Dynamic characterization of drug resistance and heterogeneity of the gastric cancer cell BGC823 using single-cell Raman spectroscopy

Quantitative Raman chemical imaging of intracellular drug-membrane aggregates and small molecule drug precipitates in cytoplasmic organelles

Raman confocal microscopes have been used to visualize the distribution of small molecule drugs within different subcellular compartments. This visualization allows the discovery, characterization, and detailed analysis of the molecular transport phenomena underpinning the Volume of Distribution - a key parameter governing the systemic pharmacokinetics of small molecule drugs. In the specific case of lipophilic small molecules with large Volumes of Distribution, chemical imaging studies using Raman confocal microscopes have revealed how weakly basic, poorly soluble drug molecules can accumulate inside cells by forming stable, supramolecular complexes in association with cytoplasmic membranes or by precipitating out within organelles. To study the self-assembly and function of the resulting intracellular drug inclusions, Raman chemical imaging methods have been developed to measure and map the mass, concentration, and ionization state of drug molecules at a microscopic, subcellular level. Beyond the field of drug delivery, Raman chemical imaging techniques relevant to the study of microscopic drug precipitates and drug-lipid complexes which form inside cells are also being developed by researchers with seemingly unrelated scientific interests. Highlighting advances in data acquisition, calibration methods, and computational data management and analysis tools, this review will cover a decade of technological developments that enable the conversion of spectral signals obtained from Raman confocal microscopes into new discoveries and information about previously unknown, concentrative drug transport pathways driven by soluble-to-insoluble phase transitions occurring within the cytoplasmic organelles of eukaryotic cells.

Sub-Cellular Dynamic Analysis of BGC823 Cells after Treatment with the Multi-Component Drug CKI Using Raman Spectroscopy

Multi-component drugs (MCDs) can induce various cellular changes covering multiple levels, from molecular and subcellular structure to cell morphology. A "non-invasive" method for comprehensively detecting the dynamic changes of cellular fine structure and chemical components on the subcellular level is highly desirable for MCD studies. In this study, the subcellular dynamic processes of gastric cancer BGC823 cells after treatment with a multi-component drug, Compound Kushen Injection (CKI), were investigated using a homemade, high-resolution, confocal Raman spectroscopy (RS) device combined with bright-field imaging. The Raman spectra of the nucleus, cytoplasm and intracellular vesicles (0.4-1 μm) were collected simultaneously for each cell treated with CKI at different times and doses. The RS measurements showed that CKI decreased the DNA signatures, which the drug is known to inhibit. Meanwhile, the CKI-induced subcellular dynamic changes in the appearance of numerous intracellular vesicles and the deconstruction of cytoplasm components were observed and discussed. The results demonstrated that high-resolution subcellular micro-Raman spectroscopy has potential for detecting fine cellular dynamic variation induced by drugs and the screening of MCDs in cancer therapy.

Raman spectroscopy combined with deep learning for rapid detection of melanoma at the single cell level

Melanoma is an aggressive and metastatic skin cancer caused by genetic mutations in melanocytes, and its incidence is increasing year by year. Understanding the gene mutation information of melanoma cases is very important for its precise treatment. The current diagnostic methods for melanoma include radiological, pharmacological, histological, cytological and molecular techniques, but the gold standard for diagnosis is still pathological biopsy, which is time consuming and destructive. Raman spectroscopy is a rapid, sensitive and nondestructive detection method. In this study, a total of 20,000 Surface-enhanced Raman scattering (SERS) spectra of melanocytes and melanoma cells were collected using a positively charged gold nanoparticles planar solid SERS substrate, and a classification network system based on convolutional neural networks (CNN) was constructed to achieve the classification of melanocytes and melanoma cells, wild-type and mutant melanoma cells and their drug resistance. Among them, the classification accuracy of melanocytes and melanoma cells was over 98%. Raman spectral differences between melanocytes and melanoma cells were analyzed and compared, and the response of cells to antitumor drugs were also evaluated. The results showed that Raman spectroscopy provided a basis for the medication of melanoma, and SERS spectra combined with CNN classification model realized classification of melanoma, which is of great significance for rapid diagnosis and identification of melanoma.

Optical tweezers and Raman spectroscopy for single-cell classification of drug resistance in acute lymphoblastic leukemia

Laser Tweezers Raman Spectroscopy (LTRS) is a combination of laser tweezers and Raman spectroscopy. It is a physical tool based on the mechanical effects of the laser, which can be used to study single living cells in suspension in a fast and non-destructive way. Our work aims to establish a methodology system based on LTRS to rapidly and non-destructively detect the resistance of acute lymphoblastic leukemia (ALL) cells and to provide a new idea for the evaluation of the resistance of ALL cells. Two specific adriamycin-resistant and parental ALL cells BALL-1 and Nalm6 were included in this study. Adriamycin resistant cells can induce the spectral differences, which can be detected by LTRS initially. To ensure the accuracy of the results, we use the principal components analysis (PCA) as well as the classification and regression trees (CRT) algorithms, which show that the specificity and sensitivity of LTRS are above 90%. In addition, to further clarify the chemoresistance status of ALL cells, we used the CRT models and receiver operating characteristic (ROC) curves which are based on the band data to look for some important bands and band intensity ratios that have strong pointing significance. Our work proves that LTRS analysis combined with multivariate statistical analyses have great potential to be a novel analytical strategy at the single-cell level for rapidly evaluating the chemoresistance status of ALL cells.

Cancer-associated fibroblasts in gastric cancer affect malignant progression via the CXCL12-CXCR4 axis

Background: Cancer-associated fibroblasts (CAFs) are principal constituents of the tumor microenvironment (TME) and play a critical role in tumor progression. The CXCL12/CXCR4 axis regulates multiple facets of the TME. The aim of this study was to determine the relationship between CXCL12 expression in CAFs and the malignant progression of gastric cancer (GC).

Methods: In the GEO (Gene Expression Omnibus) database, we performed transcriptome analysis on paired gastric cancer RNA sequencing samples, and scRNA analysis was performed on advanced malignant GC samples from the scRNA sequencing data set. Fibroblast cells were co-cultured with GC cells, and invasion, migration, epithelial-mesenchymal transformation (EMT) were determined. After blocking the expression of fibroblast CXCL12, cells were co-cultured with a GC cell line. Detection of GC cell line invasion, migration, EMT and CXCR4, Wnt5a and β-Catenin expression levels was performed. Primary CAFs and gastric normal fibroblasts were isolated and CXCL12 mRNA and protein expression were determined. In addition, a cohort of 285 GC cases was established, protein expression was evaluated immunohistochemically, and prognostic results were analyzed.

Results: GC transcriptome analysis suggested that cytokine-cytokine receptor interaction and the Wnt signaling pathway in GC tissues were significantly up-regulated. scRNA analysis of advanced malignant GC samples showed that severe intestinal metaplasia (SIM) in GC specimens of different malignant grades had obvious fibroblast clusters compared to non-atrophic gastritis (NAG) and early gastric cancer (EGC). In the SIM group, fibroblast cluster, CXCL12, CXCR4, and Wnt5a were overexpressed. Co-culturing with fibroblast cells significantly increased the invasion, migration, and EMT of GC cells, and blocking CXCL12 in CAFs disturbed the expression of Wnt5a and β-catenin. In our cohort of GC patients, high CXCL12 expression in CAFs significantly correlated with histological grade (P = 0.012) and TNM stage (P = 0.014), as well as with poor overall survival (p = 0.0107).

Conclusion: High expression of CXCL12 in CAFs in a GC microenvironment can affect the migration, invasion, and EMT of GC cells. Furthermore, it can cause poor prognosis in patients with GC.

D2O-Probed Raman Microspectroscopy Distinguishes the Metabolic Dynamics of Macromolecules in Organellar Anticancer Drug Response

To profile the metabolic dynamics responding to drugs at the single-cell/organelle resolution, rapid and economical mechanism-revealing methods are required. Here, we introduced D₂O-probed Raman microspectroscopy in combination with the multivariate curve resolution-alternating least squares (MCR-ALS or MCR) algorithm. Exploiting MCR to deconvolute each macromolecular component specifically, the method is able to track and distinguish changes in lipid and protein metabolic activities in a human cancer cell line (MCF-7) and in Saccharomyces cerevisiae, in response to the metabolism-inhibitory effect of rapamycin, which inhibits the mammalian/mechanistic target of rapamycin (mTOR) signaling. Under rapamycin, in the lipid bodies of cancer cells, metabolic activities of both protein and lipid are suppressed; in the nucleus, protein synthesis remains active, whereas lipid synthesis is inhibited; in the cytoplasm, syntheses of protein and lipid are both dose- and duration-dependent. Thus, rapamycin differentially influences protein and lipid synthesis in mTOR signaling. Moreover, the strong correlation between macromolecular-specific components of yeast and those in MCF-7 cytoplasm, nucleus, and lipid bodies revealed similarity in rapamycin response. Notably, highly metabolically active cancer cells after high-dosage rapamycin exposure (500 or 5000 × IC₅₀) were revealed, which escape detection by population-level cytotoxicity tests. Thus, by unveiling macromolecule-specific metabolic dynamics at the organelle level, the method is valuable to mechanism-based rapid screening and dissection of drug response.

The prospects of tumor chemosensitivity testing at the single-cell level

Tumor chemosensitivity testing plays a pivotal role in the optimal selection of chemotherapeutic regimens for cancer patients in a personalized manner. High-throughput drug screening approaches have been developed but they failed to take into account intratumor heterogeneity and therefore only provided limited predictive power of therapeutic response to individual cancer patients. Single cancer cell drug sensitivity testing (SCC-DST) has been recently developed to evaluate the variable sensitivity of single cells to different anti-tumor drugs. In this review, we discuss how SCC-DST overcomes the obstacles of traditional drug screening methodologies. We outline critical procedures of SCC-DST responsible for single-cell generation and sorting, cell-drug encapsulation on a microfluidic chip and detection of cell-drug interactions. In SCC-DST, droplet-based microfluidics is emerging as an important platform that integrated various assays and analyses for drug susceptibility tests for individual patients. With the advancement of technology, both fluorescence imaging and label-free analysis have been used for detecting single cell-drug interactions. We also discuss the feasibility of integrating SCC-DST with single-cell RNA sequencing to unravel the mechanisms leading to drug resistance, and utilizing artificial intelligence to facilitate the analysis of various omics data in the evaluation of drug susceptibility. SCC-DST is setting the stage for better drug selection for individual cancer patients in the era of precision medicine.

Unveiling dose- and time-dependent osteosarcoma cell responses to the γ-secretase inhibitor, DAPT, by confocal Raman microscopy

Using confocal Raman micro-spectroscopy, this study aims to elucidate the cellular responses of the γ-secretase inhibitor, N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester (DAPT), in osteosarcoma (OS) cells in a dose- and time-dependent manner. The K7M2 murine OS cell line was treated with different DAPT doses (0, 10, 20, and 40 μM) for 24 and 48 hours before investigations. Significant compositional changes (nucleic acids, protein and lipid) after DAPT treatment were addressed, which testified inhibitory effect of DAPT on the growth of OS cells. Moreover, both partial least squares-discriminant analysis (PLS-DA) and principal component analysis-linear discriminant analysis (PCA-LDA) analyses revealed governing composition variations among groups by distinguishing their spectral characteristics. Furthermore, by adopting leave-one-out cross validation method, it is shown that PLS-DA exhibited more classification capacity than PCA-LDA algorithm. Hence, by understanding the DAPT-based cellular variations, the achieved results provided an experimental foundation to establish new DAPT-based anticancer therapeutic strategies, and preclinical Raman analytical methodologies on drug-cell interactions.

Confocal Raman microspectroscopic analysis on the time-dependent impact of DAPT, a γ-secretase inhibitor, to osteosarcoma cells

Confocal Raman microspectroscopy (CRM) analysis provides subcellular compositional and morphology related information. In this study, we used CRM in conjunction with multivariate statistical analysis to elucidate the time-dependent impact of the γ-secretase inhibitor, DAPT (N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester) of osteosarcoma (OS) cells. The interactions of DAPT (20 μM) with a murine OS cell line K7M2 at 24 and 48 h were monitored. The spectral characteristics of drug action were identified to illustrate the cellular compositional alterations, showing that DAPT induced apoptosis by reducing the protein, lipid and nucleic acid content and structural changes. Multivariate algorithms, principal component analysis (PCA) and linear discriminant analysis (LDA) revealed a clear separation among cells in the untreated control (UT), 24H (DAPT-treated for 24 h), and 48H (DAPT-treated for 48 h) groups, achieving sensitivities of 100%, 96%, 100% and specificities of 98%, 100%, 100%, respectively. After point-scanned spectral imaging, K-means clustering analysis (KCA) was further used to visualize sub-cellular morphological changes and the underlying spectral characteristics in a temporal sequence. Compared with the UT group, Raman imaging results exhibited gradually increased nuclear division of OS cells with DAPT treatment duration extension, along with changes in the physiology of other organelles within the cell. By providing a unique perspective for understanding the temporary cellular responses to DAPT at molecular level, the achieved results form the foundation of strategies for the application of CRM and other Raman-based techniques for studying the therapeutic responses of other anticancer agents in cancer model systems.

Single-Cell Multimodal Analytical Approach by Integrating Raman Optical Tweezers and RNA Sequencing

Single-cell analysis has become a state-of-art approach to heterogeneity profiling in tumor cells. Herein, we realize a kind of single-cell multimodal analytical approach by combining single-cell RNA sequencing (scRNA-seq) with Raman optical tweezers (ROT), a label-free single-cell identification and isolation technique, and apply it to investigate drug sensitivity. The drug sensitivity of human BGC823 gastric cancer cells toward different drugs, paclitaxel and sodium dichloroacetate, was distinguished in the conjoint analytical way including morphology monitoring, Raman identification, and transcriptomic profiling. Each individual BGC823 cancer cell was measured by Raman spectroscopy, then nondestructively isolated out by ROT, and finally RNA-sequenced. Our results demonstrate each analytical mode can reflect cell response to the drugs from different perspectives and is consistent and complementary with each other. Therefore, we believe the multimodal analytical approach offers an access to comprehensive characterizations of the unicellular complexity, which especially makes sense for studying tumor heterogeneity or a desired special cell from a mixture cell sample such as whole blood.

Rapid and accurate identification of marine microbes with single-cell Raman spectroscopy

Rapid and accurate identification of individual microorganisms, such as pathogenic or unculturable microbes, is significant in microbiology. In this work, rapid identification of marine microorganisms by single-cell Raman spectroscopy (scRS) using one-dimensional convolutional neural networks (1DCNN) was explored. Here, single-cell Raman spectra of ten species of marine actinomycetes, two species of non-marine actinomycetes and E. coli (as a reference) were individually collected. Several common classification algorithms in chemometrics, including linear discriminant analysis with principal component analysis and a support vector machine, were applied to evaluate the 1DCNN performance based on the raw and pre-processed Raman spectra. 1DCNN showed superior performance on the raw data in terms of its accuracy and recall rate compared with other classification algorithms. Our investigation demonstrated that the scRS-integrating advanced 1DCNN classification algorithm provided a rapid and accurate approach for identifying individual microorganisms without time-consuming cell culture and sophisticated or specific techniques, which could be a useful methodology for discriminating the microbes that cannot be cultured under normal conditions, especially for 'biological risk'-related emergencies.

Confocal Raman Spectral Imaging Study of DAPT, a γ-secretase Inhibitor, Induced Physiological and Biochemical Reponses in Osteosarcoma Cells

Confocal Raman microspectral imaging was adopted to elucidate the cellular drug responses of osteosarcoma cells (OC) to N-[N-(3, 5-difluorophenyl acetyl)-L-alanyl]-sphenylglycine butyl ester (DAPT), a γ-secretase inhibitor, by identifying the drug induced subcellular compositional and structural changes. Methods: Spectral information were acquired from cultured osteosarcoma cells treated with 0 (Untreated Group, UT), 10 (10 μM DAPT treated, 10T), 20 μM (20 μM DAPT treated, 20T) DAPT for 24 hours. A one-way ANOVA and Tukey's honest significant difference (HSD) post hoc multiple test were sequentially applied to address spectral features among three groups. Multivariate algorithms such as K-means clustering analysis (KCA) and Principal component analysis (PCA) were used to highlight the structural and compositional differences, while, univariate imaging was applied to illustrate the distribution pattern of certain cellular components after drug treatment. Results: Major biochemical changes in DAPT-induced apoptosis came from changes in the content and structure of proteins, lipids, and nucleic acids. By adopted multivariate algorithms, the drug induced cellular changes was identified by the morphology and spectral characteristics between untreated cells and treated cells, testified that DAPT mainly acted in the nuclear region. With the increase of the drug concentration, the content of main subcellular compositions, such nucleic acid, protein, and lipid decreased. In an addition, DAPT-induced nuclear fragmentation and apoptosis was depicted by the univariate Raman image of major cellular components (nucleic acids, proteins and lipids). Conclusions: The achieved Raman spectral and imaging results illustrated detailed DAPT-induced subcellular compositional and structural variations as a function of drug dose. Such observations can not only explain drug therapeutic mechanisms of OC DAPT treatment, and also provide new insights for accessing the medicine curative efficacy and predicting prognosis.

Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning

Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absent. Herein, we report an analytical approach that combines single cell mass spectrometry (SCMS) based metabolomics with machine learning (ML) models to address the existing challenges. Metabolomic profiles of live cancer cells (HCT-116) with different levels (i.e., no, low, and high) of chemotherapy-induced drug resistance were measured using the Single-probe SCMS technique. A series of ML models, including random forest (RF), artificial neural network (ANN), and penalized logistic regression (LR), were constructed to predict the degrees of drug resistance of individual cells. A systematic comparison of performance was conducted among multiple models, and the method validation was carried out experimentally. Our results indicate that these ML models, especially the RF model constructed on the obtained SCMS datasets, can rapidly and accurately predict different degrees of drug resistance of live single cells. With such rapid and reliable assessment of drug resistance demonstrated at the single cell level, our method can be potentially employed to evaluate chemotherapeutic efficacy in the clinic.

Integrating a generalized data analysis workflow with the Single-probe mass spectrometry experiment for single cell metabolomics

We conducted single cell metabolomics studies of live cancer cells through online single cell mass spectrometry (SCMS) experiments combined with a generalized comprehensive data analysis workflow. The SCMS experiments were carried out using the Single-probe device coupled with a mass spectrometer to measure molecular profiles of cells in response to two mitotic inhibitors, taxol and vinblastine, under a series of treatment conditions. SCMS metabolomic data were analyzed using a comprehensive approach, including data pre-treatment, visualization, statistical analysis, machine learning, and pathway enrichment analysis. For comparative studies, traditional liquid chromatography-MS (LC-MS) experiments were conducted using lysates prepared from bulk cell samples. Metabolomic profiles of single cells were visualized through Partial Least Square-Discriminant Analysis (PLS-DA), and the phenotypic biomarkers associated with emerging phenotypes induced by drug treatment were discovered and compared through a series of rigorous statistical analysis. Species of interest were further identified at both the single cell and population levels. In addition, four biological pathways potentially involved in the drug treatment were determined through pathway enrichment analysis. Our work demonstrated the capability of comprehensive pipeline studies of single cell metabolomics. This method can be potentially applied to broader types of SCMS datasets for future pharmaceutical and chemotherapeutic research.

Nondestructive Identification and Accurate Isolation of Single Cells through a Chip with Raman Optical Tweezers

Raman optical tweezers (ROT) as a label-free technique plays an important role in single-cell study such as heterogeneity of tumor and microbial cells. Herein we designed a chip utilizing ROT to isolate a specific single cell. The chip was made from a polydimethylsiloxane (PDMS) slab and formed into a gourd-shaped reservoir with a connected channel on a cover glass. On the chip an individual cell could be isolated from a cell crowd and then extracted with ∼0.5 μL of phosphate-buffered saline (PBS) via pipet immediately after Raman spectral measurements of the same cell. As verification, we separated four different type of cells including BGC823 gastric cancer cells, erythrocytes, lymphocytes, and E. coli cells and quantifiably characterized the heterogeneity of the cancer cells, leukocyte subtype, and erythrocyte status, respectively. The average time of identifying and isolating a specific cell was 3 min. Cell morphology comparison and viability tests showed that the successful rate of single-cell isolation was about 90%. Thus, we believe our platform could further couple other single-cell techniques such as single-cell sequencing and become a multiperspective analytical approach at the level of a single cell.

Recent advances in single-cell analysis by mass spectrometry

Cells are the most basic structural units that play vital roles in the functioning of living organisms. Analysis of the chemical composition and content of a single cell plays a vital role in ensuring precise investigations of cellular metabolism, and is a crucial aspect of lipidomic and proteomic studies. In addition, structural knowledge provides a better understanding of cell behavior as well as the cellular and subcellular mechanisms. However, single-cell analysis can be very challenging due to the very small size of each cell as well as the large variety and extremely low concentrations of substances found in individual cells. On account of its high sensitivity and selectivity, mass spectrometry holds great promise as an effective technique for single-cell analysis. Numerous mass spectrometric techniques have been developed to elucidate the molecular profiles at the cellular level, including electrospray ionization mass spectrometry (ESI-MS), secondary ion mass spectrometry (SIMS), laser-based mass spectrometry and inductively coupled plasma mass spectrometry (ICP-MS). In this review, the recent advances in single-cell analysis by mass spectrometry are summarized. The strategies of different ionization modes to achieve single-cell analysis are classified and discussed in detail.

Anti-Cancer Drug Sensitivity Assay with Quantitative Heterogeneity Testing Using Single-Cell Raman Spectroscopy

A novel anti-cancer drug sensitivity testing (DST) approach was developed based on in vitro single-cell Raman spectrum intensity (RSI). Generally, the intensity of Raman spectra (RS) for a single living cell treated with drugs positively relates to the sensitivity of the cells to the drugs. In this study, five cancer cell lines (BGC 823, SGC 7901, MGC 803, AGS, and NCI-N87) were exposed to three cytotoxic compounds or to combinations of these compounds, and then they were evaluated for their responses with RSI. The results of RSI were consistent with conventional DST methods. The parametric correlation coefficient for the RSI and Methylthiazolyl tetrazolium assay (MTT) was 0.8558 ± 0.0850, and the coefficient of determination was calculated as R² = 0.9529 ± 0.0355 for fitting the dose⁻response curve. Moreover, RSI data for NCI-N87 cells treated by trastuzumab, everolimus (cytostatic), and these drugs in combination demonstrated that the RSI method was suitable for testing the sensitivity of cytostatic drugs. Furthermore, a heterogeneity coefficient H was introduced for quantitative characterization of the heterogeneity of cancer cells treated by drugs. The largest possible variance between RSs of cancer cells were quantitatively obtained using eigenvalues of principal component analysis (PCA). The ratio of H between resistant cells and sensitive cells was greater than 1.5, which suggested the H-value was effective to describe the heterogeneity of cancer cells. Briefly, the RSI method might be a powerful tool for simple and rapid detection of the sensitivity of tumor cells to anti-cancer drugs and the heterogeneity of their responses to these drugs.