Single-cell Heterogeneity Research Articles

Insights from the single-cell level: lineage trajectory and somatic-germline interactions during spermatogenesis in dwarf surfclam Mulinia lateralis

BackgroundSpermatogenesis is a complex process of cellular differentiation that commences with the division of spermatogonia stem cells, ultimately resulting in the production of functional spermatozoa. However, a substantial gap remains in our understanding of the molecular mechanisms and key driver genes that underpin this process, particularly in invertebrates. The dwarf surfclam (Mulinia lateralis) is considered an optimal bivalve model due to its relatively short generation time and ease of breeding in laboratory settings.ResultsIn this study, over 4,600 testicular cells from various samples were employed to identify single-cell heterogeneity on a more comprehensive scale. The four germ cell populations (spermatogonia, primary spermatocytes, secondary spermatocytes, and round spermatids/spermatozoa) and three somatic populations (follicle cell, hemocyte, and nerve cell) were characterized. The four types of germ cells exhibited disparate cell cycle statuses and an uninterrupted developmental trajectory, progressing from spermatogonia to spermatids/spermatozoa. Pseudotime analysis indicates that gene expression, translation, ATP metabolic process, and microtubule-based process are involved in the transition of germ cell types. Weighted gene coexpression network analysis (WGCNA) identified four modules corresponding to the four types of germ cells, as well as key transcription factors (e.g., MYC, SREBF1, SOXH) that may play a critical role in these cell types. Furthermore, our findings revealed that there is extensive bidirectional communication between the somatic cells and the germline cells, including the FGF and TGF-β signaling pathways, as well as other ligand-receptor pairs, such as NTN1-NEO1 and PLG-PLGRKT.ConclusionsThis study provides a comprehensive single-cell transcriptome landscape of the gonad, which will contribute to the understanding of germ cell fate transition during spermatogenesis, and the development of germ cell manipulation technologies in mollusks.

Highly multiplexed spatial transcriptomics in bacteria.

Single-cell decisions made in complex environments underlie many bacterial phenomena. Image-based transcriptomics approaches offer an avenue to study such behaviors, yet these approaches have been hindered by the massive density of bacterial messenger RNA. To overcome this challenge, we combined 1000-fold volumetric expansion with multiplexed error-robust fluorescence in situ hybridization (MERFISH) to create bacterial-MERFISH. This method enables high-throughput, spatially resolved profiling of thousands of operons within individual bacteria. Using bacterial-MERFISH, we dissected the response of Escherichia coli to carbon starvation, systematically mapped subcellular RNA organization, and charted the adaptation of a gut commensal Bacteroides thetaiotaomicron to micrometer-scale niches in the mammalian colon. We envision that bacterial-MERFISH will be broadly applicable to the study of bacterial single-cell heterogeneity in diverse, spatially structured, and native environments.

Multichrome encoding-based multiplexed, spatially resolved imaging reveals single-cell RNA epigenetic modifications heterogeneity

Understanding the heterogeneity of epigenetic modifications within single cells is pivotal for unraveling the nature of the complexity of gene expression and cellular function. In this study, we have developed a strategy based on multichrome encoding and “AND” Boolean logic recognition for multiplexed, spatially resolved imaging of single-cell RNA epigenetic modifications, termed as PRoximity Exchange-assisted Encoding of Multichrome (PREEM). Through the implementation of this strategy, we can now map the expression and nuclear distribution of multiple site-specific RNA N6-methyladenosine (m6A) modifications at the single-molecule resolution level in single-cells, and reveal the previously unknown heterogeneity. Notably, we demonstrate how these patterns change after treatment with various drugs. Moreover, cyclic imaging with tailed DNA self-assembly further suggest the scalability and adaptability of PREEM’s design. As an innovative epigenetic modification imaging tool, PREEM not only broadens the horizons of single-cell epigenetics research, enabling joint analysis of multiple targets beyond the limitations of imaging channels, but also reveals cell-to-cell variability, thereby enhancing our capacity to explore cellular functions.

Single-Cell Heterogeneity of EGFR Pathway Is Linked to Unique Signatures of Drug Response and Malignancy in Patient Derived Glioblastoma Stem Cells.

In glioblastoma, the therapeutically intractable and resistant phenotypes can be derived from glioma stem cells, which often have different underlying mechanisms from non-stem glioma cells. Aberrant signaling across the EGFR-PTEN-AKT-mTOR pathways have been shown as common drivers of glioblastoma. Revealing the inter and intra-cellular heterogeneity within glioma stem cell populations in relations to signaling patterns through these pathways may be key to precision diagnostic and therapeutic targeting of these cells. Single cell parallel proteomic heterogeneity profiling of the EGFR-PTEN-AKT-mTOR pathways was conducted in a panel of fifteen glioma stem cell models derived from patient glioblastoma biopsies. The analysis included 59,464 data points from 14,866 cells and identified forty-nine molecularly distinct signaling phenotypes. High content bioinformatics resolved two unique patient clusters diverging on EGFR expression and AKT/TORC1 activation. Phenotypic validation indicated drug responsive phenotypes to EGFR blocking in the high EGFR expressing cluster with lower tumor initiating potential in comparison to the AKT/TORC1 activated cluster. High EGFR expression trended with improved patient prognosis while AKT/TORC1 activated samples trended with poorer patient outcomes. Genetic heterogeneity was observed in both clusters with proneural, classical and mesenchymal subtypes observed. Quantitative single cell heterogeneity profiling reveals divergent EGFR-PTEN-AKT-mTOR pathways of patient derived glioma stem cells, which would inform future research and personalized therapeutic strategies.

Single-cell Organelle Extraction with Cellular Indexing.

Bulk methods to fractionate organelles lack the resolution to capture single-cell heterogeneity. While microfluidic approaches attempt to fractionate organelles at the cellular level, they fail to map each organelle back to its cell of origin-crucial for multiomics applications. To address this, we developed VacTrap, a high-throughput microfluidic device for isolating and spatially indexing single nuclei from mammalian cells. VacTrap consists of three aligned layers: (1) a Bis-gel microwells layer with a 'trapdoors' (BAC-gel) base, fabricated atop a through-hole glass slide; (2) a PDMS microwell layer to receive transferred nuclei; and (3) a vacuum manifold. VacTrap operation begins with cell lysis using DDF to release intact nuclei into the Bis-gel microwells, while cytoplasmic proteins are electrophoresed into the Bis-gel. Upon exposure to DTT and vacuum force, the trapdoors open, allowing nuclei to transfer to the PDMS microwells. VacTrap dissolves the trapdoors within 3-5 minutes and achieve synchronized nuclei transfer with 98% efficiency across 80% of trapdoors in a 256-microwell array, surpassing the <1% efficiency of passive transfer without vacuum. Morphology analysis confirmed preservation of organelle integrity throughout VacTrap operation. By enabling spatial indexing of nuclei back to their original cell, VacTrap provides a robust, high-throughput tool for single-cell multiomics applications.

Single-Cell Isolation Chip Integrated with Multicolor Barcode Array for High-Throughput Single-Cell Exosome Profiling in Tissue Samples.

Exosomes, functional biomarkers involved in cancer progression, have gained widespread attention for promoting tumor formation, growth, and metastasis. Current bulk exosome detections in bodily fluids enable cancer functional analysis, but average secretion levels from cell populations, losing parent cell information and ignoring exosome heterogeneity from diverse cell subgroups, necessitating an effective platform for analyzing single-cell exosome functional heterogeneity. Here, a high-throughput platform is presented, capable of efficient single-cell isolation and multi-color exosome phenotype analysis, as well as quantifying trace exosomes secreted by single cells. Photothermal-driven single-cell chips achieve significant single-cell isolation efficiency (≈97%) within 5 min, facilitating the ultra-high throughput single-cell exosome analysis. By conducting mass spectrometry and protein interaction of breast cancer exosome phenotypic proteins, key exosome phenotypes are identified. Tens of thousands of single cells from breast cancer cell lines, and clinical tissues are analyzed, revealing various subgroup differences. The study finds more CD44 and EGFR co-expressing exosome subgroups in breast cancer cell lines, while immune-evasion PD-L1 high-phenotype exosome subgroups are primarily presented in complex tumor microenvironments, especially in HER2-positive tissues. This platform offers powerful single-cell isolation, exosome quantification, and phenotypic analysis capabilities, making it a powerful tool for advancing single-cell exosome analysis in cancer research.

Visualization of Endogenous RNA G-Quadruplex in Individual Genes in Single Cells.

Single-cell RNA imaging enables the detection of endogenous RNA and the analysis of its abundance, localization, and single-cell heterogeneity, which is powerful in the study of RNA biology. G-quadruplex (G4) is a kind of nucleic secondary structure and plays important roles in various biological processes. To reveal the regulating mechanism of monogenic G4s in a specific biological pathway, it is of great important to visualize endogenous RNA G4 in individual genes. Here, we describe Module Assembled Multifunctional Probes Assay (MAMPA) for single-cell visualization of monogenic RNA G4. This method works well in various cell lines and various RNA G4 analytes and has a low detection limit at ten copies per cell.

Lensless On-Chip Chemiluminescence Imaging for High-Throughput Single-Cell Heterogeneity Analysis.

High-throughput single-cell heterogeneity imaging and analysis is essential for understanding complex biological systems and for advancing personalized precision disease diagnosis and treatment. Here, we present a miniaturized lensless chemiluminescence chip for high-throughput single-cell functional imaging with subcellular resolution. With the sensitive chemiluminescence sensing and wide field of view of contact lensless imaging, we demonstrated the chemiluminescent imaging of over 1000 single cells, and their membrane glycoprotein and the high-throughput single-cell heterogeneity of membrane protein imaging were examined for precision analysis. Furthermore, the functional adhesion and heterogeneity of single live cells were imaged and explored. This miniaturized lensless on-chip CL-CMOS imaging platform enables high-throughput single-cell imaging and analysis with high sensitivity and subcellular resolution, providing new techniques for the cellular study of biological heterogeneity and has potential application in precision disease diagnosis and treatment at the point-of-care settings.

MATES: a deep learning-based model for locus-specific quantification of transposable elements in single cell

Transposable elements (TEs) are crucial for genetic diversity and gene regulation. Current single-cell quantification methods often align multi-mapping reads to either ‘best-mapped’ or ‘random-mapped’ locations and categorize them at the subfamily levels, overlooking the biological necessity for accurate, locus-specific TE quantification. Moreover, these existing methods are primarily designed for and focused on transcriptomics data, which restricts their adaptability to single-cell data of other modalities. To address these challenges, here we introduce MATES, a deep-learning approach that accurately allocates multi-mapping reads to specific loci of TEs, utilizing context from adjacent read alignments flanking the TE locus. When applied to diverse single-cell omics datasets, MATES shows improved performance over existing methods, enhancing the accuracy of TE quantification and aiding in the identification of marker TEs for identified cell populations. This development facilitates the exploration of single-cell heterogeneity and gene regulation through the lens of TEs, offering an effective transposon quantification tool for the single-cell genomics community.

Single-cell heterogeneity in ribosome content and the consequences for the growth laws.

Across species and environments, the ribosome content of cell populations correlates with population growth rate. The robustness and universality of this correlation have led to its classification as a "growth law." This law has fueled theories about how evolution selects for microbial organisms that maximize their growth rate based on nutrient availability, and it has informed models about how individual cells regulate their growth rates and ribosomal content. However, due to methodological limitations, this growth law has rarely been studied at the level of individual cells. While populations of fast-growing cells tend to have more ribosomes than populations of slow-growing cells, it is unclear whether individual cells tightly regulate their ribosome content to match their environment. Here, we employ recent groundbreaking single-cell RNA sequencing techniques to study this growth law at the single-cell level in two different microbes, S. cerevisiae (a single-celled yeast and eukaryote) and B. subtilis (a bacterium and prokaryote). In both species, we observe significant variation in the ribosomal content of single cells that is not predictive of growth rate. Fast-growing populations include cells exhibiting transcriptional signatures of slow growth and stress, as do cells with the highest ribosome content we survey. Broadening our focus to non-ribosomal transcripts reveals subpopulations of cells in unique transcriptional states suggestive that they have evolved to do things other than maximize their rate of growth. Overall, these results indicate that single-cell ribosome levels are not finely tuned to match population growth rates or nutrient availability and cannot be predicted by a Gaussian process model that assumes measurements are sampled from a normal distribution centered on the population average. This work encourages the expansion of growth law and other models that predict how growth rates are regulated or how they evolve to consider single-cell heterogeneity. To this end, we provide extensive data and analysis of ribosomal and transcriptomic variation across thousands of single cells from multiple conditions, replicates, and species.

Dissecting the Single-Cell Diversity and Heterogeneity Underlying Cervical Precancerous Lesions and Cancer Tissues.

The underlying cellular diversity and heterogeneity from cervix precancerous lesions to cervical squamous cell carcinoma (CSCC) is investigated. Four single-cell datasets including normal tissues, normal adjacent tissues, precancerous lesions, and cervical tumors were integrated to perform disease stage analysis. Single-cell compositional data analysis (scCODA) was utilized to reveal the compositional changes of each cell type. Differentially expressed genes (DEGs) among cell types were annotated using BioCarta. An assay for transposase-accessible chromatin sequencing (ATAC-seq) analysis was performed to correlate epigenetic alterations with gene expression profiles. Lastly, a logistic regression model was used to assess the similarity between the original and new cohort data (HRA001742). After global annotation, seven distinct cell types were categorized. Eight consensus-upregulated DEGs were identified in B cells among different disease statuses, which could be utilized to predict the overall survival of CSCC patients. Inferred copy number variation (CNV) analysis of epithelial cells guided disease progression classification. Trajectory and ATAC-seq integration analysis identified 95 key transcription factors (TF) and one immunohistochemistry (IHC) testified key-node TF (YY1) involved in epithelial cells from CSCC initiation to progression. The consistency of epithelial cell subpopulation markers was revealed with single-cell sequencing, bulk sequencing, and RT-qPCR detection. KRT8 and KRT15, markers of Epi6, showed progressively higher expression with disease progression as revealed by IHC detection. The logistic regression model testified the robustness of the resemblance of clusters among the various datasets utilized in this study. Valuable insights into CSCC cellular diversity and heterogeneity provide a foundation for future targeted therapy.

Clinical cell-surface targets in metastatic and primary solid cancers.

Therapies against cell-surface targets (CSTs) represent an emerging treatment class in solid malignancies. However, high-throughput investigations of CST expression across cancer types have been reliant on data sets of mostly primary tumors, despite therapeutic use most commonly in metastatic disease. We identified a total of 818 clinical trials of CST therapies with 78 CSTs. We assembled a data set spanning RNA-seq and microarrays in 7,927 benign samples, 16,866 primary tumor samples, and 6,124 metastatic tumor samples. We also utilized single-cell RNA-seq data from 36 benign tissues and 558 primary and metastatic tumor samples, and matched RNA versus protein expression in 29 benign tissue samples, 1,075 tumor samples, and 942 cell lines. High RNA expression accurately predicted high protein expression across CST therapies in benign tissues, tumor samples, and cell lines. We compared metastatic versus primary tumor expression, identified potential opportunities for repositioning, and matched cell lines to tumor types based on CST and global RNA expression. We evaluated single-cell heterogeneity across tumors, and identified rare normal cell subpopulations that may contribute to toxicity. Finally, we identified combinations of CST therapies for which bispecific approaches could improve tumor specificity. This study helps better define the landscape of CST expression in metastatic and primary cancers.

Multi-Zone Visco-Node-Pore Sensing: A Microfluidic Platform for Multi-Frequency Viscoelastic Phenotyping of Single Cells.

This study introduces multi-zone visco-Node-Pore Sensing (mz-visco-NPS), an electronic-based microfluidic platform for single-cell viscoelastic phenotyping. mz-visco-NPS implements a series of sinusoidal-shaped contraction zones that periodically deform a cell at specific strain frequencies, leading to changes in resistance across the zones that correspond to the cell's frequency-dependent elastic G' and viscous G″ moduli. mz-visco-NPS is validated by measuring the viscoelastic changes of MCF-7 cells when their cytoskeleton is disrupted. mz-visco-NPS is also employed to measure the viscoelastic properties of human mammary epithelial cells across the entire continuum of epithelial transformation states, from average- and high-risk primary epithelial cells, to immortal non-malignant (MCF-10A), malignant (MCF-7), and metastatic (MDA-MB-231) cell lines. With a throughput of 600 cells per hour and demonstrated ease-of-use, mz-visco-NPS reveals a remarkable level of single-cell heterogeneity that would otherwise be masked by ensemble averaging.

Abstract A039: Repeat RNA mediated disruption of cellular plasticity in pancreatic cancer

Abstract Aberrant expression of repeat RNAs in pancreatic ductal adenocarcinoma (PDAC) mimics viral-like responses with implications for tumor cell state and the surrounding microenvironment. To better understand the relationship of repeat RNAs in human PDAC, we employed the NanoString CosMx™ spatial molecular imaging (SMI) platform, which utilizes a 1,000-plex RNA panel with custom repeat RNA probes targeting long interspersed nuclear element 1 (LINE-1) retrotransposon ORF1 and ORF2, HSATII satellite (SAT) repeat, and two human endogenous retroviruses (HERV-K and HERV-H) in 46 primary tumors. This analysis revealed correlations of high repeat RNA expression with alterations in the epithelial state of PDAC cells and the myofibroblast phenotype in cancer-associated fibroblasts (CAFs). The loss of cellular identity observed with dosing of extracellular vesicles (EVs) and individual repeat RNAs in PDAC and CAF cell culture models points to cell-cell intercommunication involving these viral-like elements. Differences in the PDAC and CAF response are driven by distinct innate immune signaling pathways through interferon regulatory transcription factor 3 (IRF3). Altogether, our data indicate that cell context-specific viral-like responses driven by tumor cells have broad impacts on single-cell heterogeneity within cancer cells and the pancreatic cancer microenvironment. Citation Format: Eunae You, Patrick Danaher, Chenyue Lu, Siyu Sun, Luli Zou, Ildiko Phillips, Alexandra Rojas, Natalie Ho, Yuhui Song, Michael Raabe, Katherine Xu, Peter Richieri, Hao Li, Natalie Aston, Rebecca Porter, Bidish Patel, Linda Nieman, Nathan Schurman, Briana Hudson, Khrystyna North, Sarah Church, Vikram Deshpande, Andrew Liss, Tae Kim, Yi Cui, Youngmi Kim, Benjamin Greenbaum, Martin Aryee, David Ting. Repeat RNA mediated disruption of cellular plasticity in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A039.

Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer

While heterogeneity is a key feature of cancer, understanding metabolic heterogeneity at the single-cell level remains a challenge. Here we present 13C-SpaceM, a method for spatial single-cell isotope tracing that extends the previously published SpaceM method with detection of 13C6-glucose-derived carbons in esterified fatty acids. We validated 13C-SpaceM on spatially heterogeneous models using liver cancer cells subjected to either normoxia-hypoxia or ATP citrate lyase depletion. This revealed substantial single-cell heterogeneity in labelling of the lipogenic acetyl-CoA pool and in relative fatty acid uptake versus synthesis hidden in bulk analyses. Analysing tumour-bearing brain tissue from mice fed a 13C6-glucose-containing diet, we found higher glucose-dependent synthesis of saturated fatty acids and increased elongation of essential fatty acids in tumours compared with healthy brains. Furthermore, our analysis uncovered spatial heterogeneity in lipogenic acetyl-CoA pool labelling in tumours. Our method enhances spatial probing of metabolic activities in single cells and tissues, providing insights into fatty acid metabolism in homoeostasis and disease.

ScEpiAge: an age predictor highlighting single-cell ageing heterogeneity in mouse blood

Ageing is the accumulation of changes and decline of function of organisms over time. The concept and biomarkers of biological age have been established, notably DNA methylation-based clocks. The emergence of single-cell DNA methylation profiling methods opens the possibility of studying the biological age of individual cells. Here, we generate a large single-cell DNA methylation and transcriptome dataset from mouse peripheral blood samples, spanning a broad range of ages. The number of genes expressed increases with age, but gene-specific changes are small. We next develop scEpiAge, a single-cell DNA methylation age predictor, which can accurately predict age in (very sparse) publicly available datasets, and also in single cells. DNA methylation age distribution is wider than technically expected, indicating epigenetic age heterogeneity and functional differences. Our work provides a foundation for single-cell and sparse data epigenetic age predictors, validates their functionality and highlights epigenetic heterogeneity during ageing.

Circadian period is compensated for repressor protein turnover rates in single cells

Most mammalian cells have molecular circadian clocks that generate widespread rhythms in transcript and protein abundance. While circadian clocks are robust to fluctuations in the cellular environment, little is known about the mechanisms by which the circadian period compensates for fluctuating metabolic states. Here, we exploit the heterogeneity of single cells both in circadian period and a metabolic parameter-protein stability-to study their interdependence without the need for genetic manipulation. We generated cells expressing key circadian proteins (CRYPTOCHROME1/2 (CRY1/2) and PERIOD1/2 (PER1/2)) as endogenous fusions with fluorescent proteins and simultaneously monitored circadian rhythms and degradation in thousands of single cells. We found that the circadian period compensates for fluctuations in the turnover rates of circadian repressor proteins and uncovered possible mechanisms using a mathematical model. In addition, the stabilities of the repressor proteins are circadian phase dependent and correlate with the circadian period in a phase-dependent manner, in contrast to the prevailing model.

Single-cell tumor heterogeneity landscape of hepatocellular carcinoma: unraveling the pro-metastatic subtype and its interaction loop with fibroblasts

BackgroundTumor heterogeneity presents a formidable challenge in understanding the mechanisms driving tumor progression and metastasis. The heterogeneity of hepatocellular carcinoma (HCC) in cellular level is not clear.MethodsIntegration analysis of single-cell RNA sequencing data and spatial transcriptomics data was performed. Multiple methods were applied to investigate the subtype of HCC tumor cells. The functional characteristics, translation factors, clinical implications and microenvironment associations of different subtypes of tumor cells were analyzed. The interaction of subtype and fibroblasts were analyzed.ResultsWe established a heterogeneity landscape of HCC malignant cells by integrated 52 single-cell RNA sequencing data and 5 spatial transcriptomics data. We identified three subtypes in tumor cells, including ARG1+ metabolism subtype (Metab-subtype), TOP2A+ proliferation phenotype (Prol-phenotype), and S100A6+ pro-metastatic subtype (EMT-subtype). Enrichment analysis found that the three subtypes harbored different features, that is metabolism, proliferating, and epithelial-mesenchymal transition. Trajectory analysis revealed that both Metab-subtype and EMT-subtype originated from the Prol-phenotype. Translation factor analysis found that EMT-subtype showed exclusive activation of SMAD3 and TGF-β signaling pathway. HCC dominated by EMT-subtype cells harbored an unfavorable prognosis and a deserted microenvironment. We uncovered a positive loop between tumor cells and fibroblasts mediated by SPP1-CD44 and CCN2/TGF-β-TGFBR1 interaction pairs. Inhibiting CCN2 disrupted the loop, mitigated the transformation to EMT-subtype, and suppressed metastasis.ConclusionBy establishing a heterogeneity landscape of malignant cells, we identified a three-subtype classification in HCC. Among them, S100A6+ tumor cells play a crucial role in metastasis. Targeting the feedback loop between tumor cells and fibroblasts is a promising anti-metastatic strategy.

Highly Multiplexed Spatial Transcriptomics in Bacteria.

Single-cell decisions made in complex environments underlie many bacterial phenomena. Image-based transcriptomics approaches offer an avenue to study such behaviors, yet these approaches have been hindered by the massive density of bacterial mRNA. To overcome this challenge, we combine 1000-fold volumetric expansion with multiplexed error robust fluorescence in situ hybridization (MERFISH) to create bacterial-MERFISH. This method enables high-throughput, spatially resolved profiling of thousands of operons within individual bacteria. Using bacterial-MERFISH, we dissect the response of E. coli to carbon starvation, systematically map subcellular RNA organization, and chart the adaptation of a gut commensal B. thetaiotaomicron to micron-scale niches in the mammalian colon. We envision bacterial-MERFISH will be broadly applicable to the study of bacterial single-cell heterogeneity in diverse, spatially structured, and native environments.

Designing SERS nanotags for profiling overexpressed surface markers on single cancer cells: A review

This review focuses on the chemical design and the use of Surface-Enhanced Raman Scattering (SERS)-active nanotags for measuring surface markers that can be overexpressed at the surface of single cancer cells. Indeed, providing analytical tools with true single-cell measurements capabilities is capital, especially since cancer research is increasingly leaning toward single-cell analysis, either to guide treatment decisions or to understand complex tumor behaviour including the single-cell heterogeneity and the appearance of treatment resistance. Over the past two decades, SERS nanotags have triggered significant interest in the scientific community owing their advantages over fluorescent tags, mainly because SERS nanotags resist photobleaching and exhibit sharper signal bands, which reduces possible spectral overlap and enables the discrimination between the SERS signals and the autofluorescence background from the sample itself. The extensive efforts invested in harnessing SERS nanotags for biomedical purposes, particularly in cancer research, highlight their potential as the next generation of optical labels for single-cell studies. The review unfolds in two main parts. The first part focuses on the structure of SERS nanotags, detailing their chemical composition and the role of each building block of the tags. The second part explores applications in measuring overexpressed surface markers on single-cells. The latter encompasses studies using single nanotags, multiplexed measurements, quantitative information extraction, monitoring treatment responses, and integrating phenotype measurements with SERS nanotags on single cells isolated from complex biological matrices. This comprehensive review anticipates SERS nanotags to persist as a pivotal technology in advancing single-cell analytical methods, particularly in the context of cancer research and personalized medicine.