Microbial Interaction Network Inference in Microfluidic Droplets.

Lead Contact and Materials Availability

Further information and requests for reagents should be directed to and will be fulfilled by the lead contact, Ophelia S. Venturelli ( venturelli@wisc.edu ).

Materials Availability Statement

Plasmids and strains generated in this study ( Table S9 ) are available upon request.

Experimental Model and Subject Details

General strain maintenance: Bacillus subtilis, Escherichia coli, Methylobacterium extorquens AM1, and Salmonella typhimurium LT2 All strains specified in Table S9 were maintained in 25% glycerol stocks prepared from cultures inoculated from single colonies and then stored at −80°C. Strains were recovered from glycerol stocks by inoculating liquid LB broth (Lennox, Sigma) or dilution streaking onto LB agar plates with appropriate antibiotics and culturing at 37°C, with the exception of Methylobacterium extorquens , which was streaked onto plates made with SOB Broth (Research Products International) and 15 g/L Bacteriological Agar (Bioworld) and cultured at 30°C. Precultures were inoculated either directly from glycerol stocks or from single colonies on dilution streaked plates.

Bacillus subtilis transformation

B. subtilis was inoculated into 1 mL MC medium ( Loyo and Burton, 2018 ) and incubated for 4 hours at 37°C for transformation. Plasmid DNA was f irst linearized by treatment with ScaI restriction enzyme (New England Biolabs). Next, 200 ng of plasmid DNA was added to 200 ml cell culture and incubated for 2 hours at 37°C. Tr ansformed cells were selected by plating on LB plates containing 100 mg ml −1 spectinomycin (Gold Biotechnology). Plasmid pVP038 ( Table S10 ) was transformed into B. subtilis 168, trpC2, cat to make B. subtilis 168, trpC2, cat, amyE::Pveg-gfp-spec ( Table S9 ).

Escherichia coli cloning and transformation

PCR amplifications were performed using Phusion High-Fidelity DNA polymerase (New England Biolabs) and oligonucleotides for cloning were obtained from Integrated DNA Technologies. Standard cloning methods were used to construct plasmids. Plasmids were derived from a previously built construct library ( Lee et al., 2011 ).

Plasmids in Table

S10 were transformed into strains as specified in Table S9 . Method Details Bacterial cell culturing For experiments E1–7 ( Table S1 ), strains were grown for approximately 12 hours at 37°C in LB medium, diluted 1:50 into fresh LB medium, and then grown to an OD600 of 0.3–1 as measured on a 1 cm spectrophotometer (NanoDrop Thermo Fisher Scientific). Next, the culture (3 mL) was centrifuged for 2 min at 3,500 × g and supernatant was removed. The cells were washed 4X by resuspending the pellet in 0.5 mL of minimal media and centrifuged as described above. In experiment E1 ( Table S1 ), cells were cultured in M9 supplemented with glucose (1X M9 salts, 2 mM MgSO4, 100 μM CaCl2, 0.4% glucose) and 25 g/mL chloramphenicol (Sigma). The cell cultures containing different strains were normalized to an OD600 of 0.15 and mixed in a 1:1 ratio. In experiment E2 ( Table S1 ), B. subtilis and E. coli were mixed in a 2:1 volumetric ratio to account for differences in the cell number to OD ratios. Cells were cultured in LB media containing 50 ng/mL anhydrotetracycline (aTc, Cayman Chemicals), 0.1% arabinose (Sigma) and 25 g/mL chloramphenicol. In experiments E3–E7, cells were cultured in M9 media (1X M9 salts, 2 mM MgSO4, 100 μM CaCl2) supplemented with 0.4% glucose, 0.2% lactose and/or 200 μM methionine as indicated. In experiments E8–21 ( Table S1 ), a single colony of ME, ST Lac* and EC Met- (CFP) were each inoculated into modified Hypho media ( Table S5 ) for 48 hr at 30°C with shaking. The OD600 was measured for each culture. Cultures were centrifuged for 2 minutes at 3,500 × g and resuspended in fresh modified Hypho medium to OD600 values of 0.20, 0.033, and 0.14 for ME, ST Lac* and EC Met-, respectively. These solutions were mixed in equal volume to form the community mixture. Immediately before cell encapsulation, antibiotic(s) (6 mg/mL carbenicillin disodium salt (Sigma), 10 mg/mL erythromycin (Sigma), and 2.5 mg/mL streptomycin sulfate salt (IBI Scientific)) were added to the community culture as specified and the culture was mixed by vortexing. All droplets for experiments E8–21 ( Table S1 ) were encapsulated on the same day from the same community mixture to reduce variability across experiments.

Dynamic range of cell counting

The bacterial strains EC Met- (CFP), EC WT (RFP), and ST Lac* (YFP) were grown in LB medium to early stationary phase, centrifuged at 18,000×g for 1 min, decanted, and resuspended in M9 minimal medium without glucose. Next, the cells were centrifuged at 18,000 × g for 1 min, decanted and resuspended in a smaller volume of M9 minimal medium without glucose to concentrate the cells. The OD600 values of the concentrated EC Met-, EC WT and ST Lac* cultures were 14.4, 19.6, and 6.4, respectively. Equal volumes of each culture were combined to generate the mixed culture. The mixed culture was serially diluted by a factor of 2 until a dilution of 2 −7 was reached. The diluted cultures were encapsulated separately using the droplet maker device and the resulting droplets were imaged and quantified using the computational image analysis pipeline. Fabrication of microfluidic devices Photoresist masters of 25 μm layer height were fabricated by spinning a layer of photoresist SU-8 3025 (Microchem) onto a silicon wafer (University Wafer), then baked at 95°C for 10 minutes. Following baking, photoresist master was patterned by UV photolithography over a photomask ( Data S1 , CADArt). The master was subjected to post-exposure bake at 95°C for 4 min and developed in fresh SU-8 developer (Microchem) for 6 min, prior to rinsing with isopropyl alcohol (Fischer Scientific) and baking at 150°C to remove the solvent. The microfluidic devices were fabricated by pouring poly(dimethylsiloxane) at a 11:1 polymer-to-crosslinker ratio (Dow Corning Sylgard 184) onto the master and curing at 65°C for 1 hr. The PDMS devices were excised with a scalpel and cored with a 0.75 mm biopsy core (World Precision Instruments) to create inlets and outlets. The device was then bonded to a microscope glass slide using an O 2 plasma cleaner (Harrick Plasma), and channels were treated with Aquapel (PPG Industries) to render them hydrophobic. Finally, the devices were baked at 65°C for 20 min to evaporate excess Aquapel prior to use.

Encapsulation of cells into droplets and fluorescence microscopy

To encapsulate cells into droplets, 1 mL syringes (BD Luer Lok) were fitted with 27-gauge needles and PE/2 tubing. 500 μL of the culture was loaded into a 1 mL syringe. Fluorinated oil (3M Novec 7500) was prepared with 2% ionic Krytox 157 FSH surfactant (experiments E1–E6) ( Dejournette et al., 2013 ) or 2% of a block copolymer of Jeffamine ED-900 and Krytox 157 FSH (experiments E7-E21) ( Holtze et al., 2008 ) loaded into a 1 mL syringe. The free end of the tubing was primed and inserted into the droplet-making device. Droplets were generated using flow rates of 600 μL hr −1 oil and 300 μL hr −1 cell culture at a 30 μm × 25 μm junction, which generated ~40 μm diameter droplets at 4.8 kHz. After allowing at least 20 minutes for equilibration, droplets were collected into a 1.7 mL microfuge tube for at least 15 min and incubated as specified in each experiment. Droplets were loaded into chamber microscopy slides (Invitrogen C10228 ) and imaged with a 20X objective (Nikon, MRH10201) on a Ti-E Eclipse inverted microscope (Nikon). Fluorescence was imaged using the following filters (Chroma): (1) CFP: 436nm/20nm (ex), 480nm/40nm (em); (2) GFP: 470nm/40nm (ex), 525/50nm (em); (3) RFP: 560nm/40nm (ex), 630/70nm (em); and (4) YFP: 500nm/40nm (ex), 535nm/30nm (em).

Fluorescence microscopy image analysis

Custom code in Python was used for automated cell counting in droplets and microbial interaction network inference. Droplets were identified from the phase-contrast images using the Hough transformation algorithm (OpenCV 3, Pulli et al., 2012 ). Droplets with a diameter 10% larger or smaller than 40 μm were removed from the dataset. Fluorescent cells were segmented by identifying connected regions using the SimpleBlobDetector object (OpenCV 3, Pulli et al., 2012 ). Droplets were binned by the presence or absence of each fluorescently labeled strain. For experiments E1–7 ( Table S1 ), interaction strength from strain j to strain i , where droplet d contains d k cells of strain k , was defined according to Equation 1 . (1) l o g 2 ( m e a n ( d i ∀ d | d i 〉 0 , d j > 0 ) m e a n ( d i ∀ d | d i 〉 0 , d j = 0 ) ) For experiments E8–21, interaction strengths were calculated as described in the text and summarized in Tables S6 – 8 . The impact of each species on each other species (species-species interaction) was inferred by comparing to the number of cells in single-species, no antibiotic droplets to two-species, no antibiotic droplets (6 possible interactions). The impact of each antibiotic on each species (antibiotic-species interaction) was inferred by comparing the number of cells in single-species, no antibiotic droplets to single-species, single-antibiotic droplets (9 possible interactions). The impact of each species on each antibiotic’s impact on each other species (species-antibiotic-species interaction) was inferred by comparing the number of cells in single-species, single-antibiotic droplets to two-species, single-antibiotic droplets (18 possible interactions). Finally, antibiotic-antibiotic-species interactions were inferred by comparing the number of cells in single-species, single antibiotic droplets to single-species, two-antibiotic droplets (9 possible interactions). Network schematics were drawn with Cytoscape 3.5 ( Shannon et al., 2003 ). Discrete-time Markov model of cell growth A discrete-time Markov model was developed to recapitulate the experimentally measured cell count distributions. At each time step, the propagation of each strain is determined by computing the probability of cell division (P div,i ), cell death or cell growth dormancy for the duration of the experiment (P death,i ), and remaining unchanged (P static,i ) ( Equations 2 – 4 ). (2) P d i v , i = r d i v , i o × I i i ( n i , s i i , k i i , a i i ) × I i j ( n j , s i j , k i j , a i j ) (3) P d e a t h , i = r d e a t h , i o (4) P s t a t i c , i = 1 − ( P d i v , i + P d e a t h , i ) The parameter r div,io is the basal probability of cell division for strain i . The parameter r death,io represents the probability of cell death of strain i (constant). n i denotes the number of cells of strain I and s ij defines whether the outgoing interaction of strain j (donor) to strain i is positive (s ij = 1) or negative ( s ij = −1). The parameters k ij and a ij define the sigmoidal interaction function I ij , representing the incoming interaction for strain i produced by strain j ( Equation 5 ) (5) I i j = { ( 1 + a i j ) e k i j n j 1 + a i j e k i j n j , i f s i j = + 1 ( 1 + a i j ) 1 + a i j e k i j n j , i f s i j = − 1 The negative interaction function approaches zero as a function of n j whereas the positive interaction approaches (1 + a ij )/ a ij as a function of n j . The values of a ij and r div,i are constrained such that P div,i ≤ 1 ( Equation 6 ). The self-interaction function I ii ( n i , s ii , k ii , a ii ) is less than one (s ii = −1) and approaches zero as a function of n i leading to saturation of the number of cells of strain i . The interaction function I ij , is equal to 1 when n j = 0, representing the absence of an interaction between strain i and j . In the absence of an interaction between strain i and j, P div,i is not dependent on strain j , ( s ij = −1, k ij = 0, a ij = 0). The outgoing interaction from the partner strain j , I ij ( n j , s ij , k ij , a ij ), can be positive or negative depending on the value of the parameter s ij . The parameters a ij and k ij determine the interaction sensitivity defined as the number of partner cells at the half-maximum of the interaction function, n ^ j , ( Equation 6 ), and the rate of change of the interaction as a function of the number of partner cells ( Equation 7 ). (6) n ^ j = 1 k i j ln ( 1 a i j + 2 ) (7) d I i j d n j | n ^ j = { k i j ( 1 a i j + 2 ) 4 ( a i j + 1 ) , i f s i j = 1 − k i j ( 1 a i j + 2 ) a i j 4 ( a i j + 1 ) , i f s i j = − 1 At each time step, the state transition of a cell is independent of all other cells and the cell’s prior history. The state transitions were simulated by sampling from a trinomial distribution determined by the probabilities P div,I , P death,I , and P static,i . Communities were simulated for 100 time-steps wherein each time-step corresponded to 10.8 minutes of experimental time. Variables were constrained such that the cell populations reached a steady state within the simulation time. The initial conditions for the simulations were sampled from a Poisson distribution with λ=1.5. Communities that did not contain both strains were discarded and resampled. Model parameters are listed in Table S4 .

Quantification and Statistical Analysis

All statistical analysis was performed using NumPy version 1.13.1 ( van Der Walt, et al., 2011 , Python 2 or 3 distributed through Anaconda). Statistical significance (p-value) between cell counts within droplets was computed using the two-sided Mann-Whitney U test. Error bars represent the 95% confidence interval of the mean.

Data and Code Availability

The droplet image analysis code and stochastic model are accessible on a GitHub repository at: https://github.com/ryanusahk/MINI-Drop-Supplementary-Code . The raw image files are available through Mendeley Data (doi10.17632/g5ch5r7d6m.1).

Lead Contact and Materials Availability

Further information and requests for reagents should be directed to and will be fulfilled by the lead contact, Ophelia S. Venturelli ( venturelli@wisc.edu ).

Materials Availability Statement

Plasmids and strains generated in this study ( Table S9 ) are available upon request.

Materials Availability Statement

Plasmids and strains generated in this study ( Table S9 ) are available upon request.

Experimental Model and Subject Details

General strain maintenance: Bacillus subtilis, Escherichia coli, Methylobacterium extorquens AM1, and Salmonella typhimurium LT2 All strains specified in Table S9 were maintained in 25% glycerol stocks prepared from cultures inoculated from single colonies and then stored at −80°C. Strains were recovered from glycerol stocks by inoculating liquid LB broth (Lennox, Sigma) or dilution streaking onto LB agar plates with appropriate antibiotics and culturing at 37°C, with the exception of Methylobacterium extorquens , which was streaked onto plates made with SOB Broth (Research Products International) and 15 g/L Bacteriological Agar (Bioworld) and cultured at 30°C. Precultures were inoculated either directly from glycerol stocks or from single colonies on dilution streaked plates.

Bacillus subtilis transformation

B. subtilis was inoculated into 1 mL MC medium ( Loyo and Burton, 2018 ) and incubated for 4 hours at 37°C for transformation. Plasmid DNA was f irst linearized by treatment with ScaI restriction enzyme (New England Biolabs). Next, 200 ng of plasmid DNA was added to 200 ml cell culture and incubated for 2 hours at 37°C. Tr ansformed cells were selected by plating on LB plates containing 100 mg ml −1 spectinomycin (Gold Biotechnology). Plasmid pVP038 ( Table S10 ) was transformed into B. subtilis 168, trpC2, cat to make B. subtilis 168, trpC2, cat, amyE::Pveg-gfp-spec ( Table S9 ).

Escherichia coli cloning and transformation

PCR amplifications were performed using Phusion High-Fidelity DNA polymerase (New England Biolabs) and oligonucleotides for cloning were obtained from Integrated DNA Technologies. Standard cloning methods were used to construct plasmids. Plasmids were derived from a previously built construct library ( Lee et al., 2011 ).

Plasmids in Table

S10 were transformed into strains as specified in Table S9 .

Method Details Bacterial cell culturing For experiments E1–7 ( Table S1 ), strains were grown for approximately 12 hours at 37°C in LB medium, diluted 1:50 into fresh LB medium, and then grown to an OD600 of 0.3–1 as measured on a 1 cm spectrophotometer (NanoDrop Thermo Fisher Scientific). Next, the culture (3 mL) was centrifuged for 2 min at 3,500 × g and supernatant was removed. The cells were washed 4X by resuspending the pellet in 0.5 mL of minimal media and centrifuged as described above. In experiment E1 ( Table S1 ), cells were cultured in M9 supplemented with glucose (1X M9 salts, 2 mM MgSO4, 100 μM CaCl2, 0.4% glucose) and 25 g/mL chloramphenicol (Sigma). The cell cultures containing different strains were normalized to an OD600 of 0.15 and mixed in a 1:1 ratio. In experiment E2 ( Table S1 ), B. subtilis and E. coli were mixed in a 2:1 volumetric ratio to account for differences in the cell number to OD ratios. Cells were cultured in LB media containing 50 ng/mL anhydrotetracycline (aTc, Cayman Chemicals), 0.1% arabinose (Sigma) and 25 g/mL chloramphenicol. In experiments E3–E7, cells were cultured in M9 media (1X M9 salts, 2 mM MgSO4, 100 μM CaCl2) supplemented with 0.4% glucose, 0.2% lactose and/or 200 μM methionine as indicated. In experiments E8–21 ( Table S1 ), a single colony of ME, ST Lac* and EC Met- (CFP) were each inoculated into modified Hypho media ( Table S5 ) for 48 hr at 30°C with shaking. The OD600 was measured for each culture. Cultures were centrifuged for 2 minutes at 3,500 × g and resuspended in fresh modified Hypho medium to OD600 values of 0.20, 0.033, and 0.14 for ME, ST Lac* and EC Met-, respectively. These solutions were mixed in equal volume to form the community mixture. Immediately before cell encapsulation, antibiotic(s) (6 mg/mL carbenicillin disodium salt (Sigma), 10 mg/mL erythromycin (Sigma), and 2.5 mg/mL streptomycin sulfate salt (IBI Scientific)) were added to the community culture as specified and the culture was mixed by vortexing. All droplets for experiments E8–21 ( Table S1 ) were encapsulated on the same day from the same community mixture to reduce variability across experiments.

Dynamic range of cell counting

The bacterial strains EC Met- (CFP), EC WT (RFP), and ST Lac* (YFP) were grown in LB medium to early stationary phase, centrifuged at 18,000×g for 1 min, decanted, and resuspended in M9 minimal medium without glucose. Next, the cells were centrifuged at 18,000 × g for 1 min, decanted and resuspended in a smaller volume of M9 minimal medium without glucose to concentrate the cells. The OD600 values of the concentrated EC Met-, EC WT and ST Lac* cultures were 14.4, 19.6, and 6.4, respectively. Equal volumes of each culture were combined to generate the mixed culture. The mixed culture was serially diluted by a factor of 2 until a dilution of 2 −7 was reached. The diluted cultures were encapsulated separately using the droplet maker device and the resulting droplets were imaged and quantified using the computational image analysis pipeline. Fabrication of microfluidic devices Photoresist masters of 25 μm layer height were fabricated by spinning a layer of photoresist SU-8 3025 (Microchem) onto a silicon wafer (University Wafer), then baked at 95°C for 10 minutes. Following baking, photoresist master was patterned by UV photolithography over a photomask ( Data S1 , CADArt). The master was subjected to post-exposure bake at 95°C for 4 min and developed in fresh SU-8 developer (Microchem) for 6 min, prior to rinsing with isopropyl alcohol (Fischer Scientific) and baking at 150°C to remove the solvent. The microfluidic devices were fabricated by pouring poly(dimethylsiloxane) at a 11:1 polymer-to-crosslinker ratio (Dow Corning Sylgard 184) onto the master and curing at 65°C for 1 hr. The PDMS devices were excised with a scalpel and cored with a 0.75 mm biopsy core (World Precision Instruments) to create inlets and outlets. The device was then bonded to a microscope glass slide using an O 2 plasma cleaner (Harrick Plasma), and channels were treated with Aquapel (PPG Industries) to render them hydrophobic. Finally, the devices were baked at 65°C for 20 min to evaporate excess Aquapel prior to use.

Encapsulation of cells into droplets and fluorescence microscopy

To encapsulate cells into droplets, 1 mL syringes (BD Luer Lok) were fitted with 27-gauge needles and PE/2 tubing. 500 μL of the culture was loaded into a 1 mL syringe. Fluorinated oil (3M Novec 7500) was prepared with 2% ionic Krytox 157 FSH surfactant (experiments E1–E6) ( Dejournette et al., 2013 ) or 2% of a block copolymer of Jeffamine ED-900 and Krytox 157 FSH (experiments E7-E21) ( Holtze et al., 2008 ) loaded into a 1 mL syringe. The free end of the tubing was primed and inserted into the droplet-making device. Droplets were generated using flow rates of 600 μL hr −1 oil and 300 μL hr −1 cell culture at a 30 μm × 25 μm junction, which generated ~40 μm diameter droplets at 4.8 kHz. After allowing at least 20 minutes for equilibration, droplets were collected into a 1.7 mL microfuge tube for at least 15 min and incubated as specified in each experiment. Droplets were loaded into chamber microscopy slides (Invitrogen C10228 ) and imaged with a 20X objective (Nikon, MRH10201) on a Ti-E Eclipse inverted microscope (Nikon). Fluorescence was imaged using the following filters (Chroma): (1) CFP: 436nm/20nm (ex), 480nm/40nm (em); (2) GFP: 470nm/40nm (ex), 525/50nm (em); (3) RFP: 560nm/40nm (ex), 630/70nm (em); and (4) YFP: 500nm/40nm (ex), 535nm/30nm (em).

Fluorescence microscopy image analysis

Custom code in Python was used for automated cell counting in droplets and microbial interaction network inference. Droplets were identified from the phase-contrast images using the Hough transformation algorithm (OpenCV 3, Pulli et al., 2012 ). Droplets with a diameter 10% larger or smaller than 40 μm were removed from the dataset. Fluorescent cells were segmented by identifying connected regions using the SimpleBlobDetector object (OpenCV 3, Pulli et al., 2012 ). Droplets were binned by the presence or absence of each fluorescently labeled strain. For experiments E1–7 ( Table S1 ), interaction strength from strain j to strain i , where droplet d contains d k cells of strain k , was defined according to Equation 1 . (1) l o g 2 ( m e a n ( d i ∀ d | d i 〉 0 , d j > 0 ) m e a n ( d i ∀ d | d i 〉 0 , d j = 0 ) ) For experiments E8–21, interaction strengths were calculated as described in the text and summarized in Tables S6 – 8 . The impact of each species on each other species (species-species interaction) was inferred by comparing to the number of cells in single-species, no antibiotic droplets to two-species, no antibiotic droplets (6 possible interactions). The impact of each antibiotic on each species (antibiotic-species interaction) was inferred by comparing the number of cells in single-species, no antibiotic droplets to single-species, single-antibiotic droplets (9 possible interactions). The impact of each species on each antibiotic’s impact on each other species (species-antibiotic-species interaction) was inferred by comparing the number of cells in single-species, single-antibiotic droplets to two-species, single-antibiotic droplets (18 possible interactions). Finally, antibiotic-antibiotic-species interactions were inferred by comparing the number of cells in single-species, single antibiotic droplets to single-species, two-antibiotic droplets (9 possible interactions). Network schematics were drawn with Cytoscape 3.5 ( Shannon et al., 2003 ). Discrete-time Markov model of cell growth A discrete-time Markov model was developed to recapitulate the experimentally measured cell count distributions. At each time step, the propagation of each strain is determined by computing the probability of cell division (P div,i ), cell death or cell growth dormancy for the duration of the experiment (P death,i ), and remaining unchanged (P static,i ) ( Equations 2 – 4 ). (2) P d i v , i = r d i v , i o × I i i ( n i , s i i , k i i , a i i ) × I i j ( n j , s i j , k i j , a i j ) (3) P d e a t h , i = r d e a t h , i o (4) P s t a t i c , i = 1 − ( P d i v , i + P d e a t h , i ) The parameter r div,io is the basal probability of cell division for strain i . The parameter r death,io represents the probability of cell death of strain i (constant). n i denotes the number of cells of strain I and s ij defines whether the outgoing interaction of strain j (donor) to strain i is positive (s ij = 1) or negative ( s ij = −1). The parameters k ij and a ij define the sigmoidal interaction function I ij , representing the incoming interaction for strain i produced by strain j ( Equation 5 ) (5) I i j = { ( 1 + a i j ) e k i j n j 1 + a i j e k i j n j , i f s i j = + 1 ( 1 + a i j ) 1 + a i j e k i j n j , i f s i j = − 1 The negative interaction function approaches zero as a function of n j whereas the positive interaction approaches (1 + a ij )/ a ij as a function of n j . The values of a ij and r div,i are constrained such that P div,i ≤ 1 ( Equation 6 ). The self-interaction function I ii ( n i , s ii , k ii , a ii ) is less than one (s ii = −1) and approaches zero as a function of n i leading to saturation of the number of cells of strain i . The interaction function I ij , is equal to 1 when n j = 0, representing the absence of an interaction between strain i and j . In the absence of an interaction between strain i and j, P div,i is not dependent on strain j , ( s ij = −1, k ij = 0, a ij = 0). The outgoing interaction from the partner strain j , I ij ( n j , s ij , k ij , a ij ), can be positive or negative depending on the value of the parameter s ij . The parameters a ij and k ij determine the interaction sensitivity defined as the number of partner cells at the half-maximum of the interaction function, n ^ j , ( Equation 6 ), and the rate of change of the interaction as a function of the number of partner cells ( Equation 7 ). (6) n ^ j = 1 k i j ln ( 1 a i j + 2 ) (7) d I i j d n j | n ^ j = { k i j ( 1 a i j + 2 ) 4 ( a i j + 1 ) , i f s i j = 1 − k i j ( 1 a i j + 2 ) a i j 4 ( a i j + 1 ) , i f s i j = − 1 At each time step, the state transition of a cell is independent of all other cells and the cell’s prior history. The state transitions were simulated by sampling from a trinomial distribution determined by the probabilities P div,I , P death,I , and P static,i . Communities were simulated for 100 time-steps wherein each time-step corresponded to 10.8 minutes of experimental time. Variables were constrained such that the cell populations reached a steady state within the simulation time. The initial conditions for the simulations were sampled from a Poisson distribution with λ=1.5. Communities that did not contain both strains were discarded and resampled. Model parameters are listed in Table S4 .

Supplementary Material 1 Data S1. Schematic of the Microfluidic Droplet Generation Device, Related to STAR Methods and Figure 1 Table S1.

Strains and Media

Conditions for Each Experiment, Related to Figures 2 – 4 and 6 Table S2. Interaction Magnitudes and Statistics for Experiments 1–7, Related to Figures 2 and 3 Table S3. Higher-order interaction calculations for Experiments 1–7, Related to Figure 4 Table S4. Stochastic model parameters, Related to STAR Methods and Figure 5 Table S4. Stochastic model parameters, Related to STAR Methods and Figure 5 Table S5. Modified Hypho Media Composition, Related to STAR Methods Table S6. Types of Interactions in the Antibiotic Interaction Networks, Related to Figure 6 Table S7. Interactions from Antibiotics Experiments 8–14 (30°C), Related to Figure 6 Table S8. Interactions from Antibiotics Experiments 15–21 (37°C), Related to Figure 6 Table S9.

Strains

Used in this Work, Related to STAR Methods Table S10.

Plasmids Used to Generate

Strains in Table S9 , Related to STAR Methods Table S11. Interaction Magnitudes and Statistics for Experiments 8–21, Related to Figures 6 and S10 2 3