The paper: https://www.nature.com/articles/s41586-022-05563-7

Random thoughts and observations ...

Interesting that they did this in live cells. That means using GFP tagging to label the organelles, and spinning disk microscopes to not kill them while you take pictures. It would have been far easier to fix the cells, stain them with antibodies, and use scanning confocals. They don't say why they chose this route, but i assume they thought they would get more physiologically relevant shapes from live than dead cells; not always a safe assumption given the stress that imaging imposes on cells. Especially since they used DNA and membrane stains on them while alive!

Usually, the reason you do live imaging is because you want to see the cells moving or doing stuff. Adding a time dimension to this dataset would be amazing. But they didn't do that. Maybe that comes next?

Bit of sexy machine learning (initially reported in another paper but used here):

> The tightly packed, epithelial-like nature of hiPS cells, as well as the need for highly accurate 3D cell boundaries to minimize the misassignment of cellular structures to neighbouring cells required deep-learning-based segmentation approaches to create a robust, scalable and highly accurate 3D cell and nuclear segmentation algorithm

But the core of the analysis was done with classy traditional techniques:

> We aligned all cells along their longest axis in the xy plane, preserving their biologically relevant, epithelial-like apical–basal axis. We then used a spherical harmonic expansion (SHE) 17,18 to accurately parameterize each 3D cell and nuclear shape with a set of orthogonal periodic basis set functions, defined on the surface of a sphere (Fig. 2a and Extended Data Fig. 3). The joint vectors for all cells (578 SHE coefficients) were then subjected to PCA. We found that the first eight principal components represented about 70% of the total variance in cell and nuclear shape (Fig. 2b).

However, this work follows the common pattern of heavily mathematical cell biology, in that it gives a rigorous, numerical, data-driven demonstration of the bleeding obvious:

> We performed a hierarchical clustering analysis of these correlation values to create a purely data-driven ‘average pairwise spatial interaction map’ of cellular structures. Notably, we found that the cellular structures clustered naturally into an ordered radial compartmentalization of the cell, from the centre of the nucleus outward (Fig. 3d), and also separated between the apical and basal domains of the cell. The six top-level clusters included structures localized to the nucleus, nuclear periphery, cytoplasm, apical domain (in a dispersed way), cell periphery and basal domain, respectively.

And so on in the same utterly unsurprising vein for several figures. But i will take their word for it that this is new:

> Unexpectedly, the variance in nuclear speckle (SON) volumes was most uniquely attributable to the nuclear surface area and not the nuclear volume, although speckles localize throughout the nucleoplasm. This is notable in light of the possible connection between transcript splicing (which occurs at nuclear speckles) and increased rates of nuclear export 25.

I suppose the point of this work is not to find interesting facts about the locations of some well-known proteins inside cells, but to develop and validate a tool for studying the locations of proteins inside cells. If someone is studying some new and exciting protein, they can now drop it into this pipeline and see how it's located inside the cell, in a rigorous quantitative way. If they're friends with someone at the Allen Institute, that is. Still, i'm not sure how much more actual knowledge you would gain by doing that rather than just growing a single coverslip, staining it, and spending half an hour panning around on a bog standard microscope.

Especially given how long this took:

> the three years of data acquisition of the WTC-11 hiPSC Single-Cell Image Dataset v1

!

But this is very cool, plaudits to all involved:

> Custom codes were central to the conclusions of the paper. All necessary code to reproduce the results in this paper has been deposited in GitHub. This includes code for downloading our datasets, single-cell feature extraction, cellular parameterization and organelle size scaling. Jupyter notebooks to reproduce the figures shown in the paper are also provided.

Seriously, control-F for that section, there's links to every little bit on GitHub.

Also, control-F for the supplementary videos if you don't fancy reading so many words.

Methodological nerd shit:

> The spinning-disk confocal microscopes were equipped with[...] two Orca Flash 4.0 cameras (Hamamatsu).

Those are CMOS cameras! Using something other than CCD would have been unthinkable back in my day.

> After the selection of FOV position from the well overview acquisition, the DNA of cells was first stained for 20 min with NucBlue Live (Thermo Fisher Scientific). Then the cell membrane was stained with CellMask Deep Red (CMDR, Thermo Fisher Scientific) in the continued presence of NucBlue Live for an additional 10 min, and cells were washed once before imaging for a maximum of 2.5 h.

NucBlue Live is Hoechst 33342, so nothing special. Couldn't find out exactly what CellMask Deep Red is, except that "CellMask™ plasma membrane stains are amphipathic molecules providing a lipophilic moiety for excellent membrane loading and a negatively charged hydrophilic dye for “anchoring” of the probe in the plasma membrane". I would be mildly concerned that either of these would have some toxic effects on the cells. Probably not a huge deal. Would be nice to see controls for that, though.