Effect of seasonal variation in ambient temperature on RNA quality of breast cancer tissue in a remote biobank setting

Prediction of Time-Series Transcriptomic Gene Expression Based on Long Short-Term Memory with Empirical Mode Decomposition

RNA degradation can significantly affect the results of gene expression profiling, with subsequent analysis failing to faithfully represent the initial gene expression level. It is urgent to have an artificial intelligence approach to better utilize the limited data to obtain meaningful and reliable analysis results in the case of data with missing destination time. In this study, we propose a method based on the signal decomposition technique and deep learning, named Multi-LSTM. It is divided into two main modules: One decomposes the collected gene expression data by an empirical mode decomposition (EMD) algorithm to obtain a series of sub-modules with different frequencies to improve data stability and reduce modeling complexity. The other is based on long short-term memory (LSTM) as the core predictor, aiming to deeply explore the temporal nonlinear relationships embedded in the sub-modules. Finally, the prediction results of sub-modules are reconstructed to obtain the final prediction results of time-series transcriptomic gene expression. The results show that EMD can efficiently reduce the nonlinearity of the original data, which provides reliable theoretical support to reduce the complexity and improve the robustness of LSTM models. Overall, the decomposition-combination prediction framework can effectively predict gene expression levels at unknown time points.

Effects of brain tissue section processing and storage time on gene expression

The pre-processing of samples is important factors that affect the results of the RNA-sequencing (RNA-seq) data. However, the effects of frozen sections storage conditions on the integrity of RNA and sequencing results haven't been reported. The study of frozen section protection schemes can provide reliable experimental results for single-cell and spatial transcriptome sequencing. In this study, RNA was isolated to be studied for RNA from brain section at different temperatures (RT: room temperature, -20 °C) and storage time (0 h, 2 h, 4 h, 8 h, 12 h, 16 h, 24 h, 7day, 3week, 6month). The stability of reference genes was validated using reverse transcription quantitative real-time polymerase chain reaction (qRT-PCR). The results showed that the storage at room temperature significantly affected RNA integrity number (RIN), and the RIN value was lower with the prolongation of storage, while the storage at -20 °C exerted less effect on the RIN value. Cresyl violet staining for brain tissue sections had little effect on RNA integrity. 1925, 899 and 3390 differential expression genes (DEGs) were screened at 2 h, 4 h and 8 h at room temperature, respectively. A total of 892, 478 and 619 genes were shown to be differentially expressed at -20 °C for 7d, 3w and 6 m, respectively. Among them, the expression of glycoprotein m6a (Gpm6a), calmodulin 1 (Calm1), calmodulin 1 (Calm2), thymosin, beta 4, X chromosome (Tmsb4x), ribosomal protein S21 (Rps21) and so on were correlated with RNA quality. According to the expression stability of 4 reference genes (Actb: beta-actin; Gapdh: glyceraldehyde-3-phosphate dehydrogenase; 18S: 18S ribosomal; Hprt1: hypoxanthine phosphoribosyltransferase 1), 18S is the most stable reference gene in the brain. In conclusion, the storage temperature and time of frozen sections have significant effects on RNA integrity and sequencing results. But there are still some genes that are stable and not affected by worsening of overall RNA integrity ie the decrease of RIN value. In addition, 1% cresyl violet staining can protect RNA.