Multiplexed detection of SARS-CoV-2 genomic and subgenomic RNA using in situ hybridization

The widespread Coronavirus Disease 2019 (COVID-19) is caused by infection with the novel coronavirus SARS-CoV-2. Currently, we have a limited toolset available for visualizing SARS-CoV-2 in cells and tissues, particularly in tissues from patients who died from COVID-19. Generally, single-molecule RNA FISH techniques have shown mixed results in formalin fixed paraffin embedded tissues such as those preserved from human autopsies. Here, we present a platform for preparing autopsy tissue for visualizing SARS-CoV-2 RNA using RNA FISH with amplification by hybridization chain reaction (HCR). We developed probe sets that target different regions of SARS-CoV-2 (including ORF1a and N) as well as probe sets that specifically target SARS-CoV-2 subgenomic mRNAs. We validated these probe sets in cell culture and tissues (lung, lymph node, and placenta) from infected patients. Using this technology, we observe distinct subcellular localization patterns of the ORF1a and N regions, with the ORF1a concentrated around the nucleus and the N showing a diffuse distribution across the cytoplasm. In human lung tissue, we performed multiplexed RNA FISH HCR for SARS-CoV-2 and cell-type specific marker genes. We found viral RNA in cells containing the alveolar type 2 (AT2) cell marker gene (SFTPC) and the alveolar macrophage marker gene (MARCO), but did not identify viral RNA in cells containing the alveolar type 1 (AT1) cell marker gene (AGER). Moreover, we observed distinct subcellular localization patterns of viral RNA in AT2 cells and alveolar macrophages, consistent with phagocytosis of infected cells. In sum, we demonstrate the use of RNA FISH HCR for visualizing different RNA species from SARS-CoV-2 in cell lines and FFPE autopsy specimens. Furthermore, we multiplex this assay with probes for cellular genes to determine what cell-types are infected within the lung. We anticipate that this platform could be broadly useful for studying SARS-CoV-2 pathology in tissues as well as extended for other applications including investigating the viral life cycle, viral diagnostics, and drug screening.

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289 COOPERATIVE EXPRESSION OF PLURIPOTENCY-RELATED GENES AND NEURAL CREST MARKER GENES IN PORCINE GFP-TRANSGENIC SKIN-DERIVED PROGENITORS

Reproduction Fertility and Development ◽

10.1071/rdv21n1ab289 ◽

2009 ◽

Vol 21 (1) ◽

pp. 241

Author(s):

M. T. Zhao ◽

C. S. Isom ◽

J. G. Zhao ◽

Y. H. Hao ◽

J. Ross ◽

...

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Neural Crest ◽

Therapeutic Potential ◽

Marker Gene ◽

Stem Cell Marker ◽

Marker Genes ◽

Specific Marker ◽

Marker Gene Expression ◽

Neural Crest Origin

Recently neural crest derived multipotent progenitors from skin have attracted much attention as the skin may provide an accessible, autologous source of stem cells available with therapeutic potential (Toma JG et al. 2001 Nat. Cell Biol. 3, 778–784). The multipotent property of stem cells could be tracked back to the expression of specific marker genes that are exclusively expressed in multipotent stem cells rather than any other types of differentiated cells. Here we demonstrate the property of multipotency and neural crest origin of porcine GFP-transgenic skin derived progenitors (termed pSKP) in vitro by marker gene expression analysis. The pSKP cells were isolated from the back skin of GFP transgenic fetuses by serum-free selection culture in the presence of EGF (20 ng mL–1) and bFGF (40 ng mL–1), and developed into spheres in 1–2 weeks (Dyce PW et al. 2004 Biochem. Biophy. Res. Commun. 316, 651–658). Three groups of RT-PCR primers were used on total RNA from purified pSKP cells: pluripotency related genes (Oct4, Sox2, Nanog, Stat3), neural crest marker genes (p75NGFR, Slug, Twist, Pax3, Sox9, Sox10) and lineage specific genes (GFAP, tubulin β-III, leptin). Expression of both pluripotency related genes and neural crest marker genes were detected in undifferentiated pSKP cells. In addition, transcripts for fibronectin, vimentin and nestin (neural stem cell marker) were also present. The percentage of positive cells for Oct4, fibronection and vimentin were 12.3%, 67.9% and 53.7% respectively. Differentiation assays showed the appearance of tubulin β-III positive (39.4%) and GFAP-positive (42.6%) cells in cultures by immunocytochemistry, which share the characteristics of neurons and glial cells, respectively. Thus, we confirm the multiple lineage potentials and neural crest origin of pSKP cells in the level of marker gene expression. This work was funded by National Institutes of Health National Center for Research Resources RR013438.

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GATA4 blocks squamous epithelial cell gene expression in human esophageal squamous cells

Scientific Reports ◽

10.1038/s41598-021-82557-x ◽

2021 ◽

Vol 11 (1) ◽

Cited By ~ 1

Author(s):

Roman Stavniichuk ◽

Ann DeLaForest ◽

Cayla A. Thompson ◽

James Miller ◽

Rhonda F. Souza ◽

...

Keyword(s):

Gene Expression ◽

Epithelial Cell ◽

Epithelial Cells ◽

Cell Fate ◽

Gene Amplification ◽

Marker Gene ◽

Ectopic Expression ◽

Marker Genes ◽

Cell Marker ◽

Squamous Epithelial Cell

AbstractGATA4 promotes columnar epithelial cell fate during gastric development. When ectopically expressed in the developing mouse forestomach, the tissue emerges as columnar-like rather than stratified squamous with gene expression changes that parallel those observed in the pre-malignant squamous to columnar metaplasia known as Barrett’s esophagus (BE). GATA4 mRNA up-regulation and gene amplification occur in BE and its associated cancer, esophageal adenocarcinoma (EAC), and GATA4 gene amplification correlates with poor patient outcomes. Here, we explored the effect of ectopic expression of GATA4 in mature human esophageal squamous epithelial cells. We found that GATA4 expression in esophageal squamous epithelial cells compromised squamous cell marker gene expression and up-regulated expression of the canonical columnar cell cytokeratin KRT8. We observed GATA4 occupancy in the p63, KRT5, and KRT15 promoters, suggesting that GATA4 directly represses expression of squamous epithelial cell marker genes. Finally, we verified GATA4 protein expression in BE and EAC and found that exposure of esophageal squamous epithelial cells to acid and bile, known BE risk factors, induced GATA4 mRNA expression. We conclude that GATA4 suppresses expression of genes marking the stratified squamous epithelial cell lineage and that this repressive action by GATA4 may have implications in BE and EAC.

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Detection of condition-specific marker genes from RNA-seq data with MGFR

PeerJ ◽

10.7717/peerj.6970 ◽

2019 ◽

Vol 7 ◽

pp. e6970 ◽

Cited By ~ 1

Author(s):

Khadija El Amrani ◽

Gregorio Alanis-Lobato ◽

Nancy Mah ◽

Andreas Kurtz ◽

Miguel A. Andrade-Navarro

Keyword(s):

Gene Expression ◽

Marker Gene ◽

Cell Types ◽

The Other ◽

Marker Genes ◽

Specific Marker ◽

Sample Type ◽

Rna Seq ◽

Cell Fate Decisions ◽

Daunting Task

The identification of condition-specific genes is key to advancing our understanding of cell fate decisions and disease development. Differential gene expression analysis (DGEA) has been the standard tool for this task. However, the amount of samples that modern transcriptomic technologies allow us to study, makes DGEA a daunting task. On the other hand, experiments with low numbers of replicates lack the statistical power to detect differentially expressed genes. We have previously developed MGFM, a tool for marker gene detection from microarrays, that is particularly useful in the latter case. Here, we have adapted the algorithm behind MGFM to detect markers in RNA-seq data. MGFR groups samples with similar gene expression levels and flags potential markers of a sample type if their highest expression values represent all replicates of this type. We have benchmarked MGFR against other methods and found that its proposed markers accurately characterize the functional identity of different tissues and cell types in standard and single cell RNA-seq datasets. Then, we performed a more detailed analysis for three of these datasets, which profile the transcriptomes of different human tissues, immune and human blastocyst cell types, respectively. MGFR’s predicted markers were compared to gold-standard lists for these datasets and outperformed the other marker detectors. Finally, we suggest novel candidate marker genes for the examined tissues and cell types. MGFR is implemented as a freely available Bioconductor package (https://doi.org/doi:10.18129/B9.bioc.MGFR), which facilitates its use and integration with bioinformatics pipelines.

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Spatial Maps of Hepatocellular Carcinoma Transcriptomes Highlight an Unexplored Landscape of Heterogeneity and a Novel Gene Signature for Survival

10.21203/rs.3.rs-812356/v1 ◽

2021 ◽

Author(s):

Nan Zhao ◽

Yanhui Zhang ◽

Runfen Cheng ◽

Danfang Zhang ◽

Fan Li ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Drug Targets ◽

Marker Gene ◽

Risk Groups ◽

Gene Signature ◽

Low Risk ◽

Marker Genes ◽

Specific Marker ◽

Gene Profiles

Abstract Background: Hepatocellular carcinoma (HCC) are often present with satellite nodules, rendering current curative treatments ineffective in many patients. The heterogeneity of HCC is a major challenge in personalized medicine. The emergence of spatial transcriptomics (ST) provides a powerful strategy for delineating the complex molecular landscapes of tumors. Methods: In this study, we investigated tissue-wide gene expression heterogeneity in tumor and adjacent nonneoplastic tissues using ST technology. We analyzed the transcriptomes of nearly 10820 tissue regions and identified main gene expression clusters and their specific marker genes (differentially expressed genes, DEGs) in patients. The DEGs were analyzed from two perspectives. First of all, we identified two distinct gene profiles associated with satellite nodules and conducted a more comprehensive analysis for both gene profiles. Their clinical relevance for human HCC was validated with KM Plotter. Secondly, we screened DEGs with TCGA database to divide the HCC cohort into high- and low-risk groups according to Cox analysis. HCC patients from the ICGC cohort were used for validation. Kaplan Meier analysis was used to compare the overall survival (OS) between high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors for OS. Results: Novel markers for the prediction of satellite nodules and a tumor clusters specific marker genes signature model(6 genes) for HCC prognosis was constructed, respectively. Conclusion: The establishment of marker gene proﬁles may be an important step towards an unbiased view of HCC and the 6-genes signature can be used for prognostic prediction in HCC. This analysis will help us to clarify one of the possible soucres of HCC heterogeneity, uncover pathogenic mechanisms and novel anti-tumor drug targets.

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Transcriptional activity of Serum Response Factor is mediated by the RhoA/Rho kinase pathway during HSC differentiation and influences expression of Smooth Muscle Cell marker genes

Zeitschrift für Gastroenterologie ◽

10.1055/s-2008-1037500 ◽

2008 ◽

Vol 46 (01) ◽

Author(s):

J Herrmann ◽

U Haas ◽

AM Gressner ◽

R Weiskirchen

Keyword(s):

Smooth Muscle ◽

Transcriptional Activity ◽

Serum Response Factor ◽

Rho Kinase ◽

Marker Genes ◽

Response Factor ◽

Cell Marker ◽

Serum Response ◽

Smooth Muscle Cell Marker ◽

Kinase Pathway

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scSorter: assigning cells to known cell types according to marker genes

Genome Biology ◽

10.1186/s13059-021-02281-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Hongyu Guo ◽

Jun Li

Keyword(s):

Real Data ◽

Cell Types ◽

Exact Expression ◽

Marker Genes ◽

Specific Marker ◽

Sequencing Data ◽

Reference Dataset ◽

Over Expression ◽

Higher Power ◽

Cell Type Specific

AbstractOn single-cell RNA-sequencing data, we consider the problem of assigning cells to known cell types, assuming that the identities of cell-type-specific marker genes are given but their exact expression levels are unavailable, that is, without using a reference dataset. Based on an observation that the expected over-expression of marker genes is often absent in a nonnegligible proportion of cells, we develop a method called scSorter. scSorter allows marker genes to express at a low level and borrows information from the expression of non-marker genes. On both simulated and real data, scSorter shows much higher power compared to existing methods.

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Multi-view feature selection for identifying gene markers: a diversified biological data driven approach

BMC Bioinformatics ◽

10.1186/s12859-020-03810-0 ◽

2020 ◽

Vol 21 (S18) ◽

Author(s):

Sudipta Acharya ◽

Laizhong Cui ◽

Yi Pan

Keyword(s):

Gene Expression ◽

Feature Selection ◽

Gene Selection ◽

Marker Gene ◽

Biological Data ◽

Protein Interaction Data ◽

Marker Genes ◽

Data Sets ◽

Gene Markers ◽

Multi Objective

Abstract Background In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. Results In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. Conclusion A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

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Potential roles of stem cell marker genes in axon regeneration

Experimental & Molecular Medicine ◽

10.1038/s12276-020-00553-z ◽

2021 ◽

Vol 53 (1) ◽

pp. 1-7

Author(s):

Jinyoung Lee ◽

Yongcheol Cho

Keyword(s):

Stem Cell ◽

Expression Profiling ◽

Axon Regeneration ◽

Stem Cell Marker ◽

Marker Genes ◽

Cell Marker ◽

Regenerative Capacity ◽

Regeneration Mechanism ◽

Central Nervous Systems

AbstractAxon regeneration is orchestrated by many genes that are differentially expressed in response to injury. Through a comparative analysis of gene expression profiling, injury-responsive genes that are potential targets for understanding the mechanisms underlying regeneration have been revealed. As the efficiency of axon regeneration in both the peripheral and central nervous systems can be manipulated, we suggest that identifying regeneration-associated genes is a promising approach for developing therapeutic applications in vivo. Here, we review the possible roles of stem cell marker- or stemness-related genes in axon regeneration to gain a better understanding of the regeneration mechanism and to identify targets that can enhance regenerative capacity.

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PPIT: an R package for inferring microbial taxonomy from nifH sequences

Bioinformatics ◽

10.1093/bioinformatics/btab100 ◽

2021 ◽

Author(s):

Bennett J Kapili ◽

Anne E Dekas

Keyword(s):

Gene Transfer ◽

Horizontal Gene Transfer ◽

Query Sequence ◽

Marker Gene ◽

R Package ◽

Supplementary Information ◽

Marker Genes ◽

Pairwise Identity ◽

Metabolic Marker ◽

Microbial Taxonomy

Abstract Motivation Linking microbial community members to their ecological functions is a central goal of environmental microbiology. When assigned taxonomy, amplicon sequences of metabolic marker genes can suggest such links, thereby offering an overview of the phylogenetic structure underpinning particular ecosystem functions. However, inferring microbial taxonomy from metabolic marker gene sequences remains a challenge, particularly for the frequently sequenced nitrogen fixation marker gene, nitrogenase reductase (nifH). Horizontal gene transfer in recent nifH evolutionary history can confound taxonomic inferences drawn from the pairwise identity methods used in existing software. Other methods for inferring taxonomy are not standardized and require manual inspection that is difficult to scale. Results We present Phylogenetic Placement for Inferring Taxonomy (PPIT), an R package that infers microbial taxonomy from nifH amplicons using both phylogenetic and sequence identity approaches. After users place query sequences on a reference nifH gene tree provided by PPIT (n = 6317 full-length nifH sequences), PPIT searches the phylogenetic neighborhood of each query sequence and attempts to infer microbial taxonomy. An inference is drawn only if references in the phylogenetic neighborhood are: (1) taxonomically consistent and (2) share sufficient pairwise identity with the query, thereby avoiding erroneous inferences due to known horizontal gene transfer events. We find that PPIT returns a higher proportion of correct taxonomic inferences than BLAST-based approaches at the cost of fewer total inferences. We demonstrate PPIT on deep-sea sediment and find that Deltaproteobacteria are the most abundant potential diazotrophs. Using this dataset we show that emending PPIT inferences based on visual inspection of query sequence placement can achieve taxonomic inferences for nearly all sequences in a query set. We additionally discuss how users can apply PPIT to the analysis of other marker genes. Availability PPIT is freely available to non-commercial users at https://github.com/bkapili/ppit. Installation includes a vignette that demonstrates package use and reproduces the nifH amplicon analysis discussed here. The raw nifH amplicon sequence data have been deposited in the GenBank, EMBL, and DDBJ databases under BioProject number PRJEB37167. Supplementary information Supplementary data are available at Bioinformatics online.

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