Mapping Cellular Interactions from Spatially Resolved Transcriptomics Data.

Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently, through the introduction of spatially resolved transcriptomics technologies (SRTs), especially those that achieve single cell resolution. However, significant challenges remain to analyze such highly complex data properly. Here, we introduce a Bayesian multi-instance learning framework, spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates, and most importantly the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of spacia for all three commercialized single cell resolution ST technologies: MERSCOPE/Vizgen, CosMx/Nanostring, and Xenium/10X. Spacia unveiled how endothelial cells, fibroblasts and B cells in the tumor microenvironment contribute to Epithelial-Mesenchymal Transition and lineage plasticity in prostate cancer cells. We deployed spacia in a set of pan-cancer datasets and showed that B cells also participate in PDL1/PD1 signaling in tumors. We demonstrated that a CD8+ T cell/PDL1 effectiveness signature derived from spacia analyses is associated with patient survival and response to immune checkpoint inhibitor treatments in 3,354 patients. We revealed differential spatial interaction patterns between γδ T cells and liver hepatocytes in healthy and cancerous contexts. Overall, spacia represents a notable step in advancing quantitative theories of cellular communications.

Do "Newly Born" orphan proteins resemble "Never Born" proteins? A study using three deep learning algorithms.

"Newly Born" proteins, devoid of detectable homology to any other proteins, known as orphan proteins, occur in a single species or within a taxonomically restricted gene family. They are generated by the expression of novel open reading frames, and appear throughout evolution. We were curious if three recently developed programs for predicting protein structures, namely, AlphaFold2, RoseTTAFold, and ESMFold, might be of value for comparison of such "Newly Born" proteins to random polypeptides with amino acid content similar to that of native proteins, which have been called "Never Born" proteins. The programs were used to compare the structures of two sets of "Never Born" proteins that had been expressed-Group 1, which had been shown experimentally to possess substantial secondary structure, and Group 3, which had been shown to be intrinsically disordered. Overall, although the models generated were scored as being of low quality, they nevertheless revealed some general principles. Specifically, all four members of Group 1 were predicted to be compact by all three algorithms, in agreement with the experimental data, whereas the members of Group 3 were predicted to be very extended, as would be expected for intrinsically disordered proteins, again consistent with the experimental data. These predicted differences were shown to be statistically significant by comparing their accessible surface areas. The three programs were then used to predict the structures of three orphan proteins whose crystal structures had been solved, two of which display novel folds. Surprisingly, only for the protein which did not have a novel fold, and was taxonomically restricted, rather than being a true orphan, did all three algorithms predict very similar, high-quality structures, closely resembling the crystal structure. Finally, they were used to predict the structures of seven orphan proteins with well-identified biological functions, whose 3D structures are not known. Two proteins, which were predicted to be disordered based on their sequences, are predicted by all three structure algorithms to be extended structures. The other five were predicted to be compact structures with only two exceptions in the case of AlphaFold2. All three prediction algorithms make remarkably similar and high-quality predictions for one large protein, HCO_11565, from a nematode. It is conjectured that this is due to many homologs in the taxonomically restricted family of which it is a member, and to the fact that the Dali server revealed several nonrelated proteins with similar folds. An animated Interactive 3D Complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:Proteins:3.

Synergistically Detachable Microneedle Dressing for Programmed Treatment of Chronic Wounds.

Chronic wounds such as diabetic feet undergo a lifetime risk of developing into incurable ulcers. Current treatments for chronic wounds remain unsatisfactory due to the lack of ideal wound dressings that integrate facile dressing change, long-acting treatment, and high therapeutic efficacy into one system. Herein, a synergistically detachable microneedle (MN) dressing with a dual-layer structure is presented to enable programmed treatment via one-time dressing application. Such a dual-layer dressing MN system (DDMNS) is composed of chitosan (CS) hydrogel dressing (CSHD) on top of a detachable MN patch with a CS tip and a polyvinyl pyrrolidone (PVP) backing substrate incorporated with magnesium (Mg). The synergistic detachment is achieved with the backing Mg/PVP substrate dissolving within minutes due to the local moist environment of the CSHD enhancing the reaction between Mg and inflammation microenvironment. The combined treatment of Mg and panax notoginseng saponins (PNS) loaded in DDMNS achieves antibacterial, neovascularization, and activating a benign immune response so that the three overlapping periods of the inflammation, tissue proliferation, and tissue remodeling of wound healing reach a dynamic balance. This advanced DDMNS provides a facile approach for the programmed treatment of chronic wound management indicating potential value in wound healing and other related biomedical fields.