CROSS REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to U.S. Provisional Application No. 63/374,168, entitled “SPECTRAL EMISSION BASED RESOLUTION AND CLASSIFICATION OF EXTRACELLULAR VESICLE POPULATIONS” and filed on August 31, 2022, the disclosure of which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

[0002] The subject matter described herein relates generally to the digital and computational pathology and more specifically to spectral emission-based techniques for resolving and classifying lipid containing particles such as extracellular vesicles, engineered extracellular vesicles, lipoproteins, liposomes, synthetic particles, and/or the like.

INTRODUCTION

[0003] A nanoparticle is an assembly of atoms or molecules in which at least one dimension in the 1-100 nanometer range. Nanoparticles, which includes intracellular structures (e.g., magnetosomes) as well as extracellular assemblies (e.g., lipoproteins and viruses), may be naturally occurring or manmade to serve a gamut of biological functions from mineral storage to intercellular communication. A lipidic particle refers to a type of nanoparticle having lipid constituting at least a portion of its membrane and/or cargo. Examples of lipidic particles include naturally occurring extracellular vesicles, engineered extracellular vesicles, lipoproteins, liposomes, synthetic particles, and/or the like. For example, an extracellular vesicle (EV) is a lipid-bound nanoparticle in which a lipid bilayer membrane encapsulates a variety of molecular cargo including, for example, lipids, nucleotides, carbohydrates, proteins, and/or the like. [0004] Lipidic particles such as extracellular vesicles play a vital role in intercellular communication. For example, virtually all types of cells secrete extracellular particles into the extracellular space, which then deliver a variety of molecular cargo to neighboring and/or distant cells. As such, extracellular vesicles have been implicated across a gamut of cell physiological activities including, for example, stress response, intercellular competition, lateral gene transfer (e.g., via ribonucleic acid (RNA) or deoxyribonucleic acid (DNA)), pathogenicity, detoxification, and/or the like. For instance, tumor cells produce extracellular vesicles (e.g., pro-metastatic extracellular vesicles), which spread to distant organs to prime those microenvironments for future metastatic spread of tumor cells.

SUMMARY

[0005] Systems, methods, and articles of manufacture, including computer program products, are provided for resolving and classifying lipidic particles including, for example, extracellular vesicles, engineered extracellular vesicles, lipoproteins, liposomes, synthetic particles, and/or the like. In one aspect, there is provided a system for resolving and classifying populations of lipidic particles. The system may include at least one processor and at least one memory. The at least one memory may include program code that provides operations when executed by the at least one processor. The operations may include: receiving a sample including a plurality of lipidic particles; determining, for each lipidic particle of the plurality of lipidic particles, a set of spectral emission measurements corresponding to a lipid order in a membrane of the lipidic particle; identifying, based at least on the set of spectral emission measurements associated with each lipidic particle, one or more populations of lipidic particles present in the sample; identifying a type of lipidic particles corresponding to each population of the one or more populations of lipidic particles present in the sample; and generating a first lipidic particle profile to include one or more types of lipidic particles identified as present in the sample.

[0006] In another aspect, there is provided a method for resolving and classifying lipidic particles including, for example, extracellular vesicles, engineered extracellular vesicles, lipoproteins, liposomes, synthetic particles, and/or the like. The method may include: receiving a sample including a plurality of lipidic particles; determining, for each lipidic particle of the plurality of lipidic particles, a set of spectral emission measurements corresponding to a lipid order in a membrane of the lipidic particle; identifying, based at least on the set of spectral emission measurements associated with each lipidic particle, one or more populations of lipidic particles present in the sample; identifying a type of lipidic particles corresponding to each population of the one or more populations of lipidic particles present in the sample; and generating a first lipidic particle profile to include one or more types of lipidic particles identified as present in the sample.

[0007] In another aspect, there is provided a computer program product for resolving and classifying populations of lipidic particles including, for example, extracellular vesicles, engineered extracellular vesicles, lipoproteins, liposomes, synthetic particles, and/or the like. The computer program product may include a non-transitory computer readable medium storing instructions that cause operations when executed by at least one data processor. The operations may include: receiving a sample including a plurality of lipidic particles; determining, for each lipidic particle of the plurality of lipidic particles, a set of spectral emission measurements corresponding to a lipid order in a membrane of the lipidic particle; identifying, based at least on the set of spectral emission measurements associated with each lipidic particle, one or more populations of lipidic particles present in the sample; identifying a type of lipidic particles corresponding to each population of the one or more populations of lipidic particles present in the sample; and generating a first lipidic particle profile to include one or more types of lipidic particles identified as present in the sample.

[0008] In some variations of the methods, systems, and non-transitory computer readable media, one or more of the following features can optionally be included in any feasible combination.

[0009] In some variations, the first lipidic particle profile may be generated to include a first type of lipidic particles present in the sample and/or a second type of lipidic particles absent from the sample.

[0010] In some variations, a quantity of each type of the one or more types of lipidic particles present in the sample may be determined. The first lipidic particle profile may be generated to further include the quantity of each type of lipidic particle present in the sample.

[0011] In some variations, whether a quantity of each type of the one or more types of lipidic particles present in the sample exceeds a threshold value may be determined. The first lipidic particles profile may be generated to further include a first type of lipidic particles whose quantity exceeds the threshold value and/or a second type of lipidic particles whose quantity fails to exceed the threshold value.

[0012] In some variations, a relative proportion of each type of the one or more types of lipidic particles present in the sample may be determined. The first lipidic particle profile may be generated to further include the relative proportion of each type of lipidic particles present in the sample.

[0013] In some variations, a cargo associated with at least one type of lipidic particles present in the sample may be determined. The first lipidic particle profile may be generated to further include the cargo associated with the at least one type of lipidic particles. [0014] In some variations, the first lipidic particle profile may be generated to include a presence, an absence, a quantity, and/or a relative proportion of each type of cargo associated with the at least one type of lipidic particles.

[0015] In some variations, the first lipidic particle profile may be analyzed relative to a second lipidic particle profile. At least one of a disease diagnosis, a disease progress, a treatment, and a treatment response for a patient associated with the sample may be determined based at least on the analysis.

[0016] In some variations, the second lipidic particle profile may be a non-patient specific reference lipidic particle profile representative of (i) a disease-free state, (ii) a particular stage of a disease, or (iii) an untreated state prior to an administration of the treatment

[0017] In some variations, the first lipidic particle profile may be representative of a first state of the patient prior to an administration of the treatment and the second lipidic particle profile may be representative of a second state of the patient subsequent to the administration of the treatment.

[0018] In some variations, the first lipidic particle profile may be representative of a first state of the patient at a first time and the second lipidic particle profile may be representative of a second state of the patient at a second time.

[0019] In some variations, the first lipidic particle profile may be representative of a first state of the patient being administered the treatment and the second lipidic particle profile may be representative of a second state of the patient being administered a different treatment.

[0020] In some variations, at least one of (i) a disease state for a patient associated with the sample, (ii) a response of the patient to a treatment for a disease, (iii) a first likelihood of the patient responding to the treatment, (iv) a second likelihood of the patient relapsing after the treatment, and (v) a durability of the patient’s response to the treatment may be determined based at least on the first lipidic profile of the patient.

[0021] In some variations, the set of spectral emission measurements may include a first fluorescence intensity of a first wavelength of light emitted by the lipidic particle in response being exposed to an excitation light source and a second fluorescence intensity of a second wavelength of light emitted by the lipidic particle in response to being exposed to the excitation light source.

[0022] In some variations, the set of spectral emission measurements may include a maximum, a minimum, a mean, a mode, and/or a median fluorescence intensity exhibited by the lipidic particle in response to being exposed to an excitation light source.

[0023] Tn some variations, the set of spectral emission measurement may include at least one of an area and an aspect ratio of the lipidic particle observed at each of an n quantity of wavelength of light.

[0024] In some variations, a reduced dimension representation of a dataset including the set of spectral emission measurements associated with each lipidic particle in the sample may be generated. The one or more populations of lipidic particles may be identified based at least on the reduced dimension representation of the dataset.

[0025] In some variations, a subtype of lipidic particles corresponding to each population of the one or more populations of lipidic particles present in the sample may be identified. The first lipidic particle profile may be generated to further include one or more subtypes of lipidic particles identified as present in the sample.

[0026] In some variations, the one or more types of lipidic particles may include extracellular vesicles, engineered extracellular vesicles, lipoproteins, liposomes, and/or synthetic particles. [0027] In some variations, the lipid order in the membrane of the lipidic particle may correspond to (i) a first proportion of lipid molecules, (ii) a second proportion of water molecules in the membrane of the lipidic particle, and/or (iii) a general polarity (GP) of the membrane of the lipidic particle.

[0028] Implementations of the current subject matter can include, but are not limited to, methods consistent with the descriptions provided herein as well as articles that comprise a tangibly embodied machine-readable medium operable to cause one or more machines (e.g., computers, etc.) to result in operations implementing one or more of the described features. Similarly, computer systems are also described that may include one or more processors and one or more memories coupled to the one or more processors. A memory, which can include a non- transitory computer-readable or machine-readable storage medium, may include, encode, store, or the like one or more programs that cause one or more processors to perform one or more of the operations described herein. Computer implemented methods consistent with one or more implementations of the current subject matter can be implemented by one or more data processors residing in a single computing system or multiple computing systems. Such multiple computing systems can be connected and can exchange data and/or commands or other instructions or the like via one or more connections, including, for example, to a connection over a network (e.g. the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, or the like), via a direct connection between one or more of the multiple computing systems, etc.

[0029] The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims. While certain features of the currently disclosed subject matter are described for illustrative purposes in relation to the resolution and classification of certain types of lipidic particles, such as extracellular vesicles, it should be readily understood that such features are not intended to be limiting. The claims that follow this disclosure are intended to define the scope of the protected subject matter.

DESCRIPTION OF DRAWINGS

[0030] The accompanying drawings, which are incorporated in and constitute a part of this specification, show certain aspects of the subject matter disclosed herein and, together with the description, help explain some of the principles associated with the disclosed implementations. In the drawings,

[0031] FIG. 1 depicts a system diagram illustrating an example of a spectral emission analysis system, in accordance with some example embodiments;

[0032] FIG. 2 depicts a flowchart illustrating an example of a process for resolving and classifying lipidic particles, in accordance with some example embodiments;

[0033] FIG. 3 depicts a flowchart illustrating an example of a process for analyzing a lipidic particles profile, in accordance with some example embodiments; and

[0034] FIG. 4 depicts a schematic diagram illustrating an example of a workflow for resolving and classifying lipidic particles, in accordance with some example embodiments;

[0035] FIG. 5A depicts a graph illustrating an example of the relationships between lipid order (Lo), the proportion of lipid molecules, and the proportion of water molecules in the membrane of a lipidic particle, in accordance with some example embodiments; [0036] FIG. 5B depicts a graph illustrating an example of the relationship between the general polarity (GP) value and the lipid order of the membrane of a lipidic particle, in accordance with some example embodiments;

[0037] FIG. 5C depicts a graph illustrating an example of the relationship between the proportion of lipid molecules and the spectral emission spectrum of a lipidic particle, in accordance with some example embodiments;

[0038] FIG. 6A depicts graphs illustrating the relationship between general polarity (GP) value and lipid order across lipidic particles treated with different lipophilic dyes, in accordance with some example embodiments;

[0039] FIG. 6B depicts a graph illustrating the relationship between lipid order, as quantified by general polarity (GP), and the emission spectra of different lipidic particles, in accordance with some example embodiments;

[0040] FIG. 7 depicts a screenshot illustrating an example of a user interface, in accordance with some example embodiments; and

[0041] FIG. 8 depicts a block diagram illustrating an example of a computing system, in accordance with some example embodiments.

[0042] When practical, similar reference numbers denote similar structures, features, or elements.

DETAILED DESCRIPTION

[0043] Lipidic particles, which are lipid-containing nanoparticles in which lipid forms at least a portion of the membrane and/or cargo, perform a variety of biological functions from mineral storage to intracellular communication. Examples of lipidic particles include extracellular vesicles, lipoproteins, liposomes, synthetic particles, and/or the like. For example, as a key participant in intercellular communication, extracellular vesicles are implicated across a gamut of cell physiological activities including, for example, stress response, intercellular competition, lateral gene transfer (e.g., via ribonucleic acid (RNA) or deoxyribonucleic acid (DNA)), pathogenicity, detoxification, and/or the like. Extracellular vesicles (EVs) are a heterogeneous group of membrane vesicles that are secreted by cells into the extracellular space. There is a wide range of classification and nomenclature for this heterogeneous pool of extracellular vesicles, which is mostly defined on their cell of origin, morphology, size, and cargo (content). However, broadly speaking, extracellular vesicles can be divided into two main groups depending on their fundamental mode of biogenesis: exosomes and microvesicles. In some instances and contexts, apoptotic bodies are considered a third subtype of extracellular vesicles. Exosomes, or small extracellular vesicles, range in size from 40-150 nanometers. They are generated as intraluminal vesicles (ILV) in multivesicular endosomes (MVEs) and are collectively released into the extracellular space upon fusion of the multivesicular endosomes with the plasma membrane. Microvesicles range in size from 150-500 nanometers (with exceptions up to 1000 nanometers). Microvesicles include larger extracellular vesicles such as migrasomes and oncosomes. They originate primarily through the budding of the plasma membrane. Apoptotic bodies range from 50-5000 nanometers in size and originate during plasma membrane blebbing of cells undergoing programmed cell death. Finally, plasma membrane protrusions can give rise oncosomes, which are large extracellular vesicles ranging between 1 to 10 micrometers in diameter.

[0044] The presence and/or absence of certain lipidic particles may serve as predictive and/or prognostic biomarkers. However, a sufficiently specific and sensitive biomarker may require recognizing the highly nuanced differences that exist across different types of lipidic particles. Contrastingly, conventional approaches are inexact and incapable of differentiating between populations of lipidic particles exhibiting subtle variations. For example, size-based classification of extracellular vesicles, which recognizes the four broad categories of extracellular vesicles noted above, is inadequate for resolving the highly heterogeneous populations of extracellular vesicles produced by cells in which many more than four different types of extracellular vesicles exist. In turn, a biomarker that relies on sized-based classification of extracellular vesicles may not have sufficient specificity or sensitivity to be clinically valuable. Accordingly, in some example embodiments, individual lipidic particles may be resolved and classified based on the spectral emission measurements associated with each lipidic particle. For example, a sample of one or more tissue fragments, free cells, and/or bodily fluids may contain one or more populations of lipidic particles including, for example, extracellular vesicles, lipoproteins, liposomes, synthetic particles, and/or the like. Each population of lipidic particles may correspond to a different type of lipidic particle as characterized, for example, by the organization of the lipid molecules in the lipidic particle. For instance, in some cases, one population of lipidic particles in the sample may be associated with a different lipid order metric (e.g., general polarity (GP) and/or the like) than another population of lipidic particles present in the same sample. The lipid order metric (e.g., general polarity (GP) and/or the like) of a lipidic particle may quantify the order (or disorder) of the lipid molecules forming the membrane of the lipidic particle. In some cases, the presence of more organized lipid molecules in the membrane of the lipidic particle may be indicative of a less fluid (or more rigid) membrane with a lower hydration and a higher proportion of lipid molecules (e.g., cholesterol). [0045] In some example embodiments, the sample may be treated with a dye, such as a lipophilic dye (e.g., Di-8-ANEPPS, F2N12S, DPH, laurdan, and/or the like), having a spectral emission spectrum indicative of one or more properties of each lipidic particle present in the sample. For instance, in some cases, the spectral emission spectrum of the lipophilic dye may be indicative of the lipid order (or another property) of the membrane of each lipidic particle present in the sample. Accordingly, once the sample is treated with the dye, a lipidic particle in the sample may respond to being exposed to an excitation light source by exhibiting a fluorescent emission spectrum indicative of the lipid order (or the organization of lipid molecules) in the membrane of the lipidic particle. Many more different types of lipidic particles, as characterized by variations in the organization of constituent lipid molecules, may be identified based on the fluorescent emission spectrum associated with the lipidic particle. Resolving lipidic particles in this manner may thus enable a more granular differentiation between the many different types of lipidic particles that may be present in the sample. For example, this manner of resolution may enable a differentiation between different subtypes of extracellular vesicles, which do not necessarily differ in size. Moreover, spectral emission based resolution may enable the distinction of extracellular vesicles from similarly sized, lipid containing particles such as lipoproteins, lipid droplets, exomers, and protein/lipid aggregates. Accordingly, a biomarker leveraging populations of lipidic particles resolved based on fluorescent emission spectrum may be sufficiently specific and sensitive to have clinical value.

[0046] In some example embodiments, the one or more populations of lipidic particles present in the sample may be identified based on a set of spectral emission measurements corresponding to the spectral emission spectrum associated with each lipidic particle present in the sample. For example, the set of spectral emission measurements associated with each lipidic particle may include a first fluorescence intensity of a first wavelength of light emitted by the lipidic particle in response being exposed to the excitation light source and a second fluorescence intensity of a second wavelength of light emitted by the lipidic particle in response to being exposed to the excitation light source. The spectral emission spectrum of the lipidic particle may correspond to the lipid order (or the organization of lipid molecules) in the membrane of the lipid particle. That is, a first lipidic particle exhibiting a first lipid order may have a first spectral emission spectrum while a second lipidic particle exhibiting a second lipid order may have a second spectral emission spectrum that is different from the first spectral emission spectrum of the first lipid particle. Accordingly, the first lipidic particle may be differentiated from the second lipidic particle based at least on the difference in lipid order, as indicated by the different spectral emission spectra exhibited by the first lipidic particle and the second lipidic particle.

[0047] In some example embodiments, different populations of lipidic particles present in the sample, such as extracellular vesicles, may be identified based on a reduced dimension representation of a dataset that includes, for each lipidic particle in the sample, the set of spectral emission measurements corresponding to the spectral emission spectrum of the lipidic particle. For example, the set of spectral emission measurements associated with an individual lipidic particle may include an n quantity of fluorescence intensity measurements, each of which associated with a wavelength of light in the visible spectrum (e.g., the spectrum between 350 nanometers and 800 nanometers). In some cases, the set of spectral emission measurements may also include a maximum, a minimum, a mean, a mode, and/or a median fluorescence intensity exhibited by the lipidic particle in response to being exposed to the excitation light source. Furthermore, in some cases, the set of spectral emission measurements for a lipidic particle may include, for each of the n wavelengths of light in the visible spectrum (e.g., between 350 nanometers and 800 nanometers), the area (e.g., brightfield area) and/or the aspect ratio of the lipidic particle observed at that wavelength of light. Generating the reduced dimension representation of the aforementioned dataset may include clustering the lipidic particles present in the sample based on the set of spectral emission measurements associated with each lipidic particles. Accordingly, the reduced dimension representation of the dataset may include one or more clusters of lipidic particles, with each cluster corresponding to an individual population of lipidic particles present in the sample. For instance, in some cases, the one or more clusters of lipidic particles may include one cluster corresponding to a first type of lipidic particles (e.g., extracellular vesicles) and another cluster corresponding to a second type of lipidic particles (e.g., lipoproteins, liposomes, or synthetic particles). Alternatively and/or additionally, the one or more clusters of lipidic particles may include one cluster corresponding to a first subtype of lipidic particles (e.g., a first subtype of extracellular vesicles) and another cluster corresponding to a second subtype of lipidic particles (e.g., a second subtype of extracellular vesicles).

[0048] In some example embodiments, a first lipidic particle profile may be generated based on the different types of lipidic particles, such as extracellular vesicles, engineered extracellular vesicles, lipoproteins, synthetic particles, liposomes, and/or the like, identified as present in the sample. For example, the first lipidic particle profile may include a quantity (or relative proportion) of liposomes of each lipid composition present in the sample. Alternatively and/or additionally, the first lipidic particle profile may include a quantity (or relative proportion) of synthetic particles of defined sizes that encompass the range of vesicle sizes present in the sample. In some cases, the first lipidic particle profile may also include a quantity (or relative proportion) of engineered extracellular vesicles in the sample that contain a defined cargo and/or a quantity (or relative proportion) of engineered extracellular vesicles in the sample without the defined cargo.

[0049] In some example embodiments, the first lipidic particle profile for a patient associated with the sample may be generated based on the different subtypes of lipidic particles, such as the different subtypes of extracellular vesicles, identified as present in the sample. For example, the first lipidic particle profile may include a quantity of each subtype of extracellular vesicle present in the sample. Alternatively, and/or additionally, the first lipidic particle profile may include a relative proportion of each subtype of extracellular vesicle present in the sample. In some cases, the first lipidic particle profile may include one or more subtypes of extracellular vesicles present in the sample as well as one or more subtypes of extracellular vesicles absent from the sample.

[0050] In some example embodiments, in addition to the types (or subtypes) of lipidic particles, the first lipidic particle profile of the patient may be further generated to include a cargo carried by at least one type of lipidic particles present in the sample. For example, in some cases, the cargo is detected by treating the sample with one or more of an antibody having a binding specificity towards an antigen presented by the lipidic particle, a dye, and a nucleotide- hybridization of the lipidic particles. Some lipidic particles, such as extracellular vesicle, may carry a variety of cargo including, for example, one or more lipids, nucleotides, carbohydrates, proteins, and/or the like. Accordingly, in some cases, the first lipidic particle profile of the patient may be generated to include a presence, an absence, a quantity, and/or a relative proportion of each type of cargo carried by the at least one type (or subtype) of lipidic particles present in the sample.

[0051] In some example embodiments, the first lipidic particle profile for the patient may be analyzed relative to a second lipidic particle profile in order to determine at least one of a disease diagnosis, a disease progress, a treatment, and a treatment response for the patient. For example, in some cases, the second lipidic particle profile may be a non-patient specific reference lipidic particle profile representative of a disease-free state or a particular stage of a disease, in which case a disease diagnosis and/or a disease progress for the patient may be determined by analyzing the first lipidic particle profile for the patient relative to the second lipidic particle profile. Alternatively and/or additionally, the second lipidic particle profile may be a non-patient specific reference profile representative of an untreated state prior to the administration of a treatment, in which case a treatment response for the patient may be determined by analyzing the first lipidic particle profile for the patient relative to the second lipidic particle profile.

[0052] Tn some example embodiments, instead of a non-patient specific reference lipidic particle profile, the second lipidic particle profile may be associated with a same patient as the first lipidic particle profile. For example, the first lipidic particle profile may be representative of a first state of the patient at a first time while the second lipidic particle profile may be representative of a second state of the patient at a second time. As such, a disease progress of the patient may be determined by performing a comparative analysis of the first lipidic particle profile and the second lipidic particle profile. In some cases, the first lipidic particle profile may be representative of a first state of the patient prior to an administration of a treatment while the second lipidic particle profile may be representative of a second state of the patient subsequent to the administration of the treatment. Alternatively and/or additionally, the first lipidic particle profile may be representative of a first state of the patient being administered one treatment and the second lipidic particle profile may be representative of a second state of the patient being administered a different treatment. Accordingly, a response of the patient to one or more treatments may be determined by at least analyzing the first lipidic particle profile relative to the second lipidic particle profile. [0053] FIG. 1 depicts a system diagram illustrating an example of a spectral emission analysis system 100, in accordance with some example embodiments. Referring to FIG. 1, the spectral emission analysis system 100 may include a LP profile analysis engine 110, a flow cytometer 120, and a client device 130. As shown in FIG. 1, the LP profile analysis engine 110, the flow cytometer 120, and the client device 130 may be communicatively coupled via a network 140. The client device 130 may be a processor-based device including, for example, a workstation, a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable apparatus, and/or the like. The network 140 may be a wired network and/or a wireless network including, for example, a local area network (LAN), a virtual local area network (VLAN), a wide area network (WAN), a public land mobile network (PLMN), the Internet, and/or the like.

[0054] In some example embodiments, the LP profile analysis engine 110 may resolve and classify one or more populations of lipidic particles found in a sample 115 based on the spectral emission measurements associated with each individual lipidic particle. The sample 115 may be a biological sample or a derivation of a biological sample (e.g., a purified biological sample, an enriched biological sample, and/or the like) containing biological matter such as one or more tissue fragments, free cells, bodily fluids, and/or the like. In some cases, the sample 115 may include one or more organotypic cultures and/or organoids. Alternatively and/or additionally, the sample 115 may be non-mammalian in origin such as, for example, a bacterial sample, a fungal sample, a plant sample, and/or the like. As described in more detail below, examples of lipidic particles that may be resolved and classified based on spectral emission measurements include extracellular vesicles, engineered extracellular vesicles, lipoproteins, liposomes, and synthetic particles.

[0055] The flow cytometer 120 may perform spectral flow cytometry in which one or more spectral emission measurements are performed to determine a spectral emission spectrum of each lipidic particle present in the sample 115. Referring again to FIG. 1, the flow cytometer 120 may include, for example, a flow cell 121, an excitation light source 123, and a detector 125. The flow cell 121 may include a liquid stream carrying one or more particles, such as the lipidic particles from the sample 115, in a single fde through a light beam from the excitation light source 123. In some cases, the excitation light source 123 may include one or more lamps (e.g., mercury, xenon), high-power water-cooled lasers (e.g., argon, krypton, dye laser), low-power air-cooled lasers (e.g., argon (488 nanometers), red helium-neon (633 nanometers), green helium-neon, helium cadmium (ultraviolet)), and diode laser (e.g., blue, green, red, violet).

[0056] The detector 125 may measure the fluorescence intensity of the light emitted by each lipidic particle in the sample 1 15 in response to being exposed to the excitation light source 123. In the sample shown in FIG. 1, the sample 115 may be treated with a dye, such as a lipophilic dye (e.g. Di-8-ANEPPS, F2N12S, DPH, laurdan, and/or the like), having a spectral emission spectrum corresponding to one or more properties of each lipidic particle in the sample 115. For example, in some cases, the spectral emission spectrum of the lipophilic dye may be indicative of the lipid order, fluidity, potential, and/or polarity of the membrane of each lipidic particle present in the sample 115. In this context, the term “lipid order” may refer to the organization of lipid molecules (e.g., cholesterol and/or the like) in the membrane of a lipidic particle. In some cases, the lipid order of the membrane of a lipidic particle may vary depending a first proportion (e.g., molar ratio) of lipid molecules (e.g., cholesterol and/or the like) and/or a second proportion of water molecules in the membrane of the lipidic particle. For instance, the lipid molecules in the membrane of a lipidic particle may be more ordered when there is a higher proportion of lipid molecules (and a lower proportion of water molecules) and less ordered when there is a lower proportion of lipid molecules (and a higher proportion of water molecules). Moreover, in some cases, the spectral emission spectrum of the lipophilic dye may correspond to the overall lipid composition of a lipid particle, which includes the lipids present in the membrane of the lipidic particle as well as the lipids present in the cargo carried by the lipidic particle.

[0057] Accordingly, the detector 125 may be configured to the detect dye-specific fluorescence signals emitted by each lipidic particle in the sample 115. The resulting set of spectral emission measurements for each lipidic particle in the sample 115 may include, for example, an n quantity of measurements, each of which corresponding to a wavelength of light in the visible spectrum (e.g., between 350 nanometers and 800 nanometers). For example, the set of spectral emission measurements for each lipidic particle in the sample 115 may include a first fluorescence intensity of a first wavelength of light emitted by the lipidic particle in response being exposed to the excitation light source 123 and a second fluorescence intensity of a second wavelength of light emitted by the lipidic particle in response to being exposed to the excitation light source 123. In some cases, in addition to the fluorescence intensity of each wavelength of the n wavelengths of light in the visible spectrum (e.g., between 350 nanometers an 800 nanometers), the set of spectral emission measurements for each lipidic particle in the sample 115 may also include an area (e.g., a brightfield area) and/or an aspect ratio of the lipidic particle. Furthermore, in some cases, the set of spectral emission measurements for each lipidic particle in the sample 115 may include a maximum, a minimum, a mean, a mode, and/or a median fluorescence intensity measurement exhibited by the lipidic particle in response to being exposed to the excitation light source 123.

[0058] In some example embodiments, the LP profile analysis engine 110 may identify, based at least on the set of spectral emission measurements associated with each lipidic particles in the sample 1 15, one or more populations of lipidic particles present in the sample 1 15. For example, in some cases, a single population of lipidic particles in the sample 115 may correspond to an individual type of lipidic particles such as extracellular vesicles, engineered extracellular vesicles, lipoproteins, liposomes, synthetic particles, and/or the like. In some cases, within that single population of lipidic particles present in the sample 115, two or more subpopulations of lipidic particles, each of which corresponding to a different subtype of lipidic particles, may be further differentiated based at least on the set of spectral emission measurements. As noted, in some cases, the set of spectral emission measurements associated with each lipidic particle in the sample 115 may correspond to one or more properties of the lipidic particle, such as the lipid order or fluidity of the membrane of each lipidic particle. In some cases, the lipid order of the membrane of a lipidic particle may be dependent on the composition of the membrane, including the quantity (or proportion) of lipid molecules and water molecules present in the makeup of a the membrane of the lipidic particle. Different types (or subtypes) of lipidic particles may be characterized by variations in lipid order. For instance, a first population of lipidic particles present in the sample 115 may correspond to a first type (or a first subtype) of lipidic particles having a first lipid order that differs from a second lipid order of a second population of lipidic particles of a second type (or a second subtype) present in the sample 115. Compared to conventional approaches to resolving lipidic particles, such as sized-based classification of extracellular vesicles that recognizes merely four broad categories of extracellular vesicles (e.g., microvesicles, exosomes, apoptotic bodies, and oncosomes), resolving lipidic particles based on the spectral emission spectrum of individual lipidic particles, such as a spectral emission spectrum corresponding to the lipid order of the membrane of each lipidic particle, may enable a more granular differentiation between the highly heterogeneous populations of lipidic particles present in the sample 115.

[0059] In some example embodiments, the LP profde analysis engine 110 may identify one or more populations of lipidic particles present in the sample 1 15 based on a reduced dimension representation of a dataset that includes, for each lipidic particle in the sample 115, the set of spectral emission measurements corresponding to the spectral emission spectrum of the lipidic particle. That is, the dataset may include a plurality of datapoints, each of which corresponding to the set of spectral emission measurements for a single lipidic particle. The LP profile analysis engine 110 may generate the reduced dimension representation of the dataset by applying a variety of techniques including, for example, t -distributed stochastic neighbor embeeding (t-SNE), uniform manifold approximation and projection (UMAP), principal component analysis (PCA), linear discriminant analysis (LDA), a machine learning model, and/or the like.

[0060] As noted, the set of spectral emission measurements associated with each lipidic particle in the sample 115 may include an n quantity of measurements associated with the lipidic particle. Each measurement of the n quantity of measurements may correspond to the fluorescence intensity of a different wavelength of light emitted by the lipidic particle in response to being exposed to the excitation light source 123. For example, in some cases, the n-quantity of measurements may include 5 to 25 measurements at equal or unequal intervals across the visible spectrum (e.g., between 350 nanometers and 800 nanometers). In some cases, in addition to fluorescence intensity at each of an n wavelengths of light in the visible spectrum (e.g., between 350 nanometers an 800 nanometers), the set of spectral emission measurements for each lipidic particle in the sample 115 may also include one or more additional measurements including, for example, an area (e.g., a brightfield area) of the lipidic particle, an aspect ratio of the lipidic particle, a maximum fluorescence intensity, a minimum fluorescence, a mean fluorescence, a mode fluorescence, and/or a median fluorescence intensity. Accordingly, the dataset including the set of spectral emission measurements for each lipidic particle in the sample 115 may occupy at least an n-dimensional space.

[0061] Generating the reduced dimension representation of the dataset may include embedding the dataset in a lower dimensional space than the n-dimensional space of the original dataset. Whereas the n-dimensional space of the original dataset is parameterized by an n quantity of features, which in this case corresponds to the aforementioned n quantity of spectral emission measurements, the lower dimensional space is parameterized by a fewer quantity of features. As such, in some cases, reducing the dimensionality of the dataset may uncover relationships between the datapoints in the dataset, each of which corresponding to a lipidic particle, that are obscured in a high dimensional representation. For instance, the datapoints in the n-dimensional space of the original dataset may be sparse, meaning that two datapoints corresponding to two lipidic particles having similar emission spectra may be distant from one another in the n-dimensional space of the original dataset. Contrastingly, the two datapoints corresponding the two lipidic particles having similar emission spectra (e g., indicative of similar membrane properties such as lipid order or fluidity) may be proximate to one another in the lower dimensional while two datapoints corresponding to two lipidic particles having dissimilar emission spectra (e.g., indicative of dissimilar membrane properties such as lipid order or fluidity) may remain distant from one another in the lower dimensional space. The distribution of the datapoints in the dataset, with some datapoints being more closely collocated than others, may give rise to distinct clusters in the reduced dimension representation of the dataset. As noted, in some cases, the LP profile analysis engine 110 may identify each cluster as corresponding to individual populations of lipidic particles. [0062] Increasing the quantity of measurements included in the set of spectral emission measurements associated with each lipidic particle (e.g., increasing the value of n or decreasing the size of intervals at which the spectral emission measurements are performed), which increases the dimensionality of the dataset, may also increase the resolution and sensitivity at which the LP profile analysis engine 110 is able to differentiate between the different types (or subtypes) of lipidic particles present in the sample 115. In particular, increasing the value of n and decreasing the size of intervals between successive spectral emission measurements may provide additional resolution if there is a change in the intensity of spectral emission within the smaller intervals. Furthermore, it should be appreciated that the quantity of measurements included in the set of spectral emission measurements (e.g., the value of ri) and the dimensionality of the corresponding dataset should not exceed the quantity of lipidic particles present in the sample 115 in order to avoid an overfitting of the available data.

[0063] In some cases, generating the reduced dimension representation of the dataset may include clustering the lipidic particles present in the sample 115 based on the set of spectral emission measurements associated with each lipidic particle. Each resulting cluster of lipidic particles may correspond to a single population of lipidic particles present in the sample 115. For example, the reduced dimension representation of the dataset may include a first cluster of lipidic particles corresponding to a first population of lipidic particles of a first type (or a first subtype) and a second cluster of lipidic particles corresponding to a second population of lipidic particles of a second type (or a second subtype).

[0064] In some example embodiments, the LP profile analysis engine 110 may generate, based on one or more types (or subtypes) of lipidic particles identified as present in the sample 115, a first lipidic particle profile 150a for a patient associated with the sample 115. For example, the LP profile analysis engine 110 may generate the first lipidic particle profile 150a to include a quantity of each type (or subtype) of lipidic particles present in the sample 115. Alternatively and/or additionally, the LP profile analysis engine 110 may generate the first lipidic particle profile 150a to include a relative proportion of each type (or subtype) of lipidic particles present in the sample 115. In some cases, the LP profile analysis engine 110 may generate the first lipidic particle profile 150a to include one or more types (or subtypes) of lipidic particles present in the sample 115 as well as one or more types (or subtypes) of lipidic particles absent from the sample 115.

[0065] In some cases, the LP profile analysis engine 110 may further generate the first lipidic particle profile 150a of the patient to include a cargo carried by at least one type (or subtype) of lipidic particles present in the sample 115. For example, in the case of extracellular vesicles, each extracellular vesicle in the sample 115 may be a lipid bound nanoparticle in which a lipid bilayer membrane encapsulates, absorbs, or contains a variety of molecular cargo including, for example, lipids, nucleotides, carbohydrates, proteins, and/or the like. Accordingly, the cargo carried by each extracellular vesicle may be detected by treating the sample 115 with one or more of an antibody having a binding specificity towards an antigen presented by the extracellular vesicles, a dye, and a nucleotide-hybridization of the extracellular vesicles. Moreover, in some cases, the LP profile analysis engine 110 may generate the first lipidic particle profile 150a of the patient may to include a presence, an absence, a quantity, and/or a relative proportion of each type of cargo carried by the at least one type of extracellular vesicles present in the sample 115.

[0066] In some cases, the LP profile analysis engine 110 may generate the first lipid particle profile 150a based on a variety of lipidic particles present in the sample 115. For instance, in addition to the extracellular vesicles noted above, the first lipidic particle profile 150a may be also generated based on one or more other types of lipidic particles present in the sample 115 such as engineered extracellular vesicles, liposomes, lipoproteins, synthetic particles, and/or the like. Accordingly, in some cases, the LP profile analysis engine 110 may further generate the first lipidic particle profile 150a based on one or more synthetic particles, liposomes, and engineered extracellular vesicles. For example, the first lipidic particle profile 150a may be generated to include a quantity of liposomes of each lipid composition present in the sample 115. Alternatively and/or additionally, the first lipidic particle profile 150a may be generated to include a quantity of synthetic particles of defined sizes that encompass the range of vesicle sizes present in the sample 115. Furthermore, in some instances, the first lipidic particle profile 150a may be generated to also include a quantity of engineered extracellular vesicles in the sample 115 that contain a defined cargo and/or a quantity of engineered extracellular vesicles in the sample 115 without the defined cargo.

[0067] In some example embodiments, the LP profile analysis engine 110 may derive a variety of insights by analyzing the cargo carried by the at least one type (or subtype) of lipidic particle present in the sample 115. For example, the LP profile analysis engine 110 may determine, based at least on the presence and/or absence of certain organ-specific cargo, the origin (e.g., organ site) of the at least one type (or subtype) of lipidic particles (e.g., extracellular vesicles and/or the like). The LP profile analysis engine 110 may also determine, based at least on the presence and/or absence of certain function specific cargo, an ability of the at least one type (or subtype) of lipidic particles to mediate one or more corresponding functions. In the case of a disease specific cargo, the LP profile analysis engine 110 may determine a disease state based on the presence and/or absence of such cargo. Meanwhile, the presence and/or absence of a treatment specific cargo may be used to determine whether a patient associated with the sample 115 is susceptible or responding

15 to the corresponding treatment. Finally, certain cargo may also be specific to certain types (or subtypes) of lipidic particles (e.g., extracellular vesicles), in which case the LP profile analysis engine 110 may determine the biogenesis pathway of the lipidic particle based on such cargo.

[0068] In some example embodiments, the LP profile analysis engine 110 may analyze the first lipidic particle profile 150a relative to a second lipidic particle profile 150b in order to determine at least one of a disease diagnosis, a disease progress, a treatment, and a treatment response for the patient associated with the first lipidic particle profile 150a. The second lipidic particle profile 150b may provide a variety of non-patient specific and/or patient-specific reference points. For example, in some cases, the second lipidic particle profile 150b may be a non-patient specific reference lipidic particle profile representative of a disease-free state or a particular stage of a disease, in which case the LP profile analysis engine 110 may determine a disease diagnosis and/or a disease progress for the patient by at least analyzing the first lipidic particle profile 150a of the patient relative to the second lipidic particle profile 150b. Alternatively, and/or additionally, the second lipidic particle profile 150b may be a non-patient specific reference profile representative of an untreated state prior to the administration of a treatment, in which case the LP profile analysis engine 110 may determine a treatment response for the patient by analyzing the first lipidic particle profile 150a of the patient relative to the second lipidic particle profile 150b.

[0069] In some cases, instead of a non-patient specific reference lipidic particle profile, the second lipidic particle profile 150b may be associated with a same patient as the first lipidic particle profile 150a. For example, the first lipidic particle profile 150a may be representative of a first state of the patient at a first time while the second lipidic particle profile 150b may be representative of a second state of the patient at a second time. As such, the LP profile analysis engine 110 may perform a comparative analysis of the first lipidic particle profile 150a and the second lipidic particle profile 150b in order to determine a disease progress of the patient.

[0070] In some cases, the first lipidic particle profile 150a may be representative of a first state of the patient prior to an administration of a treatment while the second lipidic particle profile 150b may be representative of a second state of the patient subsequent to the administration of the treatment. Alternatively, and/or additionally, the first lipidic particle profile 150a may be representative of a first state of the patient being administered one treatment and the second lipidic particle profile 150b may be representative of a second state of the patient being administered a different treatment. Accordingly, the LP profile analysis engine 110 may determine a response of the patient to one or more treatments by at least performing a comparative analysis of the first lipidic particle profile 150a and the second lipidic particle profile 150b.

[0071] In some example embodiments, the LP profile analysis engine 110 may generate, for display in a user interface 135 at the client device 130, a visual representation of each type of lipidic particle identified as present in the sample 1 15. For example, in some cases, the LP profile analysis engine 110 may generate, for display in the user interface 135, a visual representation of at least a portion of the first lipidic particle 150a and/or the second lipidic particle profile 150b. Accordingly, in some cases, the LP profile analysis engine 110 may generate the user interface 135 to display a visual representation (e.g., a bar graph, a pie chart, a histogram, and/or the like) indicating a quantity and/or a relative proportion of each type of lipidic particle present in the sample 115. The visual representation may include, for instance, a different visual indicator for each type (or subtype) of lipidic particle present in the sample 115 in order to enable a visual differentiation between the corresponding populations of lipidic particle. Alternatively and/or additionally, the LP profile analysis engine 110 may generate the user interface 135 to display a visual representation of a reduced dimension representation of a dataset including the set of spectral emission measurements associated with each lipidic particle present in the sample 115.

[0072] FIG. 2 depicts a flowchart illustrating an example of a process 200 for resolving and classifying lipidic particle, in accordance with some example embodiments. Referring to FIGS. 1 and 2, the process 200 may be performed by the LP profile analysis engine 110, for example, to resolve and classify the lipidic particles present in the sample 115.

[0073] At 202, the LP profile analysis engine 110 may receive a sample including a plurality of lipidic particles. For example, the LP profile analysis engine 110 may receive the sample 115, which may contain a plurality of lipidic particle. In some cases, the sample 115 may be a biological sample or a derivation of a biological sample such as a purified biological sample, an enriched biological sample, and/or the like. Moreover, the sample 115 may include a variety of biological matter including, for example, one or more tissue fragments, free cells, body fluids, and/or the like. In some example embodiments, the sample 115 may be treated such that the lipidic particles present in the sample 115 emit light when exposed to an excitation light source. For instance, the sample 115 may be treated with a dye, such as a lipophilic dye (e.g., Di-8-ANEPPS, F2N12S, DPH, laurdan, and/or the like), having a spectral emission spectrum corresponding to one or more properties of each lipidic particle. To further illustrate, FIG. 4 depicts a schematic diagram illustrating an example of a workflow 400 for resolving and classifying extracellular vesicles which, as noted, are a type of lipidic particles. In the example shown in FIG. 4, at 402, the sample 115 may undergo extracellular vesicle (EV) labeling for example, by treating the sample 115 to one or more of a lipophilic dye (e.g., Di-8-ANEPPS), an antibody with affinity towards one or more surface antigens (e.g., CD63, CD9, CD81), a genetically encoded fluorescent reporter (e.g., CD63-pHluorin), and/or the like. [0074] At 204, the LP profile analysis engine 110 may determine, for each lipidic particle of the plurality of lipidic particles, a set of spectral emission measurements corresponding to one or more properties of the lipidic particle. In some example embodiments, the sample 115 containing the plurality of lipidic particles may undergo spectral flow cytometry to determine a spectral emission spectrum of each lipidic particle present in the sample 115. In the example shown in FIG. 1, the lipidic particles in the sample 115 may be carried in a liquid stream of the flow cell 121 and passed through the light beam from the excitation light source 123 in a single file. The detector 125 of the flow cytometer 120 may measure the fluorescence intensity of the light emitted by each lipidic particle in the sample 115 in response to being exposed to the excitation light source 123. As noted, the sample 1 15 may be treated with a dye having a spectral emission spectrum corresponding to one or more properties of each lipidic particle present in the sample 115.

[0075] When the sample 115 is treated with a lipophilic dye, for example, the spectral emission spectrum of the dye may be indicative of the lipid order (or disorder) in the membrane of each lipidic particle in the sample 115. The lipid order (or disorder) in the membrane of a lipidic particle may be dependent on the lipid composition of the membrane including, for example, a first proportion of lipid molecules, a second proportion of water molecules, and/or the like. FIG. 5A depicts a graph 500 illustrating a first relationship between the lipid order (Lo) and the first proportion of lipid molecules (e.g., the molar ratio of cholesterol) in the membrane of a lipidic particle, such as an extracellular vesicle (EV) and/or the like. The graph 500 also shows a second relationship between lipid order (Lo) and the second proportion of water molecules (e.g., hydration) in the membrane of a lipidic particle. As shown in FIG. 5A, the membrane of a lipidic particle, such as an extracellular vesicle and/or the like, may exhibit a higher lipid order (Lo) where when the first proportion of lipid molecules is high but the second proportion of water molecules is low. Conversely, the membrane of a lipidic particle may exhibit a higher lipid disorder (Ld) where the first proportion of lipid molecules is low but the second proportion of water molecules is high. In some cases, the lipid order (or disorder) of the membrane of a lipidic particle may be quantified by a lipid order metric such as, for example, general polarity (GP) and/or the like. For example, the general polarity (GP) value of the membrane of a lipidic particle may be determined, in accordance with Equation (1) below, based on a first fluorescence intensity (EM ⁵¹¹ ) of a first wavelength of light (e.g., 611 nanometers) emitted by the lipidic particle in response being exposed to the excitation light source and a second fluorescence intensity (EM ⁷⁰² ) of a second wavelength of light (e.g., 702 nanometers) emitted by the lipidic particle in response to being exposed to the excitation light source. An example of the relationship between the general polarity (GP) value of the membrane of a lipidic particle and the lipid order of the membrane is illustrated in the graph 525 of FIG. 5B.

[0076] Accordingly, in some cases, the LP profile analysis engine 110 may determine, for each lipidic particle in the sample 115, a set of spectral emission measurements corresponding to the lipid order (or disorder) of the lipidic particle. FIG. 5C depicts a graph 550 illustrating the relationship between the emission spectrum of a lipidic particle and the proportion of lipid molecules (e g., cholesterol) in the membrane of the lipidic particle. As shown in FIG. 5C, the emission spectrum of the lipidic particle shifts in accordance with the proportion of lipid molecules (e.g., cholesterol) in the membrane of the lipidic particle. For example, in the graph 550, a lipidic particle having 0% lipid molecules in its membrane, a lipidic particle having 10% lipid molecules in its membrane, a lipidic particle having 25% lipid molecules in its membrane, and a lipidic particle having 45% lipid molecules in its membrane all exhibit a different emission spectra, meaning that lipidic particles having different lipid proportions in their membranes may be differentiated based on their respective emission spectra. Moreover, graph 550 shows that the melt flow index (MFI) also of the membrane of each lipidic particle also changes in accordance with the proportion of lipid molecules in the membrane.

[0077] Referring again to the example shown in FIG. 4, after the sample 115 undergoes extracellular vesicle (EV) labeling at 402, the sample 115 may undergo extracellular vesicle (EV) flow cytometry at 404 to generate, for each extracellular vesicle included in the sample 115, the set of spectral emission measurements including the multi-parametric extracellular vesicle (EV) features shown at 406. Tn some cases, the set of spectral emission measurements for each extracellular vesicle in the sample 115 may include an n quantity of measurements, each of which corresponding to the fluorescence intensity of a wavelength of light in the visible spectrum (e.g., 528 nanometers, 582 nanometers, 611 nanometers, 702 nanometers, and 773 nanometers) emitted by the extracellular vesicle in response to being exposed to the excitation light source 123 (e.g., a charge-coupled device (CCD) detector). Moreover, in some cases, the set of spectral emission measurements for each extracellular vesicle in the sample 115 may include a maximum, a minimum, a mean, a mode, and/or a median fluorescence intensity measurement exhibited by the extracellular vesicle in response to being exposed to the excitation light source 123. In some cases, in addition to the aforementioned fluorescence intensity measurements, the set of spectral emission measurements for each extracellular vesicle in the sample 115 may also include an area (e.g., a brightfield area) and/or an aspect ratio of the extracellular vesicle. As shown in FIG. 4, in some cases, the set of spectral measurements associated with each extracellular vesicle (or lipidic particle) may form the “fingerprint” of the extracellular vesicle. As explained in more detail below, one or more populations of extracellular vesicles in the sample 115 that exhibit similar properties (e.g., lipid order and/or the like) may be identified based on the fingerprint associated with each extracellular vesicle.

[0078] At 206, the LP profile analysis engine 110 may identify, based at least on the set of spectral emission measurements associated with each lipidic particle, one or more populations of lipidic particles present in the sample 115. In some example embodiments, the LP profile analysis engine 110 may identify one or more populations of lipidic particles present in the sample 115 based on a reduced dimension representation of a dataset including the set of spectral emission measurements for each lipidic particle in the sample 115. For example, in some cases, the LP profile analysis engine 1 10 may generate the reduced dimension representation of the dataset by applying one or more of t-distributed stochastic neighbor embeeding (t-SNE), uniform manifold approximation and projection (UMAP), principal component analysis (PCA), linear discriminant analysis (LDA), a machine learning model, and/or the like. Moreover, in some cases, the reduced dimension representation of the dataset may include one or more clusters of lipidic particles, each of which corresponding to an individual population of lipidic particles present in the sample 115. Tn the example shown in FIG. 4, the LP profile analysis engine 110 may apply, to the dataset including the set of spectral emission measurements for each extracellular vesicle in the sample 115, uniform manifold approximation and projection (UMAP) to generate the reduced dimension representation of the dataset shown at 408 in which extracellular vesicles having a similar emission spectra (or similar fingerprints) form one or more individual clusters. The reduced dimension representation of the dataset may include multiple clusters of extracellular vesicles including, for example, cluster #3 and cluster #4 shown at 410. [0079] At 208, the LP profile analysis engine 110 may identify a type/subtype of lipidic particle corresponding to each population/ of the one or more populations of lipidic particles present with the sample 115. In some example embodiments, each population of lipidic particles identified as present in the sample 115 may correspond to a type (or subtype) of lipidic particles characterized by one or more properties of the lipidic particle such as, for example, the lipid order (or disorder) in the membrane of the lipidic particle. For example, the LP profile analysis engine 110 may determine that the sample 115 includes a first type (or first subtype) of lipidic particles corresponding to a first population of lipidic particles having a first lipid order (or disorder) and a second type (or second subtype) of lipidic particles corresponding to a second population of lipidic particles having a second lipid order (or disorder). Tn the example depicted in FIG. 4, cluster #3 and cluster #4 shown at 410 may each correspond to a distinct type (or subtype) of extracellular vesicles present in the sample 115 as characterized, for example, by the different lipid order (or disorder) in the membrane of the extracellular vesicles. As shown in FIG. 6A, lipid order may be a stable metric for differentiating between different populations of lipidic particles such as, for example, extracellular vesicles, engineered extracellular vesicles, lipoproteins, synthetic particles, liposomes, and/or the like. As shown in FIG. 6A, across different types of lipophilic dyes including Di-4-ANNEPDQ, Di-8-ANNEPS, and F2N12S, the difference in general polarity (GP) value between a first lipidic particle having a lipid ordered (Lo) membrane and a second lipidic particle having a lipid disordered (Ld) is measurable by flow cytometry (e.g., the flow cytometer 120 of FIG. 1). That is, the emission spectra of two (or more) lipidic particles having different magnitudes of lipid order (or disorder), as quantified by general polarity (GP), may be sufficiently different such that the two (or more) lipidic particles may be differentiated based on their respective emission spectrum. FIG. 6B depicts a graph 600 that further shows the relationship between lipid order, as quantified by general polarity (GP) value, and the emission spectra (e.g., the normalized mean fluorescent intensity) of different lipidic particles.

[0080] At 210, the LP profile analysis engine 110 may generate an lipidic particle profile to include one or more types/subtypes of lipidic particles identified as present in the sample 115. In some example embodiments, the LP profile analysis engine 110 may generate, for a patient associated with the sample 115, the first lipidic particle profile 150a to include the one or more types (or subtypes) of lipidic particles identified as present in the sample 115. In some cases, the LP profile analysis engine 110 may perform a comparative analysis between the first lipidic particle profile 150a of the patient and the second lipidic particle profile 150b, which may be a patient-specific or non-patient specific lipidic particle profile, in order to determine one or more of a disease diagnosis, a disease progress, a treatment, and a treatment response for the patient. For example, the first lipidic particle profile 150a may be generated to include the one or more types (or subtypes) of lipidic particles identified as present in the sample 115 and, in some cases, one or more types of lipidic particles not present in the sample 115. Alternatively and/or additionally, the LP profile analysis engine 110 may generate the first lipidic particle profile 150a to include a quantity and/or a relative proportion of each type (or subtype) of lipidic particles present in the sample 115. In some cases, in addition to the types (or subtypes) of lipidic particles identified as present in the sample 115, the LP profile analysis engine 110 may further generate the first lipidic particle profile 150a to include a cargo associated with at least one type (or subtype) of lipidic particles present in the sample 115. For instance, the cargo that carried by the at least one type (or subtype) of extracellular vesicles present in the sample 115 may be determined by treating the sample with one or more of an antibody having a binding specificity towards an antigen present in the at least one type (or subtype) of extracellular vesicles, a dye, and a nucleotide- hybridization of the at least one type (or subtype) of extracellular vesicles. Meanwhile, the first lipidic particle profile 150a may be generated to include a presence, an absence, a quantity, and/or a relative proportion of each type of cargo, such as lipids, nucleotides, carbohydrates, and/or proteins, associated with the at least one type of extracellular vesicles.

[0081] At 212, the LP profile analysis engine 110 may generate, for display in a user interface, a visual representation of at least a portion of the lipidic particle profile. In some example embodiments, the LP profile analysis engine 110 may generate, for display in the user interface 135 at the client device 130, a visual representation of at least a portion of the first lipidic particle profile 150a generated at operation 210 and/or the second lipidic particle profile 150b used for the comparative analysis. For instance, FIG. 7 depicts a screenshot illustrating an example of the user interface 135 displaying a visual representation of the reduced dimension representation of the dataset including the set of spectral emission measurements associated with each lipidic particle present in the sample 115. In the example of the user interface 135 shown in FIG. 7, the visual representation of the reduced dimension representation of the dataset includes one or more clusters of lipidic particles, each of which corresponding a different type (or subtype) of lipidic particles present in the sample 115. The visual representation of the reduced dimension representation of the dataset may include clusters numbered 2, 5, 6, 7, 8, 9, 10, 11, 12, and 13, each of which corresponding to a type (or subtype) of lipidic particle present in the sample 115.

[0082] FIG. 3 depicts a flowchart illustrating an example of a process 300 for analyzing a lipidic particle profde, in accordance with some example embodiments. Referring to FIGS. 1 and 3, the process 400 may be performed by the LP profile analysis engine 110 to analyze, for example, the first lipidic particle profile 150a relative to the second lipidic particle profile 150b.

In some cases, the LP profile analysis engine 110 may perform a comparative analysis of the first lipidic particle profile 150a relative to the second lipidic particle profile 150b to determine, for example, at least one of a disease diagnosis, a disease progress, a treatment, and a treatment response for the patient associated with the first lipidic particle profile 150a.

[0083] At 302, the LP profile analysis engine 110 may analyze a first lipidic particle profile of a patient relative to a second lipidic particle profile. In some example embodiments, the LP profile analysis engine 110 may perform a comparative analysis between the first lipidic particle profile 150a of the patient and the second lipidic particle profile 150b. In some cases, the first lipidic particle profile 150a may be a patient-specific lipidic particle profile representative of a current state of the patient associated with the sample 115 while the second lipidic particle profile 150b may be non-patient specific reference lipidic particle profile representative of a disease free state, a particular state of a disease, or an untreated state prior to an administration of a treatment.

[0084] Alternatively, the first lipidic particle profile 150a and the second lipidic particle profile 150b may be patient-specific lipidic particle profiles representative of different states of the patient associated with the sample 115. For example, in some cases, the first lipidic particle profile 150a may be representative of a first state of the patient at a first time while the second lipidic particle profile 150b may be representative of a second state of the patient at a second time. Alternatively and/or additionally, the first lipidic particle profile 150a may be representative of a first state of the patient prior to the administration of a treatment and the second lipidic particle profile 150b may be representative of a second state of the patient subsequent to the administration of the treatment. As yet another example, the first lipidic particle profile 150a may be representative of a first state of the patient administered a first treatment and the second lipidic particle profile 150b may be representative of a second state of the patient administered a second treatment. [0085] At 304, the LP profile analysis engine 110 may determine, based at least on the analysis, at least one of a disease diagnosis, a disease progress, a treatment, and a treatment response for the patient. In some example embodiments, the LP profile analysis engine 110 may determine, based at least on the comparative analysis between the first lipidic particle profile 150a of the patient and the second lipidic particle profile 150b, at least one of a disease diagnosis, a disease progress, a treatment, and a treatment response for the patient associated with the sample 115. For example, the LP profile analysis engine 110 may determine, based at least on the comparative analysis between the first lipidic particle profile 150a of the patient and the second lipidic particle profile 150b, a first type (or first subtype) of lipidic particles that is present in the sample 1 15 of the patient and/or a second type (or second subtype) of lipidic particles that is absent from the sample 115 of the patient. In some cases, one or more of a disease diagnosis, a disease progress, a treatment, and a treatment response for the patient may be determined based on the presence of the first type (or first subtype) of lipidic particle and/or the absence of the second type (or second subtype) of lipidic particle. Alternatively and/or additionally, the LP profile analysis engine 110 may determine, based at least on the comparative analysis between the first lipidic particle profile 150a of the patient and the second lipidic particle profile 150b, a change in the quantity and/or relative proportion of one or more types (or subtypes) of lipidic particles. Accordingly, in some cases, the LP profile analysis engine 110 may determine, based at least on the change in the quantity and/or relative proportion of one or more types of lipidic particles, one or more of a disease diagnosis, a disease progress, a treatment, and a treatment response for the patient.

[0086] In view of the above-described implementations of subj ect matter this application discloses the following list of examples, wherein one feature of an example in isolation or more than one feature of said example taken in combination and, optionally, in combination with one or more features of one or more further examples are further examples also falling within the disclosure of this application:

[0087] Item 1: A computer-implemented method, comprising: receiving a sample including a plurality of lipidic particles; determining, for each lipidic particle of the plurality of lipidic particles, a set of spectral emission measurements corresponding to a lipid order in a membrane of the lipidic particle; identifying, based at least on the set of spectral emission measurements associated with each lipidic particle, one or more populations of lipidic particles present in the sample; identifying a type of lipidic particles corresponding to each population of the one or more populations of lipidic particles present in the sample; and generating a first lipidic particle profile to include one or more types of lipidic particles identified as present in the sample.

[0088] Item 2: The method of Item 1, wherein the first lipidic particle profile is generated to include a first type of lipidic particles present in the sample and/or a second type of lipidic particles absent from the sample.

[0089] Item 3: The method of any of Items 1 to 2, further comprising: determining a quantity of each type of the one or more types of lipidic particles present in the sample; and generating the first lipidic particle profile to further include the quantity of each type of lipidic particle present in the sample.

[0090] Item 4: The method of any of Items 1 to 3, further comprising: determining whether a quantity of each type of the one or more types of lipidic particles present in the sample exceeds a threshold value; and generating the first lipidic particles profile to further include a first type of lipidic particles whose quantity exceeds the threshold value and/or a second type of lipidic particles whose quantity fails to exceed the threshold value. [0091] Item 5: The method of any of Items 1 to 4, further comprising: determining a relative proportion of each type of the one or more types of lipidic particles present in the sample; and generating the first lipidic particle profile to further include the relative proportion of each type of lipidic particles present in the sample.

[0092] Item 6: The method of any of Items 1 to 5, further comprising: determining a cargo associated with at least one type of lipidic particles present in the sample; and generating the first lipidic particle profile to include the cargo associated with the at least one type of lipidic particles.

[0093] Item 7: The method of Item 6, wherein the cargo includes one or more lipids, nucleotides, carbohydrates, and proteins.

[0094] Item 8: The method of any of Items 6 to 7, wherein the first lipidic particle profile is generated to include a presence, an absence, a quantity, and/or a relative proportion of each type of cargo associated with the at least one type of lipidic particles.

[0095] Item 9: The method of any of Items 6 to 8, wherein the cargo is detected by (i) treating the sample with an antibody having a binding specificity towards an antigen present in the at least one type of lipidic particles, (ii) treating the sample with a dye, or (iii) performing a nucleotide-hybridization of the at least one type of lipidic particles.

[0096] Item 10: The method of any of Items 1 to 9, wherein the first lipidic particle profile is generated for a patient associated with the sample.

[0097] Item 11 : The method of Item 10, further comprising: analyzing the first lipidic particle profile relative to a second lipidic particle profile; and determining, based at least on the analysis, at least one of a disease diagnosis, a disease progress, a treatment, and a treatment response for the patient. [0098] Item 12: The method of Item 11, wherein the second lipidic particle profile comprises a non-patient specific reference lipidic particle profile representative of (i) a disease- free state, (ii) a particular stage of a disease, or (iii) an untreated state prior to an administration of the treatment.

[0099] Item 13: The method of any of Items 11 to 12, wherein the first lipidic particle profile is representative of a first state of the patient prior to an administration of the treatment, and wherein the second lipidic particle profile is representative of a second state of the patient subsequent to the administration of the treatment.

[0100] Item 14: The method of any of Items 11 to 13, wherein the first lipidic particle profile is representative of a first state of the patient at a first time, and wherein the second lipidic particle profile is representative of a second state of the patient at a second time.

[0101] Item 15: The method of any of Items 11 to 14, wherein the first lipidic particle profile is representative of a first state of the patient being administered the treatment, and wherein the second lipidic particle profile is representative of a second state of the patient being administered a different treatment.

[0102] Item 16: The method of any of Items 1 to 15, further comprising: determining, based at least on the first lipidic particle profile of the patient, at least one of (i) a disease state for a patient associated with the sample, (ii) a response of the patient to a treatment for a disease, (iii) a first likelihood of the patient responding to the treatment, (iv) a second likelihood of the patient relapsing after the treatment, and (v) a durability of the patient’s response to the treatment.

[0103] Item 17: The method of any of Items 1 to 16, wherein the set of spectral emission measurements include a first fluorescence intensity of a first wavelength of light emitted by the lipidic particle in response being exposed to an excitation light source, and wherein the set of spectral emission measurements further includes a second fluorescence intensity of a second wavelength of light emitted by the lipidic particle in response to being exposed to the excitation light source.

[0104] Item 18: The method of any of Items 1 to 17, wherein the set of spectral emission measurements include a maximum, a minimum, a mean, a mode, and/or a median fluorescence intensity exhibited by the lipidic particle in response to being exposed to an excitation light source.

[0105] Item 19: The method of any of Items 1 to 18, wherein the set of spectral emission measurement include at least one of an area and an aspect ratio of the lipidic particle observed at each of an n quantity of wavelength of light.

[0106] Item 20: The method of any of Items 1 to 19, wherein the set of spectral emission measurements includes an n quantity of measurements, and wherein each measurement of the n quantity of measurements corresponds to a fluorescence intensity of a different wavelength of light emitted by the n quantity of measurements in response to being exposed to an excitation light source.

[0107] Item 21 : The method of Item 20, wherein n is between 5 and 25.

[0108] Item 22: The method of any of Items 20 to 21, wherein each measurement of the n quantity of measurements is associated with a wavelength of light between 350 nanometers and 800 nanometers.

[0109] Item 23: The method of any of Items 1 to 22, wherein the sample is treated with a dye having a spectral emission spectrum indicative of the lipid order of each lipidic particle present in the sample.

[0110] Item 24: The method of any of Items 1 to 23, wherein the sample is treated with a lipophilic dye. [0111] Item 25: The method of any of Items 1 to 24, further comprising: generating a reduced dimension representation of a dataset including the set of spectral emission measurements associated with each lipidic particle in the sample; and identifying, based at least on the reduced dimension representation of the dataset, the one or more populations of lipidic particles.

[0112] Item 26: The method of Item 25, wherein the reduced dimension representation of the dataset is generated by applying t -distributed stochastic neighbor embeeding (t-SNE), uniform manifold approximation and projection (UMAP), principal component analysis (PCA), linear discriminant analysis (LDA), and/or a machine learning model.

[0113] Item 27: The method of any of Items 1 to 26, wherein the sample comprises a biological sample or a derivation of the biological sample.

[0114] Item 28: The method of any of Items 1 to 27, wherein the sample comprises one or more of a tissue fragment, a free cell, or a bodily fluid.

[0115] Item 29: The method of any of Items 1 to 28, wherein the sample comprises one or more organotypic cultures and/or organoids.

[0116] Item 30: The method of any of Items 1 to 29, wherein the sample comprises one or more of a bacterial sample, a fungal sample, or a plant sample.

[0117] Item 31 : The method of any of Items 1 to 30, further comprising: differentiating, based at least on the set of spectral emission measurements associated with each lipidic particle, between the one or more populations of lipidic particles present in the sample.

[0118] Item 32: The method of any of Items 1 to 31, further comprising: identifying a subtype of lipidic particles corresponding to each population of the one or more populations of lipidic particles present in the sample; and generating the first lipidic particle profile to further include one or more subtypes of lipidic particles identified as present in the sample. [0119] Item 33: The method of any of Items 1 to 32, wherein the one or more types of lipidic particles include extracellular vesicles, engineered extracellular vesicles, lipoproteins, liposomes, and/or synthetic particles.

[0120] Item 34: The method of any of Items 1 to 33, further comprising: generating a visual representation of each type of the one or more types of lipidic particles present in the sample.

[0121] Item 35: The method of Item 34, wherein the visual representation includes a different visual indicator for each type of lipidic particles present in the sample.

[0122] Item 36: The method of any of Items 34 to 35, wherein the visual representation includes a reduced dimension representation of a dataset including the set of spectral emission measurements associated with each lipidic particle present in the sample.

[0123] Item 37: The method of Item 36, wherein the reduced dimension representation of the dataset includes one or more clusters of lipidic particles, and wherein each cluster of the one or more clusters corresponds a different type or a different subtype of lipidic particles.

[0124] Item 38: The method of any of Items 34 to 37, wherein the visual representation indicates a quantity and/or a relative proportion of each type of the one or more types of lipidic particles present in the sample.

[0125] Item 39: The method of any of Items 1 to 38, wherein the lipid order in the membrane of the lipidic particle corresponds to a first proportion of lipid molecules and/or a second proportion of water molecules in the membrane of the lipidic particle.

[0126] Item 40: The method of any of Items 1 to 39, wherein the lipid order corresponds to a general polarity (GP) of the membrane of the lipidic particle. [0127] Item 41 : A system, comprising: at least one data processor; and at least one memory storing instructions, which when executed by the at least one data processor, result in operations comprising the method of any of Items 1 to 40.

[0128] Item 42: A non-transitory computer readable medium storing instructions, which when executed by at least one data processor, result in operations comprising the method of any of Items 1 to 40.

[0129] FIG. 8 depicts a block diagram illustrating an example of computing system 800, in accordance with some example embodiments. Referring to FIGS. 1 and 8, the computing system 800 may be used to implement the LP profile analysis engine 110, the client device 130, and/or any components therein

[0130] As shown in FIG. 5, the computing system 800 can include a processor 810, a memory 820, a storage device 830, and an input/output device 840. The processor 810, the memory 820, the storage device 830, and the input/output device 840 can be interconnected via a system bus 850. The processor 810 is capable of processing instructions for execution within the computing system 800. Such executed instructions can implement one or more components of, for example, the LP profile analysis engine 110, the client device 130, and/or the like. In some example embodiments, the processor 810 can be a single-threaded processor. Alternately, the processor 810 can be a multi -threaded processor. The processor 810 is capable of processing instructions stored in the memory 820 and/or on the storage device 830 to display graphical information for a user interface provided via the input/output device 840.

[0131] The memory 820 is a computer readable medium such as volatile or non-volatile that stores information within the computing system 800. The memory 820 can store data structures representing configuration object databases, for example. The storage device 830 is capable of providing persistent storage for the computing system 800. The storage device 830 can be a floppy disk device, a hard disk device, an optical disk device, or a tape device, or other suitable persistent storage means. The input/output device 840 provides input/output operations for the computing system 800. In some example embodiments, the input/output device 840 includes a keyboard and/or pointing device. In various implementations, the input/output device 840 includes a display unit for displaying graphical user interfaces.

[0132] According to some example embodiments, the input/output device 840 can provide input/output operations for a network device. For example, the input/output device 840 can include Ethernet ports or other networking ports to communicate with one or more wired and/or wireless networks (e.g., a local area network (LAN), a wide area network (WAN), the Internet).

[0133] In some example embodiments, the computing system 800 can be used to execute various interactive computer software applications that can be used for organization, analysis and/or storage of data in various formats. Alternatively, the computing system 800 can be used to execute any type of software applications. These applications can be used to perform various functionalities, e.g., planning functionalities (e.g., generating, managing, editing of spreadsheet documents, word processing documents, and/or any other objects, etc.), computing functionalities, communications functionalities, etc. The applications can include various add-in functionalities or can be standalone computing products and/or functionalities. Upon activation within the applications, the functionalities can be used to generate the user interface provided via the input/output device 840. The user interface can be generated and presented to a user by the computing system 800 (e.g., on a computer screen monitor, etc.). [0134] One or more aspects or features of the subject matter described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs, field programmable gate arrays (FPGAs) computer hardware, firmware, software, and/or combinations thereof. These various aspects or features can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device. The programmable system or computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0135] These computer programs, which can also be referred to as programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object- oriented programming language, and/or in assembly/machine language. As used herein, the term “machine-readable medium” refers to any computer program product, apparatus and/or device, such as for example magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs), used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor. The machine-readable medium can store such machine instructions non-transitorily, such as for example as would a non-transient solid-state memory or a magnetic hard drive or any equivalent storage medium. The machine-readable medium can alternatively or additionally store such machine instructions in a transient manner, such as for example, as would a processor cache or other random access memory associated with one or more physical processor cores.

[0136] To provide for interaction with a user, one or more aspects or features of the subject matter described herein can be implemented on a computer having a display device, such as for example a cathode ray tube (CRT) or a liquid crystal display (LCD) or a light emitting diode (LED) monitor for displaying information to the user and a keyboard and a pointing device, such as for example a mouse or a trackball, by which the user may provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well. For example, feedback provided to the user can be any form of sensory feedback, such as for example visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any form, including acoustic, speech, or tactile input. Other possible input devices include touch screens or other touch-sensitive devices such as single or multi-point resistive or capacitive track pads, voice recognition hardware and software, optical scanners, optical pointers, digital image capture devices and associated interpretation software, and the like.

[0137] In the descriptions above and in the claims, phrases such as “at least one of’ or “one or more of’ may occur followed by a conjunctive list of elements or features. The term “and/or” may also occur in a list of two or more elements or features. Unless otherwise implicitly or explicitly contradicted by the context in which it used, such a phrase is intended to mean any of the listed elements or features individually or any of the recited elements or features in combination with any of the other recited elements or features. For example, the phrases “at least one of A and B;” “one or more of A and B;” and “A and/or B” are each intended to mean “A alone, B alone, or A and B together.” A similar interpretation is also intended for lists including three or more items.

For example, the phrases “at least one of A, B, and C;” “one or more of A, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, B alone, C alone, A and B together, A and C together, B and C together, or A and B and C together.” Use of the term “based on,” above and in the claims is intended to mean, “based at least in part on,” such that an unrecited feature or element is also permissible.

[0138] The subject matter described herein can be embodied in systems, apparatus, methods, and/or articles depending on the desired configuration. The implementations set forth in the foregoing description do not represent all implementations consistent with the subject matter described herein Instead, they are merely some examples consistent with aspects related to the described subject matter. Although a few variations have been described in detail above, other modifications or additions are possible. In particular, further features and/or variations can be provided in addition to those set forth herein. For example, the implementations described above can be directed to various combinations and subcombinations of the disclosed features and/or combinations and subcombinations of several further features disclosed above. In addition, the logic flows depicted in the accompanying figures and/or described herein do not necessarily require the particular order shown, or sequential order, to achieve desirable results. Other implementations may be within the scope of the following claims.