Polarimetric imaging combining optical parameters for classification of mice non-melanoma skin cancer tissue using machine learning.

Polarimetric imaging systems combining machine learning is emerging as a promising tool for the support of diagnosis and intervention decision-making processes in cancer detection/staging. A present study proposes a novel method based on Mueller matrix imaging combining optical parameters and machine learning models for classifying the progression of skin cancer based on the identification of three different types of mice skin tissues: healthy, papilloma, and squamous cell carcinoma. Three different machine learning algorithms (K-Nearest Neighbors, Decision Tree, and Support Vector Machine (SVM)) are used to construct a classification model using a dataset consisting of Mueller matrix images and optical properties extracted from the tissue samples. The experimental results show that the SVM model is robust to discriminate among three classes in the training stage and achieves an accuracy of 94 % on the testing dataset. Overall, it is provided that polarimetric imaging systems and machine learning algorithms can dynamically combine for the reliable diagnosis of skin cancer.

What makes a bad egg? Egg transcriptome reveals dysregulation of translational machinery and novel fertility genes important for fertilization.

BACKGROUND: Egg quality can be defined as the egg ability to be fertilized and subsequently develop into a normal embryo. Previous research has shed light on factors that can influence egg quality. Large gaps however remain including a comprehensive view of what makes a bad egg. Initial development of the embryo relies on maternally-inherited molecules, such as transcripts, deposited in the egg during its formation. Bad egg quality is therefore susceptible to be associated with alteration or dysregulation of maternally-inherited transcripts. We performed transcriptome analysis on a large number (N = 136) of zebrafish egg clutches, each clutch being split to monitor developmental success and perform transcriptome analysis in parallel. We aimed at drawing a molecular portrait of the egg in order to characterize the relation between egg transcriptome and developmental success and to subsequently identify new candidate genes involved in fertility. RESULTS: We identified 66 transcript that were differentially abundant in eggs of contrasted phenotype (low or high developmental success). Statistical modeling using partial least squares regression and genetics algorithm demonstrated that gene signatures from transcriptomic data can be used to predict developmental success. The identity and function of differentially expressed genes indicate a major dysregulation of genes of the translational machinery in poor quality eggs. Two genes, otulina and slc29a1a, predominantly expressed in the ovary and dysregulated in poor quality eggs were further investigated using CRISPR/Cas9 mediated genome editing. Mutants of each gene revealed remarkable subfertility whereby the majority of their eggs were unfertilizable. The Wnt pathway appeared to be dysregulated in the otulina mutant-derived eggs. CONCLUSIONS: Here we show that egg transcriptome contains molecular signatures, which can be used to predict developmental success. Our results also indicate that poor egg quality in zebrafish is associated with a dysregulation of (i) the translational machinery genes and (ii) novel fertility genes, otulina and slc29a1a, playing an important role for fertilization. Together, our observations highlight the diversity of the possible causes of egg quality defects and reveal mechanisms of maternal origin behind the lack of fertilization and early embryonic failures that can occur under normal reproduction conditions.

Scrambled eggs: Proteomic portraits and novel biomarkers of egg quality in zebrafish (Danio rerio).

Egg quality is a complex biological trait and a major determinant of reproductive fitness in all animals. This study delivered the first proteomic portraits of egg quality in zebrafish, a leading biomedical model for early development. Egg batches of good and poor quality, evidenced by embryo survival for 24 h, were sampled immediately after spawning and used to create pooled or replicated sample sets whose protein extracts were subjected to different levels of fractionation before liquid chromatography and tandem mass spectrometry. Obtained spectra were searched against a zebrafish proteome database and detected proteins were annotated, categorized and quantified based on normalized spectral counts. Manually curated and automated enrichment analyses revealed poor quality eggs to be deficient of proteins involved in protein synthesis and energy and lipid metabolism, and of some vitellogenin products and lectins, and to have a surfeit of proteins involved in endo-lysosomal activities, autophagy, and apoptosis, and of some oncogene products, lectins and egg envelope proteins. Results of pathway and network analyses suggest that this aberrant proteomic profile results from failure of oocytes giving rise to poor quality eggs to properly transit through final maturation, and implicated Wnt signaling in the etiology of this defect. Quantitative comparisons of abundant proteins in good versus poor quality eggs revealed 17 candidate egg quality markers. Thus, the zebrafish egg proteome is clearly linked to embryo developmental potential, a phenomenon that begs further investigation to elucidate the root causes of poor egg quality, presently a serious and intractable problem in livestock and human reproductive medicine.

Sex hormone-binding globulins characterization and gonadal gene expression during sex differentiation in the rainbow trout, Oncorhynchus mykiss.

Sex hormone-binding globulin (SHBG) binds androgens and estrogens in the blood of many vertebrates, including teleost fish. In mammals, SHBG is synthetized in the liver and secreted into the blood. In fish, shbga also exhibits a hepatic expression. In salmonids, in which the gene has been duplicated, the recently discovered shbgb gene exhibits a predominantly ovarian expression. The present work aimed at gaining new insight into shbgb gene structure and expression during gonadal sex differentiation, a steroid-sensitive process, and Shbgb protein structure and binding characteristics; specifically, rainbow trout (Oncorhynchus mykiss) shbgb was analyzed. shbgb structure was analyzed in silico while expression was characterized during gonadal sex differentiation using all-male and all-female populations. We observed that shbgb gene and cognate-protein structures are similar to homologs previously described in zebrafish and mammals. The shbgb gene is predominantly expressed in differentiating female gonads, with increased expression around the end of ovarian differentiation. In the ovary, shbgb mRNA was detected in a subset of somatic cells surrounding the ovarian lamellae. Furthermore, Shbgb binds steroids with a higher selectivity than Shbga, exhibiting a higher affinity for estradiol compared to Shbga. In conclusion, Shbgb binding characteristics are clearly different from those of Shbga. Shbgb is expressed in the differentiating ovary during a period when the synthesis and action of testosterone and estradiol must be tightly regulated. This strongly suggests that Shbgb participates in the regulation of steroid metabolism and/or mediation, that is, needed during early gonadal development in rainbow trout.