scholarly journals Advances in spatial transcriptomic data analysis

2021 ◽  
Vol 31 (10) ◽  
pp. 1706-1718 ◽  
Author(s):  
Ruben Dries ◽  
Jiaji Chen ◽  
Natalie del Rossi ◽  
Mohammed Muzamil Khan ◽  
Adriana Sistig ◽  
...  
Keyword(s):  
Data Analysis ◽  
Spatial Organization ◽  
Transcriptomic Data ◽  
Tissue Organization ◽  
Gene Coverage ◽  
Health And Disease ◽  
Solid Foundation ◽  
Downstream Analysis ◽  
Subcellular Resolution ◽  
Technological Platforms

Spatial transcriptomics is a rapidly growing field that promises to comprehensively characterize tissue organization and architecture at the single-cell or subcellular resolution. Such information provides a solid foundation for mechanistic understanding of many biological processes in both health and disease that cannot be obtained by using traditional technologies. The development of computational methods plays important roles in extracting biological signals from raw data. Various approaches have been developed to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical biases. Downstream analysis tools formulate spatial organization and cell–cell communications as quantifiable properties, and provide algorithms to derive such properties. Integrative pipelines further assemble multiple tools in one package, allowing biologists to conveniently analyze data from beginning to end. In this review, we summarize the state of the art of spatial transcriptomic data analysis methods and pipelines, and discuss how they operate on different technological platforms.

Spatiotemporal patterns of fibronectin distribution during embryonic development

Development ◽  
1981 ◽  
Vol 63 (1) ◽  
pp. 193-206
Author(s):  
Michael Melnick ◽  
Tina Jaskoll ◽  
Anna G. Brownell ◽  
Mary Macdougall ◽  
Conny Bessem ◽  
...  
Keyword(s):  
Limb Development ◽  
Spatial Organization ◽  
Cartilage Matrix ◽  
Cartilage Tissue ◽  
Blue Staining ◽  
Major Constituent ◽  
Alcian Blue Staining ◽  
In Ovo ◽  
Tissue Organization ◽  
Testicular Hyaluronidase

It has been suggested that an extracellular matrix - and cell surface - associated glycoprotein, fibronectin, plays a role in the positioning of cells in morphogenesis and in the maintenance of orderly tissue organization. In the present study the appearance and distribution of fibronectin during in ovo chick limb development has been investigated by indirect immunofluorescence techniques in H.H. stages 20–30. Fibronectin is not detectable until just prior to the transition from the morphogenetic to the cytodifferentiation phase of development. Beginning at H.H. stage 25, successive nonrandom patterns of fibronectin detection and distribution, which resemble the subsequent cartilaginous elements, precede overt chondrogenesis as detected by Alcian blue staining. This corresponds to the onset of the cytodifferentiation phase of limb development. As the accumulation of acidic proteoglycan increases in the cartilage matrix and the mesenchymal cells become more round in appearance, the presence of detectable fibronectin decreases and is ultimately seen only in the perichondria and basement membrane. However, predigestion of developed cartilage tissue with testicular hyaluronidase, prior to fibronectin staining, indicated that fibronectin remains a major constituent of cartilage matrix and is apparently masked by cartilagespecific proteoglycans. This study of chick limb development is consistent with the hypothesis that fibronectin may be a molecule that facilitates the spatial organization of cartilaginous primordia cytodifferentiation.

scholarly journals Programming and simulating chemical reaction networks on a surface

2020 ◽  
Vol 17 (166) ◽  
pp. 20190790 ◽  
Author(s):  
Samuel Clamons ◽  
Lulu Qian ◽  
Erik Winfree
Keyword(s):  
Chemical Reaction ◽  
Spatial Organization ◽  
Initial Conditions ◽  
Chemical Reaction Networks ◽  
Boolean Logic ◽  
Reaction Networks ◽  
Trade Off ◽  
Molecular Systems ◽  
Chemical Complexity ◽  
Solid Foundation

Models of well-mixed chemical reaction networks (CRNs) have provided a solid foundation for the study of programmable molecular systems, but the importance of spatial organization in such systems has increasingly been recognized. In this paper, we explore an alternative chemical computing model introduced by Qian & Winfree in 2014, the surface CRN, which uses molecules attached to a surface such that each molecule only interacts with its immediate neighbours. Expanding on the constructions in that work, we first demonstrate that surface CRNs can emulate asynchronous and synchronous deterministic cellular automata and implement continuously active Boolean logic circuits. We introduce three new techniques for enforcing synchronization within local regions, each with a different trade-off in spatial and chemical complexity. We also demonstrate that surface CRNs can manufacture complex spatial patterns from simple initial conditions and implement interesting swarm robotic behaviours using simple local rules. Throughout all example constructions of surface CRNs, we highlight the trade-off between the ability to precisely place molecules and the ability to precisely control molecular interactions. Finally, we provide a Python simulator for surface CRNs with an easy-to-use web interface, so that readers may follow along with our examples or create their own surface CRN designs.

scholarly journals Vertical and horizontal integration of multi-omics data with miodin

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Benjamin Ulfenborg
Keyword(s):  
Data Analysis ◽  
R Package ◽  
Heterogeneous Data ◽  
Omics Data ◽  
Technical Expertise ◽  
Horizontal Integration ◽  
Level Data ◽  
Genomics And Proteomics ◽  
Health And Disease ◽  
Molecular Layers

Abstract Background Studies on multiple modalities of omics data such as transcriptomics, genomics and proteomics are growing in popularity, since they allow us to investigate complex mechanisms across molecular layers. It is widely recognized that integrative omics analysis holds the promise to unlock novel and actionable biological insights into health and disease. Integration of multi-omics data remains challenging, however, and requires combination of several software tools and extensive technical expertise to account for the properties of heterogeneous data. Results This paper presents the miodin R package, which provides a streamlined workflow-based syntax for multi-omics data analysis. The package allows users to perform analysis of omics data either across experiments on the same samples (vertical integration), or across studies on the same variables (horizontal integration). Workflows have been designed to promote transparent data analysis and reduce the technical expertise required to perform low-level data import and processing. Conclusions The miodin package is implemented in R and is freely available for use and extension under the GPL-3 license. Package source, reference documentation and user manual are available at https://gitlab.com/algoromics/miodin.

scholarly journals Towards Mass Spectrometry-Based Chemical Exposome: Current Approaches, Challenges, and Future Directions

Toxics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 41 ◽  
Author(s):  
Jingchuan Xue ◽  
Yunjia Lai ◽  
Chih-Wei Liu ◽  
Hongyu Ru
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Measurement Techniques ◽  
Future Directions ◽  
Computational Tools ◽  
High Throughput Data ◽  
Chemical Pollutants ◽  
Health And Disease ◽  
High Throughput Data Analysis

The proposal of the “exposome” concept represents a shift of the research paradigm in studying exposure-disease relationships from an isolated and partial way to a systematic and agnostic approach. Nevertheless, exposome implementation is facing a variety of challenges including measurement techniques and data analysis. Here we focus on the chemical exposome, which refers to the mixtures of chemical pollutants people are exposed to from embryo onwards. We review the current chemical exposome measurement approaches with a focus on those based on the mass spectrometry. We further explore the strategies in implementing the concept of chemical exposome and discuss the available chemical exposome studies. Early progresses in the chemical exposome research are outlined, and major challenges are highlighted. In conclusion, efforts towards chemical exposome have only uncovered the tip of the iceberg, and further advancement in measurement techniques, computational tools, high-throughput data analysis, and standardization may allow more exciting discoveries concerning the role of exposome in human health and disease.

scholarly journals SpatialDB: a database for spatially resolved transcriptomes

2019 ◽  
Author(s):  
Zhen Fan ◽  
Runsheng Chen ◽  
Xiaowei Chen
Keyword(s):  
Spatial Organization ◽  
Expression Patterns ◽  
Functional Enrichment ◽  
Web Interface ◽  
Current Version ◽  
Transcriptomic Data ◽  
Spatially Resolved ◽  
User Friendly ◽  
New Strategies

Abstract Spatially resolved transcriptomic techniques allow the characterization of spatial organization of cells in tissues, which revolutionize the studies of tissue function and disease pathology. New strategies for detecting spatial gene expression patterns are emerging, and spatially resolved transcriptomic data are accumulating rapidly. However, it is not convenient for biologists to exploit these data due to the diversity of strategies and complexity in data analysis. Here, we present SpatialDB, the first manually curated database for spatially resolved transcriptomic techniques and datasets. The current version of SpatialDB contains 24 datasets (305 sub-datasets) from 5 species generated by 8 spatially resolved transcriptomic techniques. SpatialDB provides a user-friendly web interface for visualization and comparison of spatially resolved transcriptomic data. To further explore these data, SpatialDB also provides spatially variable genes and their functional enrichment annotation. SpatialDB offers a repository for research community to investigate the spatial cellular structure of tissues, and may bring new insights into understanding the cellular microenvironment in disease. SpatialDB is freely available at https://www.spatialomics.org/SpatialDB.

scholarly journals Symposium 2: Modern approaches to nutritional research challenges Targeted and non-targeted approaches for metabolite profiling in nutritional research

2009 ◽  
Vol 69 (1) ◽  
pp. 95-102 ◽  
Author(s):  
John K. Lodge
Keyword(s):  
Data Analysis ◽  
Metabolic Pathways ◽  
Present Report ◽  
New Technology ◽  
Nutrient Status ◽  
Tolerance Test ◽  
Careful Consideration ◽  
Metabolite Profiles ◽  
Health And Disease ◽  
Isoflavone Metabolism

The present report discusses targeted and non-targeted approaches to monitor single nutrients and global metabolite profiles in nutritional research. Non-targeted approaches such as metabolomics allow for the global description of metabolites in a biological sample and combine an analytical platform with multivariate data analysis to visualise patterns between sample groups. In nutritional research metabolomics has generated much interest as it has the potential to identify changes to metabolic pathways induced by diet or single nutrients, to explore relationships between diet and disease and to discover biomarkers of diet and disease. Although still in its infancy, a number of studies applying this technology have been performed; for example, the first study in 2003 investigated isoflavone metabolism in females, while the most recent study has demonstrated changes to various metabolic pathways during a glucose tolerance test. As a relatively new technology metabolomics is faced with a number of limitations and challenges including the standardisation of study design and methodology and the need for careful consideration of data analysis, interpretation and identification. Targeted approaches are used to monitor single or multiple nutrient and/or metabolite status to obtain information on concentration, absorption, distribution, metabolism and elimination. Such applications are currently widespread in nutritional research and one example, using stable isotopes to monitor nutrient status, is discussed in more detail. These applications represent innovative approaches in nutritional research to investigate the role of both single nutrients and diet in health and disease.

scholarly journals Determining the optimal number of independent components for reproducible transcriptomic data analysis

BMC Genomics ◽  
2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Ulykbek Kairov ◽  
Laura Cantini ◽  
Alessandro Greco ◽  
Askhat Molkenov ◽  
Urszula Czerwinska ◽  
...  
Keyword(s):  
Data Analysis ◽  
Optimal Number ◽  
Transcriptomic Data ◽  
Independent Components
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