Growth rate and genome copy number in E. coli growing in M9glyCAAT.

(A) Illustration of 1N (CRISPRi oriC, CJW7457) and multi-N (CRISPRi ftsZ, CJW7576) cells with different numbers of chromosomes along with representative microscopy images at different time points following CRISPRi induction. Scale bars: 1 µm. (B) Plot showing representative single-cell trajectories of cell area as a function of time for the CRISPRi strains following a block in DNA replication and/or cell division. (C) Plot showing the absolute growth rate as a function of cell area for 1N (32735 datapoints from 1568 cells) and multi-N cells (14006 datapoints from 916 cells) in M9glyCAAT. Lines and shaded areas denote mean ± SD from three experiments. This also applies to the panels below. (D) Absolute and (E) relative growth rate in 1N (32735 datapoints from 1568 cells, CJW7457), multi-N (14006 datapoints from 916 cells, CJW7576), and dnaC2 1N (13933 datapoints from 1043 cells, CJW7374) cells as a function of cell area in M9glyCAAT. (F) Absolute and (G) relative growth rate in 1N (13933 datapoints from 1043 cells), 2N (6265 datapoints from 295 cells), and >2N (2116 datapoints from 95 cells) dnaC2 (CJW7374) cells as a function of cell area in M9glyCAAT.

Lower ribosome activity explains the reduced growth rate of 1N cells growing in M9glyCAAT.

(A) RpsB-msfGFP fluorescence concentration in 1N (6542 cells, CJW7478) and multi-N (10537 cells, CJW7564) cells as a function of cell area. Lines and shaded areas denote mean ± SD from three experiments. (B) Relative protein concentration of different ribosomal proteins in 1N (SJ_XTL676) and multi-N (SJ_XTL229) cells by TMT-MS. 1N-rich cells were collected 0, 120, 180, 240 and 300 min after addition of 0.2% arabinose, while multi-N cells were collected after 0, 60, and 120 min of induction. Blue and cyan represent two independent experiments. Only proteins with at least four peptide measurements are plotted. (C) Apparent diffusion coefficients (Da) of JF549-labeled RspB-HaloTag in WT (32,410 tracks from 771 cells, CJW7528), 1N (848,367 tracks from 2478 cells, CJW7529) and multi-N cells (107,095 tracks from 1139 cells, CJW7530). Only tracks of length ≥9 displacements are included. 1N cells are color-binned according to their cell area while multi-N cells contain aggregated data for ∼2-10 µm2 cell areas. (D) Da in WT cells fitted by a three-state Gaussian mixture model (GMM): 77 ± 1%, 20 ± 1%, and 3.2 ± 0.5% (±SEM) of the ribosome population, from the slowest moving to the fastest moving (32,410 tracks from 771 cells). (E) Example WT and 1N cells where active (red, slow-moving) and inactive (gray, fast-moving) ribosomes are classified according to the GMM. (F) Active (slow-moving) ribosome fraction in individual WT (237 cells) and 1N (2453 cells) cells as a function of cell area. Only cells with ≥50 tracks are included. Lines and shaded areas denote mean and 95% confidence interval (CI) of the mean from bootstrapping. (G) Same as (F) but for WT (237 cells) and multi-N (683 cells) cells. (H) Absolute growth rate of 1N and multi-N cells (Figure 1C) as a function of cell area was overlaid with the total active ribosome amount (calculated from Figure 2A, F, and G). Lines and shaded areas denote mean and 95% CI of the mean from bootstrapping. All microscopy data are from three biological replicates.

RNAP activity is reduced in 1N cells growing in M9glyCAAT.

(A) RpoC-YFP fluorescence concentration in 1N (3580 cells, CJW7477) and multi-N (5554 cells, CJW7563) cells as a function of cell area. Lines and shaded areas denote mean ± SD from three experiments. (B) Relative protein concentration of core RNAP subunits and σ70 in 1N-rich (SJ_XTL676) and multi-N (SJ_XTL229) cells by TMT-MS. 1N-rich cells were collected 0, 120, 180, 240, and 300 min after addition of 0.2% L-arabinose, while multi-N cells were collected after 0, 60, and 120 min of induction. (C) Apparent diffusion coefficients of JF549-labeled RpoC-HaloTag in WT (91,280 tracks from 1000 cells, CJW7519), 1N (175884 tracks from 1219 cells, CJW7520) and multi-N cells (186951 tracks from 1040 cells, CJW7527). Only tracks of length ≥9 displacements are included. 1N cells are binned according to cell area while multi-N cells contain aggregated data for ∼2-15 µm2 cell areas. (D) Da in WT cells fitted by a 3-state GMM: 49 ± 4%, 49 ± 4%, and 2 ± 0.1% (± SEM) of the RNAP population, from the slowest moving to the fastest moving (91,280 tracks from 1,000 cells). (E) Example WT and 1N cells where active (red, slow-moving) and inactive (gray, fast-moving) RNAPs are classified according to the GMM. (F) Active RNAP fraction in individual WT (854 cells) and 1N (1024 cells) cells as a function of cell area. Only cells with at least 50 tracks are included. Lines and shaded areas denote mean ± 95% CI of the mean from bootstrapping (3 experiments). (G) Same as (F) but for WT (854 cells) and multi-N (924 cells) cells. (H) Total amount of active RNAP in WT, 1N and multi-N cells as a function of cell area (calculated from Figure 3A, F, and G). Also, shown is a linear fit to multi-N data (f(x) = 4.16 · 104 · x, R2 0.98). Lines and shaded areas denote mean and 95% CI of the mean from bootstrapping. All microscopy data are from three biological replicates.

RNASelect and EUB338 concentration measurements in 1N and multi-N cells.

(A) Images of representative cells from a mixed population of 1N (CRISPRi oriC) and multi-N (CRISPRi ftsZ) cells. Strains CJW7457 and CJW7576 carrying HU-mCherry were used for the SYTO RNASelect staining experiment, whereas DAPI-stained strains SJ_XTL676 and SJ_XTL229 were used for the EUB338 rRNA FISH experiment. (B) Concentration distribution of SYTO RNASelect (3077 cells for each population from five biological replicates) and EUB338 (1254 cells for each population from three biological replicates) in 1N and multi-N cells. (C) The average 1N/multi-N SYTO RNASelect and EUB338 concentration ratio (gray bar) calculated from five and three biological replicates (white circles), respectively. (D) RNASelect and EUB338 concentration ratios as functions of cell area (mean ± SD from five and three biological replicates, respectively). Single exponential decay functions were fitted to the average ratios (R2 > 97%) for each indicated reporter. All concentration comparisons or ratio calculations were performed for equal numbers of 1N and multi-N cells and overlapping cell area distributions (see Materials and Methods and Figure 4 – supplement 1).

Model parameter description

Initial and optimized model parameters

Mathematical modeling of DNA limitation.

(A-C) Plots comparing simulation results of model A (solid lines) with experimental data points (dots) and averages (open squares) in the M9glyCAAT condition. The multi-N and 1N cells are indicated as blue and yellow, respectively: (A) The relation between the absolute growth rate and cell area (A). (B) The relation between the active RNAP fraction and cell area. (C) The relation between the active ribosome fraction and cell area. (D) Diagram showing how the fractions of active RNAPs and ribosomes change with DNA concentration (colored from yellow to blue). Simulated results (filled dots) are based on model A. Experimental data (points with 2D error bars) from multi-N and 1N cells were combined and shown in the same plot. (E) Plot showing the effect of DNA limitation (using the ODE model A) on the decay of DNA concentration, mRNA concentration, and relative growth rate in 1N cells. Each quantity was normalized to their value at normal cell size (cell area = 2.5 𝜇𝑚2).

Scaling of the total active RNAPs, total active ribosomes, and growth rate with cell area during genome dilution in nutrient-poor media. (A)

Plot showing the total amount of active RNAPs (calculated by multiplying the total amount of RNAPs by the fraction of active RNAPs from Figure 6 – figure supplement 1A and G) in WT (CJW7339) and 1N (CJW7457) cells grown in M9gly as a function of cell area. Also shown is a linear fit to WT data (f(x) = 3.99 · 104 · x, R2 0.90). Shaded areas denote 95% CI of the mean from bootstrapping. All data are from three biological replicates. (B) Same as (A) but for cells grown in M9ala (calculated from Figure 6 – figure supplement 1B and H). The linear fit for WT data is f(x) = 3.21 · 104 · x, R2 0.95. (C) Plot showing the total active ribosome amount of 1N and multi-N cells grown in M9gly as a function of cell area. The total amount of active ribosomes was calculated by multiplying the total amount of ribosomes by the fraction of active ribosomes (from Figure 6 – figure supplement 2A and G). Also shown is a linear fit to WT data (f(x) = 2.99 · 104 · x, R2 0.97). Lines and shaded areas denote mean and 95% CI of the mean from bootstrapping. All data are from three biological replicates. (D) Same as (C) but for cells grown in M9ala (calculated from Figure 6 – figure supplement 2B and H). Here the linear fit to the WT data is f(x) = 1.90 · 104 · x, R2 0.99. (E) Absolute growth rate in 1N (50352 datapoints from 973 cells) and WT (80269 datapoints from 12544 cells) cells in M9gly. The linear fit for WT data is f(x) = 6.50 · 10−3 · x, R2 0.99. (F) Absolute growth rate in 1N (71736 datapoints from 909 cells) and WT (63367 datapoints from 6880 cells) cells in M9ala. The linear fit for WT data is f(x) = 4.05 · 10−3 · x, R2 0.97. Lines and shaded areas denote mean ± SD from three biological replicates.

Proteome and transcriptome remodeling in 1N-rich cells.

(A) Schematic explaining the calculation of the protein slopes, which describes the scaling of the relative protein concentration (concentration of a given protein relative to the proteome) with cell area. (B) Plot showing the protein scaling (average slopes from two reproducible biological replicates, see Figure 7 – supplement 1A-B) in 1N (x-axis) and multi-N (y-axis) cells across the detected proteome (2360 proteins). The colormap corresponds to a Gaussian kernel density estimation (KDE). (C) Plot showing the first principal component (PC1, which explains 69% of the total variance considering both 1N-rich and multi-N cells) used to reduce the dimensionality of the relative protein concentration during cell growth. The x-axis corresponds to the log-transformed cell area, whereas the marker size shows the cell area increase in linear scale. (D) Correlation between average protein and RNA slopes across 2324 genes. The colormap corresponds to a KDE. (E) Relation between mRNA abundance (transcripts per million 60min after CRISPRi induction) and RNA slopes in 1N-rich cells. The colormap indicates a KDE (3446 genes in total). The binned data are also shown (orange markers: mean ± SEM, ∼380 genes per bin). The Spearman correlation (ρ = -0.04) is considered not significant (NS, p-value > 10-10). (F) Correlation between RNA slopes and mRNA degradation rate from a published dataset (Balakrishnan et al., 2022) across genes. The colormap indicates a KDE (2570 genes with positive mRNA degradation rates and quantified slopes). The binned data are also shown (orange markers: mean ± SEM, ∼280 genes per bin). A significant negative Spearman correlation (p-value < 10-10) is shown for mRNAs with a degradation rate above 0.7 min-1. (G) RNA-slope comparison between essential and non-essential genes in E. coli. Three different published sets of essential genes were used (Gerdes et al., 2003; Goodall et al., 2018; Hashimoto et al., 2005). The horizontal white lines indicate the inter-quartile range of each distribution. Mann-Whitney non-parametric tests justify the significant difference (p-value < 10-10) between the two gene groups (essential vs. non-essential genes).

Strains used in this study

. The abbreviations kan, cat, and spec refer to gene cassette insertions conferring resistance to kanamycin, chloramphenicol, and spectinomycin, respectively. These insertions are flanked by Flp site-specific recombination sites (frt) that allow the removal of the insertion using Flp recombinase from plasmid pCP20 (Cherepanov and Wackernagel, 1995).

Oligonucleotides used in this study.

The relative growth rate of WT (CJW7339) cells in M9glyCAAT grown after placing them on an agarose pad.

The plot includes 61562 data points from 11907 cells. Lines and shaded areas denote mean ± SD from three biological replicates.

Characterization of ploidy in CRISPRi oriC cells.

Representative microscopy images of CRISPRi oriC cells expressing HU-CFP and ParB-mCherry with parS site at ori1 (CJW7517) in M9glyCAAT 210 min after addition of 0.2% L-arabinose. Scale bar: 1 µm.

Effects of cell area overestimation on the relative growth rate calculation.

The simulated relative growth rate for increasing cell area considering different levels of cell area over-estimation (0 to 0.2 μm2) and a constant relative growth rate of 0.0014 1/min. In the absence of an area overestimation (0 μm2), the theoretical relative growth rate remains constant as the cell area increases, consistent with exponential growth. When a constant overestimated area is added the relative growth rate scales non-linearly with the cell area. This non-linear increase in the relative growth rate estimation may be confused with super-exponential growth, but it is in fact the result of cell area overestimation during cell segmentation in the phase contrast channel.

Relationship between growth rate and cell area in cell types of different ploidy.

(A) Absolute and (B) relative growth rate in M9glyCAAT in 1N (32735 datapoints from 1568 cells, CJW7457), multi-N (14006 datapoints from 916 cells, CJW7576), and WT (19495 datapoints from 7440 cells, CJW7339) cells as a function of cell area. Lines and shaded areas denote mean ± SD from three biological replicates.

Validation of stable growth under microscope observation and absolute growth rate determination of ppGpp0 and ΔrecA cells. (A) Plot showing the absolute growth rate of 1N (CJW7457) cells grown for 90 min in a liquid M9glyCAAT culture in the presence of 0.2% L-arabinose and then transferred to an agarose pad and imaged for 2 h (dotted line). The data for 1N (CJW7457) and multi-N (CJW7576) cells (presented in Figure 1C), which were grown on an agarose pad for 6 h, are also shown for comparison. Lines and shaded areas denote mean ± SD from two biological replicates. (B) Plot showing the absolute growth rate of ppGpp0 (CJW7518) and ΔrecA (CJW7522) cells. Lines and shaded areas denote mean ± SD from two biological replicates. Also shown are the data for 1N (CJW7457) and multi-N (CJW7576) cells from Figure 1C.

Characterization of ploidy in dnaC2 cells.

(A) Representative microscopy images of WT (top) and dnaC2 (bottom) cells expressing HU-mCherry growing under microscope observation in M9glyCAAT. DNA replication in dnaC2 cells was blocked by growing cells at 37°C for 145 min. Arrows indicate dnaC2 cells with one (1N) or two (2N) nucleoids. Scale bars: 1 µm. (B) Graph showing the percentage of dnaC2 cells (CJW7374) with 1, 2 or >2 nucleoids after growth at 37°C. Shown are aggregated data from three biological replicates.

Relationships between growth rate and cell volume across cell types of different ploidy in M9glyCAAT.

(A) Absolute and (B) relative growth rate based on cell volume in 1N (32735 datapoints from 1568 cells, CJW7457), multi-N (14006 datapoints from 916 cells, CJW7576), and dnaC2 1N (13933 datapoints from 1043 cells, CJW7374) cells as a function of cell volume. Lines and shaded areas denote mean ± SD from three experiments. The growth rates are based on volume in this figure. (C) Absolute and (D) relative growth rate in 1N (32735 datapoints from 1568 cells, CJW7457), multi-N (14006 datapoints from 916 cells, CJW7576), and WT (19495 datapoints from 7440 cells, CJW7339) cells as a function of cell area. (E) Absolute growth rate and (F) relative growth rate in 1N (13933 datapoints from 1043 cells), 2N (6265 datapoints from 295 cells), and >2N (2116 datapoints from 95 cells) dnaC2 (CJW7374) cells as a function of cell area. (G) Cell width in WT (59072 datapoints from 7640 cells, CJW7339), 1N (41313 datapoints from 1585 cells, CJW7477), and multi-N (18489 datapoints from 915 cells, CJW7563) cells as a function of cell area. Lines and shaded areas denote mean ± SD from 3 biological replicates.

DNA-dependent growth in Caulobacter crescentus.

(A) Plot showing the absolute and (B) relative growth rate of 1N (DnaA depletion, strain CJW4823, 87 cells) and multi-N (FtsZ depletion, strain CJW3673, 181 cells) C. crescentus cells as a function of cell area. Lines and shaded areas denote mean ± SD from three biological replicates. (C) Example images of 1N and multi-N C. crescentus cells with ploidy represented by the number of ParB-eCFP foci. Scale bar: 2 µm.

Diffusive characteristics of labeled ribosomes in rifampicin-treated WT cells.

(A) Plot showing the probability density of apparent diffusion coefficients (Da) of JF549-labeled RpsB-HaloTag in WT cells (CJW7528) treated with 200 µg/mL rifampicin for 30 min. Only tracks of length ≥10 are included. Also shown is Da fitted by three-state Gaussian mixture model (GMM) (mean ± SEM). Data are from three biological replicates.

Diffusive characteristics of labeled ribosomes in 1N cells as a function of cell area.

Plots showing the probability density of apparent diffusion coefficients (Da) of JF549-labeled RpsB-HaloTag in 1N cells (CJW7529) in M9glyCAAT at different cell areas. Also shown is Da fitted by three-state Gaussian mixture model (GMM). Only tracks of length ≥10 are included. Data are from three biological replicates.

Diffusive characteristics of labeled RNAPs in rifampicin-treated cells.

Plot showing the probability density of apparent diffusion coefficients (Da) of JF549-labeled RpoC-HaloTag in CJW7519 cells treated with 200 µg/mL rifampicin for 30 min. Also shown is Da fitted by three-state Gaussian mixture model (mean ± SEM). Only tracks of length ≥10 are included. Data are from three biological replicates.

Determination of the relative Rsd concentration in 1N-rich and multi-N cells as a function of cell area.

Plots showing the relative protein concentration of Rsd in 1N-rich (SJ_XTL676) and multi-N (SJ_XTL229) cells, as determined by TMT-MS. 1N-rich cells grown in M9glyCAAT were collected 0, 120, 180, 240 and 300 min, while multi-N cells were collected at 0, 60, and 120 min after 0.2% L-arabinose induction of CRISPRi. Different shades of gray represent two independent experiments.

Cell sampling to match cell size distribution in mixed populations of 1N and multi-N cells.

(A) Cell area distributions from mixed CJW7457 (CRISPRi oriC) and CJW7576 (CRISPRi ftsZ) populations stained with SYTO RNASelect (aggregated data from five biological replicates) before and after cell area sampling (cell area bins with less than 50 cells were removed from the analysis). (B) Cell area distributions from mixed SJ_XTL676 (CRISPRi oriC) and SJ_XTL229 (CRISPRi ftsZ) populations stained with the EUB338 FISH probe (aggregated data from three biological replicates) before and after cell area sampling (cell area bins with less than 25 cells were removed from the analysis).

Comparison of RpoC-HaloTag-JF549 labelling between 1N and multi-N cells.

(A) Phase contrast (left) and RpoC-HaloTag-JF549 fluorescence (right) images of two representative cells from a mixed population of 1N (CRISPRi oriC, CJW7520) and multi-N (CRISPRi ftsZ, CJW7527) cells. (B) Cell area distributions from mixed 1N and multi-N cell populations in which RpoC-HaloTag was stained with the JF549 dye (aggregated data from two biological replicates) before and after cell area sampling. (C) Distributions of RpoC-HaloTag-JF549 signal concentration for 1N and multi-N cells (748 cells for each population from two biological replicates). (D) Average 1N/multi-N RpoC-HaloTag-JF549 concentration ratio (gray bar), calculated from two biological replicates (white circles) after sampling the same number of cells per biological replicate and cell area bin for each population (panel B). (E) Plot showing the concentration ratio of fluorescently labeled RpoC derivatives between 1N and multi-N cells versus the cell area. Squares show mean ± full range of RpoC-HaloTag-JF549 signal concentration ratios from two biological replicates, whereas grey circles indicate the mean signal concentration ratio of RpoC-YFP (from data shown in Figure 3A) for comparison. A linear regression (red dashed line) was fitted to the average RpoC-HaloTag-JF549 ratios. A cell area of ∼2.8 μm2 corresponds to a 1N/multi-N RpoC-HaloTag-JF549 concentration ratio equal to 1.

EUB338 staining comparison between fast (M9glyCAAT) and slow (M9gly) growing populations.

(A) Relative cell area and EUB338 concentration distributions from wildtype MG1655 exponentially growing populations in M9glyCAAT or M9gly. Data from two biological replicates (rep) are shown (M9glyCAAT rep 1: 5867 cells, M9glyCAAT rep 2: 9728 cells, M9gly rep 1: 8301 cells, M9gly rep 2: 3766 cells). The iso-contour plots (nine levels above the 25th density percentile) include data from both biological replicates per nutrient condition. (B) Representative fields of the EUB338 fluorescence in stained fixed cells that were grown in M9glyCAAT (top) or M9gly (bottom). For comparison, the two fields of view have the same dimensions, and the fluorescence is scaled the same.

Comparison between experimental results from the M9glyCAAT condition and simulation results using model B.

(A-C) Plots comparing simulation results of model B (solid lines) with experimental data (dots) and averages (open squares). The multi-N and 1N cells are indicated as blue and yellow, respectively: (A) The relation between the absolute growth rate and cell area (A). (B) The relation between the active RNAP fraction and cell area. (C) The relation between the active ribosome fraction and cell area. (D) A two-dimensional diagram showing how the fractions of active RNAPs and ribosomes change with DNA concentration (colored from yellow to blue). Experimental data (with 2D error bars) from multi-N and 1N cells were combined and shown in the same plot.

Model A-based simulations examining the effects of varying the rates in either mRNA synthesis or mRNA degradation on the relative growth rate of 1N cells as a function of cell area.

(A) Plot showing the decay of DNA concentration (black) and of the relative growth rate (blue) in 1N cells when the rate of bulk mRNA synthesis (r1) increases or decreases by ten-fold. Each quantity was normalized to its value at normal cell size (cell area = 2.5 𝜇𝑚2). (B) Same as (A) except mRNA degradation rate 𝛿 increasing or decreasing by ten-fold.

Characterization of RNAP diffusion and active fraction in poor media conditions.

(A) Plot showing the RpoC-YFP fluorescence concentration in 1N (CJW7477) cells grown in M9gly (three experiments) as a function of cell area. Lines and shaded areas denote mean ± SD between biological replicates. (B) Same as (A) but for 1N cells grown in M9ala (three experiments). (C) Plot showing the probability densities of apparent diffusion coefficients (Da) of JF549-labeled RpoC-HaloTag in WT (CJW7519) and 1N (CJW7520) cells grown in M9gly. Only tracks of length ≥10 are included. 1N cells were binned according to cell area. (D) Same as (C) but for cells grown in M9ala. (E) Plot showing the probability density of Da of JF549-labeled RpoC-HaloTag in WT cells (CJW7519) grown in M9gly. Also shown is Da fitted by three-state Gaussian mixture model (GMM) (mean ± SEM). (F) Same as (E) but for cells grown in M9ala. (G) Plots showing the fraction of active RNAPs in individual WT (CJW7519) and 1N (CJW7520) cells (dots) grown in M9gly as a function of cell area. Only cells with ≥50 tracks are included. (H) Same as (G) but for cells grown in M9ala.

Characterization of ribosomal diffusion and active fraction in poor media conditions.

(A) Plot showing the RpsB-msfGFP fluorescence concentration in 1N (CJW7478) cells grown in M9gly (from three experiments) as a function of cell area. Lines and shaded areas denote mean ± SD between the biological replicates. (B) Same as (A) but for 1N cells grown in M9ala (from two experiments). (C) Plot showing the probability density of apparent diffusion coefficients (Da) of JF549-labeled RpsB-HaloTag across cell areas for WT (CJW7528) and 1N (CJW7529) cells grown in M9gly. Only tracks of length ≥10 are included. 1N cells were binned according to cell area. (D) Same as (C) for cells grown in M9ala. (E) Plot showing probability density of Da of JF549-labeled RpsB-HaloTag in WT cells (CJW7528) grown in M9gly. Only tracks of length ≥10 are included. Also shown is Da fitted by three-state GMM (mean ± SEM). (F) Same as (E) but for cells grown in M9ala. (G) Plot showing the fraction of active ribosomes as a function of cell area for individual WT (CJW7528) and 1N (CJW7529) cells (dots) grown in M9gly. Only cells with ≥50 tracks are included. Shaded areas denote 95% confidence interval (CI) of the mean from bootstrapping. (H) same as (G) but for cells grown in M9ala.

Comparison of protein and mRNA scaling between biological replicates.

(A) Correlation of protein slopes across the proteome (2360 proteins) between two biological replicates for 1N cells. (B) Same as (A) but for multi-N cells. (C) Correlation of RNA slopes across the genome (3446 mRNAs) of 1N cells between two biological replicates. The indicated Spearman correlation shown (ρ) is significant (p-value < 10-10).

Comparison of our data with reference datasets.

(A) Comparison between the concentrations of 3446 RNAs present in both our dataset and that of Balakrishnan et al (2022). The first time point (60 min) after CRISPRi induction was used to determine the RNA abundance in our experiments. The RNA concentration was expressed as transcripts per million reads. The indicated Spearman correlations (ρ) are significant (p-value < 10-10). Each marker represents a single gene or protein and the colormap shows the KDE. (B) Correlations between RNA slopes (from 2577 genes, ∼280 genes per bin) in 1N cells and the rate of transcription initiation in wildtype cells (Balakrishnan et al., 2022). The binned data are also shown (orange markers: mean ± SEM). The p-value of ρ is not significant (p-value > 10-10). (C) Same as (B) but for protein slopes instead of RNA slopes (2084 genes, ∼230 genes per bin). (D) Correlation between protein slopes in 1N cells and mRNA degradation rate in wildtype cells (Balakrishnan et al., 2022). The colormap corresponds to KDE (for 2078 genes with positive mRNA degradation rates). The binned data are also shown (orange markers: mean ± SEM, ∼230 genes per bin). A significant negative Spearman correlation (p-value < 10-10) is shown for genes with a degradation rate above 0.7 min-1. (E) Comparison of the mRNA degradation rates (Balakrishnan et al., 2022) between essential and non-essential genes in E. coli. Three different published sets of essential genes were used (Gerdes et al., 2003; Goodall et al., 2018; Hashimoto et al., 2005). The horizontal white lines indicate the inter-quartile range of each distribution. Mann-Whitney non-parametric tests justify the non-significant difference (p-value > 10-2) between the two gene groups (essential vs non-essential genes).

Protein slopes relative to the chromosome position of their gene in 1N-rich and multi-N cells.

(A) Relationship between the absolute distance of a gene from oriC and the slope of the protein it encodes. Data from 2268 proteins are shown (colormap: Gaussian kernel density estimation), as well as binned data in six gene distance bins (open circles, average ± SEM, ∼370 proteins per bin). For the multi-N cells, the Spearman correlation was not significant (ρ = NS, p-value > 0.01), whereas for the 1N-rich cells, there was a significant Spearman correlation (ρ = 0.23, p-value < 10-10) for genes within 1.35 Mbps of oriC (below the mid-point of the 4th gene distance bin). (B) Relationship between the absolute distance of a gene from oriC and its mRNA slope. Data from 2200 RNAs are shown (colormap: Gaussian kernel density estimation), as well as binned data in six gene distance bins (open circles, average ± SEM, ∼360 proteins per bin). The Spearman correlation is not significant (ρ = -0.02, p-value > 0.1).

Protein slopes relative to protein ion intensity for 1N-rich cells.

The summed ion intensity of each protein was divided by the protein sequence length and the quotient was log-transformed. The locations of selected proteins are annotated.