Brain ResearchNeurosciencePeer-reviewed journal article
Defining the adult hippocampal neural stem cell secretome: in vivo versus in vitro transcriptomic differences and their correlation to secreted protein levels
Jiyeon K. DenningeriD, Xi CheniD, Altan M. TurkogluiD, Patricia Sarchet, Abby R. Volk, Joshua D. RieskampiD, Pearlly S. YaniD, Elizabeth D. KirbyiD
Abstract
Recent evidence shows that 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 cellular processes like neuronal maturation and stem cell maintenance, a broad characterization of NSPC-secreted factors is lacking. Secretome profiling of low abundance stem cell populations is typically achieved via proteomic characterization of in vitro, isolated cells. Here, we analyzed the in vitro NSPC secretome using conditioned media from cultured adult mouse hippocampal NSPCs and detected over 200 different bioactive proteins with an antibody array. We next assessed the NSPC secretome on a transcriptional level with RNA sequencing (RNAseq) of cultured NSPCs. This comparison revealed that quantification of gene expression did not accurately predict relative protein abundance for several factors. Furthermore, comparing our transcriptional data with previously published single cell RNA sequencing datasets of freshly isolated hippocampal NSPCs, we found key differences in gene expression of secreted proteins between cultured and acutely isolated NSPCs. Understanding the components and functions of the NSPC secretome is essential to understanding how these cells may modulate the hippocampal neurogenic niche, as well as how they can be applied therapeutically. Cumulatively, our data emphasize the importance of using proteomic analysis in conjunction with transcriptomic studies and highlights the need for better methods of global unbiased secretome profiling.
- organism: mouse
- assay: bulk RNA-seq
- assay: proteomics
- research focus: differential expression
- approach: statistical model
- Citations
- 20
- FWCI
- 0.98
- Field percentile
- top 32%
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