A systematic evaluation of single cell RNA-seq analysis pipelines

scDAPP: a comprehensive single-cell transcriptomics analysis pipeline optimized for cross-group comparison

Single-cell transcriptomics profiling has increasingly been used to evaluate cross-group (or condition) differences in cell population and cell-type gene expression. This often leads to large datasets with complex experimental designs that need advanced comparative analysis. Concurrently, bioinformatics software and analytic approaches also become more diverse and constantly undergo improvement. Thus, there is an increased need for automated and standardized data processing and analysis pipelines, which should be efficient and flexible too. To address these, we develop the single-cell Differential Analysis and Processing Pipeline (scDAPP), a R-based workflow for comparative analysis of single cell (or nucleus) transcriptomic data between two or more groups and at the levels of single cells or 'pseudobulking' samples. The pipeline automates many steps of pre-processing using data-learnt parameters, uses previously benchmarked software, and generates comprehensive intermediate data and final results that are valuable for both beginners and experts of scRNA-seq analysis. Moreover, the analytic reports, augmented by extensive data visualization, increase the transparency of computational analysis and parameter choices, while facilitate users to go seamlessly from raw data to biological interpretation. scDAPP is freely available under the MIT license, with source code, documentation and sample data at the GitHub (https://github.com/bioinfoDZ/scDAPP).

Deciphering bone marrow engraftment after allogeneic stem cell transplantation in humans using single-cell analyses

BACKGROUNDDonor cell engraftment is a prerequisite of successful allogeneic hematopoietic stem cell transplantation. Based on peripheral blood analyses, it is characterized by early myeloid recovery and T and B cell lymphopenia. However, cellular networks associated with bone marrow engraftment of allogeneic human cells have been poorly described.METHODSMass cytometry and CITE-Seq analyses were performed on bone marrow cells 3 months after transplantation in patients with acute myelogenous leukemia.RESULTSMass cytometric analyses in 26 patients and 20 healthy controls disclosed profound alterations in myeloid and B cell progenitors, with a shift toward terminal myeloid differentiation and decreased B cell progenitors. Unsupervised analysis separated recipients into 2 groups, one of them being driven by previous graft-versus-host disease (R2 patients). We then used single-cell CITE-Seq to decipher engraftment, which resolved 36 clusters, encompassing all bone marrow cellular components. Hematopoiesis in transplant recipients was sustained by committed myeloid and erythroid progenitors in a setting of monocyte-, NK cell-, and T cell-mediated inflammation. Gene expression revealed major pathways in transplant recipients, namely, TNF-α signaling via NF-κB and the IFN-γ response. The hallmark of allograft rejection was consistently found in clusters from transplant recipients, especially in R2 recipients.CONCLUSIONBone marrow cell engraftment of allogeneic donor cells is characterized by a state of emergency hematopoiesis in the setting of an allogeneic response driving inflammation.FUNDINGThis study was supported by the French National Cancer Institute (Institut National du Cancer; PLBIO19-239) and by an unrestricted research grant by Alexion Pharmaceuticals.

Neurexin 3 is Essential for the Specific Wiring of a Color Pathway in the Mammalian Retina

Precise wiring within sensory systems is critical for the accurate transmission of information. In the visual system, S-cone photoreceptors specialize in detecting short-wavelength light, crucial to color perception and environmental cue detection. S-cones form specific synapses with S-cone bipolar cells (SCBCs), a connection that is remarkably consistent across species. Yet, the molecular mechanisms guiding this specificity remain unexplored. To address this, we used the cone-dominant ground squirrel for deep-sequencing of cone subtype transcriptomes and identified Nrxn3 as an essential molecule for the S-cone to SCBC synapse. Using transgenic mouse models, we further examined the role of Nrxn3 in S-cones and discovered a significant reduction of SCBC connections in the absence of Nrxn3. This finding extends the known functions of neurexins, typically associated with synapse regulation, by highlighting their essential role in a specific synaptic connection for the first time. Moreover, the differentially expressed genes identified here pave the way for further investigations into the unique functions of cone subtypes.

Single-Cell RNA Sequencing and Its Applications in Pituitary Research

BACKGROUND

Mounting evidence underscores the significance of cellular diversity within the endocrine system and the intricate interplay between different cell types and tissues, essential for preserving physiological balance and influencing disease trajectories. The pituitary gland, a central player in the endocrine orchestra, exemplifies this complexity with its assortment of hormone-secreting and nonsecreting cells.

SUMMARY

The pituitary gland houses several types of cells responsible for hormone production, alongside nonsecretory cells like fibroblasts and endothelial cells, each playing a crucial role in the gland's function and regulatory mechanisms. Despite the acknowledged importance of these cellular interactions, the detailed mechanisms by which they contribute to pituitary gland physiology and pathology remain largely uncharted. The last decade has seen the emergence of groundbreaking technologies such as single-cell RNA sequencing, offering unprecedented insights into cellular heterogeneity and interactions. However, the application of this advanced tool in exploring the pituitary gland's complexities has been scant. This review provides an overview of this methodology, highlighting its strengths and limitations, and discusses future possibilities for employing it to deepen our understanding of the pituitary gland and its dysfunction in disease states.

KEY MESSAGE

Single-cell RNA sequencing technology offers an unprecedented means to study the heterogeneity and interactions of pituitary cells, though its application has been limited thus far. Further utilization of this tool will help uncover the complex physiological and pathological mechanisms of the pituitary, advancing research and treatment of pituitary diseases.

Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data

Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios.

Single-Cell Sequencing: High-Resolution Analysis of Cellular Heterogeneity in Autoimmune Diseases

Autoimmune diseases (AIDs) are complex in etiology and diverse in classification but clinically show similar symptoms such as joint pain and skin problems. As a result, the diagnosis is challenging, and usually, only broad treatments can be available. Consequently, the clinical responses in patients with different types of AIDs are unsatisfactory. Therefore, it is necessary to conduct more research to figure out the pathogenesis and therapeutic targets of AIDs. This requires research technologies with strong extraction and prediction capabilities. Single-cell sequencing technology analyses the genomic, epigenomic, or transcriptomic information at the single-cell level. It can define different cell types and states in greater detail, further revealing the molecular mechanisms that drive disease progression. These advantages enable cell biology research to achieve an unprecedented resolution and scale, bringing a whole new vision to life science research. In recent years, single-cell technology especially single-cell RNA sequencing (scRNA-seq) has been widely used in various disease research. In this paper, we present the innovations and applications of single-cell sequencing in the medical field and focus on the application contributing to the differential diagnosis and precise treatment of AIDs. Despite some limitations, single-cell sequencing has a wide range of applications in AIDs. We finally present a prospect for the development of single-cell sequencing. These ideas may provide some inspiration for subsequent research.

scDAPP: a comprehensive single-cell transcriptomics analysis pipeline optimized for cross-group comparison

Single-cell transcriptomics profiling has increasingly been used to evaluate cross-group differences in cell population and cell-type gene expression. This often leads to large datasets with complex experimental designs that need advanced comparative analysis. Concurrently, bioinformatics software and analytic approaches also become more diverse and constantly undergo improvement. Thus, there is an increased need for automated and standardized data processing and analysis pipelines, which should be efficient and flexible too. To address these, we develop the single-cell Differential Analysis and Processing Pipeline (scDAPP), a R-based workflow for comparative analysis of single cell (or nucleus) transcriptomic data between two or more groups and at the levels of single cells or "pseudobulking" samples. The pipeline automates many steps of pre-processing using data-learnt parameters, uses previously benchmarked software, and generates comprehensive intermediate data and final results that are valuable for both beginners and experts of scRNA-seq analysis. Moreover, the analytic reports, augmented by extensive data visualization, increase the transparency of computational analysis and parameter choices, while facilitate users to go seamlessly from raw data to biological interpretation. Availability and Implementation: scDAPP is freely available for non-commercial usage as an R package under the MIT license. Source code, documentation and sample data are available at the GitHub (https://github.com/bioinfoDZ/scDAPP).

Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets

BACKGROUND

Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data.

MAIN BODY

The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most appropriate normalization method to use. To this aim, we first give an overview of the different single cell sequencing platforms and methods commonly used including isolation and library preparation protocols. Next, we discuss the inherent sources of variability of scRNA-seq datasets. We describe the categories of normalization methods and include examples of each. We also delineate imputation and batch-effect correction methods. Furthermore, we describe data-driven metrics commonly used to evaluate the performance of normalization methods. We also discuss common scRNA-seq methods and toolkits used for integrated data analysis.

CONCLUSIONS

According to the correction performed, normalization methods can be broadly classified as within and between-sample algorithms. Moreover, with respect to the mathematical model used, normalization methods can further be classified into: global scaling methods, generalized linear models, mixed methods, and machine learning-based methods. Each of these methods depict pros and cons and make different statistical assumptions. However, there is no better performing normalization method. Instead, metrics such as silhouette width, K-nearest neighbor batch-effect test, or Highly Variable Genes are recommended to assess the performance of normalization methods.

scBOL: a universal cell type identification framework for single-cell and spatial transcriptomics data

MOTIVATION

Over the past decade, single-cell transcriptomic technologies have experienced remarkable advancements, enabling the simultaneous profiling of gene expressions across thousands of individual cells. Cell type identification plays an essential role in exploring tissue heterogeneity and characterizing cell state differences. With more and more well-annotated reference data becoming available, massive automatic identification methods have sprung up to simplify the annotation process on unlabeled target data by transferring the cell type knowledge. However, in practice, the target data often include some novel cell types that are not in the reference data. Most existing works usually classify these private cells as one generic 'unassigned' group and learn the features of known and novel cell types in a coupled way. They are susceptible to the potential batch effects and fail to explore the fine-grained semantic knowledge of novel cell types, thus hurting the model's discrimination ability. Additionally, emerging spatial transcriptomic technologies, such as in situ hybridization, sequencing and multiplexed imaging, present a novel challenge to current cell type identification strategies that predominantly neglect spatial organization. Consequently, it is imperative to develop a versatile method that can proficiently annotate single-cell transcriptomics data, encompassing both spatial and non-spatial dimensions.

RESULTS

To address these issues, we propose a new, challenging yet realistic task called universal cell type identification for single-cell and spatial transcriptomics data. In this task, we aim to give semantic labels to target cells from known cell types and cluster labels to those from novel ones. To tackle this problem, instead of designing a suboptimal two-stage approach, we propose an end-to-end algorithm called scBOL from the perspective of Bipartite prototype alignment. Firstly, we identify the mutual nearest clusters in reference and target data as their potential common cell types. On this basis, we mine the cycle-consistent semantic anchor cells to build the intrinsic structure association between two data. Secondly, we design a neighbor-aware prototypical learning paradigm to strengthen the inter-cluster separability and intra-cluster compactness within each data, thereby inspiring the discriminative feature representations. Thirdly, driven by the semantic-aware prototypical learning framework, we can align the known cell types and separate the private cell types from them among reference and target data. Such an algorithm can be seamlessly applied to various data types modeled by different foundation models that can generate the embedding features for cells. Specifically, for non-spatial single-cell transcriptomics data, we use the autoencoder neural network to learn latent low-dimensional cell representations, and for spatial single-cell transcriptomics data, we apply the graph convolution network to capture molecular and spatial similarities of cells jointly. Extensive results on our carefully designed evaluation benchmarks demonstrate the superiority of scBOL over various state-of-the-art cell type identification methods. To our knowledge, we are the pioneers in presenting this pragmatic annotation task, as well as in devising a comprehensive algorithmic framework aimed at resolving this challenge across varied types of single-cell data. Finally, scBOL is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/aimeeyaoyao/scBOL.

Spatial analysis toolkits for RNA in situ sequencing

Spatial transcriptomics (ST) is featured by high-throughput gene expression profiling within their native cell and tissue context, offering a means to investigate gene regulatory networks in tissue microenvironment. In situ sequencing (ISS) is an imaging-based ST technology that simultaneously detects hundreds to thousands of genes at subcellular resolution. As a highly reproducible and robust technique, ISS has been widely adapted and undergone a series of technical iterations. As the interest in ISS-based spatial transcriptomic analysis grows, scalable and integrated data analysis workflows are needed to facilitate the applications of ISS in different research fields. This review presents the state-of-the-art bioinformatic toolkits for ISS data analysis, which covers the upstream and downstream analysis workflows, including image analysis, cell segmentation, clustering, functional enrichment, detection of spatially variable genes and cell clusters, spatial cell-cell interactions, and trajectory inference. To assist the community in choosing the right tools for their research, the application of each tool and its compatibility with ISS data are reviewed in detailed. Finally, future perspectives and challenges concerning how to integrate heterogeneous tools into a user-friendly analysis pipeline are discussed. This article is categorized under: RNA Methods > RNA Analyses In Vitro and In Silico.

Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets

Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.

FXYD3 functionally demarcates an ancestral breast cancer stem cell subpopulation with features of drug-tolerant persisters

The heterogeneity of cancer stem cells (CSCs) within tumors presents a challenge in therapeutic targeting. To decipher the cellular plasticity that fuels phenotypic heterogeneity, we undertook single-cell transcriptomics analysis in triple-negative breast cancer (TNBC) to identify subpopulations in CSCs. We found a subpopulation of CSCs with ancestral features that is marked by FXYD domain-containing ion transport regulator 3 (FXYD3), a component of the Na+/K+ pump. Accordingly, FXYD3+ CSCs evolve and proliferate, while displaying traits of alveolar progenitors that are normally induced during pregnancy. Clinically, FXYD3+ CSCs were persistent during neoadjuvant chemotherapy, hence linking them to drug-tolerant persisters (DTPs) and identifying them as crucial therapeutic targets. Importantly, FXYD3+ CSCs were sensitive to senolytic Na+/K+ pump inhibitors, such as cardiac glycosides. Together, our data indicate that FXYD3+ CSCs with ancestral features are drivers of plasticity and chemoresistance in TNBC. Targeting the Na+/K+ pump could be an effective strategy to eliminate CSCs with ancestral and DTP features that could improve TNBC prognosis.

A novel f-divergence based generative adversarial imputation method for scRNA-seq data analysis

Comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data can enhance our understanding of cellular diversity and aid in the development of personalized therapies for individuals. The abundance of missing values, known as dropouts, makes the analysis of scRNA-seq data a challenging task. Most traditional methods made assumptions about specific distributions for missing values, which limit their capability to capture the intricacy of high-dimensional scRNA-seq data. Moreover, the imputation performance of traditional methods decreases with higher missing rates. We propose a novel f-divergence based generative adversarial imputation method, called sc-fGAIN, for the scRNA-seq data imputation. Our studies identify four f-divergence functions, namely cross-entropy, Kullback-Leibler (KL), reverse KL, and Jensen-Shannon, that can be effectively integrated with the generative adversarial imputation network to generate imputed values without any assumptions, and mathematically prove that the distribution of imputed data using sc-fGAIN algorithm is same as the distribution of original data. Real scRNA-seq data analysis has shown that, compared to many traditional methods, the imputed values generated by sc-fGAIN algorithm have a smaller root-mean-square error, and it is robust to varying missing rates, moreover, it can reduce imputation variability. The flexibility offered by the f-divergence allows the sc-fGAIN method to accommodate various types of data, making it a more universal approach for imputing missing values of scRNA-seq data.

Development of a versatile LCM-Seq method for spatial transcriptomics of fluorescently tagged cholinergic neuron populations

Single-cell transcriptomics are powerful tools to define neuronal cell types based on co-expressed gene clusters. Limited RNA input in these technologies necessarily compromises transcriptome coverage and accuracy of differential expression analysis. We propose that bulk RNA-Seq of neuronal pools defined by spatial position offers an alternative strategy to overcome these technical limitations. We report a laser-capture microdissection (LCM)-Seq method which allows deep transcriptome profiling of fluorescently tagged neuron populations isolated with LCM from histological sections of transgenic mice. Mild formaldehyde fixation of ZsGreen marker protein, LCM sampling of ∼300 pooled neurons, followed by RNA isolation, library preparation and RNA-Seq with methods optimized for nanogram amounts of moderately degraded RNA enabled us to detect ∼15,000 different transcripts in fluorescently labeled cholinergic neuron populations. The LCM-Seq approach showed excellent accuracy in quantitative studies, allowing us to detect 2891 transcripts expressed differentially between the spatially defined and clinically relevant cholinergic neuron populations of the dorsal caudate-putamen and medial septum. In summary, the LCM-Seq method we report in this study is a versatile, sensitive, and accurate bulk sequencing approach to study the transcriptome profile and differential gene expression of fluorescently tagged neuronal populations isolated from transgenic mice with high spatial precision.

A novel f -divergence based generative adversarial imputation method for scRNA-seq data analysis

Comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data can enhance our understanding of cellular diversity and aid in the development of personalized therapies for individuals. The abundance of missing values, known as dropouts, makes the analysis of scRNA-seq data a challenging task. Most traditional methods made assumptions about specific distributions for missing values, which limit their capability to capture the intricacy of high-dimensional scRNA-seq data. Moreover, the imputation performance of traditional methods decreases with higher missing rates. We propose a novel f -divergence based generative adversarial imputation method, called sc- f GAIN, for the scRNA-seq data imputation. Our studies identify four f -divergence functions, namely cross-entropy, Kullback-Leibler (KL), reverse KL, and Jensen-Shannon, that can be effectively integrated with the generative adversarial imputation network to generate imputed values without any assumptions, and mathematically prove that the distribution of imputed data using sc- f GAIN algorithm is same as the distribution of original data. Real scRNA-seq data analysis has shown that, compared to many traditional methods, the imputed values generated by sc- f GAIN algorithm have a smaller root-mean-square error, and it is robust to varying missing rates, moreover, it can reduce imputation bias. The flexibility offered by the f -divergence allows the sc- f GAIN method to accommodate various types of data, making it a more universal approach for imputing missing values of scRNA-seq data.

iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis.

RESULTS

We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance.

CONCLUSIONS

iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.

T Cell Clonal Analysis Using Single-cell RNA Sequencing and Reference Maps

T cells are endowed with T-cell antigen receptors (TCR) that give them the capacity to recognize specific antigens and mount antigen-specific adaptive immune responses. Because TCR sequences are distinct in each naïve T cell, they serve as molecular barcodes to track T cells with clonal relatedness and shared antigen specificity through proliferation, differentiation, and migration. Single-cell RNA sequencing provides coupled information of TCR sequence and transcriptional state in individual cells, enabling T-cell clonotype-specific analyses. In this protocol, we outline a computational workflow to perform T-cell states and clonal analysis from scRNA-seq data based on the R packages Seurat, ProjecTILs, and scRepertoire. Given a scRNA-seq T-cell dataset with TCR sequence information, cell states are automatically annotated by reference projection using the ProjecTILs method. TCR information is used to track individual clonotypes, assess their clonal expansion, proliferation rates, bias towards specific differentiation states, and the clonal overlap between T-cell subtypes. We provide fully reproducible R code to conduct these analyses and generate useful visualizations that can be adapted for the needs of the protocol user. Key features Computational analysis of paired scRNA-seq and scTCR-seq data Characterizing T-cell functional state by reference-based analysis using ProjecTILs Exploring T-cell clonal structure using scRepertoire Linking T-cell clonality to transcriptomic state to study relationships between clonal expansion and functional phenotype Graphical overview.

RNA recording in single bacterial cells using reprogrammed tracrRNAs

Capturing an individual cell's transcriptional history is a challenge exacerbated by the functional heterogeneity of cellular communities. Here, we leverage reprogrammed tracrRNAs (Rptrs) to record selected cellular transcripts as stored DNA edits in single living bacterial cells. Rptrs are designed to base pair with sensed transcripts, converting them into guide RNAs. The guide RNAs then direct a Cas9 base editor to target an introduced DNA target. The extent of base editing can then be read in the future by sequencing. We use this approach, called TIGER (transcribed RNAs inferred by genetically encoded records), to record heterologous and endogenous transcripts in individual bacterial cells. TIGER can quantify relative expression, distinguish single-nucleotide differences, record multiple transcripts simultaneously and read out single-cell phenomena. We further apply TIGER to record metabolic bet hedging and antibiotic resistance mobilization in Escherichia coli as well as host cell invasion by Salmonella. Through RNA recording, TIGER connects current cellular states with past transcriptional states to decipher complex cellular responses in single cells.

Best practices for single-cell analysis across modalities

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

Evaluating imputation methods for single-cell RNA-seq data

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) enables the high-throughput profiling of gene expression at the single-cell level. However, overwhelming dropouts within data may obscure meaningful biological signals. Various imputation methods have recently been developed to address this problem. Therefore, it is important to perform a systematic evaluation of different imputation algorithms.

RESULTS

In this study, we evaluated 11 of the most recent imputation methods on 12 real biological datasets from immunological studies and 4 simulated datasets. The performance of these methods was compared, based on numerical recovery, cell clustering and marker gene analysis. Most of the methods brought some benefits on numerical recovery. To some extent, the performance of imputation methods varied among protocols. In the cell clustering analysis, no method performed consistently well across all datasets. Some methods performed poorly on real datasets but excellent on simulated datasets. Surprisingly and importantly, some methods had a negative effect on cell clustering. In marker gene analysis, some methods identified potentially novel cell subsets. However, not all of the marker genes were successfully imputed in gene expression, suggesting that imputation challenges remain.

CONCLUSIONS

In summary, different imputation methods showed different effects on different datasets, suggesting that imputation may have dataset specificity. Our study reveals the benefits and limitations of various imputation methods and provides a data-driven guidance for scRNA-seq data analysis.

The Use of Single-Cell RNA-Sequencing and Spatial Transcriptomics in Understanding the Pathogenesis and Treatment of Skin Diseases

The development of multiomic profiling tools has rapidly expanded in recent years, along with their use in profiling skin tissues in various contexts, including dermatologic diseases. Among these tools, single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST) have emerged as widely adopted and powerful assays for elucidating key cellular components and their spatial arrangement within skin disease. In this paper, we review the recent biological insights gained from the use of scRNA-seq and ST and the advantages of combining both for profiling skin diseases, including aberrant wound healing, inflammatory skin diseases, and cancer. We discuss the role of scRNA-seq and ST in improving skin disease treatments and moving toward the goal of achieving precision medicine in dermatology, whereby patients can be optimally matched to treatments that maximize therapeutic response.

A benchmark of computational pipelines for single-cell histone modification data

BACKGROUND

Single-cell histone post translational modification (scHPTM) assays such as scCUT&Tag or scChIP-seq allow single-cell mapping of diverse epigenomic landscapes within complex tissues and are likely to unlock our understanding of various mechanisms involved in development or diseases. Running scHTPM experiments and analyzing the data produced remains challenging since few consensus guidelines currently exist regarding good practices for experimental design and data analysis pipelines.

RESULTS

We perform a computational benchmark to assess the impact of experimental parameters and data analysis pipelines on the ability of the cell representation to recapitulate known biological similarities. We run more than ten thousand experiments to systematically study the impact of coverage and number of cells, of the count matrix construction method, of feature selection and normalization, and of the dimension reduction algorithm used. This allows us to identify key experimental parameters and computational choices to obtain a good representation of single-cell HPTM data. We show in particular that the count matrix construction step has a strong influence on the quality of the representation and that using fixed-size bin counts outperforms annotation-based binning. Dimension reduction methods based on latent semantic indexing outperform others, and feature selection is detrimental, while keeping only high-quality cells has little influence on the final representation as long as enough cells are analyzed.

CONCLUSIONS

This benchmark provides a comprehensive study on how experimental parameters and computational choices affect the representation of single-cell HPTM data. We propose a series of recommendations regarding matrix construction, feature and cell selection, and dimensionality reduction algorithms.

ScInfoVAE: interpretable dimensional reduction of single cell transcription data with variational autoencoders and extended mutual information regularization

Single-cell RNA-sequencing (scRNA-seq) data can serve as a good indicator of cell-to-cell heterogeneity and can aid in the study of cell growth by identifying cell types. Recently, advances in Variational Autoencoder (VAE) have demonstrated their ability to learn robust feature representations for scRNA-seq. However, it has been observed that VAEs tend to ignore the latent variables when combined with a decoding distribution that is too flexible. In this paper, we introduce ScInfoVAE, a dimensional reduction method based on the mutual information variational autoencoder (InfoVAE), which can more effectively identify various cell types in scRNA-seq data of complex tissues. A joint InfoVAE deep model and zero-inflated negative binomial distributed model design based on ScInfoVAE reconstructs the objective function to noise scRNA-seq data and learn an efficient low-dimensional representation of it. We use ScInfoVAE to analyze the clustering performance of 15 real scRNA-seq datasets and demonstrate that our method provides high clustering performance. In addition, we use simulated data to investigate the interpretability of feature extraction, and visualization results show that the low-dimensional representation learned by ScInfoVAE retains local and global neighborhood structure data well. In addition, our model can significantly improve the quality of the variational posterior.

The circadian clock is disrupted in pancreatic cancer

Disruption of the circadian clock is linked to cancer development and progression. Establishing this connection has proven beneficial for understanding cancer pathogenesis, determining prognosis, and uncovering novel therapeutic targets. However, barriers to characterizing the circadian clock in human pancreas and human pancreatic cancer-one of the deadliest malignancies-have hindered an appreciation of its role in this cancer. Here, we employed normalized coefficient of variation (nCV) and clock correlation analysis in human population-level data to determine the functioning of the circadian clock in pancreas cancer and adjacent normal tissue. We found a substantially attenuated clock in the pancreatic cancer tissue. Then we exploited our existing mouse pancreatic transcriptome data to perform an analysis of the human normal and pancreas cancer samples using a machine learning method, cyclic ordering by periodic structure (CYCLOPS). Through CYCLOPS ordering, we confirmed the nCV and clock correlation findings of an intact circadian clock in normal pancreas with robust cycling of several core clock genes. However, in pancreas cancer, there was a loss of rhythmicity of many core clock genes with an inability to effectively order the cancer samples, providing substantive evidence of a dysregulated clock. The implications of clock disruption were further assessed with a Bmal1 knockout pancreas cancer model, which revealed that an arrhythmic clock caused accelerated cancer growth and worse survival, accompanied by chemoresistance and enrichment of key cancer-related pathways. These findings provide strong evidence for clock disruption in human pancreas cancer and demonstrate a link between circadian disruption and pancreas cancer progression.

Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease

In the past decade, single-cell technologies have proliferated and improved from their technically challenging beginnings to become common laboratory methods capable of determining the expression of thousands of genes in thousands of cells simultaneously. The field has progressed by taking the CNS as a primary research subject - the cellular complexity and multiplicity of neuronal cell types provide fertile ground for the increasing power of single-cell methods. Current single-cell RNA sequencing methods can quantify gene expression with sufficient accuracy to finely resolve even subtle differences between cell types and states, thus providing a great tool for studying the molecular and cellular repertoire of the CNS and its disorders. However, single-cell RNA sequencing requires the dissociation of tissue samples, which means that the interrelationships between cells are lost. Spatial transcriptomic methods bypass tissue dissociation and retain this spatial information, thereby allowing gene expression to be assessed across thousands of cells within the context of tissue structural organization. Here, we discuss how single-cell and spatially resolved transcriptomics have been contributing to unravelling the pathomechanisms underlying brain disorders. We focus on three areas where we feel these new technologies have provided particularly useful insights: selective neuronal vulnerability, neuroimmune dysfunction and cell-type-specific treatment response. We also discuss the limitations and future directions of single-cell and spatial RNA sequencing technologies.

Expression quantitative trait locus studies in the era of single-cell omics

Genome-wide association studies have revealed that the regulation of gene expression bridges genetic variants and complex phenotypes. Profiling of the bulk transcriptome coupled with linkage analysis (expression quantitative trait locus (eQTL) mapping) has advanced our understanding of the relationship between genetic variants and gene regulation in the context of complex phenotypes. However, bulk transcriptomics has inherited limitations as the regulation of gene expression tends to be cell-type-specific. The advent of single-cell RNA-seq technology now enables the identification of the cell-type-specific regulation of gene expression through a single-cell eQTL (sc-eQTL). In this review, we first provide an overview of sc-eQTL studies, including data processing and the mapping procedure of the sc-eQTL. We then discuss the benefits and limitations of sc-eQTL analyses. Finally, we present an overview of the current and future applications of sc-eQTL discoveries.

Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single cell RNA-sequencing datasets

Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding the pathologies of diseases. However, several experimental and computational challenges remain in developing and implementing transcriptomics-based deconvolution approaches, especially those using a single cell/nuclei RNA-seq reference atlas, which are becoming rapidly available across many tissues. Notably, deconvolution algorithms are frequently developed using samples from tissues with similar cell sizes. However, brain tissue or immune cell populations have cell types with substantially different cell sizes, total mRNA expression, and transcriptional activity. When existing deconvolution approaches are applied to these tissues, these systematic differences in cell sizes and transcriptomic activity confound accurate cell proportion estimates and instead may quantify total mRNA content. Furthermore, there is a lack of standard reference atlases and computational approaches to facilitate integrative analyses, including not only bulk and single cell/nuclei RNA-seq data, but also new data modalities from spatial -omic or imaging approaches. New multi-assay datasets need to be collected with orthogonal data types generated from the same tissue block and the same individual, to serve as a "gold standard" for evaluating new and existing deconvolution methods. Below, we discuss these key challenges and how they can be addressed with the acquisition of new datasets and approaches to analysis.

scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing

BACKGROUND

Single-cell RNA sequencing is a state-of-the-art technology to understand gene expression in complex tissues. With the growing amount of data being generated, the standardization and automation of data analysis are critical to generating hypotheses and discovering biological insights.

RESULTS

Here, we present scRNASequest, a semi-automated single-cell RNA-seq (scRNA-seq) data analysis workflow which allows (1) preprocessing from raw UMI count data, (2) harmonization by one or multiple methods, (3) reference-dataset-based cell type label transfer and embedding projection, (4) multi-sample, multi-condition single-cell level differential gene expression analysis, and (5) seamless integration with cellxgene VIP for visualization and with CellDepot for data hosting and sharing by generating compatible h5ad files.

CONCLUSIONS

We developed scRNASequest, an end-to-end pipeline for single-cell RNA-seq data analysis, visualization, and publishing. The source code under MIT open-source license is provided at https://github.com/interactivereport/scRNASequest . We also prepared a bookdown tutorial for the installation and detailed usage of the pipeline: https://interactivereport.github.io/scRNAsequest/tutorial/docs/ . Users have the option to run it on a local computer with a Linux/Unix system including MacOS, or interact with SGE/Slurm schedulers on high-performance computing (HPC) clusters.

The shaky foundations of simulating single-cell RNA sequencing data

BACKGROUND

With the emergence of hundreds of single-cell RNA-sequencing (scRNA-seq) datasets, the number of computational tools to analyze aspects of the generated data has grown rapidly. As a result, there is a recurring need to demonstrate whether newly developed methods are truly performant-on their own as well as in comparison to existing tools. Benchmark studies aim to consolidate the space of available methods for a given task and often use simulated data that provide a ground truth for evaluations, thus demanding a high quality standard results credible and transferable to real data.

RESULTS

Here, we evaluated methods for synthetic scRNA-seq data generation in their ability to mimic experimental data. Besides comparing gene- and cell-level quality control summaries in both one- and two-dimensional settings, we further quantified these at the batch- and cluster-level. Secondly, we investigate the effect of simulators on clustering and batch correction method comparisons, and, thirdly, which and to what extent quality control summaries can capture reference-simulation similarity.

CONCLUSIONS

Our results suggest that most simulators are unable to accommodate complex designs without introducing artificial effects, they yield over-optimistic performance of integration and potentially unreliable ranking of clustering methods, and it is generally unknown which summaries are important to ensure effective simulation-based method comparisons.

IBRAP: integrated benchmarking single-cell RNA-sequencing analytical pipeline

Single-cell ribonucleic acid (RNA)-sequencing (scRNA-seq) is a powerful tool to study cellular heterogeneity. The high dimensional data generated from this technology are complex and require specialized expertise for analysis and interpretation. The core of scRNA-seq data analysis contains several key analytical steps, which include pre-processing, quality control, normalization, dimensionality reduction, integration and clustering. Each step often has many algorithms developed with varied underlying assumptions and implications. With such a diverse choice of tools available, benchmarking analyses have compared their performances and demonstrated that tools operate differentially according to the data types and complexity. Here, we present Integrated Benchmarking scRNA-seq Analytical Pipeline (IBRAP), which contains a suite of analytical components that can be interchanged throughout the pipeline alongside multiple benchmarking metrics that enable users to compare results and determine the optimal pipeline combinations for their data. We apply IBRAP to single- and multi-sample integration analysis using primary pancreatic tissue, cancer cell line and simulated data accompanied with ground truth cell labels, demonstrating the interchangeable and benchmarking functionality of IBRAP. Our results confirm that the optimal pipelines are dependent on individual samples and studies, further supporting the rationale and necessity of our tool. We then compare reference-based cell annotation with unsupervised analysis, both included in IBRAP, and demonstrate the superiority of the reference-based method in identifying robust major and minor cell types. Thus, IBRAP presents a valuable tool to integrate multiple samples and studies to create reference maps of normal and diseased tissues, facilitating novel biological discovery using the vast volume of scRNA-seq data available.

Generation of Schwann cell derived melanocytes from hPSCs identifies pro-metastatic factors in melanoma

The neural crest (NC) is highly multipotent and generates diverse lineages in the developing embryo. However, spatiotemporally distinct NC populations display differences in fate potential, such as increased gliogenic and parasympathetic potential from later migrating, nerve-associated Schwann cell precursors (SCPs). Interestingly, while melanogenic potential is shared by both early migrating NC and SCPs, differences in melanocyte identity resulting from differentiation through these temporally distinct progenitors have not been determined. Here, we leverage a human pluripotent stem cell (hPSC) model of NC temporal patterning to comprehensively characterize human NC heterogeneity, fate bias, and lineage development. We captured the transition of NC differentiation between temporally and transcriptionally distinct melanogenic progenitors and identified modules of candidate transcription factor and signaling activity associated with this transition. For the first time, we established a protocol for the directed differentiation of melanocytes from hPSCs through a SCP intermediate, termed trajectory 2 (T2) melanocytes. Leveraging an existing protocol for differentiating early NC-derived melanocytes, termed trajectory 1 (T1), we performed the first comprehensive comparison of transcriptional and functional differences between these distinct melanocyte populations, revealing differences in pigmentation and unique expression of transcription factors, ligands, receptors and surface markers. We found a significant link between the T2 melanocyte transcriptional signature and decreased survival in melanoma patients in the cancer genome atlas (TCGA). We performed an in vivo CRISPRi screen of T1 and T2 melanocyte signature genes in a human melanoma cell line and discovered several T2-specific markers that promote lung metastasis in mice. We further demonstrated that one of these factors, SNRPB, regulates the splicing of transcripts involved in metastasis relevant functions such as migration, cell adhesion and proliferation. Overall, this study identifies distinct developmental trajectories as a source of diversity in melanocytes and implicates the unique molecular signature of SCP-derived melanocytes in metastatic melanoma.

Single-cell RNA sequencing reveals homogeneous transcriptome patterns and low variance in a suspension CHO-K1 and an adherent HEK293FT cell line in culture conditions

Recombinant mammalian host cell lines, in particular CHO and HEK293 cells, are used for the industrial production of therapeutic proteins. Despite their well-known genomic instability, the control mechanisms that enable cells to respond to changes in the environmental conditions are not yet fully understood, nor do we have a good understanding of the factors that lead to phenotypic shifts in long-term cultures. A contributing factor could be inherent diversity in transcriptomes within a population. In this study, we used a full-length coverage single-cell RNA sequencing (scRNA-seq) approach to investigate and compare cell-to-cell variability and the impact of standardized and homogenous culture conditions on the diversity of individual cell transcriptomes, comparing suspension CHO-K1 and adherent HEK293FT cells. Our data showed a critical batch effect from the sequencing of four 96-well plates of CHO-K1 single cells stored for different periods of time, which was and may be therefore identified as a technical variable to consider in experimental planning. Besides, in an artificial and controlled culture environment such as used in routine cell culture technology, the gene expression pattern of a given population does not reveal any marker gene capable to disclose relevant cell population substructures, both for CHO-K1 cells and for HEK293FT cells. The variation observed is primarily driven by the cell cycle.

Cell-type deconvolution of bulk RNA-Seq from kidney using opensource bioinformatic tools

Traditional bulk RNA-Seq pipelines do not assess cell-type composition within heterogeneous tissues. Therefore, it is difficult to determine whether conflicting findings among samples or datasets are the result of biological differences or technical differences due to variation in sample collections. This report provides a user-friendly, open source method to assess cell-type composition in bulk RNA-Seq datasets for heterogeneous tissues using published single cell (sc)RNA-Seq data as a reference. As an example, we apply the method to analysis of kidney cortex bulk RNA-Seq data from female (N=8) and male (N=9) baboons to assess whether observed transcriptome sex differences are biological or technical, i.e., variation due to ultrasound guided biopsy collections. We found cell-type composition was not statistically different in female versus male transcriptomes based on expression of 274 kidney cell-type specific transcripts, indicating differences in gene expression are not due to sampling differences. This method of cell-type composition analysis is recommended for providing rigor in analysis of bulk RNA-Seq datasets from complex tissues. It is clear that with reduced costs, more analyses will be done using scRNA-Seq; however, the approach described here is relevant for data mining and meta analyses of the thousands of bulk RNA-Seq data archived in the NCBI GEO public database.

Modeling disrupted synapse formation in wolfram syndrome using hESCs-derived neural cells and cerebral organoids identifies Riluzole as a therapeutic molecule

Dysregulated neurite outgrowth and synapse formation underlie many psychiatric disorders, which are also manifested by wolfram syndrome (WS). Whether and how the causative gene WFS1 deficiency affects synapse formation remain elusive. By mirroring human brain development with cerebral organoids, WFS1-deficient cerebral organoids not only recapitulate the neuronal loss in WS patients, but also exhibit significantly impaired synapse formation and function associated with reduced astrocytes. WFS1 deficiency in neurons autonomously delays neuronal differentiation with altered expressions of genes associated with psychiatric disorders, and impairs neurite outgrowth and synapse formation with elevated cytosolic calcium. Intriguingly, WFS1 deficiency in astrocytes decreases the expression of glutamate transporter EAAT2 by NF-κB activation and induces excessive glutamate. When co-cultured with wildtype neurons, WFS1-deficient astrocytes lead to impaired neurite outgrowth and increased cytosolic calcium in neurons. Importantly, disrupted synapse formation and function in WFS1-deficient cerebral organoids and impaired neurite outgrowth affected by WFS1-deficient astrocytes are efficiently reversed with Riluzole treatment, by restoring EAAT2 expression in astrocytes. Furthermore, Riluzole rescues the depressive-like behavior in the forced swimming test and the impaired recognition and spatial memory in the novel object test and water maze test in Wfs1 conditional knockout mice. Altogether, our study provides novel insights into how WFS1 deficiency affects synapse formation and function, and offers a strategy to treat this disease.

Longitudinal liver sampling in patients with chronic hepatitis B starting antiviral therapy reveals hepatotoxic CD8+ T cells

Accumulation of activated immune cells results in nonspecific hepatocyte killing in chronic hepatitis B (CHB), leading to fibrosis and cirrhosis. This study aims to understand the underlying mechanisms in humans and to define whether these are driven by widespread activation or a subpopulation of immune cells. We enrolled CHB patients with active liver damage to receive antiviral therapy and performed longitudinal liver sampling using fine-needle aspiration to investigate mechanisms of CHB pathogenesis in the human liver. Single-cell sequencing of total liver cells revealed a distinct liver-resident, polyclonal CD8+ T cell population that was enriched at baseline and displayed a highly activated immune signature during liver damage. Cytokine combinations, identified by in silico prediction of ligand-receptor interaction, induced the activated phenotype in healthy liver CD8+ T cells, resulting in nonspecific Fas ligand-mediated killing of target cells. These results define a CD8+ T cell population in the human liver that can drive pathogenesis and a key pathway involved in their function in CHB patients.

MS-DAP Platform for Downstream Data Analysis of Label-Free Proteomics Uncovers Optimal Workflows in Benchmark Data Sets and Increased Sensitivity in Analysis of Alzheimer's Biomarker Data

In the rapidly moving proteomics field, a diverse patchwork of data analysis pipelines and algorithms for data normalization and differential expression analysis is used by the community. We generated a mass spectrometry downstream analysis pipeline (MS-DAP) that integrates both popular and recently developed algorithms for normalization and statistical analyses. Additional algorithms can be easily added in the future as plugins. MS-DAP is open-source and facilitates transparent and reproducible proteome science by generating extensive data visualizations and quality reporting, provided as standardized PDF reports. Second, we performed a systematic evaluation of methods for normalization and statistical analysis on a large variety of data sets, including additional data generated in this study, which revealed key differences. Commonly used approaches for differential testing based on moderated t-statistics were consistently outperformed by more recent statistical models, all integrated in MS-DAP. Third, we introduced a novel normalization algorithm that rescues deficiencies observed in commonly used normalization methods. Finally, we used the MS-DAP platform to reanalyze a recently published large-scale proteomics data set of CSF from AD patients. This revealed increased sensitivity, resulting in additional significant target proteins which improved overlap with results reported in related studies and includes a large set of new potential AD biomarkers in addition to previously reported.

What has single-cell transcriptomics taught us about long non-coding RNAs in the ventricular-subventricular zone?

Long non-coding RNA (lncRNA) function is mediated by the process of transcription or through transcript-dependent associations with proteins or nucleic acids to control gene regulatory networks. Many lncRNAs are transcribed in the ventricular-subventricular zone (V-SVZ), a postnatal neural stem cell niche. lncRNAs in the V-SVZ are implicated in neurodevelopmental disorders, cancer, and brain disease, but their functions are poorly understood. V-SVZ neurogenesis capacity declines with age due to stem cell depletion and resistance to neural stem cell activation. Here we analyzed V-SVZ transcriptomics by pooling current single-cell RNA-seq data. They showed consistent lncRNA expression during stem cell activation, lineage progression, and aging. In conjunction with epigenetic and genetic data, we predicted V-SVZ lncRNAs that regulate stem cell activation and differentiation. Some of the lncRNAs validate known epigenetic mechanisms, but most remain uninvestigated. Our analysis points to several lncRNAs that likely participate in key aspects of V-SVZ stem cell activation and neurogenesis in health and disease.

Comparison of scRNA-seq data analysis method combinations

Single-cell ribonucleic acid (RNA)-sequencing (scRNA-seq) data analysis refers to the use of appropriate methods to analyze the dataset generated by RNA-sequencing performed on the single-cell transcriptome. It usually contains three steps: normalization to eliminate the technical noise, dimensionality reduction to facilitate visual understanding and data compression and clustering to divide the data into several similarity-based clusters. In addition, the gene expression data contain a large number of zero counts. These zero counts are considered relevant to random dropout events induced by multiple factors in the sequencing experiments, such as low RNA input, and the stochastic nature of the gene expression pattern at the single-cell level. The zero counts can be eliminated only through the analysis of the scRNA-seq data, and although many methods have been proposed to this end, there is still a lack of research on the combined effect of existing methods. In this paper, we summarize the two kinds of normalization, two kinds of dimension reduction and three kinds of clustering methods widely used in the current mainstream scRNA-seq data analysis. Furthermore, we propose to combine these methods into 12 technology combinations, each with a whole set of scRNA-seq data analysis processes. We evaluated the proposed combinations using Goolam, a publicly available scRNA-seq, by comparing the final clustering results and found the most suitable collection scheme of these classic methods. Our results showed that using appropriate technology combinations can improve the efficiency and accuracy of the scRNA-seq data analysis. The combinations not only satisfy the basic requirements of noise reduction, dimension reduction and cell clustering but also ensure preserving the heterogeneity of cells in downstream analysis. The dataset, Goolam, used in the study can be obtained from the ArrayExpress database under the accession number E-MTAB-3321.

Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases

Neurodegenerative diseases affect millions of people worldwide and there are currently no cures. Two types of common neurodegenerative diseases are Alzheimer's (AD) and Parkinson's disease (PD). Single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq) have become powerful tools to elucidate the inherent complexity and dynamics of the central nervous system at cellular resolution. This technology has allowed the identification of cell types and states, providing new insights into cellular susceptibilities and molecular mechanisms underlying neurodegenerative conditions. Exciting research using high throughput scRNA-seq and snRNA-seq technologies to study AD and PD is emerging. Herein we review the recent progress in understanding these neurodegenerative diseases using these state-of-the-art technologies. We discuss the fundamental principles and implications of single-cell sequencing of the human brain. Moreover, we review some examples of the computational and analytical tools required to interpret the extensive amount of data generated from these assays. We conclude by highlighting challenges and limitations in the application of these technologies in the study of AD and PD.

From multitude to singularity: An up-to-date overview of scRNA-seq data generation and analysis

Single cell RNA sequencing (scRNA-seq) is today a common and powerful technology in biomedical research settings, allowing to profile the whole transcriptome of a very large number of individual cells and reveal the heterogeneity of complex clinical samples. Traditionally, cells have been classified by their morphology or by expression of certain proteins in functionally distinct settings. The advent of next generation sequencing (NGS) technologies paved the way for the detection and quantitative analysis of cellular content. In this context, transcriptome quantification techniques made their advent, starting from the bulk RNA sequencing, unable to dissect the heterogeneity of a sample, and moving to the first single cell techniques capable of analyzing a small number of cells (1-100), arriving at the current single cell techniques able to generate hundreds of thousands of cells. As experimental protocols have improved rapidly, computational workflows for processing the data have also been refined, opening up to novel methods capable of scaling computational times more favorably with the dataset size and making scRNA-seq much better suited for biomedical research. In this perspective, we will highlight the key technological and computational developments which have enabled the analysis of this growing data, making the scRNA-seq a handy tool in clinical applications.

Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) enables researchers to quantify transcriptomes of thousands of cells simultaneously and study transcriptomic changes between cells. scRNA-seq datasets increasingly include multisubject, multicondition experiments to investigate cell-type-specific differential states (DS) between conditions. This can be performed by first identifying the cell types in all the subjects and then by performing a DS analysis between the conditions within each cell type. Naïve single-cell DS analysis methods that treat cells statistically independent are subject to false positives in the presence of variation between biological replicates, an issue known as the pseudoreplicate bias. While several methods have already been introduced to carry out the statistical testing in multisubject scRNA-seq analysis, comparisons that include all these methods are currently lacking. Here, we performed a comprehensive comparison of 18 methods for the identification of DS changes between conditions from multisubject scRNA-seq data. Our results suggest that the pseudobulk methods performed generally best. Both pseudobulks and mixed models that model the subjects as a random effect were superior compared with the naïve single-cell methods that do not model the subjects in any way. While the naïve models achieved higher sensitivity than the pseudobulk methods and the mixed models, they were subject to a high number of false positives. In addition, accounting for subjects through latent variable modeling did not improve the performance of the naïve methods.

Meso-seq for in-depth transcriptomics in ultra-low amounts of FACS-purified neuronal nuclei

Profiling of gene expression in sparse populations of genetically defined neurons is essential for dissecting the molecular mechanisms that control the development and plasticity of neural circuits. However, current transcriptomic approaches are ill suited for detailed mechanistic studies in sparse neuronal populations, as they either are technically complex and relatively expensive (e.g., single-cell RNA sequencing [RNA-seq]) or require large amounts of input material (e.g., traditional bulk RNA-seq). Thus, we established Meso-seq, a meso-scale protocol for identifying more than 10,000 robustly expressed genes in as little as 50 FACS-sorted neuronal nuclei. We demonstrate that Meso-seq works well for multiple neuroscience applications, including transcriptomics in antibody-labeled cortical neurons in mice and non-human primates, analyses of experience-regulated gene programs, and RNA-seq from visual cortex neurons labeled ultra-sparsely with viruses. Given its simplicity, robustness, and relatively low costs, Meso-seq is well suited for molecular-mechanistic studies in ultra-sparse neuronal populations in the brain.

Dual ontogeny of disease-associated microglia and disease inflammatory macrophages in aging and neurodegeneration

Brain macrophage populations include parenchymal microglia, border-associated macrophages, and recruited monocyte-derived cells; together, they control brain development and homeostasis but are also implicated in aging pathogenesis and neurodegeneration. The phenotypes, localization, and functions of each population in different contexts have yet to be resolved. We generated a murine brain myeloid scRNA-seq integration to systematically delineate brain macrophage populations. We show that the previously identified disease-associated microglia (DAM) population detected in murine Alzheimer's disease models actually comprises two ontogenetically and functionally distinct cell lineages: embryonically derived triggering receptor expressed on myeloid cells 2 (TREM2)-dependent DAM expressing a neuroprotective signature and monocyte-derived TREM2-expressing disease inflammatory macrophages (DIMs) accumulating in the brain during aging. These two distinct populations appear to also be conserved in the human brain. Herein, we generate an ontogeny-resolved model of brain myeloid cell heterogeneity in development, homeostasis, and disease and identify cellular targets for the treatment of neurodegeneration.

RNA sequencing reveals dynamic expression of spleen lncRNAs and mRNAs in Beagle dogs infected by Toxocara canis

BACKGROUND

Toxocara canis is a cosmopolitan parasite with a significant adverse impact on the health of humans and animals. The spleen is a major immune organ that plays essential roles in protecting the host against various infections. However, its role in T. canis infection has not received much attention.

METHODS

We performed sequencing-based transcriptome profiling of long noncoding RNA (lncRNA) and messenger RNA (mRNA) expression in the spleen of Beagle puppies at 24 h post-infection (hpi), 96 hpi and 36 days post-infection (dpi). Deep sequencing of RNAs isolated from the spleen of six puppies (three infected and three control) at each time point after infection was conducted.

RESULTS

Our analysis revealed 614 differentially expressed (DE) lncRNAs and 262 DEmRNAs at 24 hpi; 726 DElncRNAs and 878 DEmRNAs at 96 hpi; and 686 DElncRNAs and 504 DEmRNAs at 36 dpi. Of those, 35 DElncRNA transcripts and 11 DEmRNAs were detected at all three time points post-infection. Many DE genes were enriched in immune response, such as ifit1, ifit2 and rorc. Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that some genes (e.g. prkx and tnfrsf11a) were involved in the T cell receptor signaling pathway, calcium signaling pathway, Ras signaling pathway and NF-κB signaling pathway.

CONCLUSIONS

The findings of this study show marked alterations in the expression profiles of spleen lncRNAs and mRNAs, with possible implications in the pathophysiology of toxocariasis.

Unveiling of brain transcriptome of masked palm civet (Paguma larvata) with chronic infection of Toxoplasma gondii

BACKGROUND

The aim of this study was to gain an understanding of the transcriptomic changes that occur in a wild species when infected with Toxoplasma gondii. The masked palm civet, an artifically domesticated animal, was used as the model of a wild species. Transcriptome analysis was used to study alterations in gene expression in the domesticated masked palm civet after chronic infection with T. gondii.

METHODS

Masked palm civets were infected with 10⁵ T. gondii cysts and their brain tissue collected after 4 months of infection. RNA sequencing (RNA-Seq) was used to gain insight into the spectrum of genes that were differentially expressed due to infection. Quantitative reverse-transcription PCR (qRT-PCR) was also used to validate the level of expression of a set of differentially expressed genes (DEGs) obtained by sequencing.

RESULTS

DEGs were screened from the sequencing results and analyzed. A total of 2808 DEGs were detected, of which 860 were upregulated and 1948 were downregulated. RNA-Seq results were confirmed by qRT-PCR. DEGs were mainly enriched in cellular process and metabolic process based on gene ontology enrichment analysis. Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that transcriptional changes in the brain of infected masked palm civets evolved over the course of infection and that DEGs were mainly enriched in the signal transduction, immune system processes, transport and catabolic pathways. Finally, 10 essential driving genes were identified from the immune signaling pathway.

CONCLUSIONS

This study revealed novel host genes which may provide target genes for the development of new therapeutics and detection methods for T. gondii infection in wild animals.

The accuracy of absolute differential abundance analysis from relative count data

Concerns have been raised about the use of relative abundance data derived from next generation sequencing as a proxy for absolute abundances. For example, in the differential abundance setting, compositional effects in relative abundance data may give rise to spurious differences (false positives) when considered from the absolute perspective. In practice however, relative abundances are often transformed by renormalization strategies intended to compensate for these effects and the scope of the practical problem remains unclear. We used simulated data to explore the consistency of differential abundance calling on renormalized relative abundances versus absolute abundances and find that, while overall consistency is high, with a median sensitivity (true positive rates) of 0.91 and specificity (1—false positive rates) of 0.89, consistency can be much lower where there is widespread change in the abundance of features across conditions. We confirm these findings on a large number of real data sets drawn from 16S metabarcoding, expression array, bulk RNA-seq, and single-cell RNA-seq experiments, where data sets with the greatest change between experimental conditions are also those with the highest false positive rates. Finally, we evaluate the predictive utility of summary features of relative abundance data themselves. Estimates of sparsity and the prevalence of feature-level change in relative abundance data give reasonable predictions of discrepancy in differential abundance calling in simulated data and can provide useful bounds for worst-case outcomes in real data.

Molecular sequence counting is a near-ubituiqous method for taking “snapshots” of the state of biological systems at the molecular level and is applied to problems as diverse as profiling gene expression and characterizing bacterial community composition. However, concerns exist about the interpretation of these data, given they are relative counts. In particular some feature-level differences between samples may be technical, not biological, stemming from compositional effects. Here, we quantify the accuracy of estimates of sample-sample differences made from relative versus “absolute” molecular count data, using a comprehensive simulation strategy and published experimental data. We find the accuracy of difference estimation is high in at least 50% of simulated and real data sets but that low accuracy outcomes are far from rare. Further, we observe similar numbers of these low accuracy cases when using any of several popular methods for estimating differences in biological count data. Our results support the use of complementary reference measures of absolute abundance (like RNA spike-ins) for normalizing next-generation sequencing data. We briefly validate the use of these reference quantities and of stringent effect size thresholds as strategies for mitigating interpretational problems with relative count data.

Transcriptome profiling of kisspeptin neurons from the mouse arcuate nucleus reveals new mechanisms in estrogenic control of fertility

Kisspeptin neurons in the mediobasal hypothalamus (MBH) are critical targets of ovarian estrogen feedback regulating mammalian fertility. To reveal molecular mechanisms underlying this signaling, we thoroughly characterized the estrogen-regulated transcriptome of kisspeptin cells from ovariectomized transgenic mice substituted with 17β-estradiol or vehicle. MBH kisspeptin neurons were harvested using laser-capture microdissection, pooled, and subjected to RNA sequencing. Estrogen treatment significantly (p.adj. < 0.05) up-regulated 1,190 and down-regulated 1,139 transcripts, including transcription factors, neuropeptides, ribosomal and mitochondrial proteins, ion channels, transporters, receptors, and regulatory RNAs. Reduced expression of the excitatory serotonin receptor-4 transcript (Htr4) diminished kisspeptin neuron responsiveness to serotonergic stimulation. Many estrogen-regulated transcripts have been implicated in puberty/fertility disorders. Patients (n = 337) with congenital hypogonadotropic hypogonadism (CHH) showed enrichment of rare variants in putative CHH-candidate genes (e.g., LRP1B, CACNA1G, FNDC3A). Comprehensive characterization of the estrogen-dependent kisspeptin neuron transcriptome sheds light on the molecular mechanisms of ovary-brain communication and informs genetic research on human fertility disorders.

Cardiomyocyte Cell-Cycle Regulation in Neonatal Large Mammals: Single Nucleus RNA-Sequencing Data Analysis via an Artificial-Intelligence–Based Pipeline

Adult mammalian cardiomyocytes have very limited capacity to proliferate and repair the myocardial infarction. However, when apical resection (AR) was performed in pig hearts on postnatal day (P) 1 (AR_P1) and acute myocardial infarction (MI) was induced on P28 (MI_P28), the animals recovered with no evidence of myocardial scarring or decline in contractile performance. Furthermore, the repair process appeared to be driven by cardiomyocyte proliferation, but the regulatory molecules that govern the AR_P1-induced enhancement of myocardial recovery remain unclear. Single-nucleus RNA sequencing (snRNA-seq) data collected from fetal pig hearts and the hearts of pigs that underwent AR_P1, MI_P28, both AR_P1 and MI, or neither myocardial injury were evaluated via autoencoder, cluster analysis, sparse learning, and semisupervised learning. Ten clusters of cardiomyocytes (CM1–CM10) were identified across all experimental groups and time points. CM1 was only observed in AR_P1 hearts on P28 and was enriched for the expression of T-box transcription factors 5 and 20 (TBX5 and TBX20, respectively), Erb-B2 receptor tyrosine kinase 4 (ERBB4), and G Protein-Coupled Receptor Kinase 5 (GRK5), as well as genes associated with the proliferation and growth of cardiac muscle. CM1 cardiomyocytes also highly expressed genes for glycolysis while lowly expressed genes for adrenergic signaling, which suggested that CM1 were immature cardiomyocytes. Thus, we have identified a cluster of cardiomyocytes, CM1, in neonatal pig hearts that appeared to be generated in response to AR injury on P1 and may have been primed for activation of CM cell-cycle activation and proliferation by the upregulation of TBX5, TBX20, ERBB4, and GRK5.

Integration of computational analysis and spatial transcriptomics in single-cell study

Recent advances of single-cell transcriptomics technologies and allied computational methodologies have revolutionized molecular cell biology. Meanwhile, pioneering explorations in spatial transcriptomics have opened avenues to address fundamental biological questions in health and diseases. Here, we review the technical attributes of single-cell RNA sequencing and spatial transcriptomics, and the core concepts of computational data analysis. We further highlight the challenges in the application of data integration methodologies and the interpretation of the biological context of the findings.