Patent Application
20230341376
The instant application contains a Sequence Listing XML, which has been submitted via EFS-Web and is hereby incorporated by reference in its entirety. The Sequence Listing XML, created on Jun. 9, 2023, is named 17958005-109198-284-seq.xml, and is 4,776 bytes in size.
The present disclosure relates generally to multiplexed vibrational probes, and more specifically, to exemplary embodiments of exemplary system, method, and computer-accessible medium for metabolic activity phenotyping of single cells with multiplexed vibrational probes.
Genetics and metabolism are certain defining characteristics of life -- it is the synthesis, transformation and degradation of biomolecules (e.g., metabolism) inside each cell that carry out the genetic blueprint. Hence, quantitative measurements of the metabolism of individual cells are essential to understand questions in diverse fields in biology and medicine. (See, e.g., Reference 1). In cancer biology, for example, it is known that the metabolic profiles can be altered in many cancer cells and serve as druggable targets. (See, e.g., Reference 2). Such altered metabolism can vary from cell to cell within tumor tissue. Termed as intratumoral heterogeneity, this chaotic nature of metabolism in individual cells can lead to heterogeneous tumor growth, invasion and drug resistance. (See, e.g., Reference 3). Therefore, the task of evaluating single-cell metabolism is becoming increasingly urgent, especially in the post-genomics era. Similar tasks are also demanded in drug discovery, particularly for those drug targeting metabolic pathways, as the rapid determination of the mechanisms of compound candidates greatly facilitates drug screening. (See, e.g., References 4, 5). While recent development of multi-parameter profiling approaches has provided important insights into the mechanism of drug action, many of these analyses are not directly linked to the drug targets, limiting their interpretability. (See, e.g., References 6-9). Therefore, a method that quantitatively provides insights into the metabolism of single cells is in demand.
In addition to the metabolic measurements in single cells, it can be desirable to profile multiple biomarkers such as surface proteins as well as other pathways such as endocytosis, e.g., both at single cell level.
Among available approaches, mass-spectrometry is widely used for metabolic profiling due to its capability of evaluating a large pool of metabolites at the same time. Yet, mass-spectrometry can be rather challenging to resolve at single-cell level. Besides, mass-spectrometry is destructive, which makes it impossible to collect cells for additional downstream analysis. On the other hand, optical procedures can be advantageous for their non-destructive nature, easy sample preparation, and high throughput capability. (See, e.g., Reference 10). Fluorescence-based flow cytometry, for example, is routinely used for immune cell phenotyping, biomarker detection with great sensitivity and specificity. However, while good at profiling surface biomarkers, fluorescence methods may have, for example, limited utility in reporting metabolism, as the fluorescent probes are too bulky to label small metabolites. On the other hand, label-free Raman microscopy generally relies on the physical and chemical properties of cells to generate information about cell phenotypes. (See, e.g., References 11, 12). However, the physical and chemical information acquired by label-free Raman microscopy is difficult to explain any changes in metabolism or surface markers, which often hinders mechanistic insights for fundamental understanding.
Label-free methods, such as Raman-based vibrational microscopy, rely on physical and chemical properties to generate information about cell phenotypes (see, e.g., Reference 52), and usually don’t demand a priori knowledge of biomarkers. For this reason, label-free Raman spectroscopy has been extensively investigated and successfully demonstrated in many diagnostic applications. (See, e.g., References 53-55). In the past decade, great efforts have been put into improving the hardware in order to increase the signal to noise ratio, (see, e.g., References 56-58), and into developing sophisticated statistical tools such as machine learning procedures to achieve better diagnostic results. (See, e.g., Reference 59). Yet challenges remain that hinder the prevalence of this powerful procedure in broader applications. Among those challenges, to achieve a robust and reproducible detection and classification classifier model is the key.
Additionally, comprehensive understanding of fundamental biology, disease mechanisms, and drug therapeutics can require the integration of information from a large number of related pathways. Multiplexed measurements are thus attracting considerable interest in single-cell biology and systems biology. (See, e.g., References A1-A3). In the context of single-cell analysis, mass cytometry is popular for large-scale proteomics. (See, e.g., Reference A4). However, mass cytometry may be restricted by its destructive nature, and thus it is incompatible with the investigation of live cells and downstream cell sorting. In contrast, fluorescence-based flow cytometry can be an optical method that can interrogate live cells non-destructively. (See, e.g., References A5, A6). However, fluorescence detection may be limited by the broad fluorescent spectra, and the associated “color barrier” constraints the multiplexing level of fluorescence to just a few channels. Although, e.g., up to 17 parameters have been reported in the literature, the implementation of multiparameter fluorescence cytometry can be rather laborious and challenging due to cumbersome and error-prone spectral compensation and high instrument complexity of multiple lasers and detectors. (See, e.g., References A5, A7). Moreover, metabolites may be difficult for fluorescence detection because of the relatively bulky size of fluorophores compared to small metabolites. (See, e.g., References A8-A10), and as a result, the crucial functional information about metabolic activities can often be missing in fluorescence-based live-cell profiling. Furthermore, fluorescence detection often suffers from auto-fluorescence and photobleaching. (See, e.g., Reference All).
It is possible to exploit Raman scattering as an alternative solution to super-multiplex live-cell profiling. Raman scattering can be a powerful optical spectroscopy that can complement fluorescence in some aspects. First, in the condensed phase, e.g., Raman spectrum can typically be 50 times narrower than that of fluorescence. Thus, Raman spectroscopy can principally circumvent the “color barrier” of fluorescence and hold great promise for super-multiplexing. (See, e.g., References A12-A15). Technically, compared to multiple lasers and detectors required in fluorescence cytometry, the single laser (which excites all Raman modes) and single Raman detector (which collects all modes) configuration can permit robust readout of multiparameter data with the one-shot acquisition. Second, e.g., Raman spectroscopy may not require bulky fluorophores, making it potentially capable of detecting small metabolites. (See, e.g., Reference A16). Third, Raman spectroscopy can be free from photobleaching.
However, Raman-based live-cell profiling is still in its early stages. (See, e.g., References A17-A20). This is largely because functional Raman probes may be lagging behind the counterparts in mass cytometry or fluorescence flow cytometry. In particular, for profiling specific protein markers, compared to the well-established fluorophore-conjugated antibodies or rare-earth meta-isotope-tagged antibodies (see, e.g., References A5, A21-A24), the existing antibody-based Raman probes may be limited by insufficient brightness and/or low level of multiplexing, despite extensive efforts in developing organic dyes, (see, e.g., References A25, A26), polymer nanoparticles, (see, e.g., References A27, A28), and metallic nanoparticles, (see, e.g., References A29, A30). In a sense, Raman spectroscopy may not fulfill its full potential without, e.g., the use of matching functional probes.
Thus, it may be beneficial to provide an exemplary system, method, and computer-accessible medium for multi-parameter phenotyping of single cells with multiplexed vibrational probes which can overcome at least some of the deficiencies described herein above.
An exemplary system, and computer-accessible medium for determining metabolic information for a cell(s) can include, for example, generating spectral information of the cell(s) using a Raman spectroscopy procedure that can be based on a profiling probe(s), and determining the metabolic, biomarker and signaling pathway information based on the spectral information. The Raman spectroscopy procedure can be a single-cell Raman spectroscopy procedure. The single-cell Raman spectroscopy procedure can be performed using a whole-cell confocal micro-Raman spectrometer. The whole-cell confocal micro-Raman spectrometer can have an illumination spot size of between about 1 µm to about 5 µm, between about 5 µm to about 10 µm, or between about 10 µm to about 100 µm. The whole-cell confocal micro-Raman spectrometer can have a pinhole size between about 50 µm to about 200 µ, between about 200 µm to about 400 µm, or between about 400 µm to about 1000 µm.
In certain exemplary embodiments of the present disclosure, the profiling probe(s) can include a Deuterium-labeled branched-chain amino acid(s), or a deuterium-labeled palmitic acid(s). The profiling probe(s) can include a molecule(s) that can be used by a living organism for biosynthesis. The molecule(s) can include (i) natural perdeuterated amino acids, (ii) partially deuterated amino acids, (iii) palmitic acid, (iv) oleic acid, (v) deuterated cholesterol, (vi) heavy water, (vii) deuterated glucose, (viii) deuterated acetate, (ix) alkyne bearing amino acids, (x) 1-homopropargylglycine, (xi) alkyne bearing fatty acids, (xii) 17-octadecynoic acid, (xiii) alkyne bearing nucleic acids, (xiv) 5-ethynyl-2′-deoxyuridine, (xv) 5-ethynyl uridine, (xvi) propargylcholine, (xvii) 3-O-propargyl-D-glucose, (xviii) Carbow orgenell dyes, (xix) Carbow-Mito, (xx) Carbow-Lyso, (xxi) Carbow-ER, (xxii) at least one drug, (xxiii) erlotinib, (xxiv) rhabduscin, (xxv) terbinafine, (xxvi) or carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone. In certain exemplary embodiments of the present disclosure, the profiling probe(s) can include, e.g., nanoparticles such as Rdots that can have Raman peak(s) that are separable from Raman peak(s) of the cells. In certain exemplary embodiments of the present disclosure, these nanoparticles can be biofunctionalized to target certain biomarkers or signaling pathways.
The exemplary profiling probe(s) can have a Raman peak(s) in a cell-silent spectral region. The cell-silent spectral region can be between 1800 cm-1 and 2800 cm-1. In certain exemplary embodiments of the present disclosure, a super-multiplexed Raman probe panel can include sharp and mutually resolvable Raman peaks to simultaneously quantify cell surface proteins, endocytosis activities, and metabolic dynamics of an individual live cell. When the exemplary super-multiplexed Raman probe panel is coupled to whole-cell spontaneous Raman micro-spectroscopy, the single-cell multiparameter measurement can enable powerful clustering, correlation, and network analysis with biological insights. The exemplary procedure can be used in, e.g., 14-plexed live-cell profiling and phenotyping under various drug perturbations.
According to certain exemplary embodiments of the present disclosure, a panel of super-multiplexed Raman probe can be provided that can target a wide range of key molecular and cellular markers. In certain exemplary embodiments of the present disclosure, by harnessing an ultra-bright Raman dots (Rdots) and with further functionalization and optimization, a panel of Rdots-conjugated antibodies and aptamers can be created. Additionally, ageneral exemplary procedure to detect cell surface proteins simultaneously in single live cells can be developed. Moreover, by leveraging the independent size and color tunability of Rdots and their good biocompatibility, profiling cellular endocytic pathways in a particle-size-dependent manner can be achieved. Furthermore, multiplexed metabolic activities can be profiled in parallel by including small vibrational probes (such as, e.g., alkyne and C-D bond) with minimal perturbation. When implemented with a tailored whole-cell spontaneous Raman micro-spectroscopy (Supplementary Video), this exemplary live-cell profiling platform can provide a strategy to acquire integrated information about cell surface protein abundance, endocytosis activities, and metabolic dynamics simultaneously at a relatively high speed (e.g., 3600 cells/hour). The application of this exemplary platform in 14-plexed live-cell profiling and phenotyping under various drug perturbations can be used, for example, in measuring multiparameter information, characterizing cell heterogeneity, revealing underlying correlation, and discovering mechanisms of drug actions. The exemplary profiling platform according to exemplary embodiments of the present disclosure can be compatible with, among others, live cell cytometry, of low instrument complexity and capable of highly multiplexed measurement in a robust manner. Additionally, the exemplary profiling platform according to the exemplary embodiments of the present disclosure can provide a valuable system for both basic single-cell biology and translation applications, such as, e.g., high-content cell sorting and drug discovery.
These and other objects, features and advantages of the exemplary embodiments of the present disclosure will become apparent upon reading the following detailed description of the exemplary embodiments of the present disclosure, when taken in conjunction with the appended claims.
Further objects, features and advantages of the present disclosure will become apparent from the following detailed description taken in conjunction with the accompanying Figures showing illustrative embodiments of the present disclosure, in which:
Throughout the drawings, the same reference numerals and characters, unless otherwise stated, are used to denote like features, elements, components or portions of the illustrated embodiments. Moreover, while the present disclosure will now be described in detail with reference to the figures, it is done so in connection with the illustrative embodiments and is not limited by the particular embodiments illustrated in the figures and the appended paragraphs.
The exemplary system, method and computer-accessible medium, according to an exemplary embodiments of the present disclosure, can be used to evaluate the metabolism in single mammalian cells by coupling multiple vibrational metabolic probes with high-throughput single-cell Raman spectroscopy, termed as metabolic activity phenotyping (“MAP”). Instrument-wise, an automatic whole-cell confocal micro-Raman spectrometer was designed, which improves the throughput of single-cell metabolism measurement over commercial confocal scope by at least 100 times. Probe-wise, MAP method takes advantage of the newly developed vibrational probes of various metabolic pathways. These exemplary probes can be chemical analogs of their native counterparts and can be incorporated into all living cells without perturbing the biological activities. While they have been originally developed for vibrational imaging with subcellular resolution, (see, e.g., References 13-15), MAP integrates multiple of them with different colors into a high-throughput platform of single-whole-cell Raman spectroscopy, for the first time.
The exemplary whole-cell Raman spectroscopy system can be used to optically integrate the Raman signal over the entire cell without the need for subcellular resolution. This can provide high speed and throughput for single cell measurement. In contrast, standard commercial systems measure the signal over a very small space. In the exemplary system/apparatus, a large illumination spot (e.g., approximately 8 µm plus or minus 15%) was used to match the size of a whole mammalian cell (e.g., when disassociated it was approximately 10 um in diameter). In contrast, standard commercial systems generally utilize very tightly focused laser illumination (less than 1 µm). The illumination spot can also be between about 1 µm (plus or minus 15%) to about 5 um (plus or minus 15%), between about 5 µm (plus or minus 15%) to about 10 µm (plus or minus 15%), or between about 10 µm (plus or minus 15%) to about 100 µm (plus or minus 15%). Moreover, to accommodate the large beam size in the exemplary system/apparatus, a quasi-confocal detection geometry was achieved by using a relatively large pinhole (e.g., approximately 300 µm plus or minus 15%) to reject as much background as possible without losing the signal. In contrast, the standard pinhole size is less than 100 µm. The pinhole size can also be between about 50 µm (plus or minus 15%) to about 200 µ (plus or minus 15%), between about 200 µm (plus or minus 15%) to about 400 µm (plus or minus 15%), or between about 400 µm (plus or minus 15%) to about 1000 µm (plus or minus 15%).
Deuterium-labeled branched-chain amino acids (“d-AA”) and deuterium-labeled palmitic acid (“d-PA”) were selected to probe the metabolic activity of protein synthesis and lipid synthesis, whose metabolites take a large portion of cellular dry mass. By applying MAP, the measurement of metabolism at the single-cell level, and metabolic heterogeneity was evaluated under various drug treatments. The exemplary reproducible classification models can be achieved and they can be more robust than label-free Raman methods against the dominating batch-to-batch variation. Additionally, the successful classification of cancer cell types and heterogeneous breast cancer subtypes were demonstrated. The capability of MAP to explore metabolic profiles at single-cell level makes it be a valuable tool for basic single-cell studies as well as drug screening applications.
A sufficiently large data set can usually be beneficial for any robust statistical models, and the major obstacle for large Raman spectra data set can be the slow data acquisition procedure. For example, the traditional Raman spectrum can be collected in the confocal mode with a tightly focused laser beam to obtain high spatial resolution. Due to the weak Raman scattering cross-sections and the limit of laser power one can use before photodamage, the acquisition time of the individual Raman spectrum per pixel can usually be around dozens of seconds or sometimes even minutes. In this exemplary manner, scanning the confocal focal spot over the entire cell volume (e.g., in order to avoid intracellular heterogeneity) can take a rather long time.
To tailor the exemplary procedure for high throughput cell phenotyping, an exemplary automatic whole-cell confocal micro-Raman spectrometer can optically integrate the signal over the entire cell without subcellular resolution. (See, e.g.,
In addition, an exemplary procedure via software can be utilized with machine vision to automatically locate the cells and acquired data across the whole coverslip. (See, e.g.,
By combining the exemplary configurations and improvements described herein, the acquisition speed was improved to facilitate the high-throughput experiment, as well as an integration of the signal within a single cell in order to measure the metabolism at single-cell level reliably without the need of scanning either laterally or axially. Quantitatively, the acquisition speed was increased by at least 100 times compared to the commercial confocal system whose applicable power can be limited due to photodamage during slow acquisition. (See, e.g.,
The differentiation of cells in the multicellular organisms can be regulated by, e.g., gene expression patterns which ultimately leads to distinctive metabolism. By probing the metabolic flux with MAP, these distinctive features can be reflected and used for robust cell phenotyping. (See, e.g., References 20, 21). Indeed, it has been recently demonstrated that heavy water labeling in microorganisms facilitated the classification of microbes. (See, e.g., Reference 22). However, heavy water incorporation can be much slower for mammalian cells than in microorganisms. Hence different metabolic probes can be beneficial.
Deuterated branch-chained amino acids and deuterated palmitic acid were supplemented to the cell culture media for 24 hours in order to probe the flux of protein and lipid synthesis. There can be several considerations for choosing these two types of probes. First, protein and lipids can be the major components of cell dry mass and their metabolism can be tightly regulated. Second, Raman spectra of the metabolites of these two deuterated probes in cells can be shifted to the cell silent region (e.g., 2000 — 2300 cm-1), which facilitated the detection with high sensitivity in a background-free manner. (See, e.g.,
According to various exemplary embodiments of the present disclosure, a home-built automatic confocal micro-Raman system can be utilized. For example, in one exemplary and non-limiting implementation, a laser at 532 nm and a 600 lines/mm grating blazed at 500 nm was installed in the spectrometer. The back aperture was underfilled of the objective to expand the illumination volume (~8 um in diameter). The exemplary system was controlled by a generated software package in the Labview environment and is capable of automatic acquisition. An exemplary brightfield image was taken, and cells in the field of view were identified and located. Then, the exemplary computer-controlled motorized stage moved the slide so that illumination spot was parked on the cell, followed by Raman spectrum acquisition. This exemplary and non-limiting process was repeated for the next cell in the field of view until all the cells were taken.
Turning
Previously, the single-cell metabolic measurement has been challenging. Previously-used methods, such as mass spectrometry, can be difficult to reveal single-cell level heterogeneity. (See, e.g., Reference 24). Label-free Raman microscopy, in principle, reveals the integrated chemical composition in single cells and therefore offers information about single-cell metabolism. However, due to the complex nature of the Raman spectra, it can be difficult to assign each Raman peak in the fingerprint region to a specific chemical compound. (See, e.g., Reference 25). In addition, when dimension reduction approaches such as PCA can be applied, which can be a common practice for data analysis, the physical meanings of the dimension reduced data can be further lost. On the other hand, since the metabolic probes used have distinctive spectra, it can be straightforward to decompose the spectra in order to obtain the concentration information of each individual probe. Here the exemplary MCR-ALS method, (see, e.g., References 26-28), was used to decompose the composite spectra into the pure spectra of the two metabolic probes, and concentration profiles could thereby be obtained.
To validate the ability to reveal metabolic activity, the exemplary MAP was applied to cultured cells treated with drugs that inhibit known metabolism. Five drugs were used as the reference: cycloheximide and blasticidin, both inhibit protein translation; Triacsin C can be an inhibitor of long fatty acyl CoA synthetase; etoposide and cisplatin can be DNA replication inhibitors. MAP probes and individual drugs were added to the seeded A375 cells and allowed to incubate for 24 hours, after which the cells were fixed and Raman spectra were taken from each individual of the fixed single cells. At least 3 batches of each drug were prepared to ensure model robustness and reproducibility. MCR-ALS on the Raman spectra taken with MAP were applied, from which the concentration profiles of d-AA metabolites and d-PA metabolites in every single cell were calculated. (See, e.g.,
Both cycloheximide and blasticidin show a significant decrease of d-AA metabolites compared to the untreated group (see, e.g.,
Cell-based assays are becoming increasingly important in drug discovery and drug studies for their better reflection of the complexity of the entire living organism than target-centric biochemical assays. (See, e.g., Reference 29). Due to the complex nature of cellular phenotypes, multiplexed measurements can be needed to comprehensively study the mechanism of the drugs in order to facilitate drug discovery. In addition, because cells can respond to the drug treatment differently because of their heterogeneous nature, which can give rise to the drug resistance, single-cell resolution can also be demanded to distinguish such heterogeneous response to better understand the efficacy. (See, e.g., Reference 4). In this regard, a procedure that can be to probe multiple metabolic states of single cells can be preferable.
As it has been shown that MAP can reliably probe the metabolic activity in single cells, a determination was made as to whether a metabolic inhibitor of a specific pathway can affect other pathways. Indeed, although triacsin C specifically inhibits long fatty acyl CoA synthetase, it shows a decrease in protein synthesis activity. This can be supportive of previous findings that suggest inhibition of lipid synthesis also leads to inhibition of cell proliferation and thereby lowers protein synthesis activity. (See, e.g., Reference 30). It was also observed that DNA replication inhibitors caused low protein synthesis activity. (See, e.g.,
Since MAP measures the metabolic activity in single cells, its potential in quantitatively characterizing single-cell metabolic heterogeneity was determined. The coefficients of variation (“Cv”) of the single-cell d-PA metabolites and d-AA metabolites distribution were calculated from the metabolic inhibitor-treated cells. (See, e.g., Reference 32). The Cv value can be a normalized measure of the dispersion of the single-cell metabolic activity distribution and thus it can be used as a parameter to quantify the metabolic heterogeneity. Compared to the untreated control group, the treatment of cycloheximide and blasticidin did not alter the lipid synthesis activity distribution. Similarly, triacsin C did not change the metabolic heterogeneity in d-PA metabolites either, indicating a uniform efficacy of the drug. Etoposide and cisplatin, however, showed a significantly decreased lipid synthesis heterogeneity. (See, e.g.,
By validating that MAP can explore the metabolic activity and metabolic heterogeneity in drug-treated cells, the exemplary MAP was applied to measure the single-cell metabolism of commonly used cancer cell lines and breast cancer cell lines. Three different cancer cell lines were used: A375, HeLa and MCF7 that were respectively derived from skin cancer, cervical cancer, and breast cancer tissues. The lipid and protein synthesis activity of these three cell lines were measured with MAP. At least three batches of cells were used to ensure statistical significance. (See, e.g.,
Breast cancer can be one of the most common malignant cancer among females. The heterogeneity of breast cancer has been well recognized, and each subtype has different prognosis and treatment response due to their unique gene expression profiles and subsequent metabolism. Therefore, MAP was applied to breast cancer cell lines to investigate their metabolism profiles. Three breast cancer cell lines were chosen for their clinical significance: MCF7, a luminal A subtype; ZR-75-1, a luminal B subtype, and MDA-MB-231, a Claudin-low subtype. (See, e.g., Reference 33). Compared to the previously shown cells derived from different tissue, the breast cancer cell lines have similar lipid and protein metabolism levels. (See, e.g.,
The Cv values of lipid and protein synthesis in these cells were calculated to demonstrate their metabolic heterogeneity. (See, e.g.,
Overall, these exemplary results demonstrate the capability of MAP to explore the metabolic profiles in single cells. Because the procedure can be intrinsically based on single cells, measurements such as metabolic heterogeneity can be easily obtained. It was also found that it can be beneficial to quantify multiple parameters at the same in order to have a better understanding of the metabolic profile of a cell. In the exemplary measurement, for example, A375 cells have similar lipid synthesis activity but distinctive activity in protein synthesis compared to MCF7 cells. Likewise, the metabolic heterogeneity levels can be different between the two cell lines in both lipid and protein metabolism. Such information could have been overlooked if only one parameter was measured. Since the probes used, as well as other Raman metabolic probes, (see, e.g., Reference 13), have sharp and distinguishable peaks, it can be beneficial to use these probes for simultaneous multi-parameter measurement.
Label-free methods, such as Raman-based vibrational microscopy, rely on physical and chemical properties to generate information about cell phenotypes. (See, e.g., Reference 12) and usually don’t demand a priori knowledge of biomarkers. For this reason, label-free Raman spectroscopy has been extensively investigated and successfully demonstrated in many diagnostic applications. (See, e.g., References 25, 34, 35). In the past decade, great efforts have been put into improving the hardware in order to increase the signal to noise ratio. (See, e.g., References 36-38), and into developing sophisticated statistical tools such as machine learning procedures to achieve better diagnostic results. (See, e.g., Reference 39). Yet challenges remain that hinder the prevalence of this powerful procedure in broader applications. Among those challenges, to achieve a robust and reproducible detection and classification classifier model can be the key.
Having shown that MAP can reveal the metabolic profiles in individual single-cells, it was determined that the method might combine the merits of the label-free Raman approaches and label-based approaches. Compared to pure physicochemical measurement, metabolic activities can reflect gene expression levels of many enzymes and proteins in the complicated metabolic network in an integrated manner, thereby revealing valuable information of cell phenotypes. In this sense, it was determined that the exemplary MAP can provide better sensitivity, reproducibility and interpretability than the conventional label-free Raman methods.
To investigate this, the model performance of three different data combination procedure (see, e.g.,
It was determined that the training and testing data can be from the same batches (see, e.g.,
These exemplary observations indicate that batch-to-batch variation can be one of the major sources of variation for label-free methods. Data leakage happens when the training and testing datasets can be derived from the same batches, which leads to an overfitted model that can fail to predict the unseen samples. This can be in agreement with the findings from the Popp group (see, e.g., Reference 41), Smith group (see, e.g., Reference 42) and Fujita group (see, e.g., Reference 43). It was determined that this can be due to the fact that very often the spectral differences between cell types to be differentiated can be subtle, which can be subject to unpredictable changes if the system and specimen conditions cannot be precisely controlled.
The exemplary MAP classifier model performance was tested using the same data combination. (See, e.g.,
In particular, such
The generality of exemplary MAP determined. Breast cancer can be one of the most common malignant cancer among females, and current diagnostic methods can be time-consuming and intrusive. (See, e.g., Reference 44). In addition, heterogeneity of breast cancer has been well recognized, and each subtype has different prognosis and treatment response. To address the screening challenge, vibrational spectroscopy has been extensively investigated for its non-intrusive nature. (See, e.g., References 44-46). Yet in previous studies using vibrational spectroscopy, the heterogeneous subtypes of breast cancer were overlooked. Therefore, three breast cancer cell lines were used for their clinical significance whose distinctive metabolism profiles have been demonstrated: MCF7, a luminal A subtype; ZR-75-1, a luminal B subtype, and MDA-MB-231, a Claudin-low subtype. (See, e.g., Reference 33). Spectra of these cells were acquired using both the label-free and MAP methods for at least 4 batches of each cell type in order to examine batch-to-batch variation.
The learning curves were generated using the three different procedures. Akin to the exemplary previous results, good classifiers were acquired with procedure 1 and 2 with both label-free and MAP approaches. (See, e.g.,
Turning to
MAP can provide a direct quantitative measurement of metabolic activities at the single-cell level. With vibrational probes, it is possible to correlate the Raman spectra to the metabolism activities of individual cells so that the measurement of metabolism activities can be achieved. Furthermore, since the procedure can be at the single-cell level by nature, information such as cell heterogeneity can be readily available. As described herein, metabolic activities in each individual cell were measured, and the heterogeneity among each cell line or among each drug treatment was revealed. Coupled with the specifically designed automatic whole-cell confocal micro-Raman spectrometer, such measurement and analysis can be carried out in a high-throughput manner (e.g., with at least 100 times improvement over the commercial confocal system).
There may exist a deep reason underlying the good performance of MAP with vibrational metabolic probes. This exemplary procedure probes metabolic activities that can be regulated by transcriptomics, which can be one of the fundamental characters of organisms. In addition, because the probes may only be added for a short period of time (e.g., less than or similar to the doubling time of the cells), MAP can detect the passage of these metabolites through reactions system over time — the metabolic flux, which can be one of the most effective methods and has been used in a diversity of fields. (See, e.g., Reference 47). Moreover, protein and lipid metabolism can be among the most complexes processes in living organisms and can be tightly regulated by a large variety of signaling pathways during different cell stages. (See, e.g., References 48, 49). Therefore, by probing these metabolisms, integrated information of many metabolic enzymes in the network can be acquired about the cells.
Two categories of deuterium-labeled metabolic probes were used. However, the possible metabolic probes may not be limited to these two. Through the course of the past decade, numerous vibrational tags that serve as metabolic probes (e.g., propargylcholine and glucose) have been developed and demonstrated individually (see, e.g., Reference 13), many of which have distinctive spectral features that facilitated spectral decomposition. It was anticipated that a more comprehensive metabolism profile can be understood if more of these probes can be used simultaneously since richer information about the metabolic activities of diverse biochemical pathways can be revealed. Moreover, technological advances in instrumentation can provide faster acquisition and higher throughput. For example, coherent Raman processes, such as stimulated Raman scattering coupled with flow-cytometry, has been demonstrated with superior acquisition speed recently. (See, e.g., References 37, 50, 51). When using together with MAP according to exemplary embodiments of the present disclosure, even higher throughput can be achieved for more accurate screening. Together, the exemplary MAP can be a powerful tool for basic single-cell biological studies.
To further validate the exemplary model according to exemplary embodiments of the present disclosure, a random permutation test. (See, e.g., Reference 62) was carried out by firstly shuffling (e.g., permuting) the labels associated with each spectrum and then re-training the model using the shuffled dataset to make prediction in order to get the accuracy. Such exemplary procedure was repeated 100,000 times to give the distribution of the accuracy under a null hypothesis. When the exemplary model from the real dataset can be reasonable, the prediction accuracy from the ‘false dataset’ can be expected to be low.
In procedure 1, e.g., only one batch of each cell type was used to form the original data pool, and the classifier was trained using randomly selected samples from the data pool. (See, e.g.,
HeLa, MCF7, A375, MDA-MB-231 were cultured in Dulbecco’s Modified Eagle’s medium (e.g., DMEM: 4.5 g/L glucose; 11965, Gibco) supplemented with 10% Fetal bovine serum (e.g., FBS; 16000, Gibco) and 1% antibiotics (e.g., Penicillin-Streptomycin-Glutamine; 10378016, Gibco). ZR-75-1 cells were cultured in RPMI 1640 medium (e.g., 11875, Gibco) supplemented with 10% Fetal bovine serum (e.g., FBS; 16000, Gibco) and 1% antibiotics (e.g., Penicillin-Streptomycin-Glutamine; 10378016, Gibco). Cells were maintained in 5% CO2 at 37° C. All cells were passaged 2-3 times a week and were never allowed to grow more than 90% confluency. Cells were tested mycoplasma negative.
HeLa cells were obtained from ATCC, MDA-MB-231 and A375 cells. MCF7 and ZR-75-1 cells were also obtained.
Freshly disassociated cells were used to fill the as much the illumination volume as possible in order to maximize Raman signal to noise ratio since disassociated cells were usually round and thicker.
For label-free samples, cells were first seeded in a 24-well plate at 50% confluency with cell culture media. After overnight culture, cells were disassociated by TrypLE (e.g., 12604, Gibco). Then cells were allowed to settle onto 18 mm round quartz coverslips (e.g., 103302-258, Electron Microscopy Sciences) that were pre-coated with poly-lysine for 10 min. Cells were subsequently washed twice with PBS (e.g., 14040, Gibco) and fixed with 4% freshly diluted paraformaldehyde (e.g., PFA; 15713S, Electron Microscopy Sciences), followed by 3 times wash with PBS. Double-sided adhesive imaging spacers (e.g., GBL654002, Sigma) were used to make ‘sandwich’ samples with glass slides.
For MAP samples, cells were first seeded in a 24-well plate at 50% confluency with cell culture media and allowed to adhere overnight. Then the culture media was replaced with MAP culture media (see Table 1 below) and cells were further cultured in MAP media for 24 hours. For the drug tests, individual drugs (e.g., concentrations in Table 2 shown below) were added to the MAP media and cells were subject to the drug treatment for 24 hours. After the 24-hour culture, cell samples were prepared the same way as described above.
Referring back to
Exemplary preprocessing protocols were used to preprocess the raw spectra. (See, e.g., References 64 and 65). Prior to the preprocessing procedures, the spectra were calibrated by 1:1 mixture of acetonitrile and toluene taken on the same day. Thereafter, first, the quartz and water background were removed via an automatic procedure. (See, e.g., Reference 66). Second, an iterative procedure was used for baseline correction. (See, e.g., Reference 67). Third, the spectra were normalized to their amide peak. And lastly, the spectra were trimmed for subsequent analysis. The silent region (e.g., 2000 — 2300 cm-1) was used for MAP analysis and fingerprint region (e.g., 600 — 1800 cm-1) for label-free analysis. (See, e.g.,
In brief, once the training datasets were obtained, principal component analysis (“PCA”) was applied, followed by linear discrimination analysis. The k-nearest neighbor (“k-NN”) classifiers were then built on the dimension reduced data. To obtain the classifiers prediction accuracy, PCA and LDA were first applied to the testing dataset, then the trained classifiers were used to predict the labels of the test data, which were then compared to their known true labels to calculate the accuracy.
PCA has previously widely used to reduce noise and to improve statistical model robustness. By applying PCA to the training data, the training spectra were decomposed into multiple representations (e.g., scores and loading vectors) that capture as much of the information as possible. The number of components n was chosen in such a way that the accumulative variance explained was greater than 99%. Then the top n PCA scores were used as the input of LDA, which can be a supervised procedure that maximizes the distance between group means while minimizing the variance within a group. LDA scores and projection vectors can be obtained by the procedure, and LDA scores were further used for constructing k-NN clustering classifiers. To predict the test dataset, mean-centered spectra in the testing sets were first projected to PCA loading vectors obtained earlier in the training stage, and the PCA scores were further projected to training LDA projection vectors to obtain LDA scores for the testing sets. Then the dimension reduced data were input to the learned k-NN model to make the prediction. The model accuracies were then calculated by calculating the proportion of correctly predicted number of cells to the total number of cells. (See, e.g.,
Despite the high potential for multiplexing, the biological application of Raman spectroscopy may be limited by its small Raman cross-section, which is typically 108 ~1014 times smaller than that of fluorescent dyes. (See, e.g., Reference A31). To amplify the signal, a procedure to prepare ultra-bright Raman dots (Rdots) by non-covalently doping polymer nanoparticles with Raman active dyes can be applied. (See, e.g., Reference A32). When coupled with Stimulated Raman Scattering (SRS) microscopy, these Rdots can show, among others, unprecedented imaging performance in immunostaining of intracellular targets. Exemplary ultra-bright Rdots can be harnessed for developing a robust and cost-effective platform for live-cell profiling. For the current exemplary application of live cell profiling, Rdots were tailored in, e.g., four aspects. First, unlike imaging intracellular targets where an extremely compact size of Rdots is necessary, the nanoparticle size requirement for profiling living cell surface proteins can be flexible. Therefore, the Rdots that were 40-120 nm in diameter were prepared to further increase their Raman brightness and facilitate the demonstration of endocytosis in a size-dependent manner. Second, the previously reported 6-color Rdots palette were expanded to 10 colors for the need of profiling cell surface proteins and endocytosis. Third, the surface functionalization has also been optimized for the new applications. Fourth, the current exemplary application of live cell profiling moved from immunostaining of fixed cells to profiling living cells.
The multi-colored Rdots were prepared via a swelling-shrinking strategy. (See, e.g., Reference A33). After the addition of swelling agent THF, adequate Carbow dyes can be incorporated into the swollen polystyrene (PS) beads. Subsequent shrinking and trapping of dyes were achieved by suspension in a large amount of water (
To profile living cell surface proteins, prepared Rdots were functionalized with antibodies or aptamers. A non-toxic and hydrophilic polymer polyethylene glycol (PEG) was employed to reduce potential nonspecific hydrophobic interaction and electrical adsorption of Rdots onto cell membrane. (See, e.g., Reference A37). As shown in
Rdots have been imaged by narrowband SRS microscope in which only one Raman channel can be imaged at a time. (See, e.g., Reference A32). In this disclosure, single-cell Raman spectra of all vibrational modes were acquired in a cost-effective manner by employing whole-cell spontaneous Raman spectroscopy. (See, e.g., Reference A38). The illumination laser spot was intentionally expanded to 8~10 µm to ensure the excitation of the whole mammalian cell. By doing so, the Raman signal over the entire cell volume can be optically integrated as an individual spectrum with rapid acquisition of multiplexed probes, suitable for high-throughput single-cell applications. To test the feasibility of the detection, PEGylated Rdots were firstly conjugated with a primary antibody against CD44, a cell surface adhesion molecule that frequently overexpressed on cancer cells. (See, e.g., Reference A39). Rdot2218-CD44 stained HeLa cells exhibit a single, narrow and strong Raman peak in the cell-silent window under this setup (see
As a proof of concept of two-channel cell profiling, HeLa and SKBR3 cell lines stained with both Rdot2153-CD55 and Rdot2079-CD44 were assessed. CD44 and CD55 are both overexpressed in HeLa cells, but only moderately expressed in SKBR3 cells. Single-cell spectra were acquired through our whole-cell Raman micro-spectroscopy (see
Multiple cell surface proteins were simultaneously profile on SKBR3 with a cocktail of functionalized Rdots probe. Seven-colored 40 nm Rdots were conjugated with recognition molecules (primary antibodies or aptamers) against nucleolin (Rdot2218-Nucleolin), EpCAM (Rdot2194-EpCAM), MUC1 (Rdot2175-MUC1), CD55 (Rdot2153-CD55), EGFR (Rdot2133-EGFR), CD44 (Rdot2079-CD44), HER2 (Rdot2052-HER2), respectively. Among them, nucleolin, EpCAM, MUC1, and HER2 were stained via aptamers and the others were by antibodies. Firstly, the specificity and semi-quantitative measurement of Rdots probes were individually confirmed, where the Raman signals were found to be correlated with the expression level of surface proteins. After these individual validations, the seven functionalized Rdots were pooled into a cocktail and stained the corresponding surface proteins simultaneously. Seven resolvable Raman peaks were clearly observed, and could be assigned to seven surface proteins, respectively (
Upon exposure to mammalian cells, PS beads have been reported to enter cells through endocytosis, subsequently reaching the endosomal components. (See, e.g., Reference A40). Although some of the endocytic pathways have been well established, their exact biological roles are still under investigation. (See, e.g., References A41, A42). Intriguingly, it has been suggested that the mechanisms and pathways by which the nanoparticles were internalized are strongly dependent on the size of particles. (See, e.g., Reference A43). Motivated by the independent size and color tunability of Rdots, cellular endocytosis pathways were profiled by incubating cells with Rdots of different sizes and colors. Rdots of 40 nm (Rdot2207), 70 nm (Rdot2118), and 120 nm (Rdot2092) in diameter were incubated with HeLa cells, and their Raman peaks are designed to be mutually resolvable. Indeed, after 6 hours, HeLa cells exhibit three resolvable Raman peaks in the cell-silent window, indicating cellular uptake for each Rdots (
For whole-cell Raman acquisition, the corresponding Raman intensity is proportional to the number of internalized Rdots, since the Raman intensity of Rdots remains stable after exposed to cellular microenvironments. We therefore quantified the peak intensity to profile the differential mechanism of Rdots internalization. To this end, cells were first pre-treated with one of three inhibitors, chlorpromazine (CPZ), nystatin, and cytochalasin D (Cyto D). CPZ is a cationic amphiphilic drug used to block clathrin-mediated endocytosis. (See, e.g., Reference A44). Nystatin can decompose cholesterol and inhibit caveolae-mediated endocytosis. (See, e.g., Reference A45). Cyto D inhibits the polymerization of actin filaments, which is required for receptor-mediated endocytosis in mammalian cells. (See, e.g., Reference A46). Additionally, cells were incubated at 4° C. to inhibit energy-dependent endocytosis. Then endocytosis was examined by the quantification of internalized Rdots at the single-cell level. A distinctive response of Rdots internalization was detected when treated with various inhibitors (
This exemplary procedure was demonstrated in evaluating endocytosis differences in HeLa, SKBR3, and COS-7 cell lines. After pre-treated with three-color/size endocytic Rdots for 6 hours, single-cell Raman spectra were acquired. The spectral feature for three cell lines appears distinct in the cell-silent region, suggesting different endocytic performances (Figure 161). Quantitative readouts of three endocytic Rdots were then analyzed and histograms were constructed from single cells (
In particular, As indicated herein, seven-colored Rdots were developed to evaluate cell surface proteins (nucleolin, EpCAM, MUC1, CD55, EGFR, CD44, and HER2) and three-colored Rdots (Rdot2207, Rdot2118, and Rdot2092) to probe cellular endocytosis in live cells. No significant signs of cytotoxicity were observed during Rdots staining and endocytic test, supporting their feasibility in the live-cell assay. To provide a more comprehensive indication of phenotypic diversity of single cells, four additional metabolic probes, 13C-EdU, 17-Octadecynoic Acid (17-ODYA), diyne-tagged CoQ analogues AltQ2 (see, e.g., Reference A35) and 13C-amino acids (13C-AA), were incorporated to enrich the Raman probe panel (see
For example, almost all of the 14 probes show individual narrow Raman peaks at the cell-silent window, enabling highly sensitive detection in a background-free manner. Meanwhile, we designed them so that their Raman peaks can be mutually separated from each other. In contrast, such a resolvability would be challenging for fluorescence microscopy with broad emission peaks. To acquire, e.g., the 14-plex Raman information all together, an exemplary whole-cell confocal Raman micro-spectroscopy can be implemented which can excite and detect all Raman probes simultaneously with a single 532 nm laser source and an EMCCD detector (see, e.g., Reference A38) (see
An exemplary 14-plex Raman probe profiling cocktail was demonstrated under whole-cell Raman micro-spectroscopy. Metabolic probes including 13C-EdU, 17-ODYA, and 13C-AA were first supplemented to cell culture media for 60 hours to report various metabolic activities. For endocytic profiling, cells were subsequently incubated with three Rdots for 6 hours. Then, seven functionalized Rdots cocktail was pooled to stain cell surface proteins simultaneously. Prior to the spectral acquisition, AltQ2 probe was added to live cells. Single-cell Raman spectra of SKBR3 cells were acquired with 1 second acquisition time (see
Cell-based profiling techniques are increasingly being used in drug discovery to monitor cell response upon drug treatment. (See, e.g., Reference A57). Optical techniques have unique advantages by allowing non-invasive, fast, and cost-effective readouts of individual living cells. (See, e.g., Reference A58). However, most high-content profiling assays have been primarily focusing on partial aspects of biological phenotypes. (See, e.g., Reference A59). Integration of different phenotypes will facilitate a more systematic understanding of the mechanism of drug candidates. (See, e.g., Reference A60). To this end, the live-cell multiparameter profiling platform is demonstrated in revealing phenotypic responses upon five chemotherapy reagents (see
A single-cell Raman spectra was decomposed to retrieve the intensities of 14 individual probes and constructed a population-averaged heatmap for all 14 phenotypic features (see
One of many advantages of single-cell measurement over the conventional population-averaged one can be due to the detailed information from a large number of individual cells. This is especially important for highly multiplexed readouts, as extensively documented in single-cell transcriptomics. (See, e.g., References A76-A78). The exemplary single-cell data according to the exemplary embodiment of the present disclosure can facilitate a performance of clustering, correlation, and/or network analysis, which can likely be beyond the population-averaged results. For clustering analysis, a two-dimensional t-SNE plot was constructed to process the high-dimensional Raman spectral readouts from all the cells (see
Pearson correlation coefficients can be computed between any pairs of 14 phenotypic features across all the measured cells (see
The correlation network of 14 phenotypic features were analyzed to reveal a systematic co-regulation and interaction across cell metabolites, endocytosis, and surface proteins (see
HeLa, SKBR3, and COS-7 cells were all obtained from the American Type Culture Collection (ATCC) and kept under standard cell culture conditions (5% CO2, 37° C.). HeLa and COS-7 cells were cultured in DMEM media (Gibco, 11965118) supplemented with 10% fetal bovine serum (FBS, Gibco, 10099141) and 1 % penicillin/streptomycin (P/S, Gibco, 15140148). SKBR3 cells were cultured in McCoy’s 5A media (Gibco, 16600082) supplemented with 10% FBS and 1% P/S.
Incorporation of Carbow dyes was achieved by swelling the 4% w/v polystyrene (PS) beads (Invitrogen, C37232, C37233, C37479) in a solvent mixture containing 160 µL 4% w/v PS beads, 160 µL reverse osmosis (RO) water and 120 µL Tetrahydrofuran (THF, Sigma, 401757)), and by adding a controlled amount of Carbow dyes to the mixture (refer to Table 1 for specific dye concentration). After 30 minutes of gentle agitation at room temperature (RT), 2 mL 20 mM phosphate buffer (PH 7.3) was subsequently added to shrink the Rdots. Excess dyes were removed by three rounds of centrifugation and resuspension in RO water using 30 K MWCO filters (Millipore, UFC9030). Rdots bioconjugation to antibodies and aptamers were carried out through carboxyl-to-amine crosslinking using the ethyl dimethylaminopropyl carbodiimide (EDC, Thermo Scientific, 22980) and sulfo-NHS (Sigma, 56485). To activate the carboxyl groups on beads surface for covalent conjugation, 200 µL 4% w/v beads were mixed vigorously with 100 µL 100 mg/mL freshly prepared EDC solution and 100 µL 150 mg/mL sulfo-NHS in MES buffer (25 mM, PH 6.0) at RT for 30 minutes. Excess EDC and sulfo-NHS were separated by two rounds of centrifugation (16000 rmp) and resuspension in RO water using 30 K MWCO filters (Millipore, UFC503024). The purified beads with activated carboxyl groups were then exposed to 200 µL 35 mM NH2-PEG-COOH (Laysan Bio Inc, NC1641410) and 200 µL 320 mM NH2-PEG-OH (Laysan Bio Inc, NC1641409) in DPBS buffer (PH 8.1) for 3 hours at RT to yield a well-shielded PEG layer. Excess PEG molecules were removed by three rounds of centrifugation and resuspension in RO water using 100 K MWCO filters (Millipore, UFC510024). For antibody conjugation, the carboxyl groups on PEGylated beads were then activated with 100 µL 100 mg/mL freshly prepared EDC solution and 100 µL 150 mg/mL sulfo-NHS in MES buffer (25 mM, PH 6.0) at RT for 30 minutes. After two rounds of centrifugation, the activated beads were then mixed with antibodies at a bead: antibody molar ratio of 1:30 and react for 3 hours in HEPES buffer (10 mM, PH 8.3) at RT. The bead-Ab conjugates were separated from free antibodies by centrifugation for 3 rounds at 16000 rpm. Then the conjugates were resuspended in DPBS buffer (Gibco, 14190136) for use. For aptamer conjugation, the activated beads were mixed with aptamer at a bead: aptamer molar ratio of 1:100 and react for 3 hours in HEPES buffer (10 mM, PH 8.3) at RT. Free aptamers were removed by four rounds of centrifugation with 100 K MWCO filters and resuspension in DPBS buffer for use (see Table 4 for antibody catalog numbers and aptamer sequences).
To assess the effect of pH conditions on Rdots stability, the Rdots were exposed to opti-MEM (Gibco, 31985062) with pH 7.0, 5.5, and 4.5. The pH of opti-MEM was adjusted by adding acid dropwise with constant stirring. The time-dependent Raman intensity was measured through our home-built Raman microscope.
To prepare 13C-AA DMEM, 4 mg/mL algae 13C amino acid mix (CLM-1548, Cambridge isotope) was dissolved in RO water supplemented with 10% FBS, 1% P/S and other components including vitamin, inorganic salts, and glucose according to DMEM media formula (Invitrogen, 11965). 17-ODYA (Tocris Bioscience, 06-171-0) was dissolved in DMSO and a working stock solution of 4 mM was prepared by 1:6 complexing to BSA (Sigma-Aldrich, A6003). 13C-EdU was synthesized as the previous reported. (See, e.g., Reference A53). AltQ2 (a generous gift from Professor Mikiko Sodeoka) was dissolved in DMSO to get a stock solution of 10 mM for use.
To evaluate the cytotoxicity of Rdots on cells, SKBR3 cells were incubated with 1 nM endocytosis beads for 6 hours and then mixed with 10 nM Rdots for surface protein labeling. Cell viability was studied using Live/Dead cell double staining by incubating with 2 µM calcein-AM (Invitrogen, C3099) and 2.5 µM propidium iodide (PI, Sigma-Aldrich, P4864) for 30 minutes at 37° C. Fluorescent images were acquired by Olympus confocal microscopy prior to viable/dead cell counting.
Cells were dissociated using trypsin-EDTA (Gibco, 25-200-056) on reaching 75% confluence and then harvested in the tube. After two rounds of washing with ice-cold DPBS, cells were resuspended on DPBS buffer with 5 mM MgCl2, 1 mg/mL yeast tRNA (Invitrogen, AM7119), and 1% BSA (Sigma-Aldrich, 05470) to reach 107 cells per mL. For aptamer annealing, aptamers conjugated Rdots were incubated on a heat block at 90° C. for 4 minutes and then slowly cool to RT. Then cells were stained with seven-colored Rdot conjugates at a concentration of 10 nM for 30 minutes on ice. Followed by three rounds of washing with DBS buffer with 5 mM MgCl2 and 1% BSA, cells were attached to a poly-L-lysine coated coverslip (Neuvitro GG12PDL) and mounted onto the microscope for Raman measurement.
The schematic of the home-built Raman microscope is shown as our previously reported. (See, e.g., Reference A38). Here, the 10X objective was underfilled to reach an illumination diameter of 8-10 µm. A motorized stage was installed to automatically park on cells identified through bright filed. The entire system was controlled through a LabVIEW-based software module (National Instrument).
The setup of SRS microscopy has been described previously. (See, e.g., Reference A26). Briefly, an integrated laser (Applied Physics and Electronics, Inc., picoEMERALD) was coupled into an inverted laser scanning confocal microscope (Olympus, FV1200). The Stokes beam (1,064 nm, 6 ps pulse width) was intensity-modulated at 8 MHz by electro-optic-modulator, and a tunable pump beam (720-990 nm, 5-6 ps pulse width) was produced by the optical parametric oscillator. The laser beams were focused on the sample through a 25x water immersion objective (Olympus, XLPlan N, 1.05 NA MP). For cellular imaging, 100 mW pump and 400 mW Stokes power were used, with 40 us time constant and the matching pixel dwell time.
SKBR3 cells were seeded onto 18 mm round quartz coverslips (Electron Microscopy Sciences, 103302-258) and then maintained in a culture environment for 48 hours to reach 90% confluence. Then the culture medium was replaced with 13C-AA DMEM containing 200 µM 17-ODYA and 50 nM 13C-EdU for 60 hours. For the drug testing samples, cells were subject to the drug treatment simultaneously (see Table 5 for specific drug concentration). Followed by three rounds of gentle washing of DPBS buffer, cells were subsequently incubated with three-colored endocytic Rdots at 1 nM in serum-free DMEM medium. 6 hours later, cell surface markers were stained with seven-colored Rdots at a concentration of 10 nM for 1 hour on ice. After that, cells were washed extensively with DPBS buffer with 5 mM MgCl2 and 1% BSA. Then cells were rinsed with DPBS buffer with 5 mM MgCl2 and 40 µM AltQ2 before mounting onto the microscope for Raman measurement. Spectral unmixing was performed through the Least Square Method (LSM).
To decipher the contribution of individual Raman probes, spectra unmixing was performed for the amide region and cell silent region, respectively. The normalized spectra of 16 components (including 14-plexed Raman probes, C=C vibration of PS bead, and 12C amide peak) in
Approximating that the value of N is negligible for the low-noise spectrum, so here we made an estimation:
As a result, Msilent-1× Isilent ≈ Msilent-1 ×Msilent ×Θsilent, which means the decomposed contribution of each component Θsilent ≈Msilent-1× Isilent. Similarly, Θamide ≈Mamide-1× Iamide. The matrix computation was carried out with MATLAB. To verify the linear unmixing model is satisfactory for multiparameter Raman spectral decomposition and robust to measurement noise, the single-cell Raman spectrum acquired was reconstructed with the calculated Θ. The Pearson correlation coefficient was employed to represent the similarity between reconstructed and ground spectrum and assess the robustness of the unmixing model.
As indicated herein above, a schematic diagram of the exemplary confocal Raman microscope according to an exemplary embodiment of the present disclosure is shown in
As shown in
Further, the exemplary processing arrangement 1905 can be provided with or include an input/output ports 1935, which can include, for example a wired network, a wireless network, the internet, an intranet, a data collection probe, a sensor, etc. As shown in
The foregoing merely illustrates the principles of the disclosure. Various modifications and alterations to the described embodiments will be apparent to those skilled in the art in view of the teachings herein. It will thus be appreciated that those skilled in the art will be able to devise numerous systems, arrangements, and procedures which, although not explicitly shown or described herein, embody the principles of the disclosure and can be thus within the spirit and scope of the disclosure. Various different exemplary embodiments can be used together with one another, as well as interchangeably therewith, as should be understood by those having ordinary skill in the art. In addition, certain terms used in the present disclosure, including the specification, drawings and claims thereof, can be used synonymously in certain instances, including, but not limited to, for example, data and information. It should be understood that, while these words, and/or other words that can be synonymous to one another, can be used synonymously herein, that there can be instances when such words can be intended to not be used synonymously. Further, to the extent that the prior art knowledge has not been explicitly incorporated by reference herein above, it is explicitly incorporated herein in its entirety. All publications referenced are incorporated herein by reference in their entireties.
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This application is a continuation of International Patent Application No. PCT/US2021/025577, filed on Apr. 2, 2021 that published as International Patent Publication No. WO 2021/202997 on Oct. 7, 2021, and also relates to and claims priority from U.S. Pat. Application No. 63/004,153, filed on Apr. 2, 2020, the entire disclosures of which are incorporated herein by reference.
This invention was made with government support under Grant No. R01EB020892, awarded by the National Institutes of Health. The government has certain rights in the invention.
| Number | Date | Country | |
|---|---|---|---|
| 63004153 | Apr 2020 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/US2021/025577 | Apr 2021 | WO |
| Child | 17958005 | US |
