Tissue Systems, Cell Signaling & Algorithms - The Raredon Lab at Yale School of Medicine

Transcript

• 00:06When we look at tissues, if you look at your skin,
• 00:08it's a very stable system of cells, right?
• 00:11It doesn't just spontaneously turn into liver tissue
• 00:13or into lung tissue or into pancreatic tissue,
• 00:16and that's kind of remarkable because the genes in the cells,
• 00:19within your skin are all the genes that are there to make liver are there.
• 00:24All the genes that are there to make lung are there.
• 00:26They're just silenced or they're turned off
• 00:28or some of them are turned on, but in specific combinations,
• 00:30and when you get a certain set of cells expressing certain genes in certain ways
• 00:34and talking to each other in certain ways,
• 00:36you get this thing that we call skin tissue.
• 00:38So that question of why,
• 00:40why are tissues stable in this way?
• 00:42That's like one of the very central questions right now within tissue systems bio.
• 00:52One of the things that my lab has sort of started to specialize in
• 00:55is the cell to cell communication within these multicellular systems.
• 00:59So like lung tissue is one that we study very deeply
• 01:02and when you look at the lung, it's a community of like 50
• 01:05to 70 unique cell types or cell states that are, that are quite distinct
• 01:08and you might have a couple dozen of those cell types really close together,
• 01:11and they're all talking to each other in these very complex ways
• 01:14and the ways that they talk to each other,
• 01:17reinforces that tissue’s stability.
• 01:19It sort of keeps lung tissue behaving like lung tissue
• 01:22and stops it from deviating out of homeostasis
• 01:24and so when we study cell to cell signaling it gives us a window
• 01:28into how these cellular communities in our body are talking to one another
• 01:32and how they're reinforcing that stability,
• 01:34and we can also see during pathology during disease,
• 01:37how this cellular communication deviates from the norm
• 01:40and the holy grail would be to be able to get involved in this conversation
• 01:44so that we can take disease tissues that we see in human beings in clinical pathology
• 01:48and actually encourage that, like steer them back to homeostasis,
• 01:51sort of tell them, oh, stop being diseased, come back to the norm,
• 01:54and this is this is what I think of now when I think about regenerative medicine.
• 02:03We've really started to develop...
• 02:04the technologists in this space are doing wizardry.
• 02:07They're doing amazing things, and they're starting to develop
• 02:09what are called spatial transcriptomics or spatial multi-omics.
• 02:12You get a you take a tissue,
• 02:14you can suction it and take a look at what's going on under a microscope
• 02:16and you can also in parallel
• 02:18you can measure using spatial transcriptomics or spatial proteomics
• 02:21the character of each individual cell that's within that slide,
• 02:25and also if your panel is enriched for ligand receptor mechanisms,
• 02:28you can get very, very detailed profiling
• 02:30of how the different cells are interacting and communicating with one another.
• 02:33So that's what my lab specializes in.
• 02:35We started to leverage these spatial transcriptomics techniques
• 02:38and take that data and try to generate really good quality data
• 02:41and from that data we developed primary source computational algorithms
• 02:45to study cell to cell communication.
• 02:47With using these data, it gives us this beautiful window into
• 02:50how cells are talking to each other, how they're influencing with one another,
• 02:54and also how, it sort of allows you this bird's eye view where it's like,
• 02:58oh, it's not just this one cell has done something deviant.
• 03:02It's that this set of cells as a whole
• 03:05has sort of lapsed into this other way of behaving and communicating,
• 03:10and that opens the window to say, oh, well, what could we do therapeutically?
• 03:13Could we block that shift that we don't want to happen?
• 03:22I'm really hoping to develop regenerative therapeutics.
• 03:24So I think that since we now have this data
• 03:27and we have these computational algorithms
• 03:29that can really teach us what's going on within tissues,
• 03:31I'm very much hoping to be able to develop therapies that can sort of,
• 03:35help disease tissues to regenerate themselves,
• 03:38that we could give safely and that we know very clearly, or we have a strong,
• 03:42you know, ability to predict how these,
• 03:44therapeutics are going to influence these communities of cells
• 03:47so that we can, you know, steer disease tissues back to homeostasis.