An Introduction to the Analysis of Single-Cell RNA-Sequencing Data

Diffusion on PCA-UMAP Manifold: The Impact of Data Structure Preservation to Denoise High-Dimensional Single-Cell RNA Sequencing Data

Single-cell transcriptomics (scRNA-seq) is revolutionizing biological research, yet it faces challenges such as inefficient transcript capture and noise. To address these challenges, methods like neighbor averaging or graph diffusion are used. These methods often rely on k-nearest neighbor graphs from low-dimensional manifolds. However, scRNA-seq data suffer from the 'curse of dimensionality', leading to the over-smoothing of data when using imputation methods. To overcome this, sc-PHENIX employs a PCA-UMAP diffusion method, which enhances the preservation of data structures and allows for a refined use of PCA dimensions and diffusion parameters (e.g., k-nearest neighbors, exponentiation of the Markov matrix) to minimize noise introduction. This approach enables a more accurate construction of the exponentiated Markov matrix (cell neighborhood graph), surpassing methods like MAGIC. sc-PHENIX significantly mitigates over-smoothing, as validated through various scRNA-seq datasets, demonstrating improved cell phenotype representation. Applied to a multicellular tumor spheroid dataset, sc-PHENIX identified known extreme phenotype states, showcasing its effectiveness. sc-PHENIX is open-source and available for use and modification.

Simultaneous isolation of intact brain cells and cell-specific extracellular vesicles from cryopreserved Alzheimer's disease cortex

BACKGROUND

The neuronal and gliaI populations within the brain are tightly interwoven, making isolation and study of large populations of a single cell type from brain tissue a major technical challenge. Concurrently, cell-type specific extracellular vesicles (EVs) hold enormous diagnostic and therapeutic potential in neurodegenerative disorders including Alzheimer's disease (AD).

NEW METHOD

Postmortem AD cortical samples were thawed and gently dissociated. Following filtration, myelin and red blood cell removal, cell pellets were immunolabeled with fluorescent antibodies and analyzed by flow cytometry. The cell pellet supernatant was applied to a triple sucrose cushion for brain EV isolation.

RESULTS

Neuronal, astrocyte and microglial cell populations were identified. Cell integrity was demonstrated using calcein AM, which is retained by cells with esterase activity and an intact membrane. For some experiments cell pellets were fixed, permeabilized, and immunolabeled for cell-specific markers. Characterization of brain small EV fractions showed the expected size, depletion of EV negative markers, and enrichment in positive and cell-type specific markers.

COMPARISON WITH EXISTING METHODS AND CONCLUSIONS

We optimized and integrated established protocols, aiming to maximize information obtained from each human autopsy brain sample. The uniqueness of our method lies in its capability to isolate cells and EVs from a single cryopreserved brain sample. Our results not only demonstrate the feasibility of isolating specific brain cell subpopulations for RNA-seq but also validate these subpopulations at the protein level. The accelerated study of EVs from human samples is crucial for a better understanding of their contribution to neuron/glial crosstalk and disease progression.

Cauchy hyper-graph Laplacian nonnegative matrix factorization for single-cell RNA-sequencing data analysis

Many important biological facts have been found as single-cell RNA sequencing (scRNA-seq) technology has advanced. With the use of this technology, it is now possible to investigate the connections among individual cells, genes, and illnesses. For the analysis of single-cell data, clustering is frequently used. Nevertheless, biological data usually contain a large amount of noise data, and traditional clustering methods are sensitive to noise. However, acquiring higher-order spatial information from the data alone is insufficient. As a result, getting trustworthy clustering findings is challenging. We propose the Cauchy hyper-graph Laplacian non-negative matrix factorization (CHLNMF) as a unique approach to address these issues. In CHLNMF, we replace the measurement based on Euclidean distance in the conventional non-negative matrix factorization (NMF), which can lessen the influence of noise, with the Cauchy loss function (CLF). The model also incorporates the hyper-graph constraint, which takes into account the high-order link among the samples. The CHLNMF model's best solution is then discovered using a half-quadratic optimization approach. Finally, using seven scRNA-seq datasets, we contrast the CHLNMF technique with the other nine top methods. The validity of our technique was established by analysis of the experimental outcomes.

Anti-stromal nanotherapeutics for hepatocellular carcinoma

Hepatocellular carcinoma (HCC), the most commonly diagnosed primary liver cancer, has become a leading cause of cancer-related death worldwide. Accumulating evidence confirms that the stromal constituents within the tumor microenvironment (TME) exacerbate HCC malignancy and set the barriers to current anti-HCC treatments. Recent developments of nano drug delivery system (NDDS) have facilitated the application of stroma-targeting therapeutics, disrupting the stromal TME in HCC. This review discusses the stromal activities in HCC development and therapy resistance. In addition, it addresses the delivery challenges of NDDS for stroma-targeting therapeutics (termed anti-stromal nanotherapeutics in this review), and provides recent advances in anti-stromal nanotherapeutics for safe, effective, and specific HCC therapy.

RNA-Seq Analysis of Mammalian Prion Disease

A protein, which can attain a prion state, differs from standard proteins in terms of structural conformation and aggregation propensity. High-throughput sequencing technology provides an opportunity to gain insight into the prion disease condition when coupled with single-cell RNA-Seq analysis to reveal transcriptional changes during prion-based pathogenicity. In this chapter, we present a protocol for RNA-Seq analysis of mammalian prion disease using a single-cell RNA sequencing dataset procured from the NCBI GEO database. This protocol is a tool that can assist researchers in characterizing mammalian prion disease in a reproducible and reusable manner. Further, the resulting output has the potential to provide transcript biomarkers for mammalian prion diseases, which can be employed for diagnostic and prognostic purposes.

The Evolving Function of Vasculature and Pro-angiogenic Therapy in Fat Grafting

Autologous fat grating is a widely-accepted method to correct soft tissue deficiency. Although fat transplantation shows excellent biocompatibility and simple applicability, the relatively low retention rate caused by fat necrosis is still a challenge. The vasculature is integral after fat grafting, serving multiple crucial functions. Rapid and effective angiogenesis within grafts is essential for supplying oxygen necessary for adipocytes' survival. It facilitates the influx of inflammatory cells to remove necrotic adipocytes and aids in the delivery of regenerative cells for adipose tissue regeneration in fat grafts. The vasculature also provides a niche for interaction between adipose progenitor cells and vascular progenitor cells, enhancing angiogenesis and adipogenesis in grafts. Various methods, such as enriching grafts with diverse pro-angiogenic cells or utilizing cell-free approaches, have been employed to enhance angiogenesis. Beige and dedifferentiated adipocytes in grafts could increase vessel density. This review aims to outline the function of vasculature in fat grafting and discuss different cell or cell-free approaches that can enhance angiogenesis following fat grafting.

Construction of a hepatocytes-related and protein kinase-related gene signature in HCC based on ScRNA-Seq analysis and machine learning algorithm

With recent advancements in single-cell sequencing and machine learning methods, new insights into hepatocellular carcinoma (HCC) progression have been provided. Protein kinase-related genes (PKRGs) affect cell growth, differentiation, apoptosis, and signaling during HCC progression, making the predictive relevance of PKRGs in HCC highly necessary for personalized medicine. In this study, we analyzed single-cell data of HCC and used the machine learning method of LASSO regression to construct PKRG prediction models in six major cell types. CDK4 and AURKB were found to be the best PKRG prognostic signature for predicting the overall survival of HCC patients (including TCGA, ICGC, and GEO datasets) in hepatocytes. Independent clinical factors were further screened out using the COX regression method, and a nomogram combining PKRGs and cancer status was created. Treatment with Palbociclib (CDK4 Inhibitor) and Barasertib (AURKB Inhibitor) inhibited HCC cell migration. Patients classified as PKRG high- or low-risk groups showed different tumor mutation burdens, immune infiltrations, and gene enrichment. The PKRG high-risk group showed higher tumor mutation burdens and gene set enrichment analysis indicated that cell cycle, base excision repair, and RNA degradation pathways were more enriched in these patients. Additionally, the PKRG high-risk group demonstrated higher infiltration levels of Naïve CD8+ T cells, Endothelial cells, M2 macrophage, and Tregs than the low-risk group. In summary, this study established the hepatocytes-related PKRG signature for prognostic stratification at the single-cell level by using machine learning algorithms in HCC and identified potential HCC treatment targets based on the PKRG signature.

SigPrimedNet: A Signaling-Informed Neural Network for scRNA-seq Annotation of Known and Unknown Cell Types

Single-cell RNA sequencing is increasing our understanding of the behavior of complex tissues or organs, by providing unprecedented details on the complex cell type landscape at the level of individual cells. Cell type definition and functional annotation are key steps to understanding the molecular processes behind the underlying cellular communication machinery. However, the exponential growth of scRNA-seq data has made the task of manually annotating cells unfeasible, due not only to an unparalleled resolution of the technology but to an ever-increasing heterogeneity of the data. Many supervised and unsupervised methods have been proposed to automatically annotate cells. Supervised approaches for cell-type annotation outperform unsupervised methods except when new (unknown) cell types are present. Here, we introduce SigPrimedNet an artificial neural network approach that leverages (i) efficient training by means of a sparsity-inducing signaling circuits-informed layer, (ii) feature representation learning through supervised training, and (iii) unknown cell-type identification by fitting an anomaly detection method on the learned representation. We show that SigPrimedNet can efficiently annotate known cell types while keeping a low false-positive rate for unseen cells across a set of publicly available datasets. In addition, the learned representation acts as a proxy for signaling circuit activity measurements, which provide useful estimations of the cell functionalities.

Denoising adaptive deep clustering with self-attention mechanism on single-cell sequencing data

A large number of works have presented the single-cell RNA sequencing (scRNA-seq) to study the diversity and biological functions of cells at the single-cell level. Clustering identifies unknown cell types, which is essential for downstream analysis of scRNA-seq samples. However, the high dimensionality, high noise and pervasive dropout rate of scRNA-seq samples have a significant challenge to the cluster analysis of scRNA-seq samples. Herein, we propose a new adaptive fuzzy clustering model based on the denoising autoencoder and self-attention mechanism called the scDASFK. It implements the comparative learning to integrate cell similar information into the clustering method and uses a deep denoising network module to denoise the data. scDASFK consists of a self-attention mechanism for further denoising where an adaptive clustering optimization function for iterative clustering is implemented. In order to make the denoised latent features better reflect the cell structure, we introduce a new adaptive feedback mechanism to supervise the denoising process through the clustering results. Experiments on 16 real scRNA-seq datasets show that scDASFK performs well in terms of clustering accuracy, scalability and stability. Overall, scDASFK is an effective clustering model with great potential for scRNA-seq samples analysis. Our scDASFK model codes are freely available at https://github.com/LRX2022/scDASFK.

scHOIS: Determining Cell Heterogeneity Through Hierarchical Clustering Based on Optimal Imputation Strategy

Advances in single-cell RNA sequencing (scRNA-seq) technology provide an unbiased and high-throughput analysis of each cell at single-cell resolution, and further facilitate the development of cellular heterogeneity analysis. Despite the promise of scRNA-seq, the data generated by this method are sparse and noisy because of the presence of dropout events, which can greatly impact downstream analyses such as differential gene expression, cell type annotation, and linage trajectory reconstruction. The development of effective and robust computational methods to address both dropout and clustering are thus urgently needed. In this study, we propose a flexible, accurate two-stage algorithm for single cell heterogeneity analysis via hierarchical clustering based on an optimal imputation strategy, called scHOIS. At the first stage, masked non-negative matrix factorization is applied to approximate the original observed scRNA-seq data, with optimal rank determined by variance analysis. At the second stage, hierarchical clustering is applied to group the imputed cells using Pearson correlation to measure similarity, with the optimal number of clusters determined by integrating three classical indexes. We performed extensive experiments on real-world datasets, which showed that scHOIS effectively and robustly distinguished cellular differences and that the clustering performance of this algorithm was superior to that of other state-of-the-art methods.

scRNA-seq data analysis method to improve analysis performance

With the development of single-cell RNA sequencing technology (scRNA-seq), we have the ability to study biological questions at the level of the individual cell transcriptome. Nowadays, many analysis tools, specifically suitable for single-cell RNA sequencing data, have been developed. In this review, the currently commonly used scRNA-seq protocols are discussed. The upstream processing flow pipeline of scRNA-seq data, including goals and popular tools for reads mapping and expression quantification, quality control, normalization, imputation, and batch effect removal is also introduced. Finally, methods to evaluate these tools in both cellular and genetic dimensions, clustering and differential expression analysis are presented.

Improved downstream functional analysis of single-cell RNA-sequence data using DGAN

The dramatic increase in the number of single-cell RNA-sequence (scRNA-seq) investigations is indeed an endorsement of the new-fangled proficiencies of next generation sequencing technologies that facilitate the accurate measurement of tens of thousands of RNA expression levels at the cellular resolution. Nevertheless, missing values of RNA amplification persist and remain as a significant computational challenge, as these data omission induce further noise in their respective cellular data and ultimately impede downstream functional analysis of scRNA-seq data. Consequently, it turns imperative to develop robust and efficient scRNA-seq data imputation methods for improved downstream functional analysis outcomes. To overcome this adversity, we have designed an imputation framework namely deep generative autoencoder network [DGAN]. In essence, DGAN is an evolved variational autoencoder designed to robustly impute data dropouts in scRNA-seq data manifested as a sparse gene expression matrix. DGAN principally reckons count distribution, besides data sparsity utilizing a gaussian model whereby, cell dependencies are capitalized to detect and exclude outlier cells via imputation. When tested on five publicly available scRNA-seq data, DGAN outperformed every single baseline method paralleled, with respect to downstream functional analysis including cell data visualization, clustering, classification and differential expression analysis. DGAN is executed in Python and is accessible at https://github.com/dikshap11/DGAN .

Determinants of renin cell differentiation: a single cell epi-transcriptomics approach

Rationale

Renin cells are essential for survival. They control the morphogenesis of the kidney arterioles, and the composition and volume of our extracellular fluid, arterial blood pressure, tissue perfusion, and oxygen delivery. It is known that renin cells and associated arteriolar cells descend from FoxD1 + progenitor cells, yet renin cells remain challenging to study due in no small part to their rarity within the kidney. As such, the molecular mechanisms underlying the differentiation and maintenance of these cells remain insufficiently understood.

Objective

We sought to comprehensively evaluate the chromatin states and transcription factors (TFs) that drive the differentiation of FoxD1 + progenitor cells into those that compose the kidney vasculature with a focus on renin cells.

Methods and Results

We isolated single nuclei of FoxD1 + progenitor cells and their descendants from FoxD1 ^cre/+ ; R26R-mTmG mice at embryonic day 12 (E12) (n _cells =1234), embryonic day 18 (E18) (n _cells =3696), postnatal day 5 (P5) (n _cells =1986), and postnatal day 30 (P30) (n _cells =1196). Using integrated scRNA-seq and scATAC-seq we established the developmental trajectory that leads to the mosaic of cells that compose the kidney arterioles, and specifically identified the factors that determine the elusive, myo-endocrine adult renin-secreting juxtaglomerular (JG) cell. We confirm the role of Nfix in JG cell development and renin expression, and identified the myocyte enhancer factor-2 (MEF2) family of TFs as putative drivers of JG cell differentiation.

Conclusions

We provide the first developmental trajectory of renin cell differentiation as they become JG cells in a single-cell atlas of kidney vascular open chromatin and highlighted novel factors important for their stage-specific differentiation. This improved understanding of the regulatory landscape of renin expressing JG cells is necessary to better learn the control and function of this rare cell population as overactivation or aberrant activity of the RAS is a key factor in cardiovascular and kidney pathologies.

Single-cell RNA-sequencing of retrieved human oocytes and eggs in clinical practice and for human ovarian cell atlasing

With the advancement of single-cell separation techniques and high-throughput sequencing platforms, single-cell RNA-sequencing (scRNA-seq) has emerged as a vital technology for understanding tissue and organ systems at cellular resolution. Through transcriptional analysis, it is possible to characterize unique or rare cell types, interpret their interactions, and reveal novel functional states or shifts in developmental stages. As such, this technology is uniquely suited for studying the cells within the human ovary. The ovary is a cellularly heterogeneous organ that houses follicles, the reproductive and endocrine unit that consists of an oocyte surrounded by hormone-producing support cells, as well as many other cell populations constituting stroma, vasculature, lymphatic, and immune components. Here we review studies that have utilized scRNA-seq technology to analyze cells from healthy human ovaries and discuss the single-cell isolation techniques used. We identified two overarching applications for scRNA-seq in the human ovary. The first applies this technology to investigate transcriptional differences in oocytes/eggs from patients undergoing in vitro fertilization treatments to ultimately improve clinical outcomes. The second utilizes scRNA-seq for the pursuit of creating a comprehensive single-cell atlas of the human ovary. The knowledge gained from these studies underscores the importance of scRNA-seq technologies in unlocking a new biological understanding of the human ovary.

Single-cell RNA sequencing analysis of T helper cell differentiation and heterogeneity

Single-cell transcriptomics has emerged as a powerful tool to investigate cells' biological landscape and focus on the expression profile of individual cells. Major advantage of this approach is an analysis of highly complex and heterogeneous cell populations, such as a specific subpopulation of T helper cells that are known to differentiate into distinct subpopulations. The need for distinguishing the specific expression profile is even more important considering the T cell plasticity. However, importantly, the universal pipelines for single-cell analysis are usually not sufficient for every cell type. Here, the aims are to analyze the diversity of T cell phenotypes employing classical in vitro cytokine-mediated differentiation of human T cells isolated from human peripheral blood by single-cell transcriptomic approach with support of labelled antibodies and a comprehensive bioinformatics analysis using combination of Seurat, Nebulosa, GGplot and others. The results showed high expression similarities between Th1 and Th17 phenotype and very distinct Th2 expression profile. In a case of Th2 highly specific marker genes SPINT2, TRIB3 and CST7 were expressed. Overall, our results demonstrate how donor difference, Th plasticity and cell cycle influence the expression profiles of distinct T cell populations. The results could help to better understand the importance of each step of the analysis when working with T cell single-cell data and observe the results in a more practical way by using our analyzed datasets.

Delving into the Heterogeneity of Different Breast Cancer Subtypes and the Prognostic Models Utilizing scRNA-Seq and Bulk RNA-Seq

Background: Breast cancer (BC) is the most common malignancy in women with high heterogeneity. The heterogeneity of cancer cells from different BC subtypes has not been thoroughly characterized and there is still no valid biomarker for predicting the prognosis of BC patients in clinical practice. Methods: Cancer cells were identified by calculating single cell copy number variation using the inferCNV algorithm. SCENIC was utilized to infer gene regulatory networks. CellPhoneDB software was used to analyze the intercellular communications in different cell types. Survival analysis, univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were used to construct subtype specific prognostic models. Results: Triple-negative breast cancer (TNBC) has a higher proportion of cancer cells than subtypes of HER2+ BC and luminal BC, and the specifically upregulated genes of the TNBC subtype are associated with antioxidant and chemical stress resistance. Key transcription factors (TFs) of tumor cells for three subtypes varied, and most of the TF-target genes are specifically upregulated in corresponding BC subtypes. The intercellular communications mediated by different receptor–ligand pairs lead to an inflammatory response with different degrees in the three BC subtypes. We establish a prognostic model containing 10 genes (risk genes: ATP6AP1, RNF139, BASP1, ESR1 and TSKU; protective genes: RPL31, PAK1, STARD10, TFPI2 and SIAH2) for luminal BC, seven genes (risk genes: ACTR6 and C2orf76; protective genes: DIO2, DCXR, NDUFA8, SULT1A2 and AQP3) for HER2+ BC, and seven genes (risk genes: HPGD, CDC42 and PGK1; protective genes: SMYD3, LMO4, FABP7 and PRKRA) for TNBC. Three prognostic models can distinguish high-risk patients from low-risk patients and accurately predict patient prognosis. Conclusions: Comparative analysis of the three BC subtypes based on cancer cell heterogeneity in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy for BC patients.

scSemiAE: a deep model with semi-supervised learning for single-cell transcriptomics

Background

With the development of modern sequencing technology, hundreds of thousands of single-cell RNA-sequencing (scRNA-seq) profiles allow to explore the heterogeneity in the cell level, but it faces the challenges of high dimensions and high sparsity. Dimensionality reduction is essential for downstream analysis, such as clustering to identify cell subpopulations. Usually, dimensionality reduction follows unsupervised approach.

Results

In this paper, we introduce a semi-supervised dimensionality reduction method named scSemiAE, which is based on an autoencoder model. It transfers the information contained in available datasets with cell subpopulation labels to guide the search of better low-dimensional representations, which can ease further analysis.

Conclusions

Experiments on five public datasets show that, scSemiAE outperforms both unsupervised and semi-supervised baselines whether the transferred information embodied in the number of labeled cells and labeled cell subpopulations is much or less.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04703-0.

Brain Vascular Microenvironments in Cancer Metastasis

Primary tumours, particularly from major solid organs, are able to disseminate into the blood and lymphatic system and spread to distant sites. These secondary metastases to other major organs are the most lethal aspect of cancer, accounting for the majority of cancer deaths. The brain is a frequent site of metastasis, and brain metastases are often fatal due to the critical role of the nervous system and the limited options for treatment, including surgery. This creates a need to further understand the complex cell and molecular biology associated with the establishment of brain metastasis, including the changes to the environment of the brain to enable the arrival and growth of tumour cells. Local changes in the vascular network, immune system and stromal components all have the potential to recruit and foster metastatic tumour cells. This review summarises our current understanding of brain vascular microenvironments, fluid circulation and drainage in the context of brain metastases, as well as commenting on current cutting-edge experimental approaches used to investigate changes in vascular environments and alterations in specialised subsets of blood and lymphatic vessel cells during cancer spread to the brain.

Laser Capture Microdissection-Based mRNA Expression Microarrays and Single-Cell RNA Sequencing in Atherosclerosis Research

A major goal of methodologies related to large scale gene expression analyses is to initiate comprehensive information on transcript signatures in single cells within the tissue's anatomy. Until now, this could be achieved in a stepwise experimental approach: (1) identify the majority of transcripts in a single cell (single cell transcriptome); (2) provide information on transcripts on multiple cell subtypes in a complex sample (cell heterogeneity); and (3) give information on each cell's spatial location within the tissue (zonation transcriptomics). Such genetic information will allow construction of functionally relevant gene expression maps of single cells of a given anatomically defined tissue compartment and thus pave the way for subsequent analyses, including their epigenetic modifications. Until today these aims have not been achieved in the area of cardiovascular disease research though steps toward these goals become apparent: laser capture microdissection (LCM)-based mRNA expression microarrays of atherosclerotic plaques were applied to gain information on local gene expression changes during disease progression, providing limited spatial resolution. Moreover, while LCM-derived tissue RNA extracts have been shown to be highly sensitive and covers a range of 10-16,000 genes per array/small amount of RNA, its original promise to isolate single cells from a tissue section turned out not to be practicable because of the inherent contamination of the cell's RNA of interest with RNA from neighboring cells. Many shortcomings of LCM-based analyses have been overcome using single-cell RNA sequencing (scRNA-seq) technologies though scRNA-seq also has several limitations including low numbers of transcripts/cell and the complete loss of spatial information. Here, we describe a protocol toward combining advantages of both techniques while avoiding their flaws.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

RNA sequencing and its applications in cancer and rare diseases

With the invention of RNA sequencing over a decade ago, diagnosis and identification of the gene-related diseases entered a new phase that enabled more accurate analysis of the diseases that are difficult to approach and analyze. RNA sequencing has availed in-depth study of transcriptomes in different species and provided better understanding of rare diseases and taxonomical classifications of various eukaryotic organisms. Development of single-cell, short-read, long-read and direct RNA sequencing using both blood and biopsy specimens of the organism together with recent advancement in computational analysis programs has made the medical professional’s ability in identifying the origin and cause of genetic disorders indispensable. Altogether, such advantages have evolved the treatment design since RNA sequencing can detect the resistant genes against the existing therapies and help medical professions to take a further step in improving methods of treatments towards higher effectiveness and less side effects. Therefore, it is of essence to all researchers and scientists to have deeper insight in all available methods of RNA sequencing while taking a step-in therapy design.

Application oriented bioaugmentation processes: Mechanism, performance improvement and scale-up

Bioaugmentation is an optimization method with great potential to improve the treatment effect by introducing specific strains into the biological treatment system. In this study, a comprehensive review of the mechanism of bioaugmentation from the aspect of microbial community structure, the optimization methods facilitating application as well as feasible approaches of scale-up application has been provided. The different contribution of indigenous and exogenous strains was critically analyzed, the relationship between microbial community variation and system performance was clarified. Operation regulation and immobilization technologies are effective methods to deal with the possible failure of bioaugmentation. The gradual expansion from lab-scale, pilot scale to full-scale, the transformation and upgrading of wastewater treatment plants through the combination of direct dosing and biofilm, and the application of side-stream reactors are feasible ways to realize the full-scale application. The future challenges and prospects in this field were also proposed.

Biological Sequence Classification: A Review on Data and General Methods

With the rapid development of biotechnology, the number of biological sequences has grown exponentially. The continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence information. There are many branches of biological sequence classification research. In this review, we mainly focus on the function and modification classification of biological sequences based on machine learning. Sequence-based prediction and analysis are the basic tasks to understand the biological functions of DNA, RNA, proteins, and peptides. However, there are hundreds of classification models developed for biological sequences, and the quite varied specific methods seem dizzying at first glance. Here, we aim to establish a long-term support website ( http://lab.malab.cn/~acy/BioseqData/home.html ), which provides readers with detailed information on the classification method and download links to relevant datasets. We briefly introduce the steps to build an effective model framework for biological sequence data. In addition, a brief introduction to single-cell sequencing data analysis methods and applications in biology is also included. Finally, we discuss the current challenges and future perspectives of biological sequence classification research.

Deep learning-based advances and applications for single-cell RNA-sequencing data analysis

The rapid development of single-cell RNA-sequencing (scRNA-seq) technology has raised significant computational and analytical challenges. The application of deep learning to scRNA-seq data analysis is rapidly evolving and can overcome the unique challenges in upstream (quality control and normalization) and downstream (cell-, gene- and pathway-level) analysis of scRNA-seq data. In the present study, recent advances and applications of deep learning-based methods, together with specific tools for scRNA-seq data analysis, were summarized. Moreover, the future perspectives and challenges of deep-learning techniques regarding the appropriate analysis and interpretation of scRNA-seq data were investigated. The present study aimed to provide evidence supporting the biomedical application of deep learning-based tools and may aid biologists and bioinformaticians in navigating this exciting and fast-moving area.

Single-cell RNA sequencing and bioinformatics as tools to decipher cancer heterogenicity and mechanisms of drug resistance

It is well known that cancer is an aggressive disease, often associated with relapse, in many cases due to drug resistance. Cancer stem cell and clonal evolution are frequently causes of innate or acquired drug resistance. Current RNA sequencing technologies do not distinguish gene expression of different cell lineages because they are based on bulk cell studies. Single-cell RNA sequencing technologies and related bioinformatics clustering and differential expression analysis represent a turning point in cancer research. They are emerging as essential tools for dissecting tumors at single-cell resolution and represent novel tools to understand carcinogenesis and drug response. In this review, we will outline the role of these new technologies in addressing cancer heterogeneity and cell lineage-dependent drug resistance.

miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data

Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a 'low-quality' cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA (mtDNA) encoded genes and (ii) if a small number of genes are detected. Current best practices use these QC metrics independently with either arbitrary, uniform thresholds (e.g. 5%) or biological context-dependent (e.g. species) thresholds, and fail to jointly model these metrics in a data-driven manner. Current practices are often overly stringent and especially untenable on certain types of tissues, such as archived tumor tissues, or tissues associated with mitochondrial function, such as kidney tissue [1]. We propose a data-driven QC metric (miQC) that jointly models both the proportion of reads mapping to mtDNA genes and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. We demonstrate how our QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses. Our software package is available at https://bioconductor.org/packages/miQC.

Preparation of single-cell suspensions of mouse glomeruli for high-throughput analysis

The kidney glomerulus is essential for proper kidney function. Until recently, technical challenges associated with glomerular isolation and subsequent dissolution into single cells have limited the detailed characterization of cells in the glomerulus. Previous techniques of kidney dissociation result in low glomerular cell yield, which limits high-throughput analysis. The ability to efficiently purify glomeruli and digest the tissue into single cells is especially important for single-cell characterization methods. Here, we present a detailed and comprehensive technique for the extraction and preparation of mouse glomerular cells, with high yield and viability. The method includes direct renal perfusion of Dynabeads via the renal artery followed by kidney dissociation and isolation of glomeruli by magnet; these steps provide a high number and purity of isolated glomeruli, which are further dissociated into single cells. The balanced representation of podocytes, mesangial and endothelial cells in single-cell suspensions of mouse glomeruli, and the high cell viability observed, confirm the efficiency of our method. With some practice, the procedure can be done in <3 h (excluding equipment setup and data analysis). This protocol provides a valuable technique for advancing future single-cell-based studies of the glomerulus in health, injury and disease.

Dissecting the human kidney allograft transcriptome: single-cell RNA sequencing

PURPOSE OF REVIEW

Single-cell RNA sequencing (scRNA-seq) has provided opportunities to interrogate kidney allografts at a hitherto unavailable molecular level of resolution. Understanding of this technology is essential to better appreciate the relevant biomedical literature.

RECENT FINDINGS

Sequencing is a technique to determine the order of nucleotides in a segment of RNA or DNA. RNA-seq of kidney allograft tissues has revealed novel mechanistic insights but does not provide information on individual cell types and cell states. scRNA-seq enables to study the transcriptome of individual cells and assess the transcriptional differences and similarities within a population of cells. Initial studies on rejecting kidney allograft tissues in humans have identified the transcriptional profile of the active players of the innate and adaptive immune system. Application of scRNA-seq in a preclinical model of kidney transplantation has revealed that allograft-infiltrating myeloid cells follow a trajectory of differentiation from monocytes to proinflammatory macrophages and exhibit distinct interactions with kidney allograft parenchymal cells; myeloid cell expression of Axl played a major role in promoting intragraft myeloid cell and T-cell differentiation.

SUMMARY

The current review discusses the technical aspects of scRNA-seq and summarizes the application of this technology to dissect the human kidney allograft transcriptome.

A review of computational strategies for denoising and imputation of single-cell transcriptomic data

MOTIVATION

The advancements of single-cell sequencing methods have paved the way for the characterization of cellular states at unprecedented resolution, revolutionizing the investigation on complex biological systems. Yet, single-cell sequencing experiments are hindered by several technical issues, which cause output data to be noisy, impacting the reliability of downstream analyses. Therefore, a growing number of data science methods has been proposed to recover lost or corrupted information from single-cell sequencing data. To date, however, no quantitative benchmarks have been proposed to evaluate such methods.

RESULTS

We present a comprehensive analysis of the state-of-the-art computational approaches for denoising and imputation of single-cell transcriptomic data, comparing their performance in different experimental scenarios. In detail, we compared 19 denoising and imputation methods, on both simulated and real-world datasets, with respect to several performance metrics related to imputation of dropout events, recovery of true expression profiles, characterization of cell similarity, identification of differentially expressed genes and computation time. The effectiveness and scalability of all methods were assessed with regard to distinct sequencing protocols, sample size and different levels of biological variability and technical noise. As a result, we identify a subset of versatile approaches exhibiting solid performances on most tests and show that certain algorithmic families prove effective on specific tasks but inefficient on others. Finally, most methods appear to benefit from the introduction of appropriate assumptions on noise distribution of biological processes.

Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA-sequencing data quality control

MOTIVATION

Quality control (QC) is a critical step in single-cell RNA-seq (scRNA-seq) data analysis. Low-quality cells are removed from the analysis during the QC process to avoid misinterpretation of the data. An important QC metric is the mitochondrial proportion (mtDNA%), which is used as a threshold to filter out low-quality cells. Early publications in the field established a threshold of 5% and since then, it has been used as a default in several software packages for scRNA-seq data analysis, and adopted as a standard in many scRNA-seq studies. However, the validity of using a uniform threshold across different species, single-cell technologies, tissues and cell types has not been adequately assessed.

RESULTS

We systematically analyzed 5 530 106 cells reported in 1349 annotated datasets available in the PanglaoDB database and found that the average mtDNA% in scRNA-seq data across human tissues is significantly higher than in mouse tissues. This difference is not confounded by the platform used to generate the data. Based on this finding, we propose new reference values of the mtDNA% for 121 tissues of mouse and 44 tissues of humans. In general, for mouse tissues, the 5% threshold performs well to distinguish between healthy and low-quality cells. However, for human tissues, the 5% threshold should be reconsidered as it fails to accurately discriminate between healthy and low-quality cells in 29.5% (13 of 44) tissues analyzed. We conclude that omitting the mtDNA% QC filter or adopting a suboptimal mtDNA% threshold may lead to erroneous biological interpretations of scRNA-seq data.

AVAILABILITYAND IMPLEMENTATION

The code used to download datasets, perform the analyzes and produce the figures is available at https://github.com/dosorio/mtProportion.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell transcriptomic analyses of cardiac immune cells reveal that Rel-driven CD72-positive macrophages induce cardiomyocyte injury

AIMS

The goal of our study was to investigate the heterogeneity of cardiac macrophages (CMφs) in mice with transverse aortic constriction (TAC) via single-cell sequencing and identify a subset of macrophages associated with heart injury.

METHODS AND RESULTS

We selected all CMφs from CD45+ cells using single-cell mRNA sequencing data. Through dimension reduction, clustering and enrichment analyses, CD72hi CMφs were identified as a subset of proinflammatory macrophages. The pseudotime trajectory and ChIP-Seq analyses identified Rel as the key transcription factor that induces CD72hi CMφ differentiation. Rel KD and Rel-/- bone marrow chimera mice subjected to TAC showed features of mitigated cardiac injury, including decreased levels of cytokines and ROS, which prohibited cardiomyocyte death. The transfer of adoptive Rel-overexpressing monocytes and CD72hi CMφ injection directly aggravated heart injury in the TAC model. The CD72hi macrophages also exerted proinflammatory and cardiac injury effects associated with myocardial infarction (MI). In humans, patients with heart failure exhibit increased CD72hi CMφ levels following dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM).

CONCLUSION

Bone marrow-derived, Rel-mediated CD72hi macrophages play a proinflammatory role, induce cardiac injury and, thus, may serve as a therapeutic target for multiple cardiovascular diseases.

TRANSLATIONAL PERSPECTIVE

Heart failure (HF) imposes an enormous clinical and economic burden worldwide and presents limited therapeutic approaches. Given the close association between inflammation and adverse outcomes, proinflammatory immune cells are considered potential therapeutic targets for HF treatment. The present studies identified a specific macrophage subset associated with myocardial injury, which may provide an alternative approach for treating cardiovascular diseases.

Discovering Cellular Mitochondrial Heteroplasmy Heterogeneity with Single Cell RNA and ATAC Sequencing

Next-generation sequencing technologies have revolutionised the study of biological systems by enabling the examination of a broad range of tissues. Its application to single-cell genomics has generated a dynamic and evolving field with a vast amount of research highlighting heterogeneity in transcriptional, genetic and epigenomic state between cells. However, compared to these aspects of cellular heterogeneity, relatively little has been gleaned from single-cell datasets regarding cellular mitochondrial heterogeneity. Single-cell sequencing techniques can provide coverage of the mitochondrial genome which allows researchers to probe heteroplasmies at the level of the single cell, and observe interactions with cellular function. In this review, we give an overview of two popular single-cell modalities-single-cell RNA sequencing and single-cell ATAC sequencing-whose throughput and widespread usage offers researchers the chance to probe heteroplasmy combined with cell state in detailed resolution across thousands of cells. After summarising these technologies in the context of mitochondrial research, we give an overview of recent methods which have used these approaches for discovering mitochondrial heterogeneity. We conclude by highlighting current limitations of these approaches and open problems for future consideration.

Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods

Single-cell transcriptomics can profile thousands of cells in a single experiment and identify novel cell types, states and dynamics in a wide variety of tissues and organisms. Standard experimental protocols and analysis workflows have been developed to create single-cell transcriptomic maps from tissues. This tutorial focuses on how to interpret these data to identify cell types, states and other biologically relevant patterns with the objective of creating an annotated map of cells. We recommend a three-step workflow including automatic cell annotation (wherever possible), manual cell annotation and verification. Frequently encountered challenges are discussed, as well as strategies to address them. Guiding principles and specific recommendations for software tools and resources that can be used for each step are covered, and an R notebook is included to help run the recommended workflow. Basic familiarity with computer software is assumed, and basic knowledge of programming (e.g., in the R language) is recommended.

Spatio-temporal landscape of mouse epididymal cells and specific mitochondria-rich segments defined by large-scale single-cell RNA-seq

Spermatozoa acquire their fertilizing ability and forward motility during epididymal transit, suggesting the importance of the epididymis. Although the cell atlas of the epididymis was reported recently, the heterogeneity of the cells and the gene expression profile in the epididymal tube are still largely unknown. Considering single-cell RNA sequencing results, we thoroughly studied the cell composition, spatio-temporal differences in differentially expressed genes (DEGs) in epididymal segments and mitochondria throughout the epididymis with sufficient cell numbers. In total, 40,623 cells were detected and further clustered into 8 identified cell populations. Focused analyses revealed the subpopulations of principal cells, basal cells, clear/narrow cells, and halo/T cells. Notably, two subtypes of principal cells, the Prc7 and Prc8 subpopulations were enriched as stereocilia-like cells according to GO analysis. Further analysis demonstrated the spatially specific pattern of the DEGs in each cell cluster. Unexpectedly, the abundance of mitochondria and mitochondrial transcription (MT) was found to be higher in the corpus and cauda epididymis than in the caput epididymis by scRNA-seq, immunostaining, and qPCR validation. In addition, the spatio-temporal profile of the DEGs from the P42 and P56 epididymis, including transiting spermatozoa, was depicted. Overall, our study presented the single-cell transcriptome atlas of the mouse epididymis and revealed the novel distribution pattern of mitochondria and key genes that may be linked to sperm functionalities in the first wave and subsequent wave of sperm, providing a roadmap to be emulated in efforts to achieve sperm maturation regulation in the epididymis.

Single-cell transcriptomics: a novel precision medicine technique in nephrology

Due to the complex structure and function of the kidneys, the mechanism of kidney disease is unclear. In particular, transcriptomics approaches at the bulk level are unable to differentiate primary autonomous responses, which lead to disease development, from secondary cell non-autonomous responses. Single-cell analysis techniques can overcome the limitations inherent in the measurement of heterogeneous cell populations and clarify the central issues in kidney biology and disease pathogenesis. Single-cell sequencing helps in identifying disease-related biomarkers and pathways, stratifying patients, and deciding on appropriate treatment methods. Here we review a variety of single-cell analysis techniques and single-cell transcriptomics studies performed in the field of nephrology. Moreover, we discuss the future prospects of single-cell analysis-based precision medicine in nephrology.

Bayesian inference of gene expression states from single-cell RNA-seq data

Despite substantial progress in single-cell RNA-seq (scRNA-seq) data analysis methods, there is still little agreement on how to best normalize such data. Starting from the basic requirements that inferred expression states should correct for both biological and measurement sampling noise and that changes in expression should be measured in terms of fold changes, we here derive a Bayesian normalization procedure called Sanity (SAmpling-Noise-corrected Inference of Transcription activitY) from first principles. Sanity estimates expression values and associated error bars directly from raw unique molecular identifier (UMI) counts without any tunable parameters. Using simulated and real scRNA-seq datasets, we show that Sanity outperforms other normalization methods on downstream tasks, such as finding nearest-neighbor cells and clustering cells into subtypes. Moreover, we show that by systematically overestimating the expression variability of genes with low expression and by introducing spurious correlations through mapping the data to a lower-dimensional representation, other methods yield severely distorted pictures of the data.

netAE: semi-supervised dimensionality reduction of single-cell RNA sequencing to facilitate cell labeling

MOTIVATION

Single-cell RNA sequencing allows us to study cell heterogeneity at an unprecedented cell-level resolution and identify known and new cell populations. Current cell labeling pipeline uses unsupervised clustering and assigns labels to clusters by manual inspection. However, this pipeline does not utilize available gold-standard labels because there are usually too few of them to be useful to most computational methods. This article aims to facilitate cell labeling with a semi-supervised method in an alternative pipeline, in which a few gold-standard labels are first identified and then extended to the rest of the cells computationally.

RESULTS

We built a semi-supervised dimensionality reduction method, a network-enhanced autoencoder (netAE). Tested on three public datasets, netAE outperforms various dimensionality reduction baselines and achieves satisfactory classification accuracy even when the labeled set is very small, without disrupting the similarity structure of the original space.

AVAILABILITY AND IMPLEMENTATION

The code of netAE is available on GitHub: https://github.com/LeoZDong/netAE.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-Cell Transcriptome Analysis of Human Adipose-Derived Stromal Cells Identifies a Contractile Cell Subpopulation

The potential for human adipose-derived stromal cells (hASCs) to be used as a therapeutic product is being assessed in multiple clinical trials. However, much is still to be learned about these cells before they can be used with confidence in the clinical setting. An inherent characteristic of hASCs that is not well understood is their heterogeneity. The aim of this exploratory study was to characterize the heterogeneity of freshly isolated hASCs after two population doublings (P2) using single-cell transcriptome analysis. A minimum of two subpopulations were identified at P2. A major subpopulation was identified as contractile cells which, based on gene expression patterns, are likely to be pericytes and/or vascular smooth muscle cells (vSMCs).

Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis

Rapid cost drops and advancements in next-generation sequencing have made profiling of cells at individual level a conventional practice in scientific laboratories worldwide. Single-cell transcriptomics [single-cell RNA sequencing (SC-RNA-seq)] has an immense potential of uncovering the novel basis of human life. The well-known heterogeneity of cells at the individual level can be better studied by single-cell transcriptomics. Proper downstream analysis of this data will provide new insights into the scientific communities. However, due to low starting materials, the SC-RNA-seq data face various computational challenges: normalization, differential gene expression analysis, dimensionality reduction, etc. Additionally, new methods like 10× Chromium can profile millions of cells in parallel, which creates a considerable amount of data. Thus, single-cell data handling is another big challenge. This paper reviews the single-cell sequencing methods, library preparation, and data generation. We highlight some of the main computational challenges that require to be addressed by introducing new bioinformatics algorithms and tools for analysis. We also show single-cell transcriptomics data as a big data problem.

Investigating Inter- and Intrasample Diversity of Single-Cell RNA Sequencing Datasets

Tumor heterogeneity can arise from a variety of extrinsic and intrinsic sources and drives unfavorable outcomes. With recent technological advances, single-cell RNA sequencing has become a way for researchers to easily assay tumor heterogeneity at the transcriptomic level with high resolution. However, ongoing research focuses on different ways to analyze this big data and how to compare across multiple different samples. In this chapter, we provide a practical guide to calculate inter- and intrasample diversity metrics from single-cell RNA sequencing datasets. These measures of diversity are adapted from commonly used metrics in statistics and ecology to quantify and compare sample heterogeneity at single-cell resolution.

New tools for 'ZEBRA-FISHING'

Zebrafish has been established as a classical model for developmental studies, yet in the past years, with the explosion of novel technological methods, the use of zebrafish as a model has expanded. One of the prominent fields that took advantage of zebrafish as a model organism early on is hematopoiesis, the process of blood cell generation from hematopoietic stem and progenitor cells (HSPCs). In zebrafish, HSPCs are born early during development in the aorta-gonad-mesonephros region and then translocate to the caudal hematopoietic tissue, where they expand and finally take residence in the kidney marrow. This journey is tightly regulated at multiple levels from extracellular signals to chromatin. In order to delineate the mechanistic underpinnings of this process, next-generation sequencing techniques could be an important ally. Here, we describe genome-wide approaches that have been undertaken to delineate zebrafish hematopoiesis.

Deciphering cell-cell interactions and communication from gene expression

Cell-cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein-protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand-receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell-cell interactions from transcriptomic data and review the methods and tools used in this context.

Spontaneous cell fusions as a mechanism of parasexual recombination in tumour cell populations

The initiation and progression of cancers reflect the underlying process of somatic evolution, in which the diversification of heritable phenotypes provides a substrate for natural selection, resulting in the outgrowth of the most fit subpopulations. Although somatic evolution can tap into multiple sources of diversification, it is assumed to lack access to (para)sexual recombination-a key diversification mechanism throughout all strata of life. On the basis of observations of spontaneous fusions involving cancer cells, the reported genetic instability of polypoid cells and the precedence of fusion-mediated parasexual recombination in fungi, we asked whether cell fusions between genetically distinct cancer cells could produce parasexual recombination. Using differentially labelled tumour cells, we found evidence of low-frequency, spontaneous cell fusions between carcinoma cells in multiple cell line models of breast cancer both in vitro and in vivo. While some hybrids remained polyploid, many displayed partial ploidy reduction, generating diverse progeny with heterogeneous inheritance of parental alleles, indicative of partial recombination. Hybrid cells also displayed elevated levels of phenotypic plasticity, which may further amplify the impact of cell fusions on the diversification of phenotypic traits. Using mathematical modelling, we demonstrated that the observed rates of spontaneous somatic cell fusions may enable populations of tumour cells to amplify clonal heterogeneity, thus facilitating the exploration of larger areas of the adaptive landscape (relative to strictly asexual populations), which may substantially accelerate a tumour's ability to adapt to new selective pressures.

DSAVE: Detection of misclassified cells in single-cell RNA-Seq data

Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE (Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets.

Single-cell transcriptome analysis reveals thyrocyte diversity in the zebrafish thyroid gland

The thyroid gland regulates growth and metabolism via production of thyroid hormone in follicles composed of thyrocytes. So far, thyrocytes have been assumed to be a homogenous population. To uncover heterogeneity in the thyrocyte population and molecularly characterize the non-thyrocyte cells surrounding the follicle, we developed a single-cell transcriptome atlas of the region containing the zebrafish thyroid gland. The 6249-cell atlas includes profiles of thyrocytes, blood vessels, lymphatic vessels, immune cells, and fibroblasts. Further, the thyrocytes show expression heterogeneity, including bimodal expression of the transcription factor pax2a. To validate thyrocyte heterogeneity, we generated a CRISPR/Cas9-based pax2a knock-in line that monitors pax2a expression in the thyrocytes. A population of pax2a-low mature thyrocytes interspersed in individual follicles can be distinguished. We corroborate heterogeneity within the thyrocyte population using RNA sequencing of pax2a-high and pax2a-low thyrocytes, which demonstrates 20% differential expression in transcriptome between the two subpopulations. Our results identify and validate transcriptional differences within the presumed homogenous thyrocyte population.

What has single-cell RNA sequencing revealed about microglial neuroimmunology?

The use of single-cell RNA sequencing (scRNA-seq) in microglial research is increasing rapidly. The basic workflow of this approach consists of isolating single cells, followed by sequencing. scRNA-seq is capable of examining microglial heterogeneity on a cellular level. However, the results gained from applying this technique suffer from discrepancies due to differences between applied methods characteristics such as the number of cells sequenced and the depth of sequencing. This review aims to shed more light on the recent developments that happened in this field and how they are related to the methods used. To do that, we track the progress and limitations of various scRNA-seq methods currently available. The review then summarizes the current knowledge gained using scRNA-seq in the field of microglia, including novel subpopulations associated with function and development under homeostasis as well during several pathological conditions such as Alzheimer, lipopolysaccharide response, and HIV in relation to the methods employed. Our review points out that despite major developments found using this technique, current scRNA-seq methods suffer from high cost, low yields, and nonstandardization of generated data. Additional development of scRNA-seq methods will raise our awareness of microglia's heterogeneity and plasticity under healthy and pathological conditions.

Single-Cell Sequencing in Human Genital Infections

Human genital infections are one of the most concerning issues worldwide and can be categorized into sexually transmitted, urinary tract and vaginal infections. These infections, if left untreated, can disseminate to the other parts of the body and cause more complicated illnesses such as pelvic inflammatory disease, urethritis, and anogenital cancers. The effective treatment against these infections is further complicated by the emergence of antimicrobial resistance in the genital infection causing pathogens. Furthermore, the development and applications of single-cell sequencing technologies have open new possibilities to study the drug resistant clones, cell to cell variations, the discovery of acquired drug resistance mutations, transcriptional diversity of a pathogen across different infection stages, to identify rare cell types and investigate different cellular states of genital infection causing pathogens, and to develop novel therapeutical strategies. In this chapter, I will provide a complete review of the applications of single-cell sequencing in human genital infections before discussing their limitations and challenges.

Reading the heart at single-cell resolution

The burgeoning field of single-cell transcriptomics augments our ability to scrutinize organ systems at unprecedented resolutions. Single-cell RNA sequencing (scRNA-seq) and analytical techniques have shed light on the cellular heterogeneity, developmental trajectories, intercellular communications of the cardiac system, and thus contributed much to the understanding of cardiac development, homeostasis and disorders. Although generalized protocols are well established for scRNA-seq pipelines, customized sample preparation, quality control, and data interpretation are still needed in cardiac research. In this article, we highlight major steps that impact data quality in scRNA-seq experiments, with particular focus on sample and data processing of cardiomyocytes. We also summarize popular applications of scRNA-seq, outlining general tools, caveats and examples in cardiac research.

Single cell approaches to address adipose tissue stromal cell heterogeneity

A central function of adipose tissue is in the management of systemic energy homeostasis that is achieved through the co-ordinated regulation of energy storage and mobilization, adipokine release, and immune functions. With the dramatic increase in the prevalence of obesity and obesity-related metabolic disease over the past 30 years, there has been extensive interest in targeting adipose tissue for therapeutic benefit. However, in order for this goal to be achieved it is essential to establish a comprehensive atlas of adipose tissue cellular composition and define mechanisms of intercellular communication that mediate pathologic and therapeutic responses. While traditional methods, such as fluorescence-activated cell sorting (FACS) and genetic lineage tracing, have greatly advanced the field, these approaches are inherently limited by the choice of markers and the ability to comprehensively identify and characterize dynamic interactions among stromal cells within the tissue microenvironment. Single cell RNA sequencing (scRNAseq) has emerged as a powerful tool for deconvolving cellular heterogeneity and holds promise for understanding the development and plasticity of adipose tissue under normal and pathological conditions. scRNAseq has recently been used to characterize adipose stem cell (ASC) populations and has provided new insights into subpopulations of macrophages that arise during anabolic and catabolic remodeling in white adipose tissue. The current review summarizes recent findings that use this technology to explore adipose tissue heterogeneity and plasticity.