popsicleR: a R Package for pre-processing and quality control analysis of single cell RNA-seq data

Comprehensive review on single-cell RNA sequencing: A new frontier in Alzheimer's disease research

Alzheimer's disease (AD) is a multifaceted neurodegenerative condition marked by gradual cognitive deterioration and the loss of neurons. While conventional bulk RNA sequencing techniques have shed light on AD pathology, they frequently obscure the cellular diversity within brain tissues. The advent of single-cell RNA sequencing (scRNA-seq) has transformed our capability to analyze the cellular composition of AD, allowing for the detection of unique cell populations, rare cell types, and gene expression alterations at an individual cell level. This review examines the use of scRNA-seq in AD research, focusing on its contributions to understanding cellular diversity, disease progression, and potential therapeutic targets. We discuss key technological innovations, data analysis techniques, and challenges associated with scRNA-seq in studying AD. Furthermore, we highlight recent studies that have utilized scRNA-seq to identify novel biomarkers, uncover disease-associated pathways, and elucidate the role of non-neuronal cells, such as microglia and astrocytes, in AD pathogenesis. By providing a comprehensive overview of advancements in scRNA-seq for unraveling cellular heterogeneity in AD, this review highlights the transformative impact of scRNA-seq on our comprehension of disease mechanisms and the creation of targeted treatments.

Single-nucleus RNA-seq identifies one galanin neuronal subtype in mouse preoptic hypothalamus activated during recovery from sleep deprivation

The preoptic area of the hypothalamus (POA) is essential for sleep regulation. However, the cellular makeup of the POA is heterogeneous, and the molecular identities of the sleep-promoting cells remain elusive. To address this question, this study compares mice during recovery sleep following sleep deprivation to mice allowed extended sleep. Single-nucleus RNA sequencing (single-nucleus RNA-seq) identifies one galanin inhibitory neuronal subtype that shows upregulation of rapid and delayed activity-regulated genes during recovery sleep. This cell type expresses higher levels of growth hormone receptor and lower levels of estrogen receptor compared to other galanin subtypes. single-nucleus RNA-seq also reveals cell-type-specific upregulation of purinergic receptor (P2ry14) and serotonin receptor (Htr2a) during recovery sleep in this neuronal subtype, suggesting possible mechanisms for sleep regulation. Studies with RNAscope validate the single-nucleus RNA-seq findings. Thus, the combined use of single-nucleus RNA-seq and activity-regulated genes identifies a neuronal subtype functionally involved in sleep regulation.

Tendon-associated gene expression precedes osteogenesis in mid-palatal suture establishment

Orthodontic maxillary expansion relies on intrinsic mid-palatal suture mechanobiology to induce guided osteogenesis, yet establishment of the mid-palatal suture within the continuous secondary palate and causes of maxillary insufficiency remain poorly understood. In contrast, advances in cranial suture research hold promise to improve surgical repair of prematurely fused cranial sutures in craniosynostosis to potentially restore the obliterated signaling environment and ensure continual success of the intervention. We hypothesized that mid-palatal suture establishment is governed by shared principles with calvarial sutures and involves functional linkage between expanding primary ossification centres with the midline mesenchyme. We characterized establishment of the mid-palatal suture from late embryonic to early postnatal timepoints. Suture establishment was visualized using histological techniques and multimodal transcriptomics. We identified that mid-palatal suture formation depends on a spatiotemporally controlled signalling milieu in which tendon-associated genes play a significant role. We mapped relationships between extracellular matrix-encoding gene expression, tenocyte markers, and novel suture patency candidate genes. We identified similar expression patterns in FaceBase-deposited scRNA-seq datasets from cranial sutures. These findings demonstrate shared biological principles for suture establishment, providing further avenues for future development and understanding of maxillofacial interventions.

Profiles of Wnt pathway gene expression during tooth morphogenesis

Mouse and human genetic studies indicate key roles of the Wnt10a ligand in odontogenesis. Previous studies have identified effectors and regulators of the Wnt signaling pathway actively expressed during key stages of tooth morphogenesis. However, limitations in multiplexing and spatial resolution hindered a more comprehensive analysis of these signaling molecules. Here, profiling of transcriptomes using fluorescent multiplex in situ hybridization and single-cell RNA-sequencing (scRNA-seq) provide robust insight into the synchronized expression patterns of Wnt10a, Dkk1, and Sost simultaneously during tooth development. First, we identified Wnt10a transcripts restricted to the epithelium at the stage of tooth bud morphogenesis, contrasting that of Sost and Dkk1 localization to the dental mesenchyme. By embryonic day 15.5 (E15.5), a marked shift of Wnt10a expression from dental epithelium to mesenchyme was noted, while Sost and Dkk1 expression remained enriched in the mesenchyme. By postnatal day 0 (P0), co-localization patterns of Wnt10a, Dkk1, and Sost were observed in both terminally differentiating and secreting odontoblasts of molars and incisors. Interestingly, Wnt10a exhibited robust expression in fully differentiated ameloblasts at the developing cusp tip of both molars and incisors, an observation not previously noted in prior studies. At P7 and 14, after the mineralization of dentin and enamel, Wnt10a expression was limited to odontoblasts. Meanwhile, Wnt modulators showed reduced or absent signals in molars. In contrast, strong signals persisted in ameloblasts (for Wnt10a) and odontoblasts (for Wnt10a, Sost, and Dkk1) towards the proximal end of incisors, near the cervical loop. Our scRNA-seq analysis used CellChat to further contextualize Wnt pathway-mediated communication between cells by examining ligand-receptor interactions among different clusters. The co-localization pattern of Wnt10a, Dkk1, and Sost in both terminally differentiating and secreting odontoblasts of molars and incisors potentially signifies the crucial ligand-modulator interaction along the gradient of cytodifferentiation starting from each cusp tip towards the apical region. These data provide cell type-specific insight into the role of Wnt ligands and mediators during epithelial-mesenchymal interactions in odontogenesis.

Scaling up single-cell RNA-seq data analysis with CellBridge workflow

SUMMARY

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression at the individual cell level, unraveling unprecedented insights into cellular heterogeneity. However, the analysis of scRNA-seq data remains a challenging and time-consuming task, often demanding advanced computational expertise, rendering it impractical for high-volume environments and applications. We present CellBridge, an automated workflow designed to simplify the standard procedures entailed in scRNA-seq data analysis, eliminating the need for specialized computational expertise. CellBridge utilizes state-of-the-art computational methods, integrating a range of advanced functionalities, covering the entire process from raw unaligned sequencing reads to cell type annotation. Hence, CellBridge accelerates the pace of discovery by seamlessly enabling insights into vast volumes of scRNA-seq data, without compromising workflow control and reproducibility.

AVAILABILITY AND IMPLEMENTATION

The source code, detailed documentation, and materials required to reproduce the results are available on GitHub and archived in Zenodo. For the CellBridge pre-processing step (v1.0.0), access the GitHub repository at https://github.com/Sanofi-Public/PMCB-ToBridge and the Zenodo archive at https://zenodo.org/records/10246161. For the CellBridge processing step (v1.0.0), visit the GitHub repository at https://github.com/Sanofi-Public/PMCB-CellBridge and the Zenodo archive at https://zenodo.org/records/10246046.

Essential procedures of single-cell RNA sequencing in multiple myeloma and its translational value

Multiple myeloma (MM) is a malignant neoplasm characterized by clonal proliferation of abnormal plasma cells. In many countries, it ranks as the second most prevalent malignant neoplasm of the hematopoietic system. Although treatment methods for MM have been continuously improved and the survival of patients has been dramatically prolonged, MM remains an incurable disease with a high probability of recurrence. As such, there are still many challenges to be addressed. One promising approach is single-cell RNA sequencing (scRNA-seq), which can elucidate the transcriptome heterogeneity of individual cells and reveal previously unknown cell types or states in complex tissues. In this review, we outlined the experimental workflow of scRNA-seq in MM, listed some commonly used scRNA-seq platforms and analytical tools. In addition, with the advent of scRNA-seq, many studies have made new progress in the key molecular mechanisms during MM clonal evolution, cell interactions and molecular regulation in the microenvironment, and drug resistance mechanisms in target therapy. We summarized the main findings and sequencing platforms for applying scRNA-seq to MM research and proposed broad directions for targeted therapies based on these findings.

A machine learning framework for scRNA-seq UMI threshold optimization and accurate classification of cell types

Recent advances in single cell RNA sequencing (scRNA-seq) technologies have been invaluable in the study of the diversity of cancer cells and the tumor microenvironment. While scRNA-seq platforms allow processing of a high number of cells, uneven read quality and technical artifacts hinder the ability to identify and classify biologically relevant cells into correct subtypes. This obstructs the analysis of cancer and normal cell diversity, while rare and low expression cell populations may be lost by setting arbitrary high cutoffs for UMIs when filtering out low quality cells. To address these issues, we have developed a novel machine-learning framework that: 1. Trains cell lineage and subtype classifier using a gold standard dataset validated using marker genes 2. Systematically assess the lowest UMI threshold that can be used in a given dataset to accurately classify cells 3. Assign accurate cell lineage and subtype labels to the lower read depth cells recovered by setting the optimal threshold. We demonstrate the application of this framework in a well-curated scRNA-seq dataset of breast cancer patients and two external datasets. We show that the minimum UMI threshold for the breast cancer dataset could be lowered from the original 1500 to 450, thereby increasing the total number of recovered cells by 49%, while achieving a classification accuracy of >0.9. Our framework provides a roadmap for future scRNA-seq studies to determine optimal UMI threshold and accurately classify cells for downstream analyses.

Fibronectin-1 is a dominant mechanism for rheumatoid arthritis via the mediation of synovial fibroblasts activity

Rheumatoid arthritis (RA) has a high incidence and adverse effects on patients, thus posing a serious threat to people’s life and health. However, the underlying mechanisms regarding the development of RA are still elusive. Herein, we aimed to evaluate the RA-associated molecular mechanisms using the scRNA-seq technique. We used the GEO database to obtain scRNA-seq datasets for synovial fibroblasts (SFs) from RA cases, and the genes were then analyzed using principal component analysis (PCA) and T-Stochastic Neighbor Embedding (TSNE) analyses. Bioinformatics evaluations were carried out for asserting the highly enriched signaling pathways linked to the marker genes, and the key genes related to RA initiation were further identified. According to the obtained results, 3 cell types (0, 1, and 2) were identified by TSNE and some marker genes were statistically upregulated in cell type 1 than the other cell types. These marker genes predominantly contributed to extracellular matrix (ECM) architecture, collagen-harboring ECM, and ECM structural components, and identified as enriched with PI3K/AKT signaling cascade. Notably, fibronectin-1 (FN-1) has been identified as a critical gene that is strongly linked to the development of SFs and has enormous promise for regulating the onset of RA. Moreover, such an investigation offers novel perspectives within onset/progression of RA, suggesting that FN-1 may be a key therapeutic target for RA therapies.