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| + | Our team was inspired by the use of robots which perform tasks that would otherwise be difficult to accomplish. For example, the Mars rovers allow us to make observations and conduct physical experiments in harsh, remote locations otherwise inaccessible to humans. In many ways, the human body represents unexplored territory on a different scale. For example, we may understand many aspects of human disease. However, certain markers of disease are extremely difficult to detect (e.g., primary tumors) and treatment of disease can be hampered by the impracticality of performing invasive surgeries. |
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| | + | The "holy grail" of nanomedicine would be to develop microscopic robots that could travel anywhere in the body and perform complex, user-defined tasks. Such devices would have several key advantages over traditional, small-molecule therapies: |
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| - | == '''Overall project''' ==
| + | * They could home to specific locations in the body (minimize off-target effects) |
| | + | * They could make decisions based on their external environment |
| | + | * They could perform more complicated functions |
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| - | Your abstract
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| - | == '''Part 1: NAVIGATION: Rewiring the cell to move toward new chemical signals'''==
| + | While the idea of microscopic, therapeutic robots may seem far-fetched, there are examples of such machines in nature. For example, neutrophils (a type of white blood cell) are capable of: |
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| - | === Motivation: ''Why is this useful?'' ===
| + | * Detecting and homing to a wide range of chemical signals, at times localizing to very specific sites of inflammation |
| - | We envision a cellular robot that could travel to practically any site in the human body. This would provide a flexible platform that could be used for a variety of therapeutic tasks. The first step toward achieving this goal is to broaden the range of possible chemotactic targets for our cells. Ideally, we could connect virtually any input to chemotaxis in a generalized way.
| + | * Triggering a variety of different pathways (extravasation, phagocytosis, apoptosis) in response to external signals |
| | + | * Navigating through different types of barriers (endothelial tissue, blood-brain barrier, etc) |
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| - | === Approach: ===
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| - | Neutrophils sense most of their chemotactic signals through [http://en.wikipedia.org/wiki/G_protein_coupled_receptor G protein-coupled receptors] (GPCRs). The spectrum of chemical signals to which these cells respond is therefore determined, at least in part, by the set of GPCRs they express. Can this spectrum be broadened arbitrarily by the introduction of new GPCRs? We tested this idea by transiently expressing [[23 exogenous GPCRs]] in HL-60 (neutrophil-like) cells.
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| | + | Taking cues from nature, we were interested in harnessing or hijacking the function of complicated, natural cellular robots such as neutrophils to perform therapeutically useful tasks. In other words, we wanted to use neutrophils (or similarly motile cells) as a chassis for engineering. Minimally, we wanted to control how and when these cells move. Over the course of the summer, were were able to: |
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| - | [[Image:new_targets.jpg|350px|thumb|center|'''Inserting new sensors:''' Exogenous GPCRs were expressed in HL-60 cells.]]
| + | * Control the '''NAVIGATION''' system of our cells (alter what the cells pursue, how strong a signal they require) |
| | + | * Control the '''SPEED''' of our cells (engineer accelerators and brakes) |
| | + | * Deliver a '''PAYLOAD''' with our cells |
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| - | These cells were then tested for their ability to migrate toward ligands for the new GPCRs in multiwell [http://en.wikipedia.org/wiki/Chemotaxis_assay#Two-chamber_techniques Boyden chamber] assays. We measured the fold change in % of cells migrating toward the new ligand (with vs without added GPCR) at the peak response. We refer to this ratio as the "Migration Index." For receptors that appeared to activate a migration response (Migration Index > 3), we also conducted time-lapse microscopy to determine whether the cell movement was directed toward the gradient of ligand.
| + | Our efforts toward these goals are described in the remainder of this site. Throughout our project, we worked with two model organisms: HL-60 (neutrophil-like) cells and ''Dictyostelium discoideum'', a soil-dwelling amoeba that feeds on bacteria. Both cell types are common models for the study of chemotaxis (directed migration toward chemical signals), and each has its distinct advantages with respect to studying specific questions. |
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| - | === Results: ===
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| - | 6 of the GPCRs we transiently expressed in our cells resulted in a Migration Index > 3.
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| | + | '''PROJECT SUMMARY''' |
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| - | [[Image:Fold_Changes.png|650px|thumb|center|'''Chasing new targets:''' HL-60 cells migrate to new GPCR ligands]] | + | Part 1 - [[Team:UCSF/Navigation|Engineering NAVIGATION]] |
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| | + | Part 2 - [[Team:UCSF/SPEED|Engineering SPEED]] |
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| | + | Part 3 - [[Team:UCSF/PAYLOAD|Carrying a PAYLOAD]] |
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| - | Here, we show an example of one of these receptors (M3/M2 chimera) mediating directional migration up a stable, linear gradient of ligand. Transfected cells are fluorescent, and the concentration of ligand increases in the direction corresponding to the top of the image:
| + | Part 4 - [[Team:UCSF/Future Applications|Future Application]] |
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| - | <html><object width="560" height="340"><param name="movie" value="http://www.youtube.com/v/rB0QQlfa7f4&hl=en&fs=1&"></param><param name="allowFullScreen" value="true"></param><param name="allowscriptaccess" value="always"></param><embed src="http://www.youtube.com/v/rB0QQlfa7f4&hl=en&fs=1&" type="application/x-shockwave-flash" allowscriptaccess="always" allowfullscreen="true" width="560" height="340"></embed></object></html>
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| - | ''Common characteristics of chemotaxis receptors:'' All 6 receptors we identified couple to the [http://en.wikipedia.org/wiki/Gi_alpha_subunit Gi] signaling pathway. The behavior of the M3/M2 chimera, however, suggests that it may be possible to convert receptors with different coupling specificities into chemotaxis receptors. To generate this chimera, the third intracellular loop (i3) from the M3 muscarinic acetylcholine receptor ([http://en.wikipedia.org/wiki/Gq_alpha_subunit Gq] coupled) was exchanged with that of M2 muscarinic receptor (Gi coupled). It has previously been shown that this chimera now couples to Gi.
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| - | [[Image:chimera.png|300px|thumb|center|i3 loop determines the coupling specificty of the M3 muscarinic acetylcholine receptor.]]
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| - | Why the i3 loop allows the M3/M2 chimeric receptor to signal to the cell's chemotaxis machinery remains a question for further study. However, the possibility exists that more Gq-coupled (and possibly [http://en.wikipedia.org/wiki/Gs_alpha_subunit Gs]-coupled) receptors could be converted in this way, thus dramatically increasing the number of potential chemotaxis targets.
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| - | === Summary and outlook: ===
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| - | We have shown that we can program our cells to migrate to new chemical signals by expressing exogenous GPCRs. One of these GPCRs, a chimeric protein, suggests that there may be a way to convert even more GPCRs into chemotaxis receptors. In the future, we are interested in understanding more about why certain receptors mediate chemotaxis while others do not. It would also be interesting to go back to the receptors that did not work, and confirm that they are functional and signaling to other known pathways.
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| - | == '''Part 2: NAVIGATION: Tuning receptor sensitivity'''==
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| - | === Motivation: ''Why is this useful?'' ===
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| - | In our experiments, we could control exactly how much ligand was presented to our cells. In "real-life," however, we would want our cellular robots to be able to respond to a variety of signal strengths: from very low to very high. To accomplish this, we would want to be able to control the ''sensitivity'' of our receptors, or how the receptor's output changes when the measured quantity of ligand changes.
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| - | === Approach: ===
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| - | We felt that one key determinant of sensitivity would be the number of receptors present at the plasma membrane of the cell. Therefore, we measured the migration response of a receptor (delta Opioid receptor) whose recycling behavior could be engineered by fusing different recycling interaction modules to the C terminus of the GPCR. We tested a number of such receptor-module fusions for migration response and compared them to receptor alone. The primary assay here was again the Boyden chamber (transwell) assay.
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| - | === Results: ===
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| - | We found that virtually any protein domain/module known to alter the recycling of delta Opioid receptor (DOR) affected cellular migration to a low concentration of ligand (1 nM DADLE). Below, we show two examples of such domains. The actin binding domain from alpha-Actinin-1 (ACTN1ABD) appears to potentiate cellular migration when compared to the wild-type receptor. On the other hand, cellular response is inhibited at this concentration when DOR is bound to a domain of EBP50 (ERMbd) that binds to ERM (ezrin/radixin/moesin) family of proteins.
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| - | [[Image:Sensitivity.png|300px|thumb|center|Recycling modules affect sensitivity of DOR]]
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| - | === Summary and outlook: ===
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| - | We have shown that we can tune the sensitivity of a receptor both up and down by fusing it to different recycling modules. Next, we would like to measure recycling directly, and determine whether changes in recycling are necessary for this difference in chemotactic response. All experiments in HL-60 cells would benefit the generation of stable cell lines, which would allow us to more precisely quantify these new behaviors.
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| - | == '''Part 3: SPEED: Engineering accelerators and brakes'''==
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| - | '''A cellular cruise control by modulating cell polarity with feedback loops'''
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| - | === Motivation: ''why is this useful?'' ===
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| - | Just like we have control over speed in a car – we can brake or accelerate – it would be useful to engineer such behavior into our cellular nanorobots.
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| - | Just think about it: We could '''speed cells up''' so that they reach their targets faster and '''stop them''' once they have arrived or do not behave properly.
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| - | === Background: ===
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| - | For these experiments we chose ''Dictyostelium discoideum'' cells to test our prototypical brakes and accelerators quickly. We expect that our brakes and accelerators can be used in a plug and play fashion because Dicty’s way of movement is very similar to a neutrophil’s:
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| - | When a receptor binds chemoattractant, it induces the conversion of PhosphatidylInositol(4,5)bisphosphate (PIP2) to PhosphatidylInositol(3,4,5)trisphosphate (PIP3) (two signaling lipids in the plasma membrane) at the front of our cells. In a '''positive feedback loop''' PIP3 triggers the formation of more PIP3 at the front while similarly PIP2 leads to more PIP2 production at the sides and rear of the cell. This system sets the axis of polarity of the cell. The PIP3 patch at the front aligns the actin network and accordingly functions as a ‘turbo boost’ pushing the cell forward.
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| - | [[Image:BACKGROUND1.jpg|350px|thumb|center|'''Polarized distribution of PIP3 and PIP2:''' A patch of PIP3 is localized at the front of a cell while PIP2 is at the back. Feedback loops are used to establish and maintain this polarized distribution. ]]
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| - | === Approach: ===
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| - | Inspired by nature we tried to build accelerators and brakes by introducing our own synthetic protein based feedback loops. We designed feedback elements by fusing localization and catalytic domains involved in PIP3 production and degradation to modulate localization and level of PIP3 and PIP2 in the cell.
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| - | Here is an example of a positive feedback loop: a PIP3 binding localization domain fused to a PIP3 producing catalytic domain could produce more PIP3 where there is already PIP3- at the front. This might strengthen polarity and accelerate a cell.
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| - | [[Image:positive_feedback_loop2.jpg|400px|thumb|center|'''Synthetic protein based feedback loops:''' One example: a positive feedback loop comprising a PIP3 binding domain fused to a PIP3 generating enzyme.]]
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| - | === Results: ===
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| - | Over the summer we assembled more than 100 fusions of localization and catalytic domains and screened whether they work. How? We measured the effect our constructs have on motility of ''Dictyostelium'' cells: stronger polarity should make cells faster while weaker polarity ought to slow them down!
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| - | [[Matrix of Fusion Constructs|Here]] is an overview of all feedback loops we screened and the effect they had on the speed of cells.
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| - | We used automated cell tracking on more than 196 hours worth of movies (note: one movie is 10 minutes!) and identified strains that moved faster or slower at a very stringent statistical cutoff (p<0.0001).
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| - | This way we were able to identify '''7 brakes and 1 accelerator!'''
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| - | Check out the movie of '''one of our strong brakes (PTEN-RasC dominant active (da))[http://www.youtube.com/watch?v=_9od33Nx06Y] compared to wildtype [http://www.youtube.com/watch?v=Vtdtf8-zSRs].'''
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| - | [[Image:HO230.jpg|400px|thumb|right|'''PTEN fused to RasC da (PIP2 binding - PIP3 generating); speed: 3.5 um/min''' Cells were plated in buffer and basic motile behavior was recorded for 10 minutes taking 1 picture every 15 seconds.]] [[Image:wt.jpg|400px|thumb|left|'''wildtype; speed: 5.9 um/min'''
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| - | Cells were plated in buffer and basic motile behavior was recorded for 10 minutes taking 1 picture every 15 seconds.]]
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| - | This is indeed an effect of fusing the particular localization to the catalytic domain as neither of them alone has such a strong effect ([[Further Results|see details]]).
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| - | We hypothesize that this construct acts as a '''negative feedback loop on PIP2''' - (generating PIP3 where PIP2 should be) thereby confusing the cell with multiple fronts:
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| - | [[Image:brake3.jpg|500px|center]]
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| - | === Summary and outlook: ===
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| - | We have screened more than 100 synthetic feedback elements for their ability to accelerate or slow down speed of cell motility. We have isolated a hand full of functional elements. Now we need to confirm the mechanism of action of these elements. In the future we would like to make them inducible by a signal from outside – like a stoplight!
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| - | =Methods=
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| - | '''Navigation:'''
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| - | *[[Media:amaxa.pdf|HL-60: Transfection Protocol]] - here is how we get our constructs into HL-60 cells
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| - | *[[Media:transwell.pdf|HL-60: Boyden Chamber (Transwell) Protocol]] - here is how we assay chemotaxis in HL-60 cells
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| - | *[[Media:ezt.pdf|HL-60: Time-lapse Microscopy Protocol]] - here is how we film HL-60 cells
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| - | '''Engineering Speed:'''
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| - | *[[Media:Aar1_shuffle.pdf|Cloning: Aar1 Shuffle]] - besides BBFRFC28, here is an actual step by step scheme we used for some of our combinatorial assembly of parts
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| - | *[[Media:DICTY TRANSFORMATION.pdf|Dicty: Transformation Protocol]] - here is how we get our constructs into Dicty
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| - | *[[Media:DICTY-analyzing_motility.pdf|Dicty: Motility Assay Protocol]] - here is how we prepare and film Dicty cells
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| - | =Selected Reading=
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| - | '''Engineering Speed:'''
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| - | G protein signaling events are activated at the leading edge of chemotactic cells. Parent CA, Blacklock BJ, Froehlich WM, Murphy DB, Devreotes PN. Cell. 1998 Oct 2;95(1):81-91.
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| - | PI3-kinase signaling contributes to orientation in shallow gradients and enhances speed in steep chemoattractant gradients. Bosgraaf L, Keizer-Gunnink I, Van Haastert PJ. J Cell Sci. 2008 Nov 1;121(Pt 21):3589-97. Epub 2008 Oct 7.
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| - | <!--- The Mission, Experiments --->
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| | {| style="color:#333333;background-color:#cccccc;" cellpadding="3" cellspacing="3" border="0" bordercolor="#231f26" width="99%" align="center" | | {| style="color:#333333;background-color:#cccccc;" cellpadding="3" cellspacing="3" border="0" bordercolor="#231f26" width="99%" align="center" |
| | !align="center"|[[Team:UCSF|Home]] | | !align="center"|[[Team:UCSF|Home]] |
Our team was inspired by the use of robots which perform tasks that would otherwise be difficult to accomplish. For example, the Mars rovers allow us to make observations and conduct physical experiments in harsh, remote locations otherwise inaccessible to humans. In many ways, the human body represents unexplored territory on a different scale. For example, we may understand many aspects of human disease. However, certain markers of disease are extremely difficult to detect (e.g., primary tumors) and treatment of disease can be hampered by the impracticality of performing invasive surgeries.
"
