Individual Cells Research Articles

Nascent RNA kinetics with complex promoter architecture: Analytic results and parameter inference.

Transcription is a stochastic process that involves several downstream operations which make it difficult to model and infer transcription kinetics from mature RNA numbers in individual cell. However, recent advances in single-cell technologies have enabled a more precise measurement of the fluctuations of nascent RNA that closely reflect transcription kinetics. In this paper we introduce a general stochastic model to mimic nascent RNA kinetics with complex promoter architecture. We derive the exact distribution and moments of nascent RNA using queuing theory techniques, which provide valuable insights into the effect of the molecular memory created by the multistep activation and deactivation on the stochastic kinetics of nascent RNA. Moreover, based on the analytical results, we develop a statistical method to infer the promoter memory from stationary nascent RNA distributions. Data analysis of synthetic data and a realistic example, the HIV-1 gene, verifies the validity of this inference method.

Individual bacterial cells can use spatial sensing of chemical gradients to direct chemotaxis on surfaces

Swimming bacteria navigate chemical gradients using temporal sensing to detect changes in concentration over time. Here we show that surface-attached bacteria use a fundamentally different mode of sensing during chemotaxis. We combined microfluidic experiments, massively parallel cell tracking and fluorescent reporters to study how Pseudomonas aeruginosa senses chemical gradients during pili-based ‘twitching’ chemotaxis on surfaces. Unlike swimming cells, we found that temporal changes in concentration did not induce motility changes in twitching cells. We then quantified the chemotactic behaviour of stationary cells by following changes in the sub-cellular localization of fluorescent proteins as cells are exposed to a gradient that alternates direction. These experiments revealed that P. aeruginosa cells can directly sense differences in concentration across the lengths of their bodies, even in the presence of strong temporal fluctuations. Our work thus overturns the widely held notion that bacterial cells are too small to directly sense chemical gradients in space.

Unraveling flavivirus pathogenesis: from bulk to single-cell RNA-sequencing strategies.

The global spread of flaviviruses has triggered major outbreaks worldwide, significantly impacting public health, society, and economies. This has intensified research efforts to understand how flaviviruses interact with their hosts and manipulate the immune system, underscoring the need for advanced research tools. RNA-sequencing (RNA-seq) technologies have revolutionized our understanding of flavivirus infections by offering transcriptome analysis to dissect the intricate dynamics of virus-host interactions. Bulk RNA-seq provides a macroscopic overview of gene expression changes in virus-infected cells, offering insights into infection mechanisms and host responses at the molecular level. Single-cell RNA sequencing (scRNAseq) provides unprecedented resolution by analyzing individual infected cells, revealing remarkable cellular heterogeneity within the host response. A particularly innovative advancement, virus-inclusive single-cell RNA sequencing (viscRNA-seq), addresses the challenges posed by non-polyadenylated flavivirus genomes, unveiling intricate details of virus-host interactions. In this review, we discuss the contributions of bulk RNA-seq, scRNA-seq, and viscRNA-seq to the field, exploring their implications in cell line experiments and studies on patients infected with various flavivirus species. Comprehensive transcriptome analyses from RNA-seq technologies are pivotal in accelerating the development of effective diagnostics and therapeutics, paving the way for innovative treatments and enhancing our preparedness for future outbreaks.

IL-1β Induces LDL Transcytosis by a Novel Pathway Involving LDLR and Rab27a.

In early atherosclerosis, circulating LDLs (low-density lipoproteins) traverse individual endothelial cells by an active process termed transcytosis. The CANTOS trial (Canakinumab Antiinflammatory Thrombosis Outcome Study) treated advanced atherosclerosis using a blocking antibody for IL-1β (interleukin-1β); this significantly reduced cardiovascular events. However, whether IL-1β regulates early disease, particularly LDL transcytosis, remains unknown. We used total internal reflection fluorescence microscopy to quantify transcytosis by human coronary artery endothelial cells exposed to IL-1β. To investigate transcytosis in vivo, we injected wild-type and knockout mice with IL-1β and LDL to visualize acute LDL deposition in the aortic arch. Exposure to picomolar concentrations of IL-1β induced transcytosis of LDL but not of albumin by human coronary artery endothelial cells. Surprisingly, expression of the 2 known receptors for LDL transcytosis, ALK-1 (activin receptor-like kinase-1) and SR-BI (scavenger receptor BI), was unchanged or decreased. Instead, IL-1β increased the expression of the LDLR (LDL receptor); this was unexpected because LDLR is not required for LDL transcytosis. Overexpression of LDLR had no effect on basal LDL transcytosis. However, knockdown of LDLR abrogated the effect of IL-1β on transcytosis rates while the depletion of Cav-1 (caveolin-1) did not. Since LDLR was necessary but overexpression had no effect, we reasoned that another player must be involved. Using public RNA sequencing data to curate a list of Rab (Ras-associated binding) GTPases affected by IL-1β, we identified Rab27a. Overexpression of Rab27a alone had no effect on basal transcytosis, but its knockdown prevented induction by IL-1β. This was phenocopied by depletion of the Rab27a effector JFC1 (synaptotagmin-like protein 1). In vivo, IL-1β increased LDL transcytosis in the aortic arch of wild-type but not Ldlr-/- or Rab27a-deficient mice. The JFC1 inhibitor nexinhib20 also blocked IL-1β-induced LDL accumulation in the aorta. IL-1β induces LDL transcytosis by a distinct pathway requiring LDLR and Rab27a; this route differs from basal transcytosis. We speculate that induction of transcytosis by IL-1β may contribute to the acceleration of early disease.

Ecological and evolutionary dynamics to design and improve ovarian cancer treatment.

Ovarian cancer ecosystems are exceedingly complex, consisting of a high heterogeneity of cancer cells. Development of drugs such as poly ADP-ribose polymerase (PARP) inhibitors, targeted therapies and immunotherapies offer more options for sequential or combined treatments. Nevertheless, mortality in metastatic ovarian cancer patients remains high because cancer cells consistently develop resistance to single and combination therapies, urging a need for treatment designs that target the evolvability of cancer cells. The evolutionary dynamics that lead to resistance emerge from the complex tumour microenvironment, the heterogeneous populations, and the individual cancer cell's plasticity. We propose that successful management of ovarian cancer requires consideration of the ecological and evolutionary dynamics of the disease. Here, we review current options and challenges in ovarian cancer treatment and discuss principles of tumour evolution. We conclude by proposing evolutionarily designed strategies for ovarian cancer, with the goal of integrating such principles with longitudinal, quantitative data to improve the treatment design and management of drug resistance. KEY POINTS/HIGHLIGHTS: Tumours are ecosystems in which cancer and non-cancer cells interact and evolve in complex and dynamic ways. Conventional therapies for ovarian cancer inevitably lead to the development of resistance because they fail to consider tumours' heterogeneity and cellular plasticity. Eco-evolutionarily designed therapies should consider cancer cell plasticity and patient-specific characteristics to improve clinical outcome and prevent relapse.

Heterogeneity of tumor microenvironment cell groups in inflammatory and adenomatous polyposis coli mutant colorectal cancer based on single cell sequencing.

The prognosis of colorectal cancer (CRC) is known to vary across different etiologies. Inflammatory bowel disease (IBD) is often identified as a factor contributing to poorer outcomes. However, the mechanisms that link IBD to a worse CRC prognosis remain to be elucidated. We aim to reveal the complex tumor microenvironment of inflammatory CRC and provide a weak theoretical basis for the treatment of different subtypes of CRC. We conducted a bioinformatics analysis using single-cell RNA sequencing (scRNA-seq) data from 8,494 individual CRC cells derived from azoxymethane (AOM)/dextran sodium sulfate (DSS) and adenomatous polyposis coli (APC) mutant datasets. The expression of implicated genes in both tumor and adjacent normal tissues was examined via immunohistochemistry and immunofluorescence. CRC from AOM/DSS treatment contained fewer immune cells relative to APC-mutant CRC. However, a macrophage subcluster enriched for inflammatory factors was more prevalent in AOM/DSS datasets. This subcluster exhibited elevated expression of APOE and BNIP3. Immunofluorescence and immunohistochemistry of patient samples confirmed that the expression of APOE and BNIP3 was higher in adjacent normal tissues compared to tumors. Our findings shed light on the heterogeneous microenvironments in IBD and APC-mutant CRC. Furthermore, we identify APOE as a potential biomarker for CRC recurrence.

Decoding the pathogenesis of spermatogenic failure in cryptorchidism through single-cell transcriptomic profiling

Cryptorchidism, commonly known as undescended testis, affects 1%-9% of male newborns, posing infertility and testis tumor risks. Despite its prevalence, the detailed pathophysiology underlying male infertility within cryptorchidism remains unclear. Here, we profile and analyze 46,644 single-cell transcriptomes from individual testicular cells obtained from adult males diagnosed with cryptorchidism and healthy controls. Spermatogenesis compromise in cryptorchidism links primarily to spermatogonium self-renewal and differentiation dysfunctions. We illuminate the involvement of testicular somatic cells, including immune cells, thereby unveiling the activation and degranulation of mast cells in cryptorchidism. Mast cells are identified as contributors to interstitial fibrosis via transforming growth factor β1 (TGF-β1) and cathepsin G secretion. Furthermore, significantly increased levels of secretory proteins indicate mast cell activation and testicular fibrosis in the seminal plasma of individuals with cryptorchidism compared to controls. These insights serve as valuable translational references, enriching our comprehension of testicular pathogenesis and informing more precise diagnosis and targeted therapeutic strategies for cryptorchidism.

Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings.

Data from the single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) are now widely available. One major computational challenge is dealing with high dimensionality and inherent sparsity, which is typically addressed by producing lower dimensional representations of single cells for downstream clustering tasks. Current approaches produce such individual cell embeddings directly through a one-step learning process. Here, we propose an alternative approach by building embedding models pre-trained on reference data. We argue that this provides a more flexible analysis workflow that also has computational performance advantages through transfer learning. We implemented our approach in scEmbed, an unsupervised machine-learning framework that learns low-dimensional embeddings of genomic regulatory regions to represent and analyze scATAC-seq data. scEmbed performs well in terms of clustering ability and has the key advantage of learning patterns of region co-occurrence that can be transferred to other, unseen datasets. Moreover, models pre-trained on reference data can be exploited to build fast and accurate cell-type annotation systems without the need for other data modalities. scEmbed is implemented in Python and it is available to download from GitHub. We also make our pre-trained models available on huggingface for public use. scEmbed is open source and available at https://github.com/databio/geniml. Pre-trained models from this work can be obtained on huggingface: https://huggingface.co/databio.

Establishment of single-cell transcriptional states during seed germination

Germination involves highly dynamic transcriptional programs as the cells of seeds reactivate and express the functions necessary for establishment in the environment. Individual cell types have distinct roles within the embryo, so must therefore have cell type-specific gene expression and gene regulatory networks. We can better understand how the functions of different cell types are established and contribute to the embryo by determining how cell type-specific transcription begins and changes through germination. Here we describe a temporal analysis of the germinating Arabidopsis thaliana embryo at single-cell resolution. We define the highly dynamic cell type-specific patterns of gene expression and how these relate to changing cellular function as germination progresses. Underlying these are unique gene regulatory networks and transcription factor activity. We unexpectedly discover that most embryo cells transition through the same initial transcriptional state early in germination, even though cell identity has already been established during embryogenesis. Cells later transition to cell type-specific gene expression patterns. Furthermore, our analyses support previous findings that the earliest events leading to the induction of seed germination take place in the vasculature. Overall, our study constitutes a general framework with which to characterize Arabidopsis cell transcriptional states through seed germination, allowing investigation of different genotypes and other plant species whose seed strategies may differ.

ScVSC: Deep Variational Subspace Clustering for Single-Cell Transcriptome Data.

Single-cell RNA sequencing (scRNA-seq) is a potent advancement for analyzing gene expression at the individual cell level, allowing for the identification of cellular heterogeneity and subpopulations. However, it suffers from technical limitations that result in sparse and heterogeneous data. Here, we propose scVSC, an unsupervised clustering algorithm built on deep representation neural networks. The method incorporates the variational inference into the subspace model, which imposes regularization constraints on the latent space and further prevents overfitting. In a series of experiments across multiple datasets, scVSC outperforms existing state-of-the-art unsupervised and semi-supervised clustering tools regarding clustering accuracy and running efficiency. Moreover, the study indicates that scVSC could visually reveal the state of trajectory differentiation, accurately identify differentially expressed genes, and further discover biologically critical pathways.

Recovering single-cell expression profiles from spatial transcriptomics with scResolve.

Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable with cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell-type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained throughscResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.

Synchronisation of thermal imaging and multi-channel EIS to interpret planar array PCB fuel cell performance

The planar design of printed circuit board (PCB) fuel cells offers the advantages of simple structure, rapid prototyping and easy manufacturing. However, uneven reactants and heat distribution in each PCB cell module often result in inconsistent overall performance. Current characterisation methods mainly focus on the study of the overall performance and lack the analysis of individual cells, which provide a limited understanding of localised electrochemical and thermal effects that might play a critical part in further optimising and diagnosing the cell performance of this category. We have developed a multi-channel impedance diagnostic technique for PCB fuel cells. Combining with thermal imaging, we could simultaneously diagnose individual open cathode cell modules by measuring the heat distribution and electrochemical impedance spectra (EIS) of 11 PCB cell modules. Hence, the correlation between heat distribution, internal resistance and performance of different sections of PCB fuel cells can be established. By comparing the fuel cell’s pristine and degraded, we demonstrated how this non-destructive technique could identify and locate cell modules with deteriorated performance, which would be beneficial to real-time diagnostics, quality control and optimisation. The multi-channel impedance measurement takes several seconds only through optimised multi-frequency perturbation and can be readily extended to stacks containing more cells by simply increasing the number of voltage sensors, which can be further adapted to other electrochemical devices.

ScEpiAge: an age predictor highlighting single-cell ageing heterogeneity in mouse blood

Ageing is the accumulation of changes and decline of function of organisms over time. The concept and biomarkers of biological age have been established, notably DNA methylation-based clocks. The emergence of single-cell DNA methylation profiling methods opens the possibility of studying the biological age of individual cells. Here, we generate a large single-cell DNA methylation and transcriptome dataset from mouse peripheral blood samples, spanning a broad range of ages. The number of genes expressed increases with age, but gene-specific changes are small. We next develop scEpiAge, a single-cell DNA methylation age predictor, which can accurately predict age in (very sparse) publicly available datasets, and also in single cells. DNA methylation age distribution is wider than technically expected, indicating epigenetic age heterogeneity and functional differences. Our work provides a foundation for single-cell and sparse data epigenetic age predictors, validates their functionality and highlights epigenetic heterogeneity during ageing.

Strain fields and solitary strain waves as determining factors for the cross-sectional geometry of mouse incisor enamel

Dental enamel in the mouse incisor is the subject of one of the most detailed histological records of cell motion and action during the formation and shaping of any organ in any species. We use the rich data to test the hypothesis that the shape of the enamel body on a perpendicular cross-section of the long, sabre-like incisor can be predicted by assuming that the formative ameloblast cells respond to strain and strain-rate cues that inform individual cells of position and time. The strain field is generated when growth of the forming enamel stretches the ameloblast population. Simultaneously, the strain is relaxed by coherent wavy cell movements. We hypothesize that wave motion arises when cells maintain homeostasis in their area density, with the rate of their recovery from a density perturbation assumed proportional to the magnitude of the perturbation. Density homeostasis gives rise to a nonlinear wave equation, which results in solitary waves propagating within computed strain fields. We predict the final thickness of the enamel by assuming ameloblasts stop generating enamel after they experience a critical strain condition. The thickness profile vs position is correctly determined to within a constant factor, which is the unknown rate constant in the wave equation. When the rate constant is calibrated by the peak amplitude of the thickness profile, the commencement of enamel formation (the onset of ameloblast secretion) vs position is then also correctly predicted by the passage of solitary waves, implying that the strain jump within the solitary wave may be the trigger for the onset of secretion.

Vaccinated Atlantic salmon (Salmo salar L.) maintain a specific antibody response throughout the seasonal fluctuations of a full commercial production cycle in sea: a case study

Vaccination is a critical prophylactic measure to prevent disease outbreaks in Atlantic salmon (Salmo salar L.) and multivalent oil-adjuvanted vaccines administered intraperitoneally (IP) are widely used for this purpose. The Atlantic salmon in this observational study were sampled during commercial production in a salmon farm in Northern Norway. Fish were sampled in the hatchery prior to vaccination, and further every 6 weeks after sea transfer up until slaughter. The key findings revealed that Atlantic salmon vaccinated IP with a commercial multivalent oil-based vaccine developed specific and long-lasting antibody responses against A. salmonicida, V. anguillarum, Y. ruckeri, and IPNV for up to 20 months post-vaccination. However, there was variation in the measured specific antibody response to the different antigens. Notably, the antibody responses against A. salmonicida and V. anguillarum antigens were higher and more prolonged compared to the antibody responses against Y. ruckeri and IPNV antigens. Histological examination showed presence of vaccine-induced granulomas in the pancreas at the injection site for up to 50 weeks post-vaccination and V. anguillarum antigens were found in the granulomas up to 44 weeks post-vaccination. There were also individual IgM-positive cells present in these vaccine-induced granulomas, and qPCR confirmed increased gene expression of membrane-bound and secretory IgM up to 37 weeks post-vaccination. Altogether the qPCR data of membrane and secretory IgM suggests local production of IgM in the vaccine-induced granulomas, while the head kidney might be responsible for the long-term secretion of IgM. Neither the low temperature the fish experienced for two winter seasons (below 5 °C) nor the seasonal changes in photoperiod affected the vaccine-induced specific antibody responses.

Variational inference of single cell time series.

Time course single-cell RNA sequencing (scRNA-seq) enables researchers to probe genome-wide expression dynamics at the the single cell scale. However, when gene expression is affected jointly by time and cellular identity, analyzing such data - including conducting cell type annotation and modeling cell type-dependent dynamics - becomes challenging. To address this problem, we propose SNOW (SiNgle cell flOW map), a deep learning algorithm to deconvolve single cell time series data into time-dependent and time-independent contributions. SNOW has a number of advantages. First, it enables cell type annotation based on the time-independent dimensions. Second, it yields a probabilistic model that can be used to discriminate between biological temporal variation and batch effects contaminating individual timepoints, and provides an approach to mitigate batch effects. Finally, it is capable of projecting cells forward and backward in time, yielding time series at the individual cell level. This enables gene expression dynamics to be studied without the need for clustering or pseudobulking, which can be error prone and result in information loss. We describe our probabilistic framework in detail and demonstrate SNOW using data from three distinct time course scRNA-seq studies. Our results show that SNOW is able to construct biologically meaningful latent spaces, remove batch effects, and generate realistic time-series at the single-cell level. By way of example, we illustrate how the latter may be used to enhance the detection of cell type-specific circadian gene expression rhythms, and may be readily extended to other time-series analyses.

RPL22L1 is a novel biomarker for prognosis and immune infiltration in lung adenocarcinoma, promoting the growth and metastasis of LUAD cells by inhibiting the MDM2/P53 signaling pathway.

The ribosomal protein L22-like1 (RPL22L1) is a constituent of the 60 S ribosomal subunit whose function in lung adenocarcinoma (LUAD) remains ambiguous. This study aims to elucidate the role of RPL22L1 in LUAD through a thorough analysis and experimental validation. Our findings indicate that RPL22L1 exhibits abnormal expression patterns in various cancer types, including LUAD. Moreover, a statistically significant association was observed between elevated levels of RPL22L1 expression in LUAD patients and several clinical parameters, such as pathological stage (p = 0.0083) and gender (p = 0.0038). The high expression of RPL22L1 in LUAD demonstrated a significant association with poorer overall survival (OS) (p = 0.005), progression-free survival (PFS) (p = 0.027), and disease-specific survival (p = 0.015). The expression of RPL22L1 in LUAD (p = 0.005) was identified as an independent prognostic factor. Additionally, RPL22L1 expression in LUAD was found to be correlated with immune infiltration, immune checkpoint genes, TMB/MSI, and mRNAsi. Notably, the expression of RPL22L1 exhibited significant negative correlations with 1-BET-762, Trametinib, and WZ3105 in LUAD. The RPL22L1 gene exhibited up-regulation in multiple individual cells of LUAD, leading to a comparatively shorter PFS in the RPL22L1 variant group as opposed to the RPL22L1 variant-free group in LUAD. Significantly increased expression of RPL22L1 was noted in LUAD cell lines, where it was found to enhance the growth and metastasis of LUAD cells by suppressing the MDM2/P53 signaling pathway. Therefore, RPL22L1 may serve as a promising prognostic biomarker and therapeutic target for patients with LUAD.

Anomaly Detection for Charging Voltage Profiles in Battery Cells in an Energy Storage Station Based on Robust Principal Component Analysis

Lithium-ion batteries, with their high energy density, long cycle life, and non-polluting advantages, are widely used in energy storage stations. Connecting lithium batteries in series to form a battery pack can achieve the required capacity and voltage. However, as the batteries are used for extended periods, some individual cells in the battery pack may experience abnormal failures, affecting the performance and safety of the battery pack. At the same time, as batteries operate in complex environments, the data collected by sensors are susceptible to random noise and drift interference, which can affect the accuracy of anomaly detection in individual battery cells. In order to solve this problem, this article proposes an anomaly detection method for battery cells based on Robust Principal Component Analysis (RPCA), taking the historical operation and maintenance data of a large-scale battery pack from an energy storage station as the research subject. Firstly, theRPCA is used to denoise the observed voltage data of the battery cells to an extreme degree, obtaining a baseline charging state curve for a cell consistency assessment. This also solves the problem of sensor outputs being affected by random noise. To further detect and identify abnormal battery cells, the RPCA is used to extract outlier components. Based on the Average Deviation-3σ principle and by utilizing Gaussian distribution probability characteristics, battery cells are conducted to screen, and the serial numbers of the anomaly cells are obtained. Finally, the effectiveness and accuracy of this anomaly detection method for battery cells are compared and verified through different statistical distributions.

Recovering biomolecular network dynamics from single-cell omics data requires three time points

Single-cell omics technologies can measure millions of cells for up to thousands of biomolecular features, enabling data-driven studies of complex biological networks. However, these high-throughput experimental techniques often cannot track individual cells over time, thus complicating the understanding of dynamics such as time trajectories of cell states. These “dynamical phenotypes” are key to understanding biological phenomena such as differentiation fates. We show by mathematical analysis that, in spite of high dimensionality and lack of individual cell traces, three time-points of single-cell omics data are theoretically necessary and sufficient to uniquely determine the network interaction matrix and associated dynamics. Moreover, we show through numerical simulations that an interaction matrix can be accurately determined with three or more time-points even in the presence of sampling and measurement noise typical of single-cell omics. Our results can guide the design of single-cell omics time-course experiments, and provide a tool for data-driven phase-space analysis.

Connecting Cell Structure and Current‐Dependent Environment Changes in CO2 Electrolysis to GDE Operation Regimes and Multi‐Cell Interaction

ABSTRACTConsecutive development of materials, components, and ultimately, devices does not appear to be a promising strategy in CO2 electroreduction because maintaining comparability and transferring results between idealized and application‐oriented systems proves challenging. A modular cell design and tracking cell conditions via sensors may be a solution. We displayed a strategy to characterize gas diffusion electrode operating regimes in a flow cell with regard to different current density ranges, as well as the impact of the flow gap design. We revealed strong interdependencies between cell components, their functions as well as individual cells when integrated into a stack. Expanding the scope and resolution of experimental data made new information on the change of system parameters in flow cells accessible.