Simultaneous Exposure of Different Nanoparticles Influences Cell Uptake

Drug delivery using nano-sized carriers holds tremendous potential for curing a range of diseases. The internalisation of nanoparticles by cells, however, remains poorly understood, restricting the possibility for optimising entrance into target cells, avoiding off-target cells and evading clearance. The majority of nanoparticle cell uptake studies have been performed in the presence of only the particle of interest; here, we instead report measurements of uptake when the cells are exposed to two different types of nanoparticles at the same time. We used carboxylated polystyrene nanoparticles of two different sizes as a model system and exposed them to HeLa cells in the presence of a biomolecular corona. Using flow cytometry, we quantify the uptake at both average and individual cell level. Consistent with previous literature, we show that uptake of the larger particles is impeded in the presence of competing smaller particles and, conversely, that uptake of the smaller particles is promoted by competing larger particles. While the mechanism(s) underlying these observations remain(s) undetermined, we are partly able to restrain the likely possibilities. In the future, these effects could conceivably be used to enhance uptake of nano-sized particles used for drug delivery, by administering two different types of particles at the same time.

Examining the Development of Chronic Thromboembolic Pulmonary Hypertension at the Single-Cell Level

Background: The mechanism of chronic thromboembolic pulmonary hypertension (CTEPH) is known to be multifactorial but remains incompletely understood. Methods: In this study, single-cell RNA sequencing, which facilitates the identification of molecular profiles of samples on an individual cell level, was applied to investigate individual cell types in pulmonary endarterectomized tissues from 5 patients with CTEPH. The order of single-cell types was then traced along the developmental trajectory of CTEPH by trajectory inference analysis, and intercellular communication was characterized by analysis of ligand-receptor pairs between cell types. Finally, comprehensive bioinformatics tools were used to analyze possible functions of branch-specific cell types and the underlying mechanisms. Results: Eleven cell types were identified, with immune-related cell types (T cells, natural killer cells, macrophages, and mast cells) distributed in the left (early) branch of the pseudotime tree, cancer stem cells, and CRISPLD2+ cells as intermediate cell types, and classic disease-related cell types (fibroblasts, smooth muscle cells, myofibroblasts, and endothelial cells) in the right (later) branch. Ligand-receptor interactions revealed close communication between macrophages and disease-related cell types as well as between smooth muscle cells and fibroblasts or endothelial cells. Moreover, the ligands and receptors were significantly enriched in key pathways such as the PI3K/Akt signaling pathway. Furthermore, highly expressed genes specific to the undefined cell type were significantly enriched in important functions associated with regulation of endoplasmic reticulum stress. Conclusions: This single-cell RNA sequencing analysis revealed the order of single cells along a developmental trajectory in CTEPH as well as close communication between different cell types in CTEPH pathogenesis.

BRCA1 and Metastasis: Outcome of Defective DNA Repair

Heritable mutations in BRCA1 and BRCA2 genes are a major risk factor for breast and ovarian cancer. Inherited mutations in BRCA1 increase the risk of developing breast cancers by up to 72% and ovarian cancers by up to 69%, when compared to individuals with wild-type BRCA1. BRCA1 and BRCA2 (BRCA1/2) are both important for homologous recombination-mediated DNA repair. The link between BRCA1/2 mutations and high susceptibility to breast cancer is well established. However, the potential impact of BRCA1 mutation on the individual cell populations within a tumor microenvironment, and its relation to increased aggressiveness of cancer is not well understood. The objective of this review is to provide significant insights into the mechanisms by which BRCA1 mutations contribute to the metastatic and aggressive nature of the tumor cells.

Automated identification of cell-type–specific genes and alternative promoters

Background: Identifying key transcriptional features, such as genes or transcripts, involved in cellular differentiation remains a challenging problem. Current methods for identifying key transcriptional features predominantly rely on pairwise comparisons among different cell types. These methods also identify long lists of differentially expressed transcriptional features. Combining the results from many such pairwise comparisons to find the transcriptional features specific only to one cell type is not straightforward. Thus, one must have a principled method for amalgamating pairwise cell type comparisons that makes full use of prior knowledge about the developmental relationships between cell types. Method: We developed Cell Lineage Analysis (CLA), a computational method which identifies transcriptional features with expression patterns that discriminate cell types, incorporating Cell Ontology knowledge on the relationship between different cell types. CLA uses random forest classification with a stratified bootstrap to increase the accuracy of binary classifiers when each cell type have a different number of samples. Regularized random forest results in a classifier that selects few but important transcriptional features. For each cell type pair, CLA runs multiple instances of regularized random forest and reports the transcriptional features consistently selected. CLA not only discriminates individual cell types but can also discriminate lineages of cell types related in the developmental hierarchy. Results: We applied CLA to Functional Annotation of the Mammalian Genome 5 (FANTOM5) data and identified discriminative transcription factor and long non-coding RNA (lncRNA) genes for 71 human cell types. With capped analysis of gene expression (CAGE) data, CLA identified individual cell-type–specific alternative promoters for cell surface markers. Compared to random forest with a standard bootstrap approach, CLA's stratified bootstrap approach improved the accuracy of gene expression classification models for more than 95% of 2060 cell type pairs examined. Applied on 10X Genomics single-cell RNA-seq data for CD14+ monocytes and FCGR3A+ monocytes, CLA selected only 13 discriminative genes. These genes included the top 9 out of 370 significantly differentially expressed genes obtained from conventional differential expression analysis methods. Discussion: Our CLA method combines tools to simplify the interpretation of transcriptome datasets from many cell types. It automates the identification of the most differentially expressed genes for each cell type pairs CLA's lineage score allows easy identification of the best transcriptional markers for each cell type and lineage in both bulk and single-cell transcriptomic data. Availability: CLA is available at https://cla.hoffmanlab.org. We deposited the version of the CLA source with which we ran our experiments at https://doi.org/10.5281/zenodo.3630670. We deposited other analysis code and results at https://doi.org/10.5281/zenodo.5735636.

Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth

Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate individual cell interactions and microenvironmental dynamics. Unfortunately, the high computational cost of modeling individual cells, the inherent stochasticity of cell dynamics, and numerous model parameters are fundamental limitations of applying such models to predict tumor dynamics. To overcome these challenges, we have developed a coarse-grained two-scale ABM (cgABM) with a reduced parameter space that allows for an accurate and efficient calibration using a set of time-resolved microscopy measurements of cancer cells grown with different initial conditions. The multiscale model consists of a reaction-diffusion type model capturing the spatio-temporal evolution of glucose and growth factors in the tumor microenvironment (at tissue scale), coupled with a lattice-free ABM to simulate individual cell dynamics (at cellular scale). The experimental data consists of BT474 human breast carcinoma cells initialized with different glucose concentrations and tumor cell confluences. The confluence of live and dead cells was measured every three hours over four days. Given this model, we perform a time-dependent global sensitivity analysis to identify the relative importance of the model parameters. The subsequent cgABM is calibrated within a Bayesian framework to the experimental data to estimate model parameters, which are then used to predict the temporal evolution of the living and dead cell populations. To this end, a moment-based Bayesian inference is proposed to account for the stochasticity of the cgABM while quantifying uncertainties due to limited temporal observational data. The cgABM reduces the computational time of ABM simulations by 93% to 97% while staying within a 3% difference in prediction compared to ABM. Additionally, the cgABM can reliably predict the temporal evolution of breast cancer cells observed by the microscopy data with an average error and standard deviation for live and dead cells being 7.61±2.01 and 5.78±1.13, respectively.

Competition for endothelial cell polarity drives vascular morphogenesis

Blood vessel formation generates unique vascular patterns in each individual. The principles governing the apparent stochasticity of this process remain to be elucidated. Using mathematical methods, we find that the transition between two fundamental vascular morphogenetic programs, sprouting angiogenesis and vascular remodeling, is established by a shift on collective front-rear polarity of endothelial cells. We demonstrate that the competition between biochemical (VEGFA) and mechanical (blood flow-induced shear stress) cues controls this collective polarity shift. Shear stress increases tension at focal adhesions overriding VEGFA-driven collective polarization, which relies on tension at adherens junctions. We propose that vascular morphogenetic cues compete to regulate individual cell polarity and migration through tension shifts that translates into tissue-level emergent behaviors, ultimately leading to uniquely organized vascular patterns.

A fast variational algorithm to detect the clonal copy number substructure of tumors from single-cell data

Here we report Single CEll Variational ANeuploidy analysis (SCEVAN), a fast variational algorithm for the deconvolution of the clonal substructure of tumors from single cell data. It uses a multichannel segmentation algorithm exploiting the assumption that all the cells in a given copy number clone share the same breakpoints. Thus, the smoothed expression profile of every individual cell constitutes part of the evidence of the copy number profile in each subclone. SCEVAN can automatically and accurately discriminate between malignant and non-malignant cells, resulting in a practical framework to analyze tumors and their microenvironment. We apply SCEVAN to several datasets encompassing 106 samples and 93,322 cells from different tumors types and technologies. We demonstrate its application to characterize the intratumor heterogeneity and geographic evolution of malignant brain tumors.

Dimension-reduction simplifies the analysis of signal crosstalk in a bacterial quorum sensing pathway

Many pheromone sensing bacteria produce and detect more than one chemically distinct signal, or autoinducer. The pathways that detect these signals are typically noisy and interlocked through crosstalk and feedback. As a result, the sensing response of individual cells is described by statistical distributions that change under different combinations of signal inputs. Here we examine how signal crosstalk reshapes this response. We measure how combinations of two homoserine lactone (HSL) input signals alter the statistical distributions of individual cell responses in the AinS/R- and LuxI/R-controlled branches of the Vibrio fischeri bioluminescence pathway. We find that, while the distributions of pathway activation in individual cells vary in complex fashion with environmental conditions, these changes have a low-dimensional representation. For both the AinS/R and LuxI/R branches, the distribution of individual cell responses to mixtures of the two HSLs is effectively one-dimensional, so that a single tuning parameter can capture the full range of variability in the distributions. Combinations of crosstalking HSL signals extend the range of responses for each branch of the circuit, so that signals in combination allow population-wide distributions that are not available under a single HSL input. Dimension reduction also simplifies the problem of identifying the HSL conditions to which the pathways and their outputs are most sensitive. A comparison of the maximum sensitivity HSL conditions to actual HSL levels measured during culture growth indicates that the AinS/R and LuxI/R branches lack sensitivity to population density except during the very earliest and latest stages of growth respectively.