Cell-to-cell variability is orchestrated by transcriptional variations participating in different biological processes. However, the dissection of transcriptional variability in specific biological process at single-cell level remains unavailable. Here, we present a deep generative model scPheno to integrate scRNA-seq with disease phenotypes to unravel the invisible phenotype-related transcriptional variations. We applied scPheno on COVID-19 blood scRNA-seq to separate transcriptional variations in regulating COVID-19 host immunity and transcriptional variations in maintaining cell-type identity. In silico, we found CLU+IFI27+S100A9+ monocyte as the efficient cellular marker for the prediction of COVID-19 diagnosis. Inspiringly, using only 4 genes upregulated in CLU+IFI27+S100A9+ monocytes can predict the COVID-19 diagnosis of individuals from different country with an accuracy up to 81.3%. We also found C1+CD163+ monocyte and 8 C1+CD163+ monocyte-upregulated genes as the efficient biomarkers for the prediction of severity assessment. Overall, scPheno is an effective method in dissecting the transcriptional basis of phenotype variations at single-cell level.
Accurately and reliably capturing actual biological signals from single-cell transcriptomics is vital for achieving legitimate scientific results, which is unfortunately hindered by the presence of various kinds of unwanted variations. Here we described a deep auto-regressive factor model known as scPhenoXMBD, demonstrated that each gene’s expression can be split into discrete components that represent biological signals and unwanted variations, which effectively mitigated the effects of unwanted variations in the data of single-cell sequencing. Using scPhenoXMBD, we evaluated various factors affecting IFNβ-stimulated immune cells and demonstrated that biological signal extraction facilitates the identification of IFNβ-responsive pathways and genes. Numerous experiments were conducted to show that scPhenoXMBDcould be utilized successfully in enhancing cell clustering stability, obtaining identical cell populations from diverse data sources, advancing the single-cell CRISPR screening of functional elements, and minimizing the influence of inter-subject discrepancies in the cell-disease relationships. scPhenoXMBDis anticipated to be a dependable and repeatable method for the precise analysis of single-cell data.
A new method called the adhesive-bonded anchorage pullout test is proposed in the paper to estimate the in-place concrete compressive strength. In this method, a threaded metal insert is anchored into the concrete by means of a high strength adhesive. To estimate the concrete compressive strength, the pullout force when the anchorage system fails is measured. The embedded depth and the diameter of the reaction ring are determined by theoretical and experimental analysis. The empirical correlation relationships between the pullout forces of this new method and the compressive strengths of concrete cubes are presented for concrete specimens with varying concrete mixes in the 10–80 MPa strength range. In order to assess the accuracy of this method on site, verification tests are undertaken on a number of concrete structures. The experimental and analytical studies show that the newly proposed method is accurate in estimating the in-place concrete compressive strength.
Glioblastoma (GBM) is the most malignant tumor in center nervous system. Clinical statistics revealed that senior GBM patients had a worse overall survival (OS) comparing with that of patients in other ages, which is mainly related with tumor microenvironment including tumor-associated immune cells in particular. However, the immune heterogeneity and age-related prognosis in GBM are under studied. Here we developed a machine learning-based method to integrate public large-scale single-cell RNA sequencing (scRNA-seq) datasets to establish a comprehensive atlas of immune cells infiltrating in cross-age GBM. We found that the compositions of the immune cells are remarkably different across ages. Brain-resident microglia constitute the majority of glioblastoma-associated macrophages (GAMs) in patients, whereas dramatic elevation of extracranial monocyte-derived macrophages (MDMs) is observed in GAMs of senior patients, which contributes to the worse prognosis of aged patients. Further analysis suggests that the increased MDMs arisen from excessive recruitment and proliferation of peripheral monocytes not only lead to the T cell function inhibition in GBM, but also stimulate tumor cells proliferation via VEGFA secretion. In summary, our work provides new cues for the correlational relationship between the immune microenvironment of GBM and aging, which might be insightful for precise and effective therapeutic interventions for senior GBM patients.
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