mirasol PRT might accelerate what has been described as the platelet storage lesion (PSL), but supportive molecular signatures have not been obtained. We aimed at dissecting the influence of both variables, that is, Mirasol PRT and storage time, at the proteome level. We present comprehensive proteomics data analysis of Control PCs and PCs treated with Mirasol PRT at storage days 1, 2, 6, and 8. Our workflow was set to perform proteomics analysis using a gel-free and label-free quantification (LFQ) approach. Semi-quantification was based on LFQ signal intensities of identified proteins using MaxQuant/Perseus software platform. Data are available via ProteomeXchange with identifier PXD008119. We identified marginal differences between Mirasol PRT and Control PCs during storage. However, those significant changes at the proteome level were specifically related to the functional aspects previously described to affect platelets upon Mirasol PRT. In addition, the effect of Mirasol PRT on the platelet proteome appeared not to be exclusively due to an accelerated or enhanced PSL. In summary, semi-quantitative proteomics allows to discern between proteome changes due to Mirasol PRT or PSL, and proves to be a methodology suitable to phenotype platelets in an unbiased manner, in various physiological contexts.</p