Summary: Researchers test the theory that a neuron’s identity can be described by the genes it expresses alone.
Source: Marine Biological Laboratory
A longstanding goal in neuroscience is to classify the brain’s many cells into discrete categories according to their function. Such categories can help researchers understand the complex neural circuits that ultimately give rise to behavior and disease. However, there’s little consensus about what metrics should define a cell’s identity.
In a new study, a collaboration born in part from the Neural Systems & Behavior (NS&B) course at the Marine Biological Laboratory tests the notion that a cell’s identity can be described solely by the genes it expresses. The study, published in Proceedings of the National Academy of Sciences, advocates a more “multimodal” approach to defining cell identity.
By using popular and powerful RNA sequencing techniques, researchers can take a snapshot of all the genes that are currently turned on inside a cell. But it’s becoming increasingly clear that such strategies may be limited in their ability to give a complete picture of cell identity, or represent changes over time.
Along with their collaborators, NS&B instructors Hans Hofmann, David Schulz, and Eve Marder put two popular RNA-based methods to the test: single-cell RNA sequencing and quantitative RT-PCR. They applied these techniques to two well-studied nerve clusters in the crab Cancer borealis — the stomatogastric and cardiac ganglia –which allowed them to compare the results from the RNA-based approaches to other known metrics of cell identity.
They found that the cell identities generated by the complete RNA profiles, or “transcriptomes,” did not match the existing cell identities they had compiled over years of observation. In fact, categorizing cells based on their entire transcriptome ultimately yielded “scrambled” identities.
However, as the researchers further refined their selection of key genes to input into their analysis, the RNA profiles began to more closely resemble the identities gleaned from other attributes, such as innervation patterns, morphology, and physiology. Thus, this multimodal approach has the potential to reveal a more accurate portrayal of cell identity than RNA sequencing alone.
According to Hofmann, most studies don’t bother to validate transcriptomic data with other metrics of cell identity like morphology and physiology. “Classification and characterization of cell types is often performed within the context of specific studies, and not based on a systematic approach,” he says. “We really have to collect a lot of additional data, even across species, to come up with a robust taxonomy of cell types.”
“RNA sequencing is tremendously promising and powerful, but this study provides a valuable and necessary check,” Schulz adds.
“Rather than relying entirely on analytics applied blindly to cell type, whenever possible it’s important to consider multiple modalities of information as well.”
The trick, Hofmann and Schulz agree, is knowing which data are indicative of cell identity, and which are simply noise that will interfere with accurate classification.
Researchers must also eventually agree on a definition of “cell identity.” Drawing firm boundaries between cell types is useful in many ways, but may ultimately be problematic.
“Soon,” Schulz says, “we’ll start to see the limitations of trying to impose very discrete categories on the spectrum of cell types within and across individuals.”
Source:
Marine Biological Laboratory
Media Contacts:
Gina Hebert – Marine Biological Laboratory
Image Source:
The image is credited to Adam Northcutt.
Original Research: Open access
“Molecular profiling of single neurons of known identity in two ganglia from the crab Cancer borealis”. Adam J. Northcutt, Daniel R. Kick, Adriane G. Otopalik, Benjamin M. Goetz, Rayna M. Harris, Joseph M. Santin, Hans A. Hofmann, Eve Marder, and David J. Schulz.
PNAS doi:10.1073/pnas.1911413116.
Abstract
Molecular profiling of single neurons of known identity in two ganglia from the crab Cancer borealis
Understanding circuit organization depends on identification of cell types. Recent advances in transcriptional profiling methods have enabled classification of cell types by their gene expression. While exceptionally powerful and high throughput, the ground-truth validation of these methods is difficult: If cell type is unknown, how does one assess whether a given analysis accurately captures neuronal identity? To shed light on the capabilities and limitations of solely using transcriptional profiling for cell-type classification, we performed 2 forms of transcriptional profiling—RNA-seq and quantitative RT-PCR, in single, unambiguously identified neurons from 2 small crustacean neuronal networks: The stomatogastric and cardiac ganglia. We then combined our knowledge of cell type with unbiased clustering analyses and supervised machine learning to determine how accurately functionally defined neuron types can be classified by expression profile alone. The results demonstrate that expression profile is able to capture neuronal identity most accurately when combined with multimodal information that allows for post hoc grouping, so analysis can proceed from a supervised perspective. Solely unsupervised clustering can lead to misidentification and an inability to distinguish between 2 or more cell types. Therefore, this study supports the general utility of cell identification by transcriptional profiling, but adds a caution: It is difficult or impossible to know under what conditions transcriptional profiling alone is capable of assigning cell identity. Only by combining multiple modalities of information such as physiology, morphology, or innervation target can neuronal identity be unambiguously determined.
