I want to make a small disclaimer that I have no formal training in most of this. I am pretty confident about my understanding of electrophysiology and some robotics but much less so about anything biochem/genetics related. If there are any mistakes I am happy to correct.

Improving Automated Patch Clamping

Patch clamping is the gold standard for electrophysiology and understanding individual cellular properties is just as important as synaptic weights for whole brain emulation. I was personally shocked to find out how sparse the current library of electrophysiology features (hereafter referred to as e-features as keeping with one of the cited papers) was. The Allen Institute has made great strides with their brain atlas. However, there is still quite limited data. Only 107 cells with a full biophysically active model (that is, they tuned a neuron model to recreate observed e-features of these cells)^[1]. I believe there are only ~500 with e-features, morphology, and whole transcriptome but no model fit^[2] . I may not be exactly correct about that and there might be some split for human vs mouse neurons but 100s is the right order of magnitude. I heard a rumor in this PhD dissertation^[3] that there were datasets with >10,000 neurons with at least e-features but when I went into the citations I could not find those. If anyone finds a larger dataset with morphology and e-features please let me know. Doing additional patch clamp experiments would fill gaps in our knowledge of different cell types’ e-features and serve as benchmarks to compare how well we can emulate neurons. It is still unclear how accurate any model has to be to capture all computationally relevant activity in the brain. Even perfect e-features + a model of both electrical and chemical synapses may not be enough.

In any case, there is a seeming lack of neuron models that capture e-features for diverse cell types and specific cells. Even if they can be captured with current models (Hodgkin-Huxley, Izhikevich), this ought to be validated on more than ~500 cells. To do this, we need robust, fast, automated patch clamping. There is work into exactly such a device. Autopatcher is a project in collaboration between researchers at MIT and Georgia Tech that can currently automatically run patch clamp experiments on up to 4 cells simultaneously in vivo. However, it is still far from perfect, successfully attaching to at least 2 cells in less than 50% of cases^[4]^[5].

Measuring mRNA Transcriptome of Cells Before and After Cryopreservation

I believe that measuring the transcriptome of individual cells could help inform recreation of e-features, this will be discussed in the next section. Taking this as given for the time being, can we measure this after cryopreservation? Backing up slightly, the transcriptome is the level of mRNA for certain genes expressed. As an example, if you look at genes coding for a specific ion channel you can infer how dense those ion channels are on that particular cell compared to other neurons which should be helpful for e-features. The Allen Institute has data of transcription levels obtained simultaneously with their patch clamp experiments^[2]. They really knocked it out of the park, they measured transcription, e-features, and filled the cells with dye to recreate the morphology. What more could you ask for if you wanted to make an emulation of a single cell? But what if you want to measure transcription of every cell after cryopreservation? Can mRNA even survive that process? I will readily admit that I did only cursory research into this part as I am not especially well versed in genetics in the first place. However, with relatively little work I was able to find a group investigating just that. Quite frankly, I don’t have the biochem/genetics background to really get into this as much as I should. But looking at their figures I think the way they said it in their conclusion is a fair summary of what I care about: “Using the here-established cryopreservation method, single-cell transcriptome profiles from cells and tissues did not differ from freshly processed material.”^[6]. Now I am not sure how well this would transfer over to preserving a whole brain as their preservation method dissociated the cells and suspended them in their solution before freezing, but I see no reason to believe that it is fundamentally impossible to cryopreserve a whole brain in such a way that the mRNA is still readable. Really I only wanted to include this to confirm to myself that cold in and of itself did not somehow degrade the mRNA.

Predicting Electrophysiology from Morphology and the Transcriptome

So, assuming we have all this we still can’t perform patch clamp experiments on every neuron in the human brain. Maybe someone will object to that but these patch clamp experiments take ~10 minutes each and you have ~80 billion neurons so that just won’t work. I did read a paper about recording electrical activity from the whole brain but I won’t actually be talking about that in this post^[7]. Morphology must be extracted from the cells if you want to do whole brain emulation. You need the connectome already and synaptic weight seems correlated with size/number of dendritic spines so getting the whole morphology of the cell is no extra cost. Ideally you would also have the transcriptome along with this depending on how well preserved the brain is or if the procedure is begun immediately post mortem. Of course, getting the transcriptome does actually incur significant additional costs in terms of the total raw data and how you have to process it. Serial electron microscopy is the gold standard for morphology, but personally I am hopeful that tissue clearing and expansion allows for optical techniques to be successful. Whatever the case, the process of forming serial sections is distinct from one that measures the transcriptome. The brain clearing technique could be some assistance in this arena. To get the mRNA you have to penetrate the cell which would be a good opportunity to also inject a unique dye into each cell which would make them both more distinct when you go to trace the connectome and allow you to keep track of which cell you extracted the nucleus from. This would be a lot of very time consuming work but would be doable on the smallest of scales with current hardware although I may be overconfident.

Fermi estimate for morphology + transcriptome of d. megalaster:

~200,000 neurons
~10 minutes to stick microneedle into cell, fill with dye, extract nucleus
~4 cells can be done in parallel
200,000*10/4 = 500,000 minutes
500,000 minutes is ~1 year

Ok so perhaps that is not viable without some OOM speedup. Let’s say you don’t need to inject each cell individually, you just want to do some nuclear stain that doesn’t damage the mRNA^[8]. You can clear, expand, and dye everything homogeneously. Then imaging on a 2P microscope you can take relatively thick sections and shuttle them off to a lower resolution microscope/micromanipulator that is dedicated to just finding the nucleus and extracting it for transcriptome analysis. You could have as many of these accessory microscopes/micromanipulators as you want until the bottleneck is the speed of imaging the brain in sufficient resolution. I’m not going to do the work to find out how long this would be for d. megalaster but I found a paper that did serial sectioning on a whole mouse brain in a week which gives me hope for a highly parallel method getting both morphology and transcriptome^[9].

Anyway, once you have all this data, is it even sufficient to predict e-features? As far as I can tell that is still somewhat of an open question. I would love nothing more than to have someone confirm or deny this for me. But with my searching I was only able to find one paper from 2020 regarding the prediction of e-features using morphology^[10]. It had some success but did not incorporate the transcriptome data, had a very small dataset of only 104 neurons, and has had no follow up as far as I can tell. I am trying to get in contact with the author but the email listed on the paper looks deactivated. Additionally, the paper from the Allen Institute about measuring e-features, fitting a model to them, and recording morphology and the transcriptome claims that predicting e-features from morphology/transcriptomics is impossible a priori but goes to great lengths showing correlations between the two. I would like to think that I missed something very obvious but it seems to me that nobody has tried to train some machine learning model to integrate morphology and transcriptome data to predict e-features and this really strange to me because it seems like it would be so useful even for non WBE related neuroscience.

Logical Next Steps

I feel like there is a lot of fertile ground here to make marginal progress. The most obvious next steps to me are:

Make automated patch clamp experiments faster and more reliable
Create a large data set of neurons with their morphologies, e-features, and transcriptome
Train a machine learning model to predict e-features from morphology, either derived parameters (soma volume, axon length, etc.) or complete 3D reconstruction of the cell given as planar images
Validate/create new cryopreservation techniques to ensure mRNA is preserved well enough to be sequenced especially with potential tissue clearing and expansion as well as various stains
Combine serial sectioning/imaging acquisition with something that can simultaneously extract mRNA for transcriptomics and label the cell uniquely or otherwise ensure both transcriptome and morphology correspond to the same cell

I am going to start working on 3 because it is the only one I can really try alone without any funding.

I would welcome any related information, especially some that shows people have been working on this problem or convincing arguments on why I shouldn't waste my time chasing this.

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Nandi A, Chartrand T, Van Geit W, Buchin A, Yao Z, Lee SY, Wei Y, Kalmbach B, Lee B, Lein E, Berg J, Sümbül U, Koch C, Tasic B, Anastassiou CA. Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types. Cell Rep. 2022 Aug 9;40(6):111176. doi: 10.1016/j.celrep.2022.111176. Erratum in: Cell Rep. 2022 Nov 8;41(6):111659. doi: 10.1016/j.celrep.2022.111659. PMID: 35947954; PMCID: PMC9793758.
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https://portal.brain-map.org/cell-types/classes/multimodal-characterization/multimodal-characterization-mouse-visual-cortex
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https://repository.gatech.edu/entities/publication/640bef49-75ce-4675-b4e8-d253a55f2b8b/full
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Landry CR, Yip MC, Zhou Y, Niu W, Wang Y, Yang B, Wen Z, Forest CR. Electrophysiological and morphological characterization of single neurons in intact human brain organoids. J Neurosci Methods. 2023 Jul 1;394:109898. doi: 10.1016/j.jneumeth.2023.109898. Epub 2023 May 24. PMID: 37236404; PMCID: PMC10483933.
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https://autopatcher.org/
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Guillaumet-Adkins, A., Rodríguez-Esteban, G., Mereu, E. et al. Single-cell transcriptome conservation in cryopreserved cells and tissues. Genome Biol 18, 45 (2017). https://doi.org/10.1186/s13059-017-1171-9
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Kleinfeld D, Luan L, Mitra PP, Robinson JT, Sarpeshkar R, Shepard K, Xie C, Harris TD. Can One Concurrently Record Electrical Spikes from Every Neuron in a Mammalian Brain? Neuron. 2019 Sep 25;103(6):1005-1015. doi: 10.1016/j.neuron.2019.08.011. Epub 2019 Sep 5. PMID: 31495645; PMCID: PMC6763354.
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Krishnaswami, S., Grindberg, R., Novotny, M. et al. Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons. Nat Protoc 11, 499–524 (2016). https://doi.org/10.1038/nprot.2016.015
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Economo MN, Clack NG, Lavis LD, Gerfen CR, Svoboda K, Myers EW, Chandrashekar J. A platform for brain-wide imaging and reconstruction of individual neurons. Elife. 2016 Jan 20;5:e10566. doi: 10.7554/eLife.10566. PMID: 26796534; PMCID: PMC4739768.
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Michiels, Mario. (2020). Electrophysiology prediction of single neurons based on their morphology. 10.1101/2020.02.04.933697.