Investigating the dynamics of microbial consortia in spatially structured environments

ABSTRACTThe spatial organization of microbial communities arises from a complex interplay of biotic and abiotic interactions and is a major determinant of ecosystem functions. We design a microfluidic platform to investigate how the spatial arrangement of microbes impacts gene expression and growth. We elucidate key biochemical parameters that dictate the mapping between spatial positioning and gene expression patterns. We show that distance can establish a low-pass filter to periodic inputs, and can enhance the fidelity of information processing. Positive and negative feedback can play disparate roles in the synchronization and robustness of a genetic oscillator distributed between two strains to spatial separation. Quantification of growth and metabolite release in an amino-acid auxotroph community demonstrates that the interaction network and stability of the community are highly sensitive to temporal perturbations and spatial arrangements. In sum, our microfluidic platform can quantify spatiotemporal parameters influencing diffusion-mediated interactions in microbial consortia.

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Spatial organization of different sigma factor activities and c-di-GMP signalling within the three-dimensional landscape of a bacterial biofilm

Open Biology ◽

10.1098/rsob.180066 ◽

2018 ◽

Vol 8 (8) ◽

pp. 180066 ◽

Cited By ~ 24

Author(s):

Gisela Klauck ◽

Diego O. Serra ◽

Alexandra Possling ◽

Regine Hengge

Keyword(s):

Gene Expression ◽

Extracellular Matrix ◽

Stationary Phase ◽

Sigma Factor ◽

Spatial Organization ◽

Expression Patterns ◽

Three Dimensional ◽

Biomechanical Properties ◽

Ribosomal Gene ◽

Bacterial Biofilm

Bacterial biofilms are large aggregates of cells embedded in an extracellular matrix of self-produced polymers. In macrocolony biofilms of Escherichia coli , this matrix is generated in the upper biofilm layer only and shows a surprisingly complex supracellular architecture. Stratified matrix production follows the vertical nutrient gradient and requires the stationary phase σ S (RpoS) subunit of RNA polymerase and the second messenger c-di-GMP. By visualizing global gene expression patterns with a newly designed fingerprint set of Gfp reporter fusions, our study reveals the spatial order of differential sigma factor activities, stringent control of ribosomal gene expression and c-di-GMP signalling in vertically cryosectioned macrocolony biofilms. Long-range physiological stratification shows a duplication of the growth-to-stationary phase pattern that integrates nutrient and oxygen gradients. In addition, distinct short-range heterogeneity occurs within specific biofilm strata and correlates with visually different zones of the refined matrix architecture. These results introduce a new conceptual framework for the control of biofilm formation and demonstrate that the intriguing extracellular matrix architecture, which determines the emergent physiological and biomechanical properties of biofilms, results from the spatial interplay of global gene regulation and microenvironmental conditions. Overall, mature bacterial macrocolony biofilms thus resemble the highly organized tissues of multicellular organisms.

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Regulatory T Cell-Related Gene Biomarkers in the Deterioration of Atherosclerosis

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.661709 ◽

2021 ◽

Vol 8 ◽

Author(s):

Meng Xia ◽

Qingmeng Wu ◽

Pengfei Chen ◽

Cheng Qian

Keyword(s):

Gene Expression ◽

Cardiovascular Events ◽

Molecular Mechanisms ◽

Inflammatory Responses ◽

Expression Patterns ◽

Interaction Network ◽

Gene Signature ◽

Smooth Muscle Contraction ◽

Gene Expression Omnibus ◽

Scoring Method

Background: Regulatory T cells (Tregs) have shown to be protective against the development of atherosclerosis, a major pathological cause for cardiovascular events. Here, we aim to explore the roles of Tregs-related genes in atherosclerosis deterioration.Methods and Results: We downloaded the gene expression profile of 29 atherosclerotic samples from the Gene Expression Omnibus database with an accession number of GSE28829. The abundance of Tregs estimated by the CIBERSORT algorithm was negatively correlated with the atherosclerotic stage. Using the limma test and correlation analysis, a total of 159 differentially expressed Tregs-related genes (DETregRGs) between early and advanced atherosclerotic plaques were documented. Functional annotation analysis using the DAVID tool indicated that the DETregRGs were mainly enriched in inflammatory responses, immune-related mechanisms, and pathways such as complement and coagulation cascades, platelet activation, leukocyte trans-endothelial migration, vascular smooth muscle contraction, and so on. A protein-protein interaction network of the DETregRGs was then constructed, and five hub genes (PTPRC, C3AR1, CD53, TLR2, and CCR1) were derived from the network with node degrees ≥20. The expression patterns of these hub DETregRGs were further validated in several independent datasets. Finally, a single sample scoring method was used to build a gene signature for the five DETregRGs, which could distinguish patients with myocardial infarction from those with stable coronary disease.Conclusion: The results of this study will improve our understanding about the Tregs-associated molecular mechanisms in the progression of atherosclerosis and facilitate the discovery of novel biomarkers for acute cardiovascular events.

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A Model of Waardenburg Syndrome Using Patient-Derived iPSCs With a SOX10 Mutation Displays Compromised Maturation and Function of the Neural Crest That Involves Inner Ear Development

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.720858 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jie Wen ◽

Jian Song ◽

Yijiang Bai ◽

Yalan Liu ◽

Xinzhang Cai ◽

...

Keyword(s):

Gene Expression ◽

Neural Crest ◽

Inner Ear ◽

Expression Patterns ◽

Interaction Network ◽

Patient Specific ◽

Waardenburg Syndrome ◽

Differentiation Potential ◽

Ear Development ◽

Inner Ear Development

Waardenburg syndrome (WS) is an autosomal dominant inherited disorder that is characterized by sensorineural hearing loss and abnormal pigmentation. SOX10 is one of its main pathogenicity genes. The generation of patient-specific induced pluripotent stem cells (iPSCs) is an efficient means to investigate the mechanisms of inherited human disease. In our work, we set up an iPSC line derived from a WS patient with SOX10 mutation and differentiated into neural crest cells (NCCs), a key cell type involved in inner ear development. Compared with control-derived iPSCs, the SOX10 mutant iPSCs showed significantly decreased efficiency of development and differentiation potential at the stage of NCCs. After that, we carried out high-throughput RNA-seq and evaluated the transcriptional misregulation at every stage. Transcriptome analysis of differentiated NCCs showed widespread gene expression alterations, and the differentially expressed genes (DEGs) were enriched in gene ontology terms of neuron migration, skeletal system development, and multicellular organism development, indicating that SOX10 has a pivotal part in the differentiation of NCCs. It’s worth noting that, a significant enrichment among the nominal DEGs for genes implicated in inner ear development was found, as well as several genes connected to the inner ear morphogenesis. Based on the protein-protein interaction network, we chose four candidate genes that could be regulated by SOX10 in inner ear development, namely, BMP2, LGR5, GBX2, and GATA3. In conclusion, SOX10 deficiency in this WS subject had a significant impact on the gene expression patterns throughout NCC development in the iPSC model. The DEGs most significantly enriched in inner ear development and morphogenesis may assist in identifying the underlying basis for the inner ear malformation in subjects with WS.

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Network-guided supervised learning on gene expression using a graph convolutional neural network

10.1101/2021.12.27.474240 ◽

2021 ◽

Author(s):

Chayaporn Suphavilai ◽

Hatairat Yingtaweesittikul

Keyword(s):

Neural Network ◽

Gene Expression ◽

Machine Learning ◽

Convolutional Neural Network ◽

Protein Interaction ◽

Expression Patterns ◽

Interaction Network ◽

Gene Expression Patterns ◽

Learning Models ◽

Machine Learning Models

Background: Transcriptomic profiles have become crucial information in understanding diseases and improving treatments. While dysregulated gene sets are identified via pathway analysis, various machine learning models have been proposed for predicting phenotypes such as disease type and drug response based on gene expression patterns. However, these models still lack interpretability, as well as the ability to integrate prior knowledge from a protein-protein interaction network. Results: We propose Grandline, a graph convolutional neural network that can integrate gene expression data and structure of the protein interaction network to predict a specific phenotype. Transforming the interaction network into a spectral domain enables convolution of neighbouring genes and pinpointing high-impact subnetworks, which allow better interpretability of deep learning models. Grandline achieves high phenotype prediction accuracy (67-85% in 8 use cases), comparable to state-of-the-art machine learning models while requiring a smaller number of parameters, allowing it to learn complex but interpretable gene expression patterns from biological datasets. Conclusion: To improve the interpretability of phenotype prediction based on gene expression patterns, we developed Grandline using graph convolutional neural network technique to integrate protein interaction information. We focus on improving the ability to learn nonlinear relationships between gene expression patterns and a given phenotype and incorporation of prior knowledge, which are the main challenges of machine learning models for biological datasets. The graph convolution allows us to aggregate information from relevant genes and reduces the number of trainable parameters, facilitating model training for a small-sized biological dataset.

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Slide-seq: A Scalable Technology for Measuring Genome-Wide Expression at High Spatial Resolution

10.1101/563395 ◽

2019 ◽

Author(s):

Samuel G. Rodriques ◽

Robert R. Stickels ◽

Aleksandrina Goeva ◽

Carly A. Martin ◽

Evan Murray ◽

...

Keyword(s):

Gene Expression ◽

Spatial Resolution ◽

Large Scale ◽

Spatial Organization ◽

Expression Patterns ◽

Cell Types ◽

Genome Wide ◽

Biological Discovery ◽

Spatial Locations ◽

Genome Wide Expression

AbstractThe spatial organization of cells in tissue has a profound influence on their function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. Here, we introduce Slide-seq, a highly scalable method that enables facile generation of large volumes of unbiased spatial transcriptomes with 10 µm spatial resolution, comparable to the size of individual cells. In Slide-seq, RNA is transferred from freshly frozen tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the spatial locations of the RNA to be inferred by sequencing. To demonstrate Slide-seq’s utility, we localized cell types identified by large-scale scRNA-seq datasets within the cerebellum and hippocampus. We next systematically characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, identifying new axes of variation across Purkinje cell compartments. Finally, we used Slide-seq to define the temporal evolution of cell-type-specific responses in a mouse model of traumatic brain injury. Slide-seq will accelerate biological discovery by enabling routine, high-resolution spatial mapping of gene expression.One Sentence SummarySlide-seq measures genome-wide expression in complex tissues at 10-micron resolution.

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

10.1101/2020.09.18.304147 ◽

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.

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Single Molecule-based FliFISH Validates Radial and Heterogeneous Gene Expression Patterns in Pancreatic Islet β Cells

10.2337/figshare.14096639.v1 ◽

2021 ◽

Author(s):

Fangjia Li ◽

Dehong Hu ◽

Cailin Dieter ◽

Charles Ansong ◽

Lori Sussel ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Islet ◽

Spatial Organization ◽

Single Cells ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Β Cells ◽

Β Cell ◽

Expression Levels ◽

Wide Range

Single cell RNA sequencing (scRNA-Seq) technologies have greatly enhanced our understanding of islet cell transcriptomes and have revealed the existence of β cell heterogeneity. However, comparison of scRNA-Seq datasets from different groups have highlighted inconsistencies in gene expression patterns, primarily due to variable detection of lower abundance transcripts. Furthermore, such analyses are unable to uncover the spatial organization of heterogeneous gene expression. Here we used fluctuation localization imaging-based fluorescence in situ hybridization (fliFISH) to quantify transcripts in single cells in mouse pancreatic islet sections. We compared the expression patterns of Insulin 2 (Ins2) with Mafa and Ucn3 – two genes expressed in β cells as they mature, as well as Rgs4 – a factor with variably reported expression in the islet. This approach accurately quantified transcripts across a wide range of expression levels - from single copies to over hundred copies per cell in one islet. Importantly, fliFISH allowed evaluation of transcript heterogeneity in the spatial context of an intact islet. These studies confirm the existence of a high degree of heterogeneous gene expression levels within the islet and highlight relative and radial expression patterns that likely reflect distinct β cell maturation states along the radial axis of the islet.

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

10.21203/rs.3.rs-647777/v1 ◽

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.

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Developmental system drift in motor ganglion patterning between distantly related tunicates

10.1101/320804 ◽

2018 ◽

Author(s):

Elijah K. Lowe ◽

Alberto Stolfi

Keyword(s):

Gene Expression ◽

Spatial Organization ◽

Molecular Mechanisms ◽

Expression Patterns ◽

Lone Pair ◽

Embryonic Cell ◽

Specific Gene ◽

Developmental System Drift ◽

Larval Nervous System ◽

Contralateral Projection

AbstractThe larval nervous system of the solitary tunicate Ciona is a simple model for the study of chordate neurodevelopment. The development and connectivity of the Ciona Motor Ganglion (MG) has been studied in fine detail, but how this important structure develops in other tunicates is not well known. By comparing gene expression patterns in the developing MG of the distantly related tunicate Molgula occidentalis, we found that its patterning is highly conserved compared to the Ciona MG. MG neuronal subtypes in Molgula were specified in the exact same positions as in Ciona, though the timing of subtype-specific gene expression onset was slightly shifted to begin earlier, relative to mitotic exit and differentiation. In transgenic Molgula embryos electroporated with Dmbx reporter plasmids, we were also able to characterize the morphology of the lone pair of descending decussating neurons (ddNs) in Molgula, revealing the same unique contralateral projection seen in Ciona ddNs and their putative vertebrate homologs the Mauthner cells. Although Dmbx expression labels the ddNs in both species, cross-species transgenic assays revealed significant changes to the cis-regulatory logic underlying Dmbx transcription. We found that Dmbx cis-regulatory DNAs from Ciona can drive highly specific reporter gene expression in Molgula ddNs, but Molgula sequences are not active in Ciona ddNs. This acute divergence in the molecular mechanisms that underlie otherwise functionally conserved cis-regulatory DNAs supports the recently proposed idea that the extreme genetic plasticity observed in tunicates may be attributed to the extreme rigidity of the spatial organization of their embryonic cell lineages.

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Consistent Biomarkers and Related Pathogenesis Underlying Asthma Revealed by Systems Biology Approach

International Journal of Molecular Sciences ◽

10.3390/ijms20164037 ◽

2019 ◽

Vol 20 (16) ◽

pp. 4037 ◽

Cited By ~ 1

Author(s):

Xiner Nie ◽

Jinyi Wei ◽

Youjin Hao ◽

Jingxin Tao ◽

Yinghong Li ◽

...

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Patterns ◽

Interaction Network ◽

Bronchial Epithelium ◽

Airway Disease ◽

Arachidonic Acid Metabolism ◽

Functional Enrichment ◽

Chemical Stimulus ◽

Extracellular Region

Asthma is a common chronic airway disease worldwide. Due to its clinical and genetic heterogeneity, the cellular and molecular processes in asthma are highly complex and relatively unknown. To discover novel biomarkers and the molecular mechanisms underlying asthma, several studies have been conducted by focusing on gene expression patterns in epithelium through microarray analysis. However, few robust specific biomarkers were identified and some inconsistent results were observed. Therefore, it is imperative to conduct a robust analysis to solve these problems. Herein, an integrated gene expression analysis of ten independent, publicly available microarray data of bronchial epithelial cells from 348 asthmatic patients and 208 healthy controls was performed. As a result, 78 up- and 75 down-regulated genes were identified in bronchial epithelium of asthmatics. Comprehensive functional enrichment and pathway analysis revealed that response to chemical stimulus, extracellular region, pathways in cancer, and arachidonic acid metabolism were the four most significantly enriched terms. In the protein-protein interaction network, three main communities associated with cytoskeleton, response to lipid, and regulation of response to stimulus were established, and the most highly ranked 6 hub genes (up-regulated CD44, KRT6A, CEACAM5, SERPINB2, and down-regulated LTF and MUC5B) were identified and should be considered as new biomarkers. Pathway cross-talk analysis highlights that signaling pathways mediated by IL-4/13 and transcription factor HIF-1α and FOXA1 play crucial roles in the pathogenesis of asthma. Interestingly, three chemicals, polyphenol catechin, antibiotic lomefloxacin, and natural alkaloid boldine, were predicted and may be potential drugs for asthma treatment. Taken together, our findings shed new light on the common molecular pathogenesis mechanisms of asthma and provide theoretical support for further clinical therapeutic studies.

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