WO2025106588A1

WO2025106588A1 - Host response assays for a point-of-care (poc) platform and methods

Info

Publication number: WO2025106588A1
Application number: PCT/US2024/055787
Authority: WO
Inventors: Abbey JACKSON; Rainer Ng; Vijay Srinivasan; Vamsee Pamula
Original assignee: Baebies Inc
Current assignee: Baebies Inc
Priority date: 2023-11-13
Filing date: 2024-11-13
Publication date: 2025-05-22
Anticipated expiration: 2026-05-13

Abstract

Host response assays for a point-of-care (POC) platform and methods are provided. Methods are provided for determining the etiology of an infection in a subject with an illness and methods of determining a clinical action based on the determination, as well as systems and microfluidics cartridges (or devices) for performing the determination.

Description

HOST RESPONSE ASSAYS FOR A POINT-OF-CARE (POC) PLATFORM AND

METHODS

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 63/548.269 filed November 13, 2023, which is incorporated by reference herein in its entirety.

TECHNICAL FIELD

[0002] The subject matter relates generally to diagnostic testing and more particularly to host response assays for a point-of-care (POC) platform and methods.

BACKGROUND

[0003] Host response-based diagnostics are a relatively new tool that may be used to determine the etiology of an illness in a subject (or patient). A host response diagnostic test ty pically measures the concentrations of specific biomarkers in a subject sample that is associated with infection by a class (or classes) of infectious agents. In one example, predefined sets of mRNA transcripts that are typically expressed by the host in response to an infection may be used as biomarkers. A number of technologies are available for measuring the expression of mRNA transcripts in a biological sample (e.g., RNA sequencing, microarray, reverse transcription polymerase chain reaction (RT-PCR)). However, various technical limitations (e.g., complexity⁷, turnaround time) and cost limitations may be barriers for using these approaches in settings that have limited resources (e.g., a point-of-care setting). Accordingly, there is a need for a relatively inexpensive, easy to use platform for performing a host response diagnostic test in a point-of-care setting.

BRIEF DESCRIPTION OF DRAWINGS

[0004] Having thus described the subject matter in general terms, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein: [0005] FIG. 1 illustrates a block diagram of an example of a microfluidics system for performing a host response assay in droplets to distinguish between bacterial versus non- bacterial illnesses;

[0006] FIG. 2A and FIG. 2B illustrate a plan view and a cross-sectional view, respectively, of an example of a microfluidics structure for performing a host response assay;

[0007] FIG. 3 illustrates a schematic diagram of an example of an electrode arrangement configured for processing a patient sample and performing a RT-qPCR protocol on a microfluidics cartridge;

[0008] FIG. 4 illustrates a flow diagram of an example of a workflow for performing a host response assay on a microfluidics cartridge;

[0009] FIG. 5 illustrates a schematic diagram of an example of a dynamic loop PCR shuttling protocol;

[00010] FIG. 6A, FIG. 6B, and FIG. 6C are plots showing a comparison of the PCR performance of four droplets cycled using a loop PCR shuttling protocol; and

[00011] FIG. 7 illustrates a flow diagram of an example of a method of determining the probability of a bacterial illness or a non-bacterial illness in a subject.

DETAILED DESCRIPTION

[00012] The subject matter now will be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the subject matter are shown. Like numbers refer to like elements throughout. The subject matter may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Indeed, many modifications and other embodiments of the subject matter set forth herein will come to mind to one skilled in the art to which the subject matter pertains having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the subject matter is not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. [00013] In some embodiments, the subject matter provides host response assays for a point-of-care (POC) platform and methods.

[00014] In some embodiments, the host response assays for a POC platform and methods are provided for using a POC platform to test a subject’s (or patient’s) gene or mRNA expression response (i.e. , host gene response or host response) to distinguish or differentiate between a bacterial infection and a non-bacterial infection in a subject with an illness. For example, pre-defined sets of mRNA transcripts that are typically expressed by the host in response to an infection may be used as biomarkers. As used herein, “gene expression” or “gene targets” may refer to mRNA transcripts for the pre-defined set of genes.

[00015] In some embodiments, the host response assays for a POC platform and methods may generally include the method steps of (a) obtaining a biological sample from a subject; (b) measuring on a POC platform gene or mRNA expression levels for pre-defined sets of targets in the subject sample; (b) processing the gene expression data for entry into a bacterial predictive algorithm and/or a non-bacterial predictive algorithm; (c) entering the processed gene expression data into the predictive algorithms for processing; and (d) using the algorithm output to determine whether the subject providing the sample has a bacterial infection, a non-bacterial infection (such as a viral or fungal infection), or a co-infection (such as infection with both a bacterial and viral agent).

[00016] In some embodiments, the host response assay s for a POC platform and methods provide a means by which gene expression levels for a pre-defined set of genes may be determined on a microfluidics cartridge using a multiplexed reverse transcription (RT)- quantitative PCR (qPCR) protocol.

[00017] In some embodiments, the host response assays for a POC platform and methods provide a POC platform including a microfluidics system, a digital microfluidics cartridge (or device), and methods for distinguishing between a bacterial and a non-bacterial illness in a patient and wherein the methods may include the method steps of (a) dispensing a plurality' of sample droplets on a microfluidics cartridge; (b) combining each sample droplet with a RT- qPCR reagent droplet comprising a set of target-specific primers and probes to yield a plurality of reaction droplets; (c) on the microfluidics cartridge, exposing the plurality of reaction droplets to two or more reaction temperatures to amplify and detect in real-time the set of expressed gene targets, thereby generating a qPCR dataset; and (d) using a computer and predictive algorithms, analyzing the qPCR dataset to determine the likelihood of a bacterial infection or a non-bacterial infection.

[00018] In some embodiments, the host response assays for a POC platform and methods provide a POC platform including a microfluidics system, a digital microfluidics cartridge (or device), and methods for distinguishing between a bacterial and a non-bacterial illness in a patient and wherein the methods may include the method steps of (a) preparing a subject sample on a microfluidics cartridge, wherein preparing the subject sample may comprise isolating nucleic acid from the sample and reverse transcribing RNA into cDNA to yield a sample droplet comprising cDNA; (b) dispensing a plurality of prepared sample droplets on the microfluidics cartridge; (c) combining each sample droplet with a qPCR reagent droplet comprising a set of target-specific primers and probes to yield a plurality of reaction droplets; (c) on the microfluidics cartridge, exposing the plurality of reaction droplets to two or more reaction temperatures to amplify and detect in real-time the set of expressed gene targets, thereby generating a qPCR dataset; and (d) using a computer and predictive algorithms, analyzing the qPCR dataset to determine the likelihood of a bacterial infection or a non- bacterial infection.

[00019] In one embodiment, the host response assays for a POC platform and methods provide a means for distinguishing between a bacterial infection and a non-bacterial infection in a subject with an illness, wherein the non-bacterial infection is a viral infection.

[00020] In one embodiment, the host response assays for a POC platform and methods provide a means for distinguishing between a bacterial infection and a non-bacterial infection in a subject with an illness, wherein the non-bacterial infection is a fungal infection.

[00021] In one embodiment, the host response assays for a POC platform and methods provide a means for distinguishing between a bacterial infection and a non-bacterial infection in a subject with an illness, wherein the non-bacterial infection is a parasitic infection.

[00022] In one embodiment, the host response assays for a POC platform and methods provide a means for detecting a co-infection in a subject with an illness such as, but not limited to, a bacterial infection, a viral infection, a fungal infection, and/or a parasitic infection. [00023] In some embodiments, the host response assays for a POC platform and methods provide sets of gene targets selected for characterizing a subject's host response to an illness. In various embodiments, a set of targets may include, for example, a set of genes typically expressed during a host response to an infectious agent (e.g., a bacteria, virus, fungus, or parasite). In some cases, a set of target genes may include genes ty pically expressed in a non-infectious illness.

[00024] In various embodiments, the sets of gene targets may be used to establish a gene expression “signature” that may be used as diagnostic, prognostic, and/or predictive markers of an illness. In one example, a gene expression signature may be used to determine the likelihood (probability) of a bacterial infection or a non-bacterial infection.

[00025] The set of targets may be arranged in groups to provide for multiplexed PCR amplification and detection of multiple expression targets in an assay. For example, a multiplexed PCR amplification and detection protocol may include assaying a plurality’ of reaction droplets, wherein two or more genes in a set of gene targets are amplified in each reaction droplet.

[00026] In one embodiment, the host response assays for a POC platform and methods provide sets of gene targets selected for distinguishing between a bacterial infection or a non- bacterial infection.

[00027] In one embodiment, sets of gene targets may be selected to distinguish between a bacterial and a viral infection.

[00028] In one embodiment, sets of gene targets may be selected to distinguish between a bacterial and a fungal infection.

[00029] In one embodiment, sets of gene targets may be selected to distinguish between a bacterial and a parasitic infection.

[00030] In one embodiment, sets of gene targets may be selected to distinguish between a bacterial, a viral, a fungal, and/or a parasitic infection.

[00031] In some embodiments, the host response assays for a POC platform and methods provide predictive algorithms for distinguishing between a bacterial infection and a non- bacterial infection in a subject with an illness. [00032] In one embodiment, the host response assays for a POC platform and methods provide a predictive algorithm for determining the likelihood (probability) of a bacterial infection in a subject with an illness, i.e., a “bacterial predictor”.

[00033] In one embodiment, the host response assays for a POC platform and methods provide a predictive algorithm for determining the likelihood (probability) of a non-bacterial infection in a subject with an illness, i.e., a “non-bacterial predictor’.

[00034] In one embodiment, the host response assays for a POC platform and methods provide a predictive algorithm for determining the likelihood (probability) of a viral infection in a subject with an illness, i.e., a “viral predictor”.

[00035] In some embodiments, the host response assays for a POC platform and methods may be used to determine a clinical action based on the gene expression signature(s) obtained from a subject (patient) sample.

[00036] In one embodiment, the host response assays for a POC platform and methods may be used to predict the likelihood of sepsis in a patient with a bacterial infection.

[00037] In one embodiment, the host response assays for a POC platform and methods may be used to determine the appropriate care setting for a patient.

[00038] In one embodiment, the host response assays for a POC platform and methods may be used to determine a treatment regime based on the gene expression signature(s) obtained from a patient sample.

[00039] In one embodiment, the host response assays for a POC platform and methods may be used to monitor the efficacy of a therapy during a treatment regimen.

[00040] In one embodiment, the host response assays for a POC platform and methods may be used to determine the next steps in a patient’s treatment protocol and/or refine the treatment course.

[00041] In some embodiments, the host response assays for a POC platform and methods provide a means for monitoring a subject (patient) during treatment of a bacterial and/or non- bacterial illness. [00042] In some embodiments, the host response assays for a POC platform and methods provide a means for predicting a likelihood of a clinical outcome based on the probability of a bacterial infection.

[00043] In one embodiment, the host response assays for a POC platform and methods provide a method of predicting a clinical outcome based on the probability of a bacterial infection may include predicting the likelihood of sepsis.

[00044] In some embodiments, the host response assays for a POC platform and methods provide a POC platform including a microfluidics system, a digital microfluidics cartridge (or device), and methods for performing a host response assay in a POC setting. A POC setting may, for example, include any setting in which a subject (or patient) sample may be collected and analyzed on-site. Examples of a POC setting include, but are not limited to, a subject’s (or patient's) home, a hospital or clinic, a military facility /site, and the like.

Microfluidic System, Cartridge, and Methods

[00045] Fluidics systems and devices are used in a variety of applications to manipulate, process and/or analyze biological materials. Examples of fluidics devices include droplet actuators, microfluidics cartridges, digital microfluidics (DMF) devices, DMF cartridges, flow cell devices, and the like. The disclosure provides a system, digital microfluidics cartridge (or device), and methods for performing a host response assay that may be used to distinguish between a bacterial and a non-bacterial infection in a subject with an illness.

[00046] Referring now to FIG. 1 is a block diagram of an example of a microfluidics system 100 for performing a host response assay in droplets to distinguish between bacterial versus non-bacterial illnesses. In this example, microfluidics system 100 may include a fluidics instrument 105. Further, fluidics instrument 105 may house a microfluidics cartridge 110 along with any supporting components. Microfluidics cartridge 110 of microfluidics system 100 may be, for example, any fluidics device or cartridge, DMF device or cartridge, droplet actuator, flow cell device or cartridge, and the like. In various embodiments, microfluidics system 100 provides microfluidics cartridge 110 that may support automated processes to manipulate, process, and/or analyze biological materials.

[00047] Microfluidics cartridge 110 may be provided, for example, as a disposable and/or reusable device or cartridge. Microfluidics cartridge 110 may be used for processing biological materials. For example, host response assays 112 may be executed on microfluidics cartridge 110 using, for example, droplet operations.

[00048] Also housed in fluidics instrument 105 of microfluidics system 100 may be a controller 114, a cartridge interface 116, certain thermal control electronics 118, one or more magnets 120. a graphical user interface (GUI) 122, and a detection system 124. Controller 114 may be electrically coupled to the various hardware components of fluidics instrument 105. For example, controller 114 may be electrically coupled to microfluidics cartridge 110, thermal control electronics 118, magnets 120, GUI 122, and detection system 124. In particular, controller 114 may be electrically coupled to microfluidics cartridge 110 via cartridge interface 116, wherein cartridge interface 116 may be. for example, a pluggable interface for connecting mechanically and electrically to microfluidics cartridge 1 10.

[00049] Most chemical and biological reactions require precise and stable temperature control for optimal efficiency and performance. Accordingly, thermal control electronics 118 may be any mechanism for controlling the operating temperature of microfluidics cartridge 110. Thermal control electronics 118 may include, for example, any thermal sensors for controlling heaters (e.g., Peltier elements and resistive heaters; (not shown)). Thermal control electronics 118 may also include coolers (not shown) arranged with respect to microfluidics cartridge 1 10.

[00050] In various embodiments, thermal control electronics 118 may be used to support a PCR protocol. A PCR protocol may, for example, include a denaturation temperature, an annealing temperature, and an extension temperature. Generally, the denaturation temperature may be a temperature sufficient to achieve denaturation of a nucleic acid template. Likewise, the annealing temperature may be any temperature sufficient to achieve annealing of nucleic acid sequences, such as an oligonucleotide primer and/or probe sequence to a single-stranded target nucleic acid. Likewise, the extension temperature may be any temperature sufficient to achieve extension of, for example, a primer oligonucleotide sequence annealed to a target sequence. Further, in some cases the annealing and extension reactions may be performed at substantially the same temperature.

[00051] Magnets 120 may be, for example, permanent magnets and/or electromagnets. In the case of electromagnets, controller 114 may be used to control the electromagnets 120. GUI 122 may be any type of digital display for conveying information to a user. In one example, GUI 122 may be the display of fluidics instrument 105. In another example, GUI 122 may be the display of any networked computing device connected to fluidics instrument 105 via a network. For example, a networked computer 132 may be connected to fluidics instrument 105 via a network 134.

[00052] Networked computer 132 may be, for example, any centralized server or cloudbased server. Network 134 may be, for example, a local area network (LAN) or wide area network (WAN) for connecting to the internet. The communications interface (not shown) of controller 114 may be any wired and/or wireless communication interface for connecting to a network (e.g., network 134). Information may be exchanged with other devices connected to the network. Though FIG. 1 shows a single networked computer 132, multiple computers (physical or virtual) may be connected in the microfluidics system 100 via network 134. For example, one computer may be used to control thermal control electronics 118. Then, another computer may be used to control electromagnets 120. Then, another computer that is optimized for storing information may be used to store data received from detection system 124. Then, yet another computer that is optimized for processing data may be used to process information received from fluidics instrument 105.

[00053] Controller 114 may, for example, be a general-purpose computer, special-purpose computer, personal computer, tablet device, smart phone, smart watch, mobile device, microprocessor, or other programmable data processing apparatus. Controller 114 may provide processing capabilities, such as storing, interpreting, and/or executing software instructions. Additionally, controller 114 may be used to control the overall operations of microfluidics system 100. The software instructions may comprise machine-readable code stored in non-transitory memory that is accessible by controller 114 for the execution of the instructions. Controller 114 may be configured and programmed to control data and/or power aspects of microfluidics system 100. Further, data storage (not shown) may be built into or provided separate from controller 114.

[00054] Further, in some embodiments, controller 114 may be external to fluidics instrument 105 (not shown in FIG. 1). The functions described above may be done remotely, for example via a mobile application running on a mobile device connected to various components (i.e., thermal controls electronics 118, among others) via a local network or other network. Output from detection system 124 may also be transmitted to an external controller 114 via such networks and displayed on a mobile application or another mobile app running on a mobile device specific for the external controller 114.

[00055] Generally, controller 114 may be used to manage any functions of microfluidics system 100. For example, controller 114 may be used to manage the operations of thermal control electronics 118, magnets 120, GUI 122, detection system 124, and any other instrumentation (not shown) in relation to microfluidics cartridge 110. Further, with respect to microfluidics cartridge 110, controller 114 may control droplet manipulation by activating/deactivating electrodes.

[00056] Detection system 124 may be, for example, an optical measurement system that includes an illumination source 126 and an optical measurement device 128. In this example, illumination source 126 and optical measurement device 128 may be arranged with respect to microfluidics cartridge 110.

[00057] In one example, detection system 124 may be provided in relation to one or more detection spots (not shown) corresponding to host response assays 112 running on microfluidics cartridge 110. For example, one or more droplet operations electrodes in relation to detection system 124 may be designated as detection spots. With respect to performing host response assays in droplets, detection system 124 may be used to detect the presence and relative amount of a detection signal in a droplet at a detection spot.

[00058] Illumination source 126 of detection system 124 may be, for example, a light source for the visible range (400-800 nm), such as, but not limited to, a white light-emitting diode (LED), a halogen bulb, an arc lamp, an incandescent lamp, lasers, and the like. Illumination source 126 is not limited to a white light source. Illumination source 126 may be any color light that is useful in microfluidics system 100. Optical measurement device 128 may be used to obtain light intensity readings. Optical measurement device 128 may be, for example, a charge coupled device, a photodetector, a spectrometer, a photodiode array, a digital camera (e.g., RGB color camera) or any combinations thereof. Further, optical measurement device 128 of detection system 124 may be a multichannel detector. Further, microfluidics system 100 is not limited to one detection system 124 only (e.g., one illumination source 126 and one optical measurement device 128 only). Microfluidics system 100 may include multiple detection systems 124 (e.g., multiple illumination sources 126 and/or multiple optical measurement devices 128). [00059] Additionally, controller 114 may further include an assay predictive algorithm 130. Assay predictive algorithm 130 may be a software component for distinguishing and/or differentiating between a bacterial infection and a non-bacterial infection in a subject with an illness.

[00060] For example, assay predictive algorithm 130 may include a “bacterial predictor” component for determining the likelihood (probability ) of a bacterial infection in a subject with an illness. Further, assay predictive algorithm 130 may include a “non-bacterial predictor” component for determining the likelihood (probability ) of a non-bacterial infection in a subject with an illness. Further, assay predictive algorithm 130 may include a “viral predictor” component for determining the likelihood (probability) of a viral infection in a subject with an illness.

[00061] Referring now to FIG. 2A and FIG. 2B is a plan view and a cross-sectional view, respectively, of an example of a microfluidics structure 200. In one example, the formation of microfluidics cartridge 110 of microfluidics system 100 may be based generally on microfluidics structure 200. FIG. 2A shows that microfluidics structure 200 may include any arrangements (e.g., lines, paths, arrays) of droplet operations electrodes 216 (i.e., electrowetting electrodes). In one embodiment, microfluidics structure 200 may include an arrangement of droplet operations electrodes 216 configured for processing a patient sample and performing a dynamic loop heating PCR protocol as described hereinbelow with reference to FIG. 3 and FIG. 4.

[00062] FIG. 2B shows that microfluidics structure 200 may include a bottom substrate 210 and a top substrate 212 separated by a droplet operations gap 214. Droplet operations gap 214 may contain filler fluid 218, such as silicone oil or hexadecane. Bottom substrate 210 may be, for example, a silicon substrate, glass, or a printed circuit board (PCB). Bottom substrate 210 may include an arrangement of droplet operations electrodes 216 (e.g.. el echo wetting electrodes). Droplet operations electrodes 21 may be formed, for example, of copper, gold, or aluminum. A dielectric layer 222 (e.g., parylene coating, silicon nitride) may be atop droplet operations electrodes 216. Top substrate 212 may be, for example, a glass or plastic substrate. Top substrate 212 may include a ground reference electrode 220. In one example, ground reference electrode 220 may be formed of indium tin oxide (ITO) and wherein ITO is substantially' transparent to light. Further, a hydrophobic layer 224 may be provided on both the side of bottom substrate 210 and the side of top substrate 212 that is facing droplet operations gap 214. Examples of hydrophobic materials or coatings may include, but are not limited to, polytetrafluoroethylene (PTFE), Cytop,

Teflon™ AF (amorphous fluoropolymer) resins, FluoroPei™ coatings, silane, and the like. Droplet operations may be conducted atop droplet operations electrodes 216 on a droplet operations surface. For example, droplet operations may be conducted atop droplet operations electrodes 216.

[00063] An aqueous droplet 250 may be present in droplet operations gap 214 of microfluidics structure 200. In one example, droplet 250 may be a droplet of a blood sample (or fraction thereof) to be evaluated. In another example, droplet 250 may be a reagent droplet for conducting a host response assay, such as a first reagent droplet or a second reagent droplet. In yet another example, droplet 250 may be a reaction droplet that may or may not include a target analyte of interest. Filler fluid 218 may fill droplet operations gap 214 and surround droplet 250.

[00064] The DMF capabilities of microfluidics cartridge 1 10 may be used for processing a patient sample and performing an RT-qPCR protocol in a host response assay. In some embodiments, a host response assay performed on a microfluidics cartridge may include: (a) collecting a subject sample and on the microfluidics cartridge processing the sample to yield a nucleic acids sample; (b) reverse transcribing RNA (e.g., mRNA) in the nucleic acid sample to yield a cDNA sample; and (c) using a set of target-specific primers and fluorogenic probes, amplifying cDNA targets in the sample to detect in real-time the presence or absence of the targets of interest.

[00065] Referring now to FIG. 3 is a schematic diagram of an example of an electrode arrangement 300 configured for processing a subject sample and performing a RT-qPCR protocol on microfluidics cartridge 110. Electrode arrangement 300 may include a diluent reservoir 310 for dispensing a diluent solution (e.g.. 0.1% Tween in nuclease-free water). Electrode arrangement 300 may include one or more reagent reservoirs 315 for holding and dispensing RT-qPCR reagents comprising reagents for reverse transcribing RNA to cDNA and amplify ing sets of target analytes. For example, each reagent reservoir 315 may include reagents for reverse transcribing RNA to cDNA and reagents for amplifying and detecting a unique set of target analytes (e.g., reverse transcriptase, primers for reverse transcription, polymerase, magnesium, dNTPs, enhancers, buffer, and target specific primers and probes for amplification). For example, a first reagent reservoir R1 may be used to provide a first set of target-specific primers and fluorogenic probes for amplifying a first set of target analytes; a second reagent reservoir R2 may be used to provide a second set of target-specific primers and fluorogenic probes for amplifying a second set of target analytes; a third reagent reservoir R3 may be used to provide a third set of target-specific primers and fluorogenic probes for amplifying a third set of target analytes; and a fourth reagent reservoir R4 may be used to provide a fourth set of target-specific primers and fluorogenic probes for amplifying a fourth set of target analytes. In this example, four (4) reagent reservoirs 315 are shown, but any number of reagent reservoirs 315 may but used to support a multiplexed RT-qPCR protocol.

[00066] Electrode arrangement 300 may include a wash reagent reservoir 320 for dispensing a wash reagent solution. In one example, the wash reagent may be a solution of 70% EtOH. Further, electrode arrangement 300 may include a sample preparation reservoir 325 for preparing and/or dispensing a sample for testing.

[00067] Electrode arrangement 300 may include a heater zone 330 for performing a multiplexed RT-qPCR amplification protocol. An arrangement of heaters/coolers (not shown) may be arranged with respect to heater zone 330 for supporting the thermal requirements of a multiplexed RT-qPCR protocol. Heater zone 330 may include an arrangement of droplet operations designed to support transporting, incubating, and detecting multiple RT-qPCR droplets.

[00068] In one embodiment, heater zone 330 may include an arrangement of droplet operations electrodes for transporting, manipulating, and detecting PCR amplification droplets in a dynamic loop PCR protocol. For example, heater zone 330 may be heated to a denaturation temperature suitable for denaturing double-stranded DNA to single-stranded DNA, and subsequently cooled to an annealing/extension temperature suitable for annealing primers and probes to target sequences for amplification and detection.

[00069] In one embodiment, heater zone 330 may include an arrangement of droplet operations electrodes configured for spatially multiplexing amplification and detection of four different reaction droplets (e.g., designated as 1, 2, 3, and 4) in a dynamic loop PCR protocol. Further, heater zone 330 may include a certain droplet operations electrode that is designated as a detection spot 332. The arrangement of droplet operations in heater zone 330 may be selected to provide for spatially separating and shuttling four reaction droplets in a loop, wherein the loop is designed to ensure that only one droplet is at the detection spot at a given time. An example of loop PCR shuttling protocol is described in more detail below with reference to FIG. 5.

[00070] Electrode arrangement 300 may further include a magnet zone (not shown) for performing magnetic bead-based protocols. In one example, a magnet zone may be configured for performing a magnetic bead washing protocol. In another example, a magnet zone may be configured for performing an elution protocol to elute nucleic acids captured on magnetically responsive beads. In some cases, a magnet zone may further include an arrangement of heaters (not shown) to support various magnetic bead-based protocols.

Host Response Assays

[00071] In various embodiments, a host response assay generally includes testing for a specific host gene expression signature that may be used to distinguish between a bacterial or non-bacterial infection in subject (patient) with an illness. The assay methods make use of RT-qPCR protocols to amplify a set of target nucleic acids (i.e., target analytes), wherein the set of targeted nucleic acids is selected to distinguish between a bacterial and anon-bacterial infection.

[00072] In various embodiments, the target analytes are RNA targets although other host immune proteins such as c-reactive protein, procalcitonin, interferon gamma-induced protein 10, and TNF-related apoptosis inducing ligand and other metabolites such as lactate can be multiplexed along with the RNA targets. For example, nucleic acid molecules in a subject (patient) sample may be isolated and the RNA sequences present in the sample subsequently reverse transcribed to generate cDNA molecules that may be targeted for amplification and detection in a host response assay.

[00073] In some embodiments, a host response assay may include: (a) collecting a sample from a subject (patient) with an illness and isolating RNA from the sample; (b) reverse transcribing RNA in the sample to yield a cDNA sample; (c) amplifying the cDNA using predefined sets of target-specific primers and fluorogenic probes, and detecting in real-time targets of interest; and (d) using predictive algorithms, calculating the likelihood of a bacterial infection and/or a non-bacterial infection.

[00074] The disclosure provides a digital microfluidics cartridge (or device) configured for processing a biological sample from a subject and measuring expression levels (i.e., mRNA) for pre-defined sets of genes in the subject sample. In various embodiments, a host response assay may be performed on a DMF platform such as a microfluidics cartridge 110 of microfluidics system 100.

[00075] In some embodiments, a host response assay may include one or more process steps in the assay workflow that are performed ■’off-cartridge" (i.e.. on-bench). For example, a subject sample may be collected and processed off-cartridge to yield a nucleic acid sample that may be loaded onto a microfluidics cartridge (e.g., microfluidics cartridge 110 of microfluidics system 100) for further analysis.

[00076] In some embodiments, a host response assay may include performing all process steps in the assay workflow on a microfluidics cartridge (e g., microfluidics cartridge 110 of microfluidics system 100).

[00077] In one embodiment, a host response assay performed on a microfluidics cartridge may be performed in about 60 minutes or less.

[00078] In one embodiment, a host response assay performed on a microfluidics cartridge may be performed in about 30 minutes or less.

[00079] In one embodiment, a host response assay performed on a microfluidics cartridge may be performed in about 15 minutes or less.

[00080] In one embodiment, a host response assay performed on a microfluidics cartridge may be performed in about 10 minutes or less.

[00081] In one embodiment, a host response assay performed on a microfluidics cartridge may be performed in about 5 minutes or less.

[00082] In one embodiment, a host response assay performed on a microfluidics cartridge may be performed in about 2 minutes or less.

[00083] In some embodiments, a host response assay may be used to distinguish between a bacterial infection and a non-bacterial infection in a subject with an illness. The assay may include: (a) preparing a subject sample on a microfluidics cartridge; (b) on the microfluidics cartridge, performing a multiplexed RT-qPCR protocol to detect in real-time sets of predefined genes indicative of a bacterial illness or an illness due to other etiologies (e.g., a non- bacterial illness) to generate gene expression signatures; and (c) using a computer, determining a likelihood of a bacterial illness or non-bacterial illness based on the gene expression signatures.

[00084] In one embodiment, a host response assay may be used to distinguish between a bacterial infection and a viral infection in a subject with an illness. The assay may include: (a) preparing a subject sample on a microfluidics cartridge; (b) on the microfluidics cartridge, performing a multiplexed RT-qPCR protocol to detect in real-time sets of pre-defined genes indicative of a bacterial or a viral illness to generate gene expression signatures; and (c) using a computer, determining a likelihood of a bacterial or viral illness based on the gene expression signatures.

[00085] In one embodiment, a host response assay may be used to distinguish between a bacterial infection and a fungal infection in a subject with an illness. The assay may include: (a) preparing a subject sample on a microfluidics cartridge; (b) on the microfluidics cartridge, performing a multiplexed RT-qPCR protocol to detect in real-time sets of pre-defined genes indicative of a bacterial or a fungal illness to generate gene expression signatures; and (c) using a computer, determining a likelihood of a bacterial or fungal illness based on the gene expression signatures.

[00086] In one embodiment, a host response assay may be used to distinguish between a bacterial infection and a parasitic infection in a subject with an illness. The assay may include: ((a) preparing a subject sample on a microfluidics cartridge; (b) on the microfluidics cartridge, performing a multiplexed RT-qPCR protocol to detect in real-time sets of predefined genes indicative of a bacterial or a parasitic illness to generate gene expression signatures; and (c) using a computer, determining a likelihood of a bacterial or parasitic illness based on the gene expression signatures.

[00087] In one embodiment, a host response assay may be used to distinguish between a bacterial infection, a viral infection, a fungal infection, and/or a parasitic infection in a subject with an illness. The assay may include: (a) preparing a subject sample on a microfluidics cartridge; (b) on the microfluidics cartridge, performing a multiplexed RT- qPCR protocol to detect in real-time sets of pre-defined genes indicative of a bacterial, a viral, a fungal, or a parasitic illness to generate gene expression signatures; and (c) using a computer, determining a likelihood of a bacterial, viral, fungal, and/or parasitic illness based on the gene expression signatures.

[00088] Referring now to FIG. 4 is a flow diagram of an example of a workflow 400 for performing a host response assay on a microfluidics cartridge. In this example, the process steps of workflow 400 may be performed on microfluidics cartridge 110 of microfluidics system 100 that includes an arrangement of droplet operations electrodes configured for processing a subject sample and performing an RT-qPCR protocol, such as electrode arrangement 300 of FIG. 3. Workflow 400 may include, but is not limited to, the following steps.

[00089] At a step 410, a subject sample is collected and loaded onto a microfluidics cartridge. In one example, the sample may be a whole blood sample. For example, a whole blood sample may be collected and loaded onto sample preparation reservoir 325 of microfluidics cartridge 110. The whole blood sample may, for example, be collected in a capillary tube and loaded directly onto the sample reservoir. In one example, about 13 pL of a whole blood sample may be used. In another example, the whole blood sample may be collected in a blood collection tube, such as a PAXgene RNA tube, and an aliquot subsequently loaded onto the sample reservoir.

[00090] Sample preparation reservoir 325 may, for example, be pre-loaded with sample preparation reagents. For example, sample preparation reservoir 325 may be pre-loaded with a lysis buffer, proteinase K, and magnetically responsive nucleic acid capture beads. In one example, lysis buffer, proteinase K, and nucleic acid capture beads from RNAdvance Blood nucleic acid extraction kit (available from Beckman Coulter) may be used.

[00091] At a step 415, a cell lysis and protein degradation protocol is performed to lyse cells and/or digest protein in the subject sample and yield a nucleic acid sample. For example, in sample preparation reservoir 325, the whole blood sample may be combined with a lysis buffer and proteinase K reagent and incubated for a period of time sufficient (e.g., about 5 minutes at room temperature) to lyse cells in the sample and release nucleic acids in the sample.

[00092] At a step 420, nucleic acids are captured on magnetic capture beads to yield a bead-bound nucleic acid droplet. For example, the nucleic acid sample may be combined using droplet operations with a capture bead reagent droplet and incubated for a period of time (e.g., about 5 minutes at room temperature) to yield a bead-bound nucleic acid droplet.

[00093] At a step 425, a bead washing protocol is performed to yield washed bead-bound nucleic acid. For example, the capture beads in the bead-bound nucleic acid droplet may be immobilized using magnets 120 of fluidics instrument 105. A magnetic bead washing protocol may then be used to wash the nucleic acid-bound beads. For example, one or more wash reagent droplets may be dispensed from wash reagent reservoir 320 and combined with the bead-bound nucleic acid droplet or used to resuspend the beads. In one example a bead washing protocol may include using a combination of “snap wash’" steps (e.g., three (3) snap wash steps) and “loop wash’⁷ steps (e.g.. two (2) loop washes). For example, a “snap wash” step may include pulling the bead pellet out of liquid and running a wash buffer droplet over immobilized beads. A “loop wash” step may include resuspending the bead pellets in a wash buffer droplet and performing a droplet mixing protocol that includes, for example, transporting the droplet in a loop across certain designated droplet operations electrodes of the cartridge before re-capturing the beads. In one example, a droplet splitting protocol may be used to perform in a loop wash protocol. For example, magnets 120 of fluidics instrument 105 may be used to immobilize the capture beads and droplet operations may be used to transport and merge a wash buffer droplet with the immobilized capture beads. At the end of the bead washing protocol, the wash buffer is removed from the washed capture beads.

[00094] At a step 430, an elution protocol is performed to elute bound nucleic acid from the capture beads to yield an eluted nucleic acid droplet comprising capture beads and unbound nucleic acid. In one example, the washed bead-bound nucleic acid droplet is transported using droplet operations to heater zone 330 and incubate at about 60 °C for a period of time sufficient (e.g., about 5 minutes or less) to release the nucleic acid from the capture beads and yield an eluted nucleic acid droplet. In another example, the washed beadbound nucleic acid droplet is incubated at room temperature for a period of time sufficient to release the nucleic acid from the capture beads and yield an eluted nucleic acid droplet.

[00095] At a step 435, capture beads in the eluted nucleic acid droplet are immobilized and a droplet splitting protocol is performed to yield a processed nucleic acid droplet for analysis. For example, magnets 120 of fluidics instrument 105 may be used to immobilize the capture beads in the eluted nucleic acid droplet. Droplet operations may then be used to split-off a bead-free droplet comprising the processed nucleic acid for analysis. [00096] At a step 440, the processed nucleic acid droplet is processed into a plurality of individual droplets for analysis. In one example, a nucleic acid droplet is diluted and a droplet splitting protocol is performed to yield a plurality of individual analysis droplets. For example, the diluent droplet may be dispensed from diluent reservoir 310 and combined using droplet operations with the processed nucleic acid droplet. In one example, the diluent may be 0. 1% Tween in nuclease-free water. Droplet operations may then be used to divide the diluted nucleic acid droplet into multiple individual nucleic acid droplets for analysis. In one example, the processed nucleic acid droplet may be diluted and split into four (4) individual nucleic acid droplets for analysis. In this example, each nucleic acid droplet may be from about 1 pL to about 2 pL in volume. In another example, a nucleic acid droplet is not diluted prior to splitting into multiple individual nucleic acid droplets. In this example, the volume of each nucleic acid droplet each nucleic acid droplet may be from about 1 pL to about 2 pL in volume.

[00097] At a step 445, each nucleic acid analysis droplet is merged with a unique RT- qPCR reagent droplet comprising a pre-defined set of primers/probes to yield individual reaction droplets. For example, each RT-qPCR reagent droplet may, for example, include enzy mes and primers for performing a reverse transcription reaction, DNA polymerase for amplification cDNA. and a unique set of gene-specific primers and fluorogenic probes for detecting in real-time a set of target genes. For example, a first reagent droplet may be dispensed from a first reagent reservoir 315 (e.g., Rl) of electrode arrangement 300 and combined using droplet operations with a first analysis droplet to yield a first reaction droplet; a second reagent droplet may be dispensed from a second reagent reservoir 315 (e g., R2) of electrode arrangement 300 and combined using droplet operations with a second analysis droplet the yield a second reaction droplet; a third reagent droplet may be dispensed from a third reagent reservoir 315 (e.g., R3) of electrode arrangement 300 and combined using droplet operations with a third analysis droplet to yield a third reaction droplet; and a fourth reagent droplet may be dispensed from a fourth reagent reservoir 315 (e.g., R4) of electrode arrangement 300 and combined using droplet operations with a fourth analysis droplet to yield a fourth reaction droplet.

[00098] At a step 450, each reaction droplet is transported to a heater zone and a reverse transcription (RT) reaction is performed to yield individual amplification droplets comprising cDNA. For example, each individual reaction droplet (e.g., n = 4 individual reaction droplets) may be transported using droplet operations to a certain droplet operations electrode in heater zone 330 of electrode arrangement 300. In one example, the RT reaction may include heating the reaction temperature to about 50 °C and incubating the reaction droplets for a period of time sufficient for reverse transcription of RNA to cDNA (e.g., about 7.5 minutes or less) to yield individual amplification droplets.

[00099] At a step 455, a PCR amplification and detection protocol is performed to detect the pre-defined sets of target genes in each amplification droplet. In one embodiment, a dynamic loop PCR protocol may be used for amplifying and detecting in real-time the sets of target genes. An example of a dynamic loop PCR protocol that may be used to detect sets of target genes is described with reference to FIG. 5

[000100] In some embodiments of workflow 400, a subject sample (e.g., a whole blood sample) may be collected and processed off-cartridge to yield a bead bound nucleic acid droplet. For example, at step 410 a whole blood sample may be collected and transferred to a tube (e.g., a microcentrifuge tube) and combined on-bench with a lysis buffer and proteinase K reagent to lyse cells in the sample and release nucleic acids, thereby yielding a nucleic acid sample. An aliquot of a capture bead reagent may then be added to the nucleic acid sample and incubated for a period of time (e.g., about 5 minutes at room temperature) to yield a bead-bound nucleic acid sample. The bead-bound nucleic acid sample may then be loaded onto a sample reservoir of a microfluidics cartridge (e.g., sample reservoir 325 or microfluidics cartridge 110) for further processing and analysis.

[000101] Referring now to FIG. 5 is a schematic diagram of an example of a dynamic loop PCR shuttling protocol 500. The heater zone may, for example, be heater zone 330 of FIG. 3. In this protocol, the heater zone includes a loop of tw elve (12) droplet operations electrodes for performing a multiplexed PCR shuttling protocol on four (4) droplets, e.g., designated by black boxes labeled 1, 2. 3, and 4. PCR shuttling protocol 500 of FIG. 5 shows the four droplets (i.e., 1, 2, 3, and 4) positioned at the four comers of heater zone 330. Heater zone 330 includes a certain droplet operations electrode that is designated as a detection spot 332. In this example, droplet 1 is positioned at detection spot 332 at the start of a PCR cycle.

[000102] In step (A), a PCR cycle is initiated. For example, heater zone 330 is heated to a denaturation temperature of about 90 °C and the four droplets (1, 2, 3, and 4) are held at the denaturation temperature for about 22 seconds or less. [000103] In step (B), an annealing reaction is initiated for the first PCR cycle. For example, heater zone 330 is cooled to an annealing temperature of about 60 °C and the four droplets (1, 2, 3, and 4) are held at the annealing temperature from about 38 to about 43 seconds or less. In another example, the annealing temperature may be about 58 °C and the four droplets (1, 2, 3, and 4) are held at the annealing temperature for about 43 seconds.

[000104] In step (C), a detection process is performed to detect in real-time a set of target analytes. For example, at the beginning of the annealing reaction, a real-time detection process is performed to detect the set of target analytes in droplet 1.

[000105] In step (D), the droplets are rotated to new positions in heater zone 330. For example, using droplet operations, droplets 1, 2, 3, and 4 are rotated counterclockwise such that droplet 2 is now positioned at detection spot 332.

[000106] In step (E), a detection process is performed to detect in real-time a set of target analytes in droplet 2.

[000107] In step (F), the droplets are rotated to new positions in heater zone 330. For example, using droplet operations, droplets 1. 2. 3, and 4 are rotated counterclockwise such that droplet 3 is now positioned at detection spot 332.

[000108] In step (G), a detection process is performed to detect in real-time a set of target analytes in droplet 3.

[000109] In step (H), the droplets are rotated to new positions in heater zone 330. For example, using droplet operations, droplets 1, 2. 3, and 4 are rotated counterclockwise such that droplet 4 is now positioned at detection spot 332.

[000110] In step (I), a detection process is performed to detect in real-time a set of target analytes in droplet 4.

[000111] In step (J), the droplets are rotated to their original positions in heater zone 330. For example, using droplet operations, droplets 1, 2, 3, and 4 are rotated counterclockwise such that droplet 1 is now re-positioned at detection spot 332. Another PCR cycle may now be initiated. For example, steps (A) through (J) of PCR shuttling protocol 500 are repeated to complete a second amplification/detection cycle. Any number of amplification/detection cycles may be performed. In one example, about 36 amplification/detection cycles may be performed.

[000112] Referring now to FIG. 6A, FIG. 6B, and FIG. 6C illustrate plots 600, 610, and 620, respectively, showing a comparison of the PCR performance of four droplets cycled using a loop PCR shuttling protocol. Plots 600, 610, and 620 may be representative of the PCR performance obtained during the droplet shuttling protocol described in FIG. 3 and FIG. 5. In this example, a single reagent mixture was used for amplification and detection of targets in each of four reaction droplets. The reagent mixture included probes labeled with three different fluorophores: FAM, TAMRA, or Cy5. Plot 600 shows the normalized RFU for FAM labeled probes; plot 610 shows the normalized RFU for TAMRA labeled probes; and plot 620 shows the normalized RFU for Cy5 probes. Each curve on a graph represents a different droplet. The data show that all four droplets performed similarly.

Methods

[000113] The disclosure provides methods for distinguishing between a bacterial illness and a non-bacterial illness a subject (patient). The methods make use of predictive algorithms (e.g., assay predictive algorithm 130) to analyze gene expression signatures in the subject’s sample, wherein the gene expression signature is used to determine the likelihood (probability) of a bacterial illness vs a non-bacterial illness.

[000114] Referring now to FIG. 7 is a flow diagram of an example of a method 700 of determining the probability’ of a bacterial illness vs a non-bacterial illness in a subject. Method 700 may include, but is not limited to, the following steps.

[000115] At a step 710, a biological sample from a subject with an illness is obtained. In one example, the sample is a whole blood sample.

[000116] At a step 715, RNA is isolated from the subject’s sample. In one example, workflow 400 of FIG. 4 (e g., step 410 through step 440) may be used to process the patient's whole blood sample and isolate RNA.

[000117] At a step 720, gene expression levels for sets of pre-defined genes are measured. For example, gene expression levels may be determined using a multiplexed RT-qPCR protocol as described with reference to workflow 400 of FIG. 4 (e.g., step 445 through step 455).

[000118] At a step 725, the measured gene expression levels are normalized to generate normalized values. For example, normalizing gene expression levels for each target in the set of targets may include subtracting an average Ct value of one or more housekeeping genes included in a target set. In some cases, if a target did not amplify, a maximum Ct value from a dataset + 1 may be used as the Ct value.

[000119] At a step 730, the normalized values are entered into a bacterial predictor. For example, the normalized values are entered into a bacterial predicator of assay predictive algorithm 130 to evaluate the normalized values for each gene in the set of targets and determine the likelihood (probability) of a bacterial illness vs a non-bacterial illness.

[000120] At a step 735, the probability result of a bacterial illness is reported. For example, the output of the bacterial predictor of assay predictive algorithm 130 may be used to determine whether the subject providing the sample has an illness of bacterial origin or non- bacterial origin, or some combination of these conditions.

Clinical Actions

[000121] A subject’s (patient’s) gene expression signature characteristic of a bacterial and/or non-bacterial illness may be used to inform an appropriate clinical action for care and treatment of the patient.

[000122] The methods of the disclosure may be used to determine a clinical action based on the gene expression signature(s) obtained from a patient sample.

[000123] In one embodiment, the methods may be used to predict the likelihood of sepsis in a patient with a bacterial infection. For example, the gene expression signature of a patient with a bacterial infection may be further evaluated to determine the likelihood that the infection may lead to sepsis.

[000124] In one embodiment, the methods may be used to determine the appropriate care setting for a patient. For example, the gene expression signature of a patient may be used to determine if the patient should be admitted to a hospital. In another example, the gene expression signature of a patient may be used to determine if the patient should be directed to an emergency department or urgent care facility. In another example, the gene expression signature of a patient may be used to determine if the appropriate method of treatment, e.g., intravenous (IV) administration of a therapy.

[000125] In one embodiment, the methods may be used to determine a treatment regime based on the gene expression signature(s) obtained from a patient sample. For example, the gene expression signature of a patient with an infection may be evaluated to determine the appropriate antibiotic therapy. In one example, a patient with a bacterial expression signature may be administered an antibacterial therapy; a patient with a viral expression signature may be administered an antiviral therapy; and a patient with a fungal expression signature may be administered an antifungal therapy.

[000126] In one embodiment, the methods may be used to monitor the efficacy of a therapy during a treatment regimen.

[000127] In one embodiment, the methods may be used to determine the next steps in a patient's treatment protocol and/or refine the treatment course.

Predictive Algorithms

[000128] The methods for distinguishing between a bacterial or non-bacterial illness in a subject make use of predictive algorithms (e.g., predictive algorithm 130). In one example, a predictive algorithm or “classifier"’ may be generated as described in US Patent Application 2018/0245154 Al. entitled “Methods to Diagnose and Treat Acute Respiratory Infections” published on August 30, 2018, which is incorporated herein by reference in its entirety.

[000129] The predictive algorithms (e.g., predictive algorithm 130) make use of gene expression signatures (e.g., mRNA transcripts) that are representative of a host response to a bacterial illness or a non-bacterial illness (e.g., viral, fungal, parasitic infection). The gene expression signature may include genes whose expression increases or decreases during the host’s response to an infection.

[000130] The disclosure provides predictive algorithms (e.g., predictive algorithm 130) that may be used to distinguish between a bacterial illness and a non-bacterial illness in a subject. Predictive algorithms may, for example, be generated using biological samples obtained from a plurality of subjects known to be suffering from a bacterial illness, or a non-bacterial illness.

[000131] In some cases, a predictive algorithm (e.g., predictive algorithm 130) may be generated using biological samples obtained from a plurality of subjects with anon-infectious illness.

[000132] In some cases, a predictive algorithm (e.g., predictive algorithm 130) may be generated using biological samples obtained from a plurality of “normal’' (e.g., healthy) subjects.

[000133] In one embodiment, a method of generating a predictive algorithm (classifier) may include the steps of: (a) obtaining a biological sample (e.g., a whole blood sample) from a plurality of subjects with a bacterial infection or a non-bacterial illness; (b) isolating RNA from the plurality of subject samples; (c) measuring gene expression levels of pre-defined sets of target genes in the samples; (d) normalizing gene expression levels to generate normalized values; (e) generating a bacterial predictor and/or a non-bacterial predictor based on the normalized values.

[000134] In one embodiment, in step (a) biological samples may be obtained from a plurality of subjects with a bacterial infection or a viral infection.

[000135] In one embodiment, in step (a) biological samples may be obtained from a plurality of subjects with a bacterial infection or a fungal infection.

[000136] In one embodiment, in step (a) biological samples may be obtained from a plurality of subjects with a bacterial infection or a parasitic infection.

[000137] In some embodiments, in step (c) pre-defined sets of target genes may include sets of genes characteristic of a bacterial infection; a viral infection; a fungal infection; and/or a parasitic infection. In some cases, a pre-defined set of target genes may include a genes characteristic of a non-infectious illness.

[000138] In one embodiment, in step (d) normalizing gene expression levels for each target in the set of targets may include subtracting an average Ct value of one or more housekeeping genes included in a target set. In some cases, if a target did not amplify, a maximum Ct value from a dataset + 1 may be used as the Ct value. [000139] In one embodiment, in step (e) generating a bacterial predictor and a non-bacterial predictor may include using logistic regression to determine a coefficient and an intercept for each target in a set of target genes that may be used model the likelihood (probability ) of a bacterial illness or a non-bacterial illness.

[000140] In one example, a model equation for predicting the probability of a bacterial infection is:

Sum coefficient x normalized Ct for each target

Samples

[000141] In various embodiments, a sample is a biological sample obtained from a subject with an illness (e.g., a patient sample). In some cases, a sample is a biological sample obtained from a healthy (“normal”) individual.

[000142] In some embodiments, the sample may be a whole blood sample. The whole blood sample may be collected using a variety of blood collection protocols. For example, a whole blood sample may be collected using a collection medium that incorporates a stabilizing reagent(s) for stabilizing nucleic acids (e.g., RNA) in cell-containing biological sample. Examples of techniques and compositions suitable for stabilizing cell-containing samples such as a whole blood sample or samples derived from whole blood have been described in US Patent 11,525,155, entitled “Stabilization of Biological Samples” published on December 13. 2022, which is incorporated herein by reference in its entirety.

[000143] In one embodiment, a whole blood sample may be collected in a blood collection tube. In one example, the blood collection tube may be a PAXgene RNA tube that includes a stabilizing reagent for stabilizing RNA in the sample.

[000144] In some embodiments, a sample may be collected and processed off-cartridge prior to loading onto a microfluidics cartridge for further processing and analysis. For example, a whole blood sample may be processed off-cartridge to yield a nucleic acid sample. The nucleic acid sample may then be loaded onto a microfluidics cartridge for further processing and analysis. [000145] In some embodiments, a whole blood sample may be collected and loaded directly onto a microfluidics cartridge for processing and analysis. For example, a whole blood sample may be collected in a capillary tube and loaded directly onto a microfluidics cartridge for processing and analysis. The volume of a whole blood sample may, for example, be selected to provide a sufficient number of white blood cells (WBCs) to yield a quantity⁷ of RNA for the analysis of transcription signatures. In one example, about 13 pL of a whole blood sample may be used to provide about 70,000 WBCs for isolation of RNA for transcription analysis. In another example, about 500 nL of a whole blood sample may be used to provide a sufficient number of WBCs to yield a quantity⁷ of RNA for transcription signature analysis. In yet another example, less than about 500 nL of a whole blood sample may be used to provide a sufficient number of WBCs to yield a quantity of RNA for transcription signature analysis. In yet another example, about 100 nL of a whole blood sample may be used to provide a sufficient number of WBCs to yield a quantity of RNA for transcription signature analysis.

[000146] In some embodiments, a sample may be a fraction of a whole blood sample, such as a peripheral blood mononuclear cell (PBMC; (e.g., leukocytes)) sample or a lysate thereof. In one example, the PBMC sample (or lysate thereof) may be prepared “off-cartridge” and subsequently loaded onto a microfluidics cartridge for analysis.

[000147] In some embodiments, a sample may be collected from a subject’s (patient’) wound. In one example, the wound sample may be collected using a swab collection protocol and an appropriate collection medium, and subsequently loaded onto a microfluidics cartridge for processing and analysis.

[000148] In some embodiments, a sample may be collected from a subject’s (patient’s) cheek. In one example, the cheek sample may be collected using a swab collection protocol and an appropriate collection medium, and subsequently loaded onto a microfluidics cartridge for processing and analysis.

[000149] In some embodiments, a sample may be obtained from other bodily fluids using an appropriate collection protocol. Examples of other bodily fluids include, but are not limited to saliva, cerebrospinal fluid, bronchoalveolar lavage (BAL) fluid, nasal aspirate, and fecal matter. [000150] In some embodiments, a sample collection protocol may include a collection medium that includes one or more additives. In one example, a blood sample collection medium may include EDTA to prevent clotting of the blood sample. In another example, a sample collection medium may include a reagent for stabilizing RNA.

Targets

[000151] Pre-defined sets of expressed gene targets that are representative of a host response to a bacterial infection or a non-bacterial infection may be used to measure gene expression levels in a subject with an illness and generate a gene expression profile.

[000152] In some embodiments, the disclosure provides sets of expressed gene targets for a host response assay that may be used to distinguish between a bacterial or a non-bacterial infection in a patient with an illness.

[000153] In some cases, a set of gene targets representative of anon-infectious illness may be used to measure gene expression levels in a subject with an illness and generate a gene expression profile.

[000154] In some cases, a set of gene targets representative of a '‘normal” (healthy) state may be used to measure gene expression levels in a subject and generate a gene expression profile.

[000155] The sets of expressed gene targets may be arranged in groups to provide for multiplexed PCR amplification and detection of two or more targets in a single reaction droplet. Each target in the set of targets may be detected using a target-specific primer and probe pairs. Grouping of targets may, for example, be selected using suitable primer/probe design software to determine which nucleic acid targets would be most compatible for multiplexed amplification and detection in the same reaction droplet. In one example Panelplex™ software (available from DNA Software, Plymouth MI) may be used to selected target-specific primer and probe pairs. During the design process, various parameters may be considered such as thermodynamic properties (e.g., denaturation and/or annealing temperatures for each primer/probe and target) and cross reaction between primer/probes sequences and nucleic acid targets. [000156] A set of expressed gene targets may include one or more ‘"housekeeping” genes that may be used as a control for normalization of the data.

[000157] In some embodiments, a set of expressed gene targets may be designed for multiplexed amplification and detection of 3 target sequences in a single reaction droplet.

[000158] In one embodiment, a set of expressed gene targets may be designed for multiplexed amplification and detection of 12 targets in 4 different reaction droplets, i.e., run in 3-plex. An example of a target set for multiplexing for 4 droplets wherein each droplet is run in 3-plex is shown in Table 1. In this example, housekeeping genes that may be used as a control for data normalization are indicated with an asterisk (*).

Table 1 : Example target set for multiplexing 12 expressed gene targets in 4 droplets (A, B, C, and D) wherein each droplet is run in 3-plex.

[000159] In one embodiment, a set of expressed gene targets may be designed for multiplexed amplification and detection of 33 targets in 11 different reaction droplets, i.e., run in 3-plex. An example of a target set for multiplexing for 11 droplets wherein each droplet is run in 3-plex is shown in Table 2. In this example, housekeeping genes that may be used as a control for data normalization are indicated with an asterisk (*).

Table 2: Example target set for multiplexing 33 expressed gene targets in 11 droplets (1 through 11) wherein each droplet is run in 3-plex.

Alternative PCRs for Spatial and Spectral Multiplexing

[000160] Various modifications and variations of the disclosed microfluidics cartridge, electrode arrangements and/or methods may be used to provide multiplexed detection of sets of expressed gene targets. [000161] In one embodiment, the number of droplet operations electrodes in a heater zone loop (e g., heater zone 330 of electrode arrangement 300 of FIG. 3) may be increased to provide a larger droplet shuttling loop and accommodate a larger number of PCR droplets.

[000162] In one embodiment, an electrode arrangement configured for performing a multiplexed PCR protocol on a microfluidics cartridge may include multiple heater zones and droplet shuttling loops to accommodate a larger number of PCR droplets.

[000163] In one embodiment, multiple detection spots may be used.

[000164] In some embodiments, a stationary PCR protocol may be used, wherein the droplets are in an array with a fluorescence imaging sensor to measure all droplets simultaneously and the heaters are cycled.

[000165] In some embodiments, a stationary PCR protocol may be used, wherein the droplets are in an array and the heaters are cycled with a multichannel moving detector to measure all droplets simultaneously.

[000166] In some embodiments, a radial PCR heating protocol may be used, wherein a central single electrode is used for anneal/detection; an outer ring of electrodes that are individually thermally controlled to reach denature/ extension temperatures for transport.

[000167] In some embodiments, a radial PCR heating protocol may be used; wherein multiple central electrodes for anneal/detection are used to image only a small area and reduce cany over between droplets; the outer ring of electrodes that are individually thermally controlled to reach denature/extension temperatures for transport.

Examples

[000168] In other embodiments, detection spot 332 and its diagonally opposite electrode can be setup to be denature electrodes set to high temperatures and the electrodes on the other two comers of the square designated as anneal/extension electrodes set to low temperatures. A droplet can be continuously cycled through the electrodes where each time a loop is completed, two cycles of PCR are completed. Detectors may be placed at each anneal/extend electrode or just at one of them. Another method is to select the distance between denature electrode set to high temperature and ‘’anneal electrode’’ set to room temperature (with no heater) such that, due to thermal losses, the droplet will be at anneal/extend temperature when it reaches the 'anneal electrode” without actively setting it a temperature with a heater.

[000169] In another embodiment, a much larger loop can be constructed to accommodate more droplets. Instead of having a detector at each of the annealing droplet locations, a camera can be used to image fluorescence from all droplets. In another example, multiple optical fibers collecting fluorescence from different electrodes can be routed to a single multichannel detector but looping is designed to ensure that only one droplet is present at a detection spot at a time. For example, in a loop of 8 electrodes driven by 4 electrical connections (every 4 electrodes are connected together on a bus), place one optical fiber on the 3^rd electrode and another one on the 8^th electrode (where 1^st electrode is denature and 3^rd electrode is extend/anneal). In this way, two droplets can be detected simultaneously in a large loop without optically interfering with one another while using the same detector.

[000170] Stationary PCR: droplets in an array and the heaters are thermally cycled - with a fluorescence imaging sensor to measure all droplets simultaneously. Droplets in an array and the heaters are thermally cycled - with a multichannel moving detector to measure all droplets serially.

[000171] Radial PCR: A central single electrode is designated for anneal/detection while an outer ring of electrodes that are individually thermally controlled to reach denature and extension temperatures are designated for transport. This method also allows a single nonmoving detector to be used by multiple thermal cycling droplets. The central electrode can comprise multiple electrodes for anneal/detection that are all imaged so only a small area needs to be imaged while this method further reduces carry over between droplets by minimizing electrode reuse; outer ring of electrodes that are individually thermally controlled to reach denature/extension temperatures for transport.

[000172] Following long-standing patent law convention, the terms “a,” “an,” and “the” refer to “one or more” when used in this application, including the claims. Thus, for example, reference to “a subject” includes a plurality of subjects, unless the context clearly is to the contrary (e.g., a plurality of subjects), and so forth.

[000173] The terms “comprise,” “comprises,” “comprising,” “include,” “includes,” and “including,” are intended to be non-limiting, such that recitation of items in a list is not to the exclusion of other like items that may be substituted or added to the listed items. [000174] Terms like “preferably.” “commonly,” and “typically” are not utilized herein to limit the scope of the claimed embodiments or to imply that certain features are critical or essential to the structure or function of the claimed embodiments. These terms are intended to highlight alternative or additional features that may or may not be utilized in a particular embodiment of the present invention.

[000175] The term “substantially” is utilized herein to represent the inherent degree of uncertainty that may be attributed to any quantitative comparison, value, measurement, or other representation and to represent the degree by which a quantitative representation may vary from a stated reference without resulting in a change in the basic function of the subject matter at issue.

[000176] Various modifications and variations of the disclosed methods, compositions and uses of the invention will be apparent to the skilled person without departing from the scope and spirit of the invention. Although the subject matter has been disclosed in connection with specific preferred aspects or embodiments, it should be understood that the subject matter as claimed should not be unduly limited to such specific aspects or embodiments.

[000177] The subject matter may be implemented using hardware, software, or a combination thereof and may be implemented in one or more computer systems or other processing systems. In one aspect, the subject matter is directed toward one or more computer systems capable of carrying out the functionality described herein.

[000178] For the purposes of this specification and appended claims, unless otherwise indicated, all numbers expressing amounts, sizes, dimensions, proportions, shapes, formulations, parameters, percentages, quantities, characteristics, and other numerical values used in the specification and claims, are to be understood as being modified in all instances by the term “about” even though the term “about” may not expressly appear with the value, amount or range. Accordingly, unless indicated to the contrary, the numerical parameters set forth in the following specification and attached claims are not and need not be exact, but may be approximate and/or larger or smaller as desired, reflecting tolerances, conversion factors, rounding off, measurement error and the like, and other factors known to those of skill in the art depending on the desired properties sought to be obtained by the presently disclosed subject matter. For example, the term “about,” when referring to a value can be meant to encompass variations of, in some embodiments ± 100%, in some embodiments ± 50%, in some embodiments ± 20%, in some embodiments ± 10%, in some embodiments ± 5%, in some embodiments ± 1%, in some embodiments ± 0.5%, and in some embodiments ± 0. 1% from the specified amount, as such variations are appropriate to perform the disclosed methods or employ the disclosed compositions.

[000179] Further, the term “about” when used in connection with one or more numbers or numerical ranges, should be understood to refer to all such numbers, including all numbers in a range and modifies that range by extending the boundaries above and below the numerical values set forth. The recitation of numerical ranges by endpoints includes all numbers, e.g., whole integers, including fractions thereof, subsumed within that range (for example, the recitation of 1 to 5 includes 1, 2. 3, 4, and 5. as well as fractions thereof, e.g.. 1.5, 2.25, 3.75. 4. 1 , and the like) and any range within that range.

[000180] Although the foregoing subject matter has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be understood by those skilled in the art that certain changes and modifications can be practiced within the scope of the appended claims.

Claims

THAT WHICH IS CLAIMED:

1. A method for performing a host response assay for a point-of-care (POC) platform, the method comprising: obtaining a biological sample from a subject; measuring, on a POC platform, gene or mRNA expression levels for one or more predefined sets of targets in the subject sample; processing the gene expression data for entry' into a bacterial predictive algorithm and/or a non-bacterial predictive algorithm; entering the processed gene expression data into the predictive algorithms for processing; and determining, based on the algorithm output, whether the subject providing the sample has a bacterial infection, a non-bacterial infection, or a co-infection.

2. The method of claim 1, wherein the gene or mRNA expression levels for a pre-defined set of genes are determined on a microfluidics cartridge using a multiplexed reverse transcription (RT)-quantitative PCR (qPCR) protocol.

3. A method for performing a host response assay for a point-of-care (POC) platform, the method comprising: providing a POC platform comprising a microfluidics system and a digital microfluidics cartridge; dispensing a plurality- of sample droplets on a microfluidics cartridge; combining each sample droplet yvith a RT-qPCR reagent droplet comprising a set of target-specific primers and probes to yield a plurality of reaction droplets; on the microfluidics cartridge, exposing the plurality of reaction droplets to two or more reaction temperatures to amplify and detect in real-time the set of expressed gene targets, thereby generating a qPCR dataset; and analyzing the qPCR dataset to determine the likelihood of a bacterial infection or a non-bacterial infection based on at least one or more predictive algorithms.

4. A method for performing a host response assay for a point-of-care (POC) platform, the method comprising: providing a POC platform comprising a microfluidics system and a digital microfluidics cartridge or device; preparing a subject sample on a microfluidics cartridge, wherein preparing the subject sample may comprise isolating nucleic acid from the sample and reverse transcribing RNA into cDNA to yield a sample droplet comprising cDNA; dispensing a plurality of prepared sample droplets on the microfluidics cartridge; combining each sample droplet with a qPCR reagent droplet comprising a set of target-specific primers and probes to yield a plurality of reaction droplets; on the microfluidics cartridge, exposing the plurality of reaction droplets to two or more reaction temperatures to amplify and detect in real-time the set of expressed gene targets, thereby generating a qPCR dataset; and analyzing the qPCR dataset to determine the likelihood of a bacterial infection or a non-bacterial infection based on one or more predictive algorithms.

5. The method of any of the previous claims further comprising distinguishing between a bacterial infection and a non-bacterial infection in a subject with an illness, wherein the non-bacterial infection is a viral infection.

6. The method of any of the previous claims further comprising distinguishing between a bacterial infection and a non-bacterial infection in a subject with an illness, wherein the non-bacterial infection is a fungal infection.

7. The method of any of the previous claims further comprising distinguishing between a bacterial infection and a non-bacterial infection in a subject with an illness, wherein the non-bacterial infection is a parasitic infection.

8. The method of any of the previous claims further comprising detecting a coinfection in a subject with an illness selected from a bacterial infection, a viral infection, a fungal infection, and/or a parasitic infection.

9. The method of any of the previous claims, wherein sets of gene targets are selected for characterizing a subject’s host response to an illness.

10. The method of claim 9, wherein the sets of gene targets are used to establish a gene expression signature that is implemented as diagnostic, prognostic, and/or predictive markers of an illness.

11. The method of claim 10, wherein the gene expression signature is used to determine the likelihood (probability) of a bacterial infection or a non-bacterial infection.

12. The method of claim 9, wherein the sets of gene targets are arranged in groups to provide for multiplexed PCR amplification and detection of multiple expression targets in an assay.

13. The method of claim 12, further comprising performing a multiplexed PCR amplification and detection protocol that includes assaying a plurality of reaction droplets, wherein two or more genes in a set of gene targets are amplified in each reaction droplet.

14. The method of claim 9, wherein the sets of gene targets are selected for distinguishing between a bacterial infection or a non-bacterial infection.

15. The method of claim 9, wherein the sets of gene targets are selected to distinguish between a bacterial and a viral infection.

16. The method of claim 9, wherein the sets of gene targets are selected to distinguish between a bacterial and a fungal infection.

17. The method of claim 9, wherein the sets of gene targets are selected to distinguish between a bacterial and a parasitic infection.

18. The method of claim 9, wherein the sets of gene targets are selected to distinguish between a bacterial, a viral, a fungal, and/or a parasitic infection.

19. The method of claim 10, further comprising determining a clinical action based on the gene expression signature obtained from a subject sample.

20. The method of claim 10, further comprising determining a treatment regime based on the gene expression signature obtained from a patient sample.