System and method for measuring cell viability with high throughput by continuous geometry

Abstract

The present invention includes systems, methods, and compositions for geometric viability assays (GVA) adapted to measure individual colony forming units (CFUs) from diluted samples using one or more variable geometry containers. The GVA system includes introducing a cell sample into a growth medium, incubating the cell sample in the growth medium using an axisymmetric variable geometry container, and capturing one or more images of the CFU in the incubated variable geometry container using an imager.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This International PCT Application claims the benefit of and priority to U.S. Provisional Application No. 63 / 334,375, filed on April 25, 2022, the description, claims, and drawings of which are incorporated herein by reference in their entirety.

[0003] Government Interest Statement

[0004] This invention was made with government support from the National Institutes of Health under Grant Nos. 1DP2GM123458 and 5T32AG000279-14. The government has certain rights in this invention. Technical Field

[0005] The present invention relates to a high throughput system for objective, normalized determination of colony forming units using a cell-based detection system. In particular, the present invention relates to a novel geometric viability assay (GVA) suitable for measuring individual colony forming units from a microbial sample (and preferably a processed microbial sample) using one or more variable geometry containers. Background Art

[0006] The accelerating development of antimicrobial resistance (AMR) is causing a global health crisis. AMR was associated with an estimated 4.95 million deaths in 2019 and is projected to cause more than 10 million deaths by 2050. New technologies and innovations are urgently needed to expand the discovery pipeline for novel antibiotics and to more rapidly identify antibiotic susceptibility in clinical samples. Standard high-throughput screens used in discovery pipelines use growth inhibition (typically measured using optical absorbance) to quantify the potential of compounds. But these assays necessarily fail to detect slow-growing, drug-resistant recalcitrant cells, which are believed to be the main source of difficult-to-treat clinical infections. Furthermore, growth inhibition cannot distinguish between compounds that are bacteriostatic (i.e., stop growing) and bactericidal (i.e., induce cell death). Therefore, assays are urgently needed to identify compounds that are bactericidal rather than just bacteriostatic under physiological conditions that induce recalcitrant bacteria (i.e., slow growth). This need is based on rapid and scalable methods to measure pathogen viability after drug treatment.

[0007] The gold standard in the field for measuring cell viability is the colony forming unit (CFU) assay. A core microbiology assay taught in undergraduate laboratories and widely used worldwide, the CFU assay uses a dilution series to quantify the number of cells that are able to form colonies. The CFU assay has been used to quantify cell viability in systems including bacteria, fungi, hematopoietic progenitor cells, and cancer cells. The dynamic range of the CFU assay is typically eight orders of magnitude. This means that in a population of 100 million cells, the CFU assay can identify as few as one or as many as 100 million resistant cells that are able to survive treatment and become the seeds of relapse. However, the CFU assay is time-consuming and generates a lot of plastic waste. For the drop CFU assay, typically 15 pipette tips are required per condition to run an 8-fold dilution and transfer to an agar pad. Time and cost make it challenging to adopt the CFU assay in high-throughput screening (HTS). Previous approaches to addressing this fundamental problem have either been to increase speed by using robotics; reduce pipetting volumes by using viability staining or droplet technology; or use cell growth to estimate the initial number of cells, similar to qPCR. However, none of these methods combine the simplicity of the CFU assay with a large dynamic range. Therefore, there has long been a need for a simple, efficient, and cost-effective solution to measure cell viability using the CFU assay. More specifically, an approach that addresses the limitations of the above techniques would include an assay that: 1) can measure viability over several orders of magnitude; 2) is independent of cell growth rate; 3) requires only one pipette tip per condition; and 4) can be easily expanded to HTS without the need for specialized equipment. Summary of the invention

[0008] In one aspect, the present invention describes an improved CFU assay, the geometric viability assay (GVA), which utilizes continuous geometry to run a dilution series with a high dynamic range in a single instance. As described herein, the systems, methods, and compositions of the GVA of the present invention greatly simplify and reduce the cost of measuring cell viability compared to traditional colony forming unit (CFU) assays while maintaining a comparative dynamic range and accuracy. The GVA of the present invention enables CFU assays to be performed at higher throughput and lower cost than traditional assays.

[0009] In a preferred aspect, the GVA of the present invention can include a sample of generated cells, which is mixed with soft agarose and cast into one or more 3-D containers with geometrically variable properties as described below. The probability of forming a colony at any position along the geometrically variable container depends on the viable cell density and the three-dimensional shape of the container. By calculating this probability and by measuring the position of a subset of colonies in the container, the number of viable cells can be calculated with high accuracy. In this way, the GVA of the present invention simplifies and reduces the cost of measuring cell viability by fifteen times (15 times), can measure viability greater than 6 orders of magnitude, and enables more efficient drug development for clinically relevant drug-resistant cells.

[0010] On the other hand, the technology of the present invention includes systems, methods and devices for the novel GVA as defined herein. In a preferred aspect, the system of the present invention includes a growth medium for diluting a cell sample, and preferably a sample of prokaryotic cells or eukaryotic cells. The system of the present invention may further include one or more variable geometry containers (preferably axisymmetric) suitable for accommodating the diluted sample in the growth medium, and an imager suitable for capturing one or more images of a subset of viable microbial CFUs present in the variable geometry container after the incubation period. In another preferred aspect, the cell sample may be treated before or in the variable geometry container of the present invention, for example, by antibiotics or other experimental perturbations.

[0011] In another aspect, the present technology includes systems, methods and apparatus for screening novel compounds using the GVA of the present invention. In this preferred aspect, a series of diluted samples of target cells, such as prokaryotic cells or eukaryotic cells, can be treated with compounds from one or more drug or compound libraries and introduced into a variable geometry container suitable for containing the treated samples, preferably axisymmetric. The imager can be adapted to capture one or more images of a subset of viable CFUs present in the variable geometry container after an incubation period, thereby showing the effect of the screened compound. In this way, the GVA of the present invention can rapidly screen for antibiotic sensitivity or resistance characteristics from one or more target bacteria and rapidly determine their minimum inhibitory concentration (MIC).

[0012] On the other hand, the technology of the present invention includes systems, methods and equipment for determining the microbial content or load of a target object (such as a surface or a biome) using the GVA of the present invention. In this preferred aspect, one or a series of samples collected from a target object (such as a surface of a production facility, an environmental sample, a food or beverage product, or a biological sample from a subject) can be diluted and introduced into a variable geometry container. The imager can be suitable for capturing one or more images of a subset of viable CFU present in a variable geometry container after an incubation period, thereby displaying the microbial load or biome characteristics present on the object. On the one hand, GVA can be run before and after treatment, such as introducing antibiotics or introducing one or more bactericidal compounds, and cleaning or treating the target object, such as with traditional or antimicrobial detergents or UV light. In this way, GVA can be used to verify cleaning and disinfection protocols in commercial and therapeutic environments (such as hospitals and biological laboratories) and environmental sites and samples.

[0013] In another aspect, the present technology includes systems, methods and apparatus for performing 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 bodies or target objects can be diluted and introduced into a variable geometry container containing a diagnostic marker, such as diagnostic agar or other compounds that can identify one or more pathologically relevant microbial species (such as human or animal pathogens). The imager can be adapted to capture one or more images of a subset of viable CFUs present in the variable geometry container after an incubation period, thereby indicating the presence of the pathogen, and further using the GVA to aid in the identification and diagnosis of a disease or condition, such as an infection with a clinical microorganism or other organism.

[0014] In another aspect, the present technology includes systems, methods, and apparatus for imaging the GVA of the present invention. The system may include an imager configured to be positioned adjacent to a light source. In a preferred aspect, the imager comprises a smartphone secured to a base and mechanically responsive to an adapter. An axisymmetric variable geometry container may be used to incubate a cell sample in a growth medium and secured to a mount that may be coupled to the adapter of the present invention. In this configuration, the mount of the present invention may be adjustable so as to position the container relative to the imager, thereby allowing the imager to capture one or more images of colony forming units (CFUs) located at an end portion of the incubated container. In an alternative aspect, the adapter may include a macro lens secured by a clip to image the container secured to the mount.

[0015] In another aspect, the present technology includes systems, methods and apparatus for imaging the GVA of the present invention. The system may include an imager, such as a camera, that may be positioned adjacent to a frame. In this embodiment, the frame of the present invention may hold one or more axisymmetric variable geometry containers suitable for incubating a cell sample in a growth medium. The frame of the present invention and / or the imager are adjustable so as to position the container relative to the imager, thereby allowing the imager to capture one or more images of colony forming units (CFU) located at the end portion of the incubated container.

[0016] In another aspect, the present technology includes systems, methods, and apparatus for an imaging platform configured to capture one or more images of a container of the present invention. In this embodiment, a processor may be responsive to an imager adapted to capture images of colony forming units (CFUs), preferably located at an end portion of an axisymmetric variable geometry container of the present invention. A computer executable program responsive to the processor may be adapted to identify one or more images of the CFUs located at the end portion of the variable geometry container. In this aspect, the computer executable program may identify a boundary of a tip of a variable geometry container and further align the tip of the container. The computer executable program may further perform colony segmentation on the image and further calculate the CFUs embedded in the growth medium based on the colony segmentation.

[0017] Further aspects of the invention will be apparent from the description, drawings and claims provided herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1A-I: Geometric viability assay (GVA). a) The probability of a colony forming at a tip distance x from a cone is proportional to the infinitesimal volume dV (cyan circles) divided by the total volume V (purple cone). Analytically, this ratio is a probability density function (PDF) as a function of x (see Supplementary Materials for derivation). b) PDFs of cylinders (red), wedges (yellow), and cones (purple) as a function of axial distance (x). c) Simulations of colony distribution in cones. d) Estimation of total CFU / mL based on the position of colonies in the cone. (Top) Shown are the colony distributions for 4 simulations spanning densities from 20 to 10,000 CFU / mL. 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 positions. e) The factor by which the calculated GVA differs from the correct value as a function of the number of colonies in equation (1). The shaded error bars represent 1 standard deviation in 1000 simulations. Colors match the simulation in panel d. f) Dilution series of E. coli embedded in 150 μL 0.5% LB-agarose in a p200 pipette tip. Red circles correspond to colonies counted using custom semi-automated segmentation software. g) CFU / mL of E. coli calculated using GVA for a 4-fold dilution series. Each point is the average of 4 replicates. Average calculated after taking the logarithm. The red line is the linear regression fit to the dilution series. If the magnitude of the GVA estimate is linear with dilution, a slope of 1 on the log-log plot is expected. h) The drop CFU and GVA estimates are significantly correlated over 6 orders of magnitude. i) GVA was performed on Gram-positive, Gram-negative, and eukaryotic cells (see Figure 10a for quantification).

[0019] Figure 2A-D : GVA dynamic range (not accuracy) depends on the optical configuration. a) Picture of an assembled pipette tip holder on an iPhone 12 with a Xenvo macro lens. The pipette images were taken in front of a white background (paper) and under ambient lighting. b) Example images of the same 2 pipette tips using a Canon EOS with a 100mm f2.8 macro lens (left) or an iPhone 12 with a Xenvo macro lens (right). CFU / mL for GVA calculations are reported below. Selected colonies for GVA calculations are circled. c) Dynamic range of iPhone GVA. E. coli was diluted 4-fold and embedded in pipette tips. After incubation, the same tip was imaged with an iPhone camera with a macro lens (green) and a mirrorless camera (purple). Each point is the average of 4 replicates calculated after taking the logarithm. The green and purple lines are linear regression fits of the dilution series. d) Pearson correlation between iPhone GVA and professional camera for all pipettes where both could be used to count colonies. Correlation coefficient calculated in logarithmic space.

[0020] Figure 3A-J : GVA reduces time and materials for viability measurements by more than 10-fold. a) (Left) Schematic diagram of the drop CFU assay assuming a tip change for each dilution step and the materials required for 96 samples. (Middle) The spiral plater spreads the sample on a solid medium plate in an Archimedean spiral. The spiral causes the sample volume to decrease with radial distance, with a reported dynamic range of 3-log. One petri dish is required for each sample. (Right) GVA uses a single pipette tip to run a 6-step dilution series. bd) Time comparison of different techniques. b) Time required to prepare solid growth medium. Preparation time for the spiral plater and drop CFU includes: 1) autoclaving agar; 2) cooling after autoclaving; 3) plate pouring; and 4) plate cooling. GVA begins by melting agarose in a microwave and then equilibrating it in a warm water bath for 1 hour. c) Sample plating from a 96-well plate. Spiral plater assay sample plating time based on industry reported values. Drop CFU was timed by an expert user using a 12-channel pipette and changing tips at each dilution and plating step. d) Time required to quantify 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 and 30 min for iPhone), image preprocessing and tip segmentation (5 min), and semi-automated colony counting of 96 pipette tips (10 min). Drop CFU colonies were counted and recorded manually. e) Number and cost of pipette tips as a function of number of samples for the three different techniques. Cost estimates are provided in Supplementary Table 1. f) Agar requirements as a function of sample count. 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 was assumed per tip. g) Number of 96 wells and Petri dishes for each condition. h) Estimated total cost of consumables per 96 samples for the three methods. GVA cost is $0.17 / sample. i) Instrument cost. Based on quotes from 3 manufacturers for the spiral seeding machine (SP) and automated imaging system. GVA instrument cost includes Canon camera and 100mm f / 2.8 macro lens. j) Difference in instrument cost for Canon and iPhone optical configurations.

[0021] Figure 4A-H: GVA has low noise characteristics and is robust to missing colonies or tip position errors. a, b) Coefficient of variation (COV) between 4 technical replicates of different numbers of CFU concentrations for GVA using Canon or Apple phone optical configurations (a) and drip CFU (b). c, d) The factor by which the GVA calculated value differs from the correct value varies with the number of missing colonies (c) or error in tip position (d) in the simulation results (see methods). The shaded error bars are the standard deviation of 1000 simulations. e, f) The same error calculation for the experimental data. The error bars represent the standard deviation between all pipette tips included in each container. g, h) Correlation between GVA and drip CFU assay as a function of count and position errors.

[0022] Figure 5A-H : GVA screening of the Enzo library identified DPI as active against stationary E. coli. a) Dose response of the three antibiotics on stationary and exponential (ex) cultures after 24 hours of treatment. Each point is the average of duplicate measurements. CFU / mL was normalized to the untreated control. b) Viability of stationary and exponential cells over time at one antibiotic concentration. c) Drug classes of the Enzo bioactive substance screening library. The size of the circular wedges is proportional to the drug class representative. The target and relative representation of each class depicted on the outer ring. d) Absolute viability of stationary (green) and exponentially growing (purple) cells after 24 hours of treatment with the Enzo library. Each condition was run in duplicate and averaged in logarithmic space. e) Scatter plot of stationary and exponential phases from the screening. The standard deviation of the DMSO control is depicted with a red cross. Annotation of selected hits. f, g, h) Mitomycin C (DNA cross-linker), phentolamine (α-adrenergic antagonist), and DPI (NADPH oxidase inhibitor) dose responses in quiescent and exponential cultures.

[0023] Figure 6A-I: DPI produces ROS, activates the SOS response and antagonizes ciprofloxacin. a) Median single-cell CellROX signal over time for DPI (blue), ciprofloxacin (orange) and untreated control (yellow). b) Efficacy of DPI under aerobic and anaerobic conditions. See Figure 18b,c for ciprofloxacin and gentamicin. c) Images of live E. coli cells stained with CellROX dye for three DPI concentrations 4 hours after DPI addition. Brightness and contrast were 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 quiescence (top panel) or exponential growth phase (bottom panel). The dose response of wild-type (WT) cells is depicted in green or purple, respectively. The shaded error bars are equal to the standard deviation in log space between 3 replicates. For other mutants, see Fig. 20 f) GVA plate assay of DPI combined with ciprofloxacin at 24 hours. Each square in the heat map is the average of replicate conditions. The color bar corresponds to the log10 (CFU / mL) of each dose combination. The left panel shows the dose response of DPI plus 1 μg / mL ciprofloxacin (cyan). For the full time series, see Fig.21 . g) GVA panel assay of DPI in combination with gentamicin at 24 hours. h) Growth inhibition panel of DPI and ciprofloxacin. The optical density was measured for each condition over 8 hours and the integrated area under the growth curve (AUGC) was plotted (color bars). i) Dose response curves for time-staggered combinations. All treatments lasted a total of 24 hours. The pre-treatment condition was 2 hours of treatment with one drug followed by 22 hours of treatment with both drugs.

[0024] Fig. 7A-F : Derivation of the PDF of a cone. a) The volume of the infinitesimal dV divided by the total volume V is equivalent to the probability of finding a colony as a function of x. From trigonometry, the infinitesimal radius (r'(x)) is a function of the radius of the base of the cone (r) divided by the height of the cone (h) times x. b) The PDF of a cone as a function of x. The overhead projection of the cone is depicted above. c) The cumulative density function (CDF) as a function of x. d) The PDF is the same for axisymmetric cones, such as the square (red) and triangular (turquoise) cones. e) Two equivalent ways to count the number of CFU in a wedge using the CDF (left) or the PDF (right). N(x) is the number of colonies counted. f) The simulated percentage of CFU / mL for GVA calculations that are within 2 times the correct value as a function of the number of colonies used for the GVA calculation. 1000 simulations were used to calculate the percentage. See Figure 1c for the simulation parameters.

[0025] Figure 8A-C: Optical configuration. a) Schematic diagram of the optical configuration for imaging pipette tips containing agarose. A mirrorless camera with a macro lens is positioned above the tip in the focal plane. Adding a z positioner stage helps to fine-tune the focus. An LED light box with a diffuser is used to illuminate the pipette laterally. This box is mounted on a stepper motor to allow imaging of 12 pipettes simultaneously. The stepper motor and camera are controlled simultaneously by LabView software. b) Picture of the optical configuration. Cyan light is used to maximize the contrast of the TTC counterstain. A foam box is used as a reflective light box and a piece of paper is used as a diffuser. The GVA sample is positioned using a 12-channel pipette and imaged using a Canon EOSRP camera with an f / 2.8 100mm macro lens. c) The pixel resolution of this configuration is 6.7 microns.

[0026] Fig. 9 : Example drop CFU plate. a) Each condition (column) was diluted with 10-fold serial dilutions (rows) and 3uL was dropped onto a 1.5% LB agar pad poured into an empty tip box. Colonies were counted for the dilution row, where individual colonies were not continuous. These counts were used to calculate CFU / mL (bottom).

[0027] Fig. 10A-D : GVA calculation results for different species. a) Estimated number of CFU / mL for different dilution series for the six species tested with GVA. b) Plates were streaked with pipette tips before rinsing or after GVA embedding. No changes in CFU / mL were observed after rinsing. c) Example GVA pipette tips for E. coli biofilm. See Methods for culture and dissociation protocols. d) Biofilm growth over time. Error bars correspond to standard deviation between ≥5 biological replicates.

[0028] Figure 11A-C : Biome sampling using GVA. a) Twenty-four locations (red dots) on a volunteer were rubbed vigorously 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 in the tip. Using this protocol, the lower limit of detection was 20 CFU / mL (dashed line). Sample replicates were incubated at 30°C or 25°C for 48 hours and then imaged. b) Example pipette tips of different sample areas reveal different colony structures and concentrations at different biome locations. All samples were stained with TTC. c) Samples from higher thermal zones (ear, axilla) grew at 30°C but not at 25°C, indicating the temperature selectivity of different species grown in pipette tips.

[0029] Figure 12A-J: The chip version of GVA uses square pyramid geometry. a, b) 3D printed molds used to create square pyramids for 12 (a) and 48 (b) conditions. c) Image of a 9-fold dilution series of E. coli culture on the GVA chip. d) CFU / mL calculated by GVA for the dilution series. Each point is the average of 4 replicates. e) Noise measured using the coefficient of variation (COV) of the chip GVA. f) Matching the droplet CFU quantification to the conditions in (d). g) Corresponding noise analysis of the droplet CFU. h) Correlation between the chip GVA and the droplet CFU exceeds 5 orders of magnitude. i, j) Chip GVA of Gram-positive (i) and eukaryotic (j) cells.

[0030] Fig.13 : Smartphone (iPhone) Pipette Tip Holder. a) 3D printed part for routinely positioning a pipette tip in front of an iPhone rear camera with a Xenvo Macro Lens (15x magnification, wide angle lens not included). The blue panel slides over the Xenvo Macro Lens clipped onto the iPhone. The green bar is screwed to 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 with a stand (yellow). Use a standard FDM print block printed with PLA.

[0031] Figure 14A-C : Sensitivity analysis of GVA calculations to errors in lost colonies and tip position. a) Heatmap of error as a function of tip position and lost colony error. b) Same analysis as in panel a, but with experimental data. CFU / mL are assigned to 1e3 to 1e5 (top row), 1e5 to 1e7 (middle row), and 1e7 to 1e9 (bottom row). The number of pipette tips included in each container is annotated by count. c) Heatmap of Pearson correlation between drop CFU and GVA for tip position and lost colony error.

[0032] Figure 15A-B : Cell counts over time in static and exponential cultures. a) Number of CFU / mL in static (a) and exponential cultures (b). To generate exponential cultures, stationary cells were diluted 1:1000 in fresh LB medium and placed in a shaking incubator (180 RPM) at 37°C for 2 hours before the start of the experiment.

[0033] Figure 16A-C:Enzo screening control. a) Library diversity of ICCB Enzo known bioactive substance library compared with Maybridge HitFinder library. Tanimoto similarity (Tanimoto similarity) between all molecules based on SMILES was calculated using RDKit software package in python. According to this distance matrix, tSNE embedding was initialized with PCA and calculated with a complexity of 50. b) For both static and exponential cultures, the distribution of CFU / mL under the conditions of the edge and center hole of the plate. Statistical test uses Mann-Whitney U test for nonparametric distribution (p value>0.05). c) Distribution of CFU / mL of different drug classes identified in Enzo library (see Figure 5c). No category differences were found when using ANOVA (p value>0.001, p value was corrected for multiple hypothesis testing). No difference was found with the control when using paired Tukey test (Tukey Test) (p value>0.01, paired Tukey test)

[0034] Fig.17 : Unvalidated hits from ICCB Enzo bioactive screening. Dose response curves for E-4031 (a) and benamil (b) against stationary or exponentially (ex) growing cultures.

[0035] Figure 18A-D : a) The duration of ROS reduction and the onset of secondary ROS peaks are related to DPI concentration. The median single-cell CellROX signal changes over time for different DPI concentrations are depicted. b, c) Dose response curves of ciprofloxacin (b) and gentamicin (c) in quiescent cells under aerobic or anaerobic conditions. Treatment lasted for 24 hours. d) The efficacy of DPI as a function of increasing concentrations of the ROS scavenger ascorbic acid (AA).

[0036] Fig.19 : Ribbon diagram of lexA-repressed genes using the PEC library (rows). GFP fluorescence (top panel of each row) is proportional to the promoter activity of each gene. The bottom panel of each row depicts the bright field image. Each column corresponds to a different time point after treatment.

[0037] Fig. 20 : Sensitivity of gene mutants to DPI in exponential and stationary phases. The wild-type reference of each mutant is depicted with a solid line. Error bars are standard deviations in logarithmic space between three biological replicates. Mutants were selected from the collection of Keio University (Keio). Kanamycin (25 μg / mL) was included in all Keio culture conditions, whether in overnight culture or during treatment with DPI to maintain gene knockout.

[0038] Fig.21 : GVA time check plate crossed with DPI for E. coli and ciprofloxacin (left panel) or gentamicin (right panel). Treatment time increases row by row. Each square in the heat map is the average value under repeated conditions. The color bar corresponds to the measured log10 (CFU / mL) of each combination. The left panel shows the trace (cyan) of the DPI dose response with 1 μg / mL ciprofloxacin or 10 μg / mL gentamicin.

[0039] Fig. 22 : GVA time check plates crossed with DPI against S. typhimurium and ciprofloxacin (left panel) or gentamicin (right panel).

[0040] Fig.23 : Schematic diagram showing the procedure for the normalization method of the traditional CFU assay using the drop plate method. Scaling up to 96 measurements, and assuming a standard 12-channel pipette, this traditional CFU requires 1440 pipette tips, approximately 50 minutes to set up and compete, and at least 8 agar plates.

[0041] Fig.24 : shows a schematic diagram of the GVA of the present invention, with an optional multi-container plate. Scaled up to 96 measurements. Scaled up to 96 measurements, and assuming a standard 12-channel pipette, the GVA of the present invention requires 96 pipette tips, about about 6 minutes to set up and compete, in a preferred embodiment, 1 to 2 reusable chips,

[0042] Fig.25 : A schematic diagram of an alternative embodiment of the present invention, GVA, is shown, wherein the variable geometry container comprises a pipette tip. Scaled up to 96 measurements. Scaled up to 96 measurements, and assuming a standard 12-channel pipette, the GVA of the present invention requires 96 pipette tips, approximately about 6 minutes.

[0043] Fig.26A-B : Shows quantified data for the example shown in Figure 10. (a) Estimated CFU as a function of dilution. (b) 3-fold replicate data showing mean CFU.

[0044] Figure 27A-B:Compounds that affect viability in exponential and stationary phases were screened using data from GVA assays in a 96-well formatted drug test. The antibiotic potential of a drug library (80 compounds) was tested in duplicate for exponential and stationary bacteria. (A) Viability reduction of compounds in exponential (top) and stationary (bottom) phase cells. Each x is the average of 2 biological replicates. (B) Scatter plot of viability screening comparing exponential and stationary activity. Each blue dot represents the average of 2 replicates treated with the compound. Red dots represent DMSO controls. The dotted line represents 3 times the standard deviation of the negative control. Screening was performed with E. coli in stationary and exponential bacterial populations. The two phases were screened in duplicate. 80 compounds and 16 controls were screened. Cells in stationary and exponential phases were treated with compounds for 4 hours and then cast into pipette tips using the GVA protocol. Both compounds, mitomycin C and diphenyl iodine, showed bactericidal activity.

[0045] Figure 28A-B Figure 1 shows the utility of the GVA assay for calculating the minimum inhibitory concentration (MIC). (A) Images of cells measured for GVA at a range of kanamycin concentrations within an agarose matrix. 10 Cells / mL and the specified amount of kanamycin were embedded in each sample. The calculated CFU from the GVA assay is also shown. (B) Graph of biological triplicate samples with kanamycin treatment. The dotted line represents the detection limit of the kanamycin assay.

[0046] Fig.29 : GVA can quickly and accurately estimate CFU. The image shows the same pipette tip imaged after 4, 6, 8 and 24 hours of incubation at 37C. Colonies are clearly visible after 6 hours, and the counts at 8 hours show a dynamic range of up to 7 levels with high accuracy (bottom heat map). For use in rapid antibiotic testing, these measurements can be obtained within 8 hours without pre-incubation steps.

[0047] Figure 30A-B : (A) Total CFU / mL estimated from the position of the colonies in the cone. (Top) Shown are the distribution of colonies across 4 simulations with densities ranging from 20 to 10,000 CFU / mL. The volume of each cone is the same as in panel c. (Bottom) GVA estimates of CFU / mL vary with the included colonies and their x-positions. (B) The factor by which the calculated GVA differs from the correct value varies with the number of colonies in formula (1). The shaded error bars represent 1 standard deviation in 1000 simulations. The colors match the simulations in panel A.

[0048] Figure 31A-B: (A) GVA worked with Gram-positive (top), Gram-negative (middle), and eukaryotic (bottom) cells. (B) Serial dilutions of S. cerevisiae (baker's yeast) grown in YEPD to determine the dynamic range of eukaryotic organisms. GVA provides an accurate estimate of yeast viability up to about 7 orders of magnitude.

[0049] Figure 32A-B : (A) Image of the active area of ​​the software-based colony counting software. The pipette tip is marked with a red vertical line and a single identified colony is marked with a red circle. The top shows the current CFU count. (B) Settings of the software algorithm that determine the experimental parameters (left) and the position of the image (center). The user can also fine-tune the colony segmentation algorithm (right) or accept the default parameters.

[0050] Fig.33 : Flowchart of GVA assay. Physical (hardware) based measurements involve positioning the sample in front of the measurement device and then imaging the tip. In the software, the user identifies a single pipette tip, aligns it to an orthogonal plane to facilitate distance calculation, selects a subset of colonies within the tip, and then uses an algorithm to estimate the number of CFUs in the entire tip.

[0051] Figure 34A-C : An illustration of the GVA assay using a paper-based readout (without an imaging system). (A) Estimated CFU and calculation of the scale assuming a 36 mm pipette tip with a 150 uL agarose volume. This image shows a serial dilution of bacteria overlaid on a paper-based scale. The CFU estimate is based on the position of the 10th colony counted by the user. (B) Comparison of CFU measurements of the GCA using a high-resolution Canon camera and macro lens (purple) to the paper method using a basic magnifier (green). The accuracy is the same as paper, but the maximum number of resolvable colonies is reduced. Comparison of the paper to camera system (bottom) shows very high correlation over the dynamic range of the paper-based measurement.

[0052] Fig.35 : Minimum inhibitory concentration (MIC) measurements are independent of the starting concentration of bacteria. Each box represents the GVA measurement with increasing amounts of antibiotic (x-axis). The number of viable cells at each concentration is plotted on the y-axis. The antibiotics are labeled at the top. Within each box, each color represents the initial cell population, from 1000 to 1,000,000 cells per milliliter. The MIC for each drug is the same regardless of the starting concentration of the cells.

[0053] Fig.36: GVA calculates the minimum inhibitory concentration for various bacterial species. Each box represents the number of CFU as the concentration of the antibiotic shown on the x-axis increases. The antibiotic logo is printed on the top of each box. Each color represents a different bacterial species. The different MICs indicate that each species has a unique antibiotic sensitivity spectrum that is easily revealed by GVA.

[0054] Fig.37 : GVA allows for rapid measurement of the minimum inhibitory concentration for a variety of bacteria. Each box represents the number of CFU as the concentration of the antibiotic shown on the x-axis increases. The antibiotic logo is printed on the top of each box. Each color represents a different bacterial species. These measurements were obtained after 12 hours of incubation and are consistent with measurements after 24 and 48 hours.

[0055] Figure 38A-B Figure 1: GVA imaging works in blood agar, a common medium for growing pathogen strains. (A) Image of E. coli embedded in blood agar at the concentration recommended by the manufacturer and grown overnight at 37°C. (B) Quantification of serial dilutions of CFU. More than 5 orders of magnitude are distinguishable in this medium, similar to the results with LB or minimal medium.

[0056] Figure 39A-B : Examples use cases enabled by GVA. (1) High throughput viability screening. The Prestwick library of compounds (1440 compounds) was run in duplicate over approximately 2 weeks (2880 CFU measurements total). 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. A panel of compounds 1 and 2 were compared over time. Multiple combinations of compounds and time were measured. (4) Pharmacogenomic characterization of antibiotic efficacy. Viability of compounds at multiple concentrations was measured against multiple genomic bases. The effects of multiple genomic modifications and antibiotic concentrations were measured.

[0057] Fig.40 : Effect of potential errors on counting accuracy. (Left) Change in Pearson correlation coefficient when colonies are not included in the count. Total counts were 30 colonies. Correlation changed by less than 1%. (Right) Change in GVA accuracy if the tip position is not assigned correctly in the software. On a 36 mm pipette tip, PCC changed by less than 1% when the tip position was offset by up to 4 mm.

[0058] Fig.41 : Shows a front perspective view of a GVA assay imaging system in one embodiment thereof.

[0059] Fig.42 : Shows a front perspective view of a frame for a GVA determination imaging system in one embodiment thereof.

[0060] Fig.43 : Shows a front perspective view of an imager stand for a GVA assay imaging system in one embodiment thereof.

[0061] Fig.44 : Shows a front perspective view of a mounting block for a GVA assay imaging system in one embodiment thereof.

[0062] Fig.45 : shows a front perspective view of a plate for a GVA assay imaging system in one embodiment thereof.

[0063] Fig.46 : Shows a top perspective view of a GVA determination imaging system utilizing a smartphone mechanically responsive to an adapter in one embodiment thereof.

[0064] Fig.47 : Shown is a front perspective view of a GVA determination imaging system utilizing a smartphone mechanically responsive to an adapter in one embodiment thereof.

[0065] Fig.48 : An adapter for a GVA determination imaging system having a vertical adjuster is shown in one embodiment thereof.

[0066] Fig.49 : Shown is a depth adjuster of a GVA determination imaging system in one embodiment thereof.

[0067] Fig.50 : Shown is a mount with a container holder for a GVA assay imaging system in one embodiment thereof.

[0068] Fig.51 : Shown is a base for a GVA assay imaging system in one embodiment thereof. DETAILED DESCRIPTION

[0069] The present invention includes a novel viability assay, generally referred to herein as a geometric viability assay (GVA). In preferred embodiments, the GVA of the present invention counts CFU in a sample (e.g., a biological, environmental, or commercial sample) based on the axial position of entrapped colonies formed in an axisymmetric variable geometry container (in preferred embodiments, the axisymmetric variable geometry container comprises a conical container as described herein). Due to the physical properties of an axisymmetric variable geometry container, the probability of colonies forming at the tip of the container is less than near the base. Analytically, this probability is proportional to the square of the vertical distance of the colony from the tip of the container. By measuring the position of a limited number of colonies in a container and using the derived probability density function, the total number of colonies in the entire container can be calculated with high accuracy.

[0070] By exploiting the potential information encoded in the colony distribution, the GVA of the present invention can accurately quantify the number of viable cells in samples ranging from 1 to 10,000,000 cells. This dynamic range can be achieved using pipette tips, a conical axisymmetric variable geometry container commonly used in microbiology. As further described below, the GVA of the present invention: 1) measures viability over >6 orders of magnitude; 2) is not dependent on the growth phase or lag phase of the cells; 3) minimizes consumables; and 4) reduces operator time by more than 30 times compared to traditional drop CFU assays. The GVA combined with the present invention enables each researcher to perform up to 2000 viability measurements per day.

[0071] The technology of the present invention includes a novel system for colony formation CFU determination. In a preferred aspect, the device of the present invention includes a variable geometry container suitable for accommodating the diluted sample in the growth medium. As used herein, "sample" or "cell sample" of the present invention can include a sample containing one or more cells to be cultured and tested. In a preferred embodiment, "cell" or "cell to be cultured or detected" can include both prokaryotic cells and eukaryotic cells, and the cell can be cultured and identified as CFU, and can preferably include Gram-positive and Gram-negative bacteria, and fungi, yeast or even algae cells. In another preferred embodiment, "cell" can include pathogenic bacteria, or complex cell samples, such as biome samples, or from a surface or object, such as other samples of food or beverage containing multiple different cells.

[0072] 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 samples from any body fluid or tissue. In certain embodiments, a biological sample may include a sample that is typically subjected to clinical or diagnostic testing. 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 parts thereof. A "subject" is any organism of interest, typically a mammalian subject, and preferably a human subject.

[0073] 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 extracted or obtained from any part of the internal or external environment. In a preferred embodiment, an "environmental sample" may include samples from water, soil, municipal waste, hazardous waste, potential pollutants, etc. In another preferred embodiment, an "environmental sample" may include a "commercial sample", which may include pollutants, such as surfaces of commercial production facilities, or objects, such as food and / or beverages, etc.

[0074] Referring to FIG. 7 , the variable geometry container of the present invention includes an opening to allow for placement of a growth medium; and a middle portion adapted to have a geometric configuration that changes size in all three dimensions; and an end portion that is narrower than the opening. The apparatus of the present invention may include a plurality of variable geometry containers, such as a plurality of variable geometry containers produced in a mold having one or more variable geometry containers, or a plurality of containers positioned adjacent to one another to allow for rapid comparison and analysis. Specifically, as used herein, a "variable geometry container" means an axisymmetric vessel or container adapted to hold a quantified amount of growth medium, wherein the dimensions of the container change size in all three dimensions. It is noteworthy that in some embodiments, the size change of the container in all three dimensions may be continuous, or discontinuous. Examples of axisymmetric variable geometry container configurations that change size in all three dimensions include, but are not limited to, the following shapes shown in FIG. 7D :

[0075]

[0076] As shown in Figures 1f and 2b, in one embodiment, the variable geometry container of the present invention may include a standard translucent pipette tip or cone that can be identified by a person of ordinary skill in the art. Similarly, as shown in Figure 2, one or more pipette tips may be connected to a standard multi-purpose laboratory pipette or pipette holder as described below, either singly or multiple. As further shown in Figures 12a-b, in one embodiment, the variable geometry container of the present invention may include a mold having one or more variable geometry containers. The mold may include a three-dimensional printed chip having multiple variable geometry containers positioned adjacent to each other. As described above, the variable geometry container of the present invention may preferably be translucent to facilitate direct image capture of all or part of the bacterial colonies formed in the growth medium.

[0077] The present technology includes a novel system and method for establishing a geometric viability assay (GVA). As used herein, "GVA" describes a CFU assay performed using a variable geometry container as described herein. In preferred aspects, the GVA of the present invention may include a cell sample, preferably a biological, environmental or commercial sample containing one or more cells that can be cultured. In preferred embodiments, the cells may include bacteria, yeast or fungal cells that can be cultured to a specific density in a liquid culture medium. In preferred embodiments, this sample may be further processed. As described herein, the term "processing" includes a step of subjecting cells to an experimental disturbance to be measured (e.g., drug treatment, culture conditions, genetic modification, etc.). In preferred embodiments, the sample is treated with one or more compounds, which are suitable for killing and / or inhibiting the growth of the sample and / or affecting one or more phenotypic or genotypic changes in the sample. In preferred embodiments, this treatment may include one or more compounds selected from the group consisting of: therapeutic compounds, antibiotics, bactericidal compounds, antibacterial compounds, anticancer compounds, antifungal agents.

[0078] In one embodiment, the sample can be diluted in a growth medium, positioned in a variable geometry container and incubated. For example, in a preferred embodiment, the sample can be diluted directly in a growth medium, such as a certain amount of molten liquid agarose, and preferably 0.5% liquid agarose to a desired CFU concentration, and the sample can be further cooled to about 37°C to allow the material to be cast into a variable geometry container. Of course, various liquid and solid growth media for prokaryotic and eukaryotic cells will be well known in the art and can be adapted for the GVA of the present invention.

[0079] In a preferred embodiment, the sample containing one or more different microorganisms can be extracted from the sample or separated and grown from the sample, and further diluted in growth mediums such as nutrient agar, which can allow microorganisms to form bacterium colonies. In the present embodiment, one or more contrast agents can be added to the growth medium, such as microbial staining agent 2,3,5-triphenyltetrazolium chloride (TTC) or cell permeability fluorescent dye, to help visualize CFU later as described below. In an alternative embodiment, the growth medium can include clinical or diagnostic indicators. For example, as shown in Figure 38, the sample containing one or more different microorganisms can be extracted from the sample or separated and grown from the sample, and further diluted in diagnostic growth mediums such as blood agar, which can allow microorganisms to form bacterium colonies. As shown in the figure, blood agar is used in clinical and diagnostic environments to grow fastidious bacteria and distinguish a kind of enrichment medium of various bacteria according to its hemolytic properties.

[0080] Growth medium containing diluted samples can be poured into variable geometry containers, e.g. Fig.24 As shown, the variable geometry container can include an exemplary axisymmetric three-dimensional cone or cone, or be poured into a container that forms a part of the chip, thereby allowing different serial dilutions or culture treatment disturbances. As shown in Figure 34a, the growth medium containing the diluted sample can be directly positioned in a standard pipette tip, which can be further connected to a standard pipette configured to accommodate one or more adjacent pipette tips. Of course, such examples are merely exemplary, because various containers or pipette holders can be used to fix containers and growth medium, and allow the incubation of the cell samples contained therein. The growth medium suspension can then be incubated to form a bacterium colony embedded in the culture medium. The time, temperature and other parameters of this incubation step depend on the cells to be grown, and those of ordinary skill in the art generally understand.

[0081] During the incubation period, the viable CFUs (e.g., microbial CFUs) present in the container can form visible entrapped colonies, which can be further imaged to identify them individually. In a preferred embodiment, image samples of the three-dimensional volume of the variable geometry container can be captured, for example, using a light source and an imager (e.g., a microscope, a digital camera, or a cell phone camera), which can further digitize and save the captured images for later analysis, as further described below.

[0082] In another example, the captured images of the container can be processed and analyzed to identify clinically or diagnostically relevant characteristics, such as the presence or absence of a pathogen, a change in the growth medium, a diagnostic chemical in the growth medium, or the activation of a marker. 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 understood by those of ordinary skill in the art.

[0083] In a preferred embodiment, the captured container image can be processed and analyzed to identify the number of CFUs along a portion of the length of the variable geometry container. In certain embodiments, the number of CFUs may be clinically or diagnostically relevant, while in other embodiments, may indicate the effect of a treatment, such as an antibiotic treatment, applied to cells in a 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 cell growth as described herein.

[0084] In a preferred embodiment, the step of identifying CFUs along a portion of the length of the variable geometry container may include manual or computer-assisted identification of individual CFUs from one or more images of the portion of the variable geometry container, and preferably the end portion as shown in Figures 1-2, 11, 34. Once the number of CFUs along a portion of the length of the variable geometry container has been identified, the number of viable CFUs in the original culture sample may be calculated and output for further analysis, etc. As described below, due to the novel features of the GVA of the present invention, it is necessary to identify the locations of approximately 10-20 colonies, preferably at the tip of the container, in order to make a reliable estimate using a probability density function as described herein.

[0085] As described above, the GVA of the present invention includes embedding cells (such as bacteria, yeast, fungi, etc.) that are capable of or reproduce to form observable colonies in a container with variable geometry, i.e., a 3-D container with an axisymmetric geometry that changes size in all three dimensions. The higher the concentration of viable cells, the more likely it is that colonies will form in a small volume area at the end of the variable geometry container or tip compared to a lower cell concentration. (See, e.g., Figures 1-2 showing the end of a variable geometry container, in this example a pipette tip) As described below, the cumulative density function (CDF) distribution of colonies along the container can be analytically calculated, which can then be used to estimate the initial number of viable cells at the time of embedding using the following function:

[0086] And CFU = N(x) / CDF(x).

[0087] Here, x is the distance from the ramp to the tip of the container, h is the total length of the container, sometimes called the "ramp", and N(x) is the number of colonies counted at position x. Therefore, by recording all colonies and their positions along the container, the CFU can be estimated.

[0088] Another aspect of the present invention includes an analytical solution that provides a second alternative for CFU estimation. Specifically, an operator can: (1) count the colonies within any given variable geometry container and record their locations; (2) use the boundaries of the nearest and farthest colonies (defined as the limits of x1 and x2); and (3) use a probability density function (PDF) to estimate CFU using the following function:

[0089] and

[0090] Here |x| is the number of colonies found between positions x1 and x2. In practice, the inventors have demonstrated that recording x positions with only 10 colonies is sufficient to estimate the CFU within a sample. Instead of counting all colonies in a dense sample, only 10 or so are sufficient. This approach—counting only colonies within a defined position (sometimes called a specific “wedge”)—allows easy measurement of CFU from GVA, even over a large potential range of values ​​(approximately 10 7 ) is also true.

[0091] In order to parallelize the GVA of the present invention and establish a simple protocol at the same time, in one embodiment, the inventors created a pattern with 12 or 48 staggered triangular slopes (Figure 12). The lower limit of CFU detection is determined by the size of the slope, where the larger the volume, the smaller the CFU value that can be detected. The upper limit of CFU depends on the distinguishability of the smallest possible colony in a given wedge. Design a series of triangular slopes to simplify mathematical calculations and manufacturing processes. In this embodiment, the spacing of the triangular slopes is 9mm and the length is 36mm. The height of the slope is selected to achieve a final sample volume of 200μL, which also sets the lower limit of detection to 5CFU / mL. This spacing is convenient for using multi-channel pipettes commonly used in many laboratories. The current design accommodates 48-well or 12-well chips for 48 or 12 simultaneous experiments, respectively.

[0092] In this example, cells are first grown in 96-well plates in the presence of a specific experimental perturbation to be measured (e.g., drug treatment). After treatment, the cells are then mixed in soft agarose and added to the wells so that the growing colonies are fixed in a 3D container. Tetrazolium chloride (TTC) can be added to the agarose to make metabolizing cells red, thereby enhancing imaging contrast, but when measuring yeast colonies, TTC is not required in view of their natural optical contrast. The total time for plating into a 48-well chip (48 experiments) for a single operator is about 6 minutes, compared to a total time of about 45 minutes in a traditional drop plate assay with the same number of experiments. Figure 12 shows an example of a potential drug screening of 80 compounds in duplicate from stationary and exponentially growing bacteria (384 tests in total).

[0093] In another preferred embodiment shown in FIG. 3A , a GVA sample of the present invention can be diluted in growth medium and loaded into one or more commercial pipette tips to produce a CFU distribution of such loaded colonies that can be analytically relevant. 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 specific compounds (such as antibiotics) at specific concentrations, which can change the growth or viability of the cells. The individual colonies within the container then represent individual cells that are able to grow at a specified concentration, and the MIC can be determined by viability of the output colonies. In this embodiment, the operator is not exposed to a potentially dangerous pathogen after it has grown to a high density on the plate.

[0094] 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 physical treatments, such as UV light or genetic modification, which alter the growth or viability of the cells. The individual colonies within the container then represent individual cells that were able to grow after a particular treatment, and the overall effect of a particular treatment can be determined by counting the output colonies.

[0095] Imaging of the GVA of the present invention can be performed in a number of different ways, including scanning on a conventional microscope with a low magnification (1x, 2x, 4x) objective or using a consumer-grade camera with a macro scanning stage. The microscope has the highest possible resolution and can therefore resolve the densest samples, while the consumer camera can scan multiple wells in parallel for the highest experimental throughput.

[0096] As shown in Figure 8, the initial CFU image data can be captured using an imager, such as a commercially available camera, in this case a DSLR mounted on a custom LED light source. The camera can use a commercial macro lens to achieve the desired spatial resolution. This imaging device can use illumination from a cyan LED to maximize the contrast of TTC staining, which enhances colony detection. The light box can be moved on a computer-controlled translation stage (Thorlabs LTS300), which can program the sample into the camera field of view. This imaging embodiment can capture 5 holes per image, and the resolution limit is approximately 1,000,000 CFU / mL.

[0097] Data acquisition can be controlled by custom scripts in Labview, Matlab or Python. Output console, camera and light source from the script. In each field of view, 2-6 images are taken with slightly different focal planes (i.e., superimposed focus). The sample is then transferred to a new set of containers and then imaged. This process is repeated in all containers, and the software can automatically image up to 4x 48-well chips (a total of 192 conditions) without user input. In a preferred embodiment, the image processing of the GVA of the present invention can: (a) create enhanced images with a high signal-to-noise ratio; (b) identify a single container, and (c) mark the presence and position of a single colony in a container. Using the colony position data, the CFU in a specific container can be calculated. All image processing can be performed by custom scripts in Matlab as described in detail below.

[0098] Enhanced focal plane imaging was achieved by combining the six acquired images using a stacking focus technique, whereby each pixel was selected from the image with the highest contrast, thereby extending the depth of field. Individual slopes within the container were then separated using a Hessian transformation followed by a linear convolution to identify the locations of the top, bottom, and sides of each container marked in the image. After separation into individual slopes within the container, the colonies were then identified manually by the user using a mouse click, or automatically using a Hessian transformation to identify circular areas. The xy coordinates were also calculated from each colony and used to estimate the PDF.

[0099] The GVA of the present invention may also be visually processed without the image capture and analysis system described above. In the embodiment shown in FIG. 34 , the GVA of the present invention may include a comparative readout system. In this embodiment, variable geometry containers (in this case, comprising a plurality of pipette tips placed adjacent to each other) may be positioned relative to a paper or digital image based readout CFU indicator (320). As shown, the present invention may include a GVA assay system (300) comprising one or more variable geometry containers (310) containing an incubated cell sample in a growth medium, and a CFU indicator (320) that is pre-calculated and labeled for estimating CFU. In this embodiment, the CFU indicator (320) of the present invention comprises a pre-calculated and labeled "ruler" portion of a 36 mm pipette tip calibrated to a 150 uL agarose volume. Similarly, as shown in FIG. 34A , a plurality of containers (310) representing serial dilutions of a bacterial sample are overlaid on the paper CFU indicator (320). The CFU estimate is based on the position of the 10th colony counted by the user. FIG34b shows a comparison of the CFU measurements of the GCA using a high-resolution Canon camera and macro lens (purple) and a paper CFU indicator (320) using a basic magnifying glass (green). As shown, the accuracy is the same as the paper CFU indicator (320), but the maximum number of distinguishable colonies is reduced. As further shown in FIG34b, a 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.

[0100] The present invention further includes systems, methods and apparatus for imaging a GVA, preferably using a digital camera. In this embodiment, a GVA imaging system (100) may include an imager (101), which may preferably include a digital camera mounted to a linear stage (102) and further supported by an imager support (109). The imager (101) may be positioned adjacent to a frame (103), which is configured to couple to the linear stage (102) and further secure a light source (104). In this configuration, the frame (103) of the present invention may secure one or more variable geometry containers (113) within the field of view of the imager (101). Fig.41 As shown in , a plurality of variable geometry containers (113) can be coupled to a container holder (105) fixed to the front end of the frame (103) so as to position the container (113) between the light source (104) and the imager (101). In this configuration, the imager (101) can capture one or more images of colony forming units (CFU) in the container, and is preferably located at the end portion of the incubated container (113) as described herein.

[0101] In one embodiment of the present invention, the imager (101) may be responsive to a zoom adjuster (107) configured to adjust the position of the imager (101) relative to the frame (103). In a preferred embodiment, the zoom adjuster (107) of the present invention may include one or more rails positioned on the linear stage (102) that may allow the imager (101) (shown here as a camera) to be slidably positioned adjacent to the frame (103) and moved toward or away from the frame to adjust the "zoom" position of the camera. Such adjustments may be made manually by an operator or may be made automatically by a controller (112), such as a printed circuit board (PBC) responsive to a processor. In another embodiment, the imager (101) may be coupled to a mounting block (111) that may be responsive to a locking switch to activate or deactivate, for example, the light source (104) and or the controller (112).

[0102] Reference again Fig.41 , the frame (103) can be responsive to a pan adjuster (107) which is configured to allow horizontal or "pan" movement of the frame (103) relative to the field of view of the imager (101). In this preferred embodiment, the frame (103) of the present invention can be secured to one or more rails on the linear stage (102), preferably via a plate (110). In this configuration, the plate can slide horizontally relative to the field of view of the imager (101). (Of course, in some embodiments, the imager (101) of the present invention can be responsive to a pan adjuster (107) and the frame (103) of the present invention can be responsive to a zoom adjuster (106)). Thus, the imager (101) and / or frame (103) of the present invention may be independently adjustable to position the variable geometry container (212) relative to the field of view of the imager (202), thereby allowing the imager to capture one or more images of colony forming units (CFU) embedded in the growth medium, preferably located at the end portion of the incubated container (212).

[0103] The present invention further includes systems, methods and apparatus for imaging a GVA, preferably using a smartphone. In this embodiment, the GVA imaging system (200) of the present invention may include an imager (202) positioned adjacent to a light source (not shown). Figures 46-47 In the preferred embodiment shown in , the imager (202) of the present invention may include a smartphone secured to a mount (204) such that the smartphone's internal camera is positioned generally vertically and preferably adjacent to a light source (not shown). The adapter (206) may be mechanically responsive to the imager (202) of the present invention, which in this embodiment is a smartphone.

[0104] again Figures 46-47As shown in , the adapter (206) of the present invention can be coupled with the rear surface imager (202) and positioned above the camera of a smartphone, which can be further enhanced by placing a macro lens (209). Fig.48 As shown in , the adapter (206) may include an aperture lens aperture (208) configured to hold the macro lens (209) of the present invention. Fig.47 As shown in , an adapter (206) having a macro lens (209) fixed within a lens aperture (208) can be positioned above a camera of an imager (202), which in this embodiment comprises a smartphone. In this configuration, the field of view of the smartphone is enhanced by the placement of the macro lens (209), thereby allowing the imager (202) to more accurately capture an image of a variable geometry container (212) positioned in the camera's field of view.

[0105] The axisymmetric variable geometry container (212) of the present invention can be used to incubate a cell sample (not shown) in a growth medium (not shown). Figures 47-48 In a preferred embodiment, the variable geometry container (212) of the present invention can be positioned within the field of view of the imager (202), preferably via a mounting member (210). The mounting member (210) of the present invention can include one or more container holders (214) configured to secure the variable geometry container (212) within the field of view of the imager (202). As shown, the container holder (214) of the present invention can include an aperture suitable for mounting a variable geometry container (212), such as a pipette tip. Of course, alternative embodiments include additional elements for securing a variable geometry container (212), such as a pipette tip within the field of view of the imager (202), such embodiments including couplers, frames, latches, etc.

[0106] The mount (210) of the present invention may be adjustable in a variety of directions and orientations to allow a user to adjust the position of the variable geometry container (212) relative to the imager (202). Figures 47-48 In the embodiment shown in , 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), which is further secured to the adapter (202) of the present invention. During operation, the mount (210) can be adjusted to change the depth of the coupled container (212) relative to the imager (202).

[0107] exist Figures 47-48In the embodiment shown in , the mount (210) can be mechanically responsive to a vertical adjuster (216). In this configuration, the mount (210) can be secured to an adapter (206) that leads to a slot (220) that is positioned approximately adjacent to the field of view of the imager (202). During operation, the mount (210) can be adjusted up and down along the slot (220) to vertically adjust the variable geometry container (212) within the field of view of the imager (202). Fig.47 As shown in , the coupling (222) of the present invention, shown here as a twist coupling in the preferred embodiment, can be used to secure the container (212) in a desired position relative to the field of view of the imager (202).

[0108] In other alternative embodiments, the mount (210) of the present invention may be horizontally adjustable, such as by an extended mount surface (210) that may be slidably coupled to the mount (210) and / or the arm (224) of the present invention. Thus, the mount (210) of the present invention may be adjustable in multiple directions to position the variable geometry container (212) relative to the field of view of the imager (202), thereby allowing the imager to capture one or more images of colony forming units (CFU), preferably located at the end portion of the incubated container (212).

[0109] In another embodiment, the present invention includes novel systems, methods and apparatus for capturing and processing images of a GVA. Fig.33 In a preferred embodiment shown in , in one embodiment, the present invention may include an imaging platform (400) configured to include a processor (402) responsive to an imager (401) such as a camera or smartphone. In this embodiment, according to step 403, the imaging platform (400) of the present invention may include a computer executable program suitable for capturing one or more images of a CFU, preferably located at the end portion of a variable geometry container. According to step 404, 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), while in an internal embodiment, the images captured according to step 403 can be transmitted and processed within the imager (401) itself. In this embodiment, the computer executable program may be configured to process the image captured by the imager (401) and: 1) identify the boundaries of the variable geometry container's tip according to step 405; 2) align the variable geometry container's tip 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 calculation results may be transmitted to a data storage module (409) for further analysis, compilation, or digital storage.

[0110] As described above, the method and system for identifying and outputting CFU units can be completed manually or by a computer executable program configured for this purpose. Therefore, the steps of imaging, identifying and calculating output as described herein can be completed in certain embodiments by causing any appropriate machine and / or device for conversion of, for example, data, data processing, data conversion, external device, operation, etc. It should also be noted that in some embodiments, software and / or software solutions can be used to achieve the objectives of the present invention and can be defined as software stored on a disk or optical disk or other appropriate physical computer-readable medium (including wireless equipment and / or smart phones). In alternative embodiments, software and / or data structures can be associated with a computer or processor combination that operates on a data structure or utilizes software. Additional embodiments may include remote transmission and / or loading and / or updating of software on a computer that may be over the Internet or by any other suitable transmission machine or device, or even executing software on a computer that causes data and / or other physical transformations as described herein.

[0111] Certain embodiments of the present technology may utilize machines and / or devices, which may include general purpose computers, computers that can execute algorithms, computer readable media, software, computer readable media that continues to be specifically programmed, computer networks, server and receiver networks, transmission elements, wireless devices and / or smart phones, Internet transmission and reception elements; cloud-based storage and transmission systems, software updateable elements; computer routines and / or subroutines, computer readable memory, data storage elements, random access memory elements and / or computer interface displays that can represent data in a physically perceptible transformation, such as intuitively displaying the processed data. In addition, as can be naturally understood, in certain embodiments, any of the steps described herein can be accomplished by various hardware applications, including keyboards, mice, computer graphical interfaces, voice activation or input, servers, receivers, and any other suitable hardware devices known to ordinary technicians in the field.

[0112] As described herein, a machine learning system or model is a trained computational model that acquires features of interest, such as the presence or absence of CFUs in a variable geometry container, and classifies them. Examples of machine learning models include neural networks (including recursive neural networks and convolutional neural networks); random forest models (including random forests); restricted Boltzmann machines, recursive tensor networks, and gradient boosted trees. The term "classifier" (or classification model) is sometimes used to describe various forms of classification models, including deep learning models (e.g., neural networks with many layers) and random forest models.

[0113] As used herein, a machine learning system may include a deep learning model that may include a function approximation method that is intended to develop a custom dictionary configured to implement a given task (whether classification or dimensionality reduction). This can be implemented in various forms such as by a neural network (e.g., a convolutional neural network). In general, although not necessarily, it includes multiple layers. Each such layer includes multiple processing nodes and each layer is processed in sequence, first processing the nodes of the layer closer to the model input layer, and then processing the nodes of the layer closer to the model output. In different embodiments, one layer feeds 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), which is a computing system that can learn the relationship between an input data set and a target data set. The name ANN comes from the desire to develop a simplified mathematical representation of a part of the human nervous system that is intended to capture its "learning" and "generalization" capabilities. ANN is an important foundation in the field of artificial intelligence. ANN is widely used in research because it can model highly nonlinear systems in which the relationship between variables is unknown or very complex. ANN is usually trained on empirically observed data sets. The dataset can conventionally be divided into training set, test set and validation set.

[0114] Having now described the inventive technology, it will be illustrated with reference to certain examples, which are included herein for illustrative purposes only and are not intended to limit the invention.

[0115] Examples

[0116] Example 1: Principle and development of the geometric viability assay (GVA).

[0117] Standard high-throughput compound screening uses growth inhibition (usually measured using optical absorbance) to quantify the ability of a compound to reduce growth. However, these assays are necessarily blind to the phenotypic heterogeneity within a population (e.g., stubborn cells) and the differences between antibacterial (i.e., preventing growth) and bactericidal (i.e., inducing cell death) antibiotics. Therefore, there is an urgent need to determine compounds that have bactericidal effects rather than just antibacterial effects under physiological conditions that induce stubborn bacteria (i.e., slow growth). This demand is based on a fast and scalable method to measure the viability of pathogens after drug treatment. Therefore, the GVA of the present invention fills an important gap in current screening technology for diagnostic and manufacturing applications, as well as biocommunity sampling and discovery of new antibiotic preparations for resistant pathogens. GVA assays can also be used to more quickly and safely test dangerous pathogens for MIC. In addition, the GVA of the present invention solves the current demand for measuring the frequency of resistant cells in anticancer drug screening, with a resolution of identifying resistant cells in ten million.

[0118] Notably, the analytical framework of the GVA of the present invention enables accurate viability estimates in practice, regardless of the optical configuration. In simulations and experiments, errors in colony counts and tip position do not have a significant impact on CFU estimates, given the experimental dynamic range. In addition, exemplary variable geometry containers such as pipette tips are not perfect cones; under high-power magnification, minor imperfections in the manufacturing process are clearly visible. Despite these practical differences, even with the use of imperfect cones, the selection of a few colonies, and approximate tip positions, the GVA can still reproducibly and accurately calculate CFU concentrations within 6 orders of magnitude. This robustness comes from exploiting the potential information encoded in the position of the colonies.

[0119] Another unexpected feature of the GVA of the present invention is the self-limiting colony size observed due to CFU density. As the concentration of colonies increases, the colony size decreases accordingly, thereby maintaining colony discreteness even in dense samples. As shown below, the colony size in the strains tested leveled off after overnight incubation and did not change after several additional days.

[0120] The physical confinement of the GVA of the present invention produces culturability limitations similar to those of traditional drop CFU assays [39, 40]. However, applicants have shown that the GVA of the present invention can be used predictably for all commonly used laboratory strains tested as well as more complex samples such as biofilms and human-related biome samples. Because GVA uses the same growth substrate (e.g., solid culture medium) as traditional 2D culture techniques, applicants, those skilled in the art, will find that procedures for selectively culturing different strains in petri dishes will be transferable to GVA. Finally, the transient heat shock in current protocols using agarose does not affect the viability of the strains tested, but may be a significant interference for some species. In such alternative embodiments, other hydrogels (e.g., sodium alginate) that are cross-linked by chemical reactions may be appropriately used.

[0121] For both the drip CFU and spiral inoculator methods, incubation time remains the rate limiting step, typically requiring at least overnight for visible colonies to appear. Similarly, incubation is also the rate limiting step for the GVA of the present invention. However, for E. coli, the applicant achieved colony detection for all CFU concentrations within 8 hours. This improvement in detection time is due to the unique optical configuration, the presence of stains, and 3D geometry that maximizes light scattering. Further time reductions can be achieved using other alternative embodiments such as fluorescence imaging. Therefore, the GVA of the present invention can also shorten the time of clinical antibiotic sensitivity analysis in addition to other clinical and / or commercial applications.

[0122] In summary, Applicants have demonstrated that the GVA of the present invention significantly reduces the time and reagents required to measure cell viability compared to existing drop CFU assays, while maintaining the same dynamic range, quantitative properties, and universality across species, making the drop CFU assay the gold standard for viability measurement in microbiology.

[0123] Example 2: GVA assay development and validation.

[0124] The most time-consuming and resource-intensive step of a typical drop CFU is the dilution series, which must be run to count individual colonies over several orders of magnitude. Applicants believe that the geometry of the cone allows for the formation of a dilution series in a single step because the cross-section at the tip is smaller than the cross-section near the base. Analytically, the probability of forming a colony at any point along the axis of the cone is proportional to the cross-sectional area at that point (Figure 1a, cyan circles). This probability is defined as a probability density function (PDF), which is equal to:

[0125]

[0126] where x is the perpendicular distance from the tip along the x-axis, and h is the total length of the cone (Fig. 1a, 7a-c; see Supplementary Materials for derivation). Equation (1) applies to any axisymmetric cone or cone (Fig. 7d). The total CFU concentration in the cone can be estimated by:

[0127]

[0128] where (x1, x2) are the positions of the first and last colonies in the subvolume counted, and V is the volume of the cone. Therefore, the highest resolvable CFU density is proportional to the dynamic range of the PDF. In contrast to variable geometry containers such as cylinders or wedges, cones achieve the largest dynamic range of the PDF by changing shape in all 3 dimensions (Figure 1b). Importantly, this probability does not depend on the radial (y, z) position of the colony within the cone, but only on the perpendicular distance from the tip along the axis (x) of the cone.

[0129] Applicants simulated the distribution of colonies in cones for different CFU / mL (Fig. 1c, d). As expected, the more CFU in the cone, the more colonies there are near the tip (Fig. 1d, top panel). Regardless of the colony density, the CFU / mL estimate quickly approaches the correct value (gray dashed line) as more colony locations are included in equation (2) (Fig. 1d, bottom panel). Notably, even with more than 10,000 colonies in the cone, the CFU estimates in 97% of the simulations based only on the locations of the first 10 colonies differed from the correct value by less than 2 times (Fig. 1e, 7f). This phenomenon of rapid convergence to the correct value is the same regardless of the CFU concentration. Therefore, by exploiting the information encoded in the geometry of the cone, it is not necessary to count all colonies to accurately calculate the colony density. This concept is similar to a 3D blood cell counter; by counting a subset of colonies within a specified volume, the total concentration can be calculated using probability.

[0130] To test the theory, the applicant used a variable geometry container in the shape of a cone, commonly found in microbiology - a pipette tip. The first experiment was a dilution series using stationary E. coli (BW25113). It is known that the CFU / mL of stationary E. coli after overnight growth is approximately 10 9 CFU / mL. The cells were serially diluted, and each dilution was then treated as a sample with an unknown concentration of viable cells. Each "sample" was thoroughly mixed with molten LB agarose (cooled to ≤50°C) to a final agarose concentration of 0.5%. Triphenyltetrazolium chloride (TTC) was included in the molten agarose to increase colony contrast. The agarose was solidified in the tip, after which the tip was ejected onto an empty tip rack (see method). The agarose-containing pipette tip was then incubated overnight at 37°C, and imaged the next day using a custom optical device and a mirrorless Canon camera (imager) (Figure 1f, see Figure 8). Consistent with the applicant's simulation, the distribution of colonies formed in the tip can be predicted based on a PDF of >6 orders of magnitude (Figure 1g, slope of about 1). It is worth noting that the final colony size decreases with increasing cell density, which avoids colony overlap even at high density. Comparison of the same batch of cells using GVA and traditional drop CFU assays showed a significant correlation between the two methods (Figure 1h, Pearson correlation coefficient = 0.98, p value = 4e-16, see e.g. Fig. 9 For example drop CFU plate).

[0131] GVA was used to count other Gram-negative (Pseudomonas aeruginosa, Salmonella typhimuirium, Pseudomonas putida) and Gram-positive bacterial strains (Bacillus subtilis) as well as eukaryotic yeast cells (Saccharomyces cerevisiae) (Figures 1i, 10a). Enclosing the colonies in the pipette tip facilitates the handling of pathogen strains because rinsing can kill all contaminated cells on the outside of the tip without affecting colony growth inside the tip (Figure 10b). GVA was also used to test the viability of E. coli biofilms over time (Figures 10c, d). Finally, Applicants tested the potential of GVA to rapidly quantify non-model bacterial species. GVA was used to measure the viability of human-related biomes (Figure 11). Wiping 24 locations (FIG. 11a) showed a large dynamic range of microbial concentrations that could grow in LB (FIG. 11b). Growing samples at different temperatures repeatedly revealed temperature-selective growth of different biomes (FIG. 11c). These experiments necessarily underestimated the number of bacteria in these biomes, as many human commensal bacteria are unculturable. However, because GVA uses solid growth media, the same selective culture techniques developed for standard petri dish plating over the past 100 years can also be used for GVA, while also enabling high-throughput monitoring of culturable biomes.

[0132] Applicants next investigated how the dynamic range and accuracy of the GVA depended on the optical configuration using a low-cost camera system (also referred to in one embodiment as an imager), in this case a smartphone with a commercial macro lens. The applicant has designed a pipette tip holder that positions a single pipette tip on a smartphone. Rear camera and macro lens front (Fig. 2a, 13). Calibration reveals that the image quality is comparable to that of a 100mm f / 2.8 macro lens. The pixel size of the camera is 6.6 microns compared to smartphones The pixel size of the smartphone is 13.7 microns. (Figure 8) The applicant believes that compared with mirrorless cameras, smartphones The smaller pixel size and lower electronic depth of the camera will reduce the smallest possible colony that can be detected. As expected, comparing images taken with the camera to those taken with the iPhone confirmed that the smartphone The colonies at the highest CFU concentration could no longer be distinguished on the screen (Figure 2b). By comparing the CFU / mL calculated by GVA for the same pipette tips of an E. coli dilution series for both the iPhone and the Canon camera, the Applicant measured a 64-fold reduction in dynamic range for the iPhone compared to the Canon camera (Figure 2c). Under this configuration, GVA remains highly linear for nearly 5 orders of magnitude (green line, slope = 1.04, R 2 =0.99). On the same pipette tip, smartphones The correlation between CFU counts for the 100 nm and 200 nm camera configurations was 0.99 ( FIG. 2 d ). Thus, Applicants have found that GVA is accurate regardless of the optical configuration, but the dynamic range is set by the maximum camera resolution.

[0133] Compared with the dripping CFU or spiral inoculation instrument method, the main advantage of GVA is that time, reagent cost and plastic waste are reduced by more than 10 times (Fig. 3). The spiral inoculation instrument is the most common commercial alternative method for CFU determination, which uses a special instrument to dilute the sample along the Archimedean spiral. In order to measure the time saved by GVA, the applicant compared the 3 steps of viability determination, which include preparing solid growth medium (Fig. 3b), diluting / plating 96 situations (Fig. 3c) and imaging / counting of bacterium colonies (Fig. 3d). The most time-saving is in the plating step. It takes 3 hours to manually plate 96 situations to drip CFU. It is reported that the current spiral inoculation instrument takes 30 seconds per plate, corresponding to 96 conditions in 48 minutes. GVA takes 5 minutes, corresponding to saving 36 times of plating time. In preparation time, GVA is also faster than the spiral inoculation instrument and the dripping CFU method. According to the time reported by the manufacturer using the automatic colony counter, the imaging and counting of the bacterium colony of the spiral inoculation instrument are the fastest. When including the time for image acquisition, pipette tip segmentation, and user-guided colony detection, the time required for GVA semi-automated colony counting was similar to the amount of time required for manual colony counting of drop CFUs. In summary, using the current instrument, a single researcher measured viability for 1,200 conditions in a single day.

[0134] The applicant then compared the three methods in terms of reagent savings and plastic waste reduction. In the drop CFU assay, since each sample must be diluted and then transferred to an agarose pad separately, the standard laboratory protocol is 15 pipette tips per sample (Figure 3e). In the GVA, a single pipette tip is used for each sample, saving a total of 15 times the pipette tips compared to the drop CFU (Figure 3e). In the spiral inoculator assay, a petri dish filled with solid growth medium is required in each case (Figure 3g). Compared to the spiral inoculator method, the required plastic is reduced from petri dishes to pipette tips. At the time of writing, adding up the costs of pipette tips, agar, and culture plates, the applicant found that the drop CFU was the most expensive of the consumables, costing an average of $222 per 96 samples, while the spiral inoculator and GVA cost $87 and $17, respectively (Figure 3h). The large amount of instrument costs offset the consumables saved by the spiral inoculator (Figure 3i). Costs were calculated based on quotes for three spiral inoculators and automated imaging systems obtained from three distributors. Instrument costs for both GVA and droplet CFU include the electronic multichannel pipette. Additional instrument costs for GVA depend on the optical configuration (Fig. 3j) and are at least an order of magnitude lower than for spiral inoculator systems. In summary, this analysis shows that GVA significantly reduces operator time, instrument and reagent costs, and the carbon footprint of viability assays.

[0135] The applicants then investigated the robustness of GVA. The applicants first measured the range of 10 2 Up to 10 7 The counting noise between the four technical replicates was calculated for CFU / mL CFU concentrations (Fig. 4a, b). The noise was calculated using the coefficient of variation (COV) between replicates. The GVA noise for both the camera and smartphone optical configurations was less than or equal to the noise of the drop CFU assay at all measured CFU concentrations. As with the drop CFU assay, the GVA noise was heteroscedastic and increased as the number of colonies decreased, as expected from a Poisson process.

[0136] After confirming the low technical noise of GVA, the applicant studied the impact of two practical errors on GVA calculation: uncertainty in the position of lost colonies and cone tips. These errors were checked using both simulation and experimental data. Predictably, the error increased as the number of lost colonies increased (Figure 4c, e), although the relative error was the same at all seeding densities. It is worth noting that removing 10 of the 15 counted colonies in the simulation data brought the estimate within 2 times, regardless of the initial CFU concentration. This robustness is outlined in the experimental data and is consistent with the observation that the positions of only 5 colonies are sufficient to calculate CFU / mL within an average of 2 times (Figure 1e). For pipette tip position errors, GVA calculations at high CFU concentrations are more sensitive to misidentification of tip positions than low cell concentrations (Figure 4d, f blue and black lines). Nevertheless, in simulations and experiments, making the tip position error by 10% (4mm for a 36mm cone) still brings the estimate within 2 times of the correct value. Finally, the correlation between drop CFU and GVA (Fig. 1h) dropped slightly from 0.98 to 0.97 (Fig. 4g, h, 14) when up to 10 colonies were lost and the tip position was misaligned by 4 mm. These simulation and experimental data highlight the robustness of GVA.

[0137] In summary, Applicants' analyses found that GVA is accurate and reliable, maintaining sensitivity within a range comparable to the gold standard droplet CFU while reducing cost and time.

[0138] Example 3: High-throughput viability screening of stationary Escherichia coli.

[0139] Previous studies have found that slow growth is a non-heritable form of antibiotic tolerance, which has won time for viable cells to develop genetic resistance. Compared with cells that grow exponentially, slow-growing cells are usually metabolically active and have weaker DNA replication. Therefore, slow-growing cells are resistant to antibiotics for DNA synthesis (fluoroquinolones), protein translation (aminoglycosides) and cell wall bioproduction (β-lactams). Growth-dependent tolerance can only be observed by measuring viability, but the cumbersome and cost of dripping CFU determinations limit extensive analysis. Using GVA, the applicant directly compared the viability of exponentially growing cells and stationary cells for three different doses of antibiotics at different time amounts. In short, the applicant tested three antibiotics in six different concentrations in duplicate for five different durations for stationary and exponential cells, and continued a total of 360 viability measurements (Fig. 5a, b). This data was obtained by a single researcher using only four tip boxes in one day. Stationary cells are more resistant to ciprofloxacin, carbenicillin and gentamicin. Specifically, for carbenicillin, the viability of stationary cells treated with 100 μg / mL carbenicillin for 24 hours decreased by less than 10 times, compared with a 10,000-fold decrease in the viability of exponential cells. The number of colonies treated with 10 μg / mL carbenicillin for exponential cells did not change during the first 6 hours, and then the viable cells increased after 24 hours of treatment, thereby indicating a slowly expanding resistant population (Fig. 5b). Ciprofloxacin at 10 μg / mL has a biphasic pharmacodynamic profile, with initial bactericidal activity within an hour, reducing the viability of both stationary and exponentially growing cultures by 10 times. However, this activity stabilizes after 6 hours and reaches the second stage of killing at 24 hours. Gentamycin at 10 μg / mL requires a full 24 hours to reduce the viability of stationary cells by more than 10 times. For untreated cultures, the applicant observed an increase in the concentration of exponentially growing cells until reaching about 10 at 6 hours. 9 The peak concentration of CFU / mL was 1.374 CFU / mL (Figure 15). Once in the stationary phase, the number of viable cells decreased over time, as reported previously. These data illustrate the utility of GVA in measuring treatment effects that are independent of growth rate.

[0140] In order to explore the potential of GVA technology in high-throughput viability measurement, the applicant screened the ICCB Enzo bioactive substance library (469 compounds) for static and exponentially growing cultures (Fig. 5c, d). There are various chemical substances in the Enzo library, including bioactive lipids, small molecule inhibitors and ion channel ligands (Fig. 5c), and span the structural diversity of large libraries such as the Maybridge HitFinder library with about 14,000 compounds (Fig. 16). The viability of BW25113 Escherichia coli treated with the Enzo library is measured in both the exponential phase and the static phase. Including control and elimination of pipette errors, 2267 situations were measured. The equivalent screening using drop CFU or spiral inoculation instrument determination will require 355 tip boxes or 2267 petri dishes respectively. GVA requires 24 tip boxes. No edge effect was observed in static plates or index plates (Mann-Whitney U test, p value>0.05, Figure 16b). The average difference between drug classes was not large (Figure 16c, p value>0.001ANOVA, multiple hypothesis testing corrected p value), and there was no significant difference compared to the control (p value>0.01, paired Tukey test). Five compounds were selected for subsequent validation (mitomycin C, phentolamine, E-4031, benamiel and diphenyl iodine), corresponding to a hit rate of about 1%. Mitomycin C is a known antibiotic that works through DNA cross-linking. As expected, the applicant found that it was more active against exponentially growing cells compared to stationary cells (Figure 5f). Phentolamine is an alpha-adrenergic receptor antagonist. Phentolamine has previously been shown to block the growth of Escherichia coli induced by norepinephrine and epinephrine, which is presumed to be achieved by antagonizing alpha-adrenergic receptors. The applicant found that at high concentrations (20 μg / mL), stationary cells were more sensitive to the effects of phentolamine than exponentially growing cells (Figure 5g), thereby confirming the different sensitivities observed in the screening. E-4031 and benamipril did not have any dose-dependent effect on viability ( Fig.17 ). Finally, the applicant discovered that the hybrid NADPH oxidase (NOX) inhibitor diphenylene iodonium (DPI) was active against both quiescent and growing cultures (Figure 5h). Previous studies have identified DPI as having antibacterial properties, however the mechanism of DPI's bactericidal activity remains unclear. The applicant is very interested in the bactericidal activity of DPI because it reduces reactive oxygen species (ROS) in eukaryotic organisms by inhibiting NOX, which is very different from the mechanism of many antibiotics that increase ROS populations.

[0141] In order to study the bactericidal mechanism of DPI, the applicant first checked the Escherichia coli ROS level when treated with DPI. The ROS level was determined by the fluorescent CellROX dye that measures cytoplasmic superoxide. After being treated with a lethal DPI dose, the single cell fluorescence over time was measured and compared with the untreated control (Fig. 6a). As expected, DPI significantly reduced ROS and reached the lowest point about 75 minutes after adding the drug (Fig. 6a compares blue and yellow lines). The depth and duration of ROS reduction are directly proportional to the DPI concentration (Fig. 18a). Surprisingly, ROS rapidly surged again after this reduction. In contrast to DPI, ciprofloxacin treatment monotonically increased ROS levels (Fig. 6a, orange line). The increase in ROS levels is the basis of the bactericidal activity of ciprofloxacin; therefore, the applicant then studied whether the ROS surge induced by DPI is also the basis of its bactericidal activity. The applicant compared the DPI sensitivity of stationary cells in aerobic and anaerobic environments. DPI has lower activity in anaerobic culture (Fig. 6b), similar to gentamicin or ciprofloxacin (Fig. 18b, c). This data shows that high levels of ROS are part of the bactericidal mechanism of DPI, although this initially reduces ROS. To further demonstrate this, adding a ROS scavenger also reduces DPI efficacy (Fig. 18d).

[0142] Moderate DPI concentrations change ROS levels, but maintain viability when measured with GVA. The applicant examined cell morphology after being treated with less than 10 μg / mL DPI for 4 hours, and observed the formation of hyphae (Fig. 6c). Filamentation is a typical sign of SOS activation, and the increase of ROS is a recognized SOS activator. Therefore, the applicant wants to know whether DPI activates SOS. The applicant uses the PEC GFP promoter library to check the promoter activity of genes downstream of lexA. LexA is the master transcriptional repressor of genes in SOS regulators such as polB, dinB, dinG and yjiH, and the activated recA automatically catalyzes degradation. Compared with the ribosomal protein control (rrnB), the applicant observed a sustained dose-dependent induction of the polB promoter (Fig. 6d, solid and dotted lines). The highest promoter activity corresponds to a moderate DPI dose (3 μg / mL), in which filamentation is observed. The applicant also observed that the DPI-dependent increase in dinB, dinG and yjiH promoter activity ( Fig.19 ). The activity of the self-repressed lexA promoter was also increased within 90 minutes of DPI addition.

[0143] Since SOS activity reduces the efficacy of other fungicides, applicants predict that in the presence of DPI, SOS activation mediated by recA is essential for maintaining viability. As predicted, recA knockout is more susceptible to DPI in both the stationary and exponential growth phases (Fig. 6e), although the increase in DPI efficacy is more pronounced in exponential growth phase cells. In contrast, knocking out other DNA repair enzymes, redox repair enzymes, or ROS scavengers does not significantly change the efficacy of DPI in either growth phase (Fig. Fig. 20 Knockdown of the yedZ and fre genes (recently identified as part of the NOX-like system in bacteria) modestly increased the efficacy of DPI against quiescent cells, indicating that these proteins are unlikely to be the primary targets of DPI in E. coli (Fig. 6e). Thus, this data shows that DPI activates SOS and that SOS activation enhances cell viability.

[0144] Therefore, the applicant wanted to know whether DPI would antagonize other antibiotics whose efficacy is reduced by the SOS response. This antagonism has been observed when ciprofloxacin is combined with metronidazole, a redox-active prodrug known to activate SOS. To detect the antagonism, the applicant used GVA to measure viability in a time-resolved, check plate assay (Figure 6f, 2i). In the check plate assay, DPI was used in combination with ciprofloxacin or gentamicin in a 6×6 dose matrix. The ease of use of GVA enables check plate sampling over time, thereby obtaining a complete pharmacokinetic profile of drug-drug interactions. DPI antagonized the effects of both ciprofloxacin and gentamicin on stationary E. coli, increasing viability by 1,000 times after 24 hours of treatment compared to either drug alone (Figure 6f, g). This antagonism was not observed in the growth inhibition assay (Figure 6h), thereby emphasizing the value of viability data when studying drug-drug interactions. DPI antagonism by ciprofloxacin and gentamicin was also observed in Salmonella typhimurium ( Fig. 22 Pretreatment of cells with DPI for 2 h before the addition of ciprofloxacin further enhanced the protective effect, whereas pretreatment with ciprofloxacin for 2 h reduced the antagonistic effect of DPI ( FIG. 6i ).

[0145] In summary, the applicant found that DPI initially reduced ROS, which then surged, thereby enhancing its bactericidal effect. As expected from previous studies on ROS lethality, the effectiveness of DPI depends on SOS activation mediated by recA. By activating SOS, DPI improves drug tolerance to fluoroquinolones and aminoglycosides, as revealed by time viability check plates.

[0146] Example 4: Materials and Methods

[0147] Derivation of the axial probability density function of the cone: Assuming that individual cells are well mixed before being suspended and cast into a 3D cone, the probability of forming a colony at a distance x from the origin is proportional to the percentage of the infinitesimal volume (dV) at x to the total volume (V). dV is defined as follows:

[0148] dV=πr′(x) 2 *dx (3)

[0149] Where r'(x) is the radius of the circle at x (Figure 7a, cyan circle). Based on the geometry in Figure 7a (right inset), the applicant found that:

[0150]

[0151] Where r is the radius of the base of the cone, and h is the height of the cone.

[0152] The probability density function (PDF) of this geometry can be solved as follows:

[0153]

[0154] where C is a normalization constant and is equal to the inverse of the volume V (i.e. ) This yields the following PDF (Figure 7b):

[0155]

[0156] The associated cumulative distribution function (CDF) can be obtained by integers (Figure 7c):

[0157]

[0158] Applicants observe here that this PDF holds true regardless of the shape of the cone or the base of the cone, as long as it is axisymmetric (Fig. 7d), because the specific geometry of dV cancels out the PDF generated by the normalization constant. According to the same derivation, the PDF of a cylinder is constant and the PDF of the 2D wedge is (Figure 1b). The PDF of the cone gives the largest dynamic range of probabilities due to the exponential of x (Figure 1b). If there is only one colony in the cone, the CDF measures the probability of finding the colony as a function of x (Figure 1b). The PDF is the probability of finding a colony at any point x for a single colony in the cone. Therefore, there are two equivalent ways to calculate the number of CFUs using either the PDF or the CDF (Figure 7e).

[0159] With the PDF, the applicant can estimate the number of CFU / mL using the formula:

[0160]

[0161] where x1 and x2 are the positions of the first and last colonies, and V is the cone volume.

[0162] With CDF:

[0163]

[0164] In practice, Applicants have found it more convenient to use the PDF estimator, as it does not rely on identifying the first colony from the tip, but is mathematically equivalent.

[0165] Strains and growth conditions: Unless otherwise specified in the text, Escherichia coli strain BW25113 was used. This strain was purchased from the Yale Coli Genetic Stock Center. Escherichia coli was grown in LB (Sigma Aldrich) in a shaking incubator at 37°C. Bacillus subtilis strain W168 was kindly provided by Garner lab and grown in LB in a shaking incubator at 37°C. Pseudomonas putida strain KT2440 was kindly provided by Jacob Fenster and grown in LB in a shaking incubator at 30°C. Salmonella typhimurium strain SL1344 was kindly provided by Corrie Detweiler and grown in LB in a shaking incubator at 37°C. Saccharomyces cerevisiae strain BY4741 was kindly provided by Roy Parker and grown in YEPD in a shaking incubator at 30°C. Pseudomonas aeruginosa strain PA01 was kindly provided by Zemer Gitai and grown in LB at 37°C in a shaking incubator. Knockouts were selected from the Keio University collection (Dharmacon). The PEC promoter library in E. coli was obtained from Dharmacon (PEC3877).

[0166] All bacteria and yeast strains are tiled on agar plates, and appropriate antibiotics are selected if necessary (kanamycin is used for Keio and PEC strains). These plates are stored in 4 ° C refrigerators for 1 month. Then single bacterium colonies are selected and grown overnight in 3-5mL cultures of 12mL culture tubes, and appropriate antibiotics are selected if necessary. Each selected bacterium colony is considered as biological repetition. Multiple measurements of the same culture are considered as technical repetitions.

[0167] Antibiotic treatment: Antibiotic treatment is usually performed in a 96-well plate with a 12-channel electronic pipette. For stationary phase treatment, bacterial cells are grown overnight (≥16 hours) in a shaking incubator (180RPM). For Pseudomonas putida only, cells are grown for 2 days. After entering the stationary phase, cells are dispersed into 96-well flat-bottom plates, 100 μL of cells per well. The drug treatment is plated into a separate 96-well round-bottom plate at 1000 times. A 100nL pin transfer is used to dilute the drug plate into the cell plate at a ratio of 1:1000. This plate is then placed in a shaking incubator for the duration of the experiment.

[0168] To measure antibiotic treatment in the exponential phase, the overnight culture was diluted 1:1000 into fresh LB. This culture was then placed in an incubator for 2 hours. After this incubation, the cells were then spread into 96-well plates and then subjected to drug treatment.

[0169] Drop CFU assay: The drop CFU assay is performed similarly to the method described in . Briefly, in a 96-well plate, 90 μL is added to all wells except row A. A volume of 100 μL of sample solution is added to row A. 10 μL of cells are taken from row A and added to row B, followed by 3 mixings. This process is repeated from B to C until the final dilution of row H corresponds to a dilution from the original sample at 1e-7. Pipette tips are changed for each row to reduce sample carryover. From each column of the dilution series, 3 μL drops are transferred to the LB-agar pad. After all the liquid has been absorbed into the agar (usually 15-30 minutes), the agar plate is inverted and placed in a 37°C static incubator overnight. The counts the next morning are done manually. The first dilution with individually distinguishable colonies is used for counting and multiplied by the corresponding dilution factor.

[0170] Embedding of GVA: The purpose of embedding is to have a uniformly mixed sample in a liquid hydrogel, which allows for rapid solidification of the 3D mold. The applicant used 0.5% agarose as a convenient hydrogel that solidifies quickly and prevents cell movement after solidification. Pipette tips (200 μL, VWR Universal) are most commonly used as reproducible and inexpensive 3D geometric scaffolds.

[0171] 1. Prepare agarose solution. Prepare a 0.66% agarose solution in the selected cell culture medium. The applicant found that the color of LB and YEPD did not affect the imaging of the pipette tip. Add agarose (0.66g) to a 100mL volume of LB and microwave heat until completely dissolved. Keep a close eye on the heating process to ensure that it does not boil. After complete dissolution, place the liquid in a 50°C hot bath to keep it liquid until ready for use. At this stage, for all bacterial experiments, tetrazolium chloride (TTC, 25μg / mL final concentration) was added to LB-agarose from a 1000-fold storage solution. Respiratory bacteria reduce tetrazolium to water-insoluble formazan, which stains the colonies red.

[0172] 2. Prepare cells. Prepare fresh 96 round bottom plates by adding 50 μL of LB or YEPD to each well. Take out the sample plate containing cells and drugs from the shaking incubator, and a needle transfer tool (2 μL hanging drop, VP409) is used to transfer 2 μL of treated cells to the 50 μL LB plate. If a time course experiment is being performed, then place the sample plate back into the shaking incubator.

[0173] 3. Embedding. In order to embed, the applicant found that the electronic multichannel pipette is the most convenient for a large number of samples. The applicant usually uses a 12-channel P200 (Eppendorf explorer, 4861000724). Before pouring the liquid agarose into the reservoir, collect the following items: a 96-well plate (from step 2) containing 20 μL of sample, a box of autoclaved P200 pipette tips, an empty P200 tip box filled with ice water, and an empty P200 tip box with 2mL of water at the bottom to accommodate the embedded cells. At this point, the liquid agarose is poured into a 100mL container for the convenience of using a multichannel pipette. Using the pipetting and mixing functions on the pipette, 150 μL of LB agarose solution is extracted from the reservoir and mixed twice with 1 row of sample plates (200 μL final volume, 0.5% final agarose concentration, diluted from the sample plate at 1:100). After mixing, take 150 μL into the same pipette tip to avoid forming bubbles. Then put these tips into an ice bath for 6 seconds to ensure that the hydrogel solidifies and plugs the tips. The tips are then ejected into an empty pipette tip box. Repeat this process for all other 7 rows in the plate. Using 150 μL and diluting 1:100 from the original sample gave a lower limit of 667 CFU / mL.

[0174] 4. Incubation. After the embedding process is completed, the tip box with the LB-agarose cell suspension is placed at room temperature for about 30 minutes to ensure that the agarose is completely solidified. The tips are then moved to a static incubator overnight to allow the colonies to grow. Applicants have found that the size of the colonies does not change after overnight incubation, so as long as the cells are kept in a hydrated environment, they can be imaged up to 4 days after embedding.

[0175] Drug screening: Screening was performed using the ICCB Enzo bioactive substance matching library (Enzo, BML-2840-0100). Overnight cultures of 60 mL LB were cultured to the stationary phase with E. coli. The next morning, 60 μL of overnight cultures (stationary phase) were added to fresh 60 mL of LB and grown for 2 hours (exponential phase) in a shaking incubator. Cells were then dispersed into 100 μL volumes in 96-well plates.

[0176] Biofilm growth and treatment: MG1655 E. coli strain was used for biofilms. Overnight cultures were incubated in LB at 1:10. 5 The biofilms were inoculated in U-bottom 96-well plates and grown in a static incubator at 37°C for 48 hours. For time experiments, separate plates were used for each time point and the biofilms were dispersed at the specified time. The reported times represent the number of hours after the initial 48-hour incubation. To disperse the biofilm, non-adherent cells were aspirated, the wells were rinsed with PBS, and fresh PBS was added to the wells. The plates were covered with foil plate seals (VWR, 60941-126) and placed on a plate shaker at 3000 rpm for 30 minutes. The dispersed cells were shaken at 10 4 dilutions were made and GVA was performed. Crystal violet staining was used to confirm proper dispersion; any replicates that were not fully dispersed were discarded.

[0177] Imaging GVA Tips: Imaging was performed on a custom imager (Figure 8) or iPhone 12 (Figure 2). For the custom instrument, 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 that could resolve the smallest colonies. For the iPhone, the parts were designed in FreeCAD and then 3D printed with PLA using a Lulzbot Taz Pro FDM printer. In a single print, all pieces could be placed on the print bed. A glue stick was used to increase the adhesion of the print bed before printing. The print bed temperature was set to 70°C for all layers, and the nozzle temperature was set to 225°C. The print speed for the initial layer was set to 10 mm / sec, and then the print speed for subsequent layers was increased to 30 mm / sec. After printing, the depth channels (green parts in Figure 2a) were tapped with a drill bit No. 8-32. After assembling the holder on the Xenvo macro lens and removing the wide angle lens, the tips were positioned in front of a white background and imaged under ambient lighting using the iPhone’s autofocus function. Three images were taken for each tip and the most focused tip was selected before processing using the Matlab application.

[0178] The digital camera is mounted above a light box that provides uniform illumination. The light box is then moved by a stepper stage so that 12 tips can be automatically imaged (3 tips per field of view, 4 fields of view). The light box consists of a foam box (McMaster Carr, #8560K257) covered by a transparent acrylic sheet. White paper is adhered to the bottom of the acrylic as a diffuser. The interior of the foam box is lined with foil (Reynolds). A high-intensity cyan LED (Luxeon Rebel, 3Up) is placed on a heat sink inside the box and powered by a constant current driver (BuckBlock, 2100mA). The foam light box is mounted on a stepper motor stage (Thorlabs, LTS300). The light box is mounted on a tripod. The camera was threaded onto a z-translator (Thorlabs, MT1), which was fixed to a right angle plate (Thorlabs, AP90). The Z positioner was used to set the distance so that 3 pipette tips could be imaged in a single field of view, and a macro lens was used to focus the pipette tips. With this camera, this corresponds to a pixel size of 5.8 μm (Figure 8c). To place the tips onto the light box, a broken 12-channel P200 headlight was used. This allowed easy loading and unloading of samples using a spring release, while also providing a standard orientation for the tips.

[0179] Images were collected using a custom Labview script to control the camera and stepper stage. Labview calls a separate program, digiCamControl (digicamcontrol.com), to access the camera functions and acquire images. Typical camera settings use a shutter speed of 1 / 100 sec, an aperture of 6.3, and an ISO of 100. Five images were collected at each field of view, and the stage was then moved to the next three pipette tips (27 mm). Images were stored directly in the instrument computer as high-resolution .jpg files. Using this instrument, a typical experiment with 96 tips can be imaged in about 7 minutes.

[0180] Image processing: The purpose of image processing is to identify and extract individual pipette tips from the collected images and to identify individual colonies. These are divided into two steps that are performed sequentially. Matlab (Mathworks, R2021b) was used for all image processing analyses. The developed application can use a compiled version for the user's operating system without a Matlab license.

[0181] 1) Pipette tip segmentation: All images from a given field of view are converted to 16-bit grayscale images. The green channels of the images are summed and used for downstream analysis. The overall orientation of the image is calculated to ensure that each tip is oriented perpendicular to the x-axis. Since there are slight variations in the tips loaded onto the light box, it is necessary to accurately calculate the distance of the colonies from the pipette tips. The Hessian of the image is calculated (fibermetric.m) and convolved with a horizontal line to locate the angle of the tip. The image is then rotated by this angle (imrotate.m) to orient the pipette vertically in the image. To determine the x pixels corresponding to the pipette tip, the Hessian is calculated again from the rotated image. Starting from the middle of the image, single line convolutions at different angles are used to calculate the left and right boundaries of the pipette tip. These lines are then extended to the bottom of the pipette tip to locate the left and right boundaries of the tip. Each of the three wells is then saved as a cell array.

[0182] 2) Semi-automatic segmentation: Colonies were segmented using a semi-automatic custom script in Matlab. From the extracted pipette tip image, the user selected one of 4 different segmentation routines corresponding to different sizes of colonies in 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 done using Matlab's Image Processing Toolbox. The user could then edit the automatic segmentation to add missing colonies or remove erroneous colonies.

[0183] The colony count and position of the first and last colonies were used in equations (1) and (2) to calculate the GVA estimate in CFU / mL. For error analysis, the factor between the GVA estimate and the correct value was calculated according to the following formula: Factor:

[0184]

[0185] This error calculation method takes into account the large dynamic range of possible CFU / mL.

[0186] Microscope measurement results: For all microscopy experiments, cells from overnight cultures were diluted 1:100 in minimal medium (PMM) and shaken at 37°C for 2 hours to ensure that the cells had entered the lag phase. After 2 hours of growth, 2 μL of diluted cell culture was added to the top of a cooled 200 μL 2% low-melting agarose pad with 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 cells was inverted and pressed to the bottom of the imaging plate. The field of view (FOV) was manually selected on the microscope. After the FOV was selected and before imaging began, the drug was added on top as before. The applicant has previously found that the drug diffuses into the entire pad in a few minutes.

[0187] Imaging was performed using a Nikon Ti2 inverted microscope running the Nikon Elements software package. Fluorescence excitation was achieved using a high-angle illumination laser source (488nm and 561nm) to minimize out-of-focus background. All images were collected using a 40x NA 0.95 air objective. Images were collected on a sCMOS camera (Hamamatsu, ORCA-Fusion) camera.

[0188] Image processing was done in Matlab (MathWorks, R2020a) and followed the general protocol described in

[18] . In brief, the illumination profiles of all images were estimated from the average of 50 images per FOV. Before correcting the images, morphological opening and blurring techniques were used to broaden the illumination pattern. After illumination correction, jitter in the movies was removed by aligning each consecutive frame using a fast 2D Fourier transform implemented in Matlab. Before segmentation, the background was locally subtracted based on an estimate of the background calculated using the morphological image opening.

[0189] Cells were segmented using a Hessian-based fiber metrology routine implemented in Matlab specifically for the identification of tubular structures. Only segmented regions that reached minimum area and intensity thresholds manually selected based on camera and laser settings were included. To remove small amounts of segmented debris, the average Euclidean distance of each cell to all other cells in the multidimensional feature space was calculated, and objects whose average distance was at or above the 95th percentile were removed. The position of a cell in feature space was defined by its segmented area, perimeter, major / minor axis length, and circularity extracted using Matlab's regionprops command.

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Claims

1. A system for a geometric viability assay (GVA), the system comprising: - introducing a cell sample into a growth medium; - a variable geometry container suitable for incubating said cell sample in said growth medium, wherein said container is axisymmetric; and - An imager adapted to capture one or more images of colony forming units (CFU) in the incubated variable geometry containers.

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

3. The system of claim 1, wherein the cell sample comprises a microbial sample selected from the group consisting of a bacterial sample, a microbiome sample, a yeast sample, and a fungal sample.

4. The system of claim 3, further comprising at least one cellular contrast agent introduced into the growth medium.

5. The system of claim 1, wherein the cell sample comprises a diluted cell sample.

6. The system of claim 1, wherein the growth medium comprises a semi-solid growth medium.

7. The system of claim 1, wherein the variable geometry container comprises a pipette tip.

8. The device of claim 1, wherein the variable geometry container is translucent.

9. The system of claim 1, wherein the variable geometry container comprises a mold having one or more variable geometry containers.

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

11. The system of claim 1, wherein the growth medium comprises liquid growth medium for serial dilution of the cell sample.

12. The system of claim 1, wherein a position of the imager is adjustable relative to the variable geometry container.

13. The system of claim 1, wherein the position of the variable geometry container is adjustable relative to the imager.

14. The system of any one of claims 12 to 13, wherein the position is selected from: a vertical position, a horizontal position, and a depth of the imager from the container.

15. The system of claim 1, wherein the imager is selected from the group consisting of: a microscope, a digital camera, and a smartphone.

16. The system of claim 1, wherein the imager is adapted to identify one or more images of the CFU located at an end portion of the variable geometry container.

17. The system of claim 1, wherein the imager comprises an imaging platform, the imaging platform comprising: - a processor and a controller responsive to said imager; - A computer executable program adapted to identify one or more images of said CFU located at an end portion of said variable geometry container.

18. The system according to 17, wherein the computer executable program is configured to: - identifying the boundaries of the tip of the variable geometry container; - aligning the tip with the variable geometry container; - Perform colony segmentation; and - Calculate CFU.

19. The system of claim 1, further comprising introducing a process into the cell sample.

20. The system of claim 19, wherein the treatment comprises one or more compounds suitable for: - killing cells in the cell sample; - inhibiting the growth of said cells in said cell sample; - altering one or more phenotypic properties of the cells in the cell sample; or - altering one or more genotypic properties of said cells in said cell sample.

21. The system of claim 20, wherein the treatment compound is selected from the group consisting of: a therapeutic compound, an antibiotic, a bactericidal compound, a bacteriostatic compound, an anticancer compound, an antifungal agent, a drug screening compound.

22. The system of claim 17, further comprising an output of the number of viable CFUs in the incubated cell sample.

23. The system of claim 1, further comprising a CFU indicator.

24. A colony forming unit (CFU) assay device comprising a variable geometry container adapted to incubate a cell sample in a growth medium, wherein the variable geometry container comprises an opening, an axisymmetric middle portion, and end portions narrower than the opening.

25. The device of claim 24, wherein the variable geometry container comprises a pipette tip.

26. The apparatus of claim 24, wherein the variable geometry container comprises a mold having one or more variable geometry containers.

27. The apparatus of claim 24, wherein the mold comprises a three-dimensional printed chip having a plurality of variable geometry containers.

28. The device of claim 24, wherein the variable geometry container is translucent.

29. The device of claim 24, wherein the variable geometry container comprises a wedge-shaped configuration, a beveled configuration, or a tapered configuration.

30. The device of claim 24, wherein the cell sample is selected from the group consisting of: a biological sample, an environmental sample, a commercial sample, a prokaryotic cell sample, or a eukaryotic cell sample.

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

32. The device of claim 24, further comprising at least one cellular contrast agent introduced into the growth medium.

33. The device of claim 24, wherein the cell sample comprises a diluted cell sample.

34. The device of claim 24, wherein the growth medium comprises a semi-solid growth medium.

35. The device of claim 24, further comprising an imager adapted to capture one or more images of colony forming units (CFU) in the incubated variable geometry container.

36. The apparatus of claim 35, wherein the imager is adjustable relative to the variable geometry container.

37. The apparatus of claim 24, wherein the variable geometry container is adjustable relative to the imager.

38. The apparatus of any one of claims 36 to 37, wherein the position is selected from: a vertical position, a horizontal position, and a depth of the imager from the container.

39. The device of any one of claims 35 to 38, wherein the imager is selected from the group consisting of: a microscope, a digital camera, and a smartphone.

40. The apparatus of any one of claims 35 to 39, wherein the imager is adapted to identify one or more images of the CFU located at an end portion of the variable geometry container.

41. The apparatus of claim 35, wherein the imager comprises an imaging platform, the imaging platform comprising: - a processor and a controller responsive to said imager; - A computer executable program adapted to identify one or more images of said CFU located at an end portion of said variable geometry container.

42. The apparatus of claim 41, wherein the computer executable program is configured to: - identifying the boundaries of the tip of the variable geometry container; - aligning the tip with the variable geometry container; - Perform colony segmentation; and - Calculate CFU.

43. The device of claim 24, further comprising a process for introducing said cell sample.

44. The apparatus of claim 43, wherein the treatment comprises one or more compounds suitable for: - killing cells in the cell sample; - inhibiting the growth of said cells in said cell sample; - altering one or more phenotypic properties of the cells in the cell sample; or - altering one or more genotypic properties of said cells in said cell sample.

45. The device of claim 44, wherein the treatment compound is selected from the group consisting of: a therapeutic compound, an antibiotic, a bactericidal compound, a bacteriostatic compound, an anticancer compound, an antifungal agent, a drug screening compound.

46. ​​The device of claim 24, further comprising a CFU indicator.

47. A system for imaging a geometric viability assay (GVA), the system comprising: - an imager positioned adjacent to the light source; - an adapter mechanically responsive to said imager; - a mounting for securing a variable geometry container suitable for incubating a cell sample in a growth medium, wherein the container is axisymmetric; and - wherein the mount is adjustable so as to position the container relative to the imager to allow the imager to capture one or more images of colony forming units (CFU) located at an end portion of the incubated container.

48. The system of 47, wherein the imager comprises a smartphone.

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

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

51. The system of 50, further comprising a macro lens positioned within the lens aperture.

52. A system according to claim 47, wherein the mounting comprises one or more container holders, each of which is configured to secure one or more variable geometry containers.

53. The system of claim 47, wherein the mount comprises a vertical adjuster.

54. The system of claim 47, wherein the mount comprises a depth adjuster.

55. The system of claim 47, further comprising an opaque background positioned behind the variable geometry container.

56. The system of claim 48, wherein the smartphone comprises an imaging platform, the imaging platform comprising: - a processor responsive to the smartphone having a controller; - A computer executable program adapted to identify one or more images of said CFU located at an end portion of said variable geometry container.

57. The system according to 56, wherein the computer executable program is configured to: - identifying the boundaries of the tip of the variable geometry container; - aligning the tip with the variable geometry container; - Perform colony segmentation; and - Calculate CFU.

58. The system of claim 47, wherein the variable geometry container comprises a pipette tip.

59. The system of claim 47, further comprising introducing a process into the cell sample.

60. The system of claim 59, wherein the treatment comprises one or more compounds suitable for: - killing cells in the cell sample; - inhibiting the growth of said cells in said cell sample; - altering one or more phenotypic properties of the cells in the cell sample; or - altering one or more genotypic properties of said cells in said cell sample.

61. The system of claim 60, wherein the treatment compound is selected from the group consisting of: a therapeutic compound, an antibiotic, a bactericidal compound, a bacteriostatic compound, an anticancer compound, an antifungal agent, a drug screening compound.

62. A system for imaging a geometric viability assay (GVA), the system comprising: - Imager; -light source; - a frame holding one or more variable geometry containers suitable for incubating a cell sample in a growth medium, wherein the containers are axisymmetric; and - wherein the frame and / or the imager are adjustable so as to position the container relative to the imager to allow the imager to capture one or more images of colony forming units (CFU) located at an end portion of the incubated container.

63. The system of 62, further comprising an opaque background positioned behind the variable geometry container.

64. The system of claim 62, wherein the imager comprises an imaging platform comprising: - a processor responsive to said imager; - A computer executable program adapted to identify one or more images of said CFU located at an end portion of said variable geometry container.

65. The system according to 64, wherein the computer executable program is configured to: - identifying the boundaries of the tip of the variable geometry container; - aligning the tip with the variable geometry container; - Perform colony segmentation; and - Calculate CFU.

66. The system of claim 62, wherein the variable geometry container comprises a pipette tip.

67. The system of claim 62, further comprising a zoom adjuster responsive to the imager.

68. The system of claim 62, further comprising a pan adjuster responsive to the frame.

69. A method of performing a geometric viability assay (GVA), the method comprising: - introducing the cell sample into a growth medium; - positioning the growth medium in a variable geometry container, wherein the container is axisymmetric; - incubating the cell sample in the variable geometry container; - imaging bacterial colonies embedded in said variable geometry containers; and - Identifying a subset of viable microbial colony forming units (CFU) in said variable geometry container.

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

71. The method of claim 69, wherein the cell sample is selected from the group consisting of: a biological sample, an environmental sample, a commercial sample, a prokaryotic cell sample, or a eukaryotic cell sample.

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

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

74. The method of claim 69, wherein the growth medium comprises a semi-solid growth medium.

75. The method of claim 69, wherein the variable geometry container comprises a pipette tip.

76. The method of claim 69, wherein the variable geometry container is translucent.

77. The method of claim 69, further comprising the step of processing the cell sample.

78. The method of claim 77, wherein the treating step comprises contacting the cell sample with one or more compounds suitable for: - killing cells in the cell sample; - inhibiting the growth of said cells in said cell sample; - altering one or more phenotypic properties of the cells in the cell sample; or - altering one or more genotypic properties of said cells in said cell sample.

79. The method of claim 78, wherein the treatment compound is selected from the group consisting of a therapeutic compound, an antibiotic, a bactericidal compound, a bacteriostatic compound, an anticancer compound, an antifungal agent, a drug screening compound.

80. The method of claim 69, wherein the imager comprises an imaging platform, the imaging platform comprising: - a processor responsive to said imager; - A computer executable program adapted to identify one or more images of said CFU located at an end portion of said variable geometry container.

81. The system according to 80, wherein the computer executable program is configured to: - identifying the boundaries of the tip of the variable geometry container; - aligning the tip with the variable geometry container; - Perform colony segmentation; and - Calculate CFU.

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