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CD28 plays a critical role in regulating immune responses both by enhancing effector T cell activation and differentiation and controlling the development and function of regulatory T cells. CD28 is expressed at the cell surface as a disulfide linked homodimer that is thought to bind ligand monovalently. How ligand binding triggers CD28 to induce intracellular signaling as well as the proximal signaling pathways that are induced are not well-understood. In addition, recent data suggest inside-out signaling initiated by the T cell antigen receptor can enhance CD28 ligand binding, possibly by inducing a rearrangement of the CD28 dimer interface to allow for bivalent binding. To understand how possible conformational changes during ligand-induced receptor triggering and inside-out signaling are mediated, we examined the CD28 transmembrane domain. We identified an evolutionarily conserved YxxxxT motif that is shared with CTLA-4 and resembles the transmembrane dimerization motif within CD3ζ. We show that the CD28 transmembrane domain can drive protein dimerization in a bacterial expression system at levels equivalent to the well-known glycophorin A transmembrane dimerization motif. In addition, ectopic expression of the CD28 transmembrane domain into monomeric human CD25 can drive dimerization in murine T cells as detected by an increase in FRET by flow cytometry. Mutation of the polar YxxxxT motif to hydrophobic leucine residues (Y145L/T150L) attenuated CD28 transmembrane mediated dimerization in both the bacterial and mammalian assays. Introduction of the Y145L/T150L mutation of the CD28 transmembrane dimerization motif into the endogenous CD28 locus by CRISPR resulted in a dramatic loss in CD28 cell surface expression. These data suggest that under physiological conditions the YxxxxT dimerization motif within the CD28 transmembrane domain plays a critical role in the assembly and/or expression of stable CD28 dimers at the cell surface.

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.