A spatially resolved single-cell lung atlas integrated with clinical and blood signatures distinguishes COVID-19 disease trajectories.

COVID-19 is characterized by a broad range of symptoms and disease trajectories. Understanding the correlation between clinical biomarkers and lung pathology during acute COVID-19 is necessary to understand its diverse pathogenesis and inform more effective treatments. Here, we present an integrated analysis of longitudinal clinical parameters, peripheral blood markers, and lung pathology in 142 Brazilian patients hospitalized with COVID-19. We identified core clinical and peripheral blood signatures differentiating disease progression between patients who recovered from severe disease compared with those who succumbed to the disease. Signatures were heterogeneous among fatal cases yet clustered into two patient groups: "early death" (<15 days until death) and "late death" (>15 days). Progression to early death was characterized systemically and in lung histopathological samples by rapid endothelial and myeloid activation and the presence of thrombi associated with SARS-CoV-2+ macrophages. In contrast, progression to late death was associated with fibrosis, apoptosis, and SARS-CoV-2+ epithelial cells in postmortem lung tissue. In late death cases, cytotoxicity, interferon, and T helper 17 (TH17) signatures were only detectable in the peripheral blood after 2 weeks of hospitalization. Progression to recovery was associated with higher lymphocyte counts, TH2 responses, and anti-inflammatory-mediated responses. By integrating antemortem longitudinal blood signatures and spatial single-cell lung signatures from postmortem lung samples, we defined clinical parameters that could be used to help predict COVID-19 outcomes.

The spatial and cellular portrait of transposable element expression during gastric cancer.

Gastric Cancer (GC) is a lethal malignancy, with urgent need for the discovery of novel biomarkers for its early detection. I previously showed that Transposable Elements (TEs) become activated in early GC (EGC), suggesting a role in gene expression. Here, I follow-up on that evidence using single-cell data from gastritis to EGC, and show that TEs are expressed and follow the disease progression, with 2,430 of them being cell populations markers. Pseudotemporal trajectory modeling revealed 111 TEs associated with the origination of cancer cells. Analysis of spatial data from GC also confirms TE expression, with 204 TEs being spatially enriched in the tumor regions and the tumor microenvironment, hinting at a role of TEs in tumorigenesis. Finally, a network of TE-mediated gene regulation was modeled, indicating that ~ 2,000 genes could be modulated by TEs, with ~ 500 of them already implicated in cancer. These results suggest that TEs might play a functional role in GC progression, and highlights them as potential biomarker for its early detection.

The single-cell transcriptome of mTECs and CD4+ thymocytes under adhesion revealed heterogeneity of mTECs and a network controlled by Aire and lncRNAs.

To further understand the impact of deficiency of the autoimmune regulator (Aire) gene during the adhesion of medullary thymic epithelial cells (mTECs) to thymocytes, we sequenced single-cell libraries (scRNA-seq) obtained from Aire wild-type (WT) (Airewt/wt ) or Aire-deficient (Airewt/mut ) mTECs cocultured with WT single-positive (SP) CD4+ thymocytes. Although the libraries differed in their mRNA and long noncoding RNA (lncRNA) profiles, indicating that mTECs were heterogeneous in terms of their transcriptome, UMAP clustering revealed that both mTEC lines expressed their specific markers, i.e., Epcam, Itgb4, Itga6, and Casp3 in resting mTECs and Ccna2, Pbk, and Birc5 in proliferative mTECs. Both cocultured SP CD4+ thymocytes remained in a homogeneous cluster expressing the Il7r and Ccr7 markers. Comparisons of the two types of cocultures revealed the differential expression of mRNAs that encode transcription factors (Zfpm2, Satb1, and Lef1), cell adhesion genes (Itgb1) in mTECs, and Themis in thymocytes, which is associated with the regulation of positive and negative selection. At the single-cell sequencing resolution, we observed that Aire acts on both Aire WT and Aire-deficient mTECs as an upstream controller of mRNAs, which encode transcription factors or adhesion proteins that, in turn, are posttranscriptionally controlled by lncRNAs, for example, Neat1, Malat1, Pvt1, and Dancr among others. Under Aire deficiency, mTECs dysregulate the expression of MHC-II, CD80, and CD326 (EPCAM) protein markers as well as metabolism and cell cycle-related mRNAs, which delay the cell cycle progression. Moreover, when adhered to mTECs, WT SP CD4+ or CD8+ thymocytes modulate the expression of cell activation proteins, including CD28 and CD152/CTLA4, and the expression of cellular metabolism mRNAs. These findings indicate a complex mechanism through which an imbalance in Aire expression can affect mTECs and thymocytes during adhesion.

Dissecting reactive astrocyte responses: lineage tracing and morphology-based clustering.

Brain damage triggers diverse cellular and molecular events, with astrocytes playing a crucial role in activating local neuroprotective and reparative signaling within damaged neuronal circuits. Here, we investigated reactive astrocytes using a multidimensional approach to categorize their responses into different subtypes based on morphology. This approach utilized the StarTrack lineage tracer, single-cell imaging reconstruction and multivariate data analysis. Our findings identified three profiles of reactive astrocyte responses, categorized by their effects on cell size- and shape- related morphological parameters: "moderate", "strong," and "very strong". We also examined the heterogeneity of astrocyte reactivity, focusing on spatial and clonal distribution. Our research revealed a notable enrichment of protoplasmic and fibrous astrocytes within the "strong" and "very strong" response subtypes. Overall, our study contributes to a better understanding of astrocyte heterogeneity in response to an injury. By characterizing the diverse reactive responses among astrocyte subpopulations, we provide insights that could guide future research aimed at identifying novel therapeutic targets to mitigate brain damage and promote neural repair.

Single-cell transcriptomic analysis of B cells reveals new insights into atypical memory B cells in COVID-19.

Here, we performed single-cell RNA sequencing of S1 and receptor binding domain protein-specific B cells from convalescent COVID-19 patients with different clinical manifestations. This study aimed to evaluate the role and developmental pathway of atypical memory B cells (MBCs) in response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The results revealed a proinflammatory signature across B cell subsets associated with disease severity, as evidenced by the upregulation of genes such as GADD45B, MAP3K8, and NFKBIA in critical and severe individuals. Furthermore, the analysis of atypical MBCs suggested a developmental pathway similar to that of conventional MBCs through germinal centers, as indicated by the expression of several genes involved in germinal center processes, including CXCR4, CXCR5, BCL2, and MYC. Additionally, the upregulation of genes characteristic of the immune response in COVID-19, such as ZFP36 and DUSP1, suggested that the differentiation and activation of atypical MBCs may be influenced by exposure to SARS-CoV-2 and that these genes may contribute to the immune response for COVID-19 recovery. Our study contributes to a better understanding of atypical MBCs in COVID-19 and the role of other B cell subsets across different clinical manifestations.

Optimizing single-tube nested PCR for enhanced genotyping of single cells and in vitro produced bovine embryos.

Single-tube nested PCR (STnPCR) is a technique that improves nested PCR, reducing potential contamination and false-positive results, enhancing the amplification sensitivity. Despite being commonly used for the detection of microorganisms, STnPCR can be a valuable tool for bovine genotyping, encompassing essential targets as ROSA26 and TSPY, pivotal in the fields of animal reproduction, genetic improvement, and transgenic research. The objective of this study was to improve and innovate STnPCR for gene detection in cattle. We aimed to detect the ROSA26 and TSPY genes using low-concentration DNA samples, including single cells, small cell groups (one to five cells), in vitro-produced embryos, and bovine tissue samples. Moreover, we refined STnPCR for gene detection in up to single cells by conducting sensitivity testing with different concentration ratios of internal and external primers. Successful amplification of the ROSA26 and TSPY genes was achieved across all tested primer concentrations, even in single cells, with more consistent results observed at lower primer concentrations. Additionally, simultaneous gene amplification was achieved through STnPCR multiplexing, representing the first study of multiplex STnPCR in cattle. These outcomes not only confirm its effectiveness in detecting genetic markers for animal genetic improvement and transgenic elements but also pave the way for its widespread adoption in reproductive studies in bovines.

Single-cell resolution characterization of myeloid-derived cell states with implication in cancer outcome.

Tumor-associated myeloid-derived cells (MDCs) significantly impact cancer prognosis and treatment responses due to their remarkable plasticity and tumorigenic behaviors. Here, we integrate single-cell RNA-sequencing data from different cancer types, identifying 29 MDC subpopulations within the tumor microenvironment. Our analysis reveals abnormally expanded MDC subpopulations across various tumors and distinguishes cell states that have often been grouped together, such as TREM2+ and FOLR2+ subpopulations. Using deconvolution approaches, we identify five subpopulations as independent prognostic markers, including states co-expressing TREM2 and PD-1, and FOLR2 and PDL-2. Additionally, TREM2 alone does not reliably predict cancer prognosis, as other TREM2+ macrophages show varied associations with prognosis depending on local cues. Validation in independent cohorts confirms that FOLR2-expressing macrophages correlate with poor clinical outcomes in ovarian and triple-negative breast cancers. This comprehensive MDC atlas offers valuable insights and a foundation for futher analyses, advancing strategies for treating solid cancers.

Unveiling the Dynamics behind Glioblastoma Multiforme Single-Cell Data Heterogeneity.

Glioblastoma Multiforme is a brain tumor distinguished by its aggressiveness. We suggested that this aggressiveness leads single-cell RNA-sequence data (scRNA-seq) to span a representative portion of the cancer attractors domain. This conjecture allowed us to interpret the scRNA-seq heterogeneity as reflecting a representative trajectory within the attractor's domain. We considered factors such as genomic instability to characterize the cancer dynamics through stochastic fixed points. The fixed points were derived from centroids obtained through various clustering methods to verify our method sensitivity. This methodological foundation is based upon sample and time average equivalence, assigning an interpretative value to the data cluster centroids and supporting parameters estimation. We used stochastic simulations to reproduce the dynamics, and our results showed an alignment between experimental and simulated dataset centroids. We also computed the Waddington landscape, which provided a visual framework for validating the centroids and standard deviations as characterizations of cancer attractors. Additionally, we examined the stability and transitions between attractors and revealed a potential interplay between subtypes. These transitions might be related to cancer recurrence and progression, connecting the molecular mechanisms of cancer heterogeneity with statistical properties of gene expression dynamics. Our work advances the modeling of gene expression dynamics and paves the way for personalized therapeutic interventions.

Single-cell analysis reveals an active and heterotrophic microbiome in the Guaymas Basin deep subsurface with significant inorganic carbon fixation by heterotrophs.

The marine subsurface is a long-term sink of atmospheric carbon dioxide with significant implications for climate on geologic timescales. Subsurface microbial cells can either enhance or reduce carbon sequestration in the subsurface, depending on their metabolic lifestyle. However, the activity of subsurface microbes is rarely measured. Here, we used nanoscale secondary ion mass spectrometry (nanoSIMS) to quantify anabolic activity in 3,203 individual cells from the thermally altered deep subsurface in the Guaymas Basin, Mexico (3-75 m below the seafloor, 0-14°C). We observed that a large majority of cells were active (83%-100%), although the rates of biomass generation were low, suggesting cellular maintenance rather than doubling. Mean single-cell activity decreased with increasing sediment depth and temperature and was most strongly correlated with porewater sulfate concentrations. Intracommunity heterogeneity in microbial activity decreased with increasing sediment depth and age. Using a dual-isotope labeling approach, we determined that all active cells analyzed were heterotrophic, deriving the majority of their cellular carbon from organic sources. However, we also detected inorganic carbon assimilation in these heterotrophic cells, likely via processes such as anaplerosis, and determined that inorganic carbon contributes at least 5% of the total biomass carbon in heterotrophs in this community. Our results demonstrate that the deep marine biosphere at Guaymas Basin is largely active and contributes to subsurface carbon cycling primarily by not only assimilating organic carbon but also fixing inorganic carbon. Heterotrophic assimilation of inorganic carbon may be a small yet significant and widespread underappreciated source of labile carbon in the global subsurface. IMPORTANCE: The global subsurface is the largest reservoir of microbial life on the planet yet remains poorly characterized. The activity of life in this realm has implications for long-term elemental cycling, particularly of carbon, as well as how life survives in extreme environments. Here, we recovered cells from the deep subsurface of the Guaymas Basin and investigated the level and distribution of microbial activity, the physicochemical drivers of activity, and the relative significance of organic versus inorganic carbon to subsurface biomass. Using a sensitive single-cell assay, we found that the majority of cells are active, that activity is likely driven by the availability of energy, and that although heterotrophy is the dominant metabolism, both organic and inorganic carbon are used to generate biomass. Using a new approach, we quantified inorganic carbon assimilation by heterotrophs and highlighted the importance of this often-overlooked mode of carbon assimilation in the subsurface and beyond.

Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets.

BACKGROUND: Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data. MAIN BODY: The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most appropriate normalization method to use. To this aim, we first give an overview of the different single cell sequencing platforms and methods commonly used including isolation and library preparation protocols. Next, we discuss the inherent sources of variability of scRNA-seq datasets. We describe the categories of normalization methods and include examples of each. We also delineate imputation and batch-effect correction methods. Furthermore, we describe data-driven metrics commonly used to evaluate the performance of normalization methods. We also discuss common scRNA-seq methods and toolkits used for integrated data analysis. CONCLUSIONS: According to the correction performed, normalization methods can be broadly classified as within and between-sample algorithms. Moreover, with respect to the mathematical model used, normalization methods can further be classified into: global scaling methods, generalized linear models, mixed methods, and machine learning-based methods. Each of these methods depict pros and cons and make different statistical assumptions. However, there is no better performing normalization method. Instead, metrics such as silhouette width, K-nearest neighbor batch-effect test, or Highly Variable Genes are recommended to assess the performance of normalization methods.

Deciphering bat influenza H18N11 infection dynamics in male Jamaican fruit bats on a single-cell level.

Jamaican fruit bats (Artibeus jamaicensis) naturally harbor a wide range of viruses of human relevance. These infections are typically mild in bats, suggesting unique features of their immune system. To better understand the immune response to viral infections in bats, we infected male Jamaican fruit bats with the bat-derived influenza A virus (IAV) H18N11. Using comparative single-cell RNA sequencing, we generated single-cell atlases of the Jamaican fruit bat intestine and mesentery. Gene expression profiling showed that H18N11 infection resulted in a moderate induction of interferon-stimulated genes and transcriptional activation of immune cells. H18N11 infection was predominant in various leukocytes, including macrophages, B cells, and NK/T cells. Confirming these findings, human leukocytes, particularly macrophages, were also susceptible to H18N11, highlighting the zoonotic potential of this bat-derived IAV. Our study provides insight into a natural virus-host relationship and thus serves as a fundamental resource for future in-depth characterization of bat immunology.

Prognostic model based on B cell marker genes for NSCLC patients under neoadjuvant immunotherapy by integrated analysis of single-cell and bulk RNA-sequencing data.

BACKGROUND: Neoadjuvant immunotherapy has evolved as an effective option to treat non-small cell lung cancer (NSCLC). B cells play essential roles in the immune system as well as cancer progression. However, the repertoire of B cells and its association with clinical outcomes remains unclear in NSCLC patients receiving neoadjuvant immunotherapy. METHODS: Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data for LUAD samples were accessed from the TCGA and GEO databases. LUAD-related B cell marker genes were confirmed based on comprehensive analysis of scRNA-seq data. We then constructed the B cell marker gene signature (BCMGS) and validated it. In addition, we evaluated the association of BCGMS with tumor immune microenvironment (TIME) characteristics. Furthermore, we validated the efficacy of BCGMS in a cohort of NSCLC patients receiving neoadjuvant immunotherapy. RESULTS: A BCMGS was constructed based on the TCGA cohort and further validated in three independent GSE cohorts. In addition, the BCMGS was proven to be significantly associated with TIME characteristics. Moreover, a relatively higher risk score indicated poor clinical outcomes and a worse immune response among NSCLC patients receiving neoadjuvant immunotherapy. CONCLUSIONS: We constructed an 18-gene prognostic signature derived from B cell marker genes based on scRNA-seq data, which had the potential to predict the prognosis and immune response of NSCLC patients receiving neoadjuvant immunotherapy.

Single-nucleus RNA sequencing demonstrates an autosomal dominant Alzheimer's disease profile and possible mechanisms of disease protection.

Highly penetrant autosomal dominant Alzheimer's disease (ADAD) comprises a distinct disease entity as compared to the far more prevalent form of AD in which common variants collectively contribute to risk. The downstream pathways that distinguish these AD forms in specific cell types have not been deeply explored. We compared single-nucleus transcriptomes among a set of 27 cases divided among PSEN1-E280A ADAD carriers, sporadic AD, and controls. Autophagy genes and chaperones clearly defined the PSEN1-E280A cases compared to sporadic AD. Spatial transcriptomics validated the activation of chaperone-mediated autophagy genes in PSEN1-E280A. The PSEN1-E280A case in which much of the brain was spared neurofibrillary pathology and harbored a homozygous APOE3-Christchurch variant revealed possible explanations for protection from AD pathology including overexpression of LRP1 in astrocytes, increased expression of FKBP1B, and decreased PSEN1 expression in neurons. The unique cellular responses in ADAD and sporadic AD require consideration when designing clinical trials.

Machine learning applications on intratumoral heterogeneity in glioblastoma using single-cell RNA sequencing data.

Artificial intelligence is revolutionizing all fields that affect people's lives and health. One of the most critical applications is in the study of tumors. It is the case of glioblastoma (GBM) that has behaviors that need to be understood to develop effective therapies. Due to advances in single-cell RNA sequencing (scRNA-seq), it is possible to understand the cellular and molecular heterogeneity in the GBM. Given that there are different cell groups in these tumors, there is a need to apply Machine Learning (ML) algorithms. It will allow extracting information to understand how cancer changes and broaden the search for effective treatments. We proposed multiple comparisons of ML algorithms to classify cell groups based on the GBM scRNA-seq data. This broad comparison spectrum can show the scientific-medical community which models can achieve the best performance in this task. In this work are classified the following cell groups: Tumor Core (TC), Tumor Periphery (TP) and Normal Periphery (NP), in binary and multi-class scenarios. This work presents the biomarker candidates found for the models with the best results. The analyses presented here allow us to verify the biomarker candidates to understand the genetic characteristics of GBM, which may be affected by a suitable identification of GBM heterogeneity. This work obtained for the four scenarios covered cross-validation results of $93.03\% \pm 5.37\%$, $97.42\% \pm 3.94\%$, $98.27\% \pm 1.81\%$ and $93.04\% \pm 6.88\%$ for the classification of TP versus TC, TP versus NP, NP versus TP and TC (TPC) and NP versus TP versus TC, respectively.

Single-cell transcriptional analysis of human endothelial colony-forming cells from patients with low VWF levels.

von Willebrand factor (VWF) plays a key role in normal hemostasis, and deficiencies of VWF lead to clinically significant bleeding. We sought to identify novel modifiers of VWF levels in endothelial colony-forming cells (ECFCs) using single-cell RNA sequencing (scRNA-seq). ECFCs were isolated from patients with low VWF levels (plasma VWF antigen levels between 30 and 50 IU/dL) and from healthy controls. Human umbilical vein endothelial cells were used as an additional control cell line. Cells were characterized for their Weibel Palade body (WPB) content and VWF release. scRNA-seq of all cell lines was performed to evaluate for gene expression heterogeneity and for candidate modifiers of VWF regulation. Candidate modifiers identified by scRNA-seq were further characterized with small-interfering RNA (siRNA) experiments to evaluate for effects on VWF. We observed that ECFCs derived from patients with low VWF demonstrated alterations in baseline WPB metrics and exhibit impaired VWF release. scRNA-seq analyses of these endothelial cells revealed overall decreased VWF transcription, mosaicism of VWF expression, and genes that are differentially expressed in low VWF ECFCs and control endothelial cells (control ECs). An siRNA screen of potential VWF modifiers provided further evidence of regulatory candidates, and 1 such candidate, FLI1, alters the transcriptional activity of VWF. In conclusion, ECFCs from individuals with low VWF demonstrate alterations in their baseline VWF packaging and release compared with control ECs. scRNA-seq revealed alterations in VWF transcription, and siRNA screening identified multiple candidate regulators of VWF.

On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers.

COVID-19 is a disease with a spectrum of clinical responses ranging from moderate to critical. To study and control its effects, a large number of researchers are focused on two substantial aims. On the one hand, the discovery of diverse biomarkers to classify and potentially anticipate the disease severity of patients. These biomarkers could serve as a medical criterion to prioritize attention to those patients with higher prone to severe responses. On the other hand, understanding how the immune system orchestrates its responses in this spectrum of disease severities is a fundamental issue required to design new and optimized therapeutic strategies. In this work, using single-cell RNAseq of bronchoalveolar lavage fluid of nine patients with COVID-19 and three healthy controls, we contribute to both aspects. First, we presented computational supervised machine-learning models with high accuracy in classifying the disease severity (moderate and severe) in patients with COVID-19 starting from single-cell data from bronchoalveolar lavage fluid. Second, we identified regulatory mechanisms from the heterogeneous cell populations in the lungs microenvironment that correlated with different clinical responses. Given the results, patients with moderate COVID-19 symptoms showed an activation/inactivation profile for their analyzed cells leading to a sequential and innocuous immune response. In comparison, severe patients might be promoting cytotoxic and pro-inflammatory responses in a systemic fashion involving epithelial and immune cells without the possibility to develop viral clearance and immune memory. Consequently, we present an in-depth landscape analysis of how transcriptional factors and pathways from these heterogeneous populations can regulate their expression to promote or restrain an effective immune response directly linked to the patients prognosis.

The Role of Somatic Mutations on the Immune Response of the Tumor Microenvironment in Prostate Cancer.

Immunotherapy has improved patient survival in many types of cancer, but for prostate cancer, initial results with immunotherapy have been disappointing. Prostate cancer is considered an immunologically excluded or cold tumor, unable to generate an effective T-cell response against cancer cells. However, a small but significant percentage of patients do respond to immunotherapy, suggesting that some specific molecular subtypes of this tumor may have a better response to checkpoint inhibitors. Recent findings suggest that, in addition to their function as cancer genes, somatic mutations of PTEN, TP53, RB1, CDK12, and DNA repair, or specific activation of regulatory pathways, such as ETS or MYC, may also facilitate immune evasion of the host response against cancer. This review presents an update of recent discoveries about the role that the common somatic mutations can play in changing the tumor microenvironment and immune response against prostate cancer. We describe how detailed molecular genetic analyses of the tumor microenvironment of prostate cancer using mouse models and human tumors are providing new insights into the cell types and pathways mediating immune responses. These analyses are helping researchers to design drug combinations that are more likely to target the molecular and immunological pathways that underlie treatment failure.

A Fluorescent Real-Time Plaque Assay Enables Single-Cell Analysis of Virus-Induced Cytopathic Effect by Live-Cell Imaging.

Conventional plaque assays rely on the use of overlays to restrict viral infection allowing the formation of distinct foci that grow in time as the replication cycle continues leading to countable plaques that are visualized with standard techniques such as crystal violet, neutral red, or immunolabeling. This classical approach takes several days until large enough plaques can be visualized and counted with some variation due to subjectivity in plaque recognition. Since plaques are clonal lesions produced by virus-induced cytopathic effect, we applied DNA fluorescent dyes with differential cell permeability to visualize them by live-cell imaging. We could observe different stages of that cytopathic effect corresponding to an early wave of cells with chromatin-condensation followed by a wave of dead cells with membrane permeabilization within plaques generated by different animal viruses. This approach enables an automated plaque identification using image analysis to increase single plaque resolution compared to crystal violet counterstaining and allows its application to plaque tracking and plaque reduction assays to test compounds for both antiviral and cytotoxic activities. This fluorescent real-time plaque assay sums to those next-generation technologies by combining this robust classical method with modern fluorescence microscopy and image analysis approaches for future applications in virology.