Processing system and system for pathogen characterization

Abstract

There is provided a processing system to characterize a predefined set of pathogens in a sample, a system, and a method.

Description

PROCESSING SYSTEM AND SYSTEM FOR PATHOGEN CHARACTERIZATIONFIELD OF THE INVENTION

[0001] The invention pertains to devices for the characterization of a given population of microorganisms, in particular, for determining and identifying a predetermined set of pathogens.BACKGROUND OF THE INVENTION

[0002] Currently, there are many commercially available solutions for pathogen identification and quantification. However, these provide slow and reactive testing services. These methods rely on manual sample collection and specialized technicians, leading to less frequent testing and delayed results. Additionally, they often rely on chemicals such as antibiotics for bacterial infections, which are detrimental for humans and the environment. While some solutions may use advanced techniques such as PCR, microfluidics, spectroscopy, aptamer usage, or microscopy methods to detect bacteria, these methods still have limitations as these also require specialized equipment and expertise, making really difficult to have a PoC device to continuously track pathogen presence.

[0003] It is an object of the present disclosure to overcome the cited problems at least partially in the prior art.SUMMARY OF THE INVENTION

[0004] In a first aspect of the disclosure, a processing system to characterize a predefined set of pathogens in a sample is disclosed. The processing system configured to: obtain a measure of at least one detectable signal based on one or more captures obtained by a sensor system, the sensor system comprising an optical sensor; the at least one detectable signal emitted by at least one genetically modified microorganism upon contact with a target molecule produced by a pathogen of the predefined set of pathogens in a first sample aliquot from the sample, wherein a different genetically modified microorganism emits a different detectable signal upon contact with at least one different target molecule; obtain at least an absorbance measure of the first sample aliquot based on one or more captures obtained by the sensor system; obtain a first characterization of the predefined set of pathogens in the sample by processing at least one the measure of the at least one detectable signal and the at least one absorbance measure of the first sample aliquot; obtain a second characterization of the predefined set of pathogens based on analysis of a macroscopic growth of at least a second sample aliquot based on two or more growth images of the at least the second sample aliquot, the two or more growth images obtained by the sensor system; and configured to output an output characterization of the predefined set of pathogens based on the first characterization and on the second characterization; the first characterization, second characterization and output characterization comprising at least one or more of: pathogen detection, pathogen identification, and pathogen quantification.

[0005] The wording characterizing or characterization may comprise one or more of: detection, identification, quantification. The characterization may further comprise determination of virulence, where virulence is a pathogen's or microorganism's ability to cause damage to a host, also herein referred to as pathogenicity. The characterization may further comprise the probability of one or more of: detection, identification, quantification, and determination of virulence.

[0006] The predefined set of pathogens may comprise one or more pathogens. The set of pathogens comprise a number of pathogens which are to be characterized in a sample.

[0007] The term “sample” may refer to any biological and non-biological substance in liquid, solid or gas state, for example, water that contains or is suspected of containing a target microorganism or pathogen. A sample may be a biological substance, for example, comprising prokaryotic and / or eukaryotic cells. A sample may comprise both non-biological and biological substances. "Sample" as used herein may also refer to a biological sample which is derived from an animal, in particular, fish, for example, feces. Examples of such samples include food such as meat, vegetable, fruits, dairy products, grains-beans, oils, and sugar; water such as municipal water, tap water and water in a swimming pool; a clinical sample; including biological fluid samples such as urine. The processing system of the disclosure may be used to detect contamination in food / water sources, and in the clinical / diagnostic setting. In some embodiments, the sample is a sample of water from aquaculture facilities. The term “aquaculture” refers to any industrial activity related to the farming of fish and crustaceans. The term relates to fish and crustacean farming in sweet or freshwater and sea water. In some embodiments, the sample derives from an aquaculture environment in which a fish of interest is being reared.

[0008] The absorbance is indicative of the cell abundance of the culture resulting from the proliferation of the genetically modified microorganisms. The absorbance of the sample may be determined by processing an image obtained by the optical sensor as will be explained below. To measure absorbance, in some examples the intensity of the wavelength of interest is monitored and an absorbance value is obtained by inverting the quantified decrease in transmitted light signal. In some embodiments, the wavelength is 660 nm.

[0009] The disclosure also contemplates that the cell abundance may be determined by measuring the turbidity of the sample at the moment when the concentration of target molecules is determined and extrapolating the turbidity value using an algorithm that correlates it with the corresponding absorbance as measured with standard spectrophotometers.

[0010] In some examples, the first characterization is obtained by processing at least the measure of the at least one detectable signal normalized to an absorbance measure of the same first sample aliquot. The first characterization may comprise at least one or more of: pathogen detection, pathogen identification, and pathogen quantification. The first characterization may comprise a probability of presence of a pathogen in a virulent stage. The first characterization is based on the detectable signal and thus, in examples, based on quorum sensing mechanisms of the microorganisms emitting the detectable signals upon contact with at least one target molecule. The first characterization may therefore be indicative of virulence or virulence degree or stage of the microorganisms in the samples.

[0011] The feature “wherein a different genetically modified microorganism emits a different detectable signal upon contact with at least one different target molecule”, in the context of the disclosure, contemplates, in someexamples, that a different genetically modified microorganism, being different from the at least one genetically modified microorganism which emits at least one detectable signal upon contact with a target molecule, contains a different inducible promoter, such that different genetically modified microorganism are different based on the presence of a given inducible promoter. In other words, each genetically modified microorganism reacts by producing a reporter in response to a given target molecule which activates a given inducible promoter. In one example, a given inducible promoter becomes activated by only one target molecule so that each genetically modified microorganism may react by producing a reporter in response to a given target molecule. In another example, a given inducible promoter becomes activated by two or more target molecules so that each genetically modified microorganism may react by producing a reporter in response to two or more given target molecules. In examples, a first genetically modified microorganism and a second genetically modified microorganism react by emitting a (possibly different) detectable signal in response to at least the same target molecule, where the first genetically modified microorganism and the second genetically modified microorganism are different between them. In another example, a first and a second (different) genetically modified microorganisms that may produce a reporter in response to two or more target molecules may respond to at least one same target molecule. Thus, a first and a second (different) genetically modified microorganisms that may produce a reporter in response to two or more target molecules may each emit a detectable signal in response to at least one same target molecule. In other words, a first and a second (different) genetically modified microorganisms may each emit a detectable signal in response to at least one same target molecule. In another example, a first and a second (different) genetically modified microorganisms that may produce a reporter in response to two or more target molecules may respond to at least one same target molecule and at least one different target molecule. In other words, a first and a second (different) genetically modified microorganisms may each emit a detectable signal in response to at least one same target molecule and at least one different target molecule. In another example, a first and a second (different) genetically modified microorganisms that may produce a reporter in response to two or more target molecules may respond to different target molecules. In other words, a first and a second (different) genetically modified microorganisms may each emit a detectable signal in response to totally different target molecules.

[0012] The processing system may comprise one or more computing devices or processing units. In some examples, a computing device has at least one processor and a memory. In some implementations, the memory can correspond to one or more volatile memory devices (e.g., random access memory (RAM) devices), one or more nonvolatile memory devices (e.g., read-only memory (ROM) devices, programmable read-only memory, and flash memory), or a combination thereof. The processor may comprise a single processing core or multiple processing cores, on a single processor chip or multiple processors chips, in direct communication with each other or in a distributed processing system, executing computer instructions, dedicated hardware, or a combination thereof. Although various operations are described as being performed iteratively, in some implementations such operations may be executed fully or partially in parallel rather than in sequential iterations.

[0013] The processing system is configured or programmed to execute computer-executable instructions to obtain a measure of at least one detectable signal, for example an intensity measure of an optical signal - or fluorescent signal, or a measure of a voltage signal or a measure of any other signal for example odor signal. The measure is based on one or more captures, for example based on one or more captures of images or captures ofthe optical signal, obtained by a sensor system being an optical sensor or camera (e.g., photographic camera or digital camera). Obtained by the sensor system may comprise captured by the sensor system. The at least one detectable signal may be emitted by the at least one genetically modified microorganism upon contact with a target molecule produced by a pathogen of the predefined set of pathogens in the first sample aliquot. In examples of the present disclosure, different genetically modified microorganisms emit fluorescent signals upon contact with different target molecules.

[0014] The first sample aliquot may be held in a chamber. Said chamber may be any type of container, such as a well. The processing system may be comprised in a same system as a container to hold the sample, or the processing system may not form part of the same system and / or be remotely located, i.e. , as a cloud server. The processing system, either locally or remotely located, may receive one or more captures from the sensor system. The processing system may receive captures or data via an input interface in communication with, for example, wired or wireless communication means, for example, copper cable, coaxial, optical fiber, data cable, wireless communication system based on wireless technologies such as WiFi or WiMAX, Tetra, or mobile communications interfaces 3G, 4G, 5G, 6G.

[0015] The processing system is further configured or programmed to execute computer-executable instructions to obtain at least an absorbance measure of the first sample aliquot based on one or more captures obtained by the sensor system. The absorbance measure may be obtained by receiving the absorbance measure from a capture by the sensor system, where the sensor system comprises a spectrophotometer. The absorbance measure may be obtained by processing an image captured by the sensor system where the sensor system comprises an optical sensor and where the absorbance measure is obtained based on the image captured by the optical sensor Processing an image captured by the sensor system to obtain an absorbance measure may comprise obtaining an image by the optical sensor; obtaining readings of absorbance by a spectrophotometer of the first sample aliquot; measuring the turbidity of the sample aliquot by processing the obtained image by analyzing the colour channel information of the image; obtaining a relationship between the absorbance readings and the measures of turbidity; fitting the relationship with a model, for example a logarithmic model; and determining a correlation between absorbance readings and the measurements of turbidity.

[0016] The processing system is further configured or programmed to execute computer-executable instructions to obtain a first characterization of the predefined set of pathogens in the first sample aliquot by processing at least the measure of the at least one detectable signal and the at least one absorbance measure of the first sample aliquot. In examples the second characterization comprises at least one of: pathogen identification, concentration of the pathogen in the sample, and probability of existence of the pathogen in the sample. In some examples the processing system is further configured to obtain the first characterization by obtaining a first characterization of the predefined set of pathogens in the sample by processing the measure of the detectable signal, and the absorbance measures, wherein the absorbance measure is obtained from the same first sample aliquot as the first sample aliquot where the measure of detectable signal is obtained from. The first characterization may be obtained by processing a normalized measure of the detectable signal, which may comprise the measure of the detectable signal of the first sample aliquot divided by the absorbance measure of the same first sample aliquot.

[0017] The sample for obtaining the first characterization may be held by a first chamber or by a first plurality of chambers. The first plurality of chambers may be comprised within a well plate, wherein each well holds an aliquot of the first sample.

[0018] The processing system is further configured to obtain a second characterization of the predefined set of pathogens based on analysis of a macroscopic growth of the pathogens of at least a second sample aliquot. In examples the second characterization comprises one or more values of: pathogen -identification-, concentration of the pathogen in the sample. In examples the second characterization further comprises a probability of existence of the pathogen in the sample. The second characterization provides information about presence and evolution of microorganisms in the sample. The second sample aliquot is allowed to rest in a selective substrate. Macroscopic growth refers to growth of microorganisms that may be seen with the naked eye. This term generally refers to growth of microorganisms forming colonies of biofilms. The macroscopic growth features to be analyzed encompass the overall appearance of a microorganism, including its shape, colony morphology, size, and color, i.e., the features that can be seen with the naked eye, as well as behavior aspects such as growth rate, selective growth at different substrates, colony interaction with other pathogen in a given sample, among others. It is possible to identify the type of microorganism by examining the gross morphological / macroscopic features of the colonies formed on an agar culture. The analysis of the macroscopic growth is based on two or more growth images of the second sample aliquot in a selective substrate. The growth images may capture different stages of growth of microorganisms or pathogens in the second sample aliquot, for example an image growth may represent the second sample aliquot after an incubation period of several hours. A further growth image may represent the second sample aliquot after a further incubation period of further several hours. The two or more growth images are obtained by the sensor system, where obtained by may comprise captured by the sensor system. The second sample aliquot may be held by a second chamber or by a second plurality of chambers.

[0019] The processing system is further configured to output a characterization of the predefined set of pathogens based on the first characterization and on the second characterization. For example, in case of coincidence of first characterization and second characterization, the characterization of the predefined set of pathogens is any of first or second characterizations. In some examples the characterization comprises at least one of identification and concentration. In examples the characterization is represented as one or more triplets of values representing [pathogen, concentration, probability of existence of the pathogen in the sample]. In case of discrepancy between first characterization and second characterization, the characterization may be chosen between the first characterization or the second characterization, or the characterization may comprise a combination of the first characterization and the second characterization. The characterization may be a selection of either the first or the second characterization based on a ratio (%) of confidence that either characterization may have, in order to provide an accurate result. In examples where the characterization may be iterated in "continuous mode” the characterizations may be fed-back with previous characterizations to increase coherence. The ratio (%) of confidence is a metric obtained through accuracy of the characterization model, which may comprise a ML model.

[0020] In examples, the first characterization may comprise 3 pathogens, with 3 concentrations and 3 probabilities of presence in the sample. The probabilities of presence in the sample in the first characterization may represent the presence of pathogens in a virulent stage. The second characterization may comprise 5 pathogens, with 5concentrations and 5 probabilities of presence in the sample. Note that the first characterization provides the pathogens which are in contact with a target molecule produced by a pathogen in the sample, for example when the pathogen is in a virulence stage, and the second characterization provides pathogen which may be present in the sample but not necessarily in a virulence stage. The output characterization may therefore comprise a combination of both first and second characterizations. In examples the combination comprises outputting a characterization with the pathogens and concentrations with the highest probability between the probabilities given by the first and second characterizations.

[0021] For example, the first and second characterizations may comprise:

[0022] First characterization (indicative of virulence or pathogenicity):[pathogen 1, concentration 1.1 , probability 50%];[pathogen 2, concentration 1.2, probability 75%];[pathogen 3, concentration 1.3, probability 15%];

[0023] Second characterization (indicative of presence of pathogens):[pathogen 1, concentration 2.1 , probability 55%];[pathogen 2, concentration 2.2, probability 70%];[pathogen 3, concentration 2.3, probability 30%];[pathogen 4, concentration 2.4, probability 45%];[pathogen 5, concentration 2.5, probability 80%];[pathogen 6, concentration 2.6, probability 90%];

[0024] The first characterization may give an additional probability of infection, and the second characterization may inform about presence and concentration of microorganisms. The first and the second characterization may provide the same triplets. In the case where the first and the second characterization do not provide the same triplets, the characterization providing the greatest probability may be the output characterization by the processing system In the case of the example, the output characterization may be obtained by applying an optional filter, for example, filtering out the predictions with a probability below 50%. The resulting characterizations may comprise the combination of pathogens of both first and second characterizations. In case of contradiction between results, as for example the presence of pathogen 1 and pathogen 2 with different concentrations and different probabilities, the concentration corresponding to the characterization with a higher probability is taken. Therefore, in the example case, the output characterization would comprise:[pathogen 1 , concentration 2.1, probability 55%];[pathogen 2, concentration 2.2, probability 70%];[pathogen 3, concentration 2.3, probability 30%];[pathogen 4, concentration 2.4, probability 45%];[pathogen 5, concentration 2.5, probability 80%];[pathogen 6, concentration 2.6, probability 90%];

[0025] In examples where probabilities are filtered, for example, probabilities under 50% are discarded, then the output characterization may comprise:[pathogen 1 , concentration 2.1, probability 55%];[pathogen 2, concentration 2.2, probability 70%];[pathogen 5, concentration 2.5, probability 80%];[pathogen 6, concentration 2.6, probability 90%].

[0026] The output characterization allows an early warning alert so that, for example, a fish farm may be treated with preventive measures rather than with reactive measures such as antibiotic treatment. A time series evaluation of the output provided by the processing system of the disclosure may alert of anomalous predictions. If for example a pathogen is detected a day and not detected the following day the pathogen may be discarded. If for example a pathogen is detected to be present on one day and in a virulence stage on the following day, measures may be taken from early stages of infection to prevent the development of diseases caused by such pathogen.

[0027] The processing system, in examples, is in communication with a user interface component for outputting a characterization of the predefined set of pathogens based on the first characterization and on the second characterization, for example a display to show the characterization.

[0028] In the present disclosure, the first characterization, as seen, is based on patterns of detectable signals, where ‘‘based on patterns of detectable signals" may comprise that the characterization of the pathogens in the sample is performed by means of a correlation between the pattern of signals obtained from the first sample aliquots of and reference patterns of target molecules produced by the pathogens in the predetermined set of pathogens. An example of correlation and pattern will be described in the following detailed description.

[0029] A detectable signal may comprise an optical signal or a fluorescence signal.

[0030] The processing system is configured to obtain the measure of the fluorescence signal and the absorbance measure, also referred to as absorbance, based on at least one image obtained by the sensor system. A plurality of measures may be used as input into a computer program which is configured to output a characterization of a predefined set of pathogens For example, 10 measures of fluorescence signals and 10 absorbance measures coming from 10 different wells may be input, in series or in parallel, into a computer program or processor and an output may be provided by the computer program or by the processor.

[0031] In examples, the processor is configured to obtain the first characterization of the predefined set of pathogens by inferring the first characterization of the predefined set of pathogens in the first sample aliquots by using a trained model, for example a random forest model, wherein using the trained model comprises inputting one or more measures of the detectable signals and one or more of absorbance measures of the same first sample aliquots into the trained model, and obtaining a first characterization of a predefined set of pathogens as the output of the trained model. Each detectable signal of the detectable signals comes or is emitted from a different first sample aliquots in a first plurality of chambers. All the detectable signals are preferably of the same type, for example, fluorescent signals.

[0032] The processing system is further configured or programmed to execute computer-executable instructions to obtain a second characterization of the predefined set of pathogens. The second characterization is based on the macroscopic growth of pathogens in second sample aliquots visible in two or more growth images obtained by the sensor system. The second characterization may comprise inferring an identification of the predefined set of pathogens in the samples using a second trained model, for example an object detection algorithm, for example one of a group comprising R-CNN, Faster R-CNN, YOLO, and SSD, wherein using the second trained modelcomprises inputting the one or more images of each second sample aliquot into the second trained model and obtaining a characterization of pathogens in the samples as the output of the second trained model. The second trained model may be trained by inputting annotated images of known colony morphologies on different agar media, so that the second model maps images containing different morphologies to predictions of pathogen identification. The two or more growth images obtained by the sensor system may be obtained at intervals of growth. For example, a first image may be taken after leaving the second sample aliquots grow for first period of growth time, comprising 5 hours (h), or 10 h, or 24 h, and a second image may be taken after leaving the second sample aliquots continue growing for a consecutive second period of growth time, comprising 5 h, or 10 h, or 24 extra hours. The second sample aliquots may grow in a second plurality of chambers.

[0033] The processing system of the disclosure allows quantifying and identifying a list of pathogens, but the presence of one pathogen, for example Vibrio ssp, may not necessarily imply that Vibrio is in a virulence stage. The first characterization, based on patterns of detectable signals in the first sample aliquots, may not detect Vibrio, but the second characterization may detect Vibrio. Iterating the characterization, which is referred to as “continuous monitoring", each iteration separated from a previous one several hours or days, may provide different first and second characterizations until both coincide. Such event may imply that a Vibrio spp. such as Vibrio alginolyticus may not be in an infectious stage at the first iteration but that an outbreak has occurred after 2 iterations, or 2 days, for example. An iterative characterization provided as output from the system after two or more characterizations advantageously allows detecting both the presence, and the development of pathogens’ virulence.

[0034] In examples, the processing system may comprise a first processor and a second processor. The first processor may be configured to obtain a measure of the detectable signal and an absorbance measure from each first sample aliquot based on one or more captures obtained by the sensor system, obtaining thereby a plurality of measures of the detectable signal and a plurality of absorbance measures; and configured to obtain a first characterization of the predefined set of pathogens in the sample aliquots by processing a plurality of measures of the detectable signal, and a plurality of absorbance measures of the same sample aliquot as the measures of the detectable signal.

[0035] The second processor, different from the first processor, may be configured to obtain the second characterization of the predefined set of pathogens based on analysis of the macroscopic growth of pathogens in second sample aliquots based on two or more growth images of the sample aliquots, the two or more growth images obtained by the sensor system.

[0036] Any of the first processor or the second processor, or a third processor may output the characterization of the predefined set of pathogens based on the first and second characterizations.

[0037] In examples, the processing system provides a disease monitoring tool for, for example, fish farmers, powered by an on-site hardware and Al tools for disease prediction and anticipation.

[0038] Early pathogen detection represents a great advantage towards preventing an outbreak. Several known prophylactic measures such as greater water renewal, water temperature reduction, and probiotic administration, have been proven to be effective in most of the cases.

[0039] In a second aspect of the disclosure, there is provided a system for characterizing a predefined set of pathogens in a sample, the system comprising:a first plurality of chambers for a first characterization of the predefined set of pathogens, wherein each of the chambers is configured to hold a first sample aliquot and each of the chambers comprises a different genetically modified microorganism to emit a detectable signal upon contact with a target molecule produced by a pathogen of the predefined set of pathogens; wherein a different genetically modified microorganism emits a different detectable signal upon contact with at least one different target molecule; a light source to illuminate the plurality of chambers at an illumination wavelength; a second plurality of chambers for a second characterization of the predefined set of pathogens, wherein each of the chambers is configured to hold a second sample aliquot, wherein each of the chambers comprises a different culture media; a sensor system comprising an optical sensor configured to: capture the detectable signal or signals; capture an absorbance of each one of the first plurality of chambers; capture one or more images of the second plurality of chambers; the processing system of the first aspect, further configured to: obtain a measure of the detectable signal from each chamber of the first plurality of chambers based on the capture of the detectable signals; obtaining thereby a plurality of measures of the detectable signal; obtain an absorbance measure from each chamber of the first plurality of chambers, based on the capture of the absorbance, obtaining thereby a plurality of absorbance measures; obtain the first characterization of the predefined set of pathogens in the sample by processing at least a part of a plurality of measures of the detectable signals and at least a part of a plurality of absorbance measures; obtain the second characterization of the predefined set of pathogens based on analysis of the macroscopic growth of second sample aliquots in the second plurality of chambers based on two or more growth images of the second sample aliquots in the second plurality of chambers, based on the capture of one or more images of the second plurality of chambers.

[0040] The sample aliquots on which the first and second characterization are based may be held by the chambers of the system of the disclosure. The first characterization is based on a pattern of detectable signals determined in the first plurality of chambers corresponding to different detectable signals coming from different chambers in the first plurality of chambers. An example of a pattern will be described in the following detailed description. The second sample aliquots may be held by the second plurality of chambers and the second characterization is based on the macroscopic growth images of the second sample aliquots in the second plurality of chambers. The first plurality of chambers and / or the second plurality of chambers may comprise 2 chambers, or 3 chambers, or 4 chambers, or 90 chambers, or 96 chambers, or more. The term "chamber" may comprise any kind of container, cuvette, space or other suitable for holding a liquid or gas sample. In some examples the chamber wall is transparent. In the case where the sample is liquid, the system may comprise membranes, for example, 0.2um Millipore membrane to filter the liquid and analyze the supernatant. One or more sample aliquots of the sample may be held by one or more chambers, for example a first plurality of chambers.

[0041] The plurality of chambers, or wells, may comprise a sample comprising more pathogens than the number of pathogens in the "predefined set of pathogens” for identification / quantification. The predefined set of pathogens comprise a specific number of pathogens which are preselected based on the use-case. For example, a predefined set of pathogens may comprise Vibrio harveyi, Aeromonas hydrophila and Photobacterium damselae and the sample may comprise Vibrio harveyi, Aeromonas hydrophila and Photobacterium damselae, Tenacibaculum maritimum and Bacillus cereus.

[0042] Each chamber may allow a predefined volume of gas or liquid or solid sample to be held, for example a chamber may present at least 5 milliliters, ml, volume. Example biosensors may comprise chambers with capacity for a volume of 200uL for the first characterization. The second characterization may be provided using chambers or wells -for macroscopic growth-, with a capacity for a sample volume of 15uL.

[0043] Each chamber of the plurality of chambers is configured for holding, each chamber, at least a part of the sample or a sample aliquot. For example, 9 chambers may hold 9 parts of a sample or 9 chambers may hold 7 sample aliquots, depending on the number of pathogens to be identified.

[0044] Each chamber is provided with at least one genetically modified microorganism which emits a detectable signal upon contact with a target molecule produced by a pathogen of the predefined set of pathogens; wherein each chamber comprises a different genetically modified microorganism.

[0045] The system for characterizing a set of pathogens of the present disclosure comprises the processing system of the disclosure, further configured to provide a first characterization, a second characterization and a characterization based on captures of different signals from a first plurality of chambers and from a second plurality of chambers. The processing system is configured to receive a capture of a plurality of detectable signals, for example a first plurality of fluorescence signals, each coming from each chamber of the first plurality of chambers The processing system is configured to receive a capture of the absorbance or optical density from each chamber of the first plurality of chambers, receiving thereby a first plurality of absorbances, for example a first plurality of absorbances. Based on the first plurality of detectable signals and on the first plurality of absorbances, the processing system is configured to provide a first characterization by processing at least a part of a plurality of measures of the detectable signals and at least a part of a plurality of absorbance measures of the same chamber.

[0046] The system for characterizing a set of pathogens of the present disclosure comprises the processing system of the disclosure, further configured to obtain the second characterization of the predefined set of pathogens based on analysis of the macroscopic growth of pathogens in the second sample aliquots in the second plurality of chambers. The processor is therefore not only configured to process two or more growth images from a second sample aliquot but to process two or more growth images from a plurality of second sample aliquots, based on the capture of one or more images of the second plurality of chambers.

[0047] In examples, each genetically modified microorganism comprises a reporter gene that is under the control of an inducible promoter, such that each first promoter is responsive to a different target molecule; wherein the reporter gene encodes a protein that emits the detectable signal. In particular examples, the genetically modified microorganism further comprises a receptor gene that is under the control of a constitutive promoter, wherein the receptor gene encodes a receptor protein which binds the target molecule to form an active transcription factor that activates the inducible promoter of the reporter gene. In more particular examples, each genetically modifiedmicroorganism comprises both a reporter gene that is under the control of an inducible promoter, such that each first promoter is responsive to a different target molecule; wherein the reporter gene encodes a protein that emits the detectable signal, and a receptor gene that is under the control of a constitutive promoter, wherein the receptor gene encodes a receptor protein which binds the target molecule to form an active transcription factor that activates the inducible promoter of the reporter gene.

[0048] The "target molecule" is a molecule produced by one or more of the pathogens of the predetermined set of pathogens and is therefore present in the first sample. The target molecules are involved in the identification, quantification, and determination of the virulence of the predefined set of pathogens. Each genetically modified microorganisms contained in each chamber is designed to react to at least one target molecule by constitutively producing a receptor protein that binds the target molecule to form an active transcription factor that activates the promoter of the reporter gene, which, in turn initiates transcription of the reporter gene resulting in expression of the protein that emits the detectable signal.

[0049] In examples, the reporter gene and its inducible promoter are comprised in a plasmid. In examples the receptor gene and its constitutive promoter are comprised in a plasmid. In examples the reporter gene, the receptor gene and their respective promoters are comprised in a plasmid. In examples the reporter gene and its inducible promoter are integrated into the microorganism’s genome. In examples the receptor gene and its constitutive promoter are integrated into the microorganism’s genome. In examples the reporter gene, the receptor gene and their respective promoters are integrated into the microorganism genome.

[0050] In examples, each chamber is provided with a culture medium suitable for the proliferation of the genetically modified microorganisms The culture medium contains the nutrients and essential amino acids required by the genetically modified microorganism to survive and proliferate. The culture medium may be the same in all chambers or wells, or it may be different. The culture medium may be lyophilized. The culture medium may or not contain salt. The culture medium in each of the first plurality of chambers may be selective. “Selective culture medium” refers to a medium that allows growth of a microorganism or group of related microorganisms while not allowing growth of any other microorganisms. In one example, the culture medium in each of the first plurality of chambers is selective in the sense that it is suitable for the proliferation of the genetically modified microorganism contained in said chamber but it does not allow for proliferation of the pathogens of the predetermined set of pathogens. In non-limiting examples the system may comprise filters to filter the sample to trap bacteria so the sample with the detectable signals “only” allows the genetically modified microorganism to grow. In non-limiting examples, the growth medium may contain an antibiotic that inhibits growth of the pathogens of the predetermined set of pathogens but does not hinder growth of the genetically modified organism. The skilled person will know how to select the culture media appropriate for each genetically modified microorganism. The growth medium may not contain salt when salt is already present in the samples or in the wells. The growth medium may be concentrated, for example 10 times, to avoid any dilution factor into the chamber. Providing each chamber with the growth medium and / or nutrients allows automatization of the characterization of pathogens with the system of the present disclosure. Adding growing medium to each chamber may require a sterile area.

[0051] Each genetically modified microorganism may respond upon the presence of different target molecules based on a specificity or nature of each pathogen from the predefined set of pathogens. Thus, the plurality of chambers, or biosensors, is designed for the detection of a predefined set of pathogens of interest to characterize.

[0052] The system may comprise one or more pumps and / or valves for guiding at least one sample aliquot into at least one chamber of the plurality of chambers. In some embodiments, system may also comprise pipes and a pump arranged for leading the sample aliquots out from one or more chambers after identification of pathogens. In cases where the system comprises membranes, the pump may be upstream or downstream the membranes.

[0053] The system further comprises a light source arranged to illuminate the first plurality of chambers and having an illumination wavelength. The light source may comprise, for example, a white light source or a blue-cyan light source. The light source may comprise a diode LED array. The light source may be positioned at a distance from the first plurality of chambers and from the second plurality of chambers, either on top or below a plane containing the plurality of chambers and / or the second plurality of chambers. The light source may comprise a bluecyan LED diode array configured to emit light with a having an illumination wavelength in the range of 400-500 nm, more particularly in the range of 445 nm - 500 nm, and more particularly in the range of 490 nm - 495 nm. The diode array may be arranged on a plane containing the plurality of chambers and the second plurality of chambers or arranged above or below said plane. The diode array may surround the plurality of chambers. In examples, a light source may, in use when the chambers comprise or hold parts of a liquid sample, illuminate the plurality of chambers from a point under the plurality of chambers. The light source may be movable in such a way that the chambers may be sequentially illuminated one by one or by groups, each group comprising two or more chambers including a group comprising the plurality of chambers. In a particular example the plurality of chambers may be comprised in a housing for pathogen characterization purposes, in which case, the diode array may be attached to one or more housing walls and arranged to illuminate the plurality of chambers. The diode array may form an angle of 30 degrees with the housing walls to allow homogeneous illumination. The system may comprise a second light source for emitting a second illumination wavelength for, for example, illumination of the plurality of chambers for measuring the optical density, for example at a different illumination wavelength of 390 nm. The system may comprise a third light source for emitting a third illumination wavelength for, for example, illumination of the plurality of chambers or the second plurality of chambers for taking the growth images, for example at a third illumination wavelength corresponding to white light, at 400 - 700 nm.

[0054] The sensor system to capture the detectable signal may comprise an optical sensor, a fluorometer, an odor sensor, a voltmeter or an amperemeter.

[0055] The sensor system configured to capture an absorbance of each one of the plurality of chambers may comprise a spectrophotometer, an optical sensor or other sensor configured to capture absorbance or optical density (OD). The optical sensor may comprise any camera or device configured to capture an image.

[0056] The sensor system may comprise a single optical sensor, for example a Raspberry Pi camera, to capture an image of the first plurality of chambers and, in such example, "capture the detectable signals” and “capture an absorbance of each one of the first plurality of chambers” comprise capturing an image of the detectable signal, e.g., an image (optical signal). Based on such image, the processing system is configured to obtain measures of the detectable signal and absorbance measures of each chamber of the first plurality of chambers. Advantageouslya cost-effective system and method to capture one or more measures of detectable signals and one or more measures of absorbance may be acquired by the sensor system comprising a single optical sensor.

[0057] In examples, the first plurality of chambers and the second plurality of chambers may be comprised in either plate. The system may comprise a housing configured to house a plate at a time, such that the plates may be positioned in and removed from the housing, depending on the characterization that the processing system is to obtain.

[0058] In examples, the detectable signal is an optical signal having an emission wavelength. In such examples, the sensor system comprises a single optical sensor to capture an image of the first plurality of chambers and of the second plurality of chambers; and the processing system is configured to obtain the measure of the detectable signal(s) and of the absorbance measure(s) from each chamber based on at least one signal image obtained by the optical sensor, for example a Raspberry Pi camera, arranged to capture images of both the plurality of chambers and the second plurality of chambers.

[0059] In examples, the protein that emits the detectable signal is a fluorescent protein, or fluorophore, the detectable signal is a fluorescence signal, and the measure of the detectable signal is a fluorescence measure, wherein the fluorescent protein is selected from the group consisting of green fluorescent protein, GFP, red fluorescent protein, RFP, yellow fluorescent protein, YFP, and combinations.

[0060] Fluorescence is the emission of light by a substance that has absorbed light or other electromagnetic radiation. It is a form of luminescence. In some cases, the emitted light has a longer emission wavelength, and therefore a lower photon energy, than the absorbed radiation. Other detectable signals may comprise colorimetric signals. In examples, the fluorescent protein has an excitation wavelength, and the light source emits light having an illumination wavelength substantially equal to the excitation wavelength, where substantially equal comprises a deviation of a 1 % from the excitation wavelength, or a 5%, or a 10% from the excitation wavelength. For example, for a fluorophore having an excitation wavelength of 488 nm, the illumination wavelength may be comprised within [470, 500] nm to illuminate the plurality of chambers and to excite the fluorophores in the plurality of chambers.

[0061] In examples where the detectable signal is a colorimetric signal, the light source may emit white light.

[0062] In examples, the system further comprises a filter configured to block the illumination wavelength, or a band-pass filter to receive light emitted by the genetically modified microorganisms, the filter arranged to be either at a position or at least moveable to a position in a light path between the plurality of chambers and the optical sensor. In examples such a filter comprises an amber filter made of 3mm methacrylate sheet which acts as a bandpass filter to receive light, for example green-fluorescent light emitted by the genetically modified microorganism.

[0063] The second plurality of chambers may comprise a culture medium suitable for the macroscopic growth, for example proliferation of colonies, of the predefined set of pathogens. Said culture medium may be different from the culture medium of the rest of chambers in the second plurality of chambers. The culture media in the second plurality of chambers may be solid media, for example, agar media. For the selection of the different culture medium in each chamber of the second plurality of chambers, the nutritional requirements of the predefined set of pathogens are to be taken into account. The culture medium in each of the second plurality of chambers may be selective. The culture medium appropriate for macroscopic growth of a pathogen or a predetermined set of pathogens can be determined by a skilled person according to their common general knowledge. The predefined set of pathogensmay be predefined depending on the sample to be analyzed, for example, pathogens likely to grow in fish farms may comprise a pathogen selected from the group consisting of Vibrio spp., Aeromonas spp., E coli, Staphylococus spp., and combinations thereof. The selective culture medium in each chamber may be selective for a pathogen selected from the group consisting of Vibrio spp., Aeromonas spp., E coli, Staphylococus spp., and combinations thereof. The culture medium in each of the second plurality of chambers may be an agar medium.

[0064] The system according to the present disclosure allows characterizing a set of predefined pathogens based on activation patterns of microorganisms depending on a virulence state or infectious stage of a predefined set of pathogens in a sample and based on the nature and amount of target molecules produced by the pathogens.

[0065] In examples, the target molecule comprises a molecule produced by a pathogen in the predefined set of pathogens when the pathogen is in a virulence state. By measuring the response of the genetically modified microorganisms in the first plurality of chambers to the target molecules, wherein the target molecules are molecules produced by the pathogens in the predefined set of pathogens when the pathogens are in a virulence state, the system allows to determine the identity and concentration of the predetermined set of pathogens in the sample, thus allowing the assessment of virulence for the identified pathogens. Therefore, the present system may determine virulent behavior of the predetermined set of pathogens with high accuracy and in a short time. Changes in the measured response to the target molecules over a time interval can be used to predict virulence outcome. Since the predetermined set of pathogens is responsible for particular pathogenic conditions, the present system provides valuable information that may help taking an informed decision on whether to initiate or continue a prophylactic or therapeutic regime for the pathogenic condition(s) or whether a current therapy is effectively treating the pathogenic condition. In examples, the target molecule is selected at least from one of the following molecules: a quorum sensing molecule, QS molecule, two-component system responses, such as histidine-kinase receptors, iron-channels, transferrin receptors, and combinations thereof In one example, the target molecule is a quorum sensing molecule. “Quorum sensing" is a process that regulates the expression of genes necessary for many bacterial activities, such as biofilm formation and / or formation of virulence factors. Quorum sensing molecules which may be indicators for bacterial virulent behavior include, for example, N-Acryl homoserine lactones (AHL), oxo acyl homoserine lactones (oxo AHL), autoinducing peptides (AlP)pyocyanin, 2-heptyl-3,4-dihydroxyquinoline (PQS), 4-hydroxy-2-heptylquinoline (HHQ), Histidine protein kinases (HPKs), signal transduction enzymes, environmental ions such as iron, magnesium, auto-inducer signals (AI-2, AI-3) and combinations thereof. In particular examples the target molecules are selected from the group consisting of N-butanoyl-HSL (C4-HSL), N- hexanoyl-HSL (C6-HSL), N-(3-hydroxyhexanoyl)-HSL (3-OH-C6HSL), N-(3-oxohexanoyl)- HSL (3-OXO- C6HSL), N-decanoyl-HSL (C10-HSL), N-(3-oxodecanoyl)- HSL (3-QXQ-C10HSL), N-(3-oxododecanoyl)- HSL (3-OXO-C12HSL), N-(3-oxoctanoyl)- HSL (3-OXO-C8HSL), histidine kinase RstB, (S)-3- hydroxytridecan-4-one (CAI-1) and combinations thereof.

[0066] The system of the disclosure enables for a fast and reliable identification of particular species of pathogens and cost-effective system and method, as will be described in the following paragraphs, for identifying and quantifying living organisms in a sample, for example, a liquid sample, and be able to assess the pathogenicity behavior of these in a given context.

[0067] In a third aspect of the disclosure, a method for characterizing a predefined set of pathogens in a sample is provided. The method comprises obtaining a measure of a detectable signal and an absorbance measure from each chamber of a first plurality of chambers, each chamber holding a first sample aliquot, the measure of a detectable signal and the absorbance measure based on one or more captures obtained by a sensor system, obtaining thereby a plurality of measures of the detectable signal and a plurality of absorbance measures; wherein each detectable signal is emitted by at least one genetically modified microorganism, in each chamber, upon contact with a target molecule produced by a pathogen of the predefined set of pathogens in the first sample aliquots; obtaining a first characterization of the predefined set of pathogens in the sample by processing a plurality of measures of the detectable signal, and a plurality of absorbance measures, wherein each absorbance measure is obtained from the same chamber as the chamber where the measure of each detectable signal is obtained from; obtaining a second characterization of the predefined set of pathogens based on analysis of macroscopic growth of second sample aliquots in a second plurality of chambers based on two or more growth images of pathogens in the second sample aliquots in the second plurality of chambers, the two or more growth images obtained by the sensor system; and outputting a characterization of the predefined set of pathogens based on the first characterization and on the second characterization.

[0068] In some examples the method comprises guiding sample aliquots into two or more chambers of the plurality of chambers. In examples the guiding is performed through guiding probes or tubes and by the use of optimal pumps. In examples, the method comprises guiding sample aliquots into all of the plurality of chambers.

[0069] In some examples, the method comprises incubating the sample aliquots for an incubation period of time, for example, 10 hours or 15 hours, or 20 hours or 24 hours The genetically modified microorganism may be incubated during the incubation period of time allowing reaction upon contact with the target molecule produced by a pathogen. Such reaction allows the genetically modified microorganism to emit the detectable signal.

[0070] In some examples, the detectable signal is captured while illuminating the sample aliquots with the light source.

[0071] In some examples, obtaining a measure of an absorbance measure from each chamber comprises measuring the optical density of each chamber. In examples, an optical sensor captures an image of the plurality of chambers and a processor obtains measures from a color channel, for example blue channel, and the optical density or absorbance is obtained. In examples the absorbance is obtained by correlating absorbance measures made by a spectrophotometer with measures taken from the processing of a color channel of the image.

[0072] In some examples the method comprises illuminating the plurality of chambers by the light source emitting an excitation wavelength. In some examples the method comprises illuminating the plurality of chambers by the light source emitting a white light.

[0073] In some examples, the detectable signal is a fluorescence signal emitted by a fluorescent protein in the chambers. In such examples, the first sensor may comprise an optical sensor and obtaining a measure offluorescence comprises, given a light source configured to illuminate at a wavelength of illumination substantially equal to the excitation wavelength of the fluorescent protein, filtering the image coming from the chambers so that only light in a wavelength band comprising the emission wavelength reaches the optical sensor acting as a bandpass filter to receive light emitted by the genetically modified microorganisms or engineered bacteria and to block the light from the light source. In such examples, the wavelength band may comprise wavelengths for example in the range "excitation wavelength” of [0,7 — 1,4].

[0074] In examples, the method for characterizing a predefined set of pathogens in a sample comprises: (i) placing a first volume of sample aliquot in each chamber of the first plurality of chambers; (ii) maintaining the first plurality of chambers under conditions suitable for the growth of the genetically modified microorganisms; (iii) obtaining a measure of a detectable signal and an absorbance measure from each chamber of the first plurality of chambers based on one or more captures obtained by a sensor system, obtaining thereby a plurality of measures of the detectable signals and a plurality of absorbance measures; (iv) obtaining a first characterization of the predefined set of pathogens in the sample by processing a plurality of measures of the detectable signals, and a plurality of absorbance measures of the same chamber; (v) placing a second volume of sample aliquot in each chamber of the second plurality of chambers; (vi) maintaining the second plurality of chambers under conditions suitable for the macroscopic growth of the predefined set of pathogens; (vii) obtaining a second characterization of the predefined set of pathogens based on analysis of macroscopic growth of the predefined set of pathogens present in the second sample aliquots in a second plurality of chambers based on two or more growth images of the second sample aliquots in the second plurality of chambers, the two or more growth images obtained by the sensor system; and (viii) outputting a characterization of the predefined set of pathogens based on the first characterization and on the second characterization

[0075] In one example, the first volume of sample aliquot in step (i) is devoid of pathogens or, alternatively, the conditions of step (ii) do not allow proliferation of the pathogens.

[0076] Advantages of the aspects of the disclosure will be illustrated in the description and figures that follow.FIGURES

[0077] Figure 1 represents an example processing system according to the present disclosure.

[0078] Figure 2 represents an example system to characterize a set of predefined pathogens according to the present disclosure.

[0079] Figure 3 represents an example system to characterize a set of predefined pathogens according to the present disclosure.

[0080] Figure 4 represents an example of correlation between three pathogens to characterize, the potential three target molecules to detect and the three receptors to build the biosensor or first plurality of chambers.

[0081] Figure 5 represents first pathogen concentration of different measures of Green Fluorescent Protein GFP of a certain pathogen in a sample.

[0082] Figure 6 shows bacterial growth in 6 chambers in the second plurality 25 of chambers.

[0083] Figure 7 represents an example of colony annotation using boxes.

[0084] Figure 8 shows an example output from a detection model indicating the presence of Vibrio alginolyticus (Valgino) pathogens with different prediction probabilities.

[0085] Figure 9 represents a top view of a system according to the disclosure with a filter.

[0086] Figure 10 shows a representation of a training process of the ML models.DETAILED DESCRIPTION

[0087] Figure 1 represents a processing system 10, comprising an input interface 11 and an output interface 12, where any of the interfaces may comprise wired or wireless interface for receiving and outputting digital data.

[0088] Figure 2 represents a system to characterize a set of predefined pathogens according to the present disclosure comprising a plurality 21 of chambers for holding, each chamber, a sample aliquot, wherein each chamber comprises at least one genetically modified microorganism 22 which emits a detectable signal upon contact with a target molecule produced by a pathogen of the predefined set of pathogens; wherein each chamber comprises a different genetically modified microorganism. The system further comprises a light source 23 arranged to illuminate the plurality of chambers and configured to illuminate at a wavelength of illumination; a sensor system 24 to capture the detectable signal or signals; the sensor system 24 may comprise a first sensor to capture one or more detectable signals, a second sensor to capture one or more absorbance measures, and a third sensor to capture macroscopic growth images. Each chamber in the second plurality 25 of chambers comprises an agar media which is different from the agar media of the rest of chambers in the second plurality 25 of chambers. The sensor system 24 is configured to capture one or more images of at least the second plurality 25 of chambers; and the processing system 10 is in data communication with the sensor system 24. Two or more genetically modified microorganisms, each of the genetically modified microorganism in a chamber, emit, upon contact with a different target molecule in a sample aliquot held by the chamber, different detectable signals, for example each of the detectable signals with a different intensity, for example depending on the number of target molecules present in each sample aliquot.

[0089] Figure 3 shows a system for pathogen characterization comprising a housing 30 configured to receive a plate. A first plate 31 may comprise the first plurality 21 of chambers, or biosensors, and a second plate 32 may comprise the second plurality 25 of chambers. A single plate may be configured to receive both the first plurality 21 of chambers and the second plurality 25 of chambers. In operation, the first plate 31 may be placed in the housing; sample aliquots of a sample may be placed in each chamber of the first plurality 21 of chambers. An incubation period may pass to let the genetically modified microorganisms react upon contact with possible different target molecules present in the sample aliquots. In examples, the different target molecules comprise molecules produced by pathogens in the predefined set of pathogens when the pathogens are in a virulence state. The genetically modified microorganism of the chambers act as biosensors. The biosensors in contact with a target molecule emit, after an incubation period, a detectable signal, for example an optical signal, or fluorescence signal, for example green-fluorescent signal. In some embodiments, the fluorescence signal and / or the absorbance measure or optical density is detected after a period of time ranging from 1 min to 12 hours, from 10 min to 11 hours, from 20 min to 10 hours, from 30 min to 9 hours, from 40 min to 8 hours, from 50 min to 7 h hours from 1 hour to 6 hours starting from the step of contacting the sample with the genetically modified bacteria. In some embodiments, the step ofdetecting the measure, e.g , intensity, of the detectable signal is carried out in a defined time interval. Atime interval for the purpose of acquiring the intensity of the signal emitted by the genetically modified microorganism according to the invention may comprise from 1 min to 12 hours, from 10 min to 11 hours, from 20 min to 10 hours, from 30 min to 9 hours, from 40 min to 8 hours, from 50 min to 7 h hours from 1 hour to 6 hours, from 1 .5 h to 5 hours, from 2 hours to 4 hours, starting from the first signal acquisition.

[0090] In some embodiments the step of detecting the optical density of the sample is carried out in a defined time interval. A time interval for the purpose of detecting the optical density of the sample according to the invention may comprise from 1 min to 12 hours, from 10 min to 11 hours, from 20 min to 10 hours, from 30 min to 9 hours, from 40 min to 8 hours, from 50 min to 7 h hours from 1 hour to 6 hours, from 1 .5 h to 5 hours, from 2 hours to 4 hours, starting from the first signal acquisition.

[0091] The sensor system 24 of figure 3 comprises an optical sensor, for example, a Raspberry Pi camera. The optical sensor captures an image of the optical signals. The processing system 10 is in direct or indirect, e.g., with intermediate communication devices, communication with the sensor system 24 by wired or wireless means. The processing system 10 may be locally or remotely, e.g., in a cloud server, in communication with the sensor system 24. The light source 23 is shown as a LED diode array configured to surround the first plate 31 to allow homogenic illumination of the chambers. The LED diode may emit light illumination at 490 nm wavelength. The processing system 10, based on the captured image of the optical signal, obtains a measure of the intensity of the optical signal of each chamber and an absorbance measure of each chamber. In the example of figure 3 "the sensor system configured to capture the detectable signals; and to capture an absorbance of each one of the first plurality of chambers” comprises an optical sensor or camera configured to capture an image of the optical signals emitted from each chamber in the first plurality 21 of chambers.

[0092] The optical signal may comprise a fluorescence signal, for example a green-fluorescent signal. The measure of the fluorescence signal may be obtained by the processing system 10 and by placing an amber filter between the first plurality 21 of chambers and the sensor system 24 comprising an optical sensor or camera, the filter to act as a bandpass to block the light of the light source.

[0093] The processing system 10 obtains a first characterization of the predefined set of pathogens in the sample by processing a plurality of measures of the optical signal, wherein the measures of the optical signal comprise the intensities of the optical signals and absorbance measures of the sample aliquots in the first 21 plurality of chambers. The first characterization will be exemplified below.

[0094] Continuing operation, the first plate 31 may be removed from the housing 30 and the second plate 32 may be placed in the housing. Second sample aliquots of the sample may be placed in each chamber of the second plurality 25 of chambers. A second incubation period or growth period may pass to let possible different microorganism present in the sample aliquots to grow in the different agars. The sensor system 24 captures a first image of the second plurality of chambers after the second incubation period. A third incubation period or growth period may pass to let the different microorganism present in the sample aliquots to further grow in the different agars. The sensor system 24 captures a second image of the second plurality of chambers after the third incubation period. Based on the first image and on the second image, the processing system 10 obtains the secondcharacterization of the predefined set of pathogens based on analysis of the macroscopic growth of the different sample aliquots in the second plurality 25 of chambers. The second characterization will be exemplified below.

[0095] First characterization: Biosensors

[0096] In an example, the first characterization, also referred to as “biosensor method” is based on engineered freeze-dried genetically modified microorganisms, for example, E.coli, which contain synthetic quorum sensing receptors. One of the main problems faced by pathogen sensing devices is their inability to predict disease infection and not just bacterial presence. In the aquaculture space, most farmers are aware about the presence of some pathogenic bacteria in seawater sources. However, they cannot predict whether these pathogenic bacteria are going to cause an infection or which conditions or parameters will promote these bacteria to end-up infecting fish. The biosensor method of this examples is based in quorum sensing receptors to discriminate the pathogenicity degree of bacteria presence in water, being able to determine different hazard thresholds.

[0097] The main advantage of detecting infection-like target molecules is that the target molecules are involved in gene expression control, functioning in a coordinated manner throughout the entire population, modifying their overall behavior and thus facilitating the execution of infectious processes. Once the appropriate target molecules are identified for the pathogens of interest, the engineered sensors are designed by constructing genetically modified organisms which are reactive to the target molecules through a two component transduction system. Several sensor combinations may be needed to identify unique pattern responses for each specific pathogen. In some cases, target molecules consist of small molecules, such as quorum sensing molecules, which can be detected through mass spectrometry techniques.

[0098] Similarly, the same process may be performed for pathogen macroscopic growth, in this case, analyzing growth media selection, growth rate, macroscopic features, e.g., hemolytic activity, and growth temperature

[0099] Afterwards, an in vivo characterization is conducted to validate the information and start feeding the algorithms with annotated pathogen images.

[0100] Based on multiple trials, different activation patterns may be defined based on i) pathogen species, and ii) pathogenicity degree. A sample from which a predefined set of pathogens is to be characterized or, in other words, a sample from which pathogenic presence and degree is to be found out, is combined with “n” genetically modified microorganisms in each chamber, also referred to as “n” different bio-sensors, each of which emits a signal. Each signal “s1 , s2, ..., sn” is associated with each biosensor, or chamber, respectively.

[0101] The first characterization of the predefined set of pathogens is obtained using a trained model, wherein using the trained model comprises inputting at least one measure of the detectable signal and at least one absorbance measure into the trained model and obtaining a first characterization of a predefined set of pathogens as the output of the trained model. Supervised data of the variables (s1 , s2, .... sn, pathogenType, pathogenConcentration) is obtained through experiments. Input features to the model to be trained comprise the signals s1, s2, .... sn; and the target variables or result to be obtained comprise pathogenType and pathogenConcentration. Figure 4 shows the correlation between pathogens, target molecules and sensor receptor responses. Figure 4 represents an example of correlation between three pathogens to characterize, the potentialthree target molecules to detect and the three receptors to build the biosensor or first plurality of chambers. As said before, "based on patterns of detectable signals” may comprise that the characterization of the pathogens in the sample is performed by means of a correlation between the pattern of signals obtained from the first sample aliquots or first plurality of chambers and reference patterns of target molecules produced by the pathogens in the predetermined set of pathogens. The set of three pathogens with the set of three target molecules are mapped to look for the most relevant target interactions. In the example of figure 4, the information which receptor xR provides may be considered irrelevant or redundant with regards to the information the receptor zR provides. In this case, the receptor xR may be discarded for optimizing the set of receptors to use in the system. The system with 2 nR receptors may be enough to detect the three pathogens at the left hand side. When more receptors than pathogens are needed, more receptors may be used. Figure 5 represents biosensors responses to pathogens, or a pathogen concentration of different measures of Green Fluorescent Protein GFP of certain pathogen(s) in a sample. Each square represents a chamber or well, where each column of squares named as LAS, TRA, LUX, RHL, AHY, CEP, CVI, RSTA or CQSS represent the chambers with respective biosensors with different quorum sensing receptors. Each line of squares named Aeromonas salmonicida, Vibrio anguillarum, Vibrio harveyi and Pseudomonas aeruginosa represents a pathogen to be identified, including marine pathogens human pathogens and plant pathogens. The colored / filled squares represent biosensor activation responses for the first pathogen concentration. Different intensities of the color of each square represent the different intensities of the signals “s1 , s2, .... sn”. The first characterization is based on the pattern defined by the represented signals “s1 , s2, .... sn”.

[0102] In an example, a single random forest algorithm is trained to predict all the variables. In an example, two separate machine learning models are trained to predict each of the target variables:

[0103] Random Forest Classification model may be used to build a prediction model for qualitative variable pathogenType

[0104] Random Forest Regression model may be used to build a prediction model for the pathogenConcentration

[0105] This knowledge may be obtained from induced disease animal models. A Random Forest Regression is used based on tabulated outputs, e.g., from less likely to more likely to cause an infection, of biosensor responses for known pathogenic concentrations to be able to perform predictions based on detectable patterns given by the signals s1 , s2, ..., sn. The utilization of Random Forest, renowned for its efficacy in both classification and regression tasks, is indicative of its suitability for addressing the complex predictive requirements of the disclosure. For training, biosensor response measured as fluorescence may be used. The feature variables may be the biosensor responses measured as optical measures and absorbance measures. Examples include 10 different biosensors or chambers, or 96 chambers. For each sample, 20 predictor values are obtained. The fluorescence measures are extracted from a picture taken in replicable conditions. The biosensor response may be received from the fluorescence emitted by one or more wells. This is to be located from an image, the following may be implemented: Segmenting the image containing a plate of 96 wells into each individual well. Extracting a measure of the fluorescence intensity associated to each well. Interpolation of values is used for predicting intermediate concentrations may be implemented. A linear mapping may be used to normalize the data. Camera parameters may be tuned to increase the range of fluorescence of the biosensors. Advantageously a greater sensitivity in the predictors is achieved. The variables may be split as 70% for the training set, 20% for the validation set and 10%for the test set (10-15%). The same process is done with absorbance or turbidity measure, to segment the image containing a plate of 96 wells into each individual well and extracting a measure of absorbance associated to each well. For measuring absorbance, the following may be implemented: (I) taking a picture of the plurality of chambers; (ii): taking two absorbance measurements: (ii.a) Readings, which is a measurement given by a spectrophotometer; and (ii.b) Measures: measuring the turbidity of each chamber via image processing of the taken picture, analyzing the information in a color channel, for example the blue channel; obtaining a relationship between the absorbance readings and the measures of turbidity. Then fitting the relationship with a model, for example a logarithmic model, allows determining how to derive absorbance values from the turbidity measurements.

[0106] Choice of algorithm: Random forest algorithm may be chosen for classification and regression purposes. This is known to be one of the strongest algorithms for detecting patterns in structured data. Gini impurity may be used to optimize the splitting of decision trees within the forest. This criterion helps determine how to split the data at each node of the decision tree during the tree-building process. Ensemble learning may be used to reduce overfitting and to improve generalization by aggregating the predictions of multiple base models. In examples, n_estimators or number of trees, for example 100, may be used, the criterion- gini’, bootstrap=True and random_state 42 may be used for reproducibility of results. As analysis and evaluation metrics, accuracy is used, as the class distribution is roughly balanced, and it provides a general measure of model performance. The confusion matrix may also be considered to provide more detailed insights into the model's performance. Scikit- learn (Python) may be used as a Machine Learning Framework. Pickle may be used for Model Serialization. The backend of the method implemented by the processing system may be powered by FastAPI as an API Framework, and mySQL for data storage.

[0107] .

[0108] The system used for the first characterization comprises, with reference to the elements shown in figure 3, a single light source 23 LED strip with an emission wavelength of 390nm. The LED strip is placed 7mm above the first plate 31 with a 30-degree angle to reach a uniform sample illumination Images and captures or measures are taken with the sensor system 24 comprising a camera placed in a top view position

[0109] For GFP measurements, an amber filter may be used, the filter comprising a 3mm methacrylate sheet between the first plurality 21 of chambers and the sensor system 24 for filtering light coming from the first plurality 21 of chambers, acting as a band-pass filter to receive light emitted by the genetically modified microorganisms or engineered bacteria and to block the light of the light source. The image obtained by the camera may be decomposed on the three RGB channels for the mathematical analysis to extract information from each chamber or well. From the green channel, the mean of percentile 90 from the highest values of intensity may be obtained as measure of the fluorescent signal, or GFP signal.

[0110] For the absorbance measure or optical density OD measure, the same light source 23 may be used without the amber filter, to obtain optical density, or bacterial growth, measurements. The first plurality 21 of chambers may be captured and the mean value of intensity of the pixels may be taken.

[0111] Figure 9 represents a top view of a system according to the disclosure where a filter moveable to a position in a light path between the plurality of chambers and the optical sensor is shown. Figure 10 shows a hole 101 configured to let light pass from the chambers (not shown) to the optical sensor (not shown) so that an image canbe captured. A mounting plate 102 supports an amber filter 103 The amber filter 103 may be moved to the position in the light path between the plurality of chambers and the optical sensor, as shown in the left hand side image of Fig. 10, to take an image for the first characterization, as it may filter the illumination wavelength so that the optical signal emitted by from the chambers is detected without noise inserted by the light source. The amber filter may be removed or moveable to a position in which the filter is not in a light path between the plurality of chambers and the optical sensor, as in the right hand side image of Fig. 10, to take images for the second characterization. The filter may manually or automatically be moveable between the two positions with the aid of a servo motor, for example. A camera (not shown) may be positioned in a sagittal position, for example 154 mm from the plurality of chambers. B) The second section shows the two configurations of photographs that are taken, one with an amber filter to take the fluorescence picture and one without filter for the optical density picture.

[0112] A light source or lighting system configuration may comprise two types of illumination. Blue-cyan LED strips with a wavelength of 390 nm and arranged at an angle of 30 degrees to allow uniform illumination of the first plurality of chambers may be used for the first characterization with the filter in the position in the light path between the plurality of chambers and the optical sensor. A white light illumination may be used for the second characterization, with the filter removed.

[0113] Figure 10 shows a representation of a training process of the ML models. Figure 10 shows two image captures or pictures of a number of biosensors, the first plurality 21 of chambers, showing 10 biosensors, at the left hand side. The two pictures obtained, each, with different illumination wavelength or using a filter for fluorescence (f1, f2... , f10) and removing the filter for obtaining absorbance (a1 , a2, ..., a10). The two pictures are analyzed by the processing system to obtain measures of fluorescence, f1 , f2, . . f10, and measures of a1 , a2, ..., a10 Normalized measures of fluorescence, i.e. , s1=f1 / a1, s2= f2 / a2, . ., s10=f10 / a10 may also be determined The genetic modification of each well from which s1 and s2 originate is different That is, pathogen 1 may emit 2 types of target molecule, e.g., target molecule 1 , target molecule 2, when it is in the infectious stage, where one genetic modification 1 in well 1 responds to target molecule 1 and another genetic modification 2 (from neighboring well 2 for example) responds to target molecule 2. Pathogen 1 may emit one or more signals and well 1 may include 1 genetic modification (or sensor) that responds to 1 target molecule, well 2 may include sensor 2 that responds to target molecule 2, etc. A ML model Regression model Random Forest, RF, is trained by inputting signal variables, s1 , s2, ..., s10, and inputting a result to be obtained by the model, for example, a pathogen type known to be present in the sample, vharveyi, with medium (Med) pathogenicity. The RF model may be trained with 10 pathogen species, 4 pathogenicity degrees, for example "none, low, medium and high”. Several replicas of the same scenario can be made to ensure reproducibility. The trained model may provide with the prediction shown during operation. Figure 10 further shows the second plurality of chambers shown below the biosensors comprising 3 chambers. The pictures represented show macroscopic growth of the microorganisms present in the sample. The DL object detection model, for example YOLOv8, provides the detection of the typed of pathogen and provides labeled images as shown, where each of the labelled image are similar to the image shown in Figure. 9. The object detection model may be trained with 200 images or more, 5 images per species and per agar type, using 3 agar types and 100 annotations per pathogen species. YOLO calculates a percentage confidence of predictions by multiplying (i) the confidence that it detected an object in a specific region of the image (based on detection accuracy), (ii) theprobability that the detected object belongs to a specific class (e.g., dog, car, person). The higher the product is, the more confident YOLO is in its prediction. In other words, if it detects an object with a high confidence and it is also very likely that it belongs to a specific class, then the confidence percentage will be high.

[0114] A method of training the Random Forest Regression model may comprise steps from 1A to 1 G:

[0115] Step 1A: Obtaining a first top view image of the first plurality of chambers, with amber filter to have the GFP image response; obtaining a second top view image without the amber filter to have the OD image.

[0116] Step 1 B: Obtaining a chamber detection. Different options may be considered. For each chamber or well, a region of interest may comprise a center pixel, a center pixel value or a full circle. For the detection of the full circle, a mean of integer circle (for circular chambers) values may be taken, and then the 90th percentile of the values of the entire circle may be determined. Chamber detection by determining the entire circle provides accurate measurements. The circle selection may be performed through a detection model based on convolutional neural networks, CNNs. From a pre-trained detection model, transfer learning technique may be used for the detection of circles corresponding to chambers in an image. Both the mean and the 90th percentile are more robust measures than the central pixel. The 90th percentile measure allows obtaining a measure that is not affected by the crescentshaped shadows appearing in lateral chambers.

[0117] Step 1C: Preparing a set of known pathogen concentrations and classifying each pathogen concentration in a pathogenicity degree level; this information may be taken from pre-established animal models. Known pathogen concentrations may be prepared using MacFarland’s standards to then plate the test sample for further colony count. This provides exact pathogen concentrations in CFU / mL.

[0118] Step 1 D: Extracting GFP measures to obtain quantitative values of GFP, taking green channel from RGB decomposition and performing the mean of percentile 90 of the highest values from a selected well area. The same procedure is done for ABS values, in this case, analyzing the Blue channel from RGB decomposition. This analysis may be established after a series of tests with known GFP and ABS concentrations and performing comparison analysis with results obtained from a conventional plate reader and from measures taken from images.

[0119] Step 1 E: Labelling the extracted information based on: i) measure values, ii) known pathogenic concentration, iii) pathogenicity degree. This information may be stored in an array with multiple observations to be able to train the already random forest classification and regression models.

[0120] Step 1 F: Some additional filters may be run to remove outliers or wrong biosensor responses before storage. The additional filters may comprise:

[0121] For controlled wells with known contaminated sample, i.e., samples which are known to contain pathogens, the output may be removed.

[0122] Outlier replicates. Each plate contains 3 to 5 replicates per test sample, to then take the mean value. If one of the replicates is considerably different to the other replicates, this measure is dismissed. Similarly, if there is a high response variability from all replicates, response is not stored and the analysis is repeated.

[0123] Biosensor activity. An activity threshold may be established, meaning that biosensors which do not reach a certain OD level (i.e., bacterial growth) are being dismissed. This provides a homogeneous dataset, capturing responses at the same bacterial growth stage. This is done as, given the variability nature of biological processes,the stored information needs to be optimal for the correct functioning of the implemented machine learning algorithm.

[0124] Step 1G: Training the Random Forest Regression model to build a prediction model for the pathogen concentration, based on the measures and on the known pathogenic concentration and known pathogenicity degree. The random forest regression model is an ensemble learning algorithm that combines multiple decision trees to extract a final output, so that pathogenicity degrees o concentrations can be correlated with the measures representing quorum sensing patterns.

[0125] A prediction method to obtain a first characterization of a predefined set of pathogens in a sample may comprise Steps 1A to 1D and further:

[0126] Step 1 H: input the measures into the Random Forest Regression model to obtain the prediction.

[0127] The prediction probability in a Random Forest may be obtained by counting how many trees voted for a specific class and dividing this number by the total number of trees in the forest. This gives a measure of the model's confidence in predicting that class for a given instance (i) tree voting: each tree in the forest votes for a class for a given instance; (ii) prediction probability: The prediction probability for each class is calculated as the percentage of trees that voted for that class.

[0128] Validation of the measurement method.

[0129] The fluorescence and absorbance values obtained from the images may be contrasted with values obtained using a plate reader (conventional reader). For each row of the plate, a graph may be shown comparing measurements extracted from the image, and from the plate reader value. In the case of fluorescence, an average deviation in the measurements of 6.6% was observed, and in the case of absorbance an average deviation of 1 74% was observed.

[0130] The first characterization of “biosensors characterization" provides information that a pathogen is involved in a pathogen infection process The absence of detectable signal in one or more chambers, for example fluorescence signal, may not imply that there is absence of one or more pathogens.

[0131] Second characterization: macroscopic growth

[0132] For the second characterization, the system of figure 3 may be used, placing the second plurality 25 of chambers in the housing 30. The sensor system 24 may take pictures of the chambers in the second plurality of chambers. The light source 23 may comprise a white LED strip, wavelength between 400-500 nm, to illuminate the chambers and capture the images to be analyzed. The light source may be placed 10mm from the second plate 32. The light source 23 may comprise a diffuser panel in front of the LED strip to avoid undesired artifacts or reflections in the final image. Images may be taken from the top view of the second plate 32, where the second plate 32 comprises a second plurality 25 of chambers comprising, in figure 3, 6-well plate filled with different agar media.

[0133] The following method steps 2A to 2F may be followed:

[0134] Step 2A: Define selective media. Different agars or selective media for selective bacterial growth may be used for different chambers in the second plurality of chambers. This allows discriminating against bacterial families.Agar is a gel-like substance derived from seaweed and is widely used in biology and microbiology for various purposes, including growing and culturing microorganisms. Some of the most common types include nutrient agar, sabouraud agar, MacConkey agar or chocolate agar. Particular agar types may be chosen depending on suitability for the growth of bacterial families of interest. Selective agars may be selected to enable robust classification of i) gram-positive and gram-negative bacteria, ii) salt-tolerant bacteria, sucrose-positive bacteria, and haemolytic bacteria as the main features to have a wide classification.

[0135] Step 2B: Let test samples grow in each of the media in each of the chambers in the second plurality of chambers. Figure 6 shows bacterial growth in 6 chambers in the second plurality 25 of chambers. For the selected agar types (or media), samples may be left at the following conditions: Temperature of 25-30 degrees, although temperature is another variable that can help on bacterial classification based on temperature growth. For example, a known pathogen in each of the selected media placed on a 6-well plate reservoir may be grown. The samples may grow for a total time of 48h, with images being taken every 10 hours to track growth progression.

[0136] Step 20. Take pictures: Pictures may be taken of the agar plates with grown colonies at specific times, for example, 24h and 48h from the start of the culture.

[0137] Step 2D: Annotate the images. Two different image annotations may be implemented: i) Colony annotation using boxes, for example in Roboflow. Figure 7 represents an example of colony annotation using boxes, defining labels which correspond to the bacterial family they belong to, e.g. vharveyi for vibrio harveyi, apiscicida for aeromonas piscicida, valgino for vibrio alginolyticus etc. ii) Plate detection, for example using the same platform as for colony annotation, e.g. Roboflow, to select the different wells in each analyzed plate, to define the region of interest to then perform colony identification.

[0138] Step 2E: Run Al algorithms which are trained with annotated images to identify bacteria based on the morphology. Transfer learning from a deep learning detection model may be used, for example, YOLO algorithm A pretrained detection model based on a convolutional neural network CNN may be used to extract the main image features, and weights may be updated to learn from the training dataset. Afterwards, a neck network may reduce the size of a feature map and may increase the resolution to achieve more accuracy on the model The final step consists of location and class prediction. The algorithm uses boxes to estimate object location, optimizing Intersection Over Unions and including the non-maximum suppression algorithm to filter overlapping bounding boxes and ensure more confident prediction outputs.

[0139] Step 2F: Read predictions made by the detection model. A segmented image with the following outputs may be obtained from the Al algorithm: a) Colony identification; colony count of each bacterial group; Prediction probability, as the algorithm gives a percentage of prediction success. Figure 8 shows an example output from a detection model indicating the presence of Valgino pathogens with different prediction probabilities. A filter may be applied to avoid low probabilities to be taken as definitive outputs, for example:- Selective media growth filter: certain bacterial families are observed to grow more abundantly on some agar types than others. Assume that the output of the deep learning model on a picture of a bacterial culture on agar-type A shows bacterial colonies of type X. This prediction is to be contrasted with the a priori knowledge of the growth of bacteria-type X on agar-type A. If bacteria X is known to not grow on agar A, this may be counted as a misprediction.- Growth speed filter: the growth rate of different bacteria-types has been documented through experiments, and it may be used to find mispredictions in the deep learning detection model. For example, if bacteria- type X growth is known to happen after 48h, a model prediction of bacterial growth of type X at 24h will be counted as a misprediction. E.g., photobacterium damselae piscicida is a slow-growth bacteria which will not grow after 48 hours.- Prediction score filter: this may be used to filter out low-scored classified predictions which can result in wrong outputs.- Temperature-sensitive growth filter: this filter may be used to incorporate additional variables which can help on pathogen discrimination.

[0140] The second characterization may provide a result that quantifies and identifies the presence of a pathogen, for example, Vibrio spp. This characterization does not necessarily imply that a living being, for example a fish, is infected with Vibrio spp. The second characterization may provide information of an existing pathogen one day and an infection outburst may occur some days after such day.

[0141] Output characterization

[0142] Information from the first characterization or biosensor output is combined with the second characterization of microbiology output to give a final output characterization or response.

[0143] The order of the first characterization and of the second characterization may be altered, for example the second characterization may be implemented before the first characterization or vice versa without affecting the output characterization. The output characterization may include i) pathogen family, ii) pathogen species and subspecies (if possible), iii) pathogen concentration, displayed as CFU / mL, iv) pathogenicity degree, which will be correlated with mortality of living beings, for example, fish, in a later stage

[0144] A prediction may advantageously be based on two or more output characterizations because different information is provided. Microbiology characterization provides more variety of pathogens and biosensor characterization provides the pathogen(s) that is(are) triggering infection.

[0145] As seen, the system comprises a specific two-component signal transduction system, the two components being the microorganism and the target molecule, involved in bacterial infection processes, wherein the two- component signal transduction system may be understood as a ligand-dependent system which produces a detectable signal upon ligand presence, for example the ligand-dependent system being a Green Fluorescent Protein (GFP).

[0146] The system may be considered a multi-sample device. The biosensors are characterized by the type of signals and the combination of such signals to identify and quantify and quantify pathogens of interest. Genetically modified bacteria may produce a Green Fluorescent Protein GFP whose intensity level depends directly on the concentration of detected target molecules, which, in turn, depends on the concentration of pathogens. The system of the disclosure is configured to absorbance measure to establish the amount of genetically modified cells.

[0147] The system to measure comprises a LED diode for illuminating the sample and a camera for detecting the fluorescence level per cell. The camera is configured to detect the diffraction of the light, which depends on the number of cells in the culture.

[0148] The fluorescence measurement will be performed with a cyan LED array (490-495nm), a pair of light filters made of blue methacrylate plastics and amber methacrylate plastics, together with a diffuser filter, and a camera configured to detect the level of GFP protein fluorescence.

[0149] A minimum number of cameras may allow an efficient image acquisition, providing a balance between the technical scope, e.g., short focal length, focal length, focus angle and adequate definition and the economic scope. The use of plastic filters, to the detriment of the usual more complex and expensive filters, entails the right choice of the type of material to be used in order to make it functional since conventional plastic filters are designed for imaging, not for GFP imaging and to compensate for the difference in complexity. Artificial Intelligence model has been trained.

[0150] The system may be configured to take photographs with definition and scan them order to obtain representative samples for further processing by means of the processing system.

[0151] The system of the disclosure may be understood as an loT (Internet of Things) device allowing for the storage of the genetically modified bacteria which react with a sample of interest, for example water, blood, urine, etc. The loT device may send the captures by the sensor system to a server or cloud server comprising the processing system for further processing. This process may be done automatically and continuously over time, providing results in real time.

[0152] The system for characterization of a predefined set of pathogens may be comprise: a first plurality of chambers, stirrer, heater, light source, optical sensor, and the processing means.

[0153] The first plurality of chambers may include 5 chambers linearly distributed or may include only 1 chamber properly sealed and formed in rows, which contains the genetically modified microorganisms, also referred as to developed freeze-dried bacteria and selected powdered nutrients or reagents selected for each day. Each genetically modified microorganism in each chamber detects a different target molecule; e.g, each row constitutes therefore a pre-determined set of specific pathogen detection subsystem.

[0154] The sample to be analyzed may be received in a chamber, where agitation may be generated by a shaker. The system may comprise agitation by means of a mechanical stirrer with modulable speed and heating by means of an electrical resistance through an electrical resistance, to raise the sample temperature to 37 °C which may be a temperature control, by means of a probe and a PID controller.

[0155] The amount of lyophilized and powdered culture medium per chamber may be calculated to ensure proliferation of the microorganisms. A proper sealing may be ensured to maintain sterility and determine the durability and storage conditions, to determine the autonomy of the device. A predetermined autonomy may be projected and ensured, for example, to achieve monthly recharge of the system with nutrients.

[0156] The system of the disclosure may comprise a reagent system to load reagents into the chambers, and the system may allow automatic and periodical water sampling, which may then be periodically injected into the different capsules by guiding means or membranes to start the detection process. Once the water is injected, a guiding system for example pumps and / or valves for guiding the samples may be cleaned to avoid clogging.

[0157] Sample aliquots may then be left at, for example, the following conditions: I) temperature at 37 °C degrees, (II) mechanical agitation to facilitate bacterial growth, iii) illumination with filters to monitor the generation of GFP in each capsule. The captures of the detectable signal, GFP signal is then sent to the processing system for analysis.

[0158] The system may comprise a SIM card to ensure connectivity, regardless of the connectivity, regardless of WIFI or Ethernet connection. This allows the communication of information about at least the captures at all times and remotely, as well as, for example, images, for later analysis.

[0159] The processing system allows, for example based on artificial intelligence, a computational analysis for extracting fluorescence patterns from the fluorescence patterns of each of the images captured by the sensor system. This pattern identification may be mainly performed by means of a classification module based on convolutional neural networks (CNN). CNN. This CNN classification module, once trained, may be used to

[0160] (i)quantification, or automatic quantification of pathogens, i.e., to quantify the presence of each type of pathogen in the predefined set as a function of the light intensity of the detectable signal from the chambers. This is possible because the bio-sensor intensity is directly proportional to the pathogen concentration. The first plurality of chambers respond with different activation curves depending on the concentration; and

[0161] (ii) identification of activation patterns in genetically modified microorganisms, i.e., live biosensors used as pathogen detection systems, e.g., the chambers respond with different activation curves depending on the concentration of the pathogen, where, depending on each activation pattern, it is possible to identify the type of pathogen(s) in the sample, the quantity thereof, and / or their virulence status.

[0162] Genetically modified microorganisms

[0163] As mentioned, the system comprises a specific two-component signal transduction system, the two components being the genetically modified microorganism and the target molecule, wherein the two-component signal transduction system may be understood as a ligand-dependent system which produces a detectable signal upon ligand presence, for example the ligand-dependent system may produce a Green Fluorescent Protein, GFP protein which emits green fluorescence.

[0164] In some examples, the genetically modified microorganism comprised in the first population of chambers are genetically modified by incorporating genes that, when expressed, result in optically detectable signals. Thus, upon detection of a specific target molecule, such as QS molecules, the genetically modified microorganism responds by producing a detectable signal, for example, light (such as fluorescence) which is to be detected by suitable optical methods. Genetic constructs for imparting bioluminescence to genetically modified microorganisms may comprise a gene cassette. As used herein, the term "cassette” may refer to a recombinant DNA construct made from a vector and inserted DNA sequences which encode the proteins that are responsible for generating bioluminescence.

[0165] In some examples, the genetically modified microorganism is a bacterium. In particular examples, the genetically modified bacterium is E. coli, including, but not limited to an E. coli harboring a pSB401 plasmid expressing LuxR of V. fischeri and E. coli JLD271 / pAL103 harboring a pAL103 plasmid expressing LuxR of V. fischeri. In some embodiments the genetically modified microorganism is V. fischeri MJ-1 , which has native luxR receptor, reporter, and binding elements.

[0166] Thus, the expression vector or nucleic acid construct used to genetically modify the microorganisms contained in the first population of chambers can include additional sequences which render the vector suitable for replication and integration in prokaryotes, eukaryotes, or preferably both (e.g., shuttle vectors). Typical cloning vectors contain transcription and translation initiation sequences (e.g., promoters, enhancers) and transcription and translation terminators (e.g., polyadenylation signals). Inherent to genetic modifications of the bacteria, is the need of selecting those bacteria that are successfully genetically modified [e.g., those bacterial cells that have acquired the plasmid(s)]. Selecting these correctly, genetically modified bacteria can be performed by, for example, antibiotic resistance genes (herein also referred to as "marker genes"). Polynucleotide vectors suitable for the genetic modification of bacteria are vectors derived from expression vectors in prokaryotes such as pUC18, pUC19, Bluescript and the derivatives thereof, mpl8, mpl9, pBR322, pMB9, ColEI, pCRI, RP4, phages and "shuttle" vectors such as pSA3 and pAT28. Genetic modification also comprises introducing in the bacteria a reporter gene, which is a polynucleotide sequences coding a protein that emits a detectable signal (the protein that emits a detectable signal is also herein referred to as "reporter"). Useful reporter genes in the context of the present invention include GFP, RFP, Dsred, lacZ, luciferase, thymidine kinase and the like. Genetic modification also comprises introducing in the bacteria a receptor gene, which is a polynucleotide sequence encoding a protein or peptide which binds the target molecule to form an active transcription factor that activates the inducible promoter of the reporter gene (herein referred to as “receptor”). The receptor gene is under the control of a constitutive promoter. Useful marker genes in the context of this invention include, for example, the neomycin resistance gene, conferring resistance to the aminoglycoside G418; the hygromycin phosphotransferase gene, conferring resistance to hygromycin; the ODC gene, conferring resistance to the inhibitor of the ornithine decarboxylase (2-(difluoromethyl)-DL-ornithine (DFMO); the dihydrofolatereductase gene, conferring resistance to methotrexate; the puromycin-N-acetyl transferase gene, conferring resistance to puromycin; the ble gene, conferring resistance to zeocin; the adenosine deaminase gene, conferring resistance to 9-beta-D-xylofuranose adenine; the cytosine deaminase gene, allowing the cells to grow in the presence of N-(phosphonacetyl)-L-aspartate; thymidine kinase, allowing the cells to grow in the presence of aminopterin; the xanthine-guanine phosphoribosyltransferase gene, allowing the cells to grow in the presence of xanthine and the absence of guanine; the trpB gene of E. coli, allowing the cells to grow in the presence of indol instead of tryptophan; the hisD gene of E.coli, allowing the cells to use histidinol instead of histidine. The reporter gene, receptor gene, and marker gene may be comprised in different vectors. Alternatively, it is possible to use a combination of the reporter gene, receptor gene and the marker gene simultaneously in the same vector, or two component genes may be included in one vector and the third in a different vector.

[0167] The polynucleotide intended for genetic modification can be introduced into the bacterial cell as naked DNA plasmids, but also using vectors by methods known in the art, including but not limited to transfection, electroporation (e.g. transcutaneous electroporation), microinjection, transduction, cell fusion, DEAE dextran, calcium phosphate precipitation, use of a gene gun, or use of a DNA vector transporter. Other molecules are also useful for facilitating transfection of a nucleic acid, such as cationic oligopeptides, peptides derived from DNA binding proteins, or cationic polymers. Another well-known method that can be used to introduce polynucleotides into host cells is particle bombardment (aka biolistic transformation). Biolistic transformation is commonly accomplished in one of several ways. One common method involves propelling inert or biologically active particlesat cells Alternatively, the vector can be introduced in cells by lipofection The use of cationic lipids can promote encapsulation of negatively charged nucleic acids, and also promote fusion with negatively charged cell membranes. Particularly useful lipid compounds and compositions for transfer of nucleic acids have been described.

[0168] On the other hand, as the skilled person in the art is aware, the choice of the vector will depend on the host cell in which it will subsequently be introduced. By way of example, the vector in which said polynucleotide is introduced can also be a yeast artificial chromosome (YAC), a bacterial artificial chromosome (BAC) or a Pl-derived artificial chromosome (PAC). The characteristics of the YAC, BAC and PAC are known by the person skilled in the art. The vector can be obtained by conventional methods known by persons skilled in the art.

[0169] The reporter gene is under the control of an inducible promoter, such that each inducible promoter is responsive to a target molecule. This means that the inducible promoter becomes activated by interaction with a specific target molecule, e.g., a OS molecule, which in turn activates the transcription of the reporter gene. Accordingly, production of the reporter protein, and thus a detectable signal, depends on activation of the inducible promoter, which initiates transcription of the reporter gene in the presence of a given microorganism, i.e., pathogen of the predefined set of pathogens, which produces the target molecule able to activate the inducible promoter. The expression "such that each inducible promoter is responsive to a different target molecule” in the present disclosure may be understood such as that the inducible promoter is responsive (i.e., induced / activated) by the complex formed by the target molecule and the receptor protein.

[0170] In one example, each genetically modified microorganism contains a different inducible promoter, such that each genetically modified microorganism in each chamber is different with respect to the other genetically modified microorganisms in the other chambers of the first population of chambers, based on the presence of a given inducible promoter. It also means that each genetically modified microorganism reacts by producing a reporter in response to a given target molecule which activates a given inducible promoter. In one example, a given inducible promoter becomes activated by only one target molecule so that each genetically modified microorganism may react by producing a reporter in response to a given target molecule In another example, a given inducible promoter becomes activated by two or more target molecules so that each genetically modified microorganism may react by producing a reporter in response to two or more given target molecules. In examples, a first genetically modified microorganism and a second genetically modified microorganism react by emitting a (possibly different) detectable signal in response to at least the same target molecule, where the first genetically modified microorganism and the second genetically modified microorganism are different between them.

[0171] As system contemplates the use of multiple genetically modified microorganisms adapted to react in response to specific target molecules present in the sample, it provides simultaneous information regarding the presence of different target molecules, that is, it provides information on a pattern of target molecules. It is understood that different patterns of target molecules may be obtained depending on the pathogens present in the sample.

[0172] As would be apparent to the skilled person, the expression "each of the chambers comprises a different genetically modified microorganism” is interpreted in the sense that the chamber contains not only a single genetically modified microorganism (one cell) but a population of one type (strain) of the genetically modifiedmicroorganism. The genetically modified microorganisms in the chamber may be lyophilized. When the aliquots of the first sample are placed in the first population of chambers, the genetically modified microorganisms are hydrated. In the presence of appropriate culture medium and conditions, the population of genetically modified microorganism proliferates and, when the specific target molecule is present in the sample, reacts by producing the reporter and, thus, the detectable signal.

[0173] Culturing conditions

[0174] In order to produce the detectable signal, he first plurality of chambers is maintained under conditions suitable for the proliferation of the genetically modified microorganisms. Said conditions are such that they ensure survival and proliferation of the genetically modified microorganisms for a period of time sufficient to provide detectable signals. This culturing (or proliferating) step is carried out for the time needed until the concentration of the genetically modified microorganisms is sufficient so that the detectable signal provided by the reporter gene is above the sensitivity threshold of the measuring device. The skilled person will understand that the conditions to be used in this step can be determined by routine experimentation considering the type of genetically modified microorganism.

[0175] In general, the culturing is maintained for a time sufficient to detect the minimal possible inoculum of pathogens in the sample. Then given that time-window, responses from the engineered constructs are directly mapped to inoculum concentrations of the analyzed sample. Optimal temperature to be used in this step may be determined for instance by performing growth curves of the genetically modified microorganism at different temperatures and recording the optical density of the culture at different time points. Similar growth curves can be obtained for each of the conditions that need to be adjusted during this step, such as culture medium, type of agitation, etcetera

[0176] In some examples, the temperature in this step is of 35 degrees centigrade to 40 degrees centigrade, such as about 37 degrees centigrade. In some examples, this culturing step is carried out for a time of from about 1 hour to about 12 hours, for example about 2 hours to about 9 hours, such as about 3 hours to about 7 hours.

[0177] Examples of suitable culture media in this step are well known to a person skilled in the art, and include Luria-Bertani medium, Brilliant green lactose bile (BGLB) broth, Lauryl tryptose (LST) broth, Lactose Broth, EC broth, Levine's eosin-methylene blue (L-EMB) agar, Tryptone (tryptophan) broth, MR-VP broth, Koser's citrate broth. Apart from allowing proliferation of the genetically modified microorganisms, the culture medium allows the diffusion of the target molecules secreted by the proliferating genetically modified microorganisms.

[0178] As outlined above, in order to maintain the concentration of target molecules constant in the chambers, in one embodiment the aliquots of the first sample placed in the first population of chambers is devoid of pathogens. This may be achieved by fractionating aliquots of the first sample prior to being added to the first plurality of chambers so that the pathogens (or any other microorganism for that matter) present in the first sample are removed and only the target molecules produced by the predefined set of pathogens is present in the sample. Typically, the fractionation is carried out by filtration or by centrifugation. In another example, the culturing conditions applied to the first population of chambers are such that they do not allow for proliferation of the pathogens. This may be achieved by including in the culture medium a substance, typically of an antibiotic, which prevents the proliferation of the pathogens but does not affect proliferation of the genetically modified microorganisms, so thatthe concentration of target molecule is maintained constant. Since the microorganisms in the first population of chambers are genetically modified, the genetic modification may include, in addition to the reporter gene which is sensitive to the target molecule, a gene which provides resistance to the antibiotic (also referred to as “marker gene”).

[0179] Target molecules

[0180] As outlined above, the target molecules are representative of the pathogens present in the sample to be analysed. In an advantageous example, the target molecule is produced by the pathogen when in a virulent state. Examples of such molecules are QS molecules, such as N-Acryl homoserine lactones (AHL), oxo acyl homoserine lactones (oxo AHL), autoinducing peptides (AlP)pyocyanin, 2-heptyl-3,4-dihydroxyquinoline (PQS), 4-hydroxy-2- heptylquinoline (HHQ), (S)-3-hydroxytridecan-4-one (CAI-1) , Auto-inducers (AI-2, AI-3), Histidine protein kinases (HPKs), signal transduction enzymes, environmental ions such as iron, magnesium, and combinations thereof.

[0181] Two-component signaling systems (TCSs), which are composed of a membrane-bound histidine kinase (HK) sensor and a cytoplasmic response regulator (RR), have been implicated in regulating bacterial responses to a variety of signals and stimuli, such as nutrients and small-molecule signals. Recognition of physical or chemical signals by the HK sensor domain typically triggers modulation of HK autophosphorylation activity. The phosphoryl group is then transferred to the RR, which is usually a DNA-binding protein that acts to alter gene expression.

[0182] As used herein, the term "acyl homoserine lactone" refers to a group of compounds that share a common homoserine lactone ring structure. Examples thereof include N-|3-oxo-hexanoyl-L-homoserine lactone, N-|3-oxo- octanoyl-L-homoserine lactone, N-|3-oxo-decanoyl-L-homoserine lactone, N-hexanoyl-L-homoserine lactone, N- butanoyl-homoserine lactone, N-p-oxo-dodecanoyl-L-homoserine lactone, but the type of acyl homoserine lactone is not limited thereto. Preferably, the acyl homoserine lactone may be hexanoyl homoserine lactone.

[0183] In some examples, the QS molecule is an acyl-homoserine lactone. AS used herein, are a class of signaling molecules involved in bacterial quorum sensing with general chemical formula I or formula II:Formula II

[0184] In some examples, the acyl-homoserine lactone (AHL) is selected from the group consisting of Oxo-C6- AHL, Oxo-08-AHL, Oxo-C12-AHL, C4-AHL, Oxo-C14-AHL, N-butyryl homoserine lactone, N-hexanoyl homoserinelactone, N-(3-oxo)-hexanoyl homoserine lactone, N-octanoyl homoserine lactone, N-(3-oxo)- octanoyl homoserine lactone, N-decanoyl homoserine lactone, N- dodecanoyl homoserine lactone, N-(3-oxo)-dodecanoyl homoserine lactone, N-tetradecanoyl homoserine lactone, N-Butyryl-DL-homoserine lactone, N-Hexanoyl-L-homoserine lactone, N-heptanoyl-L-homoserine lactone, N-Octanoyl-L-homoserine lactone, N-Decanoyl-DL-homoserine lactone, N-Dodecanoyl-DL-homoserine lactone, N-Tetradecanoyl-DL-homoserine lactone, N-(3-Hydroxybutyryl)-L- homoserine lactone, N-(3-Hydroxyhexanoyl)-L-homoserine lactone, N-(3-Hydroxyoctanoyl)-DL-homoserine lactone, N-(3-Hydroxynonanoyl)-L-Homoserine lactone, N-(3-Hydroxydecanoyl)-L-Homoserine lactone, N-(3- Hydroxyundecanoyl)-L-Homoserine lactone, N-(3-Hydroxydodecanoyl)-DL-homoserine lactone, N-(3- Hydroxytetradecanoyl)-DL-homoserine lactone, N-(3-Oxoburtyryl)-L-homoserine lactone, N-(|3-Ketocaproyl)-DL- homoserine lactone, N-(3-Oxooctanoyl)-L-homoserine lactone, N-(3-Oxononanoyl)-L-homoserine lactone, N-(3- Oxodecanoyl)-L-homoserine lactone, N-(3-Oxoundecanoyl)-L-homoserine lactone, N-(3-Oxododecanoyl)-L- homoserine lactone, N-(3-Oxotetradecanoyl)-L-homoserine lactone, and combinations thereof.

[0185] In some examples, the AHL is preferably selected from the group consisting of N-butyryl homoserine lactone, N-hexanoyl homoserine lactone, N-(3-oxo)- hexanoyl homoserine lactone, N-octanoyl homoserine lactone, N-(3-oxo)- octanoyl homoserine lactone, N-decanoyl homoserine lactone, N- dodecanoyl homoserine lactone, N- (3-oxo)-dodecanoyl homoserine lactone, N-tetradecanoyl homoserine lactone, and combinations thereof.

[0186] In some examples, the AHL characteristic of Vibrio spp. is preferably selected from the group consisting of N-Butyryl-DL-homoserine lactone, N-Hexanoyl-L-homoserine lactone, N-heptanoyl-L-homoserine lactone, N- Octanoyl-L-homoserine lactone, N-Decanoyl-DL-homoserine lactone, N-Dodecanoyl-DL-homoserine lactone, N- Tetradecanoyl-DL-homoserine lactone, N-(3-Hydroxybutyryl)-L-homoserine lactone, N-(3-Hydroxyhexanoyl)-L- homoserine lactone, N-(3-Hydroxyoctanoyl)-DL-homoserine lactone, N-(3-Hydroxynonanoyl)-L-Homoserine lactone, N-(3-Hydroxydecanoyl)-L-Homoserine lactone, N-(3-Hydroxyundecanoyl)-L-Homoserine lactone, N-(3- Hydroxydodecanoyl)-DL-homoserine lactone, N-(3-Hydroxytetradecanoyl)-DL-homoserine lactone, N-(3- Oxoburtyryl)-L-homoserine lactone, N-(|3-Ketocaproyl)-DL-homoserine lactone, N-(3-Oxooctanoyl)-L-homoserine lactone, N-(3-Oxononanoyl)-L-homoserine lactone, N-(3-Oxodecanoyl)-L-homoserine lactone, N-(3- Oxoundecanoyl)-L-homoserine lactone, N-(3-Oxododecanoyl)-L-homoserine lactone, N-(3-Oxotetradecanoyl)-L- homoserine lactone, and combinations thereof.

[0187] In some examples, the pattern of QS molecules characteristic of a Vibrio spp. is selected as described in Liu et al. (Front Cell Infect Microbiol. 2018; 8: 139) which included by reference in the present disclosure.

[0188] In some examples, system is adapted to determine simultaneously a plurality of acyl-homoserine lactones. In some embodiments, the system is adapted to 2 different Acyl-homoserine lactones, 3 different Acyl-homoserine lactones, 4 different Acyl-homoserine lactones, 5 different Acyl-homoserine lactones, 6 different Acyl-homoserine lactones, 7 different Acyl-homoserine lactones, 8 different Acyl-homoserine lactones, 9 different Acyl-homoserine lactones, 10 different Acyl-homoserine lactones, 11 different Acyl-homoserine lactones, 12 different Acyl- homoserine lactones, 13 different Acyl-homoserine lactones, 14 different Acyl-homoserine lactones, 15 different Acyl-homoserine lactones, 20 different Acyl-homoserine lactones, 25 different Acyl-homoserine lactones, 30 different Acyl-homoserine lactones, 40 different Acyl-homoserine lactones, 50 different Acyl-homoserine lactones.

[0189] Other examples of TCS include KguS / R, which responds to the presence of a-ketoglutarate (KG), OrhK / OrhR which responds under hydrogen peroxide (H2O2) exposure and activates the expression of a putative methionine sulfoxide reductase system and hemolysin (HlyA). QseC sensor kinase and the QseB response regulator is another TCS example typical from enterohaemorrhagic bacteria. TCS comprising WalK sensor kinase and WaIR response regulator is essential for cell growth of some Gram-positive bacteria, such as S. aureus, Enterococcus faecalis and B. subtilis. Some other examples include PhOP / PhoQ, Algr1 / Algr2, VanRA / anS, KinA / SpoOF, Algri / Algr2, C0IS-C0IR, PmrB-PmrA, GluK-GluR, NarX-NarL, EnvZ-OmpR, HK1 1 -RR1 1 , EvgS-EvgA, AauS-AauR, among others.

[0190] Many Gram-positive bacteria produce short peptides, termed “auto-inducing peptides (AIP)” or “auto-inducers”, that function as signaling molecules and activate a subset of genes, including those which contribute to virulence and genetic competence via particular TCSs, such as AgrC, ComD, FsrC etc.

[0191] target molecules that can be used for the present system are disclosed in the following documents, all of which are incorporated by reference: 001:10.1111 / 1462-2920.15731, DOI: 10.1128 / mbio.02569-14, DOI: 10.1128 / AEM.00477-10, and D0l:10.1371 / journal.ppat.1010005.

[0192] Pathogens

[0193] The pathogens contained in the predetermined group of pathogens may be QS molecule-producing pathogens. In one embodiment, the predetermined set of pathogens may comprise pathogens belonging to Vibrio spp., to the Aeromonas genus, Photobacterium spp, Bacillus spp, Streptococcus spp., Micrococcus spp, Vagococcus spp, Flavobacterium spp, Halomonas spp., Burkholderia spp., Chromobacterium spp., Enterobacter spp., Erwinia spp., Pseudomonas spp., Ralstonia spp., Rhizobium spp., Salmonella spp., Serratia spp., Yersinia spp., E coll spp., and / or Staphylococus spp., In some examples, the Vibrio spp is selected from the group consisting of V. aestuarianus, V. anguilarum, V. brasiliensis, V. campbellii, V. coralliilyticus, V. fischeri, V. fluvialis, V. furnissii, V. gaogenes, V. harveyi, V. mediterranei, V. metschnikovii, V. pacinii, V. proteolyticus, V. rotiferianus, V. salmonicida, V. scophthalmi, V. shiloi, V. sinaloensis, V. splendidus, V. tasmaniensis, V. tubiashii, V. vulnificus, V. xiamenensis, and combinations thereof.

[0194] In some examples, the predetermined set of pathogens may comprise pathogens of a species selected from the following Table, wherein the appropriate target molecules and receptor proteins (“receptors'') is also indicated:Pathogen Target molecule / s Receptor / sVibrio fischeri N-(3-oxohexanoyl)- HSL LuxRAeromonas hydrophila N-butanoyl-HSL AhyRAeromonas salmonicida N-butanoyl-HSL AsaRAgrobacterium tumefaciens N-(3-oxooctanoyl)- HSL TraRBacillus subtilis ComX ComPBurkholderia cepacia N-octanoyl-HSL CepRChromobacterium violaceum N-hexanoyl-HSL CciREnterobacter agglomerans N-(3-oxohexanoyl)- HSL LuxRErwinia carotovora N-(3-oxohexanoyl)- HSL LuxREdwarsiella tarda EdwREscherichia coli AI-3, LPS, irons QseC / QseB, PhoR / PhoBKlebsiella pneumoniae AI-2 Lsr, LuxSPhotobacterium damselae Environmental, Mg2+ RstABPseudomonas aereofaciens N-hexanoyl-HSL CciRPseudomonas aeruginosa N-(3-oxodode- canoyl)-HSL, N- LasR, RhIR, Pqs and Iqs butyryl-HSL,N-(3- oxododecanoyl)- HSL,Pseudomonas quinolone signal (PQS), IQSRalstonia solanacearum N-hexanoyl-HSL, N-(3- CciR, RasR hydroxydodecanoyl)-HSLRhizobium etli N-octanoyl-HSL CepRRhizobium leguminosarum N-hexanoyl-HSL, N-(3-hydroxy- CinR7-cis-tetradecenoyl)-HSLRhodobactersphaeroides 7,8-cis-N- (tetradecanoyl)-HSL GtaRSalmonella typhimurium AI-2, AI-3 LsrR, QseC / QseBSerratia liguefaciens N-butanoyl-HSL, toxins ShlA / BStaphylococcus aureus AIP, SarA AgrC / AgrA, SrrAB, SaeRSStreptococcus pneumoniae ComC ComABTenacibaculum maritimum n-butyryl-HSL, iron related signal RhIR, iron uptake receptorsVibrio anguillarum N-(3-oxododecanoyl)- HSL, N-(3- LasR, VanR oxodecanoyl)- HSLVibrio alginolyticus N-(3-oxodecanoyl)- HSL, N- RhIR, VanR butanoyl-HSLVibrio harveyi N-(3 hydroxy-butanoyl)-HSL, N- LuxM, LuxR(3-oxohexanoyl)- HSLYersinia enterocolitica N-hexanoyl-HSL, ypsR, ytbRN-(3-oxohexanoyl)- HSLThe presente disclosure also refers to use of a combination of two or more, preferably, three or more, of the target molecule-receptor(s) shown in the table above for the characterization of a predetermined set of the corresponding pathogens as shown in the tableOther receptors corresponding to pathogens of interest are shown in the table below:Organism ProteinVibrio vulnificus SmcRPseudomonas aeruginosa LasRAgrobacterium tumefaciens TraRChromobacterium violaceum CviREscherichia coli SdiAAliivibrio fischeri (Vibrio fischeri) LuxRVibrio harveyi LuxNErwinia stewartii EsaRPseudomonas chlororaphis PhzR (QscR)Brucella melitensis VjbRYersinia enterocolitica YenRAliivibrio fischeri (Vibrio fischeri) AinRAeromonas hydrophila AhyRBurkholderia ambifaria BafRErwinia carotovora CarRBurkholderia sp CepRRhizobium leguminosarum CinRRhizobium etli RaiRHafnia alvei HaIRNitrosospira multiformis NmuRPseudomonas putida PpuRRhizobium leguminosarum RhiRSinorhizobium meliloti SinRVibrio anguillarum VanRVibrio anguillarum VanNVibrio fluvialis VfqRYersinia pseudotuberculosis YpsRYersinia pseudotuberculosis YtbRAcidithiobacillus ferrooxidans AfeRBradyrhizobium japonicum BjarlPseudomonas chlororaphis AurROrganism ProteinBurkholderia pseudomallei BpsRParaburkholderia kururiensis BraRPectobacterium betavasculorum EcbREdwardsiella tarda EdwRGluconacetobacter diazotrophicus GDI2838Gluconacetobacter intermedi s G i n RHalomonas anticariensis HanRMesorhizobium loti MrlR1Mesorhizobium loti MrlR2Mesorhizobium tianshanense MrtRRhodospirillum rubrum Rru_A3395Ralstonia solanacearum SoIRSerratia plymuthica SpIRSerratia proteamaculans SprRSerratia liquefaciens SwrRBurkholderia glumae T ofRPantoea agglomerans PagRPseudomonas corrugata PcoRSerratia marcescens SpnRRhodobacter sphaeroides CerRAgrobacterium vitis (Rhizobium vitis) AvsRCitrobacter rodentium C ro RPantoea agglomerans MalTPseudomonas syringae pv. Syringae AhIRPseudomonas syringae tabaci PsyRBurkholderia vietnamiensis BviRBurkholderia cenocepacia CciRAcinetobacter baumannii AbaRPseudomonas sp CmrRPantoea ananatis EanRYersinia ruckeri YruRAzospirillum lipoferum AlpRAcinetobacter oleivorans AqsRBurkholderia plantarii PlaRAeromonas veronii AcuRNitrobacter winogradskyi NwiRAcinetobacter nosocomialis AnoRMethanosaeta harundinacea / Streptomyces filipinensis FilR1Organism ProteinVibrio harveyi LuxPSalmonella typhimurium LsrBEscherichia coli (strain K12) LsrBHelicobacter pylori TIpBAggregatibacter actinomycetemcomitans RbsBBurkholderia cenocepacia RpfRPseudomonas aeruginosa PqsR (MvfR)Burkholderia ambifaria HmqGPhotorhabdus temperata PluRPhotorhabdus asymbiotica PauRAeromonas hydrophila QseCPhotobacterium sp. CECT 9192 CqsSEscherichia coli QseFCaulobacter vibrioides CckAStaphylococcus aureus, Staphylococcus epidermidis T raPEnterococcus faecium EntKEnterococcus faecalis PrgXEnterococcus faecalis T raABacillus subtilis RapALactobacillus plantarum LamCBacillus subtilis ComPBacillus subtilis MrsK2Staphylococcus lugdunensis AgrCStaphylococcus epidermidis AgrC1Streptococcus parasanguinis FW213 AgrC 2Bacillus subtilis AimRStreptococcus gordonii ComDLactobacillus plantarum PltKBacillus subtilis, Bacillus mojavensis RapCCarnobacterium piscicola CbnKLactococcus lactis NisKStreptococcus pyogenes SrtKBacillus subtilis SpaKLactobacillus sakei StxKStreptococcus pneumoniae BipHLactobacillus plantarum PlnBBacillus cereus PIcRLactobacillus sakei SppKOrganism ProteinCarnobacterium maltaromaticum PisKBacillus subtilis RapFCarnobacterium maltaromaticum CbaK.EntKBacillus subtilis RapEStaphylococcus epidermidis SaeSLactobacillus sake! SapKStreptococcus mutans RggStreptococcus thermophilus Rgg-1Streptococcus thermophilus rgg2 (MutR family)Streptococcus thermophilus ComRBacillus thuringiensis CrylAaBacillus thuringiensis NprRClostridium perfringens VirR

[0195] Virulence

[0196] As outlined above, the present system may determine the virulence of the predetermined set of pathogens with high accuracy and in a short time. The system may be used to determine if the pathogens present in the sample are virulent or about to trigger a virulent response.

[0197] The term “virulence” is understood as a parameter that measures the degree of pathogenicity of a population of pathogens, i.e., the probability that said pathogens can effectively trigger disease in a host. It will be understood that the “virulence” of a pathogen population depends on the concentration of pathogens present in the sample Since the pathogens produce molecules that are indicative of the virulence status (i.e , target molecules), the virulence will depend on the concentration of the said target molecules, as this value is used by the pathogens to increase the expression of virulence-related genes when the concentration exceeds a certain threshold level. Accordingly, in some examples, the virulence is determined as the concentration of target molecules in the sample.

[0198] The threshold level to determine the virulence relates to a situation where the concentration value of said target molecule-producing pathogen is increased at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 100% when compared to the corresponding reference value. It is understood that a concentration value above said threshold value is indicative of a high risk of virulence outcome and / or the occurrence of target molecule-producing microorganism-related pathology in a population of animals, in particular, fish. The reference value may be determined by the skilled person by routine methods. In some examples, the reference value may correspond to the concentration value of a target molecule-producing pathogen determined in a sample associated with a high risk of virulence outcome. In other examples, the reference value may correspond to the concentration value of a target molecule-producing pathogen determined in a sample associated with a low risk of virulence outcome.

[0199] Examples of the disclosure are exemplified in the clauses that follow:Clause 1 A processing system to characterize a predefined set of pathogens in a sample, the processing system configured to: obtain a measure of at least one detectable signal based on one or more captures obtained by a sensor system; the at least one detectable signal emitted by at least one genetically modified microorganism upon contact with a target molecule produced by a pathogen of the predefined set of pathogens in a first sample aliquot from the sample, wherein a different genetically modified microorganism emits a different detectable signal upon contact with a different target molecule; or wherein the different genetically modified microorganism emits a detectable signal upon contact with a target molecule; or wherein the different genetically modified microorganism emits a detectable signal upon contact with at least a different target molecule obtain at least an absorbance measure of the sample aliquot based on one or more captures obtained by the sensor system; obtain a first characterization of the predefined set of pathogens in the sample by processing at least the measure of the at least one detectable signal and the at least one absorbance measure of the first sample aliquot; obtain a second characterization of the predefined set of pathogens based on analysis of a macroscopic growth of at least a second sample aliquot based on two or more growth images of the at least the second sample aliquot, the two or more growth images obtained by the sensor system; and configured to output a characterization of the predefined set of pathogens based on the first characterization and on the second characterization.Clause I bis. The processing system of clause 1, wherein: obtain a measure of at least one detectable signal comprises to obtain two or more measures of two or more detectable signals based on one or more captures obtained by a sensor system; the two or more detectable signals emitted by two or more genetically modified microorganisms upon contact with different target molecules produced by different pathogens of the predefined set of pathogens in two or more first sample aliquots; obtain at least an absorbance measure comprises obtain two or more absorbance measures of the two or more first sample aliquots based on one or more captures obtained by the sensor system; obtain a first characterization of the predefined set of pathogens in the sample by processing two or more measures of the detectable signals, wherein the two or more measures of the detectable signals comprises the measure of the detectable signals and two or more absorbance measures of two or more same first sample aliquots; and obtain a second characterization based on analysis of a macroscopic growth of a second sample aliquot may comprise obtain a second characterization based on analysis of a macroscopic growth of two or more second sample aliquots.Clause 2. The processing system of clause 1 or clause 1. bis wherein the detectable signal is an optical signal and the processing system is configured to obtain the measure of the detectable signal and the absorbance measure based on at least one image obtained by the sensor system.Clause 3. The processing system of any one of the clauses 1 to 2 wherein the processing system is configured to obtain the first characterization of the predefined set of pathogens by inferring the first characterization of the predefined set of pathogens in at least one sample aliquot using a trained model, wherein using the trained modelcomprises inputting at least one measure of the detectable signal and one measure of the absorbance measure into the trained model, and obtaining a first characterization of a predefined set of pathogens as the output of the trained model.Clause 4. The processing system of any one of the Clauses 1 to 3 wherein the processor is configured to obtain the second characterization of the predefined set of pathogens by inferring the second characterization using a second trained model, wherein using the second trained model comprises inputting the two or more growth images into the trained model and obtaining the second characterization of the predefined set of pathogens as the output of the second trained model.Clause 5. The processing system of any one of the Clauses 1 to 4 wherein the processor is configured to obtain a measure of the detectable signal of each chamber by obtaining the intensity of the detectable signal by imageprocessing of the one or more signal images.Clause 6. The processing system of any one of the Clauses 1 to 5 wherein the processor is configured to obtain an absorbance measure from each chamber by obtaining an intensity of a wavelength of interest by inverting a quantified decrease in light signal. In some embodiments, the wavelength is 660 nm.Clause 7: A system for characterizing a predefined set of pathogens in a sample, the system comprising: a first plurality of chambers for a first characterization of the predefined set of pathogens, wherein each of the chambers is configured to hold a sample aliquot and each of the chambers comprises a different genetically modified microorganism to emit a detectable signal upon contact with a target molecule produced by a pathogen of the predefined set of pathogens; wherein a different genetically modified microorganism emits a different detectable signal upon contact with at least one different target molecule; a light source to illuminate the plurality of chambers at an illumination wavelength; a second plurality of chambers for a second characterization of the predefined set of pathogens, wherein each of the chambers is configured to hold a sample aliquot, wherein each of the chambers comprises a different culture media; a sensor system configured to: capture the detectable signals; capture an absorbance of each one of the first plurality of chambers; capture one or more images of the second plurality of chambers; the processing system according to any one of clauses 1 to 6, further configured to: obtain a measure of the detectable signal from each chamber or from sample aliquots in each chamber; obtaining thereby a plurality of measures of the detectable signal; obtain an absorbance measure from each chamber or from sample aliquots in each chamber, obtaining thereby a plurality of absorbance measures; obtain the first characterization of the predefined set of pathogens in the sample by processing at least a part of the plurality of measures of the detectable signals and at least a part of the plurality of absorbance measures; obtain the second characterization of the predefined set of pathogens based on analysis of the macroscopic growth of different sample aliquots in the second plurality of chambers based on two or more growth images of the sample aliquots in the second plurality of chambers, the two or more growth images obtained by the sensor system.Clause 8. The system of clause 7 wherein each genetically modified microorganism comprises a reporter gene that is under the control of an inducible promoter, such that each inducible promoter is responsive to a different target molecule; wherein the reporter gene encodes a protein that emits the detectable signal.Clause 9. The system of clause 7 or 8 wherein the genetically modified microorganism further comprises a receptor gene that is under the control of a constitutive promoter, wherein the receptor gene encodes a receptor protein which binds the target molecule to form an active transcription factor that activates the inducible promoter of the reporter gene.Clause 10. The system of any one of clause 7 to 9 wherein the detectable signal is an optical signal having an emission wavelength; the sensor system comprises an optical sensor to capture an image of the first plurality of chambers and of the second plurality of chambers; and wherein the processing system is configured to obtain the measure of the detectable signal and of the absorbance measure from each chamber based on at least one signal image obtained by the optical sensor.Clause 11. The system of any clause from 7 to 10 wherein the protein that emits the detectable signal is a fluorescent protein, the detectable signal is a fluorescence signal, and the measure of the detectable signal is a fluorescence measure, wherein the fluorescent protein is optionally selected from the group consisting of green fluorescent protein, GFP, red fluorescent protein, RFP, yellow fluorescent protein, YFP, and combinations thereof. Clause 12: The system of clause 11 wherein the fluorescent protein has an excitation wavelength, and wherein the light source emits light having an illumination wavelength substantially equal to the excitation wavelength.Clause 12. A: The system of any clause from 7 to 12 wherein the light source comprises a white light source or a blue-cyan light source.Clause 12. B: The system of any clause from 7 to 12. A wherein the light source comprises a diode LED array.Clause 13. The system of any one of clauses 10 to 12.B further comprising a filter configured to block the emission wavelength, the filter arranged to be either at a position or at least moveable to a position in a light path between the plurality of chambers and the optical sensor.Clause 14. The system of any one of the clauses 7 to 13 wherein the processor is configured to obtain the first characterization of the predefined set of pathogens by inferring the first characterization of the predefined set of pathogens in the sample aliquots using a trained model, for example a Random Forest model, wherein using the trained model comprises inputting a plurality of measures of the detectable signal or signals and a plurality of absorbance measures into the trained model, and obtaining a first characterization of a predefined set of pathogens as the output of the trained model.Clause 15. The system of any one of the Clauses 7 to 14 wherein the processor is configured to obtain the second characterization of the predefined set of pathogens by inferring the second characterization using a second trained model, wherein using the second trained model comprises inputting the two or more growth images of each of the agar plates into the trained model and obtaining the second characterization of the predefined set of pathogens as the output of the second trained model.Clause 16. The system of any one of the Clauses 7 to 15 wherein the processor is configured to obtain a measure of the detectable signal of each chamber by obtaining the intensity of the detectable signal by image-processing of the one or more signal images.Clause 16 bis. The system of any one of the Clauses 7 to 16 wherein different detectable signals are detectable signals presenting, each one of them, different intensities.Clause 17. The system of any one of the Clauses 7 to 16 bis wherein the detectable signal is a fluorescence signal, the measure of the detectable signal is a green fluorescent protein, GFP; the sensor system comprises an optical sensor to capture an image of the first plurality of chambers and of the second plurality of chambers; and wherein the processing system is configured to: obtain the measure of the detectable signal; and to obtain an absorbance measure from each chamber by taking an image of the first plurality of chambers; taking readings of absorbance of each chamber in the first plurality of chambers by a spectrophotometer; measuring the turbidity of each chamber in the first plurality by processing the taken image by analyzing the blue channel information of the image; obtaining a relationship between the absorbance readings and the measures of turbidity; fitting the relationship with a model, for example a logarithmic model; and determining a correlation between absorbance readings and the measurements of turbidity.Clause 18. The system of any one of clauses 7 to 17 wherein the target molecule comprises at least one of the following molecules: a quorum sensing molecule (QS molecule).Clause 19. The system of Clause 18 wherein the target molecule is a QS molecule selected from the group consisting of N-Acryl homoserine lactones (AHL), oxo acyl homoserine lactones (oxo AHL), autoinducing peptides (AlP)pyocyanin, 2-heptyl-3,4-dihydroxyquinoline (PQS), 4-hydroxy-2-heptylquinoline (HHQ), (S)-3- hydroxytridecan-4-one (CAI-1) , Auto-inducers (AI-2, AI-3), Histidine protein kinases (HPKs), signal transduction enzymes, environmental ions such as iron, magnesium, and combinations thereof. For example, the target molecule is selected from the group consisting of C4-HSL, C6-HSL, 3-OH-C6HSL, 3-OXO-C6HSL, C10-HSL, 3- OXO-C10HSL, 3-OXO-C12HSL, 3-OXO-C8HSL, and combinations thereof.Clause 20. A method for characterizing a predefined set of pathogens in a sample, the method comprising: obtaining a measure of a detectable signal and an absorbance measure from each chamber of a plurality of chambers, each chamber a holding sample aliquot, based on one or more captures obtained, respectively, by a first sensor configured to capture the detectable signal and by a second sensor configured to capture an absorbance, obtaining thereby a plurality of measures of the detectable signal and a plurality of absorbance measures; wherein the detectable signal is emitted by at least one genetically modified microorganism, in each chamber, upon contact with a target molecule produced by a pathogen of the predefined set of pathogens in the sample aliquot; obtaining a first characterization of the predefined set of pathogens in the sample by processing a plurality of measures of the detectable signal, and a plurality of absorbance measures, wherein each absorbancemeasure is obtained from the same chamber as the chamber where the measure of each detectable signal is obtained from; obtaining a second characterization of the predefined set of pathogens based on analysis of macroscopic growth of sample aliquots in a second plurality of chambers based on two or more growth images of the sample aliquots in the second plurality of chambers, the two or more growth images obtained by an optical sensor; and outputting a characterization of the predefined set of pathogens based on the first characterization and on the second characterization.Clause 21. The method for characterizing a predefined set of pathogens in a sample according to clause 20, the method comprising:(i) placing a first volume of sample aliquot in each chamber of the first plurality of chambers;(ii) maintaining the first plurality of chambers under conditions suitable for the growth of the genetically modified microorganisms;(iii) obtaining a measure of a detectable signal and an absorbance measure from each chamber of the first plurality of chambers based on one or more captures obtained by a sensor system, obtaining thereby a plurality of measures of the detectable signals and a plurality of absorbance measures;(iv) obtaining a first characterization of the predefined set of pathogens in the sample by processing a plurality of measures of the detectable signals, and a plurality of the absorbance measure of the same chamber;(v) placing a second volume of sample aliquot in each chamber of the second plurality of chambers;(vi) maintaining the second plurality of chambers under conditions suitable for the macroscopic growth of the predefined set of pathogens;(vii) obtaining a second characterization of the predefined set of pathogens based on analysis of macroscopic growth of the predefined set of pathogens present in the second sample aliquots in a second plurality of chambers based on two or more growth images of the second sample aliquots in the second plurality of chambers, the two or more growth images obtained by the sensor system; and(viii) outputting a characterization of the predefined set of pathogens based on the first characterization and on the second characterization.Clause 22. The method according to clause 21, wherein the first volume of sample aliquot in step (i) is devoid of pathogens or, alternatively, the conditions of step (ii) do not allow proliferation of the pathogens.Clause 23. The system according to any one of clauses 1-19 or the method according to any one of clauses 20- 22, wherein the predetermined set of pathogens comprise QS-molecules-producing pathogens.Clause 24. The system or method according to clause 23, wherein the predetermined set of pathogens comprise vibrio spp.Clause 25. The system or method according to clause 23 or 24, wherein the predetermined set of pathogens comprise a microorganism selected from the group consisting of of V. anguillarium, V. mediterranei, V. harveyi, V. orientalis, V. vulnificus, V. scophtalui, V. splendidus, V. fishceri, V. salmonitica, and combinations thereof.References cited in the applicationLiu et al. (Front Cell Infect Microbiol. 2018; 8: 139).

Claims

CLAIMS1 . A processing system to characterize a predefined set of pathogens in a sample, the processing system configured to: obtain a measure of at least one detectable signal based on one or more captures obtained by a sensor system, the sensor system comprising an optical sensor; the at least one detectable signal emitted by at least one genetically modified microorganism upon contact with a target molecule produced by a pathogen of the predefined set of pathogens in a first sample aliquot of the sample, wherein a different genetically modified microorganism emits a different detectable signal upon contact with at least one different target molecule; obtain at least an absorbance measure of the first sample aliquot based on one or more captures obtained by the optical sensor; obtain a first characterization of the predefined set of pathogens in the sample by processing at least the measure of the at least one detectable signal and the at least one absorbance measure of the first sample aliquot; obtain a second characterization of the predefined set of pathogens based on analysis of a macroscopic growth of at least a second sample aliquot based on two or more growth images of the at least the second sample aliquot, the two or more growth images obtained by the optical sensor; and configured to output an output characterization of the predefined set of pathogens based on the first characterization and on the second characterization the first characterization, second characterization and output characterization comprising at least one or more of: pathogen detection, pathogen identification, and pathogen quantification.2 The processing system of claim 1 wherein the detectable signal is an optical signal and the processing system is configured to obtain the measure of the detectable signal and the absorbance measure based on at least one image obtained by the optical sensor.

3. The processing system of any one of the claims 1 or 2 wherein the processing system is configured to obtain the first characterization of the predefined set of pathogens by inferring the first characterization of the predefined set of pathogens in at least one first sample aliquot using a trained model, wherein using the trained model comprises inputting at least one measure of the detectable signal from the first sample aliquot and at least one measure of the absorbance measure from the first sample aliquot into the trained model, and obtaining a first characterization of a predefined set of pathogens as the output of the trained model.

4. The processing system of any one of the claims 1 to 3 wherein the processor is configured to obtain the second characterization of the predefined set of pathogens by inferring the second characterization using a second trained model, wherein using the second trained model comprises inputting the two or more growth images into the trained model and obtaining the second characterization of the predefined set of pathogens as the output of the second trained model.

5. A system for characterizing a predefined set of pathogens in a sample, the system comprising: a first plurality of chambers for a first characterization of the predefined set of pathogens, wherein each of the chambers is configured to hold a first sample aliquot and each of the chambers comprises a different genetically modified microorganism to emit a detectable signal upon contact with a target molecule produced by a pathogen of the predefined set of pathogens; wherein a different genetically modified microorganism emits a different detectable signal upon contact with at least one different target molecule; a light source to illuminate the plurality of chambers at an illumination wavelength; a second plurality of chambers for a second characterization of the predefined set of pathogens, wherein each of the chambers is configured to hold a second sample aliquot, the second plurality of chambers configured to thereby hold a plurality of second sample aliquots, wherein each of the chambers comprises a different culture media; a sensor system comprising an optical sensor configured to: capture the detectable signal or signals; capture an absorbance of each one of the first plurality of chambers; capture one or more images of the second plurality of chambers; the processing system according to any one of claims 1 to 4, further configured to: obtain a measure of the detectable signal from each chamber of the first plurality of chambers based on at least one capture of a plurality of detectable signals; obtaining thereby a plurality of measures of the detectable signals; obtain an absorbance measure from each chamber of the first plurality of chambers, based on at least one capture of the absorbance, obtaining thereby a plurality of absorbance measures; obtain the first characterization of the predefined set of pathogens in the sample by processing at least a part of the plurality of measures of the detectable signals and at least a part of the plurality of absorbance measures; obtain the second characterization of the predefined set of pathogens based on analysis of the macroscopic growth of pathogens in the plurality of second sample aliquots in the second plurality of chambers based on two or more growth images of the second sample aliquots in the second plurality of chambers, and based on the capture of one or more images of the second plurality of chambers.

6. The system of claim 5 wherein each genetically modified microorganism comprises: a) a reporter gene that is under the control of an inducible promoter, such that each inducible promoter is responsive to a different target molecule; wherein the reporter gene encodes a protein that emits the detectable signal, and b) a receptor gene that is under the control of a constitutive promoter, wherein the receptor gene encodes a receptor protein which binds the target molecule to form an active transcription factor that activates the inducible promoter of the reporter gene.7 The system of any one of claims 5 to 6 wherein the detectable signal is an optical signal having an emission wavelength; the sensor system comprises an optical sensor to capture an image of the first plurality of chambers and of the second plurality of chambers; and wherein the processing system is configured to obtain the measure of the detectable signal and of the absorbance measure from each chamber based on at least one signal image obtained by the optical sensor.

8. The system of claim 7 wherein the protein that emits the detectable signal is a fluorescent protein, the detectable signal is a fluorescence signal, and the measure of the detectable signal is a fluorescence measure, wherein optionally the fluorescent protein is selected from the group consisting of green fluorescent protein, GFP, red fluorescent protein, RFP, yellow fluorescent protein, YFP, and combinations thereof.

9. The system of claim 8 wherein the fluorescent protein has an excitation wavelength, and wherein the light source emits light having an illumination wavelength substantially equal to the excitation wavelength.

10. The system of any one of claims 7 to 9 further comprising a band-pass filter configured to let the emission wavelength pass, the filter arranged to be either at a position or at least moveable to a position in a light path between the plurality of chambers and the optical sensor.

11. The system of any one of the claims 5 to 10 wherein the detectable signal is a fluorescence signal, the measure of the detectable signal is a green fluorescent protein, GFP; the sensor system comprises an optical sensor to capture an image of the first plurality of chambers and of the second plurality of chambers; and wherein the processing system is further configured to: obtain the measure of the detectable signal; and to obtain an absorbance measure from each chamber by- A - obtaining an image of the first plurality of chambers;- B - obtaining readings of absorbance of each chamber in the first plurality of chambers, the readings made by a spectrophotometer;- C - measuring the turbidity of each chamber in the first plurality of chambers by processing the obtained image in A by analyzing the blue channel information of the obtained image;- D - obtaining a relationship between the absorbance readings and the measures of turbidity;- E fitting the relationship with a model; and- F -determining a correlation between absorbance readings and the measurements of turbidity.

12. The system of claim 11 wherein the target molecule is a QS molecule selected from the group consisting of N- Acryl homoserine lactones (AHL), oxo acyl homoserine lactones (oxo AHL), autoinducing peptides (AlP)pyocyanin,2-heptyl-3,4-dihydroxyquinoline (PQS), 4-hydroxy-2-heptylquinoline (HHQ), (S)-3-hydroxytridecan-4-one (CAI-1) , Auto-inducers (AI-2, AI-3), Histidine protein kinases (HPKs), signal transduction enzymes, environmental ions such as iron, magnesium, and combinations thereof.

13. A method for characterizing a predefined set of pathogens in a sample, the method comprising: obtaining a measure of a detectable signal and an absorbance measure from each chamber of a first plurality of chambers, each measure based on one or more captures obtained by a sensor system, the sensor system comprising an optical sensor, each chamber holding a first sample aliquot, obtaining thereby a plurality of measures of the detectable signal and a plurality of absorbance measures; wherein the detectable signals are emitted by at least one genetically modified microorganism, in each chamber, upon contact with a target molecule produced by a pathogen of the predefined set of pathogens in the first sample aliquots; obtaining a first characterization of the predefined set of pathogens in the sample by processing a plurality of measures of the detectable signal, and a plurality of absorbance measures, wherein each absorbance measure is obtained from the same chamber as the chamber where the measure of each detectable signal is obtained from; obtaining a second characterization of the predefined set of pathogens based on analysis of macroscopic growth of pathogens in second sample aliquots in a second plurality of chambers based on two or more growth images of pathogens in the second sample aliquots in the second plurality of chambers, the two or more growth images obtained by the optical sensor; and outputting an output characterization of the predefined set of pathogens based on the first characterization and on the second characterization; the first characterization, second characterization and output characterization comprising at least one or more of: pathogen detection, pathogen identification, and pathogen quantification14. The method for characterizing a predefined set of pathogens in a sample according to claim 13, the method comprising:(i) placing a first volume of sample aliquot in each chamber of the first plurality of chambers;(ii) maintaining the first plurality of chambers under conditions suitable for the proliferation of the genetically modified microorganisms;(iii) obtaining a measure of a detectable signal and an absorbance measure from each chamber of the first plurality of chambers based on one or more captures obtained by a sensor system comprising an optical sensor, obtaining thereby a plurality of measures of the detectable signals and a plurality of absorbance measures;(iv) obtaining a first characterization of the predefined set of pathogens in the sample by processing a plurality of measures of the detectable signal, and a plurality of absorbance measures, wherein each absorbance measure is obtained from the same chamber as the chamber where the measure of each detectable signal is obtained from;(v) placing a second volume of sample aliquot in each chamber of the second plurality of chambers;(vi) maintaining the second plurality of chambers under conditions suitable for the macroscopic growth of the predefined set of pathogens;(vii) obtaining a second characterization of the predefined set of pathogens based on analysis of macroscopic growth of the predefined set of pathogens present in the second sample aliquots in a second plurality of chambers based on two or more growth images of the second sample aliquots in the second plurality of chambers, the two or more growth images obtained by the optical sensor; and(viii) outputting a characterization of the predefined set of pathogens based on the first characterization and on the second characterization.

15. The method according to claim 14, wherein the first volume of sample aliquot in step (i) is devoid of pathogens or, alternatively, the conditions of step (ii) do not allow proliferation of the pathogens.

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