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Hyperspectral reporters for long-distance and wide-area detection of gene expression in living bacteria

Nature Biotechnology (2025)Cite this article

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Abstract

Genetically encoded reporters are suitable for short-distance imaging in the laboratory but not for scanning wide outdoor areas from a distance. Here we introduce hyperspectral reporters (HSRs) designed for hyperspectral imaging cameras that are commonly mounted on unmanned aerial vehicles and satellites. HSR genes encode enzymes that produce a molecule with a unique absorption signature that can be reliably distinguished in hyperspectral images. Quantum mechanical simulations of 20,170 metabolites identified candidate HSRs, leading to the selection of biliverdin IXα and bacteriochlorophyll a for their distinct absorption spectra and biosynthetic feasibility. These genes were integrated into chemical sensor circuits in soil (Pseudomonas putida) and aquatic (Rubrivivax gelatinosus) bacteria. The bacteria were detectable outdoors under ambient light from up to 90 m in a single 4,000-m2 hyperspectral image taken using fixed and unmanned aerial vehicle-mounted cameras. The dose–response functions of the chemical sensors were measured remotely. HSRs enable large-scale studies and applications in ecology, agriculture, environmental monitoring, forensics and defense.

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Fig. 1: Prediction of metabolite spectra.
Fig. 2: Computational HSR design.
Fig. 3: Extraction of HSR information from images.
Fig. 4: Remote detection of chemical signals using HSRs.

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Data availability

The TD-DFT results are shared in Supplementary Data 1 and as a web app that can be accessed at https://voigtlab.github.io/npspec/. Sequence data for strains and plasmids used in this work are included in Supplementary Information. Raw hyperspectral images can be accessed from Zenodo at https://doi.org/10.5281/zenodo.14756888 (ref. 124). Some images could not be uploaded due to repository storage limits. These and any additional data are available from the corresponding author upon request. Source data are provided with this paper.

Code availability

The code for predicting metabolite spectra, computing the number of enzyme steps, uniqueness and contrast, and processing HSI images is available at https://doi.org/10.5281/zenodo.14827800 (ref. 125). The code to deploy the interactive web application to visualize the spectral predictions is available at https://zenodo.org/records/14827805 (ref. 126).

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Acknowledgements

The research was funded by the US Department of Defense Newton Award (MIT 6943892) and the Ministry of Defense of Israel (MIT 4441024394). Engineering of the environmental strains and optimization of the genetically encoded sensors and HSR biosynthetic pathways was funded by the Army Research Office, a directorate of the US Army Combat Capabilities Development Command Army Research Laboratory under Cooperative Agreement Number W911NF-22-2-0210. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of DEVCOM ARL or the US Government. The US Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. We thank Headwall Photonics for aid with UAV experiments. We also thank E. Perkins and members of the Environmental Laboratory of the US Army Engineer Research and Development Center for their help in field experiments and helpful discussions. We also thank R. Daniel, O. Shoseyov, A. Rudich, S. Belsey, M. Khoury, I. Casif, E. Fleshler, L. Chitayat, M. Soreni-Harari and N. R. for their help in field experiments. We acknowledge the MIT SuperCloud Supercomputing Center for providing high-performance computing resources that have contributed to the research results reported within this paper. Y.C. was supported by the Human Frontier Science Program Long Term Fellowship (LT-702).

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  1. These authors contributed equally: Yonatan Chemla, Itai Levin.

Authors and Affiliations

  1. Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Yonatan Chemla, Itai Levin, Yueyang Fan, Anna A. Johnson & Christopher A. Voigt

  2. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Connor W. Coley

  3. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Connor W. Coley

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  1. Yonatan Chemla
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  2. Itai Levin
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  3. Yueyang Fan
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  4. Anna A. Johnson
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  6. Christopher A. Voigt
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Contributions

Y.C., I.L. and C.A.V. designed experiments and wrote the paper. Y.C., I.L., Y.F. and A.A.J. performed the experiments. C.W.C., I.L., Y.C. and C.A.V. conceptualized and performed the computational predictions and analyses.

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Correspondence to Christopher A. Voigt.

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Competing interests

Y.C., I.L. and C.A.V. are the inventors of a patent that covers some of the aspects described in the paper. C.A.V. is a founder of, and I.L. is a consultant for, Fieldstone Bio, which has an option for intellectual property from MIT regarding the research described in this paper. A.A.J., C.W.C. and Y.F. declare no competing interests.

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Extended data

Extended Data Fig. 1 Biosynthetic accessibility of metabolites across different species.

These figures were calculated the same way as Fig. 2c (Methods). Purple and blue points are biliverdin IXα and bacteriochlorophyll a, respectively. The lighter colored points are their structural analogs (Extended Data Fig. 2).

Source data

Extended Data Fig. 2 Structural analogs of biliverdin-IX α and bacteriochlorophyll a.

The color scheme corresponds to Fig. 2c and d, Extended Data Fig. 1, and Supplementary Fig. 5.

Extended Data Fig. 3 Surface cell concentration limit-of-detection of HSRs.

Note that the limit-of-detection will be dependent on the specific camera and distance from the bacteria. Known concentrations of bacteria added to filter surfaces and imaged by a VNIR hyperspectral camera (Headwall E-Series) from 0.5 m (Methods). a. RGB and classified images extracted from the HSI data for constitutively-produced SmURFP(biliverdin IXα) (E. coli pYC301). b. Dependence of signal on the cell density. The limit-of-detection is marked with the vertical dashed line. The data points represent three replicates. The mean classification score indicates that it was averaged over the area of each well. c. Statistical significance of the signal in the presence compared to the absence of cells. p-values were calculated for the mean classification scores of 0.0 CFU/mm2 and each cell density, using one-sided Student t-test with equal variance. The horizontal line is the threshold used to define significant (p-value = 0.05). d. Mean reflectance spectra as a function of cell density. The absorption peak at 925 nm is due to the filter material. e. RGB and classified images extracted from the HSI data for cells constitutively producing bacteriochlorophyll a (R. gelatinosus YF6). f. Dependence of signal on the cell density. The limit-of-detection is marked with the vertical dashed line. The data points represent three replicates. g. Statistical significance of the signal in the presence compared to the absence of cells. p-values were calculated for the mean classification scores of 0.0 CFU/mm2 and each cell density, using one-sided Student t-test with equal variance. h. Reflectance spectra as a function of cell density. The data points are three replicates.

Source data

Extended Data Fig. 4 pC-HSL response functions measured by HSR and a fluorescent reporter.

a. Schematics of genetic sensors connected to the different reporters. Plasmids are detailed in Supplementary Fig. 6 and genetic parts in Supplementary Table 1. b. The response functions as measured with the two reporters. In both cases, cells were grown with different concentrations of inducers (Methods). The pC-HSL sensor connected to YFP (E. coli pYC303) was imaged using a fluorimeter and background fluorescence (fluorescence of the pellets at inducer concentration = 0) was subtracted. The lines are fits to Eq. 6 (HSR: K = 2.2 nM, n = 1.1, YFP: K = 15.4 nM, n = 1.2). The data points are three replicates.

Source data

Extended Data Fig. 5 Dependence of HSR detection on light intensity.

R. gelatinosus YF10 cultures were grown in the presence of 3OH-C14-HSL at the indicated concentration and then spread on 7 g of sand (Methods). Light intensity was set using a tunable quartz-tungsten illumination unit (Headwall Photonics) and measured using a handheld illuminance meter. a. RGB (left) and classified (right) images at different light intensities. The images are representative of three replicates. b. Response curves measured at different light intensities. The lines are fits to Eq. 6 (parameters in table). The “mean” indicates that the classification scores were averaged over the surface of each plate. The data points are three replicates. c. Dynamic range as a function of light intensity. The dynamic range was calculated as the ratio between the mean classification score for the maximally induced and uninduced samples. The data points are three replicates. d. The half-maximum induction K as a function of light intensity. Values were calculated from the best fit of Eq. 6. Points are only shown if the fit is significant. e. Limit-of-detection as a function of light intensity. The data points are three replicates. f. The limit-of-detection was calculated as the minimum concentration above which all calculated p-values were less than 0.01. p-values were calculated at each inducer concentration between the mean classification scores of the induced replicates and uninduced replicates using the one-sided Student t-test with equal variance. Points are not shown if no concentration met the significance threshold. The data points are three replicates.

Source data

Extended Data Fig. 6 Limit-of-detection of R. gelatinosus (Bacteriochlorophyll a) on different soils.

Data are shown for R. gelatinosus YF6, which constitutively produces Bacteriochlorophyll a. a. Schematic illustrating the locations where 10 µL of cultures with varying concentrations were pipetted onto different soil types (A1, A3, A5, A7). b. RGB images from HSI data. c. Classified images from the HSI data. The dashed blue boxes indicate regions analyzed that do not contain bacteria. Classification scores in panel d are the means in the areas marked by rectangles. d. Classification scores extracted from panel c. P-values were calculated using one-sided student t-test with n = 3 (equal variance). Blue dashed regions in panels b and c only contain soil (OD600 = 0.0) and were used for the t-test. Significance scores: *** (p-value < 0.005), ** (p-value < 0.01), * (p-value < 0.05), ns (not significant). The data points are three replicates.

Source data

Extended Data Fig. 7 iPhone camera imaging of SmURFP(biliverdin IXα) on sand.

a. Representative image collected of the same samples in Supplementary Fig. 12 using the iPhone 14 Pro 48-megapixel main camera. b. Responses to pC-HSL measured from each plate from the image’s red, green, and blue channels. The means were calculated by averaging over the area of each plate. The intensity measured in each channel can range from 0 to 254. The data points are three replicates. c. The same image from panel a with brightness and contrast values increased using Adobe Photoshop (v. 25.3.1). d. Responses to pC-HSL measured from the altered image. e. The same image from panel a with the “Find edges” filter applied in Photoshop to all color channels or to single color channels after decomposition of the image into red, green, and blue color channels. No response was successfully extracted from the iPhone camera image with or without additional processing. No other manipulations or enhancements of the color channels in the RGB image led to observable HSR.

Source data

Extended Data Fig. 8 UAV acquisition of HSI data to detect bacteriochlorophyll a from 24 m.

R. gelatinosus WT, YF6 (constitutive bacteriochlorophyll a), and AJ1 (repressed bacteriochlorophyll a) cultures were grown to OD600 = 4.0 (Methods). Cultures were diluted using growth media or concentrated using centrifugation and resuspension in growth media to achieve different OD600 of culture cell density added to soil. 300 mL of each culture was then sprayed on 1.5 kg locally-sourced sandy soil inside each 35 ×30 cm plastic container. Containers were then placed in a field in a closed area near Herzliya, Israel. Imaging was performed with a VNIR-SWIR (400-2500 nm) UAV hyperspectral camera (Co-Aligned HP Hyperspectral Imaging UAV and camera, Headwall) from 24 m. Using SpectralView III, reflectance correction was performed based on the calibration tarp and geo-orthorectification was performed to correct for image distortions from the flight movement. a. Picture showing a close-up of the UAV imaging system and the containers in the background. b. RGB image from the HSI data. c. Classified image from the HSI. d. Locations of different R. gelatinosus strains and their concentrations used in the experiment. The AJ1 strain was used at OD600 = 4.0. The six concentrations in the center box correspond to the wild-type strain; the two concentrations flanking the box (left and right) correspond to the YF6 strain. e. Uncropped RGB image. f. Uncropped classified image. The image in panels e and f covers 0.2 acres (800 m2).

Extended Data Fig. 9 UAV acquisition of HSI data to measure the 3OH-C14-HSL sensor response function from 54 m using bacteriochlorophyll a.

The uncropped image corresponding to Fig. 4f is shown. R. gelatinosus YF10 cultures were grown in the presence of 3OH-C14-HSL and spread on sand inside 35 × 30 cm plastic containers (Methods). In each container, 1 kg sand was covered by 300 mL of culture (OD600 = 4.0). Containers were then placed in a field in a closed area near Rehovot, Israel for 18 hours, out of which it was exposed to the sun for 4 hours prior to imaging. The temperature range was 25 – 35 °C. Imaging was performed with a VNIR-SWIR (400 - 2500 nm) UAV hyperspectral camera (Co-Aligned HP Hyperspectral Imaging UAV and camera, Headwall) from 54 m. The image encompasses a total area of 0.74 acres (3000 m2). Image acquisition required 30 sec. Using SpectralView III, reflectance correction was performed based on the calibration tarp and geo-orthorectification was performed to correct for image distortions from the flight movement. a. RGB image extracted from the HSI data. b. Classified image extracted from HSI data. c. 864 nm image extracted from HSI data. d. Pixel absorption at 864 nm with different thresholds used to label pixels as containing HSR are shown. Values in the heatmap (panel c) greater than the threshold were labeled as containing the HSR (red) and values equal to or less than the threshold were labeled as not containing the HSR (gray). True and false positive rates were calculated as described in the methods. e. Classified HSI images are shown at different threshold values. Values in the heatmap (panel b) greater than the threshold were labeled as containing HSR (red) and values equal to or less than the threshold were labeled as not containing HSR (gray). True and false positive rates were calculated as described in the methods. The bottom images are enlarged images of the area marked by the white rectangles in the top images. The black rectangles in the bottom images mark the areas in the image where R. gelatinosus YF10 was added onto sand. The mean classification scores in Fig. 4g were calculated by averaging over the area of each rectangle.

Extended Data Fig. 10 Measurement of the 3OH-C14-HSL sensor response function from 90 m using bacteriochlorophyll a.

These data correspond to Fig. 4j using R. gelatinosus YF10. The top image shows the whole area shown in Fig. 4h. The bottom images are enlarged images of the area marked by the white rectangles in the top images. The black rectangle in the bottom images marks the area in the image where R. gelatinosus YF10 was added onto 1 kg of sand. The sand covered a 1 cm deep filter disc (60 mm) treated with 800 µL of 19 mM 3OH-C14-HSL (final concentration of 50 µM/mL culture). a. RGB image calculated from HSI data. b. 864 nm image calculated from HSI data. c. Data used to calculate the ROC curve for 864 nm (Fig. 4j). Pixel absorption at 864 nm with different thresholds used to label pixels as containing HSR are shown. Values in the heatmap (panel b) greater than the threshold were labeled as containing HSR (red) and values equal to or less than the threshold were labeled as not containing HSR (gray). Images represent four of 470,044 threshold values evaluated to compute the ROC curve (Methods). d. Data used to calculate the classified ROC curve (Fig. 4j). Classified HSI images are shown at different threshold values taken along the ROC curve. Values in the classified image (Fig. 4h) greater than the threshold were labeled as containing HSR (red) and values equal to or less than the threshold were labeled as not containing HSR (gray). Images represent four of 1,015,973 threshold values evaluated to compute the ROC curve (Methods).

Supplementary information

Supplementary Information

Supplementary Figs. 1–14, Tables 1–3 and References.

Reporting Summary

Supplementary Data 1

Predicted spectra for natural metabolites.

Source data

Source Data Figs. 1–4

Source data for Figs. 1e,f, 2c,d, 3c–g and 4c,d,g–i.

Source Data Extended Data Figs. 1 and 3–7

Source data for Extended Data Figs. 1, 3a,c,d,f–h, 4b, 5b,d–f, 6d and 7b,d.

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Chemla, Y., Levin, I., Fan, Y. et al. Hyperspectral reporters for long-distance and wide-area detection of gene expression in living bacteria. Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02622-y

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