scholarly journals Large field of view-spatially resolved transcriptomics at nanoscale resolution

2021 ◽  
Author(s):  
Ao Chen ◽  
Sha Liao ◽  
Mengnan Cheng ◽  
Kailong Ma ◽  
Liang Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Temporal Dynamics ◽  
Capture Rate ◽  
Large Field ◽  
Major Advance ◽  
Widespread Application ◽  
Spatially Resolved ◽  
Sample Throughput ◽  
Spatio Temporal

ABSTRACTHigh-throughput profiling of in situ gene expression represents a major advance towards the systematic understanding of tissue complexity. Applied with enough capture area and high sample throughput it will help to define the spatio-temporal dynamics of gene expression in tissues and organisms. Yet, current technologies have considerable bottlenecks that limit widespread application. Here, we have combined DNA nanoball (DNB) patterned array chips and in situ RNA capture to develop Stereo-seq (Spatio-Temporal Enhanced REsolution Omics-sequencing). This approach allows high sample throughput transcriptomic profiling of histological sections at unprecedented (nanoscale) resolution with areas expandable to centimeter scale, high sensitivity and homogenous capture rate. As proof of principle, we applied Stereo-seq to the adult mouse brain and sagittal sections of E11.5 and E16.5 mouse embryos. Thanks to its unique features and amenability to additional modifications, Stereo-seq can pave the way for the systematic spatially resolved-omics characterization of tissues and organisms.

scholarly journals Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression

2019 ◽  
Vol 116 (39) ◽  
pp. 19490-19499 ◽  
Author(s):  
Chenglong Xia ◽  
Jean Fan ◽  
George Emanuel ◽  
Junjie Hao ◽  
Xiaowei Zhuang
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Spatial Information ◽  
Detection Efficiency ◽  
Expression Profiles ◽  
Regulation Of Gene Expression ◽  
Rna Profiling ◽  
Spatially Resolved ◽  
Cycle Dependent

The expression profiles and spatial distributions of RNAs regulate many cellular functions. Image-based transcriptomic approaches provide powerful means to measure both expression and spatial information of RNAs in individual cells within their native environment. Among these approaches, multiplexed error-robust fluorescence in situ hybridization (MERFISH) has achieved spatially resolved RNA quantification at transcriptome scale by massively multiplexing single-molecule FISH measurements. Here, we increased the gene throughput of MERFISH and demonstrated simultaneous measurements of RNA transcripts from ∼10,000 genes in individual cells with ∼80% detection efficiency and ∼4% misidentification rate. We combined MERFISH with cellular structure imaging to determine subcellular compartmentalization of RNAs. We validated this approach by showing enrichment of secretome transcripts at the endoplasmic reticulum, and further revealed enrichment of long noncoding RNAs, RNAs with retained introns, and a subgroup of protein-coding mRNAs in the cell nucleus. Leveraging spatially resolved RNA profiling, we developed an approach to determine RNA velocity in situ using the balance of nuclear versus cytoplasmic RNA counts. We applied this approach to infer pseudotime ordering of cells and identified cells at different cell-cycle states, revealing ∼1,600 genes with putative cell cycle-dependent expression and a gradual transcription profile change as cells progress through cell-cycle stages. Our analysis further revealed cell cycle-dependent and cell cycle-independent spatial heterogeneity of transcriptionally distinct cells. We envision that the ability to perform spatially resolved, genome-wide RNA profiling with high detection efficiency and accuracy by MERFISH could help address a wide array of questions ranging from the regulation of gene expression in cells to the development of cell fate and organization in tissues.

scholarly journals From whole-mount to single-cell spatial assessment of gene expression in 3D

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Lisa N. Waylen ◽  
Hieu T. Nim ◽  
Luciano G. Martelotto ◽  
Mirana Ramialison
Keyword(s):  
Gene Expression ◽  
Cell Types ◽  
Medical Diagnostics ◽  
Three Dimensions ◽  
Expression Data ◽  
Developmental Defects ◽  
Ongoing Research ◽  
Spatially Resolved ◽  
Spatio Temporal ◽  
Gene Expression Levels

Abstract Unravelling spatio-temporal patterns of gene expression is crucial to understanding core biological principles from embryogenesis to disease. Here we review emerging technologies, providing automated, high-throughput, spatially resolved quantitative gene expression data. Novel techniques expand on current benchmark protocols, expediting their incorporation into ongoing research. These approaches digitally reconstruct patterns of embryonic expression in three dimensions, and have successfully identified novel domains of expression, cell types, and tissue features. Such technologies pave the way for unbiased and exhaustive recapitulation of gene expression levels in spatial and quantitative terms, promoting understanding of the molecular origin of developmental defects, and improving medical diagnostics.

scholarly journals A sensitive mNeonGreen reporter system to measure transcriptional dynamics in Drosophila development

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Stefano Ceolin ◽  
Monika Hanf ◽  
Marta Bozek ◽  
Andrea Ennio Storti ◽  
Nicolas Gompel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fluorescent Protein ◽  
Temporal Dynamics ◽  
Reconstruction Algorithm ◽  
Detection Sensitivity ◽  
Axis Formation ◽  
Reporter System ◽  
Enhancer Activity ◽  
Spatio Temporal ◽  
Pioneer Transcription Factor

AbstractThe gene regulatory network governing anterior–posterior axis formation in Drosophila is a well-established paradigm to study transcription in developmental biology. The rapid temporal dynamics of gene expression during early stages of development, however, are difficult to track with standard techniques. We optimized the bright and fast-maturing fluorescent protein mNeonGreen as a real-time, quantitative reporter of enhancer expression. We derive enhancer activity from the reporter fluorescence dynamics with high spatial and temporal resolution, using a robust reconstruction algorithm. By comparing our results with data obtained with the established MS2-MCP system, we demonstrate the higher detection sensitivity of our reporter. We used the reporter to quantify the activity of variants of a simple synthetic enhancer, and observe increased activity upon reduction of enhancer–promoter distance or addition of binding sites for the pioneer transcription factor Zelda. Our reporter system constitutes a powerful tool to study spatio-temporal gene expression dynamics in live embryos.

scholarly journals Regulation of spatial and temporal gene expression in an animal germline

2018 ◽  
Author(s):  
Asija Diag ◽  
Marcel Schilling ◽  
Filippos Klironomos ◽  
Salah Ayoub ◽  
Nikolaus Rajewsky
Keyword(s):  
Gene Expression ◽  
Regulatory Mechanisms ◽  
Rna Expression ◽  
Temporal Expression ◽  
Temporal Gene Expression ◽  
Cell Proliferation And Differentiation ◽  
Proliferation And Differentiation ◽  
Gene Regulatory ◽  
Spatio Temporal

SUMMARYIn animal germlines, regulation of cell proliferation and differentiation is particularly important but poorly understood. Here, using a cryo-cut approach, we mapped RNA expression along the Caenorhabditis elegans germline and, using mutants, dissected gene regulatory mechanisms that control spatio-temporal expression. We detected, at near single-cell resolution, > 10,000 mRNAs, > 300 miRNAs and numerous novel miRNAs. Most RNAs were organized in distinct spatial patterns. Germline-specific miRNAs and their targets were co-localized. Moreover, we observed differential 3’ UTR isoform usage for hundreds of mRNAs. In tumorous gld-2 gld-1 mutants, gene expression was strongly perturbed. In particular, differential 3’ UTR usage was significantly impaired. We propose that PIE-1, a transcriptional repressor, functions to maintain spatial gene expression. Our data also suggest that cpsf-4 and fipp-1 control differential 3’ UTR usage for hundreds of genes. Finally, we constructed a “virtual gonad” enabling “virtual in situ hybridizations” and access to all data (https://shiny.mdc-berlin.de/spacegerm/).

scholarly journals Spatio-temporal dynamics of gene expression of the Edn1-Dlx5/6 pathway during development of the lower jaw

genesis ◽  
2010 ◽  
Vol 48 (6) ◽  
pp. 262-373 ◽  
Author(s):  
Maxence Vieux-Rochas ◽  
Stefano Mantero ◽  
Eglantine Heude ◽  
Ottavia Barbieri ◽  
Simonetta Astigiano ◽  
...  
Keyword(s):  
Gene Expression ◽  
Temporal Dynamics ◽  
Lower Jaw ◽  
Spatio Temporal

Spatio-temporal dynamics in global rice gene expression ( Oryza sativa  L.) in response to high ammonium stress

2017 ◽  
Vol 212 ◽  
pp. 94-104 ◽  
Author(s):  
Li Sun ◽  
Dongwei Di ◽  
Guangjie Li ◽  
Herbert J. Kronzucker ◽  
Weiming Shi
Keyword(s):  
Gene Expression ◽  
Oryza Sativa ◽  
Temporal Dynamics ◽  
Oryza Sativa L ◽  
Rice Gene ◽  
Spatio Temporal ◽  
Ammonium Stress

scholarly journals Modeling Spatio-temporal Dynamics of Chromatin Marks

2017 ◽  
Author(s):  
Petko Fiziev ◽  
Jason Ernst
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Temporal Dynamics ◽  
Spatial Dynamics ◽  
Computational Method ◽  
Time Points ◽  
Signal Density ◽  
Spatial Changes ◽  
Spatio Temporal ◽  
Over Time

ABSTRACTTo model spatial changes of chromatin mark peaks over time we developed and applied ChromTime, a computational method that predicts regions for which peaks either expand or contract significantly or hold steady between time points. Predicted expanding and contracting peaks can mark regulatory regions associated with transcription factor binding and gene expression changes. Spatial dynamics of peaks provided information about gene expression changes beyond localized signal density changes. ChromTime detected asymmetric expansions and contractions, which for some marks associated with the direction of transcription. ChromTime facilitates the analysis of time course chromatin data in a range of biological systems.

scholarly journals ClusterMap: multi-scale clustering analysis of spatial gene expression

2021 ◽  
Author(s):  
Yichun He ◽  
Xin Tang ◽  
Jiahao Huang ◽  
Haowen Zhou ◽  
Kevin Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dimensional Space ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Computational Framework ◽  
Spatially Resolved ◽  
Tissue Organization

AbstractQuantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we present an unsupervised and annotation-free framework, termed ClusterMap, which incorporates physical proximity and gene identity of RNAs, formulates the task as a point pattern analysis problem, and thus defines biologically meaningful structures and groups. Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and consistently performs on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell-cell interactions, and tissue organization principles from high-dimensional transcriptomic images.

scholarly journals Spatially resolved simulations of the spread of COVID-19 in three European countries

2021 ◽  
Vol 17 (7) ◽  
pp. e1009090
Author(s):  
Andrea Parisi ◽  
Samuel P. C. Brand ◽  
Joe Hilton ◽  
Rabia Aziza ◽  
Matt J. Keeling ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
European Countries ◽  
Potential Candidate ◽  
Middle Income ◽  
Spatially Resolved ◽  
Contact Patterns ◽  
Containment Measures ◽  
Spatio Temporal ◽  
Using Data ◽  
Temporal Spread

We explore the spatial and temporal spread of the novel SARS-CoV-2 virus under containment measures in three European countries based on fits to data of the early outbreak. Using data from Spain and Italy, we estimate an age dependent infection fatality ratio for SARS-CoV-2, as well as risks of hospitalization and intensive care admission. We use them in a model that simulates the dynamics of the virus using an age structured, spatially detailed agent based approach, that explicitly incorporates governmental interventions and changes in mobility and contact patterns occurred during the COVID-19 outbreak in each country. Our simulations reproduce several of the features of its spatio-temporal spread in the three countries studied. They show that containment measures combined with high density are responsible for the containment of cases within densely populated areas, and that spread to less densely populated areas occurred during the late stages of the first wave. The capability to reproduce observed features of the spatio-temporal dynamics of SARS-CoV-2 makes this model a potential candidate for forecasting the dynamics of SARS-CoV-2 in other settings, and we recommend its application in low and lower-middle income countries which remain understudied.

scholarly journals Single-cell Stereo-seq enables cell type-specific spatial transcriptome characterization in Arabidopsis leaves

2021 ◽  
Author(s):  
Keke Xia ◽  
Hai-Xi Sun ◽  
Jie Li ◽  
Jiming Li ◽  
Yu Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Spatial Information ◽  
Transcriptome Profiling ◽  
Cell Type ◽  
Spatially Resolved ◽  
Cell Type Specific

Understanding the complex functions of plant leaves requires spatially resolved gene expression profiling with single-cell resolution. However, although in situ gene expression profiling technologies have been developed, this goal has not yet been achieved. Here, we present the first in situ single-cell transcriptome profiling in plant, scStereo-seq (single-cell SpaTial Enhanced REsolution Omics-sequencing), which enabled the bona fide single-cell spatial transcriptome of Arabidopsis leaves. We successfully characterized subtle but significant transcriptomic differences between upper and lower epidermal cells. Furthermore, with high-resolution location information, we discovered the cell type-specific spatial gene expression gradients from main vein to leaf edge. By reconstructing those spatial gradients, we show for the first time the distinct spatial developmental trajectories of vascular cells and guard cells. Our findings show the importance of incorporating spatial information for answering complex biological questions in plant, and scStereo-seq offers a powerful single cell spatially resolved transcriptomic strategy for plant biology.

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