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Oleksyn, D.

Publications and source records attributed to Oleksyn, D..

3 recordsLinked to original sources

Intravital imaging the formation and resolution of MHC class II-positive T cell activation niches

Intravital imaging has revealed many of the cellular interactions that regulate immune responses, but is limited by the number of cells that can be simultaneously identified and often restricted to analysis of a single time point. We have developed two new fluorescent reporter strains, IEbeta-mAmetrine, and CD8beta-LSSmOrange, that faithfully label cells expressing MHC class II and CD8-positive conventional T cells, respectively. These fluorescent proteins are spectrally distinct from commonly used fluorescent proteins (GFP, YFP) and so these mice can be used in combination with many previously created reporter mice. In addition, we established a protocol where we can sequentially image the same area of the ear dermis over several weeks without inducing any inflammation. We applied these techniques to IEbeta-mAmetrine mice co-expressing markers for CD11c, CXCL10, and CD4 T cells to quantify the formation of CXCL3 activation clusters, elaboration of different MHC class II positive cells within these clusters, accumulation of CD4 T cells within these clusters, and the dissipation of these T cell activation niches as the inflammatory response wanes. Summary BlurbThis study describes the development and utility of two new fluorescent reporter mice for intravital imaging of the same tissue site to follow the kinetics of an immune response.

immunology↗

T cells Use Focal Adhesions to Pull Themselves Through Confined Environments

Immune cells are highly dynamic and able to migrate through environments with diverse biochemical and mechanical composition. Their migration has classically been defined as amoeboid under the assumption that it is integrin-independent. Here we show that activated primary Th1 T cells require both confinement and extracellular matrix protein to migrate efficiently. This migration is mediated through small and dynamic focal adhesions that are composed of the same proteins associated with canonical mesenchymal focal adhesions, such as integrins, talin, and vinculin. These focal adhesions, furthermore, localize to sites of contractile traction stresses, enabling T cells to pull themselves through confined spaces. Finally, we show that Th1 T cell preferentially follows tracks of other T cells, suggesting that these adhesions are modifying the extracellular matrix to provide additional environmental guidance cues. These results demonstrate not only that the boundaries between amoeboid and mesenchymal migration modes are ambiguous, but that integrin-mediated adhesions play a key role in T cell motility. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/562587v1_ufig1.gif" ALT="Figure 1"> View larger version (78K): org.highwire.dtl.DTLVardef@8ba84aorg.highwire.dtl.DTLVardef@d5a504org.highwire.dtl.DTLVardef@44d104org.highwire.dtl.DTLVardef@1f68df5_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning

Due to the overlapping emission spectra of fluorophores, fluorescence microscopy images often have bleed-through problems, leading to a false positive detection. This problem is almost unavoidable when the samples are labeled with three or more fluorophores, and the situation is complicated even further when imaged under a multiphoton microscope. Several methods have been developed and commonly used by biologists for fluorescence microscopy spectral unmixing, such as linear unmixing, non-negative matrix factorization, deconvolution, and principal component analysis. However, they either require pre-knowledge of emission spectra or restrict the number of fluorophores to be the same as detection channels, which highly limits the real-world applications of those spectral unmixing methods. In this paper, we developed a robust and flexible spectral unmixing method: Learning Unsupervised Means of Spectra (LUMoS), which uses an unsupervised machine learning clustering method to learn individual fluorophores spectral signatures from mixed images, and blindly separate channels without restrictions on the number of fluorophores that can be imaged. This method highly expands the hardware capability of two-photon microscopy to simultaneously image more fluorophores than is possible with instrumentation alone. Experimental and simulated results demonstrated the robustness of LUMoS in multi-channel separations of two-photon microscopy images. We also extended the application of this method to background/autofluorescence removal and colocalization analysis. Lastly, we integrated this tool into ImageJ to offer an easy to use spectral unmixing tool for fluorescence imaging. LUMoS allows us to gain a higher spectral resolution and obtain a cleaner image without the need to upgrade the imaging hardware capabilities.

bioinformatics↗