Public scRNA-seq Datasets Research Articles

Identification of underexplored mesenchymal and vascular-related cell populations in human skeletal muscle.

Skeletal muscle repair and maintenance are directly and indirectly supported by interstitial cell populations such as vascular cells and fibro-adipogenic progenitors (FAPs), a subset of which express Twist2 and possess direct myogenic potential. Furthermore, work in rodents has highlighted the potential of pericytes to act as progenitor cells, giving rise to muscle cells and transdifferentiating into endothelial cells. However, less is understood about these populations in human skeletal muscle. Here, we performed single-cell RNA sequencing (scRNAseq) on ∼2,000 cells isolated from the human semitendinosus muscle of young individuals. This demonstrated the presence of a vascular-related cell type that expressed pericyte and pan-endothelial genes that we localized to large blood vessels within skeletal muscle cross sections and termed endothelial-like pericytes (ELPCs). RNA velocity analysis indicated that ELPCs may represent a "transition state" between endothelial cells and pericytes. Analysis of published scRNAseq data sets revealed evidence for ELPCs in trunk and heart musculature, which showed transcriptional similarity. In addition, we identified a subset of FAPs expressing TWIST2 mRNA and protein. Human TWIST2-expressing cells were anatomically and transcriptionally comparable to mouse Twist2 cells as they were restricted to the myofiber interstitium, expressed fibrogenic genes but lacked satellite cell markers, and colocalized with the FAPs marker PDGFRα in human muscle cross sections. Taken together, these results highlight the complexity of stromal cells residing in human skeletal muscle and support the utility of scRNAseq for discovery and characterization of poorly described cell populations.

OR25-2 RBFOX2 Copy Number Variation Disrupts Urethral Development Causing Hypospadias

Abstract Despite the frequency of hypospadias (1: 125 live male births) often the etiology is not known. The Lamb Laboratory previously determined that RNA Binding Fox-1 Homolog 2 (RBFOX2) copy number variation (CNV) is associated with clinical cases of upper and lower tract genitourinary (GU) anomalies, including hypospadias. RBFOX2 encodes a RNA binding protein that regulates mRNA splicing. The hypothesis was tested that altered RBFOX2 gene dosage impacts the developing penis transcriptome via mRNA splicing, specifically the mesenchymal to epithelial transition critical for penile urethra formation, resulting in hypospadias.Searches, in publically available and local clinical databases, centered on hypospadias occurance with gene CNV: identified the RBFOX2 locus as a gene dosage sensitve region. To identify developmental pathways regulated by RBFOX2 in the GU system, Rbfox2 haploinsufficient and null mouse models were developed, and penises were collected for RNA-seq, in situ hybridization, and micro-CT analysis of urethral development. In situ hybridization together with re-analysis of published scRNA-seq datasets were employed to define the anatomical regions of Rbfox2-specific expression in the developing mouse penis [Amato & Yao, PNAS 118(25): e2103856118, 2021; Armfield & Cohn, Dev Biol 477: 145-154.2021].Publicly available clinical data identified 17 patients (0.068% n=24,896) with pathogenic RBFOX2 CNVs: 4 were noted to have hypospadias. Results from our cohort of non-syndromic GU birth defect patient samples identified 11 patients (2.4% n=450) with RBFOX2 CNVs: 21% of whom have hypospadias. RNA immunoprecipitation agianst RBFOX2 in human kidney cells validated mRNA transcipt targets previously identified in cardiac cells [Verma 2016]. Micro-CT analysis on fetal Rbfox2-haploinsufficient mice validated the modeled clinical phenotype. Combined in situhybridization and scRNA-seq spatial analysis on mouse samples revealed, after penile sex determination (E15.5), Rbfox2 expression becomes restricted to the penile urethral mesenchyme. Differential gene expression and transcript usage analysis on the Rbfox2-loss mouse penis transcriptome identified mis-expression of gene products associated the estrogen signalling pathway.Alterations in RBFOX2 gene dosage clinically correlate with upper and lower tract genitourinary anomalies, specifically hypospadias. Data from human cell lines and mouse models seemingly indicate the phenotype seems is partially the product of an RBFOX2 role in directing the mesenchymal to epithelial transition. Specifically our data support the a direct role of RBFOX2 in balancing the competing male and female pathways at the level of estrogen and androgen signaling pathways. We are currently using immunofluorescence experiments to define the changes in androgen-receptor, estrogen-receptor, and RBFOX2 patterning throughout perinatal penis development. Presentation: Monday, June 13, 2022 11:15 a.m. - 11:30 a.m.

Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells

BackgroundSophisticated methods to properly pre-process and analyze the increasing collection of single-cell RNA sequencing (scRNA-seq) data are increasingly being developed. On the contrary, the best practices to integrate these data into metabolic networks, aiming at describing metabolic phenotypes within a heterogeneous cell population, have been poorly investigated. In this regard, a critical factor is the presence of false zero values in reactions essential for a fundamental metabolic function, such as biomass or energy production. Here, we investigate the role of denoising strategies in mitigating this problem.MethodsWe applied state-of-the-art denoising strategies - namely MAGIC, ENHANCE, and SAVER - on three public scRNA-seq datasets. We then associated a metabolic flux distribution with every single cell by embedding its noise-free transcriptomics profile in the constraints of the optimization of a core metabolic model. Finally, we used the obtained single-cell optimal metabolic fluxes as features for cluster analysis. We compared the results obtained with different techniques, and with or without the use of denoising. We also investigated the possibility of applying denoising directly on the Reaction Activity Scores, which are metabolic features extracted from the read counts, rather than on the read counts.ResultsWe show that denoising of transcriptomics data improves the clustering of single cells. We also illustrate that denoising restores important metabolic properties, such as the correlation between cell cycle phase and biomass accumulation, and between the RAS scores of reactions belonging to the same metabolic pathway. We show that MAGIC performs better than ENHANCE and SAVER, and that, denoising applied directly on the RAS matrix could be an effective alternative in removing false zero values from essential metabolic reactions.ConclusionsOur results indicate that including denoising as a pre-processing operation represents a milestone to integrate scRNA-seq data into Flux Balance Analysis simulations and to perform single-cell cluster analysis with a focus on metabolic phenotypes.

A systematic evaluation of the computational tools for ligand-receptor-based cell-cell interaction inference.

Cell–cell interactions (CCIs) are essential for multicellular organisms to coordinate biological processes and functions. One classical type of CCI interaction is between secreted ligands and cell surface receptors, i.e. ligand-receptor (LR) interactions. With the recent development of single-cell technologies, a large amount of single-cell ribonucleic acid (RNA) sequencing (scRNA-Seq) data has become widely available. This data availability motivated the single-cell-resolution study of CCIs, particularly LR-based CCIs. Dozens of computational methods and tools have been developed to predict CCIs by identifying LR-based CCIs. Many of these tools have been theoretically reviewed. However, there is little study on current LR-based CCI prediction tools regarding their performance and running results on public scRNA-Seq datasets. In this work, to fill this gap, we tested and compared nine of the most recent computational tools for LR-based CCI prediction. We used 15 well-studied scRNA-Seq samples that correspond to approximately 100K single cells under different experimental conditions for testing and comparison. Besides briefing the methodology used in these nine tools, we summarized the similarities and differences of these tools in terms of both LR prediction and CCI inference between cell types. We provided insight into using these tools to make meaningful discoveries in understanding cell communications.

Comparative transcriptional analysis of satellite glial cell injury response

Background: Satellite glial cells (SGCs) tightly surround and support primary sensory neurons in the peripheral nervous system and are increasingly recognized for their involvement in the development of neuropathic pain following nerve injury. SGCs are difficult to investigate due to their flattened shape and tight physical connection to neurons in vivo and their rapid changes in phenotype and protein expression when cultured in vitro. Consequently, several aspects of SGC function under normal conditions as well as after a nerve injury remain to be explored. The recent advance in single cell RNA sequencing (scRNAseq) technologies has enabled a new approach to investigate SGCs. Methods: In this study we used scRNAseq to investigate SGCs from mice subjected to sciatic nerve injury. We used a meta-analysis approach to compare the injury response with that found in other published datasets. Furthermore, we also used scRNAseq to investigate how cells from the dorsal root ganglion (DRG) change after 3 days in culture. Results: From our meta-analysis of the injured conditions, we find that SGCs share a common signature of 18 regulated genes following sciatic nerve crush or sciatic nerve ligation, involving transcriptional regulation of cholesterol biosynthesis. We also observed a considerable transcriptional change when culturing SGCs, suggesting that some differentiate into a specialised in vitro state while others start resembling Schwann cell-like precursors. Conclusion: By using integrated analyses of new and previously published scRNAseq datasets, this study provides a consensus view of which genes are most robustly changed in SGCs after injury. Our results are available via the Broad Institute Single Cell Portal, so that readers can explore and search for genes of interest.

Dynamic Changes in Uterine NK Cell Subset Frequency and Function Over the Menstrual Cycle and Pregnancy.

Uterine natural killer cells (uNK) play an important role in promoting successful pregnancy by regulating trophoblast invasion and spiral artery remodelling in the first trimester. Recently, single-cell RNA sequencing (scRNAseq) on first-trimester decidua showed that uNK can be divided into three subsets, which may have different roles in pregnancy. Here we present an integration of previously published scRNAseq datasets, together with novel flow cytometry data to interrogate the frequency, phenotype, and function of uNK1–3 in seven stages of the reproductive cycle (menstrual, proliferative, secretory phases of the menstrual cycle; first, second, and third trimester; and postpartum). We found that uNK1 and uNK2 peak in the first trimester, but by the third trimester, the majority of uNK are uNK3. All three subsets are most able to degranulate and produce cytokines during the secretory phase of the menstrual cycle and express KIR2D molecules, which allow them to interact with HLA-C expressed by placental extravillous trophoblast cells, at the highest frequency during the first trimester. Taken together, our findings suggest that uNK are particularly active and able to interact with placental cells at the time of implantation and that uNK1 and uNK2 may be particularly involved in these processes. Our findings are the first to establish how uNK frequency and function change dynamically across the healthy reproductive cycle. This serves as a platform from which the relationship between uNK function and impaired implantation and placentation can be investigated. This will have important implications for the study of subfertility, recurrent miscarriage, pre-eclampsia, and pre-term labour.

Abstract 2105: PD-L1 high ICOSL low Secretory dendritic cells infiltrate human solid tumors

Abstract Background: Human conventional dendritic cells (cDC) are essential for the anti-tumor immune response. Their immunogenic and tolerogenic states in cancer remain controversial. Our objective was to define the range of DC activation states in vitro and to determine their relevance ex vivo in cancer. Methods: First, in vitro, we activated healthy donor cDC with 16 different stimuli and measured cDC and T cells cytokine and chemokine secretion upon co-culture (n = 130 individual experiments) to define new maturation archetypes. Second, we established the relevance of these archetypes in cancer, ex vivo in human. We performed in depth analysis of cDC infiltrating head and neck cancer (HNSCC) by flow cytometry (n = 22 patients), transcriptomic analysis (n = 6 patients), and cDC-enriched single-cell transcriptomics (ScRNAseq) (n = 10503 cells from 2 patients). Then we performed a merged analysis of cDC from in house and public ScRNAseq datasets (n = 36 tumor samples from HNSCC, Lung, and Breast cancer, and, as comparator, n = 33 blood and juxtatumor samples, n = 19 healthy donor samples, and n = 23 samples from inflammatory diseases). Results: In vitro, we identified two archetypes of cDC activation: (i) PD-L1highICOSLlow cDC that produce large amounts of inflammatory cytokines and chemokines, labeled Secretory cDC; (ii)PD-L1lowICOSLhigh cDC that induce a broad range of Th cytokines, labeled Helper cDC. This functional dichotomy of cDC was mutually exclusive and not receptor specific. In cancer, we identified Secretory cDC, and these cells aligned with mature LAMP3+cDC. Helper cDC were not found ex vivo. Secretory cDC simultaneously expressed stimulatory (CD40, TNFRSF9 (4-1BB)), inhibitory (CD274, CD200, IDO1), and mixed (PVR) checkpoints. Secretory cDC were associated with T cell inflammation in HNSCC, triple-negative breast cancer (TNBC), and melanoma (all p < 1.10-15), with improved overall survival in HNSCC and TNBC (all p < 0.01), and with response to checkpoint blockade in 2 melanoma cohorts (all p < 0.01). Conclusion: We identify and characterize Secretory cDC in several solid cancers. This novel phenotypic and functional dichotomy of human cDC activation states has broad implications for all types of immunotherapies and provides rational for drug combinations. Citation Format: Caroline Hoffmann, Floriane Noël, Maximilien Grandclaudon, Lucile Massenet-Regad, Paula Michea, Philemon Sirven, Lilith Faucheux, Aurore Surun, Olivier Lantz, Mylene Bohec, Jian Ye, Weihua Guo, Juliette Rochefort, Jerzy Klijanienko, Sylvain Baulande, Charlotte Lecerf, Maud Kamal, Christophe Le Tourneau, Maude Guillot-Delost, Vassili Soumelis. PD-L1 high ICOSL low Secretory dendritic cells infiltrate human solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2105.

Comparative transcriptional analysis of satellite glial cell injury response

Background: Satellite glial cells (SGCs) tightly surround and support primary sensory neurons in the peripheral nervous system and are increasingly recognized for their involvement in the development of neuropathic pain following nerve injury. SGCs are difficult to investigate due to their flattened shape and tight physical connection to neurons in vivo and their rapid changes in phenotype and protein expression when cultured in vitro. Consequently, several aspects of SGC function under normal conditions as well as after a nerve injury remain to be explored. The recent advance in single cell RNA sequencing (scRNAseq) technologies has enabled a new approach to investigate SGCs. Methods: In this study we used scRNAseq to investigate SGCs from mice subjected to sciatic nerve injury. We used a meta-analysis approach to compare the injury response with that found in other published datasets. Furthermore, we also used scRNAseq to investigate how cells from the dorsal root ganglion (DRG) change after 3 days in culture. Results: From our meta-analysis of the injured conditions, we find that SGCs share a common signature of 18 regulated genes following sciatic nerve crush or sciatic nerve ligation, involving transcriptional regulation of cholesterol biosynthesis. We also observed a considerable transcriptional change when culturing SGCs, suggesting that some differentiate into a specialised in vitro state while others start resembling Schwann cell-like precursors. Conclusion: By using integrated analyses of new and previously published scRNAseq datasets, this study provides a consensus view of which genes are most robustly changed in SGCs after injury. Our results are available via the Broad Institute Single Cell Portal, so that readers can explore and search for genes of interest.

ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders.

Single-cell RNA sequencing (scRNAseq) technologies allow for measurements of gene expression at a single-cell resolution. This provides researchers with a tremendous advantage for detecting heterogeneity, delineating cellular maps or identifying rare subpopulations. However, a critical complication remains: the low number of single-cell observations due to limitations by rarity of subpopulation, tissue degradation or cost. This absence of sufficient data may cause inaccuracy or irreproducibility of downstream analysis. In this work, we present Automated Cell-Type-informed Introspective Variational Autoencoder (ACTIVA): a novel framework for generating realistic synthetic data using a single-stream adversarial variational autoencoder conditioned with cell-type information. Within a single framework, ACTIVA can enlarge existing datasets and generate specific subpopulations on demand, as opposed to two separate models [such as single-cell GAN (scGAN) and conditional scGAN (cscGAN)]. Data generation and augmentation with ACTIVA can enhance scRNAseq pipelines and analysis, such as benchmarking new algorithms, studying the accuracy of classifiers and detecting marker genes. ACTIVA will facilitate analysis of smaller datasets, potentially reducing the number of patients and animals necessary in initial studies. We train and evaluate models on multiple public scRNAseq datasets. In comparison to GAN-based models (scGAN and cscGAN), we demonstrate that ACTIVA generates cells that are more realistic and harder for classifiers to identify as synthetic which also have better pair-wise correlation between genes. Data augmentation with ACTIVA significantly improves classification of rare subtypes (more than 45% improvement compared with not augmenting and 4% better than cscGAN) all while reducing run-time by an order of magnitude in comparison to both models. The codes and datasets are hosted on Zenodo (https://doi.org/10.5281/zenodo.5879639). Tutorials are available at https://github.com/SindiLab/ACTIVA. Supplementary data are available at Bioinformatics online.

A comprehensive comparison of supervised and unsupervised methods for cell type identification in single-cell RNA-seq.

The cell type identification is among the most important tasks in single-cell RNA-sequencing (scRNA-seq) analysis. Many in silico methods have been developed and can be roughly categorized as either supervised or unsupervised. In this study, we investigated the performances of 8 supervised and 10 unsupervised cell type identification methods using 14 public scRNA-seq datasets of different tissues, sequencing protocols and species. We investigated the impacts of a number of factors, including total amount of cells, number of cell types, sequencing depth, batch effects, reference bias, cell population imbalance, unknown/novel cell type, and computational efficiency and scalability. Instead of merely comparing individual methods, we focused on factors' impacts on the general category of supervised and unsupervised methods. We found that in most scenarios, the supervised methods outperformed the unsupervised methods, except for the identification of unknown cell types. This is particularly true when the supervised methods use a reference dataset with high informational sufficiency, low complexity and high similarity to the query dataset. However, such outperformance could be undermined by some undesired dataset properties investigated in this study, which lead to uninformative and biased reference datasets. In these scenarios, unsupervised methods could be comparable to supervised methods. Our study not only explained the cell typing methods' behaviors under different experimental settings but also provided a general guideline for the choice of method according to the scientific goal and dataset properties. Finally, our evaluation workflow is implemented as a modularized R pipeline that allows future evaluation of new methods. Availability: All the source codes are available at https://github.com/xsun28/scRNAIdent.

Analysis of single cell data as it relates to aging and longevity

Age-related disability and diseases are known to be delayed in people living to 100 years or more. Changes in the immune system with age are known, including in cell type composition and gene expression differences. To further explore changes in extreme longevity subjects, we investigated peripheral blood immune system cell subpopulations across age and extreme longevity at a single cell resolution. We performed an integrative analysis of public scRNA-seq datasets to define consensus cell types of longevity and age, and classified cell types in our novel New England Centenarian Study dataset. We integrated these datasets together to investigate cell type specific differences at a composition and gene expression level. Our findings identified higher cell type diversity in extreme longevity subjects compared to younger age groups, but no significant difference among younger age groups demonstrating that overall composition differences are unique to longevity. We identified novel differences in myeloid and lymphocyte populations; Extreme longevity subjects have higher composition of CD14+ Monocytes, Natural Killer cells, and T gamma delta populations and lower composition of CD16+ Monocytes and dendritic populations. We characterized gene expression differences between extreme longevity and younger age groups and differences in aging across younger age groups. We found that extreme longevity cell type specific signatures overlapped with the aging signatures by at least 50%. We identified unique genes to extreme longevity that are enriched for pathways specific to immune activation and inflammation, suggesting a protective mechanism for centenarians through efficient activation and regulation of immune subpopulations in peripheral blood.

Heterogeneity in the Response of Different Subtypes of Drosophila melanogaster Midgut Cells to Viral Infections.

Single-cell RNA sequencing (scRNA-seq) offers the possibility to monitor both host and pathogens transcriptomes at the cellular level. Here, public scRNA-seq datasets from Drosophila melanogaster midgut cells were used to compare the differences in replication strategy and cellular responses between two fly picorna-like viruses, Thika virus (TV) and D. melanogaster Nora virus (DMelNV). TV exhibited lower levels of viral RNA accumulation but infected a higher number of cells compared to DMelNV. In both cases, viral RNA accumulation varied according to cell subtype. The cellular heat shock response to TV and DMelNV infection was cell-subtype- and virus-specific. Disruption of bottleneck genes at later stages of infection in the systemic response, as well as of translation-related genes in the cellular response to DMelNV in two cell subtypes, may affect the virus replication.

Systematic Single Cell Rna Sequencing Analysis Reveals Unique Transcriptional Regulatory Networks of Atoh1-Mediated Hair Cell Conversion in Adult Mouse Cochleae

Regeneration of mammalian cochlear hair cells (HCs) by modulating molecular pathways or transcription factors is a promising approach to hearing restoration; however, immaturity of the regenerated HCs in vivo remains a major challenge. Single cell RNA sequencing (scRNA-seq) datasets have been critical in identification of transcription factors which regulate the regenerating processes; however, systematic analysis of these datasets, albeit broad interest, has been challenging to many researchers. Here we adopted multiple high-throughput sequencing analytical tools (WGCNA, SCENIC, PAGA, ARACNE-AP and VIPER) and systematically re-analyzed a previously published scRNA-seq dataset during Atoh1-mediated supporting cell (SC) to HC conversion in adult mouse cochleae1. Instead of focusing on differentially expressed genes, our new analysis with WGCNA and SCENIC focuses on connections among co-expressed genes to establish independent expression modules. We independently confirmed the existence of multiple conversion stages during the Atoh1-mediated SC-to-HC conversion process in adult cochleae in vivo. Moreover, we re-analyzed the recently published scRNA-seq datasets of developing neonatal mouse cochleae2 and confirmed that the most differentiated converted HCs of the ectopic Atoh1 expression in adult cochleae resemble endogenous outer HCs (OHCs) at postnatal day 1. Furthermore, our new PAGA trajectory inference analysis revealed that the SC-to-HC conversion process is parallel, but not identical, to the pro-sensory cell-to-OHC path during normal development. Detailed analyses of the gene regulatory networks (GRNs) identified continuous regulatory dynamics for the conversion with multiple key modules and regulons (Nhlh1, Lhx3, Barhl1 and Nfia) that have not been identified previously. Finally, we used ARACNE-AP and VIPER for transcriptional interactions from gene expression data and discovered multiple key master regulators that will likely promote conversion to more mature OHCs. These new results will guide future experimental approaches to HC regeneration for hearing restoration in adult mammalian cochleae in vivo and provide an example as how to perform in-depth analysis with the large amount of scRNA-seq data.

REBET: a method to determine the number of cell clusters based on batch effect removal.

In single-cell RNA-seq (scRNA-seq) data analysis, a fundamental problem is to determine the number of cell clusters based on the gene expression profiles. However, the performance of current methods is still far from satisfactory, presumably due to their limitations in capturing the expression variability among cell clusters. Batch effects represent the undesired variability between data measured in different batches. When data are obtained from different labs or protocols batch effects occur. Motivated by the practice of batch effect removal, we considered cell clusters as batches. We hypothesized that the number of cell clusters (i.e. batches) could be correctly determined if the variances among clusters (i.e. batch effects) were removed. We developed a new method, namely, removal of batch effect and testing (REBET), for determining the number of cell clusters. In this method, cells are first partitioned into k clusters. Second, the batch effects among these k clusters are then removed. Third, the quality of batch effect removal is evaluated with the average range of normalized mutual information (ARNMI), which measures how uniformly the cells with batch-effects-removal are mixed. By testing a range of k values, the k value that corresponds to the lowest ARNMI is determined to be the optimal number of clusters. We compared REBET with state-of-the-art methods on 32 simulated datasets and 14 published scRNA-seq datasets. The results show that REBET can accurately and robustly estimate the number of cell clusters and outperform existing methods. Contact: H.D.L. (hongdong@csu.edu.cn) or Q.S.X. (qsxu@csu.edu.cn).

Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell-cell interactions

SummaryMechanisms underlying severe coronavirus disease 2019 (COVID-19) disease remain poorly understood. We analyze several thousand plasma proteins longitudinally in 306 COVID-19 patients and 78 symptomatic controls, uncovering immune and non-immune proteins linked to COVID-19. Deconvolution of our plasma proteome data using published scRNA-seq datasets reveals contributions from circulating immune and tissue cells. Sixteen percent of patients display reduced inflammation yet comparably poor outcomes. Comparison of patients who died to severely ill survivors identifies dynamic immune-cell-derived and tissue-associated proteins associated with survival, including exocrine pancreatic proteases. Using derived tissue-specific and cell-type-specific intracellular death signatures, cellular angiotensin-converting enzyme 2 (ACE2) expression, and our data, we infer whether organ damage resulted from direct or indirect effects of infection. We propose a model in which interactions among myeloid, epithelial, and T cells drive tissue damage. These datasets provide important insights and a rich resource for analysis of mechanisms of severe COVID-19 disease.

A Gene Rank Based Approach for Single Cell Similarity Assessment and Clustering.

Single-cell RNA sequencing (scRNA-seq) technology provides quantitative gene expression profiles at single-cell resolution. As a result, researchers have established new ways to explore cell population heterogeneity and genetic variability of cells. One of the current research directions for scRNA-seq data is to identify different cell types accurately through unsupervised clustering methods. However, scRNA-seq data analysis is challenging because of their high noise level, high dimensionality and sparsity. Moreover, the impact of multiple latent factors on gene expression heterogeneity and on the ability to accurately identify cell types remains unclear. How to overcome these challenges to reveal the biological difference between cell types has become the key to analyze scRNA-seq data. For these reasons, the unsupervised learning for cell population discovery based on scRNA-seq data analysis has become an important research area. A cell similarity assessment method plays a significant role in cell clustering. Here, we present BioRank, a new cell similarity assessment method based on annotated gene sets and gene ranks. To evaluate the performances, we cluster cells by two classical clustering algorithms based on the similarity between cells obtained by BioRank. In addition, BioRank can be used by any clustering algorithm that requires a similarity matrix. Applying BioRank to 12 public scRNA-seq datasets, we show that it is better than or at least as well as several popular similarity assessment methods for single cell clustering.

SwarnSeq: An improved statistical approach for differential expression analysis of single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a powerful technology that is capable of generating gene expression data at the resolution of individual cell. The scRNA-seq data is characterized by the presence of dropout events, which severely bias the results if they remain unaddressed. There are limited Differential Expression (DE) approaches which consider the biological processes, which lead to dropout events, in the modeling process. So, we develop, SwarnSeq, an improved method for DE, and other downstream analysis that considers the molecular capture process in scRNA-seq data modeling. The performance of the proposed method is benchmarked with 11 existing methods on 10 different real scRNA-seq datasets under three comparison settings. We demonstrate that SwarnSeq method has improved performance over the 11 existing methods. This improvement is consistently observed across several public scRNA-seq datasets generated using different scRNA-seq protocols. The external spike-ins data can be used in the SwarnSeq method to enhance its performance. Availability and implementationThe method is implemented as a publicly available R package available at https://github.com/sam-uofl/SwarnSeq.

Accurate feature selection improves single-cell RNA-seq cell clustering

Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as 'features'), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have a significant impact on the clustering accuracy. All existing scRNA-seq clustering tools include a feature selection step relying on some simple unsupervised feature selection methods, mostly based on the statistical moments of gene-wise expression distributions. In this work, we carefully evaluate the impact of feature selection on cell clustering accuracy. In addition, we develop a feature selection algorithm named FEAture SelecTion (FEAST), which provides more representative features. We apply the method on 12 public scRNA-seq datasets and demonstrate that using features selected by FEAST with existing clustering tools significantly improve the clustering accuracy.

DRscDB: A single-cell RNA-seq resource for data mining and data comparison across species

With the advent of single-cell RNA sequencing (scRNA-seq) technologies, there has been a spike in studies involving scRNA-seq of several tissues across diverse species including Drosophila. Although a few databases exist for users to query genes of interest within the scRNA-seq studies, search tools that enable users to find orthologous genes and their cell type-specific expression patterns across species are limited. Here, we built a new search database, DRscDB (https://www.flyrnai.org/tools/single_cell/web/), to address this need. DRscDB serves as a comprehensive repository for published scRNA-seq datasets for Drosophila and relevant datasets from human and other model organisms. DRscDB is based on manual curation of Drosophila scRNA-seq studies of various tissue types and their corresponding analogous tissues in vertebrates including zebrafish, mouse, and human. Of note, our search database provides most of the literature-derived marker genes, thus preserving the original analysis of the published scRNA-seq datasets. Finally, DRscDB serves as a web-based user interface that allows users to mine gene expression data from scRNA-seq studies and perform cell cluster enrichment analyses pertaining to various scRNA-seq studies, both within and across species.

Macrophage imaging and subset analysis using single-cell RNA sequencing.

Macrophages have been associated with drug response and resistance in diverse settings, thus raising the possibility of using macrophage imaging as a companion diagnostic to inform personalized patient treatment strategies. Nanoparticle-based contrast agents are especially promising because they efficiently deliver fluorescent, magnetic, and/or radionuclide labels by leveraging the intrinsic capacity of macrophages to accumulate nanomaterials in their role as professional phagocytes. Unfortunately, current clinical imaging modalities are limited in their ability to quantify broad molecular programs that may explain (a) which particular cell subsets a given imaging agent is actually labeling, and (b) what mechanistic role those cells play in promoting drug response or resistance. Highly multiplexed single-cell approaches including single-cell RNA sequencing (scRNAseq) have emerged as resources to help answer these questions. In this review, we query recently published scRNAseq datasets to support companion macrophage imaging, with particular focus on using dextran-based nanoparticles to predict the action of anti-cancer nanotherapies and monoclonal antibodies.