Methods for determining single cell energetics and metabolic flux

Abstract

This disclosure provides Single cell Isotopomer Distributions and Energetics of Translation under Energetic Stress (SIDETES), a novel strategy to deconvolve metabolic heterogeneity in complex cellular environments, such as tumor and other tumor-resident cell populations, endocrine tissues, whole blood (Peripheral Blood Mononuclear Cells (PBMCs), or lymphoid organs. SIDETES can examine energetic dependence of a substrate, an inhibitor / activator, or a genetic change on rare cells in tumors, and obtain the network response for cells sorted by energy.

Description

[0001] Docket No.: 182219.00272

[0002] METHODS FOR DETERMINING SINGLE CELL ENERGETICS AND METABOLIC FLUX

[0003] CROSS-REFERENCE TO RELATED APPLICATIONS

[0004] This application claims priority under 35 U.S.C. §119(e) to US Provisional Patent Application No. 63 / 681,574, filed August 9, 2024. The foregoing application is incorporated by reference herein in its entirety.

[0005] FIELD OF THE INVENTION

[0006] This invention relates to methods for determining single cell energetics and metabolic flux.

[0007] BACKGROUND OF THE INVENTION

[0008] Energetic metabolism profiles provide critical insights into the primary energy sources and biochemical pathways utilized by cells to generate adenosine triphosphate (ATP), reflecting both the cells’ dependency on specific pathways and their capacity to engage alternative metabolic routes. Such profiles inform the functional competence of cells under various physiological and pathological conditions, including survival and adaptation in distinct anatomical microenvironments and in response to intrinsic and extrinsic signaling cues. Understanding energetic metabolism profiles is central to elucidating the physiological states and functional phenotypes of diverse cell types. Notably, cancer stem cells, tumor cells, immune cells, and neurons exhibit distinct energetic metabolism profiles that influence their proliferative potential, differentiation capacity, and specialized functions. Furthermore, in the context of cancer, energetic metabolism profiling is particularly significant, as it informs the susceptibility of transformed cells to targeted inhibitors of specific metabolic pathways.

[0009] Existing methods for determining the energetic metabolism profile of cells fall into three principal categories, all of which rely on bulk cell culture analysis. In one category, changes in plasma membrane acidification rate and other indirect readouts — such as oxygen consumption rate — are measured to infer the activity of distinct metabolic pathways. In a second category, intracellular metabolite levels are directly measured by mass spectrometry-based techniques. A third category involves assessing the activity of metabolic enzymes in fixed cells or cell lysates. These approaches typically require large numbers of purified cells, involve extensive sample Docket No.: 182219.00272 processing, and necessitate access to specialized instrumentation and sufficient quantities of tissue, thereby limiting their applicability to rare or low-abundance samples, such as patient-derived live cells or small-volume biopsies. Moreover, there is a lack of mass spectrometric imaging methods that enable independent normalization measurements of cellular metabolites, thereby limiting accurate comparisons of metabolic flux profiles between individual cells.

[0010] Thus, an improved method for determining energetic metabolism profiles with single-cell resolution and independent normalization techniques remains needed.

[0011] SUMMARY OF THE INVENTION

[0012] This disclosure addresses the need mentioned above in a number of aspects. In one aspect, this disclosure provides a method of determining a bioenergetic state of a cell. In some embodiments, the method comprises: (a) contacting a cell with an alkyne-conjugated amino acid in a first medium for a first period of time, wherein the alkyne-conjugated amino acid is incorporated into a first set of plasma membrane proteins on cell surface of the cell during translation of the first set of plasma membrane proteins; (b) contacting the cell with an azide- conjugated amino acid in a second medium for a second period of time in presence and absence of a metabolic inhibitor, wherein the azide-conjugated amino acid is incorporated into a second set of plasma membrane proteins on the cell surface of the cell during translation of the second set of plasma membrane proteins, and wherein the metabolic inhibitor causes metabolic stress to the cell; (c) contacting the cell with an azide-bearing detection tag reactive to the alkyne-conjugated amino acid in the first set of plasma membrane proteins, using click chemistry (e.g., copper-catalyzed click chemistry) to associate the azide-bearing detection tag with the first set of plasma membrane proteins; (d) contacting a cell with an alkyne-bearing detection tag reactive to the azide-conjugated amino acid on the second set of plasma membrane proteins, using click chemistry to associate the alkyne-bearing detection tag with the second set of plasma membrane proteins; (e) determining a first signal generated from the azide-bearing detection tag associated with the first set of plasma membrane proteins, wherein the first signal generated from the azide-bearing detection tag represents a baseline protein translation state of the cell; (f) determining a second signal generated from the alkyne-bearing detection tag associated with the second set of plasma membrane proteins in the presence of the metabolic inhibitor, wherein the second signal generated from the alkyne- bearing detection tag represents a metabolically-coupled protein translation state of the cell under Docket No.: 182219.00272 the metabolic stress caused by the metabolic inhibitor; (g) assessing cell viability to discriminate live and dead cells by flow cytometry; and (h) determining a bioenergetic state (e.g., bioenergetic sensitivity index) of the cell based on: a change from the baseline protein translation state to the metabolically-coupled protein translation state under the metabolic stress by comparing the second signal to the first signal.

[0013] In some embodiments, determining a bioenergetic state (e.g., bioenergetic sensitivity index) is based on the difference between the second and first signals in live cells, which reports on a change from the baseline protein translation state (i.e., azide fluorescent reporter, or detection tag) to the metabolically-coupled protein translation state (i.e., alkyne fluorescent reporter, or detection tag) encoded in the surface proteome.

[0014] In some embodiments, prior to determining the first signal, the method comprises adding a viability dye to discriminate dead cells.

[0015] In some embodiments, the method comprises performing step (b) prior to step (a). In some embodiments, the method comprises performing step (d) prior to step (c).

[0016] In some embodiments, the azide-bearing detection tag comprises an azide-bearing fluorophore, and the alkyne-bearing detection tag comprises an alkyne-bearing fluorophore. In some embodiments, the first signal generated from the azide-bearing fluorophore or the second signal generated from the alkyne-bearing fluorophore is determined by flow cytometry. In some embodiments, the first signal generated from the azide-bearing fluorophore or the second fluorescent signal generated from the alkyne-bearing fluorophore is determined by fluorescence- activated cell sorting (FACS).

[0017] In some embodiments, the method further comprises determining a baseline protein translation rate of the cell based on the first (e.g., azide fluorescent reporter, or detection tag) signal. In some embodiments, the method further comprises determining a metabolically-coupled protein translation rate of the cell under metabolic stress based on the second (e.g., alkyne fluorescent reporter, or detection tag) signal.

[0018] In some embodiments, the azide-bearing detection tag comprises an azide-bearing mass tag. In some embodiments, the alkyne-bearing detection tag comprises an alkyne-bearing mass tag. Docket No.: 182219.00272

[0019] In some embodiments, the first signal generated from the azide-bearing detection tag or the second signal generated from the alkyne-bearing detection tag is determined by mass spectrometry.

[0020] In some embodiments, the azide-bearing mass tag or the alkyne-bearing mass tag comprises an isotopic mass tag or an isobaric mass tag. In some embodiments, the azide-bearing mass tag or the alkyne-bearing mass tag comprises an isotopic mass tag or an isobaric mass tag that contains a cleavable linker to the mass tag released.

[0021] In some embodiments, the method further comprises performing mass spectrometric imaging to normalize single cell baseline metabolite flux assessments by measuring stable isotopes incorporated into metabolites and / or lipids. In some embodiments, the method further comprises performing mass spectrometric imaging to normalize single cell baseline metabolite flux assessments by measuring the azide-bearing mass tag, and / or the alkyne-bearing mass tag incorporated into metabolites and / or lipids.

[0022] In some embodiments, the method further comprises an additional reference sample to determine baseline translation rates for the surface proteome assessed in the time period of the experiment utilizing first and second detection tag signals without the addition of any metabolic stressor. In some embodiments, the reference sample is used for calculating optimized parameters for variance stabilization across the entire dataset.

[0023] In some embodiments, the step of determining the bioenergetic state of the cell comprises comparing a baseline translation rate encoded in the first detection tag signal to the metabolically- coupled translation rate encoded in the second detection tag signal when the cell is under metabolic stress, in order to normalize for differences in baseline translation rate between cells.

[0024] In some embodiments, determining the bioenergetic state of the cell comprises analyzing the metabolic profile and / or isotopic enrichment of the cell using mass spectrometric imaging, wherein the cell is dual-labeled with an alkyne-functionalized mass tag and an azide-functionalized mass tag..

[0025] In some embodiments, determining the bioenergetic state of the cell comprises normalizing the metabolic profile of the cell by measuring the respective intensities of reporter ions corresponding to the alkyne-bearing mass tag probe and the azide-bearing mass tag probe, wherein the reporter ions are detected under basal and metabolically perturbed conditions. Docket No.: 182219.00272

[0026] In some embodiments, determining the bioenergetic state of the cell comprises detecting a stable isotope using mass spectrometric imaging, and normalizing the metabolic profile of the cell based on the measured intensities of an alkyne-bearing fluorophore and an azide-bearing fluor ophore.

[0027] In some embodiments, determining the bioenergetic state of the cell comprises measuring adenosine triphosphate (ATP) flux and / or ATP turnover rate in cells and / or tissues using mass spectrometric imaging.

[0028] In some embodiments, the step of determining the bioenergetic state of the cell comprises determining a bioenergetic sensitivity index using the first detection tag signal (baseline translation) and the second detection tag signal (metabolically-coupled translation). In some embodiments, a reference sample can be used to optimize parameters for variance stabilizing transformations (i.e., a lambda-value for Yeo-Johnson transformation), and to perform variance stabilization and standardization across the entire dataset. In some embodiments, the bioenergetic sensitivity index is calculated as a ratio or difference between the first and second signals after standardization.

[0029] In some embodiments, the cell comprises a primary or transformed (including tumor cell lines) mammalian cell. These cells can be cultured under different nutrient or oxygen conditions. In some embodiments, the cell is under metabolic stress.

[0030] In some embodiments, the first period of time or the second period of time is about 2 hours to about 8 hours.

[0031] In some embodiments, the step of determining the bioenergetic state of the cell further comprises determining the rate of biomass production, such as an amount of metabolites and lipids of the cell, as normalizing the biomass production rate for a given bioenergetic state. In some embodiments, the step of determining the bioenergetic state of the cell further comprises determining metabolic flux of the cell, as normalizing pathway flux(es) for a given bioenergetic state. In some embodiments, the step of determining the bioenergetic state of the cell further comprises determining proteomic abundance, as normalizing specific proteomic targets or categories for a given bioenergetic rate. Docket No.: 182219.00272

[0032] In some embodiments, the azide-conjugated amino acid is an azide-conjugated methionine or an analog thereof. In some embodiments, the alkyne-conjugated amino acid is an alkyne- conjugated methionine or an analog thereof.

[0033] In some embodiments, the azide-conjugated amino acid comprises azide-modified homoalanine (AHA). In some embodiments, the alkyne-conjugated amino acid comprises alkyne- modified homopropargylglycine (HPG).

[0034] In some embodiments, the first medium is free of the azide-conjugated amino acid. In some embodiments, the second medium is free of the alkyne-conjugated amino acid.

[0035] In some embodiments, the first set of plasma membrane proteins and the second set of plasma membrane proteins are the same proteins. In some embodiments, the first set of plasma membrane proteins and the second set of plasma membrane proteins that are different proteins.

[0036] In some embodiments, the method comprises purifying cells by fluorescence-activated cell sorting (FACS) based on the first and second signals and re-culturing the cells.

[0037] In some embodiments, azide-conjugated amino acids on the first and / or second sets of plasma membrane proteins on the cell surface are provided an alkyne-bearing DNA barcode, which can be used as a template in single cell transcriptomics for determining whether different transcriptional states are associated with different bioenergetic states. In some embodiments, alkyne-conjugated amino acids on the plasma membrane proteins on the cell surface are provided an azide-bearing DNA barcode, which can be used as a template in single cell transcriptomics for determining whether different transcriptional states are associated with different bioenergetic states.

[0038] In some embodiments, the azide-bearing fluorophore comprises an AZD647-azide fluorophore, an Alexa Fluor 488-azide fluorophore, an Alexa Fluor 555-azide fluorophore, an Alexa Fluor 594-azide fluorophore, an Alexa Fluor 647-azide fluorophore, an Oregon Green 488- azide fluorophore, or any other azide-modified fluorophore.

[0039] In some embodiments, the alkyne-bearing fluorophore comprises an AZD488-alkyne fluorophore, an Alexa Fluor 488-alkyne fluorophore, an Alexa Fluor 555-alkyne fluorophore, an Alexa Fluor 594-alkyne fluorophore, an Alexa Fluor 647-alkyne fluorophore, an Oregon Green 488-alkyne fluorophore, or any other alkyne-modified fluorophore. Docket No.: 182219.00272

[0040] In some embodiments, the metabolic inhibitor comprises a glycolysis inhibitor, a cellular energy inhibitor, an oxidative phosphorylation inhibitor, an amino acid metabolism inhibitor, a mitochondrial metabolism inhibitor, a lipid metabolism inhibitor, a nucleotide metabolism inhibitor, a pentose phosphate pathway inhibitor, a nitrogen metabolism inhibitor, small-molecule inhibitors of one-carbon enzymes, hexosamine biosynthetic pathway inhibitors, a redox destabilizer, or a combination thereof. In some embodiments, the inhibitor comprises a protein translation inhibitor or a metabolic stressor. In some embodiments, the metabolic stressor activates cellular metabolism. In some embodiments metabolic activators of cellular metabolism may be used or methylated / alkylated metabolites that can diffuse into different cellular metabolic compartments for anapleurotic augmentation of pathway flux.

[0041] In some embodiments, cellular energy augmenting metabolites are substituting for the metabolic inhibitor, such as methylated / alkylated pathway intermediates including methyl succinate as an example for anaerobic augmentation of the TCA cycle.

[0042] The foregoing summary is not intended to define every aspect of the disclosure, and additional aspects are described in other sections, such as the following detailed description. The entire document is intended to be related as a unified disclosure, and it should be understood that all combinations of features described herein are contemplated, even if the combination of features is not found together in the same sentence, paragraph, or section of this document. Other features and advantages of the invention will become apparent from the following detailed description. It should be understood, however, that the detailed description and the specific examples, while indicating specific embodiments of the disclosure, are given by way of illustration only, because various changes and modifications within the spirit and scope of the disclosure will become apparent to those skilled in the art from this detailed description.

[0043] BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Fig. 1 shows an overview of SIDETES (Small cell number Isotopomer Distributions and Energetics of Translation under Energetic Stress). Quadrants I, II, III, and IV represent different energy states. Here, the baseline energetics are measured by HPG incorporation, and the x-axis of the energetic quadrants represents the geometric mean fluorescent intensity (gFMI) of HPG incorporation of one particular azide-linked fluorophore. For illustration, the baseline cellular gMFIs are partitioned into a low energy state (quadrant 1) and a higher energy state (quadrant II). Docket No.: 182219.00272

[0045] AHA incorporation into the surface proteome, assessed by an alkyne linked different fluorophore, measures the cellular surface proteome response to metabolic stress. The y-axis of the energetic quadrants represents the gMFI of AHA incorporation by a second, spectrally distinct fluorophore. The second fluorescent signal partitions the cells from the baseline into quadrants III and IV. Quadrant IV reflects the bioenergetic shift from low baseline intensity to higher translation rates under energetic stress, whereas quadrant III reflects cells with high baseline intensity that retain high translation rates under metabolic stress.

[0046] Figs. 2A, 2B, 2C, 2D, and 2E show an overview of single-color surface clicking. Fig. 2A shows a schematic for single-color surface clicking. Figs. 2B, 2C, 2D, and 2E show flow cytometry histograms depicting surface labeling on Jurkat cells incubated for 2 hours with AHA (Figs. 2B and 2C) or HPG (Figs. 2D and 2E) and then clicked using our optimized protocol. Surface labeling signal in the presence of +500 pM cycloheximide (CHX) is used as a reference for inhibited translation.

[0047] Figs. 3A, 3B, 3C, 3D, and 3E show optimized clicking protocol preserves cell viability. Fig. 3A shows viability of Jurkat cells incubated in the presence of AHA or HPG for 24 hours. Fig. 3B shows proliferation of Jurkat cells incubated in the presence of AHA or HPG for 24 hours, evaluating the number of live cells. Fig. 3C shows a schematic for evaluating the impact of singlecolor clicking on Jurkat cell viability. Figs. 3D and 3E show viability of Jurkat cells after incubating in media containing AHA and clicking with Alkyne-647 dye (Fig. 3D) or media containing HPG and clicking with Azide-647 dye (Fig. 3E).

[0048] Figs. 4A, 4B, 4C, and 4D show metabolic stress shifts surface fluorescent signal. Fig. 4A shows a schematic for evaluating the impact of metabolic perturbation on single-color surface click signal in Jurkat cells. Fig. 4B shows histograms depicting Jurkat surface signal fluorescent intensity in the presence of metabolic inhibitors. Figs. 4C and 4D show Seahorse assays evaluating Extracellular Acidification Rate (ECAR) (Fig. 4C) and Oxygen Consumption Rate (OCR) (Fig. 4D) in Jurkat cells at baseline or with the addition of OA or 2-DG.

[0049] Figs. 5A, 5B, 5C, and 5D show surface clicking signal is stable over the experimental time course and correlates with bioenergetics. Fig. 5A shows a schematic for pulse-chase experiment evaluating the stability of the surface labeling signal. Figs. 5B and 5C show flow cytometry histograms (Fig. 5B) and quantification (Fig. 5C) depicting stability of the surface click signal. Docket No.: 182219.00272

[0050] Note that the -Met +AHA no click condition tells us about autofluorescence of the cells at baseline in the presence of AHA. Fig. 5D shows correlation between energy charge (and surface fluorescence after clicking (left). Correlation is between newly synthesized ATP levels measured by O18incorporation from labeled water into ATPpools and surface fluorescence after clicking (right) (see also Table 1).

[0051] Figs. 6A, 6B, 6C, 6D, 6E, and 6F show use of SIDETES for dual-color encoding of metabolically-coupled translation onto the surface proteome. Fig. 6A shows a schematic for dualcolor surface clicking on Jurkat cells. Fig. 6B shows viability of Jurkat cells after sequential clicking. Figs. 6C, 6D, 6E, and 6F show flow cytometry plots after dual labeling depicting (Fig. 6C) signal to background, (Fig. 6D) effect of labeling order, (Fig. 6E) selective decrease in HPG signal with CHX treatment, and (Fig. 6F) selective decrease in HPG signal with 2-DG treatment.

[0052] Figs. 7A, 7B, 7C, 7D, and 7E show application of SIDETES to calculate a metabolic sensitivity index. Fig. 7A shows a schematic for informatic workflow to calculate an internally normalized sensitivity index. Fig. 7B shows Scatterplots depicting flow cytometry output at different stages of the standardization pipeline. Samples are Jurkat cells cultured in conditions specified at the top of the plots. Individual points, corresponding to single cells, are colored by point density in the plot. Fig. 7C shows Violin plot depicting the internally normalized sensitivity index for Jurkat cells, where baseline translation was assessed by culturing for 4 hours in HPG, and metabolically-coupled translation was assessed by culturing for 2 hours in AHA, with the addition of different metabolic inhibitors during the final hour. Figs. 7D and 7E show Scatterplots depicting Jurkat cells treated as in (Fig. 7B), where individual points, corresponding to single cells, are colored by the calculated internally normalized sensitivity index. Figure (Fig. 7D) depicts baseline translation in both channels, and (Fig. 7E) depicts baseline translation in HPG signal and metabolically-coupled translation in AHA signal following the addition of oligomycin A (OA) and 2-deoxy glucose (2-DG).

[0053] Figs. 8A, 8B, 8C, 8D, and 8E show application of the SIDETES method to leukocytes isolated from B16-0VA tumors. Fig. 8A shows an experimental schematic for dual-color surface clicking on immune cells isolated from mice bearing Bl 6-OVA tumors to evaluate cycloheximide (CHX)-sensitive translation. Following this workflow, dual-labeled samples for each condition were run separately on the flow cytometer, and the resulting intensity plots were overlaid. Each Docket No.: 182219.00272 sample takes 30 seconds-1.5 minutes to run, depending on the cell density and volume. Fig. 8B shows flow cytometry plots after sequential dual labeling performed as in (Fig. 8A), which depict a baseline translation rate in the HPG channel (AZD594) and CHX-sensitive translation rate in the AHA channel (AZD647). Specific cell populations are indicated above each flow plot, gated using the following strategy: CD45+: Live / CD45+; Myeloid: Live / CD45+ / CD11B+CD3‘; CD3+(All T cells): Live / CD457 CD1 IB' CD3+; CD8+T cells: Live / CD457 CD1 I B’ CD37 CD8+CD4‘; CD4+T cells: Live / CD45+ / CD11B" CD3+ / CD8" CD4+. Fig. 8C shows an experimental schematic for dual-color surface clicking on immune cells as isolated from mice bearing B16-OVA tumors to evaluate whether surface translation rates differ between tissue sites. Fig. 8D shows a flow cytometry plot after sequential dual labeling performed as in (Fig. 8C), which depicts baseline translation rates in both the HPG (AZD594) and AHA (AZD647) channels. CD8+T cells isolated from different tissue locations were gated as in (Fig. 8B). Fig. 8E shows flow cytometry plot after sequential dual labeling performed as in (Fig. 8C), which depicts baseline translation rates in both the HPG (AZD594) and AHA (AZD647) channels. CD81T cells were isolated from the tumor bed, gated as in (Fig. 8B), and then a comparison drawn between cells with high versus low expression of CD44.

[0054] Figs. 9A, 9B, 9C, and 9D show application of SIDETES method to OCI-AML3 cells. Fig. 9A shows a schematic for evaluating the impact of single-color clicking on OCI-AML3 cell viability. Fig. 9B shows viability of OCI-AML3 cells after incubating in media containing AHA or clicking with Alkyne-647 dye. Fig. 9C shows a schematic for evaluating the impact of metabolic perturbation on single-color surface click signal in OCLAML3 cells. Fig. 9D shows histograms depicting OCI-AML3 surface signal fluorescent intensity in the presence of metabolic inhibitors. The autofluorescence control is the +Methionine, -AHA, no click condition.

[0055] Fig. 10 shows the advantages of the SIDETES (Stable Isotope Dual-Encoded Tag for Enhanced Spatial profiling) platform in comparison with conventional state-of-the-art imaging or labeling techniques. The figure highlights improvements in sensitivity, spatial resolution, and molecular specificity achieved by using dual mass tags and stable isotope labeling in conjunction with mass spectrometric imaging.

[0056] Fig. 11 shows the chemical structures and design of the azide-based and alkyne-based mass tags used in the SIDETES platform, including the incorporation of deuterium (2H) atoms to create Docket No.: 182219.00272 distinguishable isotopic signatures. The figure further demonstrates the generation of a positively charged ion upon successful conjugation (“clicking”) of the azide mass tag with an alkyne- modified amino acid (or vice versa) within a protein or peptide, enabling enhanced ionization and detection by mass spectrometry.

[0057] Fig. 12 shows an example of in vivo metabolic labeling of mouse liver tissue using adenosine triphosphate (ATP) and water enriched with the stable isotope oxygen- 18 (18O). The labeled substrates were administered systemically for a duration of 10 minutes prior to euthanasia. The liver tissue was then cryosectioned and analyzed using mass spectrometric imaging. The resulting image, acquired at a spatial resolution of 50 pm x 50 pm, demonstrates the distribution and incorporation of metabolic labels across the tissue slice. The Fed state has more ATP turnover than fasted state, as indicated by the enhanced number of m / z ATP(18O) -Na(adduct) 529.95 pixels vs the fasted state. Note the unlabeled ATP-Na(adduct) has a m / z of 527.95 (not shown).

[0058] Fig. 13 shows a schematic representation of a protocol for single-cell metabolic and chemical labeling. Cells are incubated with alkyne-modified or azide-modified amino acid analogues then “clicked” with a structurally compatible fluorescent detector tag, with or without incubation stable isotope-labeled metabolic substrates, allowing for the incorporation of both chemical tags and isotopic labels into cellular metabolites and proteins. The labeled cells are subsequently deposited onto a pre-coated glass slide suitable for desorption electrospray ionization (DESI) mass spectrometric imaging. This approach enables high-resolution, spatially resolved analysis of individual cells based on their cellular energetics. Mass spectrometric (MS) imaging on sorted cells deposited on the glass slides enables analysis of metabolic networks with single cell resolution, revealing metabolic heterogeneity within energy windows.

[0059] DETAILED DESCRIPTION OF THE INVENTION

[0060] The disclosed method, exemplified by Single cell Isotopomer Distributions and Energetics of Translation under Energetic Stress (SIDETES), is a novel strategy to deconvolve metabolic heterogeneity in complex cellular environments, such as tumor and other tumor-resident (e.g, immune cells, fibroblast / stromal cells) cell populations, hepatic tissues, renal tissues, cardiac tissues, nervous tissues, endocrine tissues, whole blood (Peripheral Blood Mononuclear Cells (PBMCs), or lymphoid organs. Applying single cell genomics to the tumor niche has revolutionized cancer biology by detailing the diverse cell states in tumors. However, while Docket No.: 182219.00272 unbiased approaches have transformed cancer biology by providing critical insights into rare cell states, tools to deconvolve metabolic heterogeneity are still in the early stages of development. Current experimental strategies still rely on indirect measurements of metabolic state, such as from the expression of metabolic transcripts or proteins. SIDETES can examine energetic dependence of a substrate, an inhibitor / activator, or a genetic change on rare cells in tumors and obtain the network response for cells sorted by energy, and using MS imaging, can determine the normalized metabolic or flux profile for cells sorted by energy.

[0061] The SIDETES workflow is compatible with the recovery of live cells sorted into discrete energy quadrants and can be inserted in workflows for single-cell omic technologies, such as MS imaging. SIDETES can be used as a platform technology and a flexible toolkit that can be inserted into analytical workflows that benefit from bioenergetic deconvolution, including those that require live cells. For example, CRISPR screens targeting metabolic phenotypes currently rely on indirect read-outs of proliferation or drug sensitivity. SIDETES provides an alternative enrichment strategy by sorting cells based on bioenergetic dependencies before quantifying CRISPR guide enrichment. Alternatively, SIDETES can be integrated with pooled genetic perturbation screening to probe the coupling between transcriptional changes and bioenergetic state. By causally linking bioenergetics with other cellular phenotypes and dependencies, SIDETES can uncover new mechanisms by which metabolic variation impacts tumorigenesis and response to therapy.

[0062] Methods for Determining Bioenergetic States of a Cell

[0063] Accordingly, in one aspect, this disclosure provides a method of determining a bioenergetic state of a cell.

[0064] In some embodiments, the method comprises: (a) contacting a cell with an alkyne- conjugated amino acid in a first medium for a first period of time, wherein the alkyne-conjugated amino acid is incorporated into a first set of plasma membrane proteins on cell surface of the cell during translation of the first set of plasma membrane proteins; (b) contacting the cell with an azide-conjugated amino acid in a second medium for a second period of time in presence and absence of a metabolic inhibitor, wherein the azide-conjugated amino acid is incorporated into a second set of plasma membrane proteins on the cell surface of the cell during translation of the second set of plasma membrane proteins, and wherein the metabolic inhibitor causes metabolic stress to the cell; (c) contacting the cell with an azide-bearing detection tag reactive to the alkyne- Docket No.: 182219.00272 conjugated amino acid in the first set of plasma membrane proteins, using click chemistry (e.g., copper-catalyzed, or Strain-Promoted Alkyne- Azide Cycloaddition (SPAAC), click chemistry) to associate the azide-bearing detection tag with the first set of plasma membrane proteins; (d) contacting a cell with an alkyne-bearing detection tag reactive to the azide-conjugated amino acid on the second set of plasma membrane proteins, using click chemistry to associate the alkyne- bearing detection tag with the second set of plasma membrane proteins; (e) determining a first signal generated from the azide-bearing detection tag associated with the first set of plasma membrane proteins, wherein the first signal generated from the azide-bearing detection tag represents a baseline protein translation state of the cell; (f) determining a second signal generated from the alkyne-bearing detection tag associated with the second set of plasma membrane proteins in the presence of the metabolic inhibitor, wherein the second signal generated from the alkyne- bearing detection tag represents a metabolically-coupled protein translation state of the cell under the metabolic stress caused by the metabolic inhibitor; (g) assessing cell viability to discriminate live and dead cells by flow cytometry; and (h) determining a bioenergetic state (e.g., bioenergetic sensitivity index) of the cell based on: a change from the baseline protein translation state to the metabolically-coupled protein translation state under the metabolic stress by comparing the second signal to the first signal.

[0065] In some embodiments, the order of the azide-bearing and alkyne-bearing methionine analogs, as well as the alkyne-bearing and azide-bearing detection tags, can be reversed. In some embodiments, the azide-conjugated amino acid is present in the first medium and the alkyne- conjugated amino acid is present in the second medium. In some embodiments, the alkyne- conjugated amino acid is present in the first medium and the azide-conjugated amino acid is present in the second medium.

[0066] In some embodiments, the method comprises performing step (b) prior to step (a). In some embodiments, the method comprises performing step (d) prior to step (c). In some embodiments, the precision and reproducibility of the bioenergetic sensitivity index is actualized due to high viability of the cells after the copper click induction, for which the disclosed process is essential. Copper click labeling, widely regarded to be toxic to all cells in all embodiments, has been optimized in the disclosed method so that >80% cell viability can be maintained after two sequential copper-catalyzed click reactions, while yielding baseline resolved surface signal (e.g., fluorescent signal) without fixation and permeabilization. This reaction happens with a particular Docket No.: 182219.00272 optimized concentration of copper II sulfate (10-50 micro molar), at a 5: 1 ratio of BTTAA to copper, 2.5 mM sodium ascorbate in the reaction to convert Cu II to Cu I, and only 1-3 minutes in the click reaction at room temperature. This is a major departure from classical click reactions, which are usually 30-120 minutes and is essential to our bioenergetic sensitivity index calculation.

[0067] As used herein, the term “cell” refers broadly to any prokaryotic or eukaryotic cell. Prokaryotic cells include, without limitation, members of the domains Bacteria and Archaea, which represent distinct evolutionary lineages. Prokaryotic cells are generally unicellular and exhibit relatively simple structural organization. Eukaryotic cells, as used herein, include both unicellular organisms, such as yeast, and cells derived from multicellular organisms, including but not limited to, ovary cells, epithelial cells, immune cells (e.g., T cells, B cells, macrophages), hematopoietic cells, bone marrow cells, circulating vascular progenitor cells, cardiac cells, chondrocytes, osteoblasts, beta cells, hepatocytes, and neurons. The term “cell” also includes pluripotent stem cells, which, as used herein, refers to division-competent cells capable of differentiating into one or more specialized cell types. Pluripotent stem cells include, without limitation, embryonic stem cells, adult stem cells (e.g., mesenchymal stem cells (MSCs)), and induced pluripotent stem cells (iPSCs). As used herein, pluripotent stem cells are nondifferentiated. Furthermore, the term encompasses both purified primary cells and immortalized cell lines. The term “cell” also includes cells in suspension (e.g., circulating leukocytes such as peripheral blood mononuclear cells (PBMCs)) as well as adherent cells (e.g., endothelial cells).

[0068] As used herein, the term “bioenergetic state” or “bioenergetic profile” refers to a measure of the cellular energy status, including the level of energy-yielding intermediates, such as adenosine triphosphate (ATP), and / or the adenylate energy charge, which represents a weighted ratio of high-energy adenine nucleotides (ATP and adenosine diphosphate (ADP)) to the total adenine nucleotide pool (ATP, ADP, and adenosine monophosphate (AMP)). The term “bioenergetics” generally refers to the molecular and biochemical processes by which cells convert, generate, store, and utilize energy, often through ATP production. Cellular bioenergetics regulates various physiological processes, including innate immune responses, for example through the transcriptional co-repressor C-terminal binding protein (CtBP), and tissue regeneration, including oxidative metabolism relevant to tissue repair. As used herein, the term “energetic metabolism” refers to the collection of biochemical pathways and reactions that contribute to the production, storage, or utilization of energy-related metabolites within a cell. Docket No.: 182219.00272

[0069] As used herein, the terms “azide-conjugated amino acid,” “azide-containing amino acid,” “azide-functionalized amino acid,” “azide-derivatized amino acid,” or “azide-modified amino acid” refer to an amino acid analog that is functionalized with at least one azide moiety (- s). Such amino acids are useful in click chemistry applications, including copper(I)-catalyzed azidealkyne cycloaddition (CuAAC) reactions, and Strain-Promoted Alkyne-Azide Cycloaddition (SPAAC) reactions. Azide-conjugated amino acids can be incorporated into peptides or proteins for a variety of applications, including selective labeling, manipulation of protein-protein interactions, and protein modification. These amino acids may be used as surrogates for natural amino acids, such as methionine, in biosynthetic pathways. Examples of azide-conjugated amino acids include, but are not limited to, 3-azido-D-alanine hydrochloride, 3-azido-L-alanine hydrochloride, 4-azido-L-homoalanine hydrochloride (L-AHA), 4-azido-L-phenylalanine, 6- azido-D-lysine hydrochloride, and 6-azido-L-lysine hydrochloride.

[0070] In some embodiments, the azide-conjugated amino acid is an azide-conjugated methionine or an analog thereof. However, a person of ordinary skill in the art would understand any alkyne- or azide-modified amino acid analog that can be incorporated into proteins by actively translating ribosomes can be used.

[0071] In some embodiments, the azide-conjugated amino acid comprises azide-modified homoalanine (AHA).

[0072] As used herein, the terms “alkyne-conjugated amino acid,” “alkyne-containing amino acid,” “alkyne-functionalized amino acid,” “alkyne-derivatized amino acid,” and “alkyne- modified amino acid” refer to an amino acid or amino acid analog that comprises at least one alkyne functional group. Such alkyne-containing amino acids may serve as chemical building blocks for the synthesis of modified peptides and proteins. Alkyne-containing amino acids can participate in a range of chemical transformations, including intramolecular cyclizations and conjugation reactions with labels, biomolecules, or other chemical moieties, thereby enabling the generation of structurally diverse amino acid residues and peptide architectures. Notably, the presence of an alkyne functionality enables bioorthogonal conjugation reactions, including copper(I)- or copper(0)-catalyzed azide-alkyne cycloadditions (CuAAC), commonly referred to as “click chemistry.” Alkyne-modified amino acids can be readily incorporated into peptide sequences using standard solid-phase peptide synthesis (SPPS) protocols, thereby facilitating the Docket No.: 182219.00272 synthesis and site-specific functionalization of peptides and proteins that are otherwise challenging to access. The resulting bioconjugates can be used to modulate protein function, enable imaging or detection, or permit targeted delivery.

[0073] In some embodiments, the alkyne-conjugated amino acid is an alkyne analog of methionine, such as L-homopropargylglycine (HPG) or a derivative thereof. In some embodiments, the alkyne reporter signal is replaced by a sterically strained alkyne moiety, such as dibenzocyclooctyne (DBCO), to enable copper-free SPAAC reaction click chemistry. While DBCO-based reagents eliminate the need for copper catalysis, they may exhibit increased background signal in certain cell systems.

[0074] In some embodiments, the azide-bearing detection tag comprises an azide-bearing fluorophore, and the alkyne-bearing detection tag comprises an alkyne-bearing fluorophore. In some embodiments, azide-bearing fluorophores are employed as chemical probes for labeling biomolecules containing terminal alkynes or strained alkynes. These fluorophores comprise a fluorescent dye moiety covalently linked to an azide functional group, enabling selective and efficient conjugation to alkyne-containing targets through bioorthogonal click chemistry reactions. Such labeling may be carried out using Cu(I)-catalyzed alkyne-azide cycloaddition (CuAAC) or strain-promoted azide-alkyne cycloaddition (SPAAC) reactions. The latter enables copper-free labeling suitable for live-cell or in vivo applications.

[0075] In some embodiments, the azide-bearing fluorophore comprises, consists essentially of, or consists of one or more of the following: AZD647-azide fluorophore, Alexa Fluor 488-azide fluorophore, Alexa Fluor 555-azide fluorophore, Alexa Fluor 594-azide fluorophore, Alexa Fluor 647-azide fluorophore, Oregon Green 488-azide fluorophore, or any other azide-functionalized fluorophore suitable for click labeling. The choice of fluorophore may depend on desired excitation / emission properties, photostability, solubility, or biocompatibility. Conversely, alkyne- bearing fluorophores may be utilized to label biomolecules that are functionalized with an azide moiety. These fluorophores include a fluorescent dye component conjugated to a terminal alkyne or strained alkyne, enabling site-specific labeling through CuAAC or SPAAC chemistry.

[0076] In some embodiments, the alkyne-bearing fluorophore comprises, consists essentially of, or consists of one or more of the following: AZD488-alkyne fluorophore, Alexa Fluor 488-alkyne fluorophore, Alexa Fluor 555-alkyne fluorophore, Alexa Fluor 594-alkyne fluorophore, Alexa Docket No.: 182219.00272

[0077] Fluor 647-alkyne fluorophore, Oregon Green 488-alkyne fluorophore, or any other alkyne- functionalized fluorophore compatible with click chemistry protocols. Such alkyne-bearing fluorophores may be used in fixed or live-cell imaging, high-throughput screening, or diagnostic assays.

[0078] In some embodiments, the azide-bearing detection tag comprises an azide-bearing mass tag. In some embodiments, the alkyne-bearing detection tag comprises an alkyne-bearing mass tag.

[0079] In some embodiments, the first signal generated from the azide-bearing detection tag or the second signal generated from the alkyne-bearing detection tag is determined by mass spectrometry.

[0080] In some embodiments, the azide-bearing mass tag or the alkyne-bearing mass tag comprises an isotopic mass tag or an isobaric mass tag.

[0081] In some embodiments, the method further comprises performing mass spectrometric imaging to normalize single cell baseline metabolite flux assessments by measuring stable isotopes incorporated into metabolites and / or lipids. In some embodiments, the method further comprises performing mass spectrometric imaging to normalize single cell baseline metabolite flux assessments by measuring the azide-bearing mass tag, and / or the alkyne-bearing mass tag incorporated into metabolites and / or lipids.

[0082] In certain aspects, the azide- or alkyne-modified fluorophores or mass detection tags may further comprise additional functional elements, such as cell-penetrating peptides, affinity handles (e g., biotin), or quenching groups to facilitate targeted delivery, enrichment, or signal modulation.

[0083] As used herein, the term “plasma membrane protein” refers to a protein that is at least partially embedded in or associated with the plasma membrane of a cell, such that a portion of the protein is exposed on the extracellular surface of the membrane. Plasma membrane proteins can include transmembrane proteins, lipid-anchored proteins, and peripheral membrane proteins localized at the extracellular face. In some embodiments, plasma membrane proteins comprise methionine residues located within the extracellular domain.

[0084] In certain embodiments, substitution of natural methionine with methionine analogs — such as azide-conjugated methionine analogs or alkyne-conjugated methionine analogs — can be carried out in living cells. This results in the incorporation of bioorthogonal functional groups into plasma Docket No.: 182219.00272 membrane proteins, including those presented on the external surface of the cell. Such surface- exposed modified methionines may be selectively labeled via click chemistry reactions, enabling downstream applications such as mass spectrometry analysis, imaging, or protein enrichment.

[0085] In some embodiments, the term “first set of plasma membrane proteins” and “second set of plasma membrane proteins” may refer to the same population of proteins, for example, under different metabolic conditions or time points. In other embodiments, the first and second sets may represent distinct protein populations, such as proteins expressed or modified differentially in response to stimuli, stressors, or cell cycle states.

[0086] As used herein, the term “translation” or “protein synthesis” refers to the biological process by which ribosomes decode messenger RNA (mRNA) sequences to synthesize polypeptides or proteins. Translation initiates with the incorporation of methionine as the first amino acid, typically encoded by the AUG start codon, although alternative initiation codons (e.g., GUG) may be used, particularly in prokaryotic systems. The translation apparatus includes ribosomal subunits that assemble on the mRNA, recruiting aminoacyl-tRNA molecules to decode the codons sequentially and extend the growing peptide chain. Translation terminates upon recognition of one of the canonical stop codons (UAA, UAG, or UGA), triggering peptide release. The resulting polypeptide may undergo post-translational modifications and folding to become a functional protein.

[0087] As used herein, the term “click chemistry” refers to a class of chemical reactions characterized by their modularity, high yield, specificity, and biocompatibility. Click chemistry typically involves the use of energetic and selective reagents, often described as “spring-loaded,” to covalently join molecular entities under mild conditions. Representative click reactions include, but are not limited to, copper-catalyzed azide-alkyne cycloaddition (CuAAC), strain-promoted azide-alkyne cycloaddition (SPAAC), inverse electron-demand Diels-Alder reactions (IEDDA), and thiol-ene reactions. Click chemistry is widely utilized in biomolecular labeling, conjugation, diagnostics, and drug discovery, owing to its selectivity and minimal disruption to biological systems.

[0088] As used herein, the term “metabolic stress” refers to a physiological or experimental condition in which the normal metabolic processes of a cell are perturbed. Such stress may arise from insufficient or excessive availability of nutrients, hypoxia, mitochondrial dysfunction, or Docket No.: 182219.00272 accumulation of toxic intermediates, leading to impaired energy production or redox imbalance. Cells often respond to metabolic stress by engaging adaptive mechanisms, including AMPK signaling, unfolded protein response (UPR), and metabolic reprogramming, to maintain homeostasis.

[0089] As used herein, the term “metabolic inhibitor” refers to a molecule or agent that interferes with or suppresses the activity of one or more enzymes or transporters involved in metabolic pathways. Metabolic inhibitors may act competitively or noncompetitively and can be used to alter flux through metabolic pathways, inhibit cell proliferation, or sensitize cells to other therapeutic agents. Exemplary metabolic inhibitors include 2-deoxyglucose, oligomycin, methotrexate, and mal onate.

[0090] As used herein, the term “metabolic stressor” refers to any agent, compound, condition, or treatment that induces a measurable alteration in cellular metabolism. In some embodiments, a metabolic stressor comprises methylated or alkylated metabolic intermediates, such as methyl succinate, which may serve to augment or bypass enzymatic steps in the tricarboxylic acid (TCA) cycle under anaerobic conditions. In other embodiments, the metabolic stressor comprises a customized cell culture medium that is depleted of or supplemented with particular metabolites, vitamins, or nutrients. In further embodiments, the stressor may include hypoxic conditions, exposure to oxidative agents, or compounds that modulate key metabolic enzymes or regulators. In still other embodiments, the metabolic stressor may be used to probe or sensitize metabolic vulnerabilities in cells, particularly in the context of disease models or drug screening platforms.

[0091] In some embodiments, the metabolic inhibitor comprises one or more of the following: a glycolysis inhibitor, a cellular energy inhibitor, an oxidative phosphorylation inhibitor, an amino acid metabolism inhibitor, a mitochondrial metabolism inhibitor, a lipid metabolism inhibitor, a nucleotide metabolism inhibitor, a pentose phosphate pathway (PPP) inhibitor, a nitrogen metabolism inhibitor, a redox destabilizing agent, or a combination thereof. In certain embodiments, the metabolic inhibitor comprises a protein translation inhibitor, which may act to impair cellular biosynthesis and energy homeostasis under metabolic stress.

[0092] As used herein, the term “cellular energy inhibitor” refers to any compound or composition that impairs the energy-producing capacity of a cell, particularly through inhibition of glycolysis and / or mitochondrial oxidative phosphorylation. Cellular energy inhibitors may preferentially Docket No.: 182219.00272 target cancer cells due to their heightened metabolic demand and altered energy metabolism. Nonlimiting examples of cellular energy inhibitors include, but are not limited to, 2-deoxyglucose, oligomycin, rotenone, metformin, and compounds disclosed in U.S. Patent Application Publication No. US 11,077,078 B2, the entirety of which is incorporated herein by reference for all purposes.

[0093] In some embodiments, the metabolic stressor is a compound or condition that activates or perturbs cellular metabolism, thereby sensitizing cells to detection, imaging, or therapeutic intervention. In certain embodiments, metabolic activators may be employed to transiently enhance specific metabolic pathways, including but not limited to glycolysis, glutaminolysis, fatty acid oxidation, or mitochondrial respiration.

[0094] In some embodiments, the system or method utilizes methylated or alkylated metabolites that are capable of passively diffusing across cellular membranes and entering various intracellular metabolic compartments, such as the cytosol, mitochondria, or peroxisomes. These metabolites may serve as anaplerotic substrates to augment flux through the tricarboxylic acid (TCA) cycle or other biosynthetic or catabolic pathways, facilitating pathway interrogation or functional metabolic imaging.

[0095] In some embodiments, combinations of metabolic inhibitors and activators may be administered or applied in a time-sequenced or spatially targeted manner to modulate metabolic flux and probe dynamic changes in cellular bioenergetics.

[0096] In some embodiments, the method further comprises determining a baseline protein translation rate of the cell based on the first (azide fluorescent reporter) signal. In some embodiments, the method further comprises determining a metabolically-coupled protein translation rate of the cell under metabolic stress based on the second (alkyne fluorescent reporter) signal. In some embodiments, the method further comprises an additional reference sample to determine baseline translation rates for the surface proteome assessed in the time period of the experiment utilizing first and second signals without the addition of any metabolic stressor. This reference sample is used for calculating optimized parameters for variance stabilization across the entire dataset.

[0097] In some embodiments, the method further comprises performing mass spectrometric imaging to normalize single cell baseline metabolite flux assessments by measuring stable isotopes Docket No.: 182219.00272 incorporated into metabolites and / or lipids. In some embodiments, the mass spectroscopy imaging method is MALDI-, DESI-, SIMS-, or LAESI-based. In some embodiments, the stable isotopes comprise2H,13C,15N, and18O. For example, the intensity of an alkyne labeled mass tagged amino acid analog will be compared to the intensity of an azide labeled mass tagged reporter present on or in each cell.

[0098] In some embodiments, the step of determining the bioenergetic state of the cell comprises comparing a baseline translation rate encoded in the first signal to the metabolically-coupled translation rate encoded in the second signal when the cell is under metabolic stress, in order to normalize for differences in baseline translation rate between cells.

[0099] In some embodiments, the step of determining the bioenergetic state of a cell comprises detecting a stable isotope using mass spectrometric imaging, and normalizing the metabolic profile of the cell based on the measured intensity of reporter ions derived from an alkyne-labeled mass tag probe and an azide-labeled mass tag probe. In certain embodiments, the alkyne-labeled mass tag probe reacts with an azide-labeled protein on the surface of the cell to form a product having a net positive charge, thereby enhancing ionization efficiency and improving sensitivity in the mass spectrometric imaging-based single-cell analysis. Conversely, in other embodiments, the protein may be labeled with an alkyne-labeled amino acid analogue, which reacts with an azide-labeled mass tag probe, which may include a cleavable linker.

[0100] In some embodiments, the step of determining the bioenergetic state of a cell comprises detecting a stable isotope using mass spectrometric imaging and subsequently normalizing the metabolic profile based on the intensity of an alkyne-labeled fluorescent reporter and an azide- labeled fluorescent reporter. In certain embodiments, the fluorescent reporters are conjugated to amino acids and incorporated into newly synthesized proteins. The fluorescent signal is detected using single-cell fluorescence microscopy to assess total protein translation efficiency. In some embodiments, the protein translation efficiency encompasses both surface-expressed and intracellular proteins.

[0101] In some embodiments, the step of determining the bioenergetic state of a cell comprises detecting a stable isotope using mass spectrometric imaging and sorting the cells using fluorescence-activated cell sorting (FACS). In such embodiments, the metabolic intensity is Docket No.: 182219.00272 normalized against the fluorescent intensity, thereby providing a measurement of surface protein translation efficiency for metabolic intensity normalization.

[0102] In some embodiments, the step of determining the bioenergetic state of a cell comprises performing multiplexed detection of labeled mass tag probes using mass spectrometric imaging. In these embodiments, the alkyne-labeled and / or azide-labeled mass tag probes form part of a panel of stable isotope-coded chemical tags, which are distinguished based on differences in molecular weight. The stable isotope tags may comprise one or more of deuterium (2H), carbon- 13 (13C), nitrogen- 15 (15N), and oxygen- 18 (18O) and may contain a cleavable linker.

[0103] In some embodiments, the step of determining the bioenergetic state of a cell comprises measuring the ATP flux and / or turnover rate in cells or tissues using stable isotope-labeled compounds that are detected by mass spectrometric imaging. For instance, water labeled with18O may be administered for a defined duration, such as about 1 minute to about 10 minutes (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 minutes), to enable tracing of phosphate turnover and ATP dynamics in situ.

[0104] In some embodiments, the cell comprises a primary or transformed (including tumor cell lines) mammalian cell. These cells can be cultured under different nutrient or oxygen conditions.

[0105] In some embodiments, the first period of time or the second period of time is about 1 minute to about 10 hours (e.g., 1 minute, 2 minutes, 5 minutes, 10 minutes, 30 minutes, 1 hour, 1.5 hours, 2 hours, 2.5 hours, 3 hours, 3.5 hours, 4 hours, 4.5 hours, 5 hours, 5.5 hours, 6 hours, 6.5 hours, 7 hours, 7.5 hours, 8 hours, 8.5 hours, 9 hours, 9.5 hours, 10 hours).

[0106] In some embodiments, the first or second medium is free of a corresponding unconjugated amino acid. As used herein, the term “corresponding unconjugated amino acid” refers to an unconjugated amino acid that is not reactive with azide- or alkyne-functionalized molecules. For AHA and HPG, methionine is the corresponding unconjugated amino acid.

[0107] In some embodiments, the first medium can be the same as or different from the second medium. In some embodiments, the second medium is free of the azide-conjugated amino acid.

[0108] In some embodiments, the step of determining the bioenergetic state of the cell further comprises determining an amount of metabolites and lipids of the cell. Docket No.: 182219.00272

[0109] In some embodiments, the step of determining the bioenergetic state of the cell further comprises determining metabolic flux of the cell. As used herein, the term “metabolic flux” refers to the passage of a metabolite through a reaction system over time, and flux analysis is the combination of time-course methodologies in metabolomics and computational modeling of pathways. Metabolic flux is typically normalized by cellular abundance, such as gram dry weight. The flux of metabolites through a reaction is the rate of the forward reaction minus the rate of the reverse reaction. Metabolic flux is altered under disease conditions such as cardiovascular disease and cancer.

[0110] In some embodiments, the step of determining the bioenergetic state of the cell further comprises determining proteomic abundance. Proteomic abundance is the number of copies of a protein molecule in a cell. Proteomic abundance may be calculated from the sum of all unique normalized peptide ion abundances for a specific protein. In some embodiments, the method may include comparing treated cells to untreated cells, wildtypes to mutants, or samples from diseased to non-diseased subjects, with respect to their proteomic abundance in a given defined bioenergetic state.

[0111] In some embodiments, the first signal (e.g., fluorescent signal) generated from the azide- bearing fluorophore or the second signal (e.g., fluorescent signal) generated from the alkyne- bearing fluorophore is determined by flow cytometry. Flow cytometry is a well-accepted tool in research that allows a user to analyze and sort components in a sample fluid rapidly. Flow cytometers use a carrier fluid (e.g., a sheath fluid) to pass the sample components, substantially one at a time, through a zone of illumination. Each sample component is illuminated by a light source, such as a laser, and light scattered by each sample component is detected and analyzed. The sample components can be separated based on their optical and other characteristics as they exit the illumination zone. For example, fluorescence-activated cell sorting (FACS) may be used, and it typically involves using a flow cytometer capable of simultaneous excitation and detection of multiple fluorophores. The cytometric systems may include a cytometric sample fluidic subsystem, as described below. In addition, the cytometric systems include a cytometer fluidically coupled to the cytometric sample fluidic subsystem.

[0112] In some embodiments, a fluorescent signal generated from the azide-bearing fluorophore or the second fluorescent signal generated from the alkyne-bearing fluorophore is determined by Docket No.: 182219.00272 fluorescence-activated cell sorting (FACS). In some embodiments, alkyne-functionalized mass reporter tags are employed in place of conventional alkyne-bearing fluorophores for labeling biomolecules. These alkyne-functionalized mass tags are configured to undergo a selective chemical reaction, such as a copper-catalyzed azide-alkyne cycloaddition (CuAAC) or strain- promoted azide-alkyne cycloaddition (SPAAC), with azide-conjugated amino acids incorporated into proteins localized to the plasma membrane of cells. In certain embodiments, the alkyne- functionalized mass reporter tags also react with azide-conjugated amino acids that are distributed throughout the intracellular compartments, thereby enabling comprehensive spatial analysis when analyzed using mass spectrometric imaging techniques, such as matrix-assisted laser desorption / ionization (MALDI) imaging or secondary ion mass spectrometry (SIMS).

[0113] In other embodiments, azide-functionalized mass reporter tags are utilized in place of azide-bearing fluorophores. These azide-functionalized mass tags can engage in direct chemical conjugation with alkyne-conjugated amino acids displayed on the surface of plasma membrane proteins. In certain embodiments, the azide-functionalized mass tags further react with intracellular alkyne-labeled biomolecules, allowing for global labeling and detection within the cell upon analysis via mass spectrometric imaging.

[0114] In some embodiments, the detection tags comprise one or more of an azide-bearing mass tag and / or an alkyne-bearing mass tag. The mass reporter tags may include, but are not limited to, isotopic mass tags, isobaric mass tags, affinity or enrichment tags incorporating mass-distinct identifiers (e.g., DiLeu tags (dimethyl leucine-based), NeuCode SILAC), and / or custom-designed tags optimized for enhanced ionization efficiency, fragmentation specificity, or signal detection in mass spectrometry workflows, as well as cleavable linkers. These tags facilitate the quantitative and / or qualitative differentiation of cellular or molecular targets based on their unique mass spectral signatures.

[0115] In some embodiments, the mass tags are selected to enable multiplexed detection, such that distinct biological targets or labeling events can be concurrently monitored in a single mass spectrometry experiment. In some embodiments, the mass tags are configured for cleavable release, generating signature fragment ions that can be used to quantify labeling efficiency or the abundance of specific biomolecular targets. Docket No.: 182219.00272

[0116] In some embodiments, a method for determining the bioenergetic state of a cell comprises detecting and quantifying a first signal derived from an azide-functionalized detection tag and / or a second signal derived from an alkyne-functionalized detection tag using mass spectrometry. The detection tags may be incorporated into cellular biomolecules, such as lipids, metabolites, or proteins, through metabolic labeling or chemical conjugation.

[0117] In some embodiments, the method further comprises performing mass spectrometric imaging to quantify and normalize metabolite flux in individual cells by detecting the incorporation of both azide-bearing and alkyne-bearing mass tags into cellular metabolites and / or lipid species. Such incorporation enables spatially resolved measurements of metabolic activity across different subcellular regions or cell populations within a tissue sample.

[0118] In some embodiments, determining the bioenergetic state of a cell comprises analyzing the metabolic profile and / or isotopic enrichment of the cell by mass spectrometric imaging, wherein the cell is dual-labeled with an alkyne-functionalized mass tag and an azide-functionalized mass tag. The dual labeling may allow for simultaneous or comparative analysis of distinct metabolic pathways or analyte pools within the same cell.

[0119] In some embodiments, determining the bioenergetic state of the cell comprises quantifying metabolic flux and normalizing the resulting data based on the relative signal intensities of reporter ions generated from the azide- and alkyne-functionalized detection tags. The reporter ions may be selectively produced through tandem mass spectrometry (MS / MS) or other fragmentation techniques and are detected under both basal (unperturbed) and metabolically perturbed conditions.

[0120] In some embodiments, the method comprises detecting stable isotope-labeled analytes by mass spectrometric imaging and normalizing the metabolic profile based on the respective signal intensities of reporter signals derived from an alkyne-functionalized fluorophore and an azide- functionalized fluorophore. The fluorophore tags may enable orthogonal detection or validation of the spatial distribution of metabolites or tagged analytes.

[0121] In some embodiments, determining the bioenergetic state of a cell comprises measuring adenosine triphosphate (ATP) production, ATP turnover rates, or related energetic parameters in individual cells and / or tissues using mass spectrometric imaging. Such measurements may include assessing the incorporation of18O into the phosphates of ATP from18O -labeled water, mass- Docket No.: 182219.00272 tagged ATP analogs or surrogate metabolites, thereby enabling direct visualization and quantification of cellular energy dynamics.

[0122] In some embodiments, the method further comprises comparing the metabolic profiles of individual cells or regions of interest within a sample to identify differential bioenergetic states, which may be indicative of disease states, drug responses, or environmental stress conditions. In some embodiments, alkyne-bearing DNA barcodes are substituted for alkyne-bearing fluorophores. These molecules can directly react with azide-conjugated amino acids on plasma membrane protein on the cell surface. In some embodiments, azide-bearing DNA barcodes are substituted for azide-bearing fluorophores. These molecules can directly react with alkyne- conjugated amino acids on plasma membrane protein on the cell surface. The extent to which cells are labeled with distinct barcodes reflects incorporation of the corresponding conjugated amino acid into the cell surface proteome, and can be quantified by bulk or single-cell DNA sequencing. If the azide-bearing DNA barcode is different from the alkyne-bearing DNA barcode, then sequencing relative enrichments of each barcode can quantify the relative incorporation of each conjugated amino acid into the surface proteome for baseline and metabolically-coupled translation as a parameter in single cell genomics technologies.

[0123] Diagnostic Based on Bioenergetic States

[0124] In another aspect, this disclosure provides a method for predicting the survival time of a patient suffering from cancer based on a bioenergetic state of a cell determined according to a method disclosed herein. Assessment of bioenergetic state in single circulating tumor cells may augment epigenetic predictions of diagnosis or survival. In conjunction with single cell transcriptional analysis and DNA barcoding, insights into treatment may be predicted. For example, the method may be used to predict the duration of the overall survival (OS), progression- free survival (PFS), and / or disease-free survival (DFS) of the cancer patient. Those of skill in the art will recognize that OS survival time is generally based on and expressed as the percentage of people who survive a particular type of cancer for a specific amount of time. Cancer statistics often use an overall five-year survival rate. In general, OS rates do not specify whether cancer survivors are still undergoing treatment at five years or if they have become cancer-free (achieved remission). DSF gives more specific information and is the number of people with a particular cancer who achieve remission. Also, progression-free survival (PFS) rates (the number of people Docket No.: 182219.00272 who still have cancer, but their disease does not progress) include people who may have had some success with treatment, but the cancer has not disappeared completely. As used herein, the expression “short survival time” indicates that the patient will have a survival time lower than the median (or mean) observed in the general population of patients suffering from said cancer. When the patient has a short survival time, it means that the patient will have a “poor prognosis.” Inversely, the expression “long survival time” indicates that the patient will have a survival time higher than the median (or mean) observed in the general population of patients suffering from said cancer. When the patient has a long survival time, it means that the patient will have a “good prognosis.”

[0125] As used herein, the term “cancer” refers to a group of diseases involving abnormal and uncontrolled cell growth with the potential to invade surrounding tissues and spread (metastasize) to distant parts of the body. The term encompasses both primary tumors that originate at a particular tissue or organ site, and metastatic tumors that form when cancer cells disseminate from the original site and establish growth in another location. The methods and compositions described herein may be applied to a broad range of cancer types across various tissue origins and histological classifications.

[0126] Cancers that may be treated, detected, monitored, or otherwise analyzed using the methods and compositions of the present disclosure include, without limitation, those affecting the bladder, blood, bone, bone marrow, brain, breast, colon, esophagus, gastrointestinal tract, gums, head, kidney, liver, lung, nasopharynx, neck, ovary, prostate, skin, stomach, testis, tongue, and uterus, as well as circulating tumor cells (CTCs). These cancers may be of epithelial origin (carcinomas), mesenchymal origin (sarcomas), hematopoietic origin (leukemias and lymphomas), or neuroectodermal origin (gliomas and melanomas), among others.

[0127] Examples of applicable histological types of cancer include, but are not limited to, adenocarcinomas, squamous cell carcinomas, basal cell carcinomas, transitional cell carcinomas, medullary carcinomas, lobular carcinomas, inflammatory carcinomas, and undifferentiated carcinomas such as giant and spindle cell carcinoma or small cell carcinoma. Additional examples include neuroendocrine tumors such as carcinoid tumors and pheochromocytomas, germ cell tumors such as teratomas and embryonal carcinomas, and soft tissue sarcomas such as fibrosarcoma, rhabdomyosarcoma, and liposarcoma. Central nervous system malignancies such as Docket No.: 182219.00272 astrocytoma, glioblastoma, oligodendroglioma, medulloblastoma, and meningioma are also encompassed. The disclosure further covers hematologic malignancies including Hodgkin’s and non-Hodgkin’s lymphomas, leukemia subtypes such as myeloid, lymphoid, erythroid, monocytic, and megakaryoblastic leukemias, and plasma cell disorders such as multiple myeloma.

[0128] Rare and mixed tumor types, including malignant mesothelioma, thymoma, carcinosarcoma, malignant mixed Mullerian tumors, malignant skin appendage tumors, and adrenal cortical carcinomas, are also included. The scope of applicable cancers further includes those classified by cellular phenotype, including clear cell, granular cell, chromophobe, and signet ring cell carcinomas, among others. It is contemplated that the compositions and methods disclosed herein may be used regardless of the tumor grade or stage and may be applied across both solid and hematologic malignancies.

[0129] In another aspect, the disclosed methods may be used to assess the likelihood that a subject diagnosed with cancer will develop metastatic disease. As used herein, “metastasis” or “metastatic cancer” refers to the pathological process by which malignant cells spread from the site of origin to form new tumors in distant organs or tissues. A metastatic tumor is thus defined as a tumor that originates from disseminated cancer cells that have invaded a site other than the primary tumor location. Metastasis represents one of the most clinically significant and life-threatening features of cancer progression, as patients with treated primary tumors may nonetheless succumb to metastatic disease due to its aggressive growth and resistance to therapy. Accordingly, the methods described herein may be applied not only for therapeutic or diagnostic purposes related to the primary tumor, but also for prognostic assessment of metastatic potential, stratification of patients by metastatic risk, and evaluation of therapeutic responses in subjects with known or suspected metastatic cancer.

[0130] Directional Treatment based on Bioenergetic states

[0131] In another aspect, the method may be used to predict whether a subject suffering from cancer will be eligible for therapy or predict the therapy response of a subject suffering from cancer. In some embodiments, the therapy is chemotherapy. As used herein, the term “chemotherapy” has its general meaning in the art and refers to the treatment that consists of administering to the patient a chemotherapeutic agent. In another aspect, the method may be used for prediction and prognosis of therapy efficacy. In some embodiments, the therapy is Docket No.: 182219.00272 immunotherapy. As used herein, the term “immunotherapy” refers to a therapy for a disease that relies on an immune response. In some embodiments, the immunotherapy is selected from the group consisting of checkpoint inhibitors, chimeric antigen receptors (CAR) T cells, Bi-specific T-cell engagers (BiTEs), and adoptive cell therapy. The term “CAR T cell therapy” refers to a therapy consisting of T lymphocytes expressing chimeric antigen receptors. In some embodiments, the bioenergetic index may be used to optimize CAR-T cell performance for patient precision medicine therapeutics.

[0132] In some embodiments, immunotherapy involves using an immune checkpoint inhibitor. The term “immune checkpoint inhibitor,” as used herein, refers to a substance that blocks the activity of molecules involved in attenuating the immune response. Examples of immune checkpoint inhibitors include PD-1 antagonists, PD-L1 antagonists, PD-L2 antagonists, CTLA-4 antagonists, VISTA antagonists, TIM-3 antagonists, LAG-3 antagonists, IDO antagonists, KIR2D antagonists, A2AR antagonists, B7-H3 antagonist, B7-H4 antagonist, and BTLA antagonist. The term “Bi-specific T-cell engagers” (BiTEs) refers to a therapy consisting of artificial bispecific monoclonal antibodies that are investigated for the use as anti-cancer drugs. BiTEs target the host’ s immune system, more specifically the T cells’ cytotoxic activity, against cancer cells. BiTEs form a link between T cells and tumor cells and are being tested in clinical trials. The bioenergetic index may be used to optimize T cells’ cytotoxic activity in immunotherapy regimens.

[0133] In another aspect, the present disclosure provides a method for diagnosing an inflammatory disease in a subject. The method comprises determining a bioenergetic state of one or more cells obtained from the subject using any of the methods described herein, and correlating the bioenergetic state with the presence or absence of an inflammatory disease. In certain embodiments, the bioenergetic state is assessed using mass spectrometric imaging of the cells that have been dual-labeled with alkyne and azide mass tag probes, which may reflect metabolic activity or substrate incorporation. The resulting spatial and quantitative data can be used to infer metabolic shifts characteristic of inflammatory states.

[0134] As used herein, the term “inflammatory disease” refers broadly to any disease, disorder, or condition that involves, is characterized by, or results from inflammation. This includes both acute and chronic inflammatory responses, whether localized or systemic. The term is intended to have its ordinary meaning in the art and encompasses, without limitation, allergic and hypersensitivity Docket No.: 182219.00272 disorders (e.g., systemic anaphylaxis, drug-induced allergies, food allergies, insect sting allergies), inflammatory bowel diseases (e.g., Crohn’s disease, ulcerative colitis, ileitis, enteritis), and gynecologic or urogenital inflammatory conditions (e.g., vaginitis and cystitis).

[0135] Additional examples of inflammatory diseases include inflammatory skin conditions such as psoriasis, eczema, atopic dermatitis, allergic contact dermatitis, urticaria, seborrheic dermatitis, and other inflammatory dermatoses. The term further includes vasculitic syndromes such as polyarteritis nodosa, temporal arteritis, and granulomatosis with polyangiitis, as well as rheumatologic and connective tissue diseases such as spondyloarthropathies, systemic sclerosis (scleroderma), and mixed connective tissue disease.

[0136] Respiratory diseases associated with inflammation, such as asthma, allergic rhinitis, chronic obstructive pulmonary disease (COPD), and hypersensitivity pneumonitis, are also encompassed by the term. Autoimmune disorders including, but not limited to, rheumatoid arthritis, psoriatic arthritis, osteoarthritis, systemic lupus erythematosus, multiple sclerosis, autoimmune hepatitis, autoimmune thyroiditis, type 1 diabetes mellitus, and glomerulonephritis are further examples.

[0137] The term also includes transplant rejection syndromes such as allograft rejection and graft- versus-host disease (GVHD), as well as neuroinflammatory and neurodegenerative conditions including stroke, traumatic brain injury, Alzheimer’s disease, Parkinson’s disease, encephalitis, and meningitis. Other metabolic and systemic inflammatory diseases, such as atherosclerosis, myositis, gout, and osteoporosis, as well as hepatic and renal conditions like hepatitis and nephritis, are also included.

[0138] Infectious inflammatory syndromes such as sepsis and systemic inflammatory response syndrome (SIRS) fall within the scope of the term, as do additional acute or chronic inflammatory conditions, including sarcoidosis, conjunctivitis, sinusitis, otitis media, and Behcet’s syndrome.

[0139] In some embodiments, the method further comprises comparing the bioenergetic state of the subject's cells to a reference bioenergetic state obtained from a healthy subject or population. A deviation from the reference profile may be indicative of the presence, severity, or progression of an inflammatory disease. In certain embodiments, the diagnostic information derived from the bioenergetic assessment may be used in conjunction with clinical symptoms, laboratory values, or imaging results to improve diagnostic accuracy or guide therapeutic decisions. Docket No.: 182219.00272

[0140] Additional Definitions

[0141] To promote a clear and unambiguous understanding of the present disclosure, including the compositions, methods, uses, and systems described herein, the following definitions and interpretive guidance are provided. These definitions apply unless the context of the usage clearly dictates otherwise. All technical and scientific terms used herein are intended to have the meanings commonly understood by one of ordinary skill in the art to which this disclosure pertains, unless defined otherwise below.

[0142] As used herein, the term “contacting” and its grammatical variants refer to any process by which two or more components are brought into association, including being mixed within the same solution, container, compartment, or environment. Physical interaction between the components is not required unless specifically stated. Contacting may occur in any order, combination, or sub-combination. For example, “contacting component A with components B and C” encompasses: (i) mixing A with C, followed by addition of B; (ii) mixing A and B, removing B, then adding C; or (iii) adding A to a pre-mixed solution of B and C. In the context of contacting a cell or nucleic acid with one or more reaction components (e.g., a polymerase, primer, or probe), the term includes forming mixtures with partial or complete reaction systems, either before or after addition of the cell or nucleic acid.

[0143] As used herein, the terms “determining,” “measuring,” “assessing,” and “assaying” are used interchangeably and refer to any form of evaluation, whether qualitative or quantitative. These terms include identifying whether a trait, characteristic, value, molecule, or condition is present, absent, increased, decreased, or otherwise altered. Such assessments may be relative (e.g., compared to a control or baseline) or absolute (e.g., expressed in units or concentration).

[0144] As used herein, the singular terms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0145] As used herein, the terms “including,” “comprising,” “containing,” or “having”, and grammatical variations thereof, are intended to be inclusive and open-ended. These terms encompass not only the listed elements but also unlisted elements and equivalents, unless the context clearly dictates otherwise. Docket No.: 182219.00272

[0146] As used herein, the expressions “in one embodiment,” “in various embodiments,” “in some embodiments,” or “in certain embodiments” are not intended to be limiting and may refer to the same or different embodiments. These phrases are used for clarity in describing examples and do not imply exclusivity.

[0147] As used herein, the term “and / or” (and variants such as slashes “ / ”) encompasses any one or more of the listed elements, individually or in combination. For example, “A and / or B” means A alone, B alone, or both A and B.

[0148] As used herein, the term “substantially” is not intended to exclude the possibility of “completely.” For instance, a composition that is “substantially free” of an element may, in certain embodiments, be entirely free of that element. Where appropriate, the term “substantially” may be omitted without loss of meaning.

[0149] As used herein, the term “each,” when referring to a collection of items, identifies individual items within that collection. Unless explicitly limited, “each” does not necessarily mean every item in the collection.

[0150] As used herein, the terms “about” or “approximately” are used to indicate a value or range close to the stated value. In some embodiments, “about” or “approximately” refers to values within ±25%, ±20%, ±15%, ±10%, ±5%, ±2%, or ±1% of the stated value, unless otherwise specified or clear from context. Where relevant, these terms are meant to account for experimental variability and functional equivalence.

[0151] Where ranges are provided, it is intended that all intervening values and subranges are specifically included, even if not individually recited. For example, a range of “1 to 10” includes 1, 2, 3, ..., 10 and all subranges (e.g., 3 to 7). The upper and lower limits of each stated range may independently be included or excluded and all permutations thereof are intended to be within the scope of the disclosure, unless explicitly excluded.

[0152] Any examples (e.g., those preceded by “such as,” “e.g.,” “for example,”) are intended for illustration only and do not limit the scope of the invention, unless expressly stated otherwise in the claims.

[0153] Unless otherwise specified or clearly contradicted by context, the steps of any methods described herein may be performed in any order, simultaneously or sequentially. All combinations Docket No.: 182219.00272 and sub-combinations of method steps are intended to be encompassed by the disclosure, even if not explicitly described.

[0154] All patents, patent applications, publications, journal articles, database entries, and other references cited herein are hereby incorporated by reference in their entirety, for all purposes, to the extent that they are not inconsistent with the present disclosure. The citation of such references does not constitute an admission that such references are prior art or that the applicant is not entitled to antedate such references.

[0155] The examples and embodiments disclosed herein are provided for illustrative purposes and are not intended to limit the scope of the invention. Those of ordinary skill in the art will recognize that variations, modifications, and equivalents of the disclosed subject matter may be employed without departing from the scope of the appended claims.

[0156] Examples

[0157] EXAMPLE 1

[0158] Single cell Isotopomer Distributions and Energetics of Translation under Energetic Stress (SIDETES), as described herein, is a novel methodology that can deconvolve metabolic heterogeneity whether in tumor and other tumor-resident cell populations, or other complex cell environments. SIDETES improves on the state-of-the-art in several key areas, which collectively enable the quantification of bioenergetic variation and metabolic plasticity in tumor and other tumor-resident cells approaching single cell resolution when fluorophores and FACS sorting is solely used (Fig. 10). For example, SIDETES can assess single-cell energetics in a non-invasive manner, which builds on published studies demonstrating that protein translation rates can be used as a proxy for the bioenergetic state. Dual-color labeling in SIDETES enables measurement of translation at baseline and under energetic stress in the same cell for an accurate single-cell read-out of bioenergetic state responsiveness.

[0159] In addition, by encoding a bioenergetic state into a fluorescent signal on intact cells, SIDETES enables cells to be separated into discrete bioenergetic windows by fluorescence- activated cell sorting (FACS) for downstream functional analysis, including metabolic flux with the addition of stable isotopes, proteomics, pooled genetic perturbation screens, and single-cell genomics (Fig. 1). When mass spectrometric single cell imaging is used in conjunction with FACs Docket No.: 182219.00272 sorting of single or sequentially doubly fluorescent labeled cells, the metabolic profile of each of the cells sorted into a given bioenergetic window can be determined (Fig. 13). Mass spectrometric imaging can be used as a standalone modality for assessing single or sequentially doubly fluorescent labeled cells, however, the cells are not viable at the end of the mass spectrometric imaging evaluation. Combining FACS and mass spectrometric imaging allows for complete evaluation and normalization of cells isolated in a given FACS assessed fluorescent intensity window, with the remainder of the live cells in the FACS sorted fraction be used for cell culture or other purposes.

[0160] Dual Color Click-Chemistry Protocol for Metabolic Stress Assessment by Surface Protein Translation

[0161] A novel workflow is described herein that enables labeling of the nascent surface proteome with alkyne-conjugated or azide-conjugated methionine in a time frame compatible with metabolic measurements, which provides baseline-resolved fluorescent signal and returns viable cells. Figure 2A outlines the experimental workflow for single-color labeling. Time-dependent incorporation of AHA (Fig. 2B-C) and HPG (Fig. 2D-E) into the surface proteome was observed, which is prevented by addition of the translation inhibitor cycloheximide in Jurkat cells. The background signal is extremely low using this approach (Fig. 2B-E). Culturing Jurkat cells in the absence of methionine with or without AHA and HPG has no impact on viability over 24 hours (Fig. 3 A), and minimally perturbs cell proliferation (Fig. 3B). This optimized protocol preserves cell >80% viability (Fig. 3C-E). The key critical aspect of this method is that the surface fluorescent signal correlates with energetic states. To demonstrate this, Jurkat cells were treated with metabolic inhibitors of glycolysis (2 -Deoxyglucose, 2-DG) and mitochondrial respiration (Oligomycin A, OA) (Fig. 4A), which shifted the surface fluorescent signal in a dose-dependent manner (Fig. 4B). Seahorse measurements demonstrate why the combination of 2-DG and OA is needed, as Jurkat cells can adapt by enhancing extracellular acidification when mitochondrial respiration is inhibited (Fig. 4C). Jurkat cells avidly consume glucose, because inhibiting glycolysis or mitochondrial respiration significantly reduces oxygen consumption rate (Fig. 4D). It was further demonstrated that this surface fluorescent signal is stable over the time frame of the SIDETES experiment. In a pulse-chase experiment (Fig. 5A), the surface fluorescence signal is stable for up to four hours when AHA is removed and replaced by methionine (Fig. 5B-5C). Finally, this surface signal correlates with energy charge and the amount of newly synthesized ATP levels measured by O18 Docket No.: 182219.00272 incorporation from labeled water into ATP pools (Fig. 5D), indicating that it reports on cellular bioenergetic state.

[0162] Dual-color labeling of the SIDETES workflow (Fig. 6A) enables a comparison of protein translation at baseline versus under metabolic stress. Since the chemical reactivity of AHA is different from HPG, the two methionine analogs can be sequentially incorporated to measure the rate of protein translation under two different conditions, such as in the presence and absence of metabolic stress (Fig. 1).

[0163] Fig. 6 shows an example workflow to evaluate bioenergetic dependencies in single cells in response to metabolic perturbation. For dual color labeling, methionine-free media containing AHA was first used to establish a protein translation baseline. Next, HPG replaced AHA in the presence of specific metabolic inhibitors to evaluate the impact on translation rate (Fig. 6A). Next, the Jurkat cells were subjected to sequential surface clicking reactions with AZD488-alkyne and then AZD647-azide to assess plasma membrane protein translation by flow cytometry. Then, cells were labeled with a commercial viability dye. Flow cytometry was used to assess the proportion of live cells after sequential clicking (Fig. 6B), as well as the signal in each channel, representing baseline versus metabolically-coupled translation (Figs. 6C-F). A clear, baseline-resolved signal was observed in each fluorescent channel (Fig. 6C). This signal does not depend on incubation order (if AHA versus HPG is first) (Fig. 6D). It also shifts in response to translation inhibitors (Fig. 6E) and metabolic stress with 2-DG treatment (Fig. 6F). Metabolic inhibitors, such as 2- Deoxyglucose (2DG) or oligomycin (O), can be used to evaluate, for example, bioenergetic dependence on glycolysis or respiration, respectively. While the studies outlined in Fig. 6 can directly assess cellular bioenergetic dependence on glycolysis versus mitochondrial respiration, any combination of inhibitors or metabolic stressors (such as nutrient withdrawal) can be evaluated this way.

[0164] Together, these results demonstrate that protein translation can be monitored under two conditions via the nascent surface proteome, a foundational element of the SIDETES workflow (dual color with metabolic stress, Fig. 1).

[0165] This dual color labeling protocol sequentially labels plasma membrane proteins with an alkyne-conjugated fluorophore (reactive with AHA) followed by a different azide-conjugated fluorophore (reactive with HPG). This method builds upon the observation that global translation Docket No.: 182219.00272 levels and ATP synthesis correlate, which was further shown is true for the nascently translated surface proteome (Fig. 5D), and results in two measurements of protein translation - baseline and metabolically coupled.

[0166] Table 1. Compendium of18O / 16O exchange reactions which determine ATP turnover. CK- creatine kinase, AK adenylate kinase. From (Dzeja Physiol 589.21 (2011) pp 5193-5211).

[0167] EXAMPLE 2

[0168] Calculation of an Energetic Sensitivity Index as a New Parameter in Flow Cytometry

[0169] Dual-color labeling enables us to calculate an internally normalized sensitivity index that describes how translation changes under metabolic stress. This accounts for differences in baseline translation that are unrelated to the metabolic perturbation and can vary widely among cell types. The informatic workflow is outlined in Figure 7A and involves variance stabilization / standardization and outlier filtering before calculating the numerical difference in AHA and HPG fluorescent signal from flow cytometry. This is an easily calculated parameter that uses standard export formats from common commercial flow cytometry software, such as FlowJo. The effect of each step in the informatic workflow on the dataset is shown in Figure. 7B. We perform dual color labeling on Jurkat cells as in Figure 6A and then calculated our internally normalized sensitivity index (Fig. 7C). This reveals that Jurkats are energetically resilient to inhibition of fat oxidation (etomoxir, ETO), as well as to single treatments with inhibitors of glycolysis (2-deoxyglucose, 2- DG) or mitochondrial respiration (Oligomycin A, OA) (Fig. 7C). However, combining 2-DG + OA leads to a dose-dependent reduction in the sensitivity index (Fig. 7C). This sensitivity index can be represented as a parameter on flow cytometry data for visualization as well (Fig. 7D-7E). Note that the metabolic sensitivity index reflects the stochastic nature of the data, the quantitative measures of which can be obtained from the statistical characteristics of the violin plot (Fig. 7C). For example, the median or mean of the violin plot can be used comparatively between metabolic Docket No.: 182219.00272 stressors to calculate metabolic dependencies (i.e., comparing the difference between the DMSO / DMSO control, DMS0 / 2DG+0A). The probability features of the violin plot (Fig. 7C) can be used to quantify heterogeneity and responsiveness to metabolic stress.

[0170] EXAMPLE 3

[0171] Application to Additional Cell Types and Complex Cell Mixtures

[0172] The dual-color labeling workflow was performed on CD45+ leukocytes isolated from mice bearing B16-0VA melanoma tumors (Fig. 8A). Fluorescent signals in two channels are correlated, which are selectively inhibited by the addition of a translation inhibitor during incubation with the second clickable methionine analog (Fig. 8B), which is true across all immune cell subsets that we evaluated within the tumor (Fig. 8B). In addition, CD8+ T lymphocytes isolated from three anatomical locations - tumor bed, tumor-draining lymph node, or spleen - were compared using this dual-color labeling methodology (Fig. 8C). Similarly, correlated signals in the two fluorescent channels were also observe (Fig. 8D). Importantly, there is a wide range of baseline translation rates among the same cell type that are linked to tissue of origin, demonstrated why measuring baseline translation is so important (Fig. 8D). Within tumors, CD8+ T cells were compared with high or low levels of CD44, a marker that denotes cells that are antigen-experienced. This also revealed correlated fluorescent signal, where CD44-low cells had lower baseline translation than CD44+ cells (Fig. 8E). It also revealed that a subset of CD44-low cells exhibit high baseline translation, further indicating that the approach to normalize translation rates by baseline translation is essential for accurate measurements of energetic dependencies.

[0173] Finally, the single-color workflow was tested on a second cancer cell line to establish wide applicability of this method. Fig. 9A depicts an experimental schematic using 0CI-AML3 human cancer cells. Our workflow yields live cells (Fig. 9B) and surface fluorescent signal that changes with the addition of metabolic stress (Figs. 9C-D).

[0174] In summary, it was demonstrated that the approach yields baseline-resolved signal in two fluorescent channels and returns viable cells in two human cancer cell lines and primary mouse immune cells from three different anatomical sites.

[0175] EXAMPLE 4

[0176] Integration of Dual Color Click-Chemistry with Isotopomer Flux Analysis Docket No.: 182219.00272

[0177] SIDETES preserves metabolic networks because it does not involve cell permeabilization or fixation. This is a unique feature of the method because measuring translation on the plasma membrane surface does not require cell permeabilization (as opposed to the Single Cell Energetic metabolism by profiling Translation inhibition (SCENITH) method). Therefore, this SIDETES workflow can be integrated with direct downstream quantification of metabolites, lipids, and proteomic abundances from cells parsed into energetic windows.

[0178] For example, SIDETES can be performed using any kind of metabolic stress, such as hypoxia, and then cells can be sorted by FACS into quadrants by bioenergetic state based on the fluorescent intensities reflecting AHA and HPG incorporation into the surface proteome (Fig. 1). As an example, cells can be incubated in HPG to capture baseline translation rate and then switched to AHA during metabolic stress to capture the metabolically-coupled translation rate. In this case, stable isotopically labeled metabolic tracers (such as13Ce-glucose) can be added during the second incubation with AHA. To stabilize mass isotopomer distributions (MIDs), click labeling buffer and all solutions used before and after sorting must be supplemented with 50% tracer. It has been previously reported that metabolite pool sizes can change during cell sorting, but MIDs are preserved. After sorting (2,000-5,000) cells into quadrants based on the HPG and AHA signal, cell pellets must be immediately processed for metabolomics analysis, such as washing in ice-cold 150 mM ammonium acetate to remove residual tracer and then snap-frozen. Polar metabolites can then be extracted, and peak integration, isotope correction, and flux determination carried out as previously described (Jiang, Q. et al. MBio 13, e0127422-e0127422 (2022); Tang, Y. et al. Elife 11, e73360 (2022); Fernandez, C. A., et al. J. Mass Spectrom. 31, 255-262 (1996)). The extraction method can also leave protein to be profiled for individual protein abundances for correlation with the metabolite / lipid / flux network determined for a given energetic window. The13C label can also be incorporated into the nascent proteome, which can be used to assess the synthesis of biomass. This approach can be readily extended to other metabolic pathways by selecting different combinations of stable isotope tracers, sub-lethal energetic stressors, and derivatization agents to amplify signals for specific metabolites (see Table 2, Fig. 2).

[0179] Table 2. Improvements in detection sensitivity by LC-MS with chemical derivatization (3- NPH) in standards and tissue extracts Docket No.: 182219.00272

[0180] *50 pg mouse liver tissue (+ 3-NPH)

[0181] CV = coefficient of variation (100*STDEV / mean), nd = not detected

[0182] The SIDETES application can also employ the metabolite derivatization methodology described in U.S. Patent No. 10914741, the disclosure of which is incorporated herein by reference. SIDETES can be used to examine rare cell populations in complex cellular organizations, such as tumors. This chemical derivatization methodology increases detection sensitivity across chemically diverse metabolites, enabling metabolomic / fluxomic analysis of rare cells (2000-5000 cells) parsed by bioenergetic state. The impact of chemical derivatization by 3- nitrophenylhydrazine (3-NPH) was tested on detection sensitivity for metabolite standards as well as mouse liver extracts (50 pg) by LC-MS (corresponding to -5,000 cells). 10 to >10, 000-fold increases were observed in sensitivity across chemically diverse metabolites with a coefficient of variation (CV) far below 30% (Table 2), demonstrating that cell input requirements for metabolomics / metabolite isotopomer flux analysis can be reduced in this way.

[0183] EXAMPLE 5

[0184] Integration of Dual Color Click-Chemistry with Single Cell Genomics

[0185] SIDETES can be modified to integrate directly with genomics technologies. In this case, alkyne-bearing DNA barcodes are substituted for alkyne-bearing fluorophores. These molecules can directly react with azide-conjugated amino acids on plasma membrane protein on the cell surface. Similarly, azide-bearing DNA barcodes are substituted for azide-bearing fluorophores. These molecules can directly react with alkyne-conjugated amino acids on plasma membrane protein on the cell surface. The extent to which cells are labeled with distinct barcodes reflects incorporation of the corresponding conjugated amino acid into the cell surface proteome, and can be quantified as a separate library by bulk or single-cell DNA sequencing. If the azide-bearing DNA barcode is different from the alkyne-bearing DNA barcode, then sequencing relative enrichments of each barcode can quantify the relative incorporation of each conjugated amino acid into the surface proteome for baseline and metabolically-coupled translation as a parameter in Docket No.: 182219.00272 single cell genomics technologies.

[0186] SIDETES, as disclosed herein, is a flexible methodology that can be inserted into analytical workflows that benefit from bioenergetic deconvolution. For example, CRISPR screens targeting metabolic phenotypes currently rely on indirect read-outs of proliferation or drug sensitivity. SIDETES provides an alternative enrichment strategy by sorting cells based on bioenergetic dependencies or adding a DNA barcode that reflects metabolic sensitivity before quantifying CRISPR guide enrichment. Alternatively, SIDETES can also be integrated with pooled genetic perturbation screening to probe the coupling between transcriptional changes and bioenergetic state, such as through Peturb-Seq or CombiGEM (Dixit, A. etal. Cell 167, 1853-1866. e!7 (2016); Wong, A. S. L. et al. Proc. Natl. Acad. Sci. 113, 2544-2549 (2016)).

[0187] EXAMPLE 6

[0188] Integration of Dual Color Click-Chemistry with Cell Enrichment or Purification

[0189] SIDETES can be modified for a biochemical fractionation of cells based on the density of AHA or HPG incorporation into the surface proteome. In this case, alkyne-bearing affinity purification molecules (such as biotin, strep-tags, or poly-histidine tags) are substituted for alkyne- bearing fluorophores. Likewise, azide-bearing affinity purification molecules (such as biotin, strep-tags, or poly-histidine tags) are substituted for azide-bearing fluorophores. Using standard immunoprecipitation workflows, biochemical fractionation can be performed on whole cells with increasing concentrations of competitive eluent as an alternative strategy to bin cells by the rate of surface proteome translation.

[0190] EXAMPLE 7

[0191] Combination of Single Color Click-Chemistry with Clickable Metabolic Analogues

[0192] SIDETES can be modified to substitute alkyne- or azide-modified metabolite analogues instead of AHA or alkyne-modified metabolite analogues instead of HPG to correlate protein translation rates with metabolite uptake. Examples of clickable metabolites may include: Propargyl-choline, 2- Azido-2-deoxy -D-glucose, UDP-6-azi de-glucose, UDP-N- azidoacetylgalactosamine, N6-Propargyl-ATP (N6pATP), C8-Alkyne-dUTP, and 3'-Azido-3'- deoxy adenosine.

[0193] By causally linking bioenergetics with other cellular phenotypes and dependencies, Docket No.: 182219.00272

[0194] SIDETES can uncover new mechanisms by which metabolic variation impacts tumorigenesis and response to therapy.

[0195] EXAMPLE 8

[0196] Mass Spectrometric Imaging Assessment of Bioenergetic State Using18O ATP Turnover for Identification of Rare Cells with Differences from Surrounding Parenchyma

[0197] SIDETES can be used in a single color (one fluorophore or mass tag) with mass spectrometric imaging and can be further adapted to measure differences in response to a metabolic inhibitor or activator using18O water. For measurement of bioenergetic states of cells in vivo, either an azide tagged amino acid or alkyne tagged amino acid can be administered for several hours or days (depending on the amino acid incorporation rate of the cell). This can preserve, or assess the basal energetic rate assessed via protein translation. Then a metabolic inhibitor (or activator) can be given in vivo and contrasted versus the no inhibitor control by using18O water to assess ATP turnover. Both the control and the inhibitor (or activator) treated animal should have the same basal incorporation of the alkyne tagged (or azide tagged) amino acid given. If a metabolic inhibitor or activator is chosen with the effect of rapid onset (minutes) then18O water can be administered over a period of 10 minutes before animal sacrifice. Basal energetics can then be assessed after mass spectrometric imaging easily with a complementary fluorophore (e.g., alkyne tagged amino acid, azide labeled fluorophore) and the mass spectrometric imaging can be used to assess18O incorporation into ATP, yielding ATP turnover in the metabolically inhibited (or activated) state verse the control administration of unlabeled water.

[0198] Fig. 12 illustrates the mass spectrometric assessment of ATP turnover using18O water, administered over approximately 10 minutes using a combined oral gavage and intraperitoneal (IP) injection protocol to mice that had been fasted overnight and refed ad libitum for 4 hours. No alkyne or azide tagged amino acid was given, just18O water. Cellular ATP turnover measured with18O water can be done with exact mass qTOF mass spectrometry or unit Dalton, multiple reaction monitoring (MRM) assessment of parent-daughter mass pairs. Fig. 12 depicts the exact mass determination of the mass isotopomer of ATP than has incorporated one18O atom, using a Waters Cyclic Ion Mobility Time of Flight. The substitution of O18for O16increases m / z by 2, shifting the m / z of ATP from 527.95 to 529.95. Mice that had been refed displayed more ATP turnover than the fasted state, as indicated by the enhanced number of m / z 529.95 pixels vs the fasted state. Docket No.: 182219.00272

[0199] Fig. 13 further illustrates how alkyne or azide amino acid analogs can be used in conjunction with mass spectrometric imaging to elucidate metabolic network heterogeneity. Mass spectrometry imaging visualizes metabolic networks within single cells and can be applied to cultured cells. This can be used to illuminate metabolic heterogeneity among cells sorted in a narrow energetic window vs. unsorted cells, which reflect the bulk population. This experiment reveals the metabolic strategy that each cell uses to support its energetic state. Specifically, MS imaging on FACS sorted cells, even if the intensity windows for detection of azide or alkyne amino acid analog incorporation are narrow, can still reveal metabolic heterogeneity, as energy can be generated from many catabolic pathways (such as glycolysis or the TCA cycle), and the metabolic network producing a given energetic state can have different components of energy (ATP equivalents) coming from each pathway. Rare cells are expected to have significant differences in the components of their metabolic networks from the surrounding parenchyma. Thus, even if rare cells have the same energetics as the surrounding parenchyma cells under a particular condition, the proportion of energy generated from glycolysis vs the TCA cycle, for example, can differ, which can be elucidated with MS imaging, as illustrated in Fig. 13.

[0200] The present disclosure is not to be limited in scope by the specific embodiments described herein. Indeed, various modifications of the invention, in addition to those described herein, will become apparent to those skilled in the art from the foregoing description and the accompanying figures. Such modifications are intended to fall within the scope of the appended claims.

Claims

Docket No.: 182219.00272CLAIMSWhat is claimed is:

1. A method of determining a bioenergetic state of a cell comprising:(a) contacting a cell with an alkyne-conjugated amino acid in a first medium for a first period of time, wherein the alkyne-conjugated amino acid is incorporated into a first set of plasma membrane proteins on cell surface of the cell during translation of the first set of plasma membrane proteins;(b) contacting the cell with an azide-conjugated amino acid in a second medium for a second period of time in presence and absence of a metabolic inhibitor, wherein the azide- conjugated amino acid is incorporated into a second set of plasma membrane proteins on the cell surface of the cell during translation of the second set of plasma membrane proteins, and wherein the metabolic inhibitor causes metabolic stress to the cell;(c) contacting the cell with an azide-bearing detection tag reactive to the alkyne- conjugated amino acid in the first set of plasma membrane proteins, using click chemistry to associate the azide-bearing detection tag with the first set of plasma membrane proteins;(d) contacting a cell with an alkyne-bearing detection tag reactive to the azide-conjugated amino acid on the second set of plasma membrane proteins, using click chemistry to associate the alkyne-bearing detection tag with the second set of plasma membrane proteins;(e) determining a first signal generated from the azide-bearing detection tag associated with the first set of plasma membrane proteins, wherein the first signal generated from the azide- bearing detection tag represents a baseline protein translation state of the cell;(f) determining a second signal generated from the alkyne-bearing detection tag associated with the second set of plasma membrane proteins in the presence of the metabolic inhibitor, wherein the second signal generated from the alkyne-bearing detection tag represents a metabolically-coupled protein translation state of the cell under the metabolic stress caused by the metabolic inhibitor;(g) assessing cell viability to discriminate live and dead cells by flow cytometry; and(h) determining a bioenergetic state of the cell based on: a change from the baseline protein translation state to the metabolically-coupled protein translation state under the metabolic stress by comparing the second signal to the first signal.Docket No.: 182219.002722. The method of claim 1, comprising performing step (b) prior to step (a).

3. The method of claim 1 or 2, comprising performing step (d) prior to step (c).

4. The method of any one of the preceding claims, further comprising determining a baseline protein translation rate of the cell based on the first signal generated from the azide- bearing detection tag.

5. The method of any one of the preceding claims, further comprising determining a metabolically-coupled protein translation rate of the cell under the metabolic stress based on the second signal generated from the alkyne-bearing detection tag.

6. The method of any one of the preceding claims, wherein the step of determining the bioenergetic state of the cell comprises determining a bioenergetic sensitivity index by comparing a baseline translation rate at the baseline protein translation state of the cell and a translation rate at the metabolically-coupled protein translation state of the cell under the metabolic stress.

7. The method of any one of the preceding claims, wherein the cell comprises a tumor cell, a normal cell, or an immune cell.

8. The method of any one of the preceding claims, wherein the cell is under metabolic stress.

9. The method of any one of the preceding claims, wherein the step of determining the bioenergetic state of the cell further comprises determining an amount of metabolites and lipids of the cell.Docket No.: 182219.0027210. The method of any one of the preceding claims, wherein the step of determining the bioenergetic state of the cell further comprises determining metabolic flux of the cell.

11. The method of any one of the preceding claims, wherein the step of determining the bioenergetic state of the cell further comprises determining proteomic abundance.

12. The method of any one of the preceding claims, wherein the azide-conjugated amino acid is an azide-conjugated methionine or an analog thereof.

13. The method of any one of the preceding claims, wherein the alkyne-conjugated amino acid is an alkyne-conjugated methionine or an analog thereof.

14. The method of any one of the preceding claims, wherein the first or second medium is free of a corresponding unconjugated amino acid.

15. The method of any one of claims 12-14, wherein the first or second medium is free of unconjugated methionine.

16. The method of any one of the preceding claims, wherein the second medium is free of the alkyne-conjugated amino acid.

17. The method of any one of the preceding claims, wherein the azide-bearing detection tag comprises an azide-bearing fluorophore, and the alkyne-bearing detection tag comprises an alkyne-bearing fluorophore.

18. The method of claim 17, wherein the first signal generated from the azide-bearing fluorophore or the second signal generated from the alkyne-bearing fluorophore is determined by flow cytometry.Docket No.: 182219.0027219. The method of claim 17, wherein the first signal generated from the azide-bearing fluorophore or the second signal generated from the alkyne-bearing fluorophore is determined by fluorescence-activated cell sorting (FACS).

20. The method of any one of the preceding claims, wherein the azide-bearing detection tag comprises an azide-bearing mass tag, and the alkyne-bearing detection tag comprises an alkyne- bearing mass tag.

21. The method of any one of the preceding claims, wherein the first signal generated from the azide-bearing detection tag or the second signal generated from the alkyne-bearing detection tag is determined by mass spectrometry.

22. The method of claim 20, wherein the azide-bearing mass tag or the alkyne-bearing mass tag comprises an isotopic mass tag or an isobaric mass tag.

23. The method of claim 20, further comprising performing mass spectrometric imaging to normalize single cell baseline metabolite flux assessments by measuring the azide-bearing mass tag and the alkyne-bearing mass tag incorporated into metabolites and / or lipids.

24. The method of claim 20, wherein determining the bioenergetic state of the cell comprises analyzing the metabolic profile and / or isotopic enrichment of the cell using mass spectrometric imaging, and wherein the cell is dual-labeled with the alkyne-functionalized mass tag and the azide-functionalized mass tag.

25. The method of claim 20, wherein determining the bioenergetic state of the cell comprises normalizing the metabolic profile of the cell by measuring the respective intensities of reporter ions corresponding to the alkyne-bearing mass tag probe and the azide-bearing mass tag probe, wherein the reporter ions are detected under basal and metabolically perturbed conditions.Docket No.: 182219.0027226. The method of claim 17, wherein determining the bioenergetic state of the cell comprises detecting a stable isotope using mass spectrometric imaging, and normalizing the metabolic profile of the cell based on the measured intensities of an alkyne-bearing fluorophore and an azide-bearing fluorophore.

27. The method of claim 20, wherein determining the bioenergetic state of the cell comprises measuring adenosine triphosphate (ATP) flux and / or ATP turnover rate in cells and / or tissues using mass spectrometric imaging.

28. The method of any one of the preceding claims, wherein the first set of plasma membrane proteins and the second set of plasma membrane proteins are the same proteins.

29. The method of any one of claims 1-28, wherein the first set of plasma membrane proteins and the second set of plasma membrane proteins are different proteins.

30. The method of any one of the preceding claims, wherein the azide-conjugated amino acid comprises azide-modified homoalanine (AHA).

31. The method of any one of the preceding claims, wherein the alkyne-conjugated amino acid comprises alkyne-modified homopropargylglycine (HPG).

32. The method of claim 17, wherein the azide-bearing fluorophore comprises an AZD647- azide fluorophore, an Alexa Fluor 488-azide fluorophore, an Alexa Fluor 555-azide fluorophore, an Alexa Fluor 594-azide fluorophore, an Alexa Fluor 647-azide fluorophore, or an Oregon Green 488-azide fluorophore.

33. The method of claim 17, wherein the alkyne-bearing fluorophore comprises AZD488- alkyne fluorophore, an Alexa Fluor 488-alkyne fluorophore, an Alexa Fluor 555-alkyne fluorophore, an Alexa Fluor 594-alkyne fluorophore, an Alexa Fluor 647-alkyne fluorophore, or an Oregon Green 488-alkyne fluorophore.Docket No.: 182219.0027234. The method of any one of the preceding claims, wherein the metabolic inhibitor comprises a glycolysis inhibitor, a cellular energy inhibitor, an oxidative phosphorylation inhibitor, an amino acid metabolism inhibitor, a mitochondrial metabolism inhibitor, a lipid metabolism inhibitor, a nucleotide metabolism inhibitor, a pentose phosphate pathway inhibitor, a nitrogen metabolism inhibitor, a redox destabilizer, or a combination thereof.

35. The method of any one of the preceding claims, wherein the metabolic inhibitor comprises a protein translation inhibitor or a metabolic stressor.

36. The method of claim 35, wherein the metabolic stressor activates cellular metabolism.

37. The method of any one of the preceding claims, wherein the first period of time or the second period of time is about 2 hours to about 8 hours.

38. The method of any one of the preceding claims, wherein the first period of time or the second period of time is about 2 hours.

39. The method of any one of the preceding claims, wherein the alkyne-modified detection tag or azide-modified detection tag is replaced by DNA barcodes for genomics.

40. The method of any one of the preceding claims, wherein fluorescently labeled cells are sorted and re-cultured.

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