Systems and methods for measuring cell viability at high throughput via continuous geometry

Abstract

The present invention includes systems, methods, and compositions for a geometric viability assay (GVA) adapted to measure individual colony forming units (CPUs) from diluted samples using one or more variable geometry vessels.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This international PCT application claims the benefit of and priority to U.S. Provisional Application No. 63 / 334,375, filed April 25, 2022, the specification, claims and drawings of which are incorporated herein by reference in their entirety.

[0002] Statement of Government Interests This invention was made with Government support under Grant Nos. 1DP2GM123458 and 5T32AG000279-14 from the National Institutes of Health. The Government has certain rights in this invention.

[0003] The present invention relates to a high throughput system for the objective and standardized determination of colony forming units using a cell detection system. In particular, the present invention relates to a novel geometric viability assay (GVA) adapted to measure individual colony forming units from a microbial sample, preferably a treated microbial sample, using one or more variable geometry vessels. [Background technology]

[0004] Accelerating antimicrobial resistance (AMR) is causing a global health crisis. AMR was associated with an estimated 4.95 million deaths in 2019 and is expected to cause over 10 million deaths by 2050. New technologies and innovations are urgently needed to scale the discovery pipeline for novel antibiotics and more rapidly identify antibiotic susceptibility in clinical samples. Standard high-throughput screens used in discovery pipelines use growth inhibition, commonly measured using light absorbance, to quantify compound potential. However, these assays inevitably fail to detect slow-growing drug-resistant persister cells, which are considered the primary cause of refractory clinical infections. Furthermore, growth inhibition cannot distinguish between compounds that are bacteriostatic (i.e., stop growth) and bactericidal (i.e., induce cell death) in conditions that induce persister physiology (i.e., conditions of slow growth). Thus, there is an urgent need for assays to identify compounds that are bacteriostatic, not just bacteriocidal, in conditions that induce persister physiology (i.e., conditions of slow growth). This need is based on a rapid and scalable approach to measuring pathogen viability after drug treatment.

[0005] The gold standard in the field for measuring cell viability is the colony-forming unit (CFU) assay. The CFU assay is a core microbiology assay taught in undergraduate laboratories and used worldwide, which quantifies the number of cells that can form colonies using a dilution series. CFU assays have been used to quantify cell viability in systems such as bacteria, fungi, hematopoietic progenitor cells, and cancer cells. The dynamic range of the CFU assay is typically 8 orders of magnitude. This means that in a population of 100 million cells, the CFU assay can identify as few as one resistant cell that can survive treatment and re-seeding, or up to 100 million. However, CFU assays are time-consuming and generate a large amount of plastic waste. For the drop CFU assay, typically 15 pipette tips per condition are required to perform and transfer 8-fold dilutions to the agar pad. Time and cost make it difficult to implement the CFU assay in high-throughput screens (HTS). Previous approaches to address this fundamental problem have used robotics to improve speed, viability stains or droplet technology to reduce pipetting volumes, or cell proliferation to estimate initial cell numbers, similar to qPCR. However, none of these approaches combine the simplicity of the CFU assay with its wide dynamic range. Therefore, there is a long-standing need for a simple, effective, and cost-effective solution to measure cell viability using the CFU assay. More specifically, solutions to the technical limitations outlined above include assays that: 1) can measure viability over several orders of magnitude, 2) are independent of cell proliferation rate, 3) require one pipette tip per condition, and 4) are simple enough to scale to HTS without specialized equipment. Summary of the Invention

[0006] In one aspect, the present invention describes an improved CFU assay, namely the Geometric Viability Assay (GVA), which leverages continuous geometry to perform a dilution series with high dynamic range in a single instance. As described herein, the GVA systems, methods, and compositions of the present invention significantly simplify and reduce the cost of measuring cell viability over traditional colony forming unit (CFU) assays while maintaining comparable dynamic range and precision. The GVA of the present invention allows for CFU assays with higher throughput and lower cost compared to traditional assays.

[0007] In one preferred embodiment, the GVA of the present invention may involve the generation of a cell sample that is mixed with soft agarose and cast into one or more 3D vessels with geometrically variable properties as described below. The probability of a colony forming at any position along the geometrically variable vessel is determined by the viable cell density and the three-dimensional shape of the vessel. By calculating this probability and measuring the positions of a subset of colonies within the vessel, the number of viable cells can be calculated with high accuracy. Thus, the GVA of the present invention simplifies cell viability measurements, reduces costs by fifteen-fold (15X), and can measure viability over a scale of more than six orders of magnitude, enabling more efficient drug discovery against clinically relevant drug-resistant cells.

[0008] In another aspect, the present technology includes systems, methods, and devices for the novel GVA defined herein. In a preferred embodiment, the present system includes a growth medium for diluting a cell sample, preferably a prokaryotic or eukaryotic cell sample. The present system may further include one or more variable-shape containers, preferably axially symmetric, adapted to hold the diluted sample in the growth medium, and an imaging device adapted to capture one or more images of a subset of viable microbial CFUs present in the variable-shape container after an incubation period. In another preferred embodiment, the cell sample can be treated, for example, with an antibiotic or other experimental perturbation, before or inside the variable-shape container of the present invention.

[0009] In another aspect, the technology of the present invention includes systems, methods, and devices for screening novel compounds using the GVA of the present invention. In this preferred embodiment, a dilution series sample of target cells, such as prokaryotic or eukaryotic cells, can be treated with compounds from one or more drug or compound libraries and introduced into a variable-shape vessel, preferably axially symmetric, adapted to hold the treated samples. The imaging device can be adapted to capture one or more images of a subset of viable CFUs present in the variable-shape vessel after an incubation period that shows the effect of the screened compounds. In this manner, the GVA of the present invention can rapidly screen antibiotic susceptibility or resistance profiles from one or more target bacteria and rapidly determine their minimum inhibitory concentrations (MICs).

[0010] In another aspect, the present technology includes systems, methods, and devices for determining the microbial content or load of a target object, such as a surface or biome, using the GVA of the present invention. In this preferred aspect, a sample or series of samples can be taken from a target object, such as a surface in a manufacturing facility, an environmental sample, a food or beverage product, or a biological sample from a subject, diluted and introduced into the variable-geometry vessel. The imaging device can be adapted to capture one or more images of a subset of viable CFUs present in the variable-geometry vessel after an incubation period that is indicative of the microbial load or biome characteristics present on the subject. In one aspect, the GVA can be performed before and after treatments such as introduction of antibiotics or introduction of one or more bactericidal compounds, as well as cleaning or treatment of the target object, such as conventional or with antibacterial cleaners or ultraviolet light. In this way, the GVA can be used to verify cleaning and sterilization protocols in commercial facilities, as well as in treatment facilities such as hospitals and biological laboratories, and even environmental locations and samples.

[0011] In another aspect, the technology of the present invention includes systems, methods, and devices for diagnostic analysis of biological or other samples using the GVA of the present invention. In this preferred aspect, one or a series of biological sample subjects or target objects containing diagnostic markers, such as diagnostic agar or other compounds, capable of identifying one or more pathologically relevant microbial species, such as human or animal pathogens, can be diluted and introduced into the variable-geometry vessel. The imaging device can be adapted to capture one or more images of a subset of viable CFUs present in the variable-geometry vessel after an incubation period that indicates the presence of the pathogen, and the GVA can be used to clinically aid in the identification and diagnosis of diseases or conditions, such as infections caused by microorganisms or other microorganisms.

[0012] In another aspect, the present technology includes systems, methods, and devices for imaging the GVAs of the present invention. The system can include an imaging device configured to be positioned adjacent to a light source. In one preferred aspect, the imaging device comprises a smartphone secured to a base and mechanically responsive to an adapter. An axisymmetric variable geometry vessel can be used to culture a cell sample in a growth medium and secured to a mount that can mate with an adapter of the present invention. In this configuration, the mount of the present invention is adjustable to position the vessel relative to the imaging device such that the imaging device can capture one or more images of colony forming units (CFUs) at an end portion of the culture vessel. In an alternative aspect, the adapter can include a macro lens secured by a clip to facilitate imaging of the vessel secured to the mount.

[0013] In another aspect, the present technology includes systems, methods, and devices for imaging the GVAs of the present invention. The system can include an imaging device, such as a camera, that can be adjustably positioned adjacent to a frame. In this embodiment, the frame of the present invention can mount one or more axisymmetric variable-geometry vessels adapted for culturing cell samples in a growth medium. The frame of the present invention and / or the imaging device can be adjusted to position the vessels relative to the imaging device such that the imaging device can capture one or more images of colony forming units (CFUs) at an end portion of the culture vessel.

[0014] In another aspect, the present technology includes a system, method, and device for an imaging platform configured to capture one or more images of the vessel of the present invention. In this embodiment, a processor can be responsive to an imaging device adapted to capture images of colony forming units (CFUs), preferably at the end portion of the axisymmetric deformable vessel of the present invention. A computer executable program responsive to the processor can be adapted to identify one or more images of the CFUs at the end portion of the deformable vessel. In this aspect, the computer executable program can identify the boundary of the tip of the deformable vessel and further align the tip of the vessel. The computer executable program can further perform a segmentation of colonies on the image and further calculate the CFUs embedded in the growth medium from the segmentation of the colonies.

[0015] Further aspects of the present invention will become apparent from the specification, drawings, and claims provided herein. [Brief description of the drawings]

[0016] [Figure 1]Geometric viability assay (GVA). a) The probability of a colony forming at a distance x from the tip of the cone is proportional to the microvolume dV (cyan circle) divided by the total volume V (purple cone). Analytically, this ratio is the probability density function (PDF) as a function of x (see Supplementary Material for derivation). b) PDFs for cylinders (red), wedges (yellow), and cones (purple) as a function of axial distance (x). c) Simulations of colony distribution within the cones. d) Estimation of total CFU / mL based on the location of colonies within the cones. (Top) Distribution of colonies for four simulations ranging from 20 to 10,000 CFU / mL densities. The volume of each cone is the same as in panel c. (Bottom) GVA estimates of CFU / mL as a function of the colonies included and their x-position. e) The coefficients in the GVA calculation differ from the correct values ​​in equation (1) as a function of the number of colonies. Shaded error bars represent 1 standard deviation over 1000 simulations. Colors match simulations in panel d. f) Dilution series of E. coli embedded in 150 μL of 0.5% LB-agarose in a p200 pipette tip. Red circles correspond to colonies counted using custom semi-automated segmentation software. g) E. coli CFU / mL calculated using GVA of a 4-fold dilution series. Points are the average of 4 replicates. Mean values ​​are calculated after taking the log. The red line is a linear regression fitted to the dilution series. A slope of 1 for a log-log plot would be expected if GVA estimates were scaled linearly with dilution. h) Drop CFU and GVA estimates are significantly correlated over 6 orders of magnitude. i) GVA performed on Gram-positive, Gram-negative, and eukaryotic cells (see Fig. 10a for quantification) [Diagram 2]The GVA dynamic range depends on the optical configuration, but with different accuracy. a) Photograph of the pipette tip holder assembled on an iPhone 12 with a Xenvo macro lens. The image of the pipette is taken in front of a white background (paper) with ambient lighting. b) Example images of the same two pipette tips using a Canon EOS with a 100 mm f2.8 macro lens (left) or an iPhone 12 with a Xenvo macro lens (right). The CFU / mL calculated with GVA are reported below. The colonies selected for GVA calculation are circled. c) Dynamic range of iPhone GVA. E. coli was diluted 4-fold and embedded in the pipette tip. After incubation, the same tip was photographed with the iPhone camera with a macro lens (green) and with a mirrorless camera (purple). The points are the average of 4 replicates calculated after taking the log. The green and purple lines are the linear regressions fitted to the dilution series. d) Pearson correlation of all pipettes that managed to count colonies using both the iPhone GVA and the professional camera. Correlation coefficient calculated in logarithmic space. [Diagram 3]GVA reduces the time and materials required for viability measurements by more than 10-fold. a) (Left) Schematic of the drop CFU assay and materials required for 96 samples assuming tips are replaced after each dilution step. (Middle) The spiral plater spreads the samples in an Archimedes spiral onto a solid media plate. The spiral reduces the sample volume as a function of radial distance, with a reported dynamic range of 3 logs. One Petri dish is required per sample. (Right) GVA uses one pipette tip to perform a dilution series of 6 orders. b-d) Comparison of times for the various techniques. b) Time required for preparation of solid growth media. Preparation times for the spiral plater and drop CFU include: 1) autoclaving the agar; 2) cooling after autoclaving; 3) pouring onto plates; 4) cooling of plates. GVA dissolves the agarose in a microwave oven, then equilibrates in a hot bath for 1 hour before starting. c) Sample plating from 96-well plates. Sample plating times for the spiral plater assay based on industry reported values. Drop CFU were measured by a professional user using a 12-channel pipette, changing tips at each dilution and plating step. d) Time required for quantification of 96 samples. Spiral plater times are based on industry reported values ​​using an automated colony counter. GVA times include imaging (7 min for Canon with motorized stage, 30 min for iPhone), image pre-processing and tip segmentation (5 min), and semi-automated colony counting of 96 pipette tips (10 min). Drop CFU colonies were counted manually and recorded. e) Number of pipette tips and cost as a function of sample number for the three different techniques. See Supplementary Table 1 for cost estimates. f) Amount of agar required as a function of sample number. For the drop CFU and spiral plater assays, 25 mL of 1.5% agar was assumed per 15 cm Petri dish. For GVA, 200 μL of 0.5% agarose per tip was assumed. g) Number of 96 wells and Petri dishes per condition. h) Estimated total cost of consumables per 96 samples for the three methods. GVA cost is $0.17 per sample. i) Equipment cost.Based on quotes from three manufacturers for spiral platers (SPs) and automated imaging systems. GVA equipment costs include a Canon camera and a 100mm f / 2.8 macro lens. j) Equipment cost difference between Canon and iPhone optical configurations. [Figure 4] GVA has a low noise profile and is robust to missing colonies and tip positioning errors. a, b) Coefficient of variation (COV) between four technical replicates for different numbers of CFU concentrations for GVA using Canon or iPhone optical configurations (a) and drop CFU (b). c, d) Coefficients of GVA calculations differ from the correct values ​​as a function of the number of missing colonies (c) or tip positioning errors (d) in the simulated results (see Methods). The shaded error bars are the standard deviations over 1000 simulations. e, f) The same error calculations for the experimental data. The error bars represent the standard deviation among all pipette tips (#) included in each bin. g, h) Correlation between GVA and drop CFU assays as a function of counting and positioning errors. [Diagram 5]GVA screening of the Enzo library identified DPIs as active against stationary phase E. coli. a) Dose response of three antibiotics in stationary and exponential (ex) cultures after 24 h treatment. Each point is the mean of duplicate determinations. CFU / mL was normalized to untreated control. b) Viability of stationary and exponential cells over time at a given concentration of antibiotic. c) Drug classes of the Enzo Bioactive Screening Library. The size of the doughnut wedge is proportional to the representation of the drug class. The targets and relative representation of each class are displayed in the outer ring. d) Absolute viability of stationary (green) and exponentially growing cells (purple) after 24 h treatment with the Enzo library. Each condition was run in duplicate and the averages were taken in logarithmic space. e) Scatter plot of stationary and exponential phase from the screen. The standard deviation of the DMSO control is indicated by a red cross. Selected hits are annotated. f, g, h) Dose response of mitomycin C (a DNA cross-linker), phentolamine (an α-adrenergic antagonist), and DPI (an NADPH oxidase inhibitor) in stationary and exponential cultures. [Figure 6]DPI generates ROS, activates the SOS response, and antagonizes ciprofloxacin. a) Median single-cell CellROX signal as a function of time for DPI (blue), ciprofloxacin (orange), and untreated control (yellow). b) Efficacy of DPI in aerobic and anaerobic conditions. For ciprofloxacin and gentamicin, see Fig. 18b, c. c) Images of live E. coli cells stained with CellROX dye at three DPI concentrations 4 h after DPI addition. Brightness and contrast are the same for all images. See Supplementary Movie 2. d) Measurement of polB and rrnB promoter activity normalized to t=0. e) DPI dose response of E. coli knockout mutants treated during stationary (upper panel) or exponential growth (lower panel). Dose response of wild-type (WT) cells is shown in green or purple, respectively. Shaded error bars equal standard deviation in log space between three replicates. For other variants see Figure 20. f) GVA checkerboard assay of DPI in combination with ciprofloxacin at 24 hours. Each square in the heatmap was the average of duplicate conditions. Color bars correspond to the log10(CFU / mL) of each dose combination. The left panel shows the dose response of DPI with 1 μg / mL ciprofloxacin (cyan). For the complete time series see Figure 21. g) GVA checkerboard assay of DPI in combination with gentamicin at 24 hours. h) Growth inhibition checkerboard of DPI and ciprofloxacin. Optical density was measured over 8 hours for each condition and the integrated area under the growth curve (AUGC) is shown (color bars). i) Dose-response curves of time-staggered combinations. All treatments lasted a total of 24 hours. The pre-treated condition was treated with a single drug for 2 hours, followed by treatment with both drugs for 22 hours. [Figure 7]Derivation of the PDF of the cone. a) The volume of the micro dV divided by the total volume V corresponds to the probability of finding a colony as a function of x. The radius of the micro (r'(x)) is a function of x, which is the radius of the base of the cone (r) divided by the height of the cone (h), according to trigonometry. b) PDF of the cone as a function of x. An overhead projection of the cone is shown above. c) Cumulative density function (CDF) as a function of x. d) The PDF is the same for axisymmetric cones such as square pyramids (red) and triangular pyramids (turquoise). e) Two equivalent ways of calculating the number of CFUs in a wedge using the CDF (left) or the PDF (right). N(x) is the number of colonies counted. f) Percentage of simulations using GVA calculated CFU / mL within a factor of 2 of the correct value, as a function of the number of colonies used in the GVA calculation. 1000 simulations were used to calculate the percentages. See Figure 1c for simulation parameters. [Figure 8] Optical setup. a) Schematic of the optical setup for imaging pipette tips containing agarose. A mirrorless camera with a macro lens is placed above the tip in the focal plane. The addition of a z-positioner stage helps fine-tune the focus. Pipettes are illuminated laterally using an LED light box with a diffuser. This box is attached to a stepper motor, allowing imaging of 12 pipettes at a time. The stepper motor and camera are controlled simultaneously by LabView software. b) Photograph of the optical setup. Cyan light was used to maximize the contrast of the TTC counterstain. The Styrofoam box acts as a reflective light box and the paper acts as a diffuser. GVA samples are positioned using a 12-channel pipettor and imaged using a Canon EOS RP camera with an f / 2.8 100 mm macro lens. c) The pixel resolution of this setup is 6.7 microns. [Figure 9]Example of a drop CFU plate. a) Each condition (columns) is diluted in 10-fold serial dilutions (rows) and 3 μL is spotted onto a 1.5% LB agar pad poured into an empty tip box. Colonies are counted in the dilution rows where individual colonies are isolated. These counts are used to calculate CFU / mL (bottom). [Figure 10] GVA calculations for different species. a) Estimated CFU / mL counts at different dilution series for 6 species tested with GVA. b) Plates streaked with pipette tips after GVA embedding before or after bleach wash. No change in CFU / mL was observed after bleach wash. c) Example of GVA pipette tip for E. coli biofilms. See culture methods and dissociation protocol. d) Biofilm growth over time. Error bars correspond to standard deviation between ≥5 biological replicates. [Figure 11] Sampling of biomes using GVA. a) 24 sites (red dots) on a volunteer were vigorously swabbed for 15 seconds, then placed in 1 mL of LB medium and vortexed for 10 seconds. 50 μL of sample was then mixed with 150 μL of 0.66% molten LB agar to a final concentration of 0.5% agar and allowed to gel within the tip. With this protocol, the lower limit of detection was 20 CFU / mL (dotted line). Replicates of samples were incubated at 30 °C or 25 °C for 48 h before imaging. b) Examples of pipette tips from different sample areas reveal diverse colony structures and concentrations in different biome locations. All samples were stained with TTC. c) Samples from high temperature areas (ear, armpit) grew at 30 °C but not at 25 °C. This indicates temperature selectivity of the various species grown within the pipette tips. [Figure 12]The chip version of GVA uses a square pyramid geometry. a, b) 3D printed molds to create square pyramids for conditions 12 (a) and 48 (b). c) Photographs of a 9-fold dilution series of E. coli cultures on the GVA chip. d) CFU / mL calculated with GVA using the dilution series. Each dot is the average of 4 replicates. e) Noise measured using coefficient of variation (COV) of chip GVA. f) Drop CFU quantification matched to condition (d). g) Corresponding noise analysis of drop CFU. h) Correlation over 5 orders of magnitude between chip GVA and drop CFU. i, j) Chip GVA for Gram-positive cells (i) and eukaryotic cells (j). [Figure 13] Pipette tip holder for smartphone (iPhone). a) 3D printed parts for standard placement of pipette tips in front of the iPhone rear camera with Xenvo macro lens (15x magnification without wide field lens). The blue faceplate slides onto the Xenvo macro lens clipped onto the iPhone. The green bar screws into the side channel of the blue plate. This allows the height to be adjusted by sliding the green bar in the channel. The purple extension bar slides into the green channel to adjust the imaging depth. The smartphone is held upright on a stand (yellow). Pieces printed with standard FDM printing using PLA. [Figure 14] : Sensitivity analysis of GVA calculation to errors in missed colonies and tip position. a) Heatmap of error as a function of both tip position and missed colony error. b) Same analysis as panel a, but using experimental data. CFU / mL were binned between 1e3 and 1e5 (top row), 1e5 and 1e7 (middle row), and 1e7 to 1e9 (bottom row). The number of pipette tips contained in each bin is annotated by count. c) Heatmap of Pearson correlation between drop CFU and GVA for both tip position error and missed colony error. [Figure 15]: Cell numbers over time in stationary versus exponential cultures. a) Number of CFU / mL in stationary (a) versus exponential (b) cultures. To generate exponential cultures, stationary phase cells were diluted 1:1000 in fresh LB medium and placed in a 37°C shaking incubator (180 RPM) for 2 hours before starting the experiment. [Figure 16] : Enzo screening control. a) Library diversity of the ICCB Enzo Known Bioactive library compared to the Maybridge HitFinder library. Tanimoto similarity between all molecules based on SMILES was calculated using the RDKit package in Python. From this distance matrix, tSNE embedding was initialized with PCA and calculated with perplexity 50. b) Distribution of CFU / mL for conditions at the edge and central well of the plate for both stationary and exponential cultures. For statistical testing, the Mann-Whitney U test for non-parametric distribution was used (p-value > 0.05). c) Distribution of CFU / mL for the different drug classes identified in the Enzo library (see Figure 5c). No class differences were found using ANOVA (p-value > 0.001, p-value corrected for multiple hypothesis testing). No differences from the control were found using pairwise Tukey test (p-value > 0.01, pairwise Tukey test) [Figure 17] Unvalidated hits from the ICCB Enzo bioactivity screen. E-4031 (a) and phenamil (b) dose-response curves for stationary or exponential (ex) growing cultures. [Figure 18] a) The duration of ROS reduction and onset of the secondary ROS spike depend on the DPI concentration. Median single-cell CellROX signal is shown as a function of time at different DPI concentrations. b, c) Dose-response curves of ciprofloxacin (b) and gentamicin (c) on stationary phase cells in aerobic or anaerobic conditions. Treatment was for 24 h. d) Efficacy of DPI in response to increasing concentrations of the ROS scavenger ascorbic acid (AA). [Figure 19]Strip chart (rows) of lexA-repressed genes using the PEC library. GFP fluorescence (top panel of each row) is proportional to the promoter activity of each gene. The bottom panel of each row shows a brightfield image. Columns correspond to different time points after treatment. [Figure 20] Sensitivity of gene mutants to DPI in exponential and stationary phase. Wild-type reference for each mutant is shown as a solid line. Error bars are standard deviation in log space between three biological replicates. Mutants were selected from the Keio collection. Kanamycin (25 μg / mL) was included in all Keio culture conditions, both during overnight culture and DPI treatment, to maintain the gene knockout. [Figure 21] GVA time checkerboard of DPI hybridized with ciprofloxacin (left panel) or gentamicin (right panel) against E. coli. Lower rows indicate longer treatment times. Each square in the heatmap was the average of duplicate conditions. Color bars correspond to the measured log10(CFU / mL) of each combination. The left panel shows the line trace (cyan) of the DPI dose response at 1 μg / mL ciprofloxacin or 10 μg / mL gentamicin. [Figure 22] GVA time checkerboard of DPI hybridized with ciprofloxacin (left panel) or gentamicin (right panel) against Salmonella typhimurium. [Diagram 23] The procedure for the standardized method of conventional CFU assay using the drop plate method is outlined below. When scaled to 96 measurements and assuming a standard 12-channel pipette, this conventional CFU requires 1,440 pipette tips, approximately 50 minutes for setup and competition, and at least 8 agar plates. [Figure 24] 1 shows a schematic diagram of the GVA of the present invention, optionally with a multi-vessel plate. Scaling to 96 measurements. Scaling to 96 measurements and assuming a standard 12-channel pipette, the GVA of the present invention requires 96 pipette tips to set up and run, taking approximately 6 minutes, and in the preferred embodiment, two reusable tips, one for two. [Diagram 25]1 shows a schematic diagram of an alternative embodiment GVA of the present invention in which the variable geometry vessel includes a pipette tip. Scaled to 96 measurements. Scaled to 96 measurements and assuming a standard 12 channel pipette, the GVA of the present invention requires 96 pipette tips and takes approximately 6 minutes. [Figure 26] Quantified data from the example shown in Figure 10. (a) Estimated CFU as a function of dilution. (b) Triplicate data showing mean CFU. [Figure 27] Screening of compounds affecting exponential and stationary phase viability using data from GVA assays in a 96-well format drug test. Antibiotic potential of a drug library (80 compounds) was tested in duplicate against exponential and stationary phase bacteria. (A) Reduction of viability of compounds in exponential (top) and stationary phase (bottom) cells. Each x is the average of two biological replicates. (B) Scatter plot of viability screen comparing exponential and stationary phase activity. Each blue dot represents the average of two replicates of a compound treatment. Red dot represents DMSO control. Dashed line represents 3 times the standard deviation of the negative control. Screening was performed with E. coli containing both stationary and log phase bacterial populations. Both phases were screened in duplicate. 80 compounds and 16 controls were screened. Stationary and log phase cells were treated with compounds for 4 hours and then cast onto pipette tips using the GVA protocol. Two compounds, mitomycin C and diphenyl iodine, both exhibited bactericidal activity. [Figure 28] The utility of the GVA assay to calculate minimum inhibitory concentrations (MICs) is shown. (A) Images of GVA-measured cells at various kanamycin concentrations in an agarose matrix. 1x1010 cells / mL were embedded in each sample with the indicated amount of kanamycin. CFU calculated from the GVA assay are also shown. (B) Plots of biological triplicate samples with kanamycin treatment. The dashed line indicates the detection limit of the assay for kanamycin. [Figure 29]GVA can accurately estimate CFU in a rapid time frame. Images show the same pipette tip photographed after 4, 6, 8, and 24 hours of incubation at 37°C. Colonies were clearly visible after 6 hours, and counting at 8 hours showed high precision and a dynamic range of up to 7 orders of magnitude (heatmap below). When used for rapid antibiotic testing, these measurements can be obtained within 8 hours with no pre-incubation steps required. [Diagram 30] (A) Estimation of total CFU / mL based on colony location within the cone. (Top) Distribution of colonies for four simulations spanning densities from 20 to 10,000 CFU / mL. Volumes for each cone are the same as in panel c. (Bottom) GVA estimates of CFU / mL as a function of the colonies included and their x-location. (B) Coefficients in the GVA calculation differ from the correct value in equation (1) as a function of colony number. Shaded error bars represent 1 standard deviation over 1000 simulations. Colors match simulations in panel A. [Diagram 31] (A) GVA works in Gram-positive (top), Gram-negative (middle) and eukaryotic cells (bottom). (B) Serial dilutions of Saccharomyces cerevisiae (baker's yeast) grown in YEPD to determine the dynamic range in eukaryotes. GVA accurately estimates yeast viability by up to nearly seven orders of magnitude. [Diagram 32] (A) Image of the active area of ​​the software-based colony counting software. The leading edge is marked with a red vertical line and individual identified colonies are marked with red circles. The current CFU count is displayed on top. (B) Software algorithm setup defining the experimental parameters (left) and image location (center). Users can also fine-tune the colony segmentation algorithm (right) or accept the default parameters. [Diagram 33]Flowchart of the GVA assay. In a physical (hardware) based measurement, the sample is placed in front of the measurement device, followed by an image of the tip. In the software, the user identifies individual pipette tips, aligns them in an orthogonal plane for easy distance calculations, selects a subset of colonies within the tip, and uses an algorithm to estimate the number of CFUs throughout the tip. [Diagram 34] An example of the use of the GVA assay with a paper-based readout (no imaging system required). (A) Calculations for estimating CFU and a ruler assuming a 36 mm tip with an agarose volume of 150 uL. The image shows serial dilutions of bacteria overlaid on the paper-based ruler. CFU estimates are based on the position of the 10th colony counted by the user. (B) Comparison of GCA CFU measurements using a high-resolution Canon camera and macro lens (purple) with the paper-based method using a basic magnifying glass (green). Precision is the same as in the paper, but the maximum number of isolable colonies is reduced. Comparison between the paper and camera systems (bottom) shows a very high correlation over the dynamic range of the paper-based measurement. [Diagram 35] Minimum inhibitory concentration (MIC) measurements are independent of the starting concentration of bacteria. Each box represents a GVA measurement for increasing amounts of antibiotic (x-axis). The number of viable cells at each concentration is plotted on the y-axis. Antibiotics are labeled at the top. Each color within each box represents an initial cell population of 1000 to 1,000,000 cells per milliliter. The MIC is the same for each drug regardless of the starting concentration of cells. [Diagram 36] GVA calculates the minimum inhibitory concentration for various bacterial species. Each box represents the number of CFU as a function of increasing antibiotic concentration shown on the x-axis. The identity of the antibiotic is printed at the top of each box. Each color represents a different bacterial species. Differences in MICs indicate that each species has its own antibiotic susceptibility spectrum, which is easily revealed by GVA. [Figure 37]GVA can rapidly measure the minimum inhibitory concentration of a variety of bacteria. Each box represents the number of CFU as a function of increasing antibiotic concentration shown on the x-axis. The identity of the antibiotic is printed on the top of each box. Each color represents a different bacterial species. These measurements were taken after 12 hours of incubation, with consistent measurements after 24 and 48 hours. [Figure 38] GVA imaging works in blood agar, a common medium for growing pathogenic strains. (A) Image of E. coli embedded in blood agar at the manufacturer's recommended concentration and grown overnight at 37 °C. (B) Quantification of serial dilutions of CFU. Similar to results in LB or minimal medium, this medium allows for separation over 5 orders of magnitude. [Figure 39] Examples of use cases enabled by GVA: (1) High throughput viability screen. The Prestwick library of compounds (1440) was run twice over approximately 2 weeks (total of 2880 CFU assays). This screen can help identify new antibiotic compounds or combinations. (2) Pharmacokinetic and pharmacodynamic characterization of antibiotics. Antibiotic effects were measured as a function of time, concentration, and bacterial species. (3) Drug combination matrix. Checkerboard comparing a range of compound 1 and compound 2 as a function of time. Multiple compound and time combinations were measured. (4) Pharmacogenomic characterization of antibiotic efficacy. Measure viability across multiple concentrations of compound against multiple genomic bases. Effects were measured across multiple genomic modifications and antibiotic concentrations. [Diagram 40] Effect of potential errors on counting precision. (Left) Change in Pearson r coefficient when colonies are not included in the count. Total colony count was 30. Correlation changes by less than 1%. (Right) Change in GVA precision when tip position is incorrectly assigned by the software. Even with a 36 mm pipette tip where the tip position is shifted by up to 4 mm, the PCC changes by less than 1%. [Diagram 41] FIG. 1 shows a front perspective view of a GVA assay imaging system in one embodiment. [Diagram 42] FIG. 1 shows a front perspective view of a frame for a GVA assay imaging system in one embodiment. [Diagram 43] FIG. 1 illustrates a front perspective view of an imager bracket for a GVA assay imaging system in one embodiment. [Diagram 44] FIG. 1 illustrates a front perspective view of a mounting block for a GVA assay imaging system in one embodiment. [Diagram 45] FIG. 1 shows a front perspective view of a plate for a GVA assay imaging system in one embodiment. [Figure 46] FIG. 1 shows a top perspective view of a GVA assay imaging system utilizing a smartphone that is mechanically responsive to an adapter in one embodiment. [Figure 47] FIG. 1 shows a front perspective view of a GVA assay imaging system utilizing a smartphone that is mechanically responsive to an adapter in one embodiment. [Figure 48] 1 illustrates an adapter for a GVA assay imaging system with vertical adjustment in one embodiment. [Figure 49] 1 illustrates a depth adjuster for a GVA assay imaging system in one embodiment. [Figure 50] 1 illustrates a mount with a vessel holder for a GVA assay imaging system in one embodiment. [Figure 51] 1 shows the base of a GVA assay imaging system in one embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] The present invention includes a novel viability assay, generally referred to herein as the Geometric Viability Assay (GVA). In a preferred embodiment, the GVA of the present invention calculates CFUs in a sample, such as a biological, environmental, or commercial sample, based on the axial location of embedded colonies formed within an axisymmetric variable geometry vessel, which in a preferred embodiment includes a conical vessel as described herein. Based on the physical properties of the axisymmetric variable geometry vessel, the probability of a colony forming at the tip of the vessel is lower than near the base. Analytically, this probability is proportional to the square of the perpendicular distance from the colony to the vessel tip. By measuring the location of a limited number of colonies within the vessel and utilizing the derived probability density function, the total number of colonies in the entire vessel can be calculated with high accuracy.

[0018] By exploiting the latent information encoded in the colony distribution, the GVA of the present invention can accurately quantify the number of viable cells in a sample ranging from 1 cell to 10 million. This dynamic range can be achieved using conical, axisymmetric, variable geometry vessels, i.e., pipette tips, that are common in microbiology. As further described below, the GVA of the present invention: 1) measures viability over >6 orders of magnitude; 2) is independent of the growth or lag phase of cells; 3) minimizes consumables; and 4) reduces operator time by >30-fold compared to conventional drop CFU assays. The combination of the GVA of the present invention allows a throughput of up to 2000 viability measurements per researcher per day.

[0019] The technology of the present invention includes a novel system for colony-forming CFU assay. In a preferred embodiment, the device of the present invention includes a variable geometry container adapted to hold a sample diluted in a growth medium. As used herein, a "sample" or "cell sample" of the present invention may include a sample containing one or more cells to be cultured and tested. In a preferred embodiment, a "cell" or a "cell to be cultured or detected" may include both prokaryotic and eukaryotic cells that can be cultured and identified as CFUs, preferably including Gram-positive and Gram-negative bacteria, as well as fungal, yeast, and even algae cells. In another preferred embodiment, a "cell" may include pathogenic bacteria, or complex cell samples such as biome samples, or other samples from surfaces or objects such as food or beverages that contain multiple different cells.

[0020] In another preferred embodiment, the "sample" or "cell sample" of the present invention may include a biological sample. As used herein, the term "biological sample" includes a sample from any body fluid or tissue. In certain embodiments, a biological sample may include a sample that is typically the subject of a clinical or diagnostic test. Biological samples or samples suitable for use according to the methods provided herein include, but are not limited to, blood, serum, urine, saliva, tissues, cells, and organs, or portions thereof. A "subject" is any living subject of interest, generally a mammalian subject, and preferably a human subject.

[0021] In another preferred embodiment, the "sample" of the present invention may include an "environmental sample." As used herein, an "environmental sample" refers to a sample taken or obtained from any part of an internal or external environment. In a preferred embodiment, an "environmental sample" may include samples from water, soil, municipal waste, hazardous waste, potential contaminants, and the like. In another preferred embodiment, an "environmental sample" may include a "commercial sample," which may include a fomite such as a surface of a commercial manufacturing facility, or an object such as a food and / or beverage.

[0022] With reference to FIG. 7, the variable-shape container of the present invention includes an opening that allows for the placement of growth medium, a middle portion adapted to have a geometric configuration that varies in size across all three dimensions, and an end portion that is narrower than the opening. The device of the present invention may include multiple variable-shape containers, for example, manufactured in a mold with one or more variable-shape containers, or multiple containers arranged adjacent to each other to allow for rapid comparison and analysis. Specifically, as used herein, "variable-shape container" refers to an axisymmetric container or container adapted to hold a volume of growth medium, where the dimensions of the container vary in size across all three dimensions. In particular, in certain embodiments, the container that varies in size across all three dimensions may be continuous or discontinuous. Examples of axisymmetric variable-shape container configurations that vary in size across all three dimensions include, but are not limited to, the following shapes shown in FIG. 7D: JPEG2025515459000002.jpg23153

[0023] As shown in Figures 1f and 2b, in one embodiment, the variable-shape container of the present invention can include a standard translucent pipette tip or cone as recognized by those skilled in the art. Again, as shown in Figure 2, one or more pipette tips can be coupled, singly or in multiples, to a standard multi-use laboratory pipette or pipette holder, as described below. As further shown in Figures 12a-12b, in one embodiment, the variable-shape container of the present invention can include a mold having one or more variable-shape containers. The mold can include a three-dimensional printed chip having multiple variable-shape containers arranged adjacent to each other. As mentioned above, the variable-shape container of the present invention is preferably translucent to enable direct image capture of all or a portion of the colonies formed in the growth medium.

[0024] The technology of the present invention includes novel systems and methods for establishing a geometric viability assay (GVA). As used herein, "GVA" refers to a CFU assay performed using the variable geometry vessel described herein. In a preferred aspect, the GVA of the present invention may include a cell sample, preferably a biological sample, an environmental sample, or a commercial sample, that includes one or more culturable cells. In a preferred embodiment, the cells may include bacterial, yeast, or fungal cells that can be cultured to a certain density in a liquid medium. In a preferred embodiment, the sample may be further treated. As described herein, the term "treated" includes a step of exposing the cells to an experimental perturbation to be measured (e.g., drug treatment, culture conditions, genetic modification, etc.). In a preferred embodiment, the sample is treated with one or more compounds adapted to kill and / or inhibit the growth of the sample and / or affect one or more phenotypic or genotypic changes in said sample. In a preferred embodiment, such treatment may include one or more compounds selected from the group consisting of therapeutic compounds, antibiotics, bactericidal compounds, bacteriostatic compounds, anticancer compounds, and antifungal agents.

[0025] In one embodiment, the sample can be diluted in growth medium and placed in the variable geometry vessel for incubation. For example, in one preferred embodiment, the sample can be diluted to a desired concentration of CFU directly in a growth medium such as a volume of melted liquid agarose, preferably 0.5% liquid agarose, which can be further cooled to about 37° C. and the material can be cast into the variable geometry vessel. Of course, a variety of liquid and solid growth media for prokaryotic and eukaryotic cells are known in the art and can be adapted for the GVA of the present invention.

[0026] In a preferred embodiment, a sample containing one or more different microorganisms can be taken from the sample or isolated from the sample, grown, and further diluted with a growth medium such as nutrient agar, which allows the microorganisms to form colonies. In this embodiment, one or more contrast agents, such as the microbial stain 2,3,5-triphenyltetrazolium chloride (TTC) or a cell-permeable fluorescent dye, can be added to the growth medium to aid in subsequent CFU visualization as described below. In another embodiment, the growth medium can include a clinical or diagnostic indicator. For example, as shown in FIG. 38, a sample containing one or more different microorganisms can be taken from the sample or isolated from the sample, grown, and further diluted with a diagnostic growth medium such as blood agar, which allows the microorganisms to form colonies. As shown, blood agar is an enrichment medium used in clinical and diagnostic settings to grow fastidious microorganisms and differentiate various bacteria based on hemolytic properties.

[0027] The growth medium containing the diluted sample can be cast into a variable-shape container, which can include an exemplary axisymmetric three-dimensional pyramid or cone, as shown in FIG. 24, or can be cast into a container that forms part of a chip that allows for different serial dilutions or perturbations of the culture treatment. As shown in FIG. 34a, the growth medium containing the diluted sample can be placed directly into a standard pipette tip that can be further coupled with a standard pipette configured to hold one or more adjacent pipette tips. Of course, such examples are merely illustrative and various containers or pipette holders can be used to secure the container and growth medium and allow for the culture of the cell sample contained therein. The growth medium suspension can then be cultured to form colonies embedded in the medium. The time, temperature and other parameters of this culture step depend on the cells to be grown and will be generally understood by those skilled in the art.

[0028] During incubation, viable CFUs, such as CFUs of a microorganism present within the vessel, may form visible embedded colonies that can be further imaged to allow for individual identification. In a preferred embodiment, an image sample of the three-dimensional volume of the variable geometry vessel is captured, for example using a light source and an imaging device such as a microscope, digital camera, cell phone camera, etc., and the captured images can be further digitized, stored, and analyzed as further described below.

[0029] In another example, the captured images of the containers can be processed and analyzed to identify clinically or diagnostically relevant characteristics, such as the presence or absence of a pathogen, changes in the growth medium, activation of diagnostic chemicals or markers in the growth medium, etc. Of course, in some cases, the identification of one or more of these or characteristics can be visually confirmed by an operator, such as a technician in a laboratory environment or an automated detection system, as will be appreciated by those skilled in the art.

[0030] In a preferred embodiment, the captured images of the vessel can be processed and analyzed to identify the number of CFUs along a portion of the length of the variable geometry vessel. In certain embodiments, the number of CFUs can be clinically or diagnostically relevant, while in other embodiments, it can indicate the effect of a treatment, such as an antibiotic treatment, applied to the cells in the sample. In this embodiment, the number of CFUs can be used to determine the MIC of a compound or the effectiveness of a drug screen to promote or inhibit the growth of the cells described herein.

[0031] In a preferred embodiment, identifying CFUs along a portion of the length of the variable shape vessel can include manually or computer-assisted identification of individual CFUs from one or more images of a portion of the variable shape vessel, preferably the terminal portion shown in Figures 1-2, 11, and 34. Once the number of CFUs along the portion of the length of the variable shape vessel is identified, the number of viable CFUs in the original culture sample can be calculated and output for further analysis, etc. As shown below, due to the novel features of the GVA of the present invention, identification of the location of about 10-20 colonies, preferably at the tip of the vessel, is required for reliable estimation using the probability density functions described herein.

[0032] As mentioned above, the GVA of the present invention involves embedding cells capable of forming observable colonies, such as bacteria, yeast, fungi, etc., or regenerating to form colonies, into a vessel with geometrically variable properties, i.e., a 3D vessel with an axisymmetric shape that varies in size across all three dimensions. A higher concentration of live cells increases the likelihood that colonies will form in a small volume region at the end or tip of the variable-shape vessel, compared to a lower concentration of cells. (See, for example, Figures 1-2, which show the end of a variable-shape vessel, which in this embodiment is a pipette tip.) As shown below, the cumulative density function (CDF) distribution of colonies along the vessel can be analytically calculated and then used to estimate the initial number of live cells at the time of embedding, using the following function:

number

number

[0033] Another aspect of the invention includes an analytical solution that provides a second CFU estimation alternative: (1) the operator counts colonies in any geometrically variable container and records their locations, (2) uses the boundaries of the closest and furthest colonies (limits defined as x1 and x2), and (3) uses a probability density function (PDF) to estimate the CFU using the following function:

number

number

[0034] To parallelize our GVA while establishing a simple protocol, in one embodiment, we created a pattern of 12 or 48 interleaved triangular ramps (Figure 12). The lower limit of CFU detection is set by the size of the ramp, with larger volumes allowing for smaller CFU values ​​to be detected. The upper limit of CFU is determined by the smallest possible colony isolation within a particular wedge. The series of triangular ramps was designed to simplify mathematics and manufacturing. In this embodiment, the spacing between the triangular ramps is 9 mm and their length is 36 mm. The ramp height was chosen to result in a final sample volume of 200 μL. This also sets the lower limit of detection to 5 CFU / mL. This spacing is convenient to utilize multichannel pipettes that are common in many laboratories. The current design can hold 48-well or 12-well tips, allowing for 48 or 12 simultaneous experiments, respectively.

[0035] In this embodiment, cells are first grown in 96-well plates in the presence of a particular experimental perturbation (e.g., drug treatment) to be measured. After treatment, cells are mixed in soft agarose and added to the wells so that growing colonies are immobilized in the 3D vessel. Tetrazolium chloride (TTC) can be added to the agarose to turn metabolic cells red and improve imaging contrast, but TTC is not necessary given the natural optical contrast of yeast colonies. The total plating time (48 experiments) on the 48-well chip is about 6 minutes for one operator, compared to about 45 minutes for a traditional drop plate assay with the same number of experiments. An example of a potential drug screen is shown in Figure 12 for 80 compounds in duplicate from both stationary and exponentially growing bacteria (total of 384 tests).

[0036] In another preferred embodiment shown in FIG. 3A, the GVA of the present invention can generate a CFU distribution in which a sample can be diluted with growth medium and loaded into one or more commercially available pipette tips to mount colonies in an analytically relevant manner. In this embodiment, the GVA of the present invention can also be used to identify the minimum inhibitory concentration (MIC) of a compound in a high-throughput manner. Cells can be grown and treated with a particular compound, such as an antibiotic at a particular concentration, that may alter the growth or viability of the cells. Individual colonies in the vessel then represent single cells that were able to grow at a specified concentration, and the MIC can be determined by counting the output colonies. In this embodiment, the operator is not exposed to potentially dangerous pathogens grown at high density on the plate.

[0037] In another embodiment, the GVA of the present invention can also be used to identify physical or other regulators of cell growth in a high throughput manner. In this embodiment, cells can be grown and subjected to a physical treatment, such as UV light or genetic modification, that may alter the growth or viability of the cells. Each individual colony in the vessel then represents a single cell that was able to grow after the specified treatment, and the overall effect can be determined by counting the colonies output.

[0038] Imaging of the GVAs of the present invention can be done in a number of different ways, including scanning with a conventional microscope equipped with a low magnification (1x, 2x, 4x) objective, or using a consumer camera equipped with a macroscopic scanning stage. Microscopes provide the highest possible resolution and therefore resolve the densest samples, while consumer cameras can scan multiple wells in parallel for the highest experimental throughput.

[0039] As shown in FIG. 8, initial CFU image data can be captured using an imaging device such as a commercially available camera, in this case a DSLR mounted on a custom LED light source. The camera can use a commercially available macro lens to achieve the desired spatial resolution. In this imaging setup, illumination from a cyan LED can be used to maximize the contrast of the TTC staining, thereby enhancing colony detection. The light box can be moved on a computer-controlled moving stage (Thorlabs LTS300) and samples can be programmatically moved into the field of view of the camera. This imaging embodiment can capture five wells per image with an upper limit resolution of approximately 1,000,000 CFU / mL.

[0040] Data acquisition can be controlled by custom scripts in Labview, Matlab, or Python. Output from the scripts controls the stage, camera, and light source. For each field of view, 2-6 images are taken at slightly varying focal planes (i.e., focal stacking). The sample is then moved to a new set of vessels followed by imaging. This process is repeated for all vessels, and the software can automatically image up to 4x48 well chips (192 conditions total) without user input. In a preferred embodiment, image processing of the GVA of the present invention can (a) create enhanced images with high signal-to-noise ratios, (b) identify individual vessels, and (c) mark the presence and location of individual colonies within a vessel. Colony location data can be used to calculate CFUs within a particular vessel. All image processing can be performed by custom written scripts in Matlab, as described in detail below.

[0041] Enhanced focal plane imaging is achieved by combining six images acquired using a focal stacking technique, which selects each pixel from the image with the highest contrast to extend the depth of field. A Hessian transform followed by linear convolution is then used to isolate individual slopes within the vessel and identify locations within the images that mark the top, bottom, and sides of each vessel. After isolation of the individual slopes within the vessel, colonies are identified either manually using mouse clicks by the user or automatically using a Hessian transform that identifies circular regions. The xy coordinates are also calculated from each colony and used to estimate the PDF.

[0042] The GVA of the present invention can also be processed visually without the use of the image capture and analysis system described above. In the embodiment shown in FIG. 34, the GVA of the present invention can include a comparative readout system. In this embodiment, a variable-shape container, in this case including multiple pipette tips positioned adjacent to each other, can be positioned against a paper or digital image-based readout CFU indicator (320). As shown in the figure, the present invention can include a GVA assay system (300) including one or more variable-shape containers (310) containing cell samples cultured in growth medium, and a CFU indicator (320) pre-calculated and marked to estimate CFU. In this embodiment, the CFU indicator (320) of the present invention includes a pre-calculated and marked "ruler" portion calibrated to a 36 mm tip with a 150 μL agarose volume. Again, as shown in FIG. 34A, multiple containers (310) representing serial dilutions of a bacterial sample are overlaid on a paper CFU indicator (320). The CFU estimate is based on the location of the 10th colony counted by the user. Figure 34b shows a comparison of the GCA CFU measurements (purple) with a high resolution Canon camera and macro lens using a basic magnifier to the paper CFU indicator (320) (green). As shown, the precision is the same as the paper CFU indicator (320), but the maximum number of isolable colonies is reduced. Furthermore, as shown in Figure 34b, the comparison between the paper CFU indicator (320) and the camera system (bottom) shows a very high correlation over the dynamic range of the paper-based measurement.

[0043] The present invention further includes a system, method, and apparatus for imaging a GVA, preferably using a digital camera. In this embodiment, the GVA imaging system (100) may include an imaging device (101), which may preferably include a digital camera mounted on a linear stage (102) and further supported by an imaging device bracket (109). The imaging device (101) is disposed adjacent to a frame (103) and configured to be coupled to the linear stage (102), and may further secure a light source (104). In this configuration, the frame (103) of the present invention may secure one or more variable-shape containers (113) within the field of view of the imaging device (101). As shown in FIG. 41, multiple variable-shape containers (113) may be coupled to a container holder (105) secured to the front surface of the frame (103) to position the containers (113) between the light source (104) and the imaging device (101). In this configuration, the imaging device (101) can capture one or more images of colony forming units (CFUs) within a vessel, preferably in the distal portion of a culture vessel (113) described herein.

[0044] In one embodiment of the invention, the imaging device (101) can be responsive to a zoom adjuster (107) configured to adjust the position of the imaging device (101) relative to the frame (103). In a preferred embodiment, the zoom adjuster (107) of the present invention can include one or more rails disposed on a linear stage (102) such that the imaging device (101), shown here as a camera, can be slidably disposed adjacent to the frame (103) to adjust the "zoom" position of the camera by moving it closer to or retracting it from the frame (103). Such adjustments can be made manually by an operator or automatically by a controller (112), such as a printed circuit board (PBC) responsive to a processor. In another embodiment, the imaging device (101) can be coupled to a mounting block (111) that can be responsive to a kill switch to activate or deactivate, for example, the light source (104) and / or the controller (112).

[0045] Referring again to FIG. 41, the frame (103) may be responsive to a pan adjuster (107) configured to allow horizontal, or "pan", movement of the frame (103) relative to the field of view of the imaging device (101). In this preferred embodiment, the frame (103) of the present invention may be fixed to one or more rails on the linear stage (102), preferably via a plate (110). In this configuration, the plate may slide horizontally relative to the field of view of the imaging device (101). (Of course, in certain embodiments, the imaging device (101) of the present invention may be responsive to a pan adjuster (107) and the frame (103) of the present invention may be responsive to a zoom adjuster (106).) Thus, the imaging device (101) and / or frame (103) of the present invention can be independently adjusted to position the variable-shape container (212) relative to the field of view of the imaging device (202), thereby capturing one or more images of colony forming units (CFUs) embedded in growth medium, preferably at the end portions of the culture container (212).

[0046] The present invention further includes a system, method, and apparatus for imaging a GVA, preferably using a smartphone. In this embodiment, a GVA imaging system (200) of the present invention can include an imaging device (202) disposed adjacent to a light source (not shown). In a preferred embodiment shown in Figures 46 and 47, the imaging device (202) of the present invention can include a smartphone secured to a base (204) such that an internal camera of the smartphone is disposed approximately vertically, preferably adjacent to the light source (not shown). An adapter (206) can be mechanically responsive to the imaging device (202) of the present invention, which in this embodiment is a smartphone.

[0047] As shown again in FIGS. 46-47, the adapter (206) of the present invention can be coupled to a rear imaging device (202) and placed over the camera of a smartphone, and further expanded by placing a macro lens (209). As shown in FIG. 48, the adapter (206) can include an aperture lens aperture (208) configured to secure the macro lens (209) of the present invention. As shown in particular in FIG. 47, the adapter (206) having the macro lens (209) secured within the lens aperture (208) can be placed over the camera of the imaging device (202), which in this embodiment includes a smartphone. In this configuration, the placement of the macro lens (209) expands the field of view of the smartphone, allowing the imaging device (202) to more accurately capture an image of the deformable container (212) placed within the field of view of the camera.

[0048] The axisymmetric variable-shape container (212) of the present invention can be used to culture a cell sample (not shown) in a growth medium (not shown). Referring again to FIGS. 47-48, in a preferred embodiment, the variable-shape container (212) of the present invention can be positioned within the field of view of the imaging device (202), preferably via a mount (210). The mount (210) of the present invention can include one or more container holders (214) configured to secure the variable-shape container (212) within the field of view of the imaging device (202). As shown in the figures, the container holder (214) of the present invention can include an aperture adapted to fit the variable-shape container (212), such as a pipette tip. Of course, alternative embodiments include additional elements for securing the variable-shape container (212), such as a pipette tip, within the field of view of the imaging device (202), such embodiments including couplers, frames, latches, and the like.

[0049] The mount (210) of the present invention is adjustable in multiple directions and orientations to allow a user to adjust the position of the variable-shape container (212) relative to the imaging device (202). In the embodiment shown in Figures 47-48, the mount (210) can be mechanically responsive to a depth adjuster (218). In this configuration, the mount (210) can include an arm (224) that can be slidably coupled to a receiver (226) that is further secured to the adapter (202) of the present invention. During operation, the mount (210) can be adjusted to vary the depth of the attached container (212) relative to the imaging device (202).

[0050] In the embodiment shown in Figures 47-48, the mount (210) can be mechanically responsive to a vertical adjuster (216). In this configuration, the mount (210) can be secured to the adapter (206) via a slot (220) located approximately adjacent to the field of view of the imaging device (202). In operation, the mount (210) can be adjusted up or down along the slot (220) to vertically adjust the variable geometry container (212) within the field of view of the imaging device (202). As shown in Figure 47, the coupler (222) of the present invention is shown in a preferred embodiment as a twist coupler and can be used to secure the container (212) in a desired position within the field of view of the imaging device (202).

[0051] In other alternative embodiments, the mount (210) of the present invention can be horizontally adjustable, for example, through an extended mounting surface (210) that can slidably couple with the mount (210) of the present invention and / or arm (224). Thus, the mount (210) of the present invention can be adjusted in multiple orientations to position the variable geometry vessel (212) relative to the field of view of the imaging device (202) to capture one or more images of colony forming units (CFUs), preferably at a distal portion of the culture vessel (212).

[0052] In another embodiment, the present invention includes novel systems, methods, and devices for capturing and processing images of GVA. In a preferred embodiment shown in FIG. 33, in one embodiment, the present invention includes an imaging platform (400) configured to include a processor (402) responsive to an imaging device (401), such as a camera or a smart phone. In this embodiment, the imaging platform (400) of the present invention can include a computer executable program adapted to capture one or more images of the CFU, preferably at the distal portion of the variable geometry vessel, according to step 403. These images can optionally be transmitted to a separate digital processing device, such as a computer, laptop, or tablet having a processor (402) responsive to a controller (410) according to step 404, although in an internal embodiment, the images captured according to step 403 can be transmitted and processed within the imaging device (401) itself. In this embodiment, the computer executable program can be configured to process the images captured by the imaging device (401) and 1) identify the boundary of the tip of the deformable vessel according to step 405, 2) align the tip of the deformable vessel using image transformation according to step 406, 3) perform colony segmentation according to step 407, and calculate CFU according to step 408. All processed images and CFU calculations can be sent to a data storage module (409) for further analysis, editing, or digital storage.

[0053] As mentioned above, the method and system for identifying and outputting CFU units can be accomplished manually or through a similarly configured computer executable program. As a result, the steps of imaging, identifying, and calculating the output as described herein can be performed in certain embodiments via any suitable machine and / or device that provides, for example, conversion of data, data processing, data conversion, external device, operation, etc. It is also noted that in some embodiments, software and / or software solutions can be utilized to carry out the objectives of the present invention and can be defined as software stored on a magnetic or optical disk, or other suitable physical computer readable medium, including a wireless device and / or a smartphone. In alternative embodiments, the software and / or data structures can be combined and associated with a computer or processor that operates on the data structures or utilizes the software. Further embodiments may include transmitting and / or loading and / or updating the software on a computer, perhaps via the Internet or other suitable transmitter or device, or even executing the software on a computer to effect data and / or other physical transformations as described herein.

[0054] Certain embodiments of the present technology may utilize general purpose computers, computers capable of executing algorithms, computer readable media, software, computer readable media carrying specific programming, computer networks, server and receiver networks, machines and / or devices that may include transmitting elements, wireless devices and / or smartphones, internet transmitting and receiving elements, cloud-based storage and transmission systems, software updatable elements, computer clocks and / or subroutines, computer readable memory, data storage elements, random access memory elements, and / or computer interface displays capable of representing data in a physically perceptible transformation, such as visually displaying the processed data. It will further be appreciated that any of the steps described herein may, in particular embodiments, be performed through a variety of hardware applications including a keyboard, mouse, computer graphical interface, voice activation or input, servers, receivers, and other suitable hardware devices known to those of skill in the art.

[0055] As used herein, a machine learning system or model is a trained computational model that takes a feature of interest, such as the presence of CFUs in a variable geometry vessel, and classifies it. Examples of machine learning models include neural networks, including recurrent neural networks and convolutional neural networks, random forest models, including random forests, restricted Boltzmann machines, recurrent tensor networks, and gradient boosted trees. The term "classifier" (or classification model) may be used to describe any type of classification model, including deep learning models (e.g., neural networks with many layers) and random forest models.

[0056] As used herein, a machine learning system may include a deep learning model, which may include function approximation methods aimed at developing custom dictionaries configured to accomplish a given task, such as classification or dimensionality reduction. It may be implemented in various forms, such as by neural networks (e.g., convolutional neural networks). It typically, but not necessarily, includes multiple layers. Each such layer includes multiple processing nodes, and these layers are processed in sequence, with nodes in layers closer to the model input layer being processed before nodes in layers closer to the model output. In various embodiments, one layer feeds into the next layer, and so on. The output layer may include nodes representing various classifications. In certain embodiments, a machine learning system may include an artificial neural network (ANN), a type of computational system that can learn relationships between input and target data sets. The name ANN comes from the desire to develop a simplified mathematical representation of parts of the human nervous system, aimed at capturing its "learning" and "generalization" capabilities. ANNs are a major cornerstone in the field of artificial intelligence. ANNs are widely applied in research because they can model highly nonlinear systems where the relationships between variables are unknown or highly complex. Typically, ANNs are trained based on empirically observed data sets. The data set can be traditionally divided into a training set, a testing set, and a validation set.

[0057] Having described the technology of the present invention, the same will be illustrated with reference to specific examples, which are included herein for purposes of illustration only and are not intended to be limiting of the invention. EXAMPLES

[0058] Example 1: Rationale and development of the geometric viability assay (GVA). Standard high throughput compound screens use growth inhibition, typically measured using absorbance, to quantify a compound's ability to suppress growth. However, these assays inevitably do not account for phenotypic heterogeneity within a population (e.g., persister cells) and the difference between bacteriostatic (i.e., growth-stopping) and bactericidal (i.e., cell death-inducing) antibiotics. Thus, there is an urgent need for assays to identify compounds that are bacteriostatic, and not just bacteriostatic, under conditions that induce persister bacterial physiology (i.e., slow growth). This need is based on a rapid and scalable approach to measure pathogen viability after drug treatment. Thus, the GVA of the present invention fills a critical gap in current screening technologies for diagnostic and manufacturing applications, biome sampling, and discovery of novel antibiotics against drug-resistant pathogens. The GVA assay can also be used to perform MIC testing of dangerous pathogens more quickly and safely. Furthermore, the GVA of the present invention addresses the current need to measure the frequency of drug-resistant cells in anticancer drug screening with a resolution that can identify one resistant cell in 10 million.

[0059] Notably, our analytical framework of GVA enabled viability estimation to be accurate in practice, regardless of the optical configuration. In simulations and experiments, errors in colony counting and tip position did not substantially alter CFU estimation, given the dynamic range of the experiment. Furthermore, exemplary variable-geometry vessels, such as pipette tips, are not perfect cones; small manufacturing defects were clearly visible at high magnification. Despite these real-world variations, such as using imperfect cones, selecting a few colonies, and approximating tip positions, GVA still reproducibly and accurately calculated CFU concentrations over six orders of magnitude. This robustness comes from exploiting the latent information encoded in the colony positions.

[0060] Another unexpected feature of the GVA of the present invention was the observation of self-limiting colony size as a function of CFU density. As the concentration of colonies increased, the colony size decreased accordingly, and colonies remained discrete even in dense samples. As shown below, the colony size of the strains tested plateaued after overnight incubation and did not change for several additional days.

[0061] The physical constraint of the GVA of the present invention results in culture limitations similar to those seen in conventional drop CFU assays [39, 40]. However, applicants have shown that the GVA of the present invention works as expected for all commonly used laboratory strains tested, as well as more complex samples such as biofilms and human-associated biome samples. Because GVA uses the same growth substrate (e.g., solid medium) as traditional 2D culture techniques, applicants and those skilled in the art will find that procedures for selectively culturing different strains in Petri dishes are transferable to GVA. Finally, the temporary heat shock of the current protocol using agarose did not affect the viability of the strains tested, but may be a significant perturbation for certain species. In such alternative embodiments, the use of other hydrogels that crosslink via chemical reactions (e.g., sodium alginate) may be appropriate.

[0062] In both the drop CFU and spiral plater methods, incubation time remains the rate-limiting step, usually taking at least overnight for visible colonies to appear. Similarly, incubation is also the rate-limiting step of the GVA of the present invention. However, applicants have achieved colony detection across all CFU concentrations within 8 hours for E. coli. This improvement in time to detection is due to the unique optical configuration, the presence of staining dyes, and the 3D geometry that maximizes light scattering. Further time reduction can be achieved by using fluorescence imaging, among other alternative embodiments. Thus, the GVA of the present invention can reduce the time of clinical antibiotic susceptibility profiling, among other clinical and / or commercial applications.

[0063] Overall, Applicants have demonstrated that the GVA of the present invention significantly reduces the time and reagents required to measure cell viability compared to the established drop CFU assay, while maintaining the same dynamic range, quantitation, and versatility across different species that have made the drop CFU assay the gold standard for viability measurements in microbiology.

[0064] Example 2: Development and validation of the GVA assay. The most time- and resource-intensive step in traditional drop CFU is the dilution series that must be performed to count individual colonies across several orders of magnitude. Applicants reasoned that the cone's shape allows them to create a dilution series in a single step because the cross-sectional area at the tip is smaller than that near the base. Analytically, the probability that a colony will form at any point along the axis of the cone is proportional to the cross-sectional area at that point (Figure 1a, cyan circle). This probability is defined as a probability density function (PDF) equal to:

number

number

[0065] Applicants simulated the colony distribution within the cone for different CFU / mL (Fig. 1c, d). As expected, the more CFUs in the cone, the more colonies are found near the tip (Fig. 1d, top panel). Regardless of colony density, the CFU / mL estimates converge quickly to the correct value (grey dotted line) as more colony locations are included in equation (2) (Fig. 1d, bottom panel). Remarkably, even when there are more than 10,000 colonies in the cone, the CFU estimates are less than 2-fold off the correct value in 97% of simulations based only on the first 10 colony locations (Fig. 1e, 7f). This rapid convergence to the correct value is the same regardless of CFU concentration. Thus, by utilizing the information encoded in the shape of the cone, it is not necessary to count all colonies to accurately calculate colony density. This concept is similar to a 3D hemocytometer. By counting a subset of colonies within a defined volume, the total concentration can be calculated using probability.

[0066] To test this theory, applicants used a conical variable geometry vessel, i.e., a pipette tip, which is widely used in microbiology. The first experiment was a dilution series using stationary phase E. coli (BW25113). The CFU / mL of stationary phase E. coli was approximately 10 after overnight growth. 9CFU / mL. Cells were serially diluted and each dilution was then treated as a sample of unknown concentration of live cells. Each "sample" was thoroughly mixed with melted LB agarose (cooled to ≦50°C) until the final agarose concentration was 0.5%. Triphenyltetrazolium chloride (TTC) was included in the melted agarose to enhance colony contrast. The agarose was allowed to solidify within the tip before ejecting the tip into an empty tip rack (see Methods). Pipette tips containing agarose were then incubated overnight at 37°C and imaged the next day using a custom-built optical setup with a mirrorless Canon camera (imager) (see Fig. 1f, Fig. 8). Consistent with applicant's simulations, the distribution of colonies formed at the tip was predictable over more than six orders of magnitude based on the PDF (Fig. 1g, slope ≈1). Notably, final colony size decreased with increasing cell density, preventing overlapping colonies even at high densities. Comparing the same batch of cells using GVA and the traditional drop CFU assay, we found that the two approaches were significantly correlated (Fig. 1h, Pearson r=0.98, p-value=4e-16, see Fig. 9 for e.g. drop CFU plates).

[0067] GVA was used to enumerate other Gram-negative (Pseudomonas aeruginosa, Salmonella typhimurium, Pseudomonas putida) and Gram-positive (Bacillus subtilis) strains, as well as eukaryotic yeast cells (Saccharomyces cerevisiae) (Figs. 1i, 10a). Encapsulating the colonies within the pipette tip facilitated the handling of pathogenic strains, as bleach washes could kill all contaminating cells outside the tip without affecting the growth of the colonies within the tip (Fig. 10b). Viability of E. coli biofilms over time was also tested with GVA (Figs. 10c, d). Finally, applicants tested the potential of GVA for rapid quantification of non-model bacterial species. Human-associated biome viability measurements were performed using GVA (Fig. 11). Vigorous swabbing of 24 sites (Fig. 11a) revealed a wide dynamic range of microbial concentrations capable of growth in LB (Fig. 11b). Growing replicate samples at different temperatures revealed temperature-selective growth for different biomes (Figure 11c). Because many of human symbiotic plants cannot be cultured, these experiments inevitably underestimate the bacterial populations of these biomes. However, because GVA uses solid growth media, the same selective culture techniques developed over the past 100 years for standard Petri dish plating can be utilized with GVA, while also allowing high-throughput monitoring of culturable biomes.

[0068] Next, applicants investigated how the dynamic range and accuracy of GVA depended on the optical configuration using a low-cost camera system, also referred to as the imager in one embodiment, which in this case includes a smartphone (iPhone®) with a commercially available macro lens. Applicants designed a pipette tip holder that places a single tip in front of the rear camera and macro lens of the smartphone (iPhone®) (Figures 2a, 13). Calibration revealed that the pixel size of the smartphone (iPhone®) is 13.7 microns, while the pixel size of a Canon® EOS® camera with a 100 mm f / 2.8 macro lens is 6.6 microns (Figure 8). Applicants reasoned that the small pixel size and low electronic depth of the smartphone (iPhone®) camera would reduce the minimum number of colonies detected compared to the mirrorless camera. As expected, comparing images taken with the camera and the iPhone revealed that colonies with the highest CFU concentrations were no longer separable on the iPhone (Figure 2b). Comparing GVA-calculated CFU / mL for the same pipette tip of a dilution series of E. coli using both the iPhone and the camera, Applicants measured a 64-fold reduction in dynamic range on the iPhone compared to the Canon camera (Figure 2c). However, in the iPhone configuration, GVA remained highly linear over nearly five orders of magnitude (green line, slope = 1.04, R 2 = 0.99). The correlation between CFU counting on a smartphone (iPhone®) and the camera configuration on the same pipette tip was 0.99 (Figure 2d). Thus, applicants found that GVA is accurate regardless of optical configuration, but the dynamic range is set by the maximum camera resolution.

[0069] The main advantage of GVA is a >10-fold reduction in time, reagent costs, and plastic waste compared to drop CFU or spiral plater methods (Figure 3). Spiral platers are the most common commercially available alternative to CFU assays, which utilize specialized equipment to dilute samples along an Archimedes spiral. To measure the time savings of GVA, applicants compared three steps of the viability assay, including preparation of solid growth medium (Figure 3b), dilution / plating of 96 conditions (Figure 3c), and imaging / counting of colonies (Figure 3d). The largest time savings was in the plating step. With drop CFU, it took 3 hours to manually plate 96 conditions. Current spiral plater equipment is reported to take 30 seconds per plate, which equates to 96 conditions in 48 minutes. GVA took 5 minutes, which equates to a 36-fold savings in plating time. GVA also had faster preparation times than the spiral plater and drop CFU approaches. The time to image and count colonies was fastest with the spiral plater, based on manufacturer-reported times using the automated colony counter. GVA semi-automated colony counts took similar time to manual drop CFU colony counts, including time for image acquisition, pipette tip segmentation, and user-guided colony detection. In total, one researcher measured the viability of 1,200 conditions in one day using the current equipment.

[0070] Applicants next compared the reagent savings and plastic waste reduction of the three approaches. For the drop CFU assay, 15 pipette tips per sample are standard in laboratory protocols, as each sample must be diluted and then individually transferred to an agarose pad (Figure 3e). For GVA, one pipette tip is used per sample, resulting in a 15-fold savings in pipette tips compared to the drop CFU (Figure 3e). For the spiral plater assay, a Petri dish with solid growth medium is required for each condition (Figure 3g). Compared to the spiral plater method, plastic required from the Petri dish to the pipette tips is reduced. Adding up the costs of pipette tips, agar, and culture plates at the time of writing, Applicants found that the drop CFU was the most expensive of the consumables, costing an average of $222 per 96 samples, compared to $87 and $17 for the spiral plater and GVA, respectively (Figure 3h). The savings in consumables for the spiral plater are offset by significant equipment costs (Figure 3i). Costs were calculated from quotes requested from three distributors for three spiral platers and an automated imaging system. The equipment costs for both GVA and Drop CFU include an electronic multichannel pipette. The additional equipment costs for GVA depended on the optical configuration (Figure 3j) and were at least an order of magnitude lower than the spiral plater system. In summary, this analysis showed that GVA significantly reduced operator time, equipment and reagent costs, and the carbon footprint of the viability assay.

[0071] Next, the applicants investigated the robustness of GVA. 2 ~10 7 We measured the counting noise among four technical replicates across a range of CFU concentrations in CFU / mL (Fig. 4a, b). The noise was calculated using the coefficient of variation (COV) among replicates. Across all measured CFU concentrations, the GVA noise is below that of the drop CFU assay for both the camera and smartphone optical configurations. As with the drop CFU assay, the GVA noise is heteroscedastic and increases as the number of colonies decreases as expected for a Poisson process.

[0072] After verifying that the technical noise of GVA is low, applicants investigated the impact of two types of real-world errors on GVA calculations: missing colonies and uncertainty in the location of the cone tip. These errors were examined using both simulated and experimental data. As expected, as the number of missing colonies increases, the error increases (Fig. 4c, e), but the fractional error is the same for all seeding densities. Notably, removing 10 of the 15 colonies counted in the simulated data yielded estimates within a factor of 2, regardless of the initial CFU concentration. This robustness was replicated in the experimental data, consistent with the observation that the locations of as few as five colonies are sufficient to calculate CFU / mL within an average of 2-fold (Fig. 1e). In the case of pipette tip location errors, GVA calculations at high CFU concentrations are more susceptible to tip location misidentification than at low cell concentrations (Fig. 4d, f blue and black lines). Nonetheless, missing 10% of the tip positions (4 mm for the 36 mm cone) resulted in estimates within a factor of 2 from the correct value in both simulations and experiments. Finally, the correlation between drop CFU and GVA (Fig. 1h) decreased slightly from 0.98 to 0.97 with the combination of missing up to 10 colonies and a 4 mm defect in tip positions (Figs. 4g, h, 14). These simulation and experimental data highlight the robustness of GVA.

[0073] Overall, Applicants' analysis indicates that GVA is accurate and robust, reducing costs and time while maintaining sensitivity over a range comparable to that of the Gold Standard Drop CFU.

[0074] Example 3: High-throughput viability screen for stationary phase E. coli. Previous studies have found that slow growth is a non-genetic antibiotic resistance that buys time for viable cells to develop genetic resistance. Slow-growing cells generally have reduced metabolic activity and DNA replication compared to exponentially growing cells. As a result, slow-growing cells are resistant to antibiotics that target DNA synthesis (fluoroquinolones), protein translation (aminoglycosides), and cell wall biosynthesis (beta-lactams). Growth-dependent resistance can only be observed by measuring viability, but the tediousness and expense of drop CFU assays limits extensive profiling. Using GVA, applicants directly compared the viability of exponentially growing and stationary phase cells with different doses of three antibiotics administered for various times. In total, applicants tested the three antibiotics in duplicate at six different concentrations for five different time periods on stationary and exponential cells, resulting in a total of 360 viability measurements (Figure 5a, b). The data were obtained in one day by one researcher using only four tip boxes. Stationary phase cells were more resistant to ciprofloxacin, carbenicillin, and gentamicin. Notably, for carbenicillin, stationary cells treated with 100 μg / mL carbenicillin for 24 h experienced a less than 10-fold decrease in viability compared to a 10,000-fold decrease in exponential cells. Treatment of exponential cells with 10 μg / mL carbenicillin showed no change in colony numbers for the first 6 h, but after 24 h treatment, there was an increase in viable cells, indicating a slowly expanding drug-resistant pool (Figure 5b). Ciprofloxacin at 10 μg / mL had a biphasic pharmacodynamic profile, showing initial bactericidal activity within 1 h and a 10-fold decrease in viability in both stationary and exponentially growing cultures. However, this activity stabilized over 6 h, reaching a second-phase kill by 24 h. At 10 μg / mL gentamicin, it took a full 24 hours for stationary phase cell viability to decrease by more than 10-fold. For untreated cultures, Applicants found that the concentration of exponentially growing cells decreased by approximately 10% in 6 hours. 9We observed that the number of viable cells increased until it reached a peak concentration of CFU / mL (Figure 15). Upon entering stationary phase, the number of viable cells decreased over time, as previously reported. These data illustrated the utility of GVA to measure efficacy of treatment independent of growth rate.

[0075] To explore the potential of the GVA technique for high-throughput viability measurements, applicants screened the ICCB Enzo Bioactive library (469 compounds) against static and exponentially growing cultures (Fig. 5c, d). The Enzo library contains a wide range of chemicals, including bioactive lipids, small molecule inhibitors, and ion channel ligands (Fig. 5c), spanning the structural diversity of larger libraries such as the Maybridge HitFinder library, which contains approximately 14,000 compounds (Fig. 16). Viability of BW25113 E. coli treated with the Enzo library was measured in both exponential and stationary phase. Including controls and removal of pipette error, 2267 conditions were measured. An equivalent screen using drop CFU or spiral plater assays would require 355 tip boxes or 2267 Petri dishes, respectively. 24 tip boxes were required for GVA. No edge effects were observed for either stationary or exponential plates (Mann-Whitney U test, p-value > 0.05, Figure 16b). Mean differences between drug classes were modest (Figure 16c, p-value > 0.001, ANOVA, p-value corrected for multiple hypothesis testing) and not significantly different from the control (p-value > 0.01, pairwise Tukey test). Five compounds (mitomycin C, phentolamine, E-4031, phenamil, and diphenyliodonium) corresponding to a hit rate of approximately 1% were selected for follow-up validation. Mitomycin C is a known antibiotic that acts by DNA cross-linking. As expected, applicants found that it was more active against exponentially growing cells compared to stationary phase cells (Figure 5f). Phentolamine is an α-adrenergic receptor antagonist. Phentolamine has previously been shown to block norepinephrine- and epinephrine-induced growth in E. coli, presumably by antagonizing α-adrenergic receptors. Applicants found that stationary cells at high concentrations (20 μg / mL) were more sensitive to the effects of phentolamine than exponentially growing cells (FIG. 5g), confirming the difference in sensitivity observed in the screen. E-4031 and phenamil had no dose-dependent effect on viability (FIG. 17).Finally, Applicants discovered that diphenyleneiodonium (DPI), a promiscuous NADPH oxidase (NOX) inhibitor, was active against both stationary and growing cultures (Figure 5h). Previous studies have identified DPI as having antibacterial properties; however, the mechanism of DPI bactericidal activity remains unclear. Applicants were intrigued by the bactericidal activity of DPI, which reduces reactive oxygen species (ROS) in eukaryotes by inhibiting NOX, in contrast to the mechanism of many antibiotics, which increase the ROS pool.

[0076] To investigate the bactericidal mechanism of DPI, applicants first examined E. coli ROS levels upon treatment with DPI. ROS levels were measured using the fluorescent CellROX dye, which measures cytoplasmic superoxide. After treatment with a lethal dose of DPI, single-cell fluorescence was measured over time and compared to untreated controls (Figure 6a). As expected, DPI significantly reduced ROS, reaching a nadir approximately 75 min after drug addition (compare blue and yellow lines in Figure 6a). The depth and duration of ROS reduction was proportional to DPI concentration (Figure 18a). Surprisingly, this reduction was followed by a rapid rise in ROS. In contrast to DPI, ciprofloxacin treatment resulted in a monotonically increased ROS level (Figure 6a, orange line). It is the increased ROS levels that underlie ciprofloxacin's bactericidal activity. Therefore, applicants next investigated whether the ROS spike induced by DPI also underlies its bactericidal activity. Applicants compared the DPI sensitivity of stationary phase cells in aerobic and anaerobic environments. DPI was less active in anaerobic cultures (Fig. 6b), similar to gentamicin or ciprofloxacin (Fig. 18b, c). This data suggested that high levels of ROS are part of the bactericidal mechanism of DPI, despite the initial reduction in ROS. Further supporting this, the addition of ROS scavengers also reduced DPI efficiency (Fig. 18d).

[0077] Intermediate DPI concentrations altered ROS levels, but viability was maintained as measured by GVA. Applicants examined cell morphology after 4 h treatment with DPI below 10 μg / mL and observed the formation of bacterial filaments (Figure 6c). Filament formation is a typical feature of SOS activation, and increased ROS is a well-established SOS activator. Applicants therefore wondered whether DPI was initiating SOS. Applicants used a PEC GFP promoter library to examine promoter activity of genes downstream of lexA. LexA is the master transcriptional repressor of genes in the SOS regulon, such as polB, dinB, dinG, and yjiH, and is autocatalytically degraded by activated recA. Applicants observed sustained, dose-dependent induction of the polB promoter compared to a ribosomal protein control (rrnB) (Figure 6d, compare solid and dashed lines). The highest promoter activity corresponded to the intermediate dose of DPI (3 μg / mL), where filamentation was observed. Applicants also observed a DPI-dependent increase in dinB, dinG, and yjiH promoter activity (FIG. 19). The autorepressed lexA promoter also increased its activity within 90 min after DPI addition.

[0078] Because SOS activity reduces the effectiveness of other bactericides, applicants predicted that recA-mediated SOS activation would be important for maintaining viability in the presence of DPIs. As predicted, recA knockouts rendered cells susceptible to DPIs in both stationary and exponential phases of growth (Figure 6e), but the increase in DPI potency was more pronounced in exponentially growing cells. In contrast, knockout of other DNA repair enzymes, redox repair enzymes, or ROS scavengers did not substantially alter DPI potency in either growth phase (Figure 20). Knocking out yedZ and fre, genes recently identified as part of a bacterial NOX-like system, slightly increased DPI potency against stationary cells, indicating that these proteins are unlikely to be the primary targets of DPIs in E. coli (Figure 6e). Thus, the data demonstrated that DPI activates SOS and that SOS activation enhances cell viability.

[0079] Applicants therefore wondered whether DPIs might antagonize other antibiotics whose efficacy is reduced by the SOS response. Such antagonism has been observed in combination with ciprofloxacin and metronidazole, a redox-active prodrug known to activate SOS. To test for antagonism, Applicants measured viability in a time-resolved checkerboard assay using GVA (Fig. 6f, 21). In the checkerboard assay, DPI was combined with either ciprofloxacin or gentamicin across a 6 × 6 dose matrix. The ease of GVA allowed for checkerboard sampling over time, resulting in a complete pharmacokinetic profile of the drug-drug interaction. DPI antagonized both ciprofloxacin and gentamicin against stationary-phase E. coli, increasing viability by 1,000-fold compared to either agent alone after 24 h of treatment (Fig. 6f, g). This antagonism was not observed in growth inhibition assays (Figure 6h), highlighting the value of viability data when investigating drug-drug interactions. DPI antagonism between ciprofloxacin and gentamicin was also observed with S. typhimurium (Figure 22). Pretreatment of cells with DPI for 2 h before addition of ciprofloxacin further enhanced protection, whereas pretreatment with ciprofloxacin for 2 h reduced DPI antagonism (Figure 6i).

[0080] Overall, we found that DPIs first reduced ROS, and then the ROS burst enhanced their bactericidal effect. As predicted by previous studies of ROS killing, the efficacy of DPIs was dependent on recA-mediated SOS activation. By activating SOS, DPIs led to increased drug resistance to fluoroquinolones and aminoglycosides, as revealed by time-survival checkerboards.

[0081] Example 4: Materials and Methods Derivation of the cone axial probability density function: Assuming that a single cell is well mixed, suspended, and cast into a 3D cone, the probability of a colony forming at a distance x from the origin is proportional to the fraction of the total volume (V) occupied by the infinitesimal volume (dV) at x. dV is defined as:

number

number

[0082] The probability density function (PDF) for this geometry can be solved as follows:

number

number

number

number

number

number

[0083] Using the PDF, applicants can estimate the number of CFU / mL using the following formula:

number

[0084] With CDF you can:

number

[0085] Strains and growth conditions: Unless otherwise noted in the text, E. coli strain BW25113 was used. This strain was obtained from the Yale Coli Genetic Stock Center. E. coli was grown in LB (Sigma-Aldrich) in a shaking incubator at 37°C. B. subtilis strain W168 was a gift from the Garner laboratory and was grown in LB at 37°C in a shaking incubator. Pseudomonas putida strain KT2440 was a gift from Jacob Fenster and was grown in LB at 30°C in a shaking incubator. Salmonella typhimurium strain SL1344 was a gift from Corrie Detweiler and was grown in LB at 37°C in a shaking incubator. Saccharomyces cerevisiae strain BY4741 was a gift from Roy Parker and was grown in YEPD in a shaking incubator at 30°C. Pseudomonas aeruginosa strain PA01 was a gift from Zemer Gitai and was grown in LB in a shaking incubator at 37°C. Knockouts were selected from the Keio collection (Dharmacon). The E. coli PEC promoter library was obtained from Dharmacon (PEC3877).

[0086] All bacterial and yeast strains were streaked onto agar plates with appropriate antibiotic selection as required (kanamycin for Keio and PEC strains). These plates were stored in a refrigerator at 4°C for one month. Individual colonies were then selected and grown overnight in 3–5 mL cultures in 12 mL culture tubes with appropriate antibiotic selection as required. Each selected colony was considered a biological replicate. Multiple measurements of the same culture were considered technical replicates.

[0087] Antibiotic treatments: Antibiotic treatments were typically performed in 96-well plates with a 12-channel electronic pipette. For stationary phase treatments, bacterial cells were grown overnight (16+ hours) in a shaking incubator (180 RPM). For Pseudomonas putida only, cells were grown for 2 days. Once in stationary phase, cells were dispensed into a 96-well flat-bottom plate at 100 μL per well. 1000x drug treatments were plated into a separate 96-well round-bottom plate. Using a 100 nL pin transfer, the drug plate was diluted 1:1000 into the cell plate. This plate was then placed in a shaking incubator for the duration of the experiment.

[0088] To measure antibiotic treatment in log phase, the overnight culture was diluted 1:1000 with fresh LB. The culture was then placed in an incubator for 2 hours. After this incubation, the cells were distributed into 96-well plates followed by drug treatment.

[0089] Drop CFU assay: Drop CFU assay was performed similarly to the method described. Briefly, in a 96-well plate, 90 μL was added to all wells except row A. To row A, a 100 μL volume of sample solution was added. 10 μL of cells were removed from row A and added to row B, then mixed three times. This process was repeated from B to C until the final dilution in row H corresponded to a 1e-7 dilution of the original sample. Pipette tips were changed for each row to reduce sample carryover. A 3 μL drop from each column of the dilution series was transferred to an LB agar pad. Once all the liquid was absorbed by the agar (usually 15-30 min), the agar plate was inverted and placed in a standing incubator at 37 °C overnight. Counting the following morning was done manually. The first dilution with individually detachable colonies was used for counting and multiplied by the corresponding dilution factor.

[0090] Embedding GVA: The embedding goal was to homogenously mix the sample within a liquid hydrogel that would quickly solidify into a 3D mold. Applicants used 0.5% agarose as a convenient hydrogel that solidifies rapidly and prevents cell motility once solidified. Pipette tips (200 μL, VWR Universal) were most commonly used as a reproducible and inexpensive way to embed 3D shaped scaffolds. 1. Preparation of agarose solution. A 0.66% agarose solution was prepared in the cell culture medium of choice. Applicants found that the color of LB and YEPD did not affect imaging of the pipette tip. Agarose (0.66 g) was added to a 100 mL volume of LB and heated in a microwave oven until completely dissolved. Care was taken during heating to avoid overflow. Once completely dissolved, the liquid was placed in a 50°C heat bath and maintained in a liquid state until use. At this stage, tetrazolium chloride (TTC, final concentration 25 μg / mL) was added to a 1000x stock of LB agarose for all bacterial experiments. Respiratory bacteria reduce tetrazolium to water-insoluble formazan, staining the colonies red. 2. Cell preparation. Fresh 96 round-bottom plates were prepared by adding 50 μL of LB or YEPD to each well. The sample plates containing cells and drugs were removed from the shaking incubator, and 2 μL of treated cells were transferred to the 50 μL LB plate using a pin transfer tool (2 μL hanging drop, VP409). If a time course experiment was to be performed, the sample plates were returned to the shaking incubator. 3. Embedding. For embedding, applicants found that an electronic multichannel pipettor was most convenient for large numbers of samples. Applicants typically used a 12 channel P200 (Eppendorf Explorer, 4861000724). Prior to pouring the liquid agarose into the reservoirs, the following items were gathered: 96 well plate with 20 μL of sample (from step 2), a box of autoclaved P200 pipette tips, an empty P200 tip box filled with ice water, and an empty P200 tip box with 2 mL of water in the bottom to hold the embedded cells. At this point, the liquid agarose was poured into a 100 mL reservoir for ease of use with the multichannel pipette. Using the pipette and mixing function of the pipette, 150 μL of LB agarose solution was removed from the reservoir and mixed twice with one row of the sample plate (final volume 200 μL, final agarose concentration 0.5%, 1:100 dilution from the sample plate). After mixing, 150 μL was collected into the same pipette tip, avoiding the formation of air bubbles. These tips were then placed in an ice bath for 6 seconds to allow the hydrogel to solidify and clog the tip. The tip was then expelled into an empty pipette tip box. This process was repeated for all seven additional rows in the plate. Using 150 μL and a 1:100 dilution of the original sample, the lower limit was 667 CFU / mL. 4. Incubation. Once the embedding process is complete, the tip box with the LB agarose cell suspension is left at room temperature for approximately 30 minutes to ensure the agarose completely solidifies. The tip is then transferred to a standing incubator overnight to allow the colonies to grow. Applicants have found that the size of the colonies does not change even after overnight incubation, and so they can be imaged up to 4 days after embedding, as long as the cells are maintained in a hydrated environment.

[0091] Drug Screening: Screening was performed using ICCB Enzo Bioactive hit library (Enzo, BML-2840-0100). E. coli was grown to stationary phase in 60 mL of LB overnight culture. The following morning, 60 μL of the overnight culture (stationary phase) was added to 60 mL of fresh LB and grown for 2 h in a shaking incubator (exponential phase). Cells were then dispensed in 100 μL volumes into 96-well plates.

[0092] Biofilm growth and treatment: The MG1655 E. coli strain was used for biofilms. An overnight culture was diluted 1:10 in LB. 5 Biofilms were seeded into U-bottom 96-well plates and grown for 48 h at 37°C in a stationary incubator. In transient experiments, a separate plate was used for each time point, and biofilms were dispersed at the indicated times. The reported times represent the number of hours after the initial 48 h of incubation. To disperse biofilms, non-adherent cells were aspirated, wells were washed with PBS, and fresh PBS was added to the wells. Plates were covered with foil plate seals (VWR, 60941-126) and placed on a plate shaker at 3000 rpm for 30 min. Dispersed cells were diluted to 10 4 2x dilutions were performed and GVA was performed. Crystal violet staining was used to confirm proper dispersion. Any replicates that were not completely dispersed were discarded.

[0093] Advanced GVA imaging: Imaging was performed with a custom imaging device (Figure 8) or an iPhone 12 (Figure 2). For the custom device, a mirrorless commercial camera (Canon EOS RP) with a 1:1 macro lens (Canon, f / 2.8 100mm) was used to obtain high-quality images capable of resolving the smallest colonies. For the iPhone, the parts were designed in FreeCAD and then 3D printed in PLA using a Lulzbot Taz Pro FDM printer. All pieces could fit on the print bed in one print. Glue stick was used before printing to increase adhesion on the print bed. The print bed temperature was set at 70°C for all layers, and the nozzle temperature was set at 225°C. The print speed was set at 10mm / s for the first layer and increased to 30mm / s for subsequent layers. After printing, the depth channel (green in Figure 2a) was tapped at 8–32 bits. After mounting the holder on a Xenvo macro lens with the wide-field lens removed, the tips were placed in front of a white background and imaged in ambient lighting using the autofocus feature on the iPhone. Three images were taken per tip and the most in-focus tip was selected before processing using the Matlab app.

[0094] A digital camera was mounted on top of a light box providing uniform illumination. The light box was then moved by a stepper stage to allow automatic imaging of 12 tips (3 tips per field of view, 4 fields of view). The light box consisted of a Styrofoam box covered with a clear acrylic sheet (McMaster Carr, #8560K257). A sheet of white paper was attached to the underside of the acrylic to act as a diffuser. The inside of the Styrofoam box was lined with foil (Reynolds). A high-intensity cyan LED (Luxeon Rebel, 3Up) was placed on a heat sink inside the box and powered by a constant current driver (BuckBlock, 2100mA). The Styrofoam light box was attached to a stepper motor stage (Thorlabs, LTS300). The camera was attached to a z-translator (Thorlabs, MT1) fixed to a right-angle plate (Thorlabs, AP90) using a tripod 1 / 4"-20" screw. The Z-positioner was used to set the distance such that three pipette tips could be imaged in one field of view, and a macro lens was used to focus on the tips. In our camera, this corresponded to a pixel size of 5.8 μm (Figure 8c). A broken 12-channel P200 head was used to position the tips on the light box. This allowed for easy loading and unloading of samples using a spring release, and also provided a standard orientation of the tips.

[0095] Images were collected using custom Labview scripts that controlled the camera and stepper stage. Labview was used with a separate program called digiCamControl (digicamcontrol.com) to access the camera functions and acquire images. Typical camera settings used a shutter speed of 1 / 100 sec, aperture of 6.3, and ISO 100. Five images were collected for each field of view, after which the stage moved to the next three pipette tips (27 mm). Images were stored directly in the instrument's computer as high-resolution .jpg files. With this instrument, a typical experiment of 96 tips can be imaged in approximately 7 minutes.

[0096] Image processing: The goal of image processing was to identify and extract individual pipette tips from the collected images and to identify individual colonies. These were divided into two steps, performed sequentially. Matlab (Mathworks, R2021b) was used for all image processing analysis. The developed app can be used without a Matlab license, using a compiled version specific to the user's operating system. 1) Pipette tip segmentation: All images from a given field of view were converted to a 16-bit grayscale image. The green channels of the images were summed and that image was used for downstream analysis. The overall orientation of the image was calculated to make each tip perpendicular to the x-axis. This was necessary to accurately calculate the distance of the colony from the pipette tip due to slight variations in the tips mounted on the light box. The Hessian of the image (fibermetric.m) was calculated and convolved with a horizontal line to identify the angle of the tip. The image was then rotated (imrotate.m) by this angle so that the pipette was vertical in the image. The Hessian was recalculated from the rotated image to identify the x-pixel corresponding to the pipette tip. The left and right boundaries of the pipette tip were calculated using convolution of a single line from the center of the image at various angles. These lines were then extended to the base of the pipette tip to identify the left and right boundaries of the tip. Each of the three wells was then saved to a cell array. 2) Semi-automated segmentation: Colonies were segmented using a semi-automated custom script in Matlab. From the extracted images of the pipette tip, the user selected one of four different segmentation routines corresponding to the various sizes of colonies within the pipette tip. The first routine segmented the entire pipette tip, while the last segmentation algorithm zoomed to 1 / 7 of the entire tip and segmented the first 30 colonies. Segmentation was performed using an image processing toolbox in Matlab. The user could then manage the automatic segmentation to add missing colonies or remove erroneous colonies.

[0097] The colony counts and locations of the first and last colonies were used in equations (1) and (2) to calculate the GVA estimate of CFU / mL. In the error analysis, the coefficient by which the GVA estimate differed from the correct value was calculated according to the following factorization:

number

[0098] This approach to error calculation takes into account the large dynamic range of possible CFU / mL.

[0099] Microscopy measurements: For all microscopy experiments, overnight cultured cells were diluted 1:100 in minimal medium (PMM) and shaken at 37°C for 2 hours to ensure that the cells had completed the induction phase. After 2 hours of growth, 2 μL of the diluted cell culture was added on top of a chilled 200 μL 2% low melting point agarose pad containing CellROX dye (5 μM). The agarose pad was molded to fit a 96-well square bottom plate (Brooks Automation, MGB096-1-2-LG-L). After drying for 10 minutes, the pad with attached cells was inverted and pressed into the bottom of the imaging plate. The field of view (FOV) was selected manually on the microscope. After selecting the FOV, drug was added to the top as before before imaging began. Applicants have previously found that drug diffuses through the pad in a matter of minutes.

[0100] Imaging was performed using a Nikon Ti2 inverted microscope running the Nikon Elements software package. Fluorescence excitation was achieved with a laser source (488 nm and 561 nm) using high-angle illumination to minimize out-of-focus background. All images were acquired using a 40x, NA0 95 air objective. Images were acquired with an sCMOS camera (ORCA-Fusion, Hamamatsu, Japan).

[0101] Image processing was performed in Matlab (Mathworks, R2020a) and followed the general scheme described in

[18] . Briefly, the illumination profile for every image was estimated from the average of 50 images per FOV. Before image correction, the illumination pattern was spread using morphological aperture and blurring. After illumination correction, jitter within the movie was removed by aligning each successive frame using a fast 2D Fourier transform implemented in Matlab. The background was locally subtracted based on an estimate of the background calculated using morphological image aperture before segmentation.

[0102] Cell segmentation was performed using a Hessian-based fiber-metric routine implemented in Matlab, specialized for identifying tubular structures. Segmented regions were included only if they met minimum area and intensity thresholds that were manually selected based on the camera and laser settings. To remove rare segmented debris, the average Euclidean distance of each cell from all other cells in the multidimensional feature space was calculated, and objects with an average distance above the 95th percentile were removed. Cell position in the feature space was defined by the segmented area, perimeter, major / minor axis lengths, and circularity, which were extracted using Matlab's regionprops command.

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Claims

1. A system for geometric survival assays (GVA), - Cell samples introduced into growth medium, - A variable-shaped container adapted for culturing the cell sample in the growth medium, comprising an axially symmetric variable-shaped container, A system comprising: an imaging device adapted to capture images of one or more colony-forming units (CFUs) in the cultured variable-shape container.

2. The system according to claim 1, wherein the cell sample is selected from a biological sample, an environmental sample, a commercial sample, a prokaryotic cell sample, or a eukaryotic cell sample.

3. The system according to claim 1, wherein the cell sample includes a microbial sample selected from a bacterial sample, a microbiome sample, a yeast sample, and a fungal sample.

4. The system according to claim 3, further comprising at least one cell contrast agent introduced into the growth medium.

5. The system according to claim 1, wherein the cell sample includes a diluted cell sample.

6. The system according to claim 1, wherein the growth medium includes a semi-solid growth medium.

7. The system according to claim 1, wherein the variable-shaped container is equipped with a pipette tip.

8. The system according to claim 1, wherein the variable-shape container is semi-transparent.

9. The system according to claim 1, wherein the variable-shape container comprises a mold having one or more variable-shape containers.

10. The system according to claim 9, wherein the mold comprises a three-dimensional printed chip having a plurality of variable-shaped containers.

11. The system according to claim 1, wherein the growth medium includes a liquid growth medium for stepwise dilution of the cell sample.

12. The system according to claim 1, wherein the position of the imaging device is adjustable with respect to the variable-shaped container.

13. The system according to claim 1, wherein the position of the variable-shaped container is adjustable with respect to the imaging device.

14. The system according to claim 12 or 13, wherein the position is selected from a vertical position, a horizontal position, and the depth of the imaging device from the container.

15. The system according to claim 1, wherein the imaging device is selected from a microscope, a digital camera, and a smartphone.

16. The system according to claim 1, wherein the imaging device is adapted to identify one or more images of the CFU at the end portion of the variable-shaped container.

17. The imaging device, - A processor that responds to the imaging device and controller, The system according to claim 1, comprising an imaging platform comprising: a computer executable program adapted to identify one or more images of the CFU at the end portion of the variable-shaped container.

18. The aforementioned computer executable program is: - Identify the boundary of the tip of the variable-shaped container, - Align the tip of the variable-shaped container, - Perform the division of the colony, and - The system according to claim 17, configured to calculate CFU.

19. The system according to claim 1, further comprising a treatment introduced into the cell sample.

20. The above treatment - To kill the cells in the cell sample, - Inhibits the proliferation of the cells in the cell sample, - To change one or more phenotypic characteristics of the cells in the cell sample, or - The system according to claim 19, comprising one or more compounds adapted to alter one or more genotype characteristics of the cells in the cell sample.

21. The system according to claim 20, wherein the treatment compound is selected from therapeutic compounds, antibiotics, bactericidal compounds, bacteriostatic compounds, anticancer compounds, antifungal agents, and drug-screening compounds.

22. The system according to claim 17, further comprising outputting the number of viable CFUs in the cultured cell sample.

23. The system according to claim 1, further comprising a CFU index.

24. Colony-forming unit (CFU) assay device comprising a variable-shape container adapted for culturing cell samples in growth medium, wherein the variable-shape container comprises an opening, an axially symmetric intermediate portion, and a terminal portion narrower than the opening.

25. The device according to claim 24, wherein the variable-shaped container is equipped with a pipette tip.

26. The device according to claim 24, wherein the variable-shape container comprises a mold having one or more variable-shape containers.

27. The device according to claim 24, wherein the type comprises a three-dimensional printed chip having a plurality of variable-shaped containers.

28. The device according to claim 24, wherein the variable-shape container is semi-transparent.

29. The device according to claim 24, wherein the variable-shape container includes a wedge configuration, an inclined configuration, or a conical configuration.

30. The device according to claim 24, wherein the cell sample is selected from a biological sample, an environmental sample, a commercial sample, a prokaryotic cell sample, or a eukaryotic cell sample.

31. The device according to claim 24, wherein the cell sample includes a microbial sample selected from the group consisting of bacterial samples, microbiome samples, yeast samples, and fungal samples.

32. The device according to claim 24, further comprising at least one cell contrast agent introduced into the growth medium.

33. The device according to claim 24, wherein the cell sample includes a diluted cell sample.

34. The device according to claim 24, wherein the growth medium includes a semi-solid growth medium.

35. The device according to claim 24, further comprising an imaging device adapted to capture one or more images of colony-forming units (CFUs) in the cultured variable-shape container.

36. The imaging device according to claim 35, wherein the imaging device is adjustable with respect to the variable-shaped container.

37. The device according to claim 24, wherein the variable-shape container is adjustable relative to the imaging device.

38. The device according to claim 36, wherein the position is selected from a vertical position, a horizontal position, and the depth of the imaging device from the container.

39. The imaging device is selected from a microscope, a digital camera, and a smartphone, as described in claim 35.

40. The device according to claim 35, wherein the imaging device is adapted to identify one or more images of the CFU at the end portion of the variable-shaped container.

41. The imaging device, - A processor that responds to the imaging device and controller, The device according to claim 35, comprising an imaging platform comprising: a computer executable program adapted to identify one or more images of the CFU at the end portion of the variable-shaped container.

42. The aforementioned computer executable program is: - Identify the boundary of the tip of the variable-shaped container, - Align the tip of the variable-shaped container, - Perform the division of the colony, and - The device according to claim 41, configured to calculate CFU.

43. The device according to claim 24, further comprising a treatment introduced into the cell sample.

44. The above treatment - To kill the cells in the cell sample, - Inhibits the proliferation of the cells in the cell sample, - To change one or more phenotypic characteristics of the cells in the cell sample, or - The device according to claim 43, comprising one or more compounds adapted to alter one or more genotype characteristics of the cells in the cell sample.

45. The device according to claim 44, wherein the treatment compound is selected from therapeutic compounds, antibiotics, bactericidal compounds, bacteriostatic compounds, anticancer compounds, antifungal agents, and drug-screening compounds.

46. The device according to claim 24, further comprising a CFU index.

47. A system for imaging geometric survival assays (GVA), - An imaging device positioned adjacent to the light source, - An adapter that mechanically responds to the imaging device, - A mount for fixing a variable-shaped container adapted for culturing a cell sample in a growth medium, wherein the container is axially symmetric, comprising: - The mount is adjustable to position the container relative to the imaging device so that the imaging device can capture images of one or more colony-forming units (CFUs) at the end portion of the culture vessel.

48. The system according to claim 47, wherein the imaging device includes a smartphone.

49. The system according to claim 48, wherein the smartphone is fixed to a base.

50. The system according to claim 48, wherein the adapter comprises a lens aperture positioned adjacent to the camera of the smartphone.

51. The system according to claim 50, further comprising a macro lens disposed within the lens aperture.

52. The system according to claim 47, wherein the mount comprises one or more container holders, each configured to secure one or more variable-shaped containers.

53. The system according to claim 47, wherein the mount is equipped with a vertical adjuster.

54. The system according to claim 47, wherein the mount is equipped with a depth adjuster.

55. The system according to claim 47, further comprising an opaque background positioned behind the variable-shaped container.

56. The aforementioned smartphone, - A processor that responds to the smartphone having a controller, The system according to claim 48, comprising an imaging platform comprising: a computer executable program adapted to identify one or more images of the CFU at the end portion of the variable-shaped container.

57. The aforementioned computer executable program is: - Identify the boundary of the tip of the variable-shaped container, - Align the tip of the variable-shaped container, - Perform the division of the colony, and - The system according to claim 56, configured to calculate CFU.

58. The system according to claim 47, wherein the variable-shaped container is equipped with a pipette tip.

59. The system according to claim 47, further comprising a treatment introduced into the cell sample.

60. The above treatment - To kill the cells in the cell sample, - Inhibits the proliferation of the cells in the cell sample, - To change one or more phenotypic characteristics of the cells in the cell sample, or - The system according to claim 59, comprising one or more compounds adapted to alter one or more genotype characteristics of the cells in the cell sample.

61. The system according to claim 60, wherein the treatment compound is selected from therapeutic compounds, antibiotics, bactericidal compounds, bacteriostatic compounds, anticancer compounds, antifungal agents, and drug-screening compounds.

62. A system for imaging geometric survival assays (GVA), - Imaging device, - Light source and, - A frame for fixing one or more variable-shaped containers adapted for culturing cell samples in growth medium, wherein the containers are axially symmetric, comprising: - The frame and / or the imaging device is adjustable to position the container relative to the imaging device so that the imaging device can capture images of one or more colony-forming units (CFUs) at the end portion of the culture vessel.

63. The system according to claim 62, further comprising an opaque background positioned behind the variable-shaped container.

64. The imaging device, - A processor that responds to the imaging device, The system according to claim 62, comprising an imaging platform comprising: a computer executable program adapted to identify one or more images of the CFU at the end portion of the variable-shaped container.

65. The aforementioned computer executable program is: - Identify the boundary of the tip of the variable-shaped container, - Align the tip of the variable-shaped container, - Perform the division of the colony, and - The system according to claim 64, configured to calculate CFU.

66. The system according to claim 62, wherein the variable-shaped container is equipped with a pipette tip.

67. The system according to claim 62, further comprising a zoom adjuster that responds to the imaging device.

68. The system according to claim 62, further comprising a pan adjuster that responds to the frame.

69. A method for performing a geometric survival assay (GVA), - Introducing the cell sample into the growth medium, - The growth medium is placed in a variable-shaped container, and the container is axially symmetrical. - Culturing the cell sample in the variable-shaped container, - To image the colony embedded in the variable-shaped container, and A method comprising: identifying a subset of viable microbial colony-forming units (CFUs) within the variable-shaped container.

70. The method according to claim 69, further comprising the step of outputting the number of viable CFUs in the cultured microbial sample.

71. The method according to claim 69, wherein the cell sample is selected from a biological sample, an environmental sample, a commercial sample, a prokaryotic cell sample, or a eukaryotic cell sample.

72. The method according to claim 69, wherein the cell sample comprises a microbial sample selected from a bacterial sample, a microbiome sample, a yeast sample, and a fungal sample.

73. The method according to claim 69, wherein the cell sample comprises a diluted cell sample.

74. The method according to claim 69, wherein the growth medium includes a semi-solid growth medium.

75. The method according to claim 69, wherein the variable-shaped container is equipped with a pipette tip.

76. The method according to claim 69, wherein the variable-shaped container is semi-transparent.

77. The method according to claim 69, further comprising the step of treating the cell sample.

78. The aforementioned treatment step involves the cell sample, - To kill the cells in the cell sample, - Inhibits the proliferation of the cells in the cell sample, - To change one or more phenotypic characteristics of the cells in the cell sample, or - The method according to claim 77, comprising one or more compounds adapted to alter one or more genotype characteristics of the cells in the cell sample.

79. The method according to claim 78, wherein the treatment compound is selected from therapeutic compounds, antibiotics, bactericidal compounds, bacteriostatic compounds, anticancer compounds, antifungal agents, and drug-screening compounds.

80. The imaging device, - A processor that responds to the imaging device, The method according to claim 69, comprising an imaging platform comprising: a computer executable program adapted to identify one or more images of the CFU at the end portion of the variable-shaped container.

81. The aforementioned computer executable program is: - Identify the boundary of the tip of the variable-shaped container, - Align the tip of the variable-shaped container, - Perform the division of the colony, and - The system according to claim 80, configured to calculate CFU.

Images