An ambitious project to map all the cells in the human body
nature.com
nature.com
Think of the wall of text below as context for this article, as explained by someone not inhibited by proper knowledge of what he's talking about.
We start with a tiny biology refresher. Every cell in your body is essentially a clone: barring mutations, they all have the same genetic information encoded in their DNA. We often think of DNA as nature's code. To build on this metaphor, think of your stem cells as freshly installed PCs with all possible software you could need, pre-loaded on a gigantic HDD: your genome. And just like a program on your disk, genes don't do anything until they're activated. To "run" the code in a gene, the cell makes active copies of it: RNA. In oversimplified terms, a copies of RNA represents loading a genetic program into RAM and running it. The rest of the cell is basically the wetware required to run that code, which is important too: a PC is kinda useless without IO (just look at the struggles early nerds had with simply finding a use for the Altair 8800 if you don't believe me[1]).
Anyway, going from stem cell to a specific cell-type is like setting up those identical PCs for the different things that we use PCs for by opening different programs. Unlike PCs our wetware is massively parallel: to get more performance in a specific task our cell just creates more RNA copies, "loads many copies of the same program". Because of this, we can measure the activity of a gene by measuring the number of RNA copies of it in a cell.
Now imagine we're trying to reverse engineer an alien computer, and unlike Independence Day[2], we're not dealing with Mac-compatible hardware here. In programming, when you try to reverse engineer what someone else's code does, a disassembly probably won't cut it. Similarly, knowing the genome will not be enough: to really make sense of all it all, to "debug" the DNA, we need to see that "running code" in context, in relation to the whole organism.
What molecular biologists have been doing in recent years is measure the gene expression (the number of RNA molecules) of individual genes in individual cells, across as many cells as possible, at various stages of cell development. Then they compare the gene expression levels with various algorithms to organise them. Combined with extra meta-data, like what tissue the cells came from, what cell-type we know the cell belonged to based on morphology (in plain English: its visible shape in a microscope), and at which point in development cell was harvested, we can then start painting a picture of cell development and cell types.
They take cell tissues, separate it into individual cells, then measure the expression of each gene in each cell. The techniques they have figured out techniques, like droplet-based approaches[3], to do this in bulk are mind-blowing. When given the budget, and in the hands of appropriately skilled molecular biologists, you can now "easily" measure the expression of all genes in hundreds of thousands of cells.
Doing this you can figure out the different cell types, which genes are involved them, and which stage of development cell types start (dis)appearing. But remember that we separated all of the cells, so we lost the context of the original tissue. To fix this, you can take new tissue samples, and apply techniques like smFISH or MERFISH[4] to to attach fluorescent markers to individual copies of RNA of a specific gene. Gene expression is measured just by counting the number fluorescent dots on microscope slide, each representing an individual RNA molecule. Yes, biologists actually do this (with software help of course), and it works. You end up with a map of gene expression in the tissue. Also the pictures look beautiful.
This is, in a nutshell, single-cell transcriptomics[5]. Or at least the part I am exposed to through the research group I work for. It's mind-boggling and awesome to see what has become possible in the span of just a few years, and the field hasn't plateaued yet.
Thanks to all of this data we can discover all kinds of wonderful things that were invisible before. A simple example: last year it was discovered that one type of neuron in the sympathetic nervous system turned out to have seven sub-types from the genetic point of view[6]. The hypothesis is that having many sub-types makes sure they "wire up" correctly; you wouldn't want to have goosebumps whenever your heart beats. Of course, the part of the article that went viral was that we have a type of neuron solely responsible for goose bumps and nipple erections...
Anyway, the speed and size at which data is being gathered is rapidly growing. The article even mentions this explicitly. New techniques for dealing with this growing mountain of data are developed rapidly too. For example, the research group I work for just co-published a paper with Peter Kharchenko's group, describing a technique to estimate in which direction a cell's RNA expression is moving[7]. So instead of just having scalar tSNE scatter plots, we get tSNE vector fields, showing us the direction in which cell development is moving. And the coolest part is that it can be applied to existing data sets, because it relies of various types of measurements that were already being done anyway.
I'm just standing at the sidelines of all this, being incredibly impressed and humbled that I get to try to contribute a little bit to all of this. I'm just a programmer/interaction designer. Sten Linnarsson hired me to develop a user-friendly, flexible and fast web-based data browser, the Loom Viewer, for his new Loom data format. But this post is long enough as is, to I'll reply to myself with a comment explaining that.
Anyway, this is an exciting, ambitious project, that probably will have an enormous impact on biology and medical research, on a much shorter time-scale than you think.
[1] https://en.wikipedia.org/wiki/Altair_8800, https://www.youtube.com/watch?v=1FDigtF0dRQ
[2] https://scifi.stackexchange.com/questions/15141/how-did-the-...
[3] http://mccarrolllab.com/dropseq/, https://directorsblog.nih.gov/2015/06/02/single-cell-analysi...
[4] http://thenode.biologists.com/fishing-fish-2/resources/, https://www.youtube.com/watch?v=-jIZ3bH-rAE
[5] https://en.wikipedia.org/wiki/Single-cell_transcriptomics
[6] http://ki.se/en/news/special-nerve-cells-cause-goose-bumps-a..., http://www.nature.com/neuro/journal/v19/n10/full/nn.4376.htm...
[7] https://www.biorxiv.org/content/early/2017/10/19/206052, http://velocyto.org/
Similarly, for gene expression, CSV files were pretty common, probably because it's the most "neutral" format for tabular data. Obviously, this doesn't scale if you are going to measure so much data that your table consists of tens of thousands of genes by hundreds of thousands of cells. So my boss, professor Sten Linnarsson decided that his group needed a more efficient file format. And ideally, it would become adopted by other labs, so that everyone could easily exchange information.
The result is the .loom file format[0]. It's open source, BSD licensed, and it comes with a SciPy support library out of the box. It is HDF5-based[1], which has the benefit of being an old, battle-tested system, and platform-independent.
Another problem to address is that a lot of gene data is out there, but when it comes to asking simple questions you need to do a lot of work. Researchers have to "anticipate" what kind of questions people might ask about their data and put in the effort of creating a website for it. For example, for one of the groups recent papers, they created a simple website that lets you explore the gene expression of a single gene[2]. It turned out to be very popular.
So he wanted a more generic viewer for the Loom format, that lets you quickly explore the metadata en gene data and ask simple questions (for complicated stuff you will always need to download the whole dataset). That is what I have been building.
The Loom Viewer[0] is an SPA lets you explore Loom files without having to download the whole data set. If, for example, you are interested in a few dozen genes, but the data set is a hundred thousands cells and tens of thousands of genes in size, it is both wasteful and time-consuming to have to download all of that. You can run it off-line with Loom files you created yourself, and you can use it to serve loom files to others.
The app is still very rough around the edges, but feel free to have a look.
It's a really fun project, my first dive into web-dev, which was a bit frustrating at times because I basically had to teach myself everything without any guidance or help. But I learned all kinds of cool things: the app makes use of the canvas and typed arrays for relatively fast rendering[3] and minimal bandwidth overhead (most comparable viewers download image data from the server), has off-line storage for fast gene retrieval, stores the state regarding the "view settings" in the URL so you can inherently share links, etc. The great thing about working for a professor is that he lets you indulge in building cool stuff for them.
[0] https://github.com/linnarsson-lab/loompy
[1] https://en.wikipedia.org/wiki/Hierarchical_Data_Format
[2] http://linnarssonlab.org/cortex/
[2] https://github.com/linnarsson-lab/loom-viewer
[3] In case you're wondering: "why not WebGL?" Well, because my target audience includes old professors who print out websites. I actually need to render 100 separate canvases; faking it with one WebGL overlay like regl does here: http://idyll-lang.org/idyll-regl-component/ would not survive the print command.
Start with counting how many cells of each type are present for various tissues. Do this using cadavers of various ages, etc. Pretty much all we have right now for such estimates are totally back of the napkin.
I'm confused: what do you think this project is trying to achieve if not exactly that?
I am saying to simply count the cells, so we have the most basic of information. Work on this only really began in 2013...[1] From (quickly) reading their whitepaper it isn't clear to me whether they will even get the count data.
I thought you meant figure out the number of different types of cells, but you're actually saying counting the numbers of cells of each type?
You're putting the cart before the horse, since we haven identified each cell type yet. That's what this project is doing!
What does counting cells in tissues even mean here? Which cells? If you can't even tell the cell types apart, what exactly would you be counting? Do you think we already know every kind of cell type and what does? Because we don't.
And even ignoring that: say that we figure out the liver has N hundred million cells. On average, because tissue size wildly varies per person. But hey, sure, from a Pure Science perspective we could figure that out. What does that information then tell us? Is it information in any way meaningful?
How does counting the number of cells give us a better understanding of what that tissue does? Would you suggest we try to make sense of how countries are organised and what kind of culture they contain by grouping them by population size? Apparently Ghana and Nepal are identical according to this logic, as are Cameroon and Taiwan, and Niger and Sri Lanka[0].
By comparison, determining cell types via gene expression is like measuring education levels and which percentage of the population is in which profession. I don't know about you, but when it comes to making sense of how the body works, my money is on that.
[0] https://en.wikipedia.org/wiki/List_of_countries_and_dependen...
Currently we already have a classification system for cell types that is used to distinguish between different types of cancer. They should use exactly that same system.
Of course the numbers will vary by individual. What we want to get is basic upper/lower bounds. It would probably be best to see how well # of cells correlates with weight, skin area, etc and split the data into groups based on whatever easily measured physiological parameter would work best.
Once you have number of cells at various ages, then any biological theory that requires the collective activity of many cells will need to be consistent with these values. This can produce a major constraint on much theorizing, especially in areas like cancer.
Say you have a theory that cancer is caused by multiple mutations (eg, n = 7) accumulating in a cell lineage. You have other data about mutation rates per basepair per division (eg, p = 1e-8). If this theory is correct you would need a certain number of divisions to explain the curve of cancer incidence by age for that tissue. That number of divisions can be estimated from the difference in number of cells at various ages (or at least we can get some bounds on it by ignoring cell death, etc) and compared to the value required by the cancer theory.
For example:
> Currently we already have a classification system for cell types that is used to distinguish between different types of cancer. They should use exactly that same system.
Why? You don't give any reason why this classification system is any better than any other. Why would a system of classification of cancers be useful for classifying all other cell types? Actually, asking this question is already giving this statement too much credit: which of the various classification systems are you even referring to[0]?
[0] https://www.news-medical.net/health/Cancer-Classification.as...
You are asking for something that makes no sense. EG, which is better: a winter coat or a hoody? It depends on the situation. You can use either of them, both of them together, or something else entirely.
For the purpose of my example, use whatever system SEER uses since that is the biggest database of cancer data.
>"You sound like a phycisist who thinks he can explain the world in terms of spherical cows, asserting a shit-ton of stuff without clear back-up as to why what you state is true, acting as if all kinds of nuanced issues are simple and/or solved problems, and assuming authority over a field you do not seem to be part of."
I am saying literally count cells, how many are there that look like x, y, and z? Classify x, y, z however it is already being done in the clinic, if there is more than one method... then use more than one method. Each method probably has its advantages and drawbacks.
What assumptions are you talking about? I am making pretty much none. Sit there, count the cells to the best of your ability, that's it.
In fact the currently proposed project is going to be making all sorts of assumptions behind the various assays. Each of these makes the data more questionable. That is why I want basic stuff like # of cells that is least likely to be messed up.
So if there is a cancer called "Papillary squamous cell carcinoma, non-invasive", I would want a count of "papillary squamous cells".
In this case, it’s single cell rna seq. I’d argue we never got very far with bulk measurement RNA analysis because it’s not a functional technique, rather than us not having single cell resolution.
Look at the nobel prizes. So many of them have simple genetics or biochemistry at their core. It’s because those experiments were functional.
And before you tell me we haven’t yet had enough time for gene expression studies to deliver a nobel prize, I’d say well it’s been almost 25 years. Yamanka 4 factors is less than 10. CRISPR, also less than 10 should come soon too.