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Transcriptome sequencing has emerged as an important research tool for exploring the mysteries of life at the single-cell level. However, its wide application is limited by the bias associated with the amplification reactions which is essential for library building of trace RNA. In this study, low-melting-point agarose was added to the amplification reactions to take advantage of its molecular crowding effect and polymer cross-linked structure to improve the sensitivity of the reactions and reduce bias. To further evaluate the performance of the method, it was applied to transcriptome sequencing of microregion samples from brain tissue sections of mice with Parkinson's disease at the single cell level. The results showed that agarose PCR had better performance than in-tube PCR. Further application of agarose PCR to transcriptome library sequencing could obtain data closer to that of unamplified. With the addition of low melting point agarose, the sensitivity of the amplification reaction was significantly increased, while homogeneity was increased by approximately 2-fold. Not only that, but this work also provides 11% sensitivity improvement for spatial transcriptomic study on Parkinson's disease-associated gene detection. The agarose PCR provides a new tool for efficient and homogeneous amplification of trace samples and can be widely used for spatial transcriptome library sequencing and studies.

PURPOSE: This study was conducted to verify if the cfDNA integrity (cfDI) in follicular fluid and subsequent spent embryo medium (SEM) could serve as potential non-invasive biomarker for high-grade embryo selection during IVF/ICSI. METHODS: Thirty-two follicular fluids, 32 subsequent corresponding cleavage embryo SEM, and 23 subsequent blastocyst SEM were collected from 11 patients undergoing IVF/ICSI. CfDI was measured by ALU gene amplicons with different sizes by qPCR, as the ratio of long to short fragments. RESULTS: CfDI in follicular fluid corresponding to subsequent high-grade cleavage embryos and blastocysts was significantly lower than that related to low-grade embryos (p = 0.018). Conversely, cfDI in SEM was significantly and positively correlated with high-grade embryos at both stages (p = 0.009). ROC curves of the analysis of cfDI in follicular fluid showed great potential in predicting subsequent embryogenesis and embryo grade (AUC > 0.927). Regardless of the cleavage embryo grade by morphology, cfDI in day 3 SEM could predict if the cleavage embryo could develop to a high-grade blastocyst (AUC = 0.820). A concordant shift pattern of cfDI from follicular fluid to subsequent day 3 SEM and day 5 SEM was found in 81.82% participants featured by various clinical characteristics. CONCLUSION: CfDI in follicular fluid and SEM was significantly correlated with embryogenesis and embryo grade and could serve as a potential non-invasive biomarker in high-grade embryo selection. Direct qPCR was proved as a labor-saving and sensitive method for the analysis of cfDI in low volume of SEM. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10815-021-02357-0.

Background Single-cell RNA sequencing (scRNA-seq) provides new insights to address biological and medical questions, and it will benefit more from the ultralow input RNA or subcellular sequencing. Results Here, we present a highly sensitive library construction protocol for ultralow input RNA sequencing (ulRNA-seq). We systematically evaluate experimental conditions of this protocol, such as reverse transcriptase, template-switching oligos (TSO), and template RNA structure. It was found that Maxima H Minus reverse transcriptase and rN modified TSO, as well as all RNA templates capped with m7G improved the sequencing sensitivity and low abundance gene detection ability. RNA-seq libraries were successfully prepared from total RNA samples as low as 0.5 pg, and more than 2000 genes have been identified. Conclusions The ability of low abundance gene detection and sensitivity were largely enhanced with this optimized protocol. It was also confirmed in single-cell sequencing, that more genes and cell markers were identified compared to conventional sequencing method. We expect that ulRNA-seq will sequence and transcriptome characterization for the subcellular of disease tissue, to find the corresponding treatment plan.

The transformation of nonmuscle-invasive bladder cancer (BLCa) to muscle-invasive type and distant metastasis are the two major threats to patients after surgery. Thus, it is important to identify the key genes of BLCa cell invasion and metastasis. Long noncoding RNA (lncRNA) is a potential clinical tool for cancer diagnosis and treatment. Herein, we verified that lncRNA SNHG3 is upregulated in human BLCa specimens and is proportional to poor clinical prognosis via a combination of bioinformatic analyses and wet bench experiments. Then, we constructed SNHG3 knockdown and overexpression cell models via lentiviral packaging and CRISPR-Cas9 technique. Fluorescence in situ hybridization assay showed that SNHG3 is distributed in both the nucleus and cytoplasm of BLCa cell lines. In vitro assays including CCK-8, EdU, colony formation, wound healing, transwell, and tube formation demonstrated that SNHG3 knockdown and overexpression potently inhibited and enhanced BLCa cell proliferation, migration, invasion, and angiogenesis. In addition, IVIS imaging revealed that SNHG3 knockdown could significantly inhibit M-NSG mice xenograft tumor growth. Next, RNA sequencing, bioinformatics analyses and western blots indicated that SNHG3 could promote c-MYC expression. RNA immunoprecipitation, actinomycin D assay and western blot assays suggested that SNHG3 could also bind c-MYC protein which subsequently facilitate the stabilization of BMI1 mRNA, thus enhancing BMI1 protein level. However, SNHG3 knockdown had a slightly weaker inhibitory effect on BMI1 expression than c-MYC knockdown. Further, in vitro assays demonstrated that BMI1 knockdown could suppress the SNHG3 activation-induced tumor promoting effect in BLCa cells. Overall, this study has provided new insights into the potential implication of lncRNA SNHG3 in the pathogenesis of BLCa. Importantly, SNHG3/c-MYC/BMI1 axis may be a novel target for regulating tumor growth and metastasis in BLCa patients.

Multiple displacement amplification (MDA) is a popular single-cell whole-genome amplification (WGA) technique that can greatly improve the amplification efficiency of single-cell genomes. However, there is an inherent problem that cannot be completely solved, that is, the amplification bias. We here propose an improved MDA method based on low melting agarose gel, named gelMDA. Firstly, the agarose gel and solution were characterized with SEM and fluorescent reagent. Then, we used gelMDA for cDNA amplification in library preparation of RNA-seq, and conventional MDA was used as a comparison. The sensitivity, efficiency of gelMDA, and amplification bias were evaluated with fluorescence curve, product yield, and the sequencing results. Finally, gelMDA was used for single-cell transcriptome sequencing. The results showed that the sensitivity and product yield of gelMDA were significantly higher than those of conventional MDA. A lower coefficient of variation (CV) and a higher reproducibility were obtained from gelMDA sequencing results. A region of 30 μm in diameter was amplified from the tissue sections and successfully sequenced. In conclusion, gelMDA obtained higher amplification efficiency and lower amplification bias in the present study. It suggested the great potential in single-cell RNA amplification and sequencing.

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.

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 signal decomposition technique and deep learning, named Multi-LSTM. It is divided into two main modules, one decomposes the collected gene expression sequences by empirical mode decomposition (EMD) algorithm to obtain a series of subsequences 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 subsequences. Finally, the prediction results of subsequences 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 sequences, which provides a 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.

The hippocampus is an important part of the limbic system in the human brain that has essential roles in spatial navigation and cognitive functions. It is still unknown how gene expression changes in single-cell in different spatial locations of the hippocampus of Parkinson’s disease. The purpose of this study was to analyze the gene expression features of single cells in different spatial locations of mouse hippocampus, and to explore the effects of gene expression regulation on learning and memory mechanisms. Here, we obtained 74 single-cell samples from different spatial locations in a mouse hippocampus through microdissection technology, and used single-cell RNA-sequencing and spatial transcriptome sequencing to visualize and quantify the single-cell transcriptome features of tissue sections. The results of differential expression analysis showed that the expression of Sv2b, Neurod6, Grp and Stk32b genes in a hippocampus single cell at different locations was significantly different, and the marker genes of CA1, CA3 and DG subregions were identified. The results of gene function enrichment analysis showed that the up-regulated differentially expressed genes Tubb2a, Eno1, Atp2b1, Plk2, Map4, Pex5l, Fibcd1 and Pdzd2 were mainly involved in neuron to neuron synapse, vesicle-mediated transport in synapse, calcium signaling pathway and neurodegenerative disease pathways, thus affecting learning and memory function. It revealed the transcriptome profile and heterogeneity of spatially located cells in the hippocampus of PD for the first time, and demonstrated that the impaired learning and memory ability of PD was affected by the synergistic effect of CA1 and CA3 subregions neuron genes. These results are crucial for understanding the pathological mechanism of the Parkinson’s disease and making precise treatment plans.

Cell-free DNA molecules are released into the plasma via apoptotic or necrotic events and active release mechanisms, which carry the genetic and epigenetic information of its origin tissues. However, cfDNA is the mixture of various cell fragments, and the efficient enrichment of cfDNA fragments with diagnostic value remains a great challenge for application in the clinical setting. Evidence from recent years shows that cfDNA fragmentomics’ characteristics differ in normal and diseased individuals without the need to distinguish the source of the cfDNA fragments, which makes it a promising novel biomarker. Moreover, cfDNA fragmentomics can identify tissue origins by inferring epigenetic information. Thus, further insights into the fragmentomics of plasma cfDNA shed light on the origin and fragmentation mechanisms of cfDNA during physiological and pathological processes in diseases and enhance our ability to take the advantage of plasma cfDNA as a molecular diagnostic tool. In this review, we focus on the cfDNA fragment characteristics and its potential application, such as fragment length, end motifs, jagged ends, preferred end coordinates, as well as nucleosome footprints, open chromatin region, and gene expression inferred by the cfDNA fragmentation pattern across the genome. Furthermore, we summarize the methods for deducing the tissue of origin by cfDNA fragmentomics.

The rate of pregnancy can be affected by many factors in assisted reproductive technology (ART), and one of which is the quality of embryos. Therefore, selecting the embryos with high potential is crucial for the outcome. Fifteen spent blastocyst medium (SBM) samples were collected from 14 patients who received in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI), seven from high-grade embryos and eight from low-grade embryos. Cell-free RNA (cf-RNA) profile of SBM samples were analyzed by RNA sequencing in the present study. It was found that a large amount of cf-RNA were released into SBM, including protein-coding genes (68.9%) and long noncoding RNAs (lncRNAs) (17.26%). Furthermore, a high correlation was observed between blastocyst genes and SBM genes. And the cf-mRNAs of SBM were highly fragmented, and coding sequence (CDS) and untranslated (UTR) regions were released equally. Two hundred and thirty-two differentially expressed genes were identified in high-grade SBM (hSBM) and low-grade SBM (lSBM), which could be potential biomarker in distinguishing the embryos with different quality as an alternative or supplementary approach for subjective morphology criteria. Hence, cf-RNAs sequencing revealed the characterization of circulating transcriptomes of embryos with different quality. Based on the results, the genes related to blastocyst quality were screened, including the genes closely related to translation, immune-signaling pathway, and amino acid metabolism. Overall, the present study showed the types of SBM cf-RNAs, and the integrated analysis of cf-RNAs profiling with morphology grading displayed its potential in predicting blastocyst quality. The present study provided valuable scientific basis for noninvasive embryo selection in ART by RNA-profiling analysis.

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.

The circRNAs sequencing results vary due to the different enrichment methods and their performance is needed to systematic comparison. This study investigated the effects of different circRNA enrichment methods on sequencing results, including abundance and species of circRNAs, as well as the sensitivity and precision. This experiment was carried out by following four common circRNA enrichment methods: including ribosomal RNA depletion (rRNA–), polyadenylation and poly (A+) RNA depletion followed by RNase R treatment (polyA+RNase R), rRNA–+polyA+RNase R and polyA+RNase R+ rRNA–. The results showed that polyA+RNase R+ rRNA – enrichment method obtained more circRNA number, higher sensitivity and abundance among them; polyA+RNase R method obtained higher precision. The linear RNAs can be thoroughly removed in all enrichment methods except rRNA depletion method. Overall, our results helps researchers to quickly selection a circRNA enrichment of suitable for own study among many enrichment methods, and it provides a benchmark framework for future improvements circRNA enrichment methods.