Single-cell Sequencing Techniques Research Articles

Multiplexed single-cell transcriptome analysis reveals molecular characteristics of monkey pluripotent stem cell lines.

Efforts have been made to establish various human pluripotent stem cell lines. However, such methods have not yet been duplicated in non-human primate cells. Here, we introduce a multiplexed single-cell sequencing technique to profile the molecular features of monkey pluripotent stem cells in published culture conditions. The results demonstrate suboptimized maintenance of pluripotency and show that the selected signaling pathways for resetting human stem cells can also be interpreted for establishing monkey cell lines. Overall, this work legitimates the translation of novel human cell line culture conditions to monkey cells and provides guidance for exploring chemical cocktails for monkey stem cell line derivation.

GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning.

Recent advances in single-cell sequencing techniques have enabled gene expression profiling of individual cells in tissue samples so that it can accelerate biomedical research to develop novel therapeutic methods and effective drugs for complex disease. The typical first step in the downstream analysis pipeline is classifying cell types through accurate single-cell clustering algorithms. Here, we describe a novel single-cell clustering algorithm, called GRACE (GRaph Autoencoder based single-cell Clustering through Ensemble similarity larning), that can yield highly consistent groups of cells. We construct the cell-to-cell similarity network through the ensemble similarity learning framework, and employ a low-dimensional vector representation for each cell through a graph autoencoder. Through performance assessments using real-world single-cell sequencing datasets, we show that the proposed method can yield accurate single-cell clustering results by achieving higher assessment metric scores.

CD74: a prospective marker for reactive microglia?

CD74: a prospective marker for reactive microglia?

Effects of Univariate Stiffness and Degradation of DNA Hydrogels on the Transcriptomics of Neural Progenitor Cells.

Mechanical interactions between cells and extracellular matrix (ECM) are critical for stem cell fate decision. Synthetic models of ECM, such as hydrogels, can be used to precisely manipulate the mechanical properties of the cell niche and investigate how mechanical signals regulate the cell behavior. However, it has long been a great challenge to tune solely the ECM-mimic hydrogels' mechanical signals since altering the mechanical properties of most materials is usually accompanied by chemical and topological changes. Here, we employ DNA and its enantiomers to prepare a series of hydrogels with univariate stiffness regulation, which enables a precise interpretation of the fate decision of neural progenitor cells (NPCs) in a three-dimensional environment. Using single-cell RNA sequencing techniques, Monocle pseudotime trajectory and CellphoneDB analysis, we demonstrate that the stiffness of the hydrogel alone does not influence the differentiation of NPCs, but the degradation of the hydrogel that enhances cell-cell interactions is possibly the main reason. We also find that ECM remodeling facilitates cells to sense mechanical stimuli.

Abstract 2075: Highly customizable multi-sample single cell RNA-Seq pipeline on the CGC

Abstract Single-cell (sc) transcriptomics has revolutionized our understanding of the biological characteristics and dynamics of cancer development. It can help us identify rare cell subpopulations and understand mechanisms associated with tumor genesis, progression, and response to therapy. The most important step in the analyses of any scRNA-seq dataset is subpopulation identification, usually performed via unsupervised clustering, followed by gene marker identification. We created a highly customizable workflow for sc data analysis, implemented in Common Workflow Language (CWL) on the Cancer Genomics Cloud (CGC) platform. The NCI-funded CGC platform, powered by Seven Bridges, provides a collaborative cloud base computation infrastructure that collocates computation, over 750 bioinformatics workflows, and 3+ PB data to researchers, making the analysis of large datasets accessible from any environment. The “Multi-Sample Clustering and Gene Marker Identification with Seurat 4.1.0” workflow comprises the following steps: Loading scRNA-seq Expression Datasets, Quality Control and Preprocessing, and Clustering and Identification of Gene Markers. Our solution supports gene-cell count matrices generated by several commonly used quantifiers (for example, Cell Ranger counts, Salmon Alevin, Kallisto BUStools, STAR) from single or multiple sc datasets from different batches, as well as single or multiple single-cell samples combined in a single SingleCellExperiment object. The versatility of the pipeline is obtained using several implemented options in each of the steps. Quality control can be performed manually or automatically using several options for normalization (LogNormalize, Deconvolution, SCnorm and Linnorm) and for batch effect correction (Seurat and Harmony). For clustering, the pipeline uses Seurat's graph-based approach, with options for different clustering resolutions. After performing identification of gene markers for each cluster, a researcher can test differential expression using various packages including wilcox, bimod, roc, and DESeq2. Here, we demonstrate the application of this workflow to a typical sc analysis, by processing an open access dataset of 61k cells isolated from embryonal mouse pons and forebrain, two major brain tumor locations. We used different clustering resolutions to achieve different degrees of granularity and identified cluster-specific marker genes used to identify vulnerable cell populations. To enable researchers to use this analysis as a guideline, we made this analysis available as a public project. Further development of single-cell sequencing techniques will undoubtedly improve our understanding of tumor biology and highlight promising drug targets. CGC’s cloud base computation infrastructure, along with numerous available cancer datasets and easy-to-use single-cell data processing workflows, among others, will be instrumental in this process. Citation Format: Nevena Vukojicic, Aleksandar Danicic, Zelia Worman, Rowan Beck, Dalibor Veljkovic, Marko Matic, Jack DiGiovanna, Brandi Davis-Dusenbery. Highly customizable multi-sample single cell RNA-Seq pipeline on the CGC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2075.

Data-driven microscopy allows for automated context-specific acquisition of high-fidelity image data

Light microscopy is a powerful single-cell technique that allows for quantitative spatial information at subcellular resolution. However, unlike flow cytometry and single-cell sequencing techniques, microscopy has issues achieving high-quality population-wide sample characterization while maintaining high resolution. Here, we present a general framework, data-driven microscopy (DDM) that uses real-time population-wide object characterization to enable data-driven high-fidelity imaging of relevant phenotypes based on the population context. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As a proof of concept, we develop and apply DDM with plugins for improved high-content screening and live adaptive microscopy for cell migration and infection studies that capture events of interest, rare or common, with high precision and resolution. We propose that DDM can reduce human bias, increase reproducibility, and place single-cell characteristics in the context of the sample population when interpreting microscopy data, leading to an increase in overall data fidelity.

ScTSSR2: Imputing Dropout Events for Single-Cell RNA Sequencing Using Fast Two-Side Self-Representation.

The single-cell RNA sequencing (scRNA-seq) technique begins a new era by revealing gene expression patterns at single-cell resolution, enabling studies of heterogeneity and transcriptome dynamics of complex tissues at single-cell resolution. However, existing large proportion of dropout events may hinder downstream analyses. Thus imputation of dropout events is an important step in analyzing scRNA-seq data. We develop scTSSR2, a new imputation method that combines matrix decomposition with the previously developed two-side sparse self-representation, leading to fast two-side sparse self-representation to impute dropout events in scRNA-seq data. The comparisons of computational speed and memory usage among different imputation methods show that scTSSR2 has distinct advantages in terms of computational speed and memory usage. Comprehensive downstream experiments show that scTSSR2 outperforms the state-of-the-art imputation methods. A user-friendly R package scTSSR2 is developed to denoise the scRNA-seq data to improve the data quality.

Comparative analysis of single-cell genome sequencing techniques toward the characterization of germline and somatic genomes in ciliated protists

Ciliated protists contain both germline micronucleus (MIC) and somatic macronucleus (MAC) in a single cytoplasm. Programmed genome rearrangements occur in ciliates during sexual processes, and the extent of rearrangements varies dramatically among species, which lead to significant differences in genomic architectures. However, genomic sequences remain largely unknown for most ciliates due to the difficulty in culturing and in separating the germline from the somatic genome in a single cell. Single-cell whole genome amplification (WGA) has emerged as a powerful technology to characterize the genomic heterogeneity at the single-cell level. In this study, we compared two single-cell WGA, multiple displacement amplification (MDA) and multiple annealing and looping-based amplification cycles (MALBAC) in characterizing the germline and somatic genomes in ciliates with different genomic architectures. Our results showed that: 1) MALBAC exhibits strong amplification bias towards MAC genome while MDA shows bias towards MIC genome of ciliates with extensively fragmented MAC genome; 2) both MDA and MALBAC could amplify MAC genome more efficiently in ciliates with moderately fragmented MAC genome. Moreover, we found that more sample replicates could help to obtain more genomic data. Our work provides a reference for selecting the appropriate method to characterize germline and somatic genomes of ciliates.

Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma

The high incidence of hepatocellular carcinoma (HCC) recurrence negatively impacts outcomes of patients treated with curative intent despite advances in surgical techniques and other locoregional liver-targeting therapies. Over the past few decades, the emergence of transcriptome analysis tools, including real-time quantitative reverse transcription PCR, microarrays, and RNA sequencing, has not only largely contributed to our knowledge about the pathogenesis of recurrent HCC but also led to the development of outcome prediction models based on differentially expressed gene signatures. In recent years, the single-cell RNA sequencing technique has revolutionized our ability to study the complicated crosstalk between cancer cells and the immune environment, which may benefit further investigations on the role of different immune cells in HCC recurrence and the identification of potential therapeutic targets. In the present article, we summarized the major findings yielded with these transcriptome methods within the framework of a causal model consisting of three domains: primary cancer cells; carcinogenic stimuli; and tumor microenvironment. We provided a comprehensive review of the insights that transcriptome analyses have provided into diagnostics, surveillance, and treatment of HCC recurrence.

Complex hierarchical structures in single-cell genomics data unveiled by deep hyperbolic manifold learning.

With the advances in single-cell sequencing techniques, numerous analytical methods have been developed for delineating cell development. However, most are based on Euclidean space, which would distort the complex hierarchical structure of cell differentiation. Recently, methods acting on hyperbolic space have been proposed to visualize hierarchical structures in single-cell RNA-seq (scRNA-seq) data and have been proven to be superior to methods acting on Euclidean space. However, these methods have fundamental limitations and are not optimized for the highly sparse single-cell count data. To address these limitations, we propose scDHMap, a model-based deep learning approach to visualize the complex hierarchical structures of scRNA-seq data in low-dimensional hyperbolic space. The evaluations on extensive simulation and real experiments show that scDHMap outperforms existing dimensionality-reduction methods in various common analytical tasks as needed for scRNA-seq data, including revealing trajectory branches, batch correction, and denoising the count matrix with high dropout rates. In addition, we extend scDHMap to visualize single-cell ATAC-seq data.

Immune Cell Profiling of Atopic Dermatitis Patients Before and After Treatment with Halometasone Cream Wet-Wrap Therapy by Single-Cell Sequencing.

Peripheral blood immune cell profiling of atopic dermatitis patients before and after treatment by single-cell RNA sequencing technique has not been reported. To study the immune Cell Profiling of Atopic Dermatitis Patients Before and After Treatment with Halometasone Cream Wet-Wrap Therapy. We used single cell sequencing to detect the proportion change and gene expression change of immune cells in 2 patients before and after treatment, and then used real-time PCR to confirm the mRNA level of differential genes. In this study, scRNA-seq in two patients with severe AD before and after halometasone cream wet-wrap therapy showed that in the mild severity of AD after treatment, Th2 cells were significantly decreased (41.2% vs 13.4%), Th1 and Th17 cells were increased (23.3% vs 43.7%, 2.3% vs 4.8% respectively). The proportion of Th22 cells did not change much (1.3% vs 1.9%). Tregs were significantly increased also (1.5% vs 5.0%). In the regulatory T cells, the expression of IL-27, PD-1, CD103, CTLA-4, ZNF-66, IL-β, CD7 gene was specifically increased after treatment, and CD39, P21, TOX2, CD151, CD79A, S100A12, TRAP1 gene was specifically decreased after treatment. In the TH2 cells, the expression of CD27, CD68, EZH1, RAD1, EGFR, CCR10, BCL11A, KLF4 gene was specifically increased after treatment and CCL26, CD180, IL-31, CCL22, LEF1, OX40 gene was specifically decreased after treatment. These genes may be new target for further study.

Preparation of Drosophila Ovarioles for Single-Cell RNA Sequencing.

The production of eggs in the Drosophila ovary requires complex interactions between multiple cell types that coexist within the same solid tissue. This cellular heterogeneity makes the ovary a rich subject of study, but also makes it challenging to identify transcriptional differences between individual cell types using methods such as bulk RNA sequencing. The development of single-cell RNA sequencing (scRNA-seq) techniques addresses this limitation by providing an avenue to profile genetic and functional heterogeneity at a cellular resolution. Here, we describe the isolation and preparation of the Drosophila ovary for scRNA-seq. This protocol emphasizes a short preparation time, high cell viability, prevention of RNA-degradation, and reduction of technical variation to achieve highly reproducible single-cell profiles.

The Impact of Stem/Progenitor Cells on Lymphangiogenesis in Vascular Disease.

Lymphatic vessels, as the main tube network of fluid drainage and leukocyte transfer, are responsible for the maintenance of homeostasis and pathological repairment. Recently, by using genetic lineage tracing and single-cell RNA sequencing techniques, significant cognitive progress has been made about the impact of stem/progenitor cells during lymphangiogenesis. In the embryonic stage, the lymphatic network is primarily formed through self-proliferation and polarized-sprouting from the lymph sacs. However, the assembly of lymphatic stem/progenitor cells also guarantees the sustained growth of lymphvasculogenesis to obtain the entire function. In addition, there are abundant sources of stem/progenitor cells in postnatal tissues, including circulating progenitors, mesenchymal stem cells, and adipose tissue stem cells, which can directly differentiate into lymphatic endothelial cells and participate in lymphangiogenesis. Specifically, recent reports indicated a novel function of lymphangiogenesis in transplant arteriosclerosis and atherosclerosis. In the present review, we summarized the latest evidence about the diversity and incorporation of stem/progenitor cells in lymphatic vasculature during both the embryonic and postnatal stages, with emphasis on the impact of lymphangiogenesis in the development of vascular diseases to provide a rational guidance for future research.

Deciphering postnatal limb development at single-cell resolution

The early postnatal limb developmental progression bridges embryonic and mature stages and mirrors the pathological remodeling of articular cartilage. However, compared with multitudinous research on embryonic limb development, the early postnatal stage seems relatively unnoticed. Here, a systematic work to portray the postnatal limb developmental landscape was carried out by characterization of 19,952 single cells from murine hindlimbs at 4 postnatal stages using single-cell RNA sequencing technique. By delineation of cell heterogeneity, the candidate progenitor sub-clusters marked by Cd34 and Ly6e were discovered in articular cartilage and enthesis, and three cellular developmental branches marked by Col10a1, Spp1, and Tnni2 were reflected in growth plate. The representative transcriptomes and developmental patterns were intensively explored, and the key regulation mechanisms as well as evolvement in osteoarthritis were discussed. Above all, these results expand horizons of postnatal limb developmental biology and reach the interconnections between limb development, remodeling, and regeneration.

Comparison of the Single-Cell Immune Landscape of Testudines from Different Habitats

Testudines, also known as living fossils, have evolved diversely and comprise many species that occupy a variety of ecological niches. However, the immune adaptation of testudines to the different ecological niches remains poorly understood. This study compared the composition, function, and differentiation trajectories of peripheral immune cells in testudines (Chelonia mydas, Trachemys scripta elegans, Chelonoidis carbonaria, and Pelodiscus sinensis) from different habitats using the single-cell RNA sequencing (scRNA-seq) technique. The results showed that T. scripta elegans, which inhabits freshwater and brackish environments, had the most complex composition of peripheral immune cells, with 11 distinct immune cell subsets identified in total. The sea turtle C. mydas, had the simplest composition of peripheral immune cells, with only 5 distinct immune cell clusters. Surprisingly, neither basophils were found in C. mydas nor T cells in C. carbonaria. Basophil subsets in peripheral blood were identified for the first time; two basophil subtypes (GATA2-high-basophils and GATA2-low-basophils) were observed in the peripheral blood of T. scripta elegans. In addition, ACKR4 cells, CD4 T cells, CD7 T cells, serotriflin cells, and ficolin cells were specifically identified in the peripheral blood of T. scripta elegans. Furthermore, LY6G6C cells, SPC24 cells, and NKT cells were specifically observed in C. carbonaria. Moreover, there were differences in the functional status and developmental trajectory of peripheral immune cells among the testudine species. The identification of specific features of peripheral immune cells in testudines from different habitats may enable elucidation of the adaptation mechanism of testudines to various ecological niches.

CD226 knockout reduces the development of CD8+ T by impairing the TCR sensitivity of double-positive thymocytes.

The costimulation molecule CD226 is widely involved in T cell differentiation, activation and immune functional regulation in peripheral immune tissues. CD226-deficient mice have impaired immune response capacity. The function of CD226 in regulating T cell development in the thymus, a central immune organ, is not yet fully understood. We investigated the development of thymocytes using CD226 knockout mice and single-cell sequencing techniques. CD226 began to be expressed in the second half of thymocyte development, with a gradual increase from the double-positive (DP) to single-positive (SP) phase and higher levels of CD226 on CD8+ T cells than on CD4+ T cells from the SP phase to mature T cells. In the thymus of CD226KO mice, the proportion of DPT at the quiescent phase (DPT-Q) increased, of which the Gzma+ cluster that tends to be CD8+ T cells and CD5+ cluster that is undergoing positive selection decreased dramatically. Afterward, the proportion of mature CD8+ T cells reduced dramatically. Depletion of CD226 impaired TCR activation signalling and diminished AKT/ERK/NF-κB/p38 phosphorylation levels. The diminished TCR responsiveness of DPT cells impeded their positive selection process and influenced the maturation of CD8+ T cells. In mechanism, CD226 knockout enhanced DPT cell apoptosis via impairing AKT phosphorylation. These results suggest that CD226 plays a significant role in T cell thymic development via modulation of TCR signalling, affecting CD8+ T cell maturation.

The kidney—more than the sum of its cellular parts

The kidney is a complex organ composed of a plethora of highly specialized cells that talk to each other. The tubuloglomerular feedback is a prototypical example of cell-cell interactions adapting glomerular filtration rate. With the advent of single-cell sequencing techniques, spatial transcriptomics, and bioinformatical cell-cell interaction analyses, we are now able to much better decipher the complex physiology and pathophysiology of these crosstalks, and identify new biomarkers as well as novel treatment options.

Selected Articles from This Issue

PD-1T tumor-infiltrating lymphocytes (TIL) are a dysfunctional T-cell population characterized by high PD-1 expression and tumor reactivity. To evaluate their potential as a biomarker for durable clinical benefit to PD-1 blockade, Hummelink and colleagues digitally quantified PD-1T TIL in 120 pretreatment samples from patients with advanced non-small cell lung cancer. PD-1T TIL showed a high sensitivity in predicting clinical benefit and survival to anti-PD-1 and were superior to PD-L1 or TLS as a biomarker. Importantly, the absence of PD-1T TIL reliably identified a patient group without benefit from PD-1 blockade, thereby providing a promising tool to reduce overtreatment.Immune checkpoint inhibitors (ICI) targeting programmed cell death protein 1 and its ligand (PD-1/PD-L1) have revolutionized therapeutic options for renal cell carcinoma (RCC). However, response rates to ICI vary widely (20–70%) in patients with RCC. Predicting responses using conventional tissue-based techniques evaluating PD-L1 expression is of limited value, owing to the dynamic and heterogenous nature of PD-L1 expression. Mulgaonkar and colleagues systematically examined the performance of immunoPET for PD-L1 imaging relative to immunohistochemistry in an RCC tumorgraft platform. This study demonstrates the promising potential of immunoPET for clinical real-time assessment of PD-L1 expression across tumor sites and heterogeneous intratumoral distribution.CSF1R inhibitor treatments have shown to be effective targeted therapies for TGCT. Trials are currently underway to study the effect of discontinuation and retreatment with CSF1R inhibitors. In this study, van IJzendoorn and colleagues investigate the tumor landscape in TGCT using single-cell sequencing and other molecular techniques. They show that the neoplastic cells are not targeted by CSF1R inhibitors, in contrast to what was previously postulated. The authors identify markers for the neoplastic cells and show the lack of an autocrine loop involving CSF1-CSF1R in the neoplastic cells. As a result, CSF1R inhibitor treatments are unlikely to affect the neoplastic cell population in TGCT as is currently assumed. Additionally, this work identifies two new pathways that could be targeted in future trials involving TGCT.The resistance of immunotherapy in renal cancer patients remains unclear despite the high infiltrations of CD8+T cells. Here, Benhamouda and colleagues show that baseline plasma soluble CD27 (sCD27) is associated with resistance to immunotherapy in renal cell carcinoma independently of clinical factors. sCD27 reflects the interaction between CD27 expressed by T lymphocytes and CD70 expressed by tumor cells, which associates with the apoptosis and dysfunction of T lymphocytes. Peripheral blood sCD27 could thus be considered as a marker of T-cell dysfunction due to the CD27-CD70 interaction in the tumor and predict the response to immunotherapy.

Immuno(T)herapy for age-related diseases.

During the last decade, the stimulation of T-cell function by the blockage of immunosuppressive checkpoints has experienced an outstanding impact in the treatment of cancer. Development of the chimeric antigen receptor T-cell technology has also emerged as a powerful alternative for patients suffering from oncological processes, especially those affected by hematological neoplasms. Recent evidence suggest that the use of immunotherapy could be extended to non-oncological diseases and could be especially relevant for age-associated disorders, opening exciting therapeutic options for a wide range of diseases of the elderly. Here we comment on the emergence of T-cell-based immunotherapies as feasible approaches that could revolutionize the future of GeroScience.

Chromosome-Length Haplotypes with StrandPhaseR and Strand-seq.

Dense local haplotypes can now readily be extracted from long-read or droplet-based sequence data. However, these methods struggle to combine subchromosomal haplotype blocks into global chromosome-length haplotypes. Strand-seq is a single cell sequencing technique that uses read orientation to capture sparse global phase information by sequencing only one of two DNA strands for each parental homolog. In combination with dense local haplotypes from other technologies, Strand-seq data can be used to obtain complete chromosome-length phase information. In this chapter, we run the R package StrandPhaseR to phase SNVs using publicly available sequence data for sample HG005 of the Genome in a Bottle project.