Spatially Resolved Expression of Transposable Elements in Disease and Somatic Tissue with SpatialTE

Spatial transcriptomics (ST) is transforming the way we can study gene expression and its regulation through position-specific resolution within tissues. However, as in bulk RNA-Seq, transposable elements (TEs) are not being studied due to their highly repetitive nature. In recent years, TEs have been recognized as important regulators of gene expression, and thus, TE expression analysis in a spatially resolved manner could further help to understand their role in gene regulation within tissues. We present SpatialTE, a tool to analyze TE expression from ST datasets and show its application in somatic and diseased tissues. The results indicate that TEs have spatially regulated expression patterns and that their expression profiles are spatially altered in ALS disease, indicating that TEs might perform differential regulatory functions within tissue organs. We have made SpatialTE publicly available as open-source software under an MIT license.

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Interferon-regulated genetic programs and JAK/STAT pathway activate the intronic promoter of the short ACE2 isoform in renal proximal tubules

10.1101/2021.01.15.426908 ◽

2021 ◽

Author(s):

Jakub Jankowski ◽

Hye Kyung Lee ◽

Julia Wilflingseder ◽

Lothar Hennighausen

Keyword(s):

Gene Expression ◽

Proximal Tubule ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Regulatory Elements ◽

Rna Seq ◽

Angiotensin Converting Enzyme 2 ◽

Genome Wide ◽

Cytokine Stimulation

SummaryRecently, a short, interferon-inducible isoform of Angiotensin-Converting Enzyme 2 (ACE2), dACE2 was identified. ACE2 is a SARS-Cov-2 receptor and changes in its renal expression have been linked to several human nephropathies. These changes were never analyzed in context of dACE2, as its expression was not investigated in the kidney. We used Human Primary Proximal Tubule (HPPT) cells to show genome-wide gene expression patterns after cytokine stimulation, with emphasis on the ACE2/dACE2 locus. Putative regulatory elements controlling dACE2 expression were identified using ChIP-seq and RNA-seq. qRT-PCR differentiating between ACE2 and dACE2 revealed 300- and 600-fold upregulation of dACE2 by IFNα and IFNβ, respectively, while full length ACE2 expression was almost unchanged. JAK inhibitor ruxolitinib ablated STAT1 and dACE2 expression after interferon treatment. Finally, with RNA-seq, we identified a set of genes, largely immune-related, induced by cytokine treatment. These gene expression profiles provide new insights into cytokine response of proximal tubule cells.

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Deciphering spatial domains from spatially resolved transcriptomics with adaptive graph attention auto-encoder

10.1101/2021.08.21.457240 ◽

2021 ◽

Author(s):

Kangning Dong ◽

Shihua Zhang

Keyword(s):

Gene Expression ◽

Spatial Information ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Identification Accuracy ◽

Spatial Context ◽

Gene Expressions ◽

Spatially Resolved ◽

Spatial Domains

Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining spatial context of tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to use their spatial information carefully. To this end, we developed a graph attention auto- encoder framework STGATE to accurately identify spatial domains by learning low-dimensional latent embeddings via integrating spatial information and gene expression profiles. To better characterize the spatial similarity at the boundary of spatial domains, STGATE adopts an attention mechanism to adaptively learn the similarity of neighboring spots, and an optional cell type-aware module through integrating the pre-clustering of gene expressions. We validated STGATE on diverse spatial transcriptomics datasets generated by different platforms with different spatial resolutions. STGATE could substantially improve the identification accuracy of spatial domains, and denoise the data while preserving spatial expression patterns. Importantly, STGATE could be extended to multiple consecutive sections for reducing batch effects between sections and extracting 3D expression domains from the reconstructed 3D tissue effectively.

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Identifying core biological processes distinguishing human eye tissues with precise systems-level gene expression analyses and weighted correlation networks

10.1101/136960 ◽

2017 ◽

Author(s):

John M Bryan ◽

Temesgen D Fufa ◽

Kapil Bharti ◽

Brian P Brooks ◽

Robert B Hufnagel ◽

...

Keyword(s):

Gene Expression ◽

Expression Patterns ◽

Mouse Retina ◽

Human Tissues ◽

Biological Processes ◽

Rna Seq ◽

Human Eye ◽

Correlation Networks ◽

Eye Tissues ◽

Study Gene Expression

AbstractThe human eye is built from several specialized tissues which direct, capture, and pre-process information to provide vision. The gene expression of the different eye tissues has been extensively profiled with RNA-seq across numerous studies. Large consortium projects have also used RNA-seq to study gene expression patterning across many different human tissues, minus the eye. There has not been an integrated study of expression patterns from multiple eye tissues compared to other human body tissues. We have collated all publicly available healthy human eye RNA-seq datasets as well as dozens of other tissues. We use this fully integrated dataset to probe the biological processes and pan expression relationships between the cornea, retina, RPE-choroid complex, and the rest of the human tissues with differential expression, clustering, and GO term enrichment tools. We also leverage our large collection of retina and RPE-choroid tissues to build the first human weighted gene correlation networks and use them to highlight known biological pathways and eye gene disease enrichment. We also have integrated publicly available single cell RNA-seq data from mouse retina into our framework for validation and discovery. Finally, we make all these data, analyses, and visualizations available via a powerful interactive web application (https://eyeintegration.nei.nih.gov/).

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Feasibility assessment of GI tumor stroma gene expression profiles with microarray technology in formalin-fixed, paraffin-embedded (FFPE) specimens.

Journal of Clinical Oncology ◽

10.1200/jco.2011.29.4_suppl.232 ◽

2011 ◽

Vol 29 (4_suppl) ◽

pp. 232-232

Author(s):

S. X. Wang ◽

J. Wang ◽

M. S. Beg ◽

E. Ozhegov ◽

A. X. Qu ◽

...

Keyword(s):

Gene Expression ◽

Gene Annotation ◽

Expression Profiles ◽

Expression Patterns ◽

Critical Role ◽

Gene Expression Profiles ◽

Tumor Stroma ◽

Pancreatic Tissue ◽

Feasibility Assessment ◽

Study Gene Expression

232 Background: Cancer stroma plays a critical role in tumorigenesis. Microarrays are widely used to study gene expression patterns in tumor frozen tissue to explore tumorigenesis and predict recurrence. Here we explored the feasibility of applying microarrays to tumor stroma using FFPE specimens. Methods: Ten samples were obtained from one colon and one pancreatic cancer specimen. Tumor stroma and normal stroma were microdissected. RNA was extracted, amplified, and labeled using Nugene FFPE kit, with array analysis using Affymetrix Human Gene 1.0. To assess reproducibility, each stroma sample was run twice in parallel. To test the reproducibility of sampling, the specimen was microdissected twice at a different stroma location. GenePattern software was used; correlation between parallel samples and the repeat dissection samples was calculated and ComparativeMarkerSelection program with two-sided T test was used to study gene profiles in pancreatic and colon stroma. To validate the array results, the profile differences were analyzed against public databases. Results: The correlation between the duplicate samples was in the range of 0.978-0.988. The correlation between the original versus the repeat dissection at a different stroma site was 0.953-0.967. 19,319 probes with gene annotation were examined, and pancreatic stroma was compared to colon stroma. We found that 90% of these genes were expressed at similar levels. Using a cutoff of 1.3, we identified 50 genes that were expressed greater than 30% in pancreatic stroma compared to colon stroma. We researched these 50 genes against GeneNote, GNF, Unigene and SAGE databases. Upregulation of 95% of these genes in pancreatic stroma is consistent with the public databases. Moreover, among these genes we identified PNLIP, CPB1 CPA2, PRSS1/PRSS2, AMY1A, AMY1B, AMY1C, and AMY2A, known to be highly expressed in pancreatic tissue. Conclusions: Gene expression microarray is a tool that can be effectively used to analyze normal and tumor stroma in GI cancer. FFPE tissue is suitable for such an analysis, as evidenced by high reproducibility and biologic consistency of the obtained data. No significant financial relationships to disclose.

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Bioinformatics approach to spatially resolved transcriptomics

Emerging Topics in Life Sciences ◽

10.1042/etls20210131 ◽

2021 ◽

Author(s):

Ivan Krešimir Lukić

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Patterns ◽

Spatial Dimension ◽

Medical Diagnostics ◽

Regions Of Interest ◽

Rna Seq ◽

Neoplastic Tissue ◽

Spatially Resolved ◽

Cell Assays

Spatially resolved transcriptomics encompasses a growing number of methods developed to enable gene expression profiling of individual cells within a tissue. Different technologies are available and they vary with respect to: the method used to define regions of interest, the method used to assess gene expression, and resolution. Since techniques based on next-generation sequencing are the most prevalent, and provide single-cell resolution, many bioinformatics tools for spatially resolved data are shared with single-cell RNA-seq. The analysis pipelines diverge at the level of quantification matrix, downstream of which spatial techniques require specific tools to answer key biological questions. Those questions include: (i) cell type classification; (ii) detection of genes with specific spatial distribution; (iii) identification of novel tissue regions based on gene expression patterns; (iv) cell–cell interactions. On the other hand, analysis of spatially resolved data is burdened by several specific challenges. Defining regions of interest, e.g. neoplastic tissue, often calls for manual annotation of images, which then poses a bottleneck in the pipeline. Another specific issue is the third spatial dimension and the need to expand the analysis beyond a single slice. Despite the problems, it can be predicted that the popularity of spatial techniques will keep growing until they replace single-cell assays (which will remain limited to specific cases, like blood). As soon as the computational protocol reach the maturity (e.g. bulk RNA-seq), one can foresee the expansion of spatial techniques beyond basic or translational research, even into routine medical diagnostics.

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Identification of spatial co-expression patterns and intra-tissue heterogeneity in spatially resolved transcriptomics by region-specific denoising

10.21203/rs.3.rs-647777/v1 ◽

2021 ◽

Author(s):

Linhua Wang ◽

Zhandong Liu

Keyword(s):

Gene Expression ◽

Missing Values ◽

Spatial Organization ◽

Expression Profiles ◽

New Technology ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Tissue Heterogeneity ◽

Region Detection ◽

Spatially Resolved

Abstract We are pleased to introduce a first-of-its-kind tool that combines in-silico region detection and missing value estimation for spatially resolved transcriptomics. Spatial transcriptomics by 10X Visium (ST) is a new technology used to dissect gene and cell spatial organization. Analyzing this new type of data has two main challenges: automatically annotating the major tissue regions and excessive zero values of gene expression due to high dropout rates. We developed a computational tool—MIST—that addresses both challenges by automatically identifying tissue regions and estimating missing gene-expression values for each detected region. We validated MIST detected regions across multiple datasets using manual annotation on the histological staining images as references. We also demonstrated that MIST can accurately recover ST’s missing values through hold-out experiments. Furthermore, we showed that MIST could identify intra-tissue heterogeneity and recover spatial gene-gene co-expression signals. We therefore strongly encourage using MIST before downstream ST analysis because it provides unbiased region annotations and enables accurately denoised spatial gene-expression profiles.

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Identification of unique gene expression signatures in colon cancer stroma using microarray technology.

Journal of Clinical Oncology ◽

10.1200/jco.2011.29.4_suppl.447 ◽

2011 ◽

Vol 29 (4_suppl) ◽

pp. 447-447

Author(s):

S. X. Wang ◽

J. Wang ◽

E. Ozhegov ◽

R. Srinivasan ◽

O. O. Olowokure ◽

...

Keyword(s):

Gene Expression ◽

Colon Cancer ◽

Pathway Analysis ◽

Tumor Invasion ◽

Expression Profiles ◽

Expression Patterns ◽

Tumor Stroma ◽

Gene Signature ◽

Specific Gene ◽

Study Gene Expression

447 Background: Microarrays are widely used to study gene expression patterns in cancer. In colorectal cancer, it has proven useful to predict recurrence. The majority of expression profiles are generated from the cancer itself. Given the increasing evidence of importance of the microenvironment for tumor invasion, progression and metastasis, we explored tumor stroma gene signature using microarrays. Methods: Four formalin-fixed paraffin embedded (FFPE) colon cancer specimen carrying a pathological stage of T3-4/N0-2 were retrieved. Tumor stroma and normal stroma were separated using microdissection technology. Random sampling was used to minimize sampling bias. Total RNA was extracted, amplified, and labeled using Nugene FFPE kit, with array analysis using Affymetrix Human Gene 1.0. Eight samples, four normal stroma and four tumor stroma, were analyzed and compared. Array data were balanced and analyzed using standard software. To identify gene signature differences in tumor vs normal stroma, ComparativeMarker analysis and unsupervised cluster analysis were carried out. Results: We identified a 969 Affymetrix probe set as a signature that is highly expressed in tumor stroma. The top 117 genes were further analyzed to carry out a pathway analysis. We found a strong signature evident in tumor stroma, and much of this signature comprised the genes of the extracellular matrix. The pathway analysis revealed evidence of the generalized IGF1/TGFbeta/CTGF/activin mediated effect on the stroma, raising the possibility that some of this derives from, or is accompanied by, angiotensin receptor signaling. Through literature search, we found that several upregulated genes (e.g., FAP) were reported to be associated with stroma activation in vitro and in vivo. Conclusions: In this study, we successfully applied microarrays to study reactive colon tumor stroma in FFPE samples. We identified a specific gene signature reflecting stromal reaction to tumor invasion. We further identified the potential pathway that was activated in the reactive tumor stroma. We provide evidence that microarrays are useful in stroma analysis and may help identify new stromal pathways with potential diagnostic and therapeutic value. No significant financial relationships to disclose.

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Co-Expression Network-based Analysis associated with potato initial resistance

10.1101/496075 ◽

2018 ◽

Author(s):

Lang Yan ◽

Xianjun Lai ◽

Yan Wu ◽

Xuemei Tan ◽

Yizheng Zhang ◽

...

Keyword(s):

Gene Expression ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Evolutionary Divergence ◽

Regulation Mechanism ◽

Rna Seq ◽

Functional Studies ◽

Study Gene Expression ◽

Gene Modules ◽

Dramatic Shift

RNA sequencing (RNA-seq) providing genome-wide expression datasets has been successfully used to study gene expression patterns and regulation mechanism among multiple samples. Gene co-expression networks (GCNs) studies within or across species showed that coordinated genes in expression patterns are often functionally related. For potatoes, a large amount of publicly available transcriptome datasets have been generated but an optimal GCN detecting expression patterns in different genotypes, tissues and environmental conditions, is lacking. We constructed a potato GCN using 16 published RNA-Seq datasets covering 11 cultivars from native habitat worldwide. The correlations of gene expression were assessed pair-wisely and biologically meaningful gene modules which are highly connected in GCN were identified. One of the primitively native-farmer-selected cultivars in the Andes, ssp.Andigena, had relative far distance in gene expression patterns with other modern varieties. GCN in further enriched 134 highly and specifically co-expressed genes in ssp.Andigena associated with potato disease and stress resistance, which underlying the dramatic shift in evolutionary pressures during potato artificial domestication. In total, the network was consisted of into 14 gene models that involves in a variety of plant processes, which sheds light on how gene modules organized intra- and inter-varieties in the context of evolutionary divergence and provides a basis of information resource for potato gene functional studies.

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Identification of Differentially Expressed Genes Using cDNA-AFLP During Six Days In Vitro Tuberization of Potato

Zuriat ◽

10.24198/zuriat.v14i1.6751 ◽

2015 ◽

Vol 14 (1) ◽

Author(s):

Nono Carsono ◽

Christian Bachem

Keyword(s):

Gene Expression ◽

Developmental Process ◽

Expression Patterns ◽

Induction Medium ◽

In Vitro Tuberization ◽

Storage Organ ◽

Developmentally Regulated ◽

Study Gene Expression ◽

Differential Pattern

Tuberization in potato is a complex developmental process resulting in the differentiation of stolon into the storage organ, tuber. During tuberization, change in gene expression has been known to occur. To study gene expression during tuberization over the time, in vitro tuberization system provides a suitable tool, due to its synchronous in tuber formation. An early six days axillary bud growing on tuber induction medium is a crucial development since a large number of genes change in their expression patterns during this period. In order to identify, isolate and sequencing the genes which displaying differential pattern between tuberizing and non-tuberizing potato explants during six days in vitro tuberization, cDNA-AFLP fingerprint, method for the visualization of gene expression using cDNA as template which is amplified to generate an RNA-fingerprinting, was used in this experiment. Seventeen primer combinations were chosen based on their expression profile from cDNA-AFLP fingerprint. Forty five TDFs (transcript derived fragment), which displayed differential expressions, were obtained. Tuberizing explants had much more TDFs, which developmentally regulated, than those from non tuberizing explants. Seven TDFs were isolated, cloned and then sequenced. One TDF did not find similarity in the current databases. The nucleotide sequence of TDF F showed best similarity to invertase ezymes from the databases. The homology of six TDFs with known sequences is discussed in this paper.

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Transcriptomal dissection of soybean circadian rhythmicity in two geographically, phenotypically and genetically distinct cultivars

BMC Genomics ◽

10.1186/s12864-021-07869-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yanlei Yue ◽

Ze Jiang ◽

Enoch Sapey ◽

Tingting Wu ◽

Shi Sun ◽

...

Keyword(s):

Gene Expression ◽

Circadian Clock ◽

Chromosome Structure ◽

Clock Genes ◽

Expression Profiles ◽

Circadian Rhythmicity ◽

Rna Seq ◽

Night Time ◽

Time Points ◽

Circadian Clock Genes

Abstract Background In soybean, some circadian clock genes have been identified as loci for maturity traits. However, the effects of these genes on soybean circadian rhythmicity and their impacts on maturity are unclear. Results We used two geographically, phenotypically and genetically distinct cultivars, conventional juvenile Zhonghuang 24 (with functional J/GmELF3a, a homolog of the circadian clock indispensable component EARLY FLOWERING 3) and long juvenile Huaxia 3 (with dysfunctional j/Gmelf3a) to dissect the soybean circadian clock with time-series transcriptomal RNA-Seq analysis of unifoliate leaves on a day scale. The results showed that several known circadian clock components, including RVE1, GI, LUX and TOC1, phase differently in soybean than in Arabidopsis, demonstrating that the soybean circadian clock is obviously different from the canonical model in Arabidopsis. In contrast to the observation that ELF3 dysfunction results in clock arrhythmia in Arabidopsis, the circadian clock is conserved in soybean regardless of the functional status of J/GmELF3a. Soybean exhibits a circadian rhythmicity in both gene expression and alternative splicing. Genes can be grouped into six clusters, C1-C6, with different expression profiles. Many more genes are grouped into the night clusters (C4-C6) than in the day cluster (C2), showing that night is essential for gene expression and regulation. Moreover, soybean chromosomes are activated with a circadian rhythmicity, indicating that high-order chromosome structure might impact circadian rhythmicity. Interestingly, night time points were clustered in one group, while day time points were separated into two groups, morning and afternoon, demonstrating that morning and afternoon are representative of different environments for soybean growth and development. However, no genes were consistently differentially expressed over different time-points, indicating that it is necessary to perform a circadian rhythmicity analysis to more thoroughly dissect the function of a gene. Moreover, the analysis of the circadian rhythmicity of the GmFT family showed that GmELF3a might phase- and amplitude-modulate the GmFT family to regulate the juvenility and maturity traits of soybean. Conclusions These results and the resultant RNA-seq data should be helpful in understanding the soybean circadian clock and elucidating the connection between the circadian clock and soybean maturity.

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