Live single cell mass spectrometry reveals cancer-specific metabolic profiles of circulating tumor cells

Detection of circulating tumor cells that predicts the efficacy of neoadjuvant chemotherapy for locally advanced triple-negative breast cancer

Objective

This study aims to assess the predictive potential of circulating tumor cells (CTCs) and circulating tumor stem cells (CTSCs) in locally advanced triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC) compared to the RECIST 1.1 standard.

Methods

We analyzed 112 patients with TNBC at the Liaoning Tumor Hospital. CTCs and CTSCs were evaluated before NAC, on the first NAC cycle day, and after NAC. We assessed the ability of positive CTSCs after the first cycle to predict NAC resistance (requiring regimen change) with a 91% specificity. Additionally, we analyzed CTC dynamics during the first NAC cycle to predict efficacy (often reaching MP4 or MP5) with 87% sensitivity and 80% specificity.

Results

Positive CTSCs post-first cycle predicted NAC resistance with high specificity (91%). The gradual decline in CTCs during the first NAC cycle indicated NAC efficacy, allowing the regimen to continue with a sensitivity of 87% and specificity of 80%.

Conclusion

This study suggests that positive CTSCs after the first NAC cycle predict resistance, thereby facilitating early detection (≥ 6 weeks earlier than RECIST). Gradual CTC reduction during the first cycle predicts efficacy, enabling regimen continuation. CTCs and CTSCs show promise as predictive markers for NAC efficacy in patients with locally advanced TNBC.

Uncovering pre-cytokinetic block in cancer cells under shear stress using a disturbed flow-generating device

During metastasis, cancer cells navigate through harsh conditions, including various mechanical forces in the bloodstream, highlighting the need to understand the impact of mechanical and shear stresses on cancer cells. To overcome the current methodological limitations of such research, here we present a new device that replicates similar conditions by applying shear stress on cultured cells. The device provides a less complex, easily accessible alternative to traditional fluidics while generating fluid shear stress values comparable to those in human veins and capillaries. The device allows analyses of large cell numbers in standard cell culture flasks and incubators. Using this device to explore the shear stress-induced responses of various human cell lines, we discovered a previously unknown, reversible pre-cytokinetic block occurring in cells that lose anchorage during mitosis and are kept under constant shear stress. Notably, some cancer cell lines appear to bypass this unorthodox cell-cycle block, suggesting its role as a safety checkpoint to restrict the proliferation of cancer cells in the bloodstream and their overall spreading potential. These findings provide new insights into the diverse responses of normal and cancer cells to shear stress and highlight the potential of our technology for research on circulating tumor cells and metastatic spread.

The prowess of metabolomics in cancer research: current trends, challenges and future perspectives

Cancer due to its heterogeneous nature and large prevalence has tremendous socioeconomic impacts on populations across the world. Therefore, it is crucial to discover effective panels of biomarkers for diagnosing cancer at an early stage. Cancer leads to alterations in cell growth and differentiation at the molecular level, some of which are very unique. Therefore, comprehending these alterations can aid in a better understanding of the disease pathology and identification of the biomolecules that can serve as effective biomarkers for cancer diagnosis. Metabolites, among other biomolecules of interest, play a key role in the pathophysiology of cancer whose levels are significantly altered while 'reprogramming the energy metabolism', a cellular condition favored in cancer cells which is one of the hallmarks of cancer. Metabolomics, an emerging omics technology has tremendous potential to contribute towards the goal of investigating cancer metabolites or the metabolic alterations during the development of cancer. Diverse metabolites can be screened in a variety of biofluids, and tumor tissues sampled from cancer patients against healthy controls to capture the altered metabolism. In this review, we provide an overview of different metabolomics approaches employed in cancer research and the potential of metabolites as biomarkers for cancer diagnosis. In addition, we discuss the challenges associated with metabolomics-driven cancer research and gaze upon the prospects of this emerging field.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Circulating Tumor Cells Culture: Methods, Challenges, and Clinical Applications

Circulating tumor cells (CTCs) play a pivotal role in cancer metastasis and hold considerable potential for clinical diagnosis, therapeutic monitoring, and prognostic evaluation. Nevertheless, the limited quantity of CTCs in liquid biopsy samples poses challenges for comprehensive downstream analysis. In vitro culture of CTCs can effectively address the issue of insufficient CTC numbers. Furthermore, research based on CTC cell lines serves as a valuable complement to traditional cancer cell line-based research. While numerous reports exist on CTC in vitro culture and even the establishment of CTC cell lines, the methods used vary, leading to disparate culture outcomes. This review presents the developmental history and current status of CTC in vitro culture research. Additionally, the culture strategies applied in different methods and analyzed the impact of various steps on culture outcomes are compared. Overall, the review indicates that while the short-term culture of CTCs is relatively straightforward, long-term culture success has been achieved for various specific cancer types but still faces challenges. Further optimization of efficient and widely applicable culture strategies is needed. Additionally, ongoing applications of CTC in vitro culture are summarized, highlighting the potential of expanded CTCs for drug susceptibility testing and as therapeutic tools in personalized treatment.

MetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells

Single-cell mass spectrometry (SCMS) is an emerging tool for studying cell heterogeneity according to variation of molecular species in single cells. Although it has become increasingly common to employ machine learning models in SCMS data analysis, such as the classification of cell phenotypes, the existing machine learning models often suffer from low adaptability and transferability. In addition, SCMS studies of rare cells can be restricted by limited number of cell samples. To overcome these limitations, we performed SCMS analyses of melanoma cancer cell lines with two phenotypes (i.e., primary and metastatic cells). We then developed a meta-learning-based model, MetaPhenotype, that can be trained using a small amount of SCMS data to accurately classify cells into primary or metastatic phenotypes. Our results show that compared with standard transfer learning models, MetaPhenotype can rapidly predict and achieve a high accuracy of over 90% with fewer new training samples. Overall, our work opens the possibility of accurate cell phenotype classification based on fewer SCMS samples, thus lowering the demand for sample acquisition.

Prognostic prediction signature and molecular subtype for liver cancer: A CTC/CTM‑related gene prediction model and independent external validation

Liver cancer is the second leading cause of tumor-related death worldwide, and a serious threat to lives and health. Circulating tumor cells (CTCs) facilitate the progression of various cancers, including liver cancer. The relationship between CTC/circulating tumor microemboli-related genes (CRGs) and the prognosis of liver cancer is unclear. The aim of the present study was to identify CTC/circulating tumour microemboli-related genes (CRGs) in hepatocellular carcinoma and to investigate their clinical significance. Transcriptomic data from The Cancer Genome Atlas (International Cancer Genome Consortium (ICGC) and GSE117623 databases were combined, and differentially expressed CRGs were identified. These were subsequently analyzed via least absolute shrinkage and selection operator and multivariate Cox analyses, and a five-gene risk signature was constructed. The signature was validated in the ICGC and GSE14520 dataset with survival analysis and receiver operating characteristic curve analysis. Immunocyte infiltration, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE), and the somatic mutation rate were also compared between high- and low-risk groups, based on the median predictive index, to further evaluate the immunotherapeutic value of the model. Molecular subtypes of liver cancer were characterized by the non-negative matrix factorization method and potential therapeutic compounds were evaluated for different subtypes. Nomograms were utilized to predict the prognosis of patients, and the signature was compared with previous literature models. Additionally, the biological function of one of the CRGs, tumor protein p53 inducible protein 3 (TP53I3), in liver cancer was further explored through in vitro experiments. Analysis of the prognostic characteristics of the five CRGs led to the identification of two liver cancer subtypes. Patients in the low-risk group had a longer survival compared with those in the high-risk group, and patients in the latter group were associated with a higher TMB, immunocyte infiltration and somatic mutation rate, and lower TIDE scores. The prognostic profile was validated in the ICGC and GSE14520 datasets and exhibited a good predictive performance. In vitro analysis showed that the knockdown of TP53I3 suppressed liver cancer cell proliferation. In summary, CRGs were used to develop a new prognostic signature to predict the prognosis of patients with liver cancer. This signature may be used to assess the prognosis of patients and may provide new insights for clinical management strategies. In addition, TP53I3 is potentially a therapeutic target for liver cancer.

The new advance of exosome-based liquid biopsy for cancer diagnosis

Liquid biopsy is a minimally invasive method that uses biofluid samples instead of tissue samples for cancer diagnosis. Exosomes are small extracellular vesicles secreted by donor cells and act as mediators of intercellular communication in human health and disease. Due to their important roles, exosomes have been considered as promising biomarkers for liquid biopsy. However, traditional methods for exosome isolation and cargo detection methods are time-consuming and inefficient, limiting their practical application. In the past decades, many new strategies, such as microfluidic chips, nanowire arrays and electrochemical biosensors, have been proposed to achieve rapid, accurate and high-throughput detection and analysis of exosomes. In this review, we discussed about the new advance in exosome-based liquid biopsy technology, including isolation, enrichment, cargo detection and analysis approaches. The comparison of currently available methods is also included. Finally, we summarized the advantages and limitations of the present strategies and further gave a perspective to their future translational use.

Enabling high-sensitivity live single-cell mass spectrometry using an integrated electrical lysis and nano electrospray ionization interface

BACKGROUND

Live single-cell metabolomic studies encounter inherent difficulties attributed to the limited sample volume, minimal compound quantity, and insufficient sensitivity in the Mass Spectrometry (MS) method used to obtain single-cell data. However, understanding cellular heterogeneity, functional diversity, and metabolic processes within individual cells is essential. Exploring how individual cells respond to stimuli, including drugs, environmental changes, or signaling molecules, offers insights into biology, oncology, and drug discovery. Efficient release of cell contents (lysis) is vital for accurate metabolite detection at the single-cell level. Despite this, traditional approaches in live single cell metabolomics methods do not emphasize efficient lysis to prevent sample dilution. Instead, current live single cell metabolomics methods use direct infusion to introduce the cell into the mass spectrometry without prior chromatographic separation or a lysis step, which adversely affects sensitivity and metabolic coverage.

RESULTS

To address this, we developed an integrated single-cell electrical lysis and nano spray (SCEL-nS) platform coupled to an Orbitrap MS capable of efficiently lysing a single cell after being sampled with specially manufactured micropipettes. Lysis efficiency was validated by comparing live cell stain fluorescent intensities of intact and electrically lysed cells through microscopy imaging. The SCEL-nS platform successfully induced the breakdown of a single cell, significantly reducing the live cell stain's fluorescent intensity indicating cell membrane breakdown. Additionally, SCEL-nS was validated by measuring single cells spiked with the anti-cancer drug tamoxifen by MS. SCEL-nS use resulted in statistically significant increase in the peak measured by the method compared to the traditional non-lysis method.

SIGNIFICANCE

Overall, our results demonstrate that the newly incorporated SCEL-nS platform achieved higher sensitivities compared to traditional live single cell analysis methods.

Circulating tumor cells: from new biological insights to clinical practice

The primary reason for high mortality rates among cancer patients is metastasis, where tumor cells migrate through the bloodstream from the original site to other parts of the body. Recent advancements in technology have significantly enhanced our comprehension of the mechanisms behind the bloodborne spread of circulating tumor cells (CTCs). One critical process, DNA methylation, regulates gene expression and chromosome stability, thus maintaining dynamic equilibrium in the body. Global hypomethylation and locus-specific hypermethylation are examples of changes in DNA methylation patterns that are pivotal to carcinogenesis. This comprehensive review first provides an overview of the various processes that contribute to the formation of CTCs, including epithelial-mesenchymal transition (EMT), immune surveillance, and colonization. We then conduct an in-depth analysis of how modifications in DNA methylation within CTCs impact each of these critical stages during CTC dissemination. Furthermore, we explored potential clinical implications of changes in DNA methylation in CTCs for patients with cancer. By understanding these epigenetic modifications, we can gain insights into the metastatic process and identify new biomarkers for early detection, prognosis, and targeted therapies. This review aims to bridge the gap between basic research and clinical application, highlighting the significance of DNA methylation in the context of cancer metastasis and offering new avenues for improving patient outcomes.

Deciphering metabolic crosstalk in context: lessons from inflammatory diseases

Metabolism plays a crucial role in regulating the function of immune cells in both health and disease, with altered metabolism contributing to the pathogenesis of cancer and many inflammatory diseases. The local microenvironment has a profound impact on the metabolism of immune cells. Therefore, immunological and metabolic heterogeneity as well as the spatial organization of cells in tissues should be taken into account when studying immunometabolism. Here, we highlight challenges of investigating metabolic communication. Additionally, we review the capabilities and limitations of current technologies for studying metabolism in inflamed microenvironments, including single-cell omics techniques, flow cytometry-based methods (Met-Flow, single-cell energetic metabolism by profiling translation inhibition (SCENITH)), cytometry by time of flight (CyTOF), cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq), and mass spectrometry imaging. Considering the importance of metabolism in regulating immune cells in diseased states, we also discuss the applications of metabolomics in clinical research, as well as some hurdles to overcome to implement these techniques in standard clinical practice. Finally, we provide a flowchart to assist scientists in designing effective strategies to unravel immunometabolism in disease-relevant contexts.

Exploiting the metabolic vulnerability of circulating tumour cells

Metastasis has a major part in the severity of disease and lethality of cancer. Circulating tumour cells (CTCs) represent a reservoir of metastatic precursors in circulation, most of which cannot survive due to hostile conditions in the bloodstream. Surviving cells colonise a secondary site based on a combination of physical, metabolic, and oxidative stress protection states required for that environment. Recent advances in CTC isolation methods and high-resolution 'omics technologies are revealing specific metabolic pathways that support this selection of CTCs. In this review, we discuss recent advances in our understanding of CTC biology and discoveries of adaptations in metabolic pathways during their selection. Understanding these traits and delineating mechanisms by which they confer acquired resistance or vulnerability in CTCs is crucial for developing successful prognostic and therapeutic strategies in cancer.

Single Cell mass spectrometry: Towards quantification of small molecules in individual cells

Studying cell heterogeneity can provide a deeper understanding of biological activities, but appropriate studies cannot be performed using traditional bulk analysis methods. The development of diverse single cell bioanalysis methods is in urgent need and of great significance. Mass spectrometry (MS) has been recognized as a powerful technique for bioanalysis for its high sensitivity, wide applicability, label-free detection, and capability for quantitative analysis. In this review, the general development of single cell mass spectrometry (SCMS) field is covered. First, multiple existing SCMS techniques are described and compared. Next, the development of SCMS field is discussed in a chronological order. Last, the latest quantification studies on small molecules using SCMS have been described in detail.

Recent Progress in Mass Spectrometry-Based Single-Cell Metabolic Analysis

Single-cell analysis enables the measurement of biomolecules at the level of individual cells, facilitating in-depth investigations into cellular heterogeneity and precise interpretation of the related biological mechanisms. Among these biomolecules, cellular metabolites exhibit remarkable sensitivity to environmental and biochemical changes, unveiling a hidden world underlying cellular heterogeneity and allowing for the determination of cell physiological states. However, the metabolic analysis of single cells is challenging due to the extremely low concentrations, substantial content variations, and rapid turnover rates of cellular metabolites. Mass spectrometry (MS), characterized by its high sensitivity, wide dynamic range, and excellent selectivity, is employed in single-cell metabolic analysis. This review focuses on recent advances and applications of MS-based single-cell metabolic analysis, encompassing three key steps of single-cell isolation, detection, and application. It is anticipated that MS will bring profound implications in biomedical practices, serving as advanced tools to depict the single-cell metabolic landscape.

Dissemination of Circulating Tumor Cells in Breast and Prostate Cancer: Implications for Early Detection

Burgeoning evidence suggests that circulating tumor cells (CTCs) may disseminate into blood vessels at an early stage, seeding metastases in various cancers such as breast and prostate cancer. Simultaneously, the early-stage CTCs that settle in metastatic sites [termed disseminated tumor cells (DTCs)] can enter dormancy, marking a potential source of late recurrence and therapy resistance. Thus, the presence of these early CTCs poses risks to patients but also holds potential benefits for early detection and treatment and opportunities for possibly curative interventions. This review delves into the role of early DTCs in driving latent metastasis within breast and prostate cancer, emphasizing the importance of early CTC detection in these diseases. We further explore the correlation between early CTC detection and poor prognoses, which contribute significantly to increased cancer mortality. Consequently, the detection of CTCs at an early stage emerges as a critical imperative for enhancing clinical diagnostics and allowing for early interventions.

Circulating Tumor Cells: How Far Have We Come with Mining These Seeds of Metastasis?

Circulating tumor cells (CTCs) are cancer cells that slough off from the tumor and circulate in the peripheral blood and lymphatic system as micro metastases that eventually results in macro metastases. Through a simple blood draw, sensitive CTC detection from clinical samples has proven to be a useful tool for determining the prognosis of cancer. Recent technological developments now make it possible to detect CTCs reliably and repeatedly from a simple and straightforward blood test. Multicenter trials to assess the clinical value of CTCs have demonstrated the prognostic value of these cancer cells. Studies on CTCs have filled huge knowledge gap in understanding the process of metastasis since their identification in the late 19th century. However, these rare cancer cells have not been regularly used to tailor precision medicine and or identify novel druggable targets. In this review, we have attempted to summarize the milestones of CTC-based research from the time of identification to molecular characterization. Additionally, the need for a paradigm shift in dissecting these seeds of metastasis and the possible future avenues to improve CTC-based discoveries are also discussed.

CarSitePred: an integrated algorithm for identifying carbonylated sites based on KNDUA-LNDOT resampling technique

Carbonylated sites are the determining factors for functional changes or deletions in carbonylated proteins, so identifying carbonylated sites is essential for understanding the process of protein carbonylated and exploring the pathogenesis of related diseases. The current wet experimental methods for predicting carbonylated modification sites ae not only expensive and time-consuming, but also have limited protein processing capabilities and cannot meet the needs of researchers. The identification of carbonylated sites using computational methods not only improves the functional characterization of proteins, but also provides researchers with free tools for predicting carbonylated sites. Therefore, it is essential to establish a model using computational methods that can accurately predict protein carbonylated sites. In this study, a prediction model, CarSitePred, is proposed to identify carbonylation sites. In CarSitePred, specific location amino acid hydrophobic hydrophilic, one-to-one numerical conversion of amino acids, and AlexNet convolutional neural networks convert preprocessed carbonylated sequences into valid numerical features. The K-means Normal Distribution-based Undersampling Algorithm (KNDUA) and Localized Normal Distribution Oversampling Technology (LNDOT) were firstly proposed and employed to balance the K, P, R and T carbonylation training dataset. And for the first time, carbonylation modification sites were transformed into the form of images and directly inputted into AlexNet convolutional neural network to extract features for fitting SVM classifiers. The 10-fold cross-validation and independent testing results show that CarSitePred achieves better prediction performance than the best currently available prediction models. Availability: https://github.com/zuoyun123/CarSitePred.

Biological mass spectrometry enables spatiotemporal 'omics: From tissues to cells to organelles

Biological processes unfold across broad spatial and temporal dimensions, and measurement of the underlying molecular world is essential to their understanding. Interdisciplinary efforts advanced mass spectrometry (MS) into a tour de force for assessing virtually all levels of the molecular architecture, some in exquisite detection sensitivity and scalability in space-time. In this review, we offer vignettes of milestones in technology innovations that ushered sample collection and processing, chemical separation, ionization, and 'omics analyses to progressively finer resolutions in the realms of tissue biopsies and limited cell populations, single cells, and subcellular organelles. Also highlighted are methodologies that empowered the acquisition and analysis of multidimensional MS data sets to reveal proteomes, peptidomes, and metabolomes in ever-deepening coverage in these limited and dynamic specimens. In pursuit of richer knowledge of biological processes, we discuss efforts pioneering the integration of orthogonal approaches from molecular and functional studies, both within and beyond MS. With established and emerging community-wide efforts ensuring scientific rigor and reproducibility, spatiotemporal MS emerged as an exciting and powerful resource to study biological systems in space-time.

Mass Spectrometry-based Single-Cell Lipidomics: Advancements, Challenges, and the Path Forward

In the past decade, lipidomics, now recognized as standalone subdiscipline of metabolomics, has gained considerable attention. Due to its sensitivity and unparalleled versatility, mass spectrometry (MS) has emerged as the tool of choice for lipid identification and detection. Traditional MS-based lipidomics are performed on bulk cell samples. While informative, these bulk-scale cellular lipidome measurements mask cellular heterogeneity across seemingly homogeneous populations of cells. Unfortunately, single cell lipidomics methodology and analyses are considerably behind genomics, transcriptomics, and proteomics. Therefore, the cell-to-cell heterogeneity and related function remains largely unexplored for lipidomics. Herein, we review recent advances in MS-based single cell lipidomics. We also explore the root causes for the slow development of single-cell lipidomics techniques. We aim to provide insights on the pivotal knowledge gaps that have been neglected, prohibiting the propulsion of the single-cell lipidomics field forward, while also providing our perspective towards future methodologies that can pave a path forward.

Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics

Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.

A New Era of Integration between Multiomics and Spatio-Temporal Analysis for the Translation of EMT towards Clinical Applications in Cancer

Epithelial-mesenchymal transition (EMT) is crucial to metastasis by increasing cancer cell migration and invasion. At the cellular level, EMT-related morphological and functional changes are well established. At the molecular level, critical signaling pathways able to drive EMT have been described. Yet, the translation of EMT into efficient diagnostic methods and anti-metastatic therapies is still missing. This highlights a gap in our understanding of the precise mechanisms governing EMT. Here, we discuss evidence suggesting that overcoming this limitation requires the integration of multiple omics, a hitherto neglected strategy in the EMT field. More specifically, this work summarizes results that were independently obtained through epigenomics/transcriptomics while comprehensively reviewing the achievements of proteomics in cancer research. Additionally, we prospect gains to be obtained by applying spatio-temporal multiomics in the investigation of EMT-driven metastasis. Along with the development of more sensitive technologies, the integration of currently available omics, and a look at dynamic alterations that regulate EMT at the subcellular level will lead to a deeper understanding of this process. Further, considering the significance of EMT to cancer progression, this integrative strategy may enable the development of new and improved biomarkers and therapeutics capable of increasing the survival and quality of life of cancer patients.

Chemical tagging mass spectrometry: an approach for single-cell omics

Single-cell (SC) analysis offers new insights into the study of fundamental biological phenomena and cellular heterogeneity. The superior sensitivity, high throughput, and rich chemical information provided by mass spectrometry (MS) allow MS to emerge as a leading technology for molecular profiling of SC omics, including the SC metabolome, lipidome, and proteome. However, issues such as ionization suppression, low concentration, and huge span of dynamic concentrations of SC components lead to poor MS response for certain types of molecules. It is noted that chemical tagging/derivatization has been adopted in SCMS analysis, and this strategy has been proven an effective solution to circumvent these issues in SCMS analysis. Herein, we review the basic principle and general strategies of chemical tagging/derivatization in SCMS analysis, along with recent applications of chemical derivatization to single-cell metabolomics and multiplexed proteomics, as well as SCMS imaging. Furthermore, the challenges and opportunities for the improvement of chemical derivatization strategies in SCMS analysis are discussed.

Feasibility of mass cytometry proteomic characterisation of circulating tumour cells in head and neck squamous cell carcinoma for deep phenotyping

BACKGROUND

Circulating tumour cells (CTCs) are a potential cancer biomarker, but current methods of CTC analysis at single-cell resolution are limited. Here, we describe high-dimensional single-cell mass cytometry proteomic analysis of CTCs in HNSCC.

METHODS

Parsortix microfluidic-enriched CTCs from 14 treatment-naïve HNSCC patients were analysed by mass cytometry analysis using 41 antibodies. Immune cell lineage, epithelial-mesenchymal transition (EMT), stemness, proliferation and immune checkpoint expression was assessed alongside phosphorylation status of multiple signalling proteins. Patient-matched tumour gene expression and CTC EMT profiles were compared. Standard bulk CTC RNAseq was performed as a baseline comparator to assess mass cytometry data.

RESULTS

CTCs were detected in 13/14 patients with CTC counts of 2-24 CTCs/ml blood. Unsupervised clustering separated CTCs into epithelial, early EMT and advanced EMT groups that differed in signalling pathway activation state. Patient-specific CTC cluster patterns separated into immune checkpoint low and high groups. Patient tumour and CTC EMT profiles differed. Mass cytometry outperformed bulk RNAseq to detect CTCs and characterise cell phenotype.

DISCUSSION

We demonstrate mass cytometry allows high-plex proteomic characterisation of CTCs at single-cell resolution and identify common CTC sub-groups with potential for novel biomarker development and immune checkpoint inhibitor treatment stratification.

Recent methodological advances towards single-cell proteomics

Studying the central dogma at the single-cell level has gained increasing attention to reveal hidden cell lineages and functions that cannot be studied using traditional bulk analyses. Nonetheless, most single-cell studies exploiting genomic and transcriptomic levels fail to address information on proteins that are central to many important biological processes. Single-cell proteomics enables understanding of the functional status of individual cells and is particularly crucial when the specimen is composed of heterogeneous entities of cells. With the growing importance of this field, significant methodological advancements have emerged recently. These include miniaturized and automated sample preparation, multi-omics analyses, and combined analyses of multiple techniques such as mass spectrometry and microscopy. Moreover, artificial intelligence and single-molecule detection technologies have advanced throughput and improved sensitivity limitations, respectively, over conventional methods. In this review, we summarize cutting-edge methodologies for single-cell proteomics and relevant emerging technologies that have been reported in the last 5 years, and provide an outlook on this research field.

Direct Analysis of Micro-biopsy Samples by Polarity Gradient Focusing Dip-and-Go Mass Spectrometry

Direct analysis of micro-biopsy samples by mass spectrometry at single-cell level still faces major challenges. In this work, we developed a polarity gradient focusing dip-and-go strategy (PGF-Dip&Go) during induced electrospray ionization mass spectrometry (iESI-MS) analysis for real-time enrichment and spatial separation of compounds such as lipids, alkaloids, fatty amines, and drugs. Compared with direct iESI-MS analysis, enrichment of analytes (enrichment factor of 5.0-100.0) and spatial separation between different analytes were achieved. Owing to the enrichment effect and salt cleanup effect, the sensitivity of PGF-Dip&Go has been improved by 25-10,000 times compared with direct iESI-MS. PGF-Dip&Go has been successfully applied for the analysis of lipids in a 200 pL micro-biopsy section from an individual fish egg. Lysophosphatidylcholine (LPC), phosphatidylcholine (PC), and triglyceride (TG) were significantly enriched and separated according to their polarity differences, proving the potential of PGF-Dip&Go to be a noninvasive and powerful analytical tool for in situ analysis of complex small volumes in the future.

AI-aided holographic flow cytometry for label-free identification of ovarian cancer cells in the presence of unbalanced datasets

Liquid biopsy is a valuable emerging alternative to tissue biopsy with great potential in the noninvasive early diagnostics of cancer. Liquid biopsy based on single cell analysis can be a powerful approach to identify circulating tumor cells (CTCs) in the bloodstream and could provide new opportunities to be implemented in routine screening programs. Since CTCs are very rare, the accurate classification based on high-throughput and highly informative microscopy methods should minimize the false negative rates. Here, we show that holographic flow cytometry is a valuable instrument to obtain quantitative phase-contrast maps as input data for artificial intelligence (AI)-based classifiers. We tackle the problem of discriminating between A2780 ovarian cancer cells and THP1 monocyte cells based on the phase-contrast images obtained in flow cytometry mode. We compare conventional machine learning analysis and deep learning architectures in the non-ideal case of having a dataset with unbalanced populations for the AI training step. The results show the capacity of AI-aided holographic flow cytometry to discriminate between the two cell lines and highlight the important role played by the phase-contrast signature of the cells to guarantee accurate classification.

Single-cell metabolic fingerprints discover a cluster of circulating tumor cells with distinct metastatic potential

Circulating tumor cells (CTCs) are recognized as direct seeds of metastasis. However, CTC count may not be the "best" indicator of metastatic risk because their heterogeneity is generally neglected. In this study, we develop a molecular typing system to predict colorectal cancer metastasis potential based on the metabolic fingerprints of single CTCs. After identification of the metabolites potentially related to metastasis using mass spectrometry-based untargeted metabolomics, setup of a home-built single-cell quantitative mass spectrometric platform for target metabolite analysis in individual CTCs and use of a machine learning method composed of non-negative matrix factorization and logistic regression, CTCs are divided into two subgroups, C1 and C2, based on a 4-metabolite fingerprint. Both in vitro and in vivo experiments demonstrate that CTC count in C2 subgroup is closely associated with metastasis incidence. This is an interesting report on the presence of a specific population of CTCs with distinct metastatic potential at the single-cell metabolite level.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Nanocapillary sampling coupled to liquid chromatography mass spectrometry delivers single cell drug measurement and lipid fingerprints

This work describes the development of a new approach to measure drug levels and lipid fingerprints in single living mammalian cells. Nanocapillary sampling is an approach that enables the selection and isolation of single living cells under microscope observation. Here, live single cell nanocapillary sampling is coupled to liquid chromatography for the first time. This allows molecular species to be separated prior to ionisation and improves measurement precision of drug analytes. The efficiency of transferring analytes from the sampling capillary into a vial was optimised in this work. The analysis was carried out using standard flow liquid chromatography coupled to widely available mass spectrometry instrumentation, highlighting opportunities for widespread adoption. The method was applied to 30 living cells, revealing cell-to-cell heterogeneity in the uptake of different drug molecules. Using this system, we detected 14-158 lipid features per single cell, revealing the association between bedaquiline uptake and lipid fingerprints.

Recent Advances in Methods for Circulating Tumor Cell Detection

Circulating tumor cells (CTCs) are released from primary tumors and transported through the body via blood or lymphatic vessels before settling to form micrometastases under suitable conditions. Accordingly, several studies have identified CTCs as a negative prognostic factor for survival in many types of cancer. CTCs also reflect the current heterogeneity and genetic and biological state of tumors; so, their study can provide valuable insights into tumor progression, cell senescence, and cancer dormancy. Diverse methods with differing specificity, utility, costs, and sensitivity have been developed for isolating and characterizing CTCs. Additionally, novel techniques with the potential to overcome the limitations of existing ones are being developed. This primary literature review describes the current and emerging methods for enriching, detecting, isolating, and characterizing CTCs.

Biology, vulnerabilities and clinical applications of circulating tumour cells

In recent years, exceptional technological advances have enabled the identification and interrogation of rare circulating tumour cells (CTCs) from blood samples of patients, leading to new fields of research and fostering the promise for paradigm-changing, liquid biopsy-based clinical applications. Analysis of CTCs has revealed distinct biological phenotypes, including the presence of CTC clusters and the interaction between CTCs and immune or stromal cells, impacting metastasis formation and providing new insights into cancer vulnerabilities. Here we review the progress made in understanding biological features of CTCs and provide insight into exploiting these developments to design future clinical tools for improving the diagnosis and treatment of cancer.

Development of a 3D-Printed Ionization Source for Single-Cell Analysis

Understanding the physiologies and pathologies of diseases requires a thorough understanding of metabolic heterogeneity in cells. This technical note presents a 3D printing technology for manufacturing an ionization source that is specially adapted for mass spectrometry-based single-cell analysis. This all-in-one 3D-printed electrospray ionization source integrates the sample introduction, metabolite extraction, and ionization into one device, simplifying the process of single-cell analysis and improving the reproducibility of the measurement. We successfully used it for high-throughput analysis of three types of cancer cells (around 17 cells/min) and used the t-distributed stochastic neighbor embedding algorithm to distinguish different cell types based on detected metabolites. By simply adjusting the printing parameters of the 3D-printed ionization source, it can be applied to cells with different sizes. The proposed 3D-printed ionization source promises to open new possibilities for single-cell analysis.

Microfluidic device integrating single-cell extraction and electrical lysis for mass spectrometry detection of intracellular compounds

Analysis of cellular composition and metabolism at a single-cell resolution allows gaining more information about complex relationships of cells within tissues or whole living organisms by resolving the variance stemming from the cellular heterogeneity. Mass spectrometry (MS) is a perfect analytical tool satisfying the demanding requirements of detecting and identifying compounds present in such ultralow-volume samples of high chemical complexity. However, the method of sampling and sample ionization is crucial in obtaining relevant information. In this work, we present a microfluidic sampling platform that integrates single-cell extraction from MS-incompatible media with electrical cell lysis and nanoESI-MS analysis of human erythrocytes. Hemoglobin alpha and beta chains (300 amol/cell) were successfully identified in mass spectra of single-erythrocyte lysates.

Mass spectrometry-based strategies for single-cell metabolomics

Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.

Metabolomic analysis of circulating tumor cells derived liver metastasis of colorectal cancer

Metabolic reprogramming is one of the essential features of tumor that may dramatically contribute to metastasis and collapse. The metabolic profiling is investigated on the patient derived tissue and cancer cell line derived mouse metastasis xenograft. As well-recognized "seeds" for remote metastasis of tumor, role of circulating tumor cells (CTCs) in the study of metabolic reprogramming feature of tumor is yet to be elucidated. More specifically, whether there is difference of metabolic features of liver metastasis in colorectal cancer (CRC) derived from either CTCs or cancer cell line is still unknown. In this study, comprehensive untargeted metabolomics was performed using high performance liquid chromatography-mass spectrometry (HPLC-MS) in liver metastasis tissues from CT26 cells and CTCs derived mouse models. We identified 288 differential metabolites associated with the pathways such as one carbon pool by folate, folate biosynthesis and histidine metabolism through bioinformation analysis. Multiple gene expression was upregulated in the CTCs derived liver metastasis, specifically some specific enzymes. These results indicated that the metabolite phenotype and corresponding gene expression in the CTCs derived liver metastasis tissues was different from the parental CT26 cells, displaying a specific up-regulation of mRNAs involved in the above metabolism-related pathways. The metabolic profile of CTCs was characterized on the liver metastatic process in colorectal cancer. The invasion ability and chemo drug tolerance of the CTCs derived tumor and metastasis was found to be overwhelming higher than cell line derived counterpart. Identification of the differential metabolites will lead to a better understanding of the hallmarks of the cancer progression and metastasis, which may suggest potential attractive target for treating metastatic CRC.

Single-cell mass spectrometry

Owing to recent advances in mass spectrometry (MS), tens to hundreds of proteins, lipids, and small molecules can be measured in single cells. The ability to characterize the molecular heterogeneity of individual cells is necessary to define the full assortment of cell subtypes and identify their function. We review single-cell MS including high-throughput, targeted, mass cytometry-based approaches and antibody-free methods for broad profiling of the proteome and metabolome of single cells. The advantages and disadvantages of different methods are discussed, as well as the challenges and opportunities for further improvements in single-cell MS. These methods is being used in biomedicine in several applications including revealing tumor heterogeneity and high-content drug screening.

Single cell metabolism: current and future trends

Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.

Clinical application of advanced multi-omics tumor profiling: Shaping precision oncology of the future

Next-generation DNA sequencing technology has dramatically advanced clinical oncology through the identification of therapeutic targets and molecular biomarkers, leading to the personalization of cancer treatment with significantly improved outcomes for many common and rare tumor entities. More recent developments in advanced tumor profiling now enable dissection of tumor molecular architecture and the functional phenotype at cellular and subcellular resolution. Clinical translation of high-resolution tumor profiling and integration of multi-omics data into precision treatment, however, pose significant challenges at the level of prospective validation and clinical implementation. In this review, we summarize the latest advances in multi-omics tumor profiling, focusing on spatial genomics and chromatin organization, spatial transcriptomics and proteomics, liquid biopsy, and ex vivo modeling of drug response. We analyze the current stages of translational validation of these technologies and discuss future perspectives for their integration into precision treatment.

Tracking metabolites at single-cell resolution reveals metabolic dynamics during plant mitosis

Analyzing only one cell allows the changes and characteristics of intracellular metabolites during the chromosome segregation process to be precisely captured and mitotic sub-phases to be dissected at the metabolite level.

Capillary microsampling-based single-cell metabolomics by mass spectrometry and its applications in medicine and drug discovery

Characterization of cellular metabolic states is a technical challenge in biomedicine. Cellular heterogeneity caused by inherent diversity in expression of metabolic enzymes or due to sensitivity of metabolic reactions to perturbations, necessitates single cell analysis of metabolism. Heterogeneity is typically seen in cancer and thus, single-cell metabolomics is expectedly useful in studying cancer progression, metastasis, and variations in cancer drug response. However, low sample volumes and analyte concentrations limit detection of critically important metabolites. Capillary microsampling-based mass spectrometry approaches are emerging as a promising solution for achieving single-cell omics. Herein, we focus on the recent advances in capillary microsampling-based mass spectrometry techniques for single-cell metabolomics. We discuss recent technical developments and applications to cancer medicine and drug discovery.

Single cell mass spectrometry analysis of drug-resistant cancer cells: Metabolomics studies of synergetic effect of combinational treatment

Irinotecan (IRI), a topoisomerase I inhibitor blocking DNA synthesis, is a widely used chemotherapy drug for metastatic colorectal cancer. Despite being an effective chemotherapy drug, its clinical effectiveness is limited by both intrinsic and acquired drug resistance. Previous studies indicate IRI induces cancer stemness in irinotecan-resistant (IRI-resistant) cells. Metformin, an oral antidiabetic drug, was recently reported for anticancer effects, likely due to its selective killing of cancer stem cells (CSCs). Given IRI-resistant cells exhibiting high cancer stemness, we hypothesize metformin can sensitize IRI-resistant cells and rescue the therapeutic effect. In this work, we utilized the Single-probe mass spectrometry technique to analyze live IRI-resistant cells under different treatment conditions. We discovered that metformin treatment was associated with the downregulation of lipids and fatty acids, potentially through the inhibition of fatty acid synthase (FASN). Importantly, certain species can be only detected from cells in their living status. The level of synergistic effect of metformin and IRI in their co-treatment of IRI-resistant cells was evaluated using Chou-Talalay combinational index. Using enzymatic activity assay, we determined that the co-treatment exhibit the highest FASN inhibition compared with the mono-treatment of IRI or metformin. To our knowledge, this is the first single-cell MS metabolomics study demonstrating metformin-IRI synergistic effect overcoming drug resistance in IRI-resistant cells.

Is Tissue Still the Issue? The Promise of Liquid Biopsy in Uveal Melanoma

Uveal melanoma (UM) is the second most frequent type of melanoma. Therapeutic options for UM favor minimally invasive techniques such as irradiation for vision preservation. As a consequence, no tumor material is obtained. Without available tissue, molecular analyses for gene expression, mutation or copy number analysis cannot be performed. Thus, proper patient stratification is impossible and patients’ uncertainty about their prognosis rises. Minimally invasive techniques have been studied for prognostication in UM. Blood-based biomarker analysis has become more common in recent years; however, no clinically standardized protocol exists. This review summarizes insights in biomarker analysis, addressing new insights in circulating tumor cells, circulating tumor DNA, extracellular vesicles, proteomics, and metabolomics. Additionally, medical imaging can play a significant role in staging, surveillance, and prognostication of UM and is addressed in this review. We propose that combining multiple minimally invasive modalities using tumor biomarkers should be the way forward and warrant more attention in the coming years.

Hybrid Ionization Source Combining Nanoelectrospray and Dielectric Barrier Discharge Ionization for the Simultaneous Detection of Polar and Nonpolar Compounds in Single Cells

Single-cell metabolomics is expected to deliver fast and dynamic information on cell function; therefore, it requires rapid analysis of a wide variety of very small quantities of metabolites in living cells. In this work, a hybrid ionization source that combines nanoelectrospray ionization (nanoESI) and dielectric barrier discharge ionization (DBDI) is proposed for single-cell analysis. A capillary with a 1 μm i.d. tip was inserted into cells for sampling and then directly used as the nanoESI source for ionization of polar metabolites. In addition, a DBDI source was employed as a post-ionization source to improve the ionization of apolar metabolites in cells that are not easily ionized by ESI. By increasing the voltage of the DBDI source from 0 to 3.2 kV, the classes of detected metabolites can be shifted from mostly polar to both polar and apolar to mainly apolar. Plant cells (onion) and human cells (PANC-1) were investigated in this study. After optimization, 50 compounds in onion cells and 40 compounds in PANC-1 cells were observed in ESI mode (3.5 kV) and an additional 49 compounds in onion cells and 73 compounds in PANC-1 cells were detected in ESI (3.5 kV)-DBDI (2.6 kV) hybrid mode. This hybrid ionization source improves the coverage, ionization efficiency, and limit of detection of metabolites with different polarities and could potentially contribute to the fast-growing field of single-cell metabolomics.

The Development of Single-Cell Metabolism and Its Role in Studying Cancer Emergent Properties

Metabolic reprogramming is one of the hallmarks of malignant tumors, which provides energy and material basis for tumor rapid proliferation, immune escape, as well as extensive invasion and metastasis. Blocking the energy and material supply of tumor cells is one of the strategies to treat tumor, however tumor cell metabolic heterogeneity prevents metabolic-based anti-cancer treatment. Therefore, searching for the key metabolic factors that regulate cell cancerous change and tumor recurrence has become a major challenge. Emerging technology––single-cell metabolomics is different from the traditional metabolomics that obtains average information of a group of cells. Single-cell metabolomics identifies the metabolites of single cells in different states by mass spectrometry, and captures the molecular biological information of the energy and substances synthesized in single cells, which provides more detailed information for tumor treatment metabolic target screening. This review will combine the current research status of tumor cell metabolism with the advantages of single-cell metabolomics technology, and explore the role of single-cell sequencing technology in searching key factors regulating tumor metabolism. The addition of single-cell technology will accelerate the development of metabolism-based anti-cancer strategies, which may greatly improve the prognostic survival rate of cancer patients.

Circulating tumor cells in colorectal cancer in the era of precision medicine

Colorectal cancer (CRC) is one of the main causes of cancer-related morbidity and mortality across the globe. Although serum biomarkers such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19–9 (CA-199) have been prevalently used as biomarkers in various cancers, they are neither very sensitive nor highly specific. Repeated tissue biopsies at different times of the disease can be uncomfortable for cancer patients. Additionally, the existence of tumor heterogeneity and the results of local biopsy provide limited information about the overall tumor biology. Against this backdrop, it is necessary to look for reliable and noninvasive biomarkers of CRC. Circulating tumor cells (CTCs), which depart from a primary tumor, enter the bloodstream, and imitate metastasis, have a great potential for precision medicine in patients with CRC. Various efficient CTC isolation platforms have been developed to capture and identify CTCs. The count of CTCs, as well as their biological characteristics and genomic heterogeneity, can be used for the early diagnosis, prognosis, and prediction of treatment response in CRC. This study reviewed the existing CTC isolation techniques and their applications in the clinical diagnosis and treatment of CRC. The study also presented their limitations and provided future research directions.

Detection and clinical significance of circulating tumor cells in colorectal cancer

Histopathological examination (biopsy) is the "gold standard" for the diagnosis of colorectal cancer (CRC). However, biopsy is an invasive method, and due to the temporal and spatial heterogeneity of the tumor, a single biopsy cannot reveal the comprehensive biological characteristics and dynamic changes of the tumor. Therefore, there is a need for new biomarkers to improve CRC diagnosis and to monitor and treat CRC patients. Numerous studies have shown that "liquid biopsy" is a promising minimally invasive method for early CRC detection. A liquid biopsy mainly samples circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), microRNA (miRNA) and extracellular vesicles (EVs). CTCs are malignant cells that are shed from the primary tumors and/or metastases into the peripheral circulation. CTCs carry information on both primary tumors and metastases that can reflect dynamic changes in tumors in a timely manner. As a promising biomarker, CTCs can be used for early disease detection, treatment response and disease progression evaluation, disease mechanism elucidation, and therapeutic target identification for drug development. This review will discuss currently available technologies for plasma CTC isolation and detection, their utility in the management of CRC patients and future research directions.

Whole-Exome Sequencing on Circulating Tumor Cells Explores Platinum-Drug Resistance Mutations in Advanced Non-small Cell Lung Cancer

Circulating tumor cells (CTCs) have important applications in clinical practice on early tumor diagnosis, prognostic prediction, and treatment evaluation. Platinum-based chemotherapy is a fundamental treatment for non-small cell lung cancer (NSCLC) patients who are not suitable for targeted drug therapies. However, most patients progressed after a period of treatment. Therefore, revealing the genetic information contributing to drug resistance and tumor metastasis in CTCs is valuable for treatment adjustment. In this study, we enrolled nine NSCLC patients with platinum-based chemotherapy resistance. For each patient, 10 CTCs were isolated when progression occurred to perform single cell-level whole-exome sequencing (WES). Meanwhile the patients' paired primary-diagnosed formalin-fixed and paraffin-embedded samples and progressive biopsy specimens were also selected to perform WES. Comparisons of distinct mutation profiles between primary and progressive specimens as well as CTCs reflected different evolutionary mechanisms between CTC and lymph node metastasis, embodied in a higher proportion of mutations in CTCs shared with paired progressive lung tumor and hydrothorax specimens (4.4-33.3%) than with progressive lymphatic node samples (0.6-11.8%). Functional annotation showed that CTCs not only harbored cancer-driver gene mutations, including frequent mutations of EGFR and TP53 shared with primary and/or progressive tumors, but also particularly harbored cell cycle-regulated or stem cell-related gene mutations, including SHKBP1, NUMA1, ZNF143, MUC16, ORC1, PON1, PELP1, etc., most of which derived from primary tumor samples and played crucial roles in chemo-drug resistance and metastasis for NSCLCs. Thus, detection of genetic information in CTCs is a feasible strategy for studying drug resistance and discovering new drug targets when progressive tumor specimens were unavailable.