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Subthreshold Voltage Analysis Demonstrates Neuronal Cell-Surface Sialic Acids Modulate Excitability and Network Integration

Rishikesh U. Kulkarni, Catherine L. Wang, Carolyn R. Bertozzi

Posted on: 4 May 2020

Preprint posted on 8 April 2020

Neurons have a sweet tooth: How sugar molecules modify neuronal activity and network integration.

Selected by Berrak Ugur

Categories: neuroscience, physiology

Background

Neuronal membranes are coated with various sugar molecules called glycans (Figure 1) (Kleene and Schachner, 2004). These glycans are important for neuronal function and mutations that disrupt glycans cause a wide range of neurological disorders (Freeze et al., 2015). Among the vast variety of glycans, gangliosides that are sialylated glycosphingolipids have been associated with neuron excitability, axon stability and regeneration (Schnaar et al., 2014). However, how glycans, especially gangliosides, globally act to regulate neuronal electrophysiology is not well studied. To investigate how glycans contribute to neuronal network activity and integration, Kulkarni et al. performed voltage imaging in neurons treated with different sugar removing enzymes.

Figure 1: Scheme of neuronal cell membrane showing different types of glycans. Sialic acid (Sia), magnified, is negatively charged and is important for neuronal membrane charge. https://doi.org/10.1101/2020.04.07.030866

Key Findings:

The authors hypothesized that unique sialic acid modifications on neuronal membranes may selectively modify neuronal activity. To address this hypothesis, they used different sialidase enzymes that can either selectively cleave a specific sialic acid modification (2,3-linked sialic acid) or cleave a number of different sialic acid modifications. To record neuronal activity in bulk, the authors used a fluorescent voltage indicator called BeRST1 (Huang et al., 2015) that reports neuronal membrane potential with fast kinetics and relatively low noise. As previously suggested (Isaev et al., 2007), globally removing sialic acid in mouse hippocampal neurons resulted in a decrease in firing rate and population of live cells that are firing. Interestingly, selectively removing 2,3-linked sialic acid resulted in an increase in firing rate and populations of live cells that fire. This observation, the authors claim, may indicate a change in neuronal excitability as sialic acid may modify neuronal charge.

A method to assess if neuronal excitability is altered is to record subthreshold activity that may not be strong enough to trigger neuronal firing. The authors used the aforementioned voltage imaging technique coupled with a custom software library to delineate how sialidase treatment affects subthreshold activity. By performing various control experiments, the authors confirmed that (1) input from neighboring neurons generate a variance in subthreshold traces and (2) increased synapse formation leads to an increase in firing rate in mature neurons. Next, they examined selective removal of 2,3-linked sialic acid and concluded that it led to a decrease in action potential threshold but didn’t affect overall neuronal connectivity. However, promiscuously removing sialic acid resulted in a decrease in synchronized activity. The authors reasoned that asynchronous activity may be due to disrupted network integrity that leaves smaller connected neural networks (“islands”). To seek out if this is the case, the authors analyzed covariance between measured neurons based on the assumption that neurons receiving similar inputs will reflect similar activities (see Figure 2 for the model for “connectedness”). Consistent with a previous observation, selective removal of 2,3-linked sialic acid did not alter shared variance (covariance) between neurons indicating that network integration is not affected. In contrast, removing multiple sialic acid modifications led to reduction in shared variance between neurons indicating that global removal of sialic acid alters network connectivity. Overall, the authors show that different sialic acid modifications affect different neuronal properties.

Figure 2: Model of network connectivity. Connected neurons behave similarly. Reduced connectivity may result in (a) islands of connected neurons, or b) some neurons losing their connection to the greater network. https://doi.org/10.1101/2020.04.07.030866

 Take home messages:

  • Removing neuronal cell surface sialic acid modification reduced firing of cultured hippocampal neurons.
  • Selective removal of 2,3-linked sialosides led to increased action potential firing rate but did not affect network integration.
  • Removing multiple sialic acid modifications led to a decrease in network connectivity
  • It is likely that neurons utilize different sialylation subpopulations to fine tune neuronal activity.

 What I liked about this story: First of all, I should say that I do not know much about glycobiology or the computational analyses performed in this preprint. However, I thought that a global measurement approach to understand how sialic acid modification alters neuronal function is a neat way to understand the effects of these sugar molecules. Considering how glycans are associated with various neurological disorders, this approach may also shed light on how sugars modify neural network connectivity in different disease models.

Remaining Questions

1) Have you tried measurements in neurons older than 12 DIV? I understand that they get unhealthy if you record from the start, is it possible to intervene and record at a later time point?

2) Is it possible to perform a similar analysis in hippocampal neurons obtained from a mouse model of Congenital disorders of glycosylation?

References:

Freeze, H.H., Eklund, E.A., Ng, B.G., Patterson, M.C., 2015. Neurological Aspects of Human Glycosylation Disorders. Annu. Rev. Neurosci. 38, 105–125. https://doi.org/10.1146/annurev-neuro-071714-034019

Huang, Y.-L., Walker, A.S., Miller, E.W., 2015. A Photostable Silicon Rhodamine Platform for Optical Voltage Sensing. J. Am. Chem. Soc. 137, 10767–10776. https://doi.org/10.1021/jacs.5b06644

Isaev, D., Isaeva, E., Shatskih, T., Zhao, Q., Smits, N.C., Shworak, N.W., Khazipov, R., Holmes, G.L., 2007. Role of extracellular sialic acid in regulation of neuronal and network excitability in the rat hippocampus. J. Neurosci. Off. J. Soc. Neurosci. 27, 11587–11594. https://doi.org/10.1523/JNEUROSCI.2033-07.2007

Kleene, R., Schachner, M., 2004. Glycans and neural cell interactions. Nat. Rev. Neurosci. 5, 195–208. https://doi.org/10.1038/nrn1349

Schnaar, R.L., Gerardy-Schahn, R., Hildebrandt, H., 2014. Sialic Acids in the Brain: Gangliosides and Polysialic Acid in Nervous System Development, Stability, Disease, and Regeneration. Physiol. Rev. 94, 461–518. https://doi.org/10.1152/physrev.00033.2013

Tags: glycobiology, glycotime, voltageimaging

doi: https://doi.org/10.1242/prelights.19980

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Author's response

The author team shared

Hi Berrak,

Thank you for your highlight of our work!

To address your questions:

  • Yes, we have experimented with recording anywhere from 2 DIV to 21 DIV. Given the view of 14+ DIV neurons as “mature” in terms of protein expression, we are interested in taking a more rigorous look at how culture network properties develop in the 14 DIV to 28 DIV time window. However, we also observed that, at our seeding density, cultures older than 12 DIV are very recurrent, locking them into periodic bursting activity. We hope that starting with sparser cultures will allow us to take a better look at the network-building properties of mature neurons.
  • Yes! This project actually came about due to our interest in congenital disorders of glycosylation (CDGs). Our lab has long been curious about the neurological manifestations common to most CDGs and we hypothesized that these manifestations implicate glycosylation as a key regulator of functional connectivity. However, after reading the literature on the topic, we decided that we wanted to look at both co-firing and “co-excitation” in our treatment conditions. This necessarily meant gathering both spiking and subthreshold variation in many neurons at the same time. Given that this type of data is difficult to gather on a large scale with traditional electrophysiology methods, there was not much literature about how to analyze the data we gathered. We decided to first design and validate an analysis pipeline, which resulted in the study we reported here. We look forward to applying these methods to understand the role of glycosylation in the brain.

Thanks again for your interest in our work! We hope that our analysis helps others find voltage imaging as useful as we did!

-Rishi, Carolyn, and Catherine

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