scholarly journals Bayesian segmentation of spatially resolved transcriptomics data

2020 ◽  
Author(s):  
Viktor Petukhov ◽  
Ruslan A. Soldatov ◽  
Konstantin Khodosevich ◽  
Peter V. Kharchenko
Keyword(s):  
Spatial Data ◽  
Spatial Dimension ◽  
Cell Segmentation ◽  
Tissue Slices ◽  
Accurate Identification ◽  
Fixed Tissue ◽  
Spatially Resolved ◽  
Bayesian Segmentation ◽  
Transcriptomics Data

Spatial transcriptomics is an emerging stack of technologies, which adds spatial dimension to conventional single-cell RNA-sequencing. New protocols, based on in situ sequencing or multiplexed RNA fluorescent in situ hybridization register positions of single molecules in fixed tissue slices. Analysis of such data at the level of individual cells, however, requires accurate identification of cell boundaries. While many existing methods are able to approximate cell center positions using nuclei stains, current protocols do not report robust signal on the cell membranes, making accurate cell segmentation a key barrier for downstream analysis and interpretation of the data. To address this challenge, we developed a tool for Bayesian Segmentation of Spatial Transcriptomics Data (Baysor), which optimizes segmentation considering the likelihood of transcriptional composition, size and shape of the cell. The Bayesian approach can take into account nuclear or cytoplasm staining, however can also perform segmentation based on the detected transcripts alone. We show that Baysor segmentation can in some cases nearly double the number of the identified cells, while reducing contamination. Importantly, we demonstrate that Baysor performs well on data acquired using five different spatially-resolved protocols, making it a useful general tool for analysis of high-resolution spatial data.

scholarly journals SSAM-lite: a light-weight web app for rapid analysis of spatially resolved transcriptomics data

2021 ◽  
Author(s):  
Sebastian Tiesmeyer ◽  
Shashwat Sahay ◽  
Niklas Müller-Bötticher ◽  
Roland Eils ◽  
Sebastian D. Mackowiak ◽  
...  
Keyword(s):  
Programming Languages ◽  
Single Molecule ◽  
Free Cell ◽  
Cell Segmentation ◽  
Web Browser ◽  
Spatially Resolved ◽  
Transcriptomics Data ◽  
Specialized Hardware ◽  
Computational Resources

The combination of a cell's transcriptional profile and location defines its function in a spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay of choice for characterizing cells in situ. SRT methods can resolve gene expression up to single-molecule resolution. A particular computational problem with single-molecule SRT methods is the correct aggregation of mRNA molecules into cells. Traditionally, aggregating mRNA molecules into cell-based features begins with the identification of cells via segmentation of the nucleus or the cell membrane. However, recently a number of cell-segmentation-free approaches have emerged. While these methods have been demonstrated to be more performant than segmentation-based approaches, they are still not easily accessible since they require specialized knowledge of programming languages and access to large computational resources. Here we present SSAM-lite, a tool that provides an easy-to-use graphical interface to perform rapid and segmentation-free cell-typing of SRT data in a web browser. SSAM-lite runs locally and does not require computational experts or specialized hardware. Analysis of a tissue slice of the mouse somatosensory cortex took less than a minute on a laptop with modest hardware. Parameters can interactively be optimized on small portions of the data before the entire tissue image is analyzed. A server version of SSAM-lite can be run completely offline using local infrastructure. Overall, SSAM-lite is portable, lightweight, and easy to use, thus enabling a broad audience to investigate and analyze single-molecule SRT data.

scholarly journals Cell segmentation-free inference of cell types from in situ transcriptomics data

2019 ◽  
Author(s):  
Jeongbin Park ◽  
Wonyl Choi ◽  
Sebastian Tiesmeyer ◽  
Brian Long ◽  
Lars E. Borm ◽  
...  
Keyword(s):  
Tissue Characterization ◽  
Cell Types ◽  
Cell Segmentation ◽  
Cell Type ◽  
Computational Framework ◽  
Transcriptomics Data ◽  
Novel Method ◽  
2D And 3D ◽  
Spatial Cell

AbstractMultiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a novel method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. We found that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.

scholarly journals spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data

2021 ◽  
Author(s):  
Brenda Pardo ◽  
Abby Spangler ◽  
Lukas M. Weber ◽  
Stephanie C. Hicks ◽  
Andrew E. Jaffe ◽  
...  
Keyword(s):  
Gene Expression ◽  
Spatial Data ◽  
Web Application ◽  
Bioconductor Package ◽  
Data Types ◽  
Intact Tissue ◽  
Spatially Resolved ◽  
Transcriptomics Data ◽  
Spatial Coordinates ◽  
Visualization Tools

Motivation: Spatially-resolved transcriptomics has now enabled the quantification of high-throughput and transcriptome-wide gene expression in intact tissue while also retaining the spatial coordinates. Incorporating the precise spatial mapping of gene activity advances our understanding of intact tissue-specific biological processes. In order to interpret these novel spatial data types, interactive visualization tools are necessary. Results: We describe spatialLIBD, an R/Bioconductor package to interactively explore spatially-resolved transcriptomics data generated with the 10x Genomics Visium platform. The package contains functions to interactively access, visualize, and inspect the observed spatial gene expression data and data-driven clusters identified with supervised or unsupervised analyses, either on the user's computer or through a web application. Availability: spatialLIBD is available at http://bioconductor.org/packages/spatialLIBD.

scholarly journals Cell segmentation-free inference of cell types from in situ transcriptomics data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jeongbin Park ◽  
Wonyl Choi ◽  
Sebastian Tiesmeyer ◽  
Brian Long ◽  
Lars E. Borm ◽  
...  
Keyword(s):  
Tissue Characterization ◽  
Cell Types ◽  
Cell Segmentation ◽  
Cell Type ◽  
Preoptic Region ◽  
Computational Framework ◽  
Transcriptomics Data ◽  
2D And 3D ◽  
Spatial Cell

AbstractMultiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. Here, we show that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.

scholarly journals Author Correction: Cell segmentation-free inference of cell types from in situ transcriptomics data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jeongbin Park ◽  
Wonyl Choi ◽  
Sebastian Tiesmeyer ◽  
Brian Long ◽  
Lars E. Borm ◽  
...  
Keyword(s):  
Cell Types ◽  
Cell Segmentation ◽  
Transcriptomics Data

FT-IR microspectroscopic analysis of diseased white matter brain tissues

1995 ◽  
Vol 53 ◽  
pp. 968-969
Author(s):  
Steven M. Le Vine ◽  
David L. Wetzel
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Twitcher Mouse ◽  
Grid Pattern ◽  
Marked Contrast ◽  
Spatially Resolved ◽  
Ft Ir ◽  
Ir Microspectroscopy ◽  
Chemical Evidence

In situ FT-IR microspectroscopy has allowed spatially resolved interrogation of different parts of brain tissue. In previous work the spectrrscopic features of normal barin tissue were characterized. The white matter, gray matter and basal ganglia were mapped from appropriate peak area measurements from spectra obtained in a grid pattern. Bands prevalent in white matter were mostly associated with the lipid. These included 2927 and 1469 cm-1 due to CH2 as well as carbonyl at 1740 cm-1. Also 1235 and 1085 cm-1 due to phospholipid and galactocerebroside, respectively (Figs 1and2). Localized chemical changes in the white matter as a result of white matter diseases have been studied. This involved the documentation of localized chemical evidence of demyelination in shiverer mice in which the spectra of white matter lacked the marked contrast between it and gray matter exhibited in the white matter of normal mice (Fig. 3).The twitcher mouse, a model of Krabbe’s desease, was also studied. The purpose in this case was to look for a localized build-up of psychosine in the white matter caused by deficiencies in the enzyme responsible for its breakdown under normal conditions.

scholarly journals Spatially Resolved Experimental Modal Analysis on High-Speed Composite Rotors Using a Non-Contact, Non-Rotating Sensor

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4705
Author(s):  
Julian Lich ◽  
Tino Wollmann ◽  
Angelos Filippatos ◽  
Maik Gude ◽  
Juergen Czarske ◽  
...  
Keyword(s):  
High Speed ◽  
Natural Frequencies ◽  
Mode Shapes ◽  
Fiber Reinforced ◽  
Structural Dynamic ◽  
Spatially Resolved ◽  
Glass Fiber Reinforced ◽  
Vibration Responses ◽  
And Performance

Due to their lightweight properties, fiber-reinforced composites are well suited for large and fast rotating structures, such as fan blades in turbomachines. To investigate rotor safety and performance, in situ measurements of the structural dynamic behaviour must be performed during rotating conditions. An approach to measuring spatially resolved vibration responses of a rotating structure with a non-contact, non-rotating sensor is investigated here. The resulting spectra can be assigned to specific locations on the structure and have similar properties to the spectra measured with co-rotating sensors, such as strain gauges. The sampling frequency is increased by performing consecutive measurements with a constant excitation function and varying time delays. The method allows for a paradigm shift to unambiguous identification of natural frequencies and mode shapes with arbitrary rotor shapes and excitation functions without the need for co-rotating sensors. Deflection measurements on a glass fiber-reinforced polymer disk were performed with a diffraction grating-based sensor system at 40 measurement points with an uncertainty below 15 μrad and a commercial triangulation sensor at 200 measurement points at surface speeds up to 300 m/s. A rotation-induced increase of two natural frequencies was measured, and their mode shapes were derived at the corresponding rotational speeds. A strain gauge was used for validation.

scholarly journals Spatially resolved microfluidic stimulation of lymphoid tissue ex vivo

The Analyst ◽  
2017 ◽  
Vol 142 (4) ◽  
pp. 649-659 ◽  
Author(s):  
Ashley E. Ross ◽  
Maura C. Belanger ◽  
Jacob F. Woodroof ◽  
Rebecca R. Pompano
Keyword(s):  
Lymph Node ◽  
Targeted Delivery ◽  
Lymphoid Tissue ◽  
Ex Vivo ◽  
Tissue Slices ◽  
Microfluidic Platform ◽  
Spatially Resolved ◽  
Local Stimulation ◽  
Stimulation Of

We present the first microfluidic platform for local stimulation of lymph node tissue slices and demonstrate targeted delivery of a model therapeutic.

scholarly journals HistoMosaic Detecting KRAS G12V Mutation Across Colorectal Cancer Tissue Slices through in Situ PCR

2016 ◽  
Vol 88 (5) ◽  
pp. 2792-2798 ◽  
Author(s):  
Christopher B. Raub ◽  
Chen-Chung Lee ◽  
Darryl Shibata ◽  
Clive Taylor ◽  
Emil Kartalov
Keyword(s):  
Colorectal Cancer ◽  
Cancer Tissue ◽  
Tissue Slices ◽  
In Situ Pcr ◽  
Colorectal Cancer Tissue

scholarly journals Airborne determination of the temporo-spatial distribution of benzene, toluene, nitrogen oxides and ozone in the boundary layer across Greater London, UK

2015 ◽  
Vol 15 (9) ◽  
pp. 5083-5097 ◽  
Author(s):  
M. D. Shaw ◽  
J. D. Lee ◽  
B. Davison ◽  
A. Vaughan ◽  
R. M. Purvis ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Nitrogen Oxides ◽  
Linear Regression Analysis ◽  
Proton Transfer Reaction ◽  
Mixing Ratios ◽  
Spatially Resolved ◽  
Dual Channel ◽  
Benzene Toluene

Abstract. Highly spatially resolved mixing ratios of benzene and toluene, nitrogen oxides (NOx) and ozone (O3) were measured in the atmospheric boundary layer above Greater London during the period 24 June to 9 July 2013 using a Dornier 228 aircraft. Toluene and benzene were determined in situ using a proton transfer reaction mass spectrometer (PTR-MS), NOx by dual-channel NOx chemiluminescence and O3 mixing ratios by UV absorption. Average mixing ratios observed over inner London at 360 ± 10 m a.g.l. were 0.20 ± 0.05, 0.28 ± 0.07, 13.2 ± 8.6, 21.0 ± 7.3 and 34.3 ± 15.2 ppbv for benzene, toluene, NO, NO2 and NOx respectively. Linear regression analysis between NO2, benzene and toluene mixing ratios yields a strong covariance, indicating that these compounds predominantly share the same or co-located sources within the city. Average mixing ratios measured at 360 ± 10 m a.g.l. over outer London were always lower than over inner London. Where traffic densities were highest, the toluene / benzene (T / B) concentration ratios were highest (average of 1.8 ± 0.5 ppbv ppbv-1), indicative of strong local sources. Daytime maxima in NOx, benzene and toluene mixing ratios were observed in the morning (~ 40 ppbv NOx, ~ 350 pptv toluene and ~ 200 pptv benzene) and in the mid-afternoon for ozone (~ 40 ppbv O3), all at 360 ± 10 m a.g.l.

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