| 1. |
- Andersson, Axel, et al.
(författare)
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Transcriptome-Supervised Classification of Tissue Morphology Using Deep Learning
- 2020
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Ingår i: IEEE 17th International Symposium on Biomedical Imaging (ISBI). - 9781538693308 - 9781538693315 ; , s. 1630-1633
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Konferensbidrag (refereegranskat)abstract
- Deep learning has proven to successfully learn variations in tissue and cell morphology. Training of such models typically relies on expensive manual annotations. Here we conjecture that spatially resolved gene expression, e.i., the transcriptome, can be used as an alternative to manual annotations. In particular, we trained five convolutional neural networks with patches of different size extracted from locations defined by spatially resolved gene expression. The network is trained to classify tissue morphology related to two different genes, general tissue, as well as background, on an image of fluorescence stained nuclei in a mouse brain coronal section. Performance is evaluated on an independent tissue section from a different mouse brain, reaching an average Dice score of 0.51. Results may indicate that novel techniques for spatially resolved transcriptomics together with deep learning may provide a unique and unbiased way to find genotype phenotype relationships
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| 2. |
- Bombrun, Maxime, et al.
(författare)
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Decoding gene expression in 2D and 3D
- 2017
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Ingår i: Image Analysis. - Cham : Springer. - 9783319591285 ; , s. 257-268
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Konferensbidrag (refereegranskat)abstract
- Image-based sequencing of RNA molecules directly in tissue samples provides a unique way of relating spatially varying gene expression to tissue morphology. Despite the fact that tissue samples are typically cut in micrometer thin sections, modern molecular detection methods result in signals so densely packed that optical “slicing” by imaging at multiple focal planes becomes necessary to image all signals. Chromatic aberration, signal crosstalk and low signal to noise ratio further complicates the analysis of multiple sequences in parallel. Here a previous 2D analysis approach for image-based gene decoding was used to show how signal count as well as signal precision is increased when analyzing the data in 3D instead. We corrected the extracted signal measurements for signal crosstalk, and improved the results of both 2D and 3D analysis. We applied our methodologies on a tissue sample imaged in six fluorescent channels during five cycles and seven focal planes, resulting in 210 images. Our methods are able to detect more than 5000 signals representing 140 different expressed genes analyzed and decoded in parallel.
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| 3. |
- Orkisz, Maciej, et al.
(författare)
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Voxel-wise assessment of lung aeration changes on CT images using image registration: a : application to acute respiratory distress syndrome (ARDS)
- 2019
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Ingår i: International Journal of Computer Assisted Radiology and Surgery. - : Springer Science and Business Media LLC. - 1861-6410 .- 1861-6429. ; , s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- Purpose(1) To improve the accuracy of global and regional alveolar-recruitment quantification in CT scan pairs by accounting for lung-tissue displacements and deformation, (2) To propose a method for local-recruitment calculation.MethodsRecruitment was calculated by subtracting the quantity of non-aerated lung tissues between expiration and inspiration. To assess global recruitment, lung boundaries were first interactively delineated at inspiration, and then they were warped based on automatic image registration to define the boundaries at expiration. To calculate regional recruitment, the lung mask defined at inspiration was cut into pieces, and these were also warped to encompass the same tissues at expiration. Local-recruitment map was calculated as follows: For each voxel at expiration, the matching location at inspiration was determined by image registration, non-aerated voxels were counted in the neighborhood of the respective locations, and the voxel count difference was normalized by the neighborhood size. The methods were evaluated on 120 image pairs of 12 pigs with experimental acute respiratory distress syndrome.ResultsThe dispersion of global- and regional-recruitment values decreased when using image registration, compared to the conventional approach neglecting tissue motion. Local-recruitment maps overlaid onto the original images were visually consistent, and the sum of these values over the whole lungs was very close to the global-recruitment estimate, except four outliers.ConclusionsImage registration can compensate lung-tissue displacements and deformation, thus improving the quantification of alveolar recruitment. Local-recruitment calculation can also benefit from image registration, and its values can be overlaid onto the original image to display a local-recruitment map. They also can be integrated over arbitrarily shaped regions to assess regional or global recruitment.
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| 4. |
- Partel, Gabriele, 1988-, et al.
(författare)
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Automated identification of the mouse brain’s spatial compartments from in situ sequencing data
- 2020
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Ingår i: BMC Biology. - : Springer Nature. - 1741-7007. ; 18:1
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Tidskriftsartikel (refereegranskat)abstract
- Background Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape. With the advent of in situ sequencing technologies, it is now possible to profile the gene expression of targeted genes inside preserved tissue samples and thus spatially map biological processes across anatomical compartments. This also opens up for new approaches to identifying tissue compartments.Results Here, we show how in situ sequencing data combined with dimensionality reduction and clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. We also visualize gradients in gene expression and sharp as well as smooth transitions between different compartments. We apply our method on mouse brain sections and show that computationally defined anatomical compartments are highly reproducible across individuals and have the potential to replace manual annotation based on cell and tissue morphology. Conclusion Mapping the brain based on molecular information means that we can create detailed atlases independent of angle at sectioning or variations between individuals.
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| 5. |
- Partel, Gabriele, et al.
(författare)
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Identification of spatial compartments in tissue from in situ sequencing data
- 2024
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Spatial organization of tissue characterizes biological function, and spatially resolved gene expression has the power to reveal variations of features with high resolution. Here, we propose a novel graph-based in situ sequencing decoding approach that improves recall, enabling precise spatial gene expression analysis. We apply our method on in situ sequencing data from mouse brain sections, identify spatial compartments that correspond with known brain regions, and relate them with tissue morphology.
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| 6. |
- Pereira, Carla, et al.
(författare)
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Comparison of East‐Asia and West‐Europe cohorts explains disparities in survival outcomes and highlights predictive biomarkers of early gastric cancer aggressiveness
- 2021
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Ingår i: International Journal of Cancer. - : John Wiley & Sons. - 0020-7136 .- 1097-0215. ; 150:5, s. 868-880
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Tidskriftsartikel (refereegranskat)abstract
- Surgical resection with lymphadenectomy and perioperative chemotherapy is the universal mainstay for curative treatment of gastric cancer (GC) patients with locoregional disease. However, GC survival remains asymmetric in West- and East-world regions. We hypothesize that this asymmetry derives from differential clinical management. Therefore, we collected chemo-naïve GC patients from Portugal and South Korea to explore specific immunophenotypic profiles related to disease aggressiveness and clinicopathological factors potentially explaining associated overall survival (OS) differences. Clinicopathological and survival data were collected from chemo-naïve surgical cohorts from Portugal (West-Europe cohort [WE-C]; n = 170) and South Korea (East-Asia cohort [EA-C]; n = 367) and correlated with immunohistochemical expression profiles of E-cadherin and CD44v6 obtained from consecutive tissue microarrays sections. Survival analysis revealed a subset of 12.4% of WE-C patients, whose tumors concomitantly express E-cadherin_abnormal and CD44v6_very high, displaying extremely poor OS, even at TNM stages I and II. These WE-C stage-I and -II patients tumors were particularly aggressive compared to all others, invading deeper into the gastric wall (P = .032) and more often permeating the vasculature (P = .018) and nerves (P = .009). A similar immunophenotypic profile was found in 11.9% of EA-C patients, but unrelated to survival. Tumours, from stage-I and -II EA-C patients, that display both biomarkers, also permeated more lymphatic vessels (P = .003), promoting lymph node (LN) metastasis (P = .019), being diagnosed on average 8 years earlier and submitted to more extensive LN dissection than WE-C. Concomitant E-cadherin_abnormal/CD44v6_very-high expression predicts aggressiveness and poor survival of stage-I and -II GC submitted to conservative lymphadenectomy.
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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. |
- Solorzano, Leslie, 1989-
(författare)
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Image Processing, Machine Learning and Visualization for Tissue Analysis
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Knowledge discovery for understanding mechanisms of disease requires the integration of multiple sources of data collected at various magnifications and by different imaging techniques. Using spatial information, we can build maps of tissue and cells in which it is possible to extract, e.g., measurements of cell morphology, protein expression, and gene expression. These measurements reveal knowledge about cells such as their identity, origin, density, structural organization, activity, and interactions with other cells and cell communities. Knowledge that can be correlated with survival and drug effectiveness. This thesis presents multidisciplinary projects that include a variety of methods for image and data analysis applied to images coming from fluorescence- and brightfield microscopy.In brightfield images, the number of proteins that can be observed in the same tissue section is limited. To overcome this, we identified protein expression coming from consecutive tissue sections and fused images using registration to quantify protein co-expression. Here, the main challenge was to build a framework handling very large images with a combination of rigid and non-rigid image registration. Using multiplex fluorescence microscopy techniques, many different molecular markers can be used in parallel, and here we approached the challenge to decipher cell classes based on marker combinations. We used ensembles of machine learning models to perform cell classification, both increasing performance over a single model and to get a measure of confidence of the predictions. We also used resulting cell classes and locations as input to a graph neural network to learn cell neighborhoods that may be correlated with disease.Finally, the work leading to this thesis included the creation of an interactive visualization tool, TissUUmaps. Whole slide tissue images are often enormous and can be associated with large numbers of data points, creating challenges which call for advanced methods in processing and visualization. We built TissUUmaps so that it could visualize millions of data points from in situ sequencing experiments and enable contextual study of gene expression directly in the tissue at cellular and sub-cellular resolution. We also used TissUUmaps for interactive image registration, overlay of regions of interest, and visualization of tissue and corresponding cancer grades produced by deep learning methods. The aforementioned methods and tools together provide the framework for analysing and visualizing vast and complex spatial tissue structures. These developments in understanding the spatial information of tissue in different diseases pave the way for new discoveries and improving the treatment for patients.
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| 10. |
- Solorzano, Leslie, 1989-, et al.
(författare)
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Machine learning for cell classification and neighborhood analysis in glioma tissue
- 2021
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Ingår i: Cytometry Part A. - : Wiley. - 1552-4922 .- 1552-4930. ; 99:12, s. 1176-1186
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Tidskriftsartikel (refereegranskat)abstract
- Multiplexed and spatially resolved single-cell analyses that intend to study tissue heterogeneity and cell organization invariably face as a first step the challenge of cell classification. Accuracy and reproducibility are important for the downstream process of counting cells, quantifying cell-cell interactions, and extracting information on disease-specific localized cell niches. Novel staining techniques make it possible to visualize and quantify large numbers of cell-specific molecular markers in parallel. However, due to variations in sample handling and artifacts from staining and scanning, cells of the same type may present different marker profiles both within and across samples. We address multiplexed immunofluorescence data from tissue microarrays of low-grade gliomas and present a methodology using two different machine learning architectures and features insensitive to illumination to perform cell classification. The fully automated cell classification provides a measure of confidence for the decision and requires a comparably small annotated data set for training, which can be created using freely available tools. Using the proposed method, we reached an accuracy of 83.1% on cell classification without the need for standardization of samples. Using our confidence measure, cells with low-confidence classifications could be excluded, pushing the classification accuracy to 94.5%. Next, we used the cell classification results to search for cell niches with an unsupervised learning approach based on graph neural networks. We show that the approach can re-detect specialized tissue niches in previously published data, and that our proposed cell classification leads to niche definitions that may be relevant for sub-groups of glioma, if applied to larger data sets.
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