
==== Front
Commun Biol
Commun Biol
Communications Biology
2399-3642
Nature Publishing Group UK London

39227641
6783
10.1038/s42003-024-06783-5
Article
Bacterial association with metals enables in vivo monitoring of urogenital microbiota using magnetic resonance imaging
Donnelly Sarah C. 123
Varela-Mattatall Gabriel E. 14
Hassan Salvan 1
Sun Qin 14
Gelman Neil 145
Thiessen Jonathan D. 124
Thompson R. Terry 146
Prato Frank S. 1245
http://orcid.org/0000-0002-3591-6436
Burton Jeremy P. 378
http://orcid.org/0000-0001-7285-8071
Goldhawk Donna E. dgoldhawk@lawsonimaging.ca

124
1 grid.415847.b 0000 0001 0556 2414 Imaging, Lawson Research Institute, London, Canada
2 https://ror.org/02grkyz14 grid.39381.30 0000 0004 1936 8884 Collaborative Graduate Program in Molecular Imaging, Western University, London, Canada
3 https://ror.org/02grkyz14 grid.39381.30 0000 0004 1936 8884 Microbiology & Immunology, Western University, London, Canada
4 https://ror.org/02grkyz14 grid.39381.30 0000 0004 1936 8884 Medical Biophysics, Western University, London, Canada
5 https://ror.org/02grkyz14 grid.39381.30 0000 0004 1936 8884 Medical Imaging, Western University, London, Canada
6 https://ror.org/02grkyz14 grid.39381.30 0000 0004 1936 8884 Physics & Astronomy, Western University, London, Canada
7 https://ror.org/02grkyz14 grid.39381.30 0000 0004 1936 8884 Division of Urology and Surgery, Western University, London, Canada
8 grid.415847.b 0000 0001 0556 2414 Centre for Human Microbiome Research, Lawson Research Institute, London, Canada
3 9 2024
3 9 2024
2024
7 107913 12 2023
26 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Bacteria constitute a significant part of the biomass of the human microbiota, but their interactions are complex and difficult to replicate outside the host. Exploiting the superior resolution of magnetic resonance imaging (MRI) to examine signal parameters of selected human isolates may allow tracking of their dispersion throughout the body. Here we investigate longitudinal and transverse MRI relaxation rates and found significant differences between several bacterial strains. Common commensal strains of lactobacilli display notably high MRI relaxation rates, partially explained by elevated cellular manganese content, while other species contain more iron than manganese. Lactobacillus crispatus show particularly high values, 4-fold greater than any other species; up to 60-fold greater signal than relevant tissue background; and a linear relationship between relaxation rate and fraction of live cells. Different bacterial strains have detectable, repeatable MRI relaxation rates that in the future may enable monitoring of their persistence in the human body for enhanced molecular imaging.

In vitro examination of urogenital microbiota by MRI reveals high relaxation rates in lactobacilli and correlates quantity of bacteria with MRI measures that exceed those of the healthy human bladder epithelium.

Subject terms

Nanoscale biophysics
Bacterial techniques and applications
Diagnostic markers
https://doi.org/10.13039/501100000038 Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada) ALLRP576699-22 Goldhawk Donna E. issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Microbial-host interactions are widespread and many of these are complex symbiotic relationships as seen in the human microbiota at different sites in the body, though most occur in the oral cavity, and intestinal and reproductive tracts1. These interactions cause changes in metabolism and signaling pathways, not only in health but also in disease. The many functions of microbiota include protection from pathogens, production of vitamins and metabolites for energy, and major roles in homeostasis2,3. Increasing recognition of the importance of microbiota has ramifications for autoimmune diseases (e.g. rheumatoid arthritis4, type 1 diabetes5); metabolic syndromes (e.g. type 2 diabetes6,7, coronary artery disease8); and neuropsychiatric disorders (e.g. depression9, psychosis10). However, understanding microbiota interactions is challenging due to the difficulty in replicating their complexity outside of the host. Despite this, the current gold standard for the study of the composition, properties, and functions of microbiota entails ex vivo analyzes, involving the removal of microbial samples from the mucosa and propagation in vitro for high-throughput techniques like 16S rRNA gene sequencing11, shotgun metagenomics12, or metabolomics13. These approaches are susceptible to sample degradation, contamination, or changes in bacterial growth that subsequently misrepresent the microbiota of the original sample. For example, the microbiota within feces is not representative of the intestinal mucosa14–16. Since the survival of many microbes depends on bacterial-host interactions, specific nutrient requirements, and atmospheric conditions, faithfully replicating the in vivo environment in ex vivo cultures is difficult.

Medical imaging platforms offer the possibility of studying biological events in vivo, including the microbiota. However, the ability to label microorganisms other than for research purposes has been challenging17. Some new labels have been developed that are specifically utilized by certain microbes, for example, 2-deoxy-2-[18F]fluoro-D-sorbitol for the detection of Enterobacterales using positron emission tomography (PET)18. Given the potential of nuclear medicine for streamlining medical treatment, ex vivo methods to assess microbiota might best be used as tools to validate in vivo molecular imaging.

Clinical use of magnetic resonance imaging (MRI) is widespread and yet most research employing this technology to detect bacteria relies on cell labels for contrast enhancement19–23. In addition, most MRI studies image bacterial infection indirectly by focusing on host inflammation24,25. Whether or not unlabeled bacteria could be directly detected at clinically relevant levels depends on resolving the bacterial magnetic resonance (MR) signal from surrounding mammalian tissue based on differences in relaxation rates, diffusion, or other cellular MR characteristics. For example, gram positive bacteria colonizing subcutaneous mouse tumors showed an increase in chemical exchange saturation transfer MRI relative to uninfected tumors26. In addition, the association of various bacterial types with metals both in the environment and in medicine has long been appreciated2,27,28. Ferromagnetic and paramagnetic metals like iron, manganese, nickel, cobalt, gadolinium, and dysprosium influence MRI29. Indeed, bacterial acquisition of metal cofactors like iron and manganese is widely considered a form of bacterial pathogenesis, with the genes of many siderophores clustered on virulence plasmids or chromosomal pathogenicity-associated islands to improve survival in low iron environments30,31. In gram-negative uropathogenic Escherichia coli, iron and manganese uptake are linked by expression of the sitABCD operon within their pathogenicity island(s)32. The genes in this operon include those encoding iron and manganese uptake proteins, regulated by transcription factors involved in the ferric uptake regulator system as well as the manganese transport regulator (MntR)33. Unlike many bacteria, lactobacilli do not use iron as a cofactor, relying instead on manganese34,35 and its active import through the manganese/cadmium transporter MntA36,37. Thus, bacterial acquisition and storage of ferromagnetic iron or paramagnetic manganese are important factors to consider in the context of imaging bacteria by MRI.

Recognizing the importance of microbiota in human health; the need for in vivo visualization of microbial factors that underlie disease manifestation and progression; and the superior resolution of MRI for examining soft tissues38, we sought to provide evidence on whether a range of bacteria may be detected in the future with MRI, either alone and/or in combination with hybrid modalities like PET/MRI. Given large genetic variability and differences in iron and manganese handling39, we hypothesized that MR measures could be quite different than typical tissue values and could vary drastically between bacterial species and strains. To explore these differences, we examined MR relaxation rates of various pathogenic, probiotic, and commensal bacteria. Here we show that MR relaxation rates vary among bacteria partially due to differences in metal handling. In particular, commensal Lactobacillus crispatus ATCC33820 exhibits high MR relaxation rates which correlate with fraction of cells in the MR volume. As a common species of lactobacilli in the bladder, we compared L. crispatus MR measures, obtained in the presence of cultured human bladder cells, to those acquired from regions of interest in the wall of the human bladder. Taken together, these data indicate that certain species may be ideal candidates for developing methods to track bacteria in vivo, as demonstrated using standard clinical MRI sequences.

Results

MR relaxation rates of bacteria

In healthy females, the lower urinary system is dominated by species of lactobacilli40 and mostly by strains of Lactobacillus crispatus41. Our study of species detected in the female urogenital system, which typically has relatively low microbial diversity, may offer a more simplistic microbiota for in vivo imaging. Other microbes of interest were included for MRI signal comparisons to probiotic, pathobiont, pathogenic, and commensal strains. Staphylococcus aureus has been characterized for their acquisition and handling of iron, a ferromagnetic MRI detectable metal27. Escherichia coli are interesting for MRI study owing to significant genetic polymorphisms at the strain level, bestowing characteristics representative of their adaptation to diverse habitats and their roles as commensals and pathogens, including uropathogenic E. coli.

Bacteria were cultured and analyzed in an MRI cell phantom (Fig. 1a,b,c). In this subset of bacteria, MR relaxation rates were variable (Fig. 1d,e,f; Supplementary Data 1) but frequently higher than recorded in mammalian cell types42,43. Transverse relaxation rates of bacteria were dominated by the R2 component and these measures in Lactobacillus gasseri ATCC33323 were significantly higher than other species examined (Fig. 1d,e). Both Lactobacillus rhamnosus GR-1 and E. coli BL21(DE3) displayed higher R2* transverse relaxation rate than Proteus mirabilis 296 and Staphylococcus aureus Newman (Fig. 1d). Remarkably, while multiple Lactobacillus spp. demonstrated high transverse relaxation rates (1/T2, Fig. 1e), the signal intensity of L. crispatus ATCC33820 decayed to background before the shortest echo time (TE, 13 ms), thus preventing an accurate measurement in undiluted samples. This finding confirmed that select microbes may be amenable to monitoring by MRI owing to relaxation rates that far exceed those in mammalian tissue.Fig. 1 MRI relaxation rates of different bacterial species vary widely.

a Cultures from different bacterial species were harvested, washed, and loaded into Ultem wells prior to mounting in one hemisphere of a gelatin phantom. Hemispheres were then assembled to form a 9 cm spherical cell phantom; placed in a knee coil; and scanned at 3 T by MRI. b Locator images show both sagittal (left) and axial (right) cross-sections through a gelatin cell phantom. The MR signals for relaxation measurements were acquired from a 3 mm slice (yellow lines, left panel) passing through all wells. Numbers to the left of each well designate sample identity. In this example, 1 is a plastic marker; wells 2-13 contain dilutions of L. crispatus ATCC33820 in gelatin, where 2, 6 and 10 are 1/2 dilutions; 3, 7 and 11 are 1/4 dilutions; 4, 8 and 12 are 1/8 dilutions; and 5, 9 and 13 are 1/16 dilutions; wells 14–18 contain S. aureus and E. coli mutants. c In a representative T2-weighted spin echo image, signal intensity is displayed at echo time (TE) 20 ms, with corresponding scale bar in arbitrary units. d–f In all cases, the total transverse relaxation rate R2* (d, red circles and associated lines) is dominated by the R2 component of transverse relaxation (e blue circles and associated lines). Lactobacillus gasseri displayed significantly higher R2* and R2 than any other species tested. Escherichia coli BL21(DE3) and L. rhamnosus displayed higher transverse relaxation than several species (d,e black and gray lines, respectively). Pseudomonas aeruginosa displayed higher R2 than S. aureus Newman (e red line). Longitudinal relaxation rates also varied significantly between bacterial species (f, green circles and associated lines), with L. reuteri displaying higher R1 than all other species. R1 of E. coli BL21(DE3) and E. faecalis were higher than most other species (f, gray and red lines, respectively). L. gasseri and P. mirabilis both had lower R1 than several other bacterial species (f, black and blue lines, respectively). Uncertainty in the R1 of L. rhamnosus precluded reporting. Bar graphs show the mean ± s.e.m. (n = 3–5). *p < 0.05; **p < 0.01; ***p < 0.001.

Longitudinal relaxation rates (R1) reflecting spin-lattice interactions were also distinct, with lactobacilli paradoxically among the highest and lowest recorded in this study (Fig. 1f). For Lactobacillus reuteri RC-14, R1 (3.11 ± 0.08 s−1) was significantly higher than all other species examined while for L. gasseri ATCC33323, R1 (0.69 ± 0.01 s−1) was lower than most. These data suggest that R1 in addition to R2* and R2 measurements may be useful for differentiating some lactobacilli from other bacterial species.

To position measures of bacterial relaxation rates in clinical context, in vivo MRI of the human bladder was acquired to estimate appropriate tissue background for urinary strains. Since bacterial adherence to uroepithelium44 leads to differences in microbiota of urine versus tissue mucosa45, ROI were selected along the wall of the bladder. Both T1 and T2* maps from male (Fig. 2a,b) and female (Fig. 2c,d) volunteers provided similar values for R1 (1/T1) and R2* (1/T2*; Table 1) in regions of interest contouring the bladder wall (Fig. 2 red and white arrows). A plot of R1 vs R2* comparing the five groups of bacteria indicates the relationship between family members and MR measures (Fig. 2e). In most species examined, either R1 or R2* relaxation rates are well above tissue background, as assessed in the healthy human bladder. As expected, the same is true for R1 vs R2 (Supplementary Fig. 1).Fig. 2 Relaxation rates of urinary bacterial isolates exceed in vivo measures of the healthy human bladder.

a–d Representative MR images of the healthy bladder were acquired at 3 T in male (a and b) and female c,d volunteers (n = 3). T1 images and maps (a and c) and T2* images and maps b,d show a sagittal section of the bladder with region of interest along the bladder wall outlined in white, as shown by the red arrows. Corresponding heat maps (right panels, arrows point to regions of interest outlined in black) indicate the relaxation times with matching scale bars. e A scatter plot of R1 vs R2* shows the mean relaxation rate measurements of bacteria ± s.e.m. (n = 3–5). Gram positive species are denoted by circles and gram-negative species by triangles. Bacteria are grouped into their respective families as indicated by symbol color: red, Lactobacillaceae; blue, Staphylococcaceae; green, Enterococcaceae; black, Enterobacteriaceae; gray, Pseudomonadaceae. Broken gray lines show average R1 (0.69 ± 0.09 s−1) and R2* (42.1 ± 1.89 s−1) ± s.e.m. (shaded area) in the healthy human bladder.

Table 1 In vivo bladder imaging subjects and raw data

Subject	Sex	Age	a T1 (ms)	s.d. (ms)	b T2* (ms)	s.d. (ms)	
1	M	75	1852	214	23.9	6.2	
2	M	59	1181	94.6	25.7	4.5	
3	F	29	1489	121	22	8.8	
a Mean T1 within these subjects was 1507 ± 336 ms, giving a mean R1 ± s.d. of 0.69 ± 0.15 s−1

b Mean T2* within these subjects was 23.9 ± 1.85 ms, giving a mean R2* ± s.d. of 42.1 ± 3.28 s−1

Fe and Mn quantification in various bacterial species

To better understand diverse MRI measures in urinary isolates, the influence of metal co-factors was considered. After cell lysis and quantification of total cellular protein (Fig. 3a), different bacterial strains were analyzed by ICP-MS to quantify the total cellular content of iron (Fig. 3b; Supplementary Data 2) and manganese (Fig. 3c; Supplementary Data 2). Iron was below detectable levels in all samples of Lactobacillus spp. examined (Fig. 3b), consistent with their preference for manganese34. Thus, by comparison, most bacterial species contained significantly more iron than lactobacilli (black line, p < 0.05; Kruskall-Wallis p < 0.001). Staphylococcus aureus Newman contained more iron than E. coli Nissle and Enterococcus faecalis ATCC33186 (gray line). In addition, Klebsiella pneumoniae 280 contained more iron than E. faecalis (red line).Fig. 3 Total cellular iron and manganese vary widely between bacterial species.

a Cultured bacteria were washed and pelleted prior to lysis and protein quantification followed by ICP-MS to measure elemental iron and manganese. b–c Total cellular iron and manganese content was normalized to total cellular protein. Both iron (b, blue) and manganese (c, red) content varied between bacterial species. Iron content of most bacterial species was significantly higher than that of all lactobacilli examined (b, black line) while S. aureus Newman contained more iron than E. coli Nissle and E. faecalis (b, gray line). In addition, K. pneumoniae contained more iron than E. faecalis (red line). In panel c, P. mirabilis contained less Mn than E. faecalis (red line) and all staphylococci examined (red and blue lines). Staphylococcus aureus Newman also contained more Mn than K. pneumoniae and P. aeruginosa (c, blue line); whereas all Lactobacillus samples contained more Mn than many other bacterial species examined (c, black and gray lines). Data are displayed as individual values (black circles) with mean ± s.e.m. (n = 3–6) and all comparisons reflect p < 0.05. (N.D., not detectable). d Principal component analysis of mean MR and ICP-MS measures in bacteria is evaluated after excluding three outliers by the ROUT method analyzing contribution of cases. The distance between samples on the plot represents differences in bacterial metal handling and MR measures, with 87.2% of total variance being explained by the first two components shown. The association of variables are depicted by the direction of the gray arrows. Each colored point represents a separate bacterial strain or species as the mean of 3–5 replicates of each variable measurement. Points are colored by bacterial family: blue Staphylococcaceae; green, Enterococcaceae; black, Enterobacteriaceae; gray, Pseudomonadaceae. Gram positive species are denoted by circles and gram-negative by triangles.

Total cellular content of elemental manganese varied considerably between bacteria, ranging between 0.0034–112 µg Mn (mg protein) −1 (Fig. 3c). Staphylococcus aureus Newman contained more manganese than K. pneumoniae, P. mirabilis, and Pseudomonas aeruginosa (blue line; Kruskall-Wallis p < 0.001). Proteus mirabilis 296 contained the lowest manganese content (red line) while all other significant differences were due to the high manganese content of lactobacilli. Lactobacillus reuteri RC-14, L. rhamnosus GR-1, and L. crispatus ATCC33820 had higher manganese than all non-Lactobacillus species except for staphylococci and E. faecalis (black line). Lactobacillus gasseri ATCC33323 had significantly more manganese than P. aeruginosa, K. pneumoniae, and P. mirabilis (gray line).

These data show that both iron and manganese levels vary widely between different bacterial species, raising the possibility that manganese rather than iron may contribute extensively to the high MR relaxation rates of Lactobacillus spp. Note however that L. gasseri ATCC33323, with over 10-fold less manganese than the other lactobacilli examined, exhibited a mean R2 of 74.53 ± 8.00 s−1, second only to L. crispatus ATCC33820 (discussed below). Hence elemental manganese content is not the only determinant of R2.

In a principal component analysis evaluating mean R2, R1, and elemental iron and manganese content, two principal components (relaxation rates and elemental content) account for 87.2% of the variance. No significant correlations between variables are apparent but species of lactobacilli appear as outliers (Supplementary Fig. 2 and Supplementary Table 1) based on extraordinary R2 in the case of L. gasseri and both R1 and manganese content in the case of L. reuteri. Lactobacilli were identified as statistical outliers and data were reanalyzed showing no significant correlations (Supplementary Fig. 3 and Supplementary Table 3). Staphylococcus aureus Newman also represents an outlier owing to elevated iron content and low R2. Removing these outliers (lactobacilli and S. aureus Newman) to reduce bias in the principal component analysis, confirms the positive correlations between bacterial R1 and manganese content (Fig. 3d, Supplementary Table 4).

MR relaxivity of L. crispatus ATCC33820

To examine the outstanding MR signal intensity of L. crispatus ATCC33820, given its rapid T2 decay, samples were serially diluted in 4% gelatin / PBS prior to mounting in the gelatin cell phantom (Fig. 4a). Following a 1/2 dilution, signal intensity still decayed to background levels by the third TE (25 ms) but R2 and R2* were nevertheless measurable (Fig. 4b; Supplementary Data 3). Indeed, transverse relaxation rates were more reliable as the dilution factor increased and more TE could be used for decay curve fitting. With this signal advantage, L. crispatus may be detected by MRI even when relatively few cells are present. To explore this, colony forming units (CFUs) were quantified in L. crispatus samples at each dilution, with approximately 4 billion live cells in a volume of 38 mm3 (1/32 dilutions) providing measurable signal. At 1/8 dilutions, with approximately 11 billion live cells, R2* and R2 were 71.3 ± 6.10 s−1 and 56.6 ± 3.50 s−1, respectively, well above typical measures for mammalian cells42,43 and average measures estimated in relevant tissue like the human bladder (Fig. 2). Moreover, R1 values (Table 2; Supplementary Data 3) obtained from 11 billion CFUs or more were also significantly higher than samples with fewer live cells (1/8 vs 1/16 dilutions, p < 0.001), pointing again to a threshold above which these bacteria might be distinguished from tissue background.Fig. 4 Lactobacillus crispatus ATCC33820 display high MR relaxation rates related to amount of live cells.

a Lactobacillus crispatus were cultured anaerobically, and cells were serially diluted in gelatin/PBS prior to loading into Ultem wells mounted in a gelatin phantom for MRI at 3 T. b Lactobacillus crispatus displays high transverse relaxation rates when diluted in gelatin / PBS. Box and whiskers plots show the mean R2* (red) and R2 (blue) along with max and min measurements, with biological replicates shown as black circles (n = 4–10). Colony forming units (CFUs) present within the MR slice were calculated based on CFUs estimated from initial undiluted bacterial cultures c,d Plots of R2* c and R2 d show individual MR measures as a function of number of live cells (expressed as CFUs) within each MR slice. For both R2* and R2, nonlinear regression and Spearman’s correlation provides a moderately positive correlation between CFUs and transverse relaxation rates (p < 0.001). e A scatter plot shows CFUs within the MR slice of individual samples (21 voxels in a 3 mm slice through the well is approximately 38 mm3) as a function of the percentage of cells (f) loaded into the wells after serial dilution in gelatin/PBS. The line represents a moderate, positive nonlinear correlation between CFUs and fraction of cells (p < 0.05). Irrespective of dilution factor, there is a range in the estimated number of live cells that may contribute to the MR signal in any given well of the cell phantom, with average CFUs indicated by the line of best fit. f–h Fraction of L. crispatus cells (f) is strongly correlated to R2* f, R2 g and R1 h measurements (p < 0.001).

Table 2 R1 relaxation rates for serially diluted L. crispatus

aDilution	bCFUs (x 109)	cR1 (s−1)	s.e.m. (s−1)	n value	
1/4	22.0	4.23 ^	0.57	3	
1/8	11.0	3.54 ^	0.10	9	
1/16	5.49	2.21 *	0.07	9	
1/32	4.31	1.63 *	0.07	4	
a Lactobacillus crispatus was serially diluted in 4% gelatin / PBS

b Colony forming units (CFUs) within the MR slice

c Based on Tukey’s test, mean R1 values at lower dilutions (^) are significantly different (α = 0.05) than those at higher dilutions (*).

Since number of CFUs per unit volume varies with each replicate, we explored the correlation between CFUs in the MR slice (where the signal is acquired) and its relaxation rate. Both R2* and R2 show moderate positive correlations to the number of live cells (Fig. 4c,d respectively; p < 0.001). Thus, as the number of live cells in the ROI increases, so do the MR measures. Based on the moderate, positive correlation between CFUs in the slice and fraction of cells, we can estimate the average number of CFUs required to fill the MR volume at any dilution factor (Fig. 4e). However, unlike the variable nature of estimating CFUs, R2*, R2 and R1 of L. crispatus ATCC33820 were strongly correlated to the fraction (f) of cells in the sample (Fig. 4f–h, respectively). Based on these strong positive correlations, we extrapolated the nonlinear regression equations to estimate that R2* for undiluted L. crispatus (f = 100%) is approximately 398 ± 36 s−1, R2 is approximately 359 ± 27 s−1, and R1 is approximately 13.6 ± 2.2 s−1. These estimated values are at least 4-fold higher than any other species examined. Overall, L. crispatus dilution experiments demonstrate that a commensal bacterium of the urogenital tract may be a good target for developing in vivo imaging of bacteria using MRI and its hybrid modalities. In this regard, further examination of samples containing undiluted L. crispatus (f = 100%) in a cell phantom (n = 7) confirms estimates projected for R2* using sequences with short TE < 1 ms (Table 3; Supplementary Data 4). Moreover, in the same samples, values obtained for R1 using DESPOT1 sequences are approximately 60-fold above tissue background.Table 3 Longitudinal and transverse relaxation rates of undiluted L. crispatus ATCC33820

aR1 (s-1)	bMean R1 (s−1)	cR2* (s−1)	bMean R2* (s−1)	
33.7 ± 0.9	44.7 ± 4.2	297.8 ± 3.3	350.5 ± 12.2	
47.1 ± 2.3	320.0 ± 4.7	
45.0 ± 1.6	380.7 ± 6.6	
61.4 ± 3.2	385.2 ± 4.2	
41.1 ± 1.5	357.4 ± 5.8	
53.7 ± 1.6	369.5 ± 4.4	
46.1 ± 1.2	342.6 ± 5.1	
a Mean relaxation rate ± s.e.m. across ~100 voxels (R1 area = 0.46875 mm × 0.46875 mm× 100 voxels = 21.97 mm2) acquired using DESPOT1 sequence.

b Combined mean relaxation rates ± s.e.m. from n = 7 samples.

c Mean relaxation rate ± s.e.m. from 9 voxels (R2* area = 1.021 mm × 1.021 mm × 9 voxels = 9.38 mm2) acquired using a spoiled gradient echo sequence.

MRI in a population of bacterial and mammalian cells

To explore the potential for distinguishing MR relaxation rates in two distinct cell types within a single MR slice, we examined homogeneous mixtures of human 5637 bladder cells, used to model the bladder epithelium46,47, and L. crispatus ATCC33820 (Fig. 5a): two cell types that line the urinary tract, have documented interaction46, and whose proximity may influence voxel by voxel analysis of MRI signals. Decay curves of samples at varying ratios show that transverse relaxation rates in these cell mixtures are monoexponential (Supplementary Fig. 4), indicating that these bacterial and mammalian MR signals cannot be resolved using the standard MR sequences applied here. In addition, R2* values of these mammalian and bacterial cell mixtures are similar to those of L. crispatus alone, diluted in gelatin. However, R2 values are markedly lower than those measured in bacterial cell/gelatin phantoms (Fig. 4b vs 5b). While L. crispatus diluted in gelatin has a very small R2′ component (R2*−R2, Fig. 4b), in the presence of bladder cells R2′ contributes to approximately half of the R2* value.Fig. 5 Human bladder cells attenuate R2 transverse relaxation rates of Lactobacillus crispatus ATCC33820.

a Lactobacillus crispatus and human 5637 bladder cells were cultured and washed separately before mixing to serially dilute L. crispatus with increasing numbers of bladder cells. The resulting mixtures were then loaded into wells and mounted in a spherical cell phantom for MRI at 3 T. b Box and whiskers plots show the influence of increasing proportions of bladder cells on L. crispatus transverse relaxation rates. Irrespective of dilution, R2 (blue boxes, with whiskers representing max and min measurements and biological replicates shown as black circles) comprises approximately half of the R2* signal (red boxes), with R2 being significantly lower than R2* at a 1/2 dilution (black lines, p < 0.01 based on multiple paired t-tests and Holm-Sidak’s correction). Both transverse relaxation rates decreased significantly as bacterial dilution factor increased (gray lines). c–f Based on Spearman’s correlation analyzes, fraction of L. crispatus is strongly positively correlated to R2* c, R2 d, R2′ e, and R1 f measurements. g The graph of cumulative data compares R2 (circles) and R2* (triangles) against the fraction of bacteria. Blue symbols denote L. crispatus diluted in gelatin alone; red symbols denote mixed samples of L. crispatus and bladder cells. Lines demonstrate the nonlinear regression fitted with a straight line within each sample type and MR measure, significant at p < 0.001. h The graph of cumulative data compares paired R1 and R2* measurements of L. crispatus dilutions. Red symbols show L. crispatus diluted in gelatin, with a solid black nonlinear regression curve described by the equation in red. Blue symbols show L. crispatus reduced in number by the addition of 5637 bladder cells, with a dotted black nonlinear regression curve described by the equation in blue. Dilution factor is denoted by symbol shape: 1/4, square; 1/8, closed triangle; 1/16, open triangle; 1/32, open circle. Values for bladder cells alone are indicated by a blue diamond. Broken gray lines show the average R1 (0.69 ± 0.09 s−1) and R2* (42.1 ± 1.89 s−1) ± s.e.m. (shaded area) in the healthy human bladder.

The R2* values from human 5637 bladder cells (Fig. 5b; Supplementary Data 5; 25.1 ± 3.64 s−1, right-most red bar) are in the range of many other mammalian cell types. For comparison, in human melanoma MDA-MB-435 cells the measured value for R2* is 13.70 ± 3.07 s-142 and in multi-potent mouse embryonic adenocarcinoma P19 cells R2* is 13.69 ± 0.59 s−143. These are nevertheless much lower than transverse relaxation rates of lactobacilli, with mean R2* of 210 ± 20.8 s−1 when diluted 1/2 (Fig. 4b). Whether in gelatin (Fig. 4f–h) or reduced in concentration by the addition of different amounts of bladder cells (Fig. 5c–f), fraction of L. crispatus cells is strongly and positively correlated to transverse and longitudinal relaxation rates (Supplementary Data 5), as with CFUs (Supplementary Table 5 and Supplementary Fig. 5). Comparing these nonlinear regression slopes (Fig. 4f,g vs Fig. 5c,d), demonstrates that R2 but not R2* is decreased in the presence of bladder cells (Fig. 5g; r2 > 0.82 for all correlation coefficients; refer to Fig. 4f,g). The nonlinear regression of R2 vs. f for bacteria/gelatin is significantly different than that of bacteria/bladder cell mixtures (p < 0.001). Accordingly, the ratios of R2/R2* are significantly lower for L. crispatus in the presence of bladder cells than for those bacteria diluted in gelatin alone (Table 4; p < 0.001).Table 4 R2/R2* ratios for L. crispatus ATCC33820 dilutions

aL. crispatus	50%	25%	12.5%	6.25%	3.125%	dMean R2 / R2*	s.d.	
bGelatin	1.06	0.92	1.05	0.60	0.78	0.84	0.13	
0.78	0.96	0.98	0.73	0.77	
0.87	1.01	0.96	0.61	-	
0.94	0.64	0.80	0.85	-	
0.87	0.82	0.75	0.75	-	
c5637 cells	0.45	0.54	0.53	0.48	0.89	0.48	0.13	
0.42	-	0.52	0.29	0.36	
0.50	0.48	0.43	0.50	-	
0.44	0.44	-	-	-	
a L. crispatus samples are displayed as the percent bacterial cells within the sample.

b L. crispatus cells were serially diluted in gelatin/PBS (n = 22).

c L. crispatus cell concentration was reduced by the serial addition of increasing concentrations of 5637 bladder cells in gelatin/PBS (n = 15).

d R2/R2* ratios between L. crispatus/gelatin and L. crispatus/5637 cells were compared using the unpaired Mann–Whitney test, p < 0.001.

Longitudinal relaxation rates for mixtures of L. crispatus and bladder cells were also correlated with fraction of bacteria (Fig. 5f, Supplementary Table 5). A scatter plot of R1 vs R2* demonstrated that both measures are strongly correlated; increase nonlinearly in the presence and absence of bladder cells; and even at 1/32 dilutions of L. crispatus, the signal is well above tissue background as assessed in male and female bladders (Fig. 5h). The same was true for R1 vs. R2 plots (Supplementary Fig. 6). As expected, relaxation rates in human bladder cell phantoms (blue diamonds, Fig. 5h) are within the range measured for bladder tissue (gray dashed line) and validate the cell phantom model.

Discussion

Examination of 13 distinct bacterial isolates revealed that many species have unique MR signatures, reflecting in part their intrinsic capacity for regulating Fe and Mn content. By examining bacteria with extraordinarily high transverse and longitudinal relaxation rates, MR measures were related to the quantity of live bacterial cells and hence to an estimate of CFUs needed to noninvasively detect bacteria by MRI.

Data obtained in a gelatin cell phantom indicate the feasibility of noninvasively detecting select bacteria with MRI, using clinically useful sequences. The latter balance acquisition time and image resolution against strategies for reducing motion artefacts, such as breath holding or pharmaceutical inhibition of gut peristalsis. In cultured samples of different bacteria, MRI provides various values for relaxation rates, many of which are well above those reported for mammalian cells and tissue, including human bladder cells and in vivo MRI of human bladders. These findings are not only attributed to bacterial differences in total cellular Fe and Mn content but also to the cellular arrangement of ferromagnetic compounds (refer to discussion below on R2′ magnetic field inhomogeneities). As has been observed in mammalian cells48,49, relaxation rates may reflect cellular iron handling (e.g., hepcidin regulation of iron export) while total elemental iron content remains largely unchanged.

Among lactobacilli with very little iron but substantial cellular manganese content, both transverse and longitudinal relaxation rates were variable, offering opportunities for MR sequence development that may distinguish one species from another. For example, the total elemental Mn content of L. rhamnosus and L. crispatus are similar (53 vs 63 µg Mn (mg protein)-1, respectively) despite a huge difference in R2 (48 vs an estimated 359 s-1, respectively). Other factors influencing MR measures may include bacterial size, morphology, density of cells within the ROI, water content, and presence of additional MR sensitive elements, including nickel, cobalt and gadolinium. Although gadolinium is not typically found in bacteria, the genomes of some species like Salmonella enterica, Campylobacter jejuni and Bacillus subtilis encode nickel and/or cobalt transport proteins50.

The extraordinary relaxation rates of L. crispatus ATCC33820 demonstrate the sensitivity of bacterial detection that may be achieved in select species. Using serial dilutions to titrate the signal in cell phantoms, fraction of CFUs was strongly correlated to all MR relaxation rates. Moreover, the extrapolation of transverse relaxation rates to undiluted samples was latterly verified with further optimization of MR sequence parameters for rapidly relaxing signals. Alternatively, R1 mapping would likely have greater efficacy than R2* mapping for in vivo detection and quantification of L. crispatus for several reasons. Firstly, measured R1 values for volumes containing 100% L. crispatus were found to be approximately 60-fold larger than R1 values measured from the bladder wall in human imaging; whereas an approximately 10-fold difference was found for R2*. Secondly, 3D R1 maps can be acquired more rapidly than 3D R2* maps and, in particular, 3D R1 maps can be acquired within a breath hold using DESPOT151. For example, using a TR of 3.4 ms (as per Deoni et al.51), 256 phase encodes, 30 slices, a moderate parallel imaging acceleration of 2, and Partial Fourier of 6/8 in two directions, the acquisition time for both flip angles would be equal to 3.4 ms × 256 × 30 × 6/8 × 6/8 × 0.5 × 2 ≈ 15 s. Thirdly, R2* mapping in vivo suffers from the potential influence of macroscopic magnetic field variation due to borders (e.g. air/tissue) between regions of different magnetic susceptibility; whereas this concern is very minimal with DESPOT151 due to very short echo time.

Finally, the high transverse relaxation rates of L. crispatus ATCC33820 may also facilitate detection of this bacterium even at MRI field strengths lower than 3 T, since R2* increases with field strength up to 3 T52,53 in tissues with high paramagnetic content. Lower field strengths may also favor higher R154, which could be particularly valuable for imaging lactobacilli. Furthermore, given the moderate positive correlation between MR parameters and CFUs, in vivo imaging of L. crispatus and estimation of the number of live cells within an ROI should be feasible. For L. crispatus, fewer than 100 million CFUs mm−3 provides measurable MR relaxation rates that are greater than many mammalian tissues55,56.

Translation to future clinical applications involving MR detection of bacterial cells in the human host will depend on the possible additive or attenuating effects of multiple cell types and the potential for differentiating bacterial MR signatures within these complex environments. The in vivo regions of interest include bacteria that commonly adhere to mammalian tissues via pili57 or biofilms, the latter of which also form on abiotic materials such as catheters and stents58. In addition, microbial diversity varies between each micro-environment59, such that all clinical microbiota samples represent mixtures of various species living symbiotically.

In this work, we measured relaxation rates from representative two-component mixtures with the components being L. crispatus / gelatin in one mixture and L. crispatus / bladder cells in the other. Such systems are often represented by two-compartment models. The MR behavior of these systems depends on the fraction of water in each compartment as well as on the exchange rate of water molecules between compartments. Typically, the exchange rate is categorized into three regimes: fast, intermediate, and slow. Fast exchange refers to the case where the typical lifetime (or residence time) of water molecules in each compartment is short compared to the relaxation time being considered (T1 or T2; i.e., 1/R1 or 1/R2). While the two-compartment model is useful for initial consideration, it does not account for the potential interaction of compartments. For example, L. crispatus contain high levels of paramagnetic ions (Mn) and the strong microscopic magnetic field variations produced would influence transverse relaxation (R2*, R2) of water within gel and bladder cells (i.e., Mn in one compartment would affect transverse relaxation in the other compartment). One might expect that the thick cell membrane of bacteria60 interferes with the fast exchange process between the bacterial intracellular compartment and either gelatin or bladder cell compartment. However, a previous study61 found that membrane permeability of a gram positive bacterium (Corynebacterium glutamicum) was slightly higher than that of human erythrocytes. There was evidence presented to suggest that high permeability of these bacterial cell membranes might be due to aquaporins.

Microscopic magnetic field inhomogeneities represented by R2′ (the difference between R2* and R2) may arise from potential differences in the form of metals within a cell (i.e., protein-bound, sequestered, redox active) and may inform the relation between components of transverse relaxation. Total cellular iron content varied among bacteria; however, in all lactobacilli examined in this study elemental iron fell below the detection limit. This strategy by Lactobacillus spp. of replacing iron34,39,62 with another metal ion cofactor like manganese may provide a growth advantage in the low iron environments of the urogenital tract. Interestingly, during menses when iron levels increase, some lactobacilli such as Lactobacillus iners continue to thrive while L. crispatus levels generally decrease and recover only as iron levels decrease63,64. Inter-species differences may relate to how efficiently the cell disposes of large amounts of iron that can lead to increased production of harmful free radicals which promote DNA damage and lipid peroxidation65. On the other hand, low molecular weight molecules containing manganese play a role in blocking the production of reactive oxygen species and prevent lipid peroxidation, allowing lactobacilli to safely accumulate high levels of intracellular Mn39,66. The possibility that manganese is also externally bound has not been previously reported but cannot be ruled out. Lactobacilli are known to bind heavy metals like cadmium, lead and mercury2. However, within lactobacilli the presence of both conserved manganese transporters (e.g., MtsA36,67) and manganese-dependent metabolism is consistent with a high intracellular content of elemental Mn.

Using the MR sequences reported herein, to guide sequence development for in vivo imaging, confirms that measurement of transverse relaxation in volumes containing undiluted L. crispatus requires short TE on the order of 1 ms. In healthy individuals, where bacteria are more homogeneously distributed within their niche(s)68, future advancements in MR imaging of bacteria may also benefit from hybrid platforms like PET/MRI.

Building on the evidence presented here for detection of microbiota by MRI has a number of challenges. The degree to which species examined in this study may represent general features of bacterial MRI is uncertain. The cell phantom should be useful for characterizing MR measures in a wider variety of microbiota to better understand the influence of morphology and metal ion metabolism. There are also biophysical considerations in relaxation rate measurements that need addressing. For example, unlike transverse relaxation rates (R2*, R2) in volumes containing 100% L. crispatus, R1 values extrapolated from the nonlinear relationship with fraction of cells (Fig. 5f) did not match the values obtained at extremely short T1 (Table 2). Such discrepancies in related MR parameters warrant further investigation and may be related to the different sequences used to measure R1in diluted versus undiluted samples. In any case, there may be potential opportunities for adapting clinically useful sequences for imaging a variety of bacteria. The clinical need for combating infectious diseases and anti-microbial resistance will help drive noninvasive imaging applications, notwithstanding deterrents such as cost and limited availability of medical imaging.

The study herein showed that bacteria are associated with a wide range of MR relaxation rates. One of the species studied demonstrated extremely high relaxation rates, offering the potential for in vivo detection. We thus demonstrated features of this potential, which could be optimized with further development. The transverse relaxation rates of L. crispatus ATCC33820 have a strong correlation to bacterial cell numbers in mixtures with gelatin as well as human bladder cells. This dependence may form the basis for quantification of bacterial cell numbers in vivo.

Materials And Methods

Reagents

Unless otherwise noted, all reagents were from Thermo Fisher Scientific, Mississauga, Canada and Sigma-Aldrich, Oakville, Canada.

Bacterial culture

Escherichia coli strains were grown in lysogeny broth (LB) for 16-20 h at 37 °C with shaking. Staphylococci were grown in Brain-Heart Infusion (BHI) broth for 16 h at 37 °C with shaking, or, when noted, in iron depleted RPMI-1640 medium for 24 h at 37°C with shaking. Pseudomonas aeruginosa PA01 was grown in LB for 16 h at 37 °C with shaking. All lactobacilli were grown in deMan, Rogosa and Sharpe (MRS) broth anaerobically for 24 h at 37 °C. Proteus mirabilis 296 and K. pneumoniae 280 were grown in LB, while E. faecalis ATCC33186 was grown in BHI, all anaerobically for 24 h at 37 °C.

Bacterial quantification

After bacteria were grown overnight, 200 µL of each culture was placed in a 96-well plate and measured in an Eon BioTek plate reader (Biotek, Winooski, USA) with Gen5 2.01 software to obtain OD600 measurements. This aliquot of each culture was then serially diluted 1/10 down to 10−7 and 10 µL volumes were plated in triplicate on LB/agar or BHI/agar. Proteus mirabilis samples were always plated on 6% non-swarming LB/agar. For lactobacilli, 5 µL of each dilution was plated in triplicate on pre-warmed MRS/agar plates. After drying, plates were inverted and incubated either aerobically or anaerobically (depending on the bacterium) at 37 °C for 12–24 h before counting the number of CFUs. Equation 1 was used to calculate the total number of CFUs within the full culture based on the total culture volume and volume plated.1 TotalCFUs=AverageCFUsVolumeplated*Dilutionfactor*Totalculturevolume

The remaining culture was routinely pelleted at 4500 × g for 10 min, washed three times with phosphate buffered saline pH 7.4 (PBS), resuspended as a ~75% cell slurry, and loaded into Ultem wells (Fig. 1a) by centrifugation at 4500 × g for 10 min. Supernatant was removed and more of the cell slurry was added and centrifuged until the Ultem well was full69. Total number of CFUs within the wells were estimated using Eq. 2 based on CFU counts.2 CFUsinwell=TotalCFUsTotalvolumeofcellpellet*Volumeloadedinwell

The number of CFUs within the MR slice (Fig. 1b) constitute one third of the total CFUs loaded within the well (slice thickness is 3 mm and the total inner height of the Ultem well is 9 mm). For L. crispatus dilutions, cells were cultured and quantified as described above but final cell pellets were diluted 1/2 in 4% gelatin / PBS before serially diluting 1/2 down to a final dilution of 1/32. Diluted cells were mixed gently; loaded into Ultem wells; and immediately placed at 4°C to solidify before mounting in the gelatin phantom.

Tissue culture

Human bladder epithelial cells (ATCC 5637) were cultured at 37 °C and 5% CO2 in RPMI-1640 medium containing 10% FBS and 4 U mL−1 penicillin/4 μg mL−1 streptomycin. Thawed cells were not tested for mycoplasma contamination. For passaging, plates were washed twice with PBS prior to agitating in 0.05% trypsin/ethylenediaminetetraacetic acid (EDTA) and incubating at 37 °C for 10 min before triturating. Harvested cells were centrifuged at 550 x g for 10 min at 10 °C, then washed with PBS and resuspended in fresh media for plating.

Preparation of bladder cells and L. crispatus mixtures

Human ATCC 5637 bladder cells were cultured as described above in 12 × 150 mm plates; harvested; and combined into one sample for the serial dilutions described below. At harvest, cells were collected into one tube, centrifuged to remove medium, and washed three times with PBS before obtaining cell counts by hemacytometry. Lactobacillus crispatus ATCC33820 was collected as described above, including plating for CFU determination. A suspension of L. crispatus was serially diluted 1/2 with a suspension of bladder cells using the following ratios of mammalian:bacterial cells (v/v): 15:1 (93.75%/6.25%), 7:1 (87.5%/12.5%), 3:1(75%/25%) and 1:1 (50%/50%). Samples of bladder cells alone were also prepared for MR analysis. Biological replicates at each ratio were loaded into Ultem wells by centrifugation at 550 × g; removing supernatant and loading more sample until wells were full of the cellular mixture. Total number of bladder cells as well as bacterial cells were calculated as per Eq. 2, factoring in the ratio of each cell type.

Magnetic resonance imaging in a cell phantom

Cells were loaded into Ultem wells (outer diameter 5 mm, outer height 11 mm) as described above prior to mounting in a 9 cm spherical MR phantom made of 4% gelatin (porcine type A)/PBS. MR phantoms were scanned at 3 Tesla (3 T) on a Biograph mMR (Siemens AG, Erlangen, Germany), adapting previously developed sequences42 to acquire longitudinal and transverse relaxation rates.

For initial MRI studies, a single slice, with slice thickness of 3 mm (Fig. 1b) and field of view (FOV) of 120 ×120 mm2, was used for all image acquisitions. An inversion recovery (IR) spin echo sequence was used to acquire R1 (R1 = 1/T1) measurements. Matrix size of 128 × 128 gives a voxel size of 0.9 × 0.9 × 3.0 mm3. Repetition time (TR) was 4000 ms and inversion times (TI) were 22, 200, 500, 1000, 2000 and 3900 ms. The scan time for the R1 measurements was approximately 39 min.

A single echo spin echo (SE) sequence (Fig. 1c) was applied for R2 (R2 = 1/T2) measurements and a multi-echo gradient echo (GRE) sequence for R2* (R2* = 1/T2*). The matrix size for both sequences was 192 × 192 leading to a voxel size of 0.6 × 0.6 × 3.0 mm3. For SE, measurements were obtained at the following echo time (TE): 13, 20, 25, 30, 40, 60, 80, 100, 150 and 200 ms. The quantity TR – TE was held fixed at 2000 ms70. For the GRE sequence, measurements were obtained at the following TE: 6.12, 14.64, 23.16, 31.68, 40.2, 50, 60, 70 and 79.9 ms. TR was 2000 ms; number of averages = 4; and flip angle was 60°. The scan times for R2 and R2* measurements were approximately 61 min and 26 min, respectively.

Longitudinal and transverse relaxation rates were determined as follows. For R2 and R2*, first a 21-voxel region of interest (ROI; area = 0.6 × 0.6 mm × 21 voxels = 7.56 mm2) within the sample was selected, avoiding the wall of the Ultem well48. Mean signal intensity within the ROI was determined at each TE. Decay curves of these signal intensities were plotted using Prism software (GraphPad; San Diego, CA) version 9.3.0 and a single exponential line of best fit. R2′ was calculated by subtraction (R2* − R2 = R2′). For R1, mean signal intensities at each TI were taken from a 9-voxel ROI (area = 0.9 mm × 0.9 mm × 9 voxels = 7.29 mm2) within the sample. Signal intensities at each TI were plotted using custom MATLAB code developed in MATLAB 2019 and the standard IR equation was applied for determination of R171. Relaxation rates were reported as the mean ± s.e.m. using Prism software. Our standard curve fitting procedures for R1 and R2* were unsuitable for 100% L. crispatus (f = 1.0) due to significantly shorter relaxation times. This discrepancy was evident in uncertainty measures exceeding 25%, which were excluded from further analyzes.

We performed additional experiments to improve uncertainty measures by using the following modified relaxation mapping methods and parameters. R1 mapping was performed using a 3D driven equilibrium single pulse observation of T1 (DESPOT1)51 acquisition with FOV = 120 mm; matrix size = 256 × 256; slice thickness = 3 mm; 22 slices (82% slice oversampling); TR/TE = 15 ms/2.55 ms; flip angles of 5° and 26°; and acquisition time = 5 min. Flip angles were calibrated with B1 maps acquired using the Slice Selective Pre-conditioning radiofrequency pulse method72. For analysis of longitudinal relaxation rate, an ROI of approximately 100 voxels was selected on the T1 map (produced by the Siemens console) within Nunc wells42 containing 100% L. crispatus, taking care to avoid partial volume effects near the edges of the wells. The mean T1 value and standard deviation across each ROI were recorded and used to compute R1 (1/T1). The standard deviation of each R1 value (SDR1) was computed from the standard deviation across pixels of T1 (SDT1) using the following equation.3 SDR1=R1×SDTI/T1

The standard error of the mean value of R1 (SEMR1) was determined using the following equation,4 SEMRI=SDRI/(Np)1/2

where Np is the number of pixels prior to interpolation (≈50).

R2* mapping was performed using a 3D GRE sequence acquisition with FOV = 196 mm; matrix size = 192 × 192; slice thickness = 2 mm (interpolated to 1 mm); 20 slices; TR = 50 ms; 4 signal averages; Partial Fourier of 6/8 in two directions; 12 TE values ranging from 0.97 to 19.15 ms; and acquisition time = 7.7 min. Analysis of transverse relaxation rate was performed in MATLAB. The average value of the signal in the center cell of an ROI and the surrounding 8 cells (total of 9 voxels) was computed. For each sample, the exponential fit function of MATLAB was then applied to the average signals at each TE to determine R2*71.

In vivo magnetic resonance imaging in humans

Human images were acquired with the written informed consent of all participants and approved by the Western University Health Sciences Research Ethics Board. The bladder of healthy volunteers (n = 3; Table 1) was scanned at 3 T on a Siemens Biograph mMR to acquire longitudinal and transverse relaxation rates. All images were acquired in the sagittal plane. T1 mapping was performed using DESPOT151 with two flip angles (2o, 10o) and spatial resolution of 0.7 × 1 × 3.3 mm3 (interpolated to 0.7 × 0.7 × 2 mm3). Flip angles were calibrated with B1 maps acquired using the Slice Selective Pre-conditioning radiofrequency pulse method72. The scan time for T1 imaging was approximately 1 minute. T2* mapping was performed with a 2D GRE sequence with 6 TE values ranging from 2.46 ms to 14.75 ms and spatial resolution of 1.1 × 1.5 × 4 mm3 (interpolated to 1.1 × 1.1 × 4 mm3). The scan time for T2* imaging was approximately 7 minutes. Mean T1 and T2* values were measured from a ROI in the bladder wall.

Protein quantification and mass spectrometry

Bacteria were cultured as above and centrifuged at 4500 x g for 10 min at 20 °C, washing three times with at least 10 mL PBS. Cell pellets were then collected in radioimmunoprecipitation assay (RIPA) buffer containing Complete Mini protease inhibitor cocktail (Roche Diagnostic Systems, Laval, Canada) and lysed through five cycles of freeze-thaw. Protein concentrations were measured using the bicinchoninic acid (BCA) assay and bovine serum albumin (BSA) as the standard73. Absorbance at 562 nm was determined using the Eon plate reader.

For elemental iron and manganese analysis, samples containing 1–3 mg mL−1 of protein were evaluated using inductively-coupled plasma mass spectrometry (ICP-MS, Biotron Analytical Services, Western University, London, Canada). Briefly, samples were digested with nitric acid and heat, then filtered prior to mass spectrometry. The data reported here reflect total cellular iron or manganese content normalized to total amount of bacterial protein.

Statistics and Reproducibility

Statistical analyzes were performed using GraphPad Prism version 9.3 or 10.2. All replicates shown reflect biological replicates. MR relaxation rates and metal content of bacteria were analyzed using one-way ANOVA, where significance was defined at α = 0.05, followed by Tukey’s test. Non-parametric data from ICP-MS was assessed using the Kruskal-Wallis test with uncorrected Dunn’s multiple comparisons. Differences between R2 and R2* of species were analyzed by a two-tailed paired t-test.

For principal component analyzes, covariance in mean MR and mean ICP-MS measures for bacterial strains was examined. Data were standardized and variables examined were R2, R1, [Fe] and [Mn]. Eigenvalues for all principal components shown are greater than 1.0 and each set of principal components explained at least 85% of variance in the data. Follow up principal component analyzes were completed after removing statistical outliers (Fig. 3d, Supplementary Fig. 3, Supplementary Tables 2–4) which can skew principal components and loadings, especially with a small sample size (n = 11), thereby masking trends in the greater data set. Lactobacilli were flagged as outliers based on robust regression and outlier removal (ROUT) analysis (Q = 0.5) of raw data and z-scores that were 3 s.d. away from the mean. Lactobacilli and S. aureus Newman were flagged as outliers based on ROUT outlier analysis (Q = 0.5) of the contribution of cases, which demonstrates how much a single bacterium contributes to the generation of principal components. Correlation analyzes were completed using a one-tailed Spearman’s correlation at α = 0.05 with Bonferroni correction for multiple comparisons.

Comparison of individual transverse or longitudinal relaxation rates across dilution series of L. crispatus in bladder cells was completed using one-way ANOVA and Tukey’s test. Comparison of R2 and R2* across each dilution series was analyzed by multiple paired t-tests with Holm-Sidak’s correction for multiple comparisons.

Comparison of R2 vs. f and R2* vs. f nonlinear regressions for bacteria diluted in gelatin or bladder cells was done using an extra sum-of-squares F-test. Ratios of R2/R2* were compared using the unpaired two-tailed Mann–Whitney U-test to compensate for large differences in sample size between groups. Full details of all statistical analyzes are included in Supplementary Tables 6–11.

Reporting summary

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

Supplementary information

Peer Review File

Supplementary Information

Description of Additional Supplementary File

Supplementary Data 1

Supplementary Data 2

Supplementary Data 3

Supplementary Data 4

Supplementary Data 5

Reporting Summary

Supplementary information

The online version contains supplementary material available at 10.1038/s42003-024-06783-5.

Acknowledgements

The authors thank technologists John Butler, Heather Biernaski and Yvonne Huston for medical imaging support. Dr. Kait Al and Wongsakorn Kiattiburut provided valuable discussion on principal component analysis. We also thank Dr. Venketesh Thrithamara Ranganathan for critically reviewing the manuscript. This study was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Alliance grant ALLRP 576699 – 22 awarded to FSP.

Author contributions

S.C.D, J.P.B and D.E.G. conceived and designed the experiments. S.C.D., G.V.M. and Q.S. performed the experiments. S.C.D., G.V.M., S.H., J.D.T. and N.G. analyzed the data. J.P.B., D.E.G., F.S.P. and R.T.T. contributed resources and funding to this study. D.E.G. and S.C.D. wrote the paper. All authors reviewed and edited the manuscript.

Peer review

Peer review information

: Communications Biology thanks Kim Brewer, Jeff Bulte and Brianna Kelly for their contribution to the peer review of this work. Primary Handling Editors: Ophelia Bu. A peer review file is available.

Data availability

Source data can be found in the Supplementary Data files. All other data are available from the corresponding author (or other sources, as applicable) on reasonable request.

Code availability

Some data analysis was performed using MATLAB (Mathworks). These codes are available from the corresponding author upon reasonable request.

Competing interests

The authors declare the following competing interests. The following co-authors are inventors on a patent related to imaging of bacteria: Goldhawk, D.E., Burton, J.P., Silverman, M.S., Donnelly, S.C., Thompson, R.T., Zhang, M. and Prato, F.S. Biomedical imaging of bacteria and bacteriophage. Multi-Magnetics Inc. (MMI) 2020 U.S. Provisional Patent Application # 63/016,68.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Gao Y-D Zhao Y Huang J Metabolic modeling of common Escherichia coli strains in human gut microbiome BioMed. Res Int 2014 2014 694967 10.1155/2014/694967 25126572
Gao, Y.-D., Zhao, Y. & Huang, J. Metabolic modeling of common Escherichia coli strains in human gut microbiome. BioMed. Res Int 2014, 694967 (2014).25126572 10.1155/2014/694967
2. Daisley BA Immobilization of cadmium and lead by Lactobacillus rhamnosus GR-1 mitigates apical-to-basolateral heavy metal translocation in a Caco-2 model of the intestinal epithelium Gut microbes 2019 10 321 333 10.1080/19490976.2018.1526581 30426826
Daisley, B. A. et al. Immobilization of cadmium and lead by Lactobacillus rhamnosus GR-1 mitigates apical-to-basolateral heavy metal translocation in a Caco-2 model of the intestinal epithelium. Gut microbes 10, 321–333 (2019).30426826 10.1080/19490976.2018.1526581
3. McDonald JAK Evaluation of microbial community reproducibility, stability and composition in a human distal gut chemostat model J. Microbiol Methods 2013 95 167 174 10.1016/j.mimet.2013.08.008 23994646
McDonald, J. A. K. et al. Evaluation of microbial community reproducibility, stability and composition in a human distal gut chemostat model. J. Microbiol Methods 95, 167–174 (2013).23994646 10.1016/j.mimet.2013.08.008
4. Kishikawa T Metagenome-wide association study of gut microbiome revealed novel aetiology of rheumatoid arthritis in the Japanese population Ann. Rheum. Dis. 2020 79 103 111 10.1136/annrheumdis-2019-215743 31699813
Kishikawa, T. et al. Metagenome-wide association study of gut microbiome revealed novel aetiology of rheumatoid arthritis in the Japanese population. Ann. Rheum. Dis. 79, 103–111 (2020).31699813 10.1136/annrheumdis-2019-215743
5. Vatanen T The human gut microbiome in early-onset type 1 diabetes from the TEDDY study Nature 2018 562 589 594 10.1038/s41586-018-0620-2 30356183
Vatanen, T. et al. The human gut microbiome in early-onset type 1 diabetes from the TEDDY study. Nature 562, 589–594 (2018).30356183 10.1038/s41586-018-0620-2
6. Reitmeier S Arrhythmic gut microbiome signatures predict risk of type 2 diabetes Cell Host Microbe 2020 28 258 272.e256 10.1016/j.chom.2020.06.004 32619440
Reitmeier, S. et al. Arrhythmic gut microbiome signatures predict risk of type 2 diabetes. Cell Host Microbe 28, 258–272.e256 (2020).32619440 10.1016/j.chom.2020.06.004
7. Zhang Y Gut microbiome-related effects of berberine and probiotics on type 2 diabetes (the PREMOTE study) Nat. Commun. 2020 11 5015 10.1038/s41467-020-18414-8 33024120
Zhang, Y. et al. Gut microbiome-related effects of berberine and probiotics on type 2 diabetes (the PREMOTE study). Nat. Commun. 11, 5015 (2020).33024120 10.1038/s41467-020-18414-8
8. Liu H Alterations in the gut microbiome and metabolism with coronary artery disease severity Microbiome 2019 7 68 10.1186/s40168-019-0683-9 31027508
Liu, H. et al. Alterations in the gut microbiome and metabolism with coronary artery disease severity. Microbiome 7, 68 (2019).31027508 10.1186/s40168-019-0683-9
9. Liu RT Walsh RFL Sheehan AE Prebiotics and probiotics for depression and anxiety: a systematic review and meta-analysis of controlled clinical trials Neurosci. Biobehav Rev. 2019 102 13 23 10.1016/j.neubiorev.2019.03.023 31004628
Liu, R. T., Walsh, R. F. L. & Sheehan, A. E. Prebiotics and probiotics for depression and anxiety: a systematic review and meta-analysis of controlled clinical trials. Neurosci. Biobehav Rev. 102, 13–23 (2019).31004628 10.1016/j.neubiorev.2019.03.023
10. Kraeuter AK Phillips R Sarnyai Z The gut microbiome in psychosis from mice to men: a systematic review of preclinical and clinical studies Front Psychiatry 2020 11 799 10.3389/fpsyt.2020.00799 32903683
Kraeuter, A. K., Phillips, R. & Sarnyai, Z. The gut microbiome in psychosis from mice to men: a systematic review of preclinical and clinical studies. Front Psychiatry 11, 799 (2020).32903683 10.3389/fpsyt.2020.00799
11. Al KF Burton JP Processing human urine and ureteral stents for 16S rRNA amplicon sequencing STAR Protoc. 2021 2 100435 10.1016/j.xpro.2021.100435 33899017
Al, K. F. & Burton, J. P. Processing human urine and ureteral stents for 16S rRNA amplicon sequencing. STAR Protoc. 2, 100435 (2021).33899017 10.1016/j.xpro.2021.100435
12. Di Bella JM Bao Y Gloor GB Burton JP Reid G High throughput sequencing methods and analysis for microbiome research J. Microbiol Methods 2013 95 401 414 10.1016/j.mimet.2013.08.011 24029734
Di Bella, J. M., Bao, Y., Gloor, G. B., Burton, J. P. & Reid, G. High throughput sequencing methods and analysis for microbiome research. J. Microbiol Methods 95, 401–414 (2013).24029734 10.1016/j.mimet.2013.08.011
13. Watson E Reid G Metabolomics as a clinical testing method for the diagnosis of vaginal dysbiosis Am. J. Reprod. Immunol. 2018 80 e12979 10.1111/aji.12979 29756665
Watson, E. & Reid, G. Metabolomics as a clinical testing method for the diagnosis of vaginal dysbiosis. Am. J. Reprod. Immunol. 80, e12979 (2018).29756665 10.1111/aji.12979
14. Jiao L Spatial characteristics of colonic mucosa-associated gut microbiota in humans Micro. Ecol. 2022 83 811 821 10.1007/s00248-021-01789-6
Jiao, L. et al. Spatial characteristics of colonic mucosa-associated gut microbiota in humans. Micro. Ecol. 83, 811–821 (2022).10.1007/s00248-021-01789-6
15. Martinez-Guryn K Leone V Chang EB Regional diversity of the gastrointestinal microbiome Cell Host Microbe 2019 26 314 324 10.1016/j.chom.2019.08.011 31513770
Martinez-Guryn, K., Leone, V. & Chang, E. B. Regional diversity of the gastrointestinal microbiome. Cell Host Microbe 26, 314–324 (2019).31513770 10.1016/j.chom.2019.08.011
16. Zoetendal EG Mucosa-associated bacteria in the human gastrointestinal tract are uniformly distributed along the colon and differ from the community recovered from feces Appl Environ. Microbiol 2002 68 3401 3407 10.1128/AEM.68.7.3401-3407.2002 12089021
Zoetendal, E. G. et al. Mucosa-associated bacteria in the human gastrointestinal tract are uniformly distributed along the colon and differ from the community recovered from feces. Appl Environ. Microbiol 68, 3401–3407 (2002).12089021 10.1128/AEM.68.7.3401-3407.2002
17. Ordonez AA Molecular imaging of bacterial infections: overcoming the barriers to clinical translation Sci. Transl. Med 2019 11 eaax8251 10.1126/scitranslmed.aax8251 31484790
Ordonez, A. A. et al. Molecular imaging of bacterial infections: overcoming the barriers to clinical translation. Sci. Transl. Med 11, eaax8251 (2019).31484790 10.1126/scitranslmed.aax8251
18. Ordonez AA Imaging Enterobacterales infections in patients using pathogen-specific positron emission tomography Sci. Transl. Med 2021 13 eabe9805 10.1126/scitranslmed.abe9805 33853931
Ordonez, A. A. et al. Imaging Enterobacterales infections in patients using pathogen-specific positron emission tomography. Sci. Transl. Med 13, eabe9805 (2021).33853931 10.1126/scitranslmed.abe9805
19. Hoerr V Bacteria tracking by in vivo magnetic resonance imaging BMC Biol. 2013 11 63 10.1186/1741-7007-11-63 23714179
Hoerr, V. et al. Bacteria tracking by in vivo magnetic resonance imaging. BMC Biol. 11, 63 (2013).23714179 10.1186/1741-7007-11-63
20. Li Y In situ targeted MRI detection of Helicobacter pylori with stable magnetic graphitic nanocapsules Nat. Commun. 2017 8 15653 10.1038/ncomms15653 28643777
Li, Y. et al. In situ targeted MRI detection of Helicobacter pylori with stable magnetic graphitic nanocapsules. Nat. Commun. 8, 15653 (2017).28643777 10.1038/ncomms15653
21. Li L Bacteria-targeted MRI probe-based imaging bacterial infection and monitoring antimicrobial therapy in vivo Small 2021 17 e2103627 10.1002/smll.202103627 34554653
Li, L. et al. Bacteria-targeted MRI probe-based imaging bacterial infection and monitoring antimicrobial therapy in vivo. Small 17, e2103627 (2021).34554653 10.1002/smll.202103627
22. Zhang L Gadolinium-labeled aminoglycoside and its potential application as a bacteria-targeting magnetic resonance imaging contrast agent Anal. Chem. 2018 90 1934 1940 10.1021/acs.analchem.7b04029 29293308
Zhang, L. et al. Gadolinium-labeled aminoglycoside and its potential application as a bacteria-targeting magnetic resonance imaging contrast agent. Anal. Chem. 90, 1934–1940 (2018).29293308 10.1021/acs.analchem.7b04029
23. Hill PJ Magnetic resonance imaging of tumors colonized with bacterial ferritin-expressing Escherichia coli PLoS ONE 2011 6 e25409 10.1371/journal.pone.0025409 21984917
Hill, P. J. et al. Magnetic resonance imaging of tumors colonized with bacterial ferritin-expressing Escherichia coli. PLoS ONE 6, e25409 (2011).21984917 10.1371/journal.pone.0025409
24. Andronesi OC Combined off-resonance imaging and T2 relaxation in the rotating frame for positive contrast MR imaging of infection in a murine burn model J. Magn. Reson Imaging 2010 32 1172 1183 10.1002/jmri.22349 21031524
Andronesi, O. C. et al. Combined off-resonance imaging and T2 relaxation in the rotating frame for positive contrast MR imaging of infection in a murine burn model. J. Magn. Reson Imaging 32, 1172–1183 (2010).21031524 10.1002/jmri.22349
25. Brandt CT In vivo study of experimental pneumococcal meningitis using magnetic resonance imaging BMC Med Imaging 2008 8 1 10.1186/1471-2342-8-1 18194516
Brandt, C. T. et al. In vivo study of experimental pneumococcal meningitis using magnetic resonance imaging. BMC Med Imaging 8, 1 (2008).18194516 10.1186/1471-2342-8-1
26. Liu G Noninvasive imaging of infection after treatment with tumor-homing bacteria using Chemical Exchange Saturation Transfer (CEST) MRI Magn. Reson Med 2013 70 1690 1698 10.1002/mrm.24955 24123389
Liu, G. et al. Noninvasive imaging of infection after treatment with tumor-homing bacteria using Chemical Exchange Saturation Transfer (CEST) MRI. Magn. Reson Med 70, 1690–1698 (2013).24123389 10.1002/mrm.24955
27. Haley KP Skaar EP A battle for iron: host sequestration and Staphylococcus aureus acquisition Microbes Infect. 2012 14 217 227 10.1016/j.micinf.2011.11.001 22123296
Haley, K. P. & Skaar, E. P. A battle for iron: host sequestration and Staphylococcus aureus acquisition. Microbes Infect. 14, 217–227 (2012).22123296 10.1016/j.micinf.2011.11.001
28. Ma L Terwilliger A Maresso AW Iron and zinc exploitation during bacterial pathogenesis Metallomics 2015 15 1541 1554 10.1039/C5MT00170F
Ma, L., Terwilliger, A. & Maresso, A. W. Iron and zinc exploitation during bacterial pathogenesis. Metallomics 15, 1541–1554 (2015).10.1039/C5MT00170F
29. Xiao YD MRI contrast agents: classification and application (Review) Int J. Mol. Med 2016 38 1319 1326 10.3892/ijmm.2016.2744 27666161
Xiao, Y. D. et al. MRI contrast agents: classification and application (Review). Int J. Mol. Med 38, 1319–1326 (2016).27666161 10.3892/ijmm.2016.2744
30. Gao Q Roles of iron acquisition systems in virulence of extraintestinal pathogenic Escherichia coli: salmochelin and aerobactin contribute more to virulence than heme in a chicken infection model BMC Microbiol 2012 12 143 10.1186/1471-2180-12-143 22817680
Gao, Q. et al. Roles of iron acquisition systems in virulence of extraintestinal pathogenic Escherichia coli: salmochelin and aerobactin contribute more to virulence than heme in a chicken infection model. BMC Microbiol 12, 143 (2012).22817680 10.1186/1471-2180-12-143
31. Skaar EP Raffatellu M Metals in infectious diseases and nutritional immunity Metallomics 2015 7 926 928 10.1039/C5MT90021B 26017093
Skaar, E. P. & Raffatellu, M. Metals in infectious diseases and nutritional immunity. Metallomics 7, 926–928 (2015).26017093 10.1039/C5MT90021B
32. Subashchandrabose, S. & Mobley, H. L. T. Virulence and fitness determinants of uropathogenic Escherichia coli. Microbiol Spectr 3, 10.1128/microbiolspec.UTI-0015-2012 (2015).
33. Ikeda JS Janakiraman A Kehres DG Maguire ME Slauch JM Transcriptional regulation of sitABCD of Salmonella enterica serovar Typhimurium by MntR and Fur J. Bacteriol. 2005 187 912 922 10.1128/JB.187.3.912-922.2005 15659669
Ikeda, J. S., Janakiraman, A., Kehres, D. G., Maguire, M. E. & Slauch, J. M. Transcriptional regulation of sitABCD of Salmonella enterica serovar Typhimurium by MntR and Fur. J. Bacteriol. 187, 912–922 (2005).15659669 10.1128/JB.187.3.912-922.2005
34. Archibald F Lactobacillus plantarum, an organism not requiring iron FEMS Microbiol Lett. 1983 19 29 32 10.1111/j.1574-6968.1983.tb00504.x
Archibald, F. Lactobacillus plantarum, an organism not requiring iron. FEMS Microbiol Lett. 19, 29–32 (1983).10.1111/j.1574-6968.1983.tb00504.x
35. Archibald FS Duong MN Manganese acquisition by Lactobacillus plantarum J. Bacteriol. 1984 158 1 8 10.1128/jb.158.1.1-8.1984 6715278
Archibald, F. S. & Duong, M. N. Manganese acquisition by Lactobacillus plantarum. J. Bacteriol. 158, 1–8 (1984).6715278 10.1128/jb.158.1.1-8.1984
36. Groot MN Genome-based in silico detection of putative manganese transport systems in Lactobacillus plantarum and their genetic analysis Microbiology 2005 151 1229 1238 10.1099/mic.0.27375-0 15817790
Groot, M. N. et al. Genome-based in silico detection of putative manganese transport systems in Lactobacillus plantarum and their genetic analysis. Microbiology 151, 1229–1238 (2005).15817790 10.1099/mic.0.27375-0
37. Hao Z Reiske HR Wilson DB Characterization of cadmium uptake in Lactobacillus plantarum and isolation of cadmium and manganese uptake mutants Appl Environ. Microbiol 1999 65 4741 4745 10.1128/AEM.65.11.4741-4745.1999 10543780
Hao, Z., Reiske, H. R. & Wilson, D. B. Characterization of cadmium uptake in Lactobacillus plantarum and isolation of cadmium and manganese uptake mutants. Appl Environ. Microbiol 65, 4741–4745 (1999).10543780 10.1128/AEM.65.11.4741-4745.1999
38. Goldhawk, D., Gelman, N., Thompson, R. & Prato, F. in Design and Applications of Nanoparticles in Biomedical Imaging (ed J. Bulte, and Modo, M., Eds.) 187–203 (Springer International Publishing, 2017).
39. Archibald F Manganese: its acquisition by and function in the lactic acid bacteria Crit. Rev. Microbiol 1986 13 63 109 10.3109/10408418609108735 3522109
Archibald, F. Manganese: its acquisition by and function in the lactic acid bacteria. Crit. Rev. Microbiol 13, 63–109 (1986).3522109 10.3109/10408418609108735
40. Siddiqui H Nederbragt AJ Lagesen K Jeansson SL Jakobsen KS Assessing diversity of the female urine microbiota by high throughput sequencing of 16S rDNA amplicons BMC Microbiol 2011 11 244 10.1186/1471-2180-11-244 22047020
Siddiqui, H., Nederbragt, A. J., Lagesen, K., Jeansson, S. L. & Jakobsen, K. S. Assessing diversity of the female urine microbiota by high throughput sequencing of 16S rDNA amplicons. BMC Microbiol 11, 244 (2011).22047020 10.1186/1471-2180-11-244
41. Gottschick C The urinary microbiota of men and women and its changes in women during bacterial vaginosis and antibiotic treatment Microbiome 2017 5 99 10.1186/s40168-017-0305-3 28807017
Gottschick, C. et al. The urinary microbiota of men and women and its changes in women during bacterial vaginosis and antibiotic treatment. Microbiome 5, 99 (2017).28807017 10.1186/s40168-017-0305-3
42. Sengupta A Biophysical features of MagA expression in mammalian cells: implications for MRI contrast Front Microbiol 2014 5 29 24550900
Sengupta, A. et al. Biophysical features of MagA expression in mammalian cells: implications for MRI contrast. Front Microbiol 5, 29 (2014).24550900
43. Liu L MagA expression attenuates iron export activity in undifferentiated multipotent P19 cells PLoS One 2019 14 e0217842 10.1371/journal.pone.0217842 31170273
Liu, L. et al. MagA expression attenuates iron export activity in undifferentiated multipotent P19 cells. PLoS One 14, e0217842 (2019).31170273 10.1371/journal.pone.0217842
44. Harber MJ Asscher AW Virulence of urinary pathogens Kidney Int 1985 28 717 721 10.1038/ki.1985.189 2418252
Harber, M. J. & Asscher, A. W. Virulence of urinary pathogens. Kidney Int 28, 717–721 (1985).2418252 10.1038/ki.1985.189
45. Mansour B Bladder cancer-related microbiota: examining differences in urine and tissue samples Sci. Rep. 2020 10 11042 10.1038/s41598-020-67443-2 32632181
Mansour, B. et al. Bladder cancer-related microbiota: examining differences in urine and tissue samples. Sci. Rep. 10, 11042 (2020).32632181 10.1038/s41598-020-67443-2
46. Abbasian B Potential role of extracellular ATP released by bacteria in bladder infection and contractility mSphere 2019 4 e00439 19 10.1128/mSphere.00439-19 31484739
Abbasian, B. et al. Potential role of extracellular ATP released by bacteria in bladder infection and contractility. mSphere 4, e00439–19 (2019).31484739 10.1128/mSphere.00439-19
47. Lau, M. E. & Hunstad, D. A. Quantitative assessment of human neutrophil migration across a cultured bladder epithelium. J Vis Exp, e50919, 10.3791/50919 (2013).
48. Dassanayake PSB Monocyte MRI relaxation rates are regulated by extracellular iron and hepcidin Int J. Mol. Sci. 2023 24 4036 10.3390/ijms24044036 36835448
Dassanayake, P. S. B. et al. Monocyte MRI relaxation rates are regulated by extracellular iron and hepcidin. Int J. Mol. Sci. 24, 4036 (2023).36835448 10.3390/ijms24044036
49. Alizadeh K Hepcidin-mediated iron regulation in P19 cells is detectable by magnetic resonance imaging Sci. Rep. 2020 10 3163 10.1038/s41598-020-59991-4 32081948
Alizadeh, K. et al. Hepcidin-mediated iron regulation in P19 cells is detectable by magnetic resonance imaging. Sci. Rep. 10, 3163 (2020).32081948 10.1038/s41598-020-59991-4
50. Rodionov DA Hebbeln P Gelfand MS Eitinger T Comparative and functional genomic analysis of prokaryotic nickel and cobalt uptake transporters: evidence for a novel group of ATP-binding cassette transporters J. Bacteriol. 2006 188 317 327 10.1128/JB.188.1.317-327.2006 16352848
Rodionov, D. A., Hebbeln, P., Gelfand, M. S. & Eitinger, T. Comparative and functional genomic analysis of prokaryotic nickel and cobalt uptake transporters: evidence for a novel group of ATP-binding cassette transporters. J. Bacteriol. 188, 317–327 (2006).16352848 10.1128/JB.188.1.317-327.2006
51. Deoni SC Rutt BK Peters TM Rapid combined T1 and T2 mapping using gradient recalled acquisition in the steady state Magn. Reson Med 2003 49 515 526 10.1002/mrm.10407 12594755
Deoni, S. C., Rutt, B. K. & Peters, T. M. Rapid combined T1 and T2 mapping using gradient recalled acquisition in the steady state. Magn. Reson Med 49, 515–526 (2003).12594755 10.1002/mrm.10407
52. Caravan P Farrar CT Frullano L Uppal R Influence of molecular parameters and increasing magnetic field strength on relaxivity of gadolinium- and manganese-based T1 contrast agents Contrast Media Mol. Imaging 2009 4 89 100 10.1002/cmmi.267 19177472
Caravan, P., Farrar, C. T., Frullano, L. & Uppal, R. Influence of molecular parameters and increasing magnetic field strength on relaxivity of gadolinium- and manganese-based T1 contrast agents. Contrast Media Mol. Imaging 4, 89–100 (2009).19177472 10.1002/cmmi.267
53. Gossuin Y Muller RN Gillis P Relaxation induced by ferritin: a better understanding for an improved MRI iron quantification NMR Biomed. 2004 17 427 432 10.1002/nbm.903 15526352
Gossuin, Y., Muller, R. N. & Gillis, P. Relaxation induced by ferritin: a better understanding for an improved MRI iron quantification. NMR Biomed. 17, 427–432 (2004).15526352 10.1002/nbm.903
54. Stanisz GJ T1, T2 relaxation and magnetization transfer in tissue at 3T Magn. Reson Med 2005 54 507 512 10.1002/mrm.20605 16086319
Stanisz, G. J. et al. T1, T2 relaxation and magnetization transfer in tissue at 3T. Magn. Reson Med 54, 507–512 (2005).16086319 10.1002/mrm.20605
55. Bencikova D Evaluation of a single-breath-hold radial turbo-spin-echo sequence for T2 mapping of the liver at 3T Eur. Radio. 2022 32 3388 3397 10.1007/s00330-021-08439-y
Bencikova, D. et al. Evaluation of a single-breath-hold radial turbo-spin-echo sequence for T2 mapping of the liver at 3T. Eur. Radio. 32, 3388–3397 (2022).10.1007/s00330-021-08439-y
56. Obmann VC Liver MR relaxometry at 3T - segmental normal T(1) and T(2)* values in patients without focal or diffuse liver disease and in patients with increased liver fat and elevated liver stiffness Sci. Rep. 2019 9 8106 10.1038/s41598-019-44377-y 31147588
Obmann, V. C. et al. Liver MR relaxometry at 3T - segmental normal T(1) and T(2)* values in patients without focal or diffuse liver disease and in patients with increased liver fat and elevated liver stiffness. Sci. Rep. 9, 8106 (2019).31147588 10.1038/s41598-019-44377-y
57. Ligthart K Belzer C de Vos WM Tytgat HLP Bridging bacteria and the gut: functional aspects of type IV pili Trends Microbiol 2020 28 340 348 10.1016/j.tim.2020.02.003 32298612
Ligthart, K., Belzer, C., de Vos, W. M. & Tytgat, H. L. P. Bridging bacteria and the gut: functional aspects of type IV pili. Trends Microbiol 28, 340–348 (2020).32298612 10.1016/j.tim.2020.02.003
58. Ferrieres L Hancock V Klemm P Specific selection for virulent urinary tract infectious Escherichia coli strains during catheter-associated biofilm formation FEMS Immunol. Med Microbiol 2007 51 212 219 10.1111/j.1574-695X.2007.00296.x 17645737
Ferrieres, L., Hancock, V. & Klemm, P. Specific selection for virulent urinary tract infectious Escherichia coli strains during catheter-associated biofilm formation. FEMS Immunol. Med Microbiol 51, 212–219 (2007).17645737 10.1111/j.1574-695X.2007.00296.x
59. Consortium THMP Structure, function and diversity of the healthy human microbiome Nature 2012 486 207 214 10.1038/nature11234 22699609
Consortium, T. H. M. P. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).22699609 10.1038/nature11234
60. Silhavy TJ Kahne D Walker S The bacterial cell envelope Cold Spring Harb. Perspect. Biol. 2010 2 a000414 10.1101/cshperspect.a000414 20452953
Silhavy, T. J., Kahne, D. & Walker, S. The bacterial cell envelope. Cold Spring Harb. Perspect. Biol. 2, a000414 (2010).20452953 10.1101/cshperspect.a000414
61. Schoberth SM Bär NK Krämer R Kärger J Pulsed high-field gradient in vivo NMR spectroscopy to measure diffusional water permeability in Corynebacterium glutamicum Anal. Biochem 2000 279 100 105 10.1006/abio.1999.4450 10683237
Schoberth, S. M., Bär, N. K., Krämer, R. & Kärger, J. Pulsed high-field gradient in vivo NMR spectroscopy to measure diffusional water permeability in Corynebacterium glutamicum. Anal. Biochem 279, 100–105 (2000).10683237 10.1006/abio.1999.4450
62. Imbert M Blondeau R On the iron requirement of lactobacilli grown in chemically defined medium Curr. Microbiol 1998 37 64 66 10.1007/s002849900339 9625793
Imbert, M. & Blondeau, R. On the iron requirement of lactobacilli grown in chemically defined medium. Curr. Microbiol 37, 64–66 (1998).9625793 10.1007/s002849900339
63. Santiago GL Longitudinal study of the dynamics of vaginal microflora during two consecutive menstrual cycles PLoS One 2011 6 e28180 10.1371/journal.pone.0028180 22140538
Santiago, G. L. et al. Longitudinal study of the dynamics of vaginal microflora during two consecutive menstrual cycles. PLoS One 6, e28180 (2011).22140538 10.1371/journal.pone.0028180
64. Srinivasan S Temporal variability of human vaginal bacteria and relationship with bacterial vaginosis PLoS One 2010 5 e10197 10.1371/journal.pone.0010197 20419168
Srinivasan, S. et al. Temporal variability of human vaginal bacteria and relationship with bacterial vaginosis. PLoS One 5, e10197 (2010).20419168 10.1371/journal.pone.0010197
65. Keyer K Imlay JA Superoxide accelerates DNA damage by elevating free-iron levels Proc. Natl Acad. Sci. USA 1996 93 13635 13640 10.1073/pnas.93.24.13635 8942986
Keyer, K. & Imlay, J. A. Superoxide accelerates DNA damage by elevating free-iron levels. Proc. Natl Acad. Sci. USA 93, 13635–13640 (1996).8942986 10.1073/pnas.93.24.13635
66. Sobota JM Imlay JA Iron enzyme ribulose-5-phosphate 3-epimerase in Escherichia coli is rapidly damaged by hydrogen peroxide but can be protected by manganese Proc. Natl Acad. Sci. USA 2011 108 5402 5407 10.1073/pnas.1100410108 21402925
Sobota, J. M. & Imlay, J. A. Iron enzyme ribulose-5-phosphate 3-epimerase in Escherichia coli is rapidly damaged by hydrogen peroxide but can be protected by manganese. Proc. Natl Acad. Sci. USA 108, 5402–5407 (2011).21402925 10.1073/pnas.1100410108
67. Hanks TS Differential regulation of iron- and manganese-specific MtsABC and heme-specific HtsABC transporters by the metalloregulator MtsR of group A Streptococcus Infect. Immun. 2006 74 5132 5139 10.1128/IAI.00176-06 16926405
Hanks, T. S. et al. Differential regulation of iron- and manganese-specific MtsABC and heme-specific HtsABC transporters by the metalloregulator MtsR of group A Streptococcus. Infect. Immun. 74, 5132–5139 (2006).16926405 10.1128/IAI.00176-06
68. Welch, J. L. M., Hasegawaa, Y., McNulty, N. P., Gordon, J. I., and Borisy, G. G. Spatial organization of a model 15-member human gut microbiota established in gnotobiotic mice. Proc Natl Acad Sci of the USA, E9105–E9114 (2017).
69. Donnelly, S. C. The development of bacterial magnetic resonance imaging for microbiota analyses Master of Science thesis, Western University, (2020).
70. Williams LA Neonatal brain: regional variability of in vivo MR imaging relaxation rates at 3.0 T- initial experience Radiology 2005 235 595 603 10.1148/radiol.2352031769 15858099
Williams, L. A. et al. Neonatal brain: regional variability of in vivo MR imaging relaxation rates at 3.0 T- initial experience. Radiology 235, 595–603 (2005).15858099 10.1148/radiol.2352031769
71. Hendee WR Morgan CJ Magnetic resonance imaging. Part I–physical principles West J. Med 1984 141 491 500 6506686
Hendee, W. R. & Morgan, C. J. Magnetic resonance imaging. Part I–physical principles. West J. Med 141, 491–500 (1984).6506686
72. Chung S Kim D Breton E Axel L Rapid B1+ mapping using a preconditioning RF pulse with TurboFLASH readout Magn. Reson Med 2010 64 439 446 10.1002/mrm.22423 20665788
Chung, S., Kim, D., Breton, E. & Axel, L. Rapid B1+ mapping using a preconditioning RF pulse with TurboFLASH readout. Magn. Reson Med 64, 439–446 (2010).20665788 10.1002/mrm.22423
73. Smith PK Measurement of protein using bicinchoninic acid Anal. Biochem 1985 150 76 85 10.1016/0003-2697(85)90442-7 3843705
Smith, P. K. et al. Measurement of protein using bicinchoninic acid. Anal. Biochem 150, 76–85 (1985).3843705 10.1016/0003-2697(85)90442-7
