scholarly journals Inferring transcriptomic cell states and transitions only from time series transcriptome data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kyuri Jo ◽  
Inyoung Sung ◽  
Dohoon Lee ◽  
Hyuksoon Jang ◽  
Sun Kim
Keyword(s):  
Time Series ◽  
Molecular Level ◽  
Gaussian Process Regression ◽  
Cell Types ◽  
Biological Processes ◽  
Transcriptome Data ◽  
Sequencing Data ◽  
Additional Information ◽  
Cluster Networks ◽  
Genetic Perturbations

AbstractCellular stages of biological processes have been characterized using fluorescence-activated cell sorting and genetic perturbations, charting a limited landscape of cellular states. Time series transcriptome data can help define new cellular states at the molecular level since the analysis of transcriptional changes can provide information on cell states and transitions. However, existing methods for inferring cell states from transcriptome data use additional information such as prior knowledge on cell types or cell-type-specific markers to reduce the complexity of data. In this study, we present a novel time series clustering framework to infer TRAnscriptomic Cellular States (TRACS) only from time series transcriptome data by integrating Gaussian process regression, shape-based distance, and ranked pairs algorithm in a single computational framework. TRACS determines patterns that correspond to hidden cellular states by clustering gene expression data. TRACS was used to analyse single-cell and bulk RNA sequencing data and successfully generated cluster networks that reflected the characteristics of key stages of biological processes. Thus, TRACS has a potential to help reveal unknown cellular states and transitions at the molecular level using only time series transcriptome data. TRACS is implemented in Python and available at http://github.com/BML-cbnu/TRACS/.

scholarly journals miRquant 2.0: an Expanded Tool for Accurate Annotation and Quantification of MicroRNAs and their isomiRs from Small RNA-Sequencing Data

2016 ◽  
Vol 13 (5) ◽  
Author(s):  
Matthew Kanke ◽  
Jeanette Baran-Gale ◽  
Jonathan Villanueva ◽  
Praveen Sethupathy
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Cell Types ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Sequencing Technology ◽  
Additional Information ◽  
Disease States ◽  
Non Coding Rnas ◽  
The Rich

SummarySmall non-coding RNAs, in particular microRNAs, are critical for normal physiology and are candidate biomarkers, regulators, and therapeutic targets for a wide variety of diseases. There is an ever-growing interest in the comprehensive and accurate annotation of microRNAs across diverse cell types, conditions, species, and disease states. Highthroughput sequencing technology has emerged as the method of choice for profiling microRNAs. Specialized bioinformatic strategies are required to mine as much meaningful information as possible from the sequencing data to provide a comprehensive view of the microRNA landscape. Here we present miRquant 2.0, an expanded bioinformatics tool for accurate annotation and quantification of microRNAs and their isoforms (termed isomiRs) from small RNA-sequencing data. We anticipate that miRquant 2.0 will be useful for researchers interested not only in quantifying known microRNAs but also mining the rich well of additional information embedded in small RNA-sequencing data.

scholarly journals Multiobjective triclustering of time-series transcriptome data reveals key genes of biological processes

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Anirban Bhar ◽  
Martin Haubrock ◽  
Anirban Mukhopadhyay ◽  
Edgar Wingender
Keyword(s):  
Time Series ◽  
Biological Processes ◽  
Transcriptome Data ◽  
Key Genes

scholarly journals Interpretable, scalable, and transferrable functional projection of large-scale transcriptome data using constrained matrix decomposition

2021 ◽  
Author(s):  
Nicholas Panchy ◽  
Kazuhide Watanabe ◽  
Tian Hong
Keyword(s):  
Large Scale ◽  
Epithelial Mesenchymal Transition ◽  
Single Cells ◽  
Matrix Decomposition ◽  
Biological Processes ◽  
Transcriptome Data ◽  
Sequencing Data ◽  
Experimental Conditions ◽  
Mesenchymal Transition ◽  
Low Dimensional

AbstractLarge-scale transcriptome data, such as single-cell RNA-sequencing data, have provided unprecedented resources for studying biological processes at the systems level. Numerous dimensionality reduction methods have been developed to visualize and analyze these transcriptome data. In addition, several existing methods allow inference of functional variations among samples using gene sets with known biological functions. However, it remains challenging to analyze transcriptomes with reduced dimensions that are both interpretable in terms of dimensions’ directionalities and transferrable to new data. In this study, we used gene set non-negative principal component analysis (gsPCA) and non-negative matrix factorization (gsNMF) to analyze large-scale transcriptome datasets. We found that these methods provide low-dimensional information about the progression of biological processes in a quantitative manner, and their performances are comparable to existing functional variation analysis methods in terms of distinguishing multiple cell states and samples from multiple conditions. Remarkably, upon training with a subset of data, these methods allow predictions of locations in the functional space using data from experimental conditions that are not exposed to the models. Specifically, our models predicted the extent of progression and reversion for cells in the epithelial-mesenchymal transition (EMT) continuum. These methods revealed conserved EMT program among multiple types of single cells and tumor samples. Finally, we provide several recommendations on the choice between the two linear methods and the optimal algorithmic parameters. Our methods show that simple constrained matrix decomposition can produce to low-dimensional information in functionally interpretable and transferrable space, and can be widely useful for analyzing large-scale transcriptome data.

scholarly journals In-depth transcriptomic analysis of human retina reveals molecular mechanisms underlying diabetic retinopathy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kolja Becker ◽  
Holger Klein ◽  
Eric Simon ◽  
Coralie Viollet ◽  
Christian Haslinger ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Vision Loss ◽  
Ganglion Cells ◽  
Expression Profiles ◽  
Cell Types ◽  
Sequencing Data ◽  
Disease Stages ◽  
Post Transcriptional Regulation

AbstractDiabetic Retinopathy (DR) is among the major global causes for vision loss. With the rise in diabetes prevalence, an increase in DR incidence is expected. Current understanding of both the molecular etiology and pathways involved in the initiation and progression of DR is limited. Via RNA-Sequencing, we analyzed mRNA and miRNA expression profiles of 80 human post-mortem retinal samples from 43 patients diagnosed with various stages of DR. We found differentially expressed transcripts to be predominantly associated with late stage DR and pathways such as hippo and gap junction signaling. A multivariate regression model identified transcripts with progressive changes throughout disease stages, which in turn displayed significant overlap with sphingolipid and cGMP–PKG signaling. Combined analysis of miRNA and mRNA expression further uncovered disease-relevant miRNA/mRNA associations as potential mechanisms of post-transcriptional regulation. Finally, integrating human retinal single cell RNA-Sequencing data revealed a continuous loss of retinal ganglion cells, and Müller cell mediated changes in histidine and β-alanine signaling. While previously considered primarily a vascular disease, attention in DR has shifted to additional mechanisms and cell-types. Our findings offer an unprecedented and unbiased insight into molecular pathways and cell-specific changes in the development of DR, and provide potential avenues for future therapeutic intervention.

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

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.

scholarly journals Noni fruit’s water spot appearance on the second day of harvest: a trade-off between resistance and energy

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Tian Wu ◽  
Danyan Hu ◽  
Qingfen Wang
Keyword(s):  
Statistical Analysis ◽  
Molecular Level ◽  
Morinda Citrifolia ◽  
Transcriptome Data ◽  
Trade Off ◽  
Kegg Pathways ◽  
Available Energy ◽  
Climacteric Fruit ◽  
The Relationship ◽  
Insight Into

Abstract Background Noni (Morinda citrifolia Linn.) is a tropical tree that bears climacteric fruit. Previous observations and research have shown that the second day (2 d) after harvest is the most important demarcation point when the fruit has the same appearance as the freshly picked fruit (0 d); however, they are beginning to become water spot appearance. We performed a conjoint analysis of metabolome and transcriptome data for noni fruit of 0 d and 2 d to reveal what happened to the fruit at the molecular level. Genes and metabolites were annotated to KEGG pathways and the co-annotated KEGG pathways were used as a statistical analysis. Results We found 25 pathways that were significantly altered at both metabolic and transcriptional levels, including a total of 285 differentially expressed genes (DEGs) and 11 differential metabolites through an integrative analysis of transcriptomics and metabolomics. The energy metabolism and pathways originating from phenylalanine were disturbed the most. The upregulated resistance metabolites and genes implied the increase of resistance and energy consumption in the postharvest noni fruit. Most genes involved in glycolysis were downregulated, further limiting the available energy. This lack of energy led noni fruit to water spot appearance, a prelude to softening. The metabolites and genes related to the resistance and energy interacted and restricted each other to keep noni fruit seemingly hard within two days after harvest, but actually the softening was already unstoppable. Conclusions This study provides a new insight into the relationship between the metabolites and genes of noni fruit, as well as a foundation for further clarification of the post-ripening mechanism in noni fruit.

scholarly journals TUNEL Assay: A Powerful Tool for Kidney Injury Evaluation

2021 ◽  
Vol 22 (1) ◽  
pp. 412
Author(s):  
Christopher L. Moore ◽  
Alena V. Savenka ◽  
Alexei G. Basnakian
Keyword(s):  
Cell Death ◽  
Dna Fragmentation ◽  
Cell Injury ◽  
Kidney Injury ◽  
Cell Types ◽  
Tunel Assay ◽  
Terminal Deoxynucleotidyl Transferase ◽  
Hypoxic Injury ◽  
Additional Information ◽  
Tissue Compartments

Terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay is a long-established assay used to detect cell death-associated DNA fragmentation (3’-OH DNA termini) by endonucleases. Because these enzymes are particularly active in the kidney, TUNEL is widely used to identify and quantify DNA fragmentation and cell death in cultured kidney cells and animal and human kidneys resulting from toxic or hypoxic injury. The early characterization of TUNEL as an apoptotic assay has led to numerous misinterpretations of the mechanisms of kidney cell injury. Nevertheless, TUNEL is becoming increasingly popular for kidney injury assessment because it can be used universally in cultured and tissue cells and for all mechanisms of cell death. Furthermore, it is sensitive, accurate, quantitative, easily linked to particular cells or tissue compartments, and can be combined with immunohistochemistry to allow reliable identification of cell types or likely mechanisms of cell death. Traditionally, TUNEL analysis has been limited to the presence or absence of a TUNEL signal. However, additional information on the mechanism of cell death can be obtained from the analysis of TUNEL patterns.

scholarly journals Single-cell transcriptomic analysis of mIHC images via antigen mapping

2021 ◽  
Vol 7 (10) ◽  
pp. eabc5464
Author(s):  
Kiya W. Govek ◽  
Emma C. Troisi ◽  
Zhen Miao ◽  
Rachael G. Aubin ◽  
Steven Woodhouse ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Patterns ◽  
Cell Types ◽  
Level Of Detail ◽  
Cell Populations ◽  
Sequencing Data ◽  
Spatially Resolved ◽  
Murine Spleen ◽  
Single Cell Rna Sequencing ◽  
Antibody Panel

Highly multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA sequencing data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling the systematic annotation of nuanced cell populations, spatial patterns of transcription, and interactions between cell types. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published cytometry and mIHC data of this organ.

scholarly journals Analysis of the transcriptome of bovine endometrial cells isolated by laser micro-dissection (1): specific signatures of stromal, glandular and luminal epithelial cells

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Wiruntita Chankeaw ◽  
Sandra Lignier ◽  
Christophe Richard ◽  
Theodoros Ntallaris ◽  
Mariam Raliou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Localization ◽  
Expression Profiles ◽  
Cell Types ◽  
Molecular Signature ◽  
Receptor Protein ◽  
Specific Gene ◽  
Specific Cell ◽  
Biological Processes ◽  
Endometrial Cells

Abstract Background A number of studies have examined mRNA expression profiles of bovine endometrium at estrus and around the peri-implantation period of pregnancy. However, to date, these studies have been performed on the whole endometrium which is a complex tissue. Consequently, the knowledge of cell-specific gene expression, when analysis performed with whole endometrium, is still weak and obviously limits the relevance of the results of gene expression studies. Thus, the aim of this study was to characterize specific transcriptome of the three main cell-types of the bovine endometrium at day-15 of the estrus cycle. Results In the RNA-Seq analysis, the number of expressed genes detected over 10 transcripts per million was 6622, 7814 and 8242 for LE, GE and ST respectively. ST expressed exclusively 1236 genes while only 551 transcripts were specific to the GE and 330 specific to LE. For ST, over-represented biological processes included many regulation processes and response to stimulus, cell communication and cell adhesion, extracellular matrix organization as well as developmental process. For GE, cilium organization, cilium movement, protein localization to cilium and microtubule-based process were the only four main biological processes enriched. For LE, over-represented biological processes were enzyme linked receptor protein signaling pathway, cell-substrate adhesion and circulatory system process. Conclusion The data show that each endometrial cell-type has a distinct molecular signature and provide a significantly improved overview on the biological process supported by specific cell-types. The most interesting result is that stromal cells express more genes than the two epithelial types and are associated with a greater number of pathways and ontology terms.

scholarly journals Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yurong Cheng ◽  
◽  
Pascal Schlosser ◽  
Johannes Hertel ◽  
Peggy Sekula ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Inborn Errors Of Metabolism ◽  
Cell Types ◽  
Population Variation ◽  
Whole Body ◽  
Sequencing Data ◽  
Inborn Errors ◽  
Data Links ◽  
Liver And Kidney ◽  
Constraint Based Modeling

AbstractMetabolite levels in urine may provide insights into genetic mechanisms shaping their related pathways. We therefore investigate the cumulative contribution of rare, exonic genetic variants on urine levels of 1487 metabolites and 53,714 metabolite ratios among 4864 GCKD study participants. Here we report the detection of 128 significant associations involving 30 unique genes, 16 of which are known to underlie inborn errors of metabolism. The 30 genes are strongly enriched for shared expression in liver and kidney (odds ratio = 65, p-FDR = 3e−7), with hepatocytes and proximal tubule cells as driving cell types. Use of UK Biobank whole-exome sequencing data links genes to diseases connected to the identified metabolites. In silico constraint-based modeling of gene knockouts in a virtual whole-body, organ-resolved metabolic human correctly predicts the observed direction of metabolite changes, highlighting the potential of linking population genetics to modeling. Our study implicates candidate variants and genes for inborn errors of metabolism.

Sign in / Sign up

Export Citation Format

Share Document