We would like to thank Jeff Lange, Jay Unruh, Jianzheng Wu, Tarique Khan, and Ellen Ketter for their work towards development of the assay. This work was done to fulfill, in part, requirements for PhD thesis research for T.S.K. and A.R.G as students registered with the Open University, UK, and the Stowers Institute for Medical Research Graduate School, USA, respectively. Additional assay-related information can be found at https://doi.org/10.1016/j.molcel.2018.06.016. Original data underlying this manuscript can be accessed from the Stowers Original Data Repository at http://www.stowers.org/research/publications/libpb-1372. This work was funded by NIH Director&#8217;s Early Independence Award DP5-OD009152, March of Dimes Foundation Grant No. 5-FY17-32, and the Stowers Institute for Medical Research.
Author contributions are as follows. Conceptualization: T.S.K., S.V., and R.H.; Methodology: T.S.K., S.V., A.R.G., and A.B; Investigation: S.V., T.S.K., and A.R.G.; Formal Analysis: S.V., and T.S.K. ; Data Curation: T.S.K.; Visualization: T.S.K, and S.V., Writing (Original Draft): S.V., and T.S.K.; Writing (Review, Editing): R.H., S.V., and T.S.K.; and Funding Acquisition: R.H.

1. Preparation of Saccharomyces cerevisiae for DAmFRET assay
<ol>
	<li>Transform experimentally relevant yeast strains with a 2&#181; galactose-inducible plasmid that expresses the protein of interest8 tagged either at the C or N terminus with mEos3.1 using a standard lithium acetate protocol12 .</li>
	<li>Grow the yeast in liquid culture.
	<ol>
		<li>For every query protein, inoculate yeast colonies (transformed), in triplicate, each into 200 &#181;L of appropriate non-inducing growth media (i.e., media containing 2% dextrose, yeast nitrogen base and amino acids for selection of the plasmid) in a 96-well plate (Figure 1C).</li>
		<li>Incubate cells while shaking on a plate shaker (see the Table of Materials) with 1.5 mm orbit at 1200 rpm at 30 &#176;C for 16 h.</li>
	</ol>
	</li>
	<li>Induce expression of the gene of interest (representative protocol for 16 h-some proteins may take more or less time).
	<ol>
		<li>Following 16 h of induction, spin the plate at 2200 x g for 2 min at room temperature (RT0 to pellet the samples.</li>
		<li>Remove media by forceful inversion. Resuspend cells with 200 &#181;L of appropriate induction media (i.e., media containing 2% galactose, yeast nitrogen base and amino acids for selection of the plasmid). See Figure 1C.</li>
		<li>Incubate cells while shaking at 30 &#176;C for 12 h.</li>
		<li>Centrifuge (see the Table of Materials) the plate at 2200 x g for 2 min at RT to pellet the samples. Remove media by forceful inversion. Resuspend cells with 200 &#181;L of the induction media.</li>
		<li>Incubate cells while shaking at 1200 rpm at 30 &#176;C for a further 4 h. This resuspension in fresh media should happen 4 h prior to running DAmFRET, in order to reduce autofluorescence.</li>
	</ol>
	</li>
</ol>
2. Mammalian plasmid creation
<ol>
	<li>Construct a mammalian vector that has a constitutive promoter and mEos3.1 with a placeholder to insert the gene of interest and a linker in between them. 
	NOTE: We constructed a golden gate compatible vector, M1, from a publicly available plasmid (see the Table of Materials) with the following modifications: an existing BsaI site was removed by a point mutation G to A (at position 3719 as per the deposited sequence). mEos3.1 was obtained using site-directed mutagenesis of the fluorophore at I157V. Inverted BsaI sites followed by 4x(EAAAR) linker were inserted, by Gibson assembly, in-frame between CMV promoter and mEos3.1 to produce the final M1 vector. Protein expression is driven by CMV enhancer and promoter; and transcription termination by SV40 polyadenylation signal.</li>
	<li>Create a library of inserts, compatible with the constructed vector. 
	NOTE: We order our inserts for Golden Gate assembly as synthetic linear fragments (see table of materials) flanked by BsaI sites for ligation into M1 between CMV promoter and 4x(EAAAR)-mEos3.1.</li>
</ol>
3. Mammalian cell culture and transfection
<ol>
	<li>Culture HEK293T cells in DMEM + 10% FBS + 1x PenStrep media at 37 &#176;C at 5% CO2.</li>
	<li>On the day prior to transfection, seed 5 x 105 cells in a 6-well plate with 2 mL of media.</li>
	<li>Transfect cells with 2 &#181;g of plasmid DNA using a transfection reagent (see the Table of Materials) at a ratio of 3:1 (transfection reagent:DNA). Mix the components in 150 &#181;L of reduced serum media (see the Table of Materials) and incubate it at RT for 15 min, add the mix to each well and incubate for 48 h for protein expression.</li>
	<li>Confirm protein expression after 24 h by epifluorescence microscopy (488 nm excitation, 515 nm emission).</li>
</ol>
4. Preparation of mammalian cells for DAmFRET assay
<ol>
	<li>After 48 h of protein expression, remove media from each well and carefully wash cells with 1 mL of 37 &#176;C phosphate-buffered saline (PBS). Following washing, aspirate the PBS.</li>
	<li>Add 0.5 mL of trypsin-ethylenediaminetetraacetic acid (EDTA) (0.25%) and incubate for 5 min at 37 &#176;C.</li>
	<li>Add 0.5 mL of complete DMEM media to each well, resuspend cells and ensure no large clumps are visible.</li>
	<li>Transfer the whole volume of each well into 1.5 mL tubes.</li>
	<li>Spin cells for 5 min at 1000 x g at room temperature (RT).</li>
	<li>Remove the supernatant and resuspend cell pellet in 1 mL of PBS + 10 mM EDTA.</li>
	<li>Spin the cells for 5 min at 1000 x g at RT.</li>
	<li>Remove the supernatant and resuspend the cell pellet in 1 mL 4% paraformaldehyde (PFA) + 10 mM EDTA in PBS.</li>
	<li>Fix cells for 5 min on a shake table with constant movement.</li>
	<li>Spin cells for 5 min at 1000 x g at RT.</li>
	<li>Remove the supernatant and resuspend the cell pellet in 1 mL of PBS + 10 mM EDTA.</li>
	<li>Spin cells for 5 min at 1000 x g at RT.</li>
	<li>Remove the supernatant and resuspend the cells in adequate volume of buffer for the cytometry run (for 1 well of a 96-well plate use 200 &#181;L of PBS + 10 mM EDTA).</li>
	<li>Transfer the resuspended cells to one well of a round bottom 96-well plate.</li>
</ol>
5. Photoconversion of yeast and mammalian cells for the assay
<ol>
	<li>Photoconvert samples in a microplate with no cover using a UV lamp (see the Table of Materials) fitted with a 320-500 nm (violet) filter and a beam collimator, positioned 45 cm above the plate, for a duration of 25 min while shaking. These conditions are appropriate when the beam power at the plate is 11.25 mW/cm2, with approximately 17,000 mJ/cm2 of total photon dose8.</li>
</ol>
6. DAmFRET data collection
<ol>
	<li>Assay cells using a cytometer with non-collinear 488/561 lasers. The following data are the minimum requirements for calculation of FRET.
	<ol>
		<li>Use a 488 nm excitation/ 515 nm emission channel for the collection of the donor fluorescence.</li>
		<li>Use a 488 nm excitation/ 595 nm emission channel for the collection of the fluorescence of the FRET signal.</li>
		<li>Use a 561 nm excitation/ 595 nm emission channel (non-collinear with the 488/595 channel) for collection of the acceptor fluorescence.</li>
		<li>Use a 405 nm excitation/ 457 nm emission channel for the collection of autofluorescence8.</li>
		<li>Utilize a brightfield image channel for volume calculation. 
		NOTE:Imaging cytometer settings for S. cerevisiae are as follows:60x objective at low flow rate and high sensitivity; laser powers set to 405 nm at 15 mW, 488 nm at 15 mW, 561 nm at 20 mW.Imaging cytometer settings for HEK293T cells are as follows:40x objective at low flow rate and high sensitivity; laser powers set to 405 nm at 15 mW, 488 nm at 15 mW, 561 nm at 20 mW.Also note that, our imaging cytometer was custom-designed to have spatially separated 488 nm and 561 nm lasers (i.e., on different cameras) so as to obtain clear resolution of FRET from acceptor fluorescence.</li>
	</ol>
	</li>
	<li>Collect data for 20,000-50,000 single cells per sample within a fluorescence positive gate.
	<ol>
		<li>Gate single cells by a high aspect ratio and a small cell area as opposed to clumped cells or debris which would have low aspect ratio and large or very small cell areas respectively. 
		NOTE: The equivalent parameters in a non-imaging cytometer are FSC (forward scatter) for approximate cell size and SSC (side scatter) for cell granularity. Additionally, use FSC pulse height versus pulse width as a proxy for single cell gating. Additionally, it is crucial to not collect events that have fluorescence values beyond the upper limit of sensitivity of the cytometer. In our imaging cytometer, we restrict collection of events to a raw maximum pixel value of one less than the saturation limit for every fluorescent channel we collect. Similarly, for conventional flow cytometers, data points in the highest intensity bin (which includes events that are beyond the dynamic range of detection) should not be analyzed.</li>
	</ol>
	</li>
</ol>
7. Data analysis
<ol>
	<li>Perform compensation using a non-photoconverted mEos3.1 sample for the pure donor signal and monomeric DsRed2, which has a similar spectrum to the red form of mEos3.1, for the pure acceptor signal13. For more sensitivity, ensure that the FRET detector channel is also considered as a spillover target, in the analysis program.</li>
	<li>Gate samples to select only single cells which are fluorescence positive (as in Figure 2).</li>
	<li>Calculate the AmFRET parameter as the ratio of total FRET signal (488ex/595em) divided by the total FRET acceptor (561ex/595em) signal.</li>
	<li>Visualize data using a flow cytometry software (see the Table of Materials). 
	NOTE: Cytosolic concentrations for S. cerevisiae in this study were calculated with the settings below as in Khan et al.8. Data analysis was performed using flow cytometry software (see Table of Materials).</li>
</ol>

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M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+power+of+two:+protein+dimerization+in+biology.">The power of two: protein dimerization in biology.</a> Trends in Biochemical Sciences. 29 (11), 618-625 (2004).</li><li>Matthews, J. M., Sunde, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dimers,+oligomers,+everywhere.">Dimers, oligomers, everywhere.</a> Advances in Experimental Medicine and Biology. 747, 1-18 (2012).</li><li>Glover, J. R., Kowal, A. S., Schirmer, E. C., Patino, M. M., Liu, J. J., Lindquist, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Self-seeded+fibers+formed+by+Sup35,+the+protein+determinant+of+[PSI+],+a+heritable+prion-like+factor+of+S.+cerevisiae.">Self-seeded fibers formed by Sup35, the protein determinant of [PSI+], a heritable prion-like factor of S. cerevisiae.</a> Cell. 89 (5), 811-819 (1997).</li><li>Tanaka, M., Collins, S. R., Toyama, B. H., Weissman, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+physical+basis+of+how+prion+conformations+determine+strain+phenotypes.">The physical basis of how prion conformations determine strain phenotypes.</a> Nature. 442 (7102), 585-589 (2006).</li><li>Michaels, T. C. T., Dear, A. J., Knowles, T. P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stochastic+calculus+of+protein+filament+formation+under+spatial+confinement.">Stochastic calculus of protein filament formation under spatial confinement.</a> New journal of physics. , (2018).</li><li>Khan, T., Kandola, T. 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The authors declare no competing interests.

DAmFRET is the most comprehensive method to detect protein self-assembly in vivo. DAmFRET combines direct read-out of homotypic protein-protein interactions over a wide range of concentration, with single cell resolution and high throughput. The direct read-out of DAmFRET and the fact that the fused proteins do not require a specific subcellular localization or insoluble state eliminates false positives and extends its applicability to a broad range of proteins at their native subcellular locations. Notably, by tagging organelles with fluorophores that are spectrally compatible with mEos3.1, such as T-Sapphire and mCardinal, the subcellular localization of soluble and assembled forms of proteins can be determined alongside DAmFRET.
Using the imaging flow cytometer as described here, it takes 8 h to analyze a 96-well plate with approximately 20,000 gated cells per well. However, we also routinely perform DAmFRET with standard (non-imaging) cytometers that achieve much higher throughput (up to 20 samples per minute). In fact, any flow cytometer with spatially separated 488 and 561 nm lasers that is free from log amp artifacts and has the appropriate PMTs and filters for detecting donor, FRET, and acceptor signals is sufficient to perform DAmFRET. At present, this gain in throughput comes at the expense of localization information and volume determination, such that self-assembly must then be analyzed as a function of protein expression rather than concentration. This is not a problem for qualitative analyses. Additionally, it may be possible to estimate cytosolic volume by fusing a spectrally distinct fluorophore to an endogenous "housekeeping" protein whose expression tightly correlates with cytosolic volume. 
As we have demonstrated through our deployment of DAmFRET in both yeast and mammalian cells, DAmFRET can be easily adapted to different expression systems. This enables the self-assembly of proteins to be studied in their native cellular contexts. Moreover, it offers the ability to compare protein assembly across widely divergent cell-culture models to study the conservation of mechanisms that govern protein self-assembly.

Assays to study homotypic protein interactions, or &#34;self-assembly&#34; are important because the oligomeric state and solubility of proteins dictate their function. The proteome abounds with homo-multimers1,2,3,4, whereas relatively few proteins function as monomers. Proteins can also assemble aberrantly due to stress, age, or misregulation, leading to pathological changes in activity. Identifying the factors that modulate such events, or even the physical nature of the assemblies, is often exceptionally challenging.
A growing number of proteins are now recognized to self-assemble with extraordinary cooperativity and indeterminate stoichiometry, resulting in their demixing from other cellular constituents, as protein-dense phases. These take the form of disordered condensates, such as droplets and gels, or highly ordered filaments, such as amyloid fibers. The conformational fluctuations associated with the latter render its initial formation, or nucleation, inherently probabilistic at the molecular level5,6. Because the probability of nucleation scales with volume, the formation of such assemblies can be highly stochastic in the spatial confines of living cells7,8. At the extreme of stochastic nucleation-limited phase separation are prions, highly ordered protein assemblies that only rarely nucleate spontaneously, but once formed, template their own growth indefinitely. One such protein, known as ASC, executes a digital prion-like switch in the activity of mammalian innate immune cells. ASC self-assembly is nucleated by its interaction with specific proteins that have themselves oligomerized upon binding pathogen- or danger-associated molecular patterns. The ASC assemblies in turn nucleate procaspase-1 to self-assemble and activate, leading to cytokine maturation and pyroptosis of the cell9,10. The region of ASC responsible for its assembly belongs to the death domain superfamily, which consists of over one-hundred members in the human proteome. Despite the pivotal roles of death domains in innate immunity and programmed cell death, most of them have not yet been characterized with respect to self-assembly. The discovery and characterization of additional proteins with such behavior will be greatly facilitated by a direct, single-cell readout of protein self-assembly.
Classical protein biochemistry approaches to study protein self-assembly, such as size-exclusion chromatography and ultracentrifugation, are largely limited to population level assessments. However, cell-to-cell heterogeneity resulting from nucleation-limited phase transitions cannot be modeled with this level of detail. Single-cell approaches based on fluorescence microscopy regain this capability, but lack the throughput necessary to accurately quantify nucleation or to detect rare assemblies. Moreover, soluble self-assemblies such as most enzymes and pre-amyloid oligomers, are too small and mobile to be resolved by standard light microscopy. They can be detected by more sophisticated approaches such as fluorescence correlation spectroscopy, but these are very limited in cell number and throughput.
Proximity-based assays of protein assembly, such as FRET and split fluorophore complementation, offer a potential solution to these problems. However, they generally require the use of two different constructs expressing the protein of interest fused to complementary tags-the donor and acceptor fluorophores in the case of FRET. This compromises experimental throughput and also reduces sensitivity due to cell-to-cell variation in the relative levels of donor and acceptor. To circumvent this, we designed an assay that employs a photoconvertible fluorophore, mEos3.111, which allows a single construct to express both donor- and acceptor-tagged protein. The emission spectrum of unconverted mEos3.1 (GFP-like donor) sufficiently overlaps with the excitation spectrum of photoconverted mEos3.1 (dsRed-like acceptor) to allow FRET to occur when the molecules are in close proximity (&#60;10 nm). Thus, by exposing cells to an empirically determined dose of 405 nm light, which photoconverts an optimal fraction of total mEos3.1 into the acceptor form, we achieve consistent and reproducible relative levels of donor and acceptor across multiple samples, expression levels, and experiments. We measure the acceptor fluorescence when excited either directly with 561 nm light, or indirectly (by energy transfer from the donor) with 488 nm light (i.e., sensitized emission FRET). We report protein assembly as the ratio of these two values and term it amphifluoric FRET or AmFRET.
In order to calculate protein concentration by flow cytometry, we first calculate the mean fluorescence intensity of Spc42 tagged with mEos3.1. Because yeast cells contain approximately 1000 molecules of Spc42, we then calculate the fluorescence intensity of a single fluorescent green mEos3.1 molecule. By leveraging the even photoconversion at all cellular concentrations (Figure 1E), we then correlate total mEos3.1 fluorescence values for all acceptor intensities following photoconversion. We are then able to divide the total number of moles of fluorescent proteins by the approximate cytosolic volume (as determined using imaging flow cytometry) to obtain the total cytosolic concentration of the protein of interest. For exact calculations, please see the original manuscript8.
By expressing the mEos3.1-fused protein from a 2&#181; plasmid in yeast, we probe an approximately thousand-fold range of protein concentration in every sample8. We achieve the same in HEK293T cells by virtue of variation in plasmid uptake during transfection, and hence variable copy number.
The resulting distribution of AmFRET, or DAmFRET, for many thousands of cells reveals the concentration-dependence of self-assembly in the cytosol for any protein of interest. Overall, DAmFRET represents an enabling methodology to discover and characterize protein self-assembly with an unprecedented combination of sensitivity, throughput, and reproducibility.
While using an imaging cytometer enables us to obtain in vivo protein concentration measurements, such cytometers are not yet available at most research institutions. Nevertheless, DAmFRET can be run even in a typical non-imaging cytometer to get distributions of protein assembly over the range of protein expression.

<table><tbody><tr><td>96 Well Plate</td><td>Axygen</td><td>P96-450R-C-S</td><td></td></tr><tr><td>ASC 2-92 (mammalian plasmid)</td><td></td><td>rhm1.0095</td><td>Available on request</td></tr><tr><td>ASC 2-92 (R41E) (mammalian plasmid)</td><td></td><td>rhm1.0096</td><td>Available on request</td></tr><tr><td>ASC 2-92 (R41E) (yeast plasmid)</td><td></td><td>rhx2432</td><td>Available on request</td></tr><tr><td>ASC 2-92 (yeast plasmid)</td><td></td><td>rhx2431</td><td>Available on request</td></tr><tr><td>Centrifuge</td><td>Eppendorf</td><td>5430R</td><td>&quot;centrifuge&quot; in text</td></tr><tr><td>CSM -Ura</td><td>Sunrise Science Products</td><td>1004-100</td><td></td></tr><tr><td>Dextrose</td><td>EMD Millipore</td><td>DX0145-5</td><td></td></tr><tr><td>DMEM</td><td>Gibco</td><td>11966025</td><td></td></tr><tr><td>EDTA</td><td>Sigma-Aldrich</td><td>EDS-500G</td><td></td></tr><tr><td>FCS Express 6</td><td>DeNovo</td><td>FCS Express 6 Flow</td><td>&quot;flow cytometry software&quot; in text</td></tr><tr><td>Fetal Bovine Serum</td><td>VWR Life Science</td><td>45001-108</td><td></td></tr><tr><td>Flow Cytometer</td><td>BioRad</td><td>ZE5</td><td>Non-imaging flow cytometer</td></tr><tr><td>FUGENE HD</td><td>Promega</td><td>E2311</td><td>&quot;transfection reagent&quot; in text</td></tr><tr><td>Galactose</td><td>VWR Life Science</td><td>0637-500g</td><td></td></tr><tr><td>HSPE1 (yeast plasmid)</td><td></td><td>rhx1531</td><td>Available on request</td></tr><tr><td>Imaging Flow Cytometer</td><td>EMD Millipore</td><td>ImageStream X Mark II</td><td>&quot;imaging flow cytometer&quot; in text</td></tr><tr><td>Mammalian Cells</td><td></td><td>HEK293T</td><td></td></tr><tr><td>mEos3.1 (mammalian plasmid)</td><td></td><td>rhm1</td><td>Available on request</td></tr><tr><td>mEos3.1 (yeast plasmid)</td><td></td><td>rhx0935</td><td>Available on request</td></tr><tr><td>optiMEM</td><td>Gibco</td><td>31985062</td><td>&quot;reduced serum media&quot; in text</td></tr><tr><td>PBS</td><td>VWR Life Science</td><td>45000-446</td><td></td></tr><tr><td>PenStrep</td><td>Gibco</td><td>15070063</td><td></td></tr><tr><td>PFA</td><td>Sigma-Aldrich</td><td>P6148-1KG</td><td></td></tr><tr><td>Photoconversion Lamp</td><td>OmniCure</td><td>S1000</td><td></td></tr><tr><td>Plasmid #54525</td><td>Addgene</td><td>#54525</td><td>&quot;publicly available plasmid&quot; in text</td></tr><tr><td>Titramax 1000 Plate Shaker</td><td>Heidolph Instruments</td><td>1000</td><td>&quot;plate shaker&quot; in text</td></tr><tr><td>Trypsin-EDTA</td><td>Gibco</td><td>25200056</td><td></td></tr><tr><td>Yeast Strain</td><td></td><td>rhy1713</td><td>Available on request- S288c (MAT&amp;alpha; lyp1&amp;Delta; can1&amp;Delta;::STE2pr_SpHIS5 his3&amp;Delta;1 leu2&amp;Delta;0 ura3&amp;Delta;0 met15&amp;Delta;0 cln3&amp;Delta;0::GAL1pr_WHI5_hphMX)</td></tr></tbody></table>

detecting and characterizing protein self-assembly in vivo by flow cytometry

Protein self-assembly governs protein function and compartmentalizes cellular processes in space and time. Current methods to study it suffer from low-sensitivity, indirect read-outs, limited throughput, and/or population-level rather than single-cell resolution. We designed a flow cytometry-based single methodology that addresses all of these limitations: Distributed Amphifluoric FRET or DAmFRET. DAmFRET detects and quantifies protein self-assemblies by sensitized emission FRET in vivo, enables deployment across model systems-from yeast to human cells-and achieves sensitive, single-cell, high-throughput read-outs irrespective of protein localization or solubility.

Detection of oligomers that do not form puncta
We have previously applied DAmFRET for the characterization of protein phase separation, which typically results in the formation of large protein assemblies that can also be detected by fluorescence microscopy. To demonstrate the applicability of DAmFRET to diffraction-limited protein assemblies, we analyzed a biochemically well-characterized protein that forms discrete homo-heptamers that are far too small to be visualized by light microscopy: the human co-chaperonin, HSPE114. We compared the DAmFRET profiles of yeast cells expressing mEos3.1 alone, or HSPE1-mEos3.1. The latter exhibited uniform positive AmFRET values, while the former exhibited negligible AmFRET (Figure 3A). The images of cells acquired by the imaging flow cytometer (see the Table of Materials) revealed diffuse fluorescence in all channels-donor, FRET and acceptor, for both mEos3.1- and HSPE1-mEos3.1-expressing cells (Figure 3B). To confirm this finding at higher optical resolution, we used confocal microscopy to capture z-stacks for multiple fields of cells. Indeed, the fluorescence was uniformly distributed throughout the cytosol, with no detectable puncta, whether the cells expressed HSPE1-mEos3.1 or the fluorophore alone (Figure 3C). Note that the characterized Kd of HSPE1 is around 3 &#181;M which is slightly below the sensitivity of our system, and hence we do not observe the sigmoidal relationship of FRET to concentration that would be expected for this discrete homo-oligomer. Nevertheless, we conclude that DAmFRET robustly detects soluble homo-oligomerization in vivo at single cell resolution.
Detection of nucleation-limited assemblies
To showcase the ability of DAmFRET to distinguish discrete homo-oligomers from nucleation-limited assemblies such as prions, we analyzed the human inflammasome protein ASC. Whereas WT ASCPYD forms filaments in vivo, the inactive R41E mutant of ASCPYD does not9,10. We expressed in yeast cells either mEos3.1 alone, or mEos3.1 fused to either WT or R41E ASCPYD. Yeast cells expressing the fluorophore alone or the mutant form of ASCPYD exhibited negligible AmFRET over the entire concentration range, indicating an inability to self-interact. In contrast, WT ASCPYD exhibited a DAmFRET profile with two populations: one with negligible AmFRET and the other with high AmFRET (Figure 4A). As confirmed by the fluorescence images (Figure 4B), these populations represent cells that contain only soluble protein or instead contain mostly self-assembled protein, respectively. The discontinuous relationship between the populations, and the fact they occur at overlapping concentrations indicates that a nucleation barrier stabilizes the monomeric form of the protein and can keep it from assembling over the duration of the experiment8. The gap in AmFRET between the two populations indicates that, once nucleation occurs, it near instantaneously templates other monomers to the assembled form and achieves a new steady state level of AmFRET. DAmFRET corroborated prior structural data that the point mutant ASCPYD R41E disrupted nucleation across the concentrations achievable by this expression system (Figure 4A).
Applicability of DAmFRET in mammalian cells
Although yeast cells are ideal host cells for DAmFRET, we desired to extend the applicability of DAmFRET to mammalian cells. In order to avoid cell death caused by functional ASC polymers, we tested DAmFRET in HEK293T cells that lack caspase-1 expression. We expressed the same proteins in HEK293T cells as we did in yeast cells in Figure 4A. The resulting DAmFRET profiles in HEK293T cells qualitatively resemble those in yeast cells (Figures 4A,C). DAmFRET, thus, serves as the most versatile in vivo method to detect and quantify nucleated protein self-assemblies at single cell resolution with high throughput irrespective of both the presence of microscopically visible puncta as well as the cell type.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59577/59577fig1.jpg" /> 
Figure 1: Overview of experimental design for S. cerevisiae. This figure has been adapted from Khan et al.8&#160;with permission. (A) 2&#181; plasmid map showing the open reading frame for the protein of interest, tagged with photoconvertible mEos3.1 and driven by an inducible promoter. (B) When transformed into yeast cells, the 2&#181; system of replication leads to high copy number variability among cells. That variation, combined with the transcriptional noise from the GAL1 promoter, results in a broad distribution of protein expression in a population of cells. (C) Experimental overview for yeast DAmFRET assay. To ensure healthy cells for the assay, colonies are first inoculated for proliferation over 16 h in synthetic media containing the non-inducing carbon source, dextrose. Following proliferation, cells are transferred to synthetic media containing the inducing carbon source, galactose, for 16 h. Following induction, cells are partially and uniformly photoconverted by exposure to 405 nm light. (D) Samples are then analyzed using imaging flow cytometry. The spectra and intensities of green and red mEos3.1 render them well-suited for a FRET donor and acceptor, respectively. When in close proximity to one another, as occurs in the polymer depicted in the bottom cell, the red molecules will fluorescence upon excitation of green molecules (FRET). (E) Plot of donor versus acceptor intensities showing a tight linear relationship upon photoconversion, showing that the efficiency of photoconversion is not influenced by expression level. (F) Scatter plot of mean acceptor versus donor intensities from populations of cells expressing a variety of mEos-tagged proteins, in both soluble and amyloid forms, showing that the efficiency of photoconversion is not influenced by the fusion partner or solubility (error bars indicate standard deviation of biological triplicates). <a href="https://www.jove.com/files/ftp_upload/59577/59577fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59577/59577fig2.jpg" /> 
Figure 2: DAmFRET gating strategy for imaging flow cytometry data. Gating strategy used to analyze only focused, unbudded single cells which have low autofluorescence and express the fluorescent protein. (A) Hierarchy of gates to obtain well-focused, live, single cells for analysis. (B) Histogram of gradient RMS of brightfield channel. This measurement allows for the selection of cells which are properly focused under transmitted light. (C) Density plot of circularity versus area showing gated population of unbudded spherical single cells. (D) Density plot showing autofluorescence (Ch07) versus donor fluorescence (Ch02) intensity showing separate populations of expressing cells (gated) and dark cells. (E) Scatter plot of donor versus acceptor intensities showing a clear loss in donor fluorescence in a subset of cells, resulting from FRET between the donor and acceptor fluorophores. (F) Final DAmFRET plot. Cells containing unassembled protein are centered around zero AmFRET, while cells containing self-assembled protein exhibit a positive AmFRET value. Two overlapping populations in the plot is indicative of a finite barrier to nucleating that protein into higher-order assemblies such as amyloids. <a href="https://www.jove.com/files/ftp_upload/59577/59577fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59577/59577fig3.jpg" /> 
Figure 3: Representative data of monomeric and heptameric proteins by flow cytometry and microscopy. (A) DAmFRET profile of monomeric mEos3.1 protein (left) and heptameric HSPE1 tagged with mEos3.1 (right) showing increased ratiometric FRET resulting from homotypic assembly. (B) Images from the cytometer, of cells expressing mEos3.1 (left) and HSPE1 (right) shown in all captured channels. (C) Representative images of yeast cells expressing monomeric mEos3.1 (left) and HSPE1 (right). The images are sum projections of confocal slices. At least fifty cells were imaged to corroborate this observation. <a href="https://www.jove.com/files/ftp_upload/59577/59577fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/59577/59577fig4.jpg" /> 
Figure 4: DAmFRET profiles show similar self-assembly behavior of human ASCPYD protein in S. cerevisiae and HEK293T cells. (A) Density plots showing AmFRET versus cytosolic concentration (in &#181;M) of mEos3.1 (left), or mEos3.1 fused to WT (center) or monomerizing mutant of ASCPYD (right) in S. cerevisiae. (B) Images from the cytometer, of cells from the lower and upper gates (left and center panels, respectively) expressing WT ASCPYD; and of cells expressing ASCPYD R41E of the indicated gate (right panel). (C) Density plots showing AmFRET versus acceptor intensity for the same series of proteins as in (A) but expressed in HEK293T cells. <a href="https://www.jove.com/files/ftp_upload/59577/59577fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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Detecting and Characterizing Protein Self-Assembly In Vivo by Flow Cytometry

This article describes a FRET-based flow cytometry protocol to quantify protein self-assembly in both S. cerevisiae and HEK293T cells.

Protein self-assembly governs protein function and compartmentalizes cellular processes in space and time. Current methods to study it suffer from ...

detecting-characterizing-protein-self-assembly-vivo-flow

Research

JoVE Journal

Biology

10.9K Views.  Stowers Institute for Medical Research. Inzicht in de fysieke basis van eiwit zelfassemblage in cellen wordt beperkt door gebrek aan geschikte instrumenten. De huidige methoden hebben last van lage gevoeligheid, indirecte uitlezingen, beperkte doorvoer en/of populatieniveauresolutie. In tegenstelling, onze test detecteert en kwantificeert de neiging van een eiwit voor zelfassemblage in vivo met een gevoelige, eencellige, hoge doorvoer uitlezing, ongeacht eiwitlokalisatie of oplosbaarheid.Wanneer u voor het eerst de test prob...

Video: Detecting and Characterizing Protein Self-Assembly In Vivo by Flow Cytometry

Analyzing Dynamic Protein Complexes Assembled On and Released From Biolayer Interferometry Biosensor Using Mass Spectrometry and Electron Microscopy

In vivo, proteins are often part of large macromolecular complexes where binding specificity and dynamics ultimately dictate functional outputs. In this work, the pre-endosomal anthrax toxin is assembled and transitioned into the endosomal complex. First, the N-terminal domain of a cysteine mutant lethal factor (LFN) is attached to a biolayer interferometry (BLI) biosensor through disulfide coupling in an optimal orientation, allowing protective antigen (PA) prepore to bind (Kd 1 nM). The optimally oriented LFN-PAprepore complex then binds to soluble capillary morphogenic gene-2 (CMG2) cell surface receptor (Kd 170 pM), resulting in a representative anthrax pre-endosomal complex, stable at pH 7.5. This assembled complex is then subjected to acidification (pH 5.0) representative of the late endosome environment to transition the PAprepore into the membrane inserted pore state. This PApore state results in a weakened binding between the CMG2 receptor and the LFN-PApore and a substantial dissociation of CMG2 from the transition pore. The thio-attachment of LFN to the biosensor surface is easily reversed by dithiothreitol. Reduction on the BLI biosensor surface releases the LFN-PAprepore-CMG2 ternary complex or the acid transitioned LFN-PApore complexes into microliter volumes. Released complexes are then visualized and identified using electron microscopy and mass spectrometry. These experiments demonstrate how to monitor the kinetic assembly/disassembly of specific protein complexes using label-free BLI methodologies and evaluate the structure and identity of these BLI assembled complexes by electron microscopy and mass spectrometry, respectively, using easy-to-replicate sequential procedures.

Here we present a protocol to monitor the assembly and disassembly of the anthrax toxin using biolayer interferometry (BLI). Following assembly/disassembly on the biosensor surface, the large protein complexes are released from the surface for visualization and identification of components of the complexes using electron microscopy and mass spectrometry, respectively.

In vivo, proteins are often part of large macromolecular complexes where binding specificity and dynamics ultimately dictate functional outputs. In this ...

Biochemistry

Characterization of Aquatic Biofilms with Flow Cytometry

Biofilms are dynamic consortia of microorganism that play a key role in freshwater ecosystems. By changing their community structure, biofilms respond quickly to environmental changes and can be thus used as indicators of water quality. Currently, biofilm assessment is mostly based on integrative and functional endpoints, such as photosynthetic or respiratory activity, which do not provide information on the biofilm community structure. Flow cytometry and computational visualization offer an alternative, sensitive, and easy-to-use method for assessment of the community composition, particularly of the photoautotrophic part of freshwater biofilms. It requires only basic sample preparation, after which the entire sample is run through the flow cytometer. The single-cell optical and fluorescent information is used for computational visualization and biological interpretation. Its main advantages over other methods are the speed of analysis and the high-information-content nature. Flow cytometry provides information on several cellular and biofilm traits in a single measurement: particle size, density, pigment content, abiotic content in the biofilm, and coarse taxonomic information. However, it does not provide information on biofilm composition on the species level. We see high potential in the use of the method for environmental monitoring of aquatic ecosystems and as an initial biofilm evaluation step that informs downstream detailed investigations by complementary and more detailed methods.

Flow cytometry in combination with visual clustering offers an easy-to-use and fast method for studying aquatic biofilms. It can be used for biofilm characterization, detection of changes in biofilm community structure, and detection of abiotic particles embedded in the biofilm.

Biofilms are dynamic consortia of microorganism that play a key role in freshwater ecosystems. By changing their community structure, biofilms respond ...

Environment

Sensitive Detection of Proteopathic Seeding Activity with FRET Flow Cytometry

Increasing evidence supports transcellular propagation of toxic protein aggregates, or proteopathic seeds, as a mechanism for the initiation and progression of pathology in several neurodegenerative diseases, including Alzheimer's disease and the related tauopathies. The potentially critical role of tau seeds in disease progression strongly supports the need for a sensitive assay that readily detects seeding activity in biological samples. 
By combining the specificity of fluorescence resonance energy transfer (FRET), the sensitivity of flow cytometry, and the stability of a monoclonal cell line, an ultra-sensitive seeding assay has been engineered and is compatible with seed detection from recombinant or biological samples, including human and mouse brain homogenates. The assay employs monoclonal HEK 293T cells that stably express the aggregation-prone repeat domain (RD) of tau harboring the disease-associated P301S mutation fused to either CFP or YFP, which produce a FRET signal upon protein aggregation. The uptake of proteopathic tau seeds (but not other proteins) into the biosensor cells stimulates aggregation of RD-CFP and RD-YFP, and flow cytometry sensitively and quantitatively monitors this aggregation-induced FRET. The assay detects femtomolar concentrations (monomer equivalent) of recombinant tau seeds, has a dynamic range spanning three orders of magnitude, and is compatible with brain homogenates from tauopathy transgenic mice and human tauopathy subjects. With slight modifications, the assay can also detect seeding activity of other proteopathic seeds, such as &#945;-synuclein, and is also compatible with primary neuronal cultures. The ease, sensitivity, and broad applicability of FRET flow cytometry makes it useful to study a wide range of protein aggregation disorders.

Cell-to-cell transfer of protein aggregates, or proteopathic seeds, may underlie the progression of pathology in neurodegenerative diseases. Here, a novel FRET flow cytometry assay is described that enables specific and sensitive detection of seeding activity from recombinant or biological samples.

Increasing evidence supports transcellular propagation of toxic protein aggregates, or proteopathic seeds, as a mechanism for the initiation and ...

Bioengineering

A Flow Cytometry-Based Cell Surface Protein Binding Assay for Assessing Selectivity and Specificity of an Anticancer Aptamer

A key challenge in developing an anticancer aptamer is to efficiently determine the selectivity and specificity of the developed aptamer to the target protein. Due to its several advantages over monoclonal antibodies, aptamer development has gained enormous popularity among cancer researchers. Systematic evolution of ligands by exponential enrichment (SELEX) is the most common method of developing aptamers specific for proteins of interest. Following SELEX, a quick and efficient binding assay accelerates the process of identification, confirming the selectivity and specificity of the aptamer. 
This paper explains a step-by-step flow cytometric-based binding assay of an aptamer specific for epithelial cellular adhesion molecule (EpCAM). The transmembrane glycoprotein EpCAM is overexpressed in most carcinomas and plays roles in cancer initiation, progression, and metastasis. Therefore, it is a valuable candidate for targeted drug delivery to tumors. To evaluate the selectivity and specificity of the aptamer to the membrane-bound EpCAM, EpCAM-positive and -negative cells are required. Additionally, a non-binding EpCAM aptamer with a similar length and 2-dimensional (2D) structure to the EpCAM-binding aptamer is required. The binding assay includes different buffers (blocking buffer, wash buffer, incubation buffer, and FACS buffer) and incubation steps. 
The aptamer is incubated with the cell lines. Following the incubation and washing steps, the cells will be evaluated using a sensitive flow cytometry assay. Analysis of the results shows the binding of the EpCAM-specific aptamer to EpCAM-positive cells and not the EpCAM-negative cells. In EpCAM-positive cells, this is depicted as a band shift in the binding of the EpCAM aptamer to the right compared to the non-binding aptamer control. In EpCAM-negative cells, the corresponding bands of EpCAM-binding and -non-binding aptamers overlap. This demonstrates the selectivity and specificity of the EpCAM aptamer. While this protocol is focused on the EpCAM aptamer, the protocol is applicable to other published aptamers.

A necessary step in anticancer aptamer development is to test its binding to the target. We demonstrate a flow cytometric-based assay to study this binding, emphasizing the importance of including a negative control aptamer and cancer cells that are positive or negative for that particular protein.&#160;

A key challenge in developing an anticancer aptamer is to efficiently determine the selectivity and specificity of the developed aptamer to the target ...

Cancer Research

Analyzing the Interaction of Fluorescent-Labeled Proteins with Artificial Phospholipid Microvesicles using Quantitative Flow Cytometry

In the human body, most of the major physiologic reactions involved in the immune response and blood coagulation proceed on the membranes of cells. An important first step in any membrane-dependent reaction is binding of protein on the phospholipid membrane. An approach to studying protein interaction with lipid membranes has been developed using fluorescently labeled proteins and flow cytometry. This method allows the study of protein-membrane interactions using live cells and natural or artificial phospholipid vesicles. The advantage of this method is the simplicity and availability of reagents and equipment. In this method, proteins are labeled using fluorescent dyes. However, both self-made and commercially available, fluorescently labeled proteins can be used. After conjugation with a fluorescent dye, the proteins are incubated with a source of the phospholipid membrane (microvesicles or cells), and the samples are analyzed by flow cytometry. The obtained data can be used to calculate the kinetic constants and equilibrium Kd. In addition, it is possible to estimate the approximate number of protein binding sites on the phospholipid membrane using special calibration beads.

Here, we describe a set of methods for characterizing the interaction of proteins with membranes of cells or microvesicles.

In the human body, most of the major physiologic reactions involved in the immune response and blood coagulation proceed on the membranes of cells. An ...

Flow Cytometric Characterization of Murine B Cell Development

Extensive studies have characterized the development and differentiation of murine B cells in secondary lymphoid organs. Antibodies secreted by B cells have been isolated and developed into well-established therapeutics. Validation of murine B cell development, in the context of autoimmune prone mice, or in mice with modified immune systems, is a crucial component of developing or testing therapeutic agents in mice and is an appropriate use of flow cytometry. Well established B cell flow cytometric parameters can be used to evaluate B cell development in the murine peritoneum, bone marrow, and spleen, but a number of best practices must be adhered to. In addition, flow cytometric analysis of B cell compartments should also complement additional readouts of B cell development. Data generated using this technique can further our understanding of wild type, autoimmune prone mouse models as well as humanized mice that can be used to generate antibody or antibody-like molecules as therapeutics.

We describe herein a simple analysis of the heterogeneity of the murine immune B cell compartment in the peritoneum, spleen, and bone marrow tissues by flow cytometry. The protocol can be adapted and extended to other mouse tissues.

Extensive studies have characterized the development and differentiation of murine B cells in secondary lymphoid organs. Antibodies secreted by B cells ...

Immunology and Infection

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC is based on the production of fluorescence using two non-fluorescent fragments of a fluorescent protein (Venus, a Yellow Fluorescent Protein variant, is used here). Non-fluorescent Venus fragments (VN and VC) are fused to two interacting proteins (in this case, AKAP-Lbc and PDE4D3), yielding fluorescence due to VN-AKAP-Lbc-VC-PDE4D3 interaction and the formation of a functional fluorescent protein inside cells.
BiFC provides information on the subcellular localization of protein complexes and the strength of protein interactions based on fluorescence intensity. However, BiFC analysis using microscopy to quantify the strength of protein-protein interaction is time-consuming and somewhat subjective due to heterogeneity in protein expression and interaction. By coupling flow cytometric analysis with BiFC methodology, the fluorescent BiFC protein-protein interaction signal can be accurately measured for a large quantity of cells in a short time. Here, we demonstrate an application of this methodology to map regions in PDE4D3 that are required for the interaction with AKAP-Lbc. This high throughput methodology can be applied to screening factors that regulate protein-protein interaction.

Flow cytometric analysis of Bimolecular Fluorescence Complementation provides a high throughput quantitative method to study protein-protein interaction. This methodology can be applied to mapping protein binding sites and for screening factors that regulate protein-protein interaction.

Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC ...

Immunodetection of Outer Membrane Proteins by Flow Cytometry of Isolated Mitochondria

Methods to detect and monitor mitochondrial outer membrane protein components in animal tissues are vital to study mitochondrial physiology and pathophysiology. This protocol describes a technique where mitochondria isolated from rodent tissue are immunolabeled and analyzed by flow cytometry. Mitochondria are isolated from rodent spinal cords and subjected to a rapid enrichment step so as to remove myelin, a major contaminant of mitochondrial fractions prepared from nervous tissue. Isolated mitochondria are then labeled with an antibody of choice and a fluorescently conjugated secondary antibody. Analysis by flow cytometry verifies the relative purity of mitochondrial preparations by staining with a mitochondrial specific dye, followed by detection and quantification of immunolabeled protein. This technique is rapid, quantifiable and high-throughput, allowing for the analysis of hundreds of thousands of mitochondria per sample. It is applicable to assess novel proteins at the mitochondrial surface under normal physiological conditions as well as the proteins that may become mislocalized to this organelle during pathology. Importantly, this method can be coupled to fluorescent indicator dyes to report on certain activities of mitochondrial subpopulations and is feasible for mitochondria from the central nervous system (brain and spinal cord) as well as liver.

Described here is a method to detect and quantify mitochondrial outer membrane proteins by immunolabeling of mitochondria isolated from rodent tissue and analysis by flow cytometry. This method can be extended to assess functional aspects of mitochondrial subpopulations.

Methods to detect and monitor mitochondrial outer membrane protein components in animal tissues are vital to study mitochondrial physiology and ...

Determination of the Relative Cell Surface and Total Expression of Recombinant Ion Channels Using Flow Cytometry

Inherited or de novo mutations in cation-selective channels may lead to sudden cardiac death. Alteration in the plasma membrane trafficking of these multi-spanning transmembrane proteins, with or without change in channel gating, is often postulated to contribute significantly in this process. It has thus become critical to develop a method to quantify the change of the relative cell surface expression of cardiac ion channels on a large scale. Herein, a detailed protocol is provided to determine the relative total and cell surface expression of cardiac L-type calcium channels CaV1.2 and membrane-associated subunits in tsA-201 cells using two-color fluorescent cytometry assays. Compared with other microscopy-based or immunoblotting-based qualitative methods, flow cytometry experiments are fast, reproducible, and large-volume assays that deliver quantifiable end-points on large samples of live cells (ranging from 104 to 106 cells) with similar cellular characteristics in a single flow. Constructs were designed to constitutively express mCherry at the intracellular C-terminus (thus allowing a rapid assessment of the total protein expression) and express an extracellular-facing hemagglutinin (HA) epitope to estimate the cell surface expression of membrane proteins using an anti-HA fluorescence conjugated antibody. To avoid false negative, experiments were also conducted in permeabilized cells to confirm the accessibility and proper expression of the HA epitope. The detailed procedure provides: (1) design of tagged DNA (deoxyribonucleic acid) constructs, (2) lipid-mediated transfection of constructs in tsA-201 cells, (3) culture, harvest, and staining of non-permeabilized and permeabilized cells, and (4) acquisition and analysis of fluorescent signals. Additionally, the basic principles of flow cytometry are explained and the experimental design, including the choice of fluorophores, titration of the HA antibody and control experiments, is thoroughly discussed. This specific approach offers objective relative quantification of the total and cell surface expression of ion channels that can be extended to study ion pumps and plasma membrane transporters.

Inherited cardiac arrhythmias are often caused by mutations that alter the surface delivery of one or more ion channels. Here, we adapt flow cytometry assays to provide a quantification of the relative total and cell surface protein expression of recombinant ion channels expressed in tsA-201 cells.

Inherited or de novo mutations in cation-selective channels may lead to sudden cardiac death. Alteration in the plasma membrane trafficking of these ...

Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy

Protein aggregation is a hallmark of neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and so on. To detect and analyze soluble or diffuse protein oligomers or aggregates, fluorescence correlation spectroscopy (FCS), which can detect the diffusion speed and brightness of a single particle with a single molecule sensitivity, has been used. However, the proper procedure and know-how for protein aggregation detection have not been widely shared. Here, we show a standard procedure of FCS measurement for diffusion properties of aggregation-prone proteins in cell lysate and live cells: ALS-associated 25 kDa carboxyl-terminal fragment of TAR DNA/RNA-binding protein 43 kDa (TDP25) and superoxide dismutase 1 (SOD1). The representative results show that a part of aggregates of green fluorescent protein (GFP)-tagged TDP25 was slightly included in the soluble fraction of murine neuroblastoma Neuro2a cell lysate. Moreover, GFP-tagged SOD1 carrying ALS-associated mutation shows a slower diffusion in live cells. Accordingly, we here introduce the procedure to detect the protein aggregation via its diffusion property using FCS.

We here introduce a procedure to measure protein oligomers and aggregation in cell lysate and live cells using fluorescence correlation spectroscopy.

Protein aggregation is a hallmark of neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's ...

Detecting and Characterizing Protein Self-Assembly In Vivo by Flow Cytometry

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