Method for enumerating microorganisms based on cytometry and associated microbiological control methods.

Abstract

The invention relates in particular to an enumeration method for microbiological control comprising: Analysis (101) by flow cytometry of a matrix comprising a given volume of a sample, said sample having been previously labeled by a labeling protocol; Obtaining (102) a multidimensional data table presenting objects detected during the flow cytometry analysis, characterized in that the method comprises a step of applying an enumeration algorithm comprising at least the following steps: Assignment (105) of a quantitative score within a range of values ​​from [0 to 1] to each object detected during the analysis according to its probability of belonging to a determined "microorganism" class; Summing (108) the scores obtained in the preceding step; and in that the method comprises a step of: Enumeration (109) of microorganisms from the sum of the scores. Figure 1

Description

Title of the invention: Method for enumerating microorganisms based on cytometry and associated methods of microbiological control. Technical field of the invention

[0001] The invention relates to the technical field of flow cytometry, whether flow cytometry or solid-phase cytometry, and more particularly, the invention relates to the enumeration of microorganisms and the associated microbiological control methods, which correspond either to a quantitative analysis method or a qualitative analysis method for the matrices analyzed. The invention is primarily intended for processing flow cytometry data acquired on "complex matrices" but can also be applied to "simple matrices" without limitation.

[0002] The invention is of particular interest for the enumeration of microorganisms in complex matrices by solid-phase cytometry, and preferably for the microbiological control of cell suspensions found in cell and gene therapy products (lymphocytes, various types of stem cells, dendritic cells, fibroblasts, myoblasts, chondrocytes). Definitions

[0003] In the present invention, "flow cytometry" refers to a cytometry method in which the sample, already fluorescently labeled by a labeling protocol, flows through a capillary-type fluidic channel. This capillary is often sheathed by an external liquid (called sheath liquid) according to the principle of hydrodynamic focusing. At a specific location within the capillary, a laser excites a portion of the flowing sample and several point detectors, oriented at 90° or 180° with respect to the excitation beam, and associated with specific collection objectives and optical filters, continuously detect the signals emitted by the interaction between the light and the sample (primarily Side-Scatter scattering, Forward-Scatter scattering, and fluorescent emissions).The intersection between the laser beam, the sample capillary, and the volume interrogated by the collection optics (called the interrogation point) corresponds to a very small volume, on the order of a picoliter. This configuration, with an appropriate fluid regime, allows for the simultaneous detection of all suspended particles in the sample. Flow cytometry typically allows the analysis of volumes between 100 µL and 500 µL with a Limit of Detection (LoD) around 10² CFU / ml, which depends on the matrix. An example of an instrument implementing flow cytometry is the D-COUNT® system marketed by bioMérieux, the operation of which will be explained later.

[0004] In the present invention, "solid-phase cytometry" refers to a cytometry method in which the sample to be tested is first filtered through a membrane. This technique requires that the sample be filterable and may therefore require specific lysis preparatory steps before the filtration stage. The original sample volume can be very large, up to several tens of milliliters. Furthermore, since the remaining particles are captured on the surface of the membrane, this technique allows for a more detailed analysis and, for microbiological testing applications, an ultimate detection limit of one CFU per filtered volume, enabling sterility testing (qualitative microbiological control). The filtration membrane used is a so-called "surface" membrane, where particles are retained on the upper surface and not within the membrane itself.Once the objects, initially suspended in the original sample, are retained on the membrane, various operations can be performed by applying reagents either via the upper surface or even via the inner surface of the membrane, with the reagent rising by capillary action to the upper surface. Examples include counterstaining to increase contrast, activation and resuscitation of spore-forming organisms, and fluorescent labeling. For microbiological control, the fluorescent labeling process used is similar to that used for flow cytometry, namely viability labeling using fluorogenic substrates. Once prepared, this membrane can then be analyzed on a specific instrument to detect all objects present on its surface and measure various physical parameters for each object (size, shape, fluorescence intensity).This analysis includes a first detection step, also called "screening," and an optional second, complementary confirmation step. During the first detection or "screening" step, the entire membrane is scanned using fluorescence with a specific system such as the SCANRDI® system, marketed by bioMérieux. Objects are detected, and several parameters are extracted for each, corresponding to criteria such as fluorescence intensity, the fluorescence ratio between two emission channels, and the size and shape of the objects. Business rules for discrimination are then applied to each object to classify it in a binary fashion as either "microorganism" or "non-microorganism."

[0005] For enumeration applications or qualitative applications not requiring an ultimate detection limit, the analysis stops at this first step and a result is given from an account of these objects classified as "microorganism".

[0006] For enumeration applications or qualitative applications requiring a limit of detection, the "microorganism" objects are individually confirmed in the second confirmation step. They are confirmed one after the other by fluorescence microscopy with a high-magnification objective (greater than 40X). and possibly on a stack of z-images to ensure proper focus. For sterility testing applications where this second step is planned, the settings of the first step are generally adjusted to increase sensitivity, even if it means detecting many non-microorganisms, knowing that these will be eliminated during the second step. This two-step detection analysis is presented in particular in the article: "Carro et al. iMSRC: Converting a Standard Automated Microscope into an Intelligent Screening Platform. Sci Rep 2015, 5 (1), 10502".

[0007] This analysis ensures excellent sensitivity and specificity and allows solid phase cytometry to be used in the context of sterility tests required by the pharmacopoeia before the release of injectable products in particular.

[0008] In the present invention, "sensitivity" refers to the ability of a qualitative method to detect contamination when it is present: no sample is falsely declared sterile. Furthermore, "specificity" refers to the ability of a qualitative method to correctly identify an uncontaminated sample: no false alarms with a sample that is falsely declared contaminated. The threshold of a test, that is, the value at which it is considered positive, influences the sensitivity and specificity of said test. Thus, if this threshold is lowered, the test will be more sensitive but less specific. Highly sensitive tests are particularly useful for ensuring that contamination is not present (few false negatives), while highly specific tests are useful for ensuring that contamination is indeed present (few false positives).

[0009] In the present invention, "non-microorganism" means any object detected by the cytometer that does not correspond to a predetermined class of "microorganism." In the general context and without further specification, non-microorganisms correspond to reading artifacts, air bubbles, or matrix debris.

[0010] In the present application, the terms point and object are used interchangeably depending on the context: point for the cytogram, object for the equivalent data table. A point is an object.

[0011] In the present invention, the term "data table" refers to a table generated by flow cytometry analysis, presenting the detected objects in rows and the measured parameter values ​​in columns. This data format is standardized with the "fcs" Flow Cytometry Standard format (latest version fcs 3.1). In mathematical representation, each object can be considered as a point in a multi-dimensional space, with each variable representing a dimension. Graphically, these points can be represented on a cytogram whose axes are the measured variables. Limited to a single variable (a single axis), the cytogram is equivalent to a classic histogram. Beyond three dimensions, graphical representation becomes difficult, but Conceptually, the notion of a cytogram can be preserved as a geometric analog for representing the data table, with the concepts of points, point density, and clusters. Classically, the cytogram is represented in two dimensions, and particularly when several fluorescent markers are used, it allows each marker to be represented on an axis. Even with a single marker (and an associated fluorescence emission spectrum), detection can be performed on two fluorescence channels, each with a specific spectral band, and the information related to the fluorescence emission profile of the detected objects can be used on a 2D cytogram. Cytograms can be used to identify cell populations, assess data quality, and optimize acquisition parameters. In this case, it is the data table (or its geometric analog, the cytogram) that is used as input data in the method according to the invention.We can therefore speak interchangeably of object (individual element detected by the cytometer = a line in the data table) or of point, which is the mathematical representation of this object in the cytogram.

[0012] In the present invention, "business rules" refer to the variables measured in flow cytometry that generally have a physical meaning and are directly interpretable. Thus, the "Side Scatter" parameter (laser scattering measured at 90° to the excitation) is directly related to the surface condition and granular appearance of the objects, while the "Forward Scatter" parameter (laser scattering measured at 180°) is directly related to the size of the detected objects. Fluorescence parameters are also directly interpreted according to the labeling protocol. Thus, with viability labeling via a fluorogenic substrate, the emission spectrum of the fluorophore is known a priori. If the fluorescent emission is measured on separate channels, one corresponding to the emission maximum and the other to the "tail" of the emission spectrum, the intensity ratio measured between these two channels will be constant around a predefined value for the labeled objects.On a 2D cytogram with each channel on an axis and logarithmic scales, "microorganism" objects will preferentially be positioned along the diagonal. The business rules correspond to the direct physical interpretation of the cytogram in relation to the absolute positions of the points detected in the considered coordinate systems. Technological background of the invention

[0013] Classically, in cytometry applications for microbiological control, the detected objects are first categorically classified into various types (for example, microbial cells, control beads or particles), then the "microorganism" objects corresponding to the microbial cells are counted in order to establish the enumeration result.

[0014] For qualitative applications of the presence / absence type (agrifood or cosmetic applications) or sterility testing (pharmaceutical applications), this enumeration result is then converted, via a contamination threshold defined according to the requirements specific to the field of application, into a binary result: contaminated or uncontaminated sample.

[0015] For the classification phase of these objects, there are currently two main approaches, each corresponding to a way of defining regions of the cytogram resulting from the analysis. Depending on the type of cytometry (flow or solid phase), the approach differs.

[0016] Thus,

[0017]

[0018] The first approach consists of defining these regions, also called "gates" or "windows", from predefined business rules and / or from a training set, and then classifying each of the objects according to its position with respect to these determined and fixed regions.

[0019] A first example of the application of the first approach for microbiological control is explained below.

[0020] The D-COUNT® system is a flow cytometry system for the detection and enumeration of previously fluorescently labeled microbial cells. The D-COUNT® system measures the fluorescence signature of these targets at two different wavelengths (FL1 and FL2 channels) for the same excitation and the same fluorophore. A unique feature of the D-COUNT® system is that the FL2 channel actually measures, in addition to a fluorescence signal, a fraction of the light scattered by the system's laser, via a suitable selection of optical filters (a dichroic mirror without a bandpass emission filter in front of the detector). The algorithm counts individually, as they are detected, the number of fluorescent objects within a predefined and fixed window that corresponds to the theoretical fluorescence signature of the microbial cells.This enumeration is reported per volume analyzed, and is multiplied by any dilution factor to give the final enumeration result in CFU / ml of analyzed product.

[0021] Although this first approach is robust and efficient, it presents problems with complex matrices. Indeed, despite calibration of the response, a complex matrix can modify the absolute positions of individual points and sometimes cause objects that should be considered non-microorganisms to be included in "microorganism" windows, or conversely, exclude objects that should be considered microorganisms from their "microorganism" window. These errors often require an increase in the positivity threshold for qualitative applications and therefore a deterioration in sensitivity. Furthermore, in the case of dual contamination by two distinct populations, for example bacteria and molds, the first approach does not allow for the separate counting of these two different populations, even though they may appear distinctly in a point cloud, because this approach is based on a predetermined window that is wide enough to encompass all contaminants, whatever they may be.

[0022] A second example of the application of the first approach to the case of solid-phase cytometry data corresponds to the SCANRDI® system marketed by bioMérieux, and indicated in particular for the sterility testing of pharmaceutical products. The membrane, on which the fluorophore-labeled objects are present, is scanned. Several parameters (fluorescence and / or morphology) are extracted for each detected object. Business rules are applied to each object to discriminate and retain only those that are potentially microorganisms.Finally, a confirmation step is performed for each preserved object, by capturing high-magnification (> 40X) fluorescence microscopy images.

[0023] In the case of complex matrices, particularly cellular debris matrices, a very large number of objects may be detected during the first step, which can complicate and tedious the confirmation step. Furthermore, it is very difficult to establish discrimination rules that ensure a good compromise between sensitivity and specificity. Finally, even with high-resolution confirmation images, there are many cases where it is difficult to definitively assign a binary class to objects as "microorganism" or "non-microorganism."

[0024] The second approach consists of directly dividing the initial point cloud, comprising a plurality of objects, into subgroups using a clustering method. This clustering can be performed with various machine learning algorithms and is based on the observed distribution of objects represented by points on the cytogram, without prior knowledge of the regions. Once the subgroups are formed, they can then be assigned, for example, into "microorganisms" or "particles" clusters, either according to predefined business rules or via a training dataset.

[0025] An example of the second approach is presented in patent application WO2021 / 154561. A clustering step is described, followed by a classification step for the clusters formed, taking into account the shape of the point clouds and the density of the points. The approach is refined in that the scores of individual points are taken into account through the calculation of spillover coefficients ("spillover matrix"). This ultimately allows for a more flexible adjustment of new cluster recognition envelopes and for consideration of potentially distant points. Nevertheless, despite this approach, it is the entire cluster thus formed that is subsequently assigned a class. The objects (individual points) are then categorized according to their membership in these assigned classes. Here again, even if a continuous score (between 0 and 1) is determined for each object, this is only an intermediate step to define zones and ultimately, the status of each object remains qualitative according to its belonging to a particular region.

[0026] For both approaches, once the class of each object has been assigned (either directly for the first approach, or via an intermediate step of clustering the point cloud and assigning class to subgroups for the second approach), the objects of the class "microorganism" are counted.

[0027] These approaches have drawbacks. Indeed, objects represented by isolated points or overlapping point clouds pose a critical problem, particularly for the microbiological control of complex matrices. The goal of microbiological control systems is to count all microbial cells, without exception, for a given analysis volume in order to obtain a precise quantitative result and achieve the best detection limit while minimizing false alarms. However, for complex matrices, it is not uncommon to observe displacement or distortion of point clouds with possible overlap (due to optical scattering phenomena related to the matrix) or even the appearance of new points (debris, air bubbles, interference between labeling reagents and the matrix).In this context, current methods ultimately assign a qualitative class ("microorganism" or "particle" for example) to each point (whether assigned to a cluster, overlapping several clusters or individual); and this can significantly bias the enumeration result of the sample from the reality of its microbiological state.

[0028] On the other hand, even if all the marked objects are correctly counted, since the marking principle differs from the principle of traditional microbiology methods (based on culturing for a long period of incubation), the enumeration result will not necessarily be well correlated or even comparable or interpretable in terms of "Colony Forming Unit" (CFU). Object of the invention

[0029] The invention makes it possible to resolve all or part of these drawbacks and to improve existing systems and processes.

[0030] The invention relates to a method for enumerating microbial cells based on flow cytometry or solid phase cytometry and an algorithm for significantly reducing the influence of complex matrix particles, thereby reducing false alarms and improving the sensitivity of the enumeration method, and also improving the correlation with the Colony Forming Unit (CFU) count.

[0031] To this end, the invention relates to an enumeration method for microbiological control comprising: - Flow cytometry analysis of a matrix comprising a given volume of a sample, said sample having been previously labeled according to a labeling protocol, - Obtaining a multidimensional data table showing objects detected during flow cytometry analysis,

[0032] characterized in that the method includes a step of applying an enumeration algorithm comprising at least the following steps: - Assigning a quantitative score within a value range of [0 to 1] to each object detected during the analysis based on its probability of belonging to a specific "microorganism" class, - Sum of the scores obtained in the previous step, - and in that the method includes a step: - of Enumeration of microorganisms from the sum of the scores.

[0033] The method according to the invention has many advantages, which are described below.

[0034] Indeed, assigning a quantitative score to each object allows for greater accuracy in enumeration, particularly in cases where, after fluorescent labeling of the sample, one or more microbial cells are relatively inactive and generate a weak fluorescence signal. The position of this cell / these cells as an object may be outside a cluster of objects classified as "microorganisms," despite its proximity. Consequently, the score of this object will be between 0 and 1. For example, summing the scores of three objects outside the classified cluster, each with a score of 0.33, yields a sum of 1. This probabilistic approach is scientifically relevant because it is known that not all microbial cells detected by cytometry would necessarily grow on a culture medium.

[0035] Moreover, this method is also particularly advantageous in the case of a cytometry system combining, on the same detection channel, two different pieces of information, in particular a fluorescence signal and a scattering signal, as is the case for the D-COUNT® system. Indeed, depending on the matrices addressed, the more or less scattering aspect of the sample, the position of the center of the point cloud clusters and their elongation along this axis can vary considerably with distribution tails, overlapping point cloud clusters and isolated points.

[0036] Furthermore, the method according to the invention is also of interest for enumerating microorganisms from cytometry systems with multiple detection channels and potential cross-correlations between measurements, for the automatic selection of subpopulations, by lightening in particular the constraints of compensation correction or even by eliminating them knowing that they are not based on fixed and predefined windows and that the clustering phase of the data table can be carried out on a possibly pre-processed data table.

[0037] According to one feature of the invention, the method comprises a step of: - Clustering the objects of the data table into a plurality of clusters comprising at least one cluster called a "point cloud" and / or at least one "isolated point" by means of a calculation of at least one parameter for each cluster or each isolated point, the at least one parameter being selected from a list of parameters comprising at least one position and / or shape and / or density of the cluster or isolated point, - Classification of each point cloud cluster, the class being selected from several classes including at least the "microorganism" class, the classification being carried out according to at least one parameter calculated in the previous step and business rules relating to the marking protocol; each isolated point not being classified.

[0038] Advantageously, the method according to the invention applies by default to a situation with a single reference class, the class "microorganism," and therefore a binary classification into "microorganism" and "non-microorganism" (or "particle"). In this case, each object is assigned a quantitative score between 0 and 1, with 1 corresponding to the class "microorganism" and 0 to everything else ("non-microorganism" or "particle"). This is a "one-class" classification.

[0039] According to one feature of the invention, the classification step can include a plurality of classes.

[0040] According to a feature of the invention, depending on the distance of the object from a cluster classified as a microorganism, the score "1" being assigned to objects in the cluster classified as a microorganism or closest to the cluster classified as a microorganism, the score 0 being assigned to objects furthest from the cluster classified as a microorganism, and a score between 0 and 1 is assigned to objects positioned between objects with a score of 1 and objects with a score of 0.

[0041] According to one feature of the invention, the classification step may include a "non-microorganism" class. Thus, according to the invention, the classification step consists of classifying each point cloud cluster, the class being selected from several classes including at least the "non-microorganism" class and the "microorganism" class. The classification is performed according to at least one parameter calculated in the previous step and business rules relating to the tagging protocol. Each "isolated point" cluster is not classified, and the assignment step consists of assigning a score to each object in each cluster. detected during analysis: (i) if the object is in a cluster belonging to the class "non-microorganism": the score is 0; (ii) if the object is in a cluster belonging to the class "microorganisms": the score is 1; (iii) if the object is not classified: its score is between 0 and 1 depending on its distance from a cluster including classified objects.

[0042] Advantageously, the class "non-microorganism" is a class representative of the objects of a cluster and / or isolated point that are not, and this is certain, microorganisms.

[0043] According to one feature of the invention, the classification step may include a "control beads" class. Indeed, the sample may be mixed with internal control beads before analysis and thus a point cloud may correspond to these control beads, which are not non-microorganisms.

[0044] Furthermore, the method according to the invention is advantageous when two types of microbial contamination are present in the same sample, for example bacteria and yeasts, or molds, etc. The method according to the invention can include several specific classes replacing the generic class "microorganism," which can make it possible to distinguish two different, separate point cloud clusters, which would each be classified in the class "microorganism" if the method included only one class, but which, since there are specific classes, are specifically classified into two different specific classes.

[0045] Therefore, according to the invention, in the case where several classes (other than "microorganism" or "particle / non-microorganism") are to be identified, for example two different types of microorganisms as developed above, or two different types of objects of interest, for example microorganisms and beads, the classification is said to be "multi-class" and several scores are calculated for each object, according to a complete disjunctive coding ("one hot encoding").

[0046] For example, for a class of microorganism Mb, a class of microorganism M2, and a class of bead B. Three scores Si, S2, and S3 are calculated respectively for each object, from three separate classification models, each one-class: - A first model allowing the recognition of class Mi in relation to the rest, with a score Sicompris in the range of values ​​[0;1] depending on its probability of belonging to class Mi - A second model allowing recognition of class M2 in relation to the rest, with a score S2 within the range of values ​​[0;1] depending on its probability of belonging to class M2) - A third model allowing recognition of class B in relation to the rest with therefore a score S3 included in the range of values ​​[0 ;1] depending on its probability of belonging to class B).

[0047] According to one feature of the invention, the classification models are independent; the sum of the scores for each object is not necessarily equal to 1. For example, if an object is an isolated point that has a probability of belonging to each of the three clusters with a score of 0.5, the scores of said object will be 0.5; 0.5; 0.5. On the other hand, if an object is not isolated and obtains a membership score of 1 for one cluster, the other scores will be 0.

[0048] The enumeration is then performed for each category by separately summing the Si, S2, and S3 scores of each object in the cytogram. Similarly, if an activation function is applied to the scores before the summing step, the functions are not necessarily the same for the different classes.

[0049] According to one feature of the invention, the matrix is ​​a complex matrix.

[0050] In the present invention, the term "complex matrix" refers to a matrix corresponding to a sample whose composition is not controlled and / or which contains numerous particles that can be detected and possibly mistaken for microbial cells. More generally, the term "matrix" refers to the sample to be analyzed after any preparation steps such as dilution, centrifugation, or lysis. Thus, some "complex matrices" initially become "simple matrices" after the preparation protocol. Other samples remain complex matrices even after the preparation steps.

[0051] For example, drinking water and certain physiological fluids such as urine and blood plasma are not considered "complex matrices." Some processed food products such as milk or conventional yogurts are also considered "simple matrices" (after application of a preparation protocol). Conversely, dessert creams, soups, or beverages enriched with suspended particles are "complex matrices" because, even after a preparation protocol, many particles remain suspended in the sample analyzed by cytometry. In the pharmaceutical field, cell matrices treated to make them filterable (in particular, lysed) for analysis by solid-phase cytometry contain numerous debris and are considered complex.

[0052] According to another feature of the invention, the method according to the invention includes a step of applying an activation function to the scores of each object, said activation function being configured to optimize the correlation between the enumeration of the detected microorganisms and the sum of the scores. Indeed, below a certain score value, it is not relevant to include it in the sum: ten Objects scored at 0.1 should not be equivalent to 1, whereas two objects scored at 0.5 should be.

[0053] According to one feature of the invention, the activation function is a “ReLu” (Rectified Linear Unit) type activation function. This activation function may include at least one adjustable parameter for optimizing the correlation from a training set.

[0054] According to another feature of the invention, the method according to the invention includes one or more pre-processing steps of the data table before the clustering step.

[0055] According to another feature of the invention, the pre-processing step of the sizing and / or rotation and / or distortion and / or binning type of the data table, which allows the data in the data table to be correctly proportioned to each other and which facilitates the following steps.

[0056] According to one feature of the invention, the preprocessing step can also be a dimensionality reduction process. For example, a Principal Component Analysis (PCA) technique, a t-distributed stochastic neighbor embedding (t-SNE) technique, or a Uniform Manifold Approximation and Projection (UMAP) technique. Advantageously, this reduction process is configured to reduce the dimensionality of the data and thus to mitigate collinearity problems, and is particularly relevant in the case of a high-dimensional dataset. The algorithm of the method according to the invention therefore allows, when applied to datasets with multiple collinear variables, a robust and unbiased classification of the different subpopulations.

[0057] According to one feature of the invention, the clustering of the data table is carried out using learning methods based on a density calculation of the objects in the data table.

[0058] According to one feature of the invention, the analysis step is a step in which fluorescence detection is performed, with the system according to the invention, on each object of the sample, and a plurality of parameters, such as the fluorescence intensity and / or fluorescence ratio between two emission channels of the system and / or size and / or shape of the objects are extracted for each object.

[0059] According to another feature of the invention, the cytometry analysis is a solid-phase analysis. In the case of a solid-phase cytometry analysis, the sample is first filtered on the surface of a membrane, said membrane then being analyzed by fluorescence imaging to detect objects.

[0060] Alternatively and according to the invention, the cytometry analysis is a flow cytometry.

[0061] According to one feature of the invention, the preparation step may further include substeps of lysis and / or centrifugation depending on the matrix, and / or filtration.

[0062] According to one feature of the invention, the labeling protocol is a fluorescent labeling protocol for microorganisms potentially present in the sample. The labeling protocol is carried out during sample preparation prior to flow cytometry analysis.

[0063] According to one feature of the invention, the clustering of objects in the data table can be performed using a learning method such as density analysis, and preferably HDBSCAN analysis, which is a hierarchical density-based spatial clustering of applications with noise. This technique allows, in a single phase, the establishment of both the clusters and the individual scores of each object.

[0064] The advantage of using HDBSCAN analysis for clustering is that this type of density-based clustering algorithm eliminates the need to consider the number of clusters beforehand, as well as their convexity. The principle of HDBSCAN analysis is based on the fundamentals of DBSCAN, but takes into account the different data densities. The HDBSCAN documentation provides access to numerous hyperparameters; however, we have chosen to retain only the most relevant ones to avoid an excessively lengthy hyperparameter tuning phase.

[0065] According to one feature of the invention, the hyperparameters of the algorithm for clustering are:

[0066] - nbins: no binning grid discretization.

[0067] - mincluster s^ze : minimum cluster density : proportion between 0 and 1 on the set of points representing the minimum population of a cluster. The smaller this number, the more clusters with low densities can appear.

[0068] - throutliers: probability threshold for an isolated point to belong to a cluster.

[0069] Advantageously, the algorithm outputs various clusters that have been detected and objects that do not belong to any cluster and are considered isolated points. The isolated points are assigned a probability of belonging to the various detected clusters by comparing this probability to a threshold. If the probability is greater than the threshold, then the isolated point is assigned to that cluster.

[0070] Alternatively or in addition, the clustering of objects in the data table into point clouds can be carried out using unsupervised learning methods such as parametric methods based on assumptions of distributions such as the "FlowPeaks" approach, which combines k-means and Gaussian mixtures to form proto-clusters that can then be merged, are described in an article in the journal Bioinformatics, Volume 28, Issue 15, August 2012, Pages 2052-2058. This method groups data with k-means for a high number k, then correlates these groups with a multivariate Gaussian model and finally merges the Gaussians that are close.

[0071] Alternatively or in addition, the clustering of objects in the data table into point clouds can be carried out using deep learning methods, for example in the form of a self-adaptive map.

[0072] The invention also relates to a cytometry system for microbiological analysis purposes, configured to implement the enumeration method for the microbiological control of samples according to the invention.

[0073] According to one feature of the invention, the cytometry system comprises at least one detection channel. Preferably, the cytometry system comprises only two detection channels, both measuring a fluorescence signal, which makes it possible to maintain a simple system with easy channel management, particularly because it is not necessary to resort to orthogonalization compensation methods, while maintaining equivalent performance, particularly thanks to the method according to the invention.

[0074] According to one feature of the invention, object detection is done by analyzing the signal collected on the first detection channel, also called the "trigger channel".

[0075] According to a feature of the invention, the first detection channel, called the "trigger channel", corresponds to the fluorescence measurement on the maximum emission wavelength of the fluorophore for labeling microorganisms.

[0076] Advantageously, the method according to the invention is implementable in a cytometry system comprising more than two channels, said channels being correlated to each other.

[0077] According to one feature of the invention, the second detection channel (FL2) is optically configured, by a suitable choice of filters, to combine, in a weighted sum for example, at least two optical parameters, such as fluorescence and scattering.

[0078] According to one feature of the invention, the optical parameters can be fluorescence parameters.

[0079] According to one feature of the invention, the second detection channel (not used for object detection) simultaneously collects at least two optical parameters. The use of such a second channel positioned on the Y-axis and this simultaneous measurement of two optical parameters is particularly advantageous. because it simplifies optical design with a single detector. However, it is generally along this axis that the relative position of the points can vary depending on the matrix. This variability prevents the accurate definition of gates / windows using a conventional method; but with the method according to the invention, where the classification model is derived from the relative positions of points, and quantitative scores are calculated for each object, this configuration is compatible with applications for enumerating microorganisms.

[0080] According to one feature of the invention, the second detection channel measures both fluorescence over a range beyond 540 nm and a proportion of light scattered by the laser at 488 nm. Indeed, there is intentionally no bandpass filter in front of the second channel, and since the dichroic mirror allows laser light to pass through in transmission, this information is combined with the fluorescence on the second channel.

[0081] According to one feature of the invention, the first channel detects fluorescence in a specific band corresponding to the maximum intensity of the fluorophore. For example, if the fluorophore is fluorescein, the emission peak is around 520 nm.

[0082] According to the invention, the detection of the object is considered to have taken place when the intensity measured on the first channel exceeds a threshold, the value of the intensity peak is captured in the area under the peak, with the peak height and optionally the peak width.

[0083] According to a feature of the invention, the values ​​measured on the first channel and on the second channel are correlated.

[0084] According to a feature of the invention, during object detection the signal on the second channel is acquired.

[0085] According to one feature of the invention, the trigger channel of the cytometry system does not correspond to the measurement of the diffusion of objects on this system (Side Scatter or Forward Scatter), because as the system is dedicated to the analysis of a priori complex matrices, with many objects in suspension, capturing all the objects which have a diffusion signal would saturate the detector very quickly with the risk of missing the fluorescent objects.

[0086] In fact, points positioned in a Y shape on the cytogram can be found at varying heights depending on both: - their distribution (related to the size of the objects) - their fluorescence signature (emission in the yellow / orange)

[0087] There is therefore a large individual variability of the points on this Y-axis and the position can even vary from one matrix to another. The counting strategy with a fixed window can therefore fail with points that "fall" into the window microorganisms” but which are really “particles”, because of an overall diffusion linked to the matrix which attenuates the individual diffusion value of each point.

[0088] The enumeration method according to the invention is of interest for its implementation in a more specific method used for sterility tests applied to complex matrices by solid phase cytometry.

[0089] Thus, the invention also relates to a qualitative microbiological control method characterized in that it comprises at least the following steps: - implementation of the steps of the enumeration method according to the invention,

[0090] said microbiological control method further comprising at least the following steps: • comparison with a determined contamination threshold (110), the comparison being carried out between the enumeration result obtained in the enumeration step (109) and said contamination threshold, the comparison step being implemented after the enumeration step (109), • determination of sample contamination (111) following comparison (110) if the result of the enumeration is greater than the threshold, then the sample is considered contaminated; if the result of the enumeration is less than the threshold, then the sample is considered uncontaminated.

[0091] According to a feature of the invention, the threshold determined for the control method is a threshold determined as a function of the desired false positive rate and negative rate either by optimizing the false positive rate or the false negative rate, or by combining the false positive and false negative rates by calculating the precision or the Fl-score, or by calculating from the ROC (Receiving Operating Characteristics) curve.

[0092] Fl-score refers to the harmonic mean of accuracy and recall. It combines the two measurements into one, taking into account both false positives and false negatives. Thus, if false positives are higher than false negatives, the impact is felt more in terms of accuracy than recall.

[0093] According to one feature of the invention, when the analysis is a solid-phase cytometry analysis, the solid-phase cytometry analysis includes a sample screening step. This screening step is equivalent to a step for detecting objects on the surface of the membrane containing the sample.

[0094] According to one feature of the invention, when the analysis is a solid-phase cytometry analysis, the control method includes a confirmation step for the objects detected during the screening step. This confirmation step makes it possible to distinguish even the smallest microbial cell, even one that is weakly labeled, from a particle.

[0095] According to a feature of the control method in which the analysis is a solid phase cytometry analysis, the confirmation includes a step of selecting at least one object to be confirmed, the selection step being either partial or iterative.

[0096] According to one feature of the invention, the selection step is carried out from the clusters and scores obtained from the previous steps.

[0097] According to a feature of the control method in which the analysis is a solid phase cytometry analysis, the selection is a partial selection of a number of objects based on the point clouds, preferably by a selection of at least one object per cluster formed in the clustering step.

[0098] Alternatively, the selection is an iterative selection, the objects to be confirmed are selected one by one, the objects selected first being those belonging to a microorganism cluster or whose probability of belonging to a microorganism cluster is the most probable.

[0099] According to a feature of the control method in which the analysis is a solid-phase cytometry analysis, the confirmation further comprises, after the iterative and / or partial selection step, a step of acquiring and examining one or more images for each selected object. Advantageously, the examination of each image is a high-magnification microscopic examination of the detected objects.

[0100] According to a feature of the control method in which the analysis is a solid-phase cytometry analysis, the confirmation further comprises a step of applying a classification model from one or more acquired images for each object in the acquisition step. Preferably, the model used in the classification step of the confirmation step is a supervised model.

[0101] According to a feature of the control method in which the analysis is a solid-phase cytometry analysis, the confirmation further comprises a step of applying a score to each of the selected and classified objects. Advantageously, the score is a quantitative probabilistic score.

[0102] According to a feature of the invention, an optional activation function can be applied to the score obtained in the previous step.

[0103] According to one feature of the invention, the confirmation step is implemented after the scoring application step of the enumeration method and before the activation application step or the sum step of the enumeration method.

[0104] According to a feature of the invention, the sum stage corresponds to the sum of the scores obtained during the confirmation stage.

[0105] According to a feature of the control method in which the analysis is a solid-phase cytometry analysis, and where the selection step is an iterative selection, the enumeration score is compared with the determined threshold, if the If the score is greater than the threshold, then the method is stopped. If the score is less than the threshold, the steps of selection, application of a classification model, application of a score, and summation are repeated with a second object, the steps of selection, application of a classification model, application of a score, and summation being repeated for n objects until the threshold is exceeded or all objects are confirmed. Brief description of the figures

[0106] The invention will be better understood from the following description, which relates to an embodiment according to the present invention, given by way of non-limiting example and explained with reference to the accompanying schematic figures. The accompanying schematic figures are listed below:

[0107] [Fig-1] is a 2D graphical representation of a cytogram

[0108] [Fig.2] is a diagram of the flow cytometry system according to the invention

[0109] [Fig.3] is a diagram illustrating the enumeration method according to the invention,

[0110] [Fig.4] is a diagram illustrating the microbiological control method qualitative according to the invention in a first embodiment,

[0111] [Fig.5] is a diagram detailing step 103 of the application of the algorithm in the enumeration method according to the invention,

[0112] [Fig.6] is a diagram illustrating the qualitative microbiological control method according to the invention in a second embodiment,

[0113] [Fig.7] is a diagram illustrating the qualitative microbiological control method according to the invention in a third embodiment,

[0114] [Fig.8] is a presentation of the processing of a cytogram during the method according to the invention

[0115] [Fig.9] is a comparison of the interpretation of the algorithm according to the present invention with respect to the prior art algorithm.

[0116] [Fig. 10] graphically represents examples of the activation function used in the enumeration method according to the invention. Detailed description

[0117] The cytometry system for microbiological analysis, configured to implement the enumeration method according to the invention, is partially shown in [Fig. 2]. This system 1 comprises only two fluorescence measurement detection channels, FL1 and FL2, thus maintaining a simple system with easy channel management. System 1 further comprises a plurality of mirrors 2, lenses 3, a laser 4, a filter 5, and a CCD (Charged Coupled Device) sensor 8, a light source 7, and an aperture 6. The beam 4a of the laser 4 is directed onto a matrix 10 carrying a sample above which is positioned a lens 3 of system 1.

[0118] In system 1 according to the invention, the FL1 channel detects fluorescence in a specific band corresponding to the maximum intensity of the fluorophore (for example, fluorescein with an emission peak around 520 nm), and it is on this FL1 channel that object detection is performed: as soon as the intensity measured on FL1 exceeds a threshold, the peak intensity value is captured (area under the peak, peak height, and possibly peak width), and simultaneously the signal on the second channel, FL2, is also acquired. There is no channel related to object scattering measurement (Side Scatter or Forward Scatter) on system 1 according to the invention, whereas this is the case in most cytometers, which also use the FL1 channel to trigger the capture of information on the other detectors.Since system 1 according to the invention is dedicated to the analysis of a matrix 10 containing a potentially complex sample with numerous suspended objects, capturing all objects with a scattering signal would quickly saturate the detector, risking the loss of fluorescent objects. However, object size information remains valuable, and therefore system 1 according to the invention integrates this size information into the FL2 channel, which measures both fluorescence over a range beyond 540 nm (yellow / orange) and a small proportion of light scattered by the laser 4 at approximately 488 nm. Indeed, there is intentionally no bandpass filter in front of the FL2 channel, and since the dichroic mirror 2 transmits laser light, this information is combined with the fluorescence on the FL2 channel.

[0119] The system 1 according to the invention is configured to implement several methods according to the invention, an enumeration method illustrated and described in [Fig.3] and a microbiological control method illustrated and described in Figures 4, 6 and 7. [Fig.5] being a detail of a step common to the two methods according to the invention.

[0120] The enumeration method according to the invention will now be described with reference to [Fig.3].

[0121] The enumeration method includes an analysis step (101) by flow cytometry or solid-phase cytometry of a matrix comprising a given volume of a sample. In the case of enumeration, the solid-phase cytometry analysis does not require a specific confirmation step as described with reference to Figures 6 and 7.

[0122] During cytometry analysis, the points are acquired on the fly, one after the other, depending on their passage through the "interrogation zone" of system 1, which corresponds to the intersection between the sample vein 10, the excitatory laser beam 4, and the optical collection axis ZZ. The data can therefore be processed "on the fly." "stolen" as each event is detected and / or once again all the points acquired, on the whole of the cytogram constructed.

[0123] Following this analysis step (101), a multidimensional data table (step 102) is obtained, showing objects detected during the cytometry analysis (flow or solid phase). Obtaining a data table (102) is graphically represented by a cytogram, as illustrated in [Fig. 1]. Each point of the cytogram can be graphically represented in 2D, each axis representing a measured parameter. If more than two parameters are measured for each point, several 2D graphs or even a 3D view can be used. In a 2D graphical view as in [Fig. 1], "windows" F can be defined, which are regions with predefined boundaries (polygons or ellipses) and which allow a portion of the cytogram points to be selected. Furthermore, and independently of predefined windows F, points that form clusters of points Cl can also be observed.Since the window F is predefined by its boundaries, one can conceive of a window without any points inside; conversely, a point cloud Cl is an area of ​​the cytogram necessarily containing points, more or less densely distributed. It is perfectly possible to define, based on a displayed cytogram and according to the visible point clouds, a window around a point cloud. There may also be isolated points Pt in the cytogram, not belonging to any visible group, or even points at the intersection of several point clouds Cl.

[0124] From the data in the data table obtained in step 102, an enumeration algorithm is applied, including a clustering step 103 of the objects in the data table into a plurality of clusters comprising at least one cluster called a "point cloud" Cl and / or at least one isolated point Pt. This is achieved by calculating at least one parameter for each cluster Cl or isolated point Pt, the at least one parameter being selected from a list of parameters comprising at least one position and / or one shape and / or one density. More specifically, and as illustrated in Figure 5, the isolated points Pt are assigned a probability of belonging to the different clustered point clouds Cl: if this probability is greater than the threshold, then this isolated point Pt is assigned to said point cloud (decision Y); otherwise, the point remains isolated and does not belong to any point cloud (decision N). These points are also called "outliers".

[0125] Following this clustering (103), each point cloud cluster Cl is classified (104) in a binary positive / negative manner: the class is selected from several classes including at least the "microorganism" class (positive), the classification is carried out according to at least one calculated parameter and business rules relating to the marking protocol. A so-called core area of ​​the cloud is then considered. of points. This kernel is centered on the centroid and extends along a diagonal (see [Fig. 8]). This allows us to take into account both the position of the point cloud Cl and its spread along the FL1-FL2 axis (diagonally). If part of the kernel of this point cloud Cl lies within the window of positivity, then the entire point cloud will be considered positive, and each object within it will be assigned a score of 1 in the scoring step (105). Otherwise, the point cloud Cl is considered negative, and each object within it will have a score of 0 in the scoring step (105). The scoring step (105) is a relative scoring assignment: the score of an object depends on the scores of the other objects.

[0126] With regard to outliers, also called "unclassified objects," their position relative to a predetermined positivity window is considered: i) if the unclassified object is located within the predetermined positivity window, then it is assigned a score of 1, from which the set of probabilities of belonging to negative point clouds is subtracted; ii) if the unclassified object is not located within the predetermined positivity window, then it is assigned a score of 0, to which the set of probabilities of belonging to positive point clouds is added. Thus, the score of unclassified objects (outliers) is between 0 and 1 depending on its distance from a cluster containing classified objects.

[0127] This approach makes it possible to take into account the position of unclassified objects by considering their environment and their proximity to the detected point clouds. Thus, each object, whether it is part of a cluster or not, is assigned a positivity score between 0 and 1.

[0128] After assigning scores (105), an activation function (107) can be optionally applied. This activation function (107) is shown in [Fig. 10], with the solid line representing the identity activation function f(x)=x and the dashed line representing the Heavside activation function with a threshold of 0.5, the transformed score on the y-axis, and the initial score on the x-axis. The activation function, which is configurable (during the training phase according to the desired performance, it can be viewed as a hyperparameter), optimizes the correlation between the enumeration of microorganisms by flow cytometry and the CFU value (score). Indeed, below a certain score value, the sum is not meaningful: ten objects scored at 0.1 should not be equivalent to 1, whereas two objects scored at 0.5 should be.The activation function is therefore applied before any summation of scores to avoid unrepresentative residual scores.

[0129] The next step after the scoring assignment (105) and possibly the activation function (107) is the summation of the scores (108) obtained previously. Then the enumeration (109) of microorganisms is carried out based on the summation of the scores (108). The sum (108) is by design equal to the enumeration (109): from the sum of the scores, we deduce the number of microorganisms, expressed in CFU.

[0130] Finally, an alert signal giving the result of the enumeration (112) is emitted. The signal may be audible and / or visual.

[0131] Figure 8 shows an evolution of the cytogram as a function of its different treatments. Figure 8(a) illustrates the original 2D cytogram with predefined regions. Figure 8(b) illustrates the same cytogram after the clustering phase based on the density, shape, and position of the point clouds relative to the predefined regions. Figure 8(c) illustrates the cytogram after assigning scores to each point based on the relative distance between each point.

[0132] Figures 4, 6 and 7 illustrate the steps of the qualitative microbiological control method according to the invention and according to several embodiments.

[0133] According to a first embodiment of the qualitative microbiological control method shown in [Fig.4], the analysis step (101) in cytometry is a flow cytometry analysis or solid phase analysis without confirmation.

[0134] Steps (100) to (105) and (107) to (109) are identical to those described for the enumeration method described previously with reference to [Fig.3].

[0135] Following the enumeration step (109), the control method includes a comparison step (110) with a determined contamination threshold, the comparison being carried out between the enumeration result obtained in the enumeration step (109) and said determined contamination threshold,

[0136] Finally, a step of determining the sterility or contamination of the sample following the comparison (110) is implemented: if the result of the enumeration is greater than the determined contamination threshold, then the sample is considered contaminated; if the result of the enumeration is less than the determined contamination threshold, then the sample is considered uncontaminated.

[0137] According to the second and third embodiments of the qualitative microbiological control method shown in Figures 6 and 7, respectively, the analysis step (101) in flow cytometry is a solid-phase flow cytometry analysis with a confirmation step (106). Steps (100) to (105) and (107) to (109) are identical to those described for the enumeration method described previously with reference to [Fig. 3]. According to the second and third embodiments, the control method includes a confirmation step (106) of the objects detected and scored in step (105). This confirmation step (106) makes it possible to distinguish even the smallest microbial cell, however faintly labeled, from a particle. The confirmation (106) is performed by high-magnification microscopic examination of the detected objects. The confirmation (106) can be performed selectively in two ways.

[0138] According to the second embodiment shown in [Fig. 6], confirmation (106) is performed by a partial selection (106a) of a number of objects based on the point clouds, preferably one object per cluster (the cluster being created in the clustering step (103) and classified in step (104) from the data table (102) resulting from the solid-phase cytometry analysis (101) which includes a screening step). From this partial selection (106a), all the selected objects are confirmed (106c) by acquiring one or more confirmation images for each selected object and by applying a classification model (106d) based on one or more images acquired for each object, the model being a supervised model. From this classification, a quantitative probabilistic score (106e) is deduced for each of the objects.An optional activation function (107) can be applied to the quantitative probabilistic score, then the scores (108) are summed and an enumeration result (109) is derived. The enumeration score is then compared (110) with a threshold. The threshold chosen for the control method is determined based on the desired false positive and false negative rates, either by optimizing the false positive or false negative rate, or by combining the false positive and false negative rates by calculating the precision or the Fl-score, or by calculating from the ROC (Receiving Operating Characteristics) curve. Whether the sample is contaminated or not is then determined (step 111).

[0139] According to the third embodiment shown in [Fig. 7], confirmation (106) is performed by an iterative selection (106b), starting with objects whose probability of belonging to the microorganism cluster is high, and more specifically, a first object belonging to a microorganism cluster is selected (the cluster being created in the clustering step (103) and classified in step (104) from the data table (102) resulting from the solid-phase cytometry analysis (101) comprising a screening step). From this selection (106b), the first selected object is confirmed (106c) by acquiring one or more confirmation images for each selected object and by applying a classification model (106d) based on one or more images acquired for each object, the model being a supervised model. From this classification, we deduce a probabilistic quantitative score (106e) for the first object.An optional activation function (107) can be applied to the probabilistic quantitative score, then the score (108) is summed and an enumeration result (109) is deduced. The enumeration score is then compared (110) with a threshold; if the score is greater than the threshold, the method is stopped; if the score is less than the threshold, steps 106c, 106d, 106e, 107, 108, 109 are repeated with a second object, steps 106c, 106d, 106e, 107, 109. 108, 109, being repeated for n objects until the threshold is exceeded or all objects are confirmed.

[0140] Figure 9 represents a comparison between the processing of a sample according to a known algorithm (figures (a) and (c)) and the algorithm according to the invention (figures (b) and (d)).

[0141] It can be seen that in the case of a known algorithm, different point clouds Cil, C12 are detected depending on whether they are positioned in the determined positivity window F, while for the algorithm according to the invention only one point cloud Cil is detected.

[0142] With such an approach, the known algorithm would have wrongly detected contamination, whereas the algorithm according to the invention, which is much more precise, avoids the false positive.

[0143] Of course, the invention is not limited to the embodiments described and shown in the accompanying figures. Modifications remain possible, particularly with regard to the composition of the various elements or by substitution of technical equivalents, without departing from the scope of protection of the invention.

Claims

Demands

1. Enumeration method for microbiological control comprising: - Analysis (101) by flow cytometry of a matrix comprising a given volume of a sample, said sample having been previously marked by a labeling protocol, - Obtaining (102) a multidimensional data table presenting objects detected during the flow cytometry analysis, characterized in that the method comprises a step of applying an enumeration algorithm comprising at least the following steps: - Assignment (105) of a quantitative score within a range of value from [0 to 1] to each object detected during the analysis according to its probability of belonging to a determined "microorganism" class - Sum (108) of the scores obtained in the previous step and in that the method comprises a step: - of Enumeration (109) of microorganisms from the sum of the scores.

2. Enumeration method according to claim 1, comprising the following steps implemented before the scoring assignment step (105): • Clustering (103) of the objects in the data table into a plurality of clusters comprising at least one cluster called a "point cloud" and / or at least one "isolated point" by means of a calculation of at least one parameter for each cluster or each isolated point, the at least one parameter being selected from a list of parameters comprising at least one position and / or shape and / or density of the cluster or isolated point, • Classification (104) of each point cloud cluster, the class being selected from several classes comprising at least the class "microorganism", the classification being carried out according to the at least one parameter calculated in the previous step and business rules relating to the marking protocol; each isolated point not being classified.

3. Enumeration method according to claim 2, comprising one or more pre-processing steps (100) of the data table before the clustering step.

4. Enumeration method according to claim 3, comprising at least one pre-processing step of the type sizing and / or rotation and / or distortion and / or binning of the data table.

5. Enumeration method according to any one of claims 3 or 4, comprising at least one dimensionality reduction pre-processing step.

6. An enumeration method according to any one of claims 2 to 5, wherein the clustering of the data array is carried out from learning methods based on a density calculation of the objects in the data array.

7. Enumeration method according to any one of claims 2 to 5, wherein the clustering of objects in the data table can be achieved from a learning method such as density analysis and preferably HDBSCAN analysis.

8. Enumeration method according to any one of claims 1 to 7, wherein the cytometry analysis is a solid phase analysis.

9. An enumeration method according to any one of claims 1 to 8, comprising a step of applying an activation function (107) on the scores of each object, said activation function being configured to optimize the correlation between the enumeration of detected microorganisms and the sum of the scores.

10. Cytometry system (1) for microbiological analysis purposes, configured to implement the enumeration method for the microbiological control of samples according to any one of claims 1 to 9.

11. Qualitative microbiological control method comprising the following steps: - implementation of the steps of the enumeration method according to any one of claims 1 to 9, said microbiological control method further comprising at least the following steps: • comparison with a determined contamination threshold (110), the comparison being carried out between the enumeration result obtained in the enumeration step (109) and said contamination threshold, the comparison step being carried out after the enumeration step (109), • determination of the contamination of the sample (111) following the comparison (110); if the enumeration result is greater than the threshold, then the sample is considered contaminated; if the enumeration result is less than the threshold, then the sample is considered uncontaminated.

12. Qualitative microbiological control method according to claim 11, wherein where the analysis is a solid phase cytometry analysis which includes a screening step of the sample, the control method includes a confirmation step (106) of the objects detected during the screening step.

13. Qualitative microbiological control method according to claim 12, wherein the confirmation (106) includes a selection step (106a, 106b) of at least one item to be confirmed, the selection step being either partial (106a) or iterative (106b).

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