Long non-coding RNAs (lncRNAs) are important in feather development and feathering patterns, but few studies on this subject have been conducted in chickens. To understand the follicle development and feathering phenotypes, lncRNA expression profiles in chicken wing skin were determined and combined with previously determined miRNA and mRNA expression profiles of chicken wing skin. We then predicted some regulatory networks among differentially expressed mRNAs, miRNAs and lncRNAs using bioinformatics. Compared to chickens with no feathers growing out (N1 group), 778 lncRNAs were differentially expressed in early-feathering chicks with primary feathers more than 2 mm longer than primary-covert feathers (F1 group), and 443 lncRNAs were differentially expressed in late-feathering chicks with primary feathers shorter than primary-covert feathers (L2 group). Only 45 lncRNAs were differentially expressed between F1 and L2 (fold-change > 2, q < 0.01). The targets of differentially expressed lncRNAs were involved in multiple processes related to feather growth and development. Integrated analysis of lncRNAs, miRNAs and mRNAs showed that 16 pairs negatively and 17 pairs positively interacted in feather formation. XLOC_045182 might inhibit early-and late-feather formation and feather phenotype via FK1L. 107052435 might negatively regulate feather growth and development via gga-miR-31-5p. 107052611 might restrict feather development by regulating SHH expression and XLOC_235660 might have a positive effect on feather development via FGF10.

Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.

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