# Whales vs. elephants

“THE most obvious differences between different animals are differences of size, but for some reason the zoologists have paid singularly little attention to them. In a large textbook of zoology before me I find no indication that the eagle is larger than the sparrow, or the hippopotamus bigger than the hare, though some grudging admissions are made in the case of the mouse and the whale. But yet it is easy to show that a hare could not be as large as a hippopotamus, or a while as small as a herring. For every type of animal there is a most convenient size, and a large change in size inevitably carries with it a change of form.” (J.B.S. Haldane)

It’s not often that you get to see neurons from both whales and elephants. I wonder which animal has the larger neurons? And what papers describe them? We can use neuromorphr alongside its dependency, nat to plot neurons hosted and curated by neuromorpho.org

## Find neurons

First, we need to find neuron names or neuron IDs for morphologies hosted on neuromorpho.org. These are required, because we need at least one of these two bits of information in order to read neurons from the repository.

To find these items, let us search the repository for neurons from the two groups of animal in which we are interested:

# Load package
library(neuromorphr)
library(nat)

# Hello neuromorpho.org, can you hear us?
neuromorpho_is_api_healthy() # Check to see if we can talk to the neuromorpho API, otherwise we cannot continue

# Do they have these two big beasts?
all.species = neuromorpho_field_entries(field="species")
whales = all.species[grepl("whale",all.species)]
print(whales)
elephants = all.species[grepl("elephant",all.species)]
print(elephants)

# Can we get some whale neurons
whale.df = neuromorpho_search(search_terms="species:humpback whale")

# And the elephant ones!
elephant.df = neuromorpho_search(search_terms="species:elephant")

# But we probably cannot easily compare different cell types. Which types do we have?
tw = table(whale.df$cell_type) message("whale cell types:") print(tw) te = table(elephant.df$cell_type)
message("elephant cell types:")
print(te)

# Let's take only the pyramidal cells
whale.neuron_names = subset(whale.df, grepl("pyramidal",cell_type))$neuron_name elephant.neuron_names = subset(elephant.df, grepl("pyramidal",cell_type))$neuron_name

Armed with this information, we can now read the neurons as nat package neuronlist objects. You can find a detailed explanation of this data format, and its basic manipulation with nat tools, here.

Sometimes reading neurons can take a while, because neuromorpho.org is fairly slow. We’ll want to have progress = TRUE on in the following functions, so that we get a sense for how long it’s taking.

These functions send asynchronous requests to the neuromorpho.org API in parallel in order to speed things up. In one run, batch.size requests are sent, and once this pool of requests has been processed, the progress bar will update. You can increase this value to speed up the read, but do not put it too high or neuromorpho.org will fail for capacity reasons.

# Read the whale neurons
whale.pyramidal.cells = neuromorpho_read_neurons(neuron_name = whale.neuron_names, batch.size = 2, nat = TRUE, progress = TRUE)

# And read the elephant neurons
elephant.pyramidal.cells = neuromorpho_read_neurons(neuron_name = elephant.neuron_names, batch.size = 2, nat = TRUE, progress = TRUE)

## Plot neurons

Let’s plot these neocortical pyramidal cells. It is important to note that since neurons are reconstructed from many different neural systems and species, there is no ‘standard’ orientation. Instead, neuromorpho.org’s standardisation process orients the morphologies by placing the soma in the origin of coordinates and aligning the first three principal components of all XYZ coordinates with heights, width, and depth.

We can plot in 2D

nat::nopen3d()
plot(whale.pyramidal.cells, lwd = 2, soma = TRUE, col = colorRampPalette(c("deepskyblue1","deepskyblue4"))(length(whale.pyramidal.cells)))
plot(elephant.pyramidal.cells, lwd = 2, soma = TRUE, col = colorRampPalette(c("firebrick1","firebrick4"))(length(elephant.pyramidal.cells)))

And in 3D using rgl:

# Hold right click to pan
nat::nopen3d()
plot3d(whale.pyramidal.cells, lwd = 2, soma = TRUE, col = colorRampPalette(c("deepskyblue1","deepskyblue4"))(length(whale.pyramidal.cells)))
plot3d(elephant.pyramidal.cells, lwd = 2, soma = TRUE, col = colorRampPalette(c("firebrick1","firebrick4"))(length(elephant.pyramidal.cells)))

## Of Mice and Whales

So that’s interesting. But what about a small mammal - like a mouse? Can we quickly do the same for this wee beastie?

# Find
mouse.df = neuromorpho_search(search_terms= c("species:mouse", "brain_region:neocortex")) # takes a while, there's a lot of mouse data to look through!
mouse.neuron_name = mouse.df$neuron_name[grepl("pyramidal",mouse.df$cell_type)]

mouse.pyramidal.cells = neuromorpho_read_neurons(neuron_name = mouse.neuron_name, batch.size = 2, nat = TRUE, progress = TRUE, find = FALSE)
## warnings? - sometimes a file is missing from the repository it seems, or can otherwise not be accessed, so we lost a few cells ...
## In fact, the file names on neuromorpho are not entirely consistent with one another. You can try using find = TRUE, which will
## mean that this function runs slower, but scrapes the webpages for the correct dowbnload URL, which may slightly improve neuron retrieval

# Plot
nat::nopen3d()
plot3d(whale.pyramidal.cells, lwd = 2, soma = TRUE, col = colorRampPalette(c("deepskyblue1","deepskyblue4"))(length(whale.pyramidal.cells)))
plot3d(elephant.pyramidal.cells, lwd = 2, soma = TRUE, col = colorRampPalette(c("firebrick1","firebrick4"))(length(elephant.pyramidal.cells)))
plot3d(mouse.pyramidal.cells, lwd = 2, soma = TRUE, col = colorRampPalette(c("darkolivegreen1","darkolivegreen4"))(length(mouse.pyramidal.cells)))

We can actually pull some of their metrics and plot them, to see the quantitative differences

# Put names together
metrics.neuron_name = c(whale.pyramidal.cells[,"neuron_name"], elephant.pyramidal.cells[,"neuron_name"], mouse.pyramidal.cells[,"neuron_name"])

# Pull measurements, in a data frame
measurements = neuromorpho_morphometry(metrics.neuron_name, data_frame = TRUE)

# Assign species column
measurements\$species = c(rep("whale", length(whale.pyramidal.cells)), rep("elephant", length(elephant.pyramidal.cells)), rep("mouse", length(mouse.pyramidal.cells)))

# Boxplot volume
boxplot(as.numeric(volume)~species, data=measurements, notch=FALSE,
col=(c("deepskyblue1","firebrick1","darkolivegreen1")),
main="pyramidal neuron volumes", xlab="species")

# Boxplot length
boxplot(as.numeric(length)~species, data=measurements, notch=FALSE,
col=(c("deepskyblue1","firebrick1","darkolivegreen1")),
main="pyramidal neuron length", xlab="species")

So, it seems that whale neurons are a lot more voluminous than an elephant’s, but less so in terms of cable length. Both are quite a lot larger than those of a mouse.

Of course, to be sure, we would need to control by reconstruction method and work harder to establish the exact cell type correspondences.