CXCL10+ peripheral activation niches couple preferred sites of Th1 entry with optimal APC encounter.

Correct positioning of T cells within infected tissues is critical for T cell activation and pathogen control. Upon tissue entry, effector T cells must efficiently locate antigen-presenting cells (APC) for peripheral activation. We reveal that tissue entry and initial peripheral activation of Th1 effector T cells are tightly linked to perivascular positioning of chemokine-expressing APCs. Dermal inflammation induces tissue-wide de novo generation of discrete perivascular CXCL10+ cell clusters, enriched for CD11c+MHC-II+ monocyte-derived dendritic cells. These chemokine clusters are "hotspots" for both Th1 extravasation and activation in the inflamed skin. CXCR3-dependent Th1 localization to the cluster micro-environment prolongs T-APC interactions and boosts function. Both the frequency and range of these clusters are enhanced via a T helper 1 (Th1)-intrinsic, interferon-gamma (IFNγ)-dependent positive-feedback loop. Thus, the perivascular CXCL10+ clusters act as initial peripheral activation niches, optimizing controlled activation broadly throughout the tissue by coupling Th1 tissue entry with enhanced opportunities for Th1-APC encounter.

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.