scholarly journals Progress of Science from Microscopy to Microarrays (Part 1): Diagnosis of Parasitic Diseases

2009 ◽  
Vol 1 (01) ◽  
pp. 002-006 ◽  
Author(s):  
Ayan Dey ◽  
Sarman Singh
Keyword(s):  
Gene Expression ◽  
Drug Targets ◽  
Cell Types ◽  
Immune Defense ◽  
Molecular Diagnostic ◽  
Solid Supports ◽  
Tissue Samples ◽  
Silicon Chips ◽  
Antony Van Leeuwenhoek ◽  
Diagnostic Technologies

ABSTRACTEven though description of the magnifying glass goes back to 1021 by an Arabic physicist in his book, Antony van Leeuwenhoek was the first man to improve the then simple microscope for viewing biological specimens in 1674. This suggests that every discovery has scope for improvement, be it physics or be it biology. In the field of biology, scientists have long studied gene expression as a hallmark of gene activities reflecting the current cell conditions and response to host immune defense systems. These studies have been cumbersome, technically demanding and time-consuming. Application of microarrays has revolutionized this field and help understand the simultaneous expression of thousands of genes in a single sample put onto a single solid support. It is also now possible to compare gene expression in two different cell types, different stages of life cycle or two tissue samples, such as in healthy and diseased ones. Thus microarrays are beginning to dominate other conventional and molecular diagnostic technologies. The microarrays consist of solid supports onto which the nucleic acid sequences from thousands of different genes are immobilized, or attached at fixed locations. These solid supports themselves are usually glass slides, silicon chips or nylon membranes. The nucleic acids are spotted or synthesized directly onto the support. Application of microarrays is new for parasites. Most of these applications are done for monitoring parasite gene expression, to predict the functions of uncharacterized genes, probe the physiologic adaptations made under various environmental conditions, identify virulence-associated genes and test the effects of drug targets. The best examples are vector-borne parasites, such as Plasmodium, Trypanosoma and Leishmania, in which genes expressed, during mammalian and insect host stages, have been elucidated. Microarrays have also been successfully applied to understand the factors responsible to induce transformation from tachyzoite-to-bradyzoite and vice versa in Toxoplasma gondii. Thus microarrays provide a novel tool for diagnosis, prognosis and clinical management of infectious disease.

scholarly journals Automated identification of the mouse brain’s spatial compartments from in situ sequencing data

BMC Biology ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Gabriele Partel ◽  
Markus M. Hilscher ◽  
Giorgia Milli ◽  
Leslie Solorzano ◽  
Anna H. Klemm ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mouse Brain ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Gene Markers ◽  
Smooth Transitions

Abstract Background Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape and requires manual annotation. With the advent of in situ sequencing technologies and automated approaches, it is now possible to profile the gene expression of targeted genes inside preserved tissue samples and thus spatially map biological processes across anatomical compartments. Results Here, we show how in situ sequencing data combined with dimensionality reduction and clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. We also visualize gradients in gene expression and sharp as well as smooth transitions between different compartments. We apply our method on mouse brain sections and show that a fully unsupervised approach can computationally define anatomical compartments, which are highly reproducible across individuals, using as few as 18 gene markers. We also show that morphological variation does not always follow gene expression, and different spatial compartments can be defined by various cell types with common morphological features but distinct gene expression profiles. Conclusion We show that spatial gene expression data can be used for unsupervised and unbiased annotations of mouse brain spatial compartments based only on molecular markers, without the need of subjective manual annotations based on tissue and cell morphology or matching reference atlases.

scholarly journals TAMI-22. VARIATION AMONG INTACT TISSUE SAMPLES REVEALS THE CORE TRANSCRIPTIONAL IDENTITIES OF CELL TYPES IN THE GLIOMA MICROENVIRONMENT

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii218-ii218
Author(s):  
Patrick Schupp ◽  
Michael Oldham
Keyword(s):  
Gene Expression ◽  
Human Brain ◽  
Cell Types ◽  
Surface Proteins ◽  
Specific Gene ◽  
Specific Cell ◽  
Low Grade ◽  
Tissue Samples ◽  
Normal Human Brain ◽  
Normal Human

Abstract Adult low-grade gliomas generally progress to glioblastoma, a more aggressive CNS tumor with an extremely poor prognosis. Despite intensive efforts, numerous promising glioma therapies have failed to provide survival benefits. These failures reflect many factors, including intertumoral heterogeneity and immunosuppression by the tumor microenvironment (TME). We propose a novel approach to addresses these challenges through integrative deconvolution of bulk gene expression data generated from more than 5000 human gliomas and 7000 normal human brain samples. Inherent variation in the cellular composition and cellular activities of these samples allowed us to identify highly correlated modules of genes that represent specific cell types and cell states. By comparing gene coexpression modules in glioma vs. normal human brain, we have identified cell type-specific gene expression changes in the glioma TME that are highly reproducible. In contrast to single-cell methods, which sample only a fraction of the tumor tissue and fail to capture major nonmalignant cell-types, our results derive from billions of cells and thousands of individuals and are therefore highly robust. We find that a number of genes encoding cell-surface proteins are specifically up-regulated in immune and vascular cells of the glioma TME. Surprisingly, among those genes up-regulated in glioma vasculature are multiple members of the angiotensin pathway, suggesting non-canonical roles for these proteins in the glioma setting. We propose that these proteins may form a specific ‘zip code’ for glioma within the brain’s vasculature that can be targeted directly or by conjugation with existing drugs. More generally, our analytical approach has revealed reproducible gene expression changes in specific cell types of the glioma TME that provide more stable therapeutic targets than those that are expressed by genetically mutable malignant cells. We have also discovered novel, aberrantly coexpressed genes in microglia, oligodendrocytes, and astrocytes which we are testing in state-of-the-art human brain assembloid systems.

scholarly journals Single-cell-resolution transcriptome map of human, chimpanzee, bonobo, and macaque brains

2019 ◽  
Author(s):  
Ekaterina Khrameeva ◽  
Ilia Kurochkin ◽  
Dingding Han ◽  
Patricia Guijarro ◽  
Sabina Kanton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Neuronal Cell ◽  
Cell Types ◽  
Brain Regions ◽  
Human Cognition ◽  
Human Lineage ◽  
Cell Type ◽  
Cell Nuclei ◽  
Tissue Samples ◽  
Cell Type Specific

ABSTRACTIdentification of gene expression traits unique to the human brain sheds light on the mechanisms of human cognition. Here we searched for gene expression traits separating humans from other primates by analyzing 88,047 cell nuclei and 422 tissue samples representing 33 brain regions of humans, chimpanzees, bonobos, and macaques. We show that gene expression evolves rapidly within cell types, with more than two-thirds of cell type-specific differences not detected using conventional RNA sequencing of tissue samples. Neurons tend to evolve faster in all hominids, but non-neuronal cell types, such as astrocytes and oligodendrocyte progenitors, show more differences on the human lineage, including alterations of spatial distribution across neocortical layers.

scholarly journals Unsupervised cellular phenotypic hierarchy enables spatial intratumor heterogeneity characterization, recurrence-associated microdomains discovery, and harnesses network biology from hyperplexed in-situ fluorescence images of colorectal carcinoma

2020 ◽  
Author(s):  
Samantha A. Furman ◽  
Andrew M. Stern ◽  
Shikhar Uttam ◽  
D. Lansing Taylor ◽  
Filippo Pullara ◽  
...  
Keyword(s):  
Colorectal Carcinoma ◽  
Drug Targets ◽  
Spatial Configuration ◽  
Cell Types ◽  
Network Biology ◽  
Intratumor Heterogeneity ◽  
Tissue Samples ◽  
Primary Tumors ◽  
Stem Cell Maintenance

AbstractLEAPH is an unsupervised machine learning algorithm for characterizing in situ phenotypic heterogeneity in tissue samples. LEAPH builds a phenotypic hierarchy of cell types, cell states and their spatial configurations. The recursive modeling steps involve determining cell types with low-ranked mixtures of factor analyzers and optimizing cell states with spatial regularization. We applied LEAPH to hyperplexed (51 biomarkers) immunofluorescence images of colorectal carcinoma primary tumors (N=213). LEAPH, combined with pointwise mutual information (PMI), enables the discovery of phenotypically distinct microdomains, composed of spatially configured computational phenotypes. LEAPH identified a subset of microdomains visualized as the spatial configuration of recurrence-specific signaling networks whose intracellular and intercellular interactions support cancer stem cell maintenance and immunosuppression in the evolving tumor microenvironment. The LEAPH framework, when combined with microdomain discovery and microdomain-specific network biology, has the potential to provide insights into pathophysiological mechanisms, identify novel drug targets and inform therapeutic strategies for individual patients.

scholarly journals Transcriptome-wide spatial RNA profiling maps the cellular architecture of the developing human neocortex

2021 ◽  
Author(s):  
Kenny Roberts ◽  
Alexander Aivazidis ◽  
Vitalii Kleshchevnikov ◽  
Tong Li ◽  
Robin Fropf ◽  
...  
Keyword(s):  
Gene Expression ◽  
New Technology ◽  
In Situ Hybridisation ◽  
Cell Types ◽  
Fixed Tissue ◽  
Tissue Samples ◽  
Human Neocortex ◽  
Formalin Fixed Tissue ◽  
Spatial Profiling

Spatial genomic technologies can map gene expression in tissues, but provide limited potential for transcriptome-wide discovery approaches and application to fixed tissue samples. Here, we introduce the GeoMX Whole Transcriptome Atlas (WTA), a new technology for transcriptome-wide spatial profiling of tissues with cellular resolution. WTA significantly expands the Digital Spatial Profiling approach to enable in situ hybridisation against 18,190 genes at high-throughput using a sequencing readout. We applied WTA to generate the first spatial transcriptomic map of the fetal human neocortex, validating transcriptome-wide spatial profiling on formalin-fixed tissue material and demonstrating the spatial enrichment of autism gene expression in deep cortical layers. To demonstrate the value of WTA for cell atlasing, we integrated single-cell RNA-sequencing (scRNA-seq) and WTA data to spatially map dozens of neural cell types and showed that WTA can be used to directly measure cell type specific transcriptomes in situ. Moreover, we developed computational tools for background correction of WTA data and accurate integration with scRNA-seq. Our results present WTA as a versatile transcriptome-wide discovery tool for cell atlasing and fixed tissue spatial transcriptomics.

Abstract 20006: RNA Sequencing of Human Hypertrophic Cardiomyopathy Tissue Reveals Diverse Pathways Modulating the Myocardial Tissue Microenvironment

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Alejandro de Feria ◽  
Simon Maltais ◽  
Tarek S Absi ◽  
Yan R Su ◽  
Thomas P Stricker ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hypertrophic Cardiomyopathy ◽  
Rna Sequencing ◽  
Gene Clusters ◽  
Immune Defense ◽  
Enrichment Score ◽  
Myocardial Tissue ◽  
Tissue Samples ◽  
Tissue Microenvironment ◽  
Secreted Peptides

Introduction: Hypertrophic Cardiomyopathy (HCM) is a common inherited cardiac disease and cause of sudden death in adolescents and young adults. Despite significant advances in understanding the genetic underpinnings of HCM, there remains an incomplete understanding of how myofilament mutation carriers and non-carriers ultimately develop myocardial hypertrophy. Hypothesis: Human hypertrophic cardiomyopathy tissue differentially regulates gene expression involved in cellular growth. Methods: RNA was extracted from 5 control human septal tissue samples and 8 HCM septal tissue samples. The HCM tissue samples were from both myofilament mutation positive and negative patients. RNA sequencing was performed using Illumina HiSeq 2500 at a depth of 30 million paired-end reads per sample. Sequencing data was aligned to the hg19 genome using TopHat (splice-aware aligner). Cufflinks was used to estimate transcript abundance and differential expression. Pathway enrichment analysis was carried out on significant genes (q<0.05) using DAVID (v6.7). Only pathways with an enrichment score greater than 2 were evaluated. Results: Cluster and principal component analysis showed highly distinct RNA expression signatures between control and HCM tissues. There were 1500 differentially expressed sequences (q<0.05), which included 1286 annotated genes, 168 novel isoforms, and 46 long noncoding RNA sequences. Gene functional classification revealed 40 differentially regulated gene clusters in HCM tissue compared to controls. The most enriched gene clusters induced in human HCM tissue were associated with secreted peptides involved in promoting cell growth and modulation of extracellular structure (enrichment scores > 10). Highly enriched gene clusters that were suppressed in human HCM tissue were associated with secreted peptides involved in regulating inflammation and immune defense response (enrichment scores > 5). Conclusion: Our data reveals human HCM tissue differentially regulates a diverse array of genes involved in regulation of the myocardial tissue microenvironment. The strong similarity in gene expression across HCM samples suggests shared pathophysiological mechanisms independent of the underlying genetic etiology.

scholarly journals Single-cell transcriptome conservation in a comparative analysis of fresh and cryopreserved human skin tissue: pilot in localized scleroderma

2020 ◽  
Vol 22 (1) ◽  
Author(s):  
Emily Mirizio ◽  
Tracy Tabib ◽  
Xiao Wang ◽  
Wei Chen ◽  
Christopher Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Large Scale ◽  
Single Cell Analysis ◽  
Cell Types ◽  
Skin Tissue ◽  
Localized Scleroderma ◽  
Specific Gene ◽  
Tissue Samples ◽  
Cryopreserved Skin

Abstract Background The purpose of this study was to assess variability in cell composition and cell-specific gene expression in the skin of patients with localized scleroderma (LS) utilizing CryoStor® CS10 in comparison to RPMI to produce adequate preservation of tissue samples and cell types of interest for use in large-scale multi-institutional collaborations studying localized scleroderma and other skin disorders. Methods We performed single-cell RNA sequencing on paired skin biopsy specimens from 3 patients with LS. Each patient with one sample cryopreserved in CryoStor® CS10 and one fresh in RPMI media using 10× Genomics sequencing. Results Levels of cell viability and yield were comparable between CryoStor® CS10 (frozen) and RPMI (fresh) preserved cells. Furthermore, gene expression between preservation methods was collectively significantly correlated and conserved across all 18 identified cell cluster populations. Conclusion Comparable cell population and transcript expression yields between CryoStor® CS10 and RPMI preserved cells support the utilization of cryopreserved skin tissue in single-cell analysis. This suggests that employing standardized cryopreservation protocols for the skin tissue will help facilitate multi-site collaborations looking to identify mechanisms of disease in disorders characterized by cutaneous pathology.

scholarly journals The Gene Expression Deconvolution Interactive Tool (GEDIT): Accurate Cell Type Quantification from Gene Expression Data

2019 ◽  
Author(s):  
Brian B. Nadel ◽  
David Lopez ◽  
Dennis J. Montoya ◽  
Feiyang Ma ◽  
Hannah Waddel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Reference Data ◽  
Cell Types ◽  
Expression Data ◽  
Rna Seq ◽  
Cell Type ◽  
Tissue Samples ◽  
Interactive Tool ◽  
Human And Mouse

AbstractThe cell type composition of heterogeneous tissue samples can be a critical variable in both clinical and laboratory settings. However, current experimental methods of cell type quantification (e.g. cell flow cytometry) are costly, time consuming, and can introduce bias. Computational approaches that infer cell type abundance from expression data offer an alternate solution. While these methods have gained popularity, most are limited to predicting hematopoietic cell types and do not produce accurate predictions for stromal cell types. Many of these methods are also limited to particular platforms, whether RNA-seq or specific microarrays. We present the Gene Expression Deconvolution Interactive Tool (GEDIT), a tool that overcomes these limitations, compares favorably with existing methods, and provides superior versatility. Using both simulated and experimental data, we extensively evaluate the performance of GEDIT and demonstrate that it returns robust results under a wide variety of conditions. These conditions include a variety of platforms (microarray and RNA-seq), tissue types (blood and stromal), and species (human and mouse). Finally, we provide reference data from eight sources spanning a wide variety of stromal and hematopoietic types in both human and mouse. This reference database allows the user to obtain estimates for a wide variety of tissue samples without having to provide their own data. GEDIT also accepts user submitted reference data, thus allowing the estimation of any cell type or subtype, provided that reference data is available.Author SummaryThe Gene Expression Deconvolution Interactive Tool (GEDIT) is a robust and accurate tool that uses gene expression data to estimate cell type abundances. Extensive testing on a variety of tissue types and technological platforms demonstrates that GEDIT provides greater versatility than other cell type deconvolution tools. GEDIT utilizes reference data describing the expression profile of purified cell types, and we provide in the software package a library of reference matrices from various sources. GEDIT is also flexible and allows the user to supply custom reference matrices. A GUI interface for GEDIT is available at http://webtools.mcdb.ucla.edu/, and source code and reference matrices are available at https://github.com/BNadel/GEDIT.

New cancer drug targets identified in adrenocortical carcinoma through gene expression profiling

2018 ◽  
Author(s):  
Raimunde Liang ◽  
Isabel Weigand ◽  
Barbara Altieri ◽  
Stefan Kircher ◽  
Sonja Steinhauer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Adrenocortical Carcinoma ◽  
Expression Profiling ◽  
Drug Targets ◽  
Cancer Drug

scholarly journals Control of impulsivity by Gi-protein signalling in layer-5 pyramidal neurons of the anterior cingulate cortex

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Bastiaan van der Veen ◽  
Sampath K. T. Kapanaiah ◽  
Kasyoka Kilonzo ◽  
Peter Steele-Perkins ◽  
Martin M. Jendryka ◽  
...  
Keyword(s):  
Gene Expression ◽  
Anterior Cingulate Cortex ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Pyramidal Neurons ◽  
Treatment Options ◽  
Pyramidal Cells ◽  
Cingulate Cortex ◽  
Cell Types ◽  
Anterior Cingulate

AbstractPathological impulsivity is a debilitating symptom of multiple psychiatric diseases with few effective treatment options. To identify druggable receptors with anti-impulsive action we developed a systematic target discovery approach combining behavioural chemogenetics and gene expression analysis. Spatially restricted inhibition of three subdivisions of the prefrontal cortex of mice revealed that the anterior cingulate cortex (ACC) regulates premature responding, a form of motor impulsivity. Probing three G-protein cascades with designer receptors, we found that the activation of Gi-signalling in layer-5 pyramidal cells (L5-PCs) of the ACC strongly, reproducibly, and selectively decreased challenge-induced impulsivity. Differential gene expression analysis across murine ACC cell-types and 402 GPCRs revealed that - among Gi-coupled receptor-encoding genes - Grm2 is the most selectively expressed in L5-PCs while alternative targets were scarce. Validating our approach, we confirmed that mGluR2 activation reduced premature responding. These results suggest Gi-coupled receptors in ACC L5-PCs as therapeutic targets for impulse control disorders.

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