Cell Clusters Research Articles

Distinct immune microenvironment of venous tumor thrombus in hepatocellular carcinoma at single-cell resolution.

Portal vein tumor thrombus (PVTT) worsens the prognosis of hepatocellular carcinoma by increasing intrahepatic dissemination and inducing portal vein hypertension. However, the immune characteristics of PVTT remain unclear. Therefore, this study aims to explore the immune microenvironment in PVTT. Time-of-flight mass cytometry (CyTOF) revealed that macrophages and monocytes were the dominant immune cell type in PVTT, with a higher proportion than in primary tumor (PT) and blood (54.1% vs. 26.3% and 9.1%, p<0.05). The differentially enriched clustering of inhibitory and regulatory immune cells in PVTT indicated an immune-suppressive environment. According to the single-cell RNA sequencing (scRNA-seq), TAM-C5AR1 was characterized by leukocyte chemotaxis and was the most common subpopulation in PVTT (36.7%). Multiple immunofluorescence staining showed that the C5aR+ TAM/Mφ were enriched in PVTT compared to both PT and liver, and positively correlated with C5a (r=0.559, p<0.001). Notably, THP-1 (monocyte cell line) was recruited by CSQT2 (PVTT cell line) and exhibited up-regulation of CD163, CD206, and PD-L1 upon stimulation. C5aR antagonist could reverse this. C5aR+ TAMs could also inhibit Granzyme B in CD8+ T cells. High infiltration of C5aR+ TAMs in PVTT correlated with poor differentiation (p<0.009), and was a risk factor for overall survival (p=0.003) and for re-formation of PVTT after resection (p=0.007). TAMs, especially C5aR+ TAMs, were enriched in PVTT. C5aR+ TAMs contribute to the development of PVTT and poor prognosis by reshaping the immunosuppressive environment.

Identify critical genes of breast cancer and corresponding leading natural product compounds of potential therapeutic targets.

Breast cancer is a leading cause of cancer mortality among women globally, with over 2.26 million new cases annually, according to GLOBOCAN 2020. This accounts for approximately 25% of all new female cancers and 15.5% of female cancer deaths.To address this critical public health challenge, we conducted a multi-omics study aimed at identifying hub genes, therapeutic targets, and potential natural product-based therapies. We employed weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis to pinpoint hub genes in breast cancer. Regulatory networks for these genes were constructed by re-analyzing chromatin immunoprecipitation sequencing (ChIP-seq) data from breast cancer cell lines. Additionally, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) were utilized to characterize hub gene expression profiles and their relationships with immune cell clusters and tumor microenvironments. Survival analysis based on mRNA and protein expression levels identified prognostic factors and potential therapeutic targets. Lastly, large-scale virtual screening of natural product compounds revealed leading compounds that target squalene epoxidase (SQLE). Our multi-omics analysis paves the way for more effective clinical treatments for breast cancer.

Histologic Reaction Patterns of Lymph Node Involvement in Ovarian Serous Borderline Tumors.

Lymph node (LN) involvement (LNI) is not infrequently observed in ovarian serous borderline tumors (SBTs) but is not considered equivalent to malignant tumor metastasis, as it reportedly does not impact recurrence or survival in patients with SBT. However, the reasons underlying the insignificant clinical impact of LNI remain unclear. To determine whether histologic reaction patterns (HRPs) are associated with SBT prognosis. We compared HRPs around tumor cell clusters in LNs of patients with SBT and low-grade serous carcinoma. HRPs were classified into 4 categories (HRP 1-HRP 4) based on tumor cell location in LNs, the presence of their adhesion to surrounding lymphoid tissue, or pericellular desmoplastic reactions. Although LNI itself was linked to reduced recurrence-free survival (RFS), no recurrence was observed in patients with HRP 1 or 2, characterized by freely floating tumor cells in the lumens of afferent/efferent lymphatics or intranodal sinus with surrounding free spaces. Conversely, HRP 3 or higher, characterized by firm tumor cell adhesion to lymphoid tissue (HRP 3) or peritumoral desmoplastic reaction (HRP 4), independently impacted RFS, albeit not overall survival. The prognosis of SBT with LNI is not uniformly favorable, and HRPs around the LNI significantly influence patient outcomes. Patients with firm tumor cell adhesion to surrounding tissue, with or without peritumoral desmoplastic reaction (HRP ≥3), independently experience decreased RFS, although this does not correlate with reduced overall survival.

Single-cell transcriptomics of human organoid-derived enteroendocrine cell populations from the small intestine.

Gut hormones control intestinal function, metabolism and appetite, and have been harnessed therapeutically to treat type 2 diabetes and obesity. Our understanding of the enteroendocrine axis arises largely from animal studies, but intestinal organoid models make it possible to identify, genetically modify and purify human enteroendocrine cells (EECs). This study aimed to map human EECs using single-cell RNA sequencing. Organoids derived from human duodenum and ileum were genetically modified using CRISPR-Cas9 to express the fluorescent protein Venus driven by the chromogranin-A promoter. Fluorescent cells from CHGA-Venus organoids were purified by flow cytometry and analysed by 10X single-cell RNA sequencing. Cluster analysis separated EEC populations, allowing an examination of differentially expressed hormones, nutrient-sensing machinery, transcription factors and exocytotic machinery. Bile acid receptor GPBAR1 was most highly expressed in L-cells (producing glucagon-like peptide 1 and peptide YY), long-chain fatty acid receptor FFAR1 was highest in I-cells (cholecystokinin), K-cells (glucose-dependent insulinotropic polypeptide) and L-cells, short-chain fatty acid receptor FFAR2 was highest in ileal L-cells and enterochromaffin cells, olfactory receptor OR51E1 was notably expressed in ileal enterochromaffin cells, and the glucose-sensing sodium glucose cotransporter SLC5A1 was highly and differentially expressed in K- and L-cells, reflecting their known responsiveness to ingested glucose. The organoid EEC atlas was merged with published data from human intestine and organoids, with good overlap between enteroendocrine datasets. Understanding the similarities and differences between human EEC types will facilitate the development of drugs targeting the enteroendocrine axis for the treatment of conditions such as diabetes, obesity and intestinal disorders. KEY POINTS: Gut hormones regulate intestinal function, nutrient homeostasis and metabolism and form the basis of the new classes of drugs for obesity and diabetes. As enteroendocrine cells (EECs) comprise only ∼1% of the intestinal epithelium, they are under-represented in current single-cell atlases. To identify, compare and characterise human EECs we generated chromogranin-A labelled organoids from duodenal and ileal biopsies by CRISPR-Cas9. Fluorescent chromogranin-A positive EECs were purified and analysed by single-cell RNA sequencing, revealing predominant cell clusters producing different gut hormones. Cell clusters exhibited differential expression of nutrient-sensing machinery including bile acid receptors, long- and short-chain fatty acid receptors and glucose transporters. Organoid-derived EECs mapped well onto data from native intestinal cell populations, extending coverage of EECs.

CDK9 recruits HUWE1 to degrade RARα and offers therapeutic opportunities for cutaneous T-cell lymphoma

Cutaneous T-cell lymphoma (CTCL) is a heterogeneous non-Hodgkin lymphoma originating in the skin and invading the systemic hematopoietic system. Current treatments, including chemotherapy and monoclonal antibodies yielded limited responses with high incidence of side effects, highlighting the need for targeted therapy. Screening with small inhibitors library, herein we identify cyclin dependent kinase 9 (CDK9) as a driver of CTCL growth. Single-cell RNA-seq analysis reveals a CDK9high malignant T cell cluster with a unique actively proliferating feature. Inhibition, depletion or proteolysis targeting chimera (PROTAC)-mediated degradation of CDK9 significantly reduces CTCL cell growth in vitro and in murine models. CDK9 also promotes degradation of retinoic acid receptor α (RARα) via recruiting the E3 ligase HUWE1. Co-administration of CDK9-PROTAC (GT-02897) with all-trans retinoic acid (ATRA) leads to synergistic attenuation of tumor growth in vitro and in xenograft models, providing a potential translational treatment for complete eradication of CTCL.

Integrative multi-omics analysis uncovers tumor-immune-gut axis influencing immunotherapy outcomes in ovarian cancer

Recurrent ovarian cancer patients, especially those resistant to platinum, lack effective curative treatments. To address this, we conducted a phase 2 clinical trial (NCT02853318) combining pembrolizumab with bevacizumab, to increase T cell infiltration into the tumor, and oral cyclophosphamide, to reduce the number of regulatory T cells. The trial accrued 40 heavily pretreated recurrent ovarian cancer patients. The primary endpoint, progression free survival, was extended to a median of 10.2 months. The secondary endpoints demonstrated an objective response rate of 47.5%, and disease control in 30% of patients for over a year while maintaining a good quality of life. We performed comprehensive molecular, immune, microbiome, and metabolic profiling on samples of trial patients. Here, we show increased T and B cell clusters and distinct microbial patterns with amino acid and lipid metabolism are linked to exceptional clinical responses. This study suggests the immune milieu and host-microbiome can be leveraged to improve antitumor response in future immunotherapy trials.

Single-cell transcriptome analysis reveals status changes of immune cells in chronic kidney disease

Background and aimsThe immune system plays a crucial role in the development of kidney diseases. Chronic kidney disease (CKD) can lead to various complications, potentially affecting multiple systems throughout the body. Currently, the description of the immune system in human CKD is not comprehensive enough. Constructing a CKD kidney atlas using single-cell RNA sequencing (scRNA-seq) can provide deeper insights into the composition and functional changes of immune cells in CKD, facilitating the discovery of new therapeutic targets.MethodsWe processed and integrated scRNA-seq datasets from healthy and CKD kidneys from three independent cohorts using the same approach (including 42 normal samples and 23 chronic kidney disease samples). Subsequently, we conducted gene enrichment and intercellular communication analysis to construct an immune cell atlas of the kidneys in CKD patients.ResultsWe identified nine major kidney cell clusters. Further clustering analysis of different immune cell clusters revealed that, compared to normal kidneys, CKD patients’ kidneys had decreased CD16+ NK cells while CD4+ naive helper T cells and CCR7+ DC increased. Partial activation of the WNT signaling pathway was observed in T cells and NK cells of CKD patients, while some metabolism-related genes were inhibited. Myeloid cell subgroups also exhibited abnormal signaling pathway alterations. Additionally, we discovered a unique population of SPP1 macrophages in CKD, which are recruited by chemokines released from aPT and aTAL cell subpopulations. These SPP1 macrophages may promote cellular fibrosis through the signaling of SPP1, FN1, and various receptors.ConclusionWe established a human CKD kidney immune cell atlas and identified SPP1 macrophages as a unique cell type in CKD. The interaction between SPP1 macrophages and damaged cells may serve as a potential therapeutic target for treating CKD in the future.

Video tracking of single cells to identify clustering behavior

Cancer cell clustering is a critical factor in metastasis, with cells often believed to migrate in groups as they establish themselves in new environments. This study presents preliminary findings from an in vitro experiment, suggesting that co-culturing cells provides an effective method for observing this phenomenon, even though the cells are grown as monolayers. We introduce a novel single-cell tracking approach based on graph theory to identify clusters in PC3 cells cultivated in both monoculture and co-culture with PC12 cells, using 66-h time-lapse recordings. The initial step consists of defining “linked” pairs of PC3 cells, laying the foundation for the application of graph theory. We propose two alternative definitions for cell pairings. The first method, Method 1, defines cells as “linked” at a given time t if they are close together within a defined time period before and after t. A second potential alternative method, Method 2, pairs cells if there is an overlap between the convex hulls of their respective tracks during this time period. Pairing cells enables the application of graph theory for subsequent analysis. This framework represents a cell as a vertex (node) and a relation between two cells as an edge. An interconnected set of high-degree nodes (nodes with many connections or edges) forms a subgraph, or backbone, that defines a patch (cluster) of cells. All nodes connected to this backbone are part of the subgraph. The backbone of high-degree nodes functions as a partition (or cut) of the initial graph. Two consecutive clusters in the video are considered to share the same identity if the following cluster contains at least p = 75 % of the cells from the preceding cluster, and the mean positions of their cells are within △r = 75μm. PC3 cells grown in co-culture appear to form persistent clusters exceeding 10 cells after 40–50 h incubation following seeding. In contrast, PC3 cells cultured alone (mono-culture) did not exhibit this behavior. This approach is experimental and requires further validation with a broader dataset.

Invasive micropapillary carcinoma of the breast and invasive breast carcinoma of no special type: a comparison of claudin proteins’ expression and its impact on survival

Invasive micropapillary carcinoma of the breast is characterized by clusters of cells presenting with inverted polarity. Although the apico–basal polarity is a fundamental property of the epithelium, the biological alterations leading to the inside-out pattern observed in invasive micropapillary carcinoma (IMPC) remain mostly unknown. The regulation of tight junctions in polarity formation and maintenance is acknowledged. By using immunohistochemistry, we have analysed claudin-1, -3, -4, and -7 tight junction proteins expression and their prognostic value on IMPCs and compared them to invasive breast carcinomas of no special type (IBC-NST) tumors. Our cohort consisted of 37 IMPCs, 36 IBC-NST and 9 mixed IMPC/IBC-NST tumors. Two scoring systems were used to quantify protein expression: a 4-tier scoring system and the H-score method. Distant metastasis free survival (DMFS) intervals and overal survival (OS) data were used for prognosis evaluation. The analysed samples were characterized mainly by low or no claudin-1 expression whereas claudins-3, -4 and -7 showed variable positivity. We have found no significant differences in claudin-3 and -4 protein expression between IMPC and IBC-NST groups with either scoring methods, however high claudin-7 expression was found in significantly more IMPCs than IBC-NST tumors according to the H-score system (p = 0.02). The 4-tier scoring method revealed association of claudin-7 expression with molecular tumor subtypes (p = 0.001). IMPC and IBC-NST tumors did not show difference in DMFS (p = 0.70). In the analysis of pure IMPC and IBC-NST tumors, positive/high claudin-4 protein expression was significantly associated with shorter DMFS (p = 0.02/p = 0.008, respectively according to the two scoring methods). Claudin-3 and claudin-7 expression showed no association with DMFS or OS. Changes in epithelial polarity seem not to be related to claudin-1, -3, and -4 expression. Increased claudin-4 expression may have a role in breast cancer progression.

CYRI controls epidermal wound closure and cohesion of invasive border cell cluster in Drosophila.

Cell motility is crucial for many biological processes including morphogenesis, wound healing, and cancer invasion. The WAVE regulatory complex (WRC) is a central Arp2/3 regulator driving cell motility downstream of activation by Rac GTPase. CYFIP-related Rac1 interactor (CYRI) proteins are thought to compete with WRC for interaction with Rac1 in a feedback loop regulating lamellipodia dynamics. However, the physiological role of CYRI proteins in vivo in healthy tissues is unclear. Here, we used Drosophila as a model system to study CYRI function at the cellular and organismal levels. We found that CYRI is not only a potent WRC regulator in single macrophages that controls lamellipodial spreading but also identified CYRI as a molecular brake on the Rac-WRC-Arp2/3 pathway to slow down epidermal wound healing. In addition, we found that CYRI limits invasive border cell migration by controlling cluster cohesion and migration. Thus, our data highlight CYRI as an important regulator of cellular and epithelial tissue dynamics conserved across species.

MVCLST: A spatial transcriptome data analysis pipeline for cell type classification based on multi-view comparative learning

Recent advancements in spatial transcriptomics sequencing technologies can not only provide gene expression within individual cells or cell clusters (spots) in a tissue but also pinpoint the exact location of this expression and generate detailed images of stained tissue sections, which offers invaluable insights into cell type identification and cell function exploration. However, effectively integratingthegene expression data, spatial location information, and tissue images from spatial transcriptomics data presents a significant challenge for computational methodsin cell classification. In this work, we propose MVCLST, a multi-view comparative learningmethod to analyze spatial transcriptomicsdata for accurate cell type classification. MVCLSTconstructs two views based on gene expression profiles, cell coordinates and image features. The multi-view method we proposed can significantly enhance the effectiveness of feature extraction while avoiding the impact of erroneous information in organizing image or gene expression data. The model employs four separate encoders to capture shared and unique features within each view. To ensure consistency and facilitate information exchange between the two views, MVCLST incorporates a contrastive learning loss function. The extracted shared and private features from both views are fused using corresponding decoders. Finally, the model utilizes the Leiden algorithm to clusterthe learned featuresfor cell type identification. Additionally, we establish a framework called MVCLST-CCFS for spatial transcriptomicsdata analysis based on MVCLST and consistent clustering. Our method achieves excellent results in clustering on human dorsolateral prefrontal cortex data and the mouse brain tissue data. Italso outperforms state-of-the-art techniques in the subsequent search for highly variable genes across cell types on the mouse olfactory bulbdata.

T cell receptor-centric perspective to multimodal single-cell data analysis.

The T cell receptor (TCR), despite its importance, is underutilized in single-cell analysis, with gene expression features solely driving current strategies. Here, we argue for a TCR-first approach, more suited toward T cell repertoires. To this end, we curated a large T cell atlas from 12 prominent human studies, containing in total 500,000 T cells spanning multiple diseases, including melanoma, head and neck cancer, blood cancer, and lung transplantation. Here, we identified severe limitations in cell-type annotation using unsupervised approaches and propose a more robust standard using a semi-supervised method or the TCR arrangement. We showcase the utility of a TCR-first approach through application of the STEGO.R tool for the identification of treatment-related dynamics and previously unknown public T cell clusters with potential antigen-specific properties. Thus, the paradigm shift to a TCR-first can highlight overlooked key T cell features that have the potential for improvements in immunotherapy and diagnostics.

Novel placental biomarker shows predictive potential for spontaneous preterm labor.

Human parturition involve many events among mother, fetus, and placenta, and the initiation of these events is the consequence of activation of a series of endocrine and immune responses. Multiple underlying pathways account for the cascade of events that culminate in spontaneous preterm labor. In this study, we aimed to characterize these signaling pathways of placental origin at molecular levels. We used single-cell RNA-sequencing (sc-RNA-seq) analysis to probe transcriptional heterogeneity in human placenta delivered at preterm or term and then used RNA in situ hybridization (RNA-ISH) assay on formalin-fixed paraffin-embedded placental tissues to validate these results. By using sc-RNA-seq on villous cytotrophoblast isolated from a preterm placenta, we found that signaling pathways implicated in initiation of term or preterm labor including ferroptosis, kisspeptin, and senescence were constitutively activated in distinct cellular clusters of these trophoblastic stem cells. RNA-ISH-based spatial gene expression profiling in formalin-fixed paraffin-embedded tissues revealed that Pregnancy Specific beta-1-Glycoprotein 4 (PSG4), a potent molecular driver for cellular aging, was significantly increased in preterm placentas (N=30) compared to their full-term counterparts (N=9). Furthermore, PSG4 mRNA signals were predominantly detected in the villous syncytiotrophoblast and the discontinuous monolayer of cytotrophoblast as well as the intervillous space where maternal blood circulates. Our study provides strong support for PSG4 overexpression serving as a biomarker for pregnant women at risk for preterm delivery, which can allow for development of timely and clinical preventive strategies.

Exploring the Potential of BEND7 as an Immunomodulatory Biomarker in Sepsis through Integrative Genomic and Transcriptomic Analysis.

Sepsis is a life-threatening condition driven by a dysregulated immune response to infection. Identifying the genetic factors underlying sepsis pathogenesis remains a major challenge in developing effective treatments. The Summary-data-based Mendelian Randomization method was used to integrate genome-wide association study and expression quantitative trait loci data to identify sepsis-related genes. These genes were intersected with prognostic gene sets from Gene Expression Omnibus transcriptomic datasets and validated using an independent dataset. Comprehensive single-cell RNA sequencing analysis, including cell clustering, differential expression analysis, cell-cell communication mapping, and pseudotime trajectory analysis, was performed to explore the roles of the identified genes within the sepsis microenvironment. Intersection of Summary-data-based Mendelian Randomization and Gene Expression Omnibus gene sets, followed by validation, identified two risk genes and five protective genes as significantly differentially expressed. The risk gene BEND7, predominantly expressed in platelets, was further analyzed using single-cell RNA sequencing, revealing strong interactions with immune cells, particularly monocytes and neutrophils, via the intercellular adhesion molecule signaling pathway. Functional enrichment analysis suggested that BEND7-positive platelets play a role in immune modulation and platelet activation. BEND7 was identified as a platelet-specific gene involved in immune regulation during sepsis. Targeting BEND7-positive platelets may present new therapeutic opportunities in sepsis management.

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning

Integrating diverse types of biological data is essential for a holistic understanding of cancer biology, yet it remains challenging due to data heterogeneity, complexity, and sparsity. Addressing this, our study introduces an unsupervised deep learning model, MOSA (Multi-Omic Synthetic Augmentation), specifically designed to integrate and augment the Cancer Dependency Map (DepMap). Harnessing orthogonal multi-omic information, this model successfully generates molecular and phenotypic profiles, resulting in an increase of 32.7% in the number of multi-omic profiles and thereby generating a complete DepMap for 1523 cancer cell lines. The synthetically enhanced data increases statistical power, uncovering less studied mechanisms associated with drug resistance, and refines the identification of genetic associations and clustering of cancer cell lines. By applying SHapley Additive exPlanations (SHAP) for model interpretation, MOSA reveals multi-omic features essential for cell clustering and biomarker identification related to drug and gene dependencies. This understanding is crucial for developing much-needed effective strategies to prioritize cancer targets.

Single-cell analysis combined with transcriptome sequencing identifies autophagy hub genes in macrophages after spinal cord injury

Spinal cord injury (SCI) is a neurological disease characterized by the loss of motor and sensory function below the injury level. The pathogenesis of SCI is complex, involving the recruitment of various cells that play key roles in the injury area. Single-cell RNA sequencing (scRNA-seq) can analyze cell heterogeneity and inter-cell communication. Bulk RNA-seq offers advantages such as low cost, mature technology and high throughput. Joint analysis of bulk RNA-seq and scRNA-seqis more complementary for exploring the pathophysiology of diseases. In this study, we revealed changes in cell clusters and intercellular signaling after SCI through the scRNA-seq analysis. Bioinformatics analyses and experimental verification showed that macrophages increase rapidly and become the dominant cell type after SCI. The mTOR gene is the key molecule of M1 macrophage autophagy blockade and the PI3K-AKT-mTOR signaling pathway plays an important role in blockings macrophage autophagy.

PIEZO1 overexpression in hereditary hemorrhagic telangiectasia arteriovenous malformations.

Hereditary hemorrhagic telangiectasia (HHT) is an inherited vascular disorder characterized by arteriovenous malformations (AVMs). Loss-of-function mutations in Activin receptor-like kinase 1 (ALK1) cause type 2 HHT and Alk1 knockout (KO) mice develop AVMs due to overactivation of VEGFR2/PI3K/AKT signaling pathways. However, the full spectrum of signaling alterations in Alk1 mutants remains unknown and means to combat AVM formation in patients are yet to be developed. Single-cell RNA sequencing of endothelial-specific Alk1 KO mouse retinas and controls identified a cluster of endothelial cells (ECs) that was unique to Alk1 mutants and that overexpressed fluid shear stress (FSS) signaling signatures including upregulation of the mechanosensitive ion channel PIEZO1. PIEZO1 overexpression was confirmed in human HHT lesions, and genetic and pharmacological PIEZO1 inhibition was tested in Alk1 KO mice, as well as downstream PIEZO1 signaling. Pharmacological PIEZO1 inhibition, and genetic Piezo1 deletion in Alk1 -deficient mice effectively mitigated AVM formation. Furthermore, we identified that elevated VEGFR2/AKT, ERK5-p62-KLF4, hypoxia and proliferation signaling were significantly reduced in Alk1 - Piezo1 double ECKO mice. PIEZO1 overexpression and signaling is integral to HHT2, and PIEZO1 blockade reduces AVM formation and alleviates cellular and molecular hallmarks of ALK1-deficient cells. This finding provides new insights into the mechanistic underpinnings of ALK1-related vascular diseases and identifies potential therapeutic targets to prevent AVMs.

Challenges in the Cytodiagnosis of Intraparotid Schwannoma.

Clear cell tumors of parotid gland encompass a wide spectrum of neoplasms, including benign and malignant epithelial neoplasms. Additionally, tumors from adjacent structures such as paraganglioma, and metastatic neoplasms may also show clear cells. Overlapping cytological features may cause difficulty in diagnosis. This 50-year-old female presented with inability to close the right eye for 4 years, and with an infra-auricular swelling. Fine-needle aspiration cytology (FNAC) done outside was submitted for review with a diagnosis of neuroendocrine tumor. Smears showed clusters of tumor cells with abundant vacuolated cytoplasm and uniform round to ovoid nuclei. Features were those of salivary gland neoplasm of uncertain malignant potential, that is Milan category IVB: cellular neoplasm with clear cell features, with differential diagnoses of myoepithelial cell neoplasms, acinic cell carcinoma and paraganglioma. FNAC repeated for better characterization showed similar polygonal cells, accompanied by few spindle-shaped cells with ovoid nuclei, suggestive of paraganglioma. However, DOTANOC uptake was absent. Patient underwent radical parotidectomy; intraoperative frozen section showed a spindle cell tumor. Final histopathology revealed an intraparotid facial nerve schwannoma. The tumor was multi-nodular, with some nodules being composed almost entirely of polygonal cells with clear cytoplasm, that is, Antoni B areas, and others showing typical Antoni A areas admixed with Antoni B areas. Parotid FNAC has high accuracy for diagnosis of epithelial lesions; however, mesenchymal tumors often pose a diagnostic difficulty. Intraparotid schwannoma is not uncommon and may be diagnosed easily on FNAC when morphology is classical. However, intra-tumoral heterogeneity and less common histological subtypes may present a pitfall in cytodiagnosis.

Single-cell RNA sequencing reveals key regulators and differentiation trajectory of iPSC-derived cardiomyocytes

Cardiac differentiation of human pluripotent stem cells is not only a new strategy of regenerative therapy for cardiovascular disease treatment but also provides unique opportunities for the study of in vitro disease models and human heart development. To elucidate the dynamic gene regulatory networks and pivotal regulators involved in the cardiomyocyte differentiation process, we conducted an analysis of single-cell RNA sequencing data obtained from the reprogramming of two human induced pluripotent stem cell (iPSC) lines into cardiomyocytes. The data were collected from 32,365 cells at 4 stages of this process. We successfully identified cardiomyocyte clusters and several other cell clusters with different molecular characteristics derived from iPSC and described the differentiation trajectory of cardiomyocytes during differentiation in vitro. Through differential gene analysis and SCENIC analysis, we identified several candidate genes including CREG and NR2F2 that play an important regulatory role in cardiomyocyte lineage commitment. This study provides the key differentiation trajectory of heart differentiation in vitro at single-cell resolution and reveals the molecular basis of heart development and differentiation of iPSC-derived cardiomyocytes.

Visium spatial transcriptomics and proteomics identifies novel hepatic cell populations and transcriptomic signatures of alcohol-associated hepatitis.

Alcohol-associated hepatitis (AH) is the clinical manifestation of alcohol-associated liver disease (ALD). AH is a complex disease encompassing the dysregulation of many cells and cell subpopulations. This study used a hepatic spatial transcriptomic and proteomic approach (10X Genomics Visium) to identify hepatic cell populations and their associated transcriptomic and proteomic alterations in human AH. Formalin-fixed paraffin-embedded liver tissue from AH patients (n = 2) and non-ALD controls (donors) (n = 2) were used for Visium spatial transcriptomic and proteomic analysis. AH cell clusters and cell markers were drastically different in regard to tissue pattern and number of cell types compared to non-ALD controls. Cholangiocytes, endothelial cells, macrophages, and stellate cells were more profuse in AH relative to non-ALD controls. Transcriptionally, proliferating cell nuclear antigen-positive (PCNA+) hepatocytes in AH more closely resembled cholangiocytes suggesting they were non-functional hepatocytes derived from cholangiocytes. Furthermore, mitochondria protein-coding genes were reduced in AH versus non-ALD control hepatocytes, suggesting reduced functionality and loss of regenerative mechanisms. Macrophages in AH exhibited elevated gene expression involved in exosomes as compared to non-ALD controls. The most upregulated macrophage genes observed in AH were those involved in exosome trafficking. Gene and protein signatures of disease-associated hepatocytes (ANXA2+/CXCL1+/CEACAM8+) were elevated in AH and could visually identify a pre-malignant lesion. This study identified global cell type alterations in AH and distinct transcriptomic changes between AH and non-ALD controls. These findings characterize cellular plasticity and profuse transcriptomic and proteomic changes that are apparent in AH and contribute to the identification of novel therapeutics.