Developmental Biology - Brain Cell Classification|
Identifying Every Brain Cell
Neuroscientists want to classify each of the brain's many cells...
A longstanding goal in neuroscience is to classify brain cells by function. These catagories would help researchers understand the complex neural circuits of behaviors as well as the brain's response to disease. However, there's little consensus about what defines cell identity.
A new study collaboration born from the Neural Systems & Behavior (NS&B) course at the University of Missouri-Columbia's Marine Biological Laboratory, tests the notion that a cell's identity can be described solely by the genes it expresses. The study now advocates a more "multimodal" approach to defining cell identity.
The work is published in Proceedings of the National Academy of Sciences (PNAS). By using popular and powerful RNA sequencing techniques, researchers took a snapshot of all the genes currently turned on inside a cell. To them it's becoming increasingly clear cell identity strategies may not be be giving a complete picture of true cell identity, or even represent cell change 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.
Marder and her team applied these techniques to two well studied nerve clusters in the crab Cancer borealis - (1) stomatogastric and (2) cardiac ganglia - as compared to current RNA-based approaches that measure 'cell identity.'
They found cell identities generated by complete RNA profiles, or "transcriptomes," did not match existing cell identities compiled over years of observations. In fact, categorizing cells based on a cell's entire transcriptome ultimately yielded "scrambled" identities.
They then refined the selection by key genes in their analysis — and saw RNA profiles begin to resemble cell identities gleaned from other attributes such as nerve patterns [innervation], shape [morphology] and function [physiology]. 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. 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. Rather than relying entirely on analytics applied blindly to cell type, whenever possible it's important to consider multiple modalities of information as well."
David J. Schulz PhD, Division of Biological Sciences, University of Missouri-Columbia, Missouri; Professor, Neural Systems and Behavior, Marine Biological Laboratory, Woods Hole, Massachusetts, USA.
The trick, Hofmann and Schulz agree, is knowing which data are indicative of cell identity, and which are simply noise that interferes with accurate classification.
Researchers must also eventually agree on the definition of "cell identity." Drawing firm boundaries between cell types is useful in many ways, but may ultimately be problematic.
"Soon, we'll start to see the limitations of trying to impose very discrete categories on the spectrum of cell types within and across individuals."
David J. Schulz PhD.
Single-cell transcriptional profiling has become a widespread tool in cell identification, particularly in the nervous system, based on the notion that genomic information determines cell identity. However, many cell-type classification studies are not detailed by other cellular attributes (e.g., morphology, physiology). Here, we systematically test how accurately transcriptional profiling can assign cell identity to well-studied anatomically and functionally identified neurons in 2 small neuronal networks. While these neurons clearly possess distinct patterns of gene expression across cell types, their expression profiles are not sufficient to unambiguously confirm their identity. We suggest that true cell identity can only be determined by combining gene expression data with other cellular attributes such as innervation pattern, morphology, or physiology.
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.
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.
The authors thank members of the D.J.S., H.A.H., and E.M. laboratories for helpful discussions. We thank the Genomic Sequencing and Analysis Facility (The University of Texas [UT] at Austin) for library preparation and sequencing and the bioinformatics consulting team at the UT Austin Center for Computational Biology and Bioinformatics for helpful advice. This work was supported by National Institutes of Health grant R01MH046742-29 (to E.M. and D.J.S.) and the National Institute of General Medical Sciences T32GM008396 (support for A.J.N.) and National Institute of Mental Health grant 5R25MH059472-18 and the Grass Foundation (support for Neural Systems and Behavior Course at the Marine Biological Laboratory).
The Marine Biological Laboratory (MBL) is dedicated to scientific discovery - exploring fundamental biology, understanding marine biodiversity and the environment, and informing the human condition through research and education. Founded in Woods Hole, Massachusetts in 1888, the MBL is a private, nonprofit institution and an affiliate of the University of Chicago.
About the Salk Institute for Biological Studies: Every cure has a starting point. The Salk Institute embodies Jonas Salk's mission to dare to make dreams into reality. Its internationally renowned and award-winning scientists explore the very foundations of life, seeking new understandings in neuroscience, genetics, immunology, plant biology and more. The Institute is an independent nonprofit organization and architectural landmark: small by choice, intimate by nature and fearless in the face of any challenge. Be it cancer or Alzheimer's, aging or diabetes, Salk is where cures begin. Learn more at: salk.edu.
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Jan 7 2020 Fetal Timeline Maternal Timeline News
Two well studied nerve clusters in the crab Cancer borealis
reveal that cell identities generated by complete RNA profiles or "transcriptomes,"
do not match existing cell identities.
CREDIT Woods Hole Marine Biological Laboratory.