DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data

Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications

Single-cell RNA-sequencing (scRNA-seq) technology brought about a revolutionary change in the transcriptomic world, paving the way for comprehensive analysis of cellular heterogeneity in complex biological systems. It enabled researchers to see how different cells behaved at single-cell levels, providing new insights into the process. However, despite all these advancements, scRNA-seq also experiences challenges related to the complexity of data analysis, interpretation, and multi-omics data integration. In this review, these complications were discussed in detail, directly pointing at the optimization of scRNA-seq approaches and understanding the world of single-cell and its dynamics. Different protocols and currently functional single-cell databases were also covered. This review highlights different tools for the analysis of scRNA-seq and their methodologies, emphasizing innovative techniques that enhance resolution and accuracy at a single-cell level. Various applications were explored across domains including drug discovery, tumor microenvironment (TME), biomarker discovery, and microbial profiling, and case studies were discussed to explain the importance of scRNA-seq by uncovering novel and rare cell types and their identification. This review underlines a crucial aspect of scRNA-seq in the advancement of personalized medicine and highlights its potential to understand the complexity of biological systems.

Genomic determinants of antigen expression hierarchy in African trypanosomes

Antigenic variation is an immune evasion strategy used by many different pathogens. It involves the periodic, non-random switch in the expression of different antigens throughout an infection. How the observed hierarchy in antigen expression is achieved has remained a mystery1,2. A key challenge in uncovering this process has been the inability to track transcriptome changes and potential genomic rearrangements in individual cells during a switch event. Here we report the establishment of a highly sensitive single-cell RNA sequencing approach for the model protozoan parasite Trypanosoma brucei. This approach has revealed genomic rearrangements that occur in individual cells during a switch event. Our data show that following a double-strand break in the transcribed antigen-coding gene-an important trigger for antigen switching-the type of repair mechanism and the resultant antigen expression depend on the availability of a homologous repair template in the genome. When such a template was available, repair proceeded through segmental gene conversion, creating new, mosaic antigen-coding genes. Conversely, in the absence of a suitable template, a telomere-adjacent antigen-coding gene from a different part of the genome was activated by break-induced replication. Our results show the critical role of repair sequence availability in the antigen selection mechanism. Furthermore, our study demonstrates the power of highly sensitive single-cell RNA sequencing methods in detecting genomic rearrangements that drive transcriptional changes at the single-cell level.

Recurrent patterns of widespread neuronal genomic damage shared by major neurodegenerative disorders

Amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and Alzheimer's disease (AD) are common neurodegenerative disorders for which the mechanisms driving neuronal death remain unclear. Single-cell whole-genome sequencing of 429 neurons from three C9ORF72 ALS, six C9ORF72 FTD, seven AD, and twenty-three neurotypical control brains revealed significantly increased burdens in somatic single nucleotide variant (sSNV) and insertion/deletion (sIndel) in all three disease conditions. Mutational signature analysis identified a disease-associated sSNV signature suggestive of oxidative damage and an sIndel process, affecting 28% of ALS, 79% of FTD, and 65% of AD neurons but only 5% of control neurons (diseased vs. control: OR=31.20, p = 2.35×10-10). Disease-associated sIndels were primarily two-basepair deletions resembling signature ID4, which was previously linked to topoisomerase 1 (TOP1)-mediated mutagenesis. Duplex sequencing confirmed the presence of sIndels and identified similar single-strand events as potential precursor lesions. TOP1-associated sIndel mutagenesis and resulting genome instability may thus represent a common mechanism of neurodegeneration.

QClus: a droplet filtering algorithm for enhanced snRNA-seq data quality in challenging samples

Single-nuclei RNA sequencing remains a challenge for many human tissues, as incomplete removal of background signal masks cell-type-specific signals and interferes with downstream analyses. Here, we present Quality Clustering (QClus), a droplet filtering algorithm targeted toward challenging samples. QClus uses additional metrics, such as cell-type-specific marker gene expression, to cluster nuclei and filter empty and highly contaminated droplets, providing reliable filtering of samples with varying number of nuclei and contamination levels. In a benchmarking analysis against seven alternative methods across six datasets, consisting of 252 samples and over 1.9 million nuclei, QClus achieved the highest quality in the greatest number of samples over all evaluated quality metrics and recorded no processing failures, while robustly retaining numbers of nuclei within the expected range. QClus combines high quality, automation and robustness with flexibility and user-adjustability, catering to diverse experimental needs and datasets.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Exploring transcription modalities from bimodal, single-cell RNA sequencing data

There is a growing interest in generating bimodal, single-cell RNA sequencing (RNA-seq) data for studying biological pathways. These data are predominantly utilized in understanding phenotypic trajectories using RNA velocities; however, the shape information encoded in the two-dimensional resolution of such data is not yet exploited. In this paper, we present an elliptical parametrization of two-dimensional RNA-seq data, from which we derived statistics that reveal four different modalities. These modalities can be interpreted as manifestations of the changes in the rates of splicing, transcription or degradation. We performed our analysis on a cell cycle and a colorectal cancer dataset. In both datasets, we found genes that are not picked up by differential gene expression analysis (DGEA), and are consequently unnoticed, yet visibly delineate phenotypes. This indicates that, in addition to DGEA, searching for genes that exhibit the discovered modalities could aid recovering genes that set phenotypes apart. For communities studying biomarkers and cellular phenotyping, the modalities present in bimodal RNA-seq data broaden the search space of genes, and furthermore, allow for incorporating cellular RNA processing into regulatory analyses.

High content of nuclei-free low-quality cells in reference single-cell atlases: a call for more stringent quality control using nuclear fraction

The advent of droplet-based single-cell RNA-sequencing (scRNA-seq) has dramatically increased data throughput, enabling the release of a diverse array of tissue cell atlases to the public. However, we will show that prominent initiatives such as the Human Cell Atlas [1], the Tabula Sapiens [2] and the Tabula Muris [3] contain a significant amount of contamination products (frequently affecting the whole organ) in their data portals due to suboptimal quality filtering. Our work addresses a critical gap by advocating for more stringent quality filtering, highlighting the imperative for a shift from existing standards, which currently lean towards greater permissiveness. We will show the importance of incorporating cell intronic fraction in quality control -or MALAT1 expression otherwise- showcasing its informative nature and potential to elevate cell atlas data reliability. In summary, here, we unveil the hidden intronic landscape of every tissue and highlight the importance of more rigorous single-cell RNA-sequencing quality assessment in cell atlases to enhance their applicability in diverse downstream analyses.

Comparative transcriptomic analyses of thymocytes using 10x Genomics and Parse scRNA-seq technologies

BACKGROUND

Single-cell RNA sequencing experiments commonly use 10x Genomics (10x) kits due to their high-throughput capacity and standardized protocols. Recently, Parse Biosciences (Parse) introduced an alternative technology that uses multiple in-situ barcoding rounds within standard 96-well plates. Parse enables the analysis of more cells from multiple samples in a single run without the need for additional reagents or specialized microfluidics equipment. To evaluate the performance of both platforms, we conducted a benchmark study using biological and technical replicates of mouse thymus as a complex immune tissue.

RESULTS

We found that Parse detected nearly twice the number of genes compared to 10x, with each platform detecting a distinct set of genes. The comparison of multiplexed samples generated from 10x and Parse techniques showed 10x data to have lower technical variability and more precise annotation of biological states in the thymus compared to Parse.

CONCLUSION

Our results provide a comprehensive comparison of the suitability of both single-cell platforms for immunological studies.

Concepts and new developments in droplet-based single cell multi-omics

Single cell sequencing technologies have become a fixture in the molecular profiling of cells due to their ease, flexibility, and commercial availability. In particular, partitioning individual cells inside oil droplets via microfluidic reactions enables transcriptomic or multi-omic measurements for thousands of cells in parallel. Complementing the multitude of biological discoveries from genomics analyses, the past decade has brought new capabilities from assay baselines to enable a deeper understanding of the complex data from single cell multi-omics. Here, we highlight four innovations that have improved the reliability and understanding of droplet microfluidic assays. We emphasize new developments that further orient principles of technology development and guidelines for the design, benchmarking, and implementation of new droplet-based methodologies.

A sexually dimorphic hepatic cycle of periportal VLDL generation and subsequent pericentral VLDLR-mediated re-uptake

Recent single-cell transcriptomes revealed spatiotemporal programmes of liver function on the sublobular scale. However, how sexual dimorphism affected this space-time logic remained poorly understood. We addressed this by performing scRNA-seq in the mouse liver, which revealed that sex, space and time together markedly influence xenobiotic detoxification and lipoprotein metabolism. The very low density lipoprotein receptor (VLDLR) exhibits a pericentral expression pattern, with significantly higher mRNA and protein levels in female mice. Conversely, VLDL assembly is periportally biased, suggesting a sexually dimorphic hepatic cycle of periportal formation and pericentral uptake of VLDL. In humans, VLDLR expression is also pericentral, with higher mRNA and protein levels in premenopausal women compared to similarly aged men. Individuals with low hepatic VLDLR expression show a high prevalence of atherosis in the coronary artery already at an early age and an increased incidence of heart attack.

Context-aware single-cell multiomics approach identifies cell-type-specific lung cancer susceptibility genes

Genome-wide association studies (GWAS) identified over fifty loci associated with lung cancer risk. However, underlying mechanisms and target genes are largely unknown, as most risk-associated variants might regulate gene expression in a context-specific manner. Here, we generate a barcode-shared transcriptome and chromatin accessibility map of 117,911 human lung cells from age/sex-matched ever- and never-smokers to profile context-specific gene regulation. Identified candidate cis-regulatory elements (cCREs) are largely cell type-specific, with 37% detected in one cell type. Colocalization of lung cancer candidate causal variants (CCVs) with these cCREs combined with transcription factor footprinting prioritize the variants for 68% of the GWAS loci. CCV-colocalization and trait relevance score indicate that epithelial and immune cell categories, including rare cell types, contribute to lung cancer susceptibility the most. A multi-level cCRE-gene linking system identifies candidate susceptibility genes from 57% of the loci, where most loci display cell-category-specific target genes, suggesting context-specific susceptibility gene function.

Intercellular interaction between FAP+ fibroblasts and CD150+ inflammatory monocytes mediates fibrostenosis in Crohn's disease

Crohn's disease (CD) is marked by recurring intestinal inflammation and tissue injury, often resulting in fibrostenosis and bowel obstruction, necessitating surgical intervention with high recurrence rates. To elucidate the mechanisms underlying fibrostenosis in CD, we analyzed the transcriptome of cells isolated from the transmural ileum of patients with CD, including a trio of lesions from each patient: non-affected, inflamed, and stenotic ileum samples, and compared them with samples from patients without CD. Our computational analysis revealed that profibrotic signals from a subset of monocyte-derived cells expressing CD150 induced a disease-specific fibroblast population, resulting in chronic inflammation and tissue fibrosis. The transcription factor TWIST1 was identified as a key modulator of fibroblast activation and extracellular matrix (ECM) deposition. Genetic and pharmacological inhibition of TWIST1 prevents fibroblast activation, reducing ECM production and collagen deposition. Our findings suggest that the myeloid-stromal axis may offer a promising therapeutic target to prevent fibrostenosis in CD.

The Advancement and Application of the Single-Cell Transcriptome in Biological and Medical Research

Single-cell RNA sequencing technology (scRNA-seq) has been steadily developing since its inception in 2009. Unlike bulk RNA-seq, scRNA-seq identifies the heterogeneity of tissue cells and reveals gene expression changes in individual cells at the microscopic level. Here, we review the development of scRNA-seq, which has gone through iterations of reverse transcription, in vitro transcription, smart-seq, drop-seq, 10 × Genomics, and spatial single-cell transcriptome technologies. The technology of 10 × Genomics has been widely applied in medicine and biology, producing rich research results. Furthermore, this review presents a summary of the analytical process for single-cell transcriptome data and its integration with other omics analyses, including genomes, epigenomes, proteomes, and metabolomics. The single-cell transcriptome has a wide range of applications in biology and medicine. This review analyzes the applications of scRNA-seq in cancer, stem cell research, developmental biology, microbiology, and other fields. In essence, scRNA-seq provides a means of elucidating gene expression patterns in single cells, thereby offering a valuable tool for scientific research. Nevertheless, the current single-cell transcriptome technology is still imperfect, and this review identifies its shortcomings and anticipates future developments. The objective of this review is to facilitate a deeper comprehension of scRNA-seq technology and its applications in biological and medical research, as well as to identify avenues for its future development in alignment with practical needs.

A Guide to Biodetection in Droplets

Droplet-based methods for optical biodetection enable unprecedented high-throughput experimental parameters. The methods, however, remain underused due to the accompanying multidisciplinary and complicated experimental workflows. Here, we provide a tutorial for droplet-based optical biodetection workflows with a focus on the key aspect of label selection. By discussing and guiding readers through recent state-of-the-art studies, we aim to make droplet-based approaches more accessible to the general scientific public.

Protracted neuronal recruitment in the temporal lobes of young children

The temporal lobe of the human brain contains the entorhinal cortex (EC). This region of the brain is a highly interconnected integrative hub for sensory and spatial information; it also has a key role in episodic memory formation and is the main source of cortical hippocampal inputs1-4. The human EC continues to develop during childhood5, but neurogenesis and neuronal migration to the EC are widely considered to be complete by birth. Here we show that the human temporal lobe contains many young neurons migrating into the postnatal EC and adjacent regions, with a large tangential stream persisting until the age of around one year and radial dispersal continuing until around two to three years of age. By contrast, we found no equivalent postnatal migration in rhesus macaques (Macaca mulatta). Immunostaining and single-nucleus RNA sequencing of ganglionic eminence germinal zones, the EC stream and the postnatal EC revealed that most migrating cells in the EC stream are derived from the caudal ganglionic eminence and become LAMP5+RELN+ inhibitory interneurons. These late-arriving interneurons could continue to shape the processing of sensory and spatial information well into postnatal life, when children are actively interacting with their environment. The EC is one of the first regions of the brain to be affected in Alzheimer's disease, and previous work has linked cognitive decline to the loss of LAMP5+RELN+ cells6,7. Our investigation reveals that many of these cells arrive in the EC through a major postnatal migratory stream in early childhood.

Single nuclei transcriptomics in human and non-human primate striatum in opioid use disorder

In brain, the striatum is a heterogenous region involved in reward and goal-directed behaviors. Striatal dysfunction is linked to psychiatric disorders, including opioid use disorder (OUD). Striatal subregions are divided based on neuroanatomy, each with unique roles in OUD. In OUD, the dorsal striatum is involved in altered reward processing, formation of habits, and development of negative affect during withdrawal. Using single nuclei RNA-sequencing, we identified both canonical (e.g., dopamine receptor subtype) and less abundant cell populations (e.g., interneurons) in human dorsal striatum. Pathways related to neurodegeneration, interferon response, and DNA damage were significantly enriched in striatal neurons of individuals with OUD. DNA damage markers were also elevated in striatal neurons of opioid-exposed rhesus macaques. Sex-specific molecular differences in glial cell subtypes associated with chronic stress were found in OUD, particularly female individuals. Together, we describe different cell types in human dorsal striatum and identify cell type-specific alterations in OUD.

The use of single-cell RNA-seq to study heterogeneity at varying levels of virus-host interactions

The outcome of viral infection depends on the diversity of the infecting viral population and the heterogeneity of the cell population that is infected. Until almost a decade ago, the study of these dynamic processes during viral infection was challenging and limited to certain targeted measurements. Presently, with the use of single-cell sequencing technology, the complex interface defined by the interactions of cells with infecting virus can now be studied across the breadth of the transcriptome in thousands of individual cells simultaneously. In this review, we will describe the use of single-cell RNA sequencing (scRNA-seq) to study the heterogeneity of viral infections, ranging from individual virions to the immune response between infected individuals. In addition, we highlight certain key experimental limitations and methodological decisions that are critical to analyzing scRNA-seq data at each scale.

Automatic quality control of single-cell and single-nucleus RNA-seq using valiDrops

Single-cell and single-nucleus RNA-sequencing (sxRNA-seq) measures gene expression in individual cells or nuclei enabling comprehensive characterization of cell types and states. However, isolation of cells or nuclei for sxRNA-seq releases contaminating RNA, which can distort biological signals, through, for example, cell damage and transcript leakage. Thus, identifying barcodes containing high-quality cells or nuclei is a critical analytical step in the processing of sxRNA-seq data. Here, we present valiDrops, an automated method to identify high-quality barcodes and flag dead cells. In valiDrops, barcodes are initially filtered using data-adaptive thresholding on community-standard quality metrics, and subsequently, valiDrops uses a novel clustering-based approach to identify barcodes with distinct biological signals. We benchmark valiDrops and show that biological signals from cell types and states are more distinct, easier to separate and more consistent after filtering by valiDrops compared to existing tools. Finally, we show that valiDrops can predict and flag dead cells with high accuracy. This novel classifier can further improve data quality or be used to identify dead cells to interrogate the biology of cell death. Thus, valiDrops is an effective and easy-to-use method to improve data quality and biological interpretation. Our method is openly available as an R package at www.github.com/madsen-lab/valiDrops.

Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue

We define and identify a new class of control genes for next-generation sequencing called total RNA expression genes (TREGs), which correlate with total RNA abundance in cell types of different sizes and transcriptional activity. We provide a data-driven method to identify TREGs from single-cell RNA sequencing data, allowing the estimation of total amount of RNA when restricted to quantifying a limited number of genes. We demonstrate our method in postmortem human brain using multiplex single-molecule fluorescent in situ hybridization and compare candidate TREGs against classic housekeeping genes. We identify AKT3 as a top TREG across five brain regions.

Spatiotemporal molecular dynamics of the developing human thalamus

The thalamus plays a central coordinating role in the brain. Thalamic neurons are organized into spatially distinct nuclei, but the molecular architecture of thalamic development is poorly understood, especially in humans. To begin to delineate the molecular trajectories of cell fate specification and organization in the developing human thalamus, we used single-cell and multiplexed spatial transcriptomics. We show that molecularly defined thalamic neurons differentiate in the second trimester of human development and that these neurons organize into spatially and molecularly distinct nuclei. We identified major subtypes of glutamatergic neuron subtypes that are differentially enriched in anatomically distinct nuclei and six subtypes of γ-aminobutyric acid-mediated (GABAergic) neurons that are shared and distinct across thalamic nuclei.

Context-aware single-cell multiome approach identified cell-type specific lung cancer susceptibility genes

Genome-wide association studies (GWAS) identified over fifty loci associated with lung cancer risk. However, the genetic mechanisms and target genes underlying these loci are largely unknown, as most risk-associated-variants might regulate gene expression in a context-specific manner. Here, we generated a barcode-shared transcriptome and chromatin accessibility map of 117,911 human lung cells from age/sex-matched ever- and never-smokers to profile context-specific gene regulation. Accessible chromatin peak detection identified cell-type-specific candidate cis-regulatory elements (cCREs) from each lung cell type. Colocalization of lung cancer candidate causal variants (CCVs) with these cCREs prioritized the variants for 68% of the GWAS loci, a subset of which was also supported by transcription factor abundance and footprinting. cCRE colocalization and single-cell based trait relevance score nominated epithelial and immune cells as the main cell groups contributing to lung cancer susceptibility. Notably, cCREs of rare proliferating epithelial cell types, such as AT2-proliferating (0.13%) and basal cells (1.8%), overlapped with CCVs, including those in TERT. A multi-level cCRE-gene linking system identified candidate susceptibility genes from 57% of lung cancer loci, including those not detected in tissue- or cell-line-based approaches. cCRE-gene linkage uncovered that adjacent genes expressed in different cell types are correlated with distinct subsets of coinherited CCVs, including JAML and MPZL3 at the 11q23.3 locus. Our data revealed the cell types and contexts where the lung cancer susceptibility genes are functional.

Novel human pluripotent stem cell-derived hypothalamus organoids demonstrate cellular diversity

The hypothalamus is a region of the brain that plays an important role in regulating body functions and behaviors. There is a growing interest in human pluripotent stem cells (hPSCs) for modeling diseases that affect the hypothalamus. Here, we established an hPSC-derived hypothalamus organoid differentiation protocol to model the cellular diversity of this brain region. Using an hPSC line with a tyrosine hydroxylase (TH)-TdTomato reporter for dopaminergic neurons (DNs) and other TH-expressing cells, we interrogated DN-specific pathways and functions in electrophysiologically active hypothalamus organoids. Single-cell RNA sequencing (scRNA-seq) revealed diverse neuronal and non-neuronal cell types in mature hypothalamus organoids. We identified several molecularly distinct hypothalamic DN subtypes that demonstrated different developmental maturities. Our in vitro 3D hypothalamus differentiation protocol can be used to study the development of this critical brain structure and can be applied to disease modeling to generate novel therapeutic approaches for disorders centered around the hypothalamus.

Single-cell analysis of human MAIT cell transcriptional, functional and clonal diversity

Mucosal-associated invariant T (MAIT) cells are innate-like T cells that recognize microbial metabolites through a semi-invariant T cell receptor (TCR). Major questions remain regarding the extent of human MAIT cell functional and clonal diversity. To address these, we analyzed the single-cell transcriptome and TCR repertoire of blood and liver MAIT cells and developed functional RNA-sequencing, a method to integrate function and TCR clonotype at single-cell resolution. MAIT cell clonal diversity was comparable to conventional memory T cells, with private TCR repertoires shared across matched tissues. Baseline functional diversity was low and largely related to tissue site. MAIT cells showed stimulus-specific transcriptional responses in vitro, with cells positioned along gradients of activation. Clonal identity influenced resting and activated transcriptional profiles but intriguingly was not associated with the capacity to produce IL-17. Overall, MAIT cells show phenotypic and functional diversity according to tissue localization, stimulation environment and clonotype.

Spatiotemporal molecular dynamics of the developing human thalamus

The thalamus plays a central coordinating role in the brain. Thalamic neurons are organized into spatially-distinct nuclei, but the molecular architecture of thalamic development is poorly understood, especially in humans. To begin to delineate the molecular trajectories of cell fate specification and organization in the developing human thalamus, we used single cell and multiplexed spatial transcriptomics. Here we show that molecularly-defined thalamic neurons differentiate in the second trimester of human development, and that these neurons organize into spatially and molecularly distinct nuclei. We identify major subtypes of glutamatergic neuron subtypes that are differentially enriched in anatomically distinct nuclei. In addition, we identify six subtypes of GABAergic neurons that are shared and distinct across thalamic nuclei.

One-Sentence Summary

Single cell and spatial profiling of the developing thalamus in the first and second trimester yields molecular mechanisms of thalamic nuclei development.

spinDrop: a droplet microfluidic platform to maximise single-cell sequencing information content

Droplet microfluidic methods have massively increased the throughput of single-cell sequencing campaigns. The benefit of scale-up is, however, accompanied by increased background noise when processing challenging samples and the overall RNA capture efficiency is lower. These drawbacks stem from the lack of strategies to enrich for high-quality material or specific cell types at the moment of cell encapsulation and the absence of implementable multi-step enzymatic processes that increase capture. Here we alleviate both bottlenecks using fluorescence-activated droplet sorting to enrich for droplets that contain single viable cells, intact nuclei, fixed cells or target cell types and use reagent addition to droplets by picoinjection to perform multi-step lysis and reverse transcription. Our methodology increases gene detection rates fivefold, while reducing background noise by up to half. We harness these properties to deliver a high-quality molecular atlas of mouse brain development, despite starting with highly damaged input material, and provide an atlas of nascent RNA transcription during mouse organogenesis. Our method is broadly applicable to other droplet-based workflows to deliver sensitive and accurate single-cell profiling at a reduced cost.

A contamination focused approach for optimizing the single-cell RNA-seq experiment

Droplet-based single-cell RNA-seq (scRNA-seq) data are plagued by ambient contaminations caused by nucleic acid material released by dead and dying cells. This material is mixed into the buffer and is co-encapsulated with cells, leading to a lower signal-to-noise ratio. Although there exist computational methods to remove ambient contaminations post-hoc, the reliability of algorithms in generating high-quality data from low-quality sources remains uncertain. Here, we assess data quality before data filtering by a set of quantitative, contamination-based metrics that assess data quality more effectively than standard metrics. Through a series of controlled experiments, we report improvements that can minimize ambient contamination outside of tissue dissociation, via cell fixation, improved cell loading, microfluidic dilution, and nuclei versus cell preparation; many of these parameters are inaccessible on commercial platforms. We provide end-users with insights on factors that can guide their decision-making regarding optimizations that minimize ambient contamination, and metrics to assess data quality.

SiftCell: A robust framework to detect and isolate cell-containing droplets from single-cell RNA sequence reads

Single-cell RNA sequencing (scRNA-seq) massively profiles transcriptomes of individual cells encapsulated in barcoded droplets in parallel. However, in real-world scRNA-seq data, many barcoded droplets do not contain cells, but instead, they capture a fraction of ambient RNAs released from damaged or lysed cells. A typical first step to analyze scRNA-seq data is to filter out cell-free droplets and isolate cell-containing droplets, but distinguishing them is often challenging; incorrect filtering may mislead the downstream analysis substantially. We propose SiftCell, a suite of software tools to identify and visualize cell-containing and cell-free droplets in manifold space via randomization (SiftCell-Shuffle) to classify between the two types of droplets (SiftCell-Boost) and to quantify the contribution of ambient RNAs for each droplet (SiftCell-Mix). By applying our method to datasets obtained by various single-cell platforms, we show that SiftCell provides a streamlined way to perform upstream quality control of scRNA-seq, which is more comprehensive and accurate than existing methods.

Ambient RNAs removal of cortex-specific snRNA-seq reveals Apoe+ microglia/macrophage after deeper cerebral hypoperfusion in mice

BACKGROUND

Ambient RNAs contamination in single-nuclei RNA sequencing (snRNA-seq) is a challenging problem, but the consequences of ambient RNAs contamination of damaged and/or diseased tissues are poorly understood. Cognitive impairments and white/gray matter injuries are characteristic of deeper cerebral hypoperfusion mouse models induced by bilateral carotid artery stenosis (BCAS), but the molecular mechanisms still need to be further explored. More importantly, the BCAS mice can also offer an excellent model to examine the signatures of ambient RNAs contamination in damaged tissues when performing snRNA-seq.

METHODS

After the sham and BCAS mice were established, cortex-specific single-nuclei libraries were constructed. Single-nuclei transcriptomes were described informatically by the R package Seurat, and ambient RNA markers of were identified in each library. Then, after removing ambient RNAs in each sample using the in silico approaches, the combination of CellBender and subcluster cleaning, single-nuclei transcriptomes were reconstructed. Next, the comparison of ambient RNA contamination was performed using irGSEA analysis before and after the in silico approaches. Finally, further bioinformatic analyses were performed.

RESULTS

The ambient RNAs are more predominant in the BCAS group than the sham group. The contamination mainly originated from damaged neuronal nuclei, but could be reduced largely using the in silico approaches. The integrative analysis of cortex-specific snRNA-seq data and the published bulk transcriptome revealed that microglia and other immune cells were the primary effectors. In the sequential microglia/immune subgroups analysis, the subgroup of Apoe⁺ MG/Mac (microglia/macrophages) was identified. Interestingly, this subgroup mainly participated in the pathways of lipid metabolism, associated with the phagocytosis of cell debris.

CONCLUSIONS

Taken together, our current study unravels the features of ambient RNAs in snRNA-seq datasets under diseased conditions, and the in silico approaches can effectively eliminate the incorrected cell annotation and following misleading analysis. In the future, snRNA-seq data analysis should be carefully revisited, and ambient RNAs removal needs to be taken into consideration, especially for those diseased tissues. To our best knowledge, our study also offers the first cortex-specific snRNA-seq data of deeper cerebral hypoperfusion, which provides with novel therapeutic targets.

Applications of single-cell RNA sequencing in drug discovery and development

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.

Analysis of Single-Cell RNA-seq Data

As single-cell RNA sequencing experiments continue to advance scientific discoveries across biological disciplines, an increasing number of analysis tools and workflows for analyzing the data have been developed. In this chapter, we describe a standard workflow and elaborate on relevant data analysis tools for analyzing single-cell RNA sequencing data. We provide recommendations for the appropriate use of commonly used methods, with code examples and analysis interpretations.

Neuronal ambient RNA contamination causes misinterpreted and masked cell types in brain single-nuclei datasets

Ambient RNA contamination in single-cell and single-nuclei RNA sequencing (snRNA-seq) is a significant problem, but its consequences are poorly understood. Here, we show that ambient RNAs in brain snRNA-seq datasets have a nuclear or non-nuclear origin with distinct gene set signatures. Both ambient RNA signatures are predominantly neuronal, and we find that some previously annotated neuronal cell types are distinguished by ambient RNA contamination. We detect pervasive neuronal ambient RNA contamination in all glial cell types unless glia and neurons are physically separated prior to sequencing. We demonstrate that this contamination can be removed in silico and show that previous single-nuclei RNA-seq-based annotations of immature oligodendrocytes are glial nuclei contaminated with ambient RNAs. After ambient RNA removal, we detect rare, committed oligodendrocyte progenitor cells not annotated in most previous adult human brain datasets. Together, these results provide an in-depth analysis of ambient RNA contamination in brain single-nuclei datasets.