Robust Transcriptional Profiling and Identification of Differentially Expressed Genes With Low Input RNA Sequencing of Adult Hippocampal Neural Stem and Progenitor Populations.

Multipotent neural stem cells (NSCs) are found in several isolated niches of the adult mammalian brain where they have unique potential to assist in tissue repair. Modern transcriptomics offer high-throughput methods for identifying disease or injury associated gene expression signatures in endogenous adult NSCs, but they require adaptation to accommodate the rarity of NSCs. Bulk RNA sequencing (RNAseq) of NSCs requires pooling several mice, which impedes application to labor-intensive injury models. Alternatively, single cell RNAseq can profile hundreds to thousands of cells from a single mouse and is increasingly used to study NSCs. The consequences of the low RNA input from a single NSC on downstream identification of differentially expressed genes (DEGs) remains insufficiently explored. Here, to clarify the role that low RNA input plays in NSC DEG identification, we directly compared DEGs in an oxidative stress model of cultured NSCs by bulk and single cell sequencing. While both methods yielded DEGs that were replicable, single cell sequencing using the 10X Chromium platform yielded DEGs derived from genes with higher relative transcript counts compared to non-DEGs and exhibited smaller fold changes than DEGs identified by bulk RNAseq. The loss of high fold-change DEGs in the single cell platform presents an important limitation for identifying disease-relevant genes. To facilitate identification of such genes, we determined an RNA-input threshold that enables transcriptional profiling of NSCs comparable to standard bulk sequencing and used it to establish a workflow for in vivo profiling of endogenous NSCs. We then applied this workflow to identify DEGs after lateral fluid percussion injury, a labor-intensive animal model of traumatic brain injury. Our work joins an emerging body of evidence suggesting that single cell RNA sequencing may underestimate the diversity of pathologic DEGs. However, our data also suggest that population level transcriptomic analysis can be adapted to capture more of these DEGs with similar efficacy and diversity as standard bulk sequencing. Together, our data and workflow will be useful for investigators interested in understanding and manipulating adult hippocampal NSC responses to various stimuli.

Transcriptome analysis discloses dysregulated genes in normal appearing tumor-adjacent thyroid tissues from patients with papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. The molecular characteristics of histologically normal appearing tissue adjacent to the tumor (NAT) from PTC patients are not well characterized. The aim of this study was to characterize the global gene expression profile of NAT and compare it with those of normal and tumor thyroid tissues. We performed total RNA sequencing with fresh frozen thyroid tissues from a cohort of three categories of samples including NAT, normal thyroid (N), and PTC tumor (T). Transcriptome analysis shows that NAT presents a unique gene expression profile, which was not associated with sex or the presence of lymphocytic thyroiditis. Among the differentially expressed genes (DEGs) of NAT vs N, 256 coding genes and 5 noncoding genes have been reported as cancer genes involved in cell proliferation, apoptosis, and/or tumorigenesis. Bioinformatics analysis with Ingenuity Pathway Analysis software revealed that "Cancer, Organismal Injury and Abnormalities, Cellular Response to Therapeutics, and Cellular Movement" were major dysregulated pathways in the NAT tissues. This study provides improved insight into the complexity of gene expression changes in the thyroid glands of patients with PTC.

Defining the adult hippocampal neural stem cell secretome: In vivo versus in vitro transcriptomic differences and their correlation to secreted protein levels.

Adult hippocampal neural stem and progenitor cells (NSPCs) secrete a variety of proteins that affect tissue function. Though several individual NSPC-derived proteins have been shown to impact key cellular processes, a broad characterization is lacking. Secretome profiling of low abundance stem cell populations is typically achieved via proteomic characterization of in vitro, isolated cells. Here, we identified hundreds of secreted proteins in conditioned media from in vitro adult mouse hippocampal NSPCs using an antibody array and mass spectrometry. Comparison of protein abundance between antibody array and mass spectrometry plus quantification of several key secreted proteins by ELISA revealed notable disconnect between methods in what proteins were identified as being high versus low abundance, suggesting that data from antibody arrays in particular should be approached with caution. We next assessed the NSPC secretome on a transcriptional level with single cell and bulk RNA sequencing (RNAseq) of cultured NSPCs. Comparison of RNAseq transcript levels of highly secreted proteins revealed that quantification of gene expression did not necessarily predict relative protein abundance. Interestingly, comparing our in vitro NSPC gene expression data with similar data from freshly isolated, in vivo hippocampal NSPCs revealed strong correlations in global gene expression between in vitro and in vivo NSPCs. Understanding the components and functions of the NSPC secretome is essential to understanding how these cells may modulate the hippocampal neurogenic niche. Cumulatively, our data emphasize the importance of using proteomics in conjunction with transcriptomics and highlights the need for better methods of unbiased secretome profiling.