Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.</p

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.</p

Structural Characterization of Mesoporous Silica Nanofibers Synthesized Within Porous Alumina Membranes

Mesoporous silica nanofibers were synthesized within the pores of the anodic aluminum oxide template using a simple sol–gel method. Transmission electron microscopy investigation indicated that the concentration of the structure-directing agent (EO20PO70EO20) had a significant impact on the mesostructure of mesoporous silica nanofibers. Samples with alignment of nanochannels along the axis of mesoporous silica nanofibers could be formed under the P123 concentration of 0.15 mg/mL. When the P123 concentration increased to 0.3 mg/mL, samples with a circular lamellar mesostructure could be obtained. The mechanism for the effect of the P123 concentration on the mesostructure of mesoporous silica nanofibres was proposed and discussed

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen

A major drawback of single-cell ATAC-seq (scATAC-seq) is its sparsity, i.e., open chromatin regions with no reads due to loss of DNA material during the scATAC-seq protocol. Here, we propose scOpen, a computational method based on regularized non-negative matrix factorization for imputing and quantifying the open chromatin status of regulatory regions from sparse scATAC-seq experiments. We show that scOpen improves crucial downstream analysis steps of scATAC-seq data as clustering, visualization, cis-regulatory DNA interactions, and delineation of regulatory features. We demonstrate the power of scOpen to dissect regulatory changes in the development of fibrosis in the kidney. This identifies a role of Runx1 and target genes by promoting fibroblast to myofibroblast differentiation driving kidney fibrosis

Lineage tracing reveals transient phenotypic adaptation of tubular cells during acute kidney injury

Tubular injury is the hallmark of acute kidney injury (AKI) with a tremendous impact on patients and health-care systems. During injury, any differentiated proximal tubular cell (PT) may transition into a specific injured phenotype, so-called “scattered tubular cell” (STC)-phenotype. To understand the fate of this specific phenotype, we generated transgenic mice allowing inducible, reversible, and irreversible tagging of these cells in a murine AKI model, the unilateral ischemia-reperfusion injury (IRI). For lineage tracing, we analyzed the kidneys using single-cell profiling during disease development at various time points. Labeled cells, which we defined by established endogenous markers, already appeared 8 h after injury and showed a distinct expression set of genes. We show that STCs re-differentiate back into fully differentiated PTs upon the resolution of the injury. In summary, we show the dynamics of the phenotypic transition of PTs during injury, revealing a reversible transcriptional program as an adaptive response during disease.</p