| 1. |
- 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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| 2. |
- 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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| 3. |
- 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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| 4. |
- Partel, Gabriele, et al.
(författare)
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Spage2vec: Unsupervised detection of spatial gene expression constellations
- 2024
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Investigation 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 spatial functional network 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, showing that learned representations encode meaningful biological spatial information of re-occuring gene constellations involved in cellular and subcellular processes.
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| 5. |
- Pontén, Olle, et al.
(författare)
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PACMan : A software package for automated single-cell chlorophyll fluorometry
- 2024
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Ingår i: Cytometry Part A. - : John Wiley & Sons. - 1552-4922 .- 1552-4930. ; 105:3, s. 203-213
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Tidskriftsartikel (refereegranskat)abstract
- Microalgae, small photosynthetic unicells, are of great interest to ecology, ecotoxicology and biotechnology and there is a growing need to investigate the ability of cells to photosynthesize under variable conditions. Current strategies involve hand-operated pulse-amplitude-modulated (PAM) chlorophyll fluorimeters, which can provide detailed insights into the photophysiology of entire populations- or individual cells of microalgae but are typically limited in their throughput. To increase the throughput of a commercially available MICROSCOPY-PAM system, we present the PAM Automation Control Manager (‘PACMan’), an open-source Python software package that automates image acquisition, microscopy stage control and the triggering of external hardware components. PACMan comes with a user-friendly graphical user interface and is released together with a stand-alone tool (PAMalysis) for the automated calculation of per-cell maximum quantum efficiencies (= Fv/Fm). Using these two software packages, we successfully tracked the photophysiology of >1000 individual cells of green algae (Chlamydomonas reinhardtii) and dinoflagellates (genus Symbiodiniaceae) within custom-made microfluidic devices. Compared to the manual operation of MICROSCOPY-PAM systems, this represents a 10-fold increase in throughput. During experiments, PACMan coordinated the movement of the microscope stage and triggered the MICROSCOPY-PAM system to repeatedly capture high-quality image data across multiple positions. Finally, we analyzed single-cell Fv/Fm with the manufacturer-supplied software and PAMalysis, demonstrating a median difference <0.5% between both methods. We foresee that PACMan, and its auxiliary software package will help increase the experimental throughput in a range of microalgae studies currently relying on hand-operated MICROSCOPY-PAM technologies.
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