Native N-glycome profiling of single cells and ng-level blood isolates using label-free capillary electrophoresis-mass spectrometry.

The development of reliable single-cell dispensers and substantial sensitivity improvement in mass spectrometry made proteomic profiling of individual cells achievable. Yet, there are no established methods for single-cell glycome analysis due to the inability to amplify glycans and sample losses associated with sample processing and glycan labeling. In this work, we present an integrated platform coupling online in-capillary sample processing with high-sensitivity label-free capillary electrophoresis-mass spectrometry for N-glycan profiling of single mammalian cells. Direct and unbiased quantitative characterization of single-cell surface N-glycomes are demonstrated for HeLa and U87 cells, with the detection of up to 100 N-glycans per single cell. Interestingly, N-glycome alterations are unequivocally detected at the single-cell level in HeLa and U87 cells stimulated with lipopolysaccharide. The developed workflow is also applied to the profiling of ng-level amounts (5-500 ng) of blood-derived protein, extracellular vesicle, and total plasma isolates, resulting in over 170, 220, and 370 quantitated N-glycans, respectively.

Diagnostic leukapheresis reveals distinct phenotypes of NSCLC circulating tumor cells.

BACKGROUND: Circulating tumor cells (CTCs) hold immense promise for unraveling tumor heterogeneity and understanding treatment resistance. However, conventional methods, especially in cancers like non-small cell lung cancer (NSCLC), often yield low CTC numbers, hindering comprehensive analyses. This study addresses this limitation by employing diagnostic leukapheresis (DLA) to cancer patients, enabling the screening of larger blood volumes. To leverage DLA's full potential, this study introduces a novel approach for CTC enrichment from DLAs. METHODS: DLA was applied to six advanced stage NSCLC patients. For an unbiased CTC enrichment, a two-step approach based on negative depletion of hematopoietic cells was used. Single-cell (sc) whole-transcriptome sequencing was performed, and CTCs were identified based on gene signatures and inferred copy number variations. RESULTS: Remarkably, this innovative approach led to the identification of unprecedented 3,363 CTC transcriptomes. The extensive heterogeneity among CTCs was unveiled, highlighting distinct phenotypes related to the epithelial-mesenchymal transition (EMT) axis, stemness, immune responsiveness, and metabolism. Comparison with sc transcriptomes from primary NSCLC cells revealed that CTCs encapsulate the heterogeneity of their primary counterparts while maintaining unique CTC-specific phenotypes. CONCLUSIONS: In conclusion, this study pioneers a transformative method for enriching CTCs from DLA, resulting in a substantial increase in CTC numbers. This allowed the creation of the first-ever single-cell whole transcriptome in-depth characterization of the heterogeneity of over 3,300 NSCLC-CTCs. The findings not only confirm the diagnostic value of CTCs in monitoring tumor heterogeneity but also propose a CTC-specific signature that can be exploited for targeted CTC-directed therapies in the future. This comprehensive approach signifies a major leap forward, positioning CTCs as a key player in advancing our understanding of cancer dynamics and paving the way for tailored therapeutic interventions.

Comparative single-cell multiplex immunophenotyping of therapy-naive patients with rheumatoid arthritis, systemic sclerosis, and systemic lupus erythematosus shed light on disease-specific composition of the peripheral immune system.

Introduction: Systemic autoimmune diseases (SADs) are a significant burden on the healthcare system. Understanding the complexity of the peripheral immunophenotype in SADs may facilitate the differential diagnosis and identification of potential therapeutic targets. Methods: Single-cell mass cytometric immunophenotyping was performed on peripheral blood mononuclear cells (PBMCs) from healthy controls (HCs) and therapy-naive patients with rheumatoid arthritis (RA), progressive systemic sclerosis (SSc), and systemic lupus erythematosus (SLE). Immunophenotyping was performed on 15,387,165 CD45+ live single cells from 52 participants (13 cases/group), using an antibody panel to detect 34 markers. Results: Using the t-SNE (t-distributed stochastic neighbor embedding) algorithm, the following 17 main immune cell types were determined: CD4+/CD57- T cells, CD4+/CD57+ T cells, CD8+/CD161- T cells, CD8+/CD161+/CD28+ T cells, CD8dim T cells, CD3+/CD4-/CD8- T cells, TCRγ/δ T cells, CD4+ NKT cells, CD8+ NKT cells, classic NK cells, CD56dim/CD98dim cells, B cells, plasmablasts, monocytes, CD11cdim/CD172dim cells, myeloid dendritic cells (mDCs), and plasmacytoid dendritic cells (pDCs). Seven of the 17 main cell types exhibited statistically significant frequencies in the investigated groups. The expression levels of the 34 markers in the main populations were compared between HCs and SADs. In summary, 59 scatter plots showed significant differences in the expression intensities between at least two groups. Next, each immune cell population was divided into subpopulations (metaclusters) using the FlowSOM (self-organizing map) algorithm. Finally, 121 metaclusters (MCs) of the 10 main immune cell populations were found to have significant differences to classify diseases. The single-cell T-cell heterogeneity represented 64MCs based on the expression of 34 markers, and the frequency of 23 MCs differed significantly between at least twoconditions. The CD3- non-T-cell compartment contained 57 MCs with 17 MCs differentiating at least two investigated groups. In summary, we are the first to demonstrate the complexity of the immunophenotype of 34 markers over 15 million single cells in HCs vs. therapy-naive patients with RA, SSc, and SLE. Disease specific population frequencies or expression patterns of peripheral immune cells provide a single-cell data resource to the scientific community.

Single-cell analysis of anti-BCMA CAR T cell therapy in patients with central nervous system autoimmunity.

Chimeric antigen receptor (CAR) T cell immunotherapy for the treatment of neurological autoimmune diseases is promising, but CAR T cell kinetics and immune alterations after treatment are poorly understood. Here, we performed single-cell multi-omics sequencing of paired cerebrospinal fluid (CSF) and blood samples from patients with neuromyelitis optica spectrum disorder (NMOSD) treated with anti-B cell maturation antigen (BCMA) CAR T cells. Proliferating cytotoxic-like CD8+ CAR T cell clones were identified as the main effectors in autoimmunity. Anti-BCMA CAR T cells with enhanced features of chemotaxis efficiently crossed the blood-CSF barrier, eliminated plasmablasts and plasma cells in the CSF, and suppressed neuroinflammation. The CD44-expressing early memory phenotype in infusion products was potentially associated with CAR T cell persistence in autoimmunity. Moreover, CAR T cells from patients with NMOSD displayed distinctive features of suppressed cytotoxicity compared with those from hematological malignancies. Thus, we provide mechanistic insights into CAR T cell function in patients with neurological autoimmune disease.

ARTseq-FISH reveals position-dependent differences in gene expression of micropatterned mESCs.

Differences in gene-expression profiles between individual cells can give rise to distinct cell fate decisions. Yet how localisation on a micropattern impacts initial changes in mRNA, protein, and phosphoprotein abundance remains unclear. To identify the effect of cellular position on gene expression, we developed a scalable antibody and mRNA targeting sequential fluorescence in situ hybridisation (ARTseq-FISH) method capable of simultaneously profiling mRNAs, proteins, and phosphoproteins in single cells. We studied 67 (phospho-)protein and mRNA targets in individual mouse embryonic stem cells (mESCs) cultured on circular micropatterns. ARTseq-FISH reveals relative changes in both abundance and localisation of mRNAs and (phospho-)proteins during the first 48 hours of exit from pluripotency. We confirm these changes by conventional immunofluorescence and time-lapse microscopy. Chemical labelling, immunofluorescence, and single-cell time-lapse microscopy further show that cells closer to the edge of the micropattern exhibit increased proliferation compared to cells at the centre. Together these data suggest that while gene expression is still highly heterogeneous position-dependent differences in mRNA and protein levels emerge as early as 12 hours after LIF withdrawal.

CASi: A framework for cross-timepoint analysis of single-cell RNA sequencing data.

Single-cell RNA sequencing (scRNA-seq) technology has been widely used to study the differences in gene expression at the single cell level, providing insights into the research of cell development, differentiation, and functional heterogeneity. Various pipelines and workflows of scRNA-seq analysis have been developed but few considered multi-timepoint data specifically. In this study, we develop CASi, a comprehensive framework for analyzing multiple timepoints' scRNA-seq data, which provides users with: (1) cross-timepoint cell annotation, (2) detection of potentially novel cell types emerged over time, (3) visualization of cell population evolution, and (4) identification of temporal differentially expressed genes (tDEGs). Through comprehensive simulation studies and applications to a real multi-timepoint single cell dataset, we demonstrate the robust and favorable performance of the proposal versus existing methods serving similar purposes.

Biclustering analysis on tree-shaped time-series single cell gene expression data of Caenorhabditis elegans.

BACKGROUND: In recent years, gene clustering analysis has become a widely used tool for studying gene functions, efficiently categorizing genes with similar expression patterns to aid in identifying gene functions. Caenorhabditis elegans is commonly used in embryonic research due to its consistent cell lineage from fertilized egg to adulthood. Biologists use 4D confocal imaging to observe gene expression dynamics at the single-cell level. However, on one hand, the observed tree-shaped time-series datasets have characteristics such as non-pairwise data points between different individuals. On the other hand, the influence of cell type heterogeneity should also be considered during clustering, aiming to obtain more biologically significant clustering results. RESULTS: A biclustering model is proposed for tree-shaped single-cell gene expression data of Caenorhabditis elegans. Detailedly, a tree-shaped piecewise polynomial function is first employed to fit non-pairwise gene expression time series data. Then, four factors are considered in the objective function, including Pearson correlation coefficients capturing gene correlations, p-values from the Kolmogorov-Smirnov test measuring the similarity between cells, as well as gene expression size and bicluster overlapping size. After that, Genetic Algorithm is utilized to optimize the function. CONCLUSION: The results on the small-scale dataset analysis validate the feasibility and effectiveness of our model and are superior to existing classical biclustering models. Besides, gene enrichment analysis is employed to assess the results on the complete real dataset analysis, confirming that the discovered biclustering results hold significant biological relevance.

Investigation of ENO2 as a promising novel marker for the progression of colorectal cancer with microsatellite instability-high.

BACKGROUND: Microsatellite instability-high (MSI-H) has emerged as a significant biological characteristic of colorectal cancer (CRC). Studies reported that MSI-H CRC generally had a better prognosis than microsatellite stable (MSS)/microsatellite instability-low (MSI-L) CRC, but some MSI-H CRC patients exhibited distinctive molecular characteristics and experienced a less favorable prognosis. In this study, our objective was to explore the metabolic transcript-related subtypes of MSI-H CRC and identify a biomarker for predicting survival outcomes. METHODS: Single-cell RNA sequencing (scRNA-seq) data of MSI-H CRC patients were obtained from the Gene Expression Omnibus (GEO) database. By utilizing the copy number variation (CNV) score, a malignant cell subpopulation was identified at the single-cell level. The metabolic landscape of various cell types was examined using metabolic pathway gene sets. Subsequently, functional experiments were conducted to investigate the biological significance of the hub gene in MSI-H CRC. Finally, the predictive potential of the hub gene was assessed using a nomogram. RESULTS: This study revealed a malignant tumor cell subpopulation from the single-cell RNA sequencing (scRNA-seq) data. MSI-H CRC was clustered into two subtypes based on the expression profiles of metabolism-related genes, and ENO2 was identified as a hub gene. Functional experiments with ENO2 knockdown and overexpression demonstrated its role in promoting CRC cell migration, invasion, glycolysis, and epithelial-mesenchymal transition (EMT) in vitro. High expression of ENO2 in MSI-H CRC patients was associated with worse clinical outcomes, including increased tumor invasion depth (p = 0.007) and greater likelihood of perineural invasion (p = 0.015). Furthermore, the nomogram and calibration curves based on ENO2 showed potential prognosis predictive performance. CONCLUSION: Our findings suggest that ENO2 serves as a novel prognostic biomarker and is associated with the progression of MSI-H CRC.

Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.

Single-cell RNA sequencing (scRNA-seq) experiments have become instrumental in developmental and differentiation studies, enabling the profiling of cells at a single or multiple time-points to uncover subtle variations in expression profiles reflecting underlying biological processes. Benchmarking studies have compared many of the computational methods used to reconstruct cellular dynamics; however, researchers still encounter challenges in their analysis due to uncertainty with respect to selecting the most appropriate methods and parameters. Even among universal data processing steps used by trajectory inference methods such as feature selection and dimension reduction, trajectory methods' performances are highly dataset-specific. To address these challenges, we developed Escort, a novel framework for evaluating a dataset's suitability for trajectory inference and quantifying trajectory properties influenced by analysis decisions. Escort evaluates the suitability of trajectory analysis and the combined effects of processing choices using trajectory-specific metrics. Escort navigates single-cell trajectory analysis through these data-driven assessments, reducing uncertainty and much of the decision burden inherent to trajectory inference analyses. Escort is implemented in an accessible R package and R/Shiny application, providing researchers with the necessary tools to make informed decisions during trajectory analysis and enabling new insights into dynamic biological processes at single-cell resolution.

Adult Human, but Not Rodent, Spermatogonial Stem Cells Retain States with a Foetal-like Signature.

Spermatogenesis involves a complex process of cellular differentiation maintained by spermatogonial stem cells (SSCs). Being critical to male reproduction, it is generally assumed that spermatogenesis starts and ends in equivalent transcriptional states in related species. Based on single-cell gene expression profiling, it has been proposed that undifferentiated human spermatogonia can be subclassified into four heterogenous subtypes, termed states 0, 0A, 0B, and 1. To increase the resolution of the undifferentiated compartment and trace the origin of the spermatogenic trajectory, we re-analysed the single-cell (sc) RNA-sequencing libraries of 34 post-pubescent human testes to generate an integrated atlas of germ cell differentiation. We then used this atlas to perform comparative analyses of the putative SSC transcriptome both across human development (using 28 foetal and pre-pubertal scRNA-seq libraries) and across species (including data from sheep, pig, buffalo, rhesus and cynomolgus macaque, rat, and mouse). Alongside its detailed characterisation, we show that the transcriptional heterogeneity of the undifferentiated spermatogonial cell compartment varies not only between species but across development. Our findings associate 'state 0B' with a suppressive transcriptomic programme that, in adult humans, acts to functionally oppose proliferation and maintain cells in a ready-to-react state. Consistent with this conclusion, we show that human foetal germ cells-which are mitotically arrested-can be characterised solely as state 0B. While germ cells with a state 0B signature are also present in foetal mice (and are likely conserved at this stage throughout mammals), they are not maintained into adulthood. We conjecture that in rodents, the foetal-like state 0B differentiates at birth into the renewing SSC population, whereas in humans it is maintained as a reserve population, supporting testicular homeostasis over a longer reproductive lifespan while reducing mutagenic load. Together, these results suggest that SSCs adopt differing evolutionary strategies across species to ensure fertility and genome integrity over vastly differing life histories and reproductive timeframes.

Single-Cell RNA Sequencing Reveals Cardiac Fibroblast-Specific Transcriptomic Changes in Dilated Cardiomyopathy.

Dilated cardiomyopathy (DCM) is the most common cause of heart failure, with a complex aetiology involving multiple cell types. We aimed to detect cell-specific transcriptomic alterations in DCM through analysis that leveraged recent advancements in single-cell analytical tools. Single-cell RNA sequencing (scRNA-seq) data from human DCM cardiac tissue were subjected to an updated bioinformatic workflow in which unsupervised clustering was paired with reference label transfer to more comprehensively annotate the dataset. Differential gene expression was detected primarily in the cardiac fibroblast population. Bulk RNA sequencing was performed on an independent cohort of human cardiac tissue and compared with scRNA-seq gene alterations to generate a stratified list of higher-confidence, fibroblast-specific expression candidates for further validation. Concordant gene dysregulation was confirmed in TGFß-induced fibroblasts. Functional assessment of gene candidates showed that AEBP1 may play a significant role in fibroblast activation. This unbiased approach enabled improved resolution of cardiac cell-type-specific transcriptomic alterations in DCM.

Identification and validation of a prognostic anoikis-related gene signature in papillary thyroid carcinoma by integrated analysis of single-cell and bulk RNA-sequencing.

Papillary thyroid carcinoma (PTC) prognosis may be deteriorated due to the metastases, and anoikis palys an essential role in the tumor metastasis. However, the potential effect of anoikis-related genes on the prognosis of PTC was unclear. The mRNA and clinical information were obtained from the cancer genome atlas database. Hub genes were identified and risk model was constructed using Cox regression analysis. Kaplan-Meier (K-M) curve was applied for the survival analysis. Immune infiltration and immune therapy response were calculated using CIBERSORT and TIDE. The identification of cell types and cell interaction was performed by Seurat, SingleR and CellChat packages. GO, KEGG, and GSVA were applied for the enrichment analysis. Protein-protein interaction network was constructed in STRING and Cytoscape. Drug sensitivity was assessed in GSCA. Based on bulk RNA data, we identified 4 anoikis-related risk signatures, which were oncogenes, and constructed a risk model. The enrichment analysis found high risk group was enriched in some immune-related pathways. High risk group had higher infiltration of Tregs, higher TIDE score and lower levels of monocytes and CD8 T cells. Based on scRNA data, we found that 4 hub genes were mainly expressed in monocytes and macrophages, and they interacted with T cells. Hub genes were significantly related to immune escape-related genes. Drug sensitivity analysis suggested that cyclin dependent kinase inhibitor 2A may be a better chemotherapy target. We constructed a risk model which could effectively and steadily predict the prognosis of PTC. We inferred that the immune escape may be involved in the development of PTC.

Single-cell sequencing of tumour infiltrating T cells efficiently identifies tumour-specific T cell receptors based on the T cell activation score.

Adoptively transferred T cell receptor-engineered T cells are a promising cancer treatment strategy, and the identification of tumour-specific TCRs is essential. Previous studies reported that tumour-reactive T cells and TCRs could be isolated based on the expression of activation markers. However, since T cells with different cell states could not respond uniformly to activation but show a heterogeneous expression profile of activation and effector molecules, isolation of tumour-reactive T cells based on single activation or effector molecules could result in the absence of tumour-reactive T cells; thus, combinations of multiple activation and effector molecules could improve the efficiency of isolating tumour-specific TCRs. We enrolled two patients with lung adenocarcinoma and obtained their tumour infiltrating lymphocytes (TILs) and autologous tumour cells (ATCs). TILs were cocultured with the corresponding ATCs for 12 h and subjected to single-cell RNA sequencing. First, we identified three TCRs with the highest expression levels of IFNG and TNFRSF9 mRNA for each patient, yet only the top one or two recognized the corresponding ATCs in each patient. Next, we defined the activation score based on normalized expression levels of IFNG, IL2, TNF, IL2RA, CD69, TNFRSF9, GZMB, GZMA, GZMK, and PRF1 mRNA for each T cell and then identified three TCRs with the highest activation score for each patient. We found that all three TCRs in each patient could specifically identify corresponding ATCs. In conclusion, we established an efficient approach to isolate tumour-reactive TCRs based on combinations of multiple activation and effector molecules through single-cell RNA sequencing.

Multiplexed bulk and single-cell RNA-seq hybrid enables cost-efficient disease modeling with chimeric organoids.

Disease modeling with isogenic Induced Pluripotent Stem Cell (iPSC)-differentiated organoids serves as a powerful technique for studying disease mechanisms. Multiplexed coculture is crucial to mitigate batch effects when studying the genetic effects of disease-causing variants in differentiated iPSCs or organoids, and demultiplexing at the single-cell level can be conveniently achieved by assessing natural genetic barcodes. Here, to enable cost-efficient time-series experimental designs via multiplexed bulk and single-cell RNA-seq of hybrids, we introduce a computational method in our Vireo Suite, Vireo-bulk, to effectively deconvolve pooled bulk RNA-seq data by genotype reference, and thereby quantify donor abundance over the course of differentiation and identify differentially expressed genes among donors. Furthermore, with multiplexed scRNA-seq and bulk RNA-seq, we demonstrate the usefulness and necessity of a pooled design to reveal donor iPSC line heterogeneity during macrophage cell differentiation and to model rare WT1 mutation-driven kidney disease with chimeric organoids. Our work provides an experimental and analytic pipeline for dissecting disease mechanisms with chimeric organoids.

Integrating spatial and single-cell transcriptomics reveals tumor heterogeneity and intercellular networks in colorectal cancer.

Single cell RNA sequencing (scRNA-seq), a powerful tool for studying the tumor microenvironment (TME), does not preserve/provide spatial information on tissue morphology and cellular interactions. To understand the crosstalk between diverse cellular components in proximity in the TME, we performed scRNA-seq coupled with spatial transcriptomic (ST) assay to profile 41,700 cells from three colorectal cancer (CRC) tumor-normal-blood pairs. Standalone scRNA-seq analyses revealed eight major cell populations, including B cells, T cells, Monocytes, NK cells, Epithelial cells, Fibroblasts, Mast cells, Endothelial cells. After the identification of malignant cells from epithelial cells, we observed seven subtypes of malignant cells that reflect heterogeneous status in tumor, including tumor_CAV1, tumor_ATF3_JUN | FOS, tumor_ZEB2, tumor_VIM, tumor_WSB1, tumor_LXN, and tumor_PGM1. By transferring the cellular annotations obtained by scRNA-seq to ST spots, we annotated four regions in a cryosection from CRC patients, including tumor, stroma, immune infiltration, and colon epithelium regions. Furthermore, we observed intensive intercellular interactions between stroma and tumor regions which were extremely proximal in the cryosection. In particular, one pair of ligands and receptors (C5AR1 and RPS19) was inferred to play key roles in the crosstalk of stroma and tumor regions. For the tumor region, a typical feature of TMSB4X-high expression was identified, which could be a potential marker of CRC. The stroma region was found to be characterized by VIM-high expression, suggesting it fostered a stromal niche in the TME. Collectively, single cell and spatial analysis in our study reveal the tumor heterogeneity and molecular interactions in CRC TME, which provides insights into the mechanisms underlying CRC progression and may contribute to the development of anticancer therapies targeting on non-tumor components, such as the extracellular matrix (ECM) in CRC. The typical genes we identified may facilitate to new molecular subtypes of CRC.

A time-resolved multi-omics atlas of transcriptional regulation in response to high-altitude hypoxia across whole-body tissues.

High-altitude hypoxia acclimatization requires whole-body physiological regulation in highland immigrants, but the underlying genetic mechanism has not been clarified. Here we use sheep as an animal model for low-to-high altitude translocation. We generate multi-omics data including whole-genome sequences, time-resolved bulk RNA-Seq, ATAC-Seq and single-cell RNA-Seq from multiple tissues as well as phenotypic data from 20 bio-indicators. We characterize transcriptional changes of all genes in each tissue, and examine multi-tissue temporal dynamics and transcriptional interactions among genes. Particularly, we identify critical functional genes regulating the short response to hypoxia in each tissue (e.g., PARG in the cerebellum and HMOX1 in the colon). We further identify TAD-constrained cis-regulatory elements, which suppress the transcriptional activity of most genes under hypoxia. Phenotypic and transcriptional evidence indicate that antenatal hypoxia could improve hypoxia tolerance in offspring. Furthermore, we provide time-series expression data of candidate genes associated with human mountain sickness (e.g., BMPR2) and high-altitude adaptation (e.g., HIF1A). Our study provides valuable resources and insights for future hypoxia-related studies in mammals.

Single-cell transcriptomic analysis of decidual immune cell landscape in the occurrence of adverse pregnancy outcomes induced by Toxoplasma gondii infection.

BACKGROUND: Toxoplasma gondii is an obligate intracellular parasite that can lead to adverse pregnancy outcomes, particularly in early pregnancy. Previous studies have illustrated the landscape of decidual immune cells. However, the landscape of decidual immune cells in the maternal-fetal microenvironment during T. gondii infection remains unknown. METHODS: In this study, we employed single-cell RNA sequencing to analyze the changes in human decidual immune cells following T. gondii infection. The results of scRNA-seq were further validated with flow cytometry, reverse transcription-polymerase chain reaction, western blot, and immunofluorescence staining. RESULTS: Our results showed that the proportion of 17 decidual immune cell clusters and the expression levels of 21 genes were changed after T. gondii infection. Differential gene analysis demonstrated that T. gondii infection induced the differential expression of 279, 312, and 380 genes in decidual NK cells (dNK), decidual macrophages (dMφ), and decidual T cells (dT), respectively. Our results revealed for the first time that several previously unknown molecules in decidual immune cells changed following infection. This result revealed that the function of maternal-fetal immune tolerance declined, whereas the killing ability of decidual immune cells enhanced, eventually contributing to the occurrence of adverse pregnancy outcomes. CONCLUSIONS: This study provides valuable resource for uncovering several novel molecules that play an important role in the occurrence of abnormal pregnancy outcomes induced by T. gondii infection.

Modification of Seurat v4 for the Development of a Phase Assignment Tool Able to Distinguish between G2 and Mitotic Cells.

Single-cell RNA sequencing (scRNAseq) is a rapidly advancing field enabling the characterisation of heterogeneous gene expression profiles within a population. The cell cycle phase is a major contributor to gene expression variance between cells and computational analysis tools have been developed to assign cell cycle phases to cells within scRNAseq datasets. Whilst these tools can be extremely useful, all have the drawback that they classify cells as only G1, S or G2/M. Existing discrete cell phase assignment tools are unable to differentiate between G2 and M and continuous-phase-assignment tools are unable to identify a region corresponding specifically to mitosis in a pseudo-timeline for continuous assignment along the cell cycle. In this study, bulk RNA sequencing was used to identify differentially expressed genes between mitotic and interphase cells isolated based on phospho-histone H3 expression using fluorescence-activated cell sorting. These gene lists were used to develop a methodology which can distinguish G2 and M phase cells in scRNAseq datasets. The phase assignment tools present in Seurat were modified to allow for cell cycle phase assignment of all stages of the cell cycle to identify a mitotic-specific cell population.

TGFß-Responsive Stromal Activation Occurs Early in Serrated Colorectal Carcinogenesis.

Activated TGFß signaling in the tumor microenvironment, which occurs independently of epithelial cancer cells, has emerged as a key driver of tumor progression in late-stage colorectal cancer (CRC). This study aimed to elucidate the contribution of TGFß-activated stroma to serrated carcinogenesis, representing approximately 25% of CRCs and often characterized by oncogenic BRAF mutations. We used a transcriptional signature developed based on TGFß-responsive, stroma-specific genes to infer TGFß-dependent stromal activation and conducted in silico analyses in 3 single-cell RNA-seq datasets from a total of 39 CRC samples and 12 bulk transcriptomic datasets consisting of 2014 CRC and 416 precursor samples, of which 33 were serrated lesions. Single-cell analyses validated that the signature was expressed specifically by stromal cells, effectively excluding transcriptional signals derived from epithelial cells. We found that the signature was upregulated during malignant transformation and cancer progression, and it was particularly enriched in CRCs with mutant BRAF compared to wild-type counterparts. Furthermore, across four independent precursor datasets, serrated lesions exhibited significantly higher levels of TGFß-responsive stromal activation compared to conventional adenomas. This large-scale analysis suggests that TGFß-dependent stromal activation occurs early in serrated carcinogenesis. Our study provides novel insights into the molecular mechanisms underlying CRC development via the serrated pathway.

Single-Cell Transcriptional Profile Construction of Rat Pituitary Glands before and after Sexual Maturation and Identification of Novel Marker Spp1 in Gonadotropes.

The mammalian pituitary gland drives highly conserved physiological processes such as somatic cell growth, pubertal transformation, fertility, and metabolism by secreting a variety of hormones. Recently, single-cell transcriptomics techniques have been used in pituitary gland research. However, more studies have focused on adult pituitary gland tissues from different species or different sexes, and no research has yet resolved cellular differences in pituitary gland tissue before and after sexual maturation. Here, we identified a total of 15 cell clusters and constructed single-cell transcriptional profiles of rats before and after sexual maturation. Furthermore, focusing on the gonadotrope cluster, 106 genes were found to be differentially expressed before and after sexual maturation. It was verified that Spp1, which is specifically expressed in gonadotrope cells, could serve as a novel marker for this cell cluster and has a promotional effect on the synthesis and secretion of follicle-stimulating hormone. The results provide a new resource for further resolving the regulatory mechanism of pituitary gland development and pituitary hormone synthesis and secretion.