MarkerCount: A stable, count-based cell type identifier for single-cell RNA-seq experiments

Mapping Cell Identity from scRNA-seq: A primer on computational methods

Single cell (sc) technologies mark a conceptual and methodological breakthrough in our way to study cells, the base units of life. Thanks to these technological developments, large-scale initiatives are currently ongoing aimed at mapping of all the cell types in the human body, with the ambitious aim to gain a cell-level resolution of physiological development and disease. Since its broad applicability and ease of interpretation scRNA-seq is probably the most common sc-based application. This assay uses high throughput RNA sequencing to capture gene expression profiles at the sc-level. Subsequently, under the assumption that differences in transcriptional programs correspond to distinct cellular identities, ad-hoc computational methods are used to infer cell types from gene expression patterns. A wide array of computational methods were developed for this task. However, depending on the underlying algorithmic approach and associated computational requirements, each method might have a specific range of application, with implications that are not always clear to the end user. Here we will provide a concise overview on state-of-the-art computational methods for cell identity annotation in scRNA-seq, tailored for new users and non-computational scientists. To this end, we classify existing tools in five main categories, and discuss their key strengths, limitations and range of application.

Consensus representation of multiple cell-cell graphs from gene signaling pathways for cell type annotation

BACKGROUND

Recent advancements in single-cell RNA sequencing have greatly expanded our knowledge of the heterogeneous nature of tissues. However, robust and accurate cell type annotation continues to be a major challenge, hindered by issues such as marker specificity, batch effects, and a lack of comprehensive spatial and interaction data. Traditional annotation methods often fail to adequately address the complexity of cellular interactions and gene regulatory networks.

RESULTS

We proposed scMCGraph, a comprehensive computational framework that integrates gene expression with pathway activity to accurately annotate cell types within diverse scRNA-seq datasets. Initially, our model constructs multiple pathway-specific views using various pathway databases, which reflect both gene expression and pathway activities. These pathway-specific views are then integrated into a consensus graph. The consensus graph is subsequently utilized to reconstruct the multiple pathway views. Our model demonstrated exceptional robustness and accuracy across various analyses, including cross-platform, cross-time, cross-sample, and clinical dataset evaluations.

CONCLUSIONS

scMCGraph represents a significant advance in cell type annotation. The experiments have demonstrated that introducing pathway information significantly improves the learning of cell-cell graphs, with their resulting consensus graph enhancing the predictive performance of cell type prediction. Different pathway databases provide complementary data, and an increase in the number of pathways can also boost model performance. Extensive testing shows that in various cross-dataset application scenarios, scMCGraph consistently exhibits both accuracy and robustness.

Gene choice in cancer cells is exclusive in ion transport but concurrent in DNA replication

Cancers share common cellular and physiological features. Little is known about whether distinctive gene expression patterns can be displayed at the single-cell level by gene families in cancer cells. The expression of gene homologs within a family can exhibit concurrence and exclusivity. Concurrence can promote all-or-none expression patterns of related genes and underlie alternative physiological states. Conversely, exclusive gene families express the same or similar number of homologs in each cell, allowing a broad repertoire of cell identities to be generated. We show that gene families involved in the cell-cycle and antigen presentation are expressed concurrently. Concurrence in the DNA replication complex MCM reflects the replicative status of cells, including cell lines and cancer-derived organoids. Exclusive expression requires precise regulatory mechanism, but cancer cells retain this form of control for ion homeostasis and extend it to gene families involved in cell migration. Thus, the cell adhesion-based identity of healthy cells is transformed to an identity based on migration in the population of cancer cells, reminiscent of epithelial-mesenchymal transition.

A self-training interpretable cell type annotation framework using specific marker gene

MOTIVATION

Recent advances in sequencing technology provide opportunities to study biological processes at a higher resolution. Cell type annotation is an important step in scRNA-seq analysis, which often relies on established marker genes. However, most of the previous methods divide the identification of cell types into two stages, clustering and assignment, whose performances are susceptible to the clustering algorithm, and the marker information cannot effectively guide the clustering process. Furthermore, their linear heuristic-based cell assignment process is often insufficient to capture potential dependencies between cells and types.

RESULTS

Here, we present Interpretable Cell Type Annotation based on self-training (sICTA), a marker-based cell type annotation method that combines the self-training strategy with pseudo-labeling and the nonlinear association capturing capability of Transformer. In addition, we incorporate biological priori knowledge of genes and pathways into the classifier through an attention mechanism to enhance the transparency of the model. A benchmark analysis on 11 publicly available single-cell datasets demonstrates the superiority of sICTA compared to state-of-the-art methods. The robustness of our method is further validated by evaluating the prediction accuracy of the model on different cell types for each single-cell data. Moreover, ablation studies show that self-training and the ability to capture potential dependencies between cells and cell types, both of which are mutually reinforcing, work together to improve model performance. Finally, we apply sICTA to the pancreatic dataset, exemplifying the interpretable attention matrix captured by sICTA.

AVAILABILITY AND IMPLEMENTATION

The source code of sICTA is available in public at https://github.com/nbnbhwyy/sICTA. The processed datasets can be found at https://drive.google.com/drive/folders/1jbqSxacL_IDIZ4uPjq220C9Kv024m9eL. The final version of the model will be permanently available at https://doi.org/10.5281/zenodo.13474010.

scPriorGraph: constructing biosemantic cell-cell graphs with prior gene set selection for cell type identification from scRNA-seq data

Cell type identification is an indispensable analytical step in single-cell data analyses. To address the high noise stemming from gene expression data, existing computational methods often overlook the biologically meaningful relationships between genes, opting to reduce all genes to a unified data space. We assume that such relationships can aid in characterizing cell type features and improving cell type recognition accuracy. To this end, we introduce scPriorGraph, a dual-channel graph neural network that integrates multi-level gene biosemantics. Experimental results demonstrate that scPriorGraph effectively aggregates feature values of similar cells using high-quality graphs, achieving state-of-the-art performance in cell type identification.

Neuronal Spike Shapes (NSS): A straightforward approach to investigate heterogeneity in neuronal excitability states

The mammalian brain exhibits a remarkable diversity of neurons, contributing to its intricate architecture and functional complexity. The analysis of multimodal single-cell datasets enables the investigation of cell types and states heterogeneity. In this study, we introduce the Neuronal Spike Shapes (NSS), a straightforward approach for the exploration of excitability states of neurons based on their Action Potential (AP) waveforms. The NSS method describes the AP waveform based on a triangular representation complemented by a set of derived electrophysiological (EP) features. To support this hypothesis, we validate the proposed approach on two datasets of murine cortical neurons, focusing it on GABAergic neurons. The validation process involves a combination of NSS-based clustering analysis, features exploration, Differential Expression (DE), and Gene Ontology (GO) enrichment analysis. Results show that the NSS-based analysis captures neuronal excitability states that possess biological relevance independently of cell subtype. In particular, Neuronal Spike Shapes (NSS) captures, among others, a well-characterized fast-spiking excitability state, supported by both electrophysiological and transcriptomic validation. Gene Ontology Enrichment Analysis reveals voltage-gated potassium (K+) channels as specific markers of the identified NSS partitions. This finding strongly corroborates the biological relevance of NSS partitions as excitability states, as the expression of voltage-gated K+ channels regulates the hyperpolarization phase of the AP, being directly implicated in the regulation of neuronal excitability.

Identification of ulcerative colitis-specific immune cell signatures from public single-cell RNA-seq data

BACKGROUND

Single-cell RNA-seq enabled microscopic studies on tissue microenvironment of many diseases. Inflammatory bowel disease, an autoimmune disease, is involved with various dysfunction of immune cells, for which single-cell RNA-seq may provide us a deeper insight into the causes and mechanism of this complex disease.

OBJECTIVE

In this work, we used public single-cell RNA-seq data to study tissue microenvironment around ulcerative colitis, an inflammatory bowel disease causing chronic inflammation and ulcers in large intestine.

METHODS

Since not all the datasets provide cell-type annotations, we first identified cell identities to select cell populations of our interest. Differentially expressed genes and gene set enrichment analysis was then performed to infer the polarization/activation state of macrophages and T cells. Cell-to-cell interaction analysis was also performed to discover distinct interactions in ulcerative colitis.

RESULTS

Differentially expressed genes analysis of the two datasets confirmed the regulation of CTLA4, IL2RA, and CCL5 genes in the T cell subset and regulation of S100A8/A9, CLEC10A genes in macrophages. Cell-to-cell interaction analysis showed CD4+ T cells and macrophages interact actively to each other. We also identified IL-18 pathway activation in inflammatory macrophages, evidence that CD4+ T cells induce Th1 and Th2 differentiation, and also found that macrophages regulate T cell activation through different ligand-receptor pairs, viz. CD86-CTL4, LGALS9-CD47, SIRPA-CD47, and GRN-TNFRSF1B.

CONCLUSION

Analysis of these immune cell subsets may suggest novel strategies for the treatment of inflammatory bowel disease.

A scalable sparse neural network framework for rare cell type annotation of single-cell transcriptome data

Automatic cell type annotation methods are increasingly used in single-cell RNA sequencing (scRNA-seq) analysis due to their fast and precise advantages. However, current methods often fail to account for the imbalance of scRNA-seq datasets and ignore information from smaller populations, leading to significant biological analysis errors. Here, we introduce scBalance, an integrated sparse neural network framework that incorporates adaptive weight sampling and dropout techniques for auto-annotation tasks. Using 20 scRNA-seq datasets with varying scales and degrees of imbalance, we demonstrate that scBalance outperforms current methods in both intra- and inter-dataset annotation tasks. Additionally, scBalance displays impressive scalability in identifying rare cell types in million-level datasets, as shown in the bronchoalveolar cell landscape. scBalance is also significantly faster than commonly used tools and comes in a user-friendly format, making it a superior tool for scRNA-seq analysis on the Python-based platform.

Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing

Single-cell RNA-seq enabled in-depth study on tissue micro-environment and immune-profiling, where a crucial step is to annotate cell identity. Immune cells play key roles in many diseases, whereas their activities are hard to track due to their diverse and highly variable nature. Existing cell-type identifiers had limited performance for this purpose. We present HiCAT, a hierarchical, marker-based cell-type identifier utilising gene set analysis for statistical scoring for given markers. It features successive identification of major-type, minor-type and subsets utilising subset markers structured in a three-level taxonomy tree. Comparison with manual annotation and pairwise match test showed HiCAT outperforms others in major- and minor-type identification. For subsets, we qualitatively evaluated the marker expression profile demonstrating that HiCAT provide the clearest immune-cell landscape. HiCAT was also used for immune-cell profiling in ulcerative colitis and discovered distinct features of the disease in macrophage and T-cell subsets that could not be identified previously.

Role of lymphoid lineage cells aberrantly expressing alarmins S100A8/A9 in determining the severity of COVID-19

BACKGROUND

Alarmins S100A8 and S100A9 are recognized as hallmarks of severe COVID-19 and are primarily produced in myeloid cells, such as monocytes and neutrophils. As single-cell RNA-sequencing (scRNA-seq) data from patients with COVID-19 revealed the expression of S100A8/A9 in lymphoid cells in patients with severe COVID-19.

OBJECTIVE

We investigated the characteristics of lymphoid cells expressing S100A8/A9 in COVID-19 patients.

METHODS

Publicly available scRNA-seq data from patients with mild (N = 12) or severe (N = 7) COVID-19 were reanalyzed. The data were further divided into the following two groups based on the time of sample collection (from infection-onset): within 6 days (early phase) and after 6 days (late phase). Differential expression and gene set enrichment analyses were performed between S100A8/A9^High and S100A8/A9^Low lymphoid cells. Finally, cell-cell interaction analysis was performed to investigate the role of lymphoid cells expressing high levels of S100A8/A9 in COVID-19.

RESULTS

S100A8/A9 overexpression was observed in lymphoid cells, including B cells, T cells, and NK cells, in patients with severe COVID-19 (compared to patients with mild COVID-19). Cells exhibiting strong interferon/cytokine responses were found to be associated with the severity of COVID-19. Furthermore, differences in S100A8/A9-TLR4/RAGE interactions were confirmed between patients with severe and mild disease.

CONCLUSIONS

Lymphoid cells overexpressing S100A8/A9 contribute to the dysregulation of the innate immune response in patients with severe COVID-19, specifically during the early phase of infection. This study fosters a better understanding of the hyper-induction of pro-inflammatory cytokine expression and the generation of a cytokine storm in response to COVID-19 infection.