| 1. |
- 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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| 2. |
- 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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| 3. |
- Andersson, Axel
(författare)
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Computational Methods for Image-Based Spatial Transcriptomics
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Why does cancer develop, spread, grow, and lead to mortality? To answer these questions, one must study the fundamental building blocks of all living organisms — cells. Like a well-calibrated manufacturing unit, cells follow precise instructions by gene expression to initiate the synthesis of proteins, the workforces that drive all living biochemical processes.Recently, researchers have developed techniques for imaging the expression of hundreds of unique genes within tissue samples. This information is extremely valuable for understanding the cellular activities behind cancer-related diseases. These methods, collectively known as image-based spatial transcriptomics (IST) techniques, use fluorescence microscopy to combinatorically label mRNA species (corresponding to expressed genes) in tissue samples. Here, automatic image analysis is required to locate fluorescence signals and decode the combinatorial code. This process results in large quantities of points, marking the location of expressed genes. These new data formats pose several challenges regarding visualization and automated analysis.This thesis presents several computational methods and applications related to data generated from IST methods. Key contributions include: (i) A decoding method that jointly optimizes the detection and decoding of signals, particularly beneficial in scenarios with low signal-to-noise ratios or densely packed signals; (ii) a computational method for automatically delineating regions with similar gene compositions — efficient, interactive, and scalable for exploring patterns across different scales; (iii) a software enabling interactive visualization of millions of gene markers atop Terapixel-sized images (TissUUmaps); (iv) a tool utilizing signed-graph partitioning for the automatic identification of cells, independent of the complementary nuclear stain; (v) A fast and analytical expression for a score that quantifies co-localization between spatial points (such as located genes); (vi) a demonstration that gene expression markers can train deep-learning models to classify tissue morphology.In the final contribution (vii), an IST technique features in a clinical study to spatially map the molecular diversity within tumors from patients with colorectal liver metastases, specifically those exhibiting a desmoplastic growth pattern. The study unveils novel molecular patterns characterizing cellular diversity in the transitional region between healthy liver tissue and the tumor. While a direct answer to the initial questions remains elusive, this study sheds illuminating insights into the growth dynamics of colorectal cancer liver metastases, bringing us closer to understanding the journey from development to mortality in cancer.
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| 4. |
- 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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| 5. |
- 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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| 6. |
- Bekkhus, Tove, et al.
(författare)
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Automated detection of vascular remodeling in human tumor draining lymph nodes by the deep learning tool HEV-finder
- 2022
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Ingår i: Journal of Pathology. - : John Wiley & Sons. - 0022-3417 .- 1096-9896. ; 258:1, s. 4-11
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Tidskriftsartikel (refereegranskat)abstract
- Vascular remodeling is common in human cancer and has potential as future biomarkers for prediction of disease progression and tumor immunity status. It can also affect metastatic sites, including the tumor-draining lymph nodes (TDLNs). Dilation of the high endothelial venules (HEVs) within TDLNs has been observed in several types of cancer. We recently demonstrated that it is a premetastatic effect that can be linked to tumor invasiveness in breast cancer. Manual visual assessment of changes in vascular morphology is a tedious and difficult task, limiting high-throughput analysis. Here we present a fully automated approach for detection and classification of HEV dilation. By using 12,524 manually classified HEVs, we trained a deep-learning model and created a graphical user interface for visualization of the results. The tool, named the HEV-finder, selectively analyses HEV dilation in specific regions of the lymph nodes. We evaluated the HEV-finder's ability to detect and classify HEV dilation in different types of breast cancer compared to manual annotations. Our results constitute a successful example of large-scale, fully automated, and user-independent, image-based quantitative assessment of vascular remodeling in human pathology and lay the ground for future exploration of HEV dilation in TDLNs as a biomarker.
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| 7. |
- Blamey, Ben, et al.
(författare)
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Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit
- 2021
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Ingår i: GigaScience. - : Oxford University Press. - 2047-217X. ; 10:3, s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Large streamed datasets, characteristic of life science applications, are often resource-intensive to process, transport and store. We propose a pipeline model, a design pattern for scientific pipelines, where an incoming stream of scientific data is organized into a tiered or ordered "data hierarchy". We introduce the HASTE Toolkit, a proof-of-concept cloud-native software toolkit based on this pipeline model, to partition and prioritize data streams to optimize use of limited computing resources.FINDINGS: In our pipeline model, an "interestingness function" assigns an interestingness score to data objects in the stream, inducing a data hierarchy. From this score, a "policy" guides decisions on how to prioritize computational resource use for a given object. The HASTE Toolkit is a collection of tools to adopt this approach. We evaluate with 2 microscopy imaging case studies. The first is a high content screening experiment, where images are analyzed in an on-premise container cloud to prioritize storage and subsequent computation. The second considers edge processing of images for upload into the public cloud for real-time control of a transmission electron microscope.CONCLUSIONS: Through our evaluation, we created smart data pipelines capable of effective use of storage, compute, and network resources, enabling more efficient data-intensive experiments. We note a beneficial separation between scientific concerns of data priority, and the implementation of this behaviour for different resources in different deployment contexts. The toolkit allows intelligent prioritization to be `bolted on' to new and existing systems - and is intended for use with a range of technologies in different deployment scenarios.
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| 8. |
- Chelebian, Eduard, et al.
(författare)
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DEPICTER : Deep representation clustering for histology annotation
- 2024
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Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 170
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Tidskriftsartikel (refereegranskat)abstract
- Automatic segmentation of histopathology whole -slide images (WSI) usually involves supervised training of deep learning models with pixel -level labels to classify each pixel of the WSI into tissue regions such as benign or cancerous. However, fully supervised segmentation requires large-scale data manually annotated by experts, which can be expensive and time-consuming to obtain. Non -fully supervised methods, ranging from semi -supervised to unsupervised, have been proposed to address this issue and have been successful in WSI segmentation tasks. But these methods have mainly been focused on technical advancements in algorithmic performance rather than on the development of practical tools that could be used by pathologists or researchers in real -world scenarios. In contrast, we present DEPICTER (Deep rEPresentatIon ClusTERing), an interactive segmentation tool for histopathology annotation that produces a patch -wise dense segmentation map at WSI level. The interactive nature of DEPICTER leverages self- and semi -supervised learning approaches to allow the user to participate in the segmentation producing reliable results while reducing the workload. DEPICTER consists of three steps: first, a pretrained model is used to compute embeddings from image patches. Next, the user selects a number of benign and cancerous patches from the multi -resolution image. Finally, guided by the deep representations, label propagation is achieved using our novel seeded iterative clustering method or by directly interacting with the embedding space via feature space gating. We report both real-time interaction results with three pathologists and evaluate the performance on three public cancer classification dataset benchmarks through simulations. The code and demos of DEPICTER are publicly available at https://github.com/eduardchelebian/depicter.
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| 9. |
- Chelebian, Eduard, et al.
(författare)
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Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer
- 2021
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 13:19
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Tidskriftsartikel (refereegranskat)abstract
- Simple Summary Prostate cancer has very varied appearances when examined under the microscope, and it is difficult to distinguish clinically significant cancer from indolent disease. In this study, we use computer analyses inspired by neurons, so-called 'neural networks', to gain new insights into the connection between how tissue looks and underlying genes which program the function of prostate cells. Neural networks are 'trained' to carry out specific tasks, and training requires large numbers of training examples. Here, we show that a network pre-trained on different data can still identify biologically meaningful regions, without the need for additional training. The neural network interpretations matched independent manual assessment by human pathologists, and even resulted in more refined interpretation when considering the relationship with the underlying genes. This is a new way to automatically detect prostate cancer and its genetic characteristics without the need for human supervision, which means it could possibly help in making better treatment decisions. Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H & E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H & E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out.
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| 10. |
- Dobson, Ellen T.A., et al.
(författare)
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ImageJ and CellProfiler : Complements in Open‐Source Bioimage Analysis
- 2021
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Ingår i: Current Protocols in Microbiology. - : John Wiley & Sons. - 1934-8525 .- 1088-7423. ; 1:5
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Tidskriftsartikel (refereegranskat)abstract
- ImageJ and CellProfiler have long been leading open-source platforms in the field of bioimage analysis. ImageJ's traditional strength is in single-image processing and investigation, while CellProfiler is designed for building large-scale, modular analysis pipelines. Although many image analysis problems can be well solved with one or the other, using these two platforms together in a single workflow can be powerful. Here, we share two pipelines demonstrating mechanisms for productively and conveniently integrating ImageJ and CellProfiler for (1) studying cell morphology and migration via tracking, and (2) advanced stitching techniques for handling large, tiled image sets to improve segmentation. No single platform can provide all the key and most efficient functionality needed for all studies. While both programs can be and are often used separately, these pipelines demonstrate the benefits of using them together for image analysis workflows. ImageJ and CellProfiler are both committed to interoperability between their platforms, with ongoing development to improve how both are leveraged from the other
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