THERAPEUTIC APPLICATIONS

FIELD OF THE INVENTION

The present invention relates to methods for assigning a phenotypic signature of cells in a liquid biological sample obtained from a mammal, to an innate immune response group using multivariate classification algorithms. Furthermore, the present invention relates to a method for identifying whether a mammal suffers from an inflammation-related disease or is at risk of suffering from an inflammation-related disease using the phenotypic signature. Moreover, the present invention relates to a method for stratifying a mammal suffering from an inflammation- related disease for a treatment against said inflammation-related disease. In addition, the present invention relates to a method for monitoring the progression of an inflammation-related disease in a mammal during a treatment of said mammal. Also, the present invention relates to a method for identifying a compound and/or environmental condition that induces or represses the innate immune response of cells obtained from a mammal. Furthermore, the present invention relates to a use of said phenotypic signature for diagnostic and/or drug de-risking applications.

BACKGROUND

Alzheimer’s disease and other dementia are serious morbidities affecting a large number of people worldwide. Those diseases represent social challenge and at the moment no treatment is available. Diagnosis of neurodegenerative disease as well as clinical trial studies have been hindered, in part, by the limited number of biomarker tests that can correctly diagnose these diseases, adequately stratify and enrich cohorts for patients with target pathology, reliably monitor disease progression, and the lack of affordable and non-invasive tests.

There is a growing number of scientific studies where it has been shown that inflammatory processes in the brain are involved in the development of chronic neurodegenerative diseases and that systemic/peripheral immune responses contribute to disease progression. Indeed, the brain does not keep an absolute immune privilege and peripheral inflammatory mediators, such as cytokines, danger signals or other signaling molecules in addition to infiltrating peripheral immune cells can reach the brain and the central nervous system (CNS) (Cunningham et al. 2013, Heppner et al. 2015, McManus and Heneka 2017). Peripheral inflammatory events and infections, such as respiratory tract infections, bacterial, viral and fungal infections are found to be associated with and significantly contribute to the cognitive decline and progression of dementia (McManus and Heneka 2017, Giridharan et al. 2019, Paouri and Georgopoulos 2019). Employing a biocomputational systems level approach based on constructed molecular networks and by the integration of preclinical, genetic and bioinformatic data a strong contribution of immune- regulatory pathways was assigned to late-onset Alzheimer’s disease (Zhang et al. 2013).

Up to now, numerous reports have documented altered protein levels of inflammatory mediators in the body fluids, i.e. plasma, serum and CSF of patients with Alzheimer’s disease and mild cognitive impairment (MCI) (Brosseron et al. 2014). Although many studies provided controversial or inconclusive results, they provide evidence that alterations of immunological parameters coupled to Alzheimer’s disease are reflected in the periphery. Furthermore, meta analyses provided evidence to support that systemic inflammation might be a biomarker for AD diagnosis (Su et al. 2019).

In the context of innate immunity, many recent studies provided evidence for a potential relationship of inflammasome activation and inflammation-related diseases, in particular neurological disease (Lang et al. 2018). A key player in mediating inflammatory responses in many complex age-related and inflammatory diseases including neurodegenerative disease and AD is the cytosolic innate immune signaling receptor NLRP3 (NLR-family pyrin domain containing 3) (Mangan et al. 2018). It senses pathogen- and danger-associated molecular pattern- (PAMP- and DAMP-) derived signals and assembles upon activation a multiprotein inflammasome complex leading to caspase-1 mediated proteolytic activation and release of cytokines interleukin- lbeta (IL-lbeta) and IL-18 and subsequent pyroptotic cell death. Chronic activation of the NLRP3 inflammasome triggering persistent neuroinflammation is emerging as a crucial contributing pathomechanism to the development and progression of AD (Heneka et al. 2015, Heneka et al. 2013, Halle et al. 2008) and was found in microglia to be connected to seeding and spreading of amyloid-beta pathology in an AD mouse model (Venegas et al. 2017).

Furthermore, latest evidence was provided in an AD mouse model that peripheral immune challenges could induce microglial changes resulting in a reduced microglial clearance capacity of Abeta (Tejera et al. 2019). Recently in the human system, increased expression of NLRP3 inflammasome components were found in monocytes of AD patients accompanied by increased production of cytokines IL-lbeta and IL-18 (Saresella et al. 2016). Together, these studies provide evidence that the activation of the Nlrp3 inflammasome contributes to AD pathology in the brain and that pathological changes may manifest and become detectable in peripheral blood. For inflammation-related diseases and in particular Alzheimer’s disease CSF biomarkers are successfully used in clinical diagnosis with high accuracy. However, there is the demanding need for a minimally invasive, cost-effective and reliable method to diagnose early-stage Alzheimer’s disease (Blennow 2017, Zetterberg and Burnham 2019).

Recently, ultrasensitive immunoassays have been developed based on immunoprecipitation-mass spectrometry and have successfully been used to measure plasma Abeta biomarkers and to discriminate between healthy controls and patients with AD or mild cognitive impairment (Nakamura et al. 2018). The authors could demonstrate that the assessment of plasma APP/ Abeta and Abeta40/42 ratios allow to accurately predicting brain Abeta burden (Nakamura et al. 2018, Baldacci et al. 2019, Zetterberg 2019).

Beyond that, it was recently shown that high-precision plasma Abeta42/40 ratio could accurately diagnose brain amyloidosis and therefore can be used to screen cognitively normal individuals to identify at-risk individuals for AD dementia (Schindler et al. 2019, Bendlin et al. 2019). Moreover, plasma neurofilament light chain (NfL) emerged in recent times as a reliable peripheral biomarker of neurodegenerative disease (Baldacci et al. 2019, Zetterberg 2019) showing a good diagnostic accuracy in differentiating neurodegenerative patients from controls and starting from early stages of disease progression (Khalil et al. 2018). Indeed Preische et al. could demonstrate that CSF NfL and serum NfL levels correlated and were found to be elevated at pre- symptomatic stages of familial Alzheimer’s disease (Preische et al. 2019), Thus, serum NfL assessment could predict disease progression and brain neurodegeneration at the early pre- symptomatic stages of familial AD. Altogether these facts indicate that blood-based biomarker tests can be possible.

However, there is a huge need for methods that can be used to detect reliable changes, a combination and the analysis of multiple parameters is a necessary step which is supported by the recent expert review by Penner et al. considering biomarkers in blood as a “shadow of events that are occurring in the brain” and expressed as a “fingerprint in blood” worthwhile to follow up in order to overcome the constraint in biomarker development (Penner et al. 2019).

In recent years, a number of multivariate blood-based biomarker panels have been tested and proposed for diagnosing, classifying and prognosing AD, following the hypothesis that the use of panels of markers may outperform single candidate markers (Zetterberg and Burnham 2019). However, overall there has been a lack of harmonization and replication of the findings (Zetterberg and Burnham 2019). Lately, a hypothesis-led plasma biomarker search was conducted and 53 inflammatory proteins were analyzed in healthy controls, MCI and AD individuals and a collection of analytes and markers of inflammation and complement dysregulation showed significant intergroup differences which could be replicated in an independent cohort (Morgan et al. 2019).

Finally, the Korean AD Research Platform Initiative based on Immune-Inflammatory biomarkers (K- ARPI) has launched a combinatorial approach in order to develop novel biomarkers for AD, to stratify subpopulations of patients with AD and to develop new drug candidates for delaying the progression of AD by modulating toxic immune inflammatory response (Kim et al. 2019). This strategy was based on a decision platform integrating pre-screening of patients by clinical characteristics, genetic information, cell- and image-based readouts and cytokine/immune modulator profiling, and to be combined with a computational layer of data analysis employing classification algorithms and multimodal deep learning programs. However, all methods are not particularly robust, since especially biomarkers in the blood can fluctuate due to various influences or are associated with considerable effort and/or high costs.

Apart from that, in the field of drug development, drug de-risking, drug safety and risk management play a major role. The financial factor is enormous. It is estimated that one in every 10,000 molecules entering drug research makes it to the market - and even then there is no guarantee that the expensive development costs will be recovered. Apart from the financial loss that a company has to bear, patient safety naturally plays an even greater role. A newly developed drug can fail in the clinical phases, and/or lead to unforeseeable side effects, or even trigger inflammation in patients. In addition, approved drugs also may cause inflammation in patients, due to individual intolerances depending on the patient's individual immune system. Therefore, the interest in developing respective modern diagnostic methods is increasing.

More and more, drugs are to be used in a personalized manner in order to be able to exploit the individual modes of action of a drug in a patient more specifically, and thus to avoid inflammatory reactions. Suitable biomarkers are being sought that allow for a reliable statement that can be made as to whether the drug under consideration is likely to be effective in this patient; whether the specific patient is likely to tolerate the drug under consideration; and how the drug is best dosed for this patient.

Overall, there is a lack of methods for obtaining a robust, reliable, fast, inexpensive and convenient diagnosis of inflammatory diseases, at the same time allowing the stratification of the patients and subsequent monitoring of the therapy can be carried out, so that potentially inflammation triggering drugs can be ruled out in advance through drug de-risking.

A considerable challenge of personalized medicine is the analysis of the diagnostic data obtained. For example, genetic data - obtained from procedures such as next-generation sequencing - require computationally complex data processing steps before the actual analysis of the data can take place. The subsequent analysis then requires interdisciplinary cooperation between experts from the medical profession, clinical oncologists, biologists and software engineers. The provision of personalized therapies is therefore associated with a high diagnostic effort.

Currently, automated workflows, disclosed in the prior art are optimized for homogenous readouts or for image-based readouts in human cell lines or murine cell models. These approaches are not able to link intracellular processes on single cell level with extracellular signals of the innate immune response, but this is essential to understand and evaluate the mode of action of perturbation effects in predictive drug discovery and de-risking innate immunity.

Nakahata et al. (WO2012067265) disclose a method for screening drugs for suppressing inflammasome activity, using macrophages derived from induced pluripotent stem cells having mutant NLRP3 gene and contacting them with LPS, as an external stimulus, and optionally a drug of interest, wherein the Ill-beta secretion of contacted and naive cells is analyzed.

Jankovic et al. (WO2013007763) show a method for the prevention or treatment of acne, comprising the administration of an inhibitor capable of binding to a member of the inflammasome group consisting of IL-Ib, IL-1 receptor type 1, NLRP3, ASC, caspase-1 and cathepsin B, said inhibitor being selected from inter alia an antibody, an antibody fragment, and an antibody-like molecule.

The prior art discloses a number of approaches for obtaining and evaluating quantitative data from microscopic imaging.

Loo et al. (Loo LH, Wu LF, Altschuler SJ. Image-based multivariate profiling of drug responses from single cells. Nat Methods. 2007 May;4(5):445-53. doi: 10.1038/nmethl032. Epub 2007 Apr 1. PMID: 17401369) disclose a multivariate method for the classification of untreated and treated human cancer cells based solely on phenotypic single cell measurements. Bray et al. (Bray MA, Singh S, Han H, et al. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat Protoc. 2016; 11(9): 1757-1774. doi: 10.1038/nprot.2016.105) detect phenotypic profiling methods based on microscopic images of cell components or organelles to identify biologically relevant similarities and differences between samples based on these profiles and evaluate the effects of chemical, genetic or disease disorders on the phenotypic signature.

Collinet et al. (Collinet C, et al. Systems survey of endocytosis by multiparametric image analysis. Nature. 2010 Mar 11;464(7286):243-9) reveal phenotypic profiling based on multi-parametric image analysis using automated confocal microscopy to analyze the human genome in terms of endocytosis of transferrin and epidermal growth factor.

However, methods based only on single-point readout (e.g. blood-based biomarker), such as microscopic phenotypic evaluation, are much more susceptible to failure. Therefore, the present inventors have discovered a phenotypic signature which allows remedying all the above- mentioned shortcomings in the diagnosis of inflammatory diseases, stratification of patients, monitoring the progression of diseases, and de-risking of compounds in a reliable, convenient, reproducible and cost-effective way. The results obtained by methods according to the present invention are provided by using machine learning multivariate classification algorithms that replace complex, time, and cost-intensive analysis of conventional diagnostic assays. The prior art does not disclose or propose comparable assays.

BRIEF DESCRIPTION OF THE INVENTION

The present invention provides in vitro methods for diagnostic applications to identify patients suffering from inflammation-related diseases, to stratify patients, to monitor the progression of inflammation-related diseases during treatment of patients, and to identify compounds in the context of drug-de-risking to evaluate their influence on innate immune response based on the phenotypic signature, wherein the innate immune response group of each individual sample is determined by using an advanced hierarchical cluster analysis under untreated and stimulated conditions, whereby -omics data and clinical data can optionally be integrated into the phenotypic signature. The inventive phenotypic signature at the single-cell level allows conclusion on the inflammatory state of a cell obtained from a mammal based on the inflammasome activation status. The multivariate data set (data obtained by methods according to the present invention, -omics data and clinical data) representing the numerical vector that reflects the inventive phenotypic signature of the cell provides a much more robust representation of a mammal's innate immune response since measurement errors are distributed on multiple readouts compared to above mentioned conventional assays based on single-point readout.

In a first aspect thereof, the invention solves the above problem by providing an in vitro method for assigning a phenotypic signature of cells in a liquid biological sample obtained from a mammal, to an innate immune response group said method comprising: Stimulating said cells in said biological sample comprising cells with an inducing or repressing agent of the innate immune response; Separating said cells as stimulated from at least part of the supernatant of said biological sample; Collecting at least part of said supernatant, and analyzing said supernatant for factors that induce or repress the innate immunity as released from said cells; Suitably staining of microscopically evaluable intracellular and/or extracellular structures of said stimulated cells as described herein, and subsequent microscopic evaluation of said stained intracellular and/or extracellular structures; Analyzing both of said factors as released into the supernatant and said microscopic evaluation in order to obtain a combined phenotypic signature indicating the innate immune response group of said cells; and assigning said phenotypic signature to an innate immune response group comprising the use of multivariate classification algorithms.

In a second aspect, the invention relates to an in vitro method for identifying whether a mammal suffers from an inflammation-related disease or is at risk of suffering from an inflammation-related disease, said method comprising: Providing a liquid biological sample obtained from said mammal comprising cells; Stimulating said cells in said biological sample with an inducing or repressing agent of the innate immune response; Separating said cells as stimulated from at least part of the supernatant of said biological sample; Collecting at least part of said supernatant, and analyzing said supernatant for factors that induce or repress the innate immunity as released from said cells; Suitably staining of microscopically evaluable intracellular and/or extracellular structures of said stimulated cells as described herein, and subsequent microscopic evaluation of said stained intracellular and/or extracellular structures; Analyzing both of said factors as released into the supernatant and said microscopic evaluation in order to obtain a combined phenotypic signature indicating the innate immune response group of said cells, and; Assigning said phenotypic signature to an innate immune response group comprising the use of multivariate classification algorithms, wherein a change of said phenotypic signature of said mammal when compared to said phenotypic signature in a healthy mammal identifies a mammal suffering from an inflammation- related disease or being at risk of suffering from an inflammation-related disease. In a third aspect, the invention relates to an in vitro method for stratifying a mammal suffering from an inflammation-related disease, said method comprising: Providing a liquid biological sample obtained from said mammal comprising cells; Stimulating said cells in said biological sample with an inducing or repressing agent of the innate immune response; Separating said cells as stimulated from at least part of the supernatant of said biological sample; Collecting at least part of said supernatant, and analyzing said supernatant for factors that induce or repress the innate immunity as released from said cells; Suitably staining of microscopically evaluable intracellular and/or extracellular structures of said stimulated cells as described herein, and subsequent microscopic evaluation of said stained intracellular and/or extracellular structures; Analyzing both of said factors as released into the supernatant and said microscopic evaluation in order to obtain a combined phenotypic signature indicating the innate immune response group of said cells, and; Assigning said phenotypic signature to an innate immune response group comprising the use of multivariate classification algorithms; wherein said mammal is stratified for a treatment against said inflammation-related disease, if the phenotypic signature of said mammal deviates from the same phenotypic signature in a healthy mammal.

In a fourth aspect, the invention relates to an in vitro method for monitoring the progression of an inflammation-related disease in a mammal being treated against an inflammation-related disease or being at risk of suffering from an inflammation-related disease, comprising: Providing a liquid biological sample obtained from said mammal comprising cells; Stimulating said cells in said biological sample with an inducing or repressing agent of the innate immune response; Separating said cells as stimulated from at least part of the supernatant of said biological sample; Collecting at least part of said supernatant, and analyzing said supernatant for factors that induce or repress the innate immunity as released from said cells; Suitably staining of microscopically evaluable intracellular and/or extracellular structures of said stimulated cells as described herein, and subsequent microscopic evaluation of said stained intracellular and/or extracellular structures; Analyzing both of said factors as released into the supernatant and said microscopic evaluation in order to obtain a combined phenotypic signature indicating the innate immune response group of said cells, and; Assigning said phenotypic signature to an innate immune response group comprising the use of multivariate classification algorithms; wherein, during said treatment of said mammal, the influence of said treatment on the progression of the inflammation-related disease is monitored by a change in said phenotypic signature of said mammal.

In a fifth aspect, the invention relates to an in vitro method for identifying a compound and/or environmental condition that induces or represses the innate immune response of cells in a liquid biological sample obtained from a mammal, said method comprises: Contacting said cells in said liquid biological sample with a compound and/or exposing said cells in said liquid biological sample to environmental conditions that potentially induce or repress the innate immune response of said cells; Separating said cells from at least part of the supernatant of said liquid biological sample; Collecting at least part of said supernatant, and analyzing said supernatant for factors released from said cells that induce or repress the innate immunity; Suitably staining of microscopically evaluable intracellular and/or extracellular structures of said contacted cells, and subsequent microscopic evaluation of said stained intracellular and/or extracellular structures; Analyzing of both said factors as released into the supernatant and said microscopic evaluation in order to obtain a combined phenotypic signature indicating the innate immune response group of said cells, Identifying said compound as inducing or repressing said innate immune response based on said phenotypic signature as obtained, using multivariate classification algorithms.

In a sixth aspect, the invention relates to a phenotypic signature obtained from the methods according to the present invention.

In a seventh aspect, the invention relates to a use of a phenotypic signature as described herein for determining the innate immune response group of a mammalian cell, or for identifying whether a mammal suffers from an inflammation-related disease or is at risk of suffering from an inflammation-related disease, or for stratifying a mammal suffering from an inflammation-related disease, or for monitoring the progression of an inflammation-related disease in a mammal, or for identifying a compound and/or environmental condition that induces or represses the innate immune response of mammalian cells.

DETAILED DESCRIPTION OF THE INVENTION In the following, the features of the present invention will be described further. These features are listed with specific embodiments, however, it should be understood that they may be combined in any manner and in any number to create additional embodiments. The variously described examples and preferred embodiments should not be construed to limit the present invention to only the explicitly described embodiments. This description should be understood to support and encompass embodiments which combine two or more of the explicitly described embodiments or which combine the one or more of the explicitly described embodiments with any number of the disclosed and/or preferred elements. Furthermore, any permutations and combinations of all described elements in this application should be considered disclosed by the description of the present application unless the context indicates otherwise. As mentioned above, in the first aspect thereof, the present invention relates to an in vitro method for assigning a phenotypic signature of cells in a liquid biological sample obtained from a mammal, to an innate immune response group said method comprising: Stimulating said cells in said biological sample comprising cells with an inducing or repressing agent of the innate immune response; Separating said cells as stimulated from at least part of the supernatant of said biological sample; Collecting at least part of said supernatant, and analyzing said supernatant for factors that induce or repress the innate immunity as released from said cells; Suitably staining of microscopically evaluable intracellular and/or extracellular structures of said stimulated cells as described herein, and subsequent microscopic evaluation of said stained intracellular and/or extracellular structures; Analyzing both of said factors as released into the supernatant and said microscopic evaluation in order to obtain a combined phenotypic signature indicating the innate immune response group of said cells; and assigning said phenotypic signature to an innate immune response group comprising the use of multivariate classification algorithms. Figure 2 schematically illustrates the assay workflow.

Stimulation of cells in a liquid biological sample may also covers previous priming with activators of the TLR2/4 receptor, such as for example, lipopolysaccaride (LPS) and subsequent stimulation of said primed cells with an inducing or repressing agent of the innate immune response, such as, for example, pathogen-associated molecular patterns (PAMPs) or Danger-associated molecular patterns (DAMPs). Priming of said cells induce the activation of specific cellular pathways and trigger the expression of a number of genes involved in the immune response (e.g. TNF-a). The triggered cells in said biological sample are ready to initiate an inflammatory response when stimulated with an inducing or repressing agent of the innate immune response with focus on but not limited to the toll like receptors (TLRs) and the nucleotide-binding oligomerization domain like receptors, or NOD-like receptors (NLRs). Figure 1 shows microscopic images of representative phenotypes after staining certain cellular structures (chromatin, PYCARD and TNF-alpha) of untreated peripheral blood mononuclear cells (PBMCs), PBMCs primed with LPS and PBMCs previously primed with LPS and subsequently stimulated with adenosine triphosphate (ATP), an inducing agent of the innate immune response.

As used herein, the term “phenotypic signature” covers a multivariate data set of single cells of interest in form of a numerical vector obtained by the methods according to the present invention under different functional stimuli for the innate immune system with focus on but not limited to the toll like receptors (TLRs) and the nucleotide-binding oligomerization domain-like receptors, or NOD-like receptors (NLRs), as described herein, wherein these quantitative data may be combined with other information, such as, for example, clinical data, and/or anamnesis data. The readout core is based on the integration of said different readouts including but not limited to readout of cellular phenotypic descriptors obtained by microscopic evaluation, biochemical readout including but not limited to cytokine analysis and relevant proteins, genetic readout including but not limited to transcriptomic and genomic analysis, proteomic and clinical data. A phenotypic signature consists at least of the data from the analysis of released factors in the supernatant (TNF-alpha, and IL-l-beta) and data from the microscopic evaluation (chromatin, PYCARD, TNF-alpha and CD staining).

A machine learning multivariate classification algorithm processes the data set obtained by the methods according to the present invention and generates therefrom the numerical vector representing the phenotypic signature indicating the innate immune response of said cell from said mammal or of cells from control samples. The baseline signature of such control samples is composed by data collection of control cells from samples obtained from healthy donors. This data set also represents the baseline to which all the cells in the samples tested will be referenced to. Once all data has been collected the data points of the vector undergo a statistical quality check and the most robust data points are used in the final analysis. All numerical vectors of analyzed cells are then classified using said machine learning multivariate classification algorithms to finally assign the sample to an innate immune response group (i.e. healthy or diseased). Hence, each sample is represented by one numeric vector. Figure 3 exemplarily shows this assignment of untreated, or primed, or primed and activated PBMCs to their respective phenotype.

As used herein, the term “multivariate classification algorithms” means the process of identifying similarities between the multivariate data set representing the phenotypic signature of said cells as obtained by the methods according to the present invention and the multivariate data sets representing phenotypic signatures from control cells, phenotypic signatures from untreated cells with known innate immune response, or phenotypic signatures from reference cells with known innate immune response contained in a database. Conventional multivariate classification algorithms are available for the skilled person and may be adapted for the purpose of the present invention. For example, Nicholson et al. reveals machine learning multivariate pattern analysis for classification of posttraumatic stress disorder. Apart from that, there is a variety of further documents disclosed in the prior art (Nicholson, Densmore, Kinnon, Cambridge University Press; Volume 49, Issue 12; September 2019, pp. 2049-2059: Machine learning multivariate pattern analysis predicts classification of posttraumatic stress disorder and its dissociative subtype: a multimodal neuroimaging approach).

As described above, for assigning a numerical vector of a cell of interest to an innate immune response group the data points of said numerical vector are compared with data points of numerical vectors present in a database that stores a number of data representing "innate immune response groups". The term used herein, covers the cluster in which phenotypic signatures are assigned by said multivariate algorithm due to similarities in their phenotypic signatures. For example, a cluster includes numerical vectors of healthy donors, since the phenotypic signature of healthy donors have similarities in their data sets identified by the multivariate classification algorithm. In addition to the data of the "healthy" immune status group, the database contains further data sets of sample controls, e.g. from donors with inflammation-related diseases. These samples (from healthy and diseased donors) form a reference data set or baseline. Using the multivariate classification algorithm for machine learning, each individual sample of a cell from a mammal of interest is analyzed and assigned to the respective innate immune response group (e.g. healthy, or diseased innate immune response group).

As used herein, the term “innate immune response” covers any reaction, interaction or signaling pathway associated with the innate immune system. The innate immune response also covers the innate immune status of a cell at any given time, which describes the static innate immune status of a cell at a given time with a focus on activated toll-like receptors (TLRs) and NOD-like receptors (NLRs) and the NLRP3, AIM2, and NLRC4 inflammasome activation status in addition to the immune cells usually analyzed. The term “innate immune response group” describes a group of cells, wherein all cells in said respective group have a consistent and/or similar innate immune response and/or activation state of their innate immune status.

As used herein the term “liquid biological sample” means any mammalian body fluid containing the cells to be analyzed from said mammal, which enables the methods to be carried out in accordance with the present invention. For example, such liquid biological samples may be selected from blood, serum, and saliva, and a tissue, organ or cell type blood sample but without being limited to it.

The term “in vitro” as used herein, refers to methods that are carried out externally to a mammal and in a controlled artificial environment. For example, the cells for an "in vitro" method may be taken from said mammal, or may be obtained from immortal cell lines. These methods may be performed, for example, in a test tube or Petri dish, but without being limited to these. The term “supernatant” as used herein means the nutrient medium required for the cultivation and storage of said cells of interest. The culture supernatant is in direct contact with said cells and thus contains released factors by said cells which are essential for obtaining a phenotypic signature of said cells.

In the second aspect, the invention relates to an in vitro method for identifying whether a mammal suffers from an inflammation-related disease or is at risk of suffering from an inflammation-related disease, said method comprising: Providing a liquid biological sample obtained from said mammal comprising cells; Stimulating said cells in said biological sample with an inducing or repressing agent of the innate immune response; Separating said cells as stimulated from at least part of the supernatant of said biological sample; Collecting at least part of said supernatant, and analyzing said supernatant for factors that induce or repress the innate immunity as released from said cells; Suitably staining of microscopically evaluable intracellular and/or extracellular structures of said stimulated cells as described herein, and subsequent microscopic evaluation of said stained intracellular and/or extracellular structures; Analyzing both of said factors as released into the supernatant and said microscopic evaluation in order to obtain a combined phenotypic signature indicating the innate immune response group of said cells, and; Assigning said phenotypic signature to an innate immune response group comprising the use of multivariate classification algorithms, wherein a change of said phenotypic signature of said mammal when compared to said phenotypic signature in a healthy mammal identifies a mammal suffering from an inflammation- related disease or being at risk of suffering from an inflammation-related disease.

In the third aspect, the invention relates to an in vitro method for stratifying a mammal suffering from an inflammation-related disease, said method comprising: Providing a liquid biological sample obtained from said mammal comprising cells; Stimulating said cells in said biological sample with an inducing or repressing agent of the innate immune response; Separating said cells as stimulated from at least part of the supernatant of said biological sample; Collecting at least part of said supernatant, and analyzing said supernatant for factors that induce or repress the innate immunity as released from said cells; Suitably staining of microscopically evaluable intracellular and/or extracellular structures of said stimulated cells as described herein, and subsequent microscopic evaluation of said stained intracellular and/or extracellular structures; Analyzing both of said factors as released into the supernatant and said microscopic evaluation in order to obtain a combined phenotypic signature indicating the innate immune response group of said cells, and; Assigning said phenotypic signature to an innate immune response group comprising the use of multivariate classification algorithms; wherein said mammal is stratified for a treatment against said inflammation-related disease, if the phenotypic signature of said mammal deviates from the same phenotypic signature in a healthy mammal.

In the fourth aspect, the invention relates to an in vitro method for monitoring the progression of an inflammation-related disease in a mammal being treated against an inflammation-related disease or being at risk of suffering from an inflammation-related disease, comprising: Providing a liquid biological sample obtained from said mammal comprising cells; Stimulating said cells in said biological sample with an inducing or repressing agent of the innate immune response; Separating said cells as stimulated from at least part of the supernatant of said biological sample; Collecting at least part of said supernatant, and analyzing said supernatant for factors that induce or repress the innate immunity as released from said cells; Suitably staining of microscopically evaluable intracellular and/or extracellular structures of said stimulated cells as described herein, and subsequent microscopic evaluation of said stained intracellular and/or extracellular structures; Analyzing both of said factors as released into the supernatant and said microscopic evaluation in order to obtain a combined phenotypic signature indicating the innate immune response group of said cells, and; Assigning said phenotypic signature to an innate immune response group comprising the use of multivariate classification algorithms; wherein, during said treatment of said mammal, the influence of said treatment on the progression of the inflammation-related disease is monitored by a change in said phenotypic signature of said mammal.

"Monitoring" as used in the context of the present invention means the regular provision of said liquid biological sample from said mammal, as described herein, and the performance of the methods according to the present invention to provide a reliable and complete indication of the progression of a mammalian disease during treatment. The intervals can be determined by a skilled person.

Preferred is an embodiment of the method according to the present invention, wherein said inducing or repressing agent of the innate immune response induces or represses the activity of a factor selected from the group consisting of Toll-like receptors (TLRs) and/or Nod-like receptors (NLRs), such as, for example TLR4/TLR2, NLPR3, AIM2 and/or NLRC4, and is preferably selected from lipopolysaccharide (LPS), LeuLeuOMe, Nigericin, ATP, MSU crystals, aluminium crystals, nano-Si02, CPPD crystals, cholesterol crystals, Poly(dA:dT), nanoparticles, flagellin, therapeutic antibodies, CAR-T cells, amyloid beta, Tau, alpha-synuclein, TDP43, exosomes, and nanoplastic, but without being limited to these. The term "inducing or repressing agent", as used in the context of the present invention, describes a substance which affects an increase or decrease in the innate immune response, wherein an "induction" induces intracellular and/or extracellular inflammatory signaling pathways and a "repression" inhibits intracellular and/or extracellular inflammatory signaling pathways. For example, the use of an inducing agent may trigger the expression of a number of genes involved in the innate immune response, such as, for example the gene for expression of the cytokine protein ILl-beta.

As used herein the term “toll-like receptors (TLRs)” means structures of the innate immunity and belong to a group of Pattern-Recognition Receptors (PRRs). TLRs are transmembrane proteins and are used for the recognition of PAMPs (Pathogen- Associated Molecular Patterns). PAMPs are highly conserved, pathogen-specific patterns, which are not found in mammals. Such PAMPs can be, for example, proteins, lipids and carbohydrates, such as, for example, lipopolysaccharides or flagellin that occur on the surface of the microorganisms. In contrast, so called “damage-associated molecular patterns (DAMPs)” are host molecules, such as for example heat shock protein (HSPs), or Interleukin-1 (IL-1). These are endogenous, normally intracellular molecules that enter the extracellular space when a cell is damaged. By binding to the PRRs, an inflammatory reaction is triggered.

The term “NOD-like receptors (NLRs)” as used herein covers structures of the innate immunity and also belong to a further group of PRRs. In contrast to TLRs, NLRs are present as soluble proteins in the cytosol and are responsible for recognition of intracellular PAMPs that were introduced into the cell via bacterial protein secretion or PAMPs that were taken up by immune cells via phagocytosis.

In the fifth aspect, the invention relates to an in vitro method for identifying a compound and/or environmental condition that induces or represses the innate immune response of cells in a liquid biological sample obtained from a mammal, said method comprises: Contacting said cells in said liquid biological sample with a compound and/or exposing said cells in said liquid biological sample to environmental conditions that potentially induce or repress the innate immune response of said cells; Separating said cells from at least part of the supernatant of said liquid biological sample; Collecting at least part of said supernatant, and analyzing said supernatant for factors released from said cells that induce or repress the innate immunity; Suitably staining of microscopically evaluable intracellular and/or extracellular structures of said contacted cells, and subsequent microscopic evaluation of said stained intracellular and/or extracellular structures; Analyzing of both said factors as released into the supernatant and said microscopic evaluation in order to obtain a combined phenotypic signature indicating the innate immune response group of said cells; Identifying said compound as inducing or repressing said innate immune response based on said phenotypic signature as obtained, using multivariate classification algorithms.

In the context of the present invention the term “environmental condition” covers all surrounding factors that are exposed to said cell that may have a potential effect of inducing or repressing the innate immune response of said cell. Such an environmental condition may be selected from overpressure, vacuum, heat, heavy metals, or ultraviolet radiation, but without being limited to these. As a result of stressful environment such a condition may lead, for example, to the production of heat shock proteins (HSPs). Such that HSPs can bind to several pathogen-associated molecular patterns (PAMPs) and enhance Toll-like receptor (TLR) ligand-induced stimulation.

Further preferred is an embodiment of the method according to the present invention, wherein said compound is selected from a drug, a small molecule, a polypeptide, an antibody or fragment thereof, a peptide, a polynucleotide, an oligonucleotide, nanoparticles, a carbohydrate, or lipids.

The term “drug” as used in the context of the present invention includes any substance or pharmaceutical composition intended to cure or prevent disease in a mammal. The drug may be an already known substance or pharmaceutical composition, but it also includes those substances or pharmaceutical compositions which are not yet known. The term “pharmaceutical composition” covers all medical preparations, wherein the substances do not occur in their pure form but in their form as an administrable pharmaceutical composition, since the pure substances are formulated in combination with excipients to form a medicinal product. For example, it may be necessary to coat the compound with, or co-administer the compound with, a material to prevent its inactivation, or the compound may be administered to a mammal in an appropriate carrier, for example, in liposomes, or a diluent. Pharmaceutically acceptable diluents include saline and aqueous buffer solutions, for example to bring the substance in form of tablets, capsules, injections, infusions, solutions, salves or syrups. In addition to the pure substances, excipients or the entire drug may also induce or repress the innate immune response, whereby this inducing or repressing impact can be identified with the methods according to the invention based on the phenotypic signature of said cell from said mammal.

The term "small molecules" as used herein comprises low molecular weight compounds with a low molecular mass. This refers to a class of active substances with a molecular mass not exceeding approximately 800 g-mol-1.

In the context of the present invention the term “protein” is used to denote a polymer composed of amino acid monomers joined by peptide bonds. It refers to a molecular chain of amino acids, and does not refer to a specific length of the product and if required can be modified in vivo or in vitro , for example by glycosylation, amidation, carboxylation or phosphorylation. Amino acid chains with a length of less than approx. 100 amino acids are called "peptides". The terms “peptides”, and “proteins” are included within the definition of “polypeptides”. A “peptide bond” is a covalent bond between two amino acids in which the a-amino group of one amino acid is bonded to the a-carboxyl group of the other amino acid. All amino acid or polypeptide sequences, unless otherwise designated, are written from the amino terminus (N-terminus) to the carboxy terminus (C-terminus).

The term “antibody” as referred to herein includes whole, full length antibodies and any fragment or derivative thereof in which the "antigen-binding portion" or "antigen-binding region" or single chains thereof are retained, such as a binding domain of an antibody specific for the immunogenic active moiety of the conjugate. A naturally occurring “antibody” (immunoglobulin) is a glycoprotein comprising at least two heavy (H) chains and two light (L) chains inter-connected by disulfide bonds. Each heavy chain is comprised of a heavy chain variable region (abbreviated herein as VH) and a heavy chain constant region. Heavy chains are classified as mu, delta, gamma, alpha, or epsilon, and define the antibody's isotype as IgM, IgD, IgG, IgA, and IgE, respectively. The heavy chain constant region is comprised of three domains, CHI, CH2 and CH3. Each light chain is comprised of a light chain variable region (abbreviated herein as VL) and a light chain constant region. The light chain constant region is comprised of one domain, CL. The VH and VL regions can be further subdivided into regions of hyper variability, termed complementarity determining regions (CDRs), interspersed with regions that are more conserved, termed framework regions (FR). Each VH and VL is composed of three CDRs and four FRs arranged from amino-terminus to carboxy-terminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, FR4. The heavy and light chains form two regions: the Fab (fragment, antigen binding) region, also referred to as the variable (Fv) region, and the Fc (fragment, crystallizable) region. The variable regions (Fv) of the heavy and light chains contain a binding domain that interacts with an antigen. The constant (Fc) regions of the antibodies may mediate the binding to host tissues or factors, including various cells of the immune system (e.g., effector cells) and the first component (Clq) of the classical complement system. The term "Fc" as used herein includes native and mutein forms of polypeptides derived from the Fc region of an antibody. Truncated forms of such polypeptides containing the hinge region that promotes dimerization also are included. Fusion proteins comprising Fc moieties (and oligomers formed therefrom) offer the advantage of facile purification by affinity chromatography over Protein A or Protein G columns. One suitable Fc polypeptide is derived from the human IgGl antibody.

As used herein, the term “polynucleotide” covers linear biopolymers composed of 20 or more nucleotides with alternating nucleic bases. Nucleic acid polymers with a smaller number of nucleotides are called “oligonucleotides”. Such polynucleotides may be, for example, DNA, cDNA, RNA or synthetically produced DNA or RNA or a recombinant produced chimeric nucleic acid molecule comprising one of these nucleic acids alone or in combination.

The term “nanoparticle” as used herein defines particles with a particle size smaller than 0.1 pm (< 100 nm) that are either generated naturally or can be synthetically produced. Such nanoparticles can be, for example, carbon-containing nanoparticles, metal and semi-metal oxides, semiconductors, metal sulphides or polymers, but without being limited to these.

As used herein the term “carbohydrates” covers all carbon compounds, consisting of carbon, hydrogen and oxygen, which may be present as monosaccharides or as polysaccharides.

The term "lipid" as used herein refers to the totality of fats and fat-like substances. Lipids are chemically heterogeneous substances that dissolve poorly in water, but well in non-polar solvents.

Preferred is an embodiment of the method according to the present invention, wherein said inflammation-related disease is an autoimmune disease, or preferably a neurodegenerative disease.

The term "inflammation-related diseases", as used in the context of the present invention, comprises any disease, which consequence is the activation of the immune system of a mammal. The term “inflammation-related diseases” covers “neurodegenerative diseases” and “autoimmune diseases”, but without being limited to it. As a further embodiment, the term also covers any cancer disease.

As used herein the term “autoimmune disease” covers any disease (for example diabetes) including reactions of the mammals’ body that are based on a disturbed tolerance of the immune system to substances of the own body. Such autoimmune disease may be selected from autoimmune diseases, such as idiopathic or secondary pulmonary fibrosis, liver fibrosis, non-alcoholic hepatitis (NASH), autoimmune hepatitis, giant cell hepatitis, primary sclerosing cholangitis, primary biliary cirrhosis, systemic lupus erythematosus, lupus nephritis, good pasture syndrome, arthritis, sarcoidosis, allergic bronchopulmonary aspergillosis, keloid disease, such as for example fibroproliferative dermal tumor, collagen vascular diseases, systemic sclerosis, viral induced autoimmune diseases, but without being limited to.

The term “neurodegenerative diseases” as used herein, means any disease that primarily affect the nerve cells in the mammal s‘ brain and lead to progressive degeneration and death of the nerve cells, such as, for example, Parkinson, prion diseases, motor neuron diseases (MND), Huntington disease (HD), spinocerebellar ataxia (SCA), spinal muscular atrophy (SMA), and most preferably Alzheimer diseases, but without being limited to.

Further preferred is an embodiment of the method according to the present invention, wherein in step of analyzing of both said factors as released into the supernatant and said microscopic evaluation in order to obtain a combined phenotypic signature a proteome, transcriptome, lipidome and/or genome of said cells is evaluated, and clinical data are included to supplement said combined phenotypic signature.

As used herein, the term “proteome”, refers to the totality of proteins expressed by further single cells obtained from said mammal that are evaluated parallel to the microscopic evaluation of said cell in said liquid biological sample from said mammal according to the methods of the present invention. The expressed proteins may occur as intracellular, extracellular or transmembrane proteins. Many methods for the analysis of these proteins are known to the skilled person. For example, the proteome can be analyzed with Western blot or mass spectrometry, but without being limited to these methods. The amount of cellular proteins may vary depending on the time of measurement.

The term “transcriptome” as used herein, refers to the totality of all genes transcribed in further single cells (i.e. the entirety of all RNA molecules present in said further single cells) obtained from said mammal that are evaluated parallel to the microscopic evaluation of said cell in said liquid biological sample from said mammal according to the methods of the present invention. The transcribed RNA molecules are present intracellularly. There is a number of different methods in the prior art for the analysis of cellular RNA, such as the reverse transcriptase polymerase chain reaction (RT-PCR), but without being limited to this method. The amount of RNA present in a cell can vary depending on the time of measurement.

As used herein, the term “lipidome” refers to the totality of all lipids in further single cells obtained from said mammal that are evaluated parallel to the microscopic evaluation of said cell in said liquid biological sample from said mammal according to the methods of the present invention. There are many methods known to the skilled person, such as mass spectrometry, to analyze the lipidome, but without being limited to this.

The term “genome” as used herein refers to the entirety of the genetic information in further single cells obtained from said mammal that are evaluated parallel to the microscopic evaluation of said cell in said liquid biological sample from said mammal according to the methods of the present invention. The term “genetic information” covers all genetic material contained in the cell nucleus, the mitochondria and the plastids. The prior art provides a number of methods, such as, for example fluorescence in situ hybridization (FISH) or polymerase chain reaction (PCR), but without being limited to these.

Further preferred is an embodiment of the method according to the present invention, wherein said cells are selected from immune cells, peripheral blood mononuclear cells (PBMCs), PBMCs co cultured with other cells in 2D and 3D culture, lymphocytes and monocytes.

The term “peripheral blood mononuclear cells (PBMCs)” as used herein covers mononuclear cells of the blood, which have a round cell nucleus. After providing said liquid biological sample from said mammal, PBMCs from blood, for example, may be purified by centrifugation through a hydrophilic colloid. In the methods according to the present invention, both dead and living cells may be used.

As used herein, the term “2D culture” includes conventional cell culture under in vitro conditions, wherein adherent cells spread out on a plastic or glass substrate as a so-called "monolayer" (i.e. a single-cell layer). The may grow into a dense cell layer, but stop dividing as soon as they become too confluent. In 2D culture, cells do not continue to grow on top of each other to form a multilayer.

The term “3D culture” as used herein, refers to the cultivation of said cells in a micro-structured three-dimensional cell culture under in vitro conditions. For example, a suitable matrix can be selected from hydrogels made of scaffold proteins such as collagen, gelatin methacrylate. As used in the context of the present invention, the term “co-cultured” describes the simultaneous in vitro cultivation of said different cell types in an artificial environment. For example, PBMCs from said liquid biological sample from said mammal may be embedded in 2D culture or 3D culture and cultivated simultaneously. The skilled person is aware of the required knowledge.

Further provided is an embodiment of the method according to the present invention, wherein said mammal is selected from a mouse, a rat, a monkey, a rabbit, a pig, a donkey, a cow, a horse, a sheep, and preferably a human.

Further preferred is an embodiment of the method according to the present invention, wherein said separating comprises immobilizing said cells on a solid carrier material that is transparent, preferably selected from glass or a plastic material, and preferably chemically inert.

The term "solid carrier" refers to any carrier which is insoluble and chemically inert under the reaction conditions without having an influence on cellular reaction. Furthermore, the carrier must allow the substrate to flow in and out. A number of suitable carriers are known for cell immobilization.

As used herein, the term “immobilizing” refers to the spatial fixation of said cell in the liquid biological sample from said mammal to said solid carrier, for example by the action of chemicals, such as, for example, methanol, ethanol, or acetone, but without being limited to these.

In a further preferred embodiment of the method according to the present invention said microscopically evaluable intracellular and/or extracellular structures of said cells to be stained comprise lipids, sugars, nucleic acids, functional compartments and/or intracellular and/or extracellular proteins, such as for example, PYCARD, TNF-alpha, CD14, CD19, CD3, IL-l-beta, CASP1, NFkappaB, and NLRP3. The microscopic evaluation is performed optically. The skilled person is familiar with a variety of methods that can be used for the optical detection of cellular structures. For example, neutral dyes are bound by the lipid structures, and alkaline dyes may be used for staining of acidic nucleic acids, but without being limited to these. For evaluation of intracellular and/or extracellular proteins, such as for example, PYCARD, TNF-alpha, CD14, CD 19, CD3, IL-l-beta, CASP1, NFkappaB, and NLRP3 conventional immunofluorescence may be applied, using commercially available antibodies that bind specific to the respective proteins. By adding respective second fluorochrome-labelled detection antibodies, the corresponding structures on said proteins may be detected with a fluorescence microscope according to the wavelength range of the respective used fluorochrome.

Yet preferred is an embodiment of the method according to the present invention, wherein said released factors in the supernatant to be analyzed comprise cytokines, such as for example, TNF- alpha, and IL-l-beta. As used herein, the term “released factors” covers all signal molecules that may be secreted or released by said living or death cells into the surrounding supernatant, for example hormones, neurotransmitters, nitric oxide and especially cytokines, but without being limited to these. The term "cytokines", as used in the context of the present invention, covers all proteins secreted by said cells in the course of an immunological or inflammatory reaction. The skilled person knows a number of methods for analysis of released factors, in particular cytokines, such as, for example, enzyme-linked immune absorbent spot (ELISpot), or enzyme-linked immunosorbent assay (ELISA), but without being limited to these.

In yet another preferred embodiment of the method according to the present invention, said microscopic evaluation comprises at least one of morphology, subcellular location of said stained intracellular structures, and marker intensity of said stained intracellular structures of said cells.

The term "morphology" as used in the context of the present invention describes the shape and structure of said cells to be analyzed above the molecular level.

As used herein, the term “subcellular location” means the intracellular position of the stained intracellular structures within said cells or extracellular structures on the surface of the cells.

The term “marker intensity” as used herein, describes the strength in which the detection signal is emitted. The strength of the detection signal may be proportional to the amount of detectable intracellular structures present in said cells.

Preferred is an embodiment of the method according to the present invention, wherein said evaluation of the phenotypic signature is carried out automatically.

The term “automatically” as used herein, refers to autonomous microscopic evaluation by using automated microscopy, as well as autonomous collection, storage, processing and evaluation of further data, as obtained by the methods according to the present invention, by said self-learning multivariate algorithms. Further preferred is an embodiment of the method according to the present invention, wherein the method comprises a comparison with a control from a database.

The term “comparison” as used herein, means that the numerical vector representing the phenotypic signature as obtained by the methods according to the present invention is searched for overlap with numerical vectors representing the phenotypic signatures of controls stored in said database.

As used herein, the term “controls” covers numerical vectors of phenotypic signatures of untreated control cells obtained from healthy mammals indicating the innate immune response of healthy mammals stored in a database for comparison. The controls enable matching of the numerical vector of the phenotypic signature of said cells of interest with numerical vectors of phenotypic signatures of controls whose immune status or immune response group is known to be healthy.

The term “database” as used herein, covers a proprietary database containing phenotypic signatures of mammalian cells contacted with over 20,000 small molecules and which can be used as references. The database consists of a collection of numerical vectors representing phenotypic signatures from control cells, phenotypic signatures from untreated cells with known innate immune response or phenotypic signatures from reference cells with known innate immune response. The term “data set” as used herein covers several hundreds of features.

Further preferred is an embodiment of the method according to the present invention wherein said innate immune response is determined based on changes in the phenotypic signature when compared to a control provided by a database.

"Changes" as the term is used herein, means the alteration or deviation of said phenotypic signature from a phenotypic signature from control cells, phenotypic signatures from untreated cells with known innate immune response or phenotypic signatures from reference cells with known innate immune response, as provided by a database, to determine the innate immune response, in particular the inflammasome activation status, of a cell. Each tested biological sample from said mammal is compared to the baseline controls and the multivariate classification algorithm determines the change and assigns the numeric vector, and thus the corresponding sample, to a designated innate immune response group. Preferred is an embodiment of the method according to the present invention, wherein said analysis of said innate immune response comprises the analysis of the inflammasome activation status of said cells in said liquid biological sample.

A further embodiment, relates to a method for obtaining a phenotypic signature to determine the innate immune status of cells from a liquid biological sample from a mammal, the method comprising: Collecting data from a database of the analyzed factors as released into the supernatant of stimulated or contacted cells; Collecting data from a database of said microscopic evaluation of intracellular and/or extracellular structures of said stimulated or contacted cells; Optionally, collecting data from proteome, transcriptome, lipidome and/or genome analysis of said cells and clinical data from a database; Merging of all data sets to determine the phenotypic signature.

In the sixth aspect, the invention relates to a phenotypic signature obtained from the methods according to the present invention. As described herein, the phenotypic signature is composed by different sources of information. Figure 13 shows an example of such a phenotypic signature, wherein additional information may be added according to the needs in a customized manner.

In the seventh aspect, the invention relates to a use of a phenotypic signature as described herein for determining the innate immune response group of a mammalian cell, or for identifying whether a mammal suffers from an inflammation-related disease or is at risk of suffering from an inflammation-related disease, or for stratifying a mammal suffering from an inflammation-related disease, or for monitoring the progression of an inflammation-related disease in a mammal, or for identifying a compound and/or environmental condition that induces or represses the innate immune response of mammalian cells.

The terms “of the [present] invention”, “in accordance with the invention”, “according to the invention” and the like, as used herein are intended to refer to all aspects and embodiments of the invention described and/or claimed herein.

In the context of the present invention, the terms “about” and “approximately” denote an interval of accuracy that the person skilled in the art will understand to still ensure the technical effect of the feature in question. The term typically indicates deviation from the indicated numerical value by ±20%, ±15%, ±10%, and for example ±5%. As will be appreciated by the person of ordinary skill, the specific such deviation for a numerical value for a given technical effect will depend on the nature of the technical effect. For example, a natural or biological technical effect may generally have a larger such deviation than one for a man-made or engineering technical effect. As will be appreciated by the person of ordinary skill, the specific such deviation for a numerical value for a given technical effect will depend on the nature of the technical effect. For example, a natural or biological technical effect may generally have a larger such deviation than one for a man-made or engineering technical effect. Where an indefinite or definite article is used when referring to a singular noun, e.g. "a", "an" or "the", this includes a plural of that noun unless something else is specifically stated.

It is to be understood that application of the teachings of the present invention to a specific problem or environment, and the inclusion of variations of the present invention or additional features thereto (such as further aspects and embodiments), will be within the capabilities of one having ordinary skill in the art in light of the teachings contained herein.

All references, patents, and publications cited herein are hereby incorporated by reference in their entirety.

BRIEF DESCRIPTION OF THE FIGURES

The figures show:

Figure 1: shows images of representative phenotypes of PBMC under different stimuli (i.e. LPS and LPS+ATP).

Figure 2: shows the schematic representation of the assay workflow from cell seeding to the cell fixation passing through the different phases of priming, activating and detection of the assay markers. At this stage different experimental quantitative information can be collected for different markers described (but not limited) to the one showed in the balloons.

Figure 3: shows schematic examples of expected and altered phenotype under experimental conditions.

Figure 4: shows the numerical vector describing the phenotypic signature, which is composed by different sources of information.

Figure 5: shows the structure of the pipetting robot worktable for automated performance of the assay. Figure 6: shows the pipetting scheme for a Leu Leu source plate

Figure 7: shows the pipetting scheme for a Nigericin source plate Figure 8: shows the assay test plate for incubation with leucine.

Figure 9: shows the assay test plate for incubation with nigericin.

Figure 10: shows the pipetting scheme for the blocking solutions.

Figure 11: shows the pipetting scheme for the staining solutions.

Figure 12: shows the imaging settings of 32 fields for each well.

Figure 13: shows the data analysis pipeline.

Figure 14: shows de-risking Strategy. Phenotypic signatures of compounds under test are clustered (type A, B and C) with known reference compounds/conditions (a). According to the phenotypes produced and their cluster assignments, compounds under test can be risk-assessed.

Figure 15: shows the summary of de-risking pain-killers (cyclooxygenase (COX)-Modulators) approach by using the test system of the present invention.

Figure 16: shows the results of de-risking pain-killers (cyclooxygenase (COX)-Modulators) approach by using the test system of the present invention. 54 pain-killers were tested in three independent experiments. Six representative parameters demonstrate the unique collective phenotypic signature. The results were normalized to the untreated control group. Image-based assay read outs: a) ’’Active”, ratio of cells presenting mounted inflammasome, b) “TNFaPosRate”, ratio of cells showing intracellular TNFalpha expression, c) “TNFa specs/cell”, number of TNFalpha spots within TNFalpha positive cells, d) “Cells”, total number of cells. Homogenous assay read outs: e) “TNFa sec”, TNFalpha concentration in the supernatant in ng/ml, f) ”ILlb”, ILlbeta concentration in the supernatant in ng/ml.

Figure 17: shows Unsupervised Clustering; Screen of a compound collection processed by our unique image and data analysis pipeline. A. We identified compounds having no effect on the monitored mechanism of innate immune response and cellular status of the heterotypical cell model. B. We classified compounds, which show no phenotypic alteration compared to the untreated control (full rescue). C. We disclosed novel lead structures inducing the same cellular response as our benchmark control CRID3/MCC950 (drug in development). D. Compounds, which induce new cellular phenotypes.

Figure 18: shows compound-induced phenotypic rescue; The inventive technology identifies compounds, which show complete inhibition of the innate immune response in our cell model and completely rescue the “activation phenotype” towards the “no activation phenotype”.

Figure 19: Classification of the disease status via a Support Vector Machine (SVM) classifier and based on 72 features derived from a multivariate dataset (a) Confusion matrix to evaluate the quality of the output of the classifier allowing 5 features to be selected for classification. The diagonal elements represent the number of patient samples for which the predicted label is equal to the true label, while off-diagonal elements are those that are mislabeled by the classifier (b) Classifier weights of the selected features used by the SVM classifier for the class assignment of the patient samples (c) Accuracy of the classification according to the number of features used for the classifier.

Figure 20: Higher biocompatibility profiles after surface modification. PBMCs were treated with nanoparticles and cell viability was measured via cell penetration of propidium iodide (PI). The non-modified nanoparticle induces clear dose-dependent unspecific cytotoxicity (A. and B.). Representative images (B) show Nucleus and PI staining for nanoparticles and lead candidate nanoparticles. Scale bar is 50 pm. The functionalized precursor (modified core nanoparticle) as well as the adjuvant lead candidate do not induce unspecific cytotoxicity over the tested concentration range.

Figure 21: Adjuvant lead candidate induce mild inflammasome activation. LPS primed PBMCs were treated with nanoparticles. (B) The cells were finally stained with a nuclei marker (Hoechst 33342), a marker for inflammasomes and Propidium Iodide (PI) for cell integrity. Representative images from nanoparticles and Lead candidate in 500 pg/ml dose are shown in B and C. C show an overlay of inflammasome and PI highlighting some double positive cells with a white arrow. Scale bar is 50 pm. The non-modified nanoparticle induces clear dose-dependent inflammasome activation already at low concentration (A). The functionalized precursor as well as the adjuvant lead candidate induce mild inflammasome activation at high concentration only (C). Only primed monocytes were specifically activated by the lead candidate (A). Other cell types keep their normal phenotype (C).

Figure 22: Lead candidate induce strong TNFa production in monocytes. LPS primed PBMC were treated with precursor and adjuvant lead candidate. The cells were stained with a nuclei marker (Hoechst 33342), a specific marker for T-cells and intracellular TNFa (A.l and A.2). The ratio of TNFa positive cells and the concentration of released TNFa in the supernatant were quantified (B). The adjuvant lead candidate treatment leads to a very high level of TNFa positive monocytes and TNFa release (B.). The ratio TNFa positive cells is approx. 5x fold higher than the LPS control.

Figure 23: Mode of Action - Inflammasome activation through nanoparticles is not induced via extracellular ATP. LPS primed PBMCs were treated with different doses of (i) P2X7 receptor antagonist, (ii) TLR-4 inhibitor, a Caspase I inhibitor and (iv) NLRP3 inhibitor. Subsequently 100 pg/ml nanoparticles were added. The cells were stained with a nuclei marker (Hoechst 33342), a marker for inflammasomes, TNFa and ILlb. Cytokine release of ILlb and TNFa was measured via HTRF. The ratio of inflammasome activated cells (black line), TNFa positive cells (grey solid line) and ILlb positive cells (light grey solid line) and the concentration of released TNFa (light grey dashed line) and ILlb (light grey dashed line) in the supernatant were quantified. Dose- response analyses were conducted with the different inhibitors and normalized to non-inhibitor treated conditions (A-D). The treatment with P2X7 antagonist (A) does not indicate significant changes in the measured activity parameters of the nanoparticles, indicating that the nanoparticles do not induce extracellular ATP through e.g. general cytotoxic effects. After LPS binding to TLR- 4, intracellular TNFa expression and pro-ILlb production gets activated. TLR-4 inhibition (B) shows reduction in inflammasome activation, TNFa and ILlb release, indicating that TLR-4 is involved in the NP dependent inflammasome activation mechanism. That was proven through NLRP3 inhibition of inflammasome assembly (C) and inhibition of downstream caspase-I induced release of ILlb and TNFa (D).

Figure 24: Added-Value-1. Higher precision and accuracy of the present technology compared to Luminex cytokine assays. We tested the ILlbeta- and TNFalpha release performance on the present assay platform (integrated as HTRF assays from cisbio/PerkinElmer into the inventive workflow) (B) in comparison to the Luminex cytokine assay technology (A). (1) The inventive technology resulted in a higher precision and accuracy of cytokine measures as depicted in the box plots and highlighted by the arrows. (2) Due to its higher precision and accuracy, the inventive technology allowed to disclose distinct compound (Cmpd) effects on donor-specific responses which was not achieved by the Luminex technology despite its higher nominal assay ranges as indicated by the upper and lower LoD’s (tables on the left in A, B). (3) Despite higher nominal assay ranges of Luminex cytokine tests compared to present technology cytokine release LoD’s (dotted lines), donor-specific response ranges were solely resolved by the inventive technology and could not be identified by the Luminex assay. (4) Despite higher nominal assay ranges of Luminex cytokine tests compared to present technology cytokine release LoD’s, the inventive technology covered a larger assay window as indicated by the ovals on the TNFa-release box plot y-axes in (A) and (B).

Figure 25: Added- Value-2. MoA - inflammasome complex formation addressed by the compounds CRID3 and MI-2. (A) Upon inflammasome activation by LPS+ATP (LPS Activator), a pronounced increase in ILlbeta release was obtained (DMSO control, indicated by the oval). Both CRID3 (solid rectangle) and MI-2 (dotted rectangle) treatment resulted in a complete block of ILlbeta release (black arrow). (B) Simultaneously, a complete block of inflammasome activation was caused by both compounds (black arrow) indicating a compound action at the level of inflammasome complex formation, representing further knowledge and exclusively provided by the inventive technology. (C) Furthermore, both compounds resulted in a marked increase in the percentage of ILlbeta-positive cells (additional multi-parametric readout) (black arrow) indicating the accumulation of the produced cytokine in the cell cytoplasm, again indicating further knowledge by the present assay. NLRP3 inhibitors: CRID3/MCC950, MI-2 (MALTl inhibitor), compound effect: acts at the level of inflammasome complex formation; results and conclusion by Luminex: block of ILlb release; results and conclusion by present technology: block of ILlb release, absence of inflammasome complex, increase in ILlb-positive cells percentage.

Figure 26: Added- Value-3. MoA - cytokine processing and release addressed by the compound Z-VAD-OH-FMK. (A) Upon inflammasome activation by LPS+ATP (LPS Activator), a pronounced increase in ILlbeta release was obtained (DMSO control, indicated by the oval). Treatment by the pan-CASP inhibitor Z-VAD-OH-FMK (solid rectangle) resulted in a block of ILlbeta release (black arrow). (B) However, Z-VAD-OH-FMK did not show any effect on inflammasome activation and the inflammasome activation rate remained as high as observed for the DMSO control (black arrow). Therefore, the assessment of an additional multi-parametric readout via the inventive technology provided further knowledge indicating a compound effect downstream of inflammasome complex formation and upstream of ILlbeta release. (C) In contrast to CRID3 and MI-2, Z-VAD-OH-FMK treatment resulted in a low percentage of ILlbeta-positive cells (black arrow) presenting further, specific insights into compound action, again indicating additional knowledge generated by the present platform. CASP inhibitor: Z-VAD-OH-FMK, compound effect: impact at the level of cytokine processing and release; results and conclusion by Luminex: block of ILlb release; results and conclusion by present platform: block of ILlb release, presence of inflammasome complex, low levels of ILlb-positive cells percentage.

Figure 27: Added- Value-4. MoA - signal perception at the plasma membrane addressed by the compound A-438079 (P2X7 receptor antagonist). (A) Upon inflammasome activation by LPS+ATP (LPS Activator), a pronounced increase in ILlbeta release was obtained (DMSO control, indicated by the oval). Treatment by the P2X7 receptor antagonist A-438079 (solid rectangle) resulted in a block of ILlbeta release (black arrow). (B) Simultaneously, a complete block of inflammasome activation was observed (black arrow) indicating a compound action upstream of inflammasome complex assembly, representing further knowledge and exclusively provided by the inventive technology. (C) Again, A-438079 resulted in a marked increase in the percentage of ILlbeta-positive cells (black arrow) indicating the accumulation of the produced cytokine in the cell cytoplasm. Furthermore, this additional readout provided evidence for a successful LPS-priming effect, not affected by the compound treatment. Therefore, the assessment of additional multi-parametric readouts via the present technology provided further knowledge and allows to address MoA of analyzed compounds. P2X7 receptor antagonist A-438079, impact at the level of the P2X7 receptor function for ATP at the plasma membrane. Luminex result and conclusion: block of ILlb release. Present assay result: block of ILlb release, absence of inflammasome complex, increase in ILlb-positive cells percentage, no impact on priming status via TNFa-positive cells percentage.

Figure 28: Added- Value- 5. MoA - disclosure of novel phenotypes addressed by the compound MI-2. (A) Priming of the cells by LPS treatment resulted in the expression of TNFa protein as indicated by an increase in the percentage of TNFa-positive cells shown for the condition LPS (DMSO control, indicated by the oval). Subsequent inflammasome activation (condition LPS Activator) caused a -50% reduction of the TNFa-positive cell percentage for the DMSO control. However, no difference was observed between DMSO and MI-2 treatment and the TNFa- positive cell percentage was not affected by the test compound. (B) However, the analysis of additional multi-parametric readouts revealed a robust increase in the percentage of TNFa- multi spot-positive cells caused by the MI-2 compound treatment pointing towards a specific compound-induced cellular phenotype. (C) Indeed, MI-2 treatment resulted in a multi-spot TNFa phenotype as shown by immunofluorescence analysis. Therefore, the assessment and characterization of compound effects via the present multi-parametric approach allowed and facilitated the disclosure of novel cellular phenotypes. NFkB Inhibitor: MI-2 (MALTl inhibitor); compound effect: impacts at the level of inflammasome complex formation and block of NFkB. Luminex result and conclusion: partial block of TNFa release with limited resolution. Present assay result and conclusion: partial block of TNFa release, no impact on TNFa-positive cells percentage, increase of TNFa- multi spot-positive cells percentage, and identification of prominent new image-based phenotypes.

Figure 29: Compound profiling vs. a phenotype of a selected reference condition, e.g. untreated (A) or LPS treated. (B). The present inventors calculated the validated feature set for all tested compounds. Afterwards, the present inventors compared the feature set of each compound with the feature set (phenotype) of a reference condition (A or B). by calculating the euclidian distance in the feature space. After sorting the distances, the present inventors identified compounds which induce most similar phenotypes to the selected references.

Figure 30: Ranking of compounds based on the phenotypic distance to the reference phenotype “LPS”. Phenocopying the reference condition by compound treatment is an approach to identify novel molecular entities with minimal side effects in a primary screen. The resulting graph shows the distance of compounds as identified according to the present invention (black dots) and the gold standard CRID3 (grey dots) to the reference phenotype “LPS”. The inventors identified compounds which have similar phenotypes to the “gold standard” CRID3 or are even closer to the reference phenotype.

EXAMPLES

Certain aspects and embodiments of the invention will now be illustrated by way of example and with reference to the description, figures and tables set out herein. Such examples of the methods, uses and other aspects of the present invention are representative only, and should not be taken to limit the scope of the present invention to only such representative examples.

Example 1:

PBMC- Assay to identify whether a subject suffers from an inflammation-related disease PBMC samples from 5 patients and 3 healthy donors, as controls, were provided. Subsequently, reagents, the PBMC cell suspensions in complete medium (RPMI 1640 (Biochrom Cat. No. F1215 Lot 1106E), 4 mM L-Alanyl-Glutamine (Biochrom, Cat. No. K 302, 200mM), 25 mM HEPES (LifeTechnologies, Cat. No. 15630-56, 1M), 1 mg/ml Ciprofloxacin (Kabi, Infusionslsg., 200mg/ml), 10% Hi-FBS (Sigma, Cat. No. F9665, Charge 065M3367)) and a lipopolysaccharide (LPS-EB Ultrapure Lipopolysaccharide from E. coli 0111:B4 strain- TLR4 ligand, 5 x 106 EU; InvivoGen, Cat.No. tlrl-3pelps; LOT number: L3P- L3P-38-02; 5 x 10 ⁶ EU of ultrapure LPS) stock solution in endotoxin-free water with a concentration of 5mg/ml (corresponds to 5 x 10 ⁶ EU/ml) were prepared. From this, an LPS solution 1 in complete medium with a concentration of 500 ng/ml was prepared. Subsequently, a LPS solution 2 in complete medium with a concentration of 60ng/ml was prepared. 50 mΐ/well of complete medium and LPS solutions 2 were pipetted in a 384 Deepwell (DW)-plate according to the pipetting scheme shown in Table 1 below.

Table 1 : Pipetting scheme of culture medium and LPS solution 2 in a 384-DW plate _. .

The plate together with medium and LPS solution 2 was stored at 37°C until priming of the PBMC cells.

4.7 ml of cell suspension (4.5 ml for cell seeding into the 384-DW plate and 0.2 ml for subsequent RNA sequencing) of each individual PBMC sample from each donor were prepared in a vial so that the respective cell suspension had a final concentration of 1.0 x 10 ⁶ cells/ml.

RNA-Sequencing sample preparation: To prepare the samples for RNA sequencing, 2.0 x 10 ⁵ cells were transferred from the respective cell suspension of each PBMC sample into a separate tube. The samples were sent to an external laboratory for RNA sequencing.

340 mΐ of the provided cell suspensions from the five patients and three healthy donors were pipetted per well into a 96 DW -plate according to the pipetting scheme shown in Table 2.

384-DW plate (assay plate 1 and 2) and 96-DW plate were placed on the worktable of a pipetting robot as shown in Figure 5. Each tip (of 96 heads) of a pipetting robot was loaded with 170 mΐ of cell suspension from the 96 DW-plate and 40m1 were pipetted into each of the quadrants on assay plate 1 and 2. Then the remaining 10 mΐ were pipetted into the source plate (96-DW plate). 20 mΐ of the LPS stock solution 1 (with a concentration of 60 ng/ml) were transferred to the 40 mΐ of seeded PBMC cells in the 384-DW plate to obtain a final concentration of 20 ng/ml. This was followed by a 2-hour incubation of the 384-DW plate at 37°C, whereby the inflammasomes of the cells in the 2-hour incubated plate were subsequently activated with Nigericin, and a 2.5-hour incubation of the 384-DW plate, whereby the inflammasomes of the cells in the 2.5-hour incubated plate were subsequently activated with Leu Leu.

Inflammasome activation: Leu Leu and Nigericin source plates were provided for inflammasome activation of the PBMCs. Therefore, a 1 M Leu Leu stock solution (Leu-Leu-OMe HCl Chemlmpex; Cat.No. 04578, Mw 294.82; LOT number: 000424-130420-11834) in DMSO was adjusted to a Leu Leu source solution with a final concentration of 0.8 mM (working dilutions in DPBS w/o Ca2+/Mg2+, containing 25mM HEPES (Gibco, Cat. No. 15630056, Lot. No. 1742638)) and a DMSO source solution (DMSO, AppliChem Cat. No. 3672,0100; 1 M HEPES, PBS) was provided as a control, wherein both source solutions were pipetted according to the pipetting scheme shown in Figure 6. A Nigericin stock solution with a concentration of 10 mM (Life Technologies, Cat.No. N-1495; 10 mg; Mw 724.97; LOT number: 1800830) was adjusted to a final concentration of 20 mM in complete medium and a DMSO solution was also provided as a control, wherein both source solutions were pipetted according to the pipetting scheme shown in Figure 7.

20 mΐ of the provided 20mM Nigericin solution were pipetted per well onto the Nigericin plate incubated with LPS for 2 hours (5 mM final on cells) or DMSO control from the Nigericin source DW plate, followed by incubation at 37°C for one hour as shown in Figure 9.

20m1 of the provided 0.8 mM Leu Leu solution were pipetted per well onto the Leu Leu plate incubated with LPS for 2.5 hours (0.2 mM final on cells) or DMSO control from the Leu Leu source DW plate, followed by incubation at 37°C for 0.5 hour as shown in Figure 8.

Supernatant collection and cell fixation: 30m1 of the supernatant from each well of each assay plate were collected twice on collection plates for further investigation. 20 mΐ per well of an 8% PFA solution (16% Formaldehyde (w/v), meth-free, Thermo, Cat. No. 28908; 10ml of 16% Formaldehyde (w/v, meth-free); 10ml DPBS, no Ca2+, no Mg2+ (Life Technologies, Cat. No. 14190-169) were added to the assay plates and incubated for 15 minutes fixation time at RT. In the meantime, the collection plates were centrifuged at 350 x g for 2 min. Then 8 mΐ were transferred from the collection plate to each of the 4 HTRF plates and 16pl from collection plate 1 to storage plate 1. HTRF assay plates and storage plates were sealed and frozen for later investigations. The cell fixation was finished by removing the fixation solution (2 x 20 mΐ) and adding 30 mΐ of FBS/PBS solution (5 ml of Hi-FBS (Sigma, Cat. No. F9665, Charge 065M3367) to 500ml DPBS, no Ca2+, no Mg2+ (Life Technologies, Cat. No. 14190-169)).

Immunocytochemistry assay: The following solutions were prepared.

For Cell Surface markers (CDs) staining:

-CDs Blocking Buffer (1% BS A/PBS: 5 g of Bovine Serum Albumin (BSA, low endotoxin, lyophilized powder, Sigma Cat. No. A1470, Lot# SLBN1886v) were dissolved in 500ml DPBS, no Ca2+, no Mg2+ (Life Technologies, Cat. No. 14190-169))

-commercially available Hoechst33342 stock solution (lOmg/ml): (BisBenzimide H33342 trihydrochloride, H33342, >98% (HPLC and TLC), Sigma-Aldrich, Cat No. B2261, lOOmg; lOmg of H33341 in were dissolved in 1000 mΐ DMSO (Dimethyl Sulfoxide, for cell culture, AppliChem, Cat.No. A3672,0100) to reach a stock concentration of lOmg/ml) to Hoechst dilution 1 : 2500 with a final concentration of 4 pg/ml; Hoechst33342 is a nucleic acid dye with blue fluorescence when bound to dsDNA.

-CDs staining solution (10 mΐ CD3 (Alexa Fluor® 488 anti-human CD3 Antibody; 1:500 (0.4 pg/ml)) + lOOpl CD14 (PE Mouse Anti-Human CD14; 1:50 (0.24 pg/ml)) + 50 mΐ CD19 (Alexa Fluor® 647 anti-human CD19 Antibody; 1:100 (0.5 pg/ml)) + 5 ml Hoechst/CDs Blocking Buffer).

For ASC- TNFa staining:

-ASC Blocking-Permeabilization Buffer (0.5% Triton Tx-100; 1%BSA/PBS: 2.5ml Triton™ X- 100 (molecular biology, Sigma, Cat. No.T8787, Lot#036k00851) were dissolved in 500ml 1% BSA/FBS solution)

-Hoechst dilution 1:10000 (final 1 pg/ml) for ASC staining

-ASC-TNFa staining solution (ASC 1:500: 40 pi ASC (rabbit anti-PYCARD-DY488; 1:500 (1.1 pg/ml)) + 400pl TNFa (TNF-a (D1G2) Rabbit mAb; 1:50 (0.12 pg/ml)) + 20ml Hoechst/ASC Blocking Buffer) Loading of 384 DW plates: A 384 DW plate was loaded with either 50 mΐ per well of ASC blocking pre-stabilization buffer or 50m1 per well CD blocking buffer according to the pipetting scheme shown in Figure 14. In addition, another 384 DW plate was loaded with either 50m1 per well of ASC-TNFa or CD staining solution according to the pipetting scheme shown in Figure 15.

Staining: FB S/PBS solution was removed from the assay plates and 20 mΐ Blocking Buffer per well was added. 2-minute incubation at RT followed. Blocking Buffer was removed from the assay plates and 20m1 staining solution was added per well. A further 1 hour incubation at RT in the dark followed. Washing: After incubation the staining solution was removed and washed by adding 20m1 FBS/PBS solution per well.

Image acquisition setting: The plate was analyzed in two runs.

Acquisition E01: Analysis of ASC-TNFa stained wells Acquisition E02: Analysis of CD markers stained wells

Hoechst33342; blue fluorescence dye bound to dsDNA (Ch.l): 405nm ET:100ms EG:100; ASC (Ch.2): 488nm ET: 100ms EG: 100; CD14 (Ch.3): 568nm ET:300ms EG: 100; TNFa-CD19 (Ch 4): 647nm, ET:250ms EG: 120; CD3 (Ch.5): 488nm ET:200ms EG: 100. 32 fields were imaged for each well as shown in Figure 16.

Further inflammasome activators may include the following:

Adenosine 5-triphosphate disodium salt hydrate (ATP, A6419-5G, Sigma, 0.5M NaOH (Roth Cat.No. 9356.1, MW 40.01g/mol) in sterile milliQ H ₂ 0; 500 mM ATP (A6419-5G, MW 551.14, anhydrous basis): 275.57 mg/ml in 0.5M NaOH; Working dilutions in DPBS w/o Ca2+/Mg2+, containing 25mM HEPES)

Monosodium Urate Crystals (MSU crystals) (InvivoGen, Cat. No. tlrl-msu, 5 mg; Preparation of MSU stock solution (5mg/ml): Dissolve 5 mg MSU in 1 ml sterile DPBS w/o Ca2+ Mg2+; Working dilutions in sterile DPBS w/o Ca2+ Mg2+; MSU crystals final concentration range: 250 pg/ml - 500 pg/ml.

Alum Crystals (InvivoGen, Cat. No. tlrl-alk, lg; Alum crystal stock solution (20mg/ml = 40mM): lOOmg Alum crystals in 5ml sterile endotoxin-free H20 (WFI, LifeTechnologies, Cat. No. A12873-01); Working dilutions in DPBS (w/o Ca2+/Mg2+), Alum crystals final concentration range: 100 pg/ml - 200 pg/ml final. Silica Crystals - Nano-Si02 (InvivoGen, Cat. No. tlrl-si, lOmg; Preparation of Silica crystal stock solution (5mg/ml, i.e. 83 mM): Dissolve 10mg ano-SiO2 crystals in 2 ml sterile endotoxin- free H20 (WFI, LifeTechnologies, Cat. No. A12873-01); Working dilutions in complete media; Nano-Si02 final concentration range: 10 pg/ml - 200 pg/ml)

CPPD crystals - Calcium pyrophosphate dehydrate crystals (InvivoGen, Cat. No. tlrl-cppd, 5mg; Preparation of Silica crystal stock solution (5mg/ml): Dissolve 5mg Nano-Si02 crystals in 1 ml sterile DPBS w/o Ca2+/Mg2+; Mix by vortexing and sonicate suspension for 5 minutes; Store at 4°C (short-term or -2°C (long-term); Working dilutions in sterile DPBS w/o Ca2+/Mg2+; CPPD crystals final concentration range: 1 pg/ml - 50 pg/ml.

Cholesterol crystals (Cholesterol, Sigma, Cat. No. C8667; Dissolve 12.5mg/ml cholesterol in 95% ethanol)

Poly(dA:dT) (Poly(dA:dT), InvivoGen, Cat. No. tlrl-patn-1, lmg; Dissolve lmg poly(dA:dT) in lml endotoxin-free H20; Stimulate cells with lOng/ml - lOpg/ml poly(dA dT) complex for 6- 24h)

Nanoparticles/Nanoplastic

Example 2: De-risking approach for Cox-Inhibitors (painkiller)

54 compounds were tested, which are known as cyclooxygenase (COX)-Modulators. Most of the compounds were already on market or in drug development process. A unique collective phenotypic signature of this drug class was identified. There was no alteration of assay parameters, meaning that common Cox-modulators should have no effect on the innate immune response in the test system provided by the present invention. Surprisingly, three compounds (Subrofen, Bufexamac and Xanthohumol) were identified to show alteration of the collective phenotypic signature. Subrofen, an early nonsteroidal anti-inflammatory drug (NSAID), was discontinued due to adverse side effects and showed a strong decrease of cytokine release (ILlb) in the test system of the present invention. Bufexamac was withdrawn from market 2010 due to allergic reaction and was further proposed in 2017 as co-treatment agent (TNFa binder) for modulators of TNFa synthesis. This effect was proofed by using the de-risking approach of the present invention. Xanthohumol, a chalcone isolated from Humulus lupus, is currently in clinical trial phase 1. Currently, there is no information about induced adverse effects. The results are shown in Figures 17 and 18. The adverse effects of all three compounds were not identified during the safety/toxicity assessment in preclinical lead optimization or in clinical trials. Current safety testing during preclinical drug development was not able to detect the adverse modulation of innate immune response. These results show the potential of the present invention and de-risk drugs, drug candidates, cosmetics or materials (e.g. nanomaterial) humans are unwillingly exposed to.

Example 3: Nanomaterial de-risking

Immune responses to therapeutic products like vaccinations based on nanomaterials (NM) may pose problems for both patient health and product efficacy. Nevertheless, innate immunity for NLR and in particular NLRP3 activation is often not tested routinely despite evidences that drugs can trigger these pathways and induce serious side effects. Therefore, extensive and comprehensive immunity reaction testing is a key element for drug de-risking and safety, especially for biological drugs and vaccines during the preclinical development. The present test aims to target these needs as innate immune responses and, in particular, the inflammasome activation status is largely ignored by existing tests, as cutting-edge technologies are not available on the market. Therefore, a cell-based multiplexed assay was developed that monitors the activation status of the innate immune system with a specific focus on A.) Toll-like-receptor (TLR) activation; B) NLR-receptor (NLR) activation, and in addition to the commonly tested immune cells (i.e. T-cells and B-cells), C) monocyte activation status, which is largely neglected in tests in the art.

Promising results were obtained in case of the assessment of the effects of functionalized NMs on the innate immune systems. Nano-particulate Vaccine Adjuvants on the base of nanoparticles were tested in this study. The present technology was able to demonstrate the correlation between surface modification of nanoparticles, dose response and cytotoxicity. Moreover, the strong stimulation effect of an adjuvant lead candidate on specific immune cells resulting in a mild inflammasome activation and high rates of TNF-alpha positive cells was successfully shown. Furthermore, preliminary data to assess the mode-of-action of inflammasome activation based on the core material was collected, allowing to exclude cytotoxic effects as factor for inflammasome activation. With this data set, clinical risks could be identified and formulations were selected that are safe and efficacious. The results are presented in Figures 20 to 23.

Example 4: Stratifying of patients suffering from an inflammation-related disease and healthy volunteers The inventive assay was performed using hPBMC samples from 3 healthy volunteers and hPBMC samples from 20 patients suffering from an inflammation-related disease, in particular patients suffering from Alzheimer’s disease, depression, and other dementia diseases. The assay was performed automatically as described above. It was shown that the inflammasome responses observed in hPBMCs obtained from patients suffering from an inflammation-related disease, in particular suffering from a neurodegenerative disease, were significantly altered compared to the responses of hPBMCs obtained from healthy humans, which allowed the classification into disease groups. The results are represented in Figure 19.

Example 5: Benchmark experiment

To benchmark the inventive technology in the context of established state-of-the-art cytokine assays in the field of innate immune response analyses, a product benchmarking experiment was conducted. Towards this end, a 34-plex, off-the-shelf Luminex cytokine assay kit was tested in comparison to the present assay platform which in this embodiment integrates cytokine release data with image-based readout analyses leading to increased knowledge and added scientific value.

In the benchmarking experiment, innate immune reactions were assessed by classical cytokine release assays, i.e. the Luminex assay technology, and compared to the present platform which allows an in-depth analysis of immune responses by the combination and integration of cytokine release data and image-based multi-parametric readouts. Since the present assay platform represents a functional assay, the following conditions were analyzed, untreated , LPS-priming and priming-activation by LPS+ATP. Furthermore, the effects of selected reference compounds on the innate immune responses were tested, i.e. CRID3/MCC950 (NLRP3 inflammasome inhibitor), P22077 (ubiquitin protease inhibitor), 4SC202/Domatinostat (class 1 HDAC inhibitor), MI-2 (MALTl inhibitor, NLRP3 inhibitor), A438079 (P2X7 receptor antagonist), VX-765 (CASP1 inhibitor) and Z-VAD-OH-FMK (pan-CASP inhibitor). The selection of the reference compounds was based on compound-specific effects on captured phenotypes obtained by the present technology (Table 3). Captured phenotypes obtained by multi-parametric and image-based readout constitute an added value provided by the present technology. Table 3: Reference compounds used in benchmarking experiment to demonstrate the added-value provided by the present technology via multi-parametric and image-based readouts

The present technology provides the following Added-Values which are summarized in Figures 24-28, addressing precision and accuracy of cytokine release assessment (Figure 24) and disclosing molecular mechanisms-of-action (MoA) (Figures 25-28). Added Value-1: Precision and accuracy - superior performance of present technology/FITRF (Figure 25); Added-Value-2: MoA - inflammasome complex formation (Figure 26); Added- Value-3: MoA - cytokine processing and release (Figure 27); Added- Value-4: MoA - signal perception at the plasma membrane; and Added- Value-5: MoA - disclosure/identification of novel phenotypes (Figure 28).

The present assay platform showed higher precision and accuracy of cytokine release assessment as part of the multi-parametric analysis approach compared to the Luminex technology. Higher precision and accuracy facilitated and allowed to dissect compound- and donor-specific effects. The multi-parametric approach allowed to analyze and dissect molecular mechanisms of action and provided further insights into compound effects on cellular level. The image-based and multi- parametric approach of the present platform facilitated the discovery of novel cellular phenotypes induced by compound action, disease states or other experimental conditions.

Example 6: Use of the present methods for compound characterization (Figure 29 and 30)

An image and data analysis pipeline was developed in order to predict immunoactivity modulations from phenotypic measurements with machine learning algorithms.

The method involves an image analysis pipeline, where cell and aggregate objects are detected and multiple features are measured for each object type. Object detection involves conventional image analysis approaches (filtering, thresholding, watershed algorithms) as well as custom developed AI (artificial intelligence) pixel classification software based on U-shaped neural networks to detect certain blood cell types (e.g. monocytes). Thousands of features are calculated for each object (marker intensities, shape features, texture features, localization features). All features are aggregated per well and a subset is selected for comprehensive, non-redundant and reproducible definition of the cell’s phenotype with regard to innate immunity. This phenotypic readout includes ca. 500 different features, based on morphology, subcellular location, marker intensity and local texture. These phenotypic profiles are clustered in unsupervised fashion, whereas hyper parameters (algorithm type, No of classes, ...) are optimized by maximizing clustering quality of control wells. Compared to single-point readouts, the present assay allows fine-grained categorization of cellular immunity responses and is much more robust since measurement errors are distributed on multiple readouts. The optimized feature set enables precise clustering of different control conditions (“untreated” condition, “LPS” condition, “LPS + activator” condition, “activator condition”, “solvent (DMSO)" and “gold-standard (CRID3)” condition). So, the image analysis pipeline is able to define, differentiate and compare different phenotypes in our inventive assay. The inventors used this approach to benchmark phenotypes (compound structures respectively) to a reference phenotype. A reference phenotype is a phenotype which you want to achieve (induce) by adding a drug. This can be A) the phenotype of a selected condition (“untreated” condition, “LPS” condition, “solvent” condition etc ), which describes the naive status of the phenotype before a compound treatment or B) a phenotype, induced by a selected treatment (“gold-standard”, drug class etc ). The approach B will enable the selection of novel compounds which phenocopying cellular properties of a “gold standard”, e g. a drug already used in vivo. Both approaches will help to identify novel drugs with promising cellular properties in a drug screening campaign.

As a proof of principle, the present inventors benchmarked the compound-induced phenotypes to the reference condition “LPS” and “untreated” by calculating the euclidian distance of the feature vectors in the feature space.