Abstract:Single-cell ribonucleic acid sequencing is becoming widely employed to study biological processes at a novel resolution depth. The ability to analyse transcriptomes of multiple heterogeneous cell types in parallel is especially valuable for cell-focused lung research where a variety of resident and recruited cells are essential for maintaining organ functionality. We compared the single-cell transcriptomes from publicly available and unpublished datasets of the lungs in six different species: human (Homo sapie… Show more
“…First, we combined blood transcriptomic data into a common embedding and applied a single unified gene nomenclature as described previously 36 . The merged dataset revealed species-related batch effects ( Figure 1B ).…”
Translating findings from animal models to human disease is essential for dissecting disease mechanisms, developing and testing precise therapeutic strategies. The coronavirus disease 2019 (COVID-19) pandemic has highlighted this need, particularly for models showing disease severity-dependent immune responses. Single-cell transcriptomics (scRNAseq) is well poised to reveal similarities and differences between species at the molecular and cellular level with unprecedented resolution. However, computational methods enabling detailed matching are still scarce. Here, we provide a structured scRNAseq-based approach that we applied to scRNAseq from blood leukocytes originating from humans and hamsters affected with moderate or severe COVID-19. Integration of COVID-19 patient data with two hamster models that develop moderate (Syrian hamster, Mesocricetus auratus) or severe (Roborovski hamster, Phodopus roborovskii) disease revealed that most cellular states are shared across species. A neural network-based analysis using variational autoencoders quantified the overall transcriptomic similarity across species and severity levels, showing highest similarity between neutrophils of Roborovski hamsters and severe COVID-19 patients, while Syrian hamsters better matched patients with moderate disease, particularly in classical monocytes. We further used transcriptome-wide differential expression analysis to identify which disease stages and cell types display strongest transcriptional changes. Consistently, hamster's response to COVID-19 was most similar to humans in monocytes and neutrophils. Disease-linked pathways found in all species specifically related to interferon response or inhibition of viral replication. Analysis of candidate genes and signatures supported the results. Our structured neural network-supported workflow could be applied to other diseases, allowing better identification of suitable animal models with similar pathomechanisms across species.
“…First, we combined blood transcriptomic data into a common embedding and applied a single unified gene nomenclature as described previously 36 . The merged dataset revealed species-related batch effects ( Figure 1B ).…”
Translating findings from animal models to human disease is essential for dissecting disease mechanisms, developing and testing precise therapeutic strategies. The coronavirus disease 2019 (COVID-19) pandemic has highlighted this need, particularly for models showing disease severity-dependent immune responses. Single-cell transcriptomics (scRNAseq) is well poised to reveal similarities and differences between species at the molecular and cellular level with unprecedented resolution. However, computational methods enabling detailed matching are still scarce. Here, we provide a structured scRNAseq-based approach that we applied to scRNAseq from blood leukocytes originating from humans and hamsters affected with moderate or severe COVID-19. Integration of COVID-19 patient data with two hamster models that develop moderate (Syrian hamster, Mesocricetus auratus) or severe (Roborovski hamster, Phodopus roborovskii) disease revealed that most cellular states are shared across species. A neural network-based analysis using variational autoencoders quantified the overall transcriptomic similarity across species and severity levels, showing highest similarity between neutrophils of Roborovski hamsters and severe COVID-19 patients, while Syrian hamsters better matched patients with moderate disease, particularly in classical monocytes. We further used transcriptome-wide differential expression analysis to identify which disease stages and cell types display strongest transcriptional changes. Consistently, hamster's response to COVID-19 was most similar to humans in monocytes and neutrophils. Disease-linked pathways found in all species specifically related to interferon response or inhibition of viral replication. Analysis of candidate genes and signatures supported the results. Our structured neural network-supported workflow could be applied to other diseases, allowing better identification of suitable animal models with similar pathomechanisms across species.
“…47 Moreover, circadian rhythms can be also influenced by other zeitgebers such as food, exercise, temperature, and vagal tone. 48 Recent comparative single-cell studies in humans, primates, and smaller vertebrates including mice describe the lung as a heterogenous tissue, where each cell population is highly differentiated in function. 49 It is therefore likely that also the effects of the circadian clock are highly cell-specific across all species.…”
Section: Rhythms In the Lungsmentioning
confidence: 99%
“…Previous studies in rheumatoid arthritis, for example, have already shown clinical benefits of circadian adjustment of pharmacokinetic properties of glucocorticoid treatment to the physiological circadian release rhythms 47 . Moreover, circadian rhythms can be also influenced by other zeitgebers such as food, exercise, temperature, and vagal tone 48 . Recent comparative single‐cell studies in humans, primates, and smaller vertebrates including mice describe the lung as a heterogenous tissue, where each cell population is highly differentiated in function 49 .…”
Section: The Role Of Circadian Rhythms In the Lungsmentioning
Patients admitted to the intensive care unit (ICU) are in need of continuous organ replacement strategies and specialized care, for example because of neurological dysfunction, cardio‐pulmonary instability, liver or kidney failure, trauma, hemorrhagic or septic shock or even preterm birth. The 24‐h nursing and care interventions provided to critically ill patients significantly limit resting and/or recovery phases. Consecutively, the patient's endogenous circadian rhythms are misaligned and disrupted, which in turn may interfere with their critical condition. A more thorough understanding of the complex interactions of circadian effectors and tissue‐specific molecular clocks could therefore serve as potential means for enhancing personalized treatment in critically ill patients, conceivably restoring their circadian network and thus accelerating their physical and neurocognitive recovery. This review addresses the overarching issue of how circadian rhythms are affected and disturbed in critically ill newborns and adults in the ICU, and whether the conflicting external or environmental cues in the ICU environment further promote disruption and thus severity of illness. We direct special attention to the influence of cell‐type specific molecular clocks on with severity of organ dysfunctions such as severity of brain dysfunction, pneumonia‐ or ventilator‐associated lung inflammation, cardiovascular instability, liver and kidney failure, trauma, and septic shock. Finally, we address the potential of circadian rhythm stabilization to enhance and accelerate clinical recovery.
“…Integrational approaches (including different species data) and more complex analyses have been described previously. 4 Creation of Seurat Object using SCTransform in R >library(ggplot2) >library(dplyr) >library(Seurat) >library(patchwork) >library(sctransform) >library(hdf5r) >library(glmGamPoi) >MLung_data <- Read10×_h5("…/outs/filtered_feature_bc_matrix.h5", use.names = TRUE) #In this case H5 Files are publically available to start the Pipeline. If filtered_feature_bc_matrix Objects are available start Workflow with: # >MLung_data <- Read10×(data.dir = ".../outs/filtered_feature_bc_matrix") >MLung <- CreateSeuratObject(MLung_data, project = "Mouse_lung") >MLung <- PercentageFeatureSet(MLung, pattern = "ˆmt-", col.name = "percent.mt") >MLung <- SCTransform(MLung, vars.to.regress = "percent.mt") >if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") >MLung <- SCTransform(MLung, method = "glmGamPoi", vars.to.regress = "percent.mt") >MLung <- RunPCA(MLung) >ElbowPlot(MLung, ndims = 50) #credits to: Harvard Chan Bioinformatics Core 16 : https://hbctraining.github.io/scRNA-seq/lessons/elbow_plot_metric.html >pct <- MLung[["pca"]]@stdev / sum(MLung[["pca"]]@stdev) ∗ 100 >cumu <- cumsum(pct) >co1 <- which(cumu > 90 & pct < 5)[1] >co1 >co2 <- sort(which((pct[1:length(pct) - 1] - pct[2:length(pct)]) > 0.1), decreasing = T)[1] + 1 >co2 >pcs <- min(co1, co2) >pcs >plot_df <- data.frame(pct = pct, > cumu = cumu, > rank = 1:length(pct)) >ggplot(plot_df, aes(cumu, pct, label = rank, color = rank > pcs)) + > geom_text() + > geom_vline(xintercept = 90, color = "grey") + > geom_hline(yintercept = min(pct[pct > 5]), color = "grey") + > theme_bw() >MLung <- RunUMAP(MLung, dims = 1:39) # seems to work better if maximal information according to line 27 is considered >MLung <- FindNeighbors(MLung, dims = 1:39) >MLung <- FindClusters(MLung, resolution = 0.8) # resolution = 0.8 is default setting, increase to get more clusters, decrease for less >DimPlot(MLung, label = TRUE) >#saveRDS(MLung, file = "…/MLung_seurat_from_H5.rds") Visualization of the generated Seurat Object >MLung <- readRDS("…/ MLung_seurat_from_H5.rds ") #Marker genes in Dotplot >features <- c("Marco","Adgre1", "Flt3", "Cxcr2", "Siglecf", "Ncr1", "Cd3e", "Cd79a", "Epcam", "Akap5", "Lamp3", "Foxj1", "Pecam1", "Cdh5", "Mmrn1", "Inmt", "Acta2", "Cox4i2", "Msln") >DotPlot(MLung, features = features) + RotatedAxis() >MLung$populations <- paste("C", MLung$seurat_clusters) >MLung@active.ident = factor(MLung$populations) >MLung <- RenameIdents (MLung, "C 0" = "B cells", 'C 1' = "Endothelial cells", 'C 2' = "T cells", 'C 3' = "B cells", 'C 4' = "T cells", 'C 5' = "Fibroblasts", 'C 6' = "T cells", 'C 7' = "AT2", 'C 8' = "NK cells", 'C 9' = "Fibroblasts", 'C 10' = "T cells", 'C 11' = "AT1", 'C 12' = "AM", 'C 13' = "Pericytes", 'C 14' = "Endothelial cells", 'C 15' = "PMN", 'C 16' = "Macrophages", 'C 17' = "T cells", 'C 18' = "DC", "C 19" = "T cells", "C 20" = "T cells", "C 21" = "Mesothelial cells", "C 22" = "SMC", "C 23" = "Macrophages") &g...…”
Section: Expected Outcomesmentioning
confidence: 99%
“…The data chosen for exemplary naïve murine lung cell analysis was previously published 4 and raw data files deposited under “Health Atlas: https://www.health-atlas.de/data_files/563?graph_view=tree .” Code for shown example data can be found in Code Boxes 1 and 2 .…”
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