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>

<ol>
	<li>Krissinel, E., Henrick, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inference+of+macromolecular+assemblies+from+crystalline+state.">Inference of macromolecular assemblies from crystalline state.</a> Journal of Molecular Biology. 372 (3), 774-797 (2007).</li><li>K&#252;hner, S., van Noort, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proteome+organization+in+a+genome-reduced+bacterium.">Proteome organization in a genome-reduced bacterium.</a> Science. 326 (5957), 1235-1240 (2009).</li><li>Marianayagam, N. J., Sunde, M., Matthews, J. 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. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+Nucleation+In+Reveals+the+Physical+Basis+of+Prion-like+Phase+Behavior.">Quantifying Nucleation In Reveals the Physical Basis of Prion-like Phase Behavior.</a> Molecular Cell. 71 (1), 155-168 (2018).</li><li>Cai, X., Chen, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prion-like+polymerization+underlies+signal+transduction+in+antiviral+immune+defense+and+inflammasome+activation.">Prion-like polymerization underlies signal transduction in antiviral immune defense and inflammasome activation.</a> Cell. 156 (6), 1207-1222 (2014).</li><li>Lu, A., Magupalli, V. 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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 border="0" cellpadding="0" cellspacing="0" style="width:807px;" width="808">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">96 Well Plate</td>
			<td style="width:206px;">Axygen</td>
			<td style="width:166px;">P96-450R-C-S</td>
			<td style="width:172px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">ASC 2-92 (mammalian plasmid)</td>
			<td style="width:206px;"></td>
			<td style="width:166px;">rhm1.0095</td>
			<td>Available on request</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:264px;">ASC 2-92 (R41E) (mammalian plasmid)</td>
			<td style="width:206px;"></td>
			<td style="width:166px;">rhm1.0096</td>
			<td>Available on request</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">ASC 2-92 (R41E) (yeast plasmid)</td>
			<td style="width:206px;"></td>
			<td style="width:166px;">rhx2432</td>
			<td>Available on request</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">ASC 2-92 (yeast plasmid)</td>
			<td style="width:206px;"></td>
			<td style="width:166px;">rhx2431</td>
			<td>Available on request</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">Centrifuge</td>
			<td style="width:206px;">Eppendorf</td>
			<td style="width:166px;">5430R</td>
			<td>&#34;centrifuge&#34; in text</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">CSM -Ura</td>
			<td style="width:206px;">Sunrise Science Products</td>
			<td style="width:166px;">1004-100</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:22px;width:264px;">Dextrose</td>
			<td style="width:206px;">EMD Millipore</td>
			<td style="width:166px;">DX0145-5</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">DMEM</td>
			<td style="width:206px;">Gibco</td>
			<td style="width:166px;">11966025</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">EDTA</td>
			<td style="width:206px;">Sigma-Aldrich</td>
			<td style="width:166px;">EDS-500G</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:22px;width:264px;">FCS Express 6</td>
			<td style="width:206px;">DeNovo</td>
			<td style="width:166px;">FCS Express 6 Flow</td>
			<td>&#34;flow cytometry software&#34; in text</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">Fetal Bovine Serum</td>
			<td style="width:206px;">VWR Life Science</td>
			<td style="width:166px;">45001-108</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">Flow Cytometer</td>
			<td style="width:206px;">BioRad</td>
			<td style="width:166px;">ZE5</td>
			<td>Non-imaging flow cytometer</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">FUGENE HD</td>
			<td style="width:206px;">Promega</td>
			<td style="width:166px;">E2311</td>
			<td>&#34;transfection reagent&#34; in text</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">Galactose</td>
			<td style="width:206px;">VWR Life Science</td>
			<td style="width:166px;">0637-500g</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:22px;width:264px;">HSPE1 (yeast plasmid)</td>
			<td style="width:206px;"></td>
			<td style="width:166px;">rhx1531</td>
			<td>Available on request</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:22px;width:264px;">Imaging Flow Cytometer</td>
			<td style="width:206px;">EMD Millipore</td>
			<td style="width:166px;">ImageStream X Mark II</td>
			<td>&#34;imaging flow cytometer&#34; in text</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">Mammalian Cells</td>
			<td style="width:206px;"></td>
			<td style="width:166px;">HEK293T</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">mEos3.1 (mammalian plasmid)</td>
			<td style="width:206px;"></td>
			<td style="width:166px;">rhm1</td>
			<td>Available on request</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">mEos3.1 (yeast plasmid)</td>
			<td style="width:206px;"></td>
			<td style="width:166px;">rhx0935</td>
			<td>Available on request</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">optiMEM</td>
			<td style="width:206px;">Gibco</td>
			<td style="width:166px;">31985062</td>
			<td>&#34;reduced serum media&#34; in text</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">PBS</td>
			<td style="width:206px;">VWR Life Science</td>
			<td style="width:166px;">45000-446</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">PenStrep</td>
			<td style="width:206px;">Gibco</td>
			<td style="width:166px;">15070063</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">PFA</td>
			<td style="width:206px;">Sigma-Aldrich</td>
			<td style="width:166px;">P6148-1KG</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">Photoconversion Lamp</td>
			<td style="width:206px;">OmniCure</td>
			<td style="width:166px;">S1000</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">Plasmid #54525</td>
			<td style="width:206px;">Addgene</td>
			<td style="width:166px;">#54525</td>
			<td>&#34;publicly available plasmid&#34; in text</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">Titramax 1000 Plate Shaker</td>
			<td style="width:206px;">Heidolph Instruments</td>
			<td style="width:166px;">1000</td>
			<td>&#34;plate shaker&#34; in text</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">Trypsin-EDTA</td>
			<td style="width:206px;">Gibco</td>
			<td style="width:166px;">25200056</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:264px;">Yeast Strain</td>
			<td style="width:206px;"></td>
			<td style="width:166px;">rhy1713</td>
			<td>Available on request- S288c (MAT&#945; lyp1&#916; can1&#916;::STE2pr_SpHIS5 his3&#916;1 leu2&#916;0 ura3&#916;0 met15&#916;0 cln3&#916;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://jove.unicartagenaproxy.elogim.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://jove.unicartagenaproxy.elogim.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://jove.unicartagenaproxy.elogim.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://jove.unicartagenaproxy.elogim.com/files/ftp_upload/59577/59577fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Detecting and Characterizing Protein Self-Assembly In Vivo by Flow Cytometry at JoVE.com

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

Universidad de Cartagena UDEC

Research

JoVE Journal

Biology

10.9K Views.  Stowers Institute for Medical Research. This article describes a FRET-based flow cytometry protocol to quantify protein self-assembly in both S. cerevisiae and HEK293T cells.

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

Combining QD-FRET and Microfluidics to Monitor DNA Nanocomplex Self-Assembly in Real-Time

Advances in genomics continue to fuel the development of therapeutics that can target pathogenesis at the cellular and molecular level. Typically functional inside the cell, nucleic acid-based therapeutics require an efficient intracellular delivery system. One widely adopted approach is to complex DNA with a gene carrier to form nanocomplexes via electrostatic self-assembly, facilitating cellular uptake of DNA while protecting it against degradation. The challenge lies in the rational design of efficient gene carriers, since premature dissociation or overly stable binding would be detrimental to the cellular uptake and therapeutic efficacy. Nanocomplexes synthesized by bulk mixing showed a diverse range of intracellular unpacking and trafficking behavior, which was attributed to the heterogeneity in size and stability of nanocomplexes. Such heterogeneity hinders the accurate assessment of the self-assembly kinetics and adds to the difficulty in correlating their physical properties to transfection efficiencies or bioactivities. We present a novel convergence of nanophotonics (i.e. QD-FRET) and microfluidics to characterize the real-time kinetics of the nanocomplex self-assembly under laminar flow. QD-FRET provides a highly sensitive indication of the onset of molecular interactions and quantitative measure throughout the synthesis process, whereas microfluidics offers a well-controlled microenvironment to spatially analyze the process with high temporal resolution (~milliseconds). For the model system of polymeric nanocomplexes, two distinct stages in the self-assembly process were captured by this analytic platform. The kinetic aspect of the self-assembly process obtained at the microscale would be particularly valuable for microreactor-based reactions which are relevant to many micro- and nano-scale applications. Further, nanocomplexes may be customized through proper design of microfludic devices, and the resulting QD-FRET polymeric DNA nanocomplexes could be readily applied for establishing structure-function relationships. 

We present a novel and powerful integration of nanophotonics (QD-FRET) and microfluidics to investigate the formation of polyelectrolyte polyplexes, which is expected to provide better control and synthesis of uniform and customizable polyplexes for future nucleic acid-based therapeutics.

Advances in genomics continue to fuel the development of therapeutics that can target pathogenesis at the cellular and molecular level. Typically ...

IP-FCM:  Immunoprecipitation Detected by Flow Cytometry

Immunoprecipitation detected by flow cytometry (IP-FCM) is an efficient method for detecting and quantifying protein-protein interactions. The basic principle extends that of sandwich ELISA, wherein the captured primary analyte can be detected together with other molecules physically associated within multiprotein complexes. The procedure involves covalent coupling of polystyrene latex microbeads with immunoprecipitating monoclonal antibodies (mAb) specific for a protein of interest, incubating these beads with cell lysates, probing captured protein complexes with fluorochrome-conjugated probes, and analyzing bead-associated fluorescence by flow cytometry. IP-FCM is extremely sensitive, allows analysis of proteins in their native (non-denatured) state, and is amenable to either semi-quantitative or quantitative analysis. As additional advantages, IP-FCM requires no genetic engineering or specialized equipment, other than a flow cytometer, and it can be readily adapted for high-throughput applications.

The IP-FCM method is presented, which allows a sensitive, robust, biochemical assessment of native protein-protein interactions, without requiring genetic engineering or large sample sizes.

Immunoprecipitation detected by flow cytometry (IP-FCM) is an efficient method for detecting and quantifying protein-protein interactions. The basic ...

Imaging Protein-protein Interactions in vivo

Protein-protein interactions are a hallmark of all essential cellular processes. However, many of these interactions are transient, or energetically weak, preventing their identification and analysis through traditional biochemical methods such as co-immunoprecipitation. In this regard, the genetically encodable fluorescent proteins (GFP, RFP, etc.) and their associated overlapping fluorescence spectrum have revolutionized our ability to monitor weak interactions in vivo using F&ouml;rster resonance energy transfer (FRET)1-3. Here, we detail our use of a FRET-based proximity assay for monitoring receptor-receptor interactions on the endothelial cell surface.

Imaging Protein-protein Interactions in vivo

This protocol describes how to image protein-protein interactions using a FRET-based proximity assay.

Protein-protein interactions are a hallmark of all essential cellular processes. However, many of these interactions are transient, or energetically weak, ...

Flow Cytometry Purification of Mouse Meiotic Cells

The heterogeneous nature of cell types in the testis and the absence of meiotic cell culture models have been significant hurdles to the study of the unique differentiation programs that are manifest during meiosis. Two principal methods have been developed to purify, to varying degrees, various meiotic fractions from both adult and immature animals: elutriation or Staput (sedimentation) using BSA and/or percoll gradients. Both of these methods rely on cell size and density to separate meiotic cells1-5. Overall, except for few cell populations6, these protocols fail to yield sufficient purity of the numerous meiotic cell populations that are necessary for detailed molecular analyses. Moreover, with such methods usually one type of meiotic cells can be purified at a given time, which adds an extra level of complexity regarding the reproducibility and homogeneity when comparing meiotic cell samples.
Here, we describe a refined method that allows one to easily visualize, identify, and purify meiotic cells, from germ cells to round spermatids, using FACS combined with Hoechst 33342 staining7,8. This method provides an overall snapshot of the entire meiotic process and allows one to highly purify viable cells from most stage of meiosis. These purified cells can then be analyzed in detail for molecular changes that accompany progression through meiosis, for example changes in gene expression9,10and the dynamics of nucleosome occupancy at hotspots of meiotic recombination11.

An efficient method to obtain highly purified viable meiotic fractions from mouse testis is described, which combines a refined cell dissociation protocol with fluorescent activated cell sorting (FACS). This method takes advantage of differences in the DNA content and nuclear density of discrete meiotic fractions.

The heterogeneous nature of cell types in the testis and the absence of meiotic cell culture models have been significant hurdles to the study of the ...

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of different cell types in vitro and in vivo.1-5 Although there are literally thousands of publications using such dyes, some of the most commonly encountered cell tracking applications include monitoring of:
<ol>
	<li>stem and progenitor cell quiescence, proliferation and/or differentiation6-8</li>
	<li>antigen-driven membrane transfer9 and/or precursor cell proliferation3,4,10-18 and</li>
	<li>immune regulatory and effector cell function1,18-21.</li>
</ol>
Commercially available cell tracking dyes vary widely in their chemistries and fluorescence properties but the great majority fall into one of two classes based on their mechanism of cell labeling. "Membrane dyes", typified by PKH26, are highly lipophilic dyes that partition stably but non-covalently into cell membranes1,2,11. "Protein dyes", typified by CFSE, are amino-reactive dyes that form stable covalent bonds with cell proteins4,16,18. Each class has its own advantages and limitations. The key to their successful use, particularly in multicolor studies where multiple dyes are used to track different cell types, is therefore to understand the critical issues enabling optimal use of each class2-4,16,18,24.
The protocols included here highlight three common causes of poor or variable results when using cell-tracking dyes. These are:
<ol>
	<li>Failure to achieve bright, uniform, reproducible labeling. This is a necessary starting point for any cell tracking study but requires attention to different variables when using membrane dyes than when using protein dyes or equilibrium binding reagents such as antibodies.</li>
	<li>Suboptimal fluorochrome combinations and/or failure to include critical compensation controls. Tracking dye fluorescence is typically 102 - 103 times brighter than antibody fluorescence. It is therefore essential to verify that the presence of tracking dye does not compromise the ability to detect other probes being used.</li>
	<li>Failure to obtain a good fit with peak modeling software. Such software allows quantitative comparison of proliferative responses across different populations or stimuli based on precursor frequency or other metrics. Obtaining a good fit, however, requires exclusion of dead/dying cells that can distort dye dilution profiles and matching of the assumptions underlying the model with characteristics of the observed dye dilution profile.</li>
</ol>
Examples given here illustrate how these variables can affect results when using membrane and/or protein dyes to monitor cell proliferation.

Successful use of cell tracking dyes to monitor immune cell function and proliferation involves several critical steps. We describe methods for: 1) obtaining bright, uniform, reproducible label-ing with membrane dyes; 2) selecting fluorochromes and data acquisition conditions; and 3) choosing a model to quantify cell proliferation based on dye dilution.

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of ...

Discovering Protein Interactions and Characterizing Protein Function Using HaloTag Technology

Research in proteomics has exploded in recent years with advances in mass spectrometry capabilities that have led to the characterization of numerous proteomes, including those from viruses, bacteria, and yeast.&#160; In comparison, analysis of the human proteome lags behind, partially due to the sheer number of proteins which must be studied, but also the complexity of networks and interactions these present. To specifically address the challenges of understanding the human proteome, we have developed HaloTag technology for protein isolation, particularly strong for isolation of multiprotein complexes and allowing more efficient capture of weak or transient interactions and/or proteins in low abundance. &#160;HaloTag is a genetically encoded protein fusion tag, designed for covalent, specific, and rapid immobilization or labelling of proteins with various ligands. Leveraging these properties, numerous applications for mammalian cells were developed to characterize protein function and here we present methodologies including: protein pull-downs used for discovery of novel interactions or functional assays, and cellular localization. We find significant advantages in the speed, specificity, and covalent capture of fusion proteins to surfaces for proteomic analysis as compared to other traditional non-covalent approaches. We demonstrate these and the broad utility of the technology using two important epigenetic proteins as examples, the human bromodomain protein BRD4, and histone deacetylase HDAC1.&#160; These examples demonstrate the power of this technology in enabling &#160;the discovery of novel interactions and characterizing cellular localization in eukaryotes, which will together further understanding of human functional proteomics. &#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;

HaloTag technology is a multifunctional technology which has shown significant success in isolation of both small and large protein complexes from mammalian cells.&#160; Here we highlight the advantages of this technology compared to existing alternatives and demonstrate its utility to study numerous aspects of protein function inside eukaryotic cells.

Research in proteomics has exploded in recent years with advances in mass spectrometry capabilities that have led to the characterization of numerous ...

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

Through a delicate balance between quiescence and proliferation, self renewal and production of differentiated progeny, hematopoietic stem cells (HSCs) maintain the turnover of all mature blood cell lineages. The coordination of the complex signals leading to specific HSC fates relies upon the interaction between HSCs and the intricate bone marrow microenvironment, which is still poorly understood[1-2].
We describe how by combining a newly developed specimen holder for stable animal positioning with multi-step confocal and two-photon in vivo imaging techniques, it is possible to obtain high-resolution 3D stacks containing HSPCs and their surrounding niches and to monitor them over time through multi-point time-lapse imaging. High definition imaging allows detecting ex vivo labeled hematopoietic stem and progenitor cells (HSPCs) residing within the bone marrow. Moreover, multi-point time-lapse 3D imaging, obtained with faster acquisition settings, provides accurate information about HSPC movement and the reciprocal interactions between HSPCs and stroma cells.
Tracking of HSPCs in relation to GFP positive osteoblastic cells is shown as an exemplary application of this method. This technique can be utilized to track any appropriately labeled hematopoietic or stromal cell of interest within the mouse calvarium bone marrow space.

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

The nature of the interactions between hematopoietic stem and progenitor cells (HSPCs) and bone marrow niches is poorly understood. Custom hardware modifications and a multi-step acquisition protocol allow the use of two-photon and confocal microscopy to image ex vivo labeled HSPCs homed within bone marrow areas, tracking interactions and movement.

Through a delicate balance between quiescence and proliferation, self renewal and production of differentiated progeny, hematopoietic stem cells (HSCs) ...

Cytosolic Calcium Measurements in Renal Epithelial Cells by Flow Cytometry

A variety of cellular processes, both physiological and pathophysiological, require or are governed by calcium, including exocytosis, mitochondrial function, cell death, cell metabolism and cell migration to name but a few. Cytosolic calcium is normally maintained at low nanomolar concentrations; rather it is found in high micromolar to millimolar concentrations in the endoplasmic reticulum, mitochondrial matrix and the extracellular compartment. Upon stimulation, a transient increase in cytosolic calcium serves to signal downstream events. Detecting changes in cytosolic calcium is normally performed using a live cell imaging set up with calcium binding dyes that exhibit either an increase in fluorescence intensity or a shift in the emission wavelength upon calcium binding. However, a live cell imaging set up is not freely accessible to all researchers. Alternative detection methods have been optimized for immunological cells with flow cytometry and for non-immunological adherent cells with a fluorescence microplate reader. Here, we describe an optimized, simple method for detecting changes in epithelial cells with flow cytometry using a single wavelength calcium binding dye. Adherent renal proximal tubule epithelial cells, which are normally difficult to load with dyes, were loaded with a fluorescent cell permeable calcium binding dye in the presence of probenecid, brought into suspension and calcium signals were monitored before and after addition of thapsigargin, tunicamycin and ionomycin.

Calcium is involved in numerous physiological and pathophysiological signaling pathways. Live cell imaging requires specialized equipment and can be time consuming. A quick, simple method using a flow cytometer to determine relative changes in cytosolic calcium in adherent epithelial cells brought into suspension was optimized.

A variety of cellular processes, both physiological and pathophysiological, require or are governed by calcium, including exocytosis, mitochondrial ...

Analysis of Cell Suspensions Isolated from Solid Tissues by Spectral Flow Cytometry

Flow cytometry has been used for the past 40 years to define and analyze the phenotype of lymphoid and other hematopoietic cells. Initially restricted to the analysis of a few fluorochromes, currently there are dozens of different fluorescent dyes, and up to 14-18 different dyes can be combined at a time. However, several limitations still impair the analytical capabilities. Because of the multiplicity of fluorescent probes, data analysis has become increasingly complex due to the need of large, multi-parametric compensation matrices. Moreover, mutant mouse models carrying fluorescent proteins to detect and trace specific cell types in different tissues have become available, so the analysis (by flow cytometry) of auto-fluorescent cell suspensions obtained from solid organs is required. Spectral flow cytometry, which distinguishes the shapes of emission spectra along a wide range of continuous wavelengths, addresses some of these problems. The data is analyzed with an algorithm that replaces compensation matrices and treats auto-fluorescence as an independent parameter. Thus, spectral flow cytometry should be capable of discriminating fluorochromes with similar emission peaks and can provide a multi-parametric analysis without compensation requirements.
This protocol describes the spectral flow cytometry analysis, allowing for a 21-parameter (19 fluorescent probes) characterization and the management of an auto-fluorescent signal, providing high resolution in minor population detection. The results presented here show that spectral flow cytometry presents advantages in the analysis of cell populations from tissues difficult to characterize in conventional flow cytometry, such as the heart and the intestine. Spectral flow cytometry thus demonstrates the multi-parametric analytical capacity of high-performing conventional flow cytometry without the requirement for compensation and enables auto-fluorescence management.

This article describes spectral cytometry, a new approach in flow cytometry that uses the shapes of emission spectra to distinguish fluorochromes. An algorithm replaces compensations and can treat auto-fluorescence as an independent parameter. This new approach allows for the proper analysis of cells isolated from solid organs.

Flow cytometry has been used for the past 40 years to define and analyze the phenotype of lymphoid and other hematopoietic cells. Initially restricted to ...

Measuring Endoreduplication by Flow Cytometry of Isolated Tuber Protoplasts

Endoreduplication, the replication of a cell's nuclear genome without subsequent cytokinesis, yields cells with increased DNA content and is associated with specialization, development and increase in cellular size. In plants, endoreduplication seems to facilitate the growth and expansion of certain tissues and organs. Among them is the tuber of potato (Solanum tuberosum), which undergoes considerable cellular expansion in fulfilling its function of carbohydrate storage. Thus, endoreduplication may play an important role in how tubers are able to accommodate this abundance of carbon. However, the cellular debris resulting from crude nuclear isolation methods of tubers, methods that can be used effectively with leaves, precludes the estimation of the tuber endoreduplication index (EI). This article presents a technique for assessing tuber endoreduplication through the isolation of protoplasts while demonstrating representative results obtained from different genotypes and compartmentalized tuber tissues. The major limitations of the protocol are the time and reagent costs required for sample preparation as well as relatively short lifespan of samples after lysis of protoplasts. While the protocol is sensitive to technical variation, it represents an improvement over traditional methods of nuclear isolation from these large specialized cells. Possibilities for improvements to the protocol such as recycling enzyme, the use of fixatives, and other alterations are proposed.

The protocol described herein is a method for measuring endoreduplication within tubers of potato (Solanum tuberosum). It includes plasmolysis and protoplast extraction steps to decrease the noise and debris in downstream flow cytometric analysis.

Endoreduplication, the replication of a cell's nuclear genome without subsequent cytokinesis, yields cells with increased DNA content and is associated ...

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

Summary

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Chapters in this video

Combining QD-FRET and Microfluidics to Monitor DNA Nanocomplex Self-Assembly in Real-Time

IP-FCM: Immunoprecipitation Detected by Flow Cytometry

Imaging Protein-protein Interactions in vivo

Flow Cytometry Purification of Mouse Meiotic Cells

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Discovering Protein Interactions and Characterizing Protein Function Using HaloTag Technology

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

Cytosolic Calcium Measurements in Renal Epithelial Cells by Flow Cytometry

Analysis of Cell Suspensions Isolated from Solid Tissues by Spectral Flow Cytometry

Measuring Endoreduplication by Flow Cytometry of Isolated Tuber Protoplasts