High-throughput Single-cell RNA-seq Research Articles

Performance comparison of high throughput single-cell RNA-Seq platforms in complex tissues

Single-cell transcriptomics has emerged as the preferred tool to define cell identity through the analysis of gene expression signatures. However, there are limited studies that have comprehensively compared the performance of different scRNAseq systems in complex tissues. Here, we present a systematic comparison of two well-established high throughput 3′-scRNAseq platforms: 10× Chromium and BD Rhapsody, using tumours that present high cell diversity. Our experimental design includes both fresh and artificially damaged samples from the same tumours, which also provides a comparable dataset to examine their performance under challenging conditions. The performance metrics used in this study consist of gene sensitivity, mitochondrial content, reproducibility, clustering capabilities, cell type representation and ambient RNA contamination. These analyses showed that BD Rhapsody and 10× Chromium have similar gene sensitivity, while BD Rhapsody has the highest mitochondrial content. Interestingly, we found cell type detection biases between platforms, including a lower proportion of endothelial and myofibroblast cells in BD Rhapsody and lower gene sensitivity in granulocytes for 10× Chromium. Moreover, the source of the ambient noise was different between plate-based and droplet-based platforms. In conclusion, our reported platform differential performance should be considered for the selection of the scRNAseq method during the study experimental designs.

A systematic overview of single-cell transcriptomics databases, their use cases, and limitations.

Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of transcriptomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Such platforms can help bridge the gap between computational and wet lab scientists through user-friendly web-based interfaces needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.

Investigating the impact of antibiotic-induced dysbiosis on protection from Clostridium difficile colitis by mouse colonic innate lymphoid cells.

Innate lymphoid cells (ILCs) play a critical role in maintaining intestinal health in homeostatic and diseased conditions. During Clostridium difficile infection (CDI), IL-33 activates ILC2 to protect from colonic damage and mortality. The function of IL-33 and ILC is tightly regulated by the intestinal microbiota. We set out to determine the impact of antibiotic-induced disruption of the microbiome on ILC function. Our goal was to understand antibiotic-induced changes in ILC function on susceptibility to C. difficile colitis in a mouse model. We utilized high-throughput single-cell RNAseq to investigate the phenotypic features of colonic ILC at baseline, after antibiotic administration with or without IL-33 treatment. We identified a heterogeneous landscape of colonic ILCs with gene signatures of inflammatory, anti-inflammatory, migratory, progenitor, plastic, and antigen-presenting ILCs. Antibiotic treatment decreased ILC2 while coordinately increasing ILC1 and ILC3 phenotypes. Notably, Ifng+, Ccl5+, and Il23r+ ILC increased after antibiotics. IL-33 treatment counteracted the antibiotic effect by downregulating ILC1 and ILC3 and activating ILC2. In addition, IL-33 treatment markedly induced the expression of type 2 genes, including Areg and Il5. Finally, we identified amphiregulin, produced by ILC2, as protective during C. difficile infection. Together, our data expand our understanding of how antibiotics induce susceptibility to C. difficile colitis through their impact on ILC subsets and function.IMPORTANCEClostridium difficile infection (CDI) accounts for around 500,000 symptomatic cases and over 20,000 deaths annually in the United States alone. A major risk factor of CDI is antibiotic-induced dysbiosis of the gut. Microbiota-regulated IL-33 and innate lymphoid cells (ILCs) are important in determining the outcomes of C. difficile infection. Understanding how antibiotic and IL-33 treatment alter the phenotype of colon ILCs is important to identify potential therapeutics. Here, we performed single-cell RNAseq of mouse colon ILCs collected at baseline, after antibiotic treatment, and after IL-33 treatment. We identified heterogeneous subpopulations of all three ILC subtypes in the mouse colon. Our analysis revealed several potential pathways of antibiotic-mediated increased susceptibility to intestinal infection. Our discovery that Areg is abundantly expressed by ILCs, and the protection of mice from CDI by amphiregulin treatment, suggests that the amphiregulin-epidermal growth factor receptor pathway is a potential therapeutic target for treating intestinal colitis.

Comparison of high-throughput single-cell RNA-seq methods for ex vivo drug screening.

Functional precision medicine (FPM) aims to optimize patient-specific drug selection based on the unique characteristics of their cancer cells. Recent advancements in high throughput ex vivo drug profiling have accelerated interest in FPM. Here, we present a proof-of-concept study for an integrated experimental system that incorporates ex vivo treatment response with a single-cell gene expression output enabling barcoding of several drug conditions in one single-cell sequencing experiment. We demonstrate this through a proof-of-concept investigation focusing on the glucocorticoid-resistant acute lymphoblastic leukemia (ALL) E/R+Reh cell line. Three different single-cell transcriptome sequencing (scRNA-seq) approaches were evaluated, each exhibiting high cell recovery and accurate tagging of distinct drug conditions. Notably, our comprehensive analysis revealed variations in library complexity, sensitivity (gene detection), and differential gene expression detection across the methods. Despite these differences, we identified a substantial transcriptional response to fludarabine, a highly relevant drug for treating high-risk ALL, which was consistently recapitulated by all three methods. These findings highlight the potential of our integrated approach for studying drug responses at the single-cell level and emphasize the importance of method selection in scRNA-seq studies. Finally, our data encompassing 27327 cells are freely available to extend to future scRNA-seq methodological comparisons.

Microfluidic-assisted single-cell RNA sequencing facilitates the development of neutralizing monoclonal antibodies against SARS-CoV-2.

As a class of antibodies that specifically bind to a virus and block its entry, neutralizing monoclonal antibodies (neutralizing mAbs) have been recognized as a top choice for combating COVID-19 due to their high specificity and efficacy in treating serious infections. Although conventional approaches for neutralizing mAb development have been optimized for decades, there is an urgent need for workflows with higher efficiency due to time-sensitive concerns, including the high mutation rate of SARS-CoV-2. One promising approach is the identification of neutralizing mAb candidates via single-cell RNA sequencing (RNA-seq), as each B cell has a unique transcript sequence corresponding to its secreted antibody. The state-of-the-art high-throughput single-cell sequencing technologies, which have been greatly facilitated by advances in microfluidics, have greatly accelerated the process of neutralizing mAb development. Here, we provide an overview of the general procedures for high-throughput single-cell RNA-seq enabled by breakthroughs in droplet microfluidics, introduce revolutionary approaches that combine single-cell RNA-seq to facilitate the development of neutralizing mAbs against SARS-CoV-2, and outline future steps that need to be taken to further improve development strategies for effective treatments against infectious diseases.

Multiomic Single-Cell Analysis Identifies Von Willebrand Factor and TIM3-Expressing BCR-ABL1+ Chronic Myeloid Leukemia Stem Cells

Characterization of the leukemic stem cell (LSC) population in chronic myeloid leukemia (CML) may identify therapeutic targets for sustained elimination of leukemic cells. High-throughput single-cell RNAseq (scRNAseq) has paved the way for detailed transcriptional assessment of CML LSC. Building on this technique, multiomic CITE-seq approaches carry additional benefit in that the paired protein expression information may enable identification of associated cell surface marker profiles, subsequent FACS-based isolation and functional characterization. However, detection of BCR-ABL1 transcripts, which is arguably the only unequivocal marker to distinguish CML LSC from healthy hematopoietic stem cells (HSC), remains challenging using current high-throughput 3' end capture-based scRNAseq methods. For the present study, we performed single-cell CITE-seq analysis of the expression of 597 genes and 51 proteins in >70,000 stem and progenitor cells from 16 chronic phase CML patients and five healthy donors. Furthermore, we developed and employed a strategy allowing parallel detection of BCR-ABL1 transcripts at the single-cell level. In bone marrow samples from CML patients, we observed pronounced expansion of erythroid and myeloid progenitors within the CD14 -CD34 + compartment, and substantial heterogeneity within the traditionally defined CD34 +CD38 -/low LSC compartment. In-depth analysis of the latter identified a group of immature BCR-ABL1 +cells displaying a CD45RA -cKIT -CD26 + TKI resistance phenotype sitting atop a hierarchy of myeloid progenitors. Within this group of potential LSC, expression of both previously reported ( e.g. CD25 and CD26) and unreported gene and protein markers was found to distinguish these cells from their healthy counterparts. Unlike HSC, the immature LSC showed high surface expression of TIM3 and high transcription of the von Willebrand factor gene ( VWF). While overexpression of VWF within the stem cell population may be linked to the aberrant myeloid-biased hematopoiesis characterizing the disease, the cell surface upregulation of TIM3 suggests that its targeting may have merit in CML. In conclusion, we here performed detailed multiomic characterization of CML stem and progenitor cells, paired with BCR-ABL1 detection at the single-cell level. Our findings revealed LSC-specific expression patterns that may have implications for the phenotypic definition of CML LSC. Additionally, the results identify TIM3 as a conceivable target for sustained elimination of immature LSC in CML.

An Assessment of Marrow-Infiltrating T Cells in Early Relapsed Hematologic Cancer Patients Post Allogeneic Hematopoietic Stem Cell Transplantation

Background: Relapse is the major treatment failure of allogeneic hematopoietic stem cell transplantation (HSCT) in patients with hematologic malignancies. Especially, early relapse had seriously bad prognosis. And tumor-infiltrating T cells play an important role in disease progression and recurrence. However, there is lack of data about the marrow-infiltrating T cell of early relapsed patients compared to those with complete remission (CR) at an early timepoint after HSCT. Therefore, we analyzed the differentiation stage and expression of inhibitory receptors of the marrow-infiltrating T cell in the early stages of allogeneic HSCT. Method: A total of 40 samples from patients treated at Chungnam National University Hospital were collected between April 2021 and October 2022. We used flow cytometry to classify T cells into five subtypes using CD45RA, CD95, and CCR7: naïve, stem cell memory (SCM), central memory (CM), effector memory (EM) and effector memory with positive CD45RA (EMRA). Otherwise, we analyzed regulatory T cell (Treg) by defining as CD25 highCD127 low and divided them into three categories using CD25 and CD45RA: naïve, active and non-suppressive Treg. We defined early relapse (ER) as relapse within 6 months of allogeneic HSCT. High-throughput single-cell RNAseq assays from BM lymphocytes isolated by density gradients were performed to determine the expression and cell composition of previously irradiated markers. Results: With this approach, marrow-infiltrating T cells were analyzed in 40 patients (10 patients with early relapse) with a median age of 57 years (ranged from 30 to 71). AML is the most common disease at 60%, followed by MDS (20%), ALL (15%) and PMF (2.5%). Stem cell sources were 32.5% from matched sibling donor, 37.5% from haplo-identical donor and 27.5% from matched unrelated donor. All patients received mobilized peripheral blood stem cell transplantation. Marrow-infiltrating CD3+T cell counts were lower in patients with allogeneic HSCT compared to normal subjects (CD3+T cells: 13.89% in HSCT patient and 9.116% in ER patients vs. 34.19% in normal subjects, p< 0.0001). In addition, CD3+CD8+T cells were higher in CR than ER patients (CD3+CD8+T cells: 50.39% in CR vs. 30.41% in ER patients, p< 0.0033). In HSCT patients, naïve was higher, and EM and EMRA were lower than those in normal subjects among CD4+T cells (naïve; 8.629% in ER patients and 5.119% in CR patient's vs 45.76% in normal subjects, p< 0.0001/EM; 54.04% in ER patients and 55.93% in CR patients vs 18.61 in normal subject, p= 0.0003 and p< 0.0001 respectively). Of CD3+T cells, the proportion and expression level of TIM-3 was higher ER patients than in CR patients (56.33% in ER patients vs 39.20% in CR patients, p= 0.0185/MFI; 1873 in ER patients vs. 826.7 in CR patients, p= 0.0331). Of CD4+T cells, the percentage of TIM-3 was lower in normal subjects than in allogeneic HSCT patients (7.794% in normal vs 30.11% in CR patients and 26.04% in ER patients, p= 0.0002 and p= 0.0102 respectively). In patients with ER, naïve and EM was higher than those in CR patients among CD8+T cells (naïve; 8.680% in ER patients vs 4.066% in CR patients, p= 0.4001 /EM; 54.44% in ER patients vs 44.33% in CR patients, p= 0.3039). Of CD8+ EMRA T cell, the expression of TIM-3 was higher in ER patients than in CR patients (MFI; 1441 in HSCT patients vs 421.7 in normal, p=0.1038). Otherwise, of CD3+CD4-CD8-, The percentage of TIM3 was higher in ER patients than CR patients (80.33 % in ER patients vs 62.49% in CR patients, p= 0.0333). Treg levels were no significant difference between CR and ER. We have obtained single-cell sequencing data from two CR (complete response) patients and two ER (early relapse) patients, representing each group. Since the classified groups exhibited ambiguous phenotypes, they were reclassified based on specific markers of cell groups. The reclassified groups showed a higher proportion of HCS (Hematopoietic Stem Cell)-like cells in relapsed patients (+10.14%, P=0.368) and a lower proportion of CD8+ T cells (-13.40%, p=0.068). The HSC-like cells in relapsed patients exhibited higher expression of TIM3 ligand than in CR patients Conclusion: Among the IRs, TIM3 had the highest expression of all cell groups (CD4 T cells, CD8 T cells and Treg) compared with other IRs at the early stage of allogeneic HSCT. So, TIM3 might be a potential target for preventing relapse in hematologic cancer patients after allogeneic HSCT in the future.

Discovering cell types using manifold learning and enhanced visualization of single-cell RNA-Seq data

Identifying relevant disease modules such as target cell types is a significant step for studying diseases. High-throughput single-cell RNA-Seq (scRNA-seq) technologies have advanced in recent years, enabling researchers to investigate cells individually and understand their biological mechanisms. Computational techniques such as clustering, are the most suitable approach in scRNA-seq data analysis when the cell types have not been well-characterized. These techniques can be used to identify a group of genes that belong to a specific cell type based on their similar gene expression patterns. However, due to the sparsity and high-dimensionality of scRNA-seq data, classical clustering methods are not efficient. Therefore, the use of non-linear dimensionality reduction techniques to improve clustering results is crucial. We introduce a method that is used to identify representative clusters of different cell types by combining non-linear dimensionality reduction techniques and clustering algorithms. We assess the impact of different dimensionality reduction techniques combined with the clustering of thirteen publicly available scRNA-seq datasets of different tissues, sizes, and technologies. We further performed gene set enrichment analysis to evaluate the proposed method’s performance. As such, our results show that modified locally linear embedding combined with independent component analysis yields overall the best performance relative to the existing unsupervised methods across different datasets.

Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast

Recent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in “transport”, which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.

Single-cell landscape of nuclear configuration and gene expression during stem cell differentiation and X inactivation

BackgroundMammalian development is associated with extensive changes in gene expression, chromatin accessibility, and nuclear structure. Here, we follow such changes associated with mouse embryonic stem cell differentiation and X inactivation by integrating, for the first time, allele-specific data from these three modalities obtained by high-throughput single-cell RNA-seq, ATAC-seq, and Hi-C.ResultsAllele-specific contact decay profiles obtained by single-cell Hi-C clearly show that the inactive X chromosome has a unique profile in differentiated cells that have undergone X inactivation. Loss of this inactive X-specific structure at mitosis is followed by its reappearance during the cell cycle, suggesting a “bookmark” mechanism. Differentiation of embryonic stem cells to follow the onset of X inactivation is associated with changes in contact decay profiles that occur in parallel on both the X chromosomes and autosomes. Single-cell RNA-seq and ATAC-seq show evidence of a delay in female versus male cells, due to the presence of two active X chromosomes at early stages of differentiation. The onset of the inactive X-specific structure in single cells occurs later than gene silencing, consistent with the idea that chromatin compaction is a late event of X inactivation. Single-cell Hi-C highlights evidence of discrete changes in nuclear structure characterized by the acquisition of very long-range contacts throughout the nucleus. Novel computational approaches allow for the effective alignment of single-cell gene expression, chromatin accessibility, and 3D chromosome structure.ConclusionsBased on trajectory analyses, three distinct nuclear structure states are detected reflecting discrete and profound simultaneous changes not only to the structure of the X chromosomes, but also to that of autosomes during differentiation. Our study reveals that long-range structural changes to chromosomes appear as discrete events, unlike progressive changes in gene expression and chromatin accessibility.

Genomic cytometry is maturing but has single-cell multiomics hit puberty yet?

Genomic cytometry is maturing but has single-cell multiomics hit puberty yet?

Single-Cell Methylome and Transcriptome High-Resolution Maps Define the Effects of Epigenetic Modifiers on the Landscape of Hematopoietic Differentiation

Epigenetic modifications are critical determinants of cell identity and play a central role in normal hematopoietic differentiation. Moreover, mutations in epigenetic modifiers including TET2 and IDH2 mutations, which result in a decreased ability to de-methylate DNA, are frequent in myeloid leukemias and clonal hematopoiesis of indeterminate potential. This raises a central question in normal and pathologic hematopoiesis - how do epigenetic modifiers shape the differentiation landscape?To address this question, we applied high-throughput single-cell RNA-seq (scRNAseq) to reconstruct high-resolution maps of hematopoietic differentiation, with and without disruption of central epigenetic modifiers (Tet2 and Idh2). Briefly, FACS-sorted lineage-negative bone marrow cells were isolated from sixteen-week old Mx1 -cre wildtype (Wt, n=6), Mx1 -cre Tet2fl/fl (n= 6) or Mx1 -cre Idh2R140Q/Wt (n=3) mice, 4 weeks following recombination. In total, 45,100 cells underwent droplet-based scRNAseq. Clustering of Wt cells identified 25 distinct progenitor populations, recapitulating previous reports and providing a map of normal mouse hematopoietic differentiation.Tet2knockout (KO) resulted in a 1.5-fold increase in uncommitted cluster HSC4 (Flt3hi, c-Kithi, Cd34low) (Figure A-B), consistent with a potential differentiation block resulting from Tet2-KO. Notably, the HSC differentiation block did not uniformly affect differentiation into committed progenitors, but resulted in a 2-fold decrease in erythroid precursors and a 1.5-fold increase in monocyte precursors. These findings were evident by scRNA-seq four months before this phenotype was observed by flow cytometry. While the expression profiles of these committed erythroid and monocytic progenitors were not different from Wt progenitors, the degree of priming to these fates within uncommitted Tet2-KO HSCs (HSC clusters 1-4) showed reduced priming towards the erythroid fate and increased priming towards the monocytic fate (Figure C). In contrast, mutant Idh2 resulted in different skews of hematopoiesis than Tet2-KO, including an increase in neutrophil precursors, while there was no significant change in the frequency of erythroid precursors (Figure D). These data demonstrate that Tet2 and Idh2 mutations, both of which disrupt de-methylation, have distinct effects on hematopoietic differentiation.To explore the link between aberrant DNA methylation upon Tet2 - KO and the observed differentiation skews, we performed joint single-cell methylation and RNAseq, as well as ATAC-bisulfite sequencing. As expected, Tet2-KO resulted in higher global methylation. However, the increase in methylation was stochastically distributed across the genome, resulting in higher methylation heterogeneity (Figure E), and associated with higher cell-to-cell variation in transcriptional profiles. Importantly, we found that stochastic methylation changes result in deterministic differentiation skews due to differential sensitivity to methylation of binding motifs of lineage-defining transcription factors (TFs). For instance, motifs of erythroid-related TFs (including Nrf1, E2f4 and E2f1), but not monocytic-related TFs, showed a marked enrichment of CpGs and methylation-sensitive binding (Figure F).These data demonstrate the ability of high-resolution single-cell mapping of hematopoiesis to identify the exact point of disruption of HSC differentiation due to alterations in epigenetic modifiers. Specifically, these data show that Tet2-KO results in reduced erythroid but increased monocytic priming of HSCs, leading to skewed differentiation. Moreover, single-cell epigenomic analyses identified that while the hypermethylation effect of Tet2-KO is distributed widely across the genome, the skews in lineage priming result from biases in the sensitivity of lineage-defining TFs to CpG methylation at their motifs. The skews may therefore arise not from a change in methylation of a particular locus, but rather through broad lack of demethylation in targets of erythroid priming TFs, resulting from inherent enrichment of these motifs in CpGs. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BackgroundElucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation.ResultsHere, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures.ConclusionOverall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

Lnc2Cancer 3.0: an updated resource for experimentally supported lncRNA/circRNA cancer associations and web tools based on RNA-seq and scRNA-seq data.

An updated Lnc2Cancer 3.0 (http://www.bio-bigdata.net/lnc2cancer or http://bio-bigdata.hrbmu.edu.cn/lnc2cancer) database, which includes comprehensive data on experimentally supported long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) associated with human cancers. In addition, web tools for analyzing lncRNA expression by high-throughput RNA sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) are described. Lnc2Cancer 3.0 was updated with several new features, including (i) Increased cancer-associated lncRNA entries over the previous version. The current release includes 9254 lncRNA-cancer associations, with 2659 lncRNAs and 216 cancer subtypes. (ii) Newly adding 1049 experimentally supported circRNA-cancer associations, with 743 circRNAs and 70 cancer subtypes. (iii) Experimentally supported regulatory mechanisms of cancer-related lncRNAs and circRNAs, involving microRNAs, transcription factors (TF), genetic variants, methylation and enhancers were included. (iv) Appending experimentally supported biological functions of cancer-related lncRNAs and circRNAs including cell growth, apoptosis, autophagy, epithelial mesenchymal transformation (EMT), immunity and coding ability. (v) Experimentally supported clinical relevance of cancer-related lncRNAs and circRNAs in metastasis, recurrence, circulation, drug resistance, and prognosis was included. Additionally, two flexible online tools, including RNA-seq and scRNA-seq web tools, were developed to enable fast and customizable analysis and visualization of lncRNAs in cancers. Lnc2Cancer 3.0 is a valuable resource for elucidating the associations between lncRNA, circRNA and cancer.

Abstract PO-020: Inferring copy number substructure from single-cell transcriptomics in human tumors with CopyKat

Abstract High-throughput single cell transcriptomics analysis is widely used to study human tumors, however a major challenge is distinguishing the stromal cells from the malignant cancer cells, as well as clonal substructure within tumors. To address this challenge, we developed an integrative Bayesian segmentation approach, COPYKAT to estimate genomic copy numbers at 5MB resolution from high-throughput single cell RNA-seq data. We applied COPYKAT to 39,709 single cells from 16 tumors across 4 cancer types, including premalignant and triple-negative breast cancers, pancreatic ductal adenocarcinomas, and anaplastic thyroid cancer. From these data we could accurately (98%) classify tumor cells from stromal cells. In three TNBC tumors COPYKAT resolved multiple clonal subpopulations of genotypes that differed in expression of breast cancer genes and enrichment of cancer hallmarks including EMT and hypoxia. These data show that COPYKAT can accurately resolve clonal copy number substructure in tumors and classify tumor and normal cells in a variety of human cancers. Citation Format: Ruli Gao, Shanshan Bai, Henderson Ying, Yiyun Lin, Tapsi Seth, Min Hu, Emi Sei, Alexander Davis, Fang Wang, Jennifer Rui Wang, Ken Chen, Stacey Moulder, Stephen Lai, Nicholas Navin. Inferring copy number substructure from single-cell transcriptomics in human tumors with CopyKat [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-020.

Sierra: discovery of differential transcript usage from polyA-captured single-cell RNA-seq data.

High-throughput single-cell RNA-seq (scRNA-seq) is a powerful tool for studying gene expression in single cells. Most current scRNA-seq bioinformatics tools focus on analysing overall expression levels, largely ignoring alternative mRNA isoform expression. We present a computational pipeline, Sierra, that readily detects differential transcript usage from data generated by commonly used polyA-captured scRNA-seq technology. We validate Sierra by comparing cardiac scRNA-seq cell types to bulk RNA-seq of matched populations, finding significant overlap in differential transcripts. Sierra detects differential transcript usage across human peripheral blood mononuclear cells and the Tabula Muris, and 3 ′UTR shortening in cardiac fibroblasts. Sierra is available at https://github.com/VCCRI/Sierra.

Uncovering the key dimensions of high-throughput biomolecular data using deep learning.

Recent advances in high-throughput single-cell RNA-seq have enabled us to measure thousands of gene expression levels at single-cell resolution. However, the transcriptomic profiles are high-dimensional and sparse in nature. To address it, a deep learning framework based on auto-encoder, termed DeepAE, is proposed to elucidate high-dimensional transcriptomic profiling data in an encode–decode manner. Comparative experiments were conducted on nine transcriptomic profiling datasets to compare DeepAE with four benchmark methods. The results demonstrate that the proposed DeepAE outperforms the benchmark methods with robust performance on uncovering the key dimensions of single-cell RNA-seq data. In addition, we also investigate the performance of DeepAE in other contexts and platforms such as mass cytometry and metabolic profiling in a comprehensive manner. Gene ontology enrichment and pathology analysis are conducted to reveal the mechanisms behind the robust performance of DeepAE by uncovering its key dimensions.

Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation

A comprehensive reference map of all cell types in the human body is necessary for improving our understanding of fundamental biological processes and in diagnosing and treating disease. High-throughput single-cell RNA sequencing techniques have emerged as powerful tools to identify and characterize cell types in complex and heterogeneous tissues. However, extracting intact cells from tissues and organs is often technically challenging or impossible, for example in heart or brain tissue. Single-nucleus RNA sequencing provides an alternative way to obtain transcriptome profiles of such tissues. To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3′ RNA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes. Clustering of time-series transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics. We then applied DroNc-seq to postmortem heart tissue to test its performance on heterogeneous human tissue samples. Our data confirm that DroNc-seq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.

Somatic mutations and cell identity linked by Genotyping of Transcriptomes.

Defining the transcriptomic identity of malignant cells is challenging in the absence of surface markers that distinguish cancer clones from one another or from admixed non-neoplastic cells. To address this challenge, we developed Genotyping of Transcriptomes (GoT), integrating genotyping with high-throughput droplet-based single-cell RNA-seq. We applied GoT to study how somatic mutations corrupt the complex process of human hematopoiesis, profiling 38,290 CD34+ cells from patients with CALR-mutated myeloproliferative neoplasms (MPN). High-resolution mapping of malignant vs. normal hematopoietic progenitors revealed increasing CALR-mutation fitness advantage with myeloid differentiation. We identified the unfolded protein response as a predominant outcome of CALR mutations, with significant cell identity dependency, as well as NF-κB pathway upregulation specifically in uncommitted stem cells. We further extended the GoT toolkit to genotype multiple targets and loci distant from transcript ends. Collectively, these findings revealed that the transcriptional output of MPN somatic mutations is dependent on the native cell identity.

Abstract 2477: CopyCat: estimating genomic copy number profiles from high-throughput single cell RNA seq data

Abstract CopyCat is a computational approach for estimate genomic-copy number profiles from high-throughput single cell RNAseq data that is generated by technologies, such as Drop-Seq, 10X genomics or Nanowells. CopyCat involves first normalizing and transforming unique molecular index (UMI) count data matrices to calculate single cell copy number states from low-coverage datasets using gene-bin windows across the human genome, and does not require a reference normal cell control. The resulting binned data is normalized and used for segmentation to reduce noise from individual genes that can contribute to gene dosage effects along chromosomes. We show that this methods is highly effective at distinguishing diploid and aneuploid cells in human tumors or cancer cell lines, and can efficiently detect major (>10mb) chromosomal amplifications and deletions compared to copy number data generated by DNA sequencing of match samples. We further show that this method can resolve some clonal substructure within individual tumors, when subpopulations are distinguished by large chromosomal events. Our method outputs segmented copy number profiles and predicted classifications of normal (diploid) and tumor (aneuploid) cell states for individual single cells. It is implemented in R and is available on Github. Citation Format: Ruli Gao, Nicholas Navin. CopyCat: estimating genomic copy number profiles from high-throughput single cell RNA seq data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2477.