Single-array measurements reveal non-uniform, mosaic-like chemosensory arrays in bacteria

Domain: physics
Published: 2025-12-14
Editorial outcome: accept

Abstract
Motile bacteria use supramolecular arrays to detect effector gradients in their environment. In Escherichia coli, thousands of chemoreceptor molecules with diverse sensory properties cooperatively modulate the kinase activity of these arrays and, via phosphotransfer to a diffusible response regulator, control the cell’s swimming behavior. Various methods have been used to study these sensory arrays in live cells, from population-level assays to single-cell measurements, revealing hierarchical coupling interactions that underlie their remarkable sensory properties. However, measuring the responses of individual arrays has remained a challenge. Here, by combining the kinase and response regulator into a functional hybrid protein that resides within the array, we directly measured the kinase responses of individual arrays in live cells. These measurements revealed highly diverse and growth-phase-dependent sensory properties of individual arrays. Even arrays within the same cell were not substantially correlated. Additionally, we directly observed dynamic shifts in receptor occupancy within individual arrays. Overall, these data suggest that each array contains a ‘frozen’ non-uniformity, reflecting its unique assembly history and resulting in a mosaic arrangement of cooperative signaling regions, each with distinct receptor content. Consistent with this view, measured dose-responses of individual arrays mostly exhibit low cooperativity.

Introduction
The ability of motile bacteria to swim toward beneficial effectors (attractants) and away from harmful effectors (repellents)1 influences many aspects of bacterial ecology2 and human health3,4. This behavior, termed chemotaxis, relies on sensory complexes comprising thousands of transmembrane receptor molecules that detect effector gradients along the swimming trajectory of the cell and modulate its swimming behavior accordingly. The architecture of these extended sensory arrays in E. coli is illustrated in Fig. 1a5–8. Each cell can have several arrays that preferentially nucleate at the cell poles and expand over multiple cell divisions9–11. The most abundant chemoreceptors are Tar and Tsr, which primarily detect aspartate and serine, respectively12,13. The minimal core signaling unit consists of two receptor trimers associated with a homodimeric kinase (CheA) and two adaptor proteins (CheW)14–17. These core complexes further aggregate through direct contacts between the kinase CheA.P5 domain in one complex and the CheW protein in another, forming a networked CheA–CheW baseplate and extended arrays comprising thousands of receptor molecules. Although receptor-mediated signals can be integrated within core complexes17,18, extended arrays exhibit enhanced cooperativity due to long-range allosteric interactions8,16, which lead to enhanced sensitivity and increased dynamic range19–21. Stimuli detected by the receptors modulate the kinase activity of the array, which in turn controls the phosphorylation state of the cytoplasmic response regulator CheY. In its phosphorylated form, CheYp binds to the flagellar motor and promotes random changes in swimming direction. In the steady state, these reactions are balanced by a dedicated phosphatase (CheZ), which removes phosphate groups from CheY to ensure rapid sensory responses. Utilizing this steady-state balance, kinase activity can be monitored in live cells by measuring FRET between fluorescently tagged CheY and CheZ20,22. Such FRET-based assays have been used to measure kinase responses averaged across entire cell populations, revealing key properties of these sensory complexes, including cooperative responses at multiple levels that enhance receptor detection sensitivity and dynamic range16,18–20,23–25. These measurements have also contributed to a more quantitative understanding of sensory-array signaling25–28. Kinase responses have also been detected in single cells29–32, revealing cell-to-cell variability and temporal fluctuations. However, measuring the kinase activity of individual arrays within live cells has remained a challenge.

Here, we present an assay that enables the direct measurement of kinase responses in individual sensory arrays within live bacterial cells. This assay relies on combining the kinase CheA with the response regulator CheY to create a fluorescently tagged hybrid protein (CheA::CheY–mYFP), which permanently localizes to the array. In this configuration, the affinity of CheZ for CheYp in each array provides a direct readout of its kinase activity. In addition, by also tagging either Tar or Tsr in distinct strains, we could measure their occupancy relative to CheA within individual arrays. The observations presented here suggest that receptor arrays are highly non-uniform, with a mosaic arrangement of cooperative subregions, each with distinct receptor content. This non-uniformity likely reflects the unique assembly history of each array as it develops over multiple generations. Interestingly, internal non-uniformity in array composition also implies that the size of the cooperative sub-regions, which determines the scale over which this non-uniformity is averaged, can fundamentally influence how signals are integrated within the array.

Results
Measuring kinase activity in individual arrays
As illustrated in Fig. 1a (left), ligand binding to the chemoreceptors modulates the phosphorylation rate of the CheA.P1 domain—referred to here as ‘kinase activity’—mediated by interaction of this domain with the catalytic domain complex CheA.P3-4. The diffusible response regulator CheY acquires phosphoryl groups from CheA.P1, forming CheYp33. CheYp then diffuses from the array to the cell’s flagellar motors to trigger changes in swimming behavior. These reactions are balanced by CheZ-mediated dephosphorylation of CheYp.

To follow kinase activity at the array, we made a strain (VF17) having two chromosomal modifications (Fig. 1b and Methods): First, we replaced the CheA.P2 domain with fluorescently tagged CheY, yielding a hybrid kinase–phosphotransfer protein CheA::CheY–mYFP. This design ensures that phosphotransfer from CheA.P1 to CheY occurs internally within the array, while CheYp remains part of the array. Second, we fused the phosphatase CheZ to mScarlet and introduced a point mutation (F98S) that abolishes direct interaction between CheZ and CheA, while preserving its phosphatase activity toward CheYp29,34. Thus, in this new configuration, the tagged CheZ associates with the array only through transient interactions with CheYp during catalysis (Fig. 1b). As a result, the ratio of red (CheZ–mScarlet) to yellow (CheA::CheY–mYFP) fluorescence in each array directly reflects the rate of CheYp dephosphorylation, given by [CheYp·CheZ]·Kcat, normalized by the size (CheA content) of the array. Thus, at steady state, this red-to-yellow ratio provides a readout of the kinase activity per CheA molecule in the array:

Kinase activity ≈ 'red' fluor / 'yellow' fluor = [CheYp·CheZ] / [CheA::CheY] = V de:phos:ofYp / [CheA::CheY] = V phos:ofP1 / [CheA::CheY]

where V de:phos:ofYp and V phos:ofP1 are the velocities of the dephosphorylation and kinase reactions. Notably, these relationships hold for each array, independently of other arrays that may be present in the cell.

Some inherent cross-talk between arrays within the same cell might be expected due to shared CheZ (Supplementary Notes 1). To estimate this cross-talk, we first evaluated the fraction of CheZ proteins bound to arrays in the unstimulated state (Fig. S1). We found that the amount of CheZ bound to the arrays was up to approximately 40%, and a few cells containing more than one array, in which this fraction exceeded 50%, were excluded from further analysis. Based on these data, we estimated the mutual influence of kinase activity between different arrays in the same cell to be about 10%, rising to about 15% in extreme cases where a very small array coexists with a much larger one (Fig. S1).

To verify that the hybrid protein retains its dual function, we measured FRET between CheA::CheY–mYFP and CheZ–mScarlet in cell populations of the new VF17 strain. Clear responses to an attractant ligand (serine) were indeed observed (Fig. 1c, inset), indicating that CheZ association with the array is ligand dependent. This also implies that sensory inputs modulate both CheA autophosphorylation and phosphotransfer to CheY. Because these cells lack the adaptation enzymes, the responses persisted until the ligand was removed. Moreover, the ligand dose-dependent kinase responses closely matched those of native arrays with unmodified kinase (Figs. 1c and S2), exhibiting similar Hill coefficients (slopes) and ligand sensitivities (K1/2). These results are also consistent with previous measurements of mixed arrays8,23. The minor shift in K1/2 observed in Fig. 1c likely reflects slight difference in receptor expression (Fig. S2), either due to the added label in the new VF17 strain or due to modifications in the reference strain, namely, the cheY cheZ deletions and/or the plasmid-borne expression and associated antibiotic requirements, which also affect growth rate. Finally, we confirmed that the arrays remained stable and unaffected by ligand binding (Fig. 1d, yellow). We therefore conclude that the hybrid protein doesn't significantly alter the sensory responses of the arrays.

An example of how ligand binding to the receptors modulates the association of CheZ with an individual receptor array is shown in Fig. 1d (lower images, red). The corresponding intensity profiles of CheZ–mScarlet fluorescence across the cell are shown below (red lines), corrected for photobleaching (see Methods and Fig. S3). Upon addition of serine (3 mM), CheZ dissociated from the array, indicating that the kinase activity within the array had switched off. Interestingly, despite the potential presence of all five receptor types in this strain, in the specific array shown in Fig. 1d, aspartate addition (3 mM) had no observable effect on CheZ association, implying unchanged kinase activity. Finally, in the presence of both serine and aspartate, CheZ consistently exhibited no significant affinity for the array, representing the kinase-OFF state.

To quantify CheZ association with the array, we used the peak (red) fluorescence intensity in the array and subtracted the background fluorescence at the array location due to unbound CheZ (Fig. 1d, lower plots, dashed lines), which, in turn, was evaluated based on the distribution of CheZ in the presence of both serine and aspartate (kinase-OFF state). To account for changes in the total unbound CheZ in the cell upon stimulation, the red intensity at the cell body—where no array is apparent— in the presence of both serine and aspartate was normalized to the cell body intensity in each case (Fig. 1d). While Fig. 1d captures the essence of the analysis used here to evaluate the ligand responses, the actual analysis was mostly carried out in two dimensions, as described in Supplementary Methods. Additional examples are shown in Fig. S4.

Distributions of single-array responses
As expected, the kinase activity in the unstimulated state—indicated by the amount of CheZ–mScarlet associated with the array—scales approximately linearly with the array size, indicated by the CheA::CheY–mYFP fluorescence (Fig. 2a, left). Interestingly, however, the efficiency of kinase activation, namely, the kinase output per CheA (Fig. 2a, right), is somewhat higher in arrays from cells at the early or late stationary growth phase (OD 1.7 or 3.5), compared with cells in the exponential phase (OD 0.15 or 0.45). The growth curve of these cells is shown in Fig. 2a (right plot, inset), and the number of arrays per cell is shown in Fig. S5, generally consistent with the observations in ref. 11.

The responses of the arrays shown in Fig. 1d are plotted in Fig. 2b, normalized to the unperturbed kinase activity state, indicated by the red fluorescence peak in the absence of any stimulus. These normalized responses represent the fraction of kinase activity in the array inhibited by each stimulus, and notably, they reveal the relation between the effects of these stimuli on single arrays (see Fig. S4 for additional examples). To compare different arrays, the responses of each array were represented by a single point in a ‘Ser–Asp’ response plot (Fig. 2c), where the fraction of kinase activity responsive to serine is plotted against that responsive to aspartate. These responses were measured in cell populations at either the exponential or stationary growth phase. Individual arrays exhibited a wide range of sensory properties with a semi-continuous distribution. Additionally, consistent with previous observations35, the repertoire of these sensory properties shifted from serine-biased in the exponential phase to aspartate-biased in the stationary phase. Notably, however, the orthogonal ligand in each case (aspartate and serine, respectively) still elicited a wide range of responses. The corresponding population-level (FRET) dose-response measurements of these cells also showed a similar bias shift (Fig. 2d). This shift was consistent with the weighted-average values obtained from the single-array responses (Fig. 2e), in which the contribution of each array was weighted by its peak mYFP fluorescence, used as a proxy for array size, and thus for the kinase contribution of that array. A slight shift is apparent in the stationary phase, which could result from this approximation.

Three regions in the Asp–Ser plot are notable (Fig. 2e): First, the region above the diagonal (green background), where the sum of the responses to serine and aspartate is larger than the response to both stimuli combined, implying that a fraction of the kinase molecules in the array can be inhibited by either Tar or Tsr. Second, the region along the diagonal (green line), where the sum of the individual responses equals the response to both stimuli combined. Such ‘additive’ behavior may occur if Tsr and Tar each regulate distinct subsets of kinase molecules. Third, the region below the diagonal, where the sum of the individual responses is smaller than the response to both stimuli combined, implying that some fraction of the kinase molecules can only be inhibited when both stimuli are present. Clearly, most of the single-array responses observed in Fig. 2c fall above the additivity line36, indicating robust overlap in the kinase activity controlled by Tar and Tsr within each array.

As expected, when the experiments were repeated with cells lacking the Tsr receptor, a complete aspartate bias was observed in both growth phases (Figs. S6 and 2e, black circle). This result is also consistent with the Aer, Tap, and Trg receptors having only a minor influence under the conditions tested here, in line with their typically low abundance, constituting only a few percent of the total receptor population37. These receptors however may play a more significant role in the subpopulation of arrays that fall below the ‘additivity’ line in Fig. 2c. Additionally, response patterns similar to those observed in Fig. 2c were also observed for a subpopulation of arrays that present alone in their respective cells (Fig. S7), further supporting the conclusion that the potential CheZ-mediated interference between arrays doesn't significantly alter the results shown here (see also Supplementary Notes 1).

The pairwise correlations of different arrays within the same cell are shown in Fig. 3 (upper plots), where the response of one array is plotted against the corresponding response of another. In each case, the responses are shifted by the mean and scaled by the standard deviation of the respective dataset. Also shown in Fig. 3 (lower plots, gray bars) are the distributions of the pairwise differences, d = √((Rser1 - Rser2)^2 + (Rasp1 - Rasp2)^2), where Rser and Rasp represent the responses of individual arrays (1 and 2). For comparison, the corresponding distributions obtained by randomly pairing arrays from the respective datasets, are also shown (lower plots, green bars), mostly overlapping the distribution of the same-cell pairs. Overall, these data reveal diverse and growth-phase-dependent sensory properties across chemoreceptor arrays, with arrays in the same cell not significantly more correlated than those in the general population.

Receptor content and assembly of single arrays
To monitor the fractional occupancy of each array by Tar or Tsr, we constructed strains analogous to VF17, carrying CheA::CheY–mYFP, and either Tar or Tsr tagged with mScarlet, each in a separate strain (tagged CheZ was absent in these strains). In this setup, the ratio of red (mScarlet, receptors) to yellow (mYFP, CheA) fluorescence in each array reflects the amount of Tar or Tsr relative to the size of the array baseplate (Fig. 4). By tagging only one receptor type in each strain, we reduced the total number of tagged receptors, thereby minimizing potential steric effects.

We observed that the size of the arrays and, correspondingly, the amount of Tar or Tsr tended to increase as cells approached stationary phase (Fig. 4a). Moreover, in the exponential phase, the Tar fraction in the arrays (Tar/CheA) tended to be lower than that of Tsr, whereas in the stationary phase the receptor distributions became similar (Fig. 4b). This shift in receptor occupation is consistent with previous observations35,38, albeit more moderate35,38, possibly due to different growth conditions (see Methods). Interestingly, the total receptors (Tar+Tsr) to CheA ratio also increased in the stationary phase (Fig. 4b), potentially accounting for the enhanced kinase activity observed in Fig. 2a.

To follow this shift dynamically, we tracked individual arrays in growing cells (Fig. 4c). Stationary-phase cells were placed sparsely between a coverslip and an agarose-hydrogel pad with fresh growth medium (see Methods). As cells shifted from stationary to exponential growth phase, we followed growing arrays and observed a progressive reduction in Tar occupancy in individual arrays (Fig. 4c). This shift in individual array occupancy is consistent with the overall population-level shift shown in Fig. 4b (marked by red bars). Thus, the shift in receptor occupancy between stationary and exponential growth phases observed in Fig. 4b reflects not only the formation of new arrays with altered receptor occupation but also dynamic changes of receptor occupancy within preexisting arrays. Notably, to the extent that the array structure is generally stable, a temporal shift in Tar occupancy suggests that a highly non-uniform Tar distribution can develop within the array over time.

Sensory properties are consistent with mosaic receptor arrays
To analyze the response behavior observed in Fig. 2c, we first consider the commonly used MWC model23,26,28, applied to receptor clusters composed of both Tar (nTar) and Tsr (nTsr) receptors39 (see Methods). Characterizing the receptor composition in the cluster by the total number of receptors ntot and the relative Tar content r = nTar/ntot, we note that under saturating concentrations of either aspartate or serine, the inherent cooperativity in the cluster16,25 leads to a sharp dependence of kinase activity on r (Fig. 5a and S8, and Methods: Eqs. 2–3). The asymmetry between aspartate and serine shown in Fig. 5a results from the inherently distinct signaling properties of Tar and Tsr (see refs. 18, 24, 25 and Fig. S9).

The critical r-values at which the kinase activity is inhibited by aspartate (γ) or serine (η) define three regions (Fig. 5a): (I) clusters with r < γ, where the kinase output can only be inhibited by serine; (II) clusters with γ < r < η, where the kinase output can be inhibited by either serine or aspartate; and (III) clusters with r > η, where the kinase output can only be inhibited by aspartate. This analysis shows that a single MWC-like cooperative cluster typically belongs to one of these regions, and thus maps to one of the three corners of the Ser–Asp response plot (Fig. 5a, right), in contrast to the broad-range responses observed in Fig. 2c.

On the other hand, diverse single-array responses can still be compatible with substantial response cooperativity if the arrays have non-uniform composition, as suggested by Fig. 4b, c. We therefore next considered mosaic-like arrays comprising multiple subregions, each with distinct receptor compositions (Fig. 5b). Assuming an array with N sub-regions, each with distinct Tar fraction ri and maximal kinase activity ai, the total kinase activity is given by: <A> = ∑ (ai * Pon(ri)), where Pon(ri) is the ligand-dependent probability of subregion i to be in the active state. Assuming that each sub-region is still highly cooperative and well-described by the MWC model, the response properties of such mosaic arrays would essentially depend on the distribution of r-values within the array (Fig. 5b; see also Methods). In this case, the aspartate response approximately corresponds to the fraction of subarrays with r-values in regions II and III, and the serine response to the fraction of subarrays with r-values in regions I and II. Thus, provided that the r-value distribution in the array can be sufficiently broad to encompass all three regions, arrays can occupy any location on or above the ‘additivity’ line in the Ser–Asp space. To test this proposition, we simulated array populations assembled under varying Tar and Tsr expression conditions (time-dependent ri distributions) corresponding to the ‘exponential’ and ‘stationary’ phases (see Methods). Indeed, we find that even a moderate change in the average Tar fraction (as observed in Fig. 4b) can produce a significant shift in the response properties comparable to those observed in Fig. 2c (Figs. 5c and S10).

Single-array dose-response behavior
The population-level dose-responses of VF17 cell with mixed receptor arrays (Fig. 2d) generally exhibited lower cooperativity than that observed in arrays composed of a single receptor type16,23,25. Such low cooperativity could, in principle, result from K1/2 diversity among individual arrays arising from differences in their composition. In this case, arrays could each retain high intrinsic cooperativity yet differ in sensitivity (K1/2). Alternatively, under the mosaic view of arrays (Fig. 5b), the internal diversity in receptor composition, and thus in K1/2, would be expected to produce low-cooperativity dose-responses even in individual arrays.

To test these propositions directly, we measured dose-dependent responses of single arrays (Fig. 5d, e), focusing on the aspartate response in stationary-phase cells, which showed especially low cooperativity (Hill coefficient ~ 1.4) and larger responses than those in the exponential phase (Fig. 2d). Single-array responses were measured in VF17 cells as before (Figs. 1–2), except that cells were stimulated with increasing concentrations of aspartate (see Supplementary Methods). Several examples are shown in Fig. 5d, with each plot corresponding to a different array. The responses of each array were fitted with a Hill function (black lines), and the distributions of the resulting Hill coefficients and K1/2 values are shown in Fig. 5e (gray bars). Additionally, Fig. 5e (right inset) shows the population-level (FRET) dose-response behavior of the same VF17 cells (red line; Tar+Tsr+, taken from Fig. 2d) and of similar cells but lacking the Tsr receptor (green symbols; Tar+Tsr-, MK32). The latter are expected to have more uniform array composition and indeed exhibit substantially higher cooperativity. The corresponding Hill-fit parameters for these population-level responses are indicated in the main plot as red and green arrows.

The cooperativity of most single arrays (Fig. 5e, gray bars) is comparable to that observed at the population level (red arrow) and significantly smaller than the Hill coefficient characterizing cells with more uniform arrays (Tsr- cells; green arrow). The dose-response behavior of approximately 14% of the arrays is potentially consistent with higher cooperativity, but this applied mostly to only partial kinase inhibition (see Fig. 5d, lower-right plot); however, we cannot evaluate their actual cooperativity, which would require a much denser concentration scan. The overall low cooperativity observed here at the single-array level is generally consistent with the proposed mosaic organization of receptor arrays.

Diversesensory responses can provide an advantage in complex environments
Previous studies using distinct serine and aspartate sources have shown an increased bias toward aspartate at later stages of growth35. This bias was primarily dependent on the sensitivity of the cells to each effector at very low concentrations. To further test possible behavioral consequences of the diversity and bias shift observed in the responses of the arrays (Fig. 2c), we used wild-type RP437 cells and examined how their growth phase influences their ability to overcome a repellent barrier. The barrier was created and controlled by a single source containing both MeAsp (attractant, sensed by Tar) and indole (repellent, sensed by Tsr) at the end of a long channel filled with bacterial cells (Fig. 6a)40. The combined effect of these effectors produces a region of effective repulsion that propagates away from the source, followed by an attractant region lagging behind. The effective strength of the repellent barrier could be modulated by the attractant (MeAsp) concentration in the source (Fig. 6b)40. As the repulsion lobe propagates through the channel, cells that respond effectively to the repellent (indole, Tsr) are pushed away from the source, whereas cells that respond weakly to the repellent or more strongly to the attractant (MeAsp, Tar) are more likely to cross the barrier and reach the attractant region. Indeed, previous studies have shown that Tsr⁻ cells exhibited only attractant response to Indole, whereas Tar⁻ cells exhibited a stronger repellent response40.

We now examined how the bacterial growth phase influences their ability to overcome the repellent barrier for different concentrations of attractant (MeAsp) at the source. The number of cells repelled from the source at different MeAsp concentrations is shown in Fig. 6c and S11. The swimming speed and tumble-bias of these cells at the two growth stages were generally similar (Figs. 6c and S11). We found that exponential-phase cells, with mostly Tsr-dominant array responses (Fig. 2c), were less able to overcome the repellent lobe and therefore accumulated in greater numbers away from the source (Fig. 6c). Similarly, early-stationary-phase cells, in which most arrays exhibited substantial Tar-mediated responses (Fig. 2c), were more sensitive to MeAsp addition and exhibited a sharper response to MeAsp. Overall, although cell behavior is clearly influenced by additional factors, most notably adaptation, the observed behaviors are generally consistent with the sensory properties shown in Fig. 2c.

Methods
Bacterial strains and plasmids
Strains used here are derivatives of E. coli K-12 strain RP437 (eda+)50. Their relevant genotypes are as follows: VF17 [cheA(ΔP2)::cheY–myfp, Δ(cheR, cheB, cheY), cheZ(F98S)–mscarlet-I]; MK27 [cheA(ΔP2)::cheY–myfp, Δ(cheR, cheB, cheY, cheZ), tsr–mscarlet-I]; MK30 [cheA(ΔP2)::cheY-myfp, Δ(cheR, cheB, cheY, cheZ), tar-mScarlet-I]; and MK32 [VF17+tsr::ccdB-kan]. We also used strain UU2828 [cheR, cheB, cheY, cheZ] expressing CheY-mCherry and CheZ(F98S)-mYFP from pKG110 vector (pAV109; camR, NaSal 0.8 µM). We used the mScarlet-I variant throughout this work.

Strain construction
Strain VF15 was created from strain UU2828 by replacing the cheA.P2 (158–230) region with cheY-myfp and introducing the M98L point mutation in cheA to prevent expression its short variant34. Strain VF17 was then created by inserting back cheZ(F98S)-mscarlet-I, along with its native RBS (20 bp), 18 bp downstream of tap. Strains MK27 and MK30 were constructed from strain VF15 by replacing respectively, tsr with tsr-mscarlet-I or tar with tar-mScarlet-I. Strain MK32 was created by replacing tsr 211-551 with ccdB-Kan cassette. For more details and primer list see Supplementary.

Media and growth conditions
Cells were pre-grown overnight to saturation in TB medium (10 g/l Bacto-Tryptone and 5 g/l NaCl) at 30 °C. Cells were then diluted in 10 mL minimal media (M9 salts, 0.4% glucose, 1 mM MgSO4, 0.1 mM CaCl2, 1 µM thiamine, and a mixture of amino acids (Histidine, Threonine, Methionine, and Leucine, 1 mM each), and allowed to grow overnight with shaking at 32 °C to reach stationary cultures at optical density (OD) of 3.5–4. Overnight cultures were then diluted to an OD of 0.035 in fresh minimal media and grown to the desired OD, 0.1 or 0.45 (exponential) or 1.7 or 3.5 (stationary). In the growth experiments (Fig. 4c), cells were taken directly from the overnight culture and allowed to grow on an agarose hydrogel (1.2%) made from fresh minimal media. In experiments involving UU2828/pAV109 cells (Figs. 1c and S2), following the initial overnight growth in TB, cells were diluted 1:100 in TB and allowed to grow to OD 0.45.

Imaging
Fluorescence images were obtained using a Nikon-Ti inverted microscope equipped with a 100× Plan-Fluor objective (1.3 NA), a xenon lamp (Sutter Instruments), a camera (Andor Technology), filter sets: ET560/40–EmET630/75 (red channel) and ExET490/20–EmET353/50 (yellow channel), and NIS-element software. Cells were cultured as described above. To measure kinase activity response, cells were placed between a 1.2% agarose hydrogel pad and a poly-L-lysine-coated coverslip for 20 min before transferring the coverslip to a flow chamber. Initially, cells were in motility buffer, and an image of the unstimulated state was taken. They were then exposed to different stimuli for 1 min before imaging and washed with buffer between exposures. A final image was taken after returning the cells to buffer. In the single array dose response experiments (Fig. 5d, e), cells were exposed to increasing concentrations of aspartate for 1 min before imaging. See Supplementary Methods for detailed description of the analysis. In order to evaluate receptor occupation (Fig. 4), cells were placed between a 1.2% agarose hydrogel pad and a coverslip.

Population FRET measurements
The kinase activity, averaged over cell populations, was measured by following FRET between tagged CheY and CheZ proteins20,22. Flow cell assembly and FRET measurements were done as described before19,43. Activity-dependent FRET occurs between CheA::CheY–mYFP and CheZ–mScarlet-I in the newly constructed strains (VF17 and MK32), and between CheY-mCherry and CheZ(F98S)-mYFP expressed from plasmid pKG110 in strain UU2828.

Image analysis
Images were analyzed using custom software to quantify changes in CheZ localization to the arrays, a proxy for kinase activity responses, and were manually confirmed in several cases. For each array, the maximum intensity (Ipeak,i) and the intensity in the cell body outside array regions (Ibody,i) were measured relative to intensity outside the cell in both the red (CheZ-mScarlet) and yellow (CheA::CheY-mYFP) channels, with i indicating different stimulation conditions. The yellow channel provided information about the size and stability of the arrays under the various conditions. To obtain the kinase responses, intensities from the red channel were first corrected for photobleaching according to: Icorr = I / α^(i-1), where α ranged from 0.80 to 0.87 (see Fig. S3) and i denotes the image sequence number. The added noise from this correction was estimated to be approximately 2–5% (Supplementary Notes 2). The peak amplitude of each array (Fig. 1d) was calculated as the difference between the measured array peak intensity and the background intensity at the array position contributed only by the unbound CheZ: Ipeak,i = Iarrayposition,i - Iunbound,i, where the background intensity was determined as Iunbound,i = Iser+asp,arrayposition * (Ibody,i / Ibody,ser+asp). Changes in peak intensity upon stimulation with either aspartate or serine were then defined as: R asp=ser = (Ipeak,buffer - Ipeak,asp=ser) / Ipeak,buffer. Additional source of noise (~10%) arises primarily when the response approaches 1, namely, when no clear peak can be identified under stimulation conditions (i.e., Ipeak,i ≈ 0). In these cases, the measured intensity at the array position, Ii, closely matches the background intensity at this position due to unbound CheZ, Iunbound,i, making their difference sensitive to uncertainties in identifying of the exact peak position. This effect accounts for the observed deviations of R values beyond 1 in Fig. 2c. For more details see Supplementary Methods.

MWC model for mixed clusters: dependence on Tar fraction
Following ref. 39, we considered highly cooperative receptor clusters composed of ntot receptors: Tar (nTar) and Tsr (nTsr). The kinase ‘on’ probability is then given by: Pon = 1 / (1 + e^ΔE * f(L)), where f(L) = (1+L/Kon)/(1+L/Koff). Or, by defining r = nTar/ntot:

Pon(r) = 1 / (1 + (e^ΔETar * f(asp))^r * (e^ΔETsr * f(ser))^(1-r) * ntot) (Eq. 2)

Assuming that for L > Kon, we approximate f(L) ≈ Kon/Koff, and thus:

Pon(r) = 1 / (1 + (e^ΔETar * (Kon/Koff)asp)^r * (e^ΔETsr * (Kon/Koff)ser)^(1-r) * ntot) (Eq. 3)

Simulated arrays
We numerically generated arrays with non-uniform internal compositions of Tar and Tsr using distinct distributions corresponding to exponential and stationary growth phases (Fig. S10). Receptor distributions were chosen to produce responses similar to those observed experimentally, while keeping the overall shift in the averaged Tar fraction quite moderate (Figs. 5c and S10). Each growth phase was divided to three time periods, each characterized by a unique distribution of Tar-fraction r-values (Fig. S10). The mean r-value gradually shifted over time. For simplicity, normal distributions were used. During each period, new arrays were randomly nucleated, increasing the total number of arrays 4-fold in the stationary and 8-fold in the exponential phases. At each time step, core units were added to both new and preexisting arrays, according to their nucleation point. For each addition, an r-value was randomly drawn from the corresponding distribution, and core units were added such that, on average, the ratio of Tar and Tsr reflect this value. On average, 30 core complexes were added for each choice of r-value, representing time correlation. At the end of each growth phase (stationary or exponential), core units were grouped into strongly cooperative regions, and the signaling properties of these arrays were evaluated using Eq. 1. This cycle of exponential and stationary phases was iteratively repeated several times.

Behavioral chemotaxis assay
The assay was performed as previously described in ref. 40 (see schematic in Fig. 6a). RP437 cells (UU2547, courtesy of Sandy Parkinson, University of Utah) expressing GFP from a plasmid (pSA11, pTrc99a, induced by 75 µM IPTG during the final growth step) were harvested at the desired growth stage, suspended in motility medium (with added NaCl 5 gr/l) to final density OD600 ≈ 0.1, and injected into a long channel (44 mm long, 5 mm wide, 0.2 mm high; ibidi µ-Slide), forming an initially uniform distribution. Then, one end of the channel was sealed with 1.5% (w/v) agarose hydrogel in motility buffer, and the other end (the ‘source’) was filled with 1.5% (w/v) agarose hydrogel in motility media containing 1 mM indole and varying concentrations of the non-metabolizable analogue of aspartate, α-methyl-aspartate (MeAsp). The channel was then incubated at 30 °C and after 3 h, cell redistribution was monitored by fluorescence microscopy using a 20X objective (0.5 NA). The amount of ‘repelled bacteria’ was quantified by integrating the density of cells migrating away from the source relative to the initial baseline cell density (see Fig. 6c, upper inset).

To quantify the swimming speeds at different growth stages (Fig. 6c, lower inset), cells were suspended in the same motility medium at low cell density (OD600 ≈ 0.03) and injected into an ibidi µ-Slide channel for observation. To induce smooth swimming and enable quantification of swimming velocity, both serine (2 mM) and MeAsp (10 mM), were added to the suspension. Time-lapse imaging was then performed at 10–15 frames per second using a 20× (NA 0.5) objective near the bottom surface and bacterial tracks were detected using the TrackMate plugin for ImageJ.

To evaluate tumbling bias (Fig. S11), cells were diluted in motility medium (OD 2·10−4) and placed in a µ-slide (ibidi 80326, coated with BSA). Time lapse images were captured near the top surface using a 10x (NA 0.45) objective at ≈ 15 frames per second for 100 s and analyzed in 2D51, using the algorithm described in ref. 52 to identify tumble events. Only tracks longer than 3 s were analyzed.

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Code availability
The Python code used for extracting the responses of individual arrays from microscopy images is available on Zenodo: https://doi.org/10.5281/zenodo.17621831, runs in a standard Jupyterlab environment.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript presents an elegant and technically innovative study that achieves single-array resolution of kinase activity in E. coli chemosensory systems. This is a significant advance, given that previous approachs largely relied on population-averaged or whole-cell measurements. The authors aim to understand how individual receptor arrays differ in structure and function, building on prior work showing cooperative signalling and long-range integration within arrays.\nThe study introduces a novel assay based on a hybrid protein (CheA::CheY-mYFP), which embeds a fluorescent readout of kinase activity directly into the chemosensory array. This is paired with a mutant, fluorescently tagged phosphatase (CheZ(F98S)-mScarlet) that binds only to phosphorylated CheY, enabling dynamic measurement of kinase output at individual arrays. The assay also employs dual fluorescent tagging of specific receptors (Tar or Tsr), allowing the authors to correlate array composition with signaling activity.\nThe main findings are compelling: Individual arrays display highly heterogeneous kinase activities, even within the same cell. Growth phase influences ligand sensitivity - arrays are more serine-biased in exponential phase and more aspartate-biased in stationary phase. Receptor composition shifts over time within individual arrays, and array responses within the same cell are largely uncorrelated. These observations support a mosaic model in which each array comprises sub-regions with distinct receptor composition and signaling properties. Finally, the authors employ a modified MWC model and simulations to demonstrate how receptor heterogeneity can account for the observed spread of responses across the array population.\nDespite these advances, several limitations warrant attention:\nArtificiality of the system: The use of fusion proteins and the CheZ(F98S) mutant is well-motivated, but the potential perturbation of native signaling remains a concern. Although the authors provide some validation, stronger evidence could be presented. For example, systematic comparisons of Hill coefficients and EC50 values between hybrid and wild-type systems across ligand concentrations would help. Behavioral assays, such as swimming bias or soft agar migration in ∆cheA ∆cheY backgrounds complemented with the hybrid proteins, could further demonstrate functional equivalence. Additionally, the use of adaptation-deficient strains (lacking CheR and CheB) simplifies the system but removes key feedback mechanisms. It remains unclear whether the assay could operate in adaptation-proficient strains, and whether adaptation would modulate the observed heterogeneity or subregion integration.\nCrosstalk and systemic Coupling: The authors acknowledge potential inter-array crosstalk via shared CheZ and estimate this effect to be 10–15% based on simulations (Supplementary Text and Fig. S1). While this is a thoughtful analysis, such residual coupling challenges the interpretation of fully independent array measurements. The fluorescence-based readout also depends on kinetic assumptions about CheY phosphorylation and dephosphorylation, which may vary across arrays. Although a tricky experiemnt, orthogonal CheY–CheZ pairs could help directly test array independence.\nArray dynamics and stability: Figure 4C shows that receptor composition changes over time, but the functional consequences are not explored. Do these changes influence adaptation dynamics or cellular memory? The manuscript implies structural stability of arrays, but this is not quantified. FRAP of receptor exchange would help determine turnover times and test the assumption of ‘frozen’ mosaic structures.\nUnder-analyzed data representations: Some of the central plots—particularly the Ser–Asp response maps (Fig. 2)—are rich in content but difficult to interpret quantitatively. These could benefit from further statistical interrogation, such as clustering analysis, density estimation, or PCA. Plotting correlations between response behavior and receptor composition, array size, or growth phase would also be informative.\nLack of testable predicions: The mosaic model explains the observed data well, but the paper would be stronger with explicit, testable predictions. For example, does altering receptor heterogeneity via controlled expression shifts change the dose–response behavior in predictable ways? Could modifying the cooperative subregion size (e.g., through CheW mutants) change signal integration characteristics? Currently, the model is largely descriptive rather than predictive.\nGenerality and behavioral relevance: It remains unclear how generalizable the findings are across strains or species. Testing other E. coli backgrounds, and/or linking array heterogeneity to downstream behavior—e.g., changes in chemotactic behavior —would greatly enhance biological relevance.\nSuggested addition to the Discussion: The authors convincingly demonstrate that chemoreceptor arrays exhibit mosaic, history-dependent organization. However, the Discussion would benefit from a speculative section addressing possible design principles. For example, heterogeneity might enhance robustness by broadening the detectable stimulus range, serve as a molecular memory of prior growth conditions, or enable evolutionary adaptability through modular receptor incorporation. Alternatively, mosaic assembly might reflect unavoidable biophysical constraints—such as slow turnover or irreversible array growth—that evolution has co-opted. Articulating such hypotheses would enrich the conceptual impact of the work.\nIn summary, overall a nice paper, but the authors should consider some of the above ideas to strengthen the evidence for the findings and to broaden the scope of the work.", "reviewer_2": "Summary\nThe manuscript by Frank et al. investigates the kinase responses of individual chemoreceptor arrays in live E. coli cells, by combining the CheA kinase and response regulator CheY to create a functional hybrid protein that resides within the array.\nThe manuscript addresses an important technical issue, given that detecting the responses of individual arrays remains challenging. The authors found that the sensory properties of individual arrays are highly diverse and dependent on the growth phase. They also demonstrated the dynamic shifts in receptor occupancy within individual arrays. Finally, the authors suggest that receptor arrays are non-uniform, consisting of a mosaic arrangement of sub-regions with distinct receptor content, which may reflect the unique assembly history of each array. This study enables the direct measurement of kinase responses in individual sensory arrays within live bacterial cells, and contributes to a better understanding of how arrays assemble over generations.\nComments:\n1.In Fig. 1C, the authors should specify the serine concentrations used to measure the R/Y responses. In lines 134–147 and the legend for Fig. 1D, the authors should specify the growth phase (OD600) of the E. coli cells used in these experiments.\n2.In Fig. 4D, it appears that the red fluorescence becomes weaker after returning to the buffer solution, possibly due to the bleaching effect. The author should calibrate this effect for the data shown in Figures 1 and 4.\n3.In Fig. 4C, the authors do not explain why they show additional traces of Tar occupancy in individual arrays. If the conditions used were the same as in the upper-right plot, the lower plot could be moved to the SI. There are no gray bars in the lower plot. In addition, four images are shown in the lower plot, but the author provides no explanation for them in either the main text or the figure legend.\n4.The other three types of E. coli chemoreceptors (Aer, Trg and Tap) were not considered in this study, either in experiments or modelling. Could the authors discuss how these chemoreceptors influence kinase responses and the assembly of individual arrays?\n5.Using MWC-model analysis, a link was generated between the chemoreceptor fraction and ligand responses. However, the authors did not provide experimental evidence. It would be better to provide direct experimental evidence of the correlation between kinase responses and the receptor fraction.", "reviewer_3": "Motile bacteria detect chemical gradients using large chemosensory arrays of transmembrane receptors (mostly Tar and Tsr in E. coli) associated with kinases (CheA) and a scaffolding protein (CheW in E. coli). Individual cells can have several arrays, and arrays of mother cells are passed on to daughter cells. Many studies have characterized CheA activity in single cells, quantifying cell-to-cell variability in the sensitivity and gain (slope of the dose response at EC50) of individual cells to various ligands presented in the presence or absence of background concentrations of the same or other ligands. The picture that has emerged is that such variation arises from cell-to-cell variations in expression levels of the most abundant receptors Tar and Tsr. However, so far it has not been possible to directly measure the output of individual arrays in individual cells. Thus, it remains unknown whether Tar and Tsr are well mixed within individual arrays or whether there is within-cell variation in array composition.\nThe authors developed an interesting new FRET method to measure kinase activity in individual receptor arrays, that produces similar population average responses (Figure 1C) as the traditional CheY/CheZ FRET method. The new method relies on two main modifications: replacing the P2 domain of CheA with a CheY-yfp fusion, and an F98S point mutation in CheZ to prevent its direct interaction with CheA. However, rather than using FRET to quantify kinase activity, throughout the rest of the paper the authors instead use localization of CheZ-mScarlet to assay changes in interactions with CheY upon stimulation with steps of saturating amounts of serine and aspartate.\nThe paper’s main findings are: a) substantial array-to-array variability in response magnitude to saturating steps that is growth-phase dependent; b) a growth-dependent change in the receptor composition of the arrays; in most cases each signal alone can substantially inhibit the kinase suggesting that both receptors can affect the same set of kinases (as opposed to a model where each receptor only interacts with distinct subsets of kinases); d) no strong correlation between the responses of different arrays in the same cell. Finally, the authors used modeling to argue that their data is consistent with a model in which individual arrays exhibit highly diverse local receptor compositions, but since the measurements presented do not quantify the hill coefficient of the dose responses, it was difficult to understand how the modeling related to the experiments.\nThe new experimental method discussed is impressive and has the potential to significantly shape our understanding of receptor cluster development and dynamics in bacterial chemotaxis. However, I have several concerns about the current presentation of the method, the measurements, and the interpretation of the data that preclude publication until addressed.\nMAIN CONCERNS\nEXPERIMENTAL METHOD\n1) The authors motivate well the development of a new FRET system that could potentially report kinase activity in individual arrays. They show in Figure 1C that at the population level the dose response to serine is similar to that in WT, suggesting the mutations introduced do not significantly affect kinase response (although see next question). Surprisingly however, the rest of the paper makes no use of FRET to quantify change in kinase activity. Instead kinase response is quantified in terms of the change in the localization of CheZ-mScarlet on top of the CheA::CheY-YFP puncta. I could not find in the text a motivation for using this indirect method to estimate kinase activity of individual arrays when presumably the FRET signal quantifies direct interaction between CheZ and CheY-P and therefore reflects kinase activity (as shown in Figure 1C). Could the data from Figure 2C have been obtained using single array FRET? This would be a great control experiment. Or is the signal-to-noise ratio too low with this FRET pair to enable single-array FRET?\n2) The only apparent difference between the new FRET and the traditional one is a slight leftward shift in the K1/2 in the response to serine (Figure 1C). However, since the authors stimulate cells with both serine and aspartate in Figure 2, a corresponding dose-response experiment using aspartate would be valuable. Since cheZ-mScarlet is fused at the native cheZ chromosomal locus, which is in the same operon as tar, this fluorophore fusion might influence tar expression. The lower serine K1/2 observed in VF17 relative to WT could be consistent with reduced Tar expression.\n3) The method used to measure kinase activity throughout the paper relies on measuring the degree of colocalization of CheZ-mScarlet with the array, itself labeled by CheA::CheY-YFP. This method was not clearly explained in the main text. The response is quantified as the peak amplitude in the red channel minus the amplitude of a rescaled red channel when cells are stimulated with both serine and aspartate (Figure 1D second column at the bottom). The rational for that normalization of the red channel during saturated response was not provided. I am assuming here that the dashed black line in the second plot of the last row if Figure 1D is the same as in the 3rd column, but normalized so that the profile matches with the red profile away from the peak. Also, why using the peak amplitude rather than the area between the two curves?\n4) How can the authors be certain that the observed shift in CheZ-mScarlet fluorescence peak height reflects physical localization of CheZ during catalysis and is not confounded by FRET between YFP and mScarlet?\nMEASUREMENTS\n5) figure 1D only shows an example of one array that seems to only respond to serine. Can the authors provide many examples, including some in which the array responds to both ligand and some in which the array responds to only aspartate. It is also important to show examples of cells in which there are multiple clusters.\n6) How does the relative distance of individual clusters within one cell affects the estimate of the response of one cluster? The authors discuss in the SI possible interferences due to shared CheZ-mScarlet pool, but the image analysis is not discussed. I imagine that if clusters are closed to each other, the individual peaks would appear nearly merged. How independent then are the measurements of individual arrays shown in figures 2C and 3? How did the authors deal with cells in which multiple clusters are close to each other and the red channel peaks merge or nearly merge? Where the authors able to estimate a confidence interval for the response of each individual array?\n7) Related to the previous question, Can the authors plot a histogram of the number of discrete arrays per cell? Do most cells have only one cluster or more in their data? Also what is the distribution of pairwise distances between individual arrays? These statistics would be informative to interpret the figures 2 and 3.\n8) The top two panels in Figure 4A both present measurements of CheA abundance in stationary and exponential phase, yet the distributions differ depending on whether Tar or Tsr is labeled. Do the authors attribute these differences to secondary effects of receptor labeling?\n9) In Figure 4C, is the \"array size\" shown on the left panel different from the CheA concentration shown on the right? The caption of Figure 2 suggests that “array size” corresponds to the total fluorescence intensity of the CheA::CheY-mYFP fusion. Additionally, could the authors clarify what the different colored points and lines represent in these two plots? Is it data from different cells?\n10) In Figure 4C, upper left plot, the data show that array size increases as the culture shifts from stationary to exponential phase. Yet Figure 4A, upper plot, indicates that CheA levels are higher in stationary phase than in exponential. How can these observations be reconciled?\n11) What is the purpose of the fluorescence anysotropy experiment shown in Figure S5? Is it solely intended to extract the on- and off-rates of Tsr binding to serine for use in Figure 5A? If so, how were the corresponding rates for Tar determined?\nINTERPRETATION\n12) It is unclear how the modeling in Fig 5 relates to the experiments in figure 2. There is no information about n_Tar and n_Tsr measured in the experiments because the authors only measure maximum inhibitable kinase activity, and not the hill coefficient of the dose-response. If the old high-Tsr clusters are preserved, the hill coefficient for the serine response may be preserved, even as the maximal response decreases over time. To support this interpretation, the authors would need to measure the distribution of n_Tar and n_Tsr in exponential and stationary phase.\n13) It would enhance the impact of the paper if the authors could better clarify in the discussion how their results align with or differ from previous related studies. Namely:\nDo the result that most cells have 1 or 2 clusters contradict previous results of lateral clusters:\nhttps://www.ncbi.nlm.nih.gov/pubmed/30361683\nWhat are the implications of the proposed mosaic structure of receptor arrays for signal integration, especially in the presence of multiple ligands: e.g. https://pubmed.ncbi.nlm.nih.gov/38981486/\nDoes the stability of the cluster (Fig 1D, and lines 267-269) contradict previous results that the clusters are unstable under prolonged stimulation: https://www.ncbi.nlm.nih.gov/pubmed/23551504\nMINOR COMMENTS:\n15) Why was mCherry fused to CheZ in plasmid pAV109, whereas mScarlet was used in strain VF17? Is that related to the faster maturation time of mCherry compared to mScarlet (or did the authors use a fast-maturing variant of mScarlet, like mScarlet3)? Also, In Figure 1C, the authors report a 10% change in FRET amplitude with CheZ-mScarlet - do they observe a similar change when using the CheZ-mCherry fusion?\n16) What exactly is the chemical“HTML”added to the M9 minimal media?\n17) All strains are RP437 derivatives, which is an auxotroph. How do the cells grow in M9 minimal medium without supplementation with amino acids?\n18) In lines 333 to 334, the authors mention performing FRET in VF17 using CheY-mYFP and CheZ-mCherry. Where is this experiment presented in the manuscript?\n19) Could the authors provide more detail on the imaging setup? Specifically, did they use the same excitation and emission filters for mCherry and mScarlet? Their spectral properties are somewhat different.\n20) In Figure 2C, what are the concentrations used to measure serine and aspartate responses?", "reviewer_author_response": "We very much appreciate the referees’ detailed comments and suggestions. We believe their feedback has significantly improved the manuscript.\nIn the revised version, we have included single-array dose–response measurements (Figs. 5D–E), population-level dose-response measurements directly compared with the single-array responses (Figs. 2D–E, and Fig. 5E), and measurements in Tsr⁻ cells (Figs. S5 and 2E, and Fig. 5E). We also added behavioral experiments (new Fig. 6). Finally, we expanded the analysis and included a Supplementary Methods section.\nBelow is a detailed point-by-point response to the referees’ comments:\nReviewer #1 (Remarks to the Author)\nThis manuscript presents an elegant and technically innovative study that achieves single-array resolution of kinase activity in E. coli chemosensory systems. This is a significant advance, given that previous approachs largely relied on population-averaged or whole-cell measurements. The authors aim to understand how individual receptor arrays differ in structure and function, building on prior work showing cooperative signalling and long-range integration within arrays.\nThe study introduces a novel assay based on a hybrid protein (CheA::CheY-mYFP), which embeds a fluorescent readout of kinase activity directly into the chemosensory array. This is paired with a mutant, fluorescently tagged phosphatase (CheZ(F98S)-mScarlet) that binds only to phosphorylated CheY, enabling dynamic measurement of kinase output at individual arrays. The assay also employs dual fluorescent tagging of specific receptors (Tar or Tsr), allowing the authors to correlate array composition with signaling activity.\nThe main findings are compelling: Individual arrays display highly heterogeneous kinase activities, even within the same cell. Growth phase influences ligand sensitivity - arrays are more serine-biased in exponential phase and more aspartate-biased in stationary phase. Receptor composition shifts over time within individual arrays, and array responses within the same cell are largely uncorrelated. These observations support a mosaic model in which each array comprises sub-regions with distinct receptor composition and signaling properties. Finally, the authors employ a modified MWC model and simulations to demonstrate how receptor heterogeneity can account for the observed spread of responses across the array population.\nDespite these advances, several limitations warrant attention:\nArtificiality of the system:\nThe use of fusion proteins and the CheZ(F98S) mutant is well-motivated, but the potential perturbation of native signaling remains a concern. Although the authors provide some validation, stronger evidence could be presented. For example, systematic comparisons of Hill coefficients and EC50 values between hybrid and wild-type systems across ligand concentrations would help. Behavioral assays, such as swimming bias or soft agar migration in ∆cheA ∆cheY backgrounds complemented with the hybrid proteins, could further demonstrate functional equivalence.\nThe primary new construction in this study is the tagged kinase/response-regulator fusion protein. However, independent of the expression method, because the response regulator (CheY) in this construct is anchored to the array and cannot diffuse to the flagellar motors, behavioral studies are, a priori, precluded with this strain. The closest construct that permits behavioral studies is one in which mYFP (alone) is inserted into CheA at the same position; that strain exhibits near-wild-type chemotactic behavior (~90%, ref. 11).\nTherefore, to verify that the hybrid CheA/Y protein retains signaling functionality, specifically its kinase activation and inhibition, as well as phosphotransfer to CheY and to CheZ, we used population-level FRET measurements. The Tsr-mediated serine response is the dominant response in the exponential phase and can fully inhibit kinase activity (see new Fig. 2D). Note that testing stationary-phase cells can lead to complications with the reference strain due to the plasmid-based induction. Thus, using exponential-phase cells, Fig. 1C first demonstrates ligand-controlled kinase activity in the new construct and, moreover, shows that the full dose–response behavior of the new strain closely matches that of the reference strain with native CheA: both strains display very similar response curves, with comparable slope (Hill coefficient) and sensitivity (K ). A slight shift in K is notable, and, as mentioned now in L-132, this likely reflects a few percent difference in Tar expression, arising either from the added labels in the new construct or from gene deletions and plasmid-based expression in the reference strain.\nAdditionally, the use of adaptation-deficient strains (lacking CheR and CheB) simplifies the system but removes key feedback mechanisms. It remains unclear whether the assay could operate in adaptation-proficient strains, and whether adaptation would modulate the observed heterogeneity or subregion integration.\nStrains lacking the adaptation enzymes are commonly used to study fundamental properties of signaling in chemoreceptor arrays. Adaptation is not expected to alter receptor composition or organization within the arrays, but it introduces dynamic and additional layer of heterogeneity in receptor signaling bias, factors we aimed to avoid in this study. The potential effects of adaptation are discussed in L384–399.\nWe are not aware of an inherent limitation that would prevent applying this method to study RB⁺ cells, aside from possible complications in strain construction or signal-to-noise considerations. Adaptation dynamics could be slightly altered if the interaction between CheB and CheA:P1 were affected in the new construct; however, since CheY phosphorylation is clearly maintained, measurements in RB⁺ cells should, in principle, be feasible.\nCrosstalk and systemic Coupling:\nThe authors acknowledge potential inter-array crosstalk via shared CheZ and estimate this effect to be 10–15% based on simulations (Supplementary Text and Fig. S1). While this is a thoughtful analysis, such residual coupling challenges the interpretation of fully independent array measurements.\nFirst, we note that the measured kinase activity in each array reflects the activity of that array independently of all others; the interference assessed in the Supplementary arises from genuine crosstalk between the kinase activities of different arrays. Nevertheless, we believe that, despite this inherent interference (typically <10%), the single-array measurements presented here and their main conclusions are not substantially affected. To further support this, we have now included an additional plot (new Fig. S6), analogous to that in Fig. 2C, in which arrays that are the sole array in their respective cells are highlighted. Notably, this subgroup displays a similar response pattern in both exponential and stationary phases.\nThe fluorescence-based readout also depends on kinetic assumptions about CheY phosphorylation and dephosphorylation, which may vary across arrays. Although a tricky experiemnt, orthogonal CheY–CheZ pairs could help directly test array independence.\nTo our knowledge, CheZ activity is not regulated by the receptors. This certainly true for the CheZ variant used here, which is not directly associated with the arrays. Accordingly, we are not aware of any mechanism by which the inherent catalytic properties of CheZ would systematically vary between different arrays. Likewise, to our knowledge, phosphotransfer from CheA(P1) to CheY is also not directly regulated by the receptors. In contrast, the autokinase activity of CheA is tightly regulated by the receptors and is therefore the dominant parameter likely to vary between arrays (see, for example, ref. 33). In any case, under steady-state conditions, the measured CheZ–CheYp association within each array must reflect the kinase activity in that array. We are not certain what specific experiments the referee is proposing. We certainly cannot construct dedicated Y–Z pairs for each array.\nArray dynamics and stability:\nFigure 4C shows that receptor composition changes over time, but the functional consequences are not explored. Do these changes influence adaptation dynamics or cellular memory?\nFig. 4C is presented primarily to demonstrate that the shift observed in Fig. 4B in the overall Tar–Tsr distributions arises not only from newly formed arrays, but also from pre-existing arrays in which the Tar bias has been dynamically altered. In particular, such dynamic changes in receptor occupancy, combined with the generally stable structure of the arrays, can be expected to produce non-uniform arrays (L353–369).\nIn this study, we focused on applying the new single-array method to measure the signaling properties of arrays with minimal additional modifications; namely, native (untagged) receptors, expressed under native control and thus maintaining nearly native receptor/CheA–W stoichiometry. Measuring the signaling properties of individual arrays while simultaneously monitoring their content would require more elaborate constructs with multiple components tagged simultaneously, thereby introducing additional potential complications. Regarding adaptation specifically, a notable concern is the potential influence of a C-terminal tag on receptor binding by adaptation enzymes, whose binding site is located at this position.\nThe manuscript implies structural stability of arrays, but this is not quantified. FRAP of receptor exchange would help determine turnover times and test the assumption of ‘frozen’ mosaic structures.\nThe stability of the arrays is discussed (L353–369), including reference to FRAP experiments performed on these arrays (44).\nUnder-analyzed data representations:\nSome of the central plots—particularly the Ser–Asp response maps (Fig. 2)—are rich in content but difficult to interpret quantitatively. These could benefit from further statistical interrogation, such as clustering analysis, density estimation, or PCA. Plotting correlations between response behavior and receptor composition, array size, or growth phase would also be informative.\n- Dependence on growth phase has been demonstrated (Fig. 2C).\n- Regarding composition, as noted above, we focused on measuring signaling in arrays with native (untagged) receptors. Array composition was analyzed through their natural changes between growth phases (analyzed in Fig. 4), which indeed show a pronounced shift also in single-array behaviors (Fig. 2C and 2D).\n- Added to Fig. 2C the corresponding distributions along the serine and aspartate axes.\n- Distance distributions are shown in Fig. 3.\n- Added a plot highlighting arrays that are the sole array in their respective cells (new Fig. S6).\n- Added comparison with population-level behaviors (Fig. 2D and E).\nLack of testable predictions:\nThe mosaic model explains the observed data well, but the paper would be stronger with explicit, testable predictions. For example, does altering receptor heterogeneity via controlled expression shifts change the dose–response behavior in predictable ways? Could modifying the cooperative subregion size (e.g., through CheW mutants) change signal integration characteristics? Currently, the model is largely descriptive rather than predictive.\nWe observed that the measured single-array responses are consistent with the shift in receptor composition (Tar/Tsr) that occurs between growth phases.\nWe now also show that in Δtsr cells, where arrays are expected to consist more uniformly of Tar receptors, the responses are indeed concentrated near the lower-right corner of the Asp–Ser response plot (Fig. S5 and 2E).\nNon-uniform mixed (Tar/Tsr) arrays can be expected to exhibit low cooperativity even at the single-array level.\nBy measuring dose–response curves of single arrays (Figs. 5D–E), we now demonstrate low-cooperativity responses at the single-array level (see also discussion in L370–383).\nWe explicitly discuss potential implications of non-uniform arrays including to signal integration L378-402.\nFinally, we further demonstrate potential implications for chemotaxis behavior in complex environments (see Fig. 6 and related text L309-334).\nModulating the arrays using a CheW mutant might indeed be interesting to explore; however, the expected outcome is not a priory unclear. As shown in ref. 8, adding a moderate level of CheW mutant (e.g., CheW-X2/3) in addition to the native CheW did not substantially alter the measured cooperativity at the population level. On the other hand, excess CheW can also interfere with trimer formation, kinase activation, and cluster positioning (Cardozo MJ, Massazza DA, Parkinson JS, and Studdert CA; Mol. Microbiol. 2010; see also ref. 11). Thus, using this mutant would tricky and appears to be beyond the scope of this study.\nGenerality and behavioral relevance:\nIt remains unclear how generalizable the findings are across strains or species. Testing other E. coli backgrounds, and/or linking array heterogeneity to downstream behavior—e.g., changes in chemotactic behavior —would greatly enhance biological relevance.\nAs mentioned above, we added experiments in cells lacking Tsr receptors (Fig. S6 and 2E). We also demonstrate the potential implications of the observed growth-phase–dependent signaling shift (Fig. 2) for chemotaxis behavior in complex environments (new Fig. 6). Additional implications are discussed in the revised Discussion section.\nRP437 is a commonly used E. coli strain for studying fundamental properties of chemosensory arrays. Based on the principal view proposed here, we indeed expect quantitative differences between strains, reflecting their specific receptor-expression dynamics and its dependence on growth phase. While a comparative study across strains and growth conditions would be valuable, it is beyond the scope of the present study.\nSuggested addition to the Discussion:\nThe authors convincingly demonstrate that chemoreceptor arrays exhibit mosaic, history-dependent organization. However, the Discussion would benefit from a speculative section addressing possible design principles. For example,\n➢ heterogeneity might enhance robustness by broadening the detectable stimulus range, serve as a molecular memory of prior growth conditions, or enable evolutionary adaptability through modular receptor incorporation.\n➢ Alternatively, mosaic assembly might reflect unavoidable biophysical constraints—such as slow turnover or irreversible array growth—that evolution has co-opted. Articulating such hypotheses would enrich the conceptual impact of the work.\nDiscussion was revised to include both issues and added data L370-410.\nIn summary, overall a nice paper, but the authors should consider some of the above ideas to strengthen the evidence for the findings and to broaden the scope of the work.\nReviewer #2 (Remarks to the Author):\nThe manuscript by Frank et al. investigates the kinase responses of individual chemoreceptor arrays in live E. coli cells, by combining the CheA kinase and response regulator CheY to create a functional hybrid protein that resides within the array. The manuscript addresses an important technical issue, given that detecting the responses of individual arrays remains challenging. The authors found that the sensory properties of individual arrays are highly diverse and dependent on the growth phase. They also demonstrated the dynamic shifts in receptor occupancy within individual arrays. Finally, the authors suggest that receptor arrays are non-uniform, consisting of a mosaic arrangement of sub-regions with distinct receptor content, which may reflect the unique assembly history of each array. This study enables the direct measurement of kinase responses in individual sensory arrays within live bacterial cells, and contributes to a better understanding of how arrays assemble over generations.\nComments:\n1. In Fig. 1C, the authors should specify the serine concentrations used to measure the R/Y responses. In lines 134–147 and the legend for Fig. 1D, the authors should specify the growth phase (OD600) of the E. coli cells used in these experiments.\nAdded.\n2.In Fig. 4D, it appears that the red fluorescence becomes weaker after returning to the buffer solution, possibly due to the bleaching effect. The author should calibrate this effect for the data shown in Figures 1 and 4.\nWe assume the reviewer is referring to Fig. 1D rather than Fig. 4D. Indeed, unmodified images are shown, but bleaching was taken into account in the quantitative analysis (e.g., profiles in Figs. 1 and 4C). See Materials and Methods, Supplementary Methods, and Fig. S2. This is now stated in the corresponding figure legends.\n3.In Fig. 4C, the authors do not explain why they show additional traces of Tar occupancy in individual arrays. If the conditions used were the same as in the upper-right plot, the lower plot could be moved to the SI. There are no gray bars in the lower plot. In addition, four images are shown in the lower plot, but the author provides no explanation for them in either the main text or the figure legend.\nThe upper plots show representative examples of cell growth (left) and Tar bias shift (right) of the same arrays. The lower plot includes data from multiple arrays, demonstrating that the individual traces are overall consistent with the population data in part B (red bars; the previous reference to grey was a typo). Individual traces are less easily distinguishable in this plot.\n4.The other three types of E. coli chemoreceptors (Aer, Trg and Tap) were not considered in this study, either in experiments or modelling. Could the authors discuss how these chemoreceptors influence kinase responses and the assembly of individual arrays?\nIndeed, we did not explicitly consider these receptors, as they are generally low in abundance and generally constitute only a few percent of the total receptor pool (see ref. 37), which is also consistent with our experience with this strain. Furthermore, we have now added measurements of cells lacking Tsr receptors (new Figs. S5 and 2E), indicating that in the absence of Tsr, Tar clearly completely dominates the array responses. Some influence of minor receptors may be reflected in arrays whose responses fall well below the additivity line in Fig. 2C. However, further studies are required to elucidate the role of these receptors in the context of such arrays. This point is now discussed L197–202.\n5.Using MWC-model analysis, a link was generated between the chemoreceptor fraction and ligand responses. However, the authors did not provide experimental evidence. It would be better to provide direct experimental evidence of the correlation between kinase responses and the receptor fraction.\nThe new single-array method described here (Fig. 1) was applied to arrays with native (untagged) receptors and under native expression levels (stoichiometry). At this stage, we sought to avoid potential complications associated with receptor tagging. Additionally, in non-uniform arrays, the internal spatial distribution of receptors can strongly influence the response (see discussion, L384–399) and correlates with total Tar content only at a statistical level. We therefore assessed receptor occupancy in similar but distinct strains and correlations with the shift in response patterns were established at the statistical level (see Figs. 4, 5, and S9). Using the MWC model we could relate the expected responses of non-uniform arrays to those observed experimentally (Fig. 5A-C and related text). Moreover, we now also have measured single array dose-responses that is also consistent with mosaic view of the arrays (Fig. 5D-E, related text, and Discussion L370-383).\nReviewer #3 (Remarks to the Author):\nMotile bacteria detect chemical gradients using large chemosensory arrays of transmembrane receptors (mostly Tar and Tsr in E. coli) associated with kinases (CheA) and a scaffolding protein (CheW in E. coli). Individual cells can have several arrays, and arrays of mother cells are passed on to daughter cells. Many studies have characterized CheA activity in single cells, quantifying cell-to-cell variability in the sensitivity and gain (slope of the dose response at EC50) of individual cells to various ligands presented in the presence or absence of background concentrations of the same or other ligands. The picture that has emerged is that such variation arises from cell-to-cell variations in expression levels of the most abundant receptors Tar and Tsr. However, so far it has not been possible to directly measure the output of individual arrays in individual cells. Thus, it remains unknown whether Tar and Tsr are well mixed within individual arrays or whether there is within-cell variation in array composition.\nThe authors developed an interesting new FRET method to measure kinase activity in individual receptor arrays, that produces similar population average responses (Figure 1C) as the traditional CheY/CheZ FRET method. The new method relies on two main modifications: replacing the P2 domain of CheA with a CheY-yfp fusion, and an F98S point mutation in CheZ to prevent its direct interaction with CheA. However, rather than using FRET to quantify kinase activity, throughout the rest of the paper the authors instead use localization of CheZ-mScarlet to assay changes in interactions with CheY upon stimulation with steps of saturating amounts of serine and aspartate.\nThe paper’s main findings are: a) substantial array-to-array variability in response magnitude to saturating steps that is growth-phase dependent; b) a growth-dependent change in the receptor composition of the arrays; in most cases each signal alone can substantially inhibit the kinase suggesting that both receptors can affect the same set of kinases (as opposed to a model where each receptor only interacts with distinct subsets of kinases); d) no strong correlation between the responses of different arrays in the same cell. Finally, the authors used modeling to argue that their data is consistent with a model in which individual arrays exhibit highly diverse local receptor compositions, but since the measurements presented do not quantify the hill coefficient of the dose responses, it was difficult to understand how the modeling related to the experiments.\nWe have now added single-array dose-response data demonstrating low cooperativity at the single-array level, consistent with the mosaic view of arrays; see new Fig. 5C–D, related text (L280), and the Discussion (L370-383).\nThe measurements in Fig. 2C examine the relationship between the fractions of kinase activity inhibited by each ligand, providing a distinct means of characterizing diversity among individual arrays within the effectively two-dimensional response space. These correlations are both informative and directly testable by the MWC model, (see Fig. 5A-C, and related text). At present, photobleaching limits our ability to reliably measure dose-response curves for the same individual array to multiple stimuli and reasonable ligand resolution. Thus, we focused on the serine/aspartate correlations and have now supplemented them with single-array dose–response measurements. Clearly, a variety of further experiments and analyses can be pursued using the single-array assay reported here.\nThe new experimental method discussed is impressive and has the potential to significantly shape our understanding of receptor cluster development and dynamics in bacterial chemotaxis. However, I have several concerns about the current presentation of the method, the measurements, and the interpretation of the data that preclude publication until addressed.\nMAIN CONCERNS\nEXPERIMENTAL METHOD\n1) The authors motivate well the development of a new FRET system that could potentially report kinase activity in individual arrays. They show in Figure 1C that at the population level the dose response to serine is similar to that in WT, suggesting the mutations introduced do not significantly affect kinase response (although see next question). Surprisingly however, the rest of the paper makes no use of FRET to quantify change in kinase activity. Instead kinase response is quantified in terms of the change in the localization of CheZ-mScarlet on top of the CheA::CheY-YFP puncta. I could not find in the text a motivation for using this indirect method to estimate kinase activity of individual arrays when presumably the FRET signal quantifies direct interaction between CheZ and CheY-P and therefore reflects kinase activity (as shown in Figure 1C). Could the data from Figure 2C have been obtained using single array FRET? This would be a great control experiment. Or is the signal-to-noise ratio too low with this FRET pair to enable single-array FRET?\nIndeed, in population-level measurements or even single-cell measurements, where total cellular fluorescence is measured, only FRET can directly report protein–protein interactions. However, in the present construct, it is not clear that FRET offers a significant advantage or facilitates analysis with minimal photon usage compared with the simpler ratiometric (red/yellow) method employed here. For example, the inherent redistribution of CheZ within the cell upon stimulation must still be accounted for when analyzing FRET data. Thus, under these conditions, we opted for the ratiometric method, which directly reports the fraction of CheY molecules in the array bound by CheZ. Nevertheless, FRET analysis could be further explored in future work and may offer improved signal-to-noise under certain conditions.\n2) The only apparent difference between the new FRET and the traditional one is a slight leftward shift in the K1/2 in the response to serine (Figure 1C). However, since the authors stimulate cells with both serine and aspartate in Figure 2, a corresponding dose-response experiment using aspartate would be valuable. Since cheZ-mScarlet is fused at the native cheZ chromosomal locus, which is in the same operon as tar, this fluorophore fusion might influence tar expression. The lower serine K1/2 observed in VF17 relative to WT could be consistent with reduced Tar expression.\nA direct comparison between the single-array responses and the population-level FRET behavior is now provided for both serine and aspartate under both growth conditions (new Fig. 2D–E), as well as for the single-array dose-response data in Fig. 5D–E.\nGiven that the reference measurements in Fig. 1C were performed using cells in which CheY/Z expression is plasmid-borne, we had to focus on the exponential phase, where we have greater experience and better control over plasmid induction. Moreover, as shown now in the Fig. 2D, the Tsr-mediated serine response is clearly dominant under these conditions and allows full kinase inhibition. We therefore believe that the test applied in Fig. 1C is the most appropriate for validating the function of the new CheA construct in terms of ligand-mediated kinase control and phosphotransfer to CheY and CheZ. Indeed, there appears to be a small shift in K1/2, corresponding to an estimated of few percent change in Tar occupancy (evaluated using the MWC model). This shift could indeed result from receptor tagging in the new strain, but could also result from the cheY/Z deletion and plasmid-borne expression in the reference strain.\n3) The method used to measure kinase activity throughout the paper relies on measuring the degree of colocalization of CheZ-mScarlet with the array, itself labeled by CheA::CheY-YFP. This method was not clearly explained in the main text. The response is quantified as the peak amplitude in the red channel minus the amplitude of a rescaled red channel when cells are stimulated with both serine and aspartate (Figure 1D second column at the bottom). The rational for that normalization of the red channel during saturated response was not provided. I am assuming here that the dashed black line in the second plot of the last row if Figure 1D is the same as in the 3rd column, but normalized so that the profile matches with the red profile away from the peak.\nThis normalization accounts for changes in the total free CheZ within the cell upon stimulation; therefore, the background obtained from the Ser+Asp image must be scaled accordingly. We now explain this normalization in the text.\nAlso, why using the peak amplitude rather than the area between the two curves?\nThe analysis shown in Fig. 1D is strictly useful when the array is centered at the pole and is presented primarily to illustrate the behavior and conceptual analysis. The full analysis, now explained in the Supplementary Methods, is essentially two-dimensional. Although the peak area could, in principle, be used instead of the peak value, in practice the area appeared less well-defined, more sensitive to thresholding, and potentially noisier. Unless the arrays are very large, the integrated intensity over the peak should be proportional to the peak intensity, and in any case, the relative changes upon stimulation should remain representative.\n4) How can the authors be certain that the observed shift in CheZ-mScarlet fluorescence peak height reflects physical localization of CheZ during catalysis and is not confounded by FRET between YFP and mScarlet?\nmScarlet serves as the acceptor in this pair and is directly excited when acquiring the ‘red’ images. The donor, mYFP, could in principle have its fluorescence reduced by FRET; however, based on population-level FRET measurements, such a change is estimated to be less than 10%. This minor effect would not substantially influence the observed responses, which primarily rely on the red fluorescence signal. Note that once we confirmed that the arrays—and therefore the mYFP peaks—were generically stable and unaffected by ligand, we did not normalize by mYFP. Such normalization would have factored out naturally and could only have introduced additional noise.\nMEASUREMENTS\n5) figure 1D only shows an example of one array that seems to only respond to serine. Can the authors provide many examples, including some in which the array responds to both ligand and some in which the array responds to only aspartate. It is also important to show examples of cells in which there are multiple clusters.\nAdded examples in Fig. S3.\n6) How does the relative distance of individual clusters within one cell affects the estimate of the response of one cluster? The authors discuss in the SI possible interferences due to shared CheZ-mScarlet pool, but the image analysis is not discussed. I imagine that if clusters are closed to each other, the individual peaks would appear nearly merged. How independent then are the measurements of individual arrays shown in figures 2C and 3? How did the authors deal with cells in which multiple clusters are close to each other and the red channel peaks merge or nearly merge? Where the authors able to estimate a confidence interval for the response of each individual array?\n7) Related to the previous question, Can the authors plot a histogram of the number of discrete arrays per cell? Do most cells have only one cluster or more in their data? Also what is the distribution of pairwise distances between individual arrays? These statistics would be informative to interpret the figures 2 and 3.\nWe have now added a Supplementary Methods section containing a detailed description of the analysis.\nWe added the distribution of cluster-number per cell in Fig. S4. However, notably, not all observed arrays were analyzed; clusters were excluded if they were unstable or too small, and also, if they were too close to one another; we only analyzed distinguishable arrays such that no other array was at a distance comparable with their width. Additionally, we now present in Fig. S6 a distribution similar to that shown in Fig. 2C, highlighting arrays present alone in their respective cells; this subset of arrays exhibits generally similar response behaviors. Finally, we estimate the error in the extracted responses to be up to approximately 10%, mostly due to uncertainty in the array position in some instances in the red channel (see Supplementary Methods).\n8) The top two panels in Figure 4A both present measurements of CheA abundance in stationary and exponential phase, yet the distributions differ depending on whether Tar or Tsr is labeled. Do the authors attribute these differences to secondary effects of receptor labeling?\nIndeed, both strains show the same overall trend regardless of the tag: larger arrays in the stationary phase. However, a small difference appears in the stationary-phase data near the center of the distribution (~1000), the cause of which is unclear. We cannot rule out a potential effect of receptor labelling on array size distribution. Notably, to avoid this possible complication, all kinase-response measurements were performed on arrays with untagged receptors.\n9) In Figure 4C, is the \"array size\" shown on the left panel different from the CheA concentration shown on the right? The caption of Figure 2 suggests that “array size” corresponds to the total fluorescence intensity of the CheA::CheY-mYFP fusion. Additionally, could the authors clarify what the different colored points and lines represent in these two plots? Is it data from different cells?\nIndeed, array size corresponds to CheA::CheY–mYFP fluorescence, which reflects the size of the array baseplate, while Tar occupancy is calculated as the amount of Tar (red fluorescence) divided by the array size. Different colors in the plot represent different arrays. We have clarified this now in the figure legend.\n10) In Figure 4C, upper left plot, the data show that array size increases as the culture shifts from stationary to exponential phase. Yet Figure 4A, upper plot, indicates that CheA levels are higher in stationary phase than in exponential. How can these observations be reconciled?\nFigure 4A shows the overall distribution of the array population. In Figure 4C, we tracked relatively small clusters as they grew during the exponential phase. This analysis demonstrates that the changes observed in Figure 4B are not solely the result of newly formed arrays, but also include existing arrays that dynamically altered their Tar bias. We mention this now in the text.\n11) What is the purpose of the fluorescence anysotropy experiment shown in Figure S5? Is it solely intended to extract the on- and off-rates of Tsr binding to serine for use in Figure 5A? If so, how were the corresponding rates for Tar determined?\nThe relevant Tar data were presented in ref. 24. Here, we present the corresponding data for Tsr and use them to evaluate the parameters for Tsr–serine interactions.\nINTERPRETATION\n12) It is unclear how the modeling in Fig 5 relates to the experiments in figure 2. There is no information about n_Tar and n_Tsr measured in the experiments because the authors only measure maximum inhibitable kinase activity, and not the hill coefficient of the dose-response.\nFigures 5A–B present a general analysis of the expected relationship between Tar occupancy (r) and the correlations between kinase inhibition by serine and aspartate at saturation. The key observation is that, under substantial molecular cooperativity, the predicted patterns of single-array responses differ markedly from those observed experimentally (see related text). This analysis is independent of dose-response behavior and instead relies on constraints arising from the correlations between serine and aspartate responses in each array. We also show that non-uniform arrays can indeed produce response patterns similar to those seen experimentally while maintaining substantial molecular cooperativity (Fig. 5C) and only moderate changes in the Tar occupancy distribution, generally comparable to experimental observations (Fig. 4C). Furthermore, we have now added single-array dose-response measurements (Figs. 5D–E), which also support this view (see Discussion L370-383).\nIf the old high-Tsr clusters are preserved, the hill coefficient for the serine response may be preserved, even as the maximal response decreases over time. To support this interpretation, the authors would need to measure the distribution of n_Tar and n_Tsr in exponential and stationary phase.\nIndeed, if Tar-dominant and Tsr-dominant regions within an array are well segregated, one could still expect high Hill coefficients but only partial responses of single arrays. While such segregation might occur randomly in some arrays, the continuous shift in the Tar/Tsr ratio suggests that more gradual changes are likely across different subregions of the array. Correspondingly, the single-array dose-response curves, which were now added, mostly exhibit low Hill coefficients (new Figs. 5D–E and related text). However, in a minority of arrays, we cannot rule out highly cooperative yet partial responses (e.g., Fig. 5D lower-right plot, and related text).\nHistograms of Tar and Tsr occupancy are shown in Fig. 4B for both the exponential and stationary phases. We used strains similar to those employed for kinase measurements but with either Tar or Tsr tagged in separate strains. In each case, the array fraction was determined by comparing the tagged receptor signal to the total array size (measured using the CheA tag). This approach offers two main advantages over simultaneously tagging both receptors: (i) it allows the use of strains closely matching those used for kinase measurements, and (ii) it minimizes the total receptor labeling in the array, thereby reducing potential steric interactions. It should also be noted that in non-uniform arrays, the internal spatial distribution of receptor types is important (see discussion at L384-399) but we anyway cannot directly measure it.\n13) It would enhance the impact of the paper if the authors could better clarify in the discussion how their results align with or differ from previous related studies. Namely:\nDo the result that most cells have 1 or 2 clusters contradict previous results of lateral clusters:\nhttps://www.ncbi.nlm.nih.gov/pubmed/30361683\nA histogram of cluster numbers has now been added (Fig. S4), showing a distribution generally similar to that reported previously in ref. 11 (note that we have not probed here the very early growth stage reported in ref. 11). As noted above and in the Supplementary Methods, not all arrays were included in the measurements.\nWhat are the implications of the proposed mosaic structure of receptor arrays for signal integration, especially in the presence of multiple ligands: e.g. https://pubmed.ncbi.nlm.nih.gov/38981486/\nThe Discussion section has been expanded to incorporate the new single-array dose-response data and also address the following aspects: signal integration; the coexistence of diversity and highly cooperativity arrays; the role of adaptation in controlling internal diversity and thus signal integration; and potential impact on behavior.\nDoes the stability of the cluster (Fig 1D, and lines 267-269) contradict previous results that the clusters are unstable under prolonged stimulation: https://www.ncbi.nlm.nih.gov/pubmed/23551504\nThis reference is now 43. As noted in ref 43, we did not observe disassembly of arrays and proposed that the slow anisotropy dynamics reflect some form of internal change, the nature of which remains unclear. This is discussed now (L353-369).\nMINOR COMMENTS:\n15) Why was mCherry fused to CheZ in plasmid pAV109, whereas mScarlet was used in strain VF17? Is that related to the faster maturation time of mCherry compared to mScarlet (or did the authors use a fast-maturing variant of mScarlet, like mScarlet3)? Also, In Figure 1C, the authors report a 10% change in FRET amplitude with CheZ-mScarlet - do they observe a similar change when using the CheZ-mCherry fusion?\nmScarlet-I was used in VF17 to maximize brightness and improve the signal-to-noise ratio. We did not test a similar strain with mCherry. We have extensive experience with pAV109, we therefore used it here as a reference.\n16) What exactly is the chemical“HTML”added to the M9 minimal media?\n17) All strains are RP437 derivatives, which is an auxotroph. How do the cells grow in M9 minimal medium without supplementation with amino acids?\n“HTML” refers to a mixture of the amino acids Histidine, Threonine, Methionine, and Leucine, which were added to supplement the M9 minimal medium. We clarified this now in the Materials and Methods section.\n18) In lines 333 to 334, the authors mention performing FRET in VF17 using CheY-mYFP and CheZ-mCherry. Where is this experiment presented in the manuscript?\nThe sentence was indeed unclear. We clarified now.\n19) Could the authors provide more detail on the imaging setup? Specifically, did they use the same excitation and emission filters for mCherry and mScarlet? Their spectral properties are somewhat different.\nWe used the same excitation and emission filters for both mCherry and mScarlet-I. We have added further technical details regarding the imaging setup to the Materials and Methods section.\n20) In Figure 2C, what are the concentrations used to measure serine and aspartate responses?\nThe concentration used for both serine and aspartate were 3 mM, now added this information to figure legends.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nI have read the authors’ rebuttal and examined the revised manuscript, including the newly added single-array dose–response data, the extended analysis of inter-array coupling, and the Supplementary Methods and modeling sections. I find the manuscript significantly strengthened and the concerns I previously raised have been addressed carefully and convincingly.\nNotably, the authors have added clear experimental validation comparing single-array dose–response curves to population-level measurements, confirming that their hybrid constructs produce biologically meaningful responses. They also clarified that the observed variability is consistent with the mosaic receptor composition model and demonstrated low Hill coefficients in single arrays where appropriate. Moreover, their analytic treatment of inter-array crosstalk is rigorous and shows that residual coupling is minor under their experimental conditions.\nFinally, I appreciate that the revised discussion now more explicitly addresses design principles behind the observed heterogeneity and speculates constructively on its functional and evolutionary implications. Overall, this is an impressive and well-executed study, and I recommend the manuscript for publication.\nReviewer #2 (Remarks to the Author):\nThe authors have addressed the concerns of this reviewer in a satisfactory manner.\nReviewer #3 (Remarks to the Author):\nThe authors have addressed all my initial concerns, except one (see below) and the new data substantially strengthen the manuscript. That said, the added results also raise a few additional points that I would recommend considering before publication.\nThank you for the feedback.\nThe main additions to the manuscript are:\n- We added Fig. S2, demonstrating that the aspartate K1/2 is indeed similar between the new VF17 strain and the plasmid-based FRET system with native CheA.\n- We added Fig. S11, presenting examples of the cell-density profiles observed in the behavioral experiments (Fig. 6), and also providing tumble-bias analysis showing similar distributions for both cell populations used in those experiments.\nA detailed point-by-point response is provided below.\nSingle-array dose–response experiments (figure 5)\n=================================================\nThe single-array dose-response curves are a very valuable addition. I have the following suggestions regarding their analysis and interpretation:\n• Could the authors perform an analysis similar to Figure 3, i.e. compare the K1/2 and Hill coefficient of clusters within the same cell and across the population?\nWe do not have enough same-cell dose response measurements with sufficient accuracy in the existing data to allow for a meaningful analysis. While further analysis of single-array behaviors, including that suggested by the referee, would indeed be valuable, we believe this falls beyond the scope of the present manuscript.\n• Is it technically feasible to extract the single-array K1/2 and Hill coefficient distributions for Tar+ Tsr- cells? One would expect much lower cluster-to-cluster variability than in Tar+ Tsr+ cells, given the absence of receptor mixing.\nIndeed, as shown in Fig. 5D (inset), in such uniform receptor arrays (Tar+Tsr-) the dose-response is already quite steep (Hill coefficient ~10) even at the population level, clearly suggesting a narrow distribution of K1/2 among individual arrays.\nOn the other hand, the steep dose-response behavior of these cells makes it more challenging to systematically evaluate the K1/2 distribution in single arrays, as this would require very dense and broad coverage of the doses tested, which in turn exacerbates the bleaching problem. We are working to improve this, but at present it seems too early to pursue such measurements methodically.\n• The aspartate K1/2 value the authors measure (~10 uM) is quite high for stationary phase RB- cells. A valuable and easy control would be to perform an aspartate dose-response experiment using the plasmid-expressed FRET pair. As I wrote in my first review, the authors express cheZ-mScarlet fused at the native cheZ chromosomal locus, which is in the same operon as tar. Therefore, the fluorophore fusion will likely influence tar expression. The lower serine K1/2 observed in the new strain relative to the plasmid-based induction system is consistent with reduced tar expression.\nWe have now added Fig. S2, demonstrating very similar dose response behavior for aspartate in both VF17 cells and the reference cells expressing a plasmid-based FRET pair. In particular, in both cases we find that K1/2 ~ 10 μM.\nNote that given that aspartate sensitivity is about tenfold higher than that to MeAsp, the K1/2 values observed in ref. 23 for mixed receptor arrays are in fact comparable to those found here.\nIn any case, when evaluated using MWC model (Eqs. 2–3) under the assumption of uniformly mixed arrays, the K1/2 shift observed in Fig. 1C corresponds to only a few percent (3-10%) change in the Tar fraction. Such changes might indeed be caused by the added label in the VF17 strain, but could also reflect necessary modifications in the reference strain, namely the cheYcheZ deletions and/or plasmid (cam)-borne expression, which also affect growth rate. Explicitly discussed now in lines 133–139.\nPlease include the data points for the Tar+ Tsr+ dose-response fit in the inset of panel E.\nThe data and fit are shown in Fig. 2D.\nBehavioral experiments (Figure 6)\n================================\nThe new behavioral assay is interesting, though the result is largely expected given the work of Kalinin et al. 2010. I suggest the following additions and controls to strengthen the interpretation:\n• Introduce Tar+Tsr- cells into the same device - one would expect a purely attractive response in this case.\nThese experiments were reported in ref. 40. Briefly, Tsr⁻ cells exhibited only attractant responses whereas Tar⁻ cells exhibited a stronger repellent response; this is now mentioned on lines 326-7.\n• Measure tumble bias of cells in exponential and stationary phase at zero ligand. Differences in steady-state tumble bias would influence propagation speeds. Please also provide representative plots of cell density distributions within the device for several MeAsp concentrations.\nThank you. We have now added examples of cell-distribution profiles and tumble-bias analysis in a new Fig. S11.\nMinor points\n============\n• Figure 2, panel d: the y-axis label should read “kinase activity.”\nCorrected, thank you.\n• Figure S9 legend: replace “peaked” with “picked.”\nCorrected, thank you."}, "author_response": ""}]

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