Single-cell Experiments Research Articles

High-Precision Single-Cell microRNA Therapy by a Functional Nanopipette with Sensitive Photoelectrochemical Feedback.

This work proposes the concept of single-cell microRNA (miR) therapy and proof-of-concept by engineering a nanopipette for high-precision miR-21-targeted therapy in a single HeLa cell with sensitive photoelectrochemical (PEC) feedback. Targeting the representative oncogenic miR-21, the as-functionalized nanopipette permits direct intracellular drug administration with precisely controllable dosages, and the corresponding therapeutic effects can be sensitively transduced by a PEC sensing interface that selectively responds to the indicator level of cytosolic caspase-3. The experimental results reveal that injection of ca. 4.4×10-20mol miR-21 inhibitor, i.e., 26488 copies, can cause the obvious therapeutic action in the targeted cell. This work features a solution to obtain the accurate knowledge of how a certain miR-drug with specific dosages treats the cells and thus provides an insight into futuristic high-precision clinical miR therapy using personalized medicine, provided that the prerequisite single-cell experiments are courses of personalized customization.

Unraveling the timeline of gene expression: A pseudotemporal trajectory analysis of single-cell RNA sequencing data.

Single-cell RNA sequencing (scRNA-seq) technologies have rapidly developed in recent years. The droplet-based single cell platforms enable the profiling of gene expression in tens of thousands of cells per sample. The goal of a typical scRNA-seq analysis is to identify different cell subpopulations and their respective marker genes. Additionally, trajectory analysis can be used to infer the developmental or differentiation trajectories of cells. This article demonstrates a comprehensive workflow for performing trajectory inference and time course analysis on a multi-sample single-cell RNA-seq experiment of the mouse mammary gland. The workflow uses open-source R software packages and covers all steps of the analysis pipeline, including quality control, doublet prediction, normalization, integration, dimension reduction, cell clustering, trajectory inference, and pseudo-bulk time course analysis. Sample integration and cell clustering follows the Seurat pipeline while the trajectory inference is conducted using the monocle3 package. The pseudo-bulk time course analysis uses the quasi-likelihood framework of edgeR. Cells are ordered and positioned along a pseudotime trajectory that represented a biological process of cell differentiation and development. The study successfully identified genes that were significantly associated with pseudotime in the mouse mammary gland. The demonstrated workflow provides a valuable resource for researchers conducting scRNA-seq analysis using open-source software packages. The study successfully demonstrated the usefulness of trajectory analysis for understanding the developmental or differentiation trajectories of cells. This analysis can be applied to various biological processes such as cell development or disease progression, and can help identify potential biomarkers or therapeutic targets.

Unraveling the pathogenesis of novel ANKRD1 missense mutation causing familial dilated cardiomyopathy using human-induced pluripotent stem cells derived cardiomyocytes

Abstract Introduction Familial dilated cardiomyopathy(f-DCM) can be caused by mutations in 30 different genes encoding sarcomeric, cytoskeletal, mechanotransducers and transcription factors. One of the genes ANKRD1 encode Cardiac ankyrin repeat protein (CARP) which is involved in the mechanosensory unit located on the I-band of the sarcomere. Here we demonstrate a novel mutation in ANKRD1 gene causing f-DCM, and the usage of human-induced pluripotent(hiPSC) technology unraveling the pathogenesis of the disease. Purpose of the study: 1) Establishing patient specific hiPSC-derived cardiomyocytes from a family harboring a mutation in ANKRD1 gene causing f-DCM; 2) Phenotypical assessment of the morphological and ultrastructural properties of the diseased cells upon CARP activation; 3) Functional assessment of the 3 component of excitation-contraction coupling at the single-cell and tissue level. Methods and Results Patient-specific hiPSC were generated from a family harboring missense mutation in the ANKRD1 gene, with a substitution of the amino acid arginine to cysteine at position 182 of the Cardiac ankyrin repeat protein (CARP). ANKRD1-mut hiPSC were differentiated into cardiomyocytes and later matured using hormonal treatment. Morphological assessment using immunofluorescence(IF) techniques revealed that ANKRD1-mut hiPSC-CMs demonstrate an attenuated response to hypertrophy (upon Angiotensin-2 and Phenylephrine treatment). In addition ANKRD1-mut hiPSC-CMs displayed a higher cellular death rate upon increasing substrate stiffness and starvation. Electrophysiological assessment of the ANKRD1-mut cardiac tissues exhibit no alternations in action potential durations and conduction velocities while assessment of calcium transients demonstrated slower kinetics. In addition forces were quantified at the single-cell and tissue level , and demonstrated reduced active tension values and abnormal post-extrasystolic potentiation, while only single-cell experiments have demonstrated decreased passive tensions. These phenotypical alternations might be attributed to decreased nuclear translocation of CARP upon phenylephrine treatment as detected in IF experiments. Conclusion Novel mutation in ANKRD1 were found and validated to be causing f-DCM, by revealing phenotypical alternation using hiPSC-CM technology. Increased cellular death rate and impaired nuclear translocation of CARP might be the underlying causes of the morphological and contractile abnormalities detected in-vitro, leading to systolic dysfunction in the affected family.

DeltaXpress (ΔXpress): a tool for mapping differentially correlated genes using single-cell qPCR data

BackgroundHigh-throughput experiments provide deep insight into the molecular biology of different species, but more tools need to be developed to handle this type of data. At the transcriptomics level, quantitative Polymerase Chain Reaction technology (qPCR) can be affordably adapted to produce high-throughput results through a single-cell approach. In addition to comparative expression profiles between groups, single-cell approaches allow us to evaluate and propose new dependency relationships among markers. However, this alternative has not been explored before for large-scale qPCR-based experiments.ResultsHerein, we present deltaXpress (ΔXpress), a web app for analyzing data from single-cell qPCR experiments using a combination of HTML and R programming languages in a friendly environment. This application uses cycle threshold (Ct) values and categorical information for each sample as input, allowing the best pair of housekeeping genes to be chosen to normalize the expression of target genes. ΔXpress emulates a bulk analysis by observing differentially expressed genes, but in addition, it allows the discovery of pairwise genes differentially correlated when comparing two experimental conditions. Researchers can download normalized data or use subsequent modules to map differentially correlated genes, perform conventional comparisons between experimental groups, obtain additional information about their genes (gene glossary), and generate ready-to-publication images (600 dots per inch).ConclusionsΔXpress web app is freely available to non-commercial users at https://alexismurillo.shinyapps.io/dXpress/ and can be used for different experiments in all technologies involving qPCR with at least one housekeeping region.

EAAT3 impedes oligodendrocyte remyelination in chronic cerebral hypoperfusion-induced white matter injury.

Chronic cerebral hypoperfusion-induced demyelination causes progressive white matter injury, although the pathogenic pathways are unknown. The Single Cell Portal and PanglaoDB databases were used to analyze single-cell RNA sequencing experiments to determine the pattern of EAAT3 expression in CNS cells. Immunofluorescence (IF) was used to detect EAAT3 expression in oligodendrocytes and oligodendrocyte progenitor cells (OPCs). EAAT3 levels in mouse brains were measured using a western blot at various phases of development, as well as in traumatic brain injury (TBI) and intracerebral hemorrhage (ICH) mouse models. The mouse bilateral carotid artery stenosis (BCAS) model was used to create white matter injury. IF, Luxol Fast Blue staining, and electron microscopy were used to investigate the effect of remyelination. 5-Ethynyl-2-Deoxy Uridine staining, transwell chamber assays, and IF were used to examine the effects of OPCs' proliferation, migration, and differentiation in vivo and in vitro. The novel object recognition test, the Y-maze test, the rotarod test, and the grid walking test were used to examine the impact of behavioral modifications. A considerable amount of EAAT3 was expressed in OPCs and mature oligodendrocytes, according to single-cell RNA sequencing data. During multiple critical phases of mouse brain development, there were no substantial changes in EAAT3 levels in the hippocampus, cerebral cortex, or white matter. Furthermore, neither the TBI nor ICH models significantly affected the levels of EAAT3 in the aforementioned brain areas. The chronic white matter injury caused by BCAS, on the other hand, resulted in a strikingly high level of EAAT3 expression in the oligodendroglia and white matter. Correspondingly, blocking EAAT3 assisted in the recovery of cognitive and motor impairment as well as the restoration of cerebral blood flow following BCAS. Furthermore, EAAT3 suppression was connected to improved OPCs' survival and proliferation in vivo as well as faster OPCs' proliferation, migration, and differentiation in vitro. Furthermore, this study revealed that the mTOR pathway is implicated in EAAT3-mediated remyelination. Our findings provide the first evidence that abnormally high levels of oligodendroglial EAAT3 in chronic cerebral hypoperfusion impair OPCs' pro-remyelination actions, hence impeding white matter repair and functional recovery. EAAT3 inhibitors could be useful in the treatment of ischemia demyelination.

MAGNETO: Cell type marker panel generator from single-cell transcriptomic data

Single-cell RNA sequencing experiments produce data useful to identify different cell types, including uncharacterized and rare ones. This enables us to study the specific functional roles of these cells in different microenvironments and contexts. After identifying a (novel) cell type of interest, it is essential to build succinct marker panels, composed of a few genes referring to cell surface proteins and clusters of differentiation molecules, able to discriminate the desired cells from the other cell populations. In this work, we propose a fully-automatic framework called MAGNETO, which can help construct optimal marker panels starting from a single-cell gene expression matrix and a cell type identity for each cell. MAGNETO builds effective marker panels solving a tailored bi-objective optimization problem, where the first objective regards the identification of the genes able to isolate a specific cell type, while the second conflicting objective concerns the minimization of the total number of genes included in the panel. Our results on three public datasets show that MAGNETO can identify marker panels that identify the cell populations of interest better than state-of-the-art approaches. Finally, by fine-tuning MAGNETO, our results demonstrate that it is possible to obtain marker panels with different specificity levels.

MINI-AC: inference of plant gene regulatory networks using bulk or single-cell accessible chromatin profiles.

Gene regulatory networks (GRNs) represent the interactions between transcription factors (TF) and their target genes. Plant GRNs control transcriptional programs involved in growth, development, and stress responses, ultimately affecting diverse agricultural traits. While recent developments in accessible chromatin (AC) profiling technologies make it possible to identify context-specific regulatory DNA, learning the underlying GRNs remains a major challenge. We developed MINI-AC (Motif-Informed Network Inference based on Accessible Chromatin), a method that combines AC data from bulk or single-cell experiments with TF binding site (TFBS) information to learn GRNs in plants. We benchmarked MINI-AC using bulk AC datasets from different Arabidopsis thaliana tissues and showed that it outperforms other methods to identify correct TFBS. In maize, a crop with a complex genome and abundant distal AC regions, MINI-AC successfully inferred leaf GRNs with experimentally confirmed, both proximal and distal, TF-target gene interactions. Furthermore, we showed that both AC regions and footprints are valid alternatives to infer AC-based GRNs with MINI-AC. Finally, we combined MINI-AC predictions from bulk and single-cell AC datasets to identify general and cell-type specific maize leaf regulators. Focusing on C4 metabolism, we identified diverse regulatory interactions in specialized cell types for this photosynthetic pathway. MINI-AC represents a powerful tool for inferring accurate AC-derived GRNs in plants and identifying known and novel candidate regulators, improving our understanding of gene regulation in plants.

Scellpam: an R package/C++ library to perform parallel partitioning around medoids on scRNAseq data sets

Background Partitioning around medoids (PAM) is one of the most widely used and successful clustering method in many fields. One of its key advantages is that it only requires a distance or a dissimilarity between the individuals, and the fact that cluster centers are actual points in the data set means they can be taken as reliable representatives of their classes. However, its wider application is hampered by the large amount of memory needed to store the distance matrix (quadratic on the number of individuals) and also by the high computational cost of computing such distance matrix and, less importantly, by the cost of the clustering algorithm itself.Results Therefore, new software has been provided that addresses these issues. This software, provided under GPL license and usable as either an R package or a C++ library, calculates in parallel the distance matrix for different distances/dissimilarities (L_1, L_2, Pearson, cosine and weighted Euclidean) and also implements a parallel fast version of PAM (FASTPAM1) using any data type to reduce memory usage. Moreover, the parallel implementation uses all the cores available in modern computers which greatly reduces the execution time. Besides its general application, the software is especially useful for processing data of single cell experiments. It has been tested in problems including clustering of single cell experiments with up to 289,000 cells with the expression of about 29,000 genes per cell.Conclusions Comparisons with other current packages in terms of execution time have been made. The method greatly outperforms the available R packages for distance matrix calculation and also improves the packages that implement the PAM itself. The software is available as an R package at https://CRAN.R-project.org/package=scellpam and as C++ libraries at https://github.com/JdMDE/jmatlib and https://github.com/JdMDE/ppamlib The package is useful for single cell RNA-seq studies but it is also applicable in other contexts where clustering of large data sets is required.

Global quantitative understanding of non-equilibrium cell fate decision-making in response to pheromone

Cell-cycle arrest and polarized growth are commonly used to characterize the response of yeast to pheromone. However, the quantitative decision-making processes underlying time-dependent changes in cell fate remain unclear. In this study, we conducted single-cell level experiments to observe multidimensional responses, uncovering diverse fates of yeast cells. Multiple states are revealed, along with the kinetic switching rates and pathways among them, giving rise to a quantitative landscape of mating response. To quantify the experimentally observed cell fates, we developed a theoretical framework based on non-equilibrium landscape and flux theory. Additionally, we performed stochastic simulations of biochemical reactions to elucidate signal transduction and cell growth. Notably, our experimental findings have provided the first global quantitative evidence of the real-time synchronization between intracellular signaling, physiological growth, and morphological functions. These results validate the proposed underlying mechanism governing the emergence of multiple cell fate states. This study introduces an emerging mechanistic approach to understand non-equilibrium cell fate decision-making in response to pheromone.

P02.13.A THIRD-GENERATION SEQUENCING OF BRAIN TUMORS ON SINGLE-CELL LEVEL

Abstract BACKGROUND Intratumor heterogeneity (ITH) in brain tumors has been suggested to increase tumor adaptability and thus drive tumor progression and treatment resistance. Single cell sequencing has widened the range of possibilities in detecting clonal diversity and tumor evolution. As of now, most single cell experiments rely on next-generation sequencing (NGS) platforms. However, they detect only short RNA/DNA fragments that do not show the full picture of existing transcript isoforms or genetic variants. New, long-read sequencing methods such as Oxford Nanopore Technologies (ONT) or PacBio sequencing may now enable novel approaches to single cell experiments. We here set-out to complement existing single-cell analysis workflows and data pipelines with long-read sequencing approaches. We compare and integrate the data output in order to advance the understanding of ITH in brain tumors, paradigmatically demonstrated on meningioma. MATERIAL AND METHODS We set up a pipeline for single-cell/nuclei RNA long-read sequencing on ONT devices. First, libraries as derived from the 10x genomics workflow were employed, subsequently followed by independently generated libraries. To widen the insight into ITH on multiple levels, we next assessed single-cell/nuclei DNA for small somatic variants and methylation along a modified version of this platform. RESULTS Long-read RNA-sequencing yielded results comparable to short-read sequencing. Depending on sample quality, higher reads per cell and library were required for ONT sequencing to achieve similar discrimination of clusters by expression. On the other hand, cell-type specific isoforms could be identified. Specific small somatic variants known from bulk sequencing data were identified when the region was sufficiently covered in the RNA data, which was naturally more often achieved in long read than short-read data. Thus, cell-specific variant calling was enabled in a high-throughput pipeline. Maintaining the methylation information in DNA sequencing could so far only be set-up in a plate-based approach. CONCLUSION Using these approaches, we resolved features of ITH and clonal tumor evolution, aiding us in gaining a broader understanding of brain tumor biology. Furthermore, it showed novel approaches that could possibly be implemented in routine diagnostic procedures in the future. These novel techniques could be applied to other tumor entities and possibly further omics layers such as single cell proteomics and metabolomics could be added to gain a greater understanding of different tumor types and their clonal diversity.

Adrenergic nerves regulate intestinal regeneration through IL-22 signaling from type 3 innate lymphoid cells.

The intestinal epithelium has high intrinsic turnover rate, and the precise renewal of the epithelium is dependent on the microenvironment. The intestine is innervated by a dense network of peripheral nerves that controls various aspects of intestinal physiology. However, the role of neurons in regulating epithelial cell regeneration remains largely unknown. Here, we investigated the effects of gut-innervating adrenergic nerves on epithelial cell repair following irradiation (IR)-induced injury. We observed that adrenergic nerve density in the small intestine increased post IR, while chemical adrenergic denervation impaired epithelial regeneration. Single-cell RNA sequencing experiments revealed a decrease in IL-22 signaling post IR in denervated animals. Combining pharmacologic and genetic tools, we demonstrate that β-adrenergic receptor signaling drives IL-22 production from type 3 innate lymphoid cells (ILC3s) post IR, which in turn promotes epithelial regeneration. These results define an adrenergic-ILC3 axis important for intestinal regeneration.

Multi-omics Analysis Identifies Hypoxia Subtypes and S100A2 as an Immunosuppressive Factor in Cervical Cancer.

Cervical cancer is a common gynecological oncology. Growing evidence indicates hypoxia plays an important role in tumor progression and immunity. However, no study has examined the hypoxia landscape in cervical cancer. In this study, using hierarchical clustering, we identified three hypoxia subtypes in cervical cancer samples from The Cancer Genome Atlas dataset according to formerly described hypoxia-related genes. The overall survival time, hypoxic features, genomics, and immunological characteristics of these subtypes existed distinct differences. We also created a hypoxia score by principle component analysis for dimension reduction. The hypoxiaScore was an effective prognostic biomarker validated by GSE44001 and was associated with immunotherapy response. Furthermore, combined with single-cell RNA-sequence (scRNA-seq) and experiments, S100A2 was identified as an immunosuppressive factor induced by hypoxia and regulated expression of PD-L1. S100A2 also served as an oncogene promoting the proliferation and migration of cervical cancer cells. These findings depicted a new hypoxia-based classification and identified S100A2 as a potential therapeutic target for cervical cancer, thereby advancing the understanding of immunotherapy resistance mechanisms and cervical cancer genetic markers.

Glucagon-like Peptide-1 Receptor Activation Reduces Pulmonary Vein Arrhythmogenesis and Regulates Calcium Homeostasis.

Glucagon-like peptide-1 (GLP-1) receptor agonists are associated with reduced atrial fibrillation risk, but the mechanisms underlying this association remain unclear. The GLP-1 receptor agonist directly impacts cardiac Ca2+ homeostasis, which is crucial in pulmonary vein (PV, the initiator of atrial fibrillation) arrhythmogenesis. This study investigated the effects of the GLP-1 receptor agonist on PV electrophysiology and Ca2+ homeostasis and elucidated the potential underlying mechanisms. Conventional microelectrodes and whole-cell patch clamp techniques were employed in rabbit PV tissues and single PV cardiomyocytes before and after GLP-1 (7-36) amide, a GLP-1 receptor agonist. Evaluations were conducted both with and without pretreatment with H89 (10 μM, an inhibitor of protein kinase A, PKA), KN93 (1 μM, an inhibitor of Ca2+/calmodulin-dependent protein kinase II, CaMKII), and KB-R7943 (10 μM, an inhibitor of Na+/Ca2+ exchanger, NCX). Results showed that GLP-1 (7-36) amide (at concentrations of 1, 10, and 100 nM) reduced PV spontaneous activity in a concentration-dependent manner without affecting sinoatrial node electrical activity. In single-cell experiments, GLP-1 (7-36) amide (at 10 nM) reduced L-type Ca2+ current, NCX current, and late Na+ current in PV cardiomyocytes without altering Na+ current. Additionally, GLP-1 (7-36) amide (at 10 nM) increased sarcoplasmic reticulum Ca2+ content in PV cardiomyocytes. Furthermore, the antiarrhythmic effects of GLP-1 (7-36) amide on PV automaticity were diminished when pretreated with H89, KN93, or KB-R7943. This suggests that the GLP-1 receptor agonist may exert its antiarrhythmic potential by regulating PKA, CaMKII, and NCX activity, as well as modulating intracellular Ca2+ homeostasis, thereby reducing PV arrhythmogenesis.

Leveraging Single-Cell Populations to Uncover the Genetic Basis of Complex Traits.

The ease and throughput of single-cell genomics have steadily improved, and its current trajectory suggests that surveying single-cell populations will become routine. We discuss the merger of quantitative genetics with single-cell genomics and emphasize how this synergizes with advantages intrinsic to plants. Single-cell population genomics provides increased detection resolution when mapping variants that control molecular traits, including gene expression or chromatin accessibility. Additionally, single-cell population genomics reveals the cell types in which variants act and, when combined with organism-level phenotype measurements, unveils which cellular contexts impact higher-order traits. Emerging technologies, notably multiomics, can facilitate the measurement of both genetic changes and genomic traits in single cells, enabling single-cell genetic experiments. The implementation of single-cell genetics will advance the investigation of the genetic architecture of complex molecular traits and provide new experimental paradigms to study eukaryotic genetics.

An efficient state-of-health estimation method for lithium-ion batteries based on feature-importance ranking strategy and PSO-GRNN algorithm

As a critical index in ensuring the safe and reliable deployment of lithium-ion batteries (LIBs), the estimation performance of state-of-health (SOH) seriously affects the popularization of electric vehicles (EVs). However, for the existing SOH estimation methods based on machine learning algorithms, the health features are manually extracted mainly based on personal experience, without algorithm-based automatic preferential selection. Thus, this study proposes a battery SOH estimation method based on the feature-importance ranking strategy and generalized regression neural network optimized by particle swarm optimization (PSO-GRNN). Firstly, 19 vital features were extracted from charging voltage, charging current, temperature, and incremental capacity (IC) curves. Secondly, the importance indexes for these features were calculated and optimally ranked by fusing the machine learning algorithm (random forest) and statistical analysis (Pearson correlation coefficient). Accordingly, different combinations of high-ranking features were compared to determine the best number of the final feature set. Thirdly, the SOH estimation model is built by the PSO-GRNN algorithm with optimal 7 high-ranking features. Based on the publicly available NASA datasets, the proposed SOH estimation model was validated on the data from a single-cell battery. Furthermore, the datasets from three cells with different cycling test conditions were utilized, where the data from two cells and the other cell were alternately selected as the training and test sets, respectively. The SOH estimation errors (squared absolute error and mean squared error) for single-cell and multi-cell experiments were less than 1 % and 3 %, respectively. Experimental results show that the proposed method is robust and can achieve accurate and lightweight SOH estimation.

Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender.

Droplet-based single-cell assays, including single-cell RNA sequencing (scRNA-seq), single-nucleus RNA sequencing (snRNA-seq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), generate considerable background noise counts, the hallmark of which is nonzero counts in cell-free droplets and off-target gene expression in unexpected cell types. Such systematic background noise can lead to batch effects and spurious differential gene expression results. Here we develop a deep generative model based on the phenomenology of noise generation in droplet-based assays. The proposed model accurately distinguishes cell-containing droplets from cell-free droplets, learns the background noise profile and provides noise-free quantification in an end-to-end fashion. We implement this approach in the scalable and robust open-source software package CellBender. Analysis of simulated data demonstrates that CellBender operates near the theoretically optimal denoising limit. Extensive evaluations using real datasets and experimental benchmarks highlight enhanced concordance between droplet-based single-cell data and established gene expression patterns, while the learned background noise profile provides evidence of degraded or uncaptured cell types.

Single-Cell ICP-MS in Combination with Fluorescence-Activated Cell Sorting for Investigating the Effects of Nanotransported Cisplatin(IV) Prodrugs.

The combined use of fluorescence-activated cell sorting (FACS) and single-cell inductively coupled plasma mass spectrometry (SC-ICP-MS) is reported, for the first time, in this work. It is applied to evaluate the differences between the cellular uptake of ultrasmall iron oxide nanoparticles (FeNPs) loaded with cisplatin(IV) prodrug (FeNPs-Pt(IV)) and cisplatin regarding cell viability. For this aim, FACS is applied to separate viable, apoptotic, and necrotic A2780 ovarian cancer cells after exposing them to the nanotransported prodrug and cisplatin, respectively. The different sorted cell populations are individually analyzed using quantitative SC-ICP-MS to address the intracellular amount of Pt. The highest Pt intracellular content occurs in the apoptotic cell population (about 2.1 fg Pt/cell) with a narrow intercellular distribution when using FeNPs-Pt(IV) nanoprodrug and containing the largest number of cells (75% of the total). In the case of the cisplatin-treated cells, the highest Pt content (about 1.6 fg Pt/cell) could be determined in the viable sorted cell population. The combined methodology, never explored before, permits a more accurate picture of the effect of the intracellular drug content together with the cell death mechanisms associated with the free drug and the nanotransported prodrug, respectively, and opens the door to many possible single-cell experiments in sorted cell populations.

Glyoxal as an alternative fixative for single-cell RNA sequencing.

Single-cell RNA sequencing has become an important method to identify cell types, delineate the trajectories of cell differentiation in whole organisms, and understand the heterogeneity in cellular responses. Nevertheless, sample collection and processing remain a severe bottleneck for single-cell RNA sequencing experiments. Cell isolation protocols often lead to significant changes in the transcriptomes of cells, requiring novel methods to preserve cell states. Here, we developed and benchmarked protocols using glyoxal as a fixative for single-cell RNA sequencing applications. Using Drop-seq methodology, we detected a large number of transcripts and genes from glyoxal-fixed Drosophila cells after single-cell RNA sequencing. The effective glyoxal fixation of transcriptomes in Drosophila and human cells was further supported by a high correlation of gene expression data between glyoxal-fixed and unfixed samples. Accordingly, we also found highly expressed genes overlapping to a large extent between experimental conditions. These results indicated that our fixation protocol did not induce considerable changes in gene expression and conserved the transcriptome for subsequent single-cell isolation procedures. In conclusion, we present glyoxal as a suitable fixative for Drosophila cells and potentially cells of other species that allow high-quality single-cell RNA sequencing applications.

Molecular signatures define subtypes of auditory afferents with distinct peripheral projection patterns and physiological properties

Type I spiral ganglion neurons (SGNs) are the auditory afferents that transmit sound information from cochlear inner hair cells (IHCs) to the brainstem. These afferents consist of physiological subtypes that differ in their spontaneous firing rate (SR), activation threshold, and dynamic range and have been described as low, medium, and high SR fibers. Lately, single-cell RNA sequencing experiments have revealed three molecularly defined type I SGN subtypes. The extent to which physiological type I SGN subtypes correspond to molecularly defined subtypes is unclear. To address this question, we have generated mouse lines expressing CreERT2 in SGN subtypes that allow for a physiological assessment of molecular subtypes. We show that Lypd1-CreERT2 expressing SGNs represent a well-defined group of neurons that preferentially innervate the IHC modiolar side and exhibit a narrow range of low SRs. In contrast, Calb2-CreERT2 expressing SGNs preferentially innervate the IHC pillar side and exhibit a wider range of SRs, thus suggesting that a strict stratification of all SGNs into three molecular subclasses is not obvious, at least not with the CreERT2 tools used here. Genetically marked neuronal subtypes refine their innervation specificity onto IHCs postnatally during the time when activity is required to refine their molecular phenotype. Type I SGNs thus consist of genetically defined subtypes with distinct physiological properties and innervation patterns. The molecular subtype-specific lines characterized here will provide important tools for investigating the role of the physiologically distinct type I SGNs in encoding sound signals.

Assessment of the Cardiac Noncoding Transcriptome by Single-Cell RNA Sequencing Identifies FIXER, a Conserved Profibrogenic Long Noncoding RNA.

Cardiac fibroblasts have crucial roles in the heart. In particular, fibroblasts differentiate into myofibroblasts in the damaged myocardium, contributing to scar formation and interstitial fibrosis. Fibrosis is associated with heart dysfunction and failure. Myofibroblasts therefore represent attractive therapeutic targets. However, the lack of myofibroblast-specific markers has precluded the development of targeted therapies. In this context, most of the noncoding genome is transcribed into long noncoding RNAs (lncRNAs). A number of lncRNAs have pivotal functions in the cardiovascular system. lncRNAs are globally more cell-specific than protein-coding genes, supporting their importance as key determinants of cell identity. In this study, we evaluated the value of the lncRNA transcriptome in very deep single-cell RNA sequencing. We profiled the lncRNA transcriptome in cardiac nonmyocyte cells after infarction and probed heterogeneity in the fibroblast and myofibroblast populations. In addition, we searched for subpopulation-specific markers that can constitute novel targets in therapy for heart disease. We demonstrated that cardiac cell identity can be defined by the sole expression of lncRNAs in single-cell experiments. In this analysis, we identified lncRNAs enriched in relevant myofibroblast subpopulations. Selecting 1 candidate we named FIXER (fibrogenic LOX-locus enhancer RNA), we showed that its silencing limits fibrosis and improves heart function after infarction. Mechanitically, FIXER interacts with CBX4, an E3 SUMO protein ligase and transcription factor, guiding CBX4 to the promoter of the transcription factor RUNX1 to control its expression and, consequently, the expression of a fibrogenic gene program.. FIXER is conserved in humans, supporting its translational value. Our results demonstrated that lncRNA expression is sufficient to identify the various cell types composing the mammalian heart. Focusing on cardiac fibroblasts and their derivatives, we identified lncRNAs uniquely expressed in myofibroblasts. In particular, the lncRNA FIXER represents a novel therapeutic target for cardiac fibrosis.