scholarly journals stPlus: a reference-based method for the accurate enhancement of spatial transcriptomics

2021 ◽  
Author(s):  
Shengquan Chen ◽  
Boheng Zhang ◽  
Xiaoyang Chen ◽  
Xuegong Zhang ◽  
Rui Jiang
Keyword(s):  
Gene Expression ◽  
Spatial Organization ◽  
Correlation Coefficients ◽  
Detection Sensitivity ◽  
Gene Detection ◽  
Spatially Resolved ◽  
Expression Of Genes ◽  
Number Of Genes ◽  
Joint Embedding ◽  
Spatial Cell

Motivation: Single-cell RNA sequencing (scRNA-seq) techniques have revolutionized the investigation of transcriptomic landscape in individual cells. Recent advancements in spatial transcriptomic technologies further enable gene expression profiling and spatial organization mapping of cells simultaneously. Among the technologies, imaging-based methods can offer higher spatial resolutions, while they are limited by either the small number of genes imaged or the low gene detection sensitivity. Although several methods have been proposed for enhancing spatially resolved transcriptomics, inadequate accuracy of gene expression prediction and insufficient ability of cell-population identification still impede the applications of these methods. Results: We propose stPlus, a reference-based method that leverages information in scRNA-seq data to enhance spatial transcriptomics. Based on an auto-encoder with a carefully tailored loss function, stPlus performs joint embedding and predicts spatial gene expression via a weighted k-NN. stPlus outperforms baseline methods with higher gene-wise and cell-wise Spearman correlation coefficients. We also introduce a clustering-based approach to assess the enhancement performance systematically. Using the data enhanced by stPlus, cell populations can be better identified than using the measured data. The predicted expression of genes unique to scRNA-seq data can also well characterize spatial cell heterogeneity. Besides, stPlus is robust and scalable to datasets of diverse gene detection sensitivity levels, sample sizes, and number of spatially measured genes. We anticipate stPlus will facilitate the analysis of spatial transcriptomics. Availability: stPlus with detailed documents is freely accessible at http://health.tsinghua.edu.cn/software/stPlus/ and the source code is openly available on https://github.com/xy-chen16/stPlus.

scholarly journals Missing-value imputation and in silico region detection for spatially resolved transcriptomics

2021 ◽  
Author(s):  
Linhua Wang ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
In Silico ◽  
Missing Values ◽  
Spatial Organization ◽  
Expression Profiles ◽  
New Technology ◽  
Gene Expression Profiles ◽  
Gene Interaction ◽  
Region Detection ◽  
Spatially Resolved

We are pleased to introduce a first–of–its–kind algorithm that combines in–silico region detection and spatial gene expression imputation. 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 individual tissue regions. 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 subtle intra–tissue heterogeneity and recover spatial gene–gene interaction signals. We therefore strongly encourage using MIST prior to downstream ST analysis because it provides unbiased region annotations and enables accurately de–noised spatial gene expression profiles.

scholarly journals JSTA: joint cell segmentation and cell type annotation for spatial transcriptomics

2020 ◽  
Author(s):  
Russell Littman ◽  
Zachary Hemminger ◽  
Robert Foreman ◽  
Douglas Arneson ◽  
Guanglin Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Spatial Organization ◽  
Expression Patterns ◽  
Granular Cell ◽  
Detection Sensitivity ◽  
Cell Segmentation ◽  
Specific Gene ◽  
Cell Type ◽  
Differential Gene

AbstractRNA hybridization based spatial transcriptomics provides unparalleled detection sensitivity. However, inaccuracies in segmentation of image volumes into cells cause misassignment of mRNAs which is a major source of errors. Here we develop JSTA, a computational framework for Joint cell Segmentation and cell Type Annotation that utilizes prior knowledge of cell-type specific gene expression. Simulation results show that leveraging existing cell type taxonomy increases RNA assignment accuracy by more than 45%. Using JSTA we were able to classify cells in the mouse hippocampus into 133 (sub)types revealing the spatial organization of CA1, CA3, and Sst neuron subtypes. Analysis of within cell subtype spatial differential gene expression of 80 candidate genes identified 43 with statistically significant spatial differential gene expression across 61 (sub)types. Overall, our work demonstrates that known cell type expression patterns can be leveraged to improve the accuracy of RNA hybridization based spatial transcriptomics while providing highly granular cell (sub)type information. The large number of newly discovered spatial gene expression patterns substantiates the need for accurate spatial transcriptomics measurements that can provide information beyond cell (sub)type labels.

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

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.

scholarly journals ECGpred: Correlation and Prediction of Gene Expression from Nucleotide Sequence

2008 ◽  
Vol 2 (1) ◽  
pp. 64-71 ◽  
Author(s):  
Gajendra Pal Singh Raghava ◽  
Da Jeong Hwang ◽  
Joon Hee Han
Keyword(s):  
Gene Expression ◽  
Nucleotide Sequence ◽  
Microarray Data ◽  
Correlation Coefficients ◽  
Support Vector ◽  
Reading Frame ◽  
Coding Regions ◽  
Expression Of Genes ◽  
Codon Composition ◽  
Prediction Systems

Development of gene expression prediction systems from huge amount of microarray data is an inevitable problem. In the present study a support vector machine (SVM) based method has been developed to predict expression of genes from its nucleotide sequence. In this method, SVM was trained on microarray data of genes and trained SVM was used to predict the expression of other genes of the same organism under the same condition. The SVM models were developed using nucleotide, dinucleotide, and trinucleotide composition of genes and achieved correlation coefficients (r) 0.25, 0.70, 0.82 respectively, between predicted and experimentally determined gene expression. Besides, trinucleotide composition, we also tried codon composition in each forward reading frame and achieved the correlation r = 0.86, 0.83 and 0.73 between the predicted and the actual expression using trinucleotide composition from the first, second and third frames respectively. The method was developed on 4807 genes of Saccharomyces cerevisiae obtained from Holstege et al., (1998) and evaluated using 5-fold cross validation techniques. A web server ECGpred has been developed to allow users to understand the relationship between expression and various components of genes like coding/non-coding regions, transcription factor (http://www.imtech.res.in/raghava/ecgpred/).

scholarly journals Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Madhav Mantri ◽  
Gaetano J. Scuderi ◽  
Roozbeh Abedini-Nassab ◽  
Michael F. Z. Wang ◽  
David McKellar ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cellular Differentiation ◽  
Spatial Organization ◽  
Spatial Information ◽  
Cellular Interactions ◽  
Chicken Heart ◽  
Spatially Resolved ◽  
Expression Of Genes ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA sequencing is a powerful tool to study developmental biology but does not preserve spatial information about tissue morphology and cellular interactions. Here, we combine single-cell and spatial transcriptomics with algorithms for data integration to study the development of the chicken heart from the early to late four-chambered heart stage. We create a census of the diverse cellular lineages in developing hearts, their spatial organization, and their interactions during development. Spatial mapping of differentiation transitions in cardiac lineages defines transcriptional differences between epithelial and mesenchymal cells within the epicardial lineage. Using spatially resolved expression analysis, we identify anatomically restricted expression programs, including expression of genes implicated in congenital heart disease. Last, we discover a persistent enrichment of the small, secreted peptide, thymosin beta-4, throughout coronary vascular development. Overall, our study identifies an intricate interplay between cellular differentiation and morphogenesis.

scholarly journals In vitro Maturation of Buffalo Oocytes and Expression of Selected Biomarker Candidate Genes

2021 ◽  
Author(s):  
Shilpa Doultani ◽  
Vishal S. Suthar ◽  
Chandrashekhar Mootapally ◽  
Neelam Nathani ◽  
Madhavi Joshi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Candidate Genes ◽  
In Vitro Maturation ◽  
Relative Expression ◽  
Oocyte Competence ◽  
Expression Of Genes ◽  
Number Of Genes ◽  
Lower Expression ◽  
Potential Biomarkers

Background: Number of genes expressed during in vitro maturation (IVM) of which selected genes can be used as the potential biomarkers of oocyte competence. Hence, this study was planned to evaluate selected gene expression of GDF9, HAS2, SPRY, ARHGAP22, COL18A1 and GPC4 in IVM and immature cumulus oocyte complexes (COCs). Methods: The COCs were recovered from slaughter origin ovaries of buffaloes. Of which first three grade COCs were randomly allotted in immature (IMT; n=217) and IVM group (n=272). IVM of COCs was performed under drops of BO-maturation media in CO2 incubator at 39.0°C for 24 hours. The expression of genes was evaluated using qPCR and the relative expression of each gene was calculated using the ΔΔCt method with efficiency correction. The logarithmic transformation of fold change of each candidate genes in the IVM group was computed against the IMT group using the Ct values. Result: The expression obtained for IVM group of earlier reported up-regulated genes (GDF9, HAS2, SPRY1) was higher (upto 10xfold) compared to the IMT group. Relatively lower expression was observed for the rest of the three candidate genes (ARHGAP22, COL18A1, GPC4) in the bovine transcripts of oocyte which were earlier also reported as down regulated.

scholarly journals Identification of spatial compartments in tissue from in situ sequencing data

2019 ◽  
Author(s):  
Gabriele Partel ◽  
Markus M. Hilscher ◽  
Giorgia Milli ◽  
Leslie Solorzano ◽  
Anna H. Klemm ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Resolution ◽  
Mouse Brain ◽  
Gene Expression Analysis ◽  
Spatial Organization ◽  
Biological Function ◽  
Brain Regions ◽  
Sequencing Data ◽  
Spatially Resolved

ABSTRACTSpatial organization of tissue characterizes biological function, and spatially resolved gene expression has the power to reveal variations of features with high resolution. Here, we propose a novel graph-based in situ sequencing decoding approach that improves recall, enabling precise spatial gene expression analysis. We apply our method on in situ sequencing data from mouse brain sections, identify spatial compartments that correspond with known brain regions, and relate them with tissue morphology.

scholarly journals Inferring spatially transient gene expression pattern from spatial transcriptomic studies

2020 ◽  
Author(s):  
Jan Kueckelhaus ◽  
Jasmin von Ehr ◽  
Vidhya M. Ravi ◽  
Paulina Will ◽  
Kevin Joseph ◽  
...  
Keyword(s):  
Gene Expression ◽  
Spatial Organization ◽  
Transient Gene Expression ◽  
Gene Expression Pattern ◽  
Spatially Resolved ◽  
Regional Adaptation ◽  
Spatial Trajectories ◽  
Comprehensive Characterization ◽  
Transcriptomic Studies

AbstractSpatial transcriptomic is a technology to provide deep transcriptomic profiling by preserving the spatial organization. Here, we present a framework for SPAtial Transcriptomic Analysis (SPATA, https://themilolab.github.io/SPATA), to provide a comprehensive characterization of spatially resolved gene expression, regional adaptation of transcriptional programs and transient dynamics along spatial trajectories.

scholarly journals Modulation of the ComA-Dependent Quorum Response in Bacillus subtilis by Multiple Rap Proteins and Phr Peptides

2006 ◽  
Vol 188 (14) ◽  
pp. 5273-5285 ◽  
Author(s):  
Jennifer M. Auchtung ◽  
Catherine A. Lee ◽  
Alan D. Grossman
Keyword(s):  
Gene Expression ◽  
Bacillus Subtilis ◽  
Dna Microarrays ◽  
Response Regulator ◽  
Biological Processes ◽  
Expression Of Genes ◽  
Signaling Peptides ◽  
Number Of Genes ◽  
Signaling Modules ◽  
Different Levels

ABSTRACT In Bacillus subtilis, extracellular peptide signaling regulates several biological processes. Secreted Phr signaling peptides are imported into the cell and act intracellularly to antagonize the activity of regulators known as Rap proteins. B. subtilis encodes several Rap proteins and Phr peptides, and the processes regulated by many of these Rap proteins and Phr peptides are unknown. We used DNA microarrays to characterize the roles that several rap-phr signaling modules play in regulating gene expression. We found that rapK-phrK regulates the expression of a number of genes activated by the response regulator ComA. ComA activates expression of genes involved in competence development and the production of several secreted products. Two Phr peptides, PhrC and PhrF, were previously known to stimulate the activity of ComA. We assayed the roles that PhrC, PhrF, and PhrK play in regulating gene expression and found that these three peptides stimulate ComA-dependent gene expression to different levels and are all required for full expression of genes activated by ComA. The involvement of multiple Rap proteins and Phr peptides allows multiple physiological cues to be integrated into a regulatory network that modulates the timing and magnitude of the ComA response.

scholarly journals Stress-induced differential gene expression in cardiac tissue

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ana Elisa T. S. de Carvalho ◽  
Marco A. Cordeiro ◽  
Luana S. Rodrigues ◽  
Daniela Ortolani ◽  
Regina C. Spadari
Keyword(s):  
Gene Expression ◽  
Stress Response ◽  
Left Ventricle ◽  
Differential Gene Expression ◽  
Cardiac Tissue ◽  
Stressful Situation ◽  
Adaptive Mechanism ◽  
Number Of Genes ◽  
Differential Gene ◽  
Shock Stress

AbstractThe stress response is adaptive and aims to guarantee survival. However, the persistence of a stressor can culminate in pathology. Catecholamines released as part of the stress response over activate beta adrenoceptors (β-AR) in the heart. Whether and how stress affects the expression of components of the intracellular environment in the heart is still, however, unknown. This paper used microarray to analyze the gene expression in the left ventricle wall of rats submitted to foot shock stress, treated or not treated with the selective β2-AR antagonist ICI118,551 (ICI), compared to those of non-stressed rats also treated or not with ICI, respectively. The main findings were that stress induces changes in gene expression in the heart and that β2-AR plays a role in this process. The vast majority of genes disregulated by stress were exclusive for only one of the comparisons, indicating that, in the same stressful situation, the profile of gene expression in the heart is substantially different when the β2-AR is active or when it is blocked. Stress induced alterations in the expression of such a large number of genes seems to be part of stress-induced adaptive mechanism.

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