Single-cell Sequencing Experiments Research Articles

Quantifying Landscape-Flux via Single-Cell Transcriptomics Uncovers the Underlying Mechanism of Cell Cycle.

Recent developments in single-cell sequencing technology enable the acquisition of entire transcriptome data. Understanding the underlying mechanism and identifying the driving force of transcriptional regulation governing cell function directly from these data remains challenging. This study reconstructs a continuous vector field of the cell cycle based on discrete single-cell RNA velocity to quantify the single-cell global nonequilibrium dynamic landscape-flux. It reveals that large fluctuations disrupt the global landscape and genetic perturbations alter landscape-flux, thus identifying key genes in maintaining cell cycle dynamics and predicting associated functional effects. Additionally, it quantifies the fundamental energy cost of the cell cycle initiation and unveils that sustaining the cell cycle requires curl flux and dissipation to maintain the oscillatory phase coherence. This study enables the inference of the cell cycle gene regulatory networks directly from the single-cell transcriptomic data, including the feedback mechanisms and interaction intensity. This provides a golden opportunity to experimentally verify the landscape-flux theory and also obtain its associated quantifications. It also offers a unique framework for combining the landscape-flux theory and single-cell high-through sequencing experiments for understanding the underlying mechanisms of the cell cycle and can be extended to other nonequilibrium biological processes, such as differentiation development and disease pathogenesis.

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.

A neuronal coping mechanism linking stress-induced anxiety to motivation for reward.

Stress coping involves innate and active motivational behaviors that reduce anxiety under stressful situations. However, the neuronal bases directly linking stress, anxiety, and motivation are largely unknown. Here, we show that acute stressors activate mouse GABAergic neurons in the interpeduncular nucleus (IPN). Stress-coping behavior including self-grooming and reward behavior including sucrose consumption inherently reduced IPN GABAergic neuron activity. Optogenetic silencing of IPN GABAergic neuron activation during acute stress episodes mimicked coping strategies and alleviated anxiety-like behavior. In a mouse model of stress-enhanced motivation for sucrose seeking, photoinhibition of IPN GABAergic neurons reduced stress-induced motivation for sucrose, whereas photoactivation of IPN GABAergic neurons or excitatory inputs from medial habenula potentiated sucrose seeking. Single-cell sequencing, fiber photometry, and optogenetic experiments revealed that stress-activated IPN GABAergic neurons that drive motivated sucrose seeking express somatostatin. Together, these data suggest that stress induces innate behaviors and motivates reward seeking to oppose IPN neuronal activation as an anxiolytic stress-coping mechanism.

CellSTAR: a comprehensive resource for single-cell transcriptomic annotation.

Large-scale studies of single-cell sequencing and biological experiments have successfully revealed expression patterns that distinguish different cell types in tissues, emphasizing the importance of studying cellular heterogeneity and accurately annotating cell types. Analysis of gene expression profiles in these experiments provides two essential types of data for cell type annotation: annotated references and canonical markers. In this study, the first comprehensive database of single-cell transcriptomic annotation resource (CellSTAR) was thus developed. It is unique in (a) offering the comprehensive expertly annotated reference data for annotating hundreds of cell types for the first timeand (b) enabling the collective consideration of reference data and marker genes by incorporating tens of thousands of markers. Given its unique features, CellSTAR is expected to attract broad research interests from the technological innovations in single-cell transcriptomics, the studies of cellular heterogeneity & dynamics, and so on. It is now publicly accessible without any login requirement at: https://idrblab.org/cellstar.

Lymphocytes regulate expression of the SARS-CoV-2 cell entry factor ACE2 in the pancreas of T2DM patients.

COVID-19 patients with type 2 diabetes mellitus (T2DM) show both poorer clinical outcomes and have an increased risk of death. SARS-CoV-2 virus infection requires simultaneous expression of the SARS-CoV-2 cell entry factors angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine type 2 (TMPRSS2) in the same cell. The aim of the study was to explore the underlying mechanisms of a COVID-19 infection in patients with T2DM. The distribution and expression of AEC2 and TMPRSS2 in different pancreatic cell types in clinical samples of T2DM patients and diabetic mouse models were analysed by single-cell sequencing, bioinformatics analysis and basic experiments. The results showed that ACE2 and TMPRSS2 are expressed in the ducts of the human pancreas. These findings suggest that SARS-CoV-2 can infect ductal cells in vivo through ACE2 and TMPRSS2. T2DM can promote the co-expression of ACE2 and TMPRSS2 in exocrine ducts, including in the human pancreas. We hypothesize that ACE2 expression levels are associated with increased numbers of lymphocytes in vivo. Increased blood glucose levels are associated with increased ACE2 expression and an increased number of lymphocytes. At the same time, lymphocytes can promote ACE2 expression.

LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution

BackgroundLongitudinal single-cell sequencing experiments of patient-derived models are increasingly employed to investigate cancer evolution. In this context, robust computational methods are needed to properly exploit the mutational profiles of single cells generated via variant calling, in order to reconstruct the evolutionary history of a tumor and characterize the impact of therapeutic strategies, such as the administration of drugs. To this end, we have recently developed the LACE framework for the Longitudinal Analysis of Cancer Evolution.ResultsThe LACE 2.0 release aimed at inferring longitudinal clonal trees enhances the original framework with new key functionalities: an improved data management for preprocessing of standard variant calling data, a reworked inference engine, and direct connection to public databases.ConclusionsAll of this is accessible through a new and interactive Shiny R graphical interface offering the possibility to apply filters helpful in discriminating relevant or potential driver mutations, set up inferential parameters, and visualize the results. The software is available at: github.com/BIMIB-DISCo/LACE.

Preparation of High-Viability Single-Cell Suspensions from African Turquoise Killifish Brain Tissue.

Studying the brain at the single-cell level has become increasingly popular in recent years. This, however, remains challenging, especially in emerging model organisms. To carry out single-cell sequencing, the preparation of a high-viability single-cell suspension is critical. In this protocol, we describe how to prepare a high-viability single-cell suspension starting from brain tissue of the African turquoise killifish (Nothobranchius furzeri). The protocol consists of dissection, enzymatic and mechanical dissociation of the brain tissue, and debris removal. The protocol described here has been successfully used for 10× Genomics single-cell sequencing of the telencephalon of adult killifish, which requires a cell viability of at least 70%. In addition to single-cell sequencing experiments, the single-cell suspension generated can be used for other applications, including cell culture and flow cytometry.

OPA1 supports mitochondrial dynamics and immune evasion to CD8+ T cell in lung adenocarcinoma.

Mitochondrial fusion and fission were identified to play key roles during multiple biology process. Thus, we aim to investigate the roles of OPA1 in mitochondria fusion and immune evasion of non-small cell lung cancer cells. The transcriptional activation of genes related to mitochondrial dynamics was determined by using multi-omics data in lung adenocarcinoma (LUAD). We elucidated the molecular mechanism and roles of OPA1 promoting lung cancer through single-cell sequencing and molecular biological experiments. Here, we found that copy number amplification of OPA1 and MFN1 were co-occurring and synergistically activated in tumor epithelial cells in lung cancer tissues. Both of OPA1 and MFN1 were highly expressed in LUAD tumor tissues and OPA1 high expression was associated with poor prognosis. In terms of mechanism, the damaged mitochondria activated the apoptotic signaling pathways, inducing cell cycle arrest and cell apoptosis. More interestingly, OPA1 deficiency damaged mitochondrial dynamics and further blocked the respiratory function to increase the sensitivity of tumor epithelial to CD8+ T cells in non-small cell lung cancer. Our study demonstrated the high co-occurrence of copy number amplification and co-expression of OPA1 and MFN1 in LUAD tissue, and further revealed the contribution of OPA1 in maintaining the mitochondria respiratory function and the ability of immune evasion to CD8+ T cells of LUAD.

Somatic copy number variant load in neurons of healthy controls and Alzheimer’s disease patients

The possible role of somatic copy number variations (CNVs) in Alzheimer’s disease (AD) aetiology has been controversial. Although cytogenetic studies suggested increased CNV loads in AD brains, a recent single-cell whole-genome sequencing (scWGS) experiment, studying frontal cortex brain samples, found no such evidence. Here we readdressed this issue using low-coverage scWGS on pyramidal neurons dissected via both laser capture microdissection (LCM) and fluorescence activated cell sorting (FACS) across five brain regions: entorhinal cortex, temporal cortex, hippocampal CA1, hippocampal CA3, and the cerebellum. Among reliably detected somatic CNVs identified in 1301 cells obtained from the brains of 13 AD patients and 7 healthy controls, deletions were more frequent compared to duplications. Interestingly, we observed slightly higher frequencies of CNV events in cells from AD compared to similar numbers of cells from controls (4.1% vs. 1.4%, or 0.9% vs. 0.7%, using different filtering approaches), although the differences were not statistically significant. On the technical aspects, we observed that LCM-isolated cells show higher within-cell read depth variation compared to cells isolated with FACS. To reduce within-cell read depth variation, we proposed a principal component analysis-based denoising approach that significantly improves signal-to-noise ratios. Lastly, we showed that LCM-isolated neurons in AD harbour slightly more read depth variability than neurons of controls, which might be related to the reported hyperploid profiles of some AD-affected neurons.

Abstract 1687: Cyto R1 Platform enriches samples’ viability for single-cell sequencing and downstream assays

Abstract Single-cell level investigations such as DNA and RNA sequencing, protein analyses, and phenotypic studies require an input sample with a high rate of viability (>90%). If too many dead cells are present in the input, degraded proteins and ambient DNA or RNA can increase background noise that may lead to missing identification of crucial targets. Since the cost for single-cell sequencing experiments still remains high, it is critical to ensure the input sample is optimized for high viability to ensure cost-effective and reproducible data. Current bead assays or other sample viability enrichment techniques used in preparation for single-cell analyses typically result in a significant sample loss, sometimes sacrificing 80% of the starting sample, and these preparation steps require multiple passes (> 45 minutes time) and costly kits. These beads or kits can be biased and change the cell population, subject cells to unwanted stresses, and diminish sample integrity with the time needed for preparations. Here, we investigate sample cell viability enrichment on the Cyto R1 Platform, a label-free, cell enrichment, sorting, and recovery platform. At the core, the Cyto R1 uses Cyto Chips, microfluidic technologies with electrical fields, to phenotypically enrich and sort various cells based upon cells’ unique physical structures and subcellular features. Thus, the Cyto R1 ensures native cell recovery without any unwanted cell tagging to maximize sample integrity. In this work, a variety of single cell solutions derived from T cell lymphoma tumors, ascites fluid, and organ tissues were enriched for viable cells to obtain a final sample viability over 90%. Additionally, we aimed to achieve a minimum of 50,000 viable cells, a common target for single-cell sequencing, in as rapid a timeframe as possible. In one sample origin tested, murine T cell lymphomas, the average starting viability was 65 ± 6.5%, and the final viability after enrichment on the Cyto R1 Platform was 95.8 ± 1.8%. Similarly, murine ascites cell mixtures were enriched from average viabilities below 55% up to 91.6 ± 4.2%. For all samples tested, >50,000 viable cells were obtained in under 30 minutes. This work demonstrates that the Cyto R1 can effectively and efficiently enrich a variety of starting samples for high viability (>90%) as required for downstream assays and single-cell sequencing. Citation Format: Alexandra R. Hyler, Alicia M. Cole, Ridi Barua, Amiran K. Dzutsev, Eva M. Schmelz. Cyto R1 Platform enriches samples’ viability for single-cell sequencing and downstream assays [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1687.

Identifying tumor cells at the single-cell level using machine learning

Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation—the assignment of cell type or cell state to each sequenced cell—is a challenge, especially identifying tumor cells within single-cell or spatial sequencing experiments. Here, we propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level. We test ikarus on multiple single-cell datasets, showing that it achieves high sensitivity and specificity in multiple experimental contexts.

Astrocyte Reprogramming in Stroke: Opportunities and Challenges.

Stroke is a major cause of morbidity and mortality worldwide. In the early stages of stroke, irreversible damage to neurons leads to high mortality and disability rates in patients. However, there are still no effective prevention and treatment measures for the resulting massive neuronal death in clinical practice. Astrocyte reprogramming has recently attracted much attention as an avenue for increasing neurons in mice after cerebral ischemia. However, the field of astrocyte reprogramming has recently been mired in controversy due to reports questioning whether newborn neurons are derived from astrocyte transformation. To better understand the process and controversies of astrocyte reprogramming, this review introduces the method of astrocyte reprogramming and its application in stroke. By targeting key transcription factors or microRNAs, astrocytes in the mouse brain could be reprogrammed into functional neurons. Additionally, we summarize some of the current controversies over the lack of cell lineage tracing and single-cell sequencing experiments to provide evidence of gene expression profile changes throughout the process of astrocyte reprogramming. Finally, we present recent advances in cell lineage tracing and single-cell sequencing, suggesting that it is possible to characterize the entire process of astrocyte reprogramming by combining these techniques.

Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain.

A key aspect of nearly all single-cell sequencing experiments is dissociation of intact tissues into single-cell suspensions. While many protocols have been optimized for optimal cell yield, they have often overlooked the effects that dissociation can have on ex vivo gene expression. Here, we demonstrate that use of enzymatic dissociation on brain tissue induces an aberrant ex vivo gene expression signature, most prominently in microglia, which is prevalent in published literature and can substantially confound downstream analyses. To address this issue, we present a rigorously validated protocol that preserves both in vivo transcriptional profiles and cell-type diversity and yield across tissue types and species. We also identify a similar signature in postmortem human brain single-nucleus RNA-sequencing datasets, and show that this signature is induced in freshly isolated human tissue by exposure to elevated temperatures ex vivo. Together, our results provide a methodological solution for preventing artifactual gene expression changes during fresh tissue digestion and a reference for future deeper analysis of the potential confounding states present in postmortem human samples.

Ligand-mediated PAI-1 inhibition in a mouse model of peritoneal carcinomatosis

SummaryPeritoneal carcinomatosis (PC) present a ubiquitous clinical conundrum in all intra-abdominal malignancies. Via functional and transcriptomic experiments of ascites-treated PC cells, we identify STAT3 as a key signaling pathway. Integrative analysis of publicly available databases and correlation with clinical cohorts (n = 7,359) reveal putative clinically significant activating ligands of STAT3 signaling. We further validate a 3-biomarker prognostic panel in ascites independent of clinical covariates in a prospective study (n = 149). Via single-cell sequencing experiments, we uncover that PAI-1, a key component of the prognostic biomarker panel, is largely secreted by fibroblasts and mesothelial cells. Molecular stratification of ascites using PAI-1 levels and STAT3 activation in ascites-treated cells highlight a therapeutic opportunity based on a phenomenon of paracrine addiction. These results are recapitulated in patient-derived ascites-dependent xenografts. Here, we demonstrate therapeutic proof of concept of direct ligand inhibition of a prognostic target within an enclosed biological space.

LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data

The rise of longitudinal single-cell sequencing experiments on patient-derived cell cultures, xenografts and organoids is opening new opportunities to track cancer evolution, assess the efficacy of therapies and identify resistant subclones.We introduce LACE, the first algorithmic framework that processes single-cell mutational profiles from samples collected at different time points to reconstruct longitudinal models of cancer evolution. The approach maximizes a weighted likelihood function computed on longitudinal data points to solve a Boolean matrix factorization problem, via Markov chain Monte Carlo sampling.On simulations, LACE outperforms state-of-the-art methods for both bulk and single-cell sequencing data with respect to the reconstruction of the ground-truth clonal phylogeny and dynamics, also in conditions of unbalanced datasets, significant rates of sequencing errors and sampling limitations. As the results are robust with respect to data-specific errors, LACE is effective with mutational profiles generated by calling variants from (full-length) scRNA-seq data, and this allows one to investigate the relation between genomic and phenotypic evolution of tumors at the single-cell level.Here, we apply LACE to a longitudinal scRNA-seq dataset of patient-derived xenografts of BRAFV600E/K mutant melanomas, dissecting the impact of BRAF/MEK-inhibition on clonal evolution, also in terms of clone-specific gene expression dynamics. Furthermore, the analysis of breast cancer PDXs from longitudinal targeted scDNA-sequencing experiments delivers a high-resolution temporal characterization of intra-tumor heterogeneity.

Improved nanopore direct RNA sequencing of cardiac myocyte samples by selective mt-RNA depletion

RNA sequencing is a powerful tool to analyze gene expression transcriptome wide. However, RNA sequencing in general and especially the recently developed methods of long read RNA sequencing are still low-throughput and cost-intensive. Here, one important design choice is to concentrate the sequencing capacity on specific parts of the transcriptome. Especially, abundant transcripts as ribosomal RNAs may dominate the available sequencing space, if not removed prior to sequencing. Several methods exist to reduce ribosomal RNA read numbers: either based on enrichment of the relevant fraction (polyA+ RNA) or depletion, respectively degradation of ribosomal RNAs. Furthermore, commercial kits are available to deplete globin transcripts from blood samples. However, so far, no solution exists to deal with other tissue-specific highly abundant transcripts. This is especially of interest in the heart and other muscle derived samples, where reads originating from mitochondrial RNAs make up to 30% of reads in polyA+ selected libraries and around 70% in single cell sequencing experiments. We present a simple method to diminish sequencing of mitochondrial RNAs in Oxford Nanopore direct RNA sequencing libraries by RNase H based clipping of the polyA tail. We show that mt-clipping enables enhanced detection of cytoplasmic mRNAs, among them genes involved in heart development and pathogenesis. Mt-clipping may be applied as well to other sequencing protocols that are based on oligo(dT) priming and can be easily adapted to other tissue-specific high-abundant transcripts.

Inferring gene regulation dynamics from static snapshots of gene expression variability.

Inferring functional relationships within complex networks from static snapshots of a subset of variables is a ubiquitous problem in science. For example, a key challenge of systems biology is to translate cellular heterogeneity data obtained from single-cell sequencing or flow-cytometry experiments into regulatory dynamics. We show how static population snapshots of covariability can be exploited to rigorously infer properties of gene expression dynamics when gene expression reporters probe their upstream dynamics on separate timescales. This can be experimentally exploited in dual-reporter experiments with fluorescent proteins of unequal maturation times, thus turning an experimental bug into an analysis feature. We derive correlation conditions that detect the presence of closed-loop feedback regulation in gene regulatory networks. Furthermore, we show how genes with cell-cycle-dependent transcription rates can be identified from the variability of coregulated fluorescent proteins. Similar correlation constraints might prove useful in other areas of science in which static correlation snapshots are used to infer causal connections between dynamically interacting components.

Functionally distinct POMC-expressing neuron subpopulations in hypothalamus revealed by intersectional targeting

Pro-opiomelanocortin (POMC)-expressing neurons in the arcuate nucleus of the hypothalamus represent key regulators of metabolic homeostasis. Electrophysiological and single-cell sequencing experiments have revealed a remarkable degree of heterogeneity of these neurons. However, the exact molecular basis and functional consequences of this heterogeneity have not yet been addressed. Here, we have developed new mouse models in which intersectional Cre/Dre-dependent recombination allowed for successful labeling, translational profiling and functional characterization of distinct POMC neurons expressing the leptin receptor (Lepr) and glucagon like peptide 1 receptor (Glp1r). Our experiments reveal that POMCLepr+ and POMCGlp1r+ neurons represent largely nonoverlapping subpopulations with distinct basic electrophysiological properties. They exhibit a specific anatomical distribution within the arcuate nucleus and differentially express receptors for energy-state communicating hormones and neurotransmitters. Finally, we identify a differential ability of these subpopulations to suppress feeding. Collectively, we reveal a notably distinct functional microarchitecture of critical metabolism-regulatory neurons.

TCRMatch: Predicting T-Cell Receptor Specificity Based on Sequence Similarity to Previously Characterized Receptors.

The adaptive immune system in vertebrates has evolved to recognize non-self antigens, such as proteins expressed by infectious agents and mutated cancer cells. T cells play an important role in antigen recognition by expressing a diverse repertoire of antigen-specific receptors, which bind epitopes to mount targeted immune responses. Recent advances in high-throughput sequencing have enabled the routine generation of T-cell receptor (TCR) repertoire data. Identifying the specific epitopes targeted by different TCRs in these data would be valuable. To accomplish that, we took advantage of the ever-increasing number of TCRs with known epitope specificity curated in the Immune Epitope Database (IEDB) since 2004. We compared seven metrics of sequence similarity to determine their power to predict if two TCRs have the same epitope specificity. We found that a comprehensive k-mer matching approach produced the best results, which we have implemented into TCRMatch, an openly accessible tool (http://tools.iedb.org/tcrmatch/) that takes TCR β-chain CDR3 sequences as an input, identifies TCRs with a match in the IEDB, and reports the specificity of each match. We anticipate that this tool will provide new insights into T cell responses captured in receptor repertoire and single cell sequencing experiments and will facilitate the development of new strategies for monitoring and treatment of infectious, allergic, and autoimmune diseases, as well as cancer.

XenoCell: classification of cellular barcodes in single cell experiments from xenograft samples

BackgroundSingle-cell sequencing technologies provide unprecedented opportunities to deconvolve the genomic, transcriptomic or epigenomic heterogeneity of complex biological systems. Its application in samples from xenografts of patient-derived biopsies (PDX), however, is limited by the presence of cells originating from both the host and the graft in the analysed samples; in fact, in the bioinformatics workflows it is still a challenge discriminating between host and graft sequence reads obtained in a single-cell experiment.ResultsWe have developed XenoCell, the first stand-alone pre-processing tool that performs fast and reliable classification of host and graft cellular barcodes from single-cell sequencing experiments. We show its application on a mixed species 50:50 cell line experiment from 10× Genomics platform, and on a publicly available PDX dataset obtained by Drop-Seq.ConclusionsXenoCell accurately dissects sequence reads from any host and graft combination of species as well as from a broad range of single-cell experiments and platforms. It is open source and available at https://gitlab.com/XenoCell/XenoCell.