Power analysis of single-cell RNA-sequencing experiments

SM-Omics is an automated platform for high-throughput spatial multi-omics

The spatial organization of cells and molecules plays a key role in tissue function in homeostasis and disease. Spatial transcriptomics has recently emerged as a key technique to capture and positionally barcode RNAs directly in tissues. Here, we advance the application of spatial transcriptomics at scale, by presenting Spatial Multi-Omics (SM-Omics) as a fully automated, high-throughput all-sequencing based platform for combined and spatially resolved transcriptomics and antibody-based protein measurements. SM-Omics uses DNA-barcoded antibodies, immunofluorescence or a combination thereof, to scale and combine spatial transcriptomics and spatial antibody-based multiplex protein detection. SM-Omics allows processing of up to 64 in situ spatial reactions or up to 96 sequencing-ready libraries, of high complexity, in a ~2 days process. We demonstrate SM-Omics in the mouse brain, spleen and colorectal cancer model, showing its broad utility as a high-throughput platform for spatial multi-omics.

White matter microglia heterogeneity in the CNS

Microglia, the resident myeloid cells in the central nervous system (CNS) play critical roles in shaping the brain during development, responding to invading pathogens, and clearing tissue debris or aberrant protein aggregations during ageing and neurodegeneration. The original concept that like macrophages, microglia are either damaging (pro-inflammatory) or regenerative (anti-inflammatory) has been updated to a kaleidoscope view of microglia phenotypes reflecting their wide-ranging roles in maintaining homeostasis in the CNS and, their contribution to CNS diseases, as well as aiding repair. The use of new technologies including single cell/nucleus RNA sequencing has led to the identification of many novel microglia states, allowing for a better understanding of their complexity and distinguishing regional variations in the CNS. This has also revealed differences between species and diseases, and between microglia and other myeloid cells in the CNS. However, most of the data on microglia heterogeneity have been generated on cells isolated from the cortex or whole brain, whereas white matter changes and differences between white and grey matter have been relatively understudied. Considering the importance of microglia in regulating white matter health, we provide a brief update on the current knowledge of microglia heterogeneity in the white matter, how microglia are important for the development of the CNS, and how microglial ageing affects CNS white matter homeostasis. We discuss how microglia are intricately linked to the classical white matter diseases such as multiple sclerosis and genetic white matter diseases, and their putative roles in neurodegenerative diseases in which white matter is also affected. Understanding the wide variety of microglial functions in the white matter may provide the basis for microglial targeted therapies for CNS diseases.

Discoveries in Pancreatic Physiology and Disease Biology Using Single-Cell RNA Sequencing

Transcriptome analysis is used to study gene expression in human tissues. It can promote the discovery of new therapeutic targets for related diseases by characterizing the endocrine function of pancreatic physiology and pathology, as well as the gene expression of pancreatic tumors. Compared to whole-tissue RNA sequencing, single-cell RNA sequencing (scRNA-seq) can detect transcriptional activity within a single cell. The scRNA-seq had an invaluable contribution to discovering previously unknown cell subtypes in normal and diseased pancreases, studying the functional role of rare islet cells, and studying various types of cells in diabetes as well as cancer. Here, we review the recent in vitro and in vivo advances in understanding the pancreatic physiology and pathology associated with single-cell sequencing technology, which may provide new insights into treatment strategy optimization for diabetes and pancreatic cancer.

Statistics or biology: the zero-inflation controversy about scRNA-seq data

Researchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as missing data to be corrected. To help address the controversy, here we discuss the sources of biological and non-biological zeros; introduce five mechanisms of adding non-biological zeros in computational benchmarking; evaluate the impacts of non-biological zeros on data analysis; benchmark three input data types: observed counts, imputed counts, and binarized counts; discuss the open questions regarding non-biological zeros; and advocate the importance of transparent analysis.

New horizons in the stormy sea of multimodal single-cell data integration

While measurements of RNA expression have dominated the world of single-cell analyses, new single-cell techniques increasingly allow collection of different data modalities, measuring different molecules, structural connections, and intermolecular interactions. Integrating the resulting multimodal single-cell datasets is a new bioinformatics challenge. Equally important, it is a new experimental design challenge for the bench scientist, who is not only choosing from a myriad of techniques for each data modality but also faces new challenges in experimental design. The ultimate goal is to design, execute, and analyze multimodal single-cell experiments that are more than just descriptive but enable the learning of new causal and mechanistic biology. This objective requires strict consideration of the goals behind the analysis, which might range from mapping the heterogeneity of a cellular population to assembling system-wide causal networks that can further our understanding of cellular functions and eventually lead to models of tissues and organs. We review steps and challenges toward this goal. Single-cell transcriptomics is now a mature technology, and methods to measure proteins, lipids, small-molecule metabolites, and other molecular phenotypes at the single-cell level are rapidly developing. Integrating these single-cell readouts so that each cell has measurements of multiple types of data, e.g., transcriptomes, proteomes, and metabolomes, is expected to allow identification of highly specific cellular subpopulations and to provide the basis for inferring causal biological mechanisms.

Comparison and evaluation of statistical error models for scRNA-seq

BACKGROUND

Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple sources, including biological variation in cellular state as well as technical variation introduced during experimental processing. Deconvolving these effects is a key challenge for preprocessing workflows. Recent work has demonstrated the importance and utility of count models for scRNA-seq analysis, but there is a lack of consensus on which statistical distributions and parameter settings are appropriate.

RESULTS

Here, we analyze 59 scRNA-seq datasets that span a wide range of technologies, systems, and sequencing depths in order to evaluate the performance of different error models. We find that while a Poisson error model appears appropriate for sparse datasets, we observe clear evidence of overdispersion for genes with sufficient sequencing depth in all biological systems, necessitating the use of a negative binomial model. Moreover, we find that the degree of overdispersion varies widely across datasets, systems, and gene abundances, and argues for a data-driven approach for parameter estimation.

CONCLUSIONS

Based on these analyses, we provide a set of recommendations for modeling variation in scRNA-seq data, particularly when using generalized linear models or likelihood-based approaches for preprocessing and downstream analysis.

baredSC: Bayesian approach to retrieve expression distribution of single-cell data

Background

The number of studies using single-cell RNA sequencing (scRNA-seq) is constantly growing. This powerful technique provides a sampling of the whole transcriptome of a cell. However, sparsity of the data can be a major hurdle when studying the distribution of the expression of a specific gene or the correlation between the expressions of two genes.

Results

We show that the main technical noise associated with these scRNA-seq experiments is due to the sampling, i.e., Poisson noise. We present a new tool named baredSC, for Bayesian Approach to Retrieve Expression Distribution of Single-Cell data, which infers the intrinsic expression distribution in scRNA-seq data using a Gaussian mixture model. baredSC can be used to obtain the distribution in one dimension for individual genes and in two dimensions for pairs of genes, in particular to estimate the correlation in the two genes’ expressions. We apply baredSC to simulated scRNA-seq data and show that the algorithm is able to uncover the expression distribution used to simulate the data, even in multi-modal cases with very sparse data. We also apply baredSC to two real biological data sets. First, we use it to measure the anti-correlation between Hoxd13 and Hoxa11, two genes with known genetic interaction in embryonic limb. Then, we study the expression of Pitx1 in embryonic hindlimb, for which a trimodal distribution has been identified through flow cytometry. While other methods to analyze scRNA-seq are too sensitive to sampling noise, baredSC reveals this trimodal distribution.

Conclusion

baredSC is a powerful tool which aims at retrieving the expression distribution of few genes of interest from scRNA-seq data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04507-8.

SkewC: Identifying cells with skewed gene body coverage in single-cell RNA sequencing data

The analysis and interpretation of single-cell RNA sequencing (scRNA-seq) experiments are compromised by the presence of poor-quality cells. For meaningful analyses, such poor-quality cells should be excluded as they introduce noise in the data. We introduce SkewC, a quality-assessment tool, to identify skewed cells in scRNA-seq experiments. The tool’s methodology is based on the assessment of gene coverage for each cell, and its skewness as a quality measure; the gene body coverage is a unique characteristic for each protocol, and different protocols yield highly different coverage profiles. This tool is designed to avoid misclustering or false clusters by identifying, isolating, and removing cells with skewed gene body coverage profiles. SkewC is capable of processing any type of scRNA-seq dataset, regardless of the protocol. We envision SkewC as a distinctive QC method to be incorporated into scRNA-seq QC processing to preclude the possibility of scRNA-seq data misinterpretation.

•

We developed a quality assessment method applicable to single-cell RNA-Seq data

•

SkewC relies on the use of gene coverage profile of cells as a quality measure

•

SkewC reveals two classes of cells: typical cells and skewed cells

•

Typical with prototypical coverage profiles, and skewed with skewed profiles

Prioritizing Autism Risk Genes using Personalized Graphical Models Estimated from Single Cell RNA-seq Data

Hundreds of autism risk genes have been reported recently, mainly based on genetic studies where these risk genes have more de novo mutations in autism subjects than healthy controls. However, as a complex disease, autism is likely associated with more risk genes and many of them may not be identifiable through de novo mutations. We hypothesize that more autism risk genes can be identified through their connections with known autism risk genes in personalized gene-gene interaction graphs. We estimate such personalized graphs using single cell RNA sequencing (scRNA-seq) while appropriately modeling the cell dependence and possible zero-inflation in the scRNA-seq data. The sample size, which is the number of cells per individual, ranges from 891 to 1,241 in our case study using scRNA-seq data in autism subjects and controls. We consider 1,500 genes in our analysis. Since the number of genes is larger or comparable to the sample size, we perform penalized estimation. We score each gene's relevance by applying a simple graph kernel smoothing method to each personalized graph. The molecular functions of the top-scored genes are related to autism diseases. For example, a candidate gene RYR2 that encodes protein ryanodine receptor 2 is involved in neurotransmission, a process that is impaired in ASD patients. While our method provides a systemic and unbiased approach to prioritize autism risk genes, the relevance of these genes needs to be further validated in functional studies.

Single-Cell RNA-Seq Technologies and Computational Analysis Tools: Application in Cancer Research

The recent maturation of single-cell RNA sequencing (scRNA-seq) provides unique opportunities for researchers to uncover new and potentially unexpected biological discoveries and to understand the complexity of tissues by transcriptomic profiling in individual cells. This review introduces the latest scRNA-seq techniques and platforms as well as their advantages and disadvantages. Moreover, we review computational tools and pipelines for analyzing scRNA-seq data, and their applications in cancer research, highlighting the important role of scRNA-seq techniques in this area.

Single-cell approaches to dissect adaptive immune responses involved in autoimmunity: the case of celiac disease

Single-cell analysis is a powerful technology that has found widespread use in recent years. For diseases with involvement of adaptive immunity, single-cell analysis of antigen-specific T cells and B cells is particularly informative. In autoimmune diseases, the adaptive immune system is obviously at play, yet the ability to identify the culprit T and B cells recognizing disease-relevant antigen can be difficult. Celiac disease, a widespread disorder with autoimmune components, is unique in that disease-relevant antigens for both T cells and B cells are well defined. Furthermore, the celiac disease gut lesion is readily accessible allowing for sampling of tissue-resident cells. Thus, disease-relevant T cells and B cells from the gut and blood can be studied at the level of single cells. Here we review single-cell studies providing information on such adaptive immune cells and outline some future perspectives in the area of single-cell analysis in autoimmune diseases.

Unravelling glioblastoma heterogeneity by means of single-cell RNA sequencing

Glioblastoma (GBM) is the most invasive and deadliest brain cancer in adults. Its inherent heterogeneity has been designated as the main cause of treatment failure. Thus, a deeper understanding of the diversity that shapes GBM pathobiology is of utmost importance. Single-cell RNA sequencing (scRNA-seq) technologies have begun to uncover the hidden composition of complex tumor ecosystems. Herein, a semi-systematic search of reference literature databases provided all existing publications using scRNA-seq for the investigation of human GBM. We compared and discussed findings from these works to build a more robust and unified knowledge base. All aspects ranging from inter-patient heterogeneity to intra-tumoral organization, cancer stem cell diversity, clonal mosaicism, and the tumor microenvironment (TME) are comprehensively covered in this report. Tumor composition not only differs across patients but also involves a great extent of heterogeneity within itself. Spatial and cellular heterogeneity can reveal tumor evolution dynamics. In addition, the discovery of distinct cell phenotypes might lead to the development of targeted treatment approaches. In conclusion, scRNA-seq expands our knowledge of GBM heterogeneity and helps to unravel putative therapeutic targets.

Recent advances in tissue stem cells

Stem cells are undifferentiated cells capable of self-renewal and differentiation, giving rise to specialized functional cells. Stem cells are of pivotal importance for organ and tissue development, homeostasis, and injury and disease repair. Tissue-specific stem cells are a rare population residing in specific tissues and present powerful potential for regeneration when required. They are usually named based on the resident tissue, such as hematopoietic stem cells and germline stem cells. This review discusses the recent advances in stem cells of various tissues, including neural stem cells, muscle stem cells, liver progenitors, pancreatic islet stem/progenitor cells, intestinal stem cells, and prostate stem cells, and the future perspectives for tissue stem cell research.

Functionally distinct roles for eEF2K in the control of ribosome availability and p-body abundance

Processing bodies (p-bodies) are a prototypical phase-separated RNA-containing granule. Their abundance is highly dynamic and has been linked to translation. Yet, the molecular mechanisms responsible for coordinate control of the two processes are unclear. Here, we uncover key roles for eEF2 kinase (eEF2K) in the control of ribosome availability and p-body abundance. eEF2K acts on a sole known substrate, eEF2, to inhibit translation. We find that the eEF2K agonist nelfinavir abolishes p-bodies in sensory neurons and impairs translation. To probe the latter, we used cryo-electron microscopy. Nelfinavir stabilizes vacant 80S ribosomes. They contain SERBP1 in place of mRNA and eEF2 in the acceptor site. Phosphorylated eEF2 associates with inactive ribosomes that resist splitting in vitro. Collectively, the data suggest that eEF2K defines a population of inactive ribosomes resistant to recycling and protected from degradation. Thus, eEF2K activity is central to both p-body abundance and ribosome availability in sensory neurons.

Application of single-cell RNA sequencing technology in liver diseases: a narrative review

Objective

This review aimed to summarize the application of single-cell transcriptome sequencing technology in liver diseases.

Background

The increasing application of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) in life science and biomedical research has greatly improved our understanding of cellular heterogeneity in immunology, oncology, and developmental biology. scRNA-seq has proven to be a powerful tool for identifying and classifying cell subsets, characterizing rare or small cell subsets and tracking cell differentiation along the dynamic cell stages. Globally, liver disease has high rates of morbidity and mortality, and its exact pathological mechanism remains unclear, current treatment options are limited to clearance of the underlying cause or liver transplantation, which cannot overwhelm and cure liver diseases. scRNA-seq provides many novel insights for healthy and diseased livers.

Methods

In this review, we searched for related articles in the PubMed database and summarized the advances of scRNA-seq in revealing the molecular mechanisms of liver development, regeneration, and disease. We also discussed the challenges and future application potential of scRNA-seq, which is expected to enhance the ability to explore the field of liver research and accelerate the clinical application of liver precision medicine.

Conclusions

With the continuous improvement of scRNA-seq technology, scRNA-seq is expected to unlock new avenues for liver biology exploration, liver disease diagnosis, and personalized treatment, which will pave the way for breakthrough innovation in personalized medicine.

scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies

Single cell RNA-seq has revolutionized transcriptomics by providing cell type resolution for differential gene expression and expression quantitative trait loci (eQTL) analyses. However, efficient power analysis methods for single cell data and inter-individual comparisons are lacking. Here, we present scPower; a statistical framework for the design and power analysis of multi-sample single cell transcriptomic experiments. We modelled the relationship between sample size, the number of cells per individual, sequencing depth, and the power of detecting differentially expressed genes within cell types. We systematically evaluated these optimal parameter combinations for several single cell profiling platforms, and generated broad recommendations. In general, shallow sequencing of high numbers of cells leads to higher overall power than deep sequencing of fewer cells. The model, including priors, is implemented as an R package and is accessible as a web tool. scPower is a highly customizable tool that experimentalists can use to quickly compare a multitude of experimental designs and optimize for a limited budget.

Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration

Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro-adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation, and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.

Optimization of library preparation based on SMART for ultralow RNA-seq in mice brain tissues

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) provides new insights to address biological and medical questions, and it will benefit more from the ultralow input RNA or subcellular sequencing.

RESULTS

Here, we present a highly sensitive library construction protocol for ultralow input RNA sequencing (ulRNA-seq). We systematically evaluate experimental conditions of this protocol, such as reverse transcriptase, template-switching oligos (TSO), and template RNA structure. It was found that Maxima H Minus reverse transcriptase and rN modified TSO, as well as all RNA templates capped with m7G improved the sequencing sensitivity and low abundance gene detection ability. RNA-seq libraries were successfully prepared from total RNA samples as low as 0.5 pg, and more than 2000 genes have been identified.

CONCLUSIONS

The ability of low abundance gene detection and sensitivity were largely enhanced with this optimized protocol. It was also confirmed in single-cell sequencing, that more genes and cell markers were identified compared to conventional sequencing method. We expect that ulRNA-seq will sequence and transcriptome characterization for the subcellular of disease tissue, to find the corresponding treatment plan.

The multicellular signalling network of ovarian cancer metastases

BACKGROUND

Transcoelomic spread is the major route of metastasis of ovarian high-grade serous carcinoma (HGSC) with the omentum as the major metastatic site. Its unique tumour microenvironment with its large populations of adipocytes, mesothelial cells and immune cells establishes an intercellular signaling network that is instrumental for metastatic growth yet poorly understood.

METHODS

Based on transcriptomic analysis of tumour cells, tumour-associated immune and stroma cells we defined intercellular signaling pathways for 284 cytokines and growth factors and their cognate receptors after bioinformatic adjustment for contaminating cell types. The significance of individual components of this network was validated by analysing clinical correlations and potentially pro-metastatic functions, including tumour cell migration, pro-inflammatory signal transduction and TAM expansion.

RESULTS

The data show an unexpected prominent role of host cells, and in particular of omental adipocytes, mesothelial cells and fibroblasts (CAF), in sustaining this signaling network. These cells, rather than tumour cells, are the major source of most cytokines and growth factors in the omental microenvironment (n = 176 vs. n = 13). Many of these factors target tumour cells, are linked to metastasis and are associated with a short survival. Likewise, tumour stroma cells play a major role in extracellular-matrix-triggered signaling. We have verified the functional significance of our observations for three exemplary instances. We show that the omental microenvironment (i) stimulates tumour cell migration and adhesion via WNT4 which is highly expressed by CAF; (ii) induces pro-tumourigenic TAM proliferation in conjunction with high CSF1 expression by omental stroma cells and (iii) triggers pro-inflammatory signaling, at least in part via a HSP70-NF-κB pathway.

CONCLUSIONS

The intercellular signaling network of omental metastases is majorly dependent on factors secreted by immune and stroma cells to provide an environment that supports ovarian HGSC progression. Clinically relevant pathways within this network represent novel options for therapeutic intervention.

Single cell resolution of Plasma Cell fate programming in health and disease

Long considered a homogeneous population dedicated to antibody secretion, plasma cell phenotypic and functional heterogeneity is increasingly recognised. Plasma cells were first segregated based on their maturation level, but the complexity of this subset might well be underestimated by this simple dichotomy. Indeed, in the last decade new functions have been attributed to plasma cells including but not limited to cytokine secretion. However, a proper characterization of plasma cell heterogeneity has remained elusive partly due to technical issues and cellular features that are specific to this cell type. Cell intrinsic and cell extrinsic signals could be at the origin of this heterogeneity. Recent advances in technologies like single cell RNA-seq, ATAC-seq or ChIP-seq on low cell numbers helped to elucidate the fate decision in other cell lineages and similar approaches could be implemented to evaluate the heterogeneous fate of activated B cells in health and disease. Here, we summarized published work shedding some lights on the stimuli and genetic program shaping B cell terminal differentiation at the single cell level in mice and men. We also discuss the fate and heterogeneity of plasma cells during immune responses, vaccination and in the frame of human plasma cell disorders. This article is protected by copyright. All rights reserved.

High-Dimensional Single-Cell Transcriptomics in Melanoma and Cancer Immunotherapy

Recent advances in single-cell transcriptomics have greatly improved knowledge of complex transcriptional programs, rapidly expanding our knowledge of cellular phenotypes and functions within the tumour microenvironment and immune system. Several new single-cell technologies have been developed over recent years that have enabled expanded understanding of the mechanistic cells and biological pathways targeted by immunotherapies such as immune checkpoint inhibitors, which are now routinely used in patient management with high-risk early-stage or advanced melanoma. These technologies have method-specific strengths, weaknesses and capabilities which need to be considered when utilising them to answer translational research questions. Here, we provide guidance for the implementation of single-cell transcriptomic analysis platforms by reviewing the currently available experimental and analysis workflows. We then highlight the use of these technologies to dissect the tumour microenvironment in the context of cancer patients treated with immunotherapy. The strategic use of single-cell analytics in clinical settings are discussed and potential future opportunities are explored with a focus on their use to rationalise the design of novel immunotherapeutic drug therapies that will ultimately lead to improved cancer patient outcomes.

Nestin promotes pulmonary fibrosis via facilitating recycling of TGF-β receptor I

Background

Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease that is characterised by aberrant proliferation of activated myofibroblasts and pathological remodelling of the extracellular matrix. Previous studies have revealed that the intermediate filament protein nestin plays key roles in tissue regeneration and wound healing in different organs. Whether nestin plays a critical role in the pathogenesis of IPF needs to be clarified.

Methods

Nestin expression in lung tissues from bleomycin-treated mice and IPF patients was determined. Transfection with nestin short hairpin RNA vectors in vitro that regulated transcription growth factor (TGF)-β/Smad signalling was conducted. Biotinylation assays to observe plasma membrane TβRI, TβRI endocytosis and TβRI recycling after nestin knockdown were performed. Adeno-associated virus serotype (AAV)6-mediated nestin knockdown was assessed in vivo.

Results

We found that nestin expression was increased in a murine pulmonary fibrosis model and IPF patients, and that the upregulated protein primarily localised in lung α-smooth muscle actin-positive myofibroblasts. Mechanistically, we determined that nestin knockdown inhibited TGF-β signalling by suppressing recycling of TβRI to the cell surface and that Rab11 was required for the ability of nestin to promote TβRI recycling. In vivo, we found that intratracheal administration of AAV6-mediated nestin knockdown significantly alleviated pulmonary fibrosis in multiple experimental mice models.

Conclusion

Our findings reveal a pro-fibrotic function of nestin partially through facilitating Rab11-dependent recycling of TβRI and shed new light on pulmonary fibrosis treatment.

Nestin regulates the vesicular trafficking system by promoting Rab11-dependent recycling of TβRI and thereby contributes to the progression of pulmonary fibrosis. Precise targeting of nestin may represent a potential therapeutic strategy for IPF.https://bit.ly/3zO75c3

Developmental single-cell transcriptomics in the Lytechinus variegatus sea urchin embryo

Using scRNA-seq coupled with computational approaches, we studied transcriptional changes in cell states of sea urchin embryos during development to the larval stage. Eighteen closely spaced time points were taken during the first 24 h of development of Lytechinus variegatus (Lv). Developmental trajectories were constructed using Waddington-OT, a computational approach to 'stitch' together developmental time points. Skeletogenic and primordial germ cell trajectories diverged early in cleavage. Ectodermal progenitors were distinct from other lineages by the 6th cleavage, although a small percentage of ectoderm cells briefly co-expressed endoderm markers that indicated an early ecto-endoderm cell state, likely in cells originating from the equatorial region of the egg. Endomesoderm cells also originated at the 6th cleavage and this state persisted for more than two cleavages, then diverged into distinct endoderm and mesoderm fates asynchronously, with some cells retaining an intermediate specification status until gastrulation. Seventy-nine out of 80 genes (99%) examined, and included in published developmental gene regulatory networks (dGRNs), are present in the Lv-scRNA-seq dataset and are expressed in the correct lineages in which the dGRN circuits operate.

Tutorial: Guidelines for Single-Cell RT-qPCR

Reverse transcription quantitative PCR (RT-qPCR) has delivered significant insights in understanding the gene expression landscape. Thanks to its precision, sensitivity, flexibility, and cost effectiveness, RT-qPCR has also found utility in advanced single-cell analysis. Single-cell RT-qPCR now represents a well-established method, suitable for an efficient screening prior to single-cell RNA sequencing (scRNA-Seq) experiments, or, oppositely, for validation of hypotheses formulated from high-throughput approaches. Here, we aim to provide a comprehensive summary of the scRT-qPCR method by discussing the limitations of single-cell collection methods, describing the importance of reverse transcription, providing recommendations for the preamplification and primer design, and summarizing essential data processing steps. With the detailed protocol attached in the appendix, this tutorial provides a set of guidelines that allow any researcher to perform scRT-qPCR measurements of the highest standard.

AdImpute: An Imputation Method for Single-Cell RNA-Seq Data Based on Semi-Supervised Autoencoders

Motivation: The emergence of single-cell RNA sequencing (scRNA-seq) technology has paved the way for measuring RNA levels at single-cell resolution to study precise biological functions. However, the presence of a large number of missing values in its data will affect downstream analysis. This paper presents AdImpute: an imputation method based on semi-supervised autoencoders. The method uses another imputation method (DrImpute is used as an example) to fill the results as imputation weights of the autoencoder, and applies the cost function with imputation weights to learn the latent information in the data to achieve more accurate imputation.

Results: As shown in clustering experiments with the simulated data sets and the real data sets, AdImpute is more accurate than other four publicly available scRNA-seq imputation methods, and minimally modifies the biologically silent genes. Overall, AdImpute is an accurate and robust imputation method.

Estimating cellular redundancy in networks of genetic expression

Networks of genetic expression can be modeled by hypergraphs with the additional structure that real coefficients are given to each vertex-edge incidence. The spectra, i.e. the multiset of the eigenvalues, of such hypergraphs, are known to encode structural information of the data. We show how these spectra can be used, in particular, in order to give an estimation of cellular redundancy, a novel measure of gene expression heterogeneity, of the network. We analyze some simulated and real data sets of gene expression for illustrating the new method proposed here.

Single-Cell RNA Sequencing Reveals the Migration of Osteoclasts in Giant Cell Tumor of Bone

Giant cell tumor of bone (GCTB) is benign tumor that can cause significant osteolysis and bone destruction at the epiphysis of long bones. Osteoclasts are thought to be highly associated with osteolysis in GCTB. However, the migration of osteoclasts in GCTB remains unclear. A deeper understanding of the complex tumor microenvironment is required in order to delineate the migration of osteoclasts in GCTB. In this study, samples were isolated from one patient diagnosed with GCTB. Single-cell RNA sequencing (scRNA-seq) was used to detect the heterogeneity of GCTB. Multiplex immunofluorescence staining was used to evaluate the cell subtypes identified by scRNA-seq. A total of 8,033 cells were obtained from one patient diagnosed with GCTB, which were divided into eight major cell types as depicted by a single-cell transcriptional map. The osteoclasts were divided into three subsets, and their differentiation trajectory and migration status were further analyzed. Osteoclast migration may be regulated via a series of genes associated with cell migration. Furthermore, four signaling pathways (RANKL, PARs, CD137 and SMEA3 signaling pathway) were found to be highly associated with osteoclast migration. This comprehensive single-cell transcriptome analysis of GCTB identified a series of genes associated with cell migration as well as four major signaling pathways that were highly related to the migration of osteoclasts in GCTB. Our findings broaden the understanding of GCTB bionetworks and provides a theoretical basis for anti-osteolysis therapy against GCTB in the future.

Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data

BACKGROUND

Standard preprocessing of single-cell RNA-seq UMI data includes normalization by sequencing depth to remove this technical variability, and nonlinear transformation to stabilize the variance across genes with different expression levels. Instead, two recent papers propose to use statistical count models for these tasks: Hafemeister and Satija (Genome Biol 20:296, 2019) recommend using Pearson residuals from negative binomial regression, while Townes et al. (Genome Biol 20:295, 2019) recommend fitting a generalized PCA model. Here, we investigate the connection between these approaches theoretically and empirically, and compare their effects on downstream processing.

RESULTS

We show that the model of Hafemeister and Satija produces noisy parameter estimates because it is overspecified, which is why the original paper employs post hoc smoothing. When specified more parsimoniously, it has a simple analytic solution equivalent to the rank-one Poisson GLM-PCA of Townes et al. Further, our analysis indicates that per-gene overdispersion estimates in Hafemeister and Satija are biased, and that the data are in fact consistent with the overdispersion parameter being independent of gene expression. We then use negative control data without biological variability to estimate the technical overdispersion of UMI counts, and find that across several different experimental protocols, the data are close to Poisson and suggest very moderate overdispersion. Finally, we perform a benchmark to compare the performance of Pearson residuals, variance-stabilizing transformations, and GLM-PCA on scRNA-seq datasets with known ground truth.

CONCLUSIONS

We demonstrate that analytic Pearson residuals strongly outperform other methods for identifying biologically variable genes, and capture more of the biologically meaningful variation when used for dimensionality reduction.

A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples

Comparing diverse single-cell RNA sequencing (scRNA-seq) datasets generated by different technologies and in different laboratories remains a major challenge. Here we address the need for guidance in choosing algorithms leading to accurate biological interpretations of varied data types acquired with different platforms. Using two well-characterized cellular reference samples (breast cancer cells and B cells), captured either separately or in mixtures, we compared different scRNA-seq platforms and several preprocessing, normalization and batch-effect correction methods at multiple centers. Although preprocessing and normalization contributed to variability in gene detection and cell classification, batch-effect correction was by far the most important factor in correctly classifying the cells. Moreover, scRNA-seq dataset characteristics (for example, sample and cellular heterogeneity and platform used) were critical in determining the optimal bioinformatic method. However, reproducibility across centers and platforms was high when appropriate bioinformatic methods were applied. Our findings offer practical guidance for optimizing platform and software selection when designing an scRNA-seq study.

Application of single-cell transcriptomics to kinetoplastid research

Kinetoplastid parasites are responsible for both human and animal diseases across the globe where they have a great impact on health and economic well-being. Many species and life cycle stages are difficult to study due to limitations in isolation and culture, as well as to their existence as heterogeneous populations in hosts and vectors. Single-cell transcriptomics (scRNA-seq) has the capacity to overcome many of these difficulties, and can be leveraged to disentangle heterogeneous populations, highlight genes crucial for propagation through the life cycle, and enable detailed analysis of host–parasite interactions. Here, we provide a review of studies that have applied scRNA-seq to protozoan parasites so far. In addition, we provide an overview of sample preparation and technology choice considerations when planning scRNA-seq experiments, as well as challenges faced when analysing the large amounts of data generated. Finally, we highlight areas of kinetoplastid research that could benefit from scRNA-seq technologies.

Single-cell RNA-seq analysis identifies unique chondrocyte subsets and reveals involvement of ferroptosis in human intervertebral disc degeneration

OBJECTIVE

Nucleus pulposus (NP) plays a central role in disc degeneration pathogenesis, however, as a heterogeneous tissue, cell subsets in NP and their corresponding biological process in intervertebral disc degeneration (IVDD) are unreported.

METHOD

Nucleus pulposus were isolated from normal control and IVDD, and then subjected to single-cell RNA sequencing (scRNA-seq). Unsupervised clustering of the cells based on the gene expression profiles using the Seurat package and passed to tSNE for clustering visualization. Rat model of disc degeneration was built to validate the pathways identified by scRNA-Seq.

RESULTS

Seven chondrocyte subsets were revealed in NP based on differential gene expression, among which 4 subsets (C1-C4) were reported for the first time. Furthermore, GO and KEGG analyses discovered that ferroptosis pathways were enriched. Rat model of disc degeneration was built (n = 6/group, control vs. model) to validate the pathways identified by scRNA-Seq. Iron levels of NP were significantly higher in model group than control group (means 0.712 vs. 0.248, respectively, mg/gpro, p = 0.0026), and the levels of Heme Oxygenase 1 (HO-1) were also elevated in model group (means 14.33 vs. 5.16 IOD, respectively, p = 0.0002). However, the levels of ferritin light chain (FTL) were significantly decreased in model group compared to control group (means 26.17 vs. 9.00 FTL⁺ cell number, respectively, p = 0.0011).

CONCLUSIONS

Novel chondrocyte subsets in nucleus pulposus were discovered through scRNA-Seq, which provided novel insight to understand the pathological change during the development of IVDD. Ferroptosis participated in disc degeneration pathogenesis and it might serve as a new target for intervening IVDD.

Heterogeneity of germinal center B cells: New insights from single-cell studies

Upon antigen exposure, activated B cells in antigen-draining lymphoid organs form microanatomical structures, called germinal centers (GCs), where affinity maturation occurs. Within the GC microenvironment, GC B cells undergo proliferation and B cell receptor (BCR) genes somatic hypermutation in the dark zone (DZ), and affinity-based selection in the light zone (LZ). In the current paradigm of GC dynamics, high-affinity LZ B cells may be selected by cognate T- follicular helper cells to either differentiate into plasma cells or memory B cells, or re-enter the DZ and initiate a new round of proliferation and BCR diversification, before migrating back to the LZ. Given the diversity of cell states and potential cell fates that GC B cells may adopt, the two-state DZ-LZ paradigm has been challenged by studies that explored GC B-cell heterogeneity with a variety of single-cell technologies. Here, we review studies and single-cell technologies which have allowed to refine the working model of GC B-cell cellular and molecular heterogeneity during affinity maturation. This review also covers the use of single-cell quantitative data for mathematical modeling of GC reactions, and the application of single-cell genomics to the study of GC-derived malignancies.

Single-nucleus RNA-seq2 reveals functional crosstalk between liver zonation and ploidy

Single-cell RNA-seq reveals the role of pathogenic cell populations in development and progression of chronic diseases. In order to expand our knowledge on cellular heterogeneity, we have developed a single-nucleus RNA-seq2 method tailored for the comprehensive analysis of the nuclear transcriptome from frozen tissues, allowing the dissection of all cell types present in the liver, regardless of cell size or cellular fragility. We use this approach to characterize the transcriptional profile of individual hepatocytes with different levels of ploidy, and have discovered that ploidy states are associated with different metabolic potential, and gene expression in tetraploid mononucleated hepatocytes is conditioned by their position within the hepatic lobule. Our work reveals a remarkable crosstalk between gene dosage and spatial distribution of hepatocytes.

Integrative Single-Cell Transcriptomic Analysis of Human Fetal Thymocyte Development

Intrathymic differentiation of T lymphocytes begins as early as intrauterine stage, yet the T cell lineage decisions of human fetal thymocytes at different gestational ages are not currently understood. Here, we performed integrative single-cell analyses of thymocytes across gestational ages. We identified conserved candidates underlying the selection of T cell receptor (TCR) lineages in different human fetal stages. The trajectory of early thymocyte commitment during fetal growth was also characterized. Comparisons with mouse data revealed conserved and species-specific transcriptional dynamics of thymocyte proliferation, apoptosis and selection. Genome-wide association study (GWAS) data associated with multiple autoimmune disorders were analyzed to characterize susceptibility genes that are highly expressed at specific stages during fetal thymocyte development. In summary, our integrative map describes previously underappreciated aspects of human thymocyte development, and provides a comprehensive reference for understanding T cell lymphopoiesis in a self-tolerant and functional adaptive immune system.

Modular, efficient and constant-memory single-cell RNA-seq preprocessing

We describe a workflow for preprocessing of single-cell RNA-sequencing data that balances efficiency and accuracy. Our workflow is based on the kallisto and bustools programs, and is near optimal in speed with a constant memory requirement providing scalability for arbitrarily large datasets. The workflow is modular, and we demonstrate its flexibility by showing how it can be used for RNA velocity analyses.

Follicular lymphoma dynamics

Follicular lymphoma (FL) is an indolent yet challenging disease. Despite a generally favorable response to immunochemotherapy regimens, a fraction of patients does not respond or relapses early with unfavorable prognosis. For the vast majority of those who initially respond, relapses will repeatedly occur with increasing refractoriness to available treatments. Addressing the clinical challenges in FL warrants deep understanding of the nature of treatment-resistant FL cells seeding relapses, and of the biological basis of early disease progression. Great progress has been made in the last decade in the description and interrogation of the (epi)genomic landscape of FL cells, of their major dependency to the tumor microenvironment (TME), and of the stepwise lymphomagenesis process, from healthy to subclinical disease and to overt FL. A new picture is emerging, in which an ever-evolving tumor-TME duo sparks a complex and multilayered clonal and functional heterogeneity, blurring the discovery of prognostic biomarkers, patient stratification and reliable designs of risk-adapted treatments. Novel technological approaches allowing to decipher both tumor and TME heterogeneity at the single-cell level are beginning to unravel unsuspected cell dynamics and plasticity of FL cells. The upcoming drawing of a comprehensive functional picture of FL within its ecosystem holds great promise to address the unmet medical needs of this complex lymphoma.

SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data

The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis.

Single-cell RNA sequencing (scRNA-seq) allows researchers to analyze gene expression of thousands of single cells simultaneously. However, the low amount of extracted mRNA leads to a large number of dropout events, which introduce computational challenges and hinder downstream analysis of data. To address this problem, we developed SDImpute, a novel statistical method to recover the scRNA-seq data based on cell-level and gene-level information in this manuscript. The goal of our algorithm is to denoise the scRNA-seq data while maintaining the biological nature of gene expression. Combining SDImpute with the downstream analysis tools, we considered the matched bulk expression data and known cell labels of the scRNA-seq data as criteria to design experiments to validate the performance of our method in both simulated and real datasets. Moreover, we offer an R package with detailed instructions and an example input dataset. We hope that SDImpute will be beneficial to researchers to identify mechanisms underlying some biological processes by analysis of the scRNA-seq data.

The effect of methanol fixation on single-cell RNA sequencing data

Background

Single-cell RNA sequencing (scRNA-seq) has led to remarkable progress in our understanding of tissue heterogeneity in health and disease. Recently, the need for scRNA-seq sample fixation has emerged in many scenarios, such as when samples need long-term transportation, or when experiments need to be temporally synchronized. Methanol fixation is a simple and gentle method that has been routinely applied in scRNA-sEq. Yet, concerns remain that fixation may result in biases which may change the RNA-seq outcome.

Results

We adapted an existing methanol fixation protocol and performed scRNA-seq on both live and methanol fixed cells. Analyses of the results show methanol fixation can faithfully preserve biological related signals, while the discrepancy caused by fixation is subtle and relevant to library construction methods. By grouping transcripts based on their lengths and GC content, we find that transcripts with different features are affected by fixation to different degrees in full-length sequencing data, while the effect is alleviated in Drop-seq result.

Conclusions

Our deep analysis reveals the effects of methanol fixation on sample RNA integrity and elucidates the potential consequences of using fixation in various scRNA-seq experiment designs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07744-6.

A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells

RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3' or 5' termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples.

Landscape of Exhausted Virus-Specific CD8 T Cells in Chronic LCMV Infection

A hallmark of chronic infections is the presence of exhausted CD8 T cells, characterized by a distinct transcriptional program compared with functional effector or memory cells, co-expression of multiple inhibitory receptors, and impaired effector function, mainly driven by recurrent T cell receptor engagement. In the context of chronic lymphocytic choriomeningitis virus (LCMV) infection in mice, most studies focused on studying splenic virus-specific CD8 T cells. Here, we provide a detailed characterization of exhausted CD8 T cells isolated from six different tissues during established LCMV infection, using single-cell RNA sequencing. Our data reveal that exhausted cells are heterogeneous, adopt organ-specific transcriptomic profiles, and can be divided into five main functional subpopulations: advanced exhaustion, effector-like, intermediate, proliferating, or memory-like. Adoptive transfer experiments showed that these phenotypes are plastic, suggesting that the tissue microenvironment has a major impact in shaping the phenotype and function of virus-specific CD8 T cells during chronic infection.

Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis

The high proportion of zeros in typical single-cell RNA sequencing datasets has led to widespread but inconsistent use of terminology such as dropout and missing data. Here, we argue that much of this terminology is unhelpful and confusing, and outline simple ideas to help to reduce confusion. These include: (1) observed single-cell RNA sequencing counts reflect both true gene expression levels and measurement error, and carefully distinguishing between these contributions helps to clarify thinking; and (2) method development should start with a Poisson measurement model, rather than more complex models, because it is simple and generally consistent with existing data. We outline how several existing methods can be viewed within this framework and highlight how these methods differ in their assumptions about expression variation. We also illustrate how our perspective helps to address questions of biological interest, such as whether messenger RNA expression levels are multimodal among cells.

High-throughput full-length single-cell RNA-seq automation

Existing protocols for full-length single-cell RNA sequencing produce libraries of high complexity (thousands of distinct genes) with outstanding sensitivity and specificity of transcript quantification. These full-length libraries have the advantage of allowing probing of transcript isoforms, are informative regarding single-nucleotide polymorphisms and allow assembly of the VDJ region of the T- and B-cell-receptor sequences. Since full-length protocols are mostly plate-based at present, they are also suited to profiling cell types where cell numbers are limiting, such as rare cell types during development. A disadvantage of these methods has been the scalability and cost of the experiments, which has limited their popularity as compared with droplet-based and nanowell approaches. Here, we describe an automated protocol for full-length single-cell RNA sequencing, including both an in-house automated Smart-seq2 protocol and a commercial kit-based workflow. The protocols take 3-5 d to complete, depending on the number of plates processed in a batch. We discuss these two protocols in terms of ease of use, equipment requirements, running time, cost per sample and sequencing quality. By benchmarking the lysis buffers, reverse transcription enzymes and their combinations, we have optimized the in-house automated protocol to dramatically reduce its cost. An automated setup can be adopted easily by a competent researcher with basic laboratory skills and no prior automation experience. These pipelines have been employed successfully for several research projects allied with the Human Cell Atlas initiative ( www.humancellatlas.org ).

Single-Cell Sequencing Methodologies: From Transcriptome to Multi-Dimensional Measurement

Cells are the basic building blocks of biological systems, with inherent unique molecular features and development trajectories. The study of single cells facilitates in-depth understanding of cellular diversity, disease processes, and organization of multicellular organisms. Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for the interrogation of gene expression patterns and the dynamics of single cells, allowing cellular heterogeneity to be dissected at unprecedented resolution. Nevertheless, measuring at only transcriptome level or 1D is incomplete; the cellular heterogeneity reflects in multiple dimensions, including the genome, epigenome, transcriptome, spatial, and even temporal dimensions. Hence, integrative single cell analysis is highly desired. In addition, the way to interpret sequencing data by virtue of bioinformatic tools also exerts critical roles in revealing differential gene expression. Here, a comprehensive review that summarizes the cutting-edge single-cell transcriptome sequencing methodologies, including scRNA-seq, spatial and temporal transcriptome profiling, multi-omics sequencing and computational methods developed for scRNA-seq data analysis is provided. Finally, the challenges and perspectives of this field are discussed.

Plasticity and heterogeneity of thermogenic adipose tissue

The perception of adipose tissue, both in the scientific community and in the general population, has changed dramatically in the past 20 years. While adipose tissue was thought for a long time to be a rather simple lipid storage entity, it is now recognized as a highly heterogeneous organ and a critical regulator of systemic metabolism, composed of many different subtypes of cells, with important endocrine functions. Additionally, adipose tissue is nowadays recognized to contribute to energy turnover, due to the presence of specialized thermogenic adipocytes, which can be found in many adipose depots. This review discusses the unprecedented insights that we have gained into the heterogeneity of thermogenic adipocytes and their respective precursors due to the technical developments in single-cell and nucleus technologies. These methodological advances have increased our understanding of how adipose tissue catabolic function is influenced by developmental and intercellular communication events.

Recent Advances in Cellular and Molecular Bioengineering for Building and Translation of Biological Systems

In January of 2020, the Biomedical Engineering Society (BMES)- Cellular and Molecular Bioengineering (CMBE) conference was held in Puerto Rico and themed “Vision 2020: Emerging Technologies to Elucidate the Rule of Life.” The annual BME-CMBE conference gathered worldwide leaders and discussed successes and challenges in engineering biological systems and their translation. The goal of this report is to present the research frontiers in this field and provide perspectives on successful engineering and translation towards the clinic. We hope that this report serves as a constructive guide in shaping the future of research and translation of engineered biological systems.

scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

A pressing challenge in single-cell transcriptomics is to benchmark experimental protocols and computational methods. A solution is to use computational simulators, but existing simulators cannot simultaneously achieve three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill this gap, we propose scDesign2, a transparent simulator that achieves all three goals and generates high-fidelity synthetic data for multiple single-cell gene expression count-based technologies. In particular, scDesign2 is advantageous in its transparent use of probabilistic models and its ability to capture gene correlations via copulas.

Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networks

Single-cell RNA sequencing technology promotes the profiling of single-cell transcriptomes at an unprecedented throughput and resolution. However, in scRNA-seq studies, only a low amount of sequenced mRNA in each cell leads to missing detection for a portion of mRNA molecules, i.e. the dropout problem which hinders various downstream analyses. Therefore, it is necessary to develop robust and effective imputation methods for the increasing scRNA-seq data. In this study, we have developed an imputation method (GraphSCI) to impute the dropout events in scRNA-seq data based on the graph convolution networks. Extensive experiments demonstrated that GraphSCI outperforms other state-of-the-art methods for imputation on both simulated and real scRNA-seq data. Meanwhile, GraphSCI is able to accurately infer gene-to-gene relationships and the inferred gene-to-gene relationships could also provide powerful assistance for imputation dynamically during the training process, which is a key promotion of GraphSCI compared with other imputation algorithms.

Pan-Cancer Drivers Are Recurrent Transcriptional Regulatory Heterogeneities in Early-Stage Luminal Breast Cancer

The heterogeneous composition of solid tumors is known to impact disease progression and response to therapy. Malignant cells coexist in different regulatory states that can be accessed transcriptomically by single-cell RNA sequencing, but these methods have many caveats related to sensitivity, noise, and sample handling. We revised a statistical fluctuation analysis called stochastic profiling to combine with 10-cell RNA sequencing, which was designed for laser-capture microdissection (LCM) and extended here for immuno-LCM. When applied to a cohort of late-onset, early-stage luminal breast cancers, the integrated approach identified thousands of candidate regulatory heterogeneities. Intersecting the candidates from different tumors yielded a relatively stable set of 710 recurrent heterogeneously expressed genes (RHEG), which were significantly variable in >50% of patients. RHEGs were not strongly confounded by dissociation artifacts, cell-cycle oscillations, or driving mutations for breast cancer. Rather, RHEGs were enriched for epithelial-to-mesenchymal transition genes and, unexpectedly, the latest pan-cancer assembly of driver genes across cancer types other than breast. These findings indicate that heterogeneous transcriptional regulation conceivably provides a faster, reversible mechanism for malignant cells to evaluate the effects of potential oncogenes or tumor suppressors on cancer hallmarks. SIGNIFICANCE: Profiling intratumor heterogeneity of luminal breast carcinoma cells identifies a recurrent set of genes, suggesting sporadic activation of pathways known to drive other types of cancer.See related articles by Schaff and colleagues, p. 1853 and Sutcliffe and colleagues, p. 1868.