scholarly journals CONFESS: Fluorescence-based single-cell ordering in R

2018 ◽  
Author(s):  
Efthymios Motakis ◽  
Diana H.P. Low
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Temporal Dynamics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Image Data ◽  
Cdna Libraries ◽  
Fluorescence Signal ◽  
Cell Detection ◽  
Estimation Model

AbstractModern high-throughput single-cell technologies facilitate the efficient processing of hundreds of individual cells to comprehensively study their morphological and genomic heterogeneity. Fluidigm’s C1 Auto Prep system isolates fluorescence-stained cells into specially designed capture sites, generates high-resolution image data and prepares the associated cDNA libraries for mRNA sequencing. Current statistical methods focus on the analysis of the gene expression profiles and ignore the important information carried by the images. Here we propose a new direction for single-cell data analysis and develop CONFESS, a customized cell detection and fluorescence signal estimation model for images coming from the Fluidigm C1 system. Applied to a set of HeLa cells expressing fluorescence cell cycle reporters, the method predicted the progression state of hundreds of samples and enabled us to study the spatio-temporal dynamics of the HeLa cell cycle. The output can be easily integrated with the associated single-cell RNA-seq expression profiles for deeper understanding of a given biological system. CONFESS R package is available at Bioconductor (http://bioconductor.org/packages/release/bioc/html/CONFESS.html).

scholarly journals Computational cell cycle analysis of single cell RNA-Seq data

2020 ◽  
Author(s):  
Marmar Moussa ◽  
Ion I. Măndoiu
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Analysis Tool ◽  
Rna Seq ◽  
Cell Type ◽  
Computational Cell ◽  
Analysis Pipeline ◽  
Web Based

AbstractThe variation in gene expression profiles of cells captured in different phases of the cell cycle can interfere with cell type identification and functional analysis of single cell RNA-Seq (scRNA-Seq) data. In this paper, we introduce SC1CC (SC1 Cell Cycle analysis tool), a computational approach for clustering and ordering single cell transcriptional profiles according to their progression along cell cycle phases. We also introduce a new robust metric, Gene Smoothness Score (GSS) for assessing the cell cycle based order of the cells. SC1CC is available as part of the SC1 web-based scRNA-Seq analysis pipeline, publicly accessible at https://sc1.engr.uconn.edu/.

4 Molecularly guided multiplexed digital spatial analysis reveals differential gene expression profiles in the WNT-β-catenin pathway between melanoma and prostate tumors

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A4-A4
Author(s):  
Anushka Dikshit ◽  
Dan Zollinger ◽  
Karen Nguyen ◽  
Jill McKay-Fleisch ◽  
Kit Fuhrman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Molecule ◽  
Spatial Information ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Fluorescence Assay ◽  
Differentially Expressed ◽  
Tumor Type ◽  
Prostate Tumors

BackgroundThe canonical WNT-β-catenin signaling pathway is vital for development and tissue homeostasis but becomes strongly tumorigenic when dysregulated. and alter the transcriptional signature of a cell to promote malignant transformation. However, thorough characterization of these transcriptomic signatures has been challenging because traditional methods lack either spatial information, multiplexing, or sensitivity/specificity. To overcome these challenges, we developed a novel workflow combining the single molecule and single cell visualization capabilities of the RNAscope in situ hybridization (ISH) assay with the highly multiplexed spatial profiling capabilities of the GeoMx™ Digital Spatial Profiler (DSP) RNA assays. Using these methods, we sought to spatially profile and compare gene expression signatures of tumor niches with high and low CTNNB1 expression.MethodsAfter screening 120 tumor cores from multiple tumors for CTNNB1 expression by the RNAscope assay, we identified melanoma as the tumor type with the highest CTNNB1 expression while prostate tumors had the lowest expression. Using the RNAscope Multiplex Fluorescence assay we selected regions of high CTNNB1 expression within 3 melanoma tumors as well as regions with low CTNNB1 expression within 3 prostate tumors. These selected regions of interest (ROIs) were then transcriptionally profiled using the GeoMx DSP RNA assay for a set of 78 genes relevant in immuno-oncology. Target genes that were differentially expressed were further visualized and spatially assessed using the RNAscope Multiplex Fluorescence assay to confirm GeoMx DSP data with single cell resolution.ResultsThe GeoMx DSP analysis comparing the melanoma and prostate tumors revealed that they had significantly different gene expression profiles and many of these genes showed concordance with CTNNB1 expression. Furthermore, immunoregulatory targets such as ICOSLG, CTLA4, PDCD1 and ARG1, also demonstrated significant correlation with CTNNB1 expression. On validating selected targets using the RNAscope assay, we could distinctly visualize that they were not only highly expressed in melanoma compared to the prostate tumor, but their expression levels changed proportionally to that of CTNNB1 within the same tumors suggesting that these differentially expressed genes may be regulated by the WNT-β-catenin pathway.ConclusionsIn summary, by combining the RNAscope ISH assay and the GeoMx DSP RNA assay into one joint workflow we transcriptionally profiled regions of high and low CTNNB1 expression within melanoma and prostate tumors and identified genes potentially regulated by the WNT- β-catenin pathway. This novel workflow can be fully automated and is well suited for interrogating the tumor and stroma and their interactions.GeoMx Assays are for RESEARCH ONLY, not for diagnostics.

scholarly journals The cell type-specific effect of TAp73 isoforms on the cell cycle and apoptosis

2008 ◽  
Vol 13 (3) ◽  
Author(s):  
Jitka Holcakova ◽  
Pavla Ceskova ◽  
Roman Hrstka ◽  
Petr Muller ◽  
Lenka Dubska ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Cancer Patients ◽  
Transcriptional Activity ◽  
Human Cancer ◽  
Binding Capacity ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Effect ◽  
P53 Family ◽  
Null Cell

Abstractp73, a member of the p53 family, exhibits activities similar to those of p53, including the ability to induce growth arrest and apoptosis. p73 influences chemotherapeutic responses in human cancer patients, in association with p53. Alternative splicing of the TP73 gene produces many p73 C- and N-terminal isoforms, which vary in their transcriptional activity towards p53-responsive promoters. In this paper, we show that the C-terminal spliced isoforms of the p73 protein differ in their DNA-binding capacity, but this is not an accurate predictor of transcriptional activity. In different p53-null cell lines, p73β induces either mitochondrial-associated or death receptor-mediated apoptosis, and these differences are reflected in different gene expression profiles. In addition, p73 induces cell cycle arrest and p21WAF1 expression in H1299 cells, but not in Saos-2. This data shows that TAp73 isoforms act differently depending on the tumour cell background, and have important implications for p73-mediated therapeutic responses in individual human cancer patients.

scholarly journals P02.10 FocuSCOPE: a single cell, multi-omics solution to simultaneously analyze tumor variants and microenvironment

2021 ◽  
Vol 9 (Suppl 1) ◽  
pp. A12.1-A12
Author(s):  
Y Arjmand Abbassi ◽  
N Fang ◽  
W Zhu ◽  
Y Zhou ◽  
Y Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
High Throughput ◽  
Immune Cells ◽  
Genetic Variants ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Single Cell Sequencing

Recent advances of high-throughput single cell sequencing technologies have greatly improved our understanding of the complex biological systems. Heterogeneous samples such as tumor tissues commonly harbor cancer cell-specific genetic variants and gene expression profiles, both of which have been shown to be related to the mechanisms of disease development, progression, and responses to treatment. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in tumor responses to systematic therapy such as immunotherapy or cell therapy. However, most current high-throughput single cell sequencing methods detect only gene expression levels or epigenetics events such as chromatin conformation. The information on important genetic variants including mutation or fusion is not captured. To better understand the mechanisms of tumor responses to systematic therapy, it is essential to decipher the connection between genotype and gene expression patterns of both tumor cells and cells in the tumor microenvironment. We developed FocuSCOPE, a high-throughput multi-omics sequencing solution that can detect both genetic variants and transcriptome from same single cells. FocuSCOPE has been used to successfully perform single cell analysis of both gene expression profiles and point mutations, fusion genes, or intracellular viral sequences from thousands of cells simultaneously, delivering comprehensive insights of tumor and immune cells in tumor microenvironment at single cell resolution.Disclosure InformationY. Arjmand Abbassi: None. N. Fang: None. W. Zhu: None. Y. Zhou: None. Y. Chen: None. U. Deutsch: None.

scholarly journals Single-cell immune repertoire and transcriptome sequencing reveals that clonally expanded and transcriptionally distinct lymphocytes populate the aged central nervous system in mice

2021 ◽  
Vol 288 (1945) ◽  
pp. 20202793
Author(s):  
Alexander Yermanos ◽  
Daniel Neumeier ◽  
Ioana Sandu ◽  
Mariana Borsa ◽  
Ann Cathrin Waindok ◽  
...  
Keyword(s):  
Gene Expression ◽  
Central Nervous System ◽  
Nervous System ◽  
B Cells ◽  
Single Cell ◽  
Somatic Hypermutation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Receptor ◽  
The Central Nervous System

Neuroinflammation plays a crucial role during ageing and various neurological conditions, including Alzheimer's disease, multiple sclerosis and infection. Technical limitations, however, have prevented an integrative analysis of how lymphocyte immune receptor repertoires and their accompanying transcriptional states change with age in the central nervous system. Here, we leveraged single-cell sequencing to simultaneously profile B cell receptor and T cell receptor repertoires and accompanying gene expression profiles in young and old mouse brains. We observed the presence of clonally expanded B and T cells in the central nervous system of aged male mice. Furthermore, many of these B cells were of the IgM and IgD isotypes, and had low levels of somatic hypermutation. Integrating gene expression information additionally revealed distinct transcriptional profiles of these clonally expanded lymphocytes. Our findings implicate that clonally related T and B cells in the CNS of elderly mice may contribute to neuroinflammation accompanying homeostatic ageing.

scholarly journals Circulating tumor cell characterization of lung cancer brain metastasis in the cerebrospinal fluid through single-cell transcriptome analysis

2020 ◽  
Author(s):  
Haoyu Ruan ◽  
Yihang Zhou ◽  
Jie Shen ◽  
Yue Zhai ◽  
Ying Xu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cerebrospinal Fluid ◽  
Single Cell ◽  
Tumor Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Unknown Primary ◽  
Great Promise ◽  
Cell Cycle Gene

AbstractMetastatic lung cancer accounts for about half of the brain metastases (BM). Development of leptomeningeal metastases (LM) are becoming increasingly common, and its prognosis is still poor despite the advances in systemic and local approaches. Cytology analysis in the cerebrospinal fluid (CSF) remains the diagnostic gold standard. Although several previous studies performed in CSF have offered great promise for the diagnostics and therapeutics of LM, a comprehensive characterization of circulating tumor cells (CTCs) in CSF is still lacking. To fill this critical gap of lung adenocarcinoma LM (LUAD-LM), we analyzed the transcriptomes of 1,375 cells from 5 LUAD-LM patient and 3 control samples using single-cell RNA sequencing technology. We defined CSF-CTCs based on abundant expression of epithelial markers and genes with lung origin, as well as the enrichment of metabolic pathway and cell adhesion molecules, which are crucial for the survival and metastases of tumor cells. Elevated expression of CEACAM6 and SCGB3A2 was discovered in CSF-CTCs, which could serve as candidate biomarkers of LUAD-LM. We identified substantial heterogeneity in CSF-CTCs among LUAD-LM patients and within patient among individual cells. Cell-cycle gene expression profiles and the proportion of CTCs displaying mesenchymal and cancer stem cell properties also vary among patients. In addition, CSF-CTC transcriptome profiling identified one LM case as cancer of unknown primary site (CUP). Our results will shed light on the mechanism of LUAD-LM and provide a new direction of diagnostic test of LUAD-LM and CUP cases from CSF samples.

scholarly journals The single-cell transcriptional landscape of lung carcinoid tumors

2021 ◽  
Author(s):  
Philip Bischoff ◽  
Alexandra Trinks ◽  
Jennifer Wiederspahn ◽  
Benedikt Obermayer ◽  
Jan Patrick Pett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Carcinoid Tumor ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Carcinoid Tumors ◽  
Cellular Composition ◽  
Cell Gene Expression ◽  
Cell Gene ◽  
Lung Carcinoid

AbstractLung carcinoid tumors, also referred to as pulmonary neuroendocrine tumors or lung carcinoids, are rare neoplasms of the lung with a more favorable prognosis than other subtypes of lung cancer. Still, some patients suffer from relapsed disease and metastatic spread while no consensus treatment exists for metastasized carcinoids. Several recent single-cell studies have provided detailed insights into the cellular heterogeneity of more common lung cancers, such as adeno- and squamous cell carcinoma. However, the characteristics of lung carcinoids on the single-cell level are yet completely unknown.To study the cellular composition and single-cell gene expression profiles in lung carcinoids, we applied single-cell RNA sequencing to three lung carcinoid tumor samples and normal lung tissue. The single-cell transcriptomes of carcinoid tumor cells reflected intertumoral heterogeneity associated with clinicopathological features, such as tumor necrosis and proliferation index. The immune microenvironment was specifically enriched in noninflammatory monocyte-derived myeloid cells. Tumor-associated endothelial cells were characterized by distinct gene expression profiles. A spectrum of vascular smooth muscle cells and pericytes predominated the stromal microenvironment. We found a small proportion of myofibroblasts exhibiting features reminiscent of cancer-associated fibroblasts. Stromal and immune cells exhibited potential paracrine interactions which may shape the microenvironment via NOTCH, VEGF, TGFβ and JAK/STAT signaling. Moreover, single-cell gene signatures of pericytes and myofibroblasts demonstrated prognostic value in bulk gene expression data.Here, we provide first comprehensive insights into the cellular composition and single-cell gene expression profiles in lung carcinoids, demonstrating the non-inflammatory and vessel-rich nature of their tumor microenvironment, and outlining relevant intercellular interactions which could serve as future therapeutic targets.

scholarly journals Characterization of Genes Associated with TGA7 During the Floral Transition

2020 ◽  
Author(s):  
Xiaorui Xu ◽  
Jingya Xu ◽  
Chen Yuan ◽  
Yikai Hu ◽  
Qinggang Liu ◽  
...  
Keyword(s):  
Flowering Time ◽  
Expression Profiles ◽  
Plant Defence ◽  
Gene Expression Profiles ◽  
Floral Transition ◽  
Cdna Libraries ◽  
Genetic Pathways ◽  
Time Control ◽  
Pathways Analysis

Abstract BackgroundThe TGA family has ten members and plays vital roles in plant defence and development in Arabidopsis. However, involvement of TGAs in control of flowering time remains largely unknown and requires further investigation. ResultsTo study the role of TGA7 during the floral transition, we first tested phenotypes of tga7 mutant, which displayed delay-flowering phenotype under both long-day and short-day conditions. We then performed flowering genetic pathways analysis and found that both autonomous and thermosensory pathways may affect TGA7 expression. Furthermore, to reveal differential gene expression profiles between wild-type (WT) and tga7, cDNA libraries were generated for WT and tga7 mutant seedlings at 9 DAG (days after germination). For each library, deep-sequencing produced approximately 6.67 Gb of high-quality sequences with the majority (84.55%) of mRNAs between 500 and 3000 nucleotides in length. Three hundred and twenty-five differentially expressed genes (DEGs) were identified between WT and tga7 mutant seedlings. Among them, four genes are associated with flowering time control. Differential expression of the four flowering-related DEGs was further validated by qRT-PCR.ConclusionsTransciptomic sequencing coupled with flowering genetic pathways analysis provides a framework for further studying the role of TGA7 in promoting flowering.

High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning

2021 ◽  
Author(s):  
Xiangtao Li ◽  
Shaochuan Li ◽  
Lei Huang ◽  
Shixiong Zhang ◽  
Ka-chun Wong
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Single Cell ◽  
Deep Neural Networks ◽  
Expression Profiles ◽  
Marker Gene ◽  
Gene Expression Profiles ◽  
Underlying Mechanisms ◽  
Cell Data ◽  
Gene Expression Levels

Abstract Single-cell RNA sequencing (scRNA-seq) technologies have been heavily developed to probe gene expression profiles at single-cell resolution. Deep imputation methods have been proposed to address the related computational challenges (e.g. the gene sparsity in single-cell data). In particular, the neural architectures of those deep imputation models have been proven to be critical for performance. However, deep imputation architectures are difficult to design and tune for those without rich knowledge of deep neural networks and scRNA-seq. Therefore, Surrogate-assisted Evolutionary Deep Imputation Model (SEDIM) is proposed to automatically design the architectures of deep neural networks for imputing gene expression levels in scRNA-seq data without any manual tuning. Moreover, the proposed SEDIM constructs an offline surrogate model, which can accelerate the computational efficiency of the architectural search. Comprehensive studies show that SEDIM significantly improves the imputation and clustering performance compared with other benchmark methods. In addition, we also extensively explore the performance of SEDIM in other contexts and platforms including mass cytometry and metabolic profiling in a comprehensive manner. Marker gene detection, gene ontology enrichment and pathological analysis are conducted to provide novel insights into cell-type identification and the underlying mechanisms. The source code is available at https://github.com/li-shaochuan/SEDIM.

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