Analysis of spinal segments can help develop a better understanding of the architecture of the central nervous system. More specifically, understanding the distribution of motor neurons (MN) in the mouse spinal cord and the muscles MNs respectively stimulate can help develop cell-type specific treatments for spinal cord injuries. In this study, we aimed to improve the current mouse spinal cord atlas with a focus on MN groups and their characteristics to obtain a better idea of the extent of MNs at each vertebrae. We hypothesized that by making injections of neuronal tracers into specific muscle groups, for example the triceps and quadriceps, their specific MN groups could be visualized in greater detail in the spinal cord. These injections help delineate MN groups since it is difficult to determine their beginning and end when examining the spinal cord and allow us to create a higher resolution atlas that uses the entire spinal cord as opposed to a single slice from each section. To render an atlas of the mouse spinal cord, tissue slices were assigned to regions of the spinal cord based on the tissue and region length. We then delineated gray matter regions and demarcated layers in the sections to outline MN groups. Current results lend support to the idea that injections in specific muscles will help visualize these segments in higher resolution to improve the atlas. Ultimately, we intend to use data from spinal segments to establish a 3D spinal reference atlas using the entire spinal cord.

The endometrium is the lining of the uterus and is the site of blastocyst implantation and trophoblast invasion. Upon the formation of the blastocyst, the trophectoderm region containing trophoblast cells invades the endometrial epithelium through the degradation of the extracellular matrix to fully implant into the endometrium. Little is known about the mechanisms that are involved in implantation due to ethical and accessibility challenges to human embryos, yet disruptions at this stage of pregnancy can lead to disorders such as pre-eclampsia and placenta accreta. A better understanding of implantation would allow for better approaches to preventing infertility and pregnancy loss. We hypothesize that utilizing three-dimensional (3D) bioprinting to create highly efficient implantation modules will allow for high-yielding mechanistic and real-time studies of this process. In this project, an extrusion-based bioprinter (BioX, Cellink) with a biocompatible hydrogel-based ink, methacrylamide-functionalized gelatin (GelMA), will be used to create arrays of microwell-shaped scaffolds for endometrial assembloids. Human umbilical vein endothelial cells (HUVEC) and human endometrial stromal cells (HESC) will be embedded into the scaffold at different ratios to find the optimal ratio that most mimics the formation of the perivascular niche. To model implantation, blastoids will be placed onto hormone-stimulated endometrial assembled arrays. A thin polydimethylsiloxane (PDMS) mesh will be utilized as a stencil to direct one blastoid per well. With these modules, greater insight will be obtained into the mechanisms of implantation and development, allowing for the uncovering of processes that were previously hidden.

Chemogenomics is a field of study that focuses on the interactions between chemicals and genes. Genomic data repositories such as the Gene Expression Omnibus (GEO), contain thousands of publicly available datasets. However, varying platforms, data formats, and technical differences in data pre-processing and differential gene expression analysis have prevented the systemic analysis of various datasets relevant to chemical exposures. The creation of a streamlined data curation and analysis pipeline capable of reprocessing, reorganizing, and reanalyzing existing data from GEO would make it possible to improve the interpretation of extensive amounts of data. In doing so, past studies can more readily be compared to gain a greater understanding of how gene pathways and diseases are affected by chemical exposure. This can be accomplished by partitioning data by technical platforms (single vs. two-color microarrays vs. RNA sequencing) and using the computational tools appropriate for each such as Linear Models for Microarray Data (LIMMA) for microarrays and DESeq2 for RNAsequencing (RNAseq) data. By using standardized methods to meta-analyze hundreds of these transcriptome datasets, it becomes easier to compare perturbed gene pathways and mechanisms for chemicals across studies. Doing so could help determine the impact of environmental chemical exposure on the expression of genes that are linked to specific diseases. This updated resource can serve as a starting point to perform in-vivo/vitro studies with the aim of fixing these altered mechanisms while better understanding the manner in which these chemicals affect certain tissues and create disease susceptibility in various organisms.

Found in all jawed vertebrates, the major histocompatibility gene complex (MHC) encodes a set of glycoproteins that detect and present foreign pathogens to the adaptive immune system. Driven by pathogen-mediated selection and sexual selection, the diversity of MHC glycoproteins varies across species, is associated with greater protection against pathogens, and can contribute to mate selection. There is a current lack of research on MHC genes outside of a laboratory setting. By studying the MHC of dark-eyed juncos (J. hyemalis), a species of songbirds recently introduced to urban spaces, we can gain a better understanding of selection mechanisms under natural stress and determine if urbanized juncos display high genetic variability. We used successful egg hatchings to measure sexual selection and quantified MHC class I diversity using next-generation sequencing. The urbanized juncos used for our study are located on the University of California, Los Angeles (UCLA) campus, an area where pathogens are uncommon. We observed an overall lack of MHC diversity and found no correlation between our metric for sexual selection and MHC diversity. The low MHC diversity is unexpected and could suggest a vulnerability to novel pathogens. Larger datasets are necessary to verify these findings. Future directions include expanding to MHC class II genes and obtaining rural junco populations to determine if a lack of MHC diversity is an evolutionary divergence in urban populations.