Rare Cell Types Research Articles

IgE-Binding Monocytes Have an Enhanced Ability to Produce IL-8 (CXCL8) in Animals with Naturally Occurring Allergy.

IL-8 is a potent chemokine that recruits neutrophils and basophils to promote inflammation in many species. IL-8 is produced by many cell types, including monocytes. In this study, we report a novel role for IgE-binding monocytes, a rare peripheral immune cell type, to promote allergic inflammation through IL-8 production in a horse model of natural IgE-mediated allergy. We developed a mAb with confirmed specificity for both recombinant and native equine IL-8 for flow cytometric analysis. Equine IL-8 was produced by CD14+/MHC class II+/CD16- monocytes, including a subpopulation of IgE-binding monocytes, following stimulation with LPS. In addition, IgE cross-linking induced IL-8 production by both peripheral blood basophils and IgE-binding monocytes. IL-8 production was compared between healthy horses and those with a naturally occurring IgE-mediated skin allergy, Culicoides hypersensitivity. Allergic horses had significantly higher percentages of IL-8+ IgE-binding monocytes after IgE cross-linking. In contrast, frequencies of IL-8+ basophils after IgE cross-linking were similar in all horses, regardless of allergic disease, highlighting IgE-binding monocytes as a novel source of IL-8 during allergy. We concluded that IgE-binding monocytes from allergic individuals have an increased capacity for IL-8 production and likely contribute to the recruitment of innate immune cells during IgE-mediated allergy and promotion of inflammation during repeated allergen contact.

Single-cell profiling of tumor heterogeneity and the microenvironment in advanced non-small cell lung cancer

Lung cancer is a highly heterogeneous disease. Cancer cells and cells within the tumor microenvironment together determine disease progression, as well as response to or escape from treatment. To map the cell type-specific transcriptome landscape of cancer cells and their tumor microenvironment in advanced non-small cell lung cancer (NSCLC), we analyze 42 tissue biopsy samples from stage III/IV NSCLC patients by single cell RNA sequencing and present the large scale, single cell resolution profiles of advanced NSCLCs. In addition to cell types described in previous single cell studies of early stage lung cancer, we are able to identify rare cell types in tumors such as follicular dendritic cells and T helper 17 cells. Tumors from different patients display large heterogeneity in cellular composition, chromosomal structure, developmental trajectory, intercellular signaling network and phenotype dominance. Our study also reveals a correlation of tumor heterogeneity with tumor associated neutrophils, which might help to shed light on their function in NSCLC.

Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID.

Because of the stochasticity associated with high-throughput single-cell sequencing, current methods for exploring cell-type diversity rely on clustering-based computational approaches in which heterogeneity is characterized at cell subpopulation rather than at full single-cell resolution. Here we present Cell-ID, a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell sequencing data. We applied Cell-ID to data from multiple human and mouse samples, including blood cells, pancreatic islets and airway, intestinal and olfactory epithelium, as well as to comprehensive mouse cell atlas datasets. We demonstrate that Cell-ID signatures are reproducible across different donors, tissues of origin, species and single-cell omics technologies, and can be used for automatic cell-type annotation and cell matching across datasets. Cell-ID improves biological interpretation at individual cell level, enabling discovery of previously uncharacterized rare cell types or cell states. Cell-ID is distributed as an open-source R software package.

Endocrine Pancreas Development and Dysfunction Through the Lens of Single-Cell RNA-Sequencing.

A chronic inability to maintain blood glucose homeostasis leads to diabetes, which can damage multiple organs. The pancreatic islets regulate blood glucose levels through the coordinated action of islet cell-secreted hormones, with the insulin released by β-cells playing a crucial role in this process. Diabetes is caused by insufficient insulin secretion due to β-cell loss, or a pancreatic dysfunction. The restoration of a functional β-cell mass might, therefore, offer a cure. To this end, major efforts are underway to generate human β-cells de novo, in vitro, or in vivo. The efficient generation of functional β-cells requires a comprehensive knowledge of pancreas development, including the mechanisms driving cell fate decisions or endocrine cell maturation. Rapid progress in single-cell RNA sequencing (scRNA-Seq) technologies has brought a new dimension to pancreas development research. These methods can capture the transcriptomes of thousands of individual cells, including rare cell types, subtypes, and transient states. With such massive datasets, it is possible to infer the developmental trajectories of cell transitions and gene regulatory pathways. Here, we summarize recent advances in our understanding of endocrine pancreas development and function from scRNA-Seq studies on developing and adult pancreas and human endocrine differentiation models. We also discuss recent scRNA-Seq findings for the pathological pancreas in diabetes, and their implications for better treatment.

Automated quality control and cell identification of droplet-based single-cell data using dropkick.

A major challenge for droplet-based single-cell sequencing technologies is distinguishing true cells from uninformative barcodes in data sets with disparate library sizes confounded by high technical noise (i.e., batch-specific ambient RNA). We present dropkick, a fully automated software tool for quality control and filtering of single-cell RNA sequencing (scRNA-seq) data with a focus on excluding ambient barcodes and recovering real cells bordering the quality threshold. By automatically determining data set–specific training labels based on predictive global heuristics, dropkick learns a gene-based representation of real cells and ambient noise, calculating a cell probability score for each barcode. Using simulated and real-world scRNA-seq data, we benchmarked dropkick against conventional thresholding approaches and EmptyDrops, a popular computational method, showing greater recovery of rare cell types and exclusion of empty droplets and noisy, uninformative barcodes. We show for both low- and high-background data sets that dropkick's weakly supervised model reliably learns which genes are enriched in ambient barcodes and draws a multidimensional boundary that is more robust to data set–specific variation than existing filtering approaches. dropkick provides a fast, automated tool for reproducible cell identification from scRNA-seq data that is critical to downstream analysis and compatible with popular single-cell Python packages.

Identification of a micropeptide and multiple secondary cell genes that modulate Drosophila male reproductive success

Even in well-characterized genomes, many transcripts are considered noncoding RNAs (ncRNAs) simply due to the absence of large open reading frames (ORFs). However, it is now becoming clear that many small ORFs (smORFs) produce peptides with important biological functions. In the process of characterizing the ribosome-bound transcriptome of an important cell type of the seminal fluid-producing accessory gland of Drosophila melanogaster, we detected an RNA, previously thought to be noncoding, called male-specific abdominal (msa). Notably, msa is nested in the HOX gene cluster of the Bithorax complex and is known to contain a micro-RNA within one of its introns. We find that this RNA encodes a "micropeptide" (9 or 20 amino acids, MSAmiP) that is expressed exclusively in the secondary cells of the male accessory gland, where it seems to accumulate in nuclei. Importantly, loss of function of this micropeptide causes defects in sperm competition. In addition to bringing insights into the biology of a rare cell type, this work underlines the importance of small peptides, a class of molecules that is now emerging as important actors in complex biological processes.

Effects of sheared chromatin length on ChIP-seq quality and sensitivity.

Chromatin immunoprecipitation followed by massively parallel, high throughput sequencing (ChIP-seq) is the method of choice for genome-wide identification of DNA segments bound by specific transcription factors or in chromatin with particular histone modifications. However, the quality of ChIP-seq datasets varies widely, with a substantial fraction being of intermediate to poor quality. Thus, it is important to discern and control the factors that contribute to variation in ChIP-seq. In this study, we focused on sonication, a user-controlled variable, to produce sheared chromatin. We systematically varied the amount of shearing of fixed chromatin from a mouse erythroid cell line, carefully measuring the distribution of resultant fragment lengths prior to ChIP-seq. This systematic study was complemented with a retrospective analysis of additional experiments. We found that the level of sonication had a pronounced impact on the quality of ChIP-seq signals. Over-sonication consistently reduced quality, while the impact of under-sonication differed among transcription factors, with no impact on sites bound by CTCF but frequently leading to the loss of sites occupied by TAL1 or bound by POL2. The bound sites not observed in low-quality datasets were inferred to be a mix of both direct and indirect binding. We leveraged these findings to produce a set of CTCF ChIP-seq datasets in rare, primary hematopoietic progenitor cells. Our observation that the amount of chromatin sonication is a key variable in success of ChIP-seq experiments indicates that monitoring the level of sonication can improve ChIP-seq quality and reproducibility and facilitate ChIP-seq in rare cell types.

Correction for both common and rare cell types in blood is important to identify genes that correlate with age

BackgroundAging is a multifactorial process that affects multiple tissues and is characterized by changes in homeostasis over time, leading to increased morbidity. Whole blood gene expression signatures have been associated with aging and have been used to gain information on its biological mechanisms, which are still not fully understood. However, blood is composed of many cell types whose proportions in blood vary with age. As a result, previously observed associations between gene expression levels and aging might be driven by cell type composition rather than intracellular aging mechanisms. To overcome this, previous aging studies already accounted for major cell types, but the possibility that the reported associations are false positives driven by less prevalent cell subtypes remains.ResultsHere, we compared the regression model from our previous work to an extended model that corrects for 33 additional white blood cell subtypes. Both models were applied to whole blood gene expression data from 3165 individuals belonging to the general population (age range of 18–81 years). We evaluated that the new model is a better fit for the data and it identified fewer genes associated with aging (625, compared to the 2808 of the initial model; P ≤ 2.5⨯10−6). Moreover, 511 genes (~ 18% of the 2808 genes identified by the initial model) were found using both models, indicating that the other previously reported genes could be proxies for less abundant cell types. In particular, functional enrichment of the genes identified by the new model highlighted pathways and GO terms specifically associated with platelet activity.ConclusionsWe conclude that gene expression analyses in blood strongly benefit from correction for both common and rare blood cell types, and recommend using blood-cell count estimates as standard covariates when studying whole blood gene expression.

Single cell transcriptomic analysis of murine lung development on hyperoxia-induced damage

During late lung development, alveolar and microvascular development is finalized to enable sufficient gas exchange. Impaired late lung development manifests as bronchopulmonary dysplasia (BPD) in preterm infants. Single-cell RNA sequencing (scRNA-seq) allows for assessment of complex cellular dynamics during biological processes, such as development. Here, we use MULTI-seq to generate scRNA-seq profiles of over 66,000 cells from 36 mice during normal or impaired lung development secondary to hyperoxia with validation of some of the findings in lungs from BPD patients. We observe dynamic populations of cells, including several rare cell types and putative progenitors. Hyperoxia exposure, which mimics the BPD phenotype, alters the composition of all cellular compartments, particularly alveolar epithelium, stromal fibroblasts, capillary endothelium and macrophage populations. Pathway analysis and predicted dynamic cellular crosstalk suggest inflammatory signaling as the main driver of hyperoxia-induced changes. Our data provides a single-cell view of cellular changes associated with late lung development in health and disease.

Direct Comparative Analyses of 10X Genomics Chromium and Smart-seq2

Single-cell RNA sequencing (scRNA-seq) is generally used for profiling transcriptome of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data generated by these two platforms from the same samples of CD45− cells, we systematically evaluated their features using a wide spectrum of analyses. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data more. For 10X-based data, we observed higher noise for mRNAs with low expression levels. Approximately 10%−30% of all detected transcripts by both platforms were from non-coding genes, with long non-coding RNAs (lncRNAs) accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected distinct groups of differentially expressed genes between cell clusters, indicating the different characteristics of these technologies. Our study promotes better understanding of these two platforms and offers the basis for an informed choice of these widely used technologies.

Human gastrointestinal epithelia of the esophagus, stomach, and duodenum resolved at single-cell resolution

The upper gastrointestinal tract, consisting of the esophagus, stomach, and duodenum, controls food transport, digestion, nutrient uptake, and hormone production. By single-cell analysis of healthy epithelia of these human organs, we molecularly define their distinct cell types. We identify a quiescent COL17A1high KRT15high stem/progenitor cell population in the most basal cell layer of the esophagus and detect substantial gene expression differences between identical cell types of the human and mouse stomach. Selective expression of BEST4, CFTR, guanylin, and uroguanylin identifies a rare duodenal cell type, referred to as BCHE cell, which likely mediates high-volume fluid secretion because of continual activation of the CFTR channel by guanylin/uroguanylin-mediated autocrine signaling. Serotonin-producing enterochromaffin cells in the antral stomach significantly differ in gene expression from duodenal enterochromaffin cells. We, furthermore, discover that the histamine-producing enterochromaffin-like cells in the oxyntic stomach express the luteinizing hormone, yet another member of the enteroendocrine hormone family.

Olfactory Rod Cells: A Rare Cell Type in the Larval Zebrafish Olfactory Epithelium With a Large Actin-Rich Apical Projection.

We report the presence of a rare cell type, the olfactory rod cell, in the developing zebrafish olfactory epithelium. These cells each bear a single actin-rich rod-like apical projection extending 5–10 μm from the epithelial surface. Live imaging with a ubiquitous Lifeact-RFP label indicates that the olfactory rods can oscillate. Olfactory rods arise within a few hours of the olfactory pit opening, increase in numbers and size during larval stages, and can develop in the absence of olfactory cilia. Olfactory rod cells differ in morphology from the known classes of olfactory sensory neuron, but express reporters driven by neuronal promoters. A sub-population of olfactory rod cells expresses a Lifeact-mRFPruby transgene driven by the sox10 promoter. Mosaic expression of this transgene reveals that olfactory rod cells have rounded cell bodies located apically in the olfactory epithelium and have no detectable axon. We offer speculation on the possible function of these cells in the Discussion.

Linear-time cluster ensembles of large-scale single-cell RNA-seq and multimodal data.

A fundamental task in single-cell RNA-seq (scRNA-seq) analysis is the identification of transcriptionally distinct groups of cells. Numerous methods have been proposed for this problem, with a recent focus on methods for the cluster analysis of ultralarge scRNA-seq data sets produced by droplet-based sequencing technologies. Most existing methods rely on a sampling step to bridge the gap between algorithm scalability and volume of the data. Ignoring large parts of the data, however, often yields inaccurate groupings of cells and risks overlooking rare cell types. We propose method Specter that adopts and extends recent algorithmic advances in (fast) spectral clustering. In contrast to methods that cluster a (random) subsample of the data, we adopt the idea of landmarks that are used to create a sparse representation of the full data from which a spectral embedding can then be computed in linear time. We exploit Specter's speed in a cluster ensemble scheme that achieves a substantial improvement in accuracy over existing methods and identifies rare cell types with high sensitivity. Its linear-time complexity allows Specter to scale to millions of cells and leads to fast computation times in practice. Furthermore, on CITE-seq data that simultaneously measures gene and protein marker expression, we show that Specter is able to use multimodal omics measurements to resolve subtle transcriptomic differences between subpopulations of cells.

Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration.

With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-seq) datasets, bioinformatics methods based on gene co-expression network are becoming efficient tools for analyzing scRNA-seq data, improving cell type prediction accuracy and in turn facilitating biological discovery. However, the current methods are mainly based on overall co-expression correlation and overlook co-expression that exists in only a subset of cells, thus fail to discover certain rare cell types and sensitive to batch effect. Here, we developed independent component analysis-based gene co-expression network inference (ICAnet) that decomposed scRNA-seq data into a series of independent gene expression components and inferred co-expression modules, which improved cell clustering and rare cell-type discovery. ICAnet showed efficient performance for cell clustering and batch integration using scRNA-seq datasets spanning multiple cells/tissues/donors/library types. It works stably on datasets produced by different library construction strategies and with different sequencing depths and cell numbers. We demonstrated the capability of ICAnet to discover rare cell types in multiple independent scRNA-seq datasets from different sources. Importantly, the identified modules activated in acute myeloid leukemia scRNA-seq datasets have the potential to serve as new diagnostic markers. Thus, ICAnet is a competitive tool for cell clustering and biological interpretations of single-cell RNA-seq data analysis.

Endothelium-derived stromal cells contribute to hematopoietic bone marrow niche formation

Bone marrow stromal cells (BMSCs) play pivotal roles in tissue maintenance and regeneration. Their origins, however, remain incompletely understood. Here we identify rare LNGFR+ cells in human fetal and regenerative bone marrow that co-express endothelial and stromal markers. This endothelial subpopulation displays transcriptional reprogramming consistent with endothelial-to-mesenchymal transition (EndoMT) and can generate multipotent stromal cells that reconstitute the bone marrow (BM) niche upon transplantation. Single-cell transcriptomics and lineage tracing in mice confirm robust and sustained contributions of EndoMT to bone precursor and hematopoietic niche pools. Interleukin-33 (IL-33) is overexpressed in subsets of EndoMT cells and drives this conversion process through ST2 receptor signaling. These data reveal generation of tissue-forming BMSCs from mouse and human endothelial cells and may be instructive for approaches to human tissue regeneration.

Assembly and Exploration of a Single Cell Atlas of the Drosophila Larval Ventral Cord. Identification of Rare Cell Types.

Single-cell RNA sequencing provides a new approach to an old problem: how to study cellular diversity in complex biological systems. This powerful tool has been instrumental in profiling different cell types and investigating, at the single-cell level, cell states, functions, and responses. However, mining these data requires new analytical and statistical methods for high-dimensional analyses that must be customized and adapted to specific goals. Here we present a custom multistage analysis pipeline which integrates modules contained in different R packages to ensure flexible, high-quality RNA-seq data analysis. We describe this workflow step by step, providing the codes, explaining the rationale for each function, and discussing the results and the limitations. We apply this pipeline to analyze different datasets of Drosophila larval ventral cords, identifying and describing rare cell types, such as astrocytes and neuroendocrine cells. This multistage analysis pipeline can be easily implemented by both novice and experienced scientists interested in neuronal and/or cellular diversity beyond the Drosophila model system. © 2021 US Government.

Abstract PO062: Isolation of tumor-infiltrating lymphocytes for higher sensitivity gene expression profiling

Abstract Gene expression profiling is a powerful approach to address the mechanism of action (MOA) of cancer immunotherapy-driven reprogramming of tumor-infiltrating lymphocytes (TILs) in the tumor microenvironment. However, TILs, particularly T and NK cells, are a very rare population within the tumor. Gene expression profiling using Nanostring or RNAseq is often not sensitive enough to detect expression changes in rare populations, and it is difficult to separate gene expression profiles between different cell subsets. Although single-cell RNAseq can distinguish gene expression profiles between different cell subsets, it is very costly and would require many cells to be sequenced before obtaining the rare cell type of interest. Classically, fluorescence-activated cell sorting (FACS) has been used to isolate rare populations, but this method is a long process that can affect cell phenotype and results in dramatic cell loss. Thus, we developed a protocol for rapid and reproducible magnetic bead-based isolation of tumor-infiltrating lymphocytes (TILs) for higher sensitivity MOA studies. CD8 and CD4 TILs were isolated from both MC38 and CT26.KSA tumor models with high purity and sufficient yield for further Nanostring gene expression analysis. As expected, CD8 and CD4 T cell gene expression profiles were dramatically enriched in the isolated TILs compared to the whole tumor. Thus, our protocol for isolating TILs from mouse tumors can be applied broadly to multiple tumor types and provides sufficient yield to perform downstream high-sensitivity gene expression profiling. Citation Format: Lindsay M. Webb, Bartholomew Naughton, Sireesha Yalavarthi, Clotilde Bourin, Jacques Moisan, Chunxiao Xu. Isolation of tumor-infiltrating lymphocytes for higher sensitivity gene expression profiling [abstract]. In: Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; 2020 Oct 19-20. Philadelphia (PA): AACR; Cancer Immunol Res 2021;9(2 Suppl):Abstract nr PO062.

Analysis of B Cell Receptor-Mediated Antigen Extraction by B Lymphocytes from Plasma Membrane Sheets Using Confocal Microscopy.

High-resolution confocal imaging has provided new insights in the process of receptor-mediated endocytosis in variety of cell types. We describe here the protocol for investigating B cell receptor (BCR)-mediated internalization of membrane bound antigens using confocal microscopy. We describe the method to prepare plasma membrane sheets (PMS) in a small area, bind fluorescently tagged antigens to the PMS and activate B cells on the PMS. We also describe the method for analyzing antigen internalization using confocal microscopy and computational image analysis. This protocol is useful for the study of antigen internalization by B cells and can be applied for studying receptor-mediated endocytosis in other cells as well. The setup we describe here is especially useful for studying rare cell types when the number of cells available is limiting.

Single-Cell Sequencing and Organoids: A Powerful Combination for Modelling Organ Development and Diseases.

The development and function of a particular organ and the pathogenesis of various diseases remain intimately linked to the features of each cell type in the organ. Conventional messenger RNA- or protein-based methodologies often fail to elucidate the contribution of rare cell types, including some subpopulations of stem cells, short-lived progenitors and circulating tumour cells, thus hampering their applications in studies regarding organ development and diseases. The scRNA-seq technique represents a new approach for determining gene expression variability at the single-cell level. Organoids are new preclinical models that recapitulate complete or partial features of their original organ and are thought to be superior to cell models in mimicking the sophisticated spatiotemporal processes of the development and regeneration and diseases. In this review, we highlight recent advances in the field of scRNA-seq, organoids and their current applications and summarize the advantages of using a combination of scRNA-seq and organoid technology to model diseases and organ development.

Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data

Annotating cell types is a critical step in single-cell RNA sequencing (scRNA-seq) data analysis. Some supervised or semi-supervised classification methods have recently emerged to enable automated cell type identification. However, comprehensive evaluations of these methods are lacking. Moreover, it is not clear whether some classification methods originally designed for analyzing other bulk omics data are adaptable to scRNA-seq analysis. In this study, we evaluated ten cell type annotation methods publicly available as R packages. Eight of them are popular methods developed specifically for single-cell research, including Seurat, scmap, SingleR, CHETAH, SingleCellNet, scID, Garnett, and SCINA. The other two methods were repurposed from deconvoluting DNA methylation data, i.e., linear constrained projection (CP) and robust partial correlations (RPC). We conducted systematic comparisons on a wide variety of public scRNA-seq datasets as well as simulation data. We assessed the accuracy through intra-dataset and inter-dataset predictions; the robustness over practical challenges such as gene filtering, high similarity among cell types, and increased cell type classes; as well as the detection of rare and unknown cell types. Overall, methods such as Seurat, SingleR, CP, RPC, and SingleCellNet performed well, with Seurat being the best at annotating major cell types. Additionally, Seurat, SingleR, CP, and RPC were more robust against downsampling. However, Seurat did have a major drawback at predicting rare cell populations, and it was suboptimal at differentiating cell types highly similar to each other, compared to SingleR and RPC. All the code and data are available from https://github.com/qianhuiSenn/scRNA_cell_deconv_benchmark.