Cell Type Annotation Research Articles

Combination of histological and transcriptomic approaches for cell types annotation in non-model organisms by example of spiny mice <i>Acomys cahirinus</i>

In mammals, cartilage tissue has a low potential for regeneration. Typically, the defect site is replaced by connective tissue. The Acomys cahirinus mouse is a relatively new model for studying tissue regeneration processes, specifically the elastic cartilage of the auricle. To investigate the molecular genetic mechanisms responsible for these processes and gain insight into the cellular and tissue composition of the intact auricle, we utilized the method of single-cell RNA sequencing (scRNA-seq). This method enables quantification of gene expression in the sample and modeling of cell clustering based on expression profiles. This allows for assessment of sample heterogeneity in terms of specific cell populations. Annotation of cell types, particularly in non-model organisms, should be supported by classical morphological studies to allow for more detailed identification of cell populations. This is necessary to separate clusters of cells that are grouped statistically based on similar expression profiles of a group of genes into smaller subpopulations. The objective of this study was to annotate all cell types present in the intact Acomys cahirinus auricle using a combination of transcriptomic approaches and classical histology methods. The study resulted in the annotation of 24 cell clusters based on known marker genes and by comparing genetic and morphological data.

MultiKano: an automatic cell type annotation tool for single-cell multi-omics data based on Kolmogorov-Arnold network and data augmentation.

MultiKano: an automatic cell type annotation tool for single-cell multi-omics data based on Kolmogorov-Arnold network and data augmentation.

Cell Type Differentiation Using Network Clustering Algorithms.

Single cell RNA-seq (scRNA-seq) technologies provide unprecedented resolution representing transcriptomics at the level of single cell. One of the biggest challenges in scRNA-seq data analysis is the cell type annotation, which is usually inferred by cell separation approaches. In-silico algorithms that accurately identify individual cell types in ongoing single-cell sequencing studies are crucial for unlocking cellular heterogeneity and understanding the biological basis of diseases. In this study, we focus on robustly identifying cell types in single-cell RNA sequencing data; we conduct a comparative analysis using methods established in biology, like Seurat, Leiden, and WGCNA, as well as Infomap, statistical inference via Stochastic Block Models (SBM), and single-cell Graph Neural Networks (scGNN). We also analyze preprocessing pipelines to identify and optimize key components in the process. Leveraging two independent datasets, PBMC and ROSMAP, we employ clustering algorithms on cell-cell networks derived from gene expression data. Our findings reveal that while clusters detected by WGCNA exhibit limited correspondence with cell types, those identified by multiresolution Infomap and Leiden, and SBM show a closer alignment, with Infomap standing out as a particularly effective approach. Infomap notably offers valuable insights for the precise characterization of cellular landscapes related to neurodegenration and immunology in scRNA-seq.

Spatially Aware Domain Adaptation Enables Cell Type Deconvolution from Multi-Modal Spatially Resolved Transcriptomics.

Spatially Resolved Transcriptomics (SRT) offers unprecedented opportunities to elucidate the cellular arrangements within tissues. Nevertheless, the absence of deconvolution methods that simultaneously model multi-modal features has impeded progress in understanding cellular heterogeneity in spatial contexts. To address this issue, SpaDA is developed, a novel spatially aware domain adaptation method that integrates multi-modal data (i.e., transcriptomics, histological images, and spatial locations) from SRT to accurately estimate the spatial distribution of cell types. SpaDA utilizes a self-expressive variational autoencoder, coupled with deep spatial distribution alignment, to learn and align spatial and graph representations from spatial multi-modal SRT data and single-cell RNA sequencing (scRNA-seq) data. This strategy facilitates the transfer of cell type annotation information across these two similarity graphs, thereby enhancing the prediction accuracy of cell type composition. The results demonstrate that SpaDA surpasses existing methods in cell type deconvolution and the identification of cell types and spatial domains across diverse platforms. Moreover, SpaDA excels in identifying spatially colocalized cell types and key marker genes in regions of low-quality measurements, exemplified by high-resolution mouse cerebellum SRT data. In conclusion, SpaDA offers a powerful and flexible framework for the analysis of multi-modal SRT datasets, advancing the understanding of complex biological systems.

T cell receptor-centric perspective to multimodal single-cell data analysis.

The T cell receptor (TCR), despite its importance, is underutilized in single-cell analysis, with gene expression features solely driving current strategies. Here, we argue for a TCR-first approach, more suited toward T cell repertoires. To this end, we curated a large T cell atlas from 12 prominent human studies, containing in total 500,000 T cells spanning multiple diseases, including melanoma, head and neck cancer, blood cancer, and lung transplantation. Here, we identified severe limitations in cell-type annotation using unsupervised approaches and propose a more robust standard using a semi-supervised method or the TCR arrangement. We showcase the utility of a TCR-first approach through application of the STEGO.R tool for the identification of treatment-related dynamics and previously unknown public T cell clusters with potential antigen-specific properties. Thus, the paradigm shift to a TCR-first can highlight overlooked key T cell features that have the potential for improvements in immunotherapy and diagnostics.

Single-cell multiomics reveals simvastatin inhibits pan-cancer epithelial-mesenchymal transition via the MEK/ERK pathway in XBP1+ mast cells

Distant metastasis is the leading cause of cancer-related mortality, and achieving survival benefits through advancements in systemic therapy remains challenging. Mast cells play a dual role in shaping the tumor microenvironment (TME) and influencing distant metastasis, underscoring the significant research value of targeting mast cells for systemic therapy in advanced cancer. We investigated variations in mast cell infiltration levels in primary and metastatic malignancies using immunocyte infiltration analysis. Mast cell subsets were identified from pan-cancer distant metastasis single-cell sequencing data through dimensionality reduction clustering and cell type annotation, combined with cell trajectory and communication network analyses. A prognostic model was established using WGCNA and 12 machine learning algorithms to identify potential mast cell targets. Drug sensitivity and Mendelian randomization analyses were conducted to select potential drugs targeting mast cells, and their effects on epithelial-mesenchymal transition (EMT) were validated through in vitro experiments, including wound healing, transwell, and western blot assays. Results revealed that activated mast cells show increased infiltration in metastatic tumors, correlating with poor survival duration. XBP1+ mast cells were identified as key components of the inhibitory TME, potentially involved in EMT activation. Simvastatin was identified as a potential drug, reversing EMT induced by XBP1+ mast cells in pan-cancer. Aberrant activation of MEK/ERK signaling in XBP1+ mast cells can stimulate cancer cell EMT by modulating degranulation, while Simvastatin can inhibit EMT by suppressing degranulation.

Estimating uncertainty in reference-based cell type annotation in single-cell genomics.

Estimating uncertainty in reference-based cell type annotation in single-cell genomics.

VisRNA: Interactive web server for scRNA-seq data analysis to discover therapeutic targets for non-small cell lung cancer

Abstract. Motivation: Non-small cell lung cancer (NSCLC) is a leading cause of lung cancer diagnoses and mortality worldwide. Single-cell RNA sequencing (scRNA-seq) has transformed our molecular understanding of cancer by revealing gene dysregulation in individual cells. This granularity allows the discovery of new therapeutic targets, promising NSCLC treatment strategies. Method: The study introduces VisRNA, a Python-based web application for analyzing scRNA-seq data statistically and functionally. VisRNA uses advanced machine learning algorithms to reduce multiple dimensionalities in scRNA-seq data and automatically annotate cell types. This method allows gene expression analysis across NSCLC tumor cell populations. Results: VisRNA analysis of NSCLC scRNA-seq datasets identified 14 distinct cell types with distinct gene expression patterns. VisRNA found several significantly differentially expressed genes in these cell populations that could be therapeutic targets. The application ranked drug candidates by molecular docking simulation performance, indicating potential efficacy against targets. Conclusion: VisRNA is a powerful tool for identifying new therapeutic targets and drug candidates for NSCLC, highlighting a significant advancement in scRNA-seq data analysis. VisRNA helps understand the diseases molecular basis by examining gene expression in individual NSCLC tumor cells, which may lead to better treatments.

Rediscovering publicly available single-cell data with the DISCO platform.

Single-cell RNA sequencing (scRNA-seq) has emerged as the key technique for studying transcriptomics at the single-cell level. In our previous work, we presented the DISCO database (https://www.immunesinglecell.org/) that integrates publicly available human scRNA-seq data. We now introduce an enhanced version of DISCO, which has expanded fourfold to include >100 million cells from >17 thousand samples. It provides uniformly realigned read count tables, curated metadata, integrated tissue and phenotype specific atlases, and harmonized cell type annotations. It also hosts a single-cell enhanced knowledgebase of cell type ontology and gene signatures relating to cell types and phenotypes. Lastly, it offers a suite of tools for data retrieval, integration, annotation, and mapping, allowing users to construct customized atlases and perform integrated analysis with their own data. These tools are also available in a standalone R package for offline analysis.

EPCO-11. BIASES IN HEALTHY BRAIN SINGLE-CELL DATASETS: FINDING THE BEST COMPARISON FOR GLIOBLASTOMA STUDIES

Abstract Tumor cells undergo metabolic alterations that support their proliferation and simultaneously modify immune populations, resulting in an immunosuppressive tumor microenvironment. This study aims to compare the metabolic expression of immune cells within the tumor microenvironment to those in a healthy environment. However, there is a lack of single-cell transcriptomic data for normal brain cells, necessitating an analysis of different methods to compare immune cells in tumors with those in a healthy brain. To address this, we explored cell-to-cell metabolic variability using public single-cell RNA-seq (scRNA-seq) data of glioblastoma (n=338564), and from immune cells in the brains of post-mortem individuals (n=14177), fetuses (n=40000), and epilepsy patients (n=466), as well as from immune blood cells (n=329762). We employed ssGSEA to generate activity scores for 70 key metabolic pathways from KEGG for each individual cell. Known marker genes were utilized to identify normal cell populations (macrophages, T cells, oligodendrocytes) in datasets lacking cell type annotations. Our results revealed that blood cells exhibited high metabolic activity in macrophages but showed moderate activity in other cell types. Immune blood cells also demonstrated high oxidative phosphorylation (OXPHOS) but low fatty acid activity. Fetal brain cells displayed a lack of heterogeneity and showed high fatty acid activity with low OXPHOS activity. Brain cells from post-mortem individuals displayed heterogeneity but exhibited low metabolic activity across nearly all cells and pathways. Additionally, the hierarchy of pathway activity differed from the other datasets. Epilepsy data revealed a metabolic activity pattern similar to tumor-infiltrating immune cells, with comparable activity hierarchy and heterogeneity, although the sample size was limited for detailed comparison. Understanding the differences between types of datasets is important to choose the most appropriate comparison, allowing us to observe the metabolic alterations in immune cells caused by glioblastoma and to develop better immunotherapy strategies.

Knowledge-based inductive bias and domain adaptation for cell type annotation.

Measurement techniques often result in domain gaps among batches of cellular data from a specific modality. The effectiveness of cross-batch annotation methods is influenced by inductive bias, which refers to a set of assumptions that describe the behavior of model predictions. Different annotation methods possess distinct inductive biases, leading to varying degrees of generalizability and interpretability. Given that certain cell types exhibit unique functional patterns, we hypothesize that the inductive biases of cell annotation methods should align with these biological patterns to produce meaningful predictions. In this study, we propose KIDA, Knowledge-based Inductive bias and Domain Adaptation. The knowledge-based inductive bias constrains the prediction rules learned from the reference dataset, composed of multiple batches, to functional patterns relevant to biology, thereby enhancing the generalization of the model to unseen batches. Since the query dataset also contains gaps from multiple batches, KIDA's domain adaptation employs pseudo labels for self-knowledge distillation, effectively narrowing the distribution gap between model predictions and the query dataset. Benchmark experiments demonstrate that KIDA is capable of achieving accurate cross-batch cell type annotation.

Cytometry masked autoencoder: An accurate and interpretable automated immunophenotyper

Single-cell cytometry data are crucial for understanding the role of the immune system in diseases and responses to treatment. However, traditional methods for annotating cytometry data face challenges in scalability, robustness, and accuracy. We propose a cytometry masked autoencoder (cyMAE), which automates immunophenotyping tasks including cell type annotation. The model upholds user-defined cell type definitions, facilitating interpretability and cross-study comparisons. The training of cyMAE has a self-supervised phase, which leverages large amounts of unlabeled data, followed by fine-tuning on specialized tasks using smaller amounts of annotated data. The cost of training a new model is amortized over repeated inferences on new datasets using the same panel. Through validation across multiple studies using the same panel, we demonstrate that cyMAE delivers accurate and interpretable cellular immunophenotyping and improves the prediction of subject-level metadata. This proof of concept marks a significant step forward for large-scale immunology studies.

Single-cell transcriptomics of rectal organoids from individuals with perianal fistulizing Crohn’s disease reveals patient-specific signatures

Perianal fistulizing Crohn’s disease (CD) is a severe gastrointestinal disorder causing extensive mucosal damage with limited treatment options. Severe manifestations of the disease appear at higher rates in non-Europeans but the genetic and cellular mechanisms driving the disease phenotypes remain poorly understood. Herein, we tested whether pathologic determinants in the epithelial stem cell compartment could be detected at the transcript level in rectal organoids derived from a diverse patient population. Rectal organoid and mucosal cells from endoscopic biopsies of each patient having perianal fistulizing CD or no disease controls were prepared for and sequenced at the single cell level. After cell type annotations based on expressed marker genes, samples were analyzed by principal components, for differential transcript expression, cell type proportions, and pathway enrichment. After QC, we produced 77,044 rectal organoid cells (n = 13 patients; 8 CD, 5 controls) with high quality sequences that identified 10 distinct epithelial subtypes, that we compared to 141,367 mucosal epithelial cells (n = 29 patients; 18 CD, 11 controls). Consistent with mucosal epithelial cells, rectal organoids prominently displayed disease signatures represented by the stem and transit amplifying regions of the rectal crypt, including alterations in transcriptional signatures of metabolic, epigenetic, and proliferating pathways. Organoids also retained their gender- and ancestral-specific gene expression signatures. However, they lacked many of the inflammatory signatures observed in epithelial cells from diseased mucosa. Perianal CD patient derived rectal organoids reflect gene expression signatures related to disease, gender, and ancestry, suggesting they harbor inherent properties amenable to further patient-specific, disease-related experimentation.

Cell2Sentence: Teaching Large Language Models the Language of Biology.

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into "cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the fine-tuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S fine-tuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications.

Single-cell RNA-seq analysis revealed the stemness of a specific cluster of B cells in acute lymphoblastic leukemia progression.

Childhood acute lymphoblastic leukemia (ALL) is a common pediatric cancer. The heterogeneous characterization of B cells in ALL progression poses new challenges to researchers. We used single-cell sequencing to explore the critical role of B cells in regulating the ALL immune microenvironment. We collected the single cell (sc) RNA-seq data of ALL and health sample from the gene expression omnibus (GEO) database, the "Seurat" and "harmony" R package was used for quality control and scRNA-seq analysis, in which the CellMarker2.0 database was used for cell type annotation. Subsequently, the FindAllMarkers function was used to identify the differentially expressed genes (DEGs) among various cell types and the DAVID database was applied for the biological process of DEGs. Then, the "inferCNV" package was used for copy number variation, regulons and cell communication were performed by SCENIC tool and CellChat package. The role of the target gene in regulating ALL progression was assessed using RT-qPCR, Transwell and scratch healing assays. We identified nine mainly cell clusters after scRNA-seq analysis, in which the B cells had higher infiltration proportion in the ALL samples and were sub-clustered into five cell sub-groups. The B cells 1 is closely associated with cell proliferation and stemness (TNFAIP3 and KDM5B), and the significant CNV of amplification occurred on chr6 and chr21 that supported stemness of B cells1. RXRB is a key transcription factor mediated the proliferation of B cells 1, which in turn suppressed hematopoietic stem cells (HSCs) proliferation and promoted cytotoxic NK/T cells activation through diverse cell communication ways. One of the key regulators of B cells is MYC, which promotes the migration and invasive ability of cell line leukemia cell lines. This study reveals the stemness characteristics of B cells and their critical role in ALL progression, a finding that provides new potential directions for the development of targeted therapies against ALL.

Whole-brain annotation and multi-connectome cell typing of Drosophila

The fruit fly Drosophila melanogaster has emerged as a key model organism in neuroscience, in large part due to the concentration of collaboratively generated molecular, genetic and digital resources available for it. Here we complement the approximately 140,000 neuron FlyWire whole-brain connectome1 with a systematic and hierarchical annotation of neuronal classes, cell types and developmental units (hemilineages). Of 8,453 annotated cell types, 3,643 were previously proposed in the partial hemibrain connectome2, and 4,581 are new types, mostly from brain regions outside the hemibrain subvolume. Although nearly all hemibrain neurons could be matched morphologically in FlyWire, about one-third of cell types proposed for the hemibrain could not be reliably reidentified. We therefore propose a new definition of cell type as groups of cells that are each quantitatively more similar to cells in a different brain than to any other cell in the same brain, and we validate this definition through joint analysis of FlyWire and hemibrain connectomes. Further analysis defined simple heuristics for the reliability of connections between brains, revealed broad stereotypy and occasional variability in neuron count and connectivity, and provided evidence for functional homeostasis in the mushroom body through adjustments of the absolute amount of excitatory input while maintaining the excitation/inhibition ratio. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open-source toolchain for brain-scale comparative connectomics.

SpotGF: Denoising spatially resolved transcriptomics data using an optimal transport-based gene filtering algorithm

Spatially resolved transcriptomics (SRT) combines gene expression profiles with the physical locations of cells in their native states but suffers from unpredictable spatial noise due to cell damage during cryosectioning and exposure to reagents for staining and mRNA release. To address this noise, we developed SpotGF, an algorithm for denoising SRT data using optimal transport-based gene filtering. SpotGF quantifies diffusion patterns numerically, distinguishing widespread expression genes from aggregated expression genes and filtering out the former as noise. Unlike conventional denoising methods, SpotGF preserves raw sequencing data, thereby avoiding false positives that can arise from imputation. Additionally, SpotGF demonstrates superior performance in cell clustering, identifying potential marker genes, and annotating cell types. Overall, SpotGF has the potential to become a crucial preprocessing step in the downstream analysis of SRT data. The SpotGF software is freely available at GitHub. A record of this paper's transparent peer review process is included in the supplemental information.

Identification of kidney cell types in scRNA-seq and snRNA-seq data using machine learning algorithms

IntroductionSingle-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) provide valuable insights into the cellular states of kidney cells. However, the annotation of cell types often requires extensive domain expertise and time-consuming manual curation, limiting scalability and generalizability. To facilitate this process, we tested the performance of five supervised classification methods for automatic cell type annotation. ResultsWe analyzed publicly available sc/snRNA-seq datasets from five expert-annotated studies, comprising 62,120 cells from 79 kidney biopsy samples. Datasets were integrated by harmonizing cell type annotations across studies. Five different supervised machine learning algorithms (support vector machines, random forests, multilayer perceptrons, k-nearest neighbors, and extreme gradient boosting) were applied to automatically annotate cell types using four training datasets and one testing dataset. Performance metrics, including accuracy (F1 score) and rejection rates, were evaluated. All five machine learning algorithms demonstrated high accuracies, with a median F1 score of 0.94 and a median rejection rate of 1.8 %. The algorithms performed equally well across different datasets and successfully rejected cell types that were not present in the training data. However, F1 scores were lower when models trained primarily on scRNA-seq data were tested on snRNA-seq data. ConclusionsDespite limitations including the number of biopsy samples, our findings demonstrate that machine learning algorithms can accurately annotate a wide range of adult kidney cell types in scRNA-seq/snRNA-seq data. This approach has the potential to standardize cell type annotation and facilitate further research on cellular mechanisms underlying kidney disease.

ScGAA: a general gated axial-attention model for accurate cell-type annotation of single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a key technology for investigating cell development and analysing cell diversity across various diseases. However, the high dimensionality and extreme sparsity of scRNA-seq data pose great challenges for accurate cell type annotation. To address this, we developed a new cell-type annotation model called scGAA (general gated axial-attention model for accurate cell-type annotation of scRNA-seq). Based on the transformer framework, the model decomposes the traditional self-attention mechanism into horizontal and vertical attention, considerably improving computational efficiency. This axial attention mechanism can process high-dimensional data more efficiently while maintaining reasonable model complexity. Additionally, the gated unit was integrated into the model to enhance the capture of relationships between genes, which is crucial for achieving an accurate cell type annotation. The results revealed that our improved transformer model is a promising tool for practical applications. This theoretical innovation increased the model performance and provided new insights into analytical tools for scRNA-seq data.

Vitessce: integrative visualization of multimodal and spatially resolved single-cell data.

Multiomics technologies with single-cell and spatial resolution make it possible to measure thousands of features across millions of cells. However, visual analysis of high-dimensional transcriptomic, proteomic, genome-mapped and imaging data types simultaneously remains a challenge. Here we describe Vitessce, an interactive web-based visualization framework for exploration of multimodal and spatially resolved single-cell data. We demonstrate integrative visualization of millions of data points, including cell-type annotations, gene expression quantities, spatially resolved transcripts and cell segmentations, across multiple coordinated views. The open-source software is available at http://vitessce.io .