Department of Information Technology

Quantitative Microscopy

Manual counting of objects and making measurements in microscopy images is very time consuming. Therefore, we develop automatic image analysis methods for microscopy. The images can be two, three, or four dimensional (space + time) and the sources can be light, fluorescence, or electron microscopes. The objects to measure can be biological samples such as viruses and cells, or specific molecular markers, and the purpose diagnostics or quality control. Below are short descriptions of our ongoing projects. More details can be found in the CBA project database.

Tissue Analysis

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Images result from slide scanners, high content screening, imaging flow cytometry, mass spectrometers and others. We develop image analysis methods (modern and classical) and tools to find, count, classify and segment objects of interest and find relationships between information coming from these images. Information is extracted in several levels: genetic, epigenetic, gene expression, protein expression and protein activity. Studied in different scales: single cells and tissue. Isolated or in-situ. Data analysis results are used to assist pathologists and medical doctors by efficiently displaying large amounts of data in a succinct and smart form.
More information at | TissUUmaps research

High Throughput Screening of Zebrafish

The zebrafish Danio rerio is a well-established and effective vertebrate model organism. It is highly complex and possesses discrete organs and tissues and many of the fundamental mechanisms are conserved in humans. Due to its small size, optical transparency of complex organs the zebrafish is well suited for imaging using microscopy and an ideal organism for large-scale screening. We are developing imaging systems for 3D screening of zebrafish using optical projection tomography (OPT) and analysis methods for high-throughput screening of zebrafish using different microscopy imaging modalities.

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HASTE: Hierarchical Analysis Of Spatial And Temporal Data

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The valuable information in digital images is often sparse, and the ever increasing speed at which data is collected results in data-volumes that exceed the computational resources available. The HASTE project takes a hierarchical approach to acquisition, analysis, and interpretation of image data. We develop computationally efficient measurements for data description, confidence-driven machine learning for determination of interestingness, and a theory and framework to apply intelligent spatial and temporal information hierarchies, distributing data to computational resources and storage options based on low-level image features. Read more at http://haste.research.it.uu.se/.

Sysmic: Deciphering Cancer Cell Migration by Systems Microscopy

The core biological theme of this program is cell migration; a basic but complex cellular process that is highly relevant to human cancer. This complexity is, in part, explained by plasticity in the possible cell migration strategies that cells adopt and by the fine-tuned spatiotemporal coordination of migratory forces. However, the molecular mechanisms and genetic regulation that give rise to cell migration plasticity and dynamic force control constitutes knowledge gaps that this program aim to fill. Read more at https://sysmic.ki.se.

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Automation of Electron Microscopy

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Electron microscopy allows for studying the shape and morphology of sub-cellular structures such as proteins and viruses at the nm level. Our current 2D research focuses on developing a miniaturized electron microscope, automated content driven image acquisition, and segmentation and texture measurements for virus diagnostics and nanoparticle analysis. Image to the left: Transmission electron microscopy image of a fecal gastroenteritis patient sample containing Adenovirus.

BioImage Informatics Facility

Connected to the Quantitative Microscopy group is the SciLifeLab BioImage Informatics National Facility. The Facility provides support and education in image analysis to reseachers at Swedish Life Science institutes, and has two nodes; one in Stockholm, connected to the School of Computer Science and Communication at KTH, and one in Uppsala, at the Centre for Image Analysis, Dept. of Information Technology, Uppsala University.
https://www.scilifelab.se/facilities/bioimage-informatics/

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Group members

Carolina Wählby
Professor
Ida-Maria Sintorn
Associate Professor
Petter Ranefall
Bioinformatician
Petter Ranefall, PhD
Bioinformatician with focus on research support on image analysis
Centre for Image Analysis and Science for Life Laboratory
Quantitative Microscopy Group - Carolina Wählby
Anna Klemm
Bioinformatician
Amin Allalou
Researcher
Joakim Lindblad
Researcher
Anindya Gupta
Post Doctoral
Hanqing Zhang
Post Doctoral
Damian Matuszewski
PhD Student
Amit Suveer
Assistant Undergoing Research Training
Leslie Solorzano
PhD Student
Gabriele Partel
PhD Student
Håkan Wieslander
PhD Student
Nicolas Pielawski
PhD Student
Kimmo Kartasalo
Guest Doctoral Student
Ankit Gupta
PhD Student
Axel Andersson
PhD Student

Updated  2019-05-17 11:52:22 by Nicolas Pielawski.