High-Throughput Gene Expression Profiles to Define Drug Similarity and Predict Compound Activity

We describe isogenic colony sequencing (ICO-seq), a massively-parallel strategy to assess the gene expression profiles of large numbers of genetically distinct yeast colonies.

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

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2021-itoc8.22 ◽

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.

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Microbial single-cell RNA sequencing by split-pool barcoding

Science ◽

10.1126/science.aba5257 ◽

2020 ◽

Vol 371 (6531) ◽

pp. eaba5257 ◽

Cited By ~ 2

Author(s):

Anna Kuchina ◽

Leandra M. Brettner ◽

Luana Paleologu ◽

Charles M. Roco ◽

Alexander B. Rosenberg ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Single Cell Analysis ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Growth Stages ◽

High Throughput Analysis ◽

Single Cell Rna Sequencing

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.

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Genomics and Systems Biology.

Blood ◽

10.1182/blood.v112.11.sci-51.sci-51 ◽

2008 ◽

Vol 112 (11) ◽

pp. sci-51-sci-51

Author(s):

Todd R. Golub

Keyword(s):

Gene Expression ◽

High Throughput ◽

Biological Networks ◽

High Throughput Screening ◽

Expression Profiles ◽

Low Cost ◽

Gene Expression Profiles ◽

Gene Expression Signature ◽

Small Molecule Libraries ◽

Approved Drugs

Genomics holds particular potential for the elucidation of biological networks that underlie disease. For example, gene expression profiles have been used to classify human cancers, and have more recently been used to predict graft rejection following organ transplantation. Such signatures thus hold promise both as diagnostic approaches and as tools with which to dissect biological mechanism. Such systems-based approaches are also beginning to impact the drug discovery process. For example, it is now feasible to measure gene expression signatures at low cost and high throughput, thereby allowing for the screening libraries of small molecule libraries in order to identify compounds capable of perturbing a signature of interest (even if the critical drivers of that signature are not yet known). This approach, known as Gene Expression-Based High Throughput Screening (GE-HTS), has been shown to identify candidate therapeutic approaches in AML, Ewing sarcoma, and neuroblastoma, and has identified tool compounds capable of inhibiting PDGF receptor signaling. A related approach, known as the Connectivity Map (www.broad.mit.edu/cmap) attempts to use gene expression profiles as a universal language with which to connect cellular states, gene product function, and drug action. In this manner, a gene expression signature of interest is used to computationally query a database of gene expression profiles of cells systematically treated with a large number of compounds (e.g., all off-patent FDA-approved drugs), thereby identifying potential new applications for existing drugs. Such systems level approaches thus seek chemical modulators of cellular states, even when the molecular basis of such altered states is unknown.

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The Prediction of Drug-Disease Correlation Based on Gene Expression Data

BioMed Research International ◽

10.1155/2018/4028473 ◽

2018 ◽

Vol 2018 ◽

pp. 1-6 ◽

Cited By ~ 3

Author(s):

Hui Cui ◽

Menghuan Zhang ◽

Qingmin Yang ◽

Xiangyi Li ◽

Michael Liebman ◽

...

Keyword(s):

Gene Expression ◽

High Throughput ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Drug Combinations ◽

Target Tissue ◽

Specific Patient ◽

Computational Approaches ◽

Drug Databases ◽

Optimal Drug

The explosive growth of high-throughput experimental methods and resulting data yields both opportunity and challenge for selecting the correct drug to treat both a specific patient and their individual disease. Ideally, it would be useful and efficient if computational approaches could be applied to help achieve optimal drug-patient-disease matching but current efforts have met with limited success. Current approaches have primarily utilized the measureable effect of a specific drug on target tissue or cell lines to identify the potential biological effect of such treatment. While these efforts have met with some level of success, there exists much opportunity for improvement. This specifically follows the observation that, for many diseases in light of actual patient response, there is increasing need for treatment with combinations of drugs rather than single drug therapies. Only a few previous studies have yielded computational approaches for predicting the synergy of drug combinations by analyzing high-throughput molecular datasets. However, these computational approaches focused on the characteristics of the drug itself, without fully accounting for disease factors. Here, we propose an algorithm to specifically predict synergistic effects of drug combinations on various diseases, by integrating the data characteristics of disease-related gene expression profiles with drug-treated gene expression profiles. We have demonstrated utility through its application to transcriptome data, including microarray and RNASeq data, and the drug-disease prediction results were validated using existing publications and drug databases. It is also applicable to other quantitative profiling data such as proteomics data. We also provide an interactive web interface to allow our Prediction of Drug-Disease method to be readily applied to user data. While our studies represent a preliminary exploration of this critical problem, we believe that the algorithm can provide the basis for further refinement towards addressing a large clinical need.

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NABIC Microarray: an integrated database of high-throughput data for gene expression profiles

Bioinformation ◽

10.6026/97320630011509 ◽

2015 ◽

Vol 11 (11) ◽

pp. 509-511

Author(s):

Jae-Hee Lee ◽

◽

Sang-Ho Kang ◽

Jong-Yeol Lee ◽

Chang-Kug Kim ◽

...

Keyword(s):

Gene Expression ◽

High Throughput ◽

Expression Profiles ◽

Gene Expression Profiles ◽

High Throughput Data

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Transcriptome profiling of ulcerative colitis mouse model suggests biomarkers and therapeutic targets for human colitis

10.1101/2020.08.12.225458 ◽

2020 ◽

Author(s):

Reza Yarani ◽

Oana Palasca ◽

Nadezhda T. Doncheva ◽

Christian Anthon ◽

Bartosz Pilecki ◽

...

Keyword(s):

Gene Expression ◽

Ulcerative Colitis ◽

Mouse Model ◽

High Throughput ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Unknown Etiology ◽

Small Rna Sequencing ◽

Complex Nature

1.AbstractBACKGROUND & AIMSUlcerative colitis (UC) is an inflammatory bowel disorder with unknown etiology. Given its complex nature, in vivo studies to investigate its pathophysiology is vital. Animal models play an important role in molecular profiling necessary to pinpoint mechanisms that contribute to human disease. Thus, we aim to identify common conserved gene expression signatures and differentially regulated pathways between human UC and a mouse model hereof, which can be used to identify UC patients from healthy individuals and to suggest novel treatment targets and biomarker candidates.METHODSTherefore, we performed high-throughput total and small RNA sequencing to comprehensively characterize the transcriptome landscape of the most widely used UC mouse model, the dextran sodium sulfate (DSS) model. We used this data in conjunction with publicly available human UC transcriptome data to compare gene expression profiles and pathways.RESULTSWe identified differentially regulated protein-coding genes, long non-coding RNAs and microRNAs from colon and blood of UC mice and further characterized the involved pathways and biological processes through which these genes may contribute to disease development and progression. By integrating human and mouse UC datasets, we suggest a set of 51 differentially regulated genes in UC colon and blood that may improve molecular phenotyping, aid in treatment decisions, drug discovery and the design of clinical trials.CONCLUSIONGlobal transcriptome analysis of the DSS-UC mouse model supports its use as an efficient high-throughput tool to discover new targets for therapeutic and diagnostic applications in human UC through identifying relationships between gene expression and disease phenotype.

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High-Throughput BCR Sequencing and Single-Cell Transcriptomics Reveal Distinct Transcriptional Profiles Associated with Subclonal Evolution of Follicular Lymphoma

Blood ◽

10.1182/blood-2019-130508 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 298-298

Author(s):

Cornelis A.M. van Bergen ◽

Marvyn T. Koning ◽

Edwin Quinten ◽

Agnieszka Mykowiecka ◽

Julieta Sepulveda ◽

...

Keyword(s):

Gene Expression ◽

Lymph Node ◽

Follicular Lymphoma ◽

B Cell ◽

Single Cell ◽

High Throughput ◽

Conflicts Of Interest ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Full Length

Objectives: Follicular lymphoma (FL) typically originates from premalignant mature B cells that carry the founder t(14;18) BCL2 translocation. Mutations in epigenetic modifiers and acquisition of N-glycosylation sites in CDR regions of the B-cell receptor (BCR) are recurrent secondary events in FL pathogenesis. Despite these oncogenic drivers, FL can remain indolent and clinically stable for years. The molecular events driving subclonal evolution into symptomatic progression and eventual transformation to aggressive lymphoma are insufficiently understood. FL cells are frozen in their B-cell development at the germinal center stage and undergo continuous somatic hypermutation mediated by expression of activation-induced deaminase (AID). We aim to identify crucial drivers of subclonal FL evolution by high-throughput mapping at single-cell resolution. Methods: Viable FL cells were isolated and cryopreserved from 23 histologically or immunocytologically confirmed FL samples from 13 patients with informed consent. Full-length VDJ/VJ transcripts were isolated by unbiased template-switching ARTISAN PCR and massive parallel NGS sequencing on the PacBio platform. The clonal primordial FL BCR (pBCR) was reconstructed from unmutated IGV/IGJ sequences with the CDR3 of the least mutated BCR. Since the IgTree program was unable to process the obtained numbers of BCR sequences, we developed the WILLOW algorithm for analysis of BCR evolution based on the principle of maximum parsimony and on distance from the pBCR. Intraclonal BCR variability was quantified by Shannon's diversity index. 5' single cell transcriptomics and VDJ/VJ sequencing was performed on 2 pools of highly purified FL cells from 5 lymph node biopsies on the 10x Genomics platform. Data were deconvoluted based on expressed variants by the Single Cell Sample Matcher (SCSM) algorithm. Clustering based on gene expression profiles was performed by shared nearest neighbour (SNN) modularity optimization within the R Seurat package. Genes whose expression differed significantly (adjusted p<0.05) between clusters were assigned to gene ontology terms. Results: ARTISAN PCR/PacBio NGS yielded a median of 743 full-length VDJ and VJ sequences (range 62-12782) per BCR chain with expected high intraclonal diversity (median 200 subclones, range 15-3301). WILLOW revealed dominant FL subclones with a subclonal hierarchy wherein multiple routes converged to offspring nodes with identical additional mutations rather than tree-like branching (Figure). In serial samples of 4 patients, lymph node biopsies had only marginally higher subclonal diversity than blood or bone marrow samples (p=0,055; Wilcoxon's matched-pairs signed rank test). Overall BCR mutational burden increased over time in sequential biopsies. Two cases of histological FL transformation were dominated by a single subclone (65% and 80% of all VDJ/VJ sequences, respectively) that was rare in the preceding FL BCR network (0.2% and 1.8%). Pooled transcriptomics data from 6050-6500 cells were assigned to individual samples by SCSM and revealed up to seven transcriptional clusters per FL. In 9 of 10 FL, genes assigned to immune function strongly contributed to separation into one or more clusters. Single cell VDJ/VJ sequencing yielded combined heavy and light chain BCR sequences for a median of 502 FL cells per biopsy (range 22 - 1919) that permitted mapping of subclonal evolution by WILLOW based on complete BCR information. Transcriptome clusters were not distributed evenly throughout the WILLOW FL BCR networks but rather statistically associated with distinct major FL subclones. Vice versa, major FL subclones within the same biopsy were distinguished by particular gene expression profiles. Conclusions: WILLOW facilitates mapping of subclonal FL evolution based on high-throughput BCR sequencing. FL evolution proceeds in networks rather than tree-like branching, whereby acquisition of certain combinations of several BCR mutations can occur in parallel in different trajectories. Transcriptomic profiling of single FL cells identifies distinct clusters within a single biopsy. Mapping of these clusters to the FL cell position in the subclonal FL evolutionary network identifies putative mechanisms that are associated with subclonal progression. These mechanisms involve physiological B-cell signalling pathways. Figure Disclosures No relevant conflicts of interest to declare.

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ShapeCluster: Applying parametric regression to analyse time-series gene expression data

10.1101/035782 ◽

2016 ◽

Author(s):

Philip J Law ◽

Vicky Buchanan-Wollaston ◽

Andrew Mead

Keyword(s):

Gene Expression ◽

High Throughput ◽

Time Course ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Parametric Models ◽

Linear Functions ◽

Biologically Relevant ◽

Research Areas ◽

Biological Stress

High-throughput technologies have made it possible to perform genome-scale analyses to investigate a variety of research areas. From these analyses, vast amounts of data are generated. However, these data can be noisy, which could obscure the underlying signal. Here, a high-throughput regression analysis approach was developed, where a variety of linear and nonlinear parametric models were fitted to gene expression profiles from time course experiments. These models include the logistic, Gompertz, exponential, critical exponential, linear+exponential, Gaussian and linear functions. The fitted parameters from these models reflect aspects of the model shape, and thus allowed for the interpretation of gene expression profiles in terms of the underlying biology, such as the time of initial gene expression. This provides a potentially more mechanistic ap-proach to studying the genetic responses to stimuli. Together with a cluster analysis, termed ShapeCluster, it was possible to group genes based on these aspects of the expression profiles. By investigating different combinations of parameters, this added flexibility to the analysis and allowed for the investigation of the data in multiple ways, including the identification of groups of genes that may be co-regulated, or participate in response to the biological stress in question. Clusters from these methods were assessed for significance through the use of over-represented annotation terms and motifs, and found to pro-duce biologically relevant sets of genes. The ShapeCluster package is available from https://sourceforge.net/projects/shapecluster/.

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