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

Effect of Different Staining Methods on Brain Cryosections

Staining frozen sections is often required to distinguish cell types for spatial transcriptomic studies of the brain. The impact of the staining methods on the RNA integrity of the cells becomes one of the limitations of spatial transcriptome technology with microdissection. However, there is a lack of systematic comparisons of different staining modalities for the pretreatment of frozen sections of brain tissue as well as their effects on transcriptome sequencing results. In this study, four different staining methods were analyzed for their effect on RNA integrity in frozen sections of brain tissue. Subsequently, differences in RNA quality in frozen sections under different staining conditions and their impact on transcriptome sequencing results were assessed by RNA-seq. As one of the most commonly used methods for staining pathological sections, HE staining seriously affects the RNA quality of frozen sections of brain tissue. In contrast, the homemade cresyl violet staining method developed in this study has the advantages of short staining time, low cost, and less RNA degradation. The homemade cresyl violet staining proposed in this study can be applied instead of HE staining as an advance staining step for transcriptome studies in frozen sections of brain tissue. In the future, this staining method may be suitable for wide application in brain-related studies of frozen tissue sections. Moreover, it is expected to become a routine step for staining cells before sampling in brain science.

Laser axial scanning microdissection for high-efficiency dissection from uneven biological samples

Fast and efficient separation of target samples is crucial for the application of laser-assisted microdissection in the molecular biology research field. Herein, we developed a laser axial scanning microdissection (LASM) system with an 8.6 times extended depth of focus by using an electrically tunable lens. We showed that the ablation quality of silicon wafers at different depths became homogenous after using our system. More importantly, for those uneven biological tissue sections within a height difference of no more than 19.2 µm, we have demonstrated that the targets with a size of microns at arbitrary positions can be dissected efficiently without additional focusing and dissection operations. Besides, dissection experiments on various biological samples with different embedding methods, which were widely adopted in biological experiments, also have shown the feasibility of our system.

Selection of reference genes in liproxstatin-1-treated K562 Leukemia cells via RT-qPCR and RNA sequencing

BACKGROUND

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) can accurately detect relative gene expression levels in biological samples. However, widely used reference genes exhibit unstable expression under certain conditions.

METHODS AND RESULTS

Here, we compared the expression stability of eight reference genes (RPLP0, RPS18, RPL13, EEF1A1, β-actin, GAPDH, HPRT1, and TUBB) commonly used in liproxstatin-1 (Lip-1)-treated K562 cells using RNA-sequencing and RT-qPCR. The expression of EEF1A1, ACTB, GAPDH, HPRT1, and TUBB was considerably lower in cells treated with 20 μM Lip-1 than in the control, and GAPDH also showed significant downregulation in the 10 μM Lip-1 group. Meanwhile, when we used geNorm, NormFinder, and BestKeeper to compare expression stability, we found that GAPDH and HPRT1 were the most unstable reference genes among all those tested. Stability analysis yielded very similar results when geNorm or BestKeeper was used but not when NormFinder was used. Specifically, geNorm and BestKeeper identified RPL13 and RPLP0 as the most stable genes under 20 μM Lip-1 treatment, whereas RPL13, EEF1A1, and TUBB were the most stable under 10 μM Lip-1 treatment. TUBB and EEF1A1 were the most stable genes in both treatment groups according to the results obtained using NormFinder. An assumed most stable gene was incorporated into each software to validate the accuracy. The results suggest that NormFinder is not an appropriate algorithm for this study.

CONCLUSIONS

Stable reference genes were recognized using geNorm and BestKeeper but not NormFinder. Overall, RPL13 and RPLP0 were the most stable reference genes under 20 μM Lip-1 treatment, whereas RPL13, EEF1A1, and TUBB were the most stable genes under 10 μM Lip-1 treatment.

Transcriptomic profiling of nuclei from paraformaldehyde-fixed and formalin-fixed paraffin-embedded brain tissues

BACKGROUND

Paraformaldehyde (PFA) fixation is necessary for histochemical staining, and formalin-fixed and paraffin-embedded (FFPE) tissue archives are the largest repository of clinically annotated specimens. Single-cell gene expression workflows have recently been developed for PFA-fixed and FFPE tissue specimens. However, for tissues where intact cells are hard to recover, including tissues containing highly interconnected neurons, single-nuclear transcriptomics is beneficial. Moreover, since RNA is very unstable, the effects of standard pathological practice on the transcriptome of samples obtained from such archived specimens like FFPE samples are largely anecdotal.

RESULTS

We evaluated the effects of polyformaldehyde (PFA) fixation and paraffin-embedding on transcriptional profiles of the mouse hippocampus obtained by RNA sequencing (RNA-seq). The transcriptomic signatures of nuclei isolated from fresh PFA-fixed and fresh FFPE tissues were comparable to those of cryopreserved samples. However, more differentially expressed genes were obtained for brains after PFA fixation for more than 3 days than in fresh PFA-fixed samples, especially genes involved in spliceosome and synaptic-related pathways. Importantly, the real cell states were destroyed, with oligodendrocyte precursor cells depleted in the 1day fixed hippocampus. After fixation for 3 days, the proportions of neuronal cells and oligodendrocytes decreased and microglia increased; however, relative frequencies remained constant for longer fixation durations. The storage time of FFPE samples had a negligible effect on the cell composition.

SIGNIFICANCE

This represents the first work to investigate the effects of fixation and storage time of brains on its nuclear transcriptome signatures in detail. The fixation time had more influences on the nuclear transcriptomic profiles than FFPE retention time, and the cliff-like effects appeared to occur over a fixed period of 1-3 days. These findings are expected to guide sample preparation for single-nucleus RNA-seq of FFPE samples, particularly in transcriptomic studies focused on brain diseases.

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 Freezing and Rewarming Methods on RNA Quality of Blood Samples

Background: RNA extracted from human blood has been widely applied to biological, medical, and clinical research of numerous diseases. Previous studies have demonstrated that high-quality RNA is indispensable to guarantee the reliability of downstream assays. In this study, we investigated the effects of freezing procedures, rewarming methods, and blood components on RNA quality of blood samples. Methods: Rabbit blood samples were divided into two groups: (1) whole blood (WB) and (2) blood cell components (BCC) with plasma removed. Samples were frozen using four representative freezing procedures (snap freezing in liquid nitrogen, snap freezing at -80°C, traditional slow freezing, and programmable controlled rate freezing) and rewarmed by placing at 4°C or by vortexing. RNA was extracted using the phenol-chloroform RNA extraction method and measured by an Agilent bioanalyzer. Then, human blood was used to verify the best protocol obtained from the rabbit blood experiment. Results: For the four freezing procedures, there were no differences in RNA integrity. For different rewarming methods, RNA integrity number (RIN) values of RNA extracted from frozen WB and BCC samples in the vortex group were above 9, while RNA obtained from WB showed worse quality compared with BCC in the 4°C group. For verification using human blood, RIN values of frozen human WB rewarmed by vortexing ranged from 8.0 to 9.1. Conclusions: Blood components and rewarming methods could affect the RNA quality of blood samples. For scenarios where WB samples have already been cryopreserved, the vortex rewarming method is optimal for high-quality RNA. Otherwise, we would recommend centrifuging fresh WB and cryopreserving it in the form of BCC, which showed a tendency to obtain high-quality RNA by either of the two rewarming methods.

Clinical challenges of tissue preparation for spatial transcriptome

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.

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

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.