Boosting the power of transcriptomics by developing an efficient gene expression profiling approach.
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ABSTRACT:
SUBMITTER: Wang J
PROVIDER: S-EPMC8710826 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In this Perspective, we discuss the current status and advances in spatial transcriptomics technologies, which allow high-resolution mapping of gene expression in intact cell and tissue samples. Spatial transcriptomics enables the creation of high-resolution maps of gene expression patterns within their native spatial context, adding an extra layer of information to the bulk sequencing data. Spatial transcriptomics has expanded significantly in recent years and is making a notable impact on a range of fields, including tissue architecture, developmental biology, cancer, and neurodegenerative and infectious diseases. The latest advancements in spatial transcriptomics have resulted in the development of highly multiplexed methods, transcriptomic-wide analysis, and single-cell resolution utilizing diverse technological approaches. In this Perspective, we provide a detailed analysis of the molecular foundations behind the main spatial transcriptomics technologies, including methods based on microdissection, in situ sequencing, single-molecule FISH, spatial capturing, selection of regions of interest, and single-cell or nuclei dissociation. We contextualize the detection and capturing efficiency, strengths, limitations, tissue compatibility, and applications of these techniques as well as provide information on data analysis. In addition, this Perspective discusses future directions and potential applications of spatial transcriptomics, highlighting the importance of the continued development to promote widespread adoption of these techniques within the research community.
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Project description:Single nucleotide polymorphism (SNP) set tests have been a powerful method in analyzing next-generation sequencing (NGS) data. The popular sequence kernel association test (SKAT) method tests a set of variants as random effects in the linear mixed model setting. Its P-value is calculated based on asymptotic theory that requires a large sample size. Therefore, it is known that SKAT is conservative and can lose power at small or moderate sample sizes. Given the current cost of sequencing technology, scales of NGS are still limited. In this report, we derive and implement computationally efficient, exact (nonasymptotic) score (eScore), likelihood ratio (eLRT), and restricted likelihood ratio (eRLRT) tests, ExactVCTest, that can achieve high power even when sample sizes are small. We perform simulation studies under various genetic scenarios. Our ExactVCTest (i.e., eScore, eLRT, eRLRT) exhibits well-controlled type I error. Under the alternative model, eScore P-values are universally smaller than those from SKAT. eLRT and eRLRT demonstrate significantly higher power than eScore, SKAT, and SKAT optimal (SKAT-o) across all scenarios and various samples sizes. We applied these tests to an exome sequencing study. Our findings replicate previous results and shed light on rare variant effects within genes. The software package is implemented in the open source, high-performance technical computing language Julia, and is freely available at https://github.com/Tao-Hu/VarianceComponentTest.jl Analysis of each trait in the exome sequencing data set with 399 individuals and 16,619 genes takes around 1 min on a desktop computer.
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Project description:Schwann cells develop from multipotent neural crest stem cells and are important for neuronal survival, maintenance of axonal integrity, and myelination. We used transgenic mice expressing green fluorescent protein in a tissue-specific manner to isolate viable, pure populations of neural crest stem cells and developing Schwann cells, which are not readily accessible by microdissection. Starting with the minute amounts of RNA obtained, a two-round amplification procedure was used to achieve reproducible DNA array hybridizations. We validated our screening procedure by comparisons with the literature and by in situ hybridization. Stage-to-stage comparisons and hierarchical clustering for neural crest and five stages of Schwann cell development suggest a wealth of candidates for genes involved in stem cell regulation and in early Schwann cell development. The combination of methods applied in this study should be generally useful for isolating and profiling other stem cell and difficult to isolate cell populations.
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Project description:Periodontitis is a highly prevalent chronic inflammatory disease of the periodontium, leading ultimately to tooth loss. In order to characterize the gene expression of periodontitis-affected gingival tissue, we have here simultaneously quantified and localized gene expression in periodontal tissue using spatial transcriptomics, combining RNA sequencing with histological analysis. Our analyses revealed distinct clusters of gene expression, which were identified to correspond to epithelium, inflamed areas of connective tissue, and non-inflamed areas of connective tissue. Moreover, 92 genes were identified as significantly up-regulated in inflamed areas of the gingival connective tissue compared to non-inflamed tissue. Among these, immunoglobulin lambda-like polypeptide 5 (IGLL5), signal sequence receptor subunit 4 (SSR4), marginal zone B and B1 cell specific protein (MZB1), and X-box binding protein 1 (XBP1) were the four most highly up-regulated genes. These genes were also verified as significantly higher expressed in gingival tissue of patients with periodontitis compared to healthy controls, using reverse transcription quantitative polymerase chain reaction. Moreover, the protein expressions of up-regulated genes were verified in gingival biopsies by immunohistochemistry. In summary, in this study, we report distinct gene expression signatures within periodontitis-affected gingival tissue, as well as specific genes that are up-regulated in inflamed areas compared to non-inflamed areas of gingival tissue. The results obtained from this study may add novel information on the genes and cell types contributing to pathogenesis of the chronic inflammatory disease periodontitis.
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Project description:BackgroundWith the increasing number of expression profiling technologies, researchers today are confronted with choosing the technology that has sufficient power with minimal sample size, in order to reduce cost and time. These depend on data variability, partly determined by sample type, preparation and processing. Objective measures that help experimental design, given own pilot data, are thus fundamental.ResultsRelative power and sample size analysis were performed on two distinct data sets. The first set consisted of Affymetrix array data derived from a nutrigenomics experiment in which weak, intermediate and strong PPARalpha agonists were administered to wild-type and PPARalpha-null mice. Our analysis confirms the hierarchy of PPARalpha-activating compounds previously reported and the general idea that larger effect sizes positively contribute to the average power of the experiment. A simulation experiment was performed that mimicked the effect sizes seen in the first data set. The relative power was predicted but the estimates were slightly conservative. The second, more challenging, data set describes a microarray platform comparison study using hippocampal deltaC-doublecortin-like kinase transgenic mice that were compared to wild-type mice, which was combined with results from Solexa/Illumina deep sequencing runs. As expected, the choice of technology greatly influences the performance of the experiment. Solexa/Illumina deep sequencing has the highest overall power followed by the microarray platforms Agilent and Affymetrix. Interestingly, Solexa/Illumina deep sequencing displays comparable power across all intensity ranges, in contrast with microarray platforms that have decreased power in the low intensity range due to background noise. This means that deep sequencing technology is especially more powerful in detecting differences in the low intensity range, compared to microarray platforms.ConclusionPower and sample size analysis based on pilot data give valuable information on the performance of the experiment and can thereby guide further decisions on experimental design. Solexa/Illumina deep sequencing is the technology of choice if interest lies in genes expressed in the low-intensity range. Researchers can get guidance on experimental design using our approach on their own pilot data implemented as a BioConductor package, SSPA http://bioconductor.org/packages/release/bioc/html/SSPA.html.
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Project description:Ketamine, a non-competitive N-methyl-d-aspartate (NMDA) receptor antagonist, is associated with accelerated neuronal apoptosis in the developing rodent brain. In this study, postnatal day (PND) 7 rats were treated with 20 mg/kg ketamine or saline in six successive doses (s.c.) at 2-h intervals. Brain frontal cortical areas were collected 6 h after the last dose and RNA isolated and hybridized to Illumina Rat Ref-12 Expression BeadChips containing 22,226 probes. Many of the differentially expressed genes were associated with cell death or differentiation and receptor activity. Ingenuity Pathway Analysis software identified perturbations in NMDA-type glutamate, GABA and dopamine receptor signaling. Quantitative polymerase chain reaction (Q-PCR) confirmed that NMDA receptor subunits were significantly up-regulated. Up-regulation of NMDA receptor mRNA signaling was further confirmed by in situ hybridization. These observations support our working hypothesis that prolonged ketamine exposure produces up-regulation of NMDA receptors and subsequent over-stimulation of the glutamatergic system by endogenous glutamate, triggering enhanced apoptosis in developing neurons.
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Project description:Microarray hybridization studies in Sézary syndrome (SS) have compared T lymphocytes from patients with cutaneous T-cell lymphoma with those of normal controls; a major limitation of this design is that significant inherent genetic variability of lymphocyte populations between individuals may produce differences in gene expression unrelated to disease state.The objective of this study was to minimize the heterogeneity of information derived from whole-genome expression analysis and to identify specific genetic differences between highly purified malignant and nonmalignant (control) T cells from the same patient with SS.Peripheral blood mononuclear cells were obtained from a patient with SS, stained with anti-T-cell receptor Vb (TCR-Vb) antibodies, and sorted by multiparameter flow cytometry. Malignant cells expressed the dominant TCR-Vb; control T cells lacked the dominant TCR-Vb but were otherwise phenotypically identical (CD3+CD4+CD45RO+). These cell populations were compared using the Illumina Inc. Sentrix Human-6 expression BeadChip system.Transcriptome analysis using the J5 test, which was selected for data analysis based on an efficiency analysis of competing statistical methods, showed differential expression of 44 genes between the malignant and nonmalignant cell subsets. Promyelocytic leukaemia zinc finger protein (ZBTB16) was the most profoundly upregulated gene in the malignant cell population, while interferon regulatory factor 3 (IRF3) and interferon-induced protein 35 (IFI35), which are important elements of the cellular response to viral infection, were significantly downregulated.The results of this study suggest the feasibility of this novel comparative approach to genomic profiling in SS. Using this method, we identified several differentially expressed genes and pathways not previously described in SS. While these findings require validation in larger studies, they may be important in SS pathogenesis.
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Project description:More studies have been conducted using gene expression similarity to identify functional connections among genes, diseases and drugs. Gene Set Enrichment Analysis (GSEA) is a powerful analytical method for interpreting gene expression data. However, due to its enormous computational overhead in the estimation of significance level step and multiple hypothesis testing step, the computation scalability and efficiency are poor on large-scale datasets. We proposed paraGSEA for efficient large-scale transcriptome data analysis. By optimization, the overall time complexity of paraGSEA is reduced from O(mn) to O(m+n), where m is the length of the gene sets and n is the length of the gene expression profiles, which contributes more than 100-fold increase in performance compared with other popular GSEA implementations such as GSEA-P, SAM-GS and GSEA2. By further parallelization, a near-linear speed-up is gained on both workstations and clusters in an efficient manner with high scalability and performance on large-scale datasets. The analysis time of whole LINCS phase I dataset (GSE92742) was reduced to nearly half hour on a 1000 node cluster on Tianhe-2, or within 120 hours on a 96-core workstation. The source code of paraGSEA is licensed under the GPLv3 and available at http://github.com/ysycloud/paraGSEA.
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Project description:The coordinated spatial and temporal regulation of gene expression in the vertebrate neural tube determines the identity of neural progenitors and the function and physiology of the neurons they generate. Progress has been made deciphering the gene regulatory programmes that are responsible for this process; however, the complexity of the tissue has hampered the systematic analysis of the network and the underlying mechanisms. To address this, we used single cell mRNA sequencing to profile cervical and thoracic regions of the developing mouse neural tube between embryonic days 9.5-13.5. We confirmed that the data accurately recapitulates neural tube development, allowing us to identify new markers for specific progenitor and neuronal populations. In addition, the analysis highlighted a previously underappreciated temporal component to the mechanisms that generate neuronal diversity, and revealed common features in the sequence of transcriptional events that lead to the differentiation of specific neuronal subtypes. Together, the data offer insight into the mechanisms that are responsible for neuronal specification and provide a compendium of gene expression for classifying spinal cord cell types that will support future studies of neural tube development, function and disease.
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Project description: Not available
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