An interactive single cell web portal identifies gene and cell networks in COVID-19 host responses

Single-cell profiling of bone metastasis ecosystems from multiple cancer types reveals convergent and divergent mechanisms of bone colonization

Bone is a common site for metastasis of solid cancers. The diversity of histological and molecular characteristics of bone metastases (BMs) remains poorly studied. Here, we performed single-cell RNA sequencing on 42 BMs from eight cancer types, identifying three distinct ecosystem archetypes, each characterized by an enrichment of specific immune cells: macrophages/osteoclasts, regulatory/exhausted T cells, or monocytes. We validated these archetypes by immunostaining on tissue sections and bioinformatic analysis of bulk RNA sequencing/microarray data from 158 BMs across more than 10 cancer types. Interestingly, we found only a modest correlation between the BM archetypes and the tissues of origin; BMs from the same cancer type often fell into different archetypes, while BMs from different cancer types sometimes converged on the same archetype. Additional analyses revealed parallel immunosuppression and bone remodeling mechanisms, some of which were experimentally validated. Overall, we discovered unappreciated heterogeneity of BMs across different cancers.

KAT5 regulates neurodevelopmental states associated with G0-like populations in glioblastoma

Quiescence cancer stem-like cells may play key roles in promoting tumor cell heterogeneity and recurrence for many tumors, including glioblastoma (GBM). Here we show that the protein acetyltransferase KAT5 is a key regulator of transcriptional, epigenetic, and proliferative heterogeneity impacting transitions into G0-like states in GBM. KAT5 activity suppresses the emergence of quiescent subpopulations with neurodevelopmental progenitor characteristics, while promoting GBM stem-like cell (GSC) self-renewal through coordinately regulating E2F- and MYC- transcriptional networks with protein translation. KAT5 inactivation significantly decreases tumor progression and invasive behavior while increasing survival after standard of care. Further, increasing MYC expression in human neural stem cells stimulates KAT5 activity and protein translation, as well as confers sensitivity to homoharringtonine, to similar levels to those found in GSCs and high-grade gliomas. These results suggest that the dynamic behavior of KAT5 plays key roles in G0 ingress/egress, adoption of quasi-neurodevelopmental states, and aggressive tumor growth in gliomas.

scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery

Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) produces vast amounts of individual cell profiling data. Its analysis presents a significant challenge in accurately annotating cell types and their associated biomarkers. Different pipelines based on deep neural network (DNN) methods have been employed to tackle these issues. These pipelines have arisen as a promising resource and can extract meaningful and concise features from noisy, diverse, and high-dimensional data to enhance annotations and subsequent analysis. Existing tools require high computational resources to execute large sample datasets. We have developed a cutting-edge platform known as scaLR (Single-cell analysis using low resource) that efficiently processes data into feature subsets, samples in batches to reduce the required memory for processing large datasets, and running DNN models in multiple central processing units. scaLR is equipped with data processing, feature extraction, training, evaluation, and downstream analysis. Its novel feature extraction algorithm first trains the model on a feature subset and stores the importance of the features for all the features in that subset. At the end of the training of all subsets, the top-K features are selected based on their importance. The final model is trained on top-K features; its performance evaluation and associated downstream analysis provide significant biomarkers for different cell types and diseases/traits. Our findings indicate that scaLR offers comparable prediction accuracy and requires less model training time and computational resources than existing Python-based pipelines. We present scaLR, a Python-based platform, engineered to utilize minimal computational resources while maintaining comparable execution times and analysis costs to existing frameworks.

CZI Cell Science Program, Abdulla S, Aevermann B, Assis P, Badajoz S, Bell SM, Bezzi E, Cakir B, Chaffer J, Chambers S, Cherry J, Chi T, Chien J, Dorman L, Garcia-Nieto P, Gloria N, Hastie M, Hegeman D, Hilton J, Huang T, Infeld A, Istrate AM, Jelic I, Katsuya K, Kim YJ, Liang K, Lin M, Lombardo M, Marshall B, Martin B, McDade F, Megill C, Patel N, Predeus A, Raymor B, Robatmili B, Rogers D, Rutherford E, Sadgat D, Shin A, Small C, Smith T, Sridharan P, Tarashansky A, Tavares N, Thomas H, Tolopko A, Urisko M, Yan J, Yeretssian G, Zamanian J, Mani A, Cool J, Carr A. CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data

Hundreds of millions of single cells have been analyzed using high-throughput transcriptomic methods. The cumulative knowledge within these datasets provides an exciting opportunity for unlocking insights into health and disease at the level of single cells. Meta-analyses that span diverse datasets building on recent advances in large language models and other machine-learning approaches pose exciting new directions to model and extract insight from single-cell data. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, the sheer number of datasets, data models and accessibility remains a challenge. Here, we present CZ CELLxGENE Discover (cellxgene.cziscience.com), a data platform that provides curated and interoperable single-cell data. Available via a free-to-use online data portal, CZ CELLxGENE hosts a growing corpus of community-contributed data of over 93 million unique cells. Curated, standardized and associated with consistent cell-level metadata, this collection of single-cell transcriptomic data is the largest of its kind and growing rapidly via community contributions. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to explore individual datasets, perform cross-corpus analysis, and run meta-analyses of tens of millions of cells across studies and tissues at the resolution of single cells.

Scope+: an open source generalizable architecture for single-cell RNA-seq atlases at sample and cell levels

SUMMARY

With the recent advancement in single-cell RNA-sequencing technologies and the increased availability of integrative tools, challenges arise in easy and fast access to large collections of cell atlas. Existing cell atlas portals rarely are open sourced and adaptable, and do not support meta-analysis at cell level. Here, we present an open source, highly optimized and scalable architecture, named Scope+, to allow quick access, meta-analysis and cell-level selection of the atlas data. We applied this architecture to our well-curated 5 million COVID-19 blood and immune cells, as a portal called Covidscope. We achieved efficient access to atlas-scale data via three strategies, such as cell-as-unit data modelling, novel database optimization techniques and innovative software architectural design. Scope+ serves as an open source architecture for researchers to build on with their own atlas.

AVAILABILITY AND IMPLEMENTATION

The COVID-19 web portal, data and meta-analysis are available on Covidscope (https://covidsc.d24h.hk/). User tutorials on how to implement Scope+ architecture with their atlases can be found at https://hiyin.github.io/scopeplus-user-tutorial/. Scope+ source code can be found at https://doi.org/10.5281/zenodo.14174632 and https://github.com/hiyin/scopeplus.

Integrative Analysis by Mendelian Randomization and Large-Scale Single-Cell Transcriptomics Reveals Causal Links between B Cell Subtypes and Diabetic Kidney Disease

Introduction

The increasing incidence of diabetic kidney disease (DKD) and the challenges in its management highlight the necessity for a deeper understanding of its pathogenesis. While recent studies have underscored the substantial impact of circulating immunity on the development of diabetic microvascular complications such as retinopathy and neuropathy, research on circulating immunity in DKD remains limited.

Methods

This study utilized Mendelian randomization analysis to explore the potential independent causal relationships between circulating immune cells and DKD pathogenesis. Additionally, a combination of single-cell disease relevance score (scDRS) and immune cell infiltration analysis was employed to map the circulating immunity landscape in DKD patients.

Results

Ten immune traits, including 5 of B cells, 2 of T cells, 2 of granulocytes, and one of monocytes, were defined to be associated with the pathogenesis of DKD. Notably, IgD - CD27 - B cell Absolute Count (IVW: OR, 1.102 [1.023-1.189], p = 0.011) and IgD - CD24 - B cell Absolute Count (IVW: OR, 1.106 [1.030-1.188], p = 0.005) were associated with promoting DKD pathogenesis, while CD24 + CD27 + B cell %B cell (IVW: OR, 0.943 [0.898-0.989], p = 0.016) demonstrated a protective effect against DKD onset. The presence of B cell-activating factor receptor (BAFF-R) on CD20 - CD38 - B cell (IVW: OR, 0.946 [0.904-0.989], p = 0.015) and BAFF-R on IgD - CD38 + B cell (IVW: OR, 0.902 [0.834-0.975], p = 0.009) also indicated a potential role in preventing DKD. scDRS analysis revealed that two main subsets of B cells, naïve B and memory B cells, had a higher proportion of DKD-related cells or a higher scDRS score of DKD phenotype, indicating their strong association with DKD. Furthermore, immune infiltrate deconvolution analysis showed a notable decrease in the circulating memory B cells and class-switched memory B cells in DKD patients compared to those of DM patients without DKD.

Conclusion

Our study revealed the causal relations between circulating immunity and DKD susceptibility, particularly highlighted the potential roles of B cell subtypes in DKD development. Further studies addressing the related mechanisms would broaden the current understanding of DKD pathogenesis.

Deconvolution of the tumor-educated platelet transcriptome reveals activated platelet and inflammatory cell transcript signatures

Tumor-educated platelets (TEPs) are a potential method of liquid biopsy for the diagnosis and monitoring of cancer. However, the mechanism underlying tumor education of platelets is not known, and transcripts associated with TEPs are often not tumor-associated transcripts. We demonstrated that direct tumor transfer of transcripts to circulating platelets is an unlikely source of the TEP signal. We used CDSeq, a latent Dirichlet allocation algorithm, to deconvolute the TEP signal in blood samples from patients with glioblastoma. We demonstrated that a substantial proportion of transcripts in the platelet transcriptome are derived from nonplatelet cells, and the use of this algorithm allows the removal of contaminant transcripts. Furthermore, we used the results of this algorithm to demonstrate that TEPs represent a subset of more activated platelets, which also contain transcripts normally associated with nonplatelet inflammatory cells, suggesting that these inflammatory cells, possibly in the tumor microenvironment, transfer transcripts to platelets that are then found in circulation. Our analysis suggests a useful and efficient method of processing TEP transcriptomic data to enable the isolation of a unique TEP signal associated with specific tumors.

A multi-organ map of the human immune system across age, sex and ethnicity

Understanding tissue biology's heterogeneity is crucial for advancing precision medicine. Despite the centrality of the immune system in tissue homeostasis, a detailed and comprehensive map of immune cell distribution and interactions across human tissues and demographics remains elusive. To fill this gap, we harmonised data from 12,981 single-cell RNA sequencing samples and curated 29 million cells from 45 anatomical sites to create a comprehensive compositional and transcriptional healthy map of the healthy immune system. We used this resource and a novel multilevel modelling approach to track immune ageing and test differences across sex and ethnicity. We uncovered conserved and tissue-specific immune-ageing programs, resolved sex-dependent differential ageing and identified ethnic diversity in clinically critical immune checkpoints. This study provides a quantitative baseline of the immune system, facilitating advances in precision medicine. By sharing our immune map, we hope to catalyse further breakthroughs in cancer, infectious disease, immunology and precision medicine.

Single-cell resolution of human airway epithelial cells exposed to bronchiolitis obliterans-associated chemicals

Bronchiolitis obliterans (BO) is a fibrotic lung disease characterized by progressive luminal narrowing and obliteration of the small airways. In the nontransplant population, inhalation exposure to certain chemicals is associated with BO; however, the mechanisms contributing to disease induction remain poorly understood. This study's objective was to use single-cell RNA sequencing for the identification of transcriptomic signatures common to primary human airway epithelial cells after chemical exposure to BO-associated chemicals-diacetyl or nitrogen mustard-to help explain BO induction. Primary airway epithelial cells were cultured at air-liquid interface and exposed to diacetyl, nitrogen mustard, or control vapors. Cultures were dissociated and sequenced for single-cell RNA. Differential gene expression and functional pathway analyses were compared across exposures. In total, 75,663 single cells were captured and sequenced from all exposure conditions. Unbiased clustering identified 11 discrete phenotypes, including 5 basal, 2 ciliated, and 2 secretory cell clusters. With chemical exposure, the proportion of cells assigned to keratin 5+ basal cells decreased, whereas the proportion of cells aligned to secretory cell clusters increased compared with control exposures. Functional pathway analysis identified interferon signaling and antigen processing/presentation as pathways commonly upregulated after diacetyl or nitrogen mustard exposure in a ciliated cell cluster. Conversely, the response of airway basal cells differed significantly with upregulation of the unfolded protein response in diacetyl-exposed basal cells, not seen in nitrogen mustard-exposed cultures. These new insights provide early identification of airway epithelial signatures common to BO-associated chemical exposures.NEW & NOTEWORTHY Bronchiolitis obliterans (BO) is a devastating fibrotic lung disease of the small airways, or bronchioles. This original manuscript uses single-cell RNA sequencing for identifying common signatures of chemically exposed airway epithelial cells in BO induction. Chemical exposure reduced the proportion of keratin 5+ basal cells while increasing the proportion of keratin 4+ suprabasal cells. Functional pathways contributory to these shifts differed significantly across exposures. These new results highlight similarities and differences in BO induction across exposures.

Longitudinal whole blood transcriptomic analysis characterizes neutrophil activation and interferon signaling in moderate and severe COVID-19

A maladaptive inflammatory response has been implicated in the pathogenesis of severe COVID-19. This study aimed to characterize the temporal dynamics of this response and investigate whether severe disease is associated with distinct gene expression patterns. We performed microarray analysis of serial whole blood RNA samples from 17 patients with severe COVID-19, 15 patients with moderate disease and 11 healthy controls. All study subjects were unvaccinated. We assessed whole blood gene expression patterns by differential gene expression analysis, gene set enrichment, two clustering methods and estimated relative leukocyte abundance using CIBERSORT. Neutrophils, platelets, cytokine signaling, and the coagulation system were activated in COVID-19, and this broad immune activation was more pronounced in severe vs. moderate disease. We observed two different trajectories of neutrophil-associated genes, indicating the emergence of a more immature neutrophil phenotype over time. Interferon-associated genes were strongly enriched in early COVID-19 before falling markedly, with modest severity-associated differences in trajectory. In conclusion, COVID-19 necessitating hospitalization is associated with a broad inflammatory response, which is more pronounced in severe disease. Our data suggest a progressively more immature circulating neutrophil phenotype over time. Interferon signaling is enriched in COVID-19 but does not seem to drive severe disease.

Schizophrenia-associated NRXN1 deletions induce developmental-timing- and cell-type-specific vulnerabilities in human brain organoids

De novo mutations and copy number deletions in NRXN1 (2p16.3) pose a significant risk for schizophrenia (SCZ). It is unclear how NRXN1 deletions impact cortical development in a cell type-specific manner and disease background modulates these phenotypes. Here, we leveraged human pluripotent stem cell-derived forebrain organoid models carrying NRXN1 heterozygous deletions in isogenic and SCZ patient genetic backgrounds and conducted single-cell transcriptomic analysis over the course of brain organoid development from 3 weeks to 3.5 months. Intriguingly, while both deletions similarly impacted molecular pathways associated with ubiquitin-proteasome system, alternative splicing, and synaptic signaling in maturing glutamatergic and GABAergic neurons, SCZ-NRXN1 deletions specifically perturbed developmental trajectories of early neural progenitors and accumulated disease-specific transcriptomic signatures. Using calcium imaging, we found that both deletions led to long-lasting changes in spontaneous and synchronous neuronal networks, implicating synaptic dysfunction. Our study reveals developmental-timing- and cell-type-dependent actions of NRXN1 deletions in unique genetic contexts.

Transcriptomics and RNA-Based Therapeutics as Potential Approaches to Manage SARS-CoV-2 Infection

SARS-CoV-2 is a coronavirus family member that appeared in China in December 2019 and caused the disease called COVID-19, which was declared a pandemic in 2020 by the World Health Organization. In recent months, great efforts have been made in the field of basic and clinical research to understand the biology and infection processes of SARS-CoV-2. In particular, transcriptome analysis has contributed to generating new knowledge of the viral sequences and intracellular signaling pathways that regulate the infection and pathogenesis of SARS-CoV-2, generating new information about its biology. Furthermore, transcriptomics approaches including spatial transcriptomics, single-cell transcriptomics and direct RNA sequencing have been used for clinical applications in monitoring, detection, diagnosis, and treatment to generate new clinical predictive models for SARS-CoV-2. Consequently, RNA-based therapeutics and their relationship with SARS-CoV-2 have emerged as promising strategies to battle the SARS-CoV-2 pandemic with the assistance of novel approaches such as CRISPR-CAS, ASOs, and siRNA systems. Lastly, we discuss the importance of precision public health in the management of patients infected with SARS-CoV-2 and establish that the fusion of transcriptomics, RNA-based therapeutics, and precision public health will allow a linkage for developing health systems that facilitate the acquisition of relevant clinical strategies for rapid decision making to assist in the management and treatment of the SARS-CoV-2-infected population to combat this global public health problem.

Impacts of exposure to air pollution, radon and climate drivers on the COVID-19 pandemic in Bucharest, Romania: A time series study

During the ongoing global COVID-19 pandemic disease, like several countries, Romania experienced a multiwaves pattern over more than two years. The spreading pattern of SARS-CoV-2 pathogens in the Bucharest, capital of Romania is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation. Through descriptive statistics and cross-correlation analysis applied to daily time series of observational and geospatial data, this study aims to evaluate the synergy of COVID-19 incidence and lethality with air pollution and radon under different climate conditions, which may exacerbate the coronavirus' effect on human health. During the entire analyzed period 1 January 2020-21 December 2021, for each of the four COVID-19 waves were recorded different anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere, and favorable stability conditions during fall-early winter seasons for COVID-19 disease fast-spreading, mostly during the second, and the fourth waves. As the temporal pattern of airborne SARS-CoV-2 and its mutagen variants is affected by seasonal variability of the main air pollutants and climate parameters, this paper found: 1) the daily outdoor exposures to air pollutants (particulate matter PM2.5 and PM10, nitrogen dioxide-NO₂, sulfur dioxide-SO₂, carbon monoxide-CO) and radon - ²²²Rn, are directly correlated with the daily COVID-19 incidence and mortality, and may contribute to the spread and the severity of the pandemic; 2) the daily ground ozone-O₃ levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance are anticorrelated with the daily new COVID-19 incidence and deaths, averageingful for spring-summer periods. Outdoor exposure to ambient air pollution associated with radon is a non-negligible driver of COVID-19 transmission in large metropolitan areas, and climate variables are risk factors in spreading the viral infection. The findings of this study provide useful information for public health authorities and decision-makers to develop future pandemic diseases strategies in high polluted metropolitan environments.