Various brain cells. Various gene expression profiles. (Laboratory meeting)

This article is written by Edward Alain Pajarillo, MSc, Ph.D. Research Associate @ Florida A&M University.

I have chosen 3 interesting articles to present in our laboratory meeting today. As I jump from one science to another, I am continuously learning new concepts and techniques that will be valuable for my future independent research.

Brain cell types.PNGIf there’s one take home message that I want to tell my readers, it is this: “Understanding brain development, function, and disease at the molecular level are reflected differently by which cell type you are looking at.” The brain is comprised of numerous cell types. In the central nervous system alone, we have the major types: (1) neurons and (2) glia. Neuron’s primary role is for the transmission of electrical signals back and forth, to and fro the stimuli (receiving and responding), whereas glial cells act as its general support system. Kind of like, our charging cables; the copper wire conducts the electricity (neurons) and then insulator wraps around the wire (glia). And also, to make things more complicated, there are glial cell subtypes too, which will not be the focus of this short article.

Come to my focus, analyzing and studying the gene expression profile in the brain. Just a brief introduction, gene expression is necessary for normal biological activity and body function. Period. Our body is comprised of DNA sequences (genome) that will be transcribed to RNA (transcriptome) then translated to its proper functional molecule, the protein (proteome). Thus, studying genes and their expression is vital, especially if we want to understand how the brain and other organs respond to certain stimuli, condition or disease.

Genomes Transcriptomes ProteomesStudying gene expression (RNA transcripts) is quite tedious, but we can understand function better and determine new markers for diseases. We study the whole collection of RNA – transcriptome using various technologies such as quantitative PCR, microarray or next generation sequencing (NGS) – for RNA-seq analysis. Each technology has its own purpose, advantage, and disadvantage. And we have come a long way in research when it comes to sequencing. I have studied genomes and proteomes during my Ph.D. Although I personally want to use proteomes because of the reason that it reflects (more of) the actual function in comparison to genomes and transcriptomes. Right now, I want to learn, understand and gain novel insights with NGS and studying whole transcriptomes for two reasons:

  1. They acquire high-throughput data – more data means more insights.
  2. Discover biomarkers or target genes, as well as cellular pathways in different conditions.

PublicationsThe number of publications and interest in RNA-seq and transcriptome studies has grown in the past ten years (see figure) for the brain research. Transcriptome studies have been done for many years using different technologies, and the microarray technology is a big chunk of those studies, but RNA-seq technology has been very promising and has displayed continuous growth over the years. The workflow of RNA-seq is relatively simpler and more straightforward than microarray. Simple and more output.RNA-seq workflowHowever, when we go back to the initial problem: various brain cell types or “heterogeneity” comes into play, this is so much burden for neuroscientists. This is a problem that has been under consideration for many years even in other diseases like cancer. Regardless of the sequencing technology being used. BGocke cell reportsut since we have developed separation techniques including cell sorting devices or single cell isolators, this problem could be minimized (or eliminated) and may become the standard in both in vivo and ex vivo studies in the following years. Although only a few laboratories are able to perform these experiments right now, we hope for the “best-is-yet-to-come” when we see cell sorters become more advanced and cheaper. But the question is if heterogeneity comes into play, should we consider the validity of the results acquired and interpreted in previous studies affected by it (heterogeneity)? As we can see in a recent study by Gocke, RNA-seq profiles of various brain cell types are undeniable. Earlier reports by Cahoy et al. using microarray has reported it too.cahoy top 40 genes.PNGFinally, I go back to my take home message: “Understanding brain development, function, and disease at the molecular level are reflected differently by which cell type you are looking at.” That is why there is no simple explanation to neurodegenerative diseases and even cancer because of the different cell types. However, recent quantifying technologies and cell separating devices have been helping researchers to overcome these limitations. We should be able to determine the genetic cause of neurological disorders in a few years, and hopefully, discover specific drug targets and develop the cure for these diseases, and not just manage their symptoms.

References:

 John D. Cahoy, Ben Emery, Amit Kaushal, Lynette C. Foo, Jennifer L. Zamanian, Karen S. Christopherson, Yi Xing, Jane L. Lubischer, Paul A. Krieg, Sergey A. Krupenko, Wesley J. Thompson and Ben A. Barres. 2008. A Transcriptome Database for Astrocytes, Neurons, and Oligodendrocytes: A New Resource for Understanding Brain Development and Function. Journal of Neuroscience. 28(1):264-78.

Karpagam Srinivasan, Brad A. Friedman, Jessica L. Larson, Benjamin E. Lauffer, Leonard D. Goldstein, Laurie L. Appling, Jovencio Borneo, Chungkee Poon, Terence Ho, Fang Cai, Pascal Steiner, Marcel P. van der Brug, Zora Modrusan, Joshua S. Kaminker & David V. Hansen. 2016. Untangling the brain’s neuroinflammatory and neurodegenerative transcriptional responses. Nature Communications. 7: 11295.

Ozgun Gokce, Geoffrey M. Stanley, Barbara Treutlein8, Norma F. Neff, J. Gray Camp, Robert C. Malenka, Patrick E. Rothwell, Marc V. Fuccillo, Thomas C. Südhof. 2016. Cellular Taxonomy of the Mouse Striatum as Revealed by Single-Cell RNA-Seq. Cell reports. 16(4):1126–1137.

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