WO2019175386A1 - Digital holographic microscopy for determining a viral infection status - Google Patents
Digital holographic microscopy for determining a viral infection status Download PDFInfo
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- WO2019175386A1 WO2019175386A1 PCT/EP2019/056547 EP2019056547W WO2019175386A1 WO 2019175386 A1 WO2019175386 A1 WO 2019175386A1 EP 2019056547 W EP2019056547 W EP 2019056547W WO 2019175386 A1 WO2019175386 A1 WO 2019175386A1
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
- G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
- G01N33/502—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics for testing non-proliferative effects
- G01N33/5026—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics for testing non-proliferative effects on cell morphology
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- G01N15/10—Investigating individual particles
- G01N15/14—Optical investigation techniques, e.g. flow cytometry
- G01N15/1468—Optical investigation techniques, e.g. flow cytometry with spatial resolution of the texture or inner structure of the particle
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- G03H—HOLOGRAPHIC PROCESSES OR APPARATUS
- G03H1/00—Holographic processes or apparatus using light, infrared or ultraviolet waves for obtaining holograms or for obtaining an image from them; Details peculiar thereto
- G03H1/04—Processes or apparatus for producing holograms
- G03H1/0443—Digital holography, i.e. recording holograms with digital recording means
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- G01N2015/1006—Investigating individual particles for cytology
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
- G01N2333/005—Assays involving biological materials from specific organisms or of a specific nature from viruses
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- G01N2333/11—Orthomyxoviridae, e.g. influenza virus
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- G03H1/0005—Adaptation of holography to specific applications
- G03H2001/005—Adaptation of holography to specific applications in microscopy, e.g. digital holographic microscope [DHM]
Definitions
- the present invention relate to a method of determining a viral infection status of at least one cell in cell sample using holographic information obtained from digital holographic microscopy (DHM).
- DHM digital holographic microscopy
- Determination of infectious viral diseases can be achieved using several different techniques.
- plaque assay technique is useful for viral quantification, where a monolayer of cells is infected with a virus causing the infected cells to lyse. Surrounding cells will then become infected and eventually a larger area of the monolayer will appear clear. The area known as plaques can be counted to measure viral infection. However, detection is slow since viral detection is only determined at the lysis stage.
- RNA samples include a macromolecular probe such as an antibody or using a nucleic amplification technique such as PCR.
- PCR or antibody probe requires much time and effort, while it is important for viral infections to be quickly identified.
- Antibodies also require identification of antigen marker on the cell membrane; hence different antibodies may be needed according to the cell type, presenting antigen, and infecting virus. The task of identifying suitable antigen markers is a time consuming process.
- a method for providing a viral infection status of at least one cell in a cell sample comprising receiving holographic information (304) of the cell sample obtained by digital holographic microscopy, and determining from the holographic information, the viral infection status (306) of the at least one cell, wherein the infection status (306) is determined from cellular parameter data (312) comprising one or more measured cellular parameters (308) of the cell derived from the holographic information wherein the one or more measured cellular parameters, MCPs, (308) comprises a subset of the MCPs of Table 1 , where the subset comprises one or more MCP from at least one of the groups (a) Phase Texture (F40 to F51 ), (b) Refraction Peak (F20, F33-F37), (c) Morphology (F2-F19, F70-72).
- the subset may comprise one or more (group (a)) MCPs selected from F45 (ID PhaseCorrelationFeature), F44 (ID PhaseContrastFeature) , F48 (ID
- PhaseAverageUniformityFeature F51 (ID PhaseUniformityFeature).
- the subset may comprise one or more (group (b)) MCPs selected from F20 (ID EquivalentPeakDiameterFeature), F33 (ID PeakAreaFeature), F34 (ID PeakAreaNormalizedFeature), F36 (ID PeakHeightFeature), F37 (ID PeakHeightNormalizedFeature) .
- the subset may comprise one or more (group (c)) MCPs selected from F8 (ID EquivalentCellDiameterFeature), F17 (ID RadiusMeanFeature), F8 (ID Equivalent Diameter), F3 (ID CellAreaFeature) ,F18 (ID RadiusVarianceFeature), F6 (ID ElongatednessFeature), F4 (ID CircularityFeature) .
- group (c) MCPs selected from F8 (ID EquivalentCellDiameterFeature), F17 (ID RadiusMeanFeature), F8 (ID Equivalent Diameter), F3 (ID CellAreaFeature) ,F18 (ID RadiusVarianceFeature), F6 (ID ElongatednessFeature), F4 (ID CircularityFeature) .
- the subset may further comprises one or more MCPs of group (d) Optical Height (F24- F32, F38-F39), optionally selected from F27 (ID OpticalHeightMeanFeature), F32 (ID OpticalVolumeFeature), F38 (ID OpticalHeightStandardDeviationFeature), F39 (ID OpticalHeightStandardDeviationlnMicronFeature).
- group Optical Height (F24- F32, F38-F39), optionally selected from F27 (ID OpticalHeightMeanFeature), F32 (ID OpticalVolumeFeature), F38 (ID OpticalHeightStandardDeviationFeature), F39 (ID OpticalHeightStandardDeviationlnMicronFeature).
- the subset may comprise
- F45 (ID PhaseCorrelationFeature) and F44 (ID PhaseContrastFeature),
- F45 (ID PhaseCorrelationFeature), F44 (ID PhaseContrastFeature), and F48 (ID PhaseSkewnessFeature) .
- F45 (ID PhaseCorrelationFeature), F44 (ID PhaseContrastFeature), F48 (ID PhaseSkewnessFeature) , and F20 (ID EquivalentPeakDiameterFeature),
- F45 ID PhaseCorrelationFeature F44 (ID PhaseContrastFeature), F48 (ID PhaseSkewnessFeature), F20 (ID EquivalentPeakDiameterFeature, and F33 ((ID PeakAreaFeature or F34 (ID PeakAreaNormalizedFeature )).
- a method for providing a viral infection status of at least one cell in a cell sample comprising receiving holographic information (304) of the cell sample obtained by digital holographic microscopy, and determining from the holographic information, the viral infection status (306) of the at least one cell.
- the infection status (306) may be determined from cellular parameter data (312) comprising one or more measured cellular parameters (308) of the cell derived from the holographic information.
- the one or more measured cellular parameters (308) may comprise one or more of the parameters of Table 1.
- the cellular parameter data (312) may further comprise one or more derived parameters determined from the one or more measured cellular parameters of the at least one cell, including
- the determining may comprise using a predictive model trained using a training set of cells of known viral infection status and measured cellular parameters.
- the predictive model may use a machine learning method such as a neural network method, random forest trees or deep learning method.
- the sample may be a static or flowing cellular suspension, or an adherent cell culture.
- the viral infection status of the at least one cell includes one or more of: an indication of a presence or absence of a viral infection, a probability of an infection, a degree of infection, a stage of infection, quantity of virus.
- the method may further comprise outputting a report comprising the viral infection status of the at least one cell.
- the method may be performed at a remote processing centre.
- DHM digital holographic microscope
- the DHM may comprise a light source emitting at least partially coherent light, an interferometer, and an image sensor.
- a computing device configured to perform a method as described here, said computing device configured to:
- FIG. 1 depicts an arrangement of a DHM connected to a vessel that is a bio-reactor from which sample is pumped for analysis.
- FIG. 2 depicts an arrangement of a dismountable pump assembly for a vessel.
- FIG. 3 illustrates a dismountable conduit for a DHM.
- FIG. 4 shows a DHM suitable for obtaining holographic information of an adherent cell culture.
- FIGs. 5 and 6 are flow charts illustrating different aspects of the method of the invention.
- FIG. 7 flow charts illustrating a machine learning protocol.
- FIG. 8 is a graph of showing virus load, TCD, VCD and cell viability of Batch 1 cells in a bioreactor.
- FIG. 9 is a graph of showing virus load, TCD, VCD and cell viability of Batch 2 cells in a bioreactor.
- the terms“one or more” or“at least one”, such as one or more or at least one member(s) of a group of members, is clear per se, by means of further exemplification, the term encompasses inter alia a reference to any one of said members, or to any two or more of said members, such as, e.g., any >3, >4, >5, >6 or >7 etc. of said members, and up to all said members.
- a method for providing information on viral infection status of at least one cell in a cell sample comprising receiving holographic information from said cell sample obtained by digital holographic microscopy (DHM), and determining from the holographic information, the viral infection status of the at least one cell of the cell sample .
- DLM digital holographic microscopy
- the holographic information may be used to determine cellular parameter data comprising one or more measured cellular parameter for each cell in the sample, from which the viral infection status can be determined for each cell or cells in the sample.
- the viral status may be provided in“real time”.
- real-time is a means that the status is regularly updated (e.g. 1-5 times/min) to reflect changes in virus load as cells in the sample are incubated.
- the sample may be a flowing suspension.
- the sample may be a flowing suspension pumped from and returned to a bioreactor.
- the cell sample comprises one or a plurality of cells.
- the one or a plurality of cells may be derived from a biological organism that comprises cells from said biological organism.
- the biological organism may be any for instance, animal, insect, yeast, or bacterial.
- the one or a plurality of cells is may be derived from an animal, preferably from a mammal, e.g. from a cat, a dog, a swine, a horse, a cattle, a sheep, a goat, a rabbit, a rat, a mouse, a monkey.
- the one or a plurality of cells may be derived from a human being.
- the one or a plurality of cells in the cell sample may or may not be labelled; as mentioned elsewhere in, labelling is not necessary for holographic determination of viral status.
- the cell sample may comprise a liquid suspension containing the one or a plurality of cells in a suspended state in a liquid. It is appreciated that liquid in which the cells are suspended might depend to the nature of the cell sample (e.g. body fluid, blood, excretion, bioreactor).
- the cell sample may be provided as a static (non-flowing) liquid suspension in relation to the DHM image sensor.
- the cell sample may be provided as a flow of liquid suspension in relation to the DHM image sensor e.g. pumped from and optionally returned to a vessel.
- Acquiring holographic information of a flow allows the label-free and real time measurement of viral infection status.
- Other viral detection techniques may not be sufficiently fast or accurate for the monitoring of a sample flow; techniques that rely on macromolecular probes consume large quantities of probe, and the labelled cells cannot be returned to the vessel.
- the term vessel as used herein refers to a container or a hinge system capable of holding and/or guiding a liquid wherein cells to be analysed of interest are present.
- the term vessel includes a reactor, an incubator, container, bio-reactor, fermentation reactors, water supply piping or plumbing, water canalization systems, water purification reactors, brewing reactors, micro-reactors, etc.
- the cell sample must be sufficiently transparent to allow passage of light emitted by the DHM light source. Light scattering should be sufficiently reduced in order not to significantly affect interference processes on which DHM relies. It is understood that the cell density is such that cells are not extensively overlapped e.g. light passes through the volume of observation and the incident photons on the image sensor result from an integration of all the cells in the volume.
- Exemplary elements of a DHM in combination with a flowing sample is show, for instance, in FIGs. 1 to 3.
- a system comprising a DHM (100), a reactor (50), DHM-dismountable conduit (also known as a cartridge) (150), connected to a dismountable pump assembly (120) by a tubing assembly (160) is shown in FIG. 1.
- the DHM (100) may be provided with a docking element (102) configured to receive the dismountable conduit (150) for the passage of a sample flow, which docking element (102) is configured to position a transparent part (152, FIG. 3) of the dismountable conduit in relation to the DHM image sensor and/or DHM light source for acquisition of the holographic information.
- the transparent part (152) is configured for the passage of light emitted by the DHM light source.
- the dismountable conduit (DC) (150) has a DC inlet (154) for inflow of liquid suspension, and a DC outlet (156) for its outflow.
- the dismountable conduit (150) is fluidly connected or connectable to the vessel (50) using a tubing assembly (160) comprising at least one tube (160) having a proximal end (10) and a distal end (20).
- the terms “distal” or“distal to” and “proximal” or“proximal to” is understood to mean towards (proximal) or away (distal) from the DHM.
- proximal or “proximal to” means towards the DHM and, therefore, away from the e.g. the vessel.
- dismountable or“distal to” means towards away from the DHM and away from e.g. the vessel.
- a proximal end of the tubing assembly (160) may be connected to the dismountable conduit (150).
- a distal end of the tubing assembly (160) may be connected to a dismountable pump assembly (120) configured to draw and pump fluid from the vessel (50) through the tubing assembly (160) and towards the dismountable conduit (150).
- the dismountable pump assembly (120) may comprise a fluid conducting part (122) that is an arrangement of one or more conduits having a sample outlet (124) that is connected or connectable to a distal end (20) of an outflow tube of the tubing assembly (160), and a sample inlet (126) that is in contact or contactable with the vessel (50) contents.
- the sample inlet (126) is typically disposed on an end of a projecting element (128) comprised in the fluid conducting part (122) that separates the sample inlet (126) from the sample outlet (124) and connects them.
- the length of the projecting element (128) may be determined according to the size of the vessel (50) and the height of liquid therein.
- the fluid conducting part (122) is also disposed with a flow induction mechanism (130) that when actuated, for instance by the application of torque, induces flow of liquid from the sample inlet (126) to the sample outlet (124).
- the flow induction mechanism (130) may be determined according to the sample type and pressure requires; the skilled person may choose from a variety of mechanisms such as those found in a positive displacement pump or velocity pump for instance.
- the fluid conducting part (122) may comprise a coupling (132) for attachment to a dismountable actuator (140) for the supply of force (e.g. torque) to the flow induction mechanism.
- the dismountable actuator (140) may comprise an electric motor. Sample from the vessel (50) flows under induced flow from sample inlet (126) to the sample outlet (124), through the outflow tube of tubing assembly (160) connected to the DC inlet (154) of the dismountable conduit (150).
- the fluid conducting part (122) may further comprise a sample return inlet (134) that is connected or connectable to a distal end (20) of a return flow tube of tubing assembly (160), and in fluid connection in the fluid conducting part (122) with a sample return outlet (136).
- the sample return outlet (136) may be typically disposed on the aforementioned projecting element (128) at a different position from the sample inlet (126).
- Sample returning from the dismountable conduit (150) (from the DC outlet (156)) flows through the return flow tube of tubing assembly (160) into the sample return inlet (134) of the fluid conducting part, through the sample return outlet (136) and into the vessel (50).
- the fluid conducting part (122) may be disposable.
- the fluid conducting part (122) is preferably formed from a rigid material.
- An example of a dismountable conduit, dismountable pump assembly flow system, and tubes suitable for use with a vessel and a DHM are described in WO 2014/044823 and US 2017/0205222 .
- the DHM-dismountable conduit also known as a cartridge
- dismountable pump assembly 120
- tubing assembly 160
- the DHM-dismountable conduit may be provided as a sterile disposable kit, preferably without the dismountable actuator (140).
- the sample may comprise cells on a support substrate, incorporated into a glass or plastic container, or microscope slide. Suitable container include multi-plate well, T-flask.
- the support substrate is determined such that the front focal plane of the DHM automatically falls within the cell sample, without the need of refocusing the DHM for each sample.
- the DHM may be provided with a sample holder configured to receive the support substrate in the requisite form (e.g. as a microscope slide or container).
- the sample holder may be moveable (e.g. displaceable and/or rotatable) to allow different regions of the support substrate to be acquired. In FIG.
- an exemplary DHM (100) is depicted comprising a stationary sample holder that is a stage (104) onto which the container is placed and a window (106) for the passage of light, for instance from the sample to the image sensor or of light emitted by the light source.
- the support substrate is used in adherent cell culture, wherein cells are cultured in a suitable medium and adhere to and spread over the support substrate, typically in a monolayer.
- Adherent cell culture is used to propagate certain animal (e.g. mammalian) cells that are anchorage dependent.
- the cell sample must be sufficiently transparent to allow passage of light emitted by the DHM light source. Light scattering should be sufficiently reduced in order not to significantly affect interference processes on which DHM relies. It is understood that the cell density is such that cells are not extensively overlapped e.g. light passes through the volume of observation and the incident photons on the image sensor result from an integration of all the cells in the volume.
- the present method is suitable for detection of different viral infections.
- the virus may be a DNA virus or an RNA virus.
- the virus may be any, for instance but not limited to, Epstein-Barr virus, flu virus, hepatitis B virus, herpes virus, Human T lymphotrophic virus type 1 (HTLV-I), hepatitis C virus, Adenovirus, Adeno-associated virus (AAV), lentivirus or baculovirus.
- Epstein-Barr virus flu virus
- hepatitis B virus herpes virus
- Human T lymphotrophic virus type 1 (HTLV-I) Human T lymphotrophic virus type 1
- hepatitis C virus hepatitis C virus
- Adenovirus Adeno-associated virus
- lentivirus lentivirus or baculovirus.
- Holographic information refers to information, generally being phase and amplitude information, which can be obtained through a digital holographic microscope (DHM) from the cell sample.
- said holographic information may include a digital hologram acquired by the DHM, an intensity image derived from the digital hologram, a quantitative phase contrast image derived from the digital hologram, or a combination of these.
- the intensity image and quantitative phase contrast image may be obtained from the digital hologram by hologram reconstruction.
- the images - usually the quantitative phase contrast images - are then segmented in order to delineate individual cells from the background, thereby generating a plurality of segmented images from the holographic images.
- the process of segmentation is known in the art, for instance, from watershed treatment, clustering-based image threshold and using neural networks.
- Various algorithms exist for watershed treatment including Meyer's flooding algorithm and optimal spanning forest algorithms (watershed cuts).
- Cluster-based image thresholding may employ Otsu’s method.
- the holographic information may include the segmented images.
- the measured cellular parameters are then determined for an individual cell in the image segment.
- the cellular parameter data comprises:
- MCP measured cellular parameters
- one or more derived parameters determined from the one or more measured cellular parameters e.g. transformation of two or more MCPs of the same cell, or transformation of one or more of the same MCP for two or more different cells.
- a derived parameter may include:
- the ratio may be any one of (F1 ) to (73) with any other one of (F1 ) to (F73) of Table 1.
- a ratio may be any one of (F67)-(F73) with any other of (F67)-( F73), a ratio (F68) with (F69), a ratio (F68) with (F67), a ratio any of (F68) to (F73) with (F67).
- a derived parameter - nuclear size variability - may be determined from F70 and is the standard deviation of the nuclear volumes of cells present in one or multiple fields of view.
- Another derived parameter - nuclear volume variability - may be derived from F71 and is the variability of the statistical distribution of all nuclear volumes analyzed.
- the viral infection status of the cell in the sample may be determined.
- the cellular parameter data comprises an indication of variation (e.g. nuclear size variability, nuclear volume variability) between two or more cells in the cell sample of a measured cellular parameter
- the viral infection status may regard two or more cells in the cell sample.
- the holographic information may be used to determine one or more measured cellular parameters (MCP) for an individual cell in the cell sample.
- MCP is a parameter that is measured for a cell or for a cellular component (e.g. nuclear size, optical height of any of the inner structure of the cell, for instance but not limited to the cytoplasm optical height, optical nuclear height) from holographic information.
- Table 1 lists examples (F1 to F73) of MCPs of a cell.
- MCPs Measured cellular parameters
- “No.” refers to the code used herein to identify the MCP
- “ID” refers to the attribute name used by standard software to label the MCP
- “Category” refers to the class of MCP
- “Group” refers to a more refined classification of MCP
- “Description” provides measurement information.
- an optical height is expressed in this document with reference to the optical height of the liquid medium, in which case it is proportional to the difference of the time it takes for light to cross the structure (e.g. cytoplasm, nucleus, nucleolus) in the direction of the height and the time it takes for light to cross the same distance in the liquid medium.
- an optical height is defined in this document with reference to the optical height of the liquid medium, in which case it is proportional to the difference of the time it takes for light to cross the structure (e.g. cytoplasm, nucleus, nucleolus) in the direction of the height and the time it takes for light to cross the same distance in the liquid medium.
- an optical height as the result obtained by multiplying the refractive index multiplied with the actual physical height.
- the MCP may typically be a property of a whole cell (e.g. surface, circularity) or of a component of a cell (e.g. nuclear size, optical height of any of the inner structure of the cell, for instance but not limited to the cytoplasm optical height, optical nuclear height).
- a component of a cell e.g. nuclear size, optical height of any of the inner structure of the cell, for instance but not limited to the cytoplasm optical height, optical nuclear height.
- an image of the component of the cell isolated first then the MCP determined on that isolated image.
- the skilled person knows how to process holographic images to arrive at a MCP for instance using principles of image processing as set out, for instance, in Digital Image Processing (4th Edition) by Rafael C. Gonzalez and Richard E. Woods (ISBN-13: 978-0133356724), and using a library of programming functions such as Open CV (Open Source Computer Vision).
- One or more, or all of the MCPs of Table 1 may be used to determine the viral infection status.
- a subset of the MCPs of Table 1 may be used to determine the viral infection status.
- the machine learning algorithm may determine the subset of the MCPs depending on cell type and on the infecting virus.
- the MCPs may be classified into different groups such as intensity texture, morphology, refraction peak, intensity, optical height, refraction peak, phase texture, intensity texture, fluorescence, optical.
- the group Phase Texture (F40-F51 ) contains MCPs that are a measure of variation of optical density or phase shift within the cell.
- the phase of light passing through a cell varies according to the local density within the cell. Regions of more density within the cell have a larger phase shift.
- the group Refraction Peak (F20, F33-F37) contains MCPs that are a measure of light refracted by a cell.
- light refracted by a cell emerges as a conical beam; refraction peak concerns geometric characteristics of this beam.
- the group Morphology (F2-F19, F70-72) contains MCPs that are a measure of a geometric property of a cell (e.g. radius, diameter etc).
- the group Optical Height (F24-F32, F38-F39) contains MCPs that are a measure of thickness of an objection, in direction of the incident light.
- the subset of MCPs may comprise one or more of the MCPs in at least one of the groups (a) Phase Texture (F40 to F51 ), (b) Refraction Peak (F20, F33-F37), (c) Morphology (F2- F19, F70-72), (d) Optical Height (F24-F32, F38-F39).
- the subset of MCPs may comprise one or more of the MCPs in at least one of the groups (a) Phase Texture (F40 to F51 ), (b) Refraction Peak (F20, F33-F37), (c) Morphology (F2- F19, F70-72).
- the subset of MCPs may comprise one or more of the MCPs in at least one of the groups (a) Phase Texture (F40 to F51 ), (b) Refraction Peak (F20, F33-F37), (c) Morphology (F2- F19, F70-72), and further comprise one or more MCPs of group (d) Optical Height (F24- F32, F38-F39), optionally selected from F27 (ID OpticalHeightMeanFeature), F32 (ID OpticalVolumeFeature), F38 (ID OpticalHeightStandardDeviationFeature), F39 (ID OpticalHeightStandardDeviationlnMicronFeature).
- the subset of MCPs may comprise one or more of the MCPs of at least (a) Phase Texture (F40 to F51 ) and (b) Refraction Peak (F20, F33-F37).
- the subset of MCPs may comprise one or more of the MCPs of at least (a) Phase Texture (F40 to F51 ).
- the MCPs of groups (a) to (d) may be as set in Table 1 , or be contain at least the MCP as set out in the following sections.
- the subset of MCPs may comprise one or more of the MCPs in group (a) Phase Texture (F40 to F51 ).
- the subset of MCPs may comprise one or more of the MCPs in group (a) Phase Texture selected from F45 (ID PhaseCorrelationFeature) , F44 (ID PhaseContrastFeature) ,F48 (ID PhaseSkewnessFeature) , F47 (ID PhaseHomogeneityFeature) , F43 (ID
- PhaseAverageUniformityFeature PhaseAverageUniformityFeature
- F51 ID PhaseUniformityFeature
- the subset of MCPs may comprise F45 (ID PhaseCorrelationFeature) .
- the subset of MCPs may comprise F45 (ID PhaseCorrelationFeature) and F44 (ID PhaseContrastFeature) .
- the subset of MCPs may comprise F45 (ID PhaseCorrelationFeature) and F44 (ID PhaseContrastFeature) and F48 (ID PhaseSkewnessFeature) .
- the subset of MCPs may comprise all of MCPs F45 (ID Phase Correlation), F44 (ID PhaseContrastFeature), F48 (ID PhaseSkewnessFeature), F47 (ID PhaseHomogeneityFeature), F43 (ID PhaseAverageUniformityFeature), and F51 (ID PhaseUniformityFeature) .
- the subset of MCP may comprise one or more of the MCPs in group (b) Refraction Peak (F20, F33-F37).
- the subset of MCP may comprise one or more of the MCPs in group (b) Refraction Peak selected from F20 (ID EquivalentPeakDiameterFeature), F33 (ID PeakAreaFeature), F34 (ID PeakAreaNormalizedFeature), F36 (ID PeakHeightFeature), and F37 (ID PeakHeightNormalizedFeature) .
- F33 ID PeakAreaFeature
- F34 ID PeakAreaNormalizedFeature
- F36 ID PeakHeightFeature
- F37 ID PeakHeightNormalizedFeature
- the subset of MCPs may comprise F20 (ID EquivalentPeakDiameterFeature) .
- the subset of MCPs may comprise all of MCPs F20 (ID EquivalentPeakDiameterFeature), either F33 (ID PeakAreaFeature) or F34 (ID PeakAreaNormalizedFeature), either F36 (ID PeakHeightFeature) or F37 (ID PeakHeightNormalizedFeature) .
- the subset of MCP may comprise one or more of the MCPs in group (c) Morphology (F2- F19, F70-72).
- the subset of MCP may comprise one or more of the MCPs in group (c) Morphology selected from F8 (ID EquivalentCellDiameterFeature), F17 (ID RadiusMeanFeature), F8 (ID Equivalent Diameter) or F3 (ID CellAreaFeature), F18 (ID RadiusVarianceFeature), F6 (ID ElongatednessFeature), and F4 (ID CircularityFeature).
- F8 ID EquivalentCellDiameterFeature
- F17 ID RadiusMeanFeature
- F8 ID EquivalentCellDiameterFeature
- F3 ID CellAreaFeature
- F18 ID RadiusVarianceFeature
- F6 ID ElongatednessFeature
- the subset of MCPs may comprise F8 (ID EquivalentCellDiameterFeature) or F17 (ID RadiusMeanFeature) .
- the subset of MCPs may comprise F8 (ID EquivalentCellDiameterFeature) or F17 (ID RadiusMeanFeature) or F3 (ID CellAreaFeature) .
- the subset of MCPs may comprise F8 (ID EquivalentCellDiameterFeature) or F17 (ID RadiusMeanFeature) or F3 (ID CellAreaFeature)) and (F18 (ID RadiusVarianceFeature) or F6 (ID ElongatednessFeature)) and F4 (ID CircularityFeature) .
- the subset of MCP may comprise one or more of the MCPs in group (d) Optical Height (F24-F32, F38-F39).
- the subset of MCP may comprise one or more of the MCPs in group (d) Optical Height selected from F27 (ID OpticalHeightMeanFeature), F32 (ID OpticalVolumeFeature), F38 (ID OpticalHeightStandardDeviationFeature), and F39 (ID
- F27 ID OpticalHeightMeanFeature
- F32 ID OpticalVolumeFeature
- F38 ID OpticalHeightStandardDeviationFeature
- F39 ID OpticalHeightStandardDeviationFeature
- OpticalHeightStandardDeviationlnMicronFeature are related, and one or the other may be used.
- the subset of MCPs may comprise F27 (ID OpticalHeightMeanFeature) or F32 (ID OpticalVolumeFeature).
- the subset of MCPs may comprise (F27 (ID OpticalHeightMeanFeature) or F32 (ID OpticalVolumeFeature)) and (F38 (ID OpticalHeightStandardDeviationFeature) or F39 (ID OpticalHeightStandardDeviationlnMicronFeature) ).
- the subset of MCPs may comprise F45 (ID PhaseCorrelationFeature) .
- the subset of MCPs may comprise F45 (ID PhaseCorrelationFeature) and F44 (ID PhaseContrastFeature) .
- the subset of MCPs may comprise 2 or more, preferably all of F45 (ID
- PhaseSkewnessFeature PhaseSkewnessFeature
- the subset of MCPs may comprise 3 or more, preferably all of F45 (ID
- PhaseCorrelationFeature F44 (ID PhaseContrastFeature), F48 (PhaseSkewnessFeature), and F20 (ID EquivalentPeakDiameterFeature) .
- the subset of MCPs may comprise 4 or more, preferably all of F45 (ID
- PhaseSkewnessFeature F20 (ID EquivalentPeakDiameterFeature), and F33 ((ID PeakAreaFeature) or F34 (ID PeakAreaNormalizedFeature)).
- the subset of MCPs may comprise 2, 3, 4, 5 or more, preferably all of F45 (ID PhaseCorrelationFeature), F44 (ID PhaseContrastFeature), F48 (ID
- PeakAreaFeature or F34 (ID PeakAreaNormalizedFeature)), (F36 (ID PeakHeightFeature) or F37 (ID PeakHeightNormalizedFeature)), F47 (ID PhaseHomogeneityFeature), (F17 (ID RadiusMeanFeature) or F8 (ID Equivalent Diameter)), (F3 (ID CellAreaFeature) or F8 (ID Equivalent Diameter)), F43 (ID PhaseAverageUniformityFeature), (F32 (ID OpticalVolumeFeature) or F27 (ID OpticalHeightMeanFeature)), (F18 (ID RadiusVarianceFeature) or F6 (ID ElongatednessFeature)), F4 (ID CircularityFeature), F27 (ID OpticalHeightMeanFeature), (F38 (ID OpticalHeightStandardDeviationFeature) or F39 (ID OpticalHeightStandardDeviationlnMicronFeature)), F51 (ID PhaseUniformityFeature) .
- Each MCP may have an equal weighting or a weight factor may be applied to one or more the measured cellular parameters, depending on the cell type and on the infecting virus.
- the machine learning algorithm may determine the weighting.
- Digital Holographic Microscopy is a technique which allows a recording of three dimensional information of a sample or object without the need of scanning the sample layer-by-layer. In this respect DHM is a superior technique to confocal microscopy in terms of acquisition speed.
- a holographic representation is recorded by an image sensor such a CCD or CMOS. The holographic representation may be subsequently be stored or processed on a computer.
- a light source that is coherent is used to illuminate the sample.
- the light source may be provided by a laser.
- the light form the source is split into two beams, an object beam and a reference beam.
- the object beam is sent via an optical system to the sample and interacts with it, thereby altering the phase and amplitude of the light depending on the object’s optical properties and 3D shape.
- the object beam which has been reflected on or transmitted through the sample is then made (e.g. by set of mirrors and/or beam splitters) to interfere with the reference beam, resulting in an interference pattern which is digitally recorded.
- an absorptive element can be introduced in the reference beam which decreases its amplitude to the level of the object beam, but does not alter the phase of the reference beam or at most changes the phase globally, i.e. not dependent on where and how the reference beam passes through the absorptive element.
- the recorded interference pattern contains information on the phase and amplitude changes which depend on the object’s optical properties and 3D shape.
- In-line DHM is similar to the more traditional DHM, but does not split the beam, at least not by a beam splitter or other external optical element.
- In-line DHM is most preferably used to look at a not-too-dense solution of particles, e.g. cells, in a fluid. Thereby some part of the at least partially coherent light will pass through the sample without interacting with the particles (reference beam) and interfere with light that has interacted with the particles (object beam), giving rise to an interference pattern which is recorded digitally and processed.
- In-line DHM is used in transmission mode, it needs light with a relatively large coherence length, and cannot be used if the samples are too thick or dense.
- DDHM differential DHM
- European patent EP 1 631 788 Another DHM technique called differential DHM (DDHM) is disclosed in European patent EP 1 631 788.
- DDHM is different to the other techniques in that it does not make use of reference and object beams in the traditional sense.
- the sample is illuminated by a light source that outputs at least partially coherent light for use in a reflection or in a transmission mode.
- the reflected or transmitted sample beam can be sent through an objective lens and subsequently split in two by a beam splitter and sent along different paths in a differential interferometer, e.g. of the Michelson or Mach- Zehnder type.
- a beam-bending element or tilting mechanism is inserted, e.g. a transparent wedge or a diffraction grating.
- the two beams are then made to interfere with each other in the focal plane of a focusing lens and the interference pattern in this focal plane is recorded digitally by an image sensor such a CCD or CMOS.
- the interference pattern may be stored on a digital storage medium.
- the beam-bending element due to the beam-bending element, the two beams are slightly shifted in a controlled way and the interference pattern depends on the amount of shifting. Then the beam-bending element is turned, thereby altering the amount of shifting.
- the new interference pattern is also recorded. This can be done a number N of times, and from these N interference patterns, the gradient (or spatial derivative) of the phase in the focal plane of the focusing lens can be approximately computed.
- phase-stepping method This is called the phase-stepping method, but other methods of obtaining the phase gradient are also known, such as a Fourier transform data processing technique.
- the gradient of the phase can be integrated to give the phase as a function of position.
- the amplitude of the light as a function of position can be computed from the possibly but not necessarily weighted average of the amplitudes of the N recorded interference patterns. Since phase and amplitude are thus known, the same information is obtained as in a direct holographic method (using a reference and an object beam), and a subsequent 3D reconstruction of the object can be performed.
- the light source may emit spatially and temporally partially coherent light.
- the light source may emit highly correlated laser light.
- Spatially and temporally partially coherent light may be produced by for instance a light-emitting diode (LED).
- a LED is cheaper than a laser and produces light with a spectrum centred around a known wavelength, which is spatially and temporally partially coherent, i.e. not as coherent as laser light, but still coherent enough to produce holographic images of the quality which is necessary for the applications at hand.
- LEDs also have the advantage of being available for many different wavelengths and are very small in size and easy to use or replace if necessary.
- DHM spatially and temporally partially coherent light for obtaining holographic images
- the use of DHM in a diagnostic setting has many advantages which makes it the ideal technique to implement in a setting such as in the current invention.
- a phase shift image is also created.
- the phase shift image is unique for DHM and gives quantifiable information about optical distance.
- the phase shift image forms a topography image of the object.
- An object image is calculated at a given focal distance.
- the recorded hologram contains all the necessary object wave front information, it is possible to calculate the object at any focal plane by changing the focal distance parameter in the reconstruction algorithm.
- the hologram contains all the information needed to calculate a complete image stack.
- the object wave front is recorded from multiple angles, it is possible to fully characterize the optical characteristics of the object and create tomography images of the object.
- the needed components for a DHM system are inexpensive optics and semiconductor components, such as a laser diode and an image sensor.
- the low component cost in combination with the auto focusing capabilities of DHM, make it possible to manufacture DHM systems for a very low cost. Nevertheless, the cost of a DHM may still be too high for monitoring a large amount of reactors.
- the present invention provides a system comprising one DHM and a set of electro-fluidic circuits which are capable of guiding fluid samples from multiple reactors to the DHM and preferably back.
- only one DHM is needed to monitor multiple reactors and the overall cost can be reduced.
- a DHM comprises a light source that emits coherent light or at least partially coherent light such as a LASER or LED, an interferometer which may comprise a set of mirrors and/or beam splitters, and an image sensor such as a CCD or CMOS, and a processor and a computer-readable storage medium (e.g. solid state drive, flash card, or magnetic recording device).
- a DHM may also comprise further optical components such as one or more lenses, mirrors, prisms, attenuators, etc.
- a DHM may comprise or may be connected to processing means such as a mainframe, a PC, a logical device such as a PLC, etc.
- a DHM may work in transmission and/or reflection mode, depending on the nature of the sample which is to be observed.
- a DHM may be a traditional DHM, an in-line DHM, a differential DHM, or another kind of DHM.
- the present method (300) uses holographic information (304) of a cell sample (302) to determine viral infection status (306) as shown in FIG. 5.
- a pattern in the cellular parameter data is identified that correlates to a viral infection.
- the method may use a predictive mathematical model (330) of cellular viral infection, wherein an input to the predictive model comprises the cellular parameter data (212) derived from measured cellular parameters (308) for each cell obtained from holographic information of the cell sample (304) and the output is viral infection status (306) as exemplified in the scheme (310) of FIG. 6.
- the predictive model (330) may be created using a machine learning (328) method (e.g. neural network or random forest trees, deep learning) that is trained using a training set of cells (322), wherein the viral infection status (324) and the cellular parameter data (326) is known for each cell or cells as exemplified in the scheme (320) of FIG. 7.
- the viral infection status may be determined using, for instance, an antibody optionally labelled, or using a nucleic acid amplification technique such as PCR.
- Creation of a predictive model using machine learning e.g. neural network or random forest trees, deep learning
- machine learning e.g. neural network or random forest trees, deep learning
- Deep learning is known in the art, for instance, from en.wikipedia.org/wiki/DeepJearning, a dn has been described in various publications, for instance, Deep Learning, 2016, Ian Goodfellow, Yoshua Bengio and Aaron Courville.
- a training set of cells comprises vi rally-infected cells and non-virally infected cells.
- the measured cellular parameters for individual cells in the training set of cell measured by DHM is obtained.
- the cellular parameter data comprising the measured cellular parameter and optionally the derived parameters are provided to the learning algorithm.
- the machine learning method learns how to discriminate between a virally-infected cell and a non-virally infected cell based on the holographic information, in particular on the cellular parameter data, thereby creating the predictive model.
- the machine learning method may be supervised or unsupervised.
- a confusion matrix allows an error rate to be determined.
- the viral infection status of the at least one cell in the sample may include an indication of a presence of an infection (yes/no), a probability of an infection (fraction), a degree of infection, a stage of infection, apoptotic state, capsid content.
- the viral infection status may indicate the type of virus.
- the viral infection status of at least 2 cells in the sample may be determining by statistically processing viral infection statuses for a plurality of cells in the sample.
- the method may include a step of providing the cell sample.
- the method may include a step of outputting or providing a report on the viral infection status of the at least one cell.
- a computing device configured for performing the method as described herein.
- the computing device may comprise circuitry configured for performing the method of the invention.
- the circuitry comprises a computer processor and a memory.
- the computing device may be implemented in one or multiple connected computers, as a cloud computing device, or other device containing the aforementioned circuitry.
- the computing device may receive input data comprising one or more of:
- - holographic information of the cell sample which may include one or more of:
- the input data may be electronically received e.g. over the Internet or sent via a data storage medium.
- the computing device might be at a location distant from the DHM, for instance in another facility, town, city, country etc.
- the computing device may be configured to:
- the computing device may be further configured to:
- the computing device may be further configured to determine from the holographic information, cellular parameter data comprising one or more measured parameters of a cell in the cell sample, from which the viral infection status of the at least one cell is determined.
- the computing device may be further configured to input into the predictive model the cellular parameter data, and output from the predictive model the viral infection status of the at least one cell.
- a remote processing centre may receive input data comprising one or more of:
- - holographic information of the cell sample which may include one or more of:
- the input data may be electronically received e.g. over the Internet or sent via a data storage medium.
- the remote processing centre might be at a location distant from the DHM, for instance in another facility, town, city, country etc.
- the remote processing centre typically comprises a computer having a processor and memory, for instance, a server.
- the remote processing centre might be implemented as a cloud-based computing facility.
- the remote processing centre may store the input data.
- the remote processing centre may process the input data, in particular may execute the determining step.
- the remote processing centre may have access to the predictive model.
- the remote processing centre may send output data comprising information on viral infection status of the cell or of the sample.
- the output data may comprise a report containing information on viral infection status of the cell and/or of the sample.
- a system may be provided comprising the computing device as described herein.
- the system may further comprise a DHM.
- the DHM may be a DHM as described herein.
- the DHM comprises a light source emitting at least partially coherent light, an interferometer, and an image sensor.
- the DHM may be provided with a docking element (102) as described herein configured to receive the dismountable conduit for the passage of a sample flow.
- MCPs measured cellular parameters
- Filtering was applied to improve the quality of data set according to the following criteria of MCP: cell diameter >15 (retain) - remove debris/small cells; cell diameter ⁇ 30 (retain) - remove clusters; peak height >3 (retain) - retain only cells with good viability; best depth feature ⁇ 5 (retain) - keep only cells in focus.
- 75% of the filtered data was applied to machine learning algorithms to train a classifier to obtain a random forest tree (RFT). A total of 20 trees with a depth of 10 were used. Using the trained algorithm on the remaining 25% of the filtered data, the rate of falsely predicted infected cells was found to be low and the rate of true predicted healthy cells was found to be high.
- the RFT ranked the discriminatory power of each measured cellular parameter according to Table 2.
- the method of the invention was used to monitor virus load of cells in flowing suspension from a bioreactor in real time. Two different batches of SF9 cells were grown in a bioreactor in liquid medium. The virus load of the cells was monitored using a method of the invention by pumping the suspension cells from the bioreactor through a flow cell having a transparent part for the passage of light for obtaining real-time images by DHM. The cells were returned to to the bioreactor. Simultaneously monitored were total cell density (TCD), viable cell density (VCD), and viability. At -90 hours (arrow B), an aliquot of Baculovirus virus was introduced into the bioreactor. The method of the invention detected an exponential increase in virus load (see FIGs. 8 (Batch 1 ) and 9 (Batch 2)).
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US16/976,429 US12055540B2 (en) | 2018-03-15 | 2019-03-15 | Digital holographic microscopy for determining a viral infection status |
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