| 1. |
- Ho, Andrew T. V., et al.
(author)
-
Prostaglandin E2 is essential for efficacious skeletal muscle stem-cell function, augmenting regeneration and strength
- 2017
-
In: Proceedings of the National Academy of Sciences of the United States of America. - : NATL ACAD SCIENCES. - 0027-8424 .- 1091-6490. ; 114:26, s. 6675-6684
-
Journal article (peer-reviewed)abstract
- Skeletal muscles harbor quiescent muscle-specific stem cells (MuSCs) capable of tissue regeneration throughout life. Muscle injury precipitates a complex inflammatory response in which a multiplicity of cell types, cytokines, and growth factors participate. Here we show that Prostaglandin E2 (PGE2) is an inflammatory cytokine that directly targets MuSCs via the EP4 receptor, leading to MuSC expansion. An acute treatment with PGE2 suffices to robustly augment muscle regeneration by either endogenous or transplanted MuSCs. Loss of PGE2 signaling by specific genetic ablation of the EP4 receptor in MuSCs impairs regeneration, leading to decreased muscle force. Inhibition of PGE2 production through nonsteroidal anti-inflammatory drug (NSAID) administration just after injury similarly hinders regeneration and compromises muscle strength. Mechanistically, the PGE2 EP4 interaction causes MuSC expansion by triggering a cAMP/phosphoCREB pathway that activates the proliferation-inducing transcription factor, Nurr1. Our findings reveal that loss of PGE2 signaling to MuSCs during recovery from injury impedes muscle repair and strength. Through such gain-or loss-of-function experiments, we found that PGE2 signaling acts as a rheostat for muscle stem-cell function. Decreased PGE2 signaling due to NSAIDs or increased PGE2 due to exogenous delivery dictates MuSC function, which determines the outcome of regeneration. The markedly enhanced and accelerated repair of damaged muscles following intramuscular delivery of PGE2 suggests a previously unrecognized indication for this therapeutic agent.
|
|
| 2. |
- Olofsson, Per E., et al.
(author)
-
A collagen-based microwell migration assay to study NK-target cell interactions
- 2019
-
In: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 9
-
Journal article (peer-reviewed)abstract
- Natural killer (NK) cell cytotoxicity in tissue is dependent on the ability of NK cells to migrate through the extracellular matrix (ECM) microenvironment. Traditional imaging studies of NK cell migration and cytotoxicity have utilized 2D surfaces, which do not properly reproduce the structural and mechanical cues that shape the migratory response of NK cells in vivo. Here, we have combined a microwell assay that allows long-term imaging and tracking of small, well-defined populations of NK cells with an interstitial ECM-like matrix. The assay allows for long-term imaging of NK-target cell interactions within a confined 3D volume. We found marked differences in motility between individual cells with a small fraction of the cells moving slowly and being confined to a small volume within the matrix, while other cells moved more freely. A majority of NK cells also exhibited transient variation in their motility, alternating between periods of migration arrest and movement. The assay could be used as a complement to in vivo imaging to study human NK cell heterogeneity in migration and cytotoxicity.
|
|
| 3. |
|
|
| 4. |
- Magnusson, Klas E. G., 1985-, et al.
(author)
-
Global linking of cell tracks using the Viterbi algorithm
- 2015
-
In: IEEE Transactions on Medical Imaging. - : IEEE Press. - 0278-0062 .- 1558-254X. ; 34:4, s. 911-929
-
Journal article (peer-reviewed)abstract
- Automated tracking of living cells in microscopy image sequences is an important and challenging problem. With this application in mind, we propose a global track linking algorithm, which links cell outlines generated by a segmentation algorithm into tracks. The algorithm adds tracks to the image sequence one at a time, in a way which uses information from the complete image sequence in every linking decision. This is achieved by finding the tracks which give the largest possible increases to a probabilistically motivated scoring function, using the Viterbi algorithm. We also present a novel way to alter previously created tracks when new tracks are created, thus mitigating the effects of error propagation. The algorithm can handle mitosis, apoptosis, and migration in and out of the imaged area, and can also deal with false positives, missed detections, and clusters of jointly segmented cells. The algorithm performance is demonstrated on two challenging datasets acquired using bright-field microscopy, but in principle, the algorithm can be used with any cell type and any imaging technique, presuming there is a suitable segmentation algorithm.
|
|
| 5. |
- Magnusson, Klas E. G., 1985-
(author)
-
Segmentation and tracking of cells and particles in time-lapse microscopy
- 2016
-
Doctoral thesis (other academic/artistic)abstract
- In biology, many different kinds of microscopy are used to study cells. There are many different kinds of transmission microscopy, where light is passed through the cells, that can be used without staining or other treatments that can harm the cells. There is also fluorescence microscopy, where fluorescent proteins or dyes are placed in the cells or in parts of the cells, so that they emit light of a specific wavelength when they are illuminated with light of a different wavelength. Many fluorescence microscopes can take images on many different depths in a sample and thereby build a three-dimensional image of the sample. Fluorescence microscopy can also be used to study particles, for example viruses, inside cells. Modern microscopes often have digital cameras or other equipment to take images or record time-lapse video.When biologists perform experiments on cells, they often record image sequences or sequences of three-dimensional volumes to see how the cells behave when they are subjected to different drugs, culture substrates, or other external factors. Previously, the analysis of recorded data has often been done manually, but that is very time-consuming and the results often become subjective and hard to reproduce. Therefore there is a great need for technology for automated analysis of image sequences with cells and particles inside cells. Such technology is needed especially in biological research and drug development. But the technology could also be used clinically, for example to tailor a cancer treatment to an individual patient by evaluating different treatments on cells from a biopsy.This thesis presents algorithms to find cells and particles in images, and to calculate tracks that show how they have moved during an experiment. We have developed a complete system that can find and track cells in all commonly used imaging modalities. We selected and extended a number of existing segmentation algorithms, and thereby created a complete tool to find cell outlines. To link the segmented objects into tracks, we developed a new track linking algorithm. The algorithm adds tracks one by one using dynamic programming, and has many advantages over prior algorithms. Among other things, it is fast, it calculates tracks which are optimal for the entire image sequence, and it can handle situations where multiple cells have been segmented incorrectly as one object. To make it possible to use information about the velocities of the objects in the linking, we developed a method where the positions of the objects are preprocessed using a filter before the linking is performed. This is important for tracking of some particles inside cells and for tracking of cell nuclei in some embryos. We have developed an open source software which contains all tools that are necessary to analyze image sequences with cells or particles. It has tools for segmentation and tracking of objects, optimization of settings, manual correction, and analysis of outlines and tracks. We developed the software together with biologists who used it in their research. The software has already been used for data analysis in a number of biology publications. Our system has also achieved outstanding performance in three international objective comparisons of systems for tracking of cells.
|
|
| 6. |
- Togninalli, Matteo, et al.
(author)
-
Machine learning-based classification of dual fluorescence signals reveals muscle stem cell fate transitions in response to regenerative niche factors
- 2023
-
In: NPJ REGENERATIVE MEDICINE. - : Springer Nature. - 2057-3995. ; 8:1
-
Journal article (peer-reviewed)abstract
- The proper regulation of muscle stem cell (MuSC) fate by cues from the niche is essential for regeneration of skeletal muscle. How pro-regenerative niche factors control the dynamics of MuSC fate decisions remains unknown due to limitations of population-level endpoint assays. To address this knowledge gap, we developed a dual fluorescence imaging time lapse (Dual-FLIT) microscopy approach that leverages machine learning classification strategies to track single cell fate decisions with high temporal resolution. Using two fluorescent reporters that read out maintenance of stemness and myogenic commitment, we constructed detailed lineage trees for individual MuSCs and their progeny, classifying each division event as symmetric self-renewing, asymmetric, or symmetric committed. Our analysis reveals that treatment with the lipid metabolite, prostaglandin E2 (PGE2), accelerates the rate of MuSC proliferation over time, while biasing division events toward symmetric self-renewal. In contrast, the IL6 family member, Oncostatin M (OSM), decreases the proliferation rate after the first generation, while blocking myogenic commitment. These insights into the dynamics of MuSC regulation by niche cues were uniquely enabled by our Dual-FLIT approach. We anticipate that similar binary live cell readouts derived from Dual-FLIT will markedly expand our understanding of how niche factors control tissue regeneration in real time.
|
|
| 7. |
- Ulman, V, et al.
(author)
-
An objective comparison of cell-tracking algorithms
- 2017
-
In: Nature Methods. - : NATURE PUBLISHING GROUP. - 1548-7091 .- 1548-7105. ; 14:12, s. 1141-
-
Journal article (peer-reviewed)abstract
- We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.
|
|
