P–057 Preliminary evaluation of laser confocal Raman spectroscopy as a noninvasive method for detecting sperm chromosome aneuploidy

Study question To evaluate the efficiency and accuracy of Raman microspectra in detecting sperm chromosome balance state by DNA content difference. Summary answer Raman spectroscopy can identify the difference of X and Y sperm DNA content, but the accuracy still needed to be improved for clinical application. What is known already Aneuploid sperm fertilization affects embryo quality and leads to the waste of oocytes in Assisted Reproductive Technology (ART). Raman spectroscopy can identify substances and observe molecular changes through specific spectral patterns with high specificity and has become a new hot spot in ART. Previous research has used this technology to detect embryo culture medium to evaluate the aneuploidy of embryos. The DNA content of X and Y in sperm was different, which may serve as a marker for sperm aneuploidy detection by Raman spectroscopy. Study design, size, duration The significant difference in the morphology of the sex chromosomes of X and Y spermatozoa leads to a substantial difference in the DNA content. We perform Raman spectroscopy to identify the spectral differences of the sperms, especially the differences in sperm DNA content. We further verified the accuracy with fluorescence in situ hybridization (FISH). Participants/materials, setting, methods Spermatozoa were provided by healthy donors with normal aneuploidy, and analysis parameters met the current World Health Organization (WHO, 2010) standards. Sperm heads were detected by laser confocal Raman spectroscopy and obtained the corresponding spectra. The sperm chromosome information was classified by Standard principal component analysis (PCA) and identified by fluorescence in situ hybridization (FISH). Student’s t-test and Receiver operating characteristic (ROC) curve analysis was performed for further analysis. Main results and the role of chance Standard principal component analysis (PCA) after unqualified quality control divided spermatozoa into two groups according to the calculation and calibration results, 22 cases in group A and 31 cases in group B. Then, we conducted frequency distribution histogram statistics on the above data, and the results showed that there were differences in frequency distribution at I785 = 23,750 and Area714 –1162 = 3,250,000. The FISH analysis identified sex chromosomes of 59 spermatozoa, which was not exactly one-to-one correspondence with the results of PCA analysis. Then we further analyzed the sperm of 59 cases by statistical analysis. The results showed that there were significant differences between X sperm (n = 39) and Y sperm (n = 20) at 714–1162 cm–1 and 785 (P < 0.05). ROC curve analysis was used to evaluate the sensitivity of correlation between sperm DNA content and Raman spectra. The results showed that the corresponding thresholds of I785 = 24,986.5 and Area714–1162 cm–1 = 3,748,990 were the best for distinguishing the two kinds of sperm. When the sperm’s peak value of 785 or 714–1162cm–1 exceeds the above thresholds, X-sperm’s possibility greatly increased. The AUC of the ROC curve in both cases was 0.662 and 0.696, respectively. Limitations, reasons for caution Current Raman spectroscopy requires spermatozoa elution and fixation, which damage the sperms. Furthermore, current Raman spectral data are not obtained from the whole sperm head, limiting the accuracy of this technique. Wider implications of the findings: Our results indicated that Raman spectroscopy had potential application value for sperm aneuploidy detection and could be used as a noninvasive selector for normal haploid sperms in the ART. Trial registration number LL-SC–2018–038

Frequency-separated principal component analysis of cortical population activity

A method termed frequency-separated principal component analysis (FS-PCA) is introduced for analyzing populations of simultaneously recorded neurons. This framework extends standard principal component analysis by extracting components of activity delimited to specific frequency bands. FS-PCA revealed that circuits of the primary visual cortex generate a broad range of components dominated by low-frequency activity. Furthermore, low-dimensional fluctuations in population activity modulated the response of individual neurons to sensory input.

CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-Seq data

Most existing dimensionality reduction and clustering packages for single-cell RNA-Seq (scRNA-Seq) data deal with dropouts by heavy modelling and computational machinery. Here we introduce CIDR (Clustering through Imputation and Dimensionality Reduction), an ultrafast algorithm which uses a novel yet very simple ‘implicit imputation’ approach to alleviate the impact of dropouts in scRNA-Seq data in a principled manner. Using a range of simulated and real data, we have shown that CIDR improves the standard principal component analysis and outperforms the state-of-the-art methods, namely t-SNE, ZIFA and RaceID, in terms of clustering accuracy. CIDR typically completes within seconds for processing a data set of hundreds of cells, and minutes for a data set of thousands of cells. CIDR can be downloaded at https://github.org/VCCRI/CIDR.

Performance of Statistical Process Control Tools under Different Plant Operation Strategies

The outputs of statistical process control (SPC) tools developed for fault detection are comparatively examined while applied to actual data collected in an industrial plant. The influence of added information gathered from the plant operation under different strategies is analyzed. Particularly, standard principal component analysis (PCA), kernel PCA and the Hotelling's T2 charts are inspected for a reported problem. The effect of training the tools either with an extended historic databank obtained under standard operation, or including also non-conventional conditions, is studied. The ability of the tools to provide a specific alarm and identify the responsible variable is examined by analyzing the contributions per variable to the SPE and the T2 statistics. In addition, the capacity of the tested tools to adapt to a new operation strategy is compared.