Digital microfluidic methods for pharmacotyping

Abstract

A pharmacotyping system can include a digital microfluidics (DMF) platform. The pharmacotyping system can also include an assay access mechanism that allows interrogation access to the at least one cell culture site. A method of pharmacotyping can include introducing a sample fluid into a pharmacotyping system, where the sample fluid includes non-adherent cells. The pharmacotyping system can include a digital microfluidics (DMF) platform having at least one cell culture site with a binding surface for binding with nonadherent cells or extracellular vesicles. The pharmacotyping system can also include an assay tool which interrogates the sample fluid in the at least one cell culture site. The method of pharmacotyping can also include testing the sample fluid in the at least one cell culture site using the assay tool to produce an assay output. Furthermore, the method can include providing a recommended treatment plan based on the analysis of the sample fluid using the assay output.

Description

[0001] DIGITAL MICROFLUIDIC METHODS FOR PHARMACOTYPING

[0002] CROSS REFERENCE TO RELATED APPLICATIONS

[0003] This application claims priority to of U.S. Provisional Patent Application No. 63 / 571,845, filed on March 29, 2024, which is incorporated herein by reference in its entirety.

[0004] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0005] Not applicable.

[0006] NAMES OF THE PARTIES TO A JOINT RESEARCH AGREEMENT

[0007] Not applicable.

[0008] INCORPORATION BY REFERENCE STATEMENT

[0009] Not applicable.

[0010] BACKGROUND

[0011] Pharmacotyping is the process of testing and analyzing an individual’s likely response to drugs using biological samples, such as patient-derived cells, to determine the most effective and least toxic treatment. Pharmacotyping can be a key aspect of precision medicine, as it helps tailor drug therapies based on a patient’s unique biological characteristics, which may include but are not limited to genetic information.

[0012] SUMMARY

[0013] A method of pharmacotyping can include introducing a sample fluid into a pharmacotyping system, where the sample fluid includes at least one of non-adherent cells and extracellular vesicles released from the non-adherent cells. The pharmacotyping system can include a digital microfluidics (DMF) platform having at least one cell culture site having a binding surface for binding with at least one of the non-adherent cells and the extracellular vesicles. The pharmacotyping system can also include an assay tool, which interrogates the sample fluid in the at least one cell culture site. Additionally, the method of pharmacotyping can also include testing the sample fluid in the at least one cell culture site using the assay tool to produce an assay output. Furthermore, the method can include providing a recommended treatment plan based on the analysis of the sample fluid using the assay output.

[0014] A pharmacotyping system can include a digital microfluidics (DMF) platform having at least one cell culture site with a binding surface for binding with at least one of non-adherent cells and extracellular vesicles released by the non-adherent cells. The pharmacotyping system can also include an assay access mechanism that allows interrogation access to the at least one cell culture site.

[0015] There has thus been outlined, rather broadly, the more important features of the invention so that the detailed description thereof that follows may be better understood, and so that the present contribution to the art may be better appreciated. Other features of the present invention will become clearer from the following detailed description of the invention, taken with the accompanying drawings and claims, or may be learned by the practice of the invention.

[0016] BRIEF DESCRIPTION OF THE DRAWINGS

[0017] FIG. 1 is an illustration of a DMF platform in accordance with one example.

[0018] FIG. 2 is a flow7chart of a method of pharmacotyping in accordance with one example

[0019] FIG. 3 A is an illustration of the on-chip droplet manipulation of a DMF platform in accordance with one example.

[0020] FIG. 3B is an illustration of the multi-step immunostaining protocol for phosphoprotein detection in accordance with one example.

[0021] FIG. 3C is a fluorescence scanning image of a microfluidic top plate in accordance with one example.

[0022] FIG. 3D is an illustration of the extraction of single-cell signatures through image analy sis in accordance with one example.

[0023] FIG. 3E is an illustration of a drug response analysis framework in accordance with one example.

[0024] FIG. 3F is an illustration of a drug sensitivity prediction model in accordance with one example.

[0025] FIG. 4 is a boxplot of cell retention ratios in accordance with one example.

[0026] FIG. 5A is an illustration of a scheme of cell processing steps in accordance with one example. FIG. 5B is a bar graph showing the retention ratios for CCRF-CCEM, HSB-2, and their mixtures in accordance with one example.

[0027] FIG. 5C is a scatter plot of single-cell immunocytochemistry analysis in accordance with one example.

[0028] FIG. 5D is a graph showing correlation of mean pLCK intensity between phosphoflow and the present technology.

[0029] FIG. 6A is an illustration showing differences in subcellular location and function between LCK and BCL2 proteins.

[0030] FIG. 6B shows representative fluorescence images of protein expression profdes across T-ALL cell lines in accordance with one example.

[0031] FIG. 6C is a graph showing the viability of seven T-ALL cell lines in response to the drug DASATINIB in accordance with one example.

[0032] FIG. 6D is a graph showing the viability of seven T-ALL cell lines in response to the drug VENETOCLAX in accordance with one example.

[0033] FIG. 7A is a graph that illustrates normalized mean intensity of pLCK for different T-ALL cell lines in accordance with one example.

[0034] FIG. 7B is a graph that illustrates normalized mean intensity of pBCL2 for different T-ALL cell lines in accordance with one example.

[0035] FIG. 7C is a graph showing the correlation between mean pLCK intensity and DASATINIB LC50 across T-ALL cell lines in accordance with one example.

[0036] FIG. 7D is a graph illustrating ROC curve analysis using single-cell pLCK intensity to predict drug sensitivity7in T-ALL cell lines in accordance with one example.

[0037] FIG. 7E is a graph showing the correlation between mean pBCL2 intensity and VENETOCLAX LC50 across T-ALL cell lines in accordance with one example.

[0038] FIG. 7F is a graph illustrating ROC curve analysis using single-cell pBCL2 intensity to predict drug sensitivity7in T-ALL cell lines in accordance with one example.

[0039] FIG. 8A is an illustration of the process of obtaining PDX cell samples in accordance with one example.

[0040] FIG. 8B shows representative fluorescence images of protein expression profiles across T-ALL PDX samples in accordance with one example.

[0041] FIG. 8C is a graph that represents normalized mean intensity7of pLCK for different T-ALL PDX samples in accordance with one example.

[0042] FIG. 8D is a graph that represents normalized mean intensity of pBCL2 for different T-ALL PDX samples in accordance with one example. FIG. 8E is a graph illustrating ROC curve analysis of single-cell pLCK intensity data for predicting DASATINIB sensitivity in T-ALL PDX samples in accordance with one example.

[0043] FIG. 8F is a graph illustrating ROC curve analysis of single cell pBCL2 intensity data for predicting VENETOCLAX sensitivity in T-ALL PDX sample in accordance with one example.

[0044] FIG. 8G is a boxplot showing mean pLCK intensity values in resistant versus sensitive T-ALL PDX samples in accordance with one example.

[0045] FIG. 8H is a boxplot showing mean pBCL2 intensity' values in resistant versus sensitive T-ALL PDX samples in accordance with one example.

[0046] FIG. 9A is an illustration of a construction scheme of a drug prediction model in accordance with one example.

[0047] FIG. 9B is an illustration of five-fold cross-validation metrics for DASATINIB sensitivity prediction in accordance with one example.

[0048] FIG. 9C is an illustration of five-fold cross-validation metrics for VENETOCLAX sensitivity prediction in accordance with one example.

[0049] FIG. 9D is a graph illustration ROC curve analysis showing model performance for DASATINIB and VENETOCLAX in accordance with one example.

[0050] FIG. 9E is an illustration of test set performance metrics for DASATINIB and VENETOCLAX in accordance with one example.

[0051] FIG. 9F illustrates SHAP-based feature importance and directionality for DASATINIB sensitivity predictions in accordance with one example.

[0052] FIG. 9G illustrates SHAP-based feature importance and directionality' for VENETOCLAX sensitivity predictions in accordance with one example.

[0053] FIG. 9H is an illustration of a confusion matrix for DASATINIB in accordance with one example.

[0054] FIG. 91 is an illustration of a confusion matrix for VENETOCLAX in accordance with one example.

[0055] FIG. 9J is a scatter plot of predicted probabilities for sensitivity of DASATINIB in accordance with one example.

[0056] FIG 9K is a scatter plot of predicted probabilities for sensitivity of VENETOCLAX in accordance with one example.

[0057] FIG. 10 shows an example of a study of fast phosphorylation stimuli response in single cells in accordance with one example. FIG. HA and 11B show pharmacotyping responses for T-cell response for acute lymphoblastic leukemia in accordance with one example.

[0058] These drawings are provided to illustrate various aspects of the invention and are not intended to be limiting of the scope in terms of dimensions, materials, configurations, arrangements or proportions unless otherwise limited by the claims.

[0059] DETAILED DESCRIPTION

[0060] While these exemplar}’ embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, it should be understood that other embodiments may be realized and that various changes to the invention may be made without departing from the spirit and scope of the present invention. Thus, the following more detailed description of the embodiments of the present invention is not intended to limit the scope of the invention, as claimed, but is presented for purposes of illustration only and not limitation to describe the features and characteristics of the present invention, to set forth the best mode of operation of the invention, and to sufficiently enable one skilled in the art to practice the invention. Accordingly, the scope of the present invention is to be defined solely by the appended claims.

[0061] Definitions

[0062] In describing and claiming the present invention, the following terminology will be used.

[0063] The singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a cell culture site” includes reference to one or more of such materials and reference to “the DMF channel” refers to one or more of such channels.

[0064] As used herein with respect to an identified property or circumstance, “substantially” refers to a degree of deviation that is sufficiently small so as to not measurably detract from the identified property or circumstance. The exact degree of deviation allowable may in some cases depend on the specific context.

[0065] As used herein, “adjacent” refers to the proximity of two structures or elements. Particularly, elements that are identified as being “adjacent” may be either abutting or connected. Such elements may also be near or close to each other without necessarily contacting each other. The exact degree of proximity may in some cases depend on the specific context. As used herein, the term "about" is used to provide flexibility and imprecision associated with a given term, metric or value. The degree of flexibility for a particular variable can be readily determined by one skilled in the art. However, unless otherwise enunciated, the term “about” generally connotes flexibility of less than 2%, and most often less than 1%, and in some cases less than 0.01%.

[0066] As used herein, a plurality of items, structural elements, compositional elements, and / or materials may be presented in a common list for convenience. However, these lists should be construed as though each member of the list is individually identified as a separate and unique member. Thus, no individual member of such list should be construed as a de facto equivalent of any other member of the same list solely based on their presentation in a common group without indications to the contrary.

[0067] As used herein, the term “at least one of’ is intended to be synonymous with “one or more of.” For example, “at least one of A, B and C” explicitly includes only A, only B, only C, or combinations of each.

[0068] Numerical data may be presented herein in a range format. It is to be understood that such range format is used merely for convenience and brevity and should be interpreted flexibly to include not only the numerical values explicitly recited as the limits of the range, but also to include all the individual numerical values or sub-ranges encompassed within that range as if each numerical value and sub-range is explicitly recited. For example, a numerical range of about 1 to about 4.5 should be interpreted to include not only the explicitly recited limits of 1 to about 4.5, but also to include individual numerals such as 2, 3, 4, and sub-ranges such as 1 to 3, 2 to 4, etc. The same principle applies to ranges reciting only one numerical value, such as “less than about 4.5,” which should be interpreted to include all of the above-recited values and ranges. Further, such an interpretation should apply regardless of the breadth of the range or the characteristic being described.

[0069] Any steps recited in any method or process claims may be executed in any order and are not limited to the order presented in the claims. Means-plus-function or step- plus-function limitations will only be employed where for a specific claim limitation all of the following conditions are present in that limitation: a) “means for” or “step for” is expressly recited; and b) a corresponding function is expressly recited. The structure, material or acts that support the means-plus function are expressly recited in the description herein. Accordingly, the scope of the invention should be determined solely by the appended claims and their legal equivalents, rather than by the descriptions and examples given herein.

[0070] Example Embodiments

[0071] A pharmacotyping system can include a digital microfluidics (DMF) platform and an assay access mechanism. DMF, also described as electrowetting-on-dielectric (EWOD), is a method of droplet-based fluid manipulation, where the movement of discrete droplets on a surface array of electrodes is controlled via electric fields. FIG 1 shows a DMF platform 100. In DMF, droplets 1 10 are controlled by selectively applying electric fields between a ground electrode 112, which is on a top plate 102, and actuation electrodes 114a and 114b, which are on a bottom plate 104. The actuation electrodes 114a. 114b can be horizontally separated by a gap or dielectric so that each electrode pad can act as a digitized location for creation of a local electric field. In the illustrated example, electrode 114a is actuated so that a charge builds adjacent the channel surface, which attracts preferential opposite charges and dipoles within the droplet 110, and encourages movement of the droplet 110 toward that electrode 114a. Thus, upon application of electric fields, charges accumulate, which serve as an electrostatic handle for droplet manipulation. In this manner, the degree of droplet control can depend on the number and spacing of individual electrodes within a digital array of electrodes (e.g. coplanar with electrodes 114), as well as voltage across the channel. This droplet manipulation enables automated and precise control over droplet movement, mixing, splitting, and merging, which can be especially valuable when handling small sample volumes or expensive reagents. Additionally, the bottom plate 104 can further include a dielectric insulation layer 116.

[0072] As previously described, pharmacotyping is the process of analyzing a patient’s likely response to drugs based on their biological profile, often using in vitro testing of drug efficacy on patient-derived cells. Often, this takes the form of measuring drug sensitivity of the cells of the patient. Because of its ability' to precisely manipulate small volumes of fluids, automate experiments, and integrate multiple assay types on a single platform. DMF is a valuable tool in pharmacotyping. In the present disclosure, the DMF platform is not used to treat cells with drugs, but rather to measure molecular features, such as phosphoprotein expression, that are associated w ith drug response. These features are used to predict drug sensitivity based on a trained model developed from a reference database of samples with known drug response. Moreover, because the DMF assay in this invention measures molecular features without requiring extended incubation periods associated with drug treatments, it enables significantly faster turnaround times compared to conventional drug sensitivity assays. Using DMF in pharmacotyping can provide many benefits, such as in the development of precision medicine by allowing for rapid molecular analysis-based prediction of drug response, identifying the most effective treatment based on a patient's specific biology without the need for lengthy cell viability assays involving direct drug treatment. Another benefit can be that by miniaturizing and automating the screening. DMF can reduce reagent consumption, making high-cost molecular assays more feasible for screening. It can also enable parallelized assays for multiple targets predictive of drug sensitivity on a single platform.

[0073] The DMF platform 100 can have at least one channel 122, which serves as a pathway for controlled droplet movement, transport, and interaction. In some examples, the at least one channel 122 can be a virtual channel, a structured microchannel, a capillary channel, or a combination of these channel types can be used together across different locations of the DMF platform. In a virtual channel, which has no physical walls, droplets 110 are guided along predefined electrode paths on a flat surface. In a physical microchannel, the channel is integrated with the DMF platform 100, allowing for passive fluid flow, reducing reliance on continuous electrode actuation. In a capillary channel, passive droplet transport via capillary' forces is utilized. Inside the at least one channel 122 can be a microwell 106. Micro wells 106 are small, structured wells incorporated into the DMF platform 100 to facilitate the controlled handling of cells. Micro wells 106 allow for stable positioning of droplets 1 10 within the DMF channel 122, confinement of cells or particles for extended culture, settling, and / or reaction times, and controlled diffusion and reaction kinetics in assays.

[0074] The DMF platform 100 can also include at least one cell culture site 120 that is oriented within the microwell 106 in the DMF channel 122. Cell culture sites 120 can refer to specific regions or chambers on the DMF platform 100 where cells are maintained, grown, and manipulated. These sites 120 are designed to provide a controlled microenvironment suitable for sustaining living cells. Cells can be encapsulated within individual droplets 110 that act as a microreactor, providing isolation and precise control over the microenvironment. Examples of controlling the microenvironment in cell culture sites 120 can include temperature regulation to maintain physiological conditions and humidity regulation to ensure that droplets 110 do not evaporate during long-term culture.

[0075] In some examples, the at least one cell culture site 120 can include a binding surface 124 that can be used to bind with at least one of non-adherent cells and extracellular vesicles released by the non-adherent cells. Non-adherent cells are suspension cells that do not naturally attach to surfaces, unlike adherent cells that require a substrate for growth. In DMF, there can be a few important applications of non-adherent cells. One example is single-cell analysis, which can include isolation and study of immune cells and hematopoietic progenitor cells, which naturally grow in suspension. Another example is high-throughput drug screening, which can include testing drug responses on leukemia, lymphoma, and hematopoietic stem cells, to name a few, in controlled microenvironments. A further example is synthetic biology and cell engineering, such as gene editing in floating cells within droplets. As for extracellular vesicles, these are small, membrane-bound particles that are released by cells into the extracellular environment. They can play a key role in intercellular communication by transferring proteins, lipids, RNA, and other molecules between cells. Extracellular vesicles can also influence various biological processes, including immune responses, tissue repair, and disease progression. Examples of extracellular vesicles can include exosomes, microvesicles (ectosomes), apoptotic bodies, or oncosomes.

[0076] In some examples, the at least one cell culture site 120 can include a surface modification 118. Surface modification 118 in DMF is used to augment and improve the process in many ways. In one example, modification 118 can be used to improve droplet mobility by reducing contact angle hysteresis to ensure smooth and precise droplet movement. Another way surface modification 118 can be used is to prevent biofouling by minimizing nonspecific adsorption of biomolecules, such as proteins and cells, that can interfere with assays. An additional way modification 118 can be used is to create and maintain stable hydrophobic surfaces with strategically positioned hydrophilic sites, enabling controlled formation of microwells 106 by leveraging surface energy differences during droplet movement.

[0077] The surface modification 118 in the present disclosure can be chosen to avoid adverse effects on native metabolic pathways and cell signaling. In other words, the surface modification 118 selected can provide a desired hydrophobicity for droplet actuation, yet remain inert so that chemicals are not leached or otherwise interfere with native cellular processes. Selection of a surface modification 118 can prioritize biocompatibility. This can include selecting inert materials, such as coatings that are known for their inertness and have a long history' of use in biological applications, including but not limited to polydimethylsiloxane (PDMS), silicon dioxide (glass), silicon nitride, parylene, and inert metal oxides such as indium tin oxide (ITO). This can also include using anti-fouling coatings, e.g. incorporating biocompatible polymers or zwitterionic coatings that reduce nonspecific protein adsorption and prevent unintended activation of signaling pathways. When selecting a surface modification 118 chemical and physical stability can also be considered. This can include verifying that the chosen modification 118 does not release any components that could alter metabolic pathways or disrupt cell signaling. This can also include verifying that the modification 118 is stable under the DMF’s operating conditions, such as temperature. pH. and repeated electro wetting cycles. In some examples, the surface modification 118 of the present disclosure can include one or more of charged polymers (Poly-L-lysine, Poly-D-lysine), extracellular matrix adhesion proteins (Fibronectin, Laminin), or it can rely on inherent surface properties that enable attachment through physisorption without additional modification (e.g., uncoated ITO surface).

[0078] In some examples, the surface modification 118, such as those previously listed, or inherent surface properties such as those exhibited by uncoated ITO surfaces, can be used for binding with non-adherent cells. As previously described, non-adherent cells are suspension cells that do not naturally attach to surfaces, unlike adherent cells that require a substrate for growth. Also previously described is the importance and practical applications of non-adherent cells in DMF. However, because non-adherent cells do not naturally attach to surfaces, they can present unique challenges. One example can include uncontrolled cell movement, where the suspension cells remain freely floating in droplets, making it harder to localize them for imaging or biochemical reactions. Another example can include settling and sedimentation, where cells may settle at the bottom of the droplet over time, affecting uniformity in assays. A further example can include evaporation and osmolarity control, where droplets can evaporate, leading to osmotic stress on cells. As a result, surface modification 118 or inherent surface properties can be used to promote cell adhesion and growth and to enhance cell attachment for bioassays and tissue engineering.

[0079] In one example, the pharmacotyping system can further include the non-adherent cells suspended in a carrier fluid and oriented in the at least one cell culture site 120. Additionally, at least some of the non-adherent cells can be bound to the binding surface 124. As previously mentioned, the non-adherent cells can be suspension cells. Examples of suspension cells can include immune cells such as lymphocytes, cancer cell lines such as Jurkat cells and HL-60 cells, and hybridoma cells that are used to produce monoclonal antibodies in biopharmaceuticals. In some examples, the carrier fluid can also contain extracellular vesicles released by the non-adherent cells. In further examples, at least one of the non-adherent cells and the extracellular vesicles can be bound to the binding surface 124 via physisorption via buffer sedimentation. Physisorption, also known as physical adsorption, is a process where molecules adhere to a surface through primary van der Walls forces. In such cases, no surface modification or coatings are required to provide binding sites. Rather, cells and vesicles are mechanically adhered to the surface. In buffer sedimentation, buffers maintain a stable pH and ionic strength, which can stabilize both the substrate and particles in suspension. Under gravitational or centrifugal forces, particles in the buffer can settle toward a substrate. This increases local concentration of particles near a surface. Acting together, buffer sedimentation helps drive molecules or particles toward a surface, and once close enough, the weak forces of physisorption take over, enabling the adsorption of molecules onto the surface.

[0080] Once sample materials are introduced to a cell culture site, the sample can be interrogated using a suitable assay tool. In some examples of the pharmacoty ping system, the assay access mechanism can allow interrogation access of the at least one cell culture site 120. Examples of the assay access mechanism can include at least one of a transparent window (e.g., transparent ITO-coated glass top plate allowing optical interrogation of fluorescently labeled cells), a sample port (e.g., for introducing reagents for on-chip immunocytochemistry procedures such as fixation, permeabilization. staining, and washing), optical fiber interfaces, fluidic channels, removable covers or open-access designs, electrical contact pads, or spectroscopic windows, among others. The assay access mechanism can allow an assay tool to be oriented to interrogate the cell culture site, which can be further included in the pharmacotyping system. Assay interrogation refers to the process used to analyze and measure biological or chemical reactions or sample properties occurring within droplets 110 on the DMF platform 100. This can include detecting signals from reactions, assessing assay performance, and optimizing conditions for accuracy and reproducibility. In the present disclosure, this can especially include analyzing and measuring a patient's likely response to drugs based on the biological profile of patient-derived cells by measuring molecular biomarkers (e.g.. phosphoproteins) predictive of drug sensitivity, without directly treating cells.

[0081] Many techniques can be used for assay interrogation, such as, but not limited to, fluorescence detection, colorimetric detection, radiometric detection, mass spectrometry, nanoparticle tracking analysis (NTA), chemiluminescence detection, electrochemical detection, bioluminescence detection, absorbance-based detection, surface plasmon resonance (SPR). and Raman spectroscopy. Examples of the assay tools that can be used for fluorescence detection can include one or more of fluorescence microscopes, spectrofluorometers, microplate readers, laser-induced fluorescence (LIF) systems, CCD / CMOS camera-based detectors, optical fiber-based probes, microarray scanners, tissue fluorescence scanners, and flow cytometers. Examples of the assay tools that can be used for colorimetric detection can include one or more of UV- visible spectrophotometers, microplate readers, standalone colorimeters, and lateral flow assays. Examples of the assay tools that can be used for radiometric detection can include one or more of liquid scintillation counters, gamma counters, autoradiography systems, and scintillation proximity assays (SPA). A mass spectrometer can be used to perform mass spectrometry. Examples of the assay tools that can be used for NTA can include one or more of laser illumination modules, high-sensitivity cameras and microscopy systems, particle tracking software, and other commercial NTA instruments. Examples of assay tools used for chemiluminescence detection include chemiluminescence imaging systems and microplate readers. Examples of assay tools used for electrochemical detection include potentiostats, amperometric biosensors, and voltammetric analyzers. Examples of assay tools used for bioluminescence detection include luminescence imaging systems and microplate readers. Examples of assay tools used for absorbance-based detection include UV-visible spectrophotometers and microplate readers. Examples of assay tools used for SPR include SPR biosensors and instruments. Examples of assay tools used for Raman spectroscopy include Raman microscopes and spectrometers.

[0082] In one example, the assay target or biological phenomenon measured can be phosphorylation. Phosphorylation is a post-translational modification (PTM) where a phosphate group is added to proteins (typically on serine, threonine, or tyrosine residues) by kinases. Phosphorylation regulates key cellular processes such as signal transduction, metabolism, and cell cycle control. In pharmacotyping, phosphorylation status is an important biomarker for assessing drug efficacy, especially for kinase inhibitors and signaling pathway modulators. Examples of the role of phosphorylation in pharmacotyping assays can include drug mechanism of action studies, biomarker-based drug sensitivity7testing, and resistance mechanism identification. Phosphorylation can be detected using multiple assay interrogation techniques, including western blotting, ELISAs, mass spectrometry -based phosphoproteomics, in vitro kinase activity assays, protein microarrays, as well as fluorescence-based assays such as immunofluorescence using phospho-specific antibodies, fluorescence resonance energy transfer (FRET)-based biosensors, and flow cytometry (phospho-flow).

[0083] A method of pharmacotyping 200 can include introducing a sample fluid into a pharmacotyping system 210. In some examples, the sample fluid can include at least one of non-adherent cells and extracellular vesicles released from the non-adherent cells. The pharmacotyping system can include a digital microfluidics (DMF) platform 100 and an assay tool. The DMF platform 100 can have at least one channel 122, which serves as a pathway for controlled droplet movement, transport, and interaction. In some examples, the at least one channel 122 can be a virtual channel, a structured microchannel, or a capillary channel. Inside the at least one channel 122 can be a microwell 106. As previously described, microwells 106 are small, structured wells incorporated into the DMF platform 100 to facilitate the controlled handling of cells. Microwells 106 allow for stable positioning of droplets 110 within the DMF channel 122, confinement of cells or particles for extended culture or reaction times, and controlled diffusion and reaction kinetics in assays. The DMF platform 100 can also include at least one cell culture site 120 that is within the microwell 106 in the DMF channel 122. These sites 120 are designed to provide a controlled microenvironment suitable for sustaining living cells. Additionally, these sites 120 are where the sample fluid is interrogated by the assay tool. In some examples, the at least one cell culture site 120 can include a binding surface 124 that can be used to bind with at least one of non-adherent cells and extracellular vesicles released by the non-adherent cells.

[0084] In some examples, the at least one cell culture site 120 of the DMF platform 100 can include a surface modification 118 used to augment and improve the process. As previously disclosed, non-adherent cells are suspension cells that do not naturally attach to surfaces. Because of this, non-adherent cells can present unique challenges. As a result, surface modification 118 can be used to promote cell adhesion and grow th and to enhance cell attachment for bioassays and tissue engineering. Because the sample fluid can include non-adherent cells, in addition to being chosen to avoid adverse effects on native metabolic pathways and cell signaling, the surface modification 118 can be used for binding with at least one of the non-adherent cells and the extracellular vesicles. Examples of the surface modification 118 used can include one or more of charged polymers (Poly-L-lysine, Poly-D-lysine), and extracellular matrix adhesion proteins (Fibronectin. Laminin). Additionally, in some examples, binding of non-adherent cells and extracellular vesicles can occur via physisorption to an unmodified hydrophilic surface (e.g., uncoated ITO surface), without requiring a surface modification.

[0085] As previously mentioned, the non-adherent cells included in the sample fluid can be suspension cells. In some examples, the sample fluid can also contain extracellular vesicles released by the non-adherent cells. Examples of extracellular vesicles can include exosomes, microvesicles (ectosomes), apoptotic bodies, and oncosomes. In other examples, the sample fluid can include blood-derived extracellular vesicles. In further examples, at least one of the non-adherent cells and the extracellular vesicles can be bound to the binding surface 124 by physisorption resulting from buffer sedimentation.

[0086] In some examples of the method 200, the assay tool of the pharmacotyping system can interrogate the sample fluid in the at least one cell culture site 120. As previously mentioned, many techniques can be used for assay interrogation, such as fluorescence detection, colorimetric detection, radiometric detection, mass spectrometry, and nanoparticle tracking analysis (NTA), chemiluminescence detection, electrochemical detection, bioluminescence detection, absorbance-based detection, surface plasmon resonance (SPR), and Raman spectroscopy. Examples of the assay tools that can be used for fluorescence detection can include one or more of fluorescence microscopes, spectrofluorometers, microplate readers, laser-induced fluorescence (LIF) systems, CCD / CMOS camera-based detectors, optical fiber-based probes, microarray scanners, tissue fluorescence scanners, and flow cytometers. Examples of the assay tools that can be used for colorimetric detection can include one or more of UV-visible spectrophotometers, microplate readers, standalone colorimeters, and lateral flow assays. Examples of the assay tools that can be used for radiometric detection can include one or more of liquid scintillation counters, gamma counters, autoradiography systems, and scintillation proximity assays (SPA). A mass spectrometer can be used to perform mass spectrometry. Examples of the assay tools that can be used for NTA can include one or more of laser illumination modules, high-sensitivity cameras and microscopy systems, particle tracking software, and other commercial NTA instruments. Examples of assay tools used for chemiluminescence detection include chemiluminescence imaging systems and microplate readers. Examples of assay tools used for electrochemical detection include potentiostats, amperometric biosensors, and voltammetric analyzers. Examples of assay tools used for bioluminescence detection include luminescence imaging systems and microplate readers. Examples of assay tools used for absorbance-based detection include UV-visible spectrophotometers and microplate readers. Examples of assay tools used for SPR include SPR biosensors and instruments. Examples of assay tools used for Raman spectroscopy include Raman microscopes and spectrometers. In one example, the assay can measure phosphorylation. Phosphorylation can be detected using multiple assay interrogation techniques, including western blotting, ELISAs, mass spectrometrybased phosphoproteomics, in vitro kinase activity assays, protein microarrays, as well as fluorescence-based assays such as immunofluorescence using phospho-specific antibodies. FRET-based biosensors, and flow cytometry (phospho-flow).

[0087] The method of pharmacotyping 200 can also include testing the sample fluid in the at least one cell culture site 220 using the assay to produce an assay output. As previously disclosed, assay interrogation refers to the process used to analyze and measure biological or chemical reactions occurring within droplets 110 on the DMF platform 100. This can include detecting signals from reactions, assessing assay performance, and optimizing conditions for accuracy and reproducibility. In the present example, testing the sample fluid can include analyzing and measuring a patient's likely response to drugs based on their biological profile by measuring molecular biomarkers predictive of drug sensitivity at single-cell resolution, such as phosphoprotein expression, without directly treating the patient-derived cells with drugs. This can include using at least one of the assay interrogation techniques previously described. The testing can result in different assay outputs. Examples of assay outputs can include single-cell protein expression levels, including phosphorylated proteins, and morphological features from assays such as immunocytochemistry with fluorescence imaging or intracellular flow cytometry (phospho-flow). Unlike bulk assays, single-cell analysis enables identification of heterogeneous subpopulations of cells, providing clinically relevant information that may otherwise be masked in bulk-level measurements. Moreover, compared to conventional phospho-flow, western blotting, or ELISA-based assays, the DMF platform is automated and more easily standardized, while providing single-cell precision. In some examples, the response to multiple drugs can be evaluated in parallel, in series, or both, in an array of cell cultures sites within one or more DMF channels. This allows for faster and more efficient drug sensitivity predictions enabled by the DMF platform, as responses can be predicted rapidly through a biomarker assay without requiring extended incubation periods associated with conventional drug treatment-based assays.

[0088] In some examples, the assay output can further include a plurality of image-based features. In some cases, the plurality of features can include two to twenty parameters, in other cases five to fifteen parameters. Non-limiting examples of quantitative single-cell image features and corresponding descriptions are as follows:

[0089] Integratedlntensity: The sum of the pixel intensities within an object.

[0090] Meanintensity: The average pixel intensity within an object.

[0091] Stdintensity: The standard deviation of the pixel intensities within an object.

[0092] Maxintensity: The maximal pixel intensity within an object.

[0093] Minlntensity: The minimal pixel intensity within an object.

[0094] Integratedlntensity Edge: The sum of the edge pixel intensities of an object.

[0095] Mean In tensity Edge: The average edge pixel intensity of an object.

[0096] StdlntensityEdge: The standard deviation of the edge pixel intensities of an object.

[0097] MaxIntensityEdge: The maximal edge pixel intensity of an object.

[0098] MinlntensityEdge: The minimal edge pixel intensity' of an object.

[0099] MassDisplacement: The difference between the centers of gravity in the graylevel representation of the object and the binary representation of the object.

[0100] LowerQuartilelntensity: The intensity value of the pixel for which 25% of the pixels in the object have lower values.

[0101] Medianintensity: The median intensity value within the object.

[0102] MADIntensity : The median absolute deviation (MAD) value of the intensities within the object.

[0103] UpperQuartilelntensity: The intensity value of the pixel for which 75% of the pixels in the object have lower values.

[0104] FracAtD: Fraction of total stain in an object at a given radius.

[0105] MeanFrac: Mean fractional intensity’ at a given radius; calculated as fraction of total intensity normalized by fraction of pixels at a given radius.

[0106] RadialCV: Coefficient of variation of intensity within a ring, calculated across 8 slices.

[0107] Zemike: The Zemike features characterize the distribution of intensity across the object. For instance, Zemike 1,1 has a high value if the intensity is low on one side of the object and high on the other.

[0108] Area: (2D only) The number of pixels in the region.

[0109] Volume: (3D only) The number of voxels in the region.

[0110] Perimeter: (2D only) The total number of pixels around the boundary of each region in the image. FormFactor: (2D only) Calculated as 4*7t* Area / Perimeter2Equals 1 for a perfectly circular object.

[0111] Solidity7: The proportion of the pixels in the convex hull that are also in the object.

[0112] Extent: The proportion of the pixels (2D) or voxels (3D) in the bounding box that are also in the region. Computed as the area / volume of the object divided by the area / volume of the bounding box.

[0113] EulerNumber: The number of objects in the region minus the number of holes in those objects, assuming 8 -connectivity.

[0114] BoundingBoxMinimum / Vlaximum_X / Y / Z: The minimum / maximum x-, y-, and (for 3D objects) z- coordinates of the object.

[0115] BoundingBoxArea: (2D only) The area of a box containing the object.

[0116] BoundingBoxVolume: (3D only) The volume of a box containing the object.

[0117] Eccentricity: (2D only) The eccentricity of the ellipse that has the same second- moments as the region. The eccentricity is the ration of the distance between the foci of the ellipse and its major axis length.

[0118] MajorAxisLength,: The length (in pixels) of the major axis of the ellipse that has the same normalized second central moments as the region.

[0119] MinorAxisLength: The length (in pixels) of the minor axis of the ellipse that has the same normalized second central moments as the region.

[0120] EquivalentDiameter: The diameter of a circle or sphere with the same area as the object.

[0121] Orientation: (2D only) The angle (in degrees) between the x-axis and the major axis of the ellipse that has the same second-moments as the region.

[0122] Compactness: (2D only) The mean squared distance of the object’s pixels from the centroid divided by the area.

[0123] MaximumRadius: (2D only) The maximum distance of any pixel in the object to the closes pixel outside of the object.

[0124] MedianRadius: (2D only) The median distance of any pixel in the object to the closest pixel outside of the object.

[0125] MeanRadius: (2D only) The mean distance of any pixel in the object to the closest pixel outside of the object.

[0126] MinFeretDiameter, MaxFeretDiameter: (2D only) The Feret diameter is the distance between two parallel lines tangent on either side of the object. The minimum and maximum Feret diameters are the smallest and largest possible diameters, rotating the calipers along all possible angles.

[0127] Zemike shape features: (2D only) These metrics of shape describe a binary object (or more precisely, a patch with background and an object in the center) in a basis of Zemike polynomials, using the coefficients as features.

[0128] Spatial Moment features: (2D only) A series of weighted averages representing the shape, size, rotation, and location of the object.

[0129] Central Moment features: (2D only) Similar to spatial moments, but normalized to the object’s centroid. These are therefore not influenced by an object’s size (or location).

[0130] Normalized Moment features: (2D only) Similar to central moments, but further normalized to be scale invariant. These moments are therefore not impacted by an object’s size (or location).

[0131] Hu Moment features: (2D only) Hu’s set of image moment features. These are not altered by the object’s location, size, or rotation. This means they primarily describe the shape of the object.

[0132] Inertia Tensor features: (2D only) A representation of rotation inertia of the object relative to its center.

[0133] Inertia Tensor Eigenvalues features: (2D only) Values describing the movement of the Inertia Tensor array.

[0134] NumberOfNeighbors: Number of neighbor objects.

[0135] PercentTouching: Percent of the object’s boundary pixels that touch neighbors, after the objects have been expanded to the specified distance.

[0136] FirstClosestObj ectNumber: The index of the closest object.

[0137] FirstClosestDistance: The distance to the closes object (in units of pixels).

[0138] SecondClosestObj ectNumber: The distance to the second closest object (in units of pixels).

[0139] SecondClosestDistance: The distance to the second closest object (in units of pixels).

[0140] AngleBetweenNeighbors: The angle formed with the object center as the vertex and the first and second closest object centers along the vectors.

[0141] In some examples, testing can further include inputting the assay output into a drug prediction model that is configured to predict whether a sample is sensitive or resistant to a certain drug, and / or estimate the proportion of sensitive and resistant cells within the sample. The drug prediction model can have previously undergone systematic training, validation, and testing to generate a robust predictive platform for precision drug sensitivity assessment. This can be accomplished by pairing single-cell features measured by DMF imaging assays with corresponding drug sensitivity measurements (e.g., lethal concentration 50, LC50) obtained from conventional in vitro drug-treatment assays performed separately. This pairing generates a dataset linking single-cell features to known drug sensitivity outcomes, which serves as labeled input data for training, validation, and testing of the predictive model. The drug prediction model can be created using a machine learning model, including supervised methods such as partial least squares regression, logistic regression, random forest algorithms, gradient boosting models (such as extreme gradient boosting or XGBoost), or deep learning neural networks. The trained drug prediction model can then estimate the proportion of sensitive and resistant cells within a sample for the targeted therapy medication. This enables rapid drug sensitivity7assessment directly from a DMF assay, circumventing the timeconsuming procedures required for conventional treatment-based testing, as predictions rely solely on single-cell molecular biomarkers and a previously established database. For example, cells from patients with T-cell acute lymphoblastic leukemia (T-ALL) can be analyzed using this approach.

[0142] The method of pharmacotyping 200 can further include providing a recommended treatment plan 230 based on the analysis of the sample fluid using the assay output. This can include categorizing a patient sample’s predicted drug sensitivity level based on correlation between measured molecular biomarkers (e.g., phosphoprotein expression) and known drug sensitivity thresholds or outcomes. Examples of such categorization include classifying a patient sample as "sensitive," "intermediate," or "resistant" based on biomarker expression levels compared to established cutoff values. Alternatively, categorization can involve machine learning algorithms that correlate single-cell features (e.g., biomarker intensities, cell morphology) with previously established clinical response data, assigning confidence scores or probability estimates to each predicted treatment outcome. Depending on the assay output and resulting sensitivity categorization, the corresponding treatment plan can be developed. In some examples, the treatment plan can be developed by medical professionals, a computer algorithm, artificial intelligence, clinical decision-support tools, or the like. The recommended treatment plan can include a protocol or plan for treatment with drugs, therapies, or other treatment options. Because the treatment plan can be tailored to a patient’s unique molecular and cellular profile, the therapeutic efficacy can be maximized while adverse effects can be minimized. Factors used to tailor treatment plans can include one or more of biomarker expression distributions at the single-cell level, identification of resistant or sensitive subpopulations, known drug-target interactions, patient clinical history, genetic background, and prior therapeutic responses.

[0143] In some examples, the recommended treatment plan can be for the treatment of cancer. Examples of cancer in which the recommended treatment plan can be for include, but are not limited to, bowel cancer, bladder cancer, breast cancer, brain tumors, lung cancer, head and neck cancer, prostate cancer, leukemia, lymphoma, cervical cancer, kidney cancer, melanoma, bile duct cancer, bone tumors, endometrial cancer, pancreatic cancer, liver cancer, non-melanoma skin cancer, thyroid cancer, appendix cancer, and myeloma.

[0144] Examples

[0145] Example 1 - Pharmacotyping Workflow

[0146] FIGs 3A-3F is an example of a pharmacotyping workflow. FIG. 3A illustrates a DMF platform 300 with controlled on-chip droplet manipulation, depicting the movement of the droplets, from a sample fluid reservoir 310, through the virtual microwell 320, where cell attachment occurs. FIG. 3B shows the subsequent immunostaining protocol 330 for phosphoprotein detection. This includes cell attachment 370, fixation 360, permeabilization 350. and staining 340.

[0147] The next sequence in this workflow example is fluorescence scanning of the microfluidic top plate, which is depicted in FIG. 3C. Image analysis of the fluorescence scan allows for extraction of single-cell signatures comprising quantitative protein expression and morphological features, depicted in FIG. 3D. Separately, drug sensitivity is measured by treating cells from the same samples in conventional in vitro assays (e.g., well-plate drug treatment assays) to determine sensitivity metrics such as LC50 values, which are then categorized into binary classifications ("resistant" or "sensitive") based on predetermined LC50 thresholds, as depicted in FIG. 3E. The paired dataset of single-cell features (FIG. 3D) and their corresponding binary drug sensitivity labels (FIG. 3E) is used to systematically train, validate, and test a machine learning-based drug sensitivity prediction model (FIG. 3F). This model correlates single-cell phosphoprotein and morphology features to drug sensitivity, generating a robust predictive platform for precision drug sensitivity assessment. Subsequently, new patent samples can be rapidly analyzed using only the DMF assay (completed under 4 hours) and trained predictive model, without requiring additional in vitro drug treatment (days to weeks), which represents a significant time savings.

[0148] Example 2 - Cell Attachment Optimization

[0149] FIG. 4 shows a boxplot of cell retention ratios calculated based on an initial cell seeding amount (around 900 cells). The horizontal line in each box represents the median sample value. Conditions include poly-L-lysine (PLL (100 pg / mL), laminin (12.5, 25, 50 pg / mL), poly-D-lysine (PDL) (12.5, 50. 100 pg / mL), and uncoated (buffer sedimentation). To enumerate the attached cells for each coating condition, the cells were stained with CMDR and analyzed by fluorescence slide scanner. The two top performers were buffer sedimentation and PDL 100 pg / mL.

[0150] Example 3 - Assay Sensitivity Detection

[0151] FIG. 5A shows an example scheme of cell processing steps including fixation, permeabilization, blocking, and staining with several washing steps in between. FIG. 5B shows bar plots depicting the retention ration for CCRF-CEM, HSB-2, and their mixtures (3H7C. 30 / 70%; 5H5C. 50 / 50%; 7H3C. 70 / 30% HSB-2 / CCRF-CEM) after the cell processing steps shown in FIG. 5A.

[0152] FIG. 5C shows a scatter plot of single-cell immunocytochemistry analysis of Meanintensity _pLCK for different cell mixture groups. FIG. 5D shows a simple linear correlation of mean pLCK intensity between phosphoflow and the present system, with R2= 0.9647. Statistical significance was determined using one-way ANOVA. Nonsignificant (ns) results indicate p > 0.05. Asterisks denote level of significance: *, p is less than or equal to 0.05; **, p is less than or equal to 0.01; ***, p is less than or equal to 0.001; ****, p is less than or equal to 0.0001.

[0153] Example 4 - Single Cell Protein Analysis and Drug Sensitivity Test

[0154] LCK and BCL2, shown in FIG. 6A, operate at different cellular locations with distinct functions in cancer cell survival. LCK, a membrane-bound kinase at T-cell receptors, initiates critical signaling cascades when activated. BCL2 resides at the mitochondria, preventing apoptosis by sequestering pro-apoptotic proteins. DASATINIB, a drug used in this example for treating leukemia, targets LCK by blocking its ATP-binding site, disrupting downstream signaling and cellular proliferation. VENETOCLAXVENETOCLAX, another drug used in this example, works by directly binding to BCL2, releasing sequestered pro-apoptotic proteins that trigger mitochondrial outer membrane permeabilization (MOMP). cytochrome C release, and subsequent caspase-mediate cell death. Despite their different cellular targets, both drugs ultimately converge on promoting cancer cell apoptosis through distinct mechanistic pathways.

[0155] FIG. 6B shows representative fluorescence images showing protein expression profiles across T-ALL cell lines. FIG. 6C shows DASATINIB LC50s of seven T-ALL cell lines, where the values are labeled for each cell line after their sample labels. Among the seven T-ALL cell lines tested. HSB-2 and K0PT-K1 demonstrated exceptional sensitivity to DASATINIB, with LC50 values of 0.005 nM and 0.050 nM. respectively. FIG. 6D shows VENETOCLAXVENETOCLAX LC50s of seven T-ALL cell lines, where the values are labeled for each cell line after their sample labels. Among the seven T-ALL cell lines tested, LOUCY demonstrated exceptional sensitivity to VENETOCLAX, with an LC50 value of 560 nM.

[0156] Example 5 - T-ALL Cell Line Protein Phosphorylation Expression Profiles for Prediction of Drug Sensitivity

[0157] FIG. 7 A shows the single-cell mean intensity of pLCK for different T-ALL cell lines after subtraction of respective isotype controls. KOPT-K1 and HSB-2 are the sensitive cell lines, and they have the highest pLCK expression compared to other cell lines. FIG. 7B shows the single-cell mean intensity of pBCL2 for different T-ALL cell lines after subtraction of respective isotype controls. LOUCY is the only sensitive cell line, and it has the highest pBCL2 expression compared to other cell lines. Statistical significance was determined using one-way ANOVA. Non-significant (ns) results indicate p > 0.05. Asterisks denote level of significance: *, p is less than or equal to 0.05; **, p is less than or equal to 0.01; ***, p is less than or equal to 0.001; ****, p is less than or equal to 0.0001.

[0158] FIG. 7C shows the correlation between sample-averaged pLCK intensity and DASATINIB LC50s of T-ALL cell lines, with FIG. 7D showing a receiver-operating characteristic (ROC) curve analysis of single-cell-based mean pLCK intensity for predicting drug sensitivity in T-ALL cell lines. As show n, the area under the curve (AUC) is 0.826, with a sensitivity of 0.737 and a specificity of 0.778. FIG. 7E shows the correlation between sample-averaged pBCL2 intensity and VENETOCLAX LC50s of T- ALL cell lines, with FIG. 7F showing a ROC analysis of single-cell-based mean pBCL2 intensity for predicting drug sensitivity in T-ALL cell lines. As shown, the AUC is 0.900, with a sensitivity of 0.797 and a specificity of 0.869. Thus, pBCL2 intensity can be a useful factor in sensitivity prediction of VENETOCLAX. Example 6 T-ALL PDX Protein Phosphorylation Expression Profiles for the Prediction of Drug Sensitivity

[0159] Primary human T-ALL cells were injected intravenously into immunodeficient mice to establish patient-derived xenograft (PDX) models (FIG. 8A). Peripheral blood was collected and mononuclear cells were isolated by density gradient centrifugation. Leukemic cells (CD45+ / CD7+) were subsequently purified (>90%) by fluorescence- activated cell sorting and sent for DMF-based protein analysis.

[0160] FIG. 8B shows representative fluorescence images of protein expression profiles across T-ALL PDX specimens. Single-cell-based mean intensities of pLCK (FIG. 8C) and pBCL2 (FIG. 8D) for different T-ALL PDX specimens after subtraction of respective isotype controls are shown. All of the DASATINIB sensitive cases had higher pLCK expression compared to resistant cases. All but one of the VENETOCLAX sensitive cases had higher pBCL2 expression compared to resistant cases. Statistical significance was determined using one-way ANOVA. Non-significant (ns) results indicate p > 0.05. Asterisks denote level of significance: *, p is less than or equal to 0.05; **, p is less than or equal to 0.01; ***, p is less than or equal to 0.001; ****, p is less than or equal to 0.0001.

[0161] FIG. 8E shows a ROC curve analysis of single-cell pLCK intensity' data for predicting DASATINIB sensitivity in T-ALL PDX specimens, yielding an AUC of 0.884. FIG. 8F shows a ROC curve analysis of single-cell pBCL2 intensity data for predicting VENETOCLAX sensitivity' in T-ALL PDX specimens, yielding an AUC of 0.750. FIG. 8G shows sample-averaged pLCK intensity' values in resistant versus sensitive T-ALL PDX specimens. Statistical significance was determined by unpaired t- test (**p<0.01). FIG 8H shows sample-averaged pBCL2 intensity values in resistant versus sensitive T-ALL PDX samples. Statistical significance was determined by unpaired t-test (***p<0.001).

[0162] Example 7 - T-ALL Drug Sensitivity Model

[0163] FIG. 9A shows an example of a construction scheme of an XGBoost model. In this example, the input consisted of 36,759 cells labeled as drug-resistant (0) or sensitive (1), along with 288 protein and shape features per cell. Data was split into a training set, which consisted of all cell lines plus 60% of PDX cells, and a test set, which consisted of 40% of PDX cells. Five-fold cross-validation was performed using all cell line data combined with different subsets of the training PDX data (using 36% of training PDX cells to predict the remaining 24% of training PDX cells). The final model was evaluated using the test set.

[0164] FIG. 9B shows five-fold cross-validation metrics for DASATINIB sensitivity prediction. FIG. 9C shows five-fold cross-validation metrics for VENETOCLAX sensitivity prediction. FIG. 9D shows ROC curves showing model performance for DASATINIB and VENETOCLAX. FIG. 9E shows test set performance metrics for DASATINIB and VENETOCLAX. FIGs 9F and 9G show summary plots visualizing cell feature impact and direction of influence on prediction for DASATINIB and VENETOCLAX, respectively.

[0165] FIG. 9H shows a DASATINIB confusion matrix showing 4461 / 4787 (93.2%) resistant cells and 5286 / 5676 (93.1%) sensitive cells correctly predicted, which is illustrated in FIG. 9J. FIG 91 shows a VENETOCLAX confusion matrix showing 4483 / 4682 (95.7%) resistant cells and 4939 / 5781 (85.4%) sensitive cells correctly predicted, which is illustrated in FIG. 9K.

[0166] Example 8 - Example Study

[0167] FIG. 10 shows an example study of fast phosphorylation stimuli response in single cells.

[0168] Example 9 - Pharmacotyping Responses

[0169] FIG. 11A and 1 IB show pharmacotyping responses for T-ALL.

[0170] While the flowcharts presented for this technology7may imply a specific order of execution, the order of execution may differ from what is illustrated. For example, the order of two or more blocks may be rearranged relative to the order shown. Further, two or more blocks shown in succession may be executed in parallel or with partial parallelization. In some configurations, one or more blocks shown in the flow chart may be omitted or skipped. Any number of counters, state variables, warning semaphores, or messages might be added to the logical flow for purposes of enhanced utility7, accounting, performance, measurement, troubleshooting, or for similar reasons.

[0171] Reference was made to the examples illustrated in the drawings and specific language was used herein to describe the same. It will nevertheless be understood that no limitation of the scope of the technology is thereby intended. Alterations and further modifications of the features illustrated herein and additional applications of the examples as illustrated herein are to be considered within the scope of the description.

[0172] Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more examples. In the preceding description, numerous specific details were provided, such as examples of various configurations, to provide a thorough understanding of examples of the described technology. It will be recognized, however, that the technology may be practiced without one or more of the specific details, or with other methods, components, devices, etc. In other instances, well- known structures or operations were not shown or described in detail to avoid obscuring aspects of the technology.

[0173] For example, various features and elements of the above-described invention can be used alone or in various combinations, where elements described in one example can be utilized in other described examples. More specifically, the invention can be generally described by the following clauses:

[0174] Clause 1. A method of pharmacotyping. comprising: introducing a sample fluid into a pharmacotyping system, wherein the sample fluid includes at least one of nonadherent cells and extracellular vesicles released from the non-adherent cells, and wherein the pharmacotyping system comprises: a digital microfluidics (DMF) platform having at least one cell culture site having a binding surface for binding with at least one of the non-adherent cells and the extracellular vesicles; and an assay tool which interrogates the sample fluid in the at least one cell culture site; testing the sample fluid in the at least one cell culture site using the assay tool to produce an assay output; and providing a recommended treatment plan based on the analysis of the sample fluid using the assay output.

[0175] Clause 2. The method of clause 1 , wherein the at least one cell culture site is within a microwell in a DMF channel, wherein there is at least one DMF channel in the DMF platform.

[0176] Clause 3. The method of clause 1, wherein non-adherent cells are suspension cells and the fluid sample further includes extracellular vesicles released from the non- adherent cells.

[0177] Clause 4. The method of clause 1, wherein extracellular vesicles include one or more of exosomes, micro vesicles (ectosomes), apoptotic bodies, or oncosomes.

[0178] Clause 5. The method of clause 1, wherein at least one of the non-adherent cells and the extracellular vesicles are bound to the at least one cell culture site using buffer sedimentation.

[0179] Clause 6. The method of clause 1, wherein the at least one cell culture site includes a surface modification configured to improve binding with at least one of the non-adherent cells and the extracellular vesicles. Clause 7. The method of clause 6, wherein the surface modification is chosen to avoid adverse effects on native metabolic pathways and cell signaling.

[0180] Clause 8. The method of clause 6, wherein the surface modifications include one or more of charged polymers (Poly-L-lysine, Poly-D-lysine), extracellular matrix adhesion proteins (Fibronectin, Laminin), or an unmodified surface allowing binding via physisorption (uncoated ITO surface).

[0181] Clause 9. The method of clause 1, wherein the testing includes one or more of fluorescence detection, colorimetric detection, radiometric detection, mass spectrometry, nanoparticle tracking analysis (NT A), chemiluminescence detection, electrochemical detection, bioluminescence detection, absorbance-based detection, surface plasmon resonance (SPR). Raman spectroscopy, and flow cytometry.

[0182] Clause 10. The method of clause 9, wherein the assay target or biological phenomenon measured is phosphory lation.

[0183] Clause 11. The method of clause 1, wherein the assay output is in the form of data generated by one or more assays, including western blot, enzyme-linked immunosorbent assay (ELISA), intracellular flow cytometry, immunocytochemistry, mass spectrometry, kinase activity assays, or fluorescence scanning, wherein the data includes quantitative protein expression levels, phosphory lation levels, morphological features, single-cell protein expression profdes, single-cell phosphory lation status, single-cell morphological metrics, or combinations thereof.

[0184] Clause 1 . The method of clause 1 1 , wherein the assay output further includes a plurality of assay parameters including two or more of Integratedlntensity, Meanintensity, Stdintensity, Maxintensity, Minlntensity, IntegratedlntensityEdge, MeanlntensityEdge, StdlntensityEdge, MaxIntensityEdge, Minlntensit Edge. MassDisplacement, LowerQuartilelntensity, Medianintensity, MADIntensity, UpperQuartilelntensity, FracAtD, MeanFrac, RadialCV, Zemike, Area, Volume, Perimeter, FormFactor, Solidity7, Extent, EulerNumber, BoundingBoxMinimunVMaximum_X / Y / Z, BoundingBoxArea. BoundingBoxVolume. Eccentricity. MajorAxisLength, MinorAxisLength, EquivalentDiameter, Orientation, Compactness, MaximumRadius, MedianRadius, MeanRadius, MinFeretDiameter, MaxFeretDiameter, Zemike shape features, Spatial Moment features, Central Moment features, Normalized Moment features, Hu Moment features, Inertia Tensor features. Inertia Tensor Eigenvalues features, NumberOfNeighbors, PercentTouching. FirstClosestObj ectNumber, FirstClosestDistance, SecondClosestObj ectNumber, SecondClosestDistance, and AngleBetweenNeighbors.

[0185] Clause 13. The method of clause 1, wherein testing includes predicting sensitivity to multiple drugs in parallel, series, or both in an array of cell culture sites within the at least one DMF channel, based on single-cell molecular biomarkers without requiring direct drug treatment.

[0186] Clause 14. The method of clause 1, wherein the sample fluid comprises blood- derived extracellular vesicles.

[0187] Clause 15. The method of clause 12, wherein testing further comprises inputting the assay output into a drug prediction model that is configured to predict whether a sample is sensitive or resistant to a certain drug, or estimate the proportion of sensitive and resistant cells within the sample.

[0188] Clause 16. The method of clause 15, wherein the providing the recommended treatment plan includes categorizing a patient sample's predicted drug sensitivity level based on correlation between measured molecular biomarkers and known drug sensitivity thresholds or outcomes.

[0189] Clause 17. The method of clause 1, wherein the recommended treatment plan is for the treatment of cancer.

[0190] Clause 18. The method of clause 17, wherein the cancer is leukemia.

[0191] Clause 19. A pharmacotyping system, comprising: a digital microfluidics (DMF) platform having at least one cell culture site w ith a binding surface for binding with at least one of non-adherent cells and extracellular vesicles released by the nonadherent cells; and an assay access mechanism which allows interrogation access to the at least one cell culture site.

[0192] Clause 20. The system of clause 19, wherein the DMF platform includes at least one DMF channel and wherein the at least one cell culture site is oriented within a microwell in the at least one DMF channel.

[0193] Clause 21. The system of clause 19, further comprising the non-adherent cells suspended in a carrier fluid and oriented in the at least one cell culture site, wherein at least some of the non-adherent cells are bound to the binding surface.

[0194] Clause 22. The system of clause 21, wherein the non-adherent cells are suspension cells and the carrier fluid further contains extracellular vesicles released by the non-adherent cells. Clause 23. The system of clause 22, wherein extracellular vesicles include one or more of exosomes, microvesicles (ectosomes), apoptotic bodies, or oncosomes.

[0195] Clause 24. The system of clause 19, wherein at least one of the non-adherent cells and the extracellular vesicles are bound to the binding surface via physisorption resulting from buffer sedimentation.

[0196] Clause 25. The system of clause 19, wherein the binding surface includes a surface modification configured to chemically or physically bind with at least one of non-adherent cells and extracellular vesicles released by the non-adherent cells.

[0197] Clause 26. The system of clause 25, wherein the surface modification is chosen to avoid adverse effects on native metabolic pathways and cell signaling, including selection based on inertness of materials, biocompatibility, minimal nonspecific adsorption, chemical and physical stability under assay conditions, and absence of unintended biological stimulation or interference.

[0198] Clause 27. The system of clause 25, wherein the surface modifications include one or more of charged polymer coatings (Poly-L-lysine. Poly -D-ly sine, poly ornithine); extracellular matrix protein coatings (Fibronectin. Laminin. Collagen, Gelatin, Matrigel); anti-fouling and inert coatings (polyethylene glycol (PEG)-based coatings, zwitterionic coatings, polyvinyl alcohol (PVA), poly dopamine); or by binding via physisorption to an unmodified hydrophilic surface (uncoated indium tin oxide (ITO) surface, silicon dioxide, or glass).

[0199] Clause 28. The system of clause 19, wherein the assay access mechanism is one or more of a transparent window, a sample port, optical fiber interfaces, fluidic channels, removable covers or open-access designs, electrical contact pads, or spectroscopic windows.

[0200] Clause 29. The system of clause 19, further comprising an assay tool oriented to interrogate the at least one cell culture site through the assay access mechanism.

[0201] Clause 30. The system of clause 29, wherein the assay tool includes one or more of fluorescence microscopes, spectrofluorometers, microplate readers, laser-induced fluorescence (LIF) systems, CCD / CMOS camera-based detectors, optical fiber-based probes, microarray scanners, tissue fluorescence scanners, flow cytometers, western blotting with phospho-specific antibodies, enzyme-linked immunosorbent assays (ELIS As), mass spectrometry' -based phosphoproteomics, in vitro kinase activity' assays, protein microarrays, FRET-based biosensors, UV -visible spectrophotometers, standalone colorimeters, lateral flow assays, liquid scintillation counters, gamma counters, autoradiography systems, scintillation proximity assays (SPA), chemiluminescence imaging systems, a mass spectrometer, laser illumination modules, high-sensitivity’ cameras and microscopy systems, particle tracking software, potentiostats, amperometric biosensors, voltammetric analyzers, luminescence imaging systems, surface plasmon resonance (SPR) biosensors, Raman microscopes, Raman spectrometers, and commercial NTA systems.

[0202] Clause 31. The system of clause 29, wherein the assay tool is configured to measure phosphorylation using one or more of western blotting with phospho-specific antibodies, enzyme-linked immunosorbent assays (ELISAs), mass spectrometry-based phosphoproteomics, in vitro kinase activity assays, protein microarrays, FRET-based biosensors, immunofluorescence using phospho-specific antibodies, or flow cytometry (phospho-flow).

[0203] Although the subject matter has been described in language specific to structural features and / or operations, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features and operations described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. Numerous modifications and alternative arrangements may be devised without departing from the spirit and scope of the described technology.

Claims

CLAIMSWhat is claimed is:

1. A method of pharmacotyping, comprising: introducing a sample fluid into a pharmacotyping system, wherein the sample fluid includes at least one of non-adherent cells and extracellular vesicles released from the non-adherent cells, and wherein the pharmacotyping system comprises: a digital microfluidics (DMF) platform having at least one cell culture site having a binding surface for binding with at least one of the non- adherent cells and the extracellular vesicles; and an assay tool which interrogates the sample fluid in the at least one cell culture site; testing the sample fluid in the at least one cell culture site using the assay tool to produce an assay output; and providing a recommended treatment plan based on the analysis of the sample fluid using the assay output.

2. The method of claim 1, wherein the at least one cell culture site is within a microwell in a DMF channel, wherein there is at least one DMF channel in the DMF platform.

3. The method of claim 1, wherein non-adherent cells are suspension cells and the fluid sample further includes extracellular vesicles released from the non-adherent cells.

4. The method of claim 1, wherein extracellular vesicles include one or more of exosomes, microvesicles (ectosomes), apoptotic bodies, or oncosomes.

5. The method of claim 1, wherein at least one of the non-adherent cells and the extracellular vesicles are bound to the at least one cell culture site using buffer sedimentation.

6. The method of claim 1, wherein the at least one cell culture site includes a surface modification configured to improve binding with at least one of the nonadherent cells and the extracellular vesicles.

7. The method of claim 6, wherein the surface modification is chosen to avoid adverse effects on native metabolic pathways and cell signaling, including avoiding unintended activation of immune cells or other signaling pathways.

8. The method of claim 6, wherein the surface modifications include one or more of charged polymers (Poly-L-lysine, Poly-D-lysine), extracellular matrix adhesion proteins (Fibronectin, Laminin), or an unmodified surface enabling binding via physisorption (uncoated ITO surface).

9. The method of claim 1, wherein the testing includes one or more of fluorescence detection, colorimetric detection, radiometric detection, mass spectrometry, nanoparticle tracking analysis (NTA) , chemiluminescence detection, electrochemical detection, bioluminescence detection, absorbance-based detection, surface plasmon resonance (SPR), Raman spectroscopy, and flow cytometry.

10. The method of claim 9, wherein the assay output measured is phosphorylation.

11. The method of claim 1 , wherein the assay output is in the form of data generated by one or more assays, including western blot, enzyme-linked immunosorbent assay (ELISA), intracellular flow cytometry, immunocytochemistry, mass spectrometry, kinase activity assays, and fluorescence scanning, wherein the data includes quantitative protein expression levels, phosphorylation levels, morphological features, single-cell protein expression profiles, single-cell phosphorylation status, single-cell morphological metrics, or combinations thereof.

12. The method of claim 11, wherein the assay output further includes a plurality of assay parameters including two or more of Integratedlntensity,Meanintensity, Stdintensity, Maxintensity, Minlntensity. IntegratedlntensityEdge, Meanintensity Edge, Stdintensity Edge, Maxintensity Edge, Minlntensity Edge, MassDisplacement, LowerQuartilelntensity, Medianintensity, MADIntensity, UpperQuartilelntensity. Loaction_MaxIntensity_X, Location MaxInlensity Y. FracAtD, MeanFrac, RadialCV, Zemike, Area, Volume, Perimeter, FormFactor, Solidity', Extent, EulerNumber, BoundmgBoxMmimum / Maximum_X / Y / Z. BoundingBoxArea, BoundingBoxVolume, Eccentricity, MajorAxisLength. MinorAxisLength, EquivalentDiameter, Orientation, Compactness, MaximumRadius, MedianRadius, MeanRadius, MinFeretDiameter, MaxFeretDiameter, Zemike shape features, Spatial Moment features, Central Moment features, Normalized Moment features, Hu Moment features. Inertia Tensor features, Inertia Tensor Eigenvalues features, NumberOfNeighbors, PercentTouching, FirstClosestObj ectNumber, FirstClosestDistance, SecondClosestObj ectNumber, SecondClosestDistance, and AngleBetweenNeighbors.

13. The method of claim 1, wherein testing includes predicting sensitivity to multiple drugs in parallel, series, or both in an array of cell culture sites within the at least one DMF channels, based on single-cell molecular biomarkers without requiring direct drug treatment.

14. The method of claim 1, wherein the sample fluid comprises blood-derived extracellular vesicles.

15. The method of claim 12, wherein testing further comprises inputting the assay output into a drug prediction model that is configured to predict at least one of whether a sample is sensitive or resistant to a certain drug, and estimate the proportion of sensitive and resistant cells within the sample.

16. The method of claim 15, wherein the providing the recommended treatment plan includes categorizing a patient sample’s predicted drug sensitivity level based on correlation between measured molecular biomarkers and known drug sensitivity thresholds or outcomes.

17. The method of claim 16, wherein at least one of: the certain drug is VENETOCLAX and the assay output includes pBCL2 intensity, and the certain drug is DASATINIB and the assay output includes pLCK intensity.

18. The method of claim 1, wherein the recommended treatment plan is for the treatment of cancer.

19. The method of claim 18, wherein the cancer is leukemia.

20. A pharmacotyping system, comprising: a digital microfluidics (DMF) platform having at least one cell culture site with a binding surface for binding with at least one of non-adherent cells and extracellular vesicles released by the non-adherent cells; and an assay access mechanism which allows interrogation access to the at least one cell culture site.

21. The system of claim 20, wherein the DMF platform includes at least one DMF channel and wherein the at least one cell culture site is oriented within a microwell in the at least one DMF channel.

22. The system of claim 20, further comprising the non-adherent cells suspended in a carrier fluid and oriented in the at least one cell culture site, wherein at least some of the non-adherent cells are bound to the binding surface.

23. The system of claim 22, wherein the non-adherent cells are suspension cells and the earner fluid further contains extracellular vesicles released by the non-adherent cells.

24. The system of claim 23, wherein extracellular vesicles include one or more of exosomes, microvesicles (ectosomes). apoptotic bodies, and oncosomes.

25. The system of claim 20, wherein at least one of the non-adherent cells and the extracellular vesicles are bound to the binding surface via physisorption resulting from buffer sedimentation.

26. The system of claim 20, wherein the binding surface includes a surface modification configured to chemically or physically bind with at least one of nonadherent cells and extracellular vesicles released by the non-adherent cells.

27. The system of claim 26, wherein the surface modification is chosen to avoid adverse effects on native metabolic pathways and cell signaling, including selection based on inertness of materials, biocompatibihty. minimal nonspecific adsorption, chemical and physical stability under assay conditions, and absence of unintended biological stimulation or interference.

28. The system of claim 26, wherein the surface modifications include one or more of: charged polymer coatings (Poly-L-lysine, Poly-D-lysine, polyomithine); extracellular matrix protein coatings (Fibronectin, Laminin, Collagen, Gelatin, Matrigel); anti-fouling and inert coatings (polyethylene glycol (PEG)-based coatings, zwitterionic coatings, polyvinyl alcohol (PVA), poly dopamine); or by binding via physisorption to an unmodified hydrophilic surface (uncoated indium tin oxide (ITO), silicon dioxide, or glass).

29. The system of claim 20, wherein the assay access mechanism is one or more of a transparent window, a sample port, optical fiber interfaces, fluidic channels, removable covers or open-access designs, electrical contact pads, or spectroscopic windows.

30. The system of claim 20, further comprising an assay tool oriented to interrogate the at least one cell culture site through the assay access mechanism.

31. The system of claim 30, wherein the assay tool includes one or more of fluorescence microscopes, spectrofluorometers, microplate readers, laser-inducedfluorescence (LIF) systems, CCD / CMOS camera-based detectors, optical fiber-based probes, microarray scanners, tissue fluorescence scanners, flow cytometers, western blotting with phospho-specific antibodies, enzyme-linked immunosorbent assays (ELISAs), mass spectrometry -based phosphoproteomics, in vitro kinase activity assays, protein microarrays, FRET-based biosensors, UV -visible spectrophotometers, standalone colorimeters, lateral flow assays, liquid scintillation counters, gamma counters, autoradiography systems, scintillation proximity assays (SPA), chemiluminescence imaging systems, a mass spectrometer, laser illumination modules, high-sensitivity cameras and microscopy systems, particle tracking software, potentiostats, amperometric biosensors, voltammetric analyzers, luminescence imaging systems, surface plasmon resonance (SPR) biosensors, Raman microscopes, Raman spectrometers, and commercial NTA systems.

32. The system of claim 30, wherein the assay tool is configured to measure phosphorylation using one or more of western blotting with phospho-specific antibodies, enzyme-linked immunosorbent assays (ELISAs), mass spectrometry-based phosphoproteomics, in vitro kinase activity assays, protein microarrays, FRET-based biosensors, immunofluorescence using phospho-specific antibodies, and flow cytometry (phospho-flow).

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