One of the big challenges in neurobiology is cell classification. “There are many individual cell types in the brain, and we don’t know how to identify them all,” says Kenneth S. Kosik, Harriman Professor of Neuroscience in the Department of Molecular, Cellular and Developmental Biology at UC Santa Barbara and co-director of the Neuroscience Research Institute.

The problem is compounded by the fact that the same cell type can look different depending on the method of analysis used to classify it, whether that is the shape of the cell, its gene expression profile, its electrophysiological firing pattern, or its selective vulnerability to certain diseases.

In classification paradigms that focus on which genes express themselves to define cell type, researchers have looked at messenger RNAS (mRNAs), Kosik explains. But in a project described in the paper “Regulation of Cell Type–Specific Transcriptomes by MicroRNA Networks During Human Brain Development,” he and his co-authors “brought an additional parameter — micro-RNAs (miRNAs) — to the problem of classification. Researchers had not previously looked at miRNAs at the single-cell level to get at cell-type specificity.”

The research team included Mahdi Golkaram (co-first author, PhD 2018) and UCSB colleagues Linda R. Petzold (professor, Departments of Computer Science and Mechanical Engineering), Hongjun R. Zhou (co-first author) and Kylie Huch (both in the Department of Molecular, Cellular and Developmental Biology); Tomasz J. Nowakowski (co-first author) at UC San Francisco; and Neha Rani (co-first author), a former post-doctoral fellow in the Kosik lab now at the Indian Institute of Technology in Kanpur, India.

If DNA is the architect holding the genetic blueprint for species replication, then RNA is the on-the-ground builder responsible sometimes for initiating, and other times for carrying out or delegating, multiple processes essential for creating fully realized living forms.

Key among the tasks that messenger RNA is responsible for include reading genetic information from DNA and translating it into proteins that fire up the cell-production factory. RNA also plays a role in regulating the cell-production process, an especially important function for ensuring that genes are expressed correctly and that cells are produced in the right amounts.

In its regulatory role, mRNA has an indispensable assistant — microRNA — which works to prevent over-proliferation of specific types of cells. Such overproduction, says Golkaram (PhD Mechanical Engineering 2018), currently a bioinformatics scientist at illumina, “can have implications in diseases that have prenatal origins, such as ASD [autism spectrum disorder].”

The interactions between mRNA and miRNA are the subject of the paper. The researchers sought to understand the complex time- and context-specific interplay between mRNAs and miRNAs, and how imbalances between them may relate to prenatally formed disease in the human brain.

“Better understanding how mRNAs and miRNAs work together, and how that looks in normal cell development and gene expression, would be valuable in understanding and addressing a range of prenatally developed diseases and disorders,” Golkaram says.

Generally, he explains, when mRNAs and miRNAs are in the right location and functioning correctly as a “bipartite” network, they inhibit translation of genes and production of proteins to prevent an unhealthy overabundance of any cell type. But if they're not functioning well, such as when an miRNA is suppressed and cannot inhibit gene translation, the result can be overexpression of one or more genes and uncontrolled production of certain cells. Cancer is the most well-known resulting pathology, Golkaram notes.

“In the interaction between these two distinct modes, each messenger RNA can be targeted by multiple micro-RNAs and vice versa,” Golkaram says. “But the interactions of mRNAs and miRNAs do not form a randomly connected network. The network contains communities — you can think of them as clusters — and if you look at a particular cluster that contains a set of both mRNAs and miRNAs and has been extracted from the network, you can see that the cluster is enriched to different extents in different cell types. So, the members of that cluster are the mRNAs and the miRNAs whose expression is known to be regulated up or down in those cell types. If one of your clusters is not working properly, you can tip the balance against or in favor of that cell type, and that can lead to disease.”

Biologists have studied the small, non-coding miRNAs for decades and know that, generally, they serve to suppress excess protein production by preventing mRNA from translating genetic coding. They therefore have a role in fine-tuning the signaling pathways that are related to brain development. But their function depends on context, and it changes as the brain develops and according to where they are expressed.

Until now, no technique has existed for seeing all of the mRNA and miRNA expressions in individual cells. For this project, the researchers used a single-cell qPCR method, which stands for “quantitative polymerase chain reaction.” Traditionally, qPCR has been used for populations, not individual cells, Golkaram explains. “You’re looking at a lot of cells at the same time, and the behavior you are capturing is a mean of the population. You don't have the resolution to see single cells, which is very important here because we are talking about mRNA-miRNA interactions in specific cell types. So, the mean, or average, behavior of your population is not representative.”

For the project, teams at UCSB and UCSF developed a customized qPCR process that could capture both mRNAs and miRNAs in every single cell. “That was an important step forward,” Golkaram says. “It helped us see that the expression levels of these targeting micro-RNAs is highly cell-type specific.”

The team used samples from prenatal human brain cells collected under strict ethical and legal regulations. Golkaram, working in the lab of Petzold, his PhD advisor, developed a machine learning approach to identify and extract patterns from what appears to be a chaotic, noisy system containing millions of data points representing the mRNA-miRNA interactions. They then used the patterns to develop hypotheses, which the experimental teams at UCSF/UCSB tested in the lab.

Previous, more-general, hypotheses have related to the expression of certain types of RNA being higher or lower in specific cell types. “But what we did was to look at all micro-RNAs and mRNAs at the same time, genome-wide, rather than just cherry-picking a few with high precision,” Golkaram explains.

To do that, he says, “We constructed the network by experimentally identifying the set of mRNAs that are micro-RNA targets and connecting all miRNA to their identified targets. That gave us the big picture, allowing us to look at the larger network and the different modules within it. Certain clusters are representative of distinct cell types, and certain micro-RNAs show cell-type-specific expression.

“We can say, ‘OK, in one cell type, the expression of micro RNAs will be modulated in a certain location or at a certain point in time to contribute different levels of regulation in the cluster from which they were extracted. We can therefore compare someone who has autism spectrum disorder with someone who is healthy and see that they have different levels of expression of this miRNA and different levels of regulation.

The researchers used two methods to confirm that a cluster is associated with ASD. One was to compare the mRNA-miRNA levels in that cluster in mice that carry the gene associated with ASD to the levels in healthy mice. The other was to observe significant overlap between the miRNAs and mRNAs in that cluster and those that previous studies of adult humans have shown to be involved in ASD. “We can quantify the difference in the miRNA expression and the mRNA-miRNA interactions in healthy and unhealthy brains,” Golkaram says.

Finally, he adds, “Heterogeneity of cell types is critical to an organism’s ability to survive and thrive, and regulating it is the key. If we want to look at different diseases having prenatal origins and understand what causes them, it’s important to have a mechanistic understanding of the mRNA-miRNA interactions. This study tells us that the imbalance between cell types during early development can lead to this disease, and also that this imbalance results from these two modes — mRNA and miRNA — not being correctly aligned.”