Single-cell Techniques Research Articles

The Role of Innate Immune Cells in Cardiac Injury and Repair: A Metabolic Perspective.

Recent technological advances have identified distinct subpopulations and roles of the cardiac innate immune cells, specifically macrophages and neutrophils. Studies on distinct metabolic pathways of macrophage and neutrophil in cardiac injury are expanding. Here, we elaborate on the roles of cardiac macrophages and neutrophils in concomitance with their metabolism in normal and diseased hearts. Single-cell techniques combined with fate mapping have identified the clusters of innate immune cell subpopulations present in the resting and diseased hearts. We are beginning to know about the presence of cardiac resident macrophages and their functions. Resident macrophages perform cardiac homeostatic roles, whereas infiltrating neutrophils and macrophages contribute to tissue damage during cardiac injury with eventual role in repair. Prior studies show that metabolic pathways regulate the phenotypes of the macrophages and neutrophils during cardiac injury. Profiling the metabolism of the innate immune cells, especially ofresident macrophages during chronic and acute cardiac diseases, can further the understanding of cardiac immunometabolism.

Cell Signaling in the Shoot Apical Meristem.

Distinct from animals, plants maintain organogenesis from specialized tissues termed meristems throughout life. In the shoot apex, the shoot apical meristem (SAM) produces all aerial organs, such as leaves, from its periphery. For this, the SAM needs to precisely balance stem cell renewal and differentiation, which is achieved through dynamic zonation of the SAM, and cell signaling within functional domains is key for SAM functions. The WUSCHEL-CLAVATA feedback loop plays a key role in SAM homeostasis, and recent studies have uncovered new components, expanding our understanding of the spatial expression and signaling mechanism. Advances in polar auxin transport and signaling have contributed to knowledge of the multifaceted roles of auxin in the SAM and organogenesis. Finally, single-cell techniques have expanded our understanding of the cellular functions within the shoot apex at single-cell resolution. In this review, we summarize the most up-to-date understanding of cell signaling in the SAM and focus on the multiple levels of regulation of SAM formation and maintenance.

Single-cell transcriptomic landscape of peripheral blood cells provides insights into adaptation of red-eared sliders (Trachemys scripta elegans).

Red-eared sliders (Trachemys scripta elegans), as one of the 100 most threatening aliens, have stronger immunity than the native species in response to environmental stress. Blood cells are an important component of immunity in the body. However, the blood cell researches of turtle are still in the traditional blood cell classification and morphological structure observation. Furthermore, turtle granulocytes cannot be accurately identified using traditional methods. Single-cell RNA sequencing techniques have been successfully implemented to study cells based on the mRNA expression patterns of each cell. The present study profiled the transcriptomes of peripheral blood cells in red-eared sliders to construct a single-cell transcriptional landscape of the different cell types and explored environmental adaptation mechanism from the perspective of hematology. All 14 transcriptionally distinct clusters (platelets, erythrocytes1, erythrocytes2, CSF1R monocytes, POF1B monocytes, neutrophils, GATA2high basophils, GATA2low basophils, CD4 T cells, CD7 T cells, B cells, ACKR4 cells, serotriflin cells, and ficolin cells) were identified in the peripheral blood cells of the red-eared sliders. In particular, a subtype of erythrocytes (erythrocytes1) that expressed immune signals was identified. Peripheral blood cells were grouped into three lineages: platelet, erythroid/lymphoid, and myeloid cell lineages. Furthermore, based on differentiation trajectory and up-regulated gene expression, ACKR4 cells were newly identified as lymphocytes, and serotriflin and ficolin cells as granulocytes. The single-cell transcriptional atlas of the peripheral blood cells in red-eared sliders provided in the present study will offer a comprehensive transcriptome reference for the exploration of physiological and pathological hematology in this species.

Progress and Challenges of Immunotherapy Predictive Biomarkers for Triple Negative Breast Cancer in the Era of Single-Cell Multi-Omics.

Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer with a poor prognosis. Despite conventional treatments, including surgery, radiation, and chemotherapy, the overall response rate to PD-1/PD-L1 immune checkpoint inhibitors remains low, with limited predictive significance from current biomarkers such as PD-L1 expression, tumor-infiltrating lymphocytes (TILs), and tumor mutational burden (TMB). To address this challenge, recent advancements in single-cell sequencing techniques have enabled deeper exploration of the highly complex and heterogeneous TNBC tumor microenvironment at the single-cell level, revealing promising TNBC predictive biomarkers for immune checkpoint inhibitors. In this review, we discuss the background, motivation, methodology, results, findings, and conclusion of multi-omics analyses that have led to the identification of these emerging biomarkers. Our review suggests that single-cell multi-omics analysis holds great promise for the identification of more effective biomarkers and personalized treatment strategies for TNBC patients.

Macrophage-Related Testicular Inflammation in Individuals with Idiopathic Non-Obstructive Azoospermia: A Single-Cell Analysis.

Male infertility is a global issue that seriously affects reproductive health. This study aimed to understand the underlying causes of idiopathic non-obstructive azoospermia (iNOA), which is a type of male infertility with unknown origins that accounts for 10-15% of cases. By using single-cell analysis techniques, we aimed to uncover the mechanisms of iNOA and gain insight into the cellular and molecular changes in the testicular environment. In this study, we performed bioinformatics analysis using scRNA-seq and microarray data obtained from the GEO database. The analysis included techniques such as pseudotime analysis, cell-cell communication, and hdWGCNA. Our study showed a significant difference between the iNOA and the normal groups, indicating a disorder in the spermatogenic microenvironment in iNOA. We observed a reduction in the proportion of Sertoli cells and blocked germ cell differentiation. Additionally, we found evidence of testicular inflammation related to macrophages and identified ODF2 and CABYR as potential biomarkers for iNOA.

Ultrasensitive proteomics depicted an in-depth landscape for the very early stage of mouse maternal-to-zygotic transition

Single-cell or low-input multi-omics techniques have revolutionized the study of pre-implantation embryo development. However, the single-cell or low-input proteomic research in this field is relatively underdeveloped because of the higher threshold of the starting material for mammalian embryo samples and the lack of hypersensitive proteome technology. In this study, a comprehensive solution of ultrasensitive proteome technology (CS-UPT) was developed for single-cell or low-input mouse oocyte/embryo samples. The deep coverage and high-throughput routes significantly reduced the starting material and were selected by investigators based on their demands. Using the deep coverage route, we provided the first large-scale snapshot of the very early stage of mouse maternal-to-zygotic transition, including almost 5,500 protein groups from 20 mouse oocytes or zygotes for each sample. Moreover, significant protein regulatory networks centered on transcription factors and kinases between the MII oocyte and 1-cell embryo provided rich insights into minor zygotic genome activation.

Decoding the Human Epidermal Complexity at Single-Cell Resolution.

The epidermis is one of the largest tissues in the human body, serving as a protective barrier. The basal layer of the epidermis, which consists of epithelial stem cells and transient amplifying progenitors, represents its proliferative compartment. As keratinocytes migrate from the basal layer to the skin surface, they exit the cell cycle and initiate terminal differentiation, ultimately generating the suprabasal epidermal layers. A deeper understanding of the molecular mechanisms and pathways driving keratinocytes' organization and regeneration is essential for successful therapeutic approaches. Single-cell techniques are valuable tools for studying molecular heterogeneity. The high-resolution characterization obtained with these technologies has identified disease-specific drivers and new therapeutic targets, further promoting the advancement of personalized therapies. This review summarizes the latest findings on the transcriptomic and epigenetic profiling of human epidermal cells, analyzed from human biopsy or after in vitro cultivation, focusing on physiological, wound healing, and inflammatory skin conditions.

The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI.

Growing evidence supports the critical role of tumour microenvironment (TME) in tumour progression, metastases, and treatment response. However, the in-situ interplay among various TME components, particularly between immune and tumour cells, are largely unknown, hindering our understanding of how tumour progresses and responds to treatment. While mainstream single-cell omics techniques allow deep, single-cell phenotyping, they lack crucial spatial information for in-situ cell-cell interaction analysis. On the other hand, tissue-based approaches such as hematoxylin and eosin and chromogenic immunohistochemistry staining can preserve the spatial information of TME components but are limited by their low-content staining. High-content spatial profiling technologies, termed spatial omics, have greatly advanced in the past decades to overcome these limitations. These technologies continue to emerge to include more molecular features (RNAs and/or proteins) and to enhance spatial resolution, opening new opportunities for discovering novel biological knowledge, biomarkers, and therapeutic targets. These advancements also spur the need for novel computational methods to mine useful TME insights from the increasing data complexity confounded by high molecular features and spatial resolution. In this review, we present state-of-the-art spatial omics technologies, their applications, major strengths, and limitations as well as the role of artificial intelligence (AI) in TME studies.

Electrophysiological properties and phenotype of the expiratory lateral parafacial neurons of rats

Active expiration is essential for increasing pulmonary ventilation during high chemical drive (hypercapnia). Several studies demonstrated that the lateral aspect of the parafacial (pFL) region, which contains expiratory neurons, drives abdominal muscles during active expiration in response to hypercapnia. However, the electrophysiological properties and synaptic mechanisms determining the activity of pFL expiratory neurons and the specific conditions for their emergence are not fully understood. Therefore, using whole-cell electrophysiology and single-cell qRT-PCR techniques, we evaluated the intrinsic electrophysiological properties, the phenotype, and the respiratory-related synaptic inputs to the pFL expiratory neurons, as well as the mechanisms for the expression of their expiratory activity under conditions of hypercapnia induced active expiration in in situ preparations of juvenile rats. We also evaluated whether these neurons possess intrinsic CO 2 /[H + ] sensitivity. All recorded pFL expiratory neurons were glutamatergic. In normocapnia, GABAergic and glycinergic inhibition during inspiration and expiration suppressed the activity of excitatory pFL expiratory neurons. In hypercapnia, these neurons escape glycinergic inhibition and generate burst discharges at the end of expiration. Post-inhibitory rebound, Ca V 3.2 isoform of T-type Ca 2+ channels, and intracellular [Ca 2+ ] contribute to their expiratory activity. Hypercapnia/hypocapnia or acidosis/alkalosis did not affect the activity of pFL expiratory neurons, indicating that these neurons do not possess intrinsic CO2 and pH sensitivity. Additionally, ion channels/receptors (TASK or GPR4) related to CO 2 /[H + ] sensitivity were not expressed by pFL expiratory neurons. In conclusion, the intrinsic electrophysiological properties and post-synaptic disinhibition of glutamatergic neurons both play essential roles in generating the expiratory oscillations in the pFL region during hypercapnia in rats. FAPESP and CNPq This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools.

Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.

GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning.

Recent advances in single-cell sequencing techniques have enabled gene expression profiling of individual cells in tissue samples so that it can accelerate biomedical research to develop novel therapeutic methods and effective drugs for complex disease. The typical first step in the downstream analysis pipeline is classifying cell types through accurate single-cell clustering algorithms. Here, we describe a novel single-cell clustering algorithm, called GRACE (GRaph Autoencoder based single-cell Clustering through Ensemble similarity larning), that can yield highly consistent groups of cells. We construct the cell-to-cell similarity network through the ensemble similarity learning framework, and employ a low-dimensional vector representation for each cell through a graph autoencoder. Through performance assessments using real-world single-cell sequencing datasets, we show that the proposed method can yield accurate single-cell clustering results by achieving higher assessment metric scores.

Neural cell isolation from adult macaques for high-throughput analyses and neurosphere cultures.

The low number of neural progenitor cells (NPCs) present in the adult and aged primate brains represents a challenge for generating high-yield and viable in vitro cultures of primary brain cells. Here we report a step-by-step approach for the fast and reproducible isolation of high-yield and viable primary brain cells, including mature neurons, immature cells and NPCs, from adult and aged macaques. We describe the anesthesia, transcardial perfusion and brain tissue preparation; the subsequent microdissection of the regions of interest and their enzymatic dissociation, leading to the separation of single cells. The cell isolation steps of our protocol can also be used for routine cell culturing, in particular for NPC expansion and differentiation, suitable for studies of hippocampal neurogenesis in the adult macaque brain. The purified primary brain cells are largely free from myelin debris and erythrocytes, paving the way for multiple downstream applications in vitro and in vivo. When combined with single-cell profiling techniques, this approach allows an unbiased and comprehensive mapping of cell states in the adult and aged macaque brain, which is needed to advance our understanding of human cognitive and neurological diseases. The neural cell isolation protocol requires 4 h and a team of four to six users with expertize in primary brain cell isolation to avoid tissue hypoxia during the time-sensitive steps of the procedure.

CD74: a prospective marker for reactive microglia?

CD74: a prospective marker for reactive microglia?

Effects of Univariate Stiffness and Degradation of DNA Hydrogels on the Transcriptomics of Neural Progenitor Cells.

Mechanical interactions between cells and extracellular matrix (ECM) are critical for stem cell fate decision. Synthetic models of ECM, such as hydrogels, can be used to precisely manipulate the mechanical properties of the cell niche and investigate how mechanical signals regulate the cell behavior. However, it has long been a great challenge to tune solely the ECM-mimic hydrogels' mechanical signals since altering the mechanical properties of most materials is usually accompanied by chemical and topological changes. Here, we employ DNA and its enantiomers to prepare a series of hydrogels with univariate stiffness regulation, which enables a precise interpretation of the fate decision of neural progenitor cells (NPCs) in a three-dimensional environment. Using single-cell RNA sequencing techniques, Monocle pseudotime trajectory and CellphoneDB analysis, we demonstrate that the stiffness of the hydrogel alone does not influence the differentiation of NPCs, but the degradation of the hydrogel that enhances cell-cell interactions is possibly the main reason. We also find that ECM remodeling facilitates cells to sense mechanical stimuli.

Multi-omic analyses in immune cell development with lessons learned from T cell development.

Traditionally, flow cytometry has been the preferred method to characterize immune cells at the single-cell level. Flow cytometry is used in immunology mostly to measure the expression of identifying markers on the cell surface, but-with good antibodies-can also be used to assess the expression of intracellular proteins. The advent of single-cell RNA-sequencing has paved the road to study immune development at an unprecedented resolution. Single-cell RNA-sequencing studies have not only allowed us to efficiently chart the make-up of heterogeneous tissues, including their most rare cell populations, it also increasingly contributes to our understanding how different omics modalities interplay at a single cell resolution. Particularly for investigating the immune system, this means that these single-cell techniques can be integrated to combine and correlate RNA and protein data at the single-cell level. While RNA data usually reveals a large heterogeneity of a given population identified solely by a combination of surface protein markers, the integration of different omics modalities at a single cell resolution is expected to greatly contribute to our understanding of the immune system.

Single-cell transcriptome analysis for cancer and biology of the pancreas: A review on recent progress.

Single-cell sequencing has become one of the most used techniques across the wide field of biology. It has enabled researchers to investigate the whole transcriptome at the cellular level across tissues, which unlocks numerous potentials for basic and applied studies in future diagnosis and therapy. Here, we review the impact of single-cell RNA sequencing, as the prominent single-cell technique, in pancreatic biology and cancer. We discuss the most recent findings about pancreatic physiology and pathophysiology owing to this technological advancement in the past few years. Using single-cell RNA sequencing, researchers have been able to discover cellular heterogeneity across healthy cell types, as well as cancer tissues of the pancreas. We will discuss the new immunological targets and new molecular mechanisms of progression in the microenvironment of pancreatic cancer studied using single-cell RNA sequencing. The scope is not limited to cancer tissues, and we cover novel developmental, evolutionary, physiological, and heterogenic insights that have also been achieved recently for pancreatic tissues. We cover all biological insights derived from the single-cell RNA sequencing data, discuss the corresponding pros and cons, and finally, conclude how future research can move better by utilizing single-cell analysis for pancreatic biology.

Single-Cell Techniques in Environmental Microbiology

Environmental microbiology has been an essential part of environmental research because it provides effective solutions to most pollutants. Hence, there is an interest in investigating microorganism behavior, such as observation, identification, isolation of pollutant degraders, and interactions between microbial species. To comprehensively understand cell heterogeneity, diverse approaches at the single-cell level are demanded. Thus far, the traditional bulk biological tools such as petri dishes are technically challenging for single cells, which could mask the heterogeneity. Single-cell technologies can reveal complex and rare cell populations by detecting heterogeneity among individual cells, which offers advantages of higher resolution, higher throughput, more accurate analysis, etc. Here, we overviewed several single-cell techniques on observation, isolation, and identification from aspects of methods and applications. Microscopic observation, sequencing identification, flow cytometric identification and isolation, Raman spectroscopy-based identification and isolation, and their applications are mainly discussed. Further development on multi-technique integrations at the single-cell level may highly advance the research progress of environmental microbiology, thereby giving more indication in the environmental microbial ecology.

Abstract 4185: Inclusive basic and advanced translational laboratory research competencies for research in cancer biology and therapeutics

Abstract Our Laboratory was established in 1994 at Univ. of Pennsylvania. Lab members demonstrated initial competencies by performing cell culture, western blots, immunofluorescence, and flow cytometry showing induction of p53/p21(WAF1) in cells treated with chemotherapy. Years later, our Laboratory of Translational Oncology & Experimental Cancer Therapeutics moved to Penn State Univ., Fox Chase Cancer Center/Temple Univ. and then Brown Univ. By 2020, with desire for inclusiveness (everyone succeeds), scientific rigor/reproducibility mandated by NIH, and as a training and mentoring activity (lab scientists/trainees/students mentoring others at High School level and beyond), we established a process for onboarding and training new cancer researchers. By Fall of 2022, there were 17 current Brown University undergraduate students (10 receiving research credit and 7 not receiving credit), HS students, 7 graduate students (PhD, masters, MD/PhD), and 6 medical students working with collaborating faculty at our laboratory at Brown’s Legorreta Cancer Center. After completion of biosafety training, and required trainings such as by IACUC, new lab members complete basic competencies in cell culture, cell viability, and western blot analysis that include technical, presentation quality output, and quantitative/statistical rigor to satisfy current standards for journal publication. For cell culture this includes pathogen free conditions, authentication, attention to details of routine procedures, documentation of morphology, freezing, thawing, passaging, seeding density, and managing cell populations to not run out of cells. Cell viability assessment includes attention to culture conditions, synergy analysis, data robustness, and presentation, and for western blots attention to quality of blots, protein quantification, loading, labeling, antibody specificity and sensitivity controls, presentation at 2022 standards, conventions for splicing, and issues with reproducibility including biological replicates, and generalizability. Additional and advanced competencies include RT-PCR, long-term colony assays, 3-D cultures (spheroids, organoids), transfection (overexpression, knockdown, CRISPR), co-culture and triculture with immune cells and fibroblasts, cytokine profiling, in vivo studies, in vivo imaging, immunohistochemistry, flow cytometric analysis, single cell techniques, viral infection, circulating tumor cell isolation, blood immune and cytokine analysis, and work with transgenic organoids and inducible cancer predisposing alleles. Modeling the tumor microenvironment, relevance to human cancer and translational directions are emphasized. Shared online lab resources, protocols, practices, videos, and manuscripts are available for lab members. The framework herein may be of interest to others involved in similar training programs. Citation Format: Wafik S. El-Deiry, Andrew George, Francesca Di Cristofano, Praveen Srinivasan, Lindsey Carlsen, Kelsey E. Huntington, Arielle De La Cruz, Leiqing Zhang, Marina Hahn, Shuai Zhao, Attila Seyhan, Bradley D. DeNardo, Aaron W. Maxwell, Dae Hee Kim, Alex Raufi, Hina Khan, Stephanie L. Graff, Don S. Dizon, Christopher Azzoli, Abbas E. Abbas, Roxanne Wood, Rishi R. Lulla, Howard P. Safran, Benedito A. Carneiro, Arunasalam Navaraj, Xiaobing Tian, Shengliang Zhang, Lanlan Zhou. Inclusive basic and advanced translational laboratory research competencies for research in cancer biology and therapeutics. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4185.

Abstract 2075: Highly customizable multi-sample single cell RNA-Seq pipeline on the CGC

Abstract Single-cell (sc) transcriptomics has revolutionized our understanding of the biological characteristics and dynamics of cancer development. It can help us identify rare cell subpopulations and understand mechanisms associated with tumor genesis, progression, and response to therapy. The most important step in the analyses of any scRNA-seq dataset is subpopulation identification, usually performed via unsupervised clustering, followed by gene marker identification. We created a highly customizable workflow for sc data analysis, implemented in Common Workflow Language (CWL) on the Cancer Genomics Cloud (CGC) platform. The NCI-funded CGC platform, powered by Seven Bridges, provides a collaborative cloud base computation infrastructure that collocates computation, over 750 bioinformatics workflows, and 3+ PB data to researchers, making the analysis of large datasets accessible from any environment. The “Multi-Sample Clustering and Gene Marker Identification with Seurat 4.1.0” workflow comprises the following steps: Loading scRNA-seq Expression Datasets, Quality Control and Preprocessing, and Clustering and Identification of Gene Markers. Our solution supports gene-cell count matrices generated by several commonly used quantifiers (for example, Cell Ranger counts, Salmon Alevin, Kallisto BUStools, STAR) from single or multiple sc datasets from different batches, as well as single or multiple single-cell samples combined in a single SingleCellExperiment object. The versatility of the pipeline is obtained using several implemented options in each of the steps. Quality control can be performed manually or automatically using several options for normalization (LogNormalize, Deconvolution, SCnorm and Linnorm) and for batch effect correction (Seurat and Harmony). For clustering, the pipeline uses Seurat's graph-based approach, with options for different clustering resolutions. After performing identification of gene markers for each cluster, a researcher can test differential expression using various packages including wilcox, bimod, roc, and DESeq2. Here, we demonstrate the application of this workflow to a typical sc analysis, by processing an open access dataset of 61k cells isolated from embryonal mouse pons and forebrain, two major brain tumor locations. We used different clustering resolutions to achieve different degrees of granularity and identified cluster-specific marker genes used to identify vulnerable cell populations. To enable researchers to use this analysis as a guideline, we made this analysis available as a public project. Further development of single-cell sequencing techniques will undoubtedly improve our understanding of tumor biology and highlight promising drug targets. CGC’s cloud base computation infrastructure, along with numerous available cancer datasets and easy-to-use single-cell data processing workflows, among others, will be instrumental in this process. Citation Format: Nevena Vukojicic, Aleksandar Danicic, Zelia Worman, Rowan Beck, Dalibor Veljkovic, Marko Matic, Jack DiGiovanna, Brandi Davis-Dusenbery. Highly customizable multi-sample single cell RNA-Seq pipeline on the CGC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2075.

Dynamics of Epithelial-Mesenchymal Plasticity: What Have Single-Cell Investigations Elucidated So Far?

Epithelial-mesenchymal plasticity (EMP) is a key driver of cancer metastasis and therapeutic resistance, through which cancer cells can reversibly and dynamically alter their molecular and functional traits along the epithelial-mesenchymal spectrum. While cells in the epithelial phenotype are usually tightly adherent, less metastatic, and drug-sensitive, those in the hybrid epithelial/mesenchymal and/or mesenchymal state are more invasive, migratory, drug-resistant, and immune-evasive. Single-cell studies have emerged as a powerful tool in gaining new insights into the dynamics of EMP across various cancer types. Here, we review many recent studies that employ single-cell analysis techniques to better understand the dynamics of EMP in cancer both in vitro and in vivo. These single-cell studies have underlined the plurality of trajectories cells can traverse during EMP and the consequent heterogeneity of hybrid epithelial/mesenchymal phenotypes seen at both preclinical and clinical levels. They also demonstrate how diverse EMP trajectories may exhibit hysteretic behavior and how the rate of such cell-state transitions depends on the genetic/epigenetic background of recipient cells, as well as the dose and/or duration of EMP-inducing growth factors. Finally, we discuss the relationship between EMP and patient survival across many cancer types. We also present a next set of questions related to EMP that could benefit much from single-cell observations and pave the way to better tackle phenotypic switching and heterogeneity in clinic.