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

Jia, Erteng; Zhou, Ying; Shi, Haojie; Pan, Min; Zhao, Xiangwei; Ge, Qinyu

doi:10.1016/j.aca.2020.10.046

Cited by 11 publications

(11 citation statements)

References 48 publications

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“…Such supportive effects of OCT may be invisible in the tumour with solid issues, since there was no difference between ‘dry’ and ‘dry’ lymph nodes with OCT. Our data suggest that the preservation time at –80°C should be maintained within 7 days before spatial sequencing. Gene capacities of sensitivity and resistance to preservation, storage and handling vary among individual gens per se, although the storage temperature and time of frozen sections could influence the RNA integrity and sequencing quality 34 …”

Section: Discussionmentioning

confidence: 99%

Clinical challenges of tissue preparation for spatial transcriptome

Liu

Jiang

Song

et al. 2022

Clinical & Translational Med

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Spatial transcriptomics is considered as an important part of spatiotemporal molecular images to bridge molecular information with clinical images. Of those potentials and opportunities, the excellent quality of human sample preparation and handling will ensure the precise and reliable information generated from clinical spatial transcriptome. The present study aims at defining potential factors that might influence the quality of spatial transcriptomics in lung cancer, para‐cancer, or normal tissues, pathological images of sections and the RNA integrity before spatial transcriptome sequencing. We categorised potential influencing factors from clinical aspects, including patient selection, pathological definition, surgical types, sample harvest, temporary preservation conditions and solutions, frozen approaches, transport and storage conditions and duration. We emphasis on the relationship between the combination of histological scores with RNA integrity number (RIN) and the unique molecular identifier (UMI), which is determines the quality of of spatial transcriptomics; however, we did not find significantly relevance between them. Our results showed that isolated times and dry conditions of sample are critical for the UMI and the quality of spatial transcriptomic samples. Thus, clinical procedures of sample preparation should be furthermore optimised and standardised as new standards of operation performance for clinical spatial transcriptome. Our data suggested that the temporary preservation time and condition of samples at operation room should be within 30 min and in ‘dry’ status. The direct cryo‐preservation within OCT media for human lung sample is recommended. Thus, we believe that clinical spatial transcriptome will be a decisive approach and bridge in the development of spatiotemporal molecular images and provide new insights for understanding molecular mechanisms of diseases at multi‐orientations.

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Section: Discussionmentioning

confidence: 99%

Clinical challenges of tissue preparation for spatial transcriptome

Liu

Jiang

Song

et al. 2022

Clinical & Translational Med

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“…We validated the single-cell transcriptional library of gelMDA using single cell level mouse brain tissue obtained by the dedicated sampler (ESM Fig. S7) [21,22]. Theoretically, the transcriptome is different from the genome, and the more cDNA amplified is not the better, but, hopefully, the less bias and the higher fidelity of expressed gene differences.…”

Section: Discussionmentioning

confidence: 99%

Low bias multiple displacement amplification with confinement effect based on agarose gel

et al. 2021

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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.

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“…In total, there are 60 genes corresponding to the expression values from time point 1 to time point 12, respectively. The data used in this study were partly obtained from previously published articles [ 20 ]. Time-specific clean data of the build sequencing results have been uploaded to the NCBI database.…”

Section: Methodsmentioning

confidence: 99%

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

Zhou

Jia

Shi

et al. 2022

IJMS

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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.

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

Cited by 11 publications

References 48 publications

Clinical challenges of tissue preparation for spatial transcriptome

Clinical challenges of tissue preparation for spatial transcriptome

Low bias multiple displacement amplification with confinement effect based on agarose gel

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

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