CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology

PELI2 is a negative regulator of STING signaling that is dynamically repressed during viral infection

The innate immune cGAS-STING pathway is activated by cytosolic double-stranded DNA (dsDNA), a ubiquitous danger signal, to produce interferon, a potent anti-viral and anti-cancer cytokine. However, STING activation must be tightly controlled because aberrant interferon production leads to debilitating interferonopathies. Here, we discover PELI2 as a crucial negative regulator of STING. Mechanistically, PELI2 inhibits the transcription factor IRF3 by binding to phosphorylated Thr354 and Thr356 on the C-terminal tail of STING, leading to ubiquitination and inhibition of the kinase TBK1. PELI2 sets a threshold for STING activation that tolerates low levels of cytosolic dsDNA, such as that caused by silenced TREX1, RNASEH2B, BRCA1, or SETX. When this threshold is reached, such as during viral infection, STING-induced interferon production temporarily downregulates PELI2, creating a positive feedback loop allowing a robust immune response. Lupus patients have insufficient PELI2 levels and high basal interferon production, suggesting that PELI2 dysregulation may drive the onset of lupus and other interferonopathies.

STPDA: Leveraging spatial-temporal patterns for downstream analysis in spatial transcriptomic data

Spatial transcriptomics, a groundbreaking field in cellular biology, faces the challenge of effectively deciphering complex spatial-temporal gene expression patterns. Traditional data analysis methods often fail to capture the intricate nuances of this data, limiting the depth of understanding in spatial distribution and gene interactions. In response, we present Spatial-Temporal Patterns for Downstream Analysis (STPDA), a sophisticated computational framework tailored for spatial transcriptomic data analysis. STPDA leverages high-resolution mapping to bridge the gap between genomics and histopathology, offering a comprehensive perspective on the spatial dynamics of gene expression within tissues. This approach enables a view of cellular function and organization, marking a paradigm shift in our comprehension of biological systems. By employing Autoregressive Moving Average (ARMA) and Long Short-Term Memory (LSTM) models, STPDA effectively deciphers both global and local spatio-temporal dynamics in cellular environments. This integration of spatial-temporal patterns for downstream analysis offers a transformative approach to spatial transcriptomics data analysis. STPDA excels in various single-cell analytical tasks, including the identification of ligand-receptor interactions and cell type classification. Its ability to harness spatial-temporal patterns not only matches but frequently surpasses the performance of existing state-of-the-art methods. To ensure widespread usability and impact, we have encapsulated STPDA in a scalable and accessible Python package, addressing single-cell tasks through advanced spatial-temporal pattern analysis. This development promises to enhance our understanding of cellular biology, offering novel insights and therapeutic strategies, and represents a substantial advancement in the field of spatial transcriptomics.

Uncertainty-aware single-cell annotation with a hierarchical reject option

MOTIVATION

Automatic cell type annotation methods assign cell type labels to new datasets by extracting relationships from a reference RNA-seq dataset. However, due to the limited resolution of gene expression features, there is always uncertainty present in the label assignment. To enhance the reliability and robustness of annotation, most machine learning methods address this uncertainty by providing a full reject option, i.e. when the predicted confidence score of a cell type label falls below a user-defined threshold, no label is assigned and no prediction is made. As a better alternative, some methods deploy hierarchical models and consider a so-called partial rejection by returning internal nodes of the hierarchy as label assignment. However, because a detailed experimental analysis of various rejection approaches is missing in the literature, there is currently no consensus on best practices.

RESULTS

We evaluate three annotation approaches (i) full rejection, (ii) partial rejection, and (iii) no rejection for both flat and hierarchical probabilistic classifiers. Our findings indicate that hierarchical classifiers are superior when rejection is applied, with partial rejection being the preferred rejection approach, as it preserves a significant amount of label information. For optimal rejection implementation, the rejection threshold should be determined through careful examination of a method's rejection behavior. Without rejection, flat and hierarchical annotation perform equally well, as long as the cell type hierarchy accurately captures transcriptomic relationships.

AVAILABILITY AND IMPLEMENTATION

Code is freely available at https://github.com/Latheuni/Hierarchical_reject and https://doi.org/10.5281/zenodo.10697468.

Repeated Decision Stumping Distils Simple Rules from Single-Cell Data

Single-cell data afford unprecedented insights into molecular processes. But the complexity and size of these data sets have proved challenging and given rise to a large armory of statistical and machine learning approaches. The majority of approaches focuses on either describing features of these data, or making predictions and classifying unlabeled samples. In this study, we introduce repeated decision stumping (ReDX) as a method to distill simple models from single-cell data. We develop decision trees of depth one-hence "stumps"-to identify in an inductive manner, gene products involved in driving cell fate transitions, and in applications to published data we are able to discover the key players involved in these processes in an unbiased manner without prior knowledge. Our algorithm is deliberately targeting the simplest possible candidate hypotheses that can be extracted from complex high-dimensional data. There are three reasons for this: (1) the predictions become straightforwardly testable hypotheses; (2) the identified candidates form the basis for further mechanistic model development, for example, for engineering and synthetic biology interventions; and (3) this approach complements existing descriptive modeling approaches and frameworks. The approach is computationally efficient, has remarkable predictive power, including in simulation studies where the ground truth is known, and yields robust and statistically stable predictors; the same set of candidates is generated by applying the algorithm to different subsamples of experimental data.

Automatic cell-type harmonization and integration across Human Cell Atlas datasets

Harmonizing cell types across the single-cell community and assembling them into a common framework is central to building a standardized Human Cell Atlas. Here, we present CellHint, a predictive clustering tree-based tool to resolve cell-type differences in annotation resolution and technical biases across datasets. CellHint accurately quantifies cell-cell transcriptomic similarities and places cell types into a relationship graph that hierarchically defines shared and unique cell subtypes. Application to multiple immune datasets recapitulates expert-curated annotations. CellHint also reveals underexplored relationships between healthy and diseased lung cell states in eight diseases. Furthermore, we present a workflow for fast cross-dataset integration guided by harmonized cell types and cell hierarchy, which uncovers underappreciated cell types in adult human hippocampus. Finally, we apply CellHint to 12 tissues from 38 datasets, providing a deeply curated cross-tissue database with ∼3.7 million cells and various machine learning models for automatic cell annotation across human tissues.

Multiresolution categorical regression for interpretable cell-type annotation

In many categorical response regression applications, the response categories admit a multiresolution structure. That is, subsets of the response categories may naturally be combined into coarser response categories. In such applications, practitioners are often interested in estimating the resolution at which a predictor affects the response category probabilities. In this paper, we propose a method for fitting the multinomial logistic regression model in high dimensions that addresses this problem in a unified and data-driven way. Our method allows practitioners to identify which predictors distinguish between coarse categories but not fine categories, which predictors distinguish between fine categories, and which predictors are irrelevant. For model fitting, we propose a scalable algorithm that can be applied when the coarse categories are defined by either overlapping or nonoverlapping sets of fine categories. Statistical properties of our method reveal that it can take advantage of this multiresolution structure in a way existing estimators cannot. We use our method to model cell-type probabilities as a function of a cell's gene expression profile (i.e., cell-type annotation). Our fitted model provides novel biological insights which may be useful for future automated and manual cell-type annotation methodology.

Single-cell assignment using multiple-adversarial domain adaptation network with large-scale references

The rapid accumulation of single-cell RNA-seq data has provided rich resources to characterize various human cell populations. However, achieving accurate cell-type annotation using public references presents challenges due to inconsistent annotations, batch effects, and rare cell types. Here, we introduce SELINA (single-cell identity navigator), an integrative and automatic cell-type annotation framework based on a pre-curated reference atlas spanning various tissues. SELINA employs a multiple-adversarial domain adaptation network to remove batch effects within the reference dataset. Additionally, it enhances the annotation of less frequent cell types by synthetic minority oversampling and fits query data with the reference data using an autoencoder. SELINA culminates in the creation of a comprehensive and uniform reference atlas, encompassing 1.7 million cells covering 230 distinct human cell types. We substantiate its robustness and superiority across a multitude of human tissues. Notably, SELINA could accurately annotate cells within diverse disease contexts. SELINA provides a complete solution for human single-cell RNA-seq data annotation with both python and R packages.

Clinical evidence for a role of E2F1-induced replication stress in modulating tumor mutational burden and immune microenvironment

DNA replication stress (RS) is frequently induced by oncogene activation and is believed to promote tumorigenesis. However, clinical evidence for the role of oncogene-induced RS in tumorigenesis remains scarce, and the mechanisms by which RS promotes cancer development remain incompletely understood. By performing a series of bioinformatic analyses on the oncogene E2F1, other RS-inducing factors, and replication fork processing factors in TCGA cancer database using previously established tools, we show that hyperactivity of E2F1 likely promotes the expression of several of these factors in virtually all types of cancer to induce RS and cytosolic self-DNA production. In addition, the expression of these factors positively correlates with that of ATR and Chk1 that govern the cellular response to RS, the tumor mutational load, and tumor infiltration of immune-suppressive CD4+Th2 cells and myeloid-derived suppressor cells (MDSCs). Consistently, high expression of these factors is associated with poor patient survival. Our study provides new insights into the role of E2F1-induced RS in tumorigenesis and suggests therapeutic approaches for E2F1-overexpressing cancers by targeting genomic instability, cytosolic self-DNA and the tumor immune microenvironment.

Cell-type annotation with accurate unseen cell-type identification using multiple references

The recent advances in single-cell RNA sequencing (scRNA-seq) techniques have stimulated efforts to identify and characterize the cellular composition of complex tissues. With the advent of various sequencing techniques, automated cell-type annotation using a well-annotated scRNA-seq reference becomes popular. But it relies on the diversity of cell types in the reference, which may not capture all the cell types present in the query data of interest. There are generally unseen cell types in the query data of interest because most data atlases are obtained for different purposes and techniques. Identifying previously unseen cell types is essential for improving annotation accuracy and uncovering novel biological discoveries. To address this challenge, we propose mtANN (multiple-reference-based scRNA-seq data annotation), a new method to automatically annotate query data while accurately identifying unseen cell types with the aid of multiple references. Key innovations of mtANN include the integration of deep learning and ensemble learning to improve prediction accuracy, and the introduction of a new metric that considers three complementary aspects to distinguish between unseen cell types and shared cell types. Additionally, we provide a data-driven method to adaptively select a threshold for identifying previously unseen cell types. We demonstrate the advantages of mtANN over state-of-the-art methods for unseen cell-type identification and cell-type annotation on two benchmark dataset collections, as well as its predictive power on a collection of COVID-19 datasets. The source code and tutorial are available at https://github.com/Zhangxf-ccnu/mtANN.

Genome-Wide Analysis of MYB Transcription Factors and Screening of MYBs Involved in the Red Color Formation in Rhododendron delavayi

Flower color is one of the crucial traits of ornamental plants. Rhododendron delavayi Franch. is a famous ornamental plant species distributed in the mountain areas of Southwest China. This plant has red inflorescence and young branchlets. However, the molecular basis of the color formation of R. delavayi is unclear. In this study, 184 MYB genes were identified based on the released genome of R. delavayi. These genes included 78 1R-MYB, 101 R2R3-MYB, 4 3R-MYB, and 1 4R-MYB. The MYBs were divided into 35 subgroups using phylogenetic analysis of the MYBs of Arabidopsis thaliana. The members of the same subgroup in R. delavayi had similar conserved domains and motifs, gene structures, and promoter cis-acting elements, which indicate their relatively conserved function. In addition, transcriptome based on unique molecular identifier strategy and color difference of the spotted petals, unspotted petals, spotted throat, unspotted throat, and branchlet cortex were detected. Results showed significant differences in the expression levels of R2R3-MYB genes. Weighted co-expression network analysis between transcriptome and chromatic aberration values of five types of red samples showed that the MYBs were the most important TFs involved in the color formation, of which seven were R2R3-MYB, and three were 1R-MYB. Two R2R3-MYB (DUH019226.1 and DUH019400.1) had the highest connectivity in the whole regulation network, and they were identified as hub genes for red color formation. These two MYB hub genes provide references for the study of transcriptional regulation of the red color formation of R. delavayi.

Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases

Neurodegenerative diseases affect millions of people worldwide and there are currently no cures. Two types of common neurodegenerative diseases are Alzheimer's (AD) and Parkinson's disease (PD). Single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq) have become powerful tools to elucidate the inherent complexity and dynamics of the central nervous system at cellular resolution. This technology has allowed the identification of cell types and states, providing new insights into cellular susceptibilities and molecular mechanisms underlying neurodegenerative conditions. Exciting research using high throughput scRNA-seq and snRNA-seq technologies to study AD and PD is emerging. Herein we review the recent progress in understanding these neurodegenerative diseases using these state-of-the-art technologies. We discuss the fundamental principles and implications of single-cell sequencing of the human brain. Moreover, we review some examples of the computational and analytical tools required to interpret the extensive amount of data generated from these assays. We conclude by highlighting challenges and limitations in the application of these technologies in the study of AD and PD.

Computational solutions for spatial transcriptomics

Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.

Screening the components of Saussurea involucrata for novel targets for the treatment of NSCLC using network pharmacology

BACKGROUND

Saussurea involucrata (SAIN), also known as Snow lotus (SI), is mainly distributed in high-altitude areas such as Tibet and Xinjiang in China. To identify novel targets for the prevention or treatment of lung adenocarcinoma and lung squamous cell carcinoma (LUAD&LUSC), and to facilitate better alternative new drug discovery as well as clinical application services, the therapeutic effects of SAIN on LUAD&LUSC were evaluated by gene differential analysis of clinical samples, compound target molecular docking, and GROMACS molecular dynamics simulation.

RESULTS

Through data screening, alignment, analysis, and validation it was confirmed that three of the major active ingredients in SAIN, namely quercetin (Q), luteolin (L), and kaempferol (K), mainly act on six protein targets, which mainly regulate signaling pathways in cancer, transcriptional misregulation in cancer, EGFR tyrosine kinase inhibitor resistance, adherens junction, IL-17 signaling pathway, melanoma, and non-small cell lung cancer. In addition, microRNAs in cancer exert preventive or therapeutic effects on LUAD&LUSC. Molecular dynamics (MD) simulations of Q, L, or K in complex with EGFR, MET, MMP1, or MMP3 revealed the presence of Q in a very stable tertiary structure in the human body.

CONCLUSION

There are three active compounds of Q, L, and K in SAIN, which play a role in the treatment and prevention of non-small cell lung cancer (NSCLC) by directly or indirectly regulating the expression of genes such as MMP1, MMP3, and EGFR.

PlantGF: an analysis and annotation platform for plant gene families

Gene families contain genes that come from the same ancestor and have similar sequences and structures. They perform certain specific functions within and among different species. Currently, there is no complete process or platform for the rapid analysis of plant gene families. In this study, a comprehensive query and analysis platform of plant gene families, the Plant Gene Family Platform (PlantGF), was constructed. The platform is composed of four main parts: Search, Tools, Statistics and Auxiliary. A total of 2 909 580 gene family members were identified from 138 plant species in PlantGF. The data can be queried in the Search section through a user-friendly interface. A general process for gene family analysis, having nine steps, is provided. The platform also includes four online tools (HMM-Search, BLAST, MAFFT and HMMER) in the Tools section for useful additional analyses. The statistical analysis of the relevant gene families is shown on the Statistics page. Auxiliary pages are provided for data downloading. The datasets for all 138 plant species' protein sequences and their gene families can be acquired on the Download page. A user's manual and some useful links are displayed on the Manual and Links pages, respectively. To the best of our knowledge, PlantGF is the first comprehensive platform for studying plant gene families, and it will make important contributions to plant gene family-related research. Database URL: http://biodb.sdau.edu.cn/PGF/index.html.

scMRMA: single cell multiresolution marker-based annotation

Single-cell RNA sequencing has become a powerful tool for identifying and characterizing cellular heterogeneity. One essential step to understanding cellular heterogeneity is determining cell identities. The widely used strategy predicts identities by projecting cells or cell clusters unidirectionally against a reference to find the best match. Here, we develop a bidirectional method, scMRMA, where a hierarchical reference guides iterative clustering and deep annotation with enhanced resolutions. Taking full advantage of the reference, scMRMA greatly improves the annotation accuracy. scMRMA achieved better performance than existing methods in four benchmark datasets and successfully revealed the expansion of CD8 T cell populations in squamous cell carcinoma after anti-PD-1 treatment.

Cell type ontologies of the Human Cell Atlas

Massive single-cell profiling efforts have accelerated our discovery of the cellular composition of the human body while at the same time raising the need to formalize this new knowledge. Here, we discuss current efforts to harmonize and integrate different sources of annotations of cell types and states into a reference cell ontology. We illustrate with examples how a unified ontology can consolidate and advance our understanding of cell types across scientific communities and biological domains.

Bias-invariant RNA-sequencing metadata annotation

BACKGROUND

Recent technological advances have resulted in an unprecedented increase in publicly available biomedical data, yet the reuse of the data is often precluded by experimental bias and a lack of annotation depth and consistency. Missing annotations makes it impossible for researchers to find datasets specific to their needs.

FINDINGS

Here, we investigate RNA-sequencing metadata prediction based on gene expression values. We present a deep-learning-based domain adaptation algorithm for the automatic annotation of RNA-sequencing metadata. We show, in multiple experiments, that our model is better at integrating heterogeneous training data compared with existing linear regression-based approaches, resulting in improved tissue type classification. By using a model architecture similar to Siamese networks, the algorithm can learn biases from datasets with few samples.

CONCLUSION

Using our novel domain adaptation approach, we achieved metadata annotation accuracies up to 15.7% better than a previously published method. Using the best model, we provide a list of >10,000 novel tissue and sex label annotations for 8,495 unique SRA samples. Our approach has the potential to revive idle datasets by automated annotation making them more searchable.

Leveraging the Cell Ontology to classify unseen cell types

Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution. However, joint analysis of datasets generated by independent labs remains challenging due to a lack of consistent terminology to describe cell types. Here, we present OnClass, an algorithm and accompanying software for automatically classifying cells into cell types that are part of the controlled vocabulary that forms the Cell Ontology. A key advantage of OnClass is its capability to classify cells into cell types not present in the training data because it uses the Cell Ontology graph to infer cell type relationships. Furthermore, OnClass can be used to identify marker genes for all the cell ontology categories, regardless of whether the cell types are present or absent in the training data, suggesting that OnClass goes beyond a simple annotation tool for single cell datasets, being the first algorithm capable to identify marker genes specific to all terms of the Cell Ontology and offering the possibility of refining the Cell Ontology using a data-centric approach.

Annotating cell types in human single-cell RNA-seq data with CellO

Cell type annotation is important in the analysis of single-cell RNA-seq data. CellO is a machine-learning-based tool for annotating cells using the Cell Ontology, a rich hierarchy of known cell types. We provide a protocol for using the CellO Python package to annotate human cells. We demonstrate how to use CellO in conjunction with Scanpy, a Python library for performing single-cell analysis, annotate a lung tissue data set, interpret its hierarchically structured cell type annotations, and create publication-ready figures.

For complete details on the use and execution of this protocol, please refer to Bernstein et al. (2021).

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CellO is a Python package for annotating cell types in single-cell RNA-seq data

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CellO classifies cells against the hierarchically structured Cell Ontology

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CellO can be integrated into single-cell analysis pipelines implemented with Scanpy

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We present a tutorial that classifies cells in an existing lung tumor data set

Mapping the multiscale structure of biological systems

Biological systems are by nature multiscale, consisting of subsystems that factor into progressively smaller units in a deeply hierarchical structure. At any level of the hierarchy, an ever-increasing diversity of technologies can be applied to characterize the corresponding biological units and their relations, resulting in large networks of physical or functional proximities-e.g., proximities of amino acids within a protein, of proteins within a complex, or of cell types within a tissue. Here, we review general concepts and progress in using network proximity measures as a basis for creation of multiscale hierarchical maps of biological systems. We discuss the functionalization of these maps to create predictive models, including those useful in translation of genotype to phenotype, along with strategies for model visualization and challenges faced by multiscale modeling in the near future. Collectively, these approaches enable a unified hierarchical approach to biological data, with application from the molecular to the macroscopic.

Landscape of toll-like receptors expression in tumor microenvironment of triple negative breast cancer (TNBC): Distinct roles of TLR4 and TLR8

TNBC is the most aggressive and hormone receptor-negative subtype of breast cancer with molecular heterogeneity in bulk tumors hindering effective treatment. Toll-like receptors (TLRs) have the potential to ignite diverse immune responses in the tumor microenvironment (TME). This encouraged us to screen their transcript expression in the publically available TCGA datasets. Reported molecular subtypes of TNBC may represent different TMEs and we observed differentially expressed TLRs (DETs) i.e. TLR3/4/6/8/9 have unique expression pattern in the TNBC subtypes, particularly in Immunomodulatory (IM) TNBC subtype. We then dissected expression of the DETs in immune and other components of the TME. TLR4 and TLR8 showed significant (p-value ≤ 0.05) negative partial correlation with tumor purity compared to other DETs. Interestingly, TLR4 and TLR8 expression showed a significant (adjusted p-value ≤ 0.05) correlation with different subsets of immune infiltrating cells having the highest correlation with monocytes/macrophage/dendritic cell populations mediating both innate and adaptive response in TNBC. The co-expression network identified genes correlated with these immune cells. Further, GSEA analysis of co-expressed genes showed a significant association of TLR8 partners with 'Peptide ligand binding', 'Gά-signaling', and 'Cytokine-cytokine interaction' while TLR4 associated genes correlated with 'Adaptive immune system' and 'Systemic lupus erythematosus' interactome. Finally, the expression of TLR4 protein was validated in a panel of TNBC cell lines. TLR4 expression in chemoresponsive TNBC was also validated in TNBC cell lines upon Paclitaxel (PTX) treatment. Collectively, the present study identified specific DETs in TNBC and discovered a prospective role of TLR4 and TLR8 in the maintenance of tumor-immune-microenvironment.

CHARTS: a web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq data sets

BACKGROUND

Single-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue. This is especially important in cancer, where tumor and tumor microenvironment heterogeneity directly impact development, maintenance, and progression of disease. While publicly available scRNA-seq cancer data sets offer unprecedented opportunity to better understand the mechanisms underlying tumor progression, metastasis, drug resistance, and immune evasion, much of the available information has been underutilized, in part, due to the lack of tools available for aggregating and analysing these data.

RESULTS

We present CHARacterizing Tumor Subpopulations (CHARTS), a web application for exploring publicly available scRNA-seq cancer data sets in the NCBI's Gene Expression Omnibus. More specifically, CHARTS enables the exploration of individual gene expression, cell type, malignancy-status, differentially expressed genes, and gene set enrichment results in subpopulations of cells across tumors and data sets. Along with the web application, we also make available the backend computational pipeline that was used to produce the analyses that are available for exploration in the web application.

CONCLUSION

CHARTS is an easy to use, comprehensive platform for exploring single-cell subpopulations within tumors across the ever-growing collection of public scRNA-seq cancer data sets. CHARTS is freely available at charts.morgridge.org.