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    • 专利摘要:
    The invention is a method for analyzing microorganisms (10i), the microorganisms being arranged in a sample, the sample comprising a viability marker (10v), capable of modifying an optical property of the microorganisms differently depending on whether they are dead or alive, the method comprising the illumination of the sample and the acquisition of an image of the latter by an image sensor, the image sensor then being exposed to a so-called exposure light wave. The method comprises the following steps: ▪ determination of positions (xi, yj) of different microorganisms (10i); From the acquired image (I0) by the image sensor, application of a propagation operator (h), so as to calculate at least one characteristic quantity (M, φ) of the light wave of exposure (14), at each radial position (xi, yj), and at a plurality of distances (z) from the detection plane (P0); ▪ formation of a profile (Mi (z), φi (z)), representing an evolution of the characteristic quantity between the image sensor and the sample; ▪ identification of living microorganisms (10i, a) according to each profile
    公开号:FR3066503A1
    申请号:FR1754535
    申请日:2017-05-22
    公开日:2018-11-23
    发明作者:Thomas Bordy；Olivier CIONI；Camille Deforceville；Ondrej Mandula
    申请人:Commissariat a lEnergie Atomique CEA；Commissariat a lEnergie Atomique et aux Energies Alternatives CEA；
    IPC主号:
    专利说明:

    Method for analyzing microorganisms
    Description
    TECHNICAL AREA
    The technical field of the invention is the characterization of microorganisms, in particular the characterization of yeasts or bacteria.
    PRIOR ART
    The use of microorganisms such as yeasts or bacteria, or their derivatives, finds many applications in many fields. In the food industry, for example, the use of yeasts is widespread in different sectors, such as baking, wine production, brewery or even the manufacture of dairy products. The application of yeasts or bacteria concerns many foods by means of probiotics, the latter being for example added to cereals or to animal feeds. Besides the food industry, many industrial fields can use microorganisms. This is for example agriculture or horticulture, with the development of phytosanitary products or fertilizers more respectful of the environment, or even the production of biofuels obtained from plants. Other applications relate to the field of pharmacy and medical diagnosis.
    For quality control purposes, the characterization step of such microorganisms constitutes an essential link in the production chain. Microbiological controls are frequently used, on samples taken from culture media, in order to detect and enumerate living microorganisms. Cultivation on petri dishes is still widely used, but has certain drawbacks, in particular the preparation, the duration of the analysis and the impossibility of detecting living and non-cultivable microorganisms.
    Direct counting techniques have been developed, using for example viability markers capable of varying the optical properties of microorganisms in different ways depending on whether they are dead or alive. Such markers can act on the color or a fluorescence property of the microorganisms examined. However, the detection step, carried out under a microscope, is generally long, the field of observation being weak.
    The publication Feizi Rapid, portable and cost effective yeast cell viability and concentration analysis using lensfree on-chip microscopy and machine learning, Lab Chip, 2016, 16, 43504358, describes a method of characterizing yeast Saccharomyces cerevisiae, based on lensless microscopy . The device presented is simple, and makes it possible to discriminate dead yeasts from living yeasts by offering a high field of observation. Methylene blue is mixed beforehand with the culture medium in which the examined yeasts are immersed. The method comprises the acquisition of an image of the culture medium by an image sensor, and the application of a holographic propagation operator to the acquired image by considering multiple propagation distances, so as to perform a digital focus, which makes it possible to establish a so-called optimal distance. When the optimal distance is obtained, each yeast is classified according to a living or dead category according to an indicator established from an image reconstructed at said optimal distance.
    The inventors have proposed an alternative to this method, so as to carry out a classification between dead and living microorganisms according to a simple process and inexpensive in computing time. Furthermore, as described in the description, the method allows a more successful analysis of living microorganisms.
    STATEMENT OF THE INVENTION
    A first object of the invention is a method for analyzing microorganisms, the microorganisms being arranged in a sample, the sample comprising a viability marker, capable of modifying an optical property of the microorganisms in a different manner depending on whether they are dead. or living, the process comprising the following stages:
    a) illumination of the sample using a light source, the light source emitting an incident light wave propagating towards the sample along a propagation axis;
    b) acquisition, using an image sensor, of an image of the sample, formed in a detection plane, the sample being placed between the light source and the image sensor, each image being representative of a light wave called exposure, to which the image sensor is exposed under the effect of illumination;
    the method being characterized in that it also comprises the following steps:
    c) determination of radial positions of different microorganisms in a plane parallel to the detection plane, each radial position being associated with a microorganism;
    d) from the image acquired during step b), application of a propagation operator, so as to calculate at least one quantity characteristic of the light exposure wave, at each radial position determined during step c), and at a plurality of distances from the detection plane;
    e) formation of a profile, representing an evolution of the characteristic quantity calculated during step d) along an axis parallel to the axis of propagation and passing through each radial position determined during step c), each profile being associated with a microorganism;
    f) as a function of each profile formed during step e), identification of living microorganisms.
    By characteristic quantity is meant, for example, a module or a phase of the exposure light wave, or a combination thereof.
    By application of a propagation operator from an image, it is meant that the propagation operator is applied to said image or to an image resulting from a transformation of said image, for example a square root of said image, as well as a possible normalization of the image.
    Step f) may include an identification of the dead microorganisms.
    According to one embodiment, following step f), a step g) of analyzing the faculty of the microorganism to divide, step g) comprising the following sub-steps for at least one microorganism considered as living during step f):
    gi) obtaining a first observation image of the sample, at a first instant, the observation image comprising regions of interest respectively associated with microorganisms, and detection of a region of interest associated with said microorganism;
    gii) acquisition of an image of the sample at a second instant, after the first instant, and obtaining a second observation image of the sample from the image acquired at the second instant;
    giii) detection, on the second observation image, of a region of interest corresponding to the microorganism;
    giv) comparison of the regions of interest detected during sub-steps gi) and giii);
    gv) determination of the ability of the microorganism to divide as a function of the comparison carried out during the sub-step giv).
    Step g) then makes it possible to identify, among the microorganisms identified as being living, the viable and non-cultivable microorganisms. It can be applied to each microorganism considered to be living following step f).
    During sub-steps gi) and gii), the first observation image and the second observation image can be obtained by application of a propagation operator respectively from an image acquired at the first instant and from d 'an image acquired at the second instant. The first observation image can result from the image acquired during step b).
    The time interval between the first instant and the second instant can be between 5 hours and 70 hours.
    According to one embodiment, during step d) the propagation operator is applied from the acquired image, during step b), according to a plurality of propagation distances, so as to obtain a stack complex images.
    According to another embodiment, step d) comprises the following sub-steps:
    di) application of a propagation operator, from the image acquired during step b) so as to calculate a complex image, called the reference image, representative of the sample, in a reference plane;
    dii) application of a propagation operator to the reference image, so as to obtain complex images, called secondary complex images, at different distances from the reference plane along the propagation axis, the secondary complex images and the reference image forming a stack of complex images;
    diii) determination of a radial position of microorganisms from images of the stack of complex images obtained during sub-step dii).
    According to this embodiment, the first observation image can come from the reference image, for example being the image of the module or the image of the phase of the reference image.
    Step c) can be implemented from a complex image of the complex image stack resulting from step d), or from the reference complex image, considering the module or phase of said complex image.
    According to one embodiment, the viability marker induces coloration of the microorganisms when they are dead, according to a spectral band of coloration. During step a), the illumination of the sample is carried out according to an illumination spectral band, the spectral illumination band not comprising all or part of the spectral coloring band.
    The process may include one of the following characteristics, taken in isolation or in technically feasible combinations:
    in step d), the characteristic quantity is determined from the module or from the phase of the exposure light wave, at each distance from the detection plane. Step f) comprises a classification of each profile according to a first class, corresponding to profiles characteristic of living microorganisms and a second class, corresponding to profiles characteristic of dead microorganisms. The classification of each profile can be carried out according to its form or a maximum value or a minimum value of the profile.
    No imaging optics are disposed between the sample and the image sensor.
    An optical imaging system is disposed between the sample and the image sensor, the optical system having an object focal plane, the sample lying in a plane of the sample, the plane of the sample. being offset from the object focal plane.
    Another object of the invention is a device for the analysis of microorganisms arranged in a sample, the device comprising:
    - a light source capable of emitting an incident light wave propagating towards the sample;
    - an image sensor;
    - a support, configured to hold the sample between the light source and the image sensor;
    a processor, configured to receive an image of the sample acquired by the image sensor and to implement steps c) to f) of a method according to the first object of the invention.
    According to one embodiment, no optical magnification or image formation extends between the image sensor and the sample, when the latter is held on the support. According to another embodiment, an optical imaging system is arranged between the sample and the image sensor, the optical system having an object focal plane, the sample extending in a plane of the sample. , the device being arranged so that the plane of the sample is offset from the object focal plane.
    Other advantages and characteristics will emerge more clearly from the description which follows of particular embodiments of the invention, given by way of nonlimiting examples, and represented in the figures listed below.
    FIGURES
    FIG. 1 represents an example of a device according to the invention.
    FIG. 2 illustrates the main steps of a method making it possible to identify living microorganisms as well as, among these, viable non-cultivable microorganisms.
    FIG. 3A is an image of the module of a complex image reconstructed from an image acquired, at a first instant, from a sample. This image was obtained during an experimental test carried out using a sample comprising yeasts. Figure 3B is detail of Figure 3A. FIGS. 3C and 3D respectively represent amplitude and phase profiles of a light wave reconstructed from the image of FIG. 3B, each profile passing through a radial position, in the plane of the image sensor, with which is associated with yeast.
    FIG. 4A represents a microscopic observation of yeasts. FIG. 4B shows, on a part of the image represented in FIG. 3B, the yeasts considered to be dead and those considered to be living, by implementing the invention. This image corresponds to the state of the yeasts at a first instant. FIG. 4C shows an image obtained by holographic reconstruction from an acquired image, at a second instant. It represents the state of the yeasts at the second instant. The comparison between FIGS. 4B and 4C allows the identification, among living yeasts, of viable and non-cultivable yeasts.
    EXPLANATION OF PARTICULAR EMBODIMENTS
    FIG. 1 represents an example of a device according to the invention. A light source 11 is capable of emitting a light wave 12, called the incident light wave, propagating in the direction of a sample 10, along a propagation axis Z. The light wave is emitted according to a spectral band of illumination Δλ .
    Sample 10 is a sample that we wish to characterize. It notably includes a 10m medium in which 10 έ microorganisms bathe . The 10m medium is generally a culture medium, comprising nutrients allowing the development of microorganisms. The term “microorganism” is understood to mean in particular a yeast, a bacterium, a spore, a fungus or a cell, whether it is a eukaryotic or prokaryotic cell, or a microalga.
    The sample also includes a viability marker 10 v , the latter being capable of modifying an optical property of a microorganism 10; differently depending on whether the microorganism is alive or dead. As mentioned in connection with the prior art, by modification of the visual appearance is meant for example a modification of the color of the microorganism. The use of such viability markers is well known. It can be methylene blue, or trypan blue. The modification of the optical property can also be a modification of the intensity of a fluorescence light emitted by a microorganism analyzed by using for example a viability indicator, sometimes designated by the term fluorogenic marker, examples of such markers being described in WO9855861A1, or in the publication Kwolek-Mirek M comparison of methods used for assessing the viability and vitality of yeast cells, FEMS Yeast Res 14 (2014) 1068-1079.
    In the example described below, the viability indicator 10 v is methylene blue. Under its effect, the dead microorganisms are colored according to a spectral band of coloring Δλ ', in this case blue, while the living microorganisms remain translucent.
    The sample 10 is, in this example, contained in a fluid chamber 15. The fluid chamber 15 is for example a Gene Frame® type fluid chamber of thickness e = 250 μιη. The thickness e of the sample 10, along the propagation axis, typically varies between 10 μιη and 1 cm, and is preferably between 20 μιη and 500 μιη. The sample extends along a plane Ιο> said plane of the sample, perpendicular to the axis of propagation Z. It is maintained on a support 10s at a distance d from an image sensor 16. The concentration microorganisms can vary between 500 per microliter and 5000 per microliter.
    The distance D between the light source 11 and the fluid chamber 15 is preferably greater than 1 cm. It is preferably between 2 and 30 cm. Advantageously, the light source, seen by the sample, is considered to be punctual. This means that its diameter (or its diagonal) is preferably less than a tenth, better a hundredth of the distance between the fluid chamber 15 and the light source. In Figure 1, the light source is a light emitting diode. It is generally associated with diaphragm 18, or spatial filter. The opening of the diaphragm is typically between 5 μιη and 1 mm, preferably between 50 μιη and 500 μιη. In this example, the diaphragm is supplied by Thorlabs under the reference P150S and its diameter is 150 μιη. The diaphragm can be replaced by an optical fiber, a first end of which is placed opposite the light source 11 and a second end of which is placed opposite the sample 10. The device represented in FIG. 1 also includes a diffuser 17 , disposed between the light source 11 and the diaphragm 18. The use of such a diffuser makes it possible to overcome constraints of centering of the light source 11 relative to the opening of the diaphragm 18. The function of such a diffuser is to distribute the light beam produced by an elementary light source 11 according to a cone of angle a. Preferably, the angle of diffusion a varies between 10 ° and 80 °. Alternatively, the light source can be a laser source, such as a laser diode. In this case, it is not useful to associate a spatial filter or a diffuser.
    Preferably, the emission spectral band Δλ of the incident light wave 12 has a width less than 100 nm. By spectral bandwidth is meant a width at half height of said spectral band.
    According to one embodiment, the light source 11 comprises several elementary light sources 11k, each being capable of emitting an incident light wave 12k in a spectral band AAk. Preferably, the spectral bands AAk of the different light sources 11 are different from each other.
    The sample 10 is placed between the light source 11 and the image sensor 16 previously mentioned. The latter preferably extends parallel to, or substantially parallel to, the plane along which the sample extends. The term substantially parallel means that the two elements may not be strictly parallel, an angular tolerance of a few degrees, less than 20 ° or 10 ° being allowed. In this example, the sample extends along an XY plane, perpendicular to the axis of propagation Z.
    The image sensor 16 is able to form an image I o of the sample 10 according to a detection plane P o . In the example shown, it is an image sensor comprising a pixel matrix, of CCD type or a CMOS. The detection plane P o preferably extends perpendicular to the axis of propagation Z of the incident light wave 12. The distance d between the sample 10 and the pixel matrix of the image sensor 16 is preferably between 50 μιη and 2 cm, preferably between 100 μιη and 2 mm.
    In this embodiment, it is noted that there is no optical magnification or image formation between the image sensor 16 and the sample 10. This does not prevent the possible presence of focusing microlenses at the level of each pixel of the image sensor 16, the latter having no function of enlarging the image acquired by the image sensor, their function being to optimize the detection efficiency.
    Under the effect of the incident light wave 12, the microorganismslOj present in the sample can generate a diffracted wave 13, capable of producing, at the level of the detection plane P o , interference, in particular with part of the incident light wave 12 'transmitted by the sample. Furthermore, the sample can absorb part of the incident light wave 12. Thus, the light wave 14, transmitted by the sample, and to which the image sensor 16 is exposed, designated by the term d wave , may include:
    a component 13 resulting from the diffraction of the incident light wave 12 by each microorganism in the sample;
    a component 12 ′ resulting from the transmission of the incident light wave 12 by the sample, part of the latter being able to be absorbed in the sample.
    These components form interference in the detection plane. Also, the image acquired by the image sensor comprises interference figures (or diffraction figures), each interference figure possibly being associated with a microorganism 10; of the sample.
    A processor 20, for example a microprocessor, is able to process each image I o acquired by the image sensor 16. In particular, the processor is a microprocessor connected to a programmable memory 22 in which is stored a sequence of instructions for perform the image processing and calculation operations described in this description. The processor can be coupled to a screen 24 allowing the display of images acquired by the image sensor 16 or calculated by the processor 20.
    An image I o acquired by the image sensor 16, also called a hologram, does not allow a sufficiently precise representation of the observed sample to be obtained. As described in connection with the prior art, a holographic propagation operator h can be applied to each image acquired by the image sensor, so as to calculate a quantity representative of the exposure light wave 14. II it is then possible to reconstruct a complex expression A of the light wave 14 at any point of coordinates (x, y, z) of space, and in particular in a reconstruction plane P z located at a distance z from image sensor 16, called the reconstruction distance, this reconstruction plane preferably being the plane along which the sample extends io> with:
    A (x, y, z) = I 0 (x, y, z) * h * denoting the convolution product operator, or, and preferably, A (x, y, z) = fl 0 (x, y , z) * h, or:
    A (x, y, z) = v 0 * h, I o being an average of the acquired image.
    io
    The function of the propagation operator h is to describe the propagation of light between the image sensor 16 and a point of coordinates (x, y, z), located at a distance | z | of the image sensor. It is then possible to determine the module M (x, y, z) and / or the phase φ (x, y, z) the light wave 14, at this distance | z |, called the reconstruction distance, with:
    M (x, y, z) = abs [A (x, y, z) J;
    - φ (χ, y, z) = arg [ri (x, y, z)];
    The operators abs and arg respectively designate the module and the argument.
    The propagation operator is for example the Fresnel-Helmholtz function, such that: h (x, y, z) = - ^ e '^ expC / Tr ^ ®.
    In other words, the complex expression A of the light wave 14, at any point of coordinates (x, y, z) of space, is such that: A (x, y, z) = M (x, y , z) eD < ' x ' y, z (
    In the remainder of this description, the coordinates (x, y) designate a radial position in a radial plane XY perpendicular to the axis of propagation Z. The coordinate z designates a coordinate along the axis of propagation Z.
    The complex expression A is a complex quantity whose argument and modulus are respectively representative of the phase and the intensity of the light wave 14 of exposure detected by the image sensor 16. The convolution product of the image I o by the propagation operator h makes it possible to obtain a complex image A z representing a spatial distribution of the complex expression A in a reconstruction plane P z , extending at a distance | z | of the detection plan P o . In this example, the detection plane P o has the equation z = 0. The complex image A z corresponds to a complex image of the sample in the reconstruction plane P z . It also represents a two-dimensional spatial distribution of the optical properties of the exposure wave 14. Such a process, designated by the term holographic reconstruction, makes it possible in particular to reconstruct an image of the module or of the phase of the exposure light wave 14 in the reconstruction plan
    It is possible to form images M z and <p z representing respectively the module or the phase of a complex complex image A z in a plane P z located at a distance | z | of the detection plane P o , with M z = mod (A z ) and φ ζ = arg (A z ).
    The inventors have developed a method for characterizing microorganisms, for example yeasts, this method being described in connection with FIG. 2, and of which certain results are illustrated in FIGS. 3A to 3D and 4A to 4C. These results were obtained according to the following experimental conditions:
    Sample 10: it comprises Active Dry Yeast (ADY) dry yeasts dispersed in a Sabouraud culture medium, to which a methylene blue solution (0.1 mg.ml 1 ) has been added. The concentration is around 1500 yeasts per μΙ.
    Light source 11: light emitting diode Created MC-E Color, comprising three light emitting diodes which can be simultaneously or successively activated, each diode emitting respectively in the following spectral bands Δλ:
    450nm - 465nm; 520nm - 535nm; 620nm - 630nm. In the following tests, only the 620nm - 630nm spectral band was used.
    Image sensor 16: 8-bit monochrome CMOS sensor 3884 x 2764 pixels, each pixel measuring 1.67 µm side, the detection surface extending over approximately 30 mm 2 . Given the thickness of the fluid chamber, the volume of sample sent by each image is 7.5 μl.
    Distance D between the light source 11 and the sample 10: 5 cm.
    Distance d between the sample 10 and the image sensor 16: 1000 μm.
    Thickness e of the fluid chamber 15: 250 μm.
    Diameter of the opening of the spatial filter 18: 150 μm.
    The main steps of the numbering method according to the invention are:
    The acquisition, by the image sensor, of an image I o of the sample in one or more spectral illumination bands, the image being acquired at a first instant ti.
    From the acquired image, obtaining a first observation image Z (t x ), corresponding to the first instant, then, from this observation image, the detection of regions of interest ROIi corresponding microorganisms 10; and determining their respective radial coordinates, the term radial signifying in a plane parallel to the detection plane.
    From the acquired image, the calculation of a characteristic quantity, for example the module or the phase, of the exposure light wave 14, at different distances from the sample, called reconstruction distances; this notably involves obtaining a stack of complex images representing the light wave of exposure, the complex images extending along parallel planes arranged between the detection plane P o and the plane of the sample w ·
    The formation of a profile representing an evolution of the characteristic quantity as a function of the reconstruction distance, each profile being associated with a microorganism.
    From one or more profiles associated with each microorganism, the identification of living or dead microorganisms.
    The method may include the acquisition of an image of the sample at a second instant t 2 , the second instant being after the first instant t x , and obtaining a second observation image / (t 2 ) associated with the second instant. The comparison of the observation images respectively associated with the first and the second instant makes it possible to identify, among the microorganisms considered as living, the viable non-cultivable microorganisms.
    The notion of viable and non-cultivable microorganism (VNC) is known to those skilled in the art. It is a living microorganism because it has a metabolic activity, but cannot divide in the environment in which it is immersed. It cannot therefore develop in its environment. The VNC state can be influenced by various factors, for example temperature, the content of the environment or the culture medium in certain nutrients or chemical elements, exposure to light, all of which can induce stress to the microorganism. . The identification of VNC microorganisms allows a rigorous characterization of the quality of the sample considered.
    Step 100: Acquisition of an image I o of the sample 10 by the image sensor 16, this image forming a hologram. One of the advantages of the configuration without lens, represented in FIG. 1, is the wide field observed, making it possible to simultaneously address a high volume of sample. This makes it possible to observe several microorganisms simultaneously, and thus to obtain rapid characterization of the sample. The field observed depends on the size of the image sensor, being slightly smaller than the detection surface of the latter, due to the spacing between the pixels of the sensor and the sample. The field observed is generally greater than 10 mm 2 , and is typically between 10 mm 2 and 50 mm 2 , which is significantly higher than with a microscope. In this example, this image is acquired at a first instant t ± and can be noted / oCG) ·
    Step 110: Formation of a reference image. Due to the absence of optical imaging, the acquired image I o may include a large number of interference patterns, and may not be easily exploitable to locate the microorganisms present in the observed field. The latter are more easily identifiable from a complex image reconstructed by applying a holographic propagation operator to the acquired image. In this example, this complex image is a reference image A re j, obtained by performing a holographic reconstruction in a reference plane P re f to an image obtained from the acquired image I o .
    A first solution is to apply the propagation operator to the acquired image I o , or preferably to the square root of the acquired image possibly normalized by the average value I o of the acquired image. The reference image A re f is a complex image comprising phase and amplitude information of the light wave 14 to which the image sensor is exposed 16. The reference plane is a plane advantageously perpendicular to the propagation axis Z, and / or parallel to the detection plane P o . It is preferably the plane of the sample W · Indeed, it is generally in this plane that the spatial resolution of a complex reconstructed image is the best, such a principle being at the basis of the so-called algorithms of digital focus.
    However, the acquired image does not include any information relating to the phase of the exposure wave 14. As a result, the holographic reconstruction is carried out on the basis of incomplete optical information, based solely on the intensity of the light wave collected on the image sensor. The improvement of the quality of the holographic reconstruction has been the subject of numerous developments, by implementing algorithms frequently called “phase retrieval”, allowing an estimation of the phase of the light wave at which the image sensor is exposed. This type of algorithm makes it possible to limit the reconstruction noise affecting the complex reconstructed image A re f. An example of a usable algorithm is for example described in US2012 / 0218379.
    According to one possibility, the sample is illuminated successively or simultaneously in different spectral bands and one acquires, in the detection plane P o , an image / 0 (ÙÂfc) representative of each spectral band. The algorithm makes it possible to obtain a complex image 24 re ^ (AÂ fc ) of the sample 10, in the reference plane, in each spectral band AÀ k . The complex images thus obtained can be combined, for example by averaging, in each pixel, their module and their phase, which makes it possible to form the reference image A re f. Alternatively, the complex reference image is a complex image AygfÇAX ^ in a spectral band. Such an algorithm has been described in the publication SNA Morel, A. Delon, P. Blandin, T. Bordy, O. Cioni, L. Hervé, C. Fromentin, J. Dinten, and C. Allier, Wide-Field Lensfree Imaging of Tissue Slides, in Advanced Microscopy Techniques IV; and Neurophotonics II, E. Beaurepaire, P. So, F. Pavone, and E. Hillman, eds., Vol. 9536 of SPIE Proceedings (Optical Society of America, 2015) as well as in patent application FR1554811 filed on May 28, 2015, and more specifically in iterative steps 100 to 500 described in this application. It has been shown that the use of two or three different spectral bands makes it possible to obtain a good quality of reconstruction.
    Another possibility, which corresponds to the preferred embodiment, is to reconstruct a complex reference image based on an image acquired from the sample when the latter is illuminated in a single spectral band Δλ. The complex reference image can be obtained using an iterative algorithm as described in patent application FR1652500 filed on March 23, 2016, and more precisely according to steps 110 to 160 described in said patent application.
    The coordinate z re ^ of the reference plane Pref is determined either a priori, in particular when the position of the sample is controlled relative to the image sensor 16, or by means of digital focusing. Digital focusing allows you to define a Pfocus focusing plane reconstructing several images and defining a sharpness criterion for each reconstructed image. The focal plane Pf 0CU s corresponds to that in which the reconstructed image presents an optimal criterion of sharpness. It corresponds to the plane in which a majority of the microorganisms extend. The reference image A re f is then formed in a reference plane Pref corresponding to the focusing plane Pf 0CUS · The focusing plane corresponds to a plane along which the sample extends.
    The complex image A re f is designated as a reference image because it serves as the basis for the formation of profiles on the basis of which the microorganisms of the sample are characterized. FIG. 3A shows an image of the module M re f of the complex reference image obtained by implementing the algorithm described in the previous paragraph.
    Step 120: construction of a stack of images.
    This step includes an application of the propagation operator h to the complex reference image A re f so as to calculate complex images A re ^ z , called secondary, along the propagation axis Z. Au during this step, the complex reference image A re f is propagated according to a plurality of reconstruction distances z, using a propagation operator h as defined above, so as to have a plurality of complex images, said secondary, A re ^ z reconstructed at different distances z from the reference plane Pref- Thus, this step includes the determination of a plurality of complex images A re ^ z such that:
    Aref.z A r ef * h z with z min - z - z max We thus obtain a stack of complex images A re f Zmin Α ^ Zmax .
    The values z min and. z max are the minimum and maximum coordinates, along the Z axis, between which the complex reference image is propagated. Preferably, the complex images are reconstructed according to a plurality of z coordinates between the sample 10 and the image sensor 16. The inventors considered that it was preferable to obtain secondary complex images on either side of the reference plane P re f, so that z min <z re f <
    z max . Unlike the image acquired I o by the image sensor 16, the complex reference image correctly describes the exposure light wave 14, in particular at its phase. Consequently, it is estimated that the secondary images A re ^ z , obtained by propagation of the reference image, form a good descriptor of the propagation of the light exposure wave 14 along the propagation axis Z. Thus, the secondary complex images are calculated quickly, without requiring the implementation of an iterative process such as that used to calculate the complex reference image A re f. The method consisting in applying an iterative algorithm to establish a complex reference image A re f (step 110) then, to obtain secondary complex images by application of a propagation operator h to the complex reference image, makes it possible to 'obtain a stack of complex images A re ^ z by optimizing the means of calculation.
    Preferably, two adjacent reconstruction planes are spaced from each other according to a fine mesh, for example between 5 pm and 50 pm, and for example 25 pm. It is a local propagation, since it is carried out at a distance of between 500 μm and 2 μm on either side of the reference plane P re f, for example ± 500 μm. Based on a reconstruction according to a distance of 500 μm on either side of the reference plane P-refi and a distance between two adjacent planes of 20 μm, the complex reference image A re f is propagated according to forty planes of reconstruction Pref, z> so as to form as many secondary complex images, Aref.z Alternatively, steps 110 and 120 can be replaced by a propagation from the image I o acquired during step 100, according to step 110 ′ shown in FIG. 2. The propagation can be carried out for example by applying a propagation operator to the square root of this image, possibly normalized by the mean value I o , according to different propagation distances z x , ... z n . It may be a simple application of a propagation operator h, in which case the reconstructed images A Z1 A Zn , forming the stack of complex images, may be affected by significant reconstruction noise. It can also be an implementation of an iterative holographic reconstruction algorithm, for example one of the algorithms previously mentioned, according to different propagation distances z x , ... z n . This makes it possible to obtain complex reconstructed images of good quality, but the method is more costly in terms of calculation. This is why it is preferable to implement an iterative reconstruction algorithm to form the complex reference image, according to step 110, and to propagate the complex reference image by a simple application of a propagation operator. , according to step 120. This makes it possible to obtain a good compromise between the quality of the images forming the stack of complex images and the computation time.
    Step 130: detection of microorganisms in the sample.
    This step aims to detect each microorganism present in the sample. It is performed from an observation image IÇtfi), called the first observation image, formed from an image IoÇtfi) acquired by the image sensor at a first acquisition instant iq, or from a complex image formed from it. Preferably, without this being necessary, the image IoÇtfi) corresponds to the image I o acquired during step 100 and the first observation image / (t x ) results from this acquired image I o , or of a complex image of the stack of images formed during step 120. It is preferable that the observation image IÇtfi) is established from the module and / or from the phase of a reconstructed complex image in a plane along which the sample extends. It can for example be the complex reference image A re f obtained during step 110 or a secondary complex image A re ^ z resulting from step 120. The first observation image can be the image of the module M re f of the complex reference image A re p or the image M re f Z of the module of another complex image of the stack of images formed during step 120. It can also be the image <p re f of the phase of the complex reference image A re p or the image <p re f iZ of the phase of another complex image of the image stack formed during step 120. Generally, the observation image is obtained from the module and / or from the phase of a complex image resulting from a propagation of an image I ü acquired by the image sensor 16.
    The detection of each microorganism is carried out either manually by an operator, or automatically, by a morphological analysis on the first observation image taking into account the fact that each microorganism 10; can be associated, in the observation image, with a region of interest ROfi of predetermined shape, which can be easily detected. It may for example be a circular or ellipsoidal shape, or the like. For this, the method can automatically detect each region of interest ROfi by taking into account one or more morphological criteria corresponding to a microorganism 10 Î7 for example its area and its eccentricity. At least one ROfi region of interest detected corresponds to at least one microorganism 10 ;. Algorithms based on a spatial correlation with forms of predetermined regions of interest can also be implemented. The volume of the sample 10 in the field of observation of the image sensor 16 being known, this step allows an estimation of an amount Ni or of a concentration of microorganisms 10; in the sample.
    FIG. 3A corresponds to an image of the module M re f of a complex reference image of a sample described below, in connection with the examples. Figure 3B corresponds to a detail of Figure 3A.
    Step 140: detection of the radial coordinates (% ;, yj) of each microorganism.
    The microorganisms 10; being detected, via the regions of interest ROf which are respectively associated with them on the first observation image Z (t x ), their position (x ^ yj) in the radial plane XY, that is to say say in a plane parallel to the detection plane, can be easily determined, by considering for example the centroid of the region of interest ROf corresponding to each of them.
    Step 150: formation of a profile associated with each microorganism.
    From each complex image forming the image stack, a quantity characteristic of the exposure light wave 14 is estimated, at each radial position (% ;, yj) selected during step 140, and at a plurality of distances z for reconstruction of the reference plane P re y, or of the detection plane P o , then a profile is created representing an evolution of the characteristic quantity as a function of z, along the propagation axis Z. The characteristic quantity can in particular be established from the module and from the phase of the complex expression A describing the exposure light wave 14, using the images of the complex image stack previously resulting from steps 120 or 110 ′.
    FIGS. 3C and 3D respectively represent a profile M; (z) of the module and a profile <pi (z) of the phase, passing through the radial positions (% ;, yj) selected on image 3B. Each profile is obtained from the images of the previously established complex image stack, by interpolating between the coordinates of two adjacent reconstructed images. Each profile is associated with a microorganism 10 ;.
    Etapel60: classification of each microorganism from the profiles formed during step 150.
    Tests have shown that when a viability marker is previously introduced into the sample, the profiles Aij (z) describing the evolution of the modulus of the exposure light wave can allow a classification between the dead microorganisms 10 id or living microorganisms 10; a . These profiles are shown in Figure 3C. In this figure, the coordinate z = 20 corresponds to the focusing plane, that is to say to the plane along which the microorganisms extend. A classification criterion is for example the maximum value taken by each profile Mj (z) between the detection plane and the focusing plane. In FIG. 3C, this is the maximum value on a part of each profile Mj (z) extending between the coordinates z = Oetz = 20. When the maximum value of a profile is less than a predetermined threshold, the microorganism is classified as dead. Otherwise, the microorganism is classified as living. In FIGS. 3C and 3D, the profiles corresponding to the microorganisms considered to be dead and alive are shown respectively in dotted lines (legend d) and in dashes (legend a).
    The method preferably includes steps 170 and 180 aimed at identifying, among the microorganisms considered to be living at the end of step 160, the viable non-cultivable microorganisms (VNC) previously mentioned.
    Step 170: acquisition of a delayed image I 0 Çt 2 ).
    During step 130, a first observation image / (ί χ ) was formed, representing the sample at a first instant, this first instant being noted t ± . Step 170 comprises an acquisition, at a second acquisition time t 2 , subsequent to the first acquisition time t x , of a second image I 0 Çt 2 ), called deferred image, of the sample 10 to 1 using the image sensor 16. The inventors observed that it was preferable that the time interval Δί between the first instant t ± and the second instant t 2 be greater than 4 hours, and preferably greater than 15 hours or 20 hours. For example, the time interval Δί is between 4 and 72 hours, this interval being adjusted as a function of the time necessary for a division of the microorganism considered.
    Step 180: formation of a delayed observation image and classification of living microorganisms.
    On the basis of the image acquired at the second acquisition time, a second observation image / (i 2 ) is formed. The second observation image / (i 2 ) is preferably formed in the same way as the first observation image / (ii), so that the microorganisms are comparable on these two images. From the second observation image, the microorganisms 10 are observed; α considered to be alive following step 160. Among these, we identify microorganisms whose morphology has varied compared to that observed in the first observation image / (ii). The variation in morphology, obtained by comparison between the first and the second observation image, must reflect at least one division of the microorganism during the time interval Δί. It can be determined manually or automatically, for example on the basis of a comparison, between the two observation images / (t x ) and / (t 2 ), of the area or of a shape parameter of the ROIi region of interest associated, on each of these images, with each living microorganism. For example, when the area of a region of interest ROIi has increased by more than 20% between the two images, it is considered that the microorganism 10; has split or a division is in progress. In this case, it is considered to be alive and able to divide it, therefore cultivable. Otherwise, it is considered viable non-cultivable.
    Figures 4B and 4C represent respectively:
    the first observation image / (G), described in connection with step 110, which corresponds to the module M re f of the complex reference image A re j;
    a second observation image / (t 2 ), obtained from an image / 0 (t 2 ) acquired at a second instant t 2 posterior to 10 hours at the first instant t ± . The second observation image is the modulus of a complex image obtained from image / 0 (t 2 ), in the same way as the complex reference image A re f.
    In FIG. 4B, the microorganisms 10 ί α considered to be living are surrounded by a white frame, while the microorganisms 10; d considered dead are surrounded by a white circle. At the radial position (% ;, y,) corresponding to a microorganism 10; was considered living, a comparison is made between the ROII interest region extending around the radial position of the first and second moments. On the basis of this comparison, the microorganisms 10 ία1 considered as living and cultivable are surrounded by a solid frame in FIG. 4C while the microorganisms 10 2 α2 considered as viable non-cultivable are surrounded by a dotted frame.
    The invention then allows a count of three categories of microorganisms: dead, living and cultivable, viable non-cultivable.
    The inventors have found that the performance of the process is increased when the spectral band of illumination Δλ is different from the spectral band of coloration Δλ 'induced by the viability marker. Thus, when the viability marker is methylene blue, the spectral illumination band is preferably located in red or in infrared. Thus, it is preferable that the spectral band of illumination and the spectral band of coloring do not overlap, or overlap marginally. By marginally overlapping is meant that the intersection between the two spectral bands is less than 10% or 20% of one of the two spectral bands.
    The process described above was compared with a visual characterization of the microorganisms, the latter being observed under the microscope by an operator. FIG. 4A represents a microscope view at the second instant t 2 . Visual characterization was used as a reference method, to qualify the process according to the invention.
    The following table shows the percentages of microorganisms considered dead, live and culturable, viable non-culturable, respectively obtained as described above (first line) and the microscope (2 nd line).
    dead living cultivable viable non-cultivable algorithm 15 34 51 reference 12 24 64
    The high proportion of viable non-cultivable yeasts is explained by a high temperature to which the sample was brought, generating a stress favoring the viable non-cultivable state.
    The reliability of the process is therefore comparable to a conventional visual characterization carried out under a microscope. However, due to the configuration in lensless imaging, the field observed is clearly greater than the field observed resulting from the use of a microscope. The invention has for example enabled a simultaneous characterization of 11,574 yeasts distributed in the field of observation. The performance of the invention is therefore superior to the visual process in terms of the quantity of microorganisms characterized per unit of time. In addition, the algorithm can be automated, reliability being demonstrated by the values in the previous table.
    According to a variant, an image-forming optic is disposed between the sample and the image sensor, the image sensor being located in a so-called defocused configuration, the focal plane of the optics being offset from the plane according to which extends the sample according to a so-called defocus distance. The defocus distance can be between 5 pm and 5 mm, and preferably between 10 pm and 2 mm. In the same way as in a configuration without a lens, such a configuration makes it possible to obtain an image in which each microorganism appears in the form of a diffraction figure, interference occurring between the light wave emitted by the source. of light and propagating to the image sensor and a diffraction wave generated by each microorganism. The method described in connection with steps 100 to 180 is applicable to images acquired according to such a configuration. However, a configuration in lensless imaging is preferred, by the greater field of observation that it provides.
    Although described with respect to a characterization of yeasts, for quality control purposes, the invention applies to other organisms such as those previously listed, since it is desired to obtain a rapid and reliable analysis in an important field of observation.
    权利要求:
    Claims (15)
    [1" id="c-fr-0001]
    1. Method for the analysis of microorganisms (10;), the microorganisms being placed in a sample, the sample comprising a viability marker (10 v ), capable of modifying an optical property of the microorganisms in different ways depending on whether they are dead or alive, the process comprising the following stages:
    a) illumination of the sample using a light source (11), the light source emitting an incident light wave (12) propagating towards the sample (10) along a propagation axis (Z) ;
    b) acquisition, using an image sensor (16), of an image (/ 0 ) of the sample (10), formed in a detection plane (P o ), the sample being disposed between the light source (11) and the image sensor (16), each image being representative of a light wave (14) of exposure, to which the image sensor (16) is exposed under the effect of illumination;
    the method being characterized in that it also comprises the following steps:
    c) determining radial positions (% ;, yj of different microorganisms (10;) in a plane parallel to the detection plane, each radial position being associated with a microorganism;
    d) from the image acquired (/ 0 ) during step b), application of a propagation operator (h), so as to calculate at least one characteristic quantity (Μ, φ) of the wave exposure light (14), at each radial position (X;, yj) determined during step c), and at a plurality of distances (z) from the detection plane (P o );
    e) formation of a profile (M; (z), ç ; (z)), representing an evolution of the characteristic quantity calculated during step d) along an axis parallel to the axis of propagation (Z) and passing through each radial position (X;, yj) determined during step c), each profile being associated with a microorganism (10;);
    f) as a function of each profile formed during step e), identification of living microorganisms (10 ία ).
    [2" id="c-fr-0002]
    2. Method according to claim 1, comprising, following step f), a step g) of analysis of the ability of the microorganism to divide, step g) comprising the following substeps for at least one microorganism considered alive during step f):
    gi) obtaining a first observation image (/ (ti)) of the sample, at a first instant (t x ), the observation image comprising regions of interest (KO / j) respectively associated microorganisms (ΙΟ;) and detection of a region of interest (ROIi) associated with said microorganism (10d a );
    gii) acquisition of an image of the sample (/ 0 (^ 2)) θ at a second instant (t 2 ), the second instant being after the first instant, and obtaining a second observation image (/ ( t 2 )) of the sample from the image (/ 0 (/ 2)) of the sample acquired at the second instant;
    giii) detection, on the second observation image, of a region of interest (ROIi) corresponding to the microorganism (10j a );
    giv) comparison of the regions of interest detected during sub-steps gi) and giii);
    gv) determination of the ability of the microorganism to divide as a function of the comparison carried out during the sub-step giv).
    [3" id="c-fr-0003]
    3. Method according to claim 2, in which the sub-step gv) comprises the identification, among living microorganisms (10 ία ), of viable and non-cultivable microorganisms (10ί, α2 ) ·
    [4" id="c-fr-0004]
    4. Method according to claim 2 or claim 3, wherein during sub-steps gi) and gii), the first observation image (/ (tj) and the second observation image (/ (t 2 )) are obtained by applying a propagation operator (h) respectively from an image (/ 0 (/ 1)) acquired at the first instant and from the image (/ 0 (/ 2)) acquired at the second moment.
    [5" id="c-fr-0005]
    5. Method according to any one of claims 2 to 4 in which during the sub-step gi), the first observation image (/ (t x )) is obtained from the acquired image (/ 0 ) during step b).
    [6" id="c-fr-0006]
    6. Method according to any one of claims 2 to 5, wherein the time interval (Δ /) between the first instant and the second instant is between 5 hours and 70 hours.
    [7" id="c-fr-0007]
    7. Method according to any one of the preceding claims, in which during step d) the propagation operator is applied from the image acquired (/ 0 ) during step b), according to a plurality of propagation distances, so as to obtain a stack of complex images.
    [8" id="c-fr-0008]
    8. Method according to any one of claims 1 to 6 in which step d) comprises: the following substeps:
    di) application of a propagation operator (h), from the image (/ 0 ) acquired during step b) so as to calculate a complex image (A re f), called the reference image, representative of the sample, in a reference plane (/ Ve /) '>
    dii) application of a propagation operator (h) to the reference image (A re f), so as to obtain complex images, called secondary complex images GVe / .z), θ different distances (z) from the plane of reference (P re ^) along the propagation axis (Z), the secondary complex images and the reference image forming a stack of complex images;
    diii) determination of a radial position (X;, yj) of microorganisms (10;) from the images of the stack of complex images obtained during the sub-step dii).
    [9" id="c-fr-0009]
    9. Method according to any one of the preceding claims, in which the viability marker (10 v ) induces coloring of the microorganisms when they are dead, according to a spectral coloring band (Δλ 1 ), and in which during the step a), the illumination of the sample is carried out according to a spectral illumination band (Δλ), the spectral illumination band not comprising all or part of the spectral coloring band.
    [10" id="c-fr-0010]
    10. Method according to any one of the preceding claims, in which in step d), the characteristic quantity is determined from the module or from the phase of the exposure light wave, at each distance from the detection plane. .
    [11" id="c-fr-0011]
    11. Method according to any one of the preceding claims, in which step f) comprises a classification of each profile (M; (z), </>; (z)) according to a first class, corresponding to characteristic profiles. of living microorganisms (10 ία ) and a second class, corresponding to characteristic profiles of dead microorganisms (io iid ).
    [12" id="c-fr-0012]
    12. The method of claim 11 wherein the classification of each profile (M; (z), </> j (z)) is carried out according to its form or a maximum value or a minimum value of the profile .
    [13" id="c-fr-0013]
    13. Method according to any one of the preceding claims, in which no image-forming optics are arranged between the sample and the image sensor.
    [14" id="c-fr-0014]
    14. Method according to any one of claims 1 to 12, in which an optical image-forming system is arranged between the sample and the image sensor, the optical system having an object focal plane, the sample s extending in a plane of the sample, the plane of the sample being offset with respect to the object focal plane.
    [15" id="c-fr-0015]
    15. Device for the analysis of microorganisms (10;) arranged in a sample (10), the device comprising:
    - a light source (11) capable of emitting an incident light wave (12) propagating towards the sample (10);
    - an image sensor (16);
    - a support (10s), configured to hold the sample (10) between the light source
    5 (11) and the image sensor (16);
    a processor (20), configured to receive an image (/ 0 ) of the sample acquired by the image sensor (16) and to implement steps c) to f) of a method according to any one of claims 1 to 14.
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    同族专利:
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    EP3631416A1|2020-04-08|
    US20200200672A1|2020-06-25|
    WO2018215337A1|2018-11-29|
    FR3066503B1|2021-05-07|
    引用文献:
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    法律状态:
    2018-05-28| PLFP| Fee payment|Year of fee payment: 2 |
    2018-11-23| PLSC| Publication of the preliminary search report|Effective date: 20181123 |
    2019-05-31| PLFP| Fee payment|Year of fee payment: 3 |
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    2021-05-31| PLFP| Fee payment|Year of fee payment: 5 |
    优先权:
    申请号 | 申请日 | 专利标题
    FR1754535A|FR3066503B1|2017-05-22|2017-05-22|MICROORGANISMS ANALYSIS PROCESS|
    FR1754535|2017-05-22|FR1754535A| FR3066503B1|2017-05-22|2017-05-22|MICROORGANISMS ANALYSIS PROCESS|
    US16/614,971| US20200200672A1|2017-05-22|2018-05-18|Method for analysing microorganisms|
    PCT/EP2018/063085| WO2018215337A1|2017-05-22|2018-05-18|Method for analysing microorganisms|
    EP18723872.0A| EP3631416B1|2017-05-22|2018-05-18|Method for analysing microorganisms|
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