| 1. |
- Andersson, Axel, et al.
(författare)
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Cell Segmentation of in situ Transcriptomics Data using Signed Graph Partitioning
- 2023
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Ingår i: Graph-Based Representations in Pattern Recognition. - Cham : Springer. - 9783031427947 - 9783031427954 ; , s. 139-148
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Konferensbidrag (refereegranskat)abstract
- The locations of different mRNA molecules can be revealed by multiplexed in situ RNA detection. By assigning detected mRNA molecules to individual cells, it is possible to identify many different cell types in parallel. This in turn enables investigation of the spatial cellular architecture in tissue, which is crucial for furthering our understanding of biological processes and diseases. However, cell typing typically depends on the segmentation of cell nuclei, which is often done based on images of a DNA stain, such as DAPI. Limiting cell definition to a nuclear stain makes it fundamentally difficult to determine accurate cell borders, and thereby also difficult to assign mRNA molecules to the correct cell. As such, we have developed a computational tool that segments cells solely based on the local composition of mRNA molecules. First, a small neural network is trained to compute attractive and repulsive edges between pairs of mRNA molecules. The signed graph is then partitioned by a mutex watershed into components corresponding to different cells. We evaluated our method on two publicly available datasets and compared it against the current state-of-the-art and older baselines. We conclude that combining neural networks with combinatorial optimization is a promising approach for cell segmentation of in situ transcriptomics data. The tool is open-source and publicly available for use at https://github.com/wahlby-lab/IS3G.
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| 2. |
- Andersson, Axel, et al.
(författare)
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Points2Regions : Fast, interactive clustering of imaging-based spatial transcriptomics data
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Imaging-based spatial transcriptomics techniques generate image data that, once processed, results in a set of spatial points with categorical labels for different mRNA species. A crucial part of analyzing downstream data involves the analysis of these point patterns. Here, biologically interesting patterns can be explored at different spatial scales. Molecular patterns on a cellular level would correspond to cell types, whereas patterns on a millimeter scale would correspond to tissue-level structures. Often, clustering methods are employed to identify and segment regions with distinct point-patterns. Traditional clustering techniques for such data are constrained by reliance on complementary data or extensive machine learning, limiting their applicability to tasks on a particular scale. This paper introduces 'Points2Regions', a practical tool for clustering spatial points with categorical labels. Its flexible and computationally efficient clustering approach enables pattern discovery across multiple scales, making it a powerful tool for exploratory analysis. Points2Regions has demonstrated efficient performance in various datasets, adeptly defining biologically relevant regions similar to those found by scale-specific methods. As a Python package integrated into TissUUmaps and a Napari plugin, it offers interactive clustering and visualization, significantly enhancing user experience in data exploration. In essence, Points2Regions presents a user-friendly and simple tool for exploratory analysis of spatial points with categorical labels.
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| 3. |
- Beháňová, Andrea, et al.
(författare)
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gACSON software for automated segmentation and morphology analyses of myelinated axons in 3D electron microscopy
- 2022
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Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier. - 0169-2607 .- 1872-7565. ; 220
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Tidskriftsartikel (refereegranskat)abstract
- Background and Objective: Advances in electron microscopy (EM) now allow three-dimensional (3D) imaging of hundreds of micrometers of tissue with nanometer-scale resolution, providing new opportunities to study the ultrastructure of the brain. In this work, we introduce a freely available Matlab-based gACSON software for visualization, segmentation, assessment, and morphology analysis of myelinated axons in 3D-EM volumes of brain tissue samples.Methods: The software is equipped with a graphical user interface (GUI). It automatically segments the intra-axonal space of myelinated axons and their corresponding myelin sheaths and allows manual segmentation, proofreading, and interactive correction of the segmented components. gACSON analyzes the morphology of myelinated axons, such as axonal diameter, axonal eccentricity, myelin thickness, or gratio.Results: We illustrate the use of the software by segmenting and analyzing myelinated axons in six 3DEM volumes of rat somatosensory cortex after sham surgery or traumatic brain injury (TBI). Our results suggest that the equivalent diameter of myelinated axons in somatosensory cortex was decreased in TBI animals five months after the injury.Conclusion: Our results indicate that gACSON is a valuable tool for visualization, segmentation, assessment, and morphology analysis of myelinated axons in 3D-EM volumes. It is freely available at https://github.com/AndreaBehan/g-ACSON under the MIT license.
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| 4. |
- Beháňová, Andrea, et al.
(författare)
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Spatial Statistics for Understanding Tissue Organization
- 2022
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Ingår i: Frontiers in Physiology. - : Frontiers Media S.A.. - 1664-042X. ; 13
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Forskningsöversikt (refereegranskat)abstract
- Interpreting tissue architecture plays an important role in gaining a better understanding of healthy tissue development and disease. Novel molecular detection and imaging techniques make it possible to locate many different types of objects, such as cells and/or mRNAs, and map their location across the tissue space. In this review, we present several methods that provide quantification and statistical verification of observed patterns in the tissue architecture. We categorize these methods into three main groups: Spatial statistics on a single type of object, two types of objects, and multiple types of objects. We discuss the methods in relation to four hypotheses regarding the methods' capability to distinguish random and non-random distributions of objects across a tissue sample, and present a number of openly available tools where these methods are provided. We also discuss other spatial statistics methods compatible with other types of input data.
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| 5. |
- Beháňová, Andrea, et al.
(författare)
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Visualization and quality control tools for large-scale multiplex tissue analysis in TissUUmaps3
- 2023
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Ingår i: Biological Imaging. - : Cambridge University Press (CUP). - 2633-903X. ; 3
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Tidskriftsartikel (refereegranskat)abstract
- Large-scale multiplex tissue analysis aims to understand processes such as development and tumor formation by studying the occurrence and interaction of cells in local environments in, for example, tissue samples from patient cohorts. A typical procedure in the analysis is to delineate individual cells, classify them into cell types, and analyze their spatial relationships. All steps come with a number of challenges, and to address them and identify the bottlenecks of the analysis, it is necessary to include quality control tools in the analysis workflow. This makes it possible to optimize the steps and adjust settings in order to get better and more precise results. Additionally, the development of automated approaches for tissue analysis requires visual verification to reduce skepticism with regard to the accuracy of the results. Quality control tools could be used to build users’ trust in automated approaches. In this paper, we present three plugins for visualization and quality control in large-scale multiplex tissue analysis of microscopy images. The first plugin focuses on the quality of cell staining, the second one was made for interactive evaluation and comparison of different cell classification results, and the third one serves for reviewing interactions of different cell types.
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| 6. |
- Latini, Francesco, M.D. 1982-, et al.
(författare)
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The link between gliomas infiltration and white matter architecture investigated with electron microscopy and diffusion tensor imaging
- 2021
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Ingår i: NeuroImage. - : Elsevier. - 2213-1582. ; 31, s. 102735-
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Tidskriftsartikel (refereegranskat)abstract
- Diffuse low-grade gliomas display preferential locations in eloquent and secondary associative brain areas. The reason for this tendency is still unknown. We hypothesized that the intrinsic architecture and water diffusion properties of the white matter bundles in these regions may facilitate gliomas infiltration. Magnetic resonance imaging of one hundred and two low-grade gliomas patients were normalized to/and segmented in MNI space to create a probabilistic infiltration weighted gradient map. Diffusion tensor imaging (DTI)- based parameters were derived for five major white matter bundles, displaying high- and low grade of infiltration, (corpus callosum, cingulum, arcuate fasciculus, inferior fronto-occipital fasciculus and cortico-spinal tract), averaged over 20 healthy individuals acquired from the Human connectome project (HCP) database. Transmission electron microscopy (TEM) was used to analyze fiber density, diameter and g-ratio in 100 human white matter regions, sampled from cadaver specimens, reflecting areas with different gliomas infiltration frequency. Histological results and DTI-based parameters were compared in anatomical regions of high- and low grade of infiltration respectively. We detected differences in the infiltration frequency of five major white matter bundles. Regional differences within the same white matter bundles were detected by both TEM- and DTI analysis. Regions with high infiltration frequency (HIF) displayed a higher fiber density, smaller fiber diameter but higher myelin thickness and lower axial diffusivity compare compared with low infiltration frequency (LIF) regions. Our results seem to indicate that the fiber diameter, myelin thickness and the possible organization of the fibers are different in HIF compared to LIF regions and may be linked to the preferential location of diffuse low-grade gliomas.
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| 7. |
- Pielawski, Nicolas, et al.
(författare)
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TissUUmaps 3 : Improvements in interactive visualization, exploration, and quality assessment of large-scale spatial omics data
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background and Objectives: Spatially resolved techniques for exploring the molecular landscape of tissue samples, such as spatial transcriptomics, often result in millions of data points and images too large to view on a regular desktop computer, limiting the possibilities in visual interactive data exploration. TissUUmaps is a free, open-source browser-based tool for GPU-accelerated visualization and interactive exploration of 107+ data points overlaying tissue samples.Methods: Herein we describe how TissUUmaps 3 provides instant multiresolution image viewing and can be customized, shared, and also integrated into Jupyter Notebooks. We introduce new modules where users can visualize markers and regions, explore spatial statistics, perform quantitative analyses of tissue morphology, and assess the quality of decoding in situ transcriptomics data.Results: We show that thanks to targeted optimizations the time and cost associated with interactive data exploration were reduced, enabling TissUUmaps 3 to handle the scale of today’s spatial transcriptomics methods.Conclusion: TissUUmaps 3 provides significantly improved performance for large multiplex datasets as compared to previous versions. We envision TissUUmaps to contribute to broader dissemination and flexible sharing of large-scale spatial omics data.
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| 8. |
- Pielawski, Nicolas, et al.
(författare)
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TissUUmaps 3 : Improvements in interactive visualization, exploration, and quality assessment of large-scale spatial omics data
- 2023
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Ingår i: Heliyon. - : Elsevier BV. - 2405-8440. ; 9:5
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Tidskriftsartikel (refereegranskat)abstract
- Background and objectives: Spatially resolved techniques for exploring the molecular landscape of tissue samples, such as spatial transcriptomics, often result in millions of data points and images too large to view on a regular desktop computer, limiting the possibilities in visual interactive data exploration. TissUUmaps is a free, open-source browser-based tool for GPU-accelerated visualization and interactive exploration of 107+ data points overlaying tissue samples.Methods: Herein we describe how TissUUmaps 3 provides instant multiresolution image viewing and can be customized, shared, and also integrated into Jupyter Notebooks. We introduce new modules where users can visualize markers and regions, explore spatial statistics, perform quantitative analyses of tissue morphology, and assess the quality of decoding in situ transcriptomics data.Results: We show that thanks to targeted optimizations the time and cost associated with interactive data exploration were reduced, enabling TissUUmaps 3 to handle the scale of today's spatial transcriptomics methods.Conclusion: TissUUmaps 3 provides significantly improved performance for large multiplex datasets as compared to previous versions. We envision TissUUmaps to contribute to broader dissemination and flexible sharing of largescale spatial omics data.
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| 9. |
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