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Selective sequestration of signalling proteins in a membraneless organelle reinforces the spatial regulation of asymmetry in Caulobacter crescentus

Nature Microbiology volume 5, pages 418–429 (2020)Cite this article

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Abstract

Selective recruitment and concentration of signalling proteins within membraneless compartments is a ubiquitous mechanism for subcellular organization1,2,3. The dynamic flow of molecules into and out of these compartments occurs on faster timescales than for membrane-enclosed organelles, presenting a possible mechanism to control spatial patterning within cells. Here, we combine single-molecule tracking and super-resolution microscopy, light-induced subcellular localization, reaction-diffusion modelling and a spatially resolved promoter activation assay to study signal exchange in and out of the 200 nm cytoplasmic pole-organizing protein popZ (PopZ) microdomain at the cell pole of the asymmetrically dividing bacterium Caulobacter crescentus4,5,6,7,8. Two phospho-signalling proteins, the transmembrane histidine kinase CckA and the cytoplasmic phosphotransferase ChpT, provide the only phosphate source for the cell fate-determining transcription factor CtrA9,10,11,12,13,14,15,16,17,18. We find that all three proteins exhibit restricted rates of entry into and escape from the microdomain as well as enhanced phospho-signalling within, leading to a submicron gradient of activated CtrA-P19 that is stable and sublinear. Entry into the microdomain is selective for cytosolic proteins and requires a binding pathway to PopZ. Our work demonstrates how nanoscale protein assemblies can modulate signal propagation with fine spatial resolution, and that in Caulobacter, this modulation serves to reinforce asymmetry and differential cell fate of the two daughter cells.

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Fig. 1: The phospho-signalling cascade that activates a cell fate transcription factor is sequestered within the PopZ microdomain.
Fig. 2: Entry into the PopZ microdomain is selective and is regulated by binding.
Fig. 3: Dynamics within the PopZ microdomain modulate the gradient of active CtrA-P.
Fig. 4: Coordinated signalling within a membraneless microdomain at the cell pole facilitates asymmetric patterning of the CtrA-P global regulator.

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Data availability

The BacTRIP sequencing data for the mapping, transcription and normalization datasets have been uploaded to the National Center for Biotechnology Sequence Read Archive at https://www.ncbi.nlm.nih.gov/sra/ under accession no. PRJNA595753. The lists of plasmids and strains generated as part of this study are available in Supplementary Tables 2 and 3. A complete list of parameters used for modelling the CtrA activation pathway is available in Supplementary Table 7. Source data for most panels in the main and Extended Data figures is available online in the Source Data. The remaining data supporting the findings of this study are available from the corresponding author on request.

Code availability

The code that supports the findings of this study, including the analysis of single-molecule tracks, reaction-diffusion model of CtrA activation pathway and analysis of the BacTRIP data are available from the corresponding author on request.

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Acknowledgements

We thank J.W. Kern for help with plasmid design and construction, M.D. Melfi for providing the RT–qPCR protocol, A. Lovell and M.R. Eckart at the Stanford Protein and Nucleic Acid Facility for support in using MySeq and RT–qPCR and for running the surface plasmon resonance experiments, J.M. Schrader for sharing unpublished data on mRNA half-life in Caulobacter, A. Olson and the Stanford Neuroscience Microscopy Service (supported by grant no. NIH NS069375) for providing equipment for and assistance with the photobleaching experiments and A.H. Squires for critical feedback on the manuscript. We also thank H.H. McAdams for helpful discussions on the modelling of signal transduction and all members of the Shapiro and Moerner laboratories for helpful discussions throughout the project. We acknowledge support from the Gordon and Betty Moore Function (award no. GBMF 2550.03) to the Life Sciences Research Foundation (to K.L.), the Weizmann Institute of Science National Postdoctoral Award Program for Advancing Women in Science (to K.L.) and from the National Institute of General Medical Sciences of the National Institutes of Health under award nos. T32GM007276 to T.H.M, R01-GM086196 to W.E.M. and L.S., R35-GM118067 to W.E.M. and R35-GM118071 to L.S. L.S. is a Chan Zuckerberg Biohub Investigator. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Author notes

  1. Lexy von Diezmann

    Present address: Center for Cell and Genome Science, University of Utah, Salt Lake City, UT, USA

  2. Lexy von Diezmann

    Present address: School of Biological Sciences, University of Utah, Salt Lake City, UT, USA

  3. Thomas H. Mann

    Present address: NOMIS Center for Immunobiology and Microbial Pathogenesis, Salk Institute for Biological Studies, La Jolla, CA, USA

  4. These authors contributed equally: Keren Lasker, Lexy von Diezmann.

Authors and Affiliations

  1. Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA

    Keren Lasker, Xiaofeng Zhou, Daniel G. Ahrens, Thomas H. Mann & Lucy Shapiro

  2. Department of Chemistry, Stanford University, Stanford, CA, USA

    Lexy von Diezmann & W. E. Moerner

  3. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Lucy Shapiro

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Contributions

This study was conceived by K.L. and L.v.D. The study was designed by K.L., L.v.D., W.E.M. and L.S. The single-molecule experiments and Monte Carlo diffusion simulations were performed and analysed by L.v.D. The diffraction-limited imaging and photobleaching experiments were performed and analysed by K.L. and L.v.D. The qPCR, surface plasmon resonance and western blot data were collected by K.L. and D.G.A. and analysed by K.L. The integrative reaction-diffusion modelling was designed and interpreted by K.L. and L.v.D., and implemented and performed by K.L. BacTRIP was designed, implemented and analysed by K.L. and X.Z. The paper was written by K.L., L.v.D., T.H.M., W.E.M. and L.S.

Corresponding author

Correspondence to Lucy Shapiro.

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Extended data

Extended Data Fig. 1 Diffraction-limited profiles of CckA-eYFP, ChpT-eYFP, CtrA-eYFP-14 localization, ChpT/PopZ interaction, and PopZ-eYFP dynamics.

a. Fluorescent constructs used to image the co-localization of CckA, ChpT, and CtrA with respect to PopZ (cf. Supplementary Table 1). pamcherry-popZ, cckA-eyfp and chpT-eyfp are integrated at the native promotor/locus. CtrA-eYFP-14 (sandwich fusion with C-terminal degradation tag) is driven by a PxylX promoter and is expressed from a high copy plasmid. b. CtrA-eYFP-14 has similar cell cycle behavior as CtrA. Shown is a time-resolved western blot of CtrA protein expressed from CtrA promoter and CtrA-eYFP-14 protein expressed from a vanillate-regulated promoter probed with anti-CtrA antibody. The KL6039 strain was induced with 1 mM vanillate for 1.5 hours prior to induction and during recovery. Data are representative of three biological replicates. c. Diffraction-limited images of predivisional Caulobacter cells expressing CckA-eYFP, ChpT-eYFP, or CtrA-eYFP-14 (top row) and PAmCherry-PopZ (middle row); new poles marked with a white arrow. Scale bars: 2 µm. Fluorescence intensity profiles, including percentage of CckA, ChpT, or CtrA signal at new and old poles, shown along normalized cell length (n = 55, 129, 143 cells respectively). d. Purified WT ChpT binds directly to PopZ, as measured by surface plasmon resonance (left), while purified CtrA does not (right). n equals three biologically independent samples. e. Recovery following targeted photobleaching of a portion of an extended PopZ microdomain. ΔpopZ cells expressing popZ-eyfp from a high copy plasmid were imaged for many frames of laser scanning confocal microscopy following targeted photobleaching with high-intensity 514 nm laser light. (left) The fraction of signal in the bleached region compared to signal in the entire microdomain is shown in red, with exponential fit in black. Shown is the mean ± s.d.; n equal to 6 cells. (right) Representative field of view from the PopZ FRAP experiment. A bleached cell and its recovery dynamics is shown next to an unbleached cell. Scale bars: 2 μm.

Source data

Extended Data Fig. 2 CckA dynamics within the poles.

a-c, simulations of membrane motion (cf. Supplementary Note 1.9). a. A single Brownian trajectory on the cell surface. The 2D projection of the trajectory exhibits erroneously low diffusivity at the poles and equator of the cell, where 3D information is lost. b. Left: mean-squared-displacement analysis of 3D diffusion accurately infers the diffusion coefficient of motion on the cell membrane, while restriction to 2D measurements induces systematic errors. Right: comparison of D values estimated from subtrajectories inside and outside the poles. Relative D underestimation inside the poles was ≤ 8% for the measured values of polar D for CckA-eYFP (0.02-0.04 µm2/s). c. Experimental approach of 3D tracking using the DH-PSF. Left, 4 second CckA-eYFP trajectory overlaid on brightfield image of a Caulobacter cell. Middle, translation and rotation in the double-helix PSF is used to detect 2D and 3D motion. Right, the corresponding 3D trajectory along the circumference of the cell. Scale bar: 500 nm. d. Caulobacter cells expressing CckA-eYFP imaged with confocal microscopy following targeted photobleaching. Signal at the old and new pole regions is defined as the fraction of total cell fluorescence (upper plot, errorbars show ± 95% CI). After 10 minutes, the initial ratio of old to new pole signal is restored (lower plot). Average of n = 6 cells. Scale bar: 2 μm. e. Fluorescence decay in cells with photobleaching only from imaging, (n = 9, cyan), cells that were uniformly bleached with the photobleaching laser (n = 3, red), and with targeted photobleaching at the old poles (n = 6, magenta) (same statistics as d.). f-g. data shown in d. if using a correction factor from ‘unbleached’ or ‘completely bleached’ cells. Regardless of the correction factor used, new poles exhibit loss and old poles exhibit recovery. Errorbars: 95% CI. h. Representative images of cells showing the StpX-mChy marker used to identify stalks, that is the old pole, and the conditions necessary for inclusion as ‘predivisional,’ as consistently observed in all cells. Scale bar: 2 μm. i. Predicted concentrations from reaction-diffusion simulations of CckA motion with fast and slow unbinding to the microdomain overlaid with data of (d). CckA molecules at the old pole were ‘bleached’ by marking the molecules as dark CckA molecules.

Extended Data Fig. 3 Super-resolved distribution of PopZ, CckA, ChpT, and CtrA within the poles.

a. Single-molecule distributions of CckA and PopZ localizations averaged from old poles (n = 29 poles) and new poles (n = 13) of predivisional cells. Blue arrows show the measured diameters of CckA. Scale bar: 200 nm. b. Projection of all single-molecule localizations of CckA and PopZ in the new pole, shown facing down the cell axis (531 and 477 localizations, respectively). Top and right: 1D histogram of CckA and PopZ with 25 nm bins. Centre: mean. The half widths at half maximum (HWHM) are calculated for CckA from the standard deviations of molecule positions along the Y and Z axes, with magnitudes shown by blue arrows (error: 95% CI from localization resampling). c. 1D profiles and 2D histograms (25 nm bin size) of CtrA, ChpT, and PopZ localizations averaged with respect to PopZ (60, 27, and 27 cells respectively for each reconstruction). Scale bar: 200 nm.

Extended Data Fig. 4 Selective polar permeability and motion of cytoplasmic proteins.

a-c. Simulations of freely diffusing cytoplasmic molecules with D = 1.8 μm2/s are observed in the poles (Supplementary Note 2.9). A step size of 0.5 ms was used, and to match experimental conditions, trajectories were reconstructed at 20 ms sampling rate (mimicking motion blur) and 40 nm localization error. a. The XY components of a single 3D trajectory, corresponding to localizations of a single freely-diffusing molecule. b. The histogram of localizations from 1,000 simulated 2-second trajectories, with poles defined as extending200 and 250 nm away from the tip (consistent with definitions used for experimental data) marked. c. 1D profile of histogram shown in (b). Approximately 4.1% and 5.5% of localizations would be expected to appear within the new and old poles (orange), respectively, if molecular motion were unobstructed. d. White light image and 2D histogram (40 nm bin size) of fPIF-eYFP and PAmCherry-PopZ single-molecule localizations. Scale bar: 2 μm. Consistent in 16 cells analyzed (Fig. 2b). e. White light image and 2D histogram (40 nm bin size) of eYFP and PAmCherry-PopZ single-molecule localizations. Scale bar: 2 μm. Consistent in 17 cells analyzed (Fig. 2b). f. 1D MSD values in the body of predivisional Caulobacter cells as shown in Fig. 1h, including eYFP and fPIF. 95% CI shown. g. Fitting the MSD data to an equation for anomalous diffusion was not able to precisely specify D and α due to the short track durations available. Points: fits values of D and α generated from 200 resamples of CtrA and ChpT tracks either from the cell body or from the poles. Blue lines mark combinations of D and α that would yield apparent diffusion coefficients Dapp for a given short-time approximation, calculated as \(D_{app} = D\tau ^{1 - \alpha }\) for Dapp = 1.8 μm2/s and τ = 20 ms (the frame integration time, right line) or τ = 100 ms (the maximum time included in the fit, left line). The trajectories measured are consistent with a short-time value of Dapp = 1.8 μm2/s and suggest that α < 0.7. In contrast, diffusion within the poles appeared relatively Brownian, with 0.75 ≤ α ≤ 0.9. The MSD curve of ChpT at the poles cannot be accurately fit due to confinement effects.

Extended Data Fig. 5 Correlative imaging of a membrane-bound, pole-insensitive construct and PopZ.

a. Diffraction-limited epifluorescence data of the FixL(TM-PAS)-eYFP construct (the transmembrane and PAS domains of Caulobacter FixL fused to eYFP) and PAmCherryPopZ. Overlap of PopZ and FixL(TM-PAS)-eYFP at the poles was consistently observed in 20 cells. Scale bar: 2 μm b. Brightfield image and 2-color histogram of single-molecule localizations of FixL(TM-PAS)-eYFP and PAmCherry-PopZ. Scale bar: 2 μm. c. A selection of five single-molecule tracks of the upper cell in b., shown relative to the PAmCherry-PopZ super-resolution reconstruction (red density). Detailed single-molecule analysis showed consistent entry into the membrane proximal to PopZ for the 4 cells analyzed. Scale bar: 200 nm.

Extended Data Fig. 6 Setup of the reaction-diffusion simulation of CtrA activation pathway.

a. A simplified schematic of the reaction and diffusion events used in our CtrA activation model. Dotted red arrows indicate CckA, ChpT, and CckA binding to either the new pole (NP) or the old pole (OP) species. The width of the arrow represents the strength of binding, with thicker arrows for tighter binding. The colored edges out of CckA (blue), ChpT (orange), and CtrA (green) boxes indicate diffusion inside the pole and in the body of the cell. The length of the arrow represents the rate of diffusion, with shorter arrows representing slower diffusion. Phosphotransfer reactions are shown in black. Each arrow is labeled with a reaction number from 1 to 39. Information on each reaction is found in Supplementary Table 7. b. A complete schematic of the phosphotransfer reactions including binding and transfer events. Reaction numbers match the simplified schematic in (a). c. Concentrations of each molecular species in the system were modeled as a function of position along the long axis of the cell and time. The governing partial differential equation (PDE) for each species was approximated as a set of ordinary differential equations (ODEs). The spatial dimension of a 4 μm cell was discretized into n = 80 bins, each representing a 0.05 μm Δx section of the cell. Each species is broken into n sub-species, one per bin. The simulation advances in 0.1 second steps. We use the second-order center difference formula to approximate the Laplacian operator and convert each PDE into a set of ODEs (Supplementary Note 2.2, equation S7). d. Simulation results with wildtype parameters (Fig. 3a) showing steady-state protein concentrations for CckA (blue), ChpT (orange), and CtrA (green) as a function of distance from the new pole in the predivisional cell.

Extended Data Fig. 7 Reaction-diffusion simulation of the CtrA activation pathway.

a. Global analysis of the effect of diffusion coefficients and reaction rates on CtrA~P amplitude and distribution. Sensitivity of CtrA~P amplitude (upper heat map) and CtrA~P distribution (lower heat map) to changes in (i) the rate of CckA, ChpT, and CtrA binding to either the new pole or old pole microdomains, (ii) diffusion coefficients of either CckA, ChpT, or CtrA, and (iii) phosphotransfer rates. Each heat map rectangle specifies one parameter (for example top/left rectangle specifies sensitivity to changes in the binding rate between CckA and the new pole microdomain). The color of the rectangle indicates the normalized sensitivity value (from low sensitivity (blue) to high sensitivity (red)) to changes in parameter value. b. Detailed results for changes in 8 parameters. For each parameter the fraction of total CtrA~P (top) and distribution of CtrA~P along the long axis of the cell (bottom) is shown for steady-state. Gray arrows signify the increasing value of the parameter. c. Contribution of the rate of phosphotrasfer from CckA to ChpT on the distribution of CtrA~P. We simulated the distribution of CtrA~P as a function of three different phosphorylation rates of CckA by ChpT: 10/s forward and 1/s back (blue), 100/s forward and 10/s back (green), and 100/s forward and 1/s back (brown). We varied the binding affinity between ChpT and PopZ between 0 and 10 μM, as well as the auto-phosphorylation rate of CckA: slow by setting CckA kHalf to 150 (left) as well as fast by setting the kHalf to 5 (right).

Extended Data Fig. 8 Diffraction-limited profiles of CckA-eYFP, ChpT-eYFP, CtrA-eYFP-14 localization and expression of CtrA~P-regulated genes in PopZ overexpression background.

a. Overexpression of PopZ does not affect CckA and ChpT abundance but does result in a reduction in CtrA abundance. Mixed population western blot of CckA-eYFP protein expressed from the cckA promoter, ChpT-eYFP expressed from the chpT promoter, and CtrA-14-eYFP protein expressed from a vanillate-regulated promoter. CckA-eYFP and ChpT-eYFP were probed with anti-YFP antibody while CtrA-14-eYFP was probed with anti-CtrA antibody. The protein abundance was measured in both wildtype (I) and PopZ overexpression (II) conditions. n equals three biologically independent experiments repeated with similar results. b. Diffraction-limited images of predivisional Caulobacter cells expressing CckA-eYFP, ChpT-eYFP, or CtrA-eYFP-14 (top row) and PAmCherry-PopZ (middle row) in a PopZ overexpression background; new poles marked with a white arrow. Scale bars: 2 µm. The percentages of total fluorescent signal at the new and old poles are indicated in blue, orange, and green for CckA, ChpT, and CtrA. Fluorescence intensity profiles shown along normalized cell length (n = 45, 52, 61 cells respectively). c. Overexpression of PopZ leads to a reduction in the expression of CtrA regulated genes. RT-qPCR of mRNA levels of five CtrA regulated genes at the 90-minute time point. Transcription was measured for wildtype cells (left) as well as cells overexpressing PopZ (right). Shown is the mean ± s.d.; n equals three biologically independent experiments repeated with similar results.

Source data

Extended Data Fig. 9 Spatially and temporally resolved transcriptional activity.

a. Time-resolved RT-qPCR of mRNA from an eyfp gene under the control of the CtrA-regulated P350 promoter integrated at four loci of the chromosome (L1, L3, L7, and L8, cf. Figure 3). Shown is mean ± s.d.; n = 3. Below plot: Replication times of these loci during the cell cycle, and cell cycle schematic of loci/dividing cells. Chromosome: black ovals. Red areas indicate polar PopZ microdomain, green tone indicates CtrA~P levels. Transcription is normalized to the five-minutes time point of L1. b. Time-resolved RT-qPCR of eyfp expressed from PxylX::eyfp at chromosomal loci L1 and L8 demonstrates a copy number effect. The observed increase at L1 and L8 represents the effect of chromosome replication, as xylose concentration and promotor activity are not expected to vary over time. Shown is mean ± s.d.; n = 3. c. eyfp transcription measured in cells at the 90 minute cell cycle time point at L1, L3, L7, and L8 in five genetic backgrounds. In the first three backgrounds, eyfp expression is controlled by the CtrA~P-regulated P350 promoter in the presence of: native wildtype ctrA (green), the phosphomimetic ctrA(D51E) driven by PxylX in a delta ctrA background (black), or ctrA driven by PxylX in a delta ctrA background (red). In the remaining backgrounds, eyfp expression is driven by the CtrA~P-regulated promoter PpilA in the presence of native wildtype ctrA (blue), and the xylose-driven promoter PxylX in the presence of xylose (gray). The wildtype ctrA, the phosphomimetic ctrA D51E, and eyfp driven by PxylX (green, black, and gray) are a subset of the data presented in Fig. 3b. The position-specific transcriptional output present for the CtrA~P-regulated P350 promoter in native conditions is lost when the sole CtrA copy is xylose-induced phosphomimetic CtrA(D51E) (black) but is recovered with xylose-inducible wildtype ctrA (red). Shown is mean + s.d.; n = 3. d. Simulated CtrA~P distribution profiles and their effect on transcription from P350. (left), possible CtrA~P profiles simulated with altered binding coefficient value between ChpT and PopZ at the new pole (cf. Extended Data Fig. 7a). Profile 4 (light brown) reflects wildtype parameters (Fig. 3a). (right) calculated transcript levels from P350 for each CtrA~P profile (Methods). Profile 4 was generated using wild-type parameters and best matched the experimental data (black, Fig. 3b).

Source data

Extended Data Fig. 10 High throughput assay to probe gene expression as a function of genomic position.

a. Graphical representation of the BacTRIP method. (I) A library of reporter plasmids generates a library of barcoded Caulobacter loci (purple, green, and red). Light gray: Tn5 mediated transposon. This includes a 5’ mosaic end (ME) followed by a SalI site, kanamycin resistance cassette (KanR), the origin of replication (ori), a promoter (gray), a gene of interest (yellow), a transcribed barcode (colors), a stop codon, the metK gene terminator from Caulobacter, and the 3’ ME. Transformation of the reporter plasmid library into Caulobacter introduces at most one barcode into each cell at a random locus (light brown box). Each barcoded strain reports local transcriptional activity at its locus. (II) DNA. Genomic DNA is used both to determine the integration locus (left) and the number of times each barcode was integrated (right). To determine the locus of each integrated barcode we digested the extracted DNA with SalI and used reverse PCR with mapping primers (Supplementary Table 8) to amplify fragments with an ori element for sequencing. To determine the number of times each barcode was integrated we amplified and sequenced the extracted DNA with counting primers (Supplementary Table 8). (III) RNA. We produced cDNA for sequencing from extracted, reverse-transcribed mRNA. The resulting quantity of inferred mRNA abundance was normalized by total copies of the barcode present in the library. b. BacTRIP results. We used three different CtrA binding sites to probe the wildtype CCNA_00350 promoter (PWT350), an engineered promoter in which we replaced the CtrA binding motif (red) of P350 with a consensus motif (PAT350), as well as a mutant site that is not recognized by CtrA (PMT350). We synchronized the three populations of cells and preformed BacTRIP on predivisional cells at the 90 minute time point. The bar graphs show abundance of 161, 156, and 148 barcoded eyfp as a function of locus distance from the new pole for pTripTn5- PWT350::eyfp, pTripTn5- PAT350::eyfp, and pTripTn5- PMT350::eyfp.

Supplementary information

Supplementary Information

Supplementary Notes 1–4, Supplementary Tables 1–8 and Supplementary References.

Reporting Summary

Supplementary Video 1

Single-molecule tracks of CtrA-eYFP-14. Exemplary raw data used to produce single-molecule trajectories of CtrA-eYFP-14. First track: entry to the new pole. (Data plotted in Fig. 1f, left). Second track: exit from the old pole. As noted in Methods, while not all single molecules used to produce tracks persisted this long before photobleaching, these results (reversible slowing of proteins within the poles) were consistently observed in 89 cells for CtrA-eYFP-14.

Source data

Source Data Fig. 1

Statistical Source Data for panels g and h.

Source Data Fig. 2

Statistical Source Data for panels b, d and e.

Source Data Fig. 3

Statistical Source Data for panel b.

Source Data Extended Data Fig. 1

SPR data for panel d and Statistical Source Data for panel e.

Source Data Extended Data Fig. 1

Full blots for panel b.

Source Data Extended Data Fig. 8

Representative RT-qPCR data for panel c.

Source Data Extended Data Fig. 8

Full blots for panel a.

Source Data Extended Data Fig. 9

Statistical Source Data for panels a–c.

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Lasker, K., von Diezmann, L., Zhou, X. et al. Selective sequestration of signalling proteins in a membraneless organelle reinforces the spatial regulation of asymmetry in Caulobacter crescentus. Nat Microbiol 5, 418–429 (2020). https://doi.org/10.1038/s41564-019-0647-7

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