WO2021205483A1

WO2021205483A1 - Method and system to detect and classify bacteria and other cells in a urine sample

Info

Publication number: WO2021205483A1
Application number: PCT/IT2020/000033
Authority: WO
Inventors: Paolo Galiano; Danut Adrian Cojoc; Maria Augusta DO REGO BARROS FERNANDES DE LIMA
Original assignee: Consiglio Nazionale delle Richerche CNR; Alifax SRL
Current assignee: Consiglio Nazionale delle Richerche CNR; Alifax SRL
Priority date: 2020-04-08
Filing date: 2020-04-08
Publication date: 2021-10-14
Anticipated expiration: 2022-10-08

Abstract

The current invention concerns a method and a system to detect and classify bacteria and other cells, in primary urine samples and classify the samples. The method comprises providing a liquid urine sample; obtaining optical phase information from the liquid urine sample by digital holographic (DHM) and speckle microscopy (SM); localizing and tracking of the cells flowing in fluidic cell sample; deriving cellular parameters (e.g. size, shape, volume, aspect ratio) from optical phase information, and classifying the cells on the base of the extracted cellular parameters.

Description

“METHOD AND SYSTEM TO DETECT AND CLASSIFY BACTERIA AND OTHER CELLS IN A URINE SAMPLE”

FIELD OF THE INVENTION

The invention pertains to the technical field of diagnostics, and more specifically to a method and system for determining specific parameters of a liquid urine sample by obtaining optical phase information (OPI) and speckle pattern information (SPI) by digital holographic microscopy (DHM) and speckle microscopy (SM). The method offers a non- destructive manner of analyzing bacteria and other cells in urine samples and can be used for the detection of bacteria, leucocytes, erythrocytes and epithelial cells and classification of the urine samples. OPI and SPI are obtained and compared to a set of values in a database relating to cellular parameters. The present invention also discloses a system that employs the method by DHM and SM of the invention as well as a method for updating and/or improving a database comprising set of parameters linked to OPI and the database related thereof.

BACKGROUND OF THE INVENTION

It is known that the analysis of urine samples, or urinalysis, is a very common clinical exam used to easily obtain diagnostic information on health status of a subject.

In particular, urinalysis is useful to detect bacterial or fungi/yeast infections of urinary system.

Several systems have been developed to test urine samples.

A review of similar systems and techniques is given in this section.

There are some commercial systems for urinalysis:

1. Sys ex UF-lOOOi

(https : //www . sysmex . com/us/en/Products/Urinaly s i s/Documents/UF 1000i%20Br ochure.pdf) uses fluorescence flow cytometry with diode laser, hydrodynamic focusing and regular conductometry. It offers two channels: one for bacteria and one for sediment particles. Sample preparation is required. The particles are focused in a thin microjet, allowing that particles are intercepted by a laser beam one by one. Fluorescence and flowing time signals are collected by photodetectors and analyzed. Particles are classified based on several criteria: outer shape, inner complexity, surface structure, DNA content. It is claimed to reduce the detection time for tract infections from hours to one minute, at a rate of up to 100 samples/hour. Parameters: WBC, RBC, EC, CAST, BACT, SRC, YLC, SPERM, X’TAL, P.CAST, MUCUS, conductivity, RBC info, UTI info.

2. The Iris iRICELL1500 from Beckman Coulter (https://www.beckmancoulter.com/it/products/urinalysis/iricell) integrates urine chemistry and microscopy into a fully automated walk-away solution that is easy to use and maintain. Iris optimizes and advances urinalysis and body fluid testing through its proprietary Digital Flow Morphology (DFM) technology using Auto- Particle Recognition (APR) Software for standardized and accurate results. Automatically classify 12 particles and sub-classify 27 particles with DFM. APR system reduces subjectivity. Process preservative tube samples on the iQ200 for greater flexibility.

Traditional cytofluorimeters and flow microscopes as those presented above provide single cell fluorescence signal and ID or 2D spatial information on the objects suspended in a flow. This is not always enough because it does not provide information on the sample height and volume.

Digital holographic microscopy (DHM) is an imaging technique that provides phase information (optical path) and hence three-dimensional (3D) information on the sample [1995_JMicrosc_Zicha]. The hologram is first recorded on a digital camera as the interference pattern between the reference beam and the object beam diffracted by the sample. The recorded hologram is then digitally processed to reconstruct the intensity and the phase of the object beam. The phase contains the 3D information in the optical path term, which is the product between the refractive index of the material and the geometrical path of the light.

Recently several DHM instruments with different optical configurations have been commercially proposed for living cells studies (most of them for cells on substrate cultures):

- Holmarc, https://www.holmarc.com/imaging_measuing_instruments.php, DHMs for quantitative study of living cells, imaging of various cell types, including SKOV-3 ovarian cancer cells, fibroblast cells, testate amoeba, diatom skeletons, and red blood cells.

Lyncee tec, https://www.lynceetec.com/category/biological_imaging/, DHMs for quantitative phase measurements (QPM) of living cell cultures up to confluence, without the use of any contrast agent and with very low illumination power, high content screening, time lapse measurements and diagnostic. Used in conjunction with the optional fluorescence module, it enables simultaneous DHM and fluorescence measurements.

- Phase Holographic Imaging AB (PHI), https://phiab.com/, HoloMonitor for quantitative study of living cells, cells proliferation, cell migration, time lapse imaging cytometry.

- Ovizio Imaging systems, http://www.ovizio.com/en/Technology, proposes differential DHM with the QMod microscope for holographic and fluorescence microscopy of living cells and the iLine F microscope for in-line, label-free suspension cell counting, which can be combined with a bioreactor.

The Ovizio system has been recently employed for label-free leukemia detection by DHM [2018_AdvScience_Ugele]. Classification of the cells in healthy and leukemic cells is performed by extracting cell parameters as the optical volume and height, based on the phase information obtained by DHM, and using Principal Component Analysis (PCA). The technique requires sample preparation and 2D hydrodynamic focusing of the sample flow to about 10 microns as required by the depth of focus of the system. Different versions of DHM have been proposed in literature in the last years for new applications in biomedicine. Thus, quantitative phase microscopy has been proposed to extract spatial signatures of cancer cells

[2017_Cytometry_A_Roitshtain]. A set of 15 parameters derived from the cellular 3D morphology and texture has been extracted for cells in suspension (without flow) from different cancer lines to constitute the signature of a cell type. Inline DHM as a label free technique for detecting tumor cells in a background of blood cells, based on three features (size, maximum intensity and mean intensity), is presented in two recently published studies

[2017_LabChip_S ingh] . Neither methods nor instruments based on DHM or and SM have been proposed for urinalysis.

The purpose of the present invention is to provide a method and system for determining specific parameters of a liquid urine sample by obtaining optical phase information (OPI) by digital holographic microscopy (DHM) and speckle pattern information (SPI) by speckle microscopy (SM). A further purpose is to provide a system configured to analyze and classify urine samples into negative and positive samples.

The Applicant has devised, tested and embodied the present invention to overcome the shortcomings of the state of the art and to obtain these and other purposes and advantages.

SUMMARY OF THE INVENTION

The present invention is set forth and characterized in the independent claims, while the dependent claims describe other characteristics of the invention or variants to the main inventive idea.

The present invention provides for a free-label method and system for analyzing liquid urine samples in a non-destructive, fast, inexpensive and objective manner and to detect the presence of bacteria in three (low / medium / high) levels, detect and count leukocytes, erythrocytes and epithelial cells present in the sample. In the current invention, said liquid urine sample are analyzed by a digital holographic microscope (DHM) allowing to obtain also speckle pattern information (SPI)).

The practitioner is provided with a digital report, comprising diagnostic information on the presence, concentration and size of bacteria in the urine sample as well as a set of cellular parameters related to cells present in the sample. This will give the practitioner or pathologist the chance to evaluate the raw sample in an unbiased manner, by taking the provided cell sample parameters into account. Diagnosis can be solely based on the report provided by the system or, if desirable, the practitioner or pathologist can proceed by more conventional means of diagnosing. DHM and SM provide the detection of negative and positive samples by a fast screening of urine samples. DHM provides both a highly specific and sensitive method for analyzing urine samples, which is often a problem for other analytical methods currently known.

DHM enables the study of living cells without the need for markers or dyes and enables quantitative analysis of the morphology of cells suspended in fluid as well as various sub-sections of said cells by obtaining a three-dimensional image from the optical phase information. The possibilities of DHM have increased during the last years due to an increase in the development of digital sensors and computers. The method allows to visualize cells without any staining up to a degree of sub-cellular distinguishability which allows efficiently segmenting the cells, counting their number and reliably classify them according to their type.

SM allows studying behavior of bunch of small particles and enables qualitative analysis of the bacteria concentration. In this regard SM has been improved in the last years by the availability of cameras with higher sensitivity and resolution. SM, in the context of the current invention, is a complementary technique to DHM, speckle pattern information is employed when the bacteria concentration is high and DHM cannot work.

The SPI, derived from the SM, has been found by the inventors to be a highly reliable parameter for detecting bacteriuria with high concentration. SPI is defined by intensity distribution and granularity. These two parameters determine direct sample classification in high concentration bacteria.

In a preferred example of the method according to the current invention, the optical path difference (OPD) is derived from the optical phase information (OPI). OPD has been found by inventors to be a consistent parameter, which is correlated with other parameters extracted from the holographic information, allows to evaluate the presence of bacteria and its concentration over the sample and to classify the sample in one of the three bacteria concentration categories: low/medium/high.

In a preferred embodiment of the method according to the current invention, at least one cellular parameter is derived from holographic information. The cellular size and shape, derived from the holographic information, have been found by the inventors to be highly reliable parameters for cell detection and identification of cell types. Cellular size is characterized by a set of sub-parameters: maximum lengths in two orthogonal directions measured on the two-dimensional projection of the cell hologram, cell area from the same projection, cell maximum height from the OPI. The second parameter, the cell shape, is also characterized by a set of sub-parameters: cell volume calculated from the 3D reconstruction from OPI, ratio between cell volume and cell area, nucleus volume, ratio between cell volume and nucleus volume, cell height variation. A scoring vector SV is appointed to the cells of said cell sample, based on said cellular parameters. Said SV determines the recognition and classification of said cells

The scoring vector SV above defined is a vector that comprises elements calculated using the cellular parameters described above and determined during measurement and processing.

The elements can be: size, shape, volume area, height, nucleus volume.

For each type of cell a reference vector is defined and compared by correlation with the measured vector. The result is called Scoring Vector SV. This is used to validate the measurement according to the values of each element of the SV. The criterium depends on the type of measurement and tolerance.

In a preferred embodiment of the method according to this invention, for samples classified as positive (i.e. medium or high bacteria concentration), Gram positive and Gram negative bacteria are discriminated from the holographic information, analyzing the size and the shape of bacteria in a given volume of a static sample. This allows to choose the specific antibiotics for the two classes of bacteria and check the effect of antibiotics according to the bacteria type. The effect after treatment with antibiotics is evaluated from the bacteria’s vitality determined by morphological parameters (changes in the size and shape parameters) and dynamic parameters (diffusion coefficient, spatial and time variation of the intensity of the scattered light) of the bacteria freely moving in the urine sample. A relevant number of bacteria are analyzed and a score vector, including the parameters above mentioned, the treatment length and antibiotic concentration is elaborated to evaluate the antibiotics effect.

This procedure leads important advantages with respect to any clinical antibiogram test. In fact, in the present method the antibiogram test can be performed in a continuous flow on the same urine sample used in the system by the use of DHM.

In a preferred embodiment, after deriving the sample parameters from the Speckle Pattern Information (SPI) and Optical Phase Information (OPI) contained in the recorded digital hologram, a scoring vector SV is appointed to each sample, whereby the SV determines the classification of said urine sample, cell content and cell type. By doing so, each urine sample is objectively evaluated, ensuring furthermore that all urine samples essential for diagnosis have been evaluated in the same, objective manner. This is a benefit when compared to the microscopy analysis of samples by a practitioner, as these analysis are time consuming and dependent on the skills and knowledge of the practitioner, as well as to the employed method of analysis and the handling the sample underwent prior to this analysis. Preferably, a practitioner will be provided with a digital report on the classification of said urine sample and cells. After receiving said digital report and diagnostic information stated therein, said practitioner can decide whether it is necessary or not to perform extra analyses. By providing the practitioner with the digital report time-consuming procedures may be avoided, moreover saving costly man-hours for a diagnostic laboratory or service.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other characteristics of the present invention will become apparent from the following description of some embodiments, given as a non-restrictive example with reference to the attached drawings wherein:

- Figure 1 depicts a schematic overview of one embodiment of the current invention, where flowing urine is analyzed;

- Figure 2 depicts a schematic overview of the processing for sample classification;

- Figure 3 depicts an example of recorded digital hologram and the reconstructed optical path difference OPD of 1 microLiter urine sample, where two bacteria (marked by squares) are identified;

- Figure 4 depicts a recorded hologram and the reconstruction of the OPD for a erythrocyte detected in the urine sample. The cell height is measured;

- Figure 5 depicts a recorded hologram and the reconstruction of the OPD for a leukocyte detected in the urine sample. The cell height is measured;

- Figure 6 depicts a recorded hologram and the reconstruction of the OPD for an epithelial cell detected in the urine sample. The cell height is measured;

- Figure 7 depicts a recorded hologram and the reconstruction of a Candida yeast cell detected in the urine sample. The cell height is measured;

- Figure 8 depicts a recorded hologram and the reconstruction of a positive sample with high concentration of bacteria. Both the hologram and reconstruction show speckle like structure; - Figure 9 depicts a recorded hologram of crystals detected in urine sample.

- Figure 10 depicts the classification results obtained with the invention for urine samples previously analyzed by flow-cytometry. Dotted lines define the 6 groups of samples. The description of samples is given in table 1. It is understood that elements and characteristics of one embodiment can conveniently be incorporated into other embodiments without further clarifications.

DETAILED DESCRIPTION OF THE INVENTION

We will now refer in detail to the various embodiments of the present invention, of which one or more examples are shown in the attached drawings. Each example is supplied by way of illustration of the invention, as well their description and phraseology or terminology and shall not be understood as a limitation thereof or of the invention per se.

The present invention provides for a method and system for determining specific parameters of a liquid urine sample by obtaining optical phase information (OPI) and speckle pattern information (SPI) by digital holographic microscopy (DHM) and speckle microscopy (SM). The method offers a non destructive manner of analyzing bacteria and other cells in urine samples and can be used for the detection of bacteria, leucocytes, erythrocytes and epithelial cells and classification of the urine samples.

A description of the system and the method is given in this section, outlining the most innovative and original elements thereof.

A method to detect bacteria and other cells in a urine sample, the method comprising the following steps: a) providing a urine sample; b) analyzing said urine sample by digital holographic microscopy (DHM) to obtain optical phase information (OPI); c) deriving at least one cellular parameter from said analysis, and; d) classifying bacteria and other cells of said urine sample based on said at least one cellular parameter derived from said analysis obtained from said DHM.

A system 10 according to the present invention is a custom inverted microscope combined with a custom digital holographic microscope (DHM). The system 10 is depicted schematically in Figure 1. In figure 1 the cover of the system 10 is not showed to make visible the internal elements installed on a support 12 and a base 14,

The inventive system 10 for the classification of bacteria and cells in a urine sample comprises, as main components: a) a digital holographic microscope (DHM) comprising an interferometer like subsystem, composed by: coherent light source 22 with fiber coupler and splitters 22a and 22b, and an imaging subsystem composed by the objective lens 26, tube lens 28 and digital recording device 32 connected to a server; b) at least one exchangeable sample vial or sample carrier or capillary 16 comprising a urine sample; and c) a computer 40 and software capable of acquiring data and providing a digital report related to said urine sample, wherein the digital report related to said cell sample comprises classification of said cells of the cell sample, wherein the classification of said cells is based on at least one cellular parameter derived from optical phase information of the urine sample obtained from the DHM, and wherein the at least one cellular parameter comprises a characteristic the cells.

The sample carrier or capillary 16, is, for example a glass capillary, with an inner diameter between 0.3 to 1 mm. The urine sample is flowed through the sample capillary 16 by a drawing device 18, for example a syringe pump, at flow rates going from 10 to 200 microliter / min.

A vial distributor 20, fixed or rotating, may cooperate with the drawing device 18 to feed exchangeable sample vials containing urine samples.

Opposite to the drawing device 18, the sample capillary 16 may finish in a waste collector 21, e.g. a tank to collect analyzed urine. The coherent light source 22, e.g. a laser diode (LD) emitting coherent laser- light, is coupled in a single mode fiber and it is used to illuminate the sample flowing in the sample capillary 16 for the digital holographic mode.

According to an aspect of the invention, a non-coherent light source 23, e.g. a white LED, used to illuminate the urine sample for brightfield imaging, may be also provided.

The two illumination modes are coupled towards the urine sample by a dichroic mirror 24 and work alternatively with timing controlled by LED 23 and Laser Diode 22 drivers from a control unit 19. The combination of illuminations of the sample represents the first original solution allowing to observe the sample both in brightfield and holographic imaging.

The objective lens (OL) 26 is an aspheric lens with a focal fob=4.5 mm which, together with a tube lens (TL) 28 of focal length fTL= 150 mm, provide a magnification M= 35X of the microscope. A set of two lenses with focal lengths fob= 3.3 mm and respectively fTL= 200 mm can be used to get a maximum magnification M= 60X. With respect to a classic microscope, the system 10 uses a simple lens. This allows having a compact system and reducing the internal reflections present in a microscope objective with more lenses that would disturb the interference pattern of the recorded digital hologram. Although the aspheric lens does not have the same optical performance as a microscope objective, the compromise is good since its numerical aperture NA= 0.5 provides the same spatial resolution. This represents the second original element in our setup.

The objective lens 26 and the tube lens 28 may be associated to slides 30 that allow focal length variation.

To record brightfield images a second digital recording device 34, different of that used to record holograms, can be provided.

The digital recording devices are digital cameras such as CCD-camera or CMOS-camera.

In a preferred embodiment, the brightfield image is formed on a first CMOS camera 34 with 8 bit depth / pixel, while the hologram is recorded on a CMOS second camera 32 with 16 bit/pixel (high dynamic range) and fast acquisition (up to 500 frames/second). The camera used for brightfield recording may be connected to the server as well.

Both cameras 32, 34 are placed at a focal fTL distance from the tube lens 28, the two optical paths being separated by a dichroic mirror 36. Brightfield imaging allows fast inspection of the sample previously to the holographic imaging and decision to go through this second step. Moreover, brightfield imaging of the sample can be recorded also during the holographic recording, providing supplementary information on the sample. The hologram is obtained by interference of a first laser beam generated from source 22a passing through the sample and a second laser beam generated from source 22b, called reference beam, which is provided by the same diode laser 22 and coupled in optical fiber 25. The reference beam 22b is introduced in the optical path of the microscope by a cube beam splitter 38, placed between the objective lens 26 and the tube lens 28. The end of the optical fiber 25 is positioned at a focal distance fTL from the tube lens 28. This solution represents the third original element, that simplifies the setup. In the known standard setups, the reference beam is introduced after the tube lens requiring an additional optical element, which complicates the alignment and perturbates the quality of the hologram due to the internal reflections.

The fourth original element is related to the holographic principle and the way in which the optical Fourier transforms are implemented. The hologram is formed by interference between the object beam (laser beam passing through the sample) and the reference beam. To obtain useful holograms, the axes of the two beams should be slightly tilted and this is controlled by the angle of the cube beam splitter. The second condition is that the optical setup should perform two Fourier transforms of the sample. In a classic setup, the sample is illuminated with a plane wave and the first Fourier transform is obtained in the focal plane of the objective lens. The second Fourier transform is performed by the tube lens, and it is obtained on the sensor of the CMOS camera (in the focal plane of the TL). The tube lens and the objective lens should be confocal, i.e. the focal planes are in the same plane. However, in this implementation one needs a lens between the exit of the fiber and the sample to firstly collimate the beam. This has two disadvantages: the space between the fiber and the sample will be reduced and it will be more difficult to handle the sample, and the lens will introduce alignment problems and internal reflections that will disturb the hologram quality. The solution of the present application uses the fact that a Fourier transform can be obtained also with spherical illumination. The exit of the fiber is micron size, hence we can consider it as a point source generating spherical beam/wave. The Fourier transform is obtained in a plane after the objective lens, conjugated with the plane of the exit of the fiber. Since the focal length fob is short, this plane is close to the focal plane of the objective lens with the exit of the fiber being at two focal distances, instead of one, from the objective lens.

The hologram in this configuration is an interference pattern containing also a blurred image of the sample (Figure 4). However, the image is not in focus and not clearly represented. Therefore, the brightfield image, providing an image in focus, is useful to monitor the sample. Instead, the hologram contains more information about the object, i.e. besides the intensity information present in the brightfield image it contains the phase information. This phase information is reconstructed numerically from the intensity information of the recorded digital hologram. It provides the user with additional useful information about the sample, as the height and volume which cannot be by brightfield imaging. In order to analyze a relevant volume of the sample the urine sample is flowed through the capillary and the holograms are acquired at a frame rate of 100 fps. The stack of holograms can be organized in a movie .avi extension. Lower or higher frequencies can be chosen for the frame rate, according to the application. Processing the digital holographic video and extracting parameters for sample classification

Specific algorithms and software in Matlab have been elaborated to process the data. The flowchart is depicted in Figure 2 and the Matlab code is available as a main program with a set of functions. The main mathematical operations are two Fourier transforms, with which we numerically propagate back the recorded hologram towards the sample plane and reconstruct the phase information in form of optical phase difference. Knowing the refractive index of the urine and the cells, optical phase difference is then converted in optical path difference (OPD). Other operations are specific to image processing and include filtering and segmentation.

If the numerical reconstruction does not provide clear phase functions after two-three iterations, the speckle effect in the hologram and in the OPD image is analysed. The speckle is evaluated by the granularity and intensity distribution and allocating a score. A high score means high speckle effect, the granularity is small and intensity distribution randomly distributed over the image. The evaluation is performed over all the holograms and reconstructions, and an average score is calculated. A high score indicates a high bacteria concentration and the sample is classified as positive. It is the abundant presence of bacteria (turbulence) which creates the speckle effect and makes it difficult reconstructing correctly the holograms. This is an original, fast and reliable assay to classify positive plus samples. If the numerical reconstruction provides clear phase functions, the OPD is analysed and the OPD coefficient is calculated for all the frames. The coefficient over a frame is determined selecting six sub-regions of size 30x30 microns, calculating firstly the mean OPD for each, and then the mean over six and standard deviation (SD). If the sub-regions are all similar (SD < imposed value) the frame coefficient fOPD is calculated. The mean value over all the frames gives the cOPD. This coefficient is compared against a pre-imposed value. In the experiments with urine samples it was considered cOPD < 0.5 for negative samples (bacteriuria < 10 u / μL), cOPD > 1.5 for positive plus (bacteriuria > 1000 u / μL) and 0.5 < cOPD < 1.5 for positive samples, (bacteuriuria between 10 and 1000 u / μL ). To detect the presence of cells, the sub-regions in the frame where OPD is considerably bigger than the mean of the six sub-regions were analyzed. From their 3D shapes cells were localized, the cell height reconstructed and cells recognized. Examples of holograms and reconstructions for bacteria, red blood cell, white blood cell, epithelial cells, candida yeast cells, are given in the Figures 4-8. Cell recognition is performed by the operator or with an automatic recognition procedure to be developed. A special case of hologram of crystals flowing in the sample is shown in Figure 9. The procedure and parameters described above are original and represent the key of the data processing for sample classification.

For samples classified as positive (i.e. medium or high bacteria concentration), Gram positive, and Gram negative bacteria can be discriminated from the holographic information, analyzing the size and the shape of bacteria in a given volume of a static sample. This allows to choose the specific antibiotics for the two classes of bacteria and check the effect of antibiotics according to the bacteria type. The effect after treatment with antibiotics is evaluated from the bacteria’s vitality determined by morphological parameters (changes in the size and shape parameters) and dynamic parameters (diffusion coefficient, spatial and time variation of the intensity of the scattered light) of the bacteria freely moving in the urine sample. A relevant number of bacteria are analyzed and a score vector, including the parameters above mentioned, the treatment length and antibiotic concentration is elaborated to evaluate the antibiotics effect.

A method for updating and/or improving a database comprising thresholds linked to holographic information, comprising the steps of: characterized in that. a) obtaining holographic information linked to a sample wherein said holographic information is obtained using digital holographic microscopy (DHM); b) deriving at least one parameter from said holographic information, wherein said at least one parameter comprises optical path difference of cells in the sample; c) comparing said parameter to said thresholds stored in database; d) classifying objects in the sample based on said comparison of said parameter, and said thresholds; e) reporting said classification of said objects; f) obtaining feedback with regards to said classification; and g) updating said database on the basis of said feedback.

A database of objects comprising a set of thresholds, queries and holographic information and/or parameters related to a characteristic of the morphology of cells in a urine sample, wherein a) said holographic information is obtained from a sample using a digital holographic microscope and/or parameters derived thereof; b) said thresholds and queries are related to the analysis of said holographic information and/or parameters; wherein the database further comprises c) classifications of the objects derived from said holographic information obtained from the sample using the digital holographic microscope and or parameters derived thereof; d) validation of susceptibility test of any antibiotic agent based on morphological cell analysis before and after sample treatment. d) image identification; e) identification information.

Examples Preliminary results have been obtained with urine samples analyzed by flow- cytometry. The type of samples, organized in 6 groups (G 1 to G6), and the results obtained with the invention are depicted in Table 1, and graphically in Figure 10. The results obtained with the invention confirm the analysis results obtained by flow cytometry in most of the cases. There are few differences, which might be assigned to the level of accuracy, but also to the possible errors in flow cytometry.

84 samples in 2 x 3 = 6 groups Leukocyturia:

Positive > 10 u/uL Negative < 10 u/uL Bacterium:

Negative < 10 u/uL Positive > 10 u/uL Positive + > 1000 u/uL

It is clear that modifications and/or additions of parts and steps may be made to the system and method to detect bacteria and other cells in a urine sample as described heretofore, without departing from the field and scope of the present invention.

In the following claims, the sole purpose of the references in brackets is to facilitate reading: they must not be considered as restrictive factors with regard to the field of protection claimed in the specific claims. BIBLIOGRAPHY

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Claims

1. A method to detect bacteria and other cells in a urine sample, the method comprising the following steps: a) providing a urine sample; b) analyzing said urine sample by digital holographic microscopy (DHM) to obtain optical phase information (OPI); c) deriving at least one cellular parameter from said analysis, and; d) classifying bacteria and other cells of said urine sample based on said at least one cellular parameter derived from said analysis obtained from said DHM.

2. A method according to claim 1, wherein said classification occurs by appointing a scoring vector (S V) based on said cellular parameters.

3. A method according to claims 1 or 2, wherein said analysis further allow obtaining speckle pattern information (SPI).

4. A method according to any of the claims from 1 to 3, further providing to illuminate said urine sample with non-coherent light in order to obtain brightfield information.

5. A method according to claim 4, wherein said obtaining brightfield information occurs during obtaining said optical phase information.

6. A method according to claim 1, wherein said at least one cellular parameter comprises optical path difference of the cells.

7. A method according to claim 1, wherein said at least one cellular parameter further comprises any ratio of the optical path difference of the nucleus.

8. A method according to claim 1, characterized in that said at least one cellular parameter derived from obtained optical phase information further comprises the cellular optical height.

9. A method according to claim 1, characterized in that said at least one cellular parameter derived from obtained optical phase information further comprises cell nucleus diameter, cell size, cell form and cell morphology.

10. A method according to claim 1, comprising a step of detecting leukocyte cells in the urine sample based on said cellular parameters.

I I. A method according to claim 1, comprising a step of detecting red blood cells in the urine sample based on said cellular parameters.

12. A method according to claim 1, comprising a step of detecting bacteria in the urine sample based on said cellular parameters.

13. A method according to claim 12, wherein said detection allow the discrimination of Gram positive bacteria and Gram negative bacteria.

14. A method according to claim 1, comprising a step of detecting epithelial cells in the urine sample based on said cellular parameters.

15. A method according to claim 1, further comprising classifying said cells of the cell sample based upon comparison of said at least one cellular parameter and a threshold database.

16. A method according to claim 1, further comprising the step of identifying the cellular type of said cells in the sample, prior to said classifying cells.

17. A method according to any of the claims 1 to 13, wherein the analysis is made with flowing urine sample.

18. A system for the classification of bacteria and cells in a urine sample comprising a) a digital holographic microscope (DHM) comprising an interferometer like subsystem, composed by: coherent light source (22) with fiber coupler and splitters (22a, 22b), and an imaging subsystem composed by the objective lens (26), tube lens (28) and digital recording devices (32) connected to a server; b) at least one exchangeable sample vial or sample carrier or capillary (16) comprising a cell sample; and c) a computer/software (40) and a software capable of acquiring data and providing a digital report related to said urine sample, wherein the digital report related to said cell sample comprises classification of said cells of the cell sample, wherein the classification of said cells is based on at least one cellular parameter derived from optical phase information of the urine sample obtained from the DHM, and wherein the at least one cellular parameter comprises a characteristic the cells.

19. A system according to claim 18, the urine is flowed through said sample capillary (16) by a drawing device (18).

20. A system according to claim 18 or 19, further comprising a non-coherent light source (23) emitting a non-coherent white light to illuminate said urine sample.

21. A system according to claim 19, wherein said coherent light source (22) and said non-coherent light source (23) are coupled towards the urine sample by a dichroic mirror (24).

22. A system according to claim 21, wherein said first light source (22) and said second light source (23) work alternatively with timing controlled by a control unit (19).

23. A system according to any claim from 18 to 22, wherein the system uses spherical lens (26, 28) to provide urine sample magnification.

24. A system according to any claim from 18 to 23, wherein the end of the optical fiber (25) is positioned at a focal distance from the tube lens (28).

25. A system according to any claim from 18 to 24, wherein said coherent light source is emitted by a optical fiber (25) providing a spherical beam obtaining a Fourier transform in a plane after a objective lens (26), conjugated with the plane of the exit of the fiber.

26. A system according to claim 18, whereby said server is provided with algorithms for the comparison of said cellular parameters with a threshold database.

27. A system according to any of the claim 18, whereby said exchangeable sample vial or sample carrier comprises identifying marks.

28. A system according to claim 18, whereby the urine sample is a liquid cell sample.

29. A system according to any claim from 18 to 28, whereby the cells are held in suspension in the liquid urine sample.

30. A method for updating and/or improving a database comprising thresholds linked to holographic information, comprising the steps of: characterized in that. a) obtaining holographic information linked to a sample wherein said holographic information is obtained using digital holographic microscopy (DHM); b) deriving at least one parameter from said holographic information, wherein said at least one parameter comprises optical path difference of cells in the sample; c) comparing said parameter to said thresholds stored in database; d) classifying objects in the sample based on said comparison of said parameter, and said thresholds; e) reporting said classification of said objects; f) obtaining feedback with regards to said classification; and g) updating said database on the basis of said feedback.

31. A method according to claim 30, further comprising storing identification information linked to said sample.

32. A database of objects comprising a set of thresholds, queries and holographic information and/or parameters related to a characteristic of the morphology of cells in a urine sample, wherein a) said holographic information is obtained from a sample using a digital holographic microscope and/or parameters derived thereof; b) said thresholds and queries are related to the analysis of said holographic information and/or parameters; wherein the database further comprises c) classifications of the objects derived from said holographic information obtained from the sample using the digital holographic microscope and/or parameters derived thereof; d) validation of susceptibility test of any antibiotic agent based on morphological cell analysis before and after sample treatment. d) image identification; e) identification information.

33. A database according to claim 32, whereby said characteristic comprises optical path difference of the cells in the sample.