Single-cell Sequencing Technology Research Articles

Modeling single cell trajectory using forward-backward stochastic differential equations.

Recent advances in single-cell sequencing technology have provided opportunities for mathematical modeling of dynamic developmental processes at the single-cell level, such as inferring developmental trajectories. Optimal transport has emerged as a promising theoretical framework for this task by computing pairings between cells from different time points. However, optimal transport methods have limitations in capturing nonlinear trajectories, as they are static and can only infer linear paths between endpoints. In contrast, stochastic differential equations (SDEs) offer a dynamic and flexible approach that can model non-linear trajectories, including the shape of the path. Nevertheless, existing SDE methods often rely on numerical approximations that can lead to inaccurate inferences, deviating from true trajectories. To address this challenge, we propose a novel approach combining forward-backward stochastic differential equations (FBSDE) with a refined approximation procedure. Our FBSDE model integrates the forward and backward movements of two SDEs in time, aiming to capture the underlying dynamics of single-cell developmental trajectories. Through comprehensive benchmarking on multiple scRNA-seq datasets, we demonstrate the superior performance of FBSDE compared to other methods, highlighting its efficacy in accurately inferring developmental trajectories.

ScPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism.

With the rapid advancement of single-cell sequencing technology, it becomes gradually possible to delve into the cellular responses to various external perturbations at the gene expression level. However, obtaining perturbed samples in certain scenarios may be considerably challenging, and the substantial costs associated with sequencing also curtail the feasibility of large-scale experimentation. A repertoire of methodologies has been employed for forecasting perturbative responses in single-cell gene expression. However, existing methods primarily focus on the average response of a specific cell type to perturbation, overlooking the single-cell specificity of perturbation responses and a more comprehensive prediction of the entire perturbation response distribution. Here we present scPRAM, a method for predicting Perturbation Responses in single-cell gene expression based on Attention Mechanisms. Leveraging variational autoencoders and optimal transport, scPRAM aligns cell states before and after perturbation, followed by accurate prediction of gene expression responses to perturbations for unseen cell types through attention mechanisms. Experiments on multiple real perturbation datasets involving drug treatments and bacterial infections demonstrate that scPRAM attains heightened accuracy in perturbation prediction across cell types, species, and individuals, surpassing existing methodologies. Furthermore, scPRAM demonstrates outstanding capability in identifying differentially expressed genes under perturbation, capturing heterogeneity in perturbation responses across species, and maintaining stability in the presence of data noise and sample size variations. https://github.com/jiang-q19/scPRAM and https://doi.org/10.5281/zenodo.10935038. Supplementary data are available at Bioinformatics online.

Single-cell transcriptomics reveals activation of endothelial cell and identifies LHPP as a potential target in ulcerative colitis

This study delves into Ulcerative colitis (UC), a persistent gastrointestinal disorder marked by inflammation and ulcers, significantly elevating colorectal cancer risk. The emergence of single-cell RNA sequencing (scRNA-seq) technology has opened new avenues for dissecting the intricate cellular dynamics and molecular mechanisms at play in UC pathology. By analyzing scRNA-seq data from individuals with UC, our study has revealed a consistent enhancement of inflammatory response pathways throughout the course of the disease, alongside detailing the characteristics of endothelial cell damage within colitis environments. A noteworthy finding is the downregulation of Phospholysine Phosphohistidine Inorganic Pyrophosphate Phosphatase (LHPP), which exhibited a inversely correlate with STAT3 expression levels. The markedly reduced expression of LHPP in both the tissues and plasma of UC patients positions LHPP as a compelling target for therapeutic intervention. Our findings highlight the pivotal role LHPP could play in moderating inflammation, spotlighting its potential as a crucial molecular target in the quest to understand and treat UC.

Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning.

Single-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-cell RNA sequencing techniques to identify cell clusters, the information contained in mono-omic data is often limited, leading to suboptimal clustering performance. The emergence of single-cell multi-omics sequencing technologies enables the integration of multiple omics data for identifying cell clusters, but how to integrate different omics data effectively remains challenging. Additionally, designing a clustering method that performs well across various types of multi-omics data poses a persistent challenge due to the data's inherent characteristics. In this paper, we propose a graph-regularized multi-view ensemble clustering (GRMEC-SC) model for single-cell clustering. Our proposed approach can adaptively integrate multiple omics data and leverage insights from multiple base clustering results. We extensively evaluate our method on five multi-omics datasets through a series of rigorous experiments. The results of these experiments demonstrate that our GRMEC-SC model achieves competitive performance across diverse multi-omics datasets with varying characteristics. Implementation of GRMEC-SC, along with examples, can be found on the GitHub repository: https://github.com/polarisChen/GRMEC-SC. Supplementary data are available at Bioinformatics online.

SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication.

Recent advances in single-cell RNA sequencing technology have eased analyses of signaling networks of cells. Recently, cell-cell interaction has been studied based on various link prediction approaches on graph-structured data. These approaches have assumptions about the likelihood of node interaction, thus showing high performance for only some specific networks. Subgraph-based methods have solved this problem and outperformed other approaches by extracting local subgraphs from a given network. In this work, we present a novel method, called Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication (SEGCECO), which uses an attributed graph convolutional neural network to predict cell-cell communication from single-cell RNA-seq data. SEGCECO captures the latent and explicit attributes of undirected, attributed graphs constructed from the gene expression profile of individual cells. High-dimensional and sparse single-cell RNA-seq data make converting the data into a graphical format a daunting task. We successfully overcome this limitation by applying SoptSC, a similarity-based optimization method in which the cell-cell communication network is built using a cell-cell similarity matrix which is learned from gene expression data. We performed experiments on six datasets extracted from the human and mouse pancreas tissue. Our comparative analysis shows that SEGCECO outperforms latent feature-based approaches, and the state-of-the-art method for link prediction, WLNM, with 0.99 ROC and 99% prediction accuracy. The datasets can be found at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84133 and the code is publicly available at Github https://github.com/sheenahora/SEGCECO and Code Ocean https://codeocean.com/capsule/8244724/tree.

Analysis of the effect of CCR7 on the microenvironment of mouse oral squamous cell carcinoma by single-cell RNA sequencing technology

BackgroundStudies have shown that CCR7, an important inflammatory factor, can promote the proliferation and metastasis of oral squamous cell carcinoma (OSCC), but its role in the tumor microenvironment (TME) remains unclear. This paper explores the role of CCR7 in the TME of OSCC.MethodsIn this work, we constructed CCR7 gene knockout mice and OSCC mouse models. Single-cell RNA sequencing (scRNA-seq) and bioinformatics were used to analyze the differences in the OSCC microenvironment between three CCR7 gene knockout mice (KO) and three wild-type mice (WT). Immunohistochemistry, immunofluorescence staining, and flow cytometry were used to analyze the expression of key genes in significantly different cell types between the KO and WT groups. An in vitro experiment was used to verify the effect of CCR7 on M2 macrophage polarization.ResultsIn the mouse OSCC models, the tumor growth rate in the KO group was significantly lower than that in the WT group. Eight main cell types (including tumor cells, fibroblasts, macrophages, granulocytes, T cells, endothelial cells, monocytes, and B cells) were identified by Seurat analysis. The scRNA-seq results showed that the proportion of tumor cells was lower, but the proportion of inflammatory cells was significantly higher in the KO group than in the WT group. CellPhoneDB analysis results indicated a strong interaction relationship between tumor cells and macrophages, T cells, fibroblasts, and endothelial cells. Functional enrichment results indicated that the expression level of the Dusp1 gene in the KO group was generally higher than that in the WT group in various cell types. Macrophage subclustering results indicated that the proportion of M2 macrophages in the KO group was lower than that in the WT group. In vitro experimental results showed that CCR7 can promote M2 macrophage polarization, thus promoting the proliferation, invasion and migration of OSCC cells.ConclusionsCCR7 gene knockout can significantly inhibit the growth of mouse oral squamous cell carcinoma by promoting the polarization of M2 macrophages.

Investigation of cellular communication and signaling pathways in tumor microenvironment for high TP53-expressing osteosarcoma cells through single-cell RNA sequencing.

Osteosarcoma (OS) stands as the most prevalent primary bone cancer in children and adolescents, and its limited treatment options often result in unsatisfactory outcomes, particularly for metastatic cases. The tumor microenvironment (TME) has been recognized as a crucial determinant in OS progression. However, the intercellular dynamics between high TP53-expressing OS cells and neighboring cell types within the TME are yet to be thoroughly understood. In our study, we harnessed the single-cell RNA sequencing (scRNA-seq) technology in combination with the computational tool-Cellchat, aiming to elucidate the intercellular communication networks present within OS. Through meticulous quantitative inference and subsequent analysis of these networks, we succeeded in identifying significant signaling pathways connecting high TP53-expressing OS cells with proximate cell types, namely Macrophages, Monocytes, Endothelial Cells, and PVLs. This research brings forth a nuanced understanding of the intricate patterns and coordination involved in the TME's intercellular communication signals. These findings not only provide profound insights into the molecular mechanisms underpinning OS but also indicate potential therapeutic targets that could revolutionize treatment strategies.

Abstract 865: Single-cell sequencing identification of malignant cell subpopulations linked to recurrence in hepatocellular carcinoma

Abstract Introduction: Hepatocellular Carcinoma (HCC) is a highly malignant and challenging-to-treat tumor, with early recurrence affecting up to 35-50% of patients within two years after surgery, significantly impacting prognosis. Identifying high-risk patients for recurrence is critical for guiding postoperative adjuvant therapy. The rapidly advancing field of single-cell RNA sequencing technology offers an unprecedented opportunity to discover clinically relevant cell subpopulations. Here, we harnessed single-cell techniques to unveil a cell subpopulation closely linked with HCC recurrence, highlighting its clinical significance. Methods and Results: We conducted single-cell RNA sequencing on four HCC samples and harmonized data from three public datasets (GSE112271, GSE125449, CNP0000650) to elucidate the heterogeneity of malignant cells in HCC. Our investigation revealed a distinctive HCC subpopulation enriched in patients with early recurrence (11.4% vs. 3.8%, P=0.02). This subpopulation exhibited up-regulated STMN1 and EZH2, signifying high proliferative potential and a propensity for epithelial-mesenchymal transition (EMT). Applying the CIBERSORTx algorithm to the TCGA-LIHC dataset (N=323 patients), we inferred the proportion of this recurrence subpopulation among all malignant cells. Notably, patients with a higher proportion of this subpopulation (N=160) demonstrated significantly shorter recurrence-free survival (31.2 vs. 42.9 months, P=0.002), reduced median survival (45.9 vs 55.7 months, P=0.03) and a higher rate of microvascular invasion (35% vs. 23%, P=0.01). To validate this recurrence-related subpopulation in clinical samples, we conducted multiplex immunohistochemistry staining on paraffin-embedded (FFPE) HCC samples (including CD45, CD31, ARG1, STMN1, EZH2) and used machine learning for its identification. Our findings revealed that patients with a higher proportion of this recurrence subpopulation (N=40) experienced significantly shorter median survival (38.5 vs. 51.0 months, P<0.005) and progression-free survival (25.1 vs. 33.4 months, P=0.01) compared to a lower proportion (N=40). Multivariate Cox regression analysis confirmed that the proportion of this recurrence subpopulation is an independent predictor of worse prognosis (P=0.003). Conclusion: Through the power of single-cell RNA sequencing technology, we have unearthed and characterized a malignant cell subpopulation intricately connected with HCC recurrence. Importantly, this subpopulation can be identified in FFPE tissue samples and correlates robustly with patient prognosis. Our study underscores the transformative possibility of single-cell techniques in developing cell subpopulation-based biomarkers, emphasizing their clinical relevance and potential utility in guiding postoperative adjuvant therapy for HCC patients. Citation Format: Yucheng Dong, Xin Guo, Xuezhu Wang, Fan Yang, Yongchang Zheng, Yang Chen. Single-cell sequencing identification of malignant cell subpopulations linked to recurrence in hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 865.

Abstract 7607: Single-cell genomics study before and after neoadjuvant therapy for triple-negative breast cancer

Abstract Breast cancer has become the most common malignant tumor globally. Neoadjuvant chemotherapy (NAC) has become an important treatment option for locally advanced breast cancer, capable of eliminating circulating tumor cells (micrometastases) and achieving downstaging objectives. However, even after completing neoadjuvant chemotherapy or receiving local treatment, some patients may still have residual lesions, which are a key factor in distant recurrence and metastasis. The existing clinical biomarker tests such as CEA, CA-153, and imaging methods are unable to accurately assess the condition early on. Although tissue pathology biopsy of tumor specimens is the gold standard for clinical pathological diagnosis, it also has certain limitations. Traditional high-throughput sequencing technology for breast cancer primarily identifies "driver genes" associated with the occurrence and development of breast cancer. However, it has limitations in identifying genomic structural changes and subclones of breast cancer, and it overlooks the heterogeneity among tumor cells. The emergence of single-cell RNA sequencing (scRNA-seq) technology can detect these heterogeneous individuals, decode the heterogeneity of breast cancer cells, refine molecular subtypes, and open up new pathways to overcome drug resistance. We collected samples from 6 patients with locally advanced triple-negative breast cancer (ER-, PR-, HER2-negative, defined as HER2 immunohistochemistry 0, 1+ or 2+ but FISH-negative). We compared the cell subgroups and their gene expression in biopsy specimens before neoadjuvant chemotherapy with those in post-treatment specimens obtained during surgery, analyzing changes in cell composition in the tumor microenvironment before and after immunotherapy to identify specific cell types potentially contributing to the outcome of immunotherapy, particularly cell subtypes associated with T-cell clonal proliferation. We found that in the group with good neoadjuvant treatment response (Miller-Payne grade 4-5), B cells, CD4+ T cells, CD8+ T cells, and Tregs cells were all significantly downregulated, while endothelial cells and aSMA-positive fibroblast cells were significantly upregulated. By comparing changes in the immune microenvironment between patients with and without T-cell clonal proliferation before and after treatment, we revealed the differentiation patterns of various immune cells in immunotherapy and potential mechanisms of action. Citation Format: Yingkuan Shao, Kailun Xu. Single-cell genomics study before and after neoadjuvant therapy for triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7607.

Single-cell Profiling of Reprogrammed Human Neural Stem Cells Unveils High Similarity to Neural Progenitors in the Developing Central Nervous System.

Similar to induced pluripotent cells (iPSCs), induced neural stem cells (iNSCs) can be directly converted from human somatic cells such as dermal fibroblasts and peripheral blood monocytes. While previous studies have demonstrated the resemblance of iNSCs to neural stem cells derived from primary sources and embryonic stem cells, respectively, a comprehensive analysis of the correlation between iNSCs and their physiological counterparts remained to be investigated. Nowadays, single-cell sequencing technologies provide unique opportunities for in-depth cellular benchmarking of complex cell populations. Our study involves the comprehensive profiling of converted human iNSCs at a single-cell transcriptomic level, alongside conventional methods, like flow cytometry and immunofluorescence stainings. Our results show that the iNSC conversion yields a homogeneous cell population expressing bona fide neural stem cell markers. Extracting transcriptomic signatures from published single cell transcriptomic atlas data and comparison to the iNSC transcriptome reveals resemblance to embryonic neuroepithelial cells of early neurodevelopmental stages observed in vivo at 5 weeks of development. Our data underscore the physiological relevance of directly converted iNSCs, making them a valuable in vitro system for modeling human central nervous system development and establishing translational applications in cell therapy and compound screening.

Abstract 5666: Exploring spatially resolved intra-tumoral heterogeneity of glioblastoma and neuronal mechanisms facilitating brain invasion

Abstract Glioblastoma (GBM) is a highly lethal brain cancer, comprising about half of all central nervous system malignancies. Its recurrence and resistance to aggressive treatments, such as surgery, radiation, and chemotherapy, underscore its biological complexity. A key feature of GBM is intra-tumoral heterogeneity (ITH), stemming from a diversity of transcriptional cell states within the tumor. While genetic contributions to ITH are well-researched, the role of non-genetic factors, especially in promoting transitions between the various transcriptional cell states (referred to as plasticity), is less understood. This plasticity enables GBM cells to survive treatment pressures, necessitating a deeper dive into the epigenetic underpinnings that govern it and the role of the tumor microenvironment (TME) in influencing it. We proposed that the TME significantly impacts the epigenetic landscape of both malignant and surrounding non-malignant cells, thereby promoting ITH and cell state plasticity. To assess this, we analyzed samples from different anatomical areas of GBM tumors collected using MRI-guided selection. Utilizing advanced multimodal single-cell sequencing technologies, we profiled the transcriptomes and epigenomes of individual cells, revealing their spatially dependent phenotypic and epigenetic diversity. Our results indicated a higher enrichment of progenitor-like malignant cells, resembling neural or oligodendrocyte progenitors, around the tumor margins. In contrast, the core mainly contained differentiated cells with mesenchymal-like characteristics. Intriguingly, progenitor-like cells in the peri-tumoral zone expressed a unique neuronal activity program, including elevated levels of PRC2 complex targets, which are critical for maintaining the pluripotency and self-renewal capabilities of neural stem cells. Additionally, our analysis of cell-cell interactions between neurons and malignant cells identified ligand-receptor pairs crucial for axon guidance, neurogenesis, and tumor invasion. Proneural factors, critical for neural differentiation, were more accessible in peri-tumoral progenitor-like cells, suggesting that malignant cells might exploit neurodevelopmental routes for growth and invasion. This work underscores the significant spatial cellular heterogeneity of GBM and the variation in epigenetic states across the tumor, demonstrating how GBM cells can hijack neurodevelopmental pathways to foster invasion. Although surgical resection can target the tumor core, our insights into the invasive mechanisms of malignant cells provide a foundation for developing targeted therapies to combat the highly invasive cells persisting in regions adjacent to the tumor. Citation Format: Federico Gaiti, Yiyan Wu, Sheila Mansouri, Benson Wu, Yosef Ellenbogen, Xuyao Li, Parnian Habibi, Joan Kant, Pathum Kossinna, Sandra Ruth Lau Rodriguez, Gelareh Zadeh. Exploring spatially resolved intra-tumoral heterogeneity of glioblastoma and neuronal mechanisms facilitating brain invasion [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5666.

The significance of single-cell transcriptome analysis in epididymis research.

As a crucial component of the male reproductive system, the epididymis plays multiple roles, including sperm storage and secretion of nutritive fluids for sperm development and maturation. The acquisition of fertilization capacity by sperm occurs during their transport through the epididymis. Compared with the testis, little has been realized about the importance of the epididymis. However, with the development of molecular biology and single-cell sequencing technology, the importance of the epididymis for male fertility should be reconsidered. Recent studies have revealed that different regions of the epididymis exhibit distinct functions and cell type compositions, which are likely determined by variations in gene expression patterns. In this research, we primarily focused on elucidating the cellular composition and region-specific gene expression patterns within different segments of the epididymis and provided detailed insights into epididymal function in male fertility.

Potential anti-tumor effects of regulatory T cells in the tumor microenvironment: a review.

Regulatory T cells (Tregs) expressing the transcription factor FoxP3 are essential for maintaining immunological balance and are a significant component of the immunosuppressive tumor microenvironment (TME). Single-cell RNA sequencing (ScRNA-seq) technology has shown that Tregs exhibit significant plasticity and functional diversity in various tumors within the TME. This results in Tregs playing a dual role in the TME, which is not always centered around supporting tumor progression as typically believed. Abundant data confirms the anti-tumor activities of Tregs and their correlation with enhanced patient prognosis in specific types of malignancies. In this review, we summarize the potential anti-tumor actions of Tregs, including suppressing tumor-promoting inflammatory responses and boosting anti-tumor immunity. In addition, this study outlines the spatial and temporal variations in Tregs function to emphasize that their predictive significance in malignancies may change. It is essential to comprehend the functional diversity and potential anti-tumor effects of Tregs to improve tumor therapy strategies.

Cell Heterogeneity and Variability in Peripheral Nerve after Injury.

With the development of single-cell sequencing technology, the cellular composition of more and more tissues is being elucidated. As the whole nervous system has been extensively studied, the cellular composition of the peripheral nerve has gradually been revealed. By summarizing the current sequencing data, we compile the heterogeneities of cells that have been reported in the peripheral nerves, mainly the sciatic nerve. The cellular variability of Schwann cells, fibroblasts, immune cells, and endothelial cells during development and disease has been discussed in this review. The discovery of the architecture of peripheral nerves after injury benefits the understanding of cellular complexity in the nervous system, as well as the construction of tissue engineering nerves for nerve repair and axon regeneration.

Comparative analysis of 10X Chromium vs. BD Rhapsody whole transcriptome single-cell sequencing technologies in complex human tissues

The development of single-cell omics tools has enabled scientists to study the tumor microenvironment (TME) in unprecedented detail. However, each of the different techniques may have its unique strengths and limitations. Here we directly compared two commercially available high-throughput single-cell RNA sequencing (scRNA-seq) technologies - droplet-based 10X Chromium vs. microwell-based BD Rhapsody - using paired samples from patients with localized prostate cancer (PCa) undergoing a radical prostatectomy.Although high technical consistency was observed in unraveling the whole transcriptome, the relative abundance of cell populations differed. Cells with low mRNA content such as T cells were underrepresented in the droplet-based system, at least partly due to lower RNA capture rates. In contrast, microwell-based scRNA-seq recovered less cells of epithelial origin. Moreover, we discovered platform-dependent variabilities in mRNA quantification and cell-type marker annotation. Overall, our study provides important information for selection of the appropriate scRNA-seq platform and for the interpretation of published results.

Role of crosstalk between synovial cells and chondrocytes in osteoarthritis (Review).

Osteoarthritis (OA) is a low-grade, nonspecific inflammatory disease that affects the entire joint. This condition is characterized by synovitis, cartilage erosion, subchondral bone defects, and subpatellar fat pad damage. There is mounting evidence demonstrating the significance of crosstalk between synovitis and cartilage destruction in the development of OA. To comprehensively explore the phenotypic alterations of synovitis and cartilage destruction, it is important to elucidate the crosstalk mechanisms between chondrocytes and synovial cells. Furthermore, the updated iteration of single-cell sequencing technology reveals the interaction between chondrocyte and synovial cells. In the present review, the histological and pathological alterations between cartilage and synovium during OA progression are described, and the mode of interaction and molecular mechanisms between synovial cells and chondrocytes in OA, both of which affect the OA process mainly by altering the inflammatory environment and cellular state, are elucidated. Finally, the current OA therapeutic approaches are summarized and emerging therapeutic targets are reviewed in an attempt to provide potential insights into OA treatment.

SCancerRNA: Expression at the Single Cell Level and Interaction Resource of Non-coding RNA Biomarkers for Cancers

Abstract Non-coding RNAs (ncRNAs) participate in multiple biological processes associated with cancer as tumor suppressors or oncogenic drivers. Due to their high stability in plasma, urine, and many other fluids, ncRNAs have the potential to serve as key biomarkers for early diagnosis and screening of cancers. During cancer progression, tumor heterogeneity plays a crucial role, and it is particularly important to understand the gene expression patterns of individual cells. With the development of single-cell RNA sequencing (scRNA-seq) technologies, uncovering gene expression in different cell types for human cancers has become feasible by profiling transcriptomes at the cellular level. However, a well-organized and comprehensive online resource that provides access to the expression of genes corresponding to ncRNA biomarkers in different cell types at the single cell level is not available yet. Therefore, we developed the SCancerRNA database to summarize experimentally supported data on long ncRNA, microRNA, PIWI-interacting RNA, small nucleolar RNA, and circular RNA biomarkers, as well as data on their differential expression at the cellular level. Furthermore, we collected biological functions and clinical applications of biomarkers to facilitate the application of ncRNA biomarkers to cancer diagnosis, as well as monitoring of progression and targeted therapies. SCancerRNA also allows users to explore interaction networks of different types of ncRNAs, and build computational models in the future. SCancerRNA is freely accessible at http://www.scancerrna.com/BioMarker.

Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS.

The molecular identity of a cell results from a complex interplay between heterogeneous molecular layers. Recent advances in single-cell sequencing technologies have opened the possibility to measure such molecular layers of regulation. Here, we present HuMMuS, a new method for inferring regulatory mechanisms from single-cell multi-omics data. Differently from the state-of-the-art, HuMMuS captures cooperation between biological macromolecules and can easily include additional layers of molecular regulation.We benchmarked HuMMuS with respect to the state-of-the-art on both paired and unpaired multi-omics datasets. Our results proved the improvements provided by HuMMuS in terms of TF targets, TF binding motifs and regulatory regions prediction. Finally, once applied to snmC-seq, scATAC-seq and scRNA-seq data from mouse brain cortex, HuMMuS enabled to accurately cluster scRNA profiles and to identify potential driver TFs. HuMMuS is available at https://github.com/cantinilab/HuMMuS. Supplementary data are available at Bioinformatics online.

Hepatic organoids move from adolescence to maturity.

Since organoids were developed 15 years ago, they are now in their adolescence as a research tool. The ability to generate 'tissue in a dish' has created enormous opportunities for biomedical research. We examine the contributions that hepatic organoids have made to three areas of liver research: as a source of cells and tissue for basic research, for drug discovery and drug safety testing, and for understanding disease pathobiology. We discuss the features that enable hepatic organoids to provide useful models for human liver diseases and identify four types of advances that will enable them to become a mature (i.e., adult) research tool over the next 5 years. During this period, advances in single-cell RNA sequencing and CRISPR technologies coupled with improved hepatic organoid methodology, which enables them to have a wider range of cell types that are present in liver and to be grown in microwells, will generate discoveries that will dramatically advance our understanding of liver development and the pathogenesis of liver diseases. It will generate also new approaches for treating liver fibrosis, which remains a major public health problem with few treatment options.

Analysis of single nuclear chromatin accessibility reveals unique myeloid populations in human pancreatic ductal adenocarcinoma.

A better understanding of the pancreatic ductal adenocarcinoma (PDAC) immune microenvironment is critical to developing new treatments and improving outcomes. Myeloid cells are of particular importance for PDAC progression; however, the presence of heterogenous subsets with different ontogeny and impact, along with some fluidity between them, (infiltrating monocytes vs. tissue-resident macrophages; M1 vs. M2) makes characterisation of myeloid populations challenging. Recent advances in single cell sequencing technology provide tools for characterisation of immune cell infiltrates, and open chromatin provides source and function data for myeloid cells to assist in more comprehensive characterisation. Thus, we explore single nuclear assay for transposase accessible chromatin (ATAC) sequencing (snATAC-Seq), a method to analyse open gene promoters and transcription factor binding, as an important means for discerning the myeloid composition in human PDAC tumours. Frozen pancreatic tissues (benign or PDAC) were prepared for snATAC-Seq using 10× Chromium technology. Signac was used for preliminary analysis, clustering and differentially accessible chromatin region identification. The genes annotated in promoter regions were used for Gene Ontology (GO) enrichment and cell type annotation. Gene signatures were used for survival analysis with The Cancer Genome Atlas (TCGA)-pancreatic adenocarcinoma (PAAD) dataset. Myeloid cell transcription factor activities were higher in tumour than benign pancreatic samples, enabling us to further stratify tumour myeloid populations. Subcluster analysis revealed eight distinct myeloid populations. GO enrichment demonstrated unique functions for myeloid populations, including interleukin-1b signalling (recruited monocytes) and intracellular protein transport (dendritic cells). The identified gene signature for dendritic cells influenced survival (hazard ratio=.63, p=.03) in the TCGA-PAAD dataset, which was unique to PDAC. These data suggest snATAC-Seq as a method for analysis of frozen human pancreatic tissues to distinguish myeloid populations. An improved understanding of myeloid cell heterogeneity and function is important for developing new treatment targets in PDAC.