WO2022241246A1 - Techniques pour optimiser la détection de particules capturées par un système microfluidique - Google Patents

Techniques pour optimiser la détection de particules capturées par un système microfluidique Download PDF

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Publication number
WO2022241246A1
WO2022241246A1 PCT/US2022/029240 US2022029240W WO2022241246A1 WO 2022241246 A1 WO2022241246 A1 WO 2022241246A1 US 2022029240 W US2022029240 W US 2022029240W WO 2022241246 A1 WO2022241246 A1 WO 2022241246A1
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image
sample
microorganisms
objects
particles
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PCT/US2022/029240
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English (en)
Inventor
Monika WEBER
David FRAEBEL
Robert Weber
Slawomir ANTOSZCZYK
Fred FARBER
Elliot Swart
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Fluid-Screen, Inc.
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Publication of WO2022241246A1 publication Critical patent/WO2022241246A1/fr

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    • G01N15/02—Investigating particle size or size distribution
    • G01N15/0205—Investigating particle size or size distribution by optical means
    • G01N15/0227—Investigating particle size or size distribution by optical means using imaging; using holography
    • G—PHYSICS
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    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/06—Investigating concentration of particle suspensions
    • G01N15/0606—Investigating concentration of particle suspensions by collecting particles on a support
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    • G01N15/10—Investigating individual particles
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06V20/698—Matching; Classification
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01L—CHEMICAL OR PHYSICAL LABORATORY APPARATUS FOR GENERAL USE
    • B01L3/00—Containers or dishes for laboratory use, e.g. laboratory glassware; Droppers
    • B01L3/50—Containers for the purpose of retaining a material to be analysed, e.g. test tubes
    • B01L3/502—Containers for the purpose of retaining a material to be analysed, e.g. test tubes with fluid transport, e.g. in multi-compartment structures
    • B01L3/5027—Containers for the purpose of retaining a material to be analysed, e.g. test tubes with fluid transport, e.g. in multi-compartment structures by integrated microfluidic structures, i.e. dimensions of channels and chambers are such that surface tension forces are important, e.g. lab-on-a-chip
    • B01L3/502715—Containers for the purpose of retaining a material to be analysed, e.g. test tubes with fluid transport, e.g. in multi-compartment structures by integrated microfluidic structures, i.e. dimensions of channels and chambers are such that surface tension forces are important, e.g. lab-on-a-chip characterised by interfacing components, e.g. fluidic, electrical, optical or mechanical interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B01L—CHEMICAL OR PHYSICAL LABORATORY APPARATUS FOR GENERAL USE
    • B01L3/00—Containers or dishes for laboratory use, e.g. laboratory glassware; Droppers
    • B01L3/50—Containers for the purpose of retaining a material to be analysed, e.g. test tubes
    • B01L3/502—Containers for the purpose of retaining a material to be analysed, e.g. test tubes with fluid transport, e.g. in multi-compartment structures
    • B01L3/5027—Containers for the purpose of retaining a material to be analysed, e.g. test tubes with fluid transport, e.g. in multi-compartment structures by integrated microfluidic structures, i.e. dimensions of channels and chambers are such that surface tension forces are important, e.g. lab-on-a-chip
    • B01L3/502761—Containers for the purpose of retaining a material to be analysed, e.g. test tubes with fluid transport, e.g. in multi-compartment structures by integrated microfluidic structures, i.e. dimensions of channels and chambers are such that surface tension forces are important, e.g. lab-on-a-chip specially adapted for handling suspended solids or molecules independently from the bulk fluid flow, e.g. for trapping or sorting beads, for physically stretching molecules
    • G—PHYSICS
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    • G01N15/01—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials specially adapted for biological cells, e.g. blood cells
    • G—PHYSICS
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    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N2015/1006—Investigating individual particles for cytology
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    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
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    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing

Definitions

  • Detection and identification of particles e.g., bacterial and viral pathogens
  • cell containing solutions e.g., blood, urine, CSF, mammalian cell culture, CHO cell matrix, CAR-T drug product, CAR-T specimen, CAR-NK drug product, body fluids, apheresis samples or samples related to immunotherapy
  • protein containing solutions e.g., for pharmaceuticals during manufacturing, drug product, drug substance
  • analyte extraction from microbiome samples, water, sterile fluids and other fluids is possible by employing isolation on cultural media and metabolic fingerprinting methods.
  • Immunoassay and nucleic acid-based assays are now widely accepted techniques, providing more sensitive and specific detection and quantification of bacteria.
  • Dielectrophoresis relates to a force in an electric field gradient on objects having dielectric moments.
  • DEP has shown promise for particle separation, but has not yet been applied in clinical settings, pharmaceutical quality assurance settings, or immunotherapy.
  • DEP uses a natural or induced dipole to cause a net force on a particle in a region having an electric field gradient. The force depends on the Clausius-Mossotti factor associated with the particle.
  • data analysis techniques which differentiate between different particles (e.g., bacterial cells and debris) in an image.
  • the techniques may be performed by identifying one or more characteristics (e.g., intensity, size, shape) of the particles which may be used to classify the particle (e.g., using thresholds, ranges, to determine how to classify a particle).
  • the techniques may provide for removing particles classified as debris from a data set.
  • the techniques may provide for separating particles classified as debris into a separate data set.
  • computer vision may be implemented to detect objects in an image.
  • the objects may be classified as a microbe of interest or any other object.
  • Such techniques may be compared with manual inspection to evaluate the accuracy of the computer vision techniques.
  • a method may be provided for combining the results of multiple quantification techniques.
  • two or more techniques e.g., the same algorithm with two sets of parameters or two different algorithms
  • the two values may be averaged to obtain an improved value for a quantification of microbes in a sample.
  • a machine learning algorithm may be provided for quantification of microbes, for example, according to the techniques described herein.
  • a negative control scan may indicate the presence of particles on an electrode surface in the absence of any sample.
  • the negative control scan may be subtracted from data obtained during sample processing.
  • a sample may be first processed by a microfluidic system.
  • the microfluidic system and/or techniques used to analyze the data obtained from the microfluidic system may be optimized to eliminate false negative results.
  • the sample may be determined to be ready for real time release, or further testing may be performed (e.g., bacterial cultures).
  • a method for classifying a particle in an image as belonging to a first group of particles, the particle being represented in the image as a plurality of contiguous pixels of the image comprises determining whether an intensity value for the particle in the image is greater than a first threshold value, determining whether at least one morphological characteristic of the particle in the image satisfies a first set of criteria associated with particles in the first group of particles, classifying the particle as belonging to the first group of particles when the intensity value for the particle in the image is greater than the threshold value and the at least one morphological characteristic of the particle in the image satisfies the first set of criteria, and outputting a result of the classifying of the particle as belonging to the first group of particles.
  • the morphological characteristic comprises a size and/or shape of the particle.
  • the size of the particle comprises a number of contiguous pixels representing the particle in the image.
  • the size of the particle is determined based on at least one measurement selected from the group consisting of an area, a circumference, a diameter, an extent, and a bisector.
  • the shape of the particle is determined based on at least one measurement selected from the group consisting of an aspect ratio, an elongation value, a convexity value, a shape factor, and a sphericity value.
  • the particle comprises one of a microorganism or debris.
  • the microorganism comprises one of bacteria, yeast, or mold.
  • the first group of particles comprise microorganisms.
  • the image includes particles belonging to the first group of particles and particles belonging to a second group of particles, and the method further comprises classifying the particle as belonging to the second group of particles when the intensity value for the particle in the image is greater than the threshold value and the at least one morphological characteristic of the particle in the image does not satisfy the first set of criteria.
  • the second group of particles comprises debris.
  • the second group of particles comprises components other than microorganisms.
  • the method further comprises when the particle is classified as belonging to the first group of particles, further classifying the particle as belonging to a first subgroup or a second subgroup of the first group of particles at least in part by determining whether the value for the at least one morphological characteristic satisfies a second set of criteria, and classifying the particle as belonging to the first subgroup when the at least one morphological characteristic satisfies the second set of criteria.
  • the first subgroup of the first group of particles comprises a first type of bacteria and the second subgroup of the first group of particles comprises a second type of bacteria different than the first type of bacteria.
  • outputting a result of the classifying of the particle as belonging to the first group of particles comprises generating an overlay visualization indicating that the particle belongs to the first group of particles, and displaying the overlay visualization and the image on a display.
  • the image is a fluorescence image.
  • the method further comprises quantifying a number of particles classified as belonging to the first group of particles, and outputting the number of particles classified as belonging to the first group of particles.
  • the method further comprises inputting a sample comprising the particle into a microfluidic passage of a microfluidic device, immobilizing the particle onto a surface of an electrode disposed in the microfluidic passage with at least one dielectrophoretic force, and capturing the image while the particle is immobilized on the surface of the electrode.
  • a system for classifying a particle in an image as belonging to a first group of particles, the particle being represented in the image as a plurality of contiguous pixels of the image comprises a microfluidic passage for receiving a sample, the sample comprising the particle, at least one electrode disposed in the microfluidic passage, the at least one electrode configured to immobilize, when activated, the particle onto a surface of the at least one electrode using dielectrophoresis, an optical system configured to capture the image while the particle is immobilized on the surface of the at least one electrode, and at least one computing device.
  • the at least one computing device is configured to determine, based on the image obtained by the optical device, whether an intensity value for the particle in the image is greater than a first threshold value, determine whether at least one morphological characteristic of the particle in the image satisfies a set of criteria associated with particles in the first group of particles, classify the particle as belonging to the first group of particles when the intensity value for the particle in the image is greater than the threshold value and the at least one morphological characteristic of the particle in the image satisfies the set of criteria, and output a result of the classifying of the particle as belonging to the first group of particles.
  • the morphological characteristic comprises a size and/or shape of the particle.
  • the size of the particle comprises a number of contiguous pixels representing the particle in the image.
  • the size of the particle is determined based on at least one measurement selected from the group consisting of an area, a circumference, a diameter, an extent, and a bisector.
  • the shape of the particle is determined based on at least one measurement selected from the group consisting of an aspect ratio, an elongation value, a convexity value, a shape factor, and a sphericity value.
  • the particle comprises one of a microorganism or debris.
  • the microorganism comprises one of bacteria, yeast, or mold.
  • the first group of particles comprise microorganisms.
  • the image includes particles belonging to the first group of particles and particles belonging to a second group of particles, and the at least one computing device being further configured to classify the particle as belonging to the second group of particles when the intensity value for the particle in the image is greater than the threshold value and the at least one morphological characteristic of the particle in the image does not satisfy the first set of criteria.
  • the second group of particles comprises debris.
  • the second group of particles comprises components other than microorganisms.
  • the at least one computing device when the particle is classified as belonging to the first group of particles, is further configured to classify the particle as belonging to a first subgroup or a second subgroup of the first group of particles at least in part by determining whether the value for the at least one morphological characteristic satisfies a second set of criteria, and classifying the particle as belonging to the first subgroup when the at least one morphological characteristic satisfies the second set of criteria.
  • the first subgroup of the first group of particles comprises a first type of bacteria and the second subgroup of the first group of particles comprises a second type of bacteria different than the first type of bacteria.
  • outputting a result of the classifying of the particle as belonging to the first group of particles comprises generating an overlay visualization indicating that the particle belongs to the first group of particles, and displaying the overlay visualization and the image on a display.
  • the image is a fluorescence image.
  • the at least one computing device is further configured to quantify a number of particles classified as belonging to the first group of particles, and output the number of particles classified as belonging to the first group of particles.
  • a method for tuning detector parameters of an automated detector used to automatically detect microorganisms in images comprises processing a plurality of first images with the automated detector to detect a first set of objects in each of the plurality of first images, each first set of objects including particles of interest and other objects, determining detector performance data of the automated detector based on the first set of objects for each of the plurality of first images and a corresponding second set of objects determined from manual identification of objects in each of the plurality of first images, the detector performance data including information about different types of errors generated by the automated detector, tuning the detector parameters of the automated detector based, at least in part, on the detector performance data, processing a second image with the automated detector having tuned detector parameters to detect a third set of objects in the second image, and outputting information about the detected objects in the second image.
  • the third set of obj ects includes the particles of interest and other obj ects, and the method further comprises quantifying an amount of particles of interest in the third set of objects.
  • determining detector performance data of the automated detector based on the first set of objects for each of the plurality of first images and a corresponding second set of objects determined from manual identification of objects in each of the plurality of first images comprises comparing object coordinates of objects in the first set of objects and objects in the second set of objects to identify objects detected in both the first and second sets for each of the plurality of first images.
  • determining detector performance data further comprises computing a pairwise distance matrix between the object coordinates in the first set of objects and object coordinates in the second set of objects, and identifying, based on the computed pairwise distance matrix, objects with a matching set of object coordinates.
  • identifying objects with a matching set of coordinates comprises using a Hungarian algorithm to identify the objects with a matching set of coordinates.
  • tuning the detector parameters of the automated detector is further based, at least in part, on user input assigning weights assigned to each of the different types of errors generated by the automated detector.
  • a method for determining a quantity of microorganisms in a set of image tiles comprises receiving a set of image tiles, the set including a first image tile and a second image tile having an overlap region with the first image tile, detecting, with an automated detector, a first set of microorganisms within the first image tile and a second set of microorganisms within the second image tile, wherein each of the microorganisms in the first set is associated with first object coordinates and each of the of microorganisms in the second set is associated with second object coordinates, identifying, based on first object coordinates and the second object coordinates, object coordinate pairs within the overlap region, determining the quantity of microorganisms in the set of image tiles based on a number of microorganisms in the first set of microorganisms, a number of microorganisms in the second set of microorganisms and a number of object coordinate pairs identified in the overlap region, and outputting the quantify of
  • identifying, based on first object coordinates and the second object coordinates, object coordinate pairs within the overlap region comprises identifying an object coordinate pair when the first object coordinates and the second object coordinates are less than a threshold distance apart. In one aspect, identifying, based on first object coordinates and the second object coordinates, object coordinate pairs within the overlap region comprises transforming the first object coordinates into a common coordinate space with the second object coordinates, and identifying an object coordinate pair based on the transformed first object coordinates and the second object coordinates. In one aspect, identifying, based on first object coordinates and the second object coordinates, object coordinate pairs within the overlap region comprises using a Hungarian algorithm to identify the object coordinate pairs.
  • a method for determining a quantity of microorganisms in a sample comprises identifying, based on an image of the sample and a first set of criteria including a first intensity threshold and one or more first morphological criteria, a first set of objects in the sample that have an intensity value in the image above the first intensity threshold and that have a shape characteristic and/or a size characteristic in the image that satisfies the one or more first morphological criteria, the objects in the first set of objects being classified as belonging to a first group of particles associated with the microorganisms of the sample, identifying, based on the image of the sample and a second set of criteria including a second threshold intensity and/or one or more second morphological criteria, a second set of objects in the sample that have an intensity value in the image above the intensity threshold and/or that have a shape characteristic and/or a size characteristic in the image that satisfies the one or more second morph
  • determining the quantity of microorganisms in the sample based on a combination of the first count and the second count comprises determining an average of the first count and the second count. In one aspect, determining an average of the first count and the second count comprises determining a weighted average of the first count and the second count. In one aspect, the first set of criteria is selected to minimize false positive detection errors.
  • a method for determining a quantity of microorganisms in a sample comprises identifying, based on an image of the sample and a first set of criteria including a first intensity threshold and one or more first morphological criteria, a first set of objects in the sample that have an intensity value in the image above the first intensity threshold and that have a shape characteristic and/or a size characteristic in the image that satisfies the one or more first morphological criteria, the objects in the first set of objects being classified as belonging to a first group of particles associated with the microorganisms of the sample, providing, as input to a trained statistical model, the image of the sample, wherein the trained statistical model was trained to identify microorganisms in an image based on a plurality of labeled images of samples including microorganisms and other components, determining the quantity of microorganisms in the sample by determining, as a first count, a number of objects in the
  • determining the quantity of microorganisms in the sample based on a combination of the first count and the second count comprises determining an average of the first count and the second count. In one aspect, determining an average of the first count and the second count comprises determining a weighted average of the first count and the second count. In one aspect, the first set of criteria is selected to minimize false positive detection errors.
  • a method for determining a quantity of microorganisms in a sample comprises providing, as input to a trained statistical model, an image of the sample, wherein the trained statistical model was trained to identify microorganisms in an image based on a plurality of labeled images of samples including microorganisms and other components, determining based on a labeled image output from the trained statistical model, the quantity of the microorganisms in the sample, and outputting the determined quantity of microorganisms in the sample.
  • the trained statistical model was trained to identify a first type of microorganisms in an image
  • the image provided as input to the trained statistical model is an image of a sample including a second type of microorganisms
  • determining the quantity of microorganisms in the sample comprises determining the quantity of the second type of microorganisms in the sample.
  • the image is a fluorescent image.
  • the image is a multi-channel image. In one aspect, the multi-channel image includes a bright field channel and two fluorescent channels.
  • the trained statistical model includes at least one trained neural network.
  • the microorganisms are Aspergillus spores.
  • the microorganisms in the sample include a first type of microorganisms and a second type of microorganisms, the image provided as input to the trained statistical model includes both the first type of microorganisms and the second type of microorganisms, and the trained statistical model is trained to label in the output image, only the first type of microorganisms and not the second type of microorganisms.
  • the first type of microorganisms have a different morphology than the first type of microorganisms.
  • a system for determining a quantity of microorganisms in a sample the sample including microorganisms and other components.
  • the system comprises a microfluidic passage for receiving the sample, at least one electrode disposed in the microfluidic passage, the at least one electrode configured to capture, when activated, particles of the sample on a surface of the at least one electrode using dielectrophoresis, an optical system configured to capture at least one image of the surface of the at least one electrode, and at least one computing device.
  • the at least one computing device is configured to provide, as input to a trained statistical model, an image of the sample, wherein the trained statistical model was trained to identify microorganisms in an image based on a plurality of labeled images of samples including microorganisms and other components, determine based on a labeled image output from the trained statistical model, the quantity of the microorganisms in the sample, and output the determined quantity of microorganisms in the sample.
  • the trained statistical model was trained to identify a first type of microorganisms in an image
  • the image provided as input to the trained statistical model is an image of a sample including a second type of microorganisms
  • determining the quantity of microorganisms in the sample comprises determining the quantity of the second type of microorganisms in the sample.
  • the image is a fluorescent image.
  • the image is a multi-channel image. In one aspect, the multi-channel image includes a bright field channel and two fluorescent channels.
  • the trained statistical model includes at least one trained neural network.
  • the microorganisms are Aspergillus spores.
  • the microorganisms in the sample include a first type of microorganisms and a second type of microorganisms, the image provided as input to the trained statistical model includes both the first type of microorganisms and the second type of microorganisms, and the trained statistical model is trained to label in the output image, only the first type of microorganisms and not the second type of microorganisms.
  • the first type of microorganisms have a different morphology than the first type of microorganisms.
  • a method for improved detection and/or quantification of microorganisms in a sample comprises passing a first fluid comprising a stain through a microfluidic passage of a microfluidic device, the microfluidic passage including at least one electrode disposed therein, passing a second fluid through the microfluidic passage after passing the first fluid through the microfluidic passage, the second fluid comprising a controlled solution without the stain, capturing, with an optical system, a first image including a surface of the at least one electrode after passing the second fluid through the microfluidic passage, passing the sample through the microfluidic passage after capturing the first image, wherein the microorganisms in the sample are captured on a surface of the at least one electrode using dielectrophoresis as the sample is passed through the microfluidic passage, passing the first fluid through the microfluidic passage while the microorganisms remain captured on the surface of the at least one electrode
  • the method further comprises passing the second fluid through the microfluidic passage prior to passing the first fluid through the microfluidic passage prior to capturing the first image. In one aspect, the method further comprises passing the second fluid through the microfluidic passage prior to passing the first fluid through the microfluidic passage and after passing the sample through the microfluidic passage. In one aspect, passing the first fluid, the second fluid, or the sample through the microfluidic passage comprises pumping the first fluid, the second fluid, or the sample through the microfluidic passage.
  • the method further comprises applying at least one first voltage to the at least one electrode as the sample is passed through the microfluidic passage, the at least one first voltage having first characteristics, and applying at least one second voltage to the at least one electrode as the second fluid is passed through the microfluidic passage after the sample is passed through the microfluidic passage, the at least one second voltage having second characteristics different than the first characteristics.
  • the first characteristics include a first amplitude and a first frequency for the at least one first voltage
  • the second characteristics include a second amplitude and a second frequency for the at least one second voltage.
  • the second amplitude is lower than the first amplitude.
  • the second frequency is different than the first frequency.
  • detecting and/or quantifying microorganisms in the sample based on the first image and the second image comprises subtracting the first image from the second image to generate a third image, and detecting microorganisms in the sample based on the third image. In one aspect, detecting and/or quantifying microorganisms in the sample based on the first image and the second image comprises subtracting the first image from the second image to generate a third image, and quantifying microorganisms in the sample based on the third image.
  • detecting and/or quantifying microorganisms in the sample based on the first image and the second image comprises identifying in the first image, a first set of objects, identifying in the second image, a second set objects, removing the first set of objects from the second set of objects, and detecting microorganisms in the sample based on the second set of objects after the first set of objects has been removed.
  • detecting and/or quantifying microorganisms in the sample based on the first image and the second image comprises quantifying in the first image, a number of first objects, quantifying in the second image, a number of second objects, and quantifying microorganisms in the sample based, at least in part, on the number of first objects and the number of second objects.
  • quantifying in the first image, a number of first objects comprises applying a classifier to the first image to detect the first objects.
  • quantifying, in the second image, a number of second objects comprises applying the classifier to the second image to detect the second objects.
  • the stain is configured to selectively interact with the microorganisms in the sample.
  • the stain is metabolic stain configured to enter inside of the microorganisms in the sample.
  • a system for detecting and/or quantifying microorganisms in a sample the sample including microorganisms and other components.
  • the system comprises a microfluidic passage for receiving the sample, a pump configured to pump a first fluid comprising a stain, a second fluid comprising a controlled solution without the stain, or the sample through the microfluidic passage, at least one electrode disposed in the microfluidic passage, the at least one electrode configured to capture, when activated, particles of the sample on a surface of the at least one electrode using dielectrophoresis, an optical system configured to capture a first image of the surface of the at least one electrode prior to pumping the sample through the microfluidic passage, and a second image of the surface of the at least one electrode after pumping the sample through the microfluidic passage, and at least one computing device.
  • the at least one computing device is configured to detect and/or quantify microorganisms in the sample based on the first image and the second image.
  • detecting and/or quantifying microorganisms in the sample based on the first image and the second image comprises subtracting the first image from the second image to generate a third image, and detecting microorganisms in the sample based on the third image. In one aspect, detecting and/or quantifying microorganisms in the sample based on the first image and the second image comprises subtracting the first image from the second image to generate a third image, and quantifying microorganisms in the sample based on the third image.
  • detecting and/or quantifying microorganisms in the sample based on the first image and the second image comprises identifying in the first image, a first set of objects, identifying in the second image, a second set objects, removing the first set of objects from the second set of objects, and detecting microorganisms in the sample based on the second set of objects after the first set of objects has been removed.
  • detecting and/or quantifying microorganisms in the sample based on the first image and the second image comprises quantifying in the first image, a number of first objects, quantifying in the second image, a number of second objects, and quantifying microorganisms in the sample based, at least in part, on the number of first objects and the number of second objects.
  • quantifying in the first image, a number of first objects comprises applying a classifier to the first image to detect the first objects.
  • quantifying, in the second image, a number of second objects comprises applying the classifier to the second image to detect the second objects.
  • the stain is configured to selectively interact with the microorganisms in the sample.
  • the stain is metabolic stain configured to enter inside of the microorganisms in the sample.
  • a method for improved detection and/or quantification of microorganisms in a sample comprising passing a first fluid comprising a first stain through a microfluidic passage of a microfluidic device, the microfluidic passage including at least one electrode disposed therein, passing a second fluid through the microfluidic passage after passing the first fluid through the microfluidic passage, the second fluid comprising a controlled solution without the first stain, passing the sample through the microfluidic passage after passing the second fluid through the microfluidic passage, wherein the microorganisms in the sample are captured on a surface of the at least one electrode using dielectrophoresis as the sample is passed through the microfluidic passage, passing a third fluid comprising a second stain through the microfluidic passage while the microorganisms remain captured on the surface of the at least one electrode, passing the second fluid through the microfluidic passage after passing the third fluid through
  • the method further comprises passing the second fluid through the microfluidic passage prior to passing the first fluid through the microfluidic passage. In one aspect, the method further comprises passing the second fluid through the microfluidic passage prior to passing the third fluid through the microfluidic passage. In one aspect, passing the first fluid, the second fluid, the third fluid, or the sample through the microfluidic passage comprises pumping the first fluid, the second fluid, the third fluid, or the sample through the microfluidic passage.
  • the method further comprises applying at least one first voltage to the at least one electrode as the sample is passed through the microfluidic passage, the at least one first voltage having first characteristics, and applying at least one second voltage to the at least one electrode as the third fluid is passed through the microfluidic passage, the at least one second voltage having second characteristics different than the first characteristics.
  • the first characteristics include a first amplitude and a first frequency for the at least one first voltage
  • the second characteristics include a second amplitude and a second frequency for the at least one second voltage.
  • the second amplitude is lower than the first amplitude.
  • the second frequency is different than the first frequency.
  • detecting and/or quantifying microorganisms in the sample based on the image comprises detecting microorganisms in the sample as objects in the image stained with the second stain but not the first stain. In one aspect, detecting and/or quantifying microorganisms in the sample based on the image further comprises quantifying the detected microorganisms in the sample.
  • the optical system includes a first filter and a second filter, and capturing an image of the surface of the at least one electrode comprises capturing the image using the first filter and the second filter.
  • the second stain is configured to selectively interact with the microorganisms in the sample.
  • the stain is metabolic stain configured to enter inside of the microorganisms in the sample.
  • a system for detecting and/or quantifying microorganisms in a sample the sample including microorganisms and other components.
  • the system comprises a microfluidic passage for receiving the sample, a pump configured to pump a first fluid comprising a first stain, a second fluid comprising a controlled solution without the first stain, a third fluid comprising a third stain, or the sample through the microfluidic passage, at least one electrode disposed in the microfluidic passage, the at least one electrode configured to capture, when activated, particles of the sample on a surface of the at least one electrode using dielectrophoresis, an optical system configured to capture an image of the surface of the at least one electrode after pumping the sample through the microfluidic passage, and at least one computing device configured to detect and/or quantify microorganisms in the sample based on the image.
  • detecting and/or quantifying microorganisms in the sample based on the image comprises detecting microorganisms in the sample as objects in the image stained with the second stain but not the first stain. In one aspect, detecting and/or quantifying microorganisms in the sample based on the image further comprises quantifying the detected microorganisms in the sample.
  • the optical system includes a first filter and a second filter, and wherein capturing an image of the surface of the at least one electrode comprises capturing the image using the first filter and the second filter.
  • the second stain is configured to selectively interact with the microorganisms in the sample.
  • the stain is metabolic stain configured to enter inside of the microorganisms in the sample.
  • a method for determining a quantity of microorganisms in a sample comprising treating the sample with an exonuclease configured to digest the nucleic acids to oligonucleotides less than five bases long to produce a clarified sample, passing the clarified sample through a microfluidic passage having at least one electrode disposed therein, wherein the microorganisms in the sample are captured on a surface of the at least one electrode using dielectrophoresis as the clarified sample is passed through the microfluidic passage, capturing, with an optical system, at least one image of the surface of the at least one electrode, and quantifying microorganisms in the sample based on the at least one image.
  • the sample is a sample from a bioreactor.
  • capturing the at least one image comprises capturing a first image prior to passing the clarified sample through the microfluidic passage and capturing a second image after passing the clarified sample through the microfluidic passage, and quantifying microorganisms in the sample comprises quantifying microorganisms in the sample based on the first image and the second image.
  • the method further comprises passing a fluid comprising a stain through the microfluidic passage prior to capturing the at least one image.
  • a method for determining a quantity of microorganisms in a sample comprising incubating the sample with activated carbon to adsorb the at least one surfactant in the sample to produce a clarified sample, passing the clarified sample through a microfluidic passage having at least one electrode disposed therein, wherein the microorganisms in the sample are captured on a surface of the at least one electrode using dielectrophoresis as the clarified sample is passed through the microfluidic passage, capturing, with an optical system, at least one image of the surface of the at least one electrode, and quantifying microorganisms in the sample based on the at least one image.
  • the sample is a sample from a bioreactor.
  • capturing the at least one image comprises capturing a first image prior to passing the clarified sample through the microfluidic passage and capturing a second image after passing the clarified sample through the microfluidic passage, and quantifying microorganisms in the sample comprises quantifying microorganisms in the sample based on the first image and the second image.
  • the method further comprises passing a fluid comprising a stain through the microfluidic passage prior to capturing the at least one image.
  • a method for identifying whether a sample is sterile comprises passing a first portion of the sample through a microfluidic passage having at least one electrode disposed therein, generating, with the at least one electrode, at least one dielectrophoretic force that acts on the first portion of the sample, determining, based on a response of the first portion of the sample to the at least one dielectrophoretic force that acts on the first portion of the sample, whether the first portion of the sample comprises one or more microorganisms, and when it is determined that the first portion of the sample does not comprise the one or more microorganisms, labeling the sample as sterile.
  • the method further comprises providing an indication that the sample is sterile, and releasing the sample from sterility testing. In one aspect, the method further comprises when it is determined that the first portion of the sample does comprise the one or more microorganisms, labeling the sample as not sterile and providing an indication that a second portion of the sample is to be further processed. In one aspect, the one or more microorganisms comprise bacteria, mold, and/or yeast.
  • a system for identifying whether a sample is sterile is provided.
  • the system comprises a microfluidic passage for receiving a first portion of the sample, at least one electrode disposed in the microfluidic passage, the at least one electrode configured to capture, when activated, a plurality of particles of the first portion of the sample on a surface of the at least one electrode using dielectrophoresis, an optical system configured to capture at least one image of the surface of the at least one electrode, and at least one computing device configured to determine, based on the at least one image, whether the first portion of the sample comprises one or more microorganisms, and when it is determined that the first portion of the sample does not comprise the one or more microorganisms, labeling the sample as sterile.
  • the at least one computing device is further configured to provide an indication that the sample is sterile, and release the sample from sterility testing. In one aspect, the at least one computing device is further configured to when it is determined that the first portion of the sample does comprise the one or more microorganisms, label the sample as not sterile and provide an indication that a second portion of the sample is to be further processed. In one aspect, the one or more microorganisms comprise bacteria, mold, and/or yeast.
  • a method for identifying whether a sample is in a condition for release comprises passing a first portion of the sample into a microfluidic passage having at least one electrode disposed therein, generating, with the at least one electrode, at least one dielectrophoretic force that acts on the first portion of the sample, determining a number of microorganisms immobilized on a surface of the at least one electrode subsequent to the generating the at least one dielectrophoretic force, determining, based on the number of microorganisms immobilized on the surface of the at least one electrode, a concentration of microorganisms in the sample, and when it is determined that the concentration of microorganisms in the sample is less than a threshold concentration, labeling the sample as being in the condition for release.
  • a system for identifying whether a sample is in a condition for release comprises a microfluidic passage for receiving a first portion of the sample, at least one electrode disposed in the microfluidic passage, the at least one electrode configured to capture, when activated, a plurality of particles of the first portion of the sample on a surface of the at least one electrode using dielectrophoresis, an optical system configured to capture at least one image of the surface of the at least one electrode, and at least one computing device configured to analyze the at least one image to determine a number of microorganisms immobilized on a surface of the at least one electrode subsequent to the generating the at least one dielectrophoretic force, determine whether the number of microorganisms immobilized on the surface of the at least one electrode is less than a threshold, and when it is determined that the number of microorganisms immobilized on the surface of the at least one electrode is less than the threshold, labeling the sample as being in the condition for release.
  • FIG. 1 schematically illustrates a system for separation, detection, and quantification of particles in a sample, according to some embodiments of the present technology
  • FIG. 2 illustrates a microfluidic system for separation, detection, and quantification of particles in a sample, according to some embodiments of the present technology
  • FIG. 3 illustrates a static system for separation, detection, and quantification of particles in a sample, according to some embodiments of the present technology
  • FIG. 4 is a flowchart of a process for classifying particles in an image, according to some embodiments of the present technology
  • FIG. 5A shows a multi-channel image of an electrode system that may be analyzed, according to some embodiments of the present technology
  • FIG. 5B shows a segmented image based on the fluorescent channel of the image in FIG. 5 A, according to some embodiments of the present technology
  • FIGS. 6A-6C show images segmented using different intensity threshold values, according to some embodiments of the present technology
  • FIGS. 7A-7E show multi-channel images of an electrode system having a segmentation overlay presented thereon, according to some embodiments of the present technology
  • FIG. 8A shows a fluorescence image captured by an optical system of microfluidic system, according to some embodiments of the present technology
  • FIG. 8B shows the image of FIG. 8A with a rescaled intensity value, according to some embodiments of the present technology
  • FIG. 8C shows fluorescence images in which a plurality of morphological-based filters have been applied, according to some embodiments of the present technology
  • FIG. 9A shows an illustration of a portion of a software tool used to determine measurement information for a particle in an image, according to some embodiments of the present technology
  • FIG. 9B shows a segmented image after application of a size-based filter, according to some embodiments of the present technology
  • FIGS. 10A and 10B show histograms of object area and object brightness, according to some embodiments of the present technology
  • FIG. 11 shows a segmented image after application of a size-based filter, according to some embodiments of the present technology
  • FIG. 12 shows the results of applying an intensity-based filter and a morphological- based filter, according to some embodiments of the present technology
  • FIGS. 13A and 13B show histograms of object area and object brightness, according to some embodiments of the present technology
  • FIG. 14A shows an image in which all bright objects and cell-shaped objects have been detected using a two-detector techniques, according to some embodiments of the present technology
  • FIG. 14B shows an image corresponding to the image in FIG. 14A after some duplicate cells are removed, according to some embodiments of the present technology
  • FIGS. 15A and 15B schematically show images for comparing automated detection and manual classification results, according to some embodiments of the present technology
  • FIG. 15C shows an image of detector performance evaluation results, according to some embodiments of the present technology
  • FIGS. 16A-16D show images after applying different error weighting strategies, according to some embodiments of the present technology
  • FIG. 17 is a flowchart of an automated process for tuning detector parameters, according to some embodiments of the present technology.
  • FIG. 18 schematically shows acquisition of a grid of partially-overlapping image tiles, which may be analyzed, according to some embodiments of the present technology
  • FIG. 19 shows an image of a grid of partially-overlapping image tiles in which particles in the overlap regions are identified and compared, according to some embodiments of the present technology
  • FIG. 20 is a flowchart of a process for quantifying particles (e.g., microorganisms) in s labeled image output from a trained statistical model, according to some embodiments of the present technology
  • FIG. 21 A is a multi-channel image of a spiral electrode system that may be analyzed, according to some embodiments of the present technology
  • FIG. 21B is a zoomed in version of the image of FIG. 21 A;
  • FIG. 22 is a bar plot showing the stability of the output of using a trained statistical model to perform classification, according to some embodiments of the present technology
  • FIG. 23 is a bar plot showing a comparison of manual counting and automated counting using a trained statistical model, according to some embodiments of the present technology
  • FIG. 24 shows a comparison of different mistake types present when an automated counting technique was used, according to some embodiments of the present technology
  • FIG. 25 is a stacked bar chart showing manual checks of automated detection errors, according to some embodiments of the present technology.
  • FIG. 26 is a plot showing bacterial capture results plotted in a row to identify a crossover frequency, according to some embodiments of the present technology
  • FIG. 27A-27C show the results of an automated counting model configured to analyze three-channel images, according to some embodiments of the present technology
  • FIGS. 28A-28B show images of using a trained statistical model to detect particles in an image, according to some embodiments of the present technology
  • FIG. 29A schematically shows components of a microfluidic system, according to some embodiments of the present technology
  • FIGS. 29B-29C schematically illustrate acts for detecting and quantifying microorganisms in a sample using a negative scan image, according to some embodiments of the present technology
  • FIGS. 29D-29E schematically illustrate acts for detecting and quantifying microorganisms in a sample using an integrated image, according to some embodiments of the present technology
  • FIG. 30 is a flowchart of a process for detecting and quantifying microorganisms in a sample using a negative scan image, according to some embodiments of the present technology
  • FIG. 31 shows images regarding how negative control scan can reduce the probability of false positive errors, according to some embodiments of the present technology
  • FIG. 32 is a flowchart of a process for detecting and quantifying microorganisms in a sample using an integrated image, according to some embodiments of the present technology
  • FIG. 33 is a flowchart of a process for using exonuclease to provide a clarified sample prior to processing the sample with a microfluidic device, according to some embodiments of the present technology;
  • FIGS. 34-34D show test and control images of using or not using exonuclease to provide samples for processing with a microfluidic device, according to some embodiments of the present technology
  • FIG. 35 is a flowchart of a process for performing sterility testing using a microfluidic device, according to some embodiments of the present technology
  • FIG. 36 schematically shows how a microfluidic device according to some embodiments of the present technology may be used to perform sterility testing; and [0107]
  • FIG. 37 is a flowchart of a process for performing bioburden testing, according to some embodiments of the present technology.
  • aspects of the technology described herein relate to an apparatus and methods for detecting, separating, quantifying, and/or enriching biological organisms (e.g., bacteria) present in a fluid sample.
  • biological organisms e.g., bacteria
  • the technology described herein provides techniques for rapid detection, separation, purification, and/or quantification of microorganisms in a sample using a microfluidic device comprising one or more electrodes configured to generate dielectrophoretic forces that act on the sample.
  • Microbial contamination is a serious and growing global threat to human health and economic development, including pharmaceutical manufacturing and immunotherapy manufacturing and treatment.
  • An example of a conventional technique to assess the presence and degree of microbial contamination in a sample is the Plate Counting Method (PCM).
  • PCM typically includes at least four steps. In step 1, a sample to be analyzed is manually placed in each of multiple test tubes, and the sample in each test tube is diluted to a desired concentration using a buffer solution. In step 2, the diluted samples are plated onto petri dishes containing agar media. Petri dishes including dilution media only (i.e., without the sample) are also plated for use as controls for comparison against the plated diluted samples.
  • step 3 the plurality of plated samples, the dilution media plates, and empty agar plates are cultured for 24 hours to 14 days to enable microbial particles to grow on the media within the petri dishes.
  • step 4 the number of bacterial colonies on each of the plates cultured in step 3 is determined, for example, using a microscope.
  • PCM is routinely used in medical, pharmacological and food industries to identify bacterial contamination.
  • PCM is slow, only moderately sensitive, labor intensive and prone to human errors.
  • the entire PCM process takes 1-14 days, includes several manual steps in which human intervention is needed, and requires a large number of plated samples at different dilutions and controls. There is therefore a need for new technologies that allow for faster, more sensitive and/or more reliable assessment of microbial contamination in fluid samples.
  • DEP Dielectrophoresis
  • Some embodiments of the technology described herein relate to a novel DEP bacterial capture and separation technique (also referred to herein as “Fluid- Screen” or “FS”) that addresses at least some of the limitations of prior DEP techniques.
  • FS Fluid- Screen
  • a fluid sample containing bacteria is processed in a microfluidic system that includes a microfluidic device.
  • the sample may be subjected to DEP forces to enable separation, detection, enrichment and/or quantification of microorganisms in the fluid sample.
  • a microfluidic system suitable for use in accordance with the techniques described herein, include the Fluid-Screen Microfluidic System, aspects of which are described in U.S. Patent Application No.
  • FIG. 1 illustrates an example system 100 for detecting bacteria in a sample, in accordance with some embodiments.
  • the system 100 comprises a microfluidic device 104 in communication with a computing device 110.
  • microfluidic device 104 may be any suitable device, examples of which are provided herein.
  • microfluidic device 104 is implemented as a microfluidic chip having one or more passages (e.g., microfluidic channels or chambers) through which a fluid sample 102 is provided for analysis.
  • passages e.g., microfluidic channels or chambers
  • microfluidic channel or simply “channel” is used herein to describe a passage through which fluid flows through microfluidic device 104, it should be appreciated that a fluid passage having any suitable dimensions may be used as said channel, and embodiments are not limited in this respect.
  • Microfluidic device 104 may comprise a single channel or multiple channels configured to receive a single sample 102 (e.g., to perform different analyses on the sample) or multiple channels configured to receive different samples for analysis. In embodiments having multiple channels, the microfluidic device may be configured to process the single sample or multiple samples in parallel (e.g., at the same or substantially the same time).
  • sample 102 may include any fluid containing bacteria or other microorganism of interest.
  • the sample comprises a biological fluid such as saliva, urine, blood, water, any other fluid such as an environmental sample or potentially contaminated fluid, protein matrices, mammalian cell culture, immunotherapy drug product, cell and gene therapy drug product, cell and gene therapy drug sample, bacterial culture, growth media, active pharmaceutical ingredients, enzyme products, or substances used in biomanufacturing, etc.
  • microfluidic device 104 includes at least one electrode 106.
  • the at least one electrode 106 may be configured to receive one or more voltages to generate positive and/or negative dielectrophoresis (DEP) force(s) that act on a sample arranged proximate to the at least one electrode.
  • the at least one electrode 106 may be configured to receive one or more voltages (e.g., one or more AC voltages) to generate at least one dielectrophoresis force that acts on the sample.
  • the at least one DEP force may cause certain components of the sample to move relative to (e.g., be attracted to or repulsed from) a surface of the at least one electrode 106.
  • bacteria and other components of the sample 102 may move freely relative to the surface of the electrode.
  • at least some components (e.g., bacteria) in the sample may be attracted to the electrode surface.
  • the small size of bacteria presents an obstacle to optical observation and quantification of bacteria in the sample.
  • the inventors have recognized that activation of the at least one electrode 106 results in an electric field that may be used to selectively trap bacteria on the surface of the electrode(s).
  • capturing bacteria on the surface of the electrode(s) may prevent the bacteria from moving in and out of focus of the optical system to enable real-time bacteria detection and quantification, a process referred to herein as “on-chip quantification.”
  • the electric field used to capture the bacteria concentrates the bacteria, which enables imaging with fluorescence microcopy or another optical detection technique. Accordingly, bacterial capture using the techniques described herein allows for detection and quantification of bacteria at significantly lower limits compared to some conventional methods, such as the PCM technique described above.
  • the ability to detect and/or quantify bacteria in a sample, even in small amounts, may be useful in applications including, but not limited to, biomanufacturing, gene therapy, analysis of patient samples, vaccine development and/or biothreat detection, antibiotic susceptibility testing.
  • the at least one DEP force acting on the sample may cause bacteria (or certain bacteria) to separate from other components of the sample (e.g., via positive DEP). Bacteria in the sample may be attracted to the surface of the at least one electrode 106 allowing for enhanced detection and/or quantification, despite the small size and/or small amount of the bacteria in the sample.
  • microfluidic device 104 is illustrated as having a single electrode, it should be understood that in some embodiments, microfluidic device 104 comprises multiple electrodes arranged in any suitable configuration.
  • System 100 may further comprise a computing device 110 configured to control one or more aspects of microfluidic device 104.
  • computing device 110 may be configured to direct the sample 102 into a channel of the microfluidic device.
  • computing device 110 is configured to control the at least one electrode 106 to generate the at least one DEP force acting on the sample 102.
  • computing device 110 may cause one or more components of an optical system (not shown) to perform one or more of detection or quantification of the bacteria or other microorganisms in the sample.
  • Non-limiting examples of a computing device 110 that may be used in accordance with some embodiments are further described herein.
  • FIG. 2 illustrates an example microfluidic system 200 for detecting the presence of microorganisms (e.g., bacteria) in a sample, in accordance with some embodiments.
  • System 200 includes microfluidic device 208 (e.g., indicated in FIG. 2 as a microfluidic chip) that includes one or more electrodes for generating DEP forces that act on a sample 204 provided as input to the system.
  • Sample 204 may contain microorganisms for which separation, detection, enrichment, and/or quantification may be performed.
  • the one or more electrodes may be arranged in any suitable configuration within the microfluidic device 208.
  • the electrodes may be arranged in one-dimension along the flow direction of the fluid, perpendicular to fluid flow direction or on a diagonal relative to the fluid flow direction.
  • a multidimensional (e.g., 2- dimensional, 3 -dimensional) array of electrodes may be used.
  • a dense array of electrodes arranged both along the direction of fluid flow and perpendicular to the direction of fluid flow may be used.
  • a flow system 202 is provided.
  • the flow system 202 may provide a solution for transporting the sample 204 to the microfluidic device 208.
  • a first pump 206 may be used to pump the solution and the sample 204 to the microfluidic device 208 at a predetermined flow rate.
  • First pump 206 may be of any suitable type. In some embodiments, first pump 206 is omitted and sample 204 is manually loaded (e.g., using a pipette or capillary flow) as input to one or more channels of microfluidic device 208.
  • Microfluidic device 208 is configured to receive sample 204 for processing.
  • Microfluidic device 208 may include one or more passages through which the sample 204 flows.
  • the one or more passages may include at least one electrode formed therein or adjacent thereto.
  • the at least one electrode may be formed within a passage.
  • the at least one electrode when activated, is configured to generate an electric field that acts on the sample 204 as it flows through the one or more passages.
  • An electrical system 212 e.g., a signal generator or controller
  • Microfluidic system 200 may include an optical system 210 to facilitate analysis of the sample 204 by performing on-chip quantification.
  • optical system 210 may comprise one or more optical sensors (e.g., a red-green-blue camera) for viewing and/or imaging the sample.
  • the optical sensor(s) may provide for enhanced detection and/or quantification of the captured microorganisms and/or the other components of the sample 204 relative to detection and quantification techniques that require separate culturing of captured microorganisms or an effluent sample from the device.
  • Any suitable optical sensor(s) may be used.
  • the optical sensor(s) comprises a digital camera.
  • the optical sensor(s) comprises electronic sensors including CMOS compatible technology.
  • the optical sensor(s) comprise fiber optics. However, any suitable optical sensor(s) may be used.
  • bacteria in the sample are stained
  • optical system 210 is configured to perform microscopy
  • optical system 210 is configured to capture one or more images (e.g., color images) of the at least one electrode while the sample is flowing through the microfluidic device 208.
  • the detector comprises nanowire and/or nanoribbon sensors.
  • the field of view of the optical system 210 at a particular magnification is insufficient to capture the entire surface of the one or more electrodes.
  • the optical system 210 may be configured to capture multiple partially overlapping images that collectively cover the entire surface of the one or more electrodes. The multiple captured images may then be analyzed to detect and/or quantify the microorganisms of interest in the sample.
  • Microfluidic system 200 also includes computer 230 configured to control an operation of optical system 210 and/or to receive images from optical system 210 and to perform processing on the received images (e.g., to count a number of microorganisms trapped by the microfluidic device 208).
  • the received images are analyzed to determine the number of microorganisms captured by the at least one electrode. For instance, microorganisms may be identified in the received images as spots (e.g., fluorescent spots) located on the edges of the electrodes. In this way, a captured target microorganism species may be differentiated from other components in the sample that are not captured and may appear as floating above the at least one electrode or located between electrodes.
  • the sample 204 may be removed from the microfluidic device 208.
  • a second pump 216 may be provided for pumping the sample 204 out of the microfluidic device 208.
  • the second pump 216 may be of any suitable type.
  • microfluidic system 200 comprises a flow sensor 214 for measuring a flow rate at which the sample 204 is removed from the microfluidic device 208.
  • the flow sensor 214 and the second pump 216 may be in communication to control a flow rate at which the sample 204 is removed from the microfluidic device 208.
  • microfluidic system 200 may be used for separating bacteria from other components (or for separating certain bacteria from other bacteria) in sample 204.
  • Microfluidic system 200 comprises a waste region 218 arranged to receive other components of the sample 204 which have been separated from the bacteria by the microfluidic device 208 and subsequently removed from the sample 204, for example, using the second pump 216.
  • analysis of the fluid collected in waste region 218 may be referred to as analysis of the “effluent sample.”
  • Microfluidic system 200 may further include effluent region 220 for receiving a purified version of sample 204 containing substantially only target microorganisms that were captured using microfluidic device 208.
  • an amount of time needed to process a sample using microfluidic system 200 is substantially less than an amount of time required to process a sample using a conventional PCM sample processing system.
  • processing a sample using system 200 may include at least three steps.
  • a sample is provided as input to microfluidic system 208 and bacteria are captured from the sample in the presence of an applied electric field.
  • automated on-chip quantification is performed, for example, using computer 230 to analyze one or more images recorded by optical system 210.
  • step 270 further analysis may be performed on waste 218 and/or effluent sample 220, as desired.
  • sample 204 may be manually provided as input to microfluidic device 208 for analysis.
  • one or more droplets of sample 204 may be provided as input to microfluidic device 208 using a pipette or other suitable technique.
  • FIG. 3 illustrates a microfluidic system 300 for detecting microorganisms in a sample, according to some embodiments.
  • microfluidic system 300 may include many of the same components as microfluidic system 200, but may omit certain components of the microfluidic system 200, such as the first pump 206, which are not needed when the sample is manually provided as input to the microfluidic device 308 (indicated in FIG. 3 as a static microfluidic chip).
  • Existing techniques for detecting microorganisms in fluid samples may be inefficient in several ways including, but not limited to, their inability to detect low levels of contaminant and/or their inability to culture certain types of microorganisms.
  • existing detection methods may take days to provide results. While faster methods such as quantitative polymerase chain reaction (qPCR) can reduce the response time to a few hours, such methods require complex sample preparation, high costs, have limited portability, and cannot be used for process streamlining.
  • qPCR quantitative polymerase chain reaction
  • FIG. 4 illustrates a process 400 for classifying a particle in an image as belonging to a first group of particles or a second group of particles.
  • act 410 image of a surface of one or more electrodes of a microfluidic system (e.g., microfluidic systems as shown in FIGS. 2 and 3) is received.
  • a microfluidic system e.g., microfluidic systems as shown in FIGS. 2 and 3
  • a microfluidic device of the microfluidic system may be controlled to generate a positive dielectrophoretic force that results in one or more types of particles (e.g., bacteria) to be attracted to the surface of the electrode(s). While the particles are attracted to the electrode surface, one or more images of the electrode surface may captured by an optical system of the microfluidic system. Such an image may be received in act 410 by a computing device (e.g., computing device 110, computer 230, computer 330) for analysis.
  • FIG. 5A provides an example of such a type of image. In the example of FIG.
  • FIG. 5 A a multi-channel image (one fluorescence channel and one bright-field channel) of electrodes on which Bacillus megaterium cells have been captured using a microfluidic device is shown.
  • the B. megaterium cells were stained with the fluorescent stain sybr green, and imaged at 20x magnification using an Olympus BX-63 microscope.
  • the techniques described here are not limited to processing images captured using any particular type of optical system.
  • process 400 After receiving the image in act 410, process 400 proceeds to act 412, where it is determined whether there are more particles in the image to classify. If it is determined in act 412 that there are additional particles to classify, process 400 proceeds to act 414, where it is determined whether an intensity value associated with a particle to be classified is above a threshold value. If it is determined in act 414 that the intensity value associated with the particle is not greater than the threshold value, process 400 returns to act 412, where it is determined whether there are more particles to classify. In some embodiments, rather than comparing the intensity value for each particle in an image with a threshold value, the entire image may be segmented based on intensity such that all pixels having an intensity less than the threshold value are disregarded. Intensity -based image segmentation in accordance with some embodiments is described in more detail below.
  • process 400 proceeds to act 416, where it is determined whether at least one morphological characteristic associated with the particle satisfies a first set of criteria (e.g., size and/or shape criteria). If it is determined in act 416 that the at least one morphological characteristic of the particle does not satisfy the first set of criteria, process 400 proceeds to act
  • the particle is classified as belonging to a second group of particles (e.g., debris).
  • a second group of particles e.g., debris
  • Process 400 then returns to act 412, where it is determined whether there are more particles in the image to classify and the process repeats. If it is determined in act 416 that the at least one morphological characteristic of the particle satisfies the first set of criteria, process 400 proceeds to act 420, where the particle is classified as belonging to the first group of particles (e.g., cells).
  • the first group of particles e.g., cells
  • Process 400 then proceeds to act 422 where it is determined whether the particle classified as belonging to the first group of particles is to be classified into a subgroup (e.g., certain types of cells). If it is determined in act 422 that sub-group classification is not desired, process 400 returns to act 412, where it is determined whether there are more particles in the image to classify and the process repeats. If it is determined in act 422 that sub-group classification is desired, process 400 proceeds to act 424, where it is determined whether one or more morphological characteristics of the particle satisfy a second set of criteria (e.g., size and/or shape criteria) associated with a sub-group of the first group of particles.
  • a subgroup e.g., certain types of cells
  • Process 400 then returns to act 412, where it is determined whether there are more particles in the image to classify and the process repeats until it is determined in act 412 that there are no further particles in the image to classify, and process 400 proceeds to act 426, where information determined based on the particle classification is output. For instance, all particles identified as belonging to the first group of particles may be identified and/or quantified, and an output of the identification and/or the quantification may be provided to a user, examples of which are described below.
  • the cell-specific detection and cell-debris classification techniques described herein include data from an experiment in which Bacillus megaterium cells were captured on electrodes of a microfluidic device (e.g., the microfluidic device shown in FIG. 2 or 3), stained with the fluorescent stain sybr green, and imaged at 20x magnification using an Olympus BX-63 microscope as the optical system.
  • a microfluidic device e.g., the microfluidic device shown in FIG. 2 or 3
  • stained with the fluorescent stain sybr green and imaged at 20x magnification using an Olympus BX-63 microscope as the optical system.
  • Olympus BX-63 microscope Olympus BX-63 microscope
  • image segmentation is the process of separating regions of pixels based on their intensity values.
  • FIG. 5B shows the fluorescence channel of the multi-channel image of FIG. 5 A following segmentation.
  • segmentation is performed using grayscale images, fluorescence images, or other color images (using each color channel (e.g., red, green, blue) or alternative representations like hue, saturation, value).
  • the minimum intensity value used to differentiate objects from the background may be a useful parameter in detecting only the desired objects (e.g., captured particles) during image segmentation, while excluding all other objects.
  • Intensity values from an 8-bit camera range from 0 to 255.
  • all particles of interest e.g., bacteria
  • some debris e.g., dust
  • pixel noise e.g., pixel noise
  • other objects e.g., microfluidic device features.
  • FIG. 6B by increasing the minimum intensity threshold value from 2 to 5, much of the pixel noise is removed, but the particles of interest and some of the debris remains.
  • the particles of interest are substantially brighter compared to the debris, nearly all of the debris and microfluidic device features may be removed by setting a much higher intensity threshold value for segmentation.
  • the segmented image shown in FIG. 6C was generated by setting the intensity threshold value for segmentation at a value of 75. By performing segmentation that restricts detection to very bright objects, much of the debris in the image can be excluded.
  • the original fluorescence image is presented in green as indicated, while objects detected by particle-specific detection using intensity -based segmentation have a pink overlay as indicated.
  • the images shown in FIGS. 7A-7E are multi-channel images that include a fluorescence channel and a bright field channel to visualize the electrodes in the image.
  • intensity-based segmentation is applied to an image in some embodiments to include particles of interest (e.g., cells) and exclude debris and other objects that are not of interest.
  • particles of interest e.g., cells
  • debris and other objects that are not of interest.
  • a series of acts may be performed to determine whether intensity -based segmentation can be used to distinguish particles of interest and debris in a fluorescence image.
  • the series of acts may include:
  • particles of interest e.g., cells
  • intensity -based image segmentation may not be sufficient to distinguish particles of interest and other objects (e.g., debris) in the image.
  • morphological features of the particles of interest may be used to classify particles in an image. For instance, as discussed above in connection with FIG. 4, at least one morphological characteristic of the particle to be classified in the image may be compared against a set of criteria (e.g., size and/or shape criteria) to determine whether to classify a particle as belonging to a first group of particles (e.g., cells).
  • classification based on morphological characteristics of particles in the image may be implemented using one or more filters that describe distinctive morphological characteristics of the particles of interest.
  • filters that describe distinctive morphological characteristics of the particles of interest.
  • a plurality of morphological-based filters were applied to distinguish microbes of interest from unwanted objects (e.g., debris) in the image.
  • unwanted objects e.g., debris
  • Morphological- based filters may be defined in any suitable way depending on the morphology of the particles of interest.
  • automated techniques including, but not limited to, applying a variance maximization technique (like principal component analysis or singular value decomposition) to the space of object measurements followed by a clustering technique (e.g. k- means clustering) is used to define suitable filters.
  • appropriate filters used to remove unwanted objects in accordance with some embodiments may be defined based on one or more morphological characteristics of the particles of interest in the image and/or the unwanted objects (e.g., debris) to be excluded. Particles of interest that have distinctive morphological characteristics in terms of size and/or shape, when compared to the debris in the image, are better candidates for defining morphological-based filters to distinguish the particles of interest from debris automatically with little or no manual inspection required.
  • the following steps are performed:
  • each object e.g., size (e.g., area, circumference, diameter, extent, bisector, ferret) and each object’s shape (e.g., aspect ratio, elongation, convexity, shape factor [circularity], sphericity) may be selected.
  • size e.g., area, circumference, diameter, extent, bisector, ferret
  • shape e.g., aspect ratio, elongation, convexity, shape factor [circularity], sphericity
  • Inspect particles of interest e.g., cells
  • This understanding may be achieved, at least in part, using software tools that allow for object interrogation and measurement determination us to click on an object in the image and displays its measurements, an example of which is shown in FIG. 9A.
  • Identify a suitable set of measurements to distinguish cells from debris For example, one or more measurements for which cells and debris occupy different ranges of values from one another may be determined. In the above example, two distinct types of debris were identified: very small specks much smaller than a cell and, occasionally, large pieces of dust much larger than a cell. Due to this characterization of the debris, a size-based filter was applied to distinguish cells from debris. Additionally, the small specks of debris were nearly perfectly round, while all the cells had a somewhat elongated sausage-like shape. Due to this characterization of the cells, a shape-based morphological filter was applied. In some embodiments, using a minimal set of measurements that distinguishes particles of interest (e.g., cells) and debris may be used.
  • particles of interest e.g., cells
  • cutoff values for each measurement filter are identified. Ideally, cutoff values that include all cells and exclude all debris would be identified. In some embodiments, the cutoff values may be determined by identifying, for each measurement filter being used, a transition point for the measurement value between cells to debris. In some embodiments, the identification is performed automatically.
  • this step is accomplished with an overlay on the original image that superimposes the detected objects in a distinctive color, subject to the filtering requirements that have been defined, allowing for a visualization of which objects remain following application of the defined morphological filters.
  • FIG. 9B shows an example, in which after application of a size filter, a large piece of debris is shown as being successfully excluded.
  • distributions of object properties may be determined to evaluate the effect of applying different amounts of image segmentation and morphological filtering.
  • Table 2 shows the number of objects detected using very general detection of all bright objects to a more specific detection geared towards only detecting cells. The increasing specificity is apparent by the drastically reduced number of objects in each set as more restrictive segmentation/filtering is applied.
  • FIGS. 10A and 10B illustrate histograms of object area and object brightness for the four data sets represented in Table 2. It can be observed that when including the debris and pixel noise (i.e., “All Objects”), the data is dominated by small, dim objects. By contrast, when restricting detection based on intensity thresholds and applying appropriate morphological filters, more accurate measurements of the characteristic size and brightness of the cells are revealed.
  • particles of interest e.g., cells
  • particles that have intensity values above a threshold value e.g., that meet the intensity -based segmentation criterion
  • a first set of morphological criteria may be classified as belonging to a second group of particles (debris)
  • particles that satisfy the first set of criteria may be classified as belonging to the first group of particles (cells).
  • both particles of interest and debris may be segmented and separated into classes based on their intensity and/or morphological characteristics, enabling for measurements distribution analysis for each class separately.
  • an intensity-based filter and a morphological filter may be applied to an original image to exclude pixel noise.
  • Pixel noise refers to small groups of pixels slightly above background intensity, but not caused by any physical object of the microfluidic device
  • pixel noise originates from electrical fluctuations at the level of the optical system’s sensor and is typically present in any image obtained by a digital image sensor
  • a minimum intensity value of 5 was set to detect both cells and debris in an image.
  • a size-based morphological filter was imposed to require the object size to be at least 5 pixels (e.g., to exclude pixel noise). The result of applying these two constraints to an original image is shown in FIG. 11, in which 6655 objects were identified. Determining the lower intensity threshold (5 in the example above) was determined using the following steps:
  • the threshold value may be set high enough to exclude pixel noise, but low enough to not exclude particles of interest (e.g., cells) or debris in the image.
  • one or more cell-specific filters determined based on knowledge about the particles of interest may be used to classify those particles from debris. For instance, in the example described herein for classifying B. megaterium cells, the following criteria may be used to define a cell-specific filter: object area 10-4000pm 2 and mean intensity value > 30. In this example, using this classification resulted in 3102 of the 6655 objects (47%) being classified as B. megaterium cells and other objects being classified as debris. As described above, in some embodiments the process of determining classifiers is performed using automated technique designed to identify meaningful axes of separation of object measurements and cluster objects in that space.
  • FIG. 12 shows the results of classifying objects identified in FIG. 11. Objects classified as cells are shown in green, whereas objects classified as debris are shown in white. [0149] In this example, rather than discarding (filtering out) objects classified as debris, those objects are labelled as a separate class, which can be characterized. Because object boundaries may depend strongly on the intensity threshold used for segmentation, object measurements can vary between a cell-specific detection with a high threshold and an all-bright- objects detection with a low threshold.
  • An example workflow for cell-specific detection in accordance with some embodiments is as follows:
  • distributions of object properties may be determined separately for each class, as shown in FIGS. 13A and 13B.
  • FIGS. 13A and 13B it was observed that that B. megaterium cells exhibit a characteristic size distribution centered around lOOpm 2 and have a mean intensity value around 60.
  • debris objects are characterized as being substantially smaller and dimmer (i.e., have lower intensity values) compared to cells as shown in the plots of FIGS. 13A and 13B, respectively.
  • some embodiments of the present technology relate to novel computational methods to enable real-time detection and quantification of particles of interest (e.g., microbes, cells) captured by one or more electrodes of a microfluidic device (e.g., a microfluidic device described in connection with FIG. 2 or 3).
  • detector performance is optimized through comparison of the automated results with manually curated data.
  • a pair of object detectors are configured to analyze fluorescence microscope images to identify microbial cells and other visible objects. To evaluate detector performance, the results of these detectors were compared against a manual (i.e., human) evaluation of the same images. Using numerical optimization, detector parameters were tuned to accommodate different applications.
  • raw data produced by a microfluidic system is a grid of multiple (e.g., hundreds or thousands) of partially- overlapping images (e.g., fluorescence microscope images) covering the capture area of a microfluidic device within the microfluidic system.
  • partially- overlapping images e.g., fluorescence microscope images
  • some embodiments relate to an automated computer vision technique to identify and enumerate objects visible in the images.
  • object detection is achieved by identifying contiguous regions of bright pixels in an image.
  • contours e.g., the boundary between dark and light regions
  • Closed contours of a given size and shape may be identified as potential object, subject to a stability requirement across multiple thresholds.
  • the tuning the parameters of the automated computer generation technique is important to ensure particles of interest (e.g., cells, microbes) in the images are accurately classified.
  • the series of binary images may be created using a specified minimum, maximum and step size for intensity thresholding.
  • intermediate contours used to filter objects based on their shape and/or size may use minimum and maximum allowed values for area, circularity, inertia and/or convexity.
  • the stability of intermediate contours which may be used to determine the final objects can be specified by a repeatability across thresholds and minimum distance cutoff for the center of the objects. The objects that a given detector finds (or fails to find) may depend, at least in part, on appropriately specifying values for each of these parameters.
  • the first detector (referred to herein as the “all objects” detector) is configured to identify all objects in the image above a background intensity value.
  • the second detector (referred to herein as the “all cells” detector) is configured to apply an identical intensity threshold filters as the all objects detector, but is further configured to additional require an additional morphological filter (e.g., shape filter) such that intermediate contours have a specific shape, quantified, for example, by particular ranges of circularity, inertia, and convexity. The particular ranges can be tuned to accommodate the characteristic shape of a given particle of interest (e.g., a particular microbe or cell).
  • shape filter e.g., shape filter
  • FIG. 14A illustrates an image in which all bright objects (teal circles, as indicated) and all cell-shaped objects (pink circles, as indicated) in a fluorescence microscope image have been automatically detected using the two detector approach described herein.
  • all cells are detected twice (once in the all objects detector and once in the all cells detector).
  • some embodiments relate to performing duplicate removal to remove doubly -counted cells from the all objects detector, and instead categorize the objects in the image as cell-shaped objects and other objects for subsequent analysis (e.g., quantification), as shown in FIG. 14B.
  • the positions of the detected objects output from each of the detectors are determined, in an ideal case, objects identified by both detectors could be identified as those that have identical positions in the image.
  • the two detectors may be configured to use different parameters (e.g., different intermediate contours) to determine the final set of objects, the object positions across detectors may not match exactly.
  • some embodiments are configured to implement a best (e.g., close, but not exact) matching between the object positions in the two data sets output from the detectors.
  • the result of applying the best matching technique is two sets of coordinates - a first set of cell-shaped objects detected in the image, in which each cell is only represented once, and a second set of other objects, in which each other object is only represented once.
  • the results output from the automated detection technique described above is automatically compared with manual classification to evaluate the accuracy of the automated dual-detector technique.
  • manual enumeration and classification were performed using the Cell Counter plugin of the open source ImageJ (available from https://imagej.nih.gov/ij) application. Images were inspected under various brightness and contrast settings to identify bright objects as well as very dim objects, often barely above background intensity. Objects were manually classified as cells or other objects. Object coordinates corresponding to the detected cells and other objects were exported in xml format for subsequent use. Automated classification of the same images was performed using the above-described dual-detector technique, with the output of the object coordinates for detected cells and other objects also being recorded.
  • a comparison of the manually classified objects and the automatically classified objects was performed by identifying matchings that minimize the distance between objects from the two sets of coordinates (automated and manual).
  • the Hungarian algorithm was used to match objects from automatic detection to those from manual enumeration.
  • FIG. 15A shows that the best matching techniques (e.g., using the Hungarian algorithm) finds the best possible matchings between two sets of coordinates, the objects found by automatic detection, and those labelled by manual classification.
  • the best matching approach enables object matching even when coordinates do not match perfectly because of, for example, a discrepancy between where a human selects an object location and where the automated detector calculates the center of the object.
  • candidate matching objects having a distance above a threshold distance were excluded to address the possibility that the two data sets may have detected different objects due, for example, to a detection error.
  • a threshold distance e.g., a typical length of a cell or other particle of interest
  • FIG. 15A shows that the two objects detected in the lower portion of the image are separated by a substantial distance.
  • FIG. 15B shows that in some embodiments, pairings further than a cutoff (threshold) distance (e.g., the two objects detected in the lower portion of the image) are excluded as matching objects.
  • the result of the best matching process may be an accurate quantification of unmatched objects from manual classification (false negatives) and unmatched objects from automated detection (false positives).
  • best matching between two sets of coordinates A and M is performed according to the following steps:
  • the automated detector performance may be evaluated by considering a set of objects A produced by the automated dual-detector system described herein comprising automatically detected cells (Ac) and automatically detected other objects (Ao), and a set of objects from manual classification, M, with some labelled as cells (Me) and some labelled as other objects (Mo). Best matching according to the techniques described above may be performed, and each pairing, as well as each unpaired object, may represent a distinct outcome. Objects present in the manual classification but not detected using the automated detection are considered as false negatives, whereas automatically detected objects absent from the manual classification are considered as false positives. The complete set of possible outcomes is:
  • FIG. 15C shows a graphical depiction of detector performance evaluation in accordance with some embodiments.
  • an automated dual-detector system located cells (identified as “AUTO CELLS”) and other objects (identified as “AUTO OTHER”) in an image.
  • Manual classification of cells identified as “MANUAL CELLS” and other objects (identified as “MANUAL OTHER”) was also performed. Best matching using the techniques described herein between the automated detection objects and manually classified objects was performed, and pairings represented by lines between circle centers are shown. Unmatched objects (false negatives/positives) are represented by bold circles. In the example shown in FIG. 15C, there were seven correct pairings, two false classifications, and two false positives.
  • some embodiments are configured to automatically tune automated detector parameters based, at least in part, on the automated detector performance determined using the techniques described herein. For instance, a quantity that describes detector performance on a given image may first be determined by, for example, counting each type of mistake represented in the detector performance measure, and weighting each mistake based on how undesirable the mistake is for a given application. For example, for some applications, failing to detect a cell entirely (a false negative cell error) may be a more important error than other types of errors. False negative other errors may be slightly more permissible, since an object annotated as a non-cell is less likely to pose a serious contamination threat.
  • False positive errors may be even more permissible, as they may require further manual inspection of the images even though they may not contain particles of interest. False classifications may represent the least severe type of error, since these objects are still detected and therefore can be flagged for manual review.
  • a set of weights are assigned to different types of errors, and example of which is shown below in Table 3.
  • Table 3 Example weights assigned to different error types.
  • the weights assigned to each of the different error types may be used to construct a score.
  • a larger score may indicate worse agreement between automated detection and manual classification for a given image (i.e., a high score represents poor detector performance.
  • the score By defining a function (the score) of many variables (the automated detector parameters), numerical optimization is used in some embodiments to determine an optimum set of automated detector parameters by, for example, searching for a global minimum in the data. In some embodiments, such numerical optimization is performed by using simulations to explore various combinations of detector parameters with the goal of minimizing the score for a given set of images (a training data set). Such an approach enables automated identification of detector parameters for any given type of experiment (e.g., a given microorganism in a given media) and a given set of preferences regarding which types of errors are more permissible than others (the weights).
  • numerical optimization is performed using differential evolution, which simulates an evolutionary process where a population of candidate solutions (combinations of automated detector parameters) is initially spread out in the parameter space, evaluated on their fitness (the score), and mutated to incorporate the traits of other candidates. Eventually the candidate solutions converge to the likely global minimum in the function to obtain an optimized set of automated detector parameters for a given application. Provided that the images obtained in subsequent experiments are similar enough to the images in the training data set, manual classification and numerical optimization using differential evolution may need only be performed once to optimize automated detector parameters for each new type of application.
  • the rate of false positive cells is given by the number of false positive cells compared to the total number of automatically detected cells in the data set. With these definitions, the total score and mistake distribution over the 15 image set was determined, with the results shown in Table 4. As shown, the improvement in score was largely driven by a reduction in false negatives.
  • Table 4 Total score and mistake distribution using the weights in Table 3 [0168]
  • a unique strength of the detector parameter optimization technique described herein is the ability of the technique to modulate detector performance to favor certain types of errors by optimizing detector parameters to a differently weighted score. For instance, although false negative cell errors are weighted most strongly in the example provided herein, other applications may instead weight false positives or false classifications more strongly, and such application-specific preferences can be coded into the optimization process by using a different set of weights assigned to the types of errors. For example, consider the following four examples of different weights:
  • Table 6 Distribution of mistake types for detectors tuned using the different weighting schemes shown in Table 5
  • FIGS. 16A- D represent graphically the results of optimizing automated detector parameters using the four differently weighted score schemes shown in Table 5. It was found that weighting strategies focused on preventing false positives (FIGS. 16A-B) do so by allowing a larger number of false negatives. Conversely, a weighting strategy focused more heavily on preventing false negatives (FIGS. 16C-D) can do so, but (at least in this example) at the expense of allowing two additional false positives errors.
  • some embodiments are directed to techniques for optimizing parameters of an automated detector.
  • the techniques use a score that characterizes detector performance compared to manual classification of an image and weights that enable the score to punish certain types of mistakes more severely than others.
  • Differential evolution is a numerical optimization technique used in some embodiments to minimize the score on a training set of manually classified images to determine the optimized parameters. Performing differential evolution using differently weighted scores shifts the distribution of mistakes in an expected way for a particular application.
  • FIG. 17 illustrates a process 1700 for tuning parameters of an automated detector in accordance with some embodiments.
  • a first image (or multiple first images) is processed using an automated detector to detect a first set of objects (e.g., cells and other objects) in the first image.
  • Process 1700 then proceeds to act 1712, where detector performance data of the automated detector is determined. As described above, the detector performance data may be based on the first set of objects detected to by the automated detector and a second set of objects detected using manual classification.
  • the detector performance data may include information about different types of errors produced by the automated detector.
  • Process 1700 then proceeds to act 1714, where the detector parameters of the automated detector are tuned based on the detector performance data. For instance, user input specifying weights for different types of errors may be used in combination with the detector performance data to tune the detector parameters for a particular application.
  • Process 1700 then proceeds to act 1716, where the automated detector having tuned detector parameters is used to process a second image to detect a third set of objects in the second image.
  • Process 1700 may then proceed to act 1718, where information about the detected objects in the third set of objects may be output. For instance, a quantity of objects in the third set may be determined and output.
  • the output produced by a microfluidic system after capture, staining, and imaging of microorganisms in a fluid sample may include a grid of multiple (e.g., hundreds or thousands) of image tiles (e.g., fluorescence microscope images) covering the capture area of a microfluidic device within the microfluidic system as an automated x-y stage of the microfluidic system moves the sample to acquire the grid of image tiles.
  • image tiles e.g., fluorescence microscope images
  • each newly added tile (center, C) may be compared to its already -processed neighbors for objects in the overlap regions that may have already been detected. Because of the direction the scan progresses, image tiles as neighbors to the northwest, north, northeast, and west (NW, N, NE, W) have already been processed when tile C is obtained.
  • the same particle of interest e.g., cell or other microorganism
  • the same particle of interest may appear in two images (for the horizontal and vertical overlap regions) or even four images (for the comer overlap regions).
  • some embodiments are directed to a computational solution that accounts for the particles in the overlap regions.
  • the unique contribution of each image tile is identified as the image tile is newly added to the grid of image tiles. Because the image tiles in the grid overlap, all already-processed neighbors of the newly added tile may be checked for objects that have already been detected. In some embodiments, a technique for determining matching coordinates
  • the coordinates of an object in one image tile may not be directly comparable to the coordinates or same object in a neighboring tile.
  • an object in the overlap region between image tiles C and W will be near the left edge of image C and near the right edge of image W.
  • a set of coordinate transformations can be defined.
  • a particular challenge in performing overlap compensation is that particles in the overlap regions may not be detected in all of the overlapping images. For instance, imperfections in the optical system may lead to objects appearing differently depending on, for example, whether they appear in the left or right half of an image. Therefore, a particle in the overlap region may have a somewhat different appearance in each of the overlapping images and may not be detected by the automated detector in all of the images. In some embodiments, using a larger overlap region between images reduces the probability of false negatives due to such a detector failure.
  • the objects detected in a newly added image tile may be checked against the set of all objects that were detected one or more times in the already- processed neighboring image tiles. Drawing inspiration from the mathematics of set theory, this is precisely the set union of the already processed neighboring image tiles. In this framework, the best matching technique can be applied to intersections of images. Therefore, in some embodiments, a union within tolerance between coordinate sets A and B is determined as follows:
  • the result will be a set of objects detected in A or B or both, but importantly, the objects detected in both images will only be included in this set (the union) once.
  • the union of already-processed neighbors (U) By computing best matching between object coordinates in the newly added tile (C) and object coordinates in the union of its already-processed neighbors (U), the objects in C that have already been detected in an overlap region can be identified. However, such objects are only counted once, even if they are shared with more than one neighboring image, which is important to obtain accurate counts. Then, the unmatched objects in C contain the unique contribution of that image tile’s contribution to the overall grid of image tiles. By summing up the unique contributions of each image tile, an accurate total count over the entire grid can be determined.
  • some microfluidic systems acquire overlapping images to avoid false negatives while imaging a large area of a microfluidic device of the microfluidic system.
  • Cells or other objects in image overlap regions may be detected in any number of the overlapping images.
  • Coordinate transformations based on the optical system stage movement allow for comparison between object locations/coordinates between overlapping images.
  • the best matching technique described herein for comparing object coordinates in an image may be extended to identify matching objects in overlap regions, and the unique contribution of each image tile can be obtained based on the extended best matching technique as described above.
  • one or more images captured by an optical system of a microfluidic system are analyzed using a detector configured to apply particular intensity-based segmentation and/or morphological criteria (e.g., size and/or shape criteria) to the image(s) to detect, classify, and/or quantify particles of interest (e.g., cells or other microorganisms) in the image(s).
  • a detector configured to apply particular intensity-based segmentation and/or morphological criteria (e.g., size and/or shape criteria) to the image(s) to detect, classify, and/or quantify particles of interest (e.g., cells or other microorganisms) in the image(s).
  • one or more neural networks are used alone or in combination with an automated detector to detect, classify, and or quantify particles of interest in one or more images.
  • Some aspects of the technology provide for training neural networks on a panel of selected bacterial species, on indicator bacteria, and/or a broad range of bacterial morphologies to be able to differentiate between debris and microorganisms, where microorganisms may be bacteria, spores, viruses, fungus, mold, yeast, etc. Some aspects of the technology provide for applying the techniques described herein in food safety testing to detect, for example, indicator microorganisms, e.g., E. cob, Salmonella, Listeria etc. Some aspects of the technology provide for applying the techniques described herein for universal capture of microorganisms and artificial intelligence with neural networks trained to recognize specific morphology of indicator bacteria for detection and/or quantification. In some embodiments, the frequency of microorganism capture may be modulated for amplitude and/or frequency. In some embodiments, the techniques described herein may be applied to enhance specificity.
  • Aspergillus spores have a very distinctive morphology. Some aspects of the technology provide for use of the techniques described herein to detect and quantify Aspergillus spores (e.g., based on morphology to differentiate them from debris and other microorganisms). In some embodiments, there is provided a database of training data used for specific recognition of a number of microorganisms. For example, the techniques described herein may be applied to detect and quantify a panel of (e.g., 13 or more, or less) indicator water-borne pathogens that cause infections in humans.
  • a panel of e.g., 13 or more, or less
  • FIG. 20 illustrates a process 2000 for quantifying microorganisms in a sample based on one or more images provided as input to trained neural network, in accordance with some embodiments.
  • an image associated with the sample is provided as input to a trained statistical model (e.g., a trained neural network, a trained machine learning model).
  • the trained statistical model may have been trained to identify microorganisms in an image based on a plurality of labeled images associated with samples including microorganisms and other components.
  • the images on which the statistical model was trained may include images of one or more electrodes of a microfluidic device (e.g., the microfluidic devices described in connection with the microfluidic systems shown in FIG.
  • Process 2000 then proceeds to act 2020, where the microorganisms in the sample are quantified based, at least in part, on an output (e.g., a labeled image) of the trained statistical model.
  • the trained statistical model may be trained to one or more microorganisms of interest to be detected, and a number of detected microorganisms may be counted to determine the amount of microorganisms in the sample.
  • Process 2000 then proceeds to act 2020, where the determined quantity of microorganisms in the same is output.
  • an indication e.g., an alarm, a visual indication, or any other suitable indication
  • an indication e.g., an alarm, a visual indication, or any other suitable indication
  • Example of using trained statistical model to detect microorganisms in sample [0184] For the bacterial cultures E. coli GFP S06 were grown on Nutrient Agar (Teknova) overnight at 37° C. Colonies were picked, resuspended in O.OOlx PBS, and diluted to OD 0.07. The E. coli sample was stained with 1:1000 dilution of SYBR Green I Nucleic Acid Stain (Invitrogen). B. subtilis 66333 (ATCC) were grown on Nutrient Agar (Teknova) overnight at 30° C. Colonies were picked, resuspended in O.OOlx PBS, and diluted to OD 0.06. The B.
  • subtilis sample was stained with 1:1000 dilution of SYBR Green I Nucleic Acid Stain (Invitrogen).
  • B. subtilis were instead incubated with 10 uM SYTO 9 dye and 60 uM propidium iodide from the LIVE/DEAD Bac Light Bacterial Viability Kit (molecular probes).
  • B. subtilis 6633 ATCC cells were grown overnight with shaking (200 rpm) at 30° C in nutrient broth (Teknova). Cells were back diluted to OD 0.2 and grown to OD 0.8 before resuspension in Difco sporulation medium (DSM), (8 mg/ml nutrient broth, 1 mg/ml KC1, 1 mM MgS04, 10 uM MnCl 2 , 500 uM CaCl 2 , 1 mM FeS04). Cells were allowed to sporulate and incubate in DSM for 48 hrs at 30° C with 200 rpm shaking.
  • DSM Difco sporulation medium
  • spores were washed 3x and resuspended in O.OOlx PBS.
  • the unpurified spore sample was stained with 1 : 1000 dilution of SYBR Green I Nucleic Acid Stain (Invitrogen) to label and differentiate remaining vegetative cells from unstained spores.
  • microfluidic device e.g., the microfluidic device described in connection with the microfluidic system in FIG. 3
  • inlet/outlet holes were sealed with tape to prevent capillary motion.
  • the device was placed on a SIB V3.0M 2018 BX53 GND No.l stage board and electrical connections were applied using a BRIDGE CP 1.0 14x28_F bridge.
  • the stage board was attached to a SDG 5162 (FGEN-Siglent) function generator and custom LZY-22 ⁇ (LZY-FS-02) amplifier using SMA 24" cables.
  • a 1 MHz, 20 Vpp signal was applied to the electrode to capture bacteria on electrode surface, as described herein.
  • a frequency scan was performed by controlling a function generator to sequentially apply frequencies from 100 MHz to 1 MHz in 1 MHz steps at 4 Vpp.
  • the microfluidic stage board was attached to an IX3-SSU automated microscope stage.
  • E. coli were imaged with an optical system that included an Olympus BX63 widefield microscope using a pE-300 ULTRA LED light source (CoolLED) and a 40x objective.
  • SYBR Green stained E. coli and B. subtilis were visualized with 485nm excitation and 510nm emission wavelengths.
  • electrode position was imaged with transmitted light bright field microscopy.
  • LIVE/DEAD stained E. coli was visualized with 536 nm excitation and 617 nm emission wavelengths for PI and 485 nm excitation and 510 nm emission wavelengths for SYTO 9.
  • FIGS. 21A-B show a zoomed in region from the displayed box on the full spiral image displayed in FIG. 21A.
  • the image was acquired with a 40x objective from an E. coli sample captured when a 1MHz, 20 Vpp voltage was applied to the electrode.
  • FIG. 22 shows bacterial counts for six images run through detection with a trained statistical model three times.
  • FIG. 23 shows manual vs. automated cell counts in accordance with some embodiments. Due to the inherent subjectivity associated with manual counts, there is variability both within the manual counts performed by different people and between manual counts and the automated counts.
  • CVs coefficients of variation
  • FIG. 24 shows classifications for different E. coli automated detection errors. Automatically labeled images were checked manually to determine when and how E. coli detection errors were made, with three types of errors above being represented in FIG. 24.
  • FIG. 25 shows manual checks of E. coli automated detection errors. The number of each merge, missing, and wrong errors was compared to the total number of bacteria detected in 25 images.
  • the trained statistical model to perform automated counting model is configured to process many images from scan experiments, where a sequence of frequencies is applied consecutively, and the bacterial response to each frequency is identified.
  • a sigmoidal curve was generated, which can be used to identify a crossover frequency ( ⁇ 50 MHz in the example of FIG. 26) from negative dielectrophoresis to positive dielectrophoresis.
  • FIG. 26 show the results of processing 100 image from a frequency scan with the automated counting model. Total bacteria captured by the microfluidic device was determined automatically for each image.
  • the trained automated counting model was configured to analyze three channel images, with one bright field and two fluorescence channels.
  • FIGS. 27 A- 27C show the results of the automated counting model update to analyze three channel images in accordance with some embodiments.
  • FIG. 27A shows LIVE/DEAD stained/?, subtilis bright field, Syto-9, and PI channels merged.
  • FIG. 27B shows bacteria detected in the Syto-9 channel.
  • FIG. 27C shows bacteria detected in the PI channel (detected bacteria overlaid with unique color label).
  • Each of the images shown in FIGS. 27A-27C were acquired with a 40x objective from a B. subtilis sample captured at 1 MHz, 20 Vpp with a microfluidic device.
  • FIG. 27 A bright field imaging is represented in gray while Syto-9 signal is represented in green and PI signal represented in red.
  • FIGS. 27B and 27C detected bacteria are each overlaid with unique color labels and thin white lines represent regions at the edge of the electrode.
  • the statistical model trained on an E. coli training data set was used to detect bacteria in B. subtilis images to see a model trained to detect one type of microorganism would successfully detect another organism having similar morphology (in this case, another rod-shaped microorganism). Images of SybrGreen stained B. subtilis were processed, and it was observed that the model trained on E. coli images could also successfully identify B. subtilis cells, as shown in FIG. 28A. This same trained model was also applied to FM-464 stained B. subtilis spores and it was observed that the trained model could also successfully identify fluorescently stained/?, subtilis spores as shown in FIG. 28B.
  • FIG. 28A shows images displaying B. subtilis vegetative cells detected and labelled by an automated model trained to detect E. coli.
  • the image on right is a zoomed in region from the box on the full spiral image displayed on the left.
  • the image on the left was acquired with a 40x objective from a/?, subtilis sample captured at 1 MHz, 20 Vpp on a microfluidic device. Detected bacteria are each overlaid with unique color labels and thin white lines represent regions at the edge of the electrode.
  • FIG. 28B shows image displaying B. subtilis spores detected and labelled an automated model trained to detect E. coli.
  • the image on right is a zoomed in region from the box on the full spiral image displayed on the left.
  • the image on the left was acquired with a 40x objective from a B. subtilis spore sample captured at 1 MHz, 20 Vpp on a microfluidic device. Detected spores are each overlaid with unique color labels and thin white lines represent the region at the edges of the electrode.
  • the output from multiple detectors of the same type may be implemented on a single set of data.
  • the results of the two detectors may be combined and compared to obtain a combined result (e.g., a count of microbes in the sample) of increased accuracy.
  • the first set of parameters may be chosen in such a way as to recognize all microbes of one type (e.g., bacteria) in the sample and to ensure there are no false negatives. For example, if it is unclear whether the object is a debris particle or a bacterium, then the detector recognizes the object as a bacterium. The result of the detection and subsequent quantification is a number of objects recognized as bacteria Nl.
  • the second set of parameters may be chosen in such a way as to recognize only bacteria in the sample and to minimize false positives. For example, if it is unclear whether the object is a debris particle or a bacterium, then the detector recognizes the object as debris. The result of the detection and subsequent quantification is a number of objects recognized as bacteria N2.
  • the outputs of the detectors may be combined in any suitable way. For instance, the positions of objects that were detected differently in both models may be investigated. For example, if there is a bacterium that has two segments the first detector may count it multiple times, whereas the second detector may count it once.
  • Nl and N2 may be associated with different weights such that the average is a weighted average.
  • combined output may be configured to ensure zero false negatives.
  • the techniques described above for detecting, classifying, and/or quantifying particles of interest (e.g., microorganisms) in a sample take as their starting point one or more images acquired from an optical system of a microfluidic system that includes a microfluidic device configured to capture the particles of interest on one or more electrodes using dielectrophoresis.
  • the inventors have recognized and appreciated that the accuracy of microorganism detection and/or quantification based on images captured by a microfluidic system may be improved by acquiring a negative control scan to be performed prior to processing a sample with the microfluidic device.
  • the negative control scan may be used when analyzing the results of sample processing by subtracting the data obtained via the negative control scan from data obtained when processing the sample.
  • FIG. 29A schematically illustrates components of a microfluidic system that includes a microfluidic device configured to process fluid samples in accordance with some embodiments.
  • a container 3 having a sample disposed therein may include bacteria 1 (e.g., E. coli.
  • the bacteria may include one species of bacteria or a mix of different species of bacteria. It should be appreciated that although bacteria are described as the microorganisms of interest in this example, other microorganisms may additionally or alternatively be included in the sample and processed with the microfluidic system shown in FIG. 29 A.
  • Container 2 includes a controlled solution (e.g., deionized water, water, buffer solution, sterile buffer, etc.).
  • Container 4 includes a stain.
  • multiple containers may be used, each of which contains a different stain.
  • each of containers 2, 3, and 4 is shown as being coupled to the inlet 5 of a microfluidic passage 6 within a microfluidic device, it should be appreciated that any of containers 2, 3, or 4 may be replaced via a fluid line (e.g., a pipe, a tube, etc.).
  • a fluid line e.g., a pipe, a tube, etc.
  • Electrode systems 7, 8 and 9 are electrically coupled to a controller 20 via contact 13, which is configured to provide an electrical signal (e.g., a voltage with amplitude VI and frequency fl) to the electrode systems 7, 8, 9.
  • Controller 20 e.g., a signal generator
  • the electrical signal can be applied to electrodes of opposite polarity as +V1, fl and -VI, fl or as VI, fl and 0V. Multiple signals can be applied to the electrodes.
  • the electrical signal is applied to the electrode systems 7, 8, 9, those electrode systems are described herein as being “activated.”
  • electrode systems 7, 8, 9 are configured to generate corresponding electric fields 10, 11, 12.
  • the lines of electric fields 10, 11, 12 also schematically show the trajectories of bacterial motion directed towards the electrodes (without differentiating which area of the electrode) due to the applied electric field and corresponding positive dielectrophoretic force.
  • the microfluidic system shown in FIG. 29 A also includes an optical system 18 including a fluorescent light source 17 (e.g., an LED) configured to facilitate imaging by optical system 18.
  • fluorescent light source 17 may be configured to excite a fluorophore attached to a labeled bacteria to capture fluorescent images.
  • fluorescent light source 17 may be configured to facilitate bright field imaging, for example, when the bacteria in the sample are unlabeled.
  • Optical system 18 may include an optical sensor with a fluorescent light detector, such as a fluorescent microscope or a LED light source with an objective and a detector (e.g., an image sensor). Optical system 18 may be configured to image the electrode system and record the fluorescent signal and/or an image, example of which are described throughout the application. The optical system 18 may be configured to image multiple parts of the electrode system (e.g., to generate a grid of image tiles as discussed in connection with FIG. 18. In some embodiments, the microfluidics device including the microfluidics passage 6 and the electrode systems 7, 8, 9 may be placed on a stage that enables movement in x,y direction to perform a multi-image scan. The microfluidics system shown in FIG.
  • 29A further includes a computer 19 (e.g., at least one hardware processor) configured to process one or more images captured by the optical system 18.
  • computer 19 may be configured to detect bacteria in fluorescent and/or bright field images and/or quantify bacteria using one or more of the techniques described herein.
  • FIG. 30 is a flowchart of a process 3000 for detecting and/or quantifying microorganisms in a sample based, at least in part, on a negative control image in accordance with some embodiments.
  • FIGS. 29B and 29C schematically illustrate some of the acts described in process 3000 and are provided further illustration.
  • a fluid from container 2 containing a controlled solution
  • the electrical signal from the controller 20 may be on or off. When the electrical signal is on, the electric field 10, 11, 12 is generated by the corresponding electrode systems 7, 8, 9.
  • Process 3000 then proceeds to act 3010, where the fluid with stain from container 4 is passed through the microfluidic passage.
  • Process 3000 then proceeds to act 3012, where the fluid from container 2 is again passed through the microfluidic passage to rinse the stain from the microfluidic passage.
  • Process 3000 then proceeds to act 3014, where one or more first “negative scan” images of all or a portion of the surface of electrodes 7, 8, 9 is captured by optical system 18. If there are any microorganisms on the chip causing contamination and/or any other contaminants such as dust, debris or particles 21, they are recorded in the negative scan. [0206] With reference to FIG.
  • process 3000 proceeds to act 3016, where the sample in container 3 is passed through the microfluidic passage.
  • the electrical signal from the controller 20 is on to generate the electric field 10, 11, 12.
  • the electrical signal is applied at high amplitude to increase the capture efficiency of bacteria on the electrode systems 7, 8, 9.
  • the bacteria from the sample are shown as moving in the direction 22 towards the electrode system 7 and are being captured on the electrode surface due to a positive dielectrophoretic force.
  • the entire volume of the sample that needs to be analyzed is passed through the microfluidic passage.
  • process 3000 proceeds to act 3018, where the fluid with controlled solution from container 2 is passed through the microfluidic passage to rinse the microfluidic passage. During the rinse, the electric field 10, 11, 12 may remain on to ensure that the microorganisms captured on the electrodes remain captured.
  • Process 3000 then proceeds to act 3020, where the fluid with stain from container 4 is passed through the microfluidic passage.
  • Process 3000 then proceeds to act 3022, where the controlled solution is passed through the microfluidic passage to rinse the stain from the electrode systems 7, 8, 9.
  • the electric field remains on to ensure that the microorganisms captured on the electrodes remain captured.
  • the amplitude of the electric field may be reduced to avoid affecting the fluorescent signal from stained microorganisms.
  • Process 3000 then proceeds to act 3024, where one or more second “positive scan” images of all or a portion of the surface of electrodes 7, 8, 9 is captured by optical system 18. If there are any microorganisms captured by the electrodes, they are recorded in the positive scan.
  • Process 3000 then proceeds to act 3026, where the microorganisms in the sample are detected and/or quantified based on the first negative scan image(s) and the second positive scan image(s). For example, particles that emit fluorescent light (signal) in the negative scan and positive scan image(s) are recognized using one or more of the particle detection techniques described herein. Particles present in the negative scan image(s) are subtracted from particles present in the positive scan image(s). The subtraction may be performed in any suitable way. For instance, in some embodiments, the subtraction is based on particle count. In other embodiments, the subtraction is based on background subtraction of the negative scan images from the positive scan.
  • FIG. 31 shows images regarding how negative control scans can reduce the probability of false positives errors in some embodiments.
  • a negative control scan was performed by processing 4mL of O.OOlxPBS through the microfluidic device under identical flow and electrical conditions used for microbial capture (300pL/min, 1MHz, 50Vpp), followed by lmL of the fluorescent stain SYBR Green I.
  • the electrode area of the microfluidic device was scanned at 20x magnification in bright field and green fluorescence channels.
  • the positive scan images on the right show microscope images after capture of 4mL of E. coli GFP S06 at 100-1000 cells/mL.
  • the capture and imaging was performed using identical conditions to the capture and imaging of the negative control scan, with the only difference being the addition of bacteria to the sample.
  • some electrodes show no debris in the negative control scan (see image on top left).
  • the positive scan see image on top right
  • some electrodes do show debris in the negative control scan (see image on bottom left).
  • the positive scan see image on bottom right
  • two or more stains may be used.
  • a benefit of using two or more stains is only a single integrated scan with two sets of stains (colors) may be captured, instead requiring a separate negative scan and positive scan as described in connection with process 3000. Capturing a single integrated scan may, in some instances, significantly reduce the sample processing time.
  • container 4A contains a first stain (stain A) and container 4B contains a second stain (stain B).
  • FIG. 32 illustrates a process 3200 for detecting and/or quantifying microorganisms in a sample based on a single image in accordance with some embodiments.
  • FIGS. 29D and 29E schematically illustrate some of the acts described in process 3200 and are provided further illustration.
  • a fluid from container 2 containing a controlled solution
  • the electrical signal from the controller 20 may be on or off. When the electrical signal is on, the electric field 10, 11, 12 is generated by the corresponding electrode systems 7, 8, 9.
  • Process 3200 then proceeds to act 3210, where the fluid with a first stain from container 4 A is passed through the microfluidic passage.
  • the stain A stains particles present in the microfluidic passage in the absence of microorganisms from the actual sample to be processed.
  • Non-limiting examples of such particles include contaminants present in a negative control, e.g., dust, debris, particle, microorganisms.
  • Process 3200 then proceeds to act 3212, where the fluid from container 2 is again passed through the microfluidic passage to rinse the first stain from the microfluidic passage.
  • process 3200 then proceeds to act 3214, where the sample in container 3 is passed through the microfluidic passage.
  • the electrical signal from the controller 20 is on to generate the electric field 10, 11, 12.
  • the electrical signal is applied at high amplitude to increase the capture efficiency of bacteria on the electrode systems 7, 8, 9.
  • the bacteria from the sample are shown as moving in the direction 22 towards the electrode systems 7, 8, 9 and are being captured on the electrode surfaces due to a positive dielectrophoretic force.
  • the entire volume of the sample that needs to be analyzed is passed through the microfluidic passage.
  • process 3200 proceeds to act 3016, where the fluid with the second stain in container 4B is passed through the microfluidic passage.
  • the second stain (stain B) is configured to label the microorganisms in the sample.
  • Process 3200 then proceeds to act 3218, where the fluid with the controlled solution from container 2 is passed through the microfluidic passage to rinse the microfluidic passage. During the rinse, the electric field 10, 11, 12 may remain on to ensure that the microorganisms captured on the electrodes remain captured.
  • Process 3200 then proceeds to act 3220, where one or more “integrated” images of all or a portion of the surface of electrodes 7, 8, 9 is captured by optical system 18.
  • the optical system may include at least two filters to record the signal from particles stained with stain A and stain B to generate the integrated scan. If there are any microorganisms captured by the electrodes, they are recorded in the integrated scan.
  • Process 3200 then proceeds to act 3222, where the microorganisms in the sample are detected and/or quantified based on the image(s) from the integrated scan. For example, particles that emit fluorescent light (signal) in the integrated scan are recognized using one or more of the particle detection techniques described herein.
  • a third stain, stain C that is specific to the target microorganisms may be applied.
  • the stain C may be selected such that the stain C does not non-specifically adhere to the surface of debris particles, so that it does not produce a false positive signal from stained non-microorganisms.
  • a metabolic stain is used to, for example, eliminate the problem of non-specific stain adhesion to debris or nonmicroorganism surfaces as the metabolic stain is configured to enter the inside of cells but not the inside of debris particles.
  • an autofluorescent signal originating from microorganisms but not from debris particles may be used to detect microorganisms in captured images. Use of autofluorescent signals may lower the false positive rate originating from a fluorescent signal from non-microorganisms that were stained through non-specific surface adhesion.
  • the surface of the microfluidic passage is coated with a surfactant or another substance to limit non-specific surface adhesion of particles other that the microorganisms present in the sample. Such embodiments may lower noise for optical detection with optical system 18.
  • the techniques described herein are applied to samples that do not have large particles of the size of bacteria, yeast, mold, viruses and or other microorganisms. For instance, such applications include, but are not limited to, microorganism detection and/or quantification from drug substance, from drug product, from water, or from buffer solutions.
  • bacteria in a sample may be encapsulated in an outer shell to enhance dielectrophoresis capture on the electrode system.
  • the techniques described herein may be used to, among other things, rapidly detect and automatically quantify any microorganisms in mammalian cell culture medium from bioreactors (as well as other solutions).
  • Cell culture media from bioreactors typically contain very high number of cells per mL, even up to 10 10 /mL. Because of the high concentration of cells per mL, such mediums contain a large number of degraded cells, and thus a large amount of both DNA and RNA nucleic acids released from such cells and free-floating organelles. The presence of such nucleic acids often cause problems for many analytic techniques like PCR, sequencing, etc.
  • nucleic acids especially in high concentration
  • a microfluidic device e.g., the microfluidic devices described in connection with the microfluidic systems shown in FIG. 2 or 3
  • a stain such as Sybr Green I (green fluorescent dye which has the feature of intercalating to nucleic acid molecules)
  • Sybr Green I green fluorescent dye which has the feature of intercalating to nucleic acid molecules
  • nucleic acid molecules covering the entire microfluidic device surface negatively affect: (i) capture of bacteria by coating electrodes on the chip, possibly weakening the electric field; (ii) released captured bacteria from chip for further analysis for e.g. sequencing, bacterial DNA can contain trace amount of mammalian cells nucleic acids which may give a false result; and (iii) automatic detection of bacteria and image analysis of the scanned chip due to the strong green fluorescent background.
  • some embodiments are directed to treatment of mammalian cell culture from bioreactors with commercially available exonuclease (e.g., Pierce Universal Nuclease for Cell Lysis) as a single step reaction before cell culture media are analyzed using a microfluidic device in accordance with the techniques described herein.
  • exonuclease is used to clarify or remove free-floating nucleic acids by digestion of the nucleic acids directly in mammalian cell culture media.
  • Exonuclease completely digests nucleic acids to oligonucleotides that are less than five bases long, which helps to improve many processes, e.g.
  • FIG. 33 illustrates a process 3300 for improved quantification of microorganisms in a sample (e.g., from a bioreactor) using a microfluidic device in accordance with some embodiments.
  • a sample e.g., from a bioreactor
  • the sample is treated with exonuclease to produce a clarified sample.
  • Process 3300 then proceeds to act 3320, where the clarified sample is passed through the microfluidic passage of a microfluidic device to capture microorganisms in the clarified sample on an electrode surface of the microfluidic device.
  • Process 3300 then proceeds to act 3330, where one or more images of the electrode surface are captured.
  • Process 3300 then proceeds to act 3340, where the captured one or more images are analyzed to quantify an amount of microorganisms in the sample. For instance one or more of the techniques described herein may be used to quantify the amount of microorganism in the sample based on the captured image(s).
  • Example protocols may be used to quantify the amount of microorganism in the sample based on the captured image(s).
  • CHO (Chinese Hamster Ovary) Media [0219] The CHO culture was sampled from a bioreactor, aliquoted to 2- or 15-mL sterile tube and stored in 4°C. Before processing with a microfluidic device and treatment with exonuclease (e.g., Pierce Universal Nuclease), CHO cultured medium was incubated 30 min at room temperature (RT), then diluted 1:100 in a sterile phosphate buffered saline (PBS) pH 7.4 without calcium chloride and magnesium chloride diluted 1:1000 UltraPure Distilled Water to achieve a working range of conductivity.
  • PBS sterile phosphate buffered saline
  • the conductivity of the diluted CHO cultured medium in PBS 1:1000 in DI water was in the range 190-210 ⁇ S/cm and was measured at room temperature (RT) using pH/mV/conductivity meter ACCUMET XL200. Aliquots of diluted PBS were stored at 4°C. PBS (FS standard buffer) [0220] All dilutions of CHO processed media were prepared in a sterile phosphate buffered saline (PBS) pH 7.4 w/o calcium chloride and magnesium chloride (Life Technologies, USA) diluted 1:1000 UltraPure Distilled Water (DI water, Life Technologies, USA) in aseptic condition.
  • PBS sterile phosphate buffered saline
  • DI water UltraPure Distilled Water
  • the conductivity of the PBS 1:1000 in DI water was in the range 19-23 ⁇ S/cm and was measured at RT using pH/mV/conductivity meter Accumet® XL200. Aliquots of diluted PBS were stored at 4°C.
  • nuclease was stored and used according to manufacturer protocol. Briefly, nuclease during sample preparation was transferred from -20°C and kept in ice. A tube was open inside of a biosafety cabinet and 1 ⁇ L of nuclease was added directly to 10 mL of RT mammalian cells medium to achieve 25 units/mL final concentration, where one unit corresponds to the amount of enzyme required to produce a change of 1.0 in the absorbance at 260nm of sonicated Herring DNA over 30 minutes at 37°C. To stop enzymatic reaction, the sample was transferred to the ice.
  • CHO culture medium was transferred from 4°C to RT and incubated on the bench for 30 min. Before processing using the microfluidic device, CHO cultured medium was diluted 1:100 in a sterile PBS pH 7.4 diluted 1:1000 UltraPure Distilled Water and was treated with nuclease. Nuclease treatment does not affect conductivity of tested sample as shown in Table 7. All experiments were conducted at room temperature. The prepared sample was ready for processing using a microfluidic device. The control sample was prepared in the same way omitting treatment with nuclease.
  • Example 1 Removing of free-floating nucleic acids in CHO cells culture from bioreactor using nuclease
  • exonuclease is an efficient method to remove free floating genetic material (DNA, RNA) from bioreactor culture of CHO cells.
  • Two samples were prepared - a first sample without nuclease treatment and a second sample with nuclease treatment.
  • 1 mL of SybrGreen I solution in UltraPure DI water was passed through the microfluidic passage of the microfluidic device, with the resultant image shown in FIG. 34A.
  • 1 mL of CHO cells culture from bioreactor not treated with nuclease and an additional 1 mL of SybrGreen I solution in UltraPure DI water Fluorescent visualization based an image captured by the optical system of the microfluidic system revealed a large number of small particulates and large streaks that covered most parts of the imaged microfluidic device as shown in FIG.
  • Fluorescent visualization based an image captured by the optical system of the microfluidic system revealed that the large number of small particulates and large streaks that covered most parts of the microfluidic device in the image in FIG. 34B were absent from the image in which the sample was first processed with exonuclease as shown in FIG. 34D.
  • exonuclease e.g., Pierce Universal Nuclease for Cell Lysis
  • exonuclease e.g., Pierce Universal Nuclease for Cell Lysis
  • Antifoams can be classified as either hydrophobic solids dispersed in carrier oil, aqueous suspensions/emulsions, liquid single components or solids and may contain surfactants.
  • Nonionic surfactant e.g., Polysorbate 80
  • QACs quaternary ammonium compounds
  • iodine iodine
  • parabens according to USP ⁇ 61> Microbiological examination of nonsterile
  • microbial enumeration tests microbial enumeration tests.
  • the presence of the surfactants in cells culture medium analyzed with a microfluidic device using one or more of the techniques described herein can be problematic due to bubbles or even unstable foam forming when the sample is passed through the microfluidic passage of the microfluidic device.
  • bubbles or light/sporadic foaming can affect the bacteria capture process/efficiency, resulting in inaccurate results.
  • some embodiments provide for adsorption of surfactants of mammalian or cell culture from bioreactors onto activated carbon before culture media are analyzed using a microfluidic device.
  • CHO Chonese Hamster Ovary Media
  • the CHO culture was sampled from bioreactor, aliquoted to 2- or 15-mL sterile tube and stored in 4°C.
  • the conductivity of the cell culture medium was in the range 10-12 mS/cm and was measured at RT using pH/mV/conductivity meter Accumet® XL200.
  • PBS FS standard buffer
  • All dilutions of CHO processed media were prepared in a sterile phosphate buffered saline (PBS) pH 7.4 without calcium chloride and magnesium chloride (Life Technologies, USA) diluted 1:1000 UltraPure Distilled Water (DI water, Life Technologies, USA) in aseptic condition.
  • PBS sterile phosphate buffered saline
  • DI water UltraPure Distilled Water
  • the conductivity of the PBS 1:1000 in DI water was in the range 19-23 ⁇ S/cm and was measured at RT using pH/mV/conductivity meter Accumet® XL200. Aliquots of diluted PBS were stored at 4°C.
  • the CHO culture medium was transferred from 4°C to RT and incubated on the bench for 30 min. Before processing using the microfluidic device, CHO cultured medium was incubated with active charcoal for 30 min in RT. Charcoal was separated from cell culture medium by filtration or decanted, then the microorganism was spiked. All experiments were conducted at RT. Such a prepared sample was then ready for processing using the microfluidic device. A control sample was prepared in the same way omitting incubation with activated charcoal.
  • Microfluidic device [0234] All tests were conducted on a flow-based microfluidic system (e.g., the microfluidic system shown in FIG. 2) and were performed at RT. To evaluate the benefits from incubation of mammalian cell culture medium from bioreactor with activated charcoal, filtrated/decanted media with spiked microorganism were processed through the microfluidic device, visualized, and quantified using the microfluidic system shown in FIG. 2.
  • USP United States Pharmacopeia
  • testing fluid samples for sterility requires detecting the presence of even one microorganism, according to the USP.
  • the sterility test requires detecting any type of bacterial contamination, yeast, mold and fungus.
  • Conventional sterility tests are complicated and labor intensive. For instance, sterility testing according to typical methods requires specialized laboratory infrastructure, trained personnel and is prone to human error. For in-process control in biomanufacturing, bioburden tests are used with an expectation that no colonies grow on plates. Sterility testing only requires detection and does not require quantification.
  • improved screening techniques that enable real-time information about the presence of microorganisms in a sample in a shorter amount of time (e.g., less than 8 hours) than conventional sterility testing technique.
  • This information may be used to determine if there is microbial contamination present in a sample, and may be used, for example, for real time drug release (RTR) testing.
  • RTR real time drug release
  • This information may alternatively be used for final product release testing, for informing decisions about the next steps in product manufacturing and/or for use in process control.
  • FIG. 35 illustrates a process 3500 for performing a sterility test on a sample in accordance with some embodiments.
  • a first portion of the sample is passed through a microfluidic passage of a microfluidic system.
  • Process 3500 then proceeds to act 3520, where a dielectrophoretic force is generated within the microfluidic passage as the sample is passed through the microfluidic passage, wherein the dielectrophoretic force acts on the first portion of the sample.
  • Process 3500 then proceeds to act 3530, where based on the response of the first portion of the sample to the dielectrophoretic force, it is determined whether the first portion of the sample includes microorganisms.
  • Process 3500 then proceeds to act 3540, where the sample is labeled as sterile when it is determined in act 3530 that the first portion of the sample does not include microorganisms.
  • the microfluidic system that includes the microfluidic device is designed in such a way that the false negative rate is zero, for example, at least in part due to the 100% capture efficiency of the microfluidic system and/or use of the data analysis techniques described herein.
  • Some aspects provide for screening for contamination in manufacturing processes based on microorganisms capture on electrodes and optical inspection. Some aspects provide for screening for disease agents in human samples based on microorganisms capture on electrodes and optical inspection. Based on the use of a microfluidic system to perform screening, this approach allows for eliminating a significant amount of bacterial culture plates and thereby reduces the testing time and cost, for example, in an amount equivalent to the number of samples that are able to be processed without bacterial culture.
  • the microfluidic system may be operated and/or data obtained from the system may be analyzed, in a manner that eliminates false negative results (e.g., the failure to detect the presence of a bacteria). This optimization may be at the cost of increasing the number of false positive results. Given this optimization, if the result of the test is negative, the sample is considered to contain zero microorganisms and does not require further sterility or bioburden testing. A negative test result may then be a basis for real time drug release.
  • a false positive result may be subsequently tested in the customary manner (e.g., with a bacterial culture). Because a significant number of samples will return no false positives, the method enables the elimination of the need for performing bacterial cultures of a large number of samples. Accordingly, the amount of time needed to perform screening is greatly reduced. For example, where the false positive rate is not higher than 50%, a reduction by a factor of two of the time and testing costs for the manufacturer can be achieved.
  • some embodiments related to sterility testing may include the following outcomes: 1) The apparatus processes the sample. If the test result is negative, that means the sample is free from contamination. The sample may be considered ready for real time drug release. 2) If the test result is positive, that sample is either contaminated or the test result is a false positive. To determine which is the case, the remainder of the unprocessed original sample is processed using a conventional technique or another method.
  • an optical system may be configured to use different magnification objectives for detecting, quantifying and/or classifying microorganisms using a microfluidic system that includes a microfluidic device, examples of which are described herein.
  • magnification objectives for detecting, quantifying and/or classifying microorganisms using a microfluidic system that includes a microfluidic device, examples of which are described herein.
  • a lower magnification objective which allows for faster scanning of the chip area but is sufficient to detect microorganisms and quantify them may be used.
  • a higher magnification objective of the optical system may be used to selectively scan spots that were detected using the lower magnification objective.
  • a sample e.g., associated with a drug
  • a sample is tested with a microfluidic system using one or more of the techniques described herein.
  • the captured microorganisms can be scanned with higher magnification and the resulting images can be identified to see if they match any of the images in the database in an attempt to identify the microorganisms causing the contamination.
  • FIG. 37 illustrates a process 3700 for determining whether a sample is in condition for release (e.g., for pharmaceutical manufacturing).
  • a first portion of the sample is passed through a microfluidic passage of a microfluidic system.
  • Process 3700 then proceeds to act 3720, where a dielectrophoretic force is generated within the microfluidic passage as the sample is passed through the microfluidic passage, wherein the dielectrophoretic force acts on the first portion of the sample.
  • Process 3700 then proceeds to act 3730, where a concentration of microorganisms in the first portion of the sample as immobilized on the surface of the electrode due to the dielectrophoretic force is determined.
  • Process 3700 then proceeds to act 3740, where it is determined whether the concentration of microorganisms is less than a threshold concentration. If it is determined in act 3740 that the concentration of microorganisms is not less than the threshold value, process 3700 proceeds to act 3750 where it is determined that the sample is not in condition for release. Otherwise, if it is determined in act 3740 that the concentration of microorganisms is less than the threshold concentration, process 3700 proceeds to act 3760, where it is determined that the sample is in condition for release.
  • One or more aspects and embodiments of the present disclosure involving the performance of processes or methods may utilize program instructions executable by a device (e.g., a computer, a processor, or other device) to perform, or control performance of, the processes or methods.
  • a device e.g., a computer, a processor, or other device
  • inventive concepts may be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement one or more of the various embodiments described above.
  • the computer readable medium or media can be transportable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various ones of the aspects described above.
  • computer readable media
  • program or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects as described above.
  • one or more computer programs that when executed perform methods of the present disclosure need not reside on a single computer or processor but may be distributed in a modular fashion among a number of different computers or processors to implement various aspects of the present disclosure.
  • Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices.
  • program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types.
  • functionality of the program modules may be combined or distributed as desired in various embodiments.
  • data structures may be stored in computer-readable media in any suitable form.
  • data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields.
  • any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.
  • the above-described embodiments of the present technology can be implemented in any of numerous ways.
  • the embodiments may be implemented using hardware, software or a combination thereof.
  • the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers.
  • any component or collection of components that perform the functions described above can be generically considered as a controller that controls the above-described function.
  • a controller can be implemented in numerous ways, such as with dedicated hardware, or with general purpose hardware (e.g., one or more processor) that is programmed using microcode or software to perform the functions recited above and may be implemented in a combination of ways when the controller corresponds to multiple components of a system.
  • a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer, as non-limiting examples. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smartphone or any other suitable portable or fixed electronic device.
  • PDA Personal Digital Assistant
  • a computer may have one or more input and output devices. These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output. Examples of input devices that can be used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, a computer may receive input information through speech recognition or in other audible formats.
  • Such computers may be interconnected by one or more networks in any suitable form, including a local area network or a wide area network, such as an enterprise network, and intelligent network (IN) or the Internet.
  • networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.
  • some aspects may be embodied as one or more methods.
  • the acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.
  • a reference to “A and/or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.
  • the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements.
  • This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified.
  • “at least one of A and B” can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.
  • ⁇ 20% of a target value in some embodiments within ⁇ 10% of a target value in some embodiments, within ⁇ 5% of a target value in some embodiments, within ⁇ 2% of a target value in some embodiments.
  • the terms “approximately” and “about” may include the target value.

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Abstract

L'invention concerne des procédés et un appareil de détection, de classification et/ou de quantification d'une particule dans une image. Un procédé consiste à déterminer si une valeur d'intensité pour la particule dans l'image est supérieure à une première valeur seuil, à déterminer si au moins une caractéristique morphologique de la particule dans l'image satisfait un premier ensemble de critères associés à des particules dans un premier groupe de particules, à classer la particule comme appartenant au premier groupe de particules lorsque la valeur d'intensité pour la particule dans l'image est supérieure à la valeur seuil et la ou les caractéristiques morphologiques de la particule dans l'image satisfont au premier ensemble de critères, et à délivrer un résultat de la classification de la particule comme appartenant au premier groupe de particules.
PCT/US2022/029240 2021-05-13 2022-05-13 Techniques pour optimiser la détection de particules capturées par un système microfluidique WO2022241246A1 (fr)

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AU2022279500B1 (en) * 2022-12-01 2023-11-23 Provectus IP Pty Ltd Automated cell analyser and method

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AU2022279500B1 (en) * 2022-12-01 2023-11-23 Provectus IP Pty Ltd Automated cell analyser and method
WO2024112995A1 (fr) * 2022-12-01 2024-06-06 Provectus IP Pty Ltd Analyseur de cellules automatisé et procédé

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