Enumeration of leukocyte infiltration in solid tumors by confocal laser scanning microscopy

Protective effects of yacon syrup powder on colonic interleukin-23 and leukocyte infiltration profile in TNBS-induced colitis mouse model

BACKGROUND: Inflammatory Bowel Disease (IBD) causes gastrointestinal tract dysregulation and triggers inflammation. Severe inflammatory events often lead to leukocyte infiltration. Yacon is known to have anti-inflammatory properties, making it a potential alternative treatment for colitis. AIMS: To investigate colonic Interleukin-23 (IL-23) level and leukocyte infiltration profile in 2,4,6-Trinitrobenzene Sulfonic Acid (TNBS)-induced colitis mouse model treated with yacon syrup powder. METHODS: Thirty male BALB/c mice (5 weeks old, body weight: 30.02±1.28 g) were randomly divided into 6 groups (K1-K6). Intra-rectal administration of TNBS was performed to K2-K6 to make the colitis model. K4-K6 were given yacon syrup powder daily for 14 days with the dose of 0.165, 0.331, and 0.662 g/30 g body weight (BW) respectively. IL-23 levels of colonic tissue were measured using ELISA. Leukocyte infiltration profile was calculated through microscopic observation of histology slides and presented in percentage and degree. RESULTS: Significantly different IL-23 levels were observed among the groups (P <  0.001). The percentage and degree of leukocyte infiltration in yacon-treated groups showed a significant decrease compared to colitis groups (P <  0.001). CONCLUSION: Yacon syrup powder provides beneficial effects on colitis mice by lowering the IL-23 level and number of leukocytes at the inflammation site.

A deep convolutional neural network approach for astrocyte detection

Astrocytes are involved in various brain pathologies including trauma, stroke, neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases, or chronic pain. Determining cell density in a complex tissue environment in microscopy images and elucidating the temporal characteristics of morphological and biochemical changes is essential to understand the role of astrocytes in physiological and pathological conditions. Nowadays, manual stereological cell counting or semi-automatic segmentation techniques are widely used for the quantitative analysis of microscopy images. Detecting astrocytes automatically is a highly challenging computational task, for which we currently lack efficient image analysis tools. We have developed a fast and fully automated software that assesses the number of astrocytes using Deep Convolutional Neural Networks (DCNN). The method highly outperforms state-of-the-art image analysis and machine learning methods and provides precision comparable to those of human experts. Additionally, the runtime of cell detection is significantly less than that of other three computational methods analysed, and it is faster than human observers by orders of magnitude. We applied our DCNN-based method to examine the number of astrocytes in different brain regions of rats with opioid-induced hyperalgesia/tolerance (OIH/OIT), as morphine tolerance is believed to activate glia. We have demonstrated a strong positive correlation between manual and DCNN-based quantification of astrocytes in rat brain.

Automatic Counting of Microglial Cells in Healthy and Glaucomatous Mouse Retinas

Proliferation of microglial cells has been considered a sign of glial activation and a hallmark of ongoing neurodegenerative diseases. Microglia activation is analyzed in animal models of different eye diseases. Numerous retinal samples are required for each of these studies to obtain relevant data of statistical significance. Because manual quantification of microglial cells is time consuming, the aim of this study was develop an algorithm for automatic identification of retinal microglia. Two groups of adult male Swiss mice were used: age-matched controls (naïve, n = 6) and mice subjected to unilateral laser-induced ocular hypertension (lasered; n = 9). In the latter group, both hypertensive eyes and contralateral untreated retinas were analyzed. Retinal whole mounts were immunostained with anti Iba-1 for detecting microglial cell populations. A new algorithm was developed in MATLAB for microglial quantification; it enabled the quantification of microglial cells in the inner and outer plexiform layers and evaluates the area of the retina occupied by Iba-1+ microglia in the nerve fiber-ganglion cell layer. The automatic method was applied to a set of 6,000 images. To validate the algorithm, mouse retinas were evaluated both manually and computationally; the program correctly assessed the number of cells (Pearson correlation R = 0.94 and R = 0.98 for the inner and outer plexiform layers respectively). Statistically significant differences in glial cell number were found between naïve, lasered eyes and contralateral eyes (P<0.05, naïve versus contralateral eyes; P<0.001, naïve versus lasered eyes and contralateral versus lasered eyes). The algorithm developed is a reliable and fast tool that can evaluate the number of microglial cells in naïve mouse retinas and in retinas exhibiting proliferation. The implementation of this new automatic method can enable faster quantification of microglial cells in retinal pathologies.

A semi-automated technique for labeling and counting of apoptosing retinal cells

BACKGROUND

Retinal ganglion cell (RGC) loss is one of the earliest and most important cellular changes in glaucoma. The DARC (Detection of Apoptosing Retinal Cells) technology enables in vivo real-time non-invasive imaging of single apoptosing retinal cells in animal models of glaucoma and Alzheimer's disease. To date, apoptosing RGCs imaged using DARC have been counted manually. This is time-consuming, labour-intensive, vulnerable to bias, and has considerable inter- and intra-operator variability.

RESULTS

A semi-automated algorithm was developed which enabled automated identification of apoptosing RGCs labeled with fluorescent Annexin-5 on DARC images. Automated analysis included a pre-processing stage involving local-luminance and local-contrast "gain control", a "blob analysis" step to differentiate between cells, vessels and noise, and a method to exclude non-cell structures using specific combined 'size' and 'aspect' ratio criteria. Apoptosing retinal cells were counted by 3 masked operators, generating 'Gold-standard' mean manual cell counts, and were also counted using the newly developed automated algorithm. Comparison between automated cell counts and the mean manual cell counts on 66 DARC images showed significant correlation between the two methods (Pearson's correlation coefficient 0.978 (p < 0.001), R Squared = 0.956. The Intraclass correlation coefficient was 0.986 (95% CI 0.977-0.991, p < 0.001), and Cronbach's alpha measure of consistency = 0.986, confirming excellent correlation and consistency. No significant difference (p = 0.922, 95% CI: -5.53 to 6.10) was detected between the cell counts of the two methods.

CONCLUSIONS

The novel automated algorithm enabled accurate quantification of apoptosing RGCs that is highly comparable to manual counting, and appears to minimise operator-bias, whilst being both fast and reproducible. This may prove to be a valuable method of quantifying apoptosing retinal cells, with particular relevance to translation in the clinic, where a Phase I clinical trial of DARC in glaucoma patients is due to start shortly.

Commercial DNA extraction kits impact observed microbial community composition in permafrost samples

The total community genomic DNA (gDNA) from permafrost was extracted using four commercial DNA extraction kits. The gDNAs were compared using quantitative real-time PCR (qPCR) targeting 16S rRNA genes and bacterial diversity analyses obtained via 454 pyrosequencing of the 16S rRNA (V3 region) amplified in single or nested PCR. The FastDNA(®) SPIN (FDS) Kit provided the highest gDNA yields and 16S rRNA gene concentrations, followed by MoBio PowerSoil(®) (PS) and MoBio PowerLyzer™ (PL) kits. The lowest gDNA yields and 16S rRNA gene concentrations were from the Meta-G-Nome™ (MGN) DNA Isolation Kit. Bacterial phyla identified in all DNA extracts were similar to that found in other soils and were dominated by Actinobacteria, Firmicutes, Gemmatimonadetes, Proteobacteria, and Acidobacteria. Weighted UniFrac and statistical analyses indicated that bacterial community compositions derived from FDS, PS, and PL extracts were similar to each other. However, the bacterial community structure from the MGN extracts differed from other kits exhibiting higher proportions of easily lysed β- and γ-Proteobacteria and lower proportions of Actinobacteria and Methylocystaceae important in carbon cycling. These results indicate that gDNA yields differ between the extraction kits, but reproducible bacterial community structure analysis may be accomplished using gDNAs from the three bead-beating lysis extraction kits.

Immunophenotypic identification and characterization of tumor cells and infiltrating cell populations in meningiomas

Meningiomas are primary tumors of the central nervous system composed of both neoplastic and other infiltrating cells. We determined the cellular composition of 51 meningioma samples by multiparameter flow cytometric (MFC) immunophenotyping and investigated the potential relationship between mRNA and protein expression levels of neoplastic cells. For immunophenotypic, morphologic, and cytogenetic characterization of individual cell populations, a large panel of markers was used together with phagocytic/endocytic functional assays and MFC sorting. Overall, our results revealed coexistence of CD45(-) neoplastic cells and CD45(+) immune infiltrating cells in all meningiomas. Infiltrating cells included tissue macrophages, with an HLA-DR(+)CD14(+)CD45(+)CD68(+)CD16(-/+)CD33(-/+) phenotype and high phagocytic/endocytic activity, and a small proportion of cytotoxic lymphocytes (mostly T CD8(+) and natural killer cells). Tumor cells expressed multiple cell adhesion proteins, tetraspanins, HLA-I/HLA-DR molecules, complement regulatory proteins, cell surface ectoenzymes, and growth factor receptors. Noteworthy, the relationship between mRNA and protein levels was variable, depending on the proteins evaluated and the level of infiltration by immune cells. In summary, our results indicate that MFC immunophenotyping provides a reliable tool for the characterization of the patterns of protein expression of different cell populations coexisting in meningioma samples, with a more accurate measure of gene expression profiles of tumor cells at the functional/protein level than conventional mRNA microarray, independently of the degree of infiltration of the tumor by immune cells.

Location and Density of Immune Cells in Precursor Lesions and Cervical Cancer

Only a small proportion of women infected with Human Papillomavirus (HPV) develop cervical cancer. Host immune response seems to play a role eliminating the viral infection and preventing progression to cancer. Characterization of tumor infiltrating lymphocytes (TILs) in cervical pre-neoplastic lesions and cervical cancer may be helpful to understand the mechanisms that mediate this protection. The aim of this study was to determine if there are differences in the localization and density (cells/mm(2)) of CD8+ T-cells, CD4+ T-cells and Tregs (CD25 + Foxp3+) in cervical pre-neoplastic lesions and cervical cancer. Immunohistochemical analysis of sections of 96 (26 CIN1, 21 CIN2, 25 CIN3, and 24 SCC) samples revealed that regardless of CIN grades, CD8+ T-cells are more abundant than CD4+, CD25+ and Foxp3+ cells in both the stroma and epithelium. There was a higher density of CD8+ cells in the stroma of cervical cancer compared to CIN3 (OR = 4.20, 95% CI 1.2-15), CIN2 (OR = 7.86, 95% CI 1.7-36.4) and CIN1 (OR = 4.25, 95% CI 1.1-17). Studies evaluating whether these cells are recruited before or after cancer progression will be helpful to understand the role of these cells in the natural history of HPV-induced lesions.