Transcriptional Inflammatory Signature in Healthy Donors and Different Radiotherapy Cancer Patients.

Cancer and ionizing radiation exposure are associated with inflammation. To identify a set of radiation-specific signatures of inflammation-associated genes in the blood of partially exposed radiotherapy patients, differential expression of 249 inflammatory genes was analyzed in blood samples from cancer patients and healthy individuals. The gene expression analysis on a cohort of 63 cancer patients (endometrial, head and neck, and prostate cancer) before and during radiotherapy (24 h, 48 h, ~1 week, ~4-8 weeks, and 1 month after the last fraction) identified 31 genes and 15 up- and 16 down-regulated genes. Transcription variability under normal conditions was determined using blood drawn on three separate occasions from four healthy donors. No difference in inflammatory expression between healthy donors and cancer patients could be detected prior to radiotherapy. Remarkably, repeated sampling of healthy donors revealed an individual endogenous inflammatory signature. Next, the potential confounding effect of concomitant inflammation was studied in the blood of seven healthy donors taken before and 24 h after a flu vaccine or ex vivo LPS (lipopolysaccharide) treatment; flu vaccination was not detected at the transcriptional level and LPS did not have any effect on the radiation-induced signature identified. Finally, we identified a radiation-specific signature of 31 genes in the blood of radiotherapy patients that were common for all cancers, regardless of the immune status of patients. Confirmation via MQRT-PCR was obtained for BCL6, MYD88, MYC, IL7, CCR4 and CCR7. This study offers the foundation for future research on biomarkers of radiation exposure, radiation sensitivity, and radiation toxicity for personalized radiotherapy treatment.

GMM-Based Expanded Feature Space as a Way to Extract Useful Information for Rare Cell Subtypes Identification in Single-Cell Mass Cytometry.

Cell subtype identification from mass cytometry data presents a persisting challenge, particularly when dealing with millions of cells. Current solutions are consistently under development, however, their accuracy and sensitivity remain limited, particularly in rare cell-type detection due to frequent downsampling. Additionally, they often lack the capability to analyze large data sets. To overcome these limitations, a new method was suggested to define an extended feature space. When combined with the robust clustering algorithm for big data, it results in more efficient cell clustering. Each marker's intensity distribution is presented as a mixture of normal distributions (Gaussian Mixture Model, GMM), and the expanded space is created by spanning over all obtained GMM components. The projection of the initial flow cytometry marker domain into the expanded space employs GMM-based membership functions. An evaluation conducted on three established cellular identification algorithms (FlowSOM, ClusterX, and PARC) utilizing the most substantial publicly available annotated dataset by Samusik et al. demonstrated the superior performance of the suggested approach in comparison to the standard. Although our approach identified 20 cell clusters instead of the expected 24, their intra-cluster homogeneity and inter-cluster differences were superior to the 24-cluster FlowSOM-based solution.

DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data.

BACKGROUND: Investigating molecular heterogeneity provides insights into tumour origin and metabolomics. The increasing amount of data gathered makes manual analyses infeasible-therefore, automated unsupervised learning approaches are utilised for discovering tissue heterogeneity. However, automated analyses require experience setting the algorithms' hyperparameters and expert knowledge about the analysed biological processes. Moreover, feature engineering is needed to obtain valuable results because of the numerous features measured. RESULTS: We propose DiviK: a scalable stepwise algorithm with local data-driven feature space adaptation for segmenting high-dimensional datasets. The algorithm is compared to the optional solutions (regular k-means, spatial and spectral approaches) combined with different feature engineering techniques (None, PCA, EXIMS, UMAP, Neural Ions). Three quality indices: Dice Index, Rand Index and EXIMS score, focusing on the overall composition of the clustering, coverage of the tumour region and spatial cluster consistency, are used to assess the quality of unsupervised analyses. Algorithms were validated on mass spectrometry imaging (MSI) datasets-2D human cancer tissue samples and 3D mouse kidney images. DiviK algorithm performed the best among the four clustering algorithms compared (overall quality score 1.24, 0.58 and 162 for d(0, 0, 0), d(1, 1, 1) and the sum of ranks, respectively), with spectral clustering being mostly second. Feature engineering techniques impact the overall clustering results less than the algorithms themselves (partial [Formula: see text] effect size: 0.141 versus 0.345, Kendall's concordance index: 0.424 versus 0.138 for d(0, 0, 0)). CONCLUSIONS: DiviK could be the default choice in the exploration of MSI data. Thanks to its unique, GMM-based local optimisation of the feature space and deglomerative schema, DiviK results do not strongly depend on the feature engineering technique applied and can reveal the hidden structure in a tissue sample. Additionally, DiviK shows high scalability, and it can process at once the big omics data with more than 1.5 mln instances and a few thousand features. Finally, due to its simplicity, DiviK is easily generalisable to an even more flexible framework. Therefore, it is helpful for other -omics data (as single cell spatial transcriptomic) or tabular data in general (including medical images after appropriate embedding). A generic implementation is freely available under Apache 2.0 license at https://github.com/gmrukwa/divik .

Are we confident that final-year medical students know at least basics about diabetes?: A preliminary report from the multicenter, survey-based Diabetes Know-Me study.

BACKGROUND: We present the results of the pilot study of a multinational "Diabetes Know-Me" project investigating knowledge regarding diabetes of medical students. This is the first collaborative project of the ISPAD JENIOUS group. METHODS: Students of the final year of medical studies from six countries answered a 25-question survey regarding basic knowledge concerning diabetes (1091 surveys handed out, response rate 86%). RESULTS: Among the responders (58% female) 90% confirmed attending diabetology classes; 11% planned to specialize in diabetology. There were significant differences between countries in the median score of correct answers ranging from 10/25 to 22/25. Attending diabetes classes (20 vs. 13/25, p < 0.0001) was the strongest factor associated with improved knowledge about diabetes (other factors analyzed were: gender, familiar/personal experience of diabetes, interest to specialize in diabetology). CONCLUSIONS: Basic knowledge about diabetes remains a challenge. Participating in classes concerning diabetes contributed the most to the diabetes-related knowledge among students of the final year of medical faculties.

Analysis of the Applicability of microRNAs in Peripheral Blood Leukocytes as Biomarkers of Sensitivity and Exposure to Fractionated Radiotherapy towards Breast Cancer.

Biomarkers for predicting individual response to radiation and for dose verification are needed to improve radiotherapy. A biomarker should optimally show signal fidelity, meaning that its level is stable and proportional to the absorbed dose. miRNA levels in human blood serum were suggested as promising biomarkers. The aim of the present investigation was to test the miRNA biomarker in leukocytes of breast cancer patients undergoing external beam radiotherapy. Leukocytes were isolated from blood samples collected prior to exposure (control); on the day when a total dose of 2 Gy, 10 Gy, or 20 Gy was reached; and one month after therapy ended (46-50 Gy in total). RNA sequencing was performed and univariate analysis was used to analyse the effect of the radiation dose on the expression of single miRNAs. To check if combinations of miRNAs can predict absorbed dose, a multinomial logistic regression model was built using a training set from eight patients (representing 40 samples) and a validation set with samples from the remaining eight patients (15 samples). Finally, Broadside, an explorative interaction mining tool, was used to extract sets of interacting miRNAs. The most prominently increased miRNA was miR-744-5p, followed by miR-4461, miR-34a-5p, miR-6513-5p, miR-1246, and miR-454-3p. Decreased miRNAs were miR-3065-3p, miR-103a-2-5p, miR-30b-3p, and miR-5690. Generally, most miRNAs showed a relatively strong inter-individual variability and different temporal patterns over the course of radiotherapy. In conclusion, miR-744-5p shows promise as a stable miRNA marker, but most tested miRNAs displayed individual signal variability which, at least in this setting, may exclude them as sensitive biomarkers of radiation response.

IFNG, FCER1A, PCDHB10 expression as a new potential marker of efficacy in grass pollen allergen-specific immunotherapy.

INTRODUCTION: Allergen-specific immunotherapy (AIT) is the core treatment in allergic rhinitis and asthma. Although widely used, some patients do not benefit from treatment and there is no efficacy objective marker. AIM: To define the profile of gene transcripts during the build-up phase of AIT and their comparison to the control group and then search for a viable efficacy marker in relation to patient symptoms. MATERIAL AND METHODS: AIT was administered in 22 patients allergic to grass pollen. Analysis of 15 selected transcript expression was performed in whole blood samples taken before AIT (sample A) and after reaching the maintenance dose (sample B). The control group included 25 healthy volunteers (sample C). The primary endpoint was Relative Quantification. The gene expression analysis was followed by clinical evaluation with the use of Allergy Control Score (ACS). RESULTS: Comparison between samples A and B of gene expression showed a significant increase in IFNG expression (p = 0.03). In relation to the control group, pretreatment samples from patients showed higher levels of AFAP1L1 (p = 0.006), COMMD8 (p = 0.001), PIK3CD (p = 0.027) and TWIST2 (p = 0.0003) in univariate analysis. A generalized linear regression model was built according to the Bayesian Information Criterion based on the IFNG, FCER1A and PCDHB10 expression pattern for prediction of the AIT outcome. The model showed a correlation in predicted and observed changes in ACS. CONCLUSIONS: There is a significant change in the expression of IFNG during the build-up phase of AIT. The authors propose an in vitro model of AIT efficacy prediction for further validation.

Kras mutations and PU.1 promoter methylation are new pathways in murine radiation-induced AML.

Therapy-related and more specifically radiotherapy-associated acute myeloid leukaemia (AML) is a well-recognized potential complication of cytotoxic therapy for the treatment of a primary cancer. The CBA mouse model is used to study radiation leukaemogenesis mechanisms with Sfpi1/PU.1 deletion and point mutation already identified as driving events during AML development. To identify new pathways, we analysed 123 mouse radiation-induced AML (rAML) samples for the presence of mutations identified previously in human AML and found three genes to be mutated; Sfpi1 R235 (68%), Flt3-ITD (4%) and Kras G12 (3%), of which G12R was previously unreported. Importantly, a significant decrease in Sfpi1 gene expression is found almost exclusively in rAML samples without an Sfpi1 R235 mutation and is specifically associated with up-regulation of mir-1983 and mir-582-5p. Moreover, this down-regulation of Sfpi1 mRNA is negatively correlated with DNA methylation levels at specific CpG sites upstream of the Sfpi1 transcriptional start site. The down regulation of Sfpi1/PU.1 has also been reported in human AML cases revealing one common pathway of myeloid disruption between mouse and human AML where dysregulation of Sfpi1/PU.1 is a necessary step in AML development.

BatchI: Batch effect Identification in high-throughput screening data using a dynamic programming algorithm.

MOTIVATION: In contemporary biological experiments, bias, which interferes with the measurements, requires attentive processing. Important sources of bias in high-throughput biological experiments are batch effects and diverse methods towards removal of batch effects have been established. These include various normalization techniques, yet many require knowledge on the number of batches and assignment of samples to batches. Only few can deal with the problem of identification of batch effect of unknown structure. For this reason, an original batch identification algorithm through dynamical programming is introduced for omics data that may be sorted on a timescale. RESULTS: BatchI algorithm is based on partitioning a series of high-throughput experiment samples into sub-series corresponding to estimated batches. The dynamic programming method is used for splitting data with maximal dispersion between batches, while maintaining minimal within batch dispersion. The procedure has been tested on a number of available datasets with and without prior information about batch partitioning. Datasets with a priori identified batches have been split accordingly, measured with weighted average Dice Index. Batch effect correction is justified by higher intra-group correlation. In the blank datasets, identified batch divisions lead to improvement of parameters and quality of biological information, shown by literature study and Information Content. The outcome of the algorithm serves as a starting point for correction methods. It has been demonstrated that omitting the essential step of batch effect control may lead to waste of valuable potential discoveries. AVAILABILITY AND IMPLEMENTATION: The implementation is available within the BatchI R package at http://zaed.aei.polsl.pl/index.php/pl/111-software. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gene set enrichment for reproducible science: comparison of CERNO and eight other algorithms.

MOTIVATION: Analysis of gene set (GS) enrichment is an essential part of functional omics studies. Here, we complement the established evaluation metrics of GS enrichment algorithms with a novel approach to assess the practical reproducibility of scientific results obtained from GS enrichment tests when applied to related data from different studies. RESULTS: We evaluated eight established and one novel algorithm for reproducibility, sensitivity, prioritization, false positive rate and computational time. In addition to eight established algorithms, we also included Coincident Extreme Ranks in Numerical Observations (CERNO), a flexible and fast algorithm based on modified Fisher P-value integration. Using real-world datasets, we demonstrate that CERNO is robust to ranking metrics, as well as sample and GS size. CERNO had the highest reproducibility while remaining sensitive, specific and fast. In the overall ranking Pathway Analysis with Down-weighting of Overlapping Genes, CERNO and over-representation analysis performed best, while CERNO and GeneSetTest scored high in terms of reproducibility. AVAILABILITY AND IMPLEMENTATION: tmod package implementing the CERNO algorithm is available from CRAN (cran.r-project.org/web/packages/tmod/index.html) and an online implementation can be found at http://tmod.online/. The datasets analyzed in this study are widely available in the KEGGdzPathwaysGEO, KEGGandMetacoreDzPathwaysGEO R package and GEO repository. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Classification of Thyroid Tumors Based on Mass Spectrometry Imaging of Tissue Microarrays; a Single-Pixel Approach.

The primary diagnosis of thyroid tumors based on histopathological patterns can be ambiguous in some cases, so proper classification of thyroid diseases might be improved if molecular biomarkers support cytological and histological assessment. In this work, tissue microarrays representative for major types of thyroid malignancies-papillary thyroid cancer (classical and follicular variant), follicular thyroid cancer, anaplastic thyroid cancer, and medullary thyroid cancer-and benign thyroid follicular adenoma and normal thyroid were analyzed by mass spectrometry imaging (MSI), and then different computation approaches were implemented to test the suitability of the registered profiles of tryptic peptides for tumor classification. Molecular similarity among all seven types of thyroid specimens was estimated, and multicomponent classifiers were built for sample classification using individual MSI spectra that corresponded to small clusters of cells. Moreover, MSI components showing the most significant differences in abundance between the compared types of tissues detected and their putative identity were established by annotation with fragments of proteins identified by liquid chromatography-tandem mass spectrometry in corresponding tissue lysates. In general, high accuracy of sample classification was associated with low inter-tissue similarity index and a high number of components with significant differences in abundance between the tissues. Particularly, high molecular similarity was noted between three types of tumors with follicular morphology (adenoma, follicular cancer, and follicular variant of papillary cancer), whose differentiation represented the major classification problem in our dataset. However, low level of the intra-tissue heterogeneity increased the accuracy of classification despite high inter-tissue similarity (which was exemplified by normal thyroid and benign adenoma). We compared classifiers based on all detected MSI components (n = 1536) and the subset of the most abundant components (n = 147). Despite relatively higher contribution of components with significantly different abundance and lower overall inter-tissue similarity in the latter case, the precision of classification was generally higher using all MSI components. Moreover, the classification model based on individual spectra (a single-pixel approach) outperformed the model based on mean spectra of tissue cores. Our result confirmed the high feasibility of MSI-based approaches to multi-class detection of cancer types and proved the good performance of sample classification based on individual spectra (molecular image pixels) that overcame problems related to small amounts of heterogeneous material, which limit the applicability of classical proteomics.

Novel TG-FGFR1 and TRIM33-NTRK1 transcript fusions in papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) is most common among all thyroid cancers. Multiple genomic alterations occur in PTC, and gene rearrangements are one of them. Here we screened 14 tumors for novel fusion transcripts by RNA-Seq. Two samples harboring RET/PTC1 and RET/PTC3 rearrangements were positive controls whereas the remaining ones were negative regarding the common PTC alterations. We used Sanger sequencing to validate potential fusions. We detected 2 novel potentially oncogenic transcript fusions: TG-FGFR1 and TRIM33-NTRK1. We detected 4 novel fusion transcripts of unknown significance accompanying the TRIM33-NTRK1 fusion: ZSWIM5-TP53BP2, TAF4B-WDR1, ABI2-MTA3, and ARID1B-PSMA1. Apart from confirming the presence of RET/PTC1 and RET/PTC3 in positive control samples, we also detected known oncogenic fusion transcripts in remaining samples: TFG-NTRK1, ETV6-NTRK3, MKRN1-BRAF, EML4-ALK, and novel isoform of CCDC6-RET.

Combining CDKN1A gene expression and genome-wide SNPs in a twin cohort to gain insight into the heritability of individual radiosensitivity.

Individual variability in response to radiation exposure is recognised and has often been reported as important in treatment planning. Despite many efforts to identify biomarkers allowing the identification of radiation sensitive patients, it is not yet possible to distinguish them with certainty before the beginning of the radiotherapy treatment. A comprehensive analysis of genome-wide single-nucleotide polymorphisms (SNPs) and a transcriptional response to ionising radiation exposure in twins have the potential to identify such an individual. In the present work, we investigated SNP profile and CDKN1A gene expression in blood T lymphocytes from 130 healthy Caucasians with a complex level of individual kinship (unrelated, mono- or dizygotic twins). It was found that genetic variation accounts for 66% (95% CI 37-82%) of CDKN1A transcriptional response to radiation exposure. We developed a novel integrative multi-kinship strategy allowing investigating the role of genome-wide polymorphisms in transcriptomic radiation response, and it revealed that rs205543 (ETV6 gene), rs2287505 and rs1263612 (KLF7 gene) are significantly associated with CDKN1A expression level. The functional analysis revealed that rs6974232 (RPA3 gene), involved in mismatch repair (p value = 9.68e-04) as well as in RNA repair (p value = 1.4e-03) might have an important role in that process. Two missense polymorphisms with possible deleterious effect in humans were identified: rs1133833 (AKIP1 gene) and rs17362588 (CCDC141 gene). In summary, the data presented here support the validity of this novel integrative data analysis strategy to provide insights into the identification of SNPs potentially influencing radiation sensitivity. Further investigations in radiation response research at the genomic level should be therefore continued to confirm these findings.

Remission phase in children diagnosed with type 1 diabetes in years 2012 to 2013 in Silesia, Poland: An observational study.

BACKGROUND/OBJECTIVE: The study aimed to analyze the frequency of partial remission (PR) and its association with chosen clinical and laboratory factors among pediatric patients with newly diagnosed type 1 diabetes (T1D). The long-term effect of PR on chosen parameters was also investigated. METHODS: In 194 patients (95 girls) aged 8.1 ± 4.3 years, we analyzed data at T1D onset: glycemia, pH, C-peptide, antibodies, weight, and concomitant autoimmune diseases. Anthropometric parameters, daily insulin requirement (DIR), and HbA1c 2 and 4 years after T1D diagnosis were also analyzed. We determined PR based on HbA1c and DIR measurements at least every 3 months. RESULTS: PR occurred in 59% of patients. Remitters had significantly higher pH (7.33 vs 7.28, P = 0.03), weight SD score (SDS) (0.25 vs -0.24, P = 0.002), and body mass index SDS (0.19 vs -0.66, P = 0.02) compared with non-remitters. Concomitant diseases correlated negatively with PR. Multivariate analysis indicated only pH at onset was an independent predictor of PR. pH was the most important factor associated with the beginning of PR. There was a positive correlation between the start and duration of PR. Four years after T1D onset remitters had lower HbA1c (7.24% vs 8.05%, 53 vs 63.9 mmol/mol, P < 0.001) and DIR (0.81 vs 1.08, P = 0.005). CONCLUSIONS: PR occurred quite often and developed more frequently in children with higher: weight and BMI SDS, but the main factor influencing PR presence and duration was higher pH at T1D onset. There was a beneficial impact of PR on HbA1c and DIR after 4 years of treatment.

Lung cancer survival and comorbidities in lung cancer screening participants of the Gdansk screening cohort.

BACKGROUND: In 2010, the World Health Organisation recommended implementation of screening programmes in four groups of diseases-neoplasms, cardiovascular diseases (CVD), diabetes mellitus (DM) and chronic obstructive pulmonary disease (COPD). It is due to the fact that they share the same, modifiable risk factors. METHODS: Between 2009 and 2011, 8637 heavy smokers (aged 50-75, smoking history >20 pack-years) were screened in the Pomeranian Pilot Lung Cancer Screening Programme (PPP) in Gdansk, Poland. We looked at 5-year follow-up and analysed the medical events and comorbidities of all participants. One health care provider in the Polish health care system provides a unique opportunity to gather most reliable data on all medical events in each person. RESULTS: In 52.0% of lung cancer screening participants CVD (33.5%), DM (26.0%) and COPD (21.0%) were diagnosed. Prevalence of these diseases is higher in lung cancer patients than in the non-cancer screening group (P < 0.0001). One hundred and seven (1.2%) lung cancers were diagnosed during PPP programme performance and another 382 cases (4.4%) in the 5-year follow-up, so the potential mean annual lung cancer detection rate is 0.77%. CONCLUSIONS: Lung cancer screening programme offers a great potential for joint screening of lung cancer, CVD, diabetes and COPD.

Heart irradiation reduces microvascular density and accumulation of HSPA1 in mice.

PURPOSE: Improvement of radiotherapy techniques reduces the exposure of normal tissues to ionizing radiation. However, the risk of radiation-related late effects remains elevated. In the present study, we investigated long-term effects of radiation on heart muscle morphology. MATERIALS AND METHODS: We established a mouse model to study microvascular density (MVD), deposition of collagen fibers, and changes in accumulation of heat shock 70 kDa protein 1 (HSPA1) in irradiated heart tissue. Hearts of C57BL/6 mice received a single dose of X­ray radiation in the range 0.2-16 Gy. Analyses were performed 20, 40, and 60 weeks after irradiation. RESULTS: Reduction in MD was revealed as a long-term effect observed 20-60 weeks after irradiation. Moreover, a significant and dose-dependent increase in accumulation of HSPA1, both cytoplasmic and nuclear, was observed in heart tissues collected 20 weeks after irradiation. We also noticed an increase in collagen deposition in hearts treated with higher doses. CONCLUSIONS: This study shows that some changes induced by radiation in the heart tissue, such as reduction in microvessel density, increase in collagen deposition, and accumulation of HSPA1, are observed as long-term effects which might be associated with late radiation cardiotoxicity.

ATP4A autoimmunity in pediatric patients with type 1 diabetes and its relationship to blood count, iron metabolism, and vitamin B12.

OBJECTIVE: We aimed to assess the prevalence of autoantibodies against the 4A subunit of the gastric proton pump (ATP4A) in pediatric type 1 diabetes (T1D) patients and explore the relationship between ATP4A positivity and blood cell count, iron turnover, and vitamin B12 concentration. SUBJECTS: The study included 94 (59% female) T1D children (aged 12.5 ± 4.1 years, T1D duration 4.2 ± 3.6 years, HbA1c 7.3 ± 1.5% (57 ± 12.6 mmol/mol) with no other autoimmune diseases. METHODS: ATP4A antibodies were measured in T1D patients using a radioimmunoprecipitation assay. Blood cell count, iron concentration, total iron binding capacity, ferritin, transferrin, hepcidin, and vitamin B12 concentration were measured in all the study participants. RESULTS: A total of 16 (17%) children were ATP4A positive. Serum concentrations of ferritin were significantly lower in ATP4A positive than in antibody negative subjects (P = .034). Overall the levels of ATP4A antibodies (ATP4A Index) correlated positively with the age at T1D diagnosis (r = 0.228, P = .026) and negatively with ferritin levels (r = -0.215, P = .037). In ATP4A positive patients, the ATP4A Index correlated positively with age at diagnosis (r = 0.544, P = .032) and negatively with vitamin B12 levels (r = -0.685, P = .004). CONCLUSIONS: ATP4A antibodies were present in a significant proportion of children with T1D. Higher ATP4A levels in T1D children are associated with lower, yet still fitting within the normal range, levels of vitamin B12, and ferritin. Routine screening of T1D children for gastric autoimmunity (ATP4A) should be considered with follow-up of those positive for vitamin B12 and iron deficiency.

Low-dose radiation accelerates aging of the T-cell receptor repertoire in CBA/Ca mice.

While the biological effects of high-dose-ionizing radiation on human health are well characterized, the consequences of low-dose radiation exposure remain poorly defined, even though they are of major importance for radiological protection. Lymphocytes are very radiosensitive, and radiation-induced health effects may result from immune cell loss and/or immune system impairment. To decipher the mechanisms of effects of low doses, we analyzed the modulation of the T-cell receptor gene repertoire in mice exposed to a single low (0.1 Gy) or high (1 Gy) dose of radiation. High-throughput T-cell receptor gene profiling was used to visualize T-lymphocyte dynamics over time in control and irradiated mice. Radiation exposure induces "aging-like" effects on the T-cell receptor gene repertoire, detectable as early as 1 month post-exposure and for at least 6 months. Surprisingly, these effects are more pronounced in animals exposed to 0.1 Gy than to 1 Gy, where partial correction occurs over time. Importantly, we found that low-dose radiation effects are partially due to the hematopoietic stem cell impairment. Collectively, our findings show that acute low-dose radiation exposure specifically results in long-term alterations of the T-lymphocyte repertoire.

Ranking metrics in gene set enrichment analysis: do they matter?

BACKGROUND: There exist many methods for describing the complex relation between changes of gene expression in molecular pathways or gene ontologies under different experimental conditions. Among them, Gene Set Enrichment Analysis seems to be one of the most commonly used (over 10,000 citations). An important parameter, which could affect the final result, is the choice of a metric for the ranking of genes. Applying a default ranking metric may lead to poor results. METHODS AND RESULTS: In this work 28 benchmark data sets were used to evaluate the sensitivity and false positive rate of gene set analysis for 16 different ranking metrics including new proposals. Furthermore, the robustness of the chosen methods to sample size was tested. Using k-means clustering algorithm a group of four metrics with the highest performance in terms of overall sensitivity, overall false positive rate and computational load was established i.e. absolute value of Moderated Welch Test statistic, Minimum Significant Difference, absolute value of Signal-To-Noise ratio and Baumgartner-Weiss-Schindler test statistic. In case of false positive rate estimation, all selected ranking metrics were robust with respect to sample size. In case of sensitivity, the absolute value of Moderated Welch Test statistic and absolute value of Signal-To-Noise ratio gave stable results, while Baumgartner-Weiss-Schindler and Minimum Significant Difference showed better results for larger sample size. Finally, the Gene Set Enrichment Analysis method with all tested ranking metrics was parallelised and implemented in MATLAB, and is available at https://github.com/ZAEDPolSl/MrGSEA . CONCLUSIONS: Choosing a ranking metric in Gene Set Enrichment Analysis has critical impact on results of pathway enrichment analysis. The absolute value of Moderated Welch Test has the best overall sensitivity and Minimum Significant Difference has the best overall specificity of gene set analysis. When the number of non-normally distributed genes is high, using Baumgartner-Weiss-Schindler test statistic gives better outcomes. Also, it finds more enriched pathways than other tested metrics, which may induce new biological discoveries.

Molecular profiles of thyroid cancer subtypes: Classification based on features of tissue revealed by mass spectrometry imaging.

Determination of the specific type of thyroid cancer is crucial for the prognosis and selection of treatment of this malignancy. However, in some cases appropriate classification is not possible based on histopathological features only, and it might be supported by molecular biomarkers. Here we aimed to characterize molecular profiles of different thyroid malignancies using mass spectrometry imaging (MSI) which enables the direct annotation of molecular features with morphological pictures of an analyzed tissue. Fifteen formalin-fixed paraffin-embedded tissue specimens corresponding to five major types of thyroid cancer were analyzed by MALDI-MSI after in-situ trypsin digestion, and the possibility of classification based on the results of unsupervised segmentation of MALDI images was tested. Novel method of semi-supervised detection of the cancer region of interest (ROI) was implemented. We found strong separation of medullary cancer from malignancies derived from thyroid epithelium, and separation of anaplastic cancer from differentiated cancers. Reliable classification of medullary and anaplastic cancers using an approach based on automated detection of cancer ROI was validated with independent samples. Moreover, extraction of spectra from tumor areas allowed the detection of molecular components that differentiated follicular cancer and two variants of papillary cancer (classical and follicular). We concluded that MALDI-MSI approach is a promising strategy in the search for biomarkers supporting classification of thyroid malignant tumors. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Tissue fixed with formalin and processed without paraffin embedding is suitable for imaging of both peptides and lipids by MALDI-IMS.

Type and quality of sample preparation have significant impact on imaging mass spectrometry results. Though imaging of fresh-frozen tissues is considered to give the best results, they are incompatible with clinical practice, since routine diagnostics is most frequently performed using formalin-fixed tissues, and formalin-fixed paraffin-embedded material is a gold standard in histopathology. We aimed to assess utility of formalin-fixed tissue specimen processed without paraffin embedding (i.e., deep-frozen and cryo-sectioned) for MALDI imaging of both peptides and lipids. Peptide and lipid imaging was performed in fresh-frozen, FFPE and formalin-fixed/frozen samples of a mouse kidney, then composition of the resulting spectra was compared. We demonstrated similarity of spectra registered during peptide imaging in FFPE and formalin-fixed/frozen tissues, and similarity of spectra registered during lipid imaging in fresh-frozen and formalin-fixed/frozen material. Furthermore, molecular images of formalin-fixed/frozen tissue resembled the features of both fresh-frozen and FFPE tissue in the case of peptide imaging, and the features of fresh-frozen tissue in the case of lipid imaging. We conclude that tissue preserved by formalin fixation and processed without paraffin embedding can be considered as an alternative to both fresh-frozen and FFPE material.