Evaluating spatially variable gene detection methods for spatial transcriptomics data.

BACKGROUND: The identification of genes that vary across spatial domains in tissues and cells is an essential step for spatial transcriptomics data analysis. Given the critical role it serves for downstream data interpretations, various methods for detecting spatially variable genes (SVGs) have been proposed. However, the lack of benchmarking complicates the selection of a suitable method. RESULTS: Here we systematically evaluate a panel of popular SVG detection methods on a large collection of spatial transcriptomics datasets, covering various tissue types, biotechnologies, and spatial resolutions. We address questions including whether different methods select a similar set of SVGs, how reliable is the reported statistical significance from each method, how accurate and robust is each method in terms of SVG detection, and how well the selected SVGs perform in downstream applications such as clustering of spatial domains. Besides these, practical considerations such as computational time and memory usage are also crucial for deciding which method to use. CONCLUSIONS: Our study evaluates the performance of each method from multiple aspects and highlights the discrepancy among different methods when calling statistically significant SVGs across diverse datasets. Overall, our work provides useful considerations for choosing methods for identifying SVGs and serves as a key reference for the future development of related methods.

Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data.

Inferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims to unravel the complex relationships between genes and their regulators. Deciphering these networks plays a critical role in understanding the underlying regulatory crosstalk that drives many cellular processes and diseases. Recent advances in sequencing technology have led to the development of state-of-the-art GRN inference methods that exploit matched single-cell multi-omic data. By employing diverse mathematical and statistical methodologies, these methods aim to reconstruct more comprehensive and precise gene regulatory networks. In this review, we give a brief overview on the statistical and methodological foundations commonly used in GRN inference methods. We then compare and contrast the latest state-of-the-art GRN inference methods for single-cell matched multi-omics data, and discuss their assumptions, limitations and opportunities. Finally, we discuss the challenges and future directions that hold promise for further advancements in this rapidly developing field.

Vascular senescence and leak are features of the early breakdown of the blood-brain barrier in Alzheimer's disease models.

Alzheimer's disease (AD) is an age-related disease, with loss of integrity of the blood-brain barrier (BBB) being an early feature. Cellular senescence is one of the reported nine hallmarks of aging. Here, we show for the first time the presence of senescent cells in the vasculature in AD patients and mouse models of AD. Senescent endothelial cells and pericytes are present in APP/PS1 transgenic mice but not in wild-type littermates at the time of amyloid deposition. In vitro, senescent endothelial cells display altered VE-cadherin expression and loss of cell junction formation and increased permeability. Consistent with this, senescent endothelial cells in APP/PS1 mice are present at areas of vascular leak that have decreased claudin-5 and VE-cadherin expression confirming BBB breakdown. Furthermore, single cell sequencing of endothelial cells from APP/PS1 transgenic mice confirms that adhesion molecule pathways are among the most highly altered pathways in these cells. At the pre-plaque stage, the vasculature shows significant signs of breakdown, with a general loss of VE-cadherin, leakage within the microcirculation, and obvious pericyte perturbation. Although senescent vascular cells were not directly observed at sites of vascular leak, senescent cells were close to the leak area. Thus, we would suggest in AD that there is a progressive induction of senescence in constituents of the neurovascular unit contributing to an increasing loss of vascular integrity. Targeting the vasculature early in AD, either with senolytics or with drugs that improve the integrity of the BBB may be valid therapeutic strategies.

Flavobacterium sp. strain GJW24 ameliorates drought resistance in Arabidopsis and Brassica.

Candidate strains that contribute to drought resistance in plants have been previously screened using approximately 500 plant growth-promoting rhizobacteria (PGPR) obtained from Gotjawal, South Korea, to further understand PGPR associated with plant drought tolerance. In this study, a selected PGPR candidate, Flavobacterium sp. strain GJW24, was employed to enhance plant drought tolerance. GJW24 application to Arabidopsis increased its survival rate under drought stress and enhanced stomatal closure. Furthermore, GJW24 promoted Arabidopsis survival under salt stress, which is highly associated with drought stress. GJW24 ameliorated the drought/salt tolerance of Brassica as well as Arabidopsis, indicating that the drought-resistance characteristics of GJW24 could be applied to various plant species. Transcriptome sequencing revealed that GJW24 upregulated a large portion of drought- and drought-related stress-inducible genes in Arabidopsis. Moreover, Gene Ontology analysis revealed that GJW24-upregulated genes were highly related to the categories involved in root system architecture and development, which are connected to amelioration of plant drought resistance. The hyper-induction of many drought/salt-responsive genes by GJW24 in Arabidopsis and Brassica demonstrated that the drought/salt stress tolerance conferred by GJW24 might be achieved, at least in part, through regulating the expression of the corresponding genes. This study suggests that GJW24 can be utilized as a microbial agent to offset the detrimental effects of drought stress in plants.

PAD2: interactive exploration of transcription factor genomic colocalization using ChIP-seq data.

Characterizing transcription factor (TF) genomic colocalization is essential for identifying cooperative binding of TFs in controlling gene expression. Here, we introduce a protocol for using PAD2, an interactive web application that enables the investigation of colocalization of various TFs and chromatin-regulating proteins from mouse embryonic stem cells at various functional genomic regions. We describe steps for accessing and searching the PAD2 database and selecting and submitting genomic regions. We then detail protein colocalization analysis using heatmap and ranked correlation plot. For complete details on the use and execution of this protocol, please refer to Kim et al. (2022).1.

Comprehensive characterization of fetal and mature retinal cell identity to assess the fidelity of retinal organoids.

Characterizing cell identity in complex tissues such as the human retina is essential for studying its development and disease. While retinal organoids derived from pluripotent stem cells have been widely used to model development and disease of the human retina, there is a lack of studies that have systematically evaluated the molecular and cellular fidelity of the organoids derived from various culture protocols in recapitulating their in vivo counterpart. To this end, we performed an extensive meta-atlas characterization of cellular identities of the human eye, covering a wide range of developmental stages. The resulting map uncovered previously unknown biomarkers of major retinal cell types and those associated with cell-type-specific maturation. Using our retinal-cell-identity map from the fetal and adult tissues, we systematically assessed the fidelity of the retinal organoids in mimicking the human eye, enabling us to comprehensively benchmark the current protocols for retinal organoid generation.

Thinking process templates for constructing data stories with SCDNEY.

Background: Globally, scientists now have the ability to generate a vast amount of high throughput biomedical data that carry critical information for important clinical and public health applications. This data revolution in biology is now creating a plethora of new single-cell datasets. Concurrently, there have been significant methodological advances in single-cell research. Integrating these two resources, creating tailor-made, efficient, and purpose-specific data analysis approaches can assist in accelerating scientific discovery. Methods: We developed a series of living workshops for building data stories, using Single-cell data integrative analysis (scdney). scdney is a wrapper package with a collection of single-cell analysis R packages incorporating data integration, cell type annotation, higher order testing and more. Results: Here, we illustrate two specific workshops. The first workshop examines how to characterise the identity and/or state of cells and the relationship between them, known as phenotyping. The second workshop focuses on extracting higher-order features from cells to predict disease progression. Conclusions: Through these workshops, we not only showcase current solutions, but also highlight critical thinking points. In particular, we highlight the Thinking Process Template that provides a structured framework for the decision-making process behind such single-cell analyses. Furthermore, our workshop will incorporate dynamic contributions from the community in a collaborative learning approach, thus the term 'living'.

Integrative analysis reveals histone demethylase LSD1 promotes RNA polymerase II pausing.

Lysine-specific demethylase 1 (LSD1) is well-known for its role in decommissioning enhancers during mouse embryonic stem cell (ESC) differentiation. Its role in gene promoters remains poorly understood despite its widespread presence at these sites. Here, we report that LSD1 promotes RNA polymerase II (RNAPII) pausing, a rate-limiting step in transcription regulation, in ESCs. We found the knockdown of LSD1 preferentially affects genes with higher RNAPII pausing. Next, we demonstrate that the co-localization sites of LSD1 and MYC, a factor known to regulate pause-release, are enriched for other RNAPII pausing factors. We show that LSD1 and MYC directly interact and MYC recruitment to genes co-regulated with LSD1 is dependent on LSD1 but not vice versa. The co-regulated gene set is significantly enriched for housekeeping processes and depleted of transcription factors compared to those bound by LSD1 alone. Collectively, our integrative analysis reveals a pleiotropic role of LSD1 in promoting RNAPII pausing.

Time-resolved phosphoproteome and proteome analysis reveals kinase signaling on master transcription factors during myogenesis.

Myogenesis is governed by signaling networks that are tightly regulated in a time-dependent manner. Although different protein kinases have been identified, knowledge of the global signaling networks and their downstream substrates during myogenesis remains incomplete. Here, we map the myogenic differentiation of C2C12 cells using phosphoproteomics and proteomics. From these data, we infer global kinase activity and predict the substrates that are involved in myogenesis. We found that multiple mitogen-activated protein kinases (MAPKs) mark the initial wave of signaling cascades. Further phosphoproteomic and proteomic profiling with MAPK1/3 and MAPK8/9 specific inhibitions unveil their shared and distinctive roles in myogenesis. Lastly, we identified and validated the transcription factor nuclear factor 1 X-type (NFIX) as a novel MAPK1/3 substrate and demonstrated the functional impact of NFIX phosphorylation on myogenesis. Altogether, these data characterize the dynamics, interactions, and downstream control of kinase signaling networks during myogenesis on a global scale.

Differentiation of brain and retinal organoids from confluent cultures of pluripotent stem cells connected by nerve-like axonal projections of optic origin.

Advances in the study of neurological conditions have been possible because of pluripotent stem cell technologies and organoids. Studies have described the generation of neural ectoderm-derived retinal and brain structures from pluripotent stem cells. However, the field is still troubled by technical challenges, including high culture costs and variability. Here, we describe a simple and economical protocol that reproducibly gives rise to the neural retina and cortical brain regions from confluent cultures of stem cells. The spontaneously generated cortical organoids are transcriptionally comparable with organoids generated by other methods. Furthermore, these organoids showed spontaneous functional network activity and proteomic analysis confirmed organoids maturity. The generation of retinal and brain organoids in close proximity enabled their mutual isolation. Suspension culture of this complex organoid system demonstrated the formation of nerve-like structures connecting retinal and brain organoids, which might facilitate the investigation of neurological diseases of the eye and brain.

Functional analysis of the stable phosphoproteome reveals cancer vulnerabilities.

MOTIVATION: The advance of mass spectrometry-based technologies enabled the profiling of the phosphoproteomes of a multitude of cell and tissue types. However, current research primarily focused on investigating the phosphorylation dynamics in specific cell types and experimental conditions, whereas the phosphorylation events that are common across cell/tissue types and stable regardless of experimental conditions are, so far, mostly ignored. RESULTS: Here, we developed a statistical framework to identify the stable phosphoproteome across 53 human phosphoproteomics datasets, covering 40 cell/tissue types and 194 conditions/treatments. We demonstrate that the stably phosphorylated sites (SPSs) identified from our statistical framework are evolutionarily conserved, functionally important and enriched in a range of core signaling and gene pathways. Particularly, we show that SPSs are highly enriched in the RNA splicing pathway, an essential cellular process in mammalian cells, and frequently disrupted by cancer mutations, suggesting a link between the dysregulation of RNA splicing and cancer development through mutations on SPSs. AVAILABILITY AND IMPLEMENTATION: The source code for data analysis in this study is available from Github repository https://github.com/PYangLab/SPSs under the open-source license of GPL-3. The data used in this study are publicly available (see Section 2.8). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcriptomic Analysis Identifies A Tolerogenic Dendritic Cell Signature.

Dendritic cells (DC) are central to regulating innate and adaptive immune responses. Strategies that modify DC function provide new therapeutic opportunities in autoimmune diseases and transplantation. Current pharmacological approaches can alter DC phenotype to induce tolerogenic DC (tolDC), a maturation-resistant DC subset capable of directing a regulatory immune response that are being explored in current clinical trials. The classical phenotypic characterization of tolDC is limited to cell-surface marker expression and anti-inflammatory cytokine production, although these are not specific. TolDC may be better defined using gene signatures, but there is no consensus definition regarding genotypic markers. We address this shortcoming by analyzing available transcriptomic data to yield an independent set of differentially expressed genes that characterize human tolDC. We validate this transcriptomic signature and also explore gene differences according to the method of tolDC generation. As well as establishing a novel characterization of tolDC, we interrogated its translational utility in vivo, demonstrating this geneset was enriched in the liver, a known tolerogenic organ. Our gene signature will potentially provide greater understanding regarding transcriptional regulators of tolerance and allow researchers to standardize identification of tolDC used for cellular therapy in clinical trials.

Loss-of-function of ARABIDOPSIS F-BOX PROTEIN HYPERSENSITIVE TO ABA 1 enhances drought tolerance and delays germination.

ABA is a phytohormone involved in diverse plant events such as seed germination and drought response. An F-box protein functions as a substrate receptor of the SCF complex and is responsible for ubiquitination of target proteins, triggering their subsequent degradation mediated by ubiquitin proteasome system. Here, we have isolated a gene named ARABIDOPSIS F-BOX PROTEIN HYPERSENSITIVE TO ABA 1 (AFA1) that was upregulated by ABA. AFA1 interacted with adaptor proteins of the SCF complex, implying its role as a substrate receptor of the complex. Its loss of function mutants, afa1 seedlings, exhibited ABA-hypersensitivity, including delayed germination in the presence of ABA. Moreover, loss of AFA1 led to increased drought tolerance in adult plants. Microarray data with ABA treatments indicated that 129 and 219 genes were upregulated or downregulated, respectively, by more than three times in afa1 relative to the wild type. Among the upregulated genes in afa1, the expression of 28.7% was induced by more than three times in the presence of ABA, while only 9.3% was repressed to the same extent. These data indicate that AFA1 is involved in the downregulation of many ABA-inducible genes, in accordance with the ABA-hypersensitive phenotype of afa1. Epistasis analysis showed that AFA1 could play a role upstream of ABI4 and ABI5 in the ABA signaling for germination inhibition. Collectively, our findings suggest that AFA1 is a novel F-box protein that negatively regulates ABA signaling.

Protocol for the processing and downstream analysis of phosphoproteomic data with PhosR.

Analysis of phosphoproteomic data requires advanced computational methodologies. To this end, we developed PhosR, a set of tools and methodologies implemented in R to allow the comprehensive analysis of phosphoproteomic data. PhosR enables processing steps such as imputation, normalization, and functional analysis such as kinase activity inference and signalome construction. Together, PhosR facilitates interpretation and discovery from large-scale phosphoproteomic data sets. For complete details on the use and execution of this protocol, please refer to Kim et al. (2021).

Defining cell identity beyond the premise of differential gene expression.

Identifying genes that define cell identity is a requisite step for characterising cell types and cell states and predicting cell fate choices. By far, the most widely used approach for this task is based on differential expression (DE) of genes, whereby the shift of mean expression are used as the primary statistics for identifying gene transcripts that are specific to cell types and states. While DE-based methods are useful for pinpointing genes that discriminate cell types, their reliance on measuring difference in mean expression may not reflect the biological attributes of cell identity genes. Here, we highlight the quest for non-DE methods and provide an overview of these methods and their applications to identify genes that define cell identity and functionality.

Towards Solving Health Inequities: A Method to Identify Ideological Operation in Global Health Programs.

The function of ideology is to naturalize and maintain unequal relations of power. Making visible how ideology operates is necessary for solving health inequities grounded in inequities of resources and power. However, discerning ideology is difficult because it operates implicitly. It is not necessarily explicit in one's stated aims or beliefs. Philosopher Slavoj Zizek conceptualizes ideology as a belief in overarching unity or harmony that obfuscates immanent tension within a system. Drawing from Zizek's conceptualization of ideology, we identify what may be considered as 'symptoms' of ideological practice: (1) the recurrent nature of a problem, and (2) the implicit externalization of the cause. Our aim is to illustrate a method to identify ideological operation in health programs on the basis of its symptoms, using three case studies of persistent global health problems: inequitable access to vaccines, antimicrobial resistance, and health inequities across racialized communities. Our proposed approach for identifying ideology allows one to identify ideological practices that could not be identified by particular ideological contents. It also safeguards us from an illusory search for an emancipatory content. Critiquing ideology in general reveals possibilities that are otherwise kept invisible and unimaginable, and may help us solve recalcitrant problems such as health inequities.

Signaling Heterogeneity is Defined by Pathway Architecture and Intercellular Variability in Protein Expression.

Insulin's activation of PI3K/Akt signaling, stimulates glucose uptake by enhancing delivery of GLUT4 to the cell surface. Here we examined the origins of intercellular heterogeneity in insulin signaling. Akt activation alone accounted for ~25% of the variance in GLUT4, indicating that additional sources of variance exist. The Akt and GLUT4 responses were highly reproducible within the same cell, suggesting the variance is between cells (extrinsic) and not within cells (intrinsic). Generalized mechanistic models (supported by experimental observations) demonstrated that the correlation between the steady-state levels of two measured signaling processes decreases with increasing distance from each other and that intercellular variation in protein expression (as an example of extrinsic variance) is sufficient to account for the variance in and between Akt and GLUT4. Thus, the response of a population to insulin signaling is underpinned by considerable single-cell heterogeneity that is largely driven by variance in gene/protein expression between cells.