| 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. |
- Marco Salas, Sergio, et al.
(författare)
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De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks
- 2022
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Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 18:8
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Tidskriftsartikel (refereegranskat)abstract
- With the emergence of high throughput single cell techniques, the understanding of the molecular and cellular diversity of mammalian organs have rapidly increased. In order to understand the spatial organization of this diversity, single cell data is often integrated with spatial data to create probabilistic cell maps. However, targeted cell typing approaches relying on existing single cell data achieve incomplete and biased maps that could mask the true diversity present in a tissue slide. Here we applied a de novo technique to spatially resolve and characterize cellular diversity of in situ sequencing data during human heart development. We obtained and made accessible well defined spatial cell-type maps of fetal hearts from 4.5 to 9 post conception weeks, not biased by probabilistic cell typing approaches. With our analysis, we could characterize previously unreported molecular diversity within cardiomyocytes and epicardial cells and identified their characteristic expression signatures, comparing them with specific subpopulations found in single cell RNA sequencing datasets. We further characterized the differentiation trajectories of epicardial cells, identifying a clear spatial component on it. All in all, our study provides a novel technique for conducting de novo spatial-temporal analyses in developmental tissue samples and a useful resource for online exploration of cell-type differentiation during heart development at sub-cellular image resolution.
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| 3. |
- 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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| 4. |
- Partel, Gabriele, 1988-, et al.
(författare)
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Graph-based image decoding for multiplexed in situ RNA detection
- 2021
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Ingår i: 2020 25th International Conference on Pattern Recognition (ICPR). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728188089 ; , s. 3783-3790
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Konferensbidrag (refereegranskat)abstract
- Image-based multiplexed in situ RNA detectionmakes it possible to map the spatial gene expression of hundreds to thousands of genes in parallel, and thus discern at the sametime a large numbers of different cell types to better understand tissue development, heterogeneity, and disease. Fluorescent signals are detected over multiple fluorescent channels and imaging rounds and decoded in order to identify RNA molecules in their morphological context. Here we present a graph-based decoding approach that models the decoding process as a network flow problem jointly optimizing observation likelihoods and distances of signal detections, thus achieving robustness with respect to noise and spatial jitter of the fluorescent signals. We evaluated our method on synthetic data generated at different experimental conditions, and on real data of in situ RNA sequencing, comparng results with respect to alternative and gold standard imagede coding pipelines.
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| 5. |
- Partel, Gabriele, 1988-
(författare)
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Image and Data Analysis for Spatially Resolved Transcriptomics : Decrypting fine-scale spatial heterogeneity of tissue's molecular architecture
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Our understanding of the biological complexity in multicellular organisms has progressed at tremendous pace in the last century and even more in the last decades with the advent of sequencing technologies that make it possible to interrogate the genome and transcriptome of individual cells. It is now possible to even spatially profile the transcriptomic landscape of tissue architectures to study the molecular organization of tissue heterogeneity at subcellular resolution. Newly developed spatially resolved transcriptomic techniques are producing large amounts of high-dimensional image data with increasing throughput, that need to be processed and analysed for extracting biological relevant information that has the potential to lead to new knowledge and discoveries. The work included in this thesis aims to provide image and data analysis tools for serving this new developing field of spatially resolved transcriptomics to fulfill its purpose. First, an image analysis workflow is presented for processing and analysing images acquired with in situ sequencing protocols, aiming to extract and decode molecular features that map the spatial transcriptomic landscape in tissue sections. This thesis also presents computational methods to explore and analyse the decoded spatial gene expression for studying the spatial molecular heterogeneity of tissue architectures at different scales. In one case, it is demonstrated how dimensionality reduction and clustering of the decoded gene expression spatial profiles can be exploited and used to identify reproducible spatial compartments corresponding to know anatomical regions across mouse brain sections from different individuals. And lastly, this thesis presents an unsupervised computational method that leverages advanced deep learning techniques on graphs to model the spatial gene expression at cellular and subcellular resolution. It provides a low dimensional representation of spatial organization and interaction, finding functional units that in many cases correspond to different cell types in the local tissue environment, without the need for cell segmentation.
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| 6. |
- Partel, Gabriele, 1988-, et al.
(författare)
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Spage2vec : Unsupervised representation of localized spatial gene expression signatures
- 2021
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Ingår i: The FEBS Journal. - : John Wiley & Sons. - 1742-464X .- 1742-4658. ; 288:6, s. 1859-1870
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Tidskriftsartikel (refereegranskat)abstract
- Investigations of spatial cellular composition of tissue architectures revealed by multiplexed in situ RNA detection often rely on inaccurate cell segmentation or prior biological knowledge from complementary single cell sequencing experiments. Here we present spage2vec, an unsupervised segmentation free approach for decrypting the spatial transcriptomic heterogeneity of complex tissues at subcellular resolution. Spage2vec represents the spatial transcriptomic landscape of tissue samples as a graph and leverages a powerful machine learning graph representation technique to create a lower dimensional representation of local spatial gene expression. We apply spage2vec to mouse brain data from three different in situ transcriptomic assays and to a spatial gene expression dataset consisting of hundreds of individual cells. We show that learned representations encode meaningful biological spatial information of re-occuring localized gene expression signatures involved in cellular and subcellular processes.
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| 7. |
- Solorzano, Leslie, 1989-, et al.
(författare)
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TissUUmaps : interactive visualization of large-scale spatial gene expression and tissue morphology data
- 2020
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Ingår i: Bioinformatics. - : OXFORD UNIV PRESS. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 36:15, s. 4363-4365
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Visual assessment of scanned tissue samples and associated molecular markers, such as gene expression, requires easy interactive inspection at multiple resolutions. This requires smart handling of image pyramids and efficient distribution of different types of data across several levels of detail.Results: We present TissUUmaps, enabling fast visualization and exploration of millions of data points overlaying a tissue sample. TissUUmaps can be used both as a web service or locally in any computer, and regions of interest as well as local statistics can be extracted and shared among users.
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