True-scale biomimetic multi-generation airway platforms of the human bronchial epithelium for in vitro cytotoxicity screening

Lung exposure to inhaled particulate matter may injure the epithelial tissue and lead to a loss of function in affected regions via inflammation for example. Screening for the critical contaminate concentrations may provide essential information towards damage assessment and epithelial healing. To date, most approaches have typically relied on traditional in vitro well plate assays or alternatively in vivo animal experiments. Yet, such methods manifest some outstanding disadvantages such as the inability to capture physiological flow and aerosol deposition characteristics as well as significant differences in anatomy, immune system and inflammatory responses compared to humans. The advent of organ-on-chip platforms has shown promising results to reconcile many such drawbacks. In an attempt to provide an attractive in vitro gateway to monitor airway health, we discuss here a novel biomimetic platform which emulates the bronchial epithelium of a human upper airway, allowing to study organ-level characteristics in a homeostatic cellular microenvironment. This device reconstitutes a multi-generation pulmonary epithelial airway environment, capturing realistic respiratory transport phenomena and critical cellular barrier functions at an air-liquid interface (ALI), in analogy to the bronchial lumen. As a proof of concept, we demonstrate its feasibility for in vitro based assays by exposing the device to cytotoxic aerosolized particles under respiratory flow conditions. Subsequently, we investigate the cytotoxic effects of these particles including cellular viability, cytokine and mucus secretion as a function of local particle deposition patterns. Ultimately, our bronchial airway models are intended to provide off-the-shelf in vitro kits geared for the end-user interested in a wide range of broader biological assays that may be attractive for cytotoxicity and drug screening. Please click Additional Files below to see the full abstract

Microfluidic acini-on-chip platforms as a tool to study bacterial lung exposure

Bacterial invasion of the respiratory system leads to complex immune responses involving many cell types. In the alveolar regions, the first line of defense includes the alveolar epithelium, secreted surfactant, alveolar lining fluid and alveolar macrophages. The epithelium consists of alveolar type I and type II cells. Both cell types are known to have immuno-modulatory functions characterized by the secretion of pro-inflammatory cytokines. Epithelial in vitro models offer attractive platforms to investigate biological functionality, but have typically relied on traditional well plate assays that come short of mimicking the complexity of the airway environment and do not capture physiological flows or relevant anatomical features. In the last decade, microfluidics have gained significant momentum in laying the foundations for constructing in vitro models that mimic physiologically-relevant organ functions. Here we propose to use acinus-on-chip platforms that mimic more closely native acinar microflows at true scale in a multi-generation alveolated tree. Acinar chips are cultured with human Alveolar Epithelial Lentivirus immortalized (hAELVi) cells at an air-liquid interface (ALI); such cells show alveolar type I like characteristics and maintained barrier function, leading to high trans-epithelial electrical resistance (TEER) in analogy to primary cells harvested from human tissue. To model bacterial infection, i.e. a strong stimulator of the innate arm of the immune system, lipopolysaccharides (LPS) will be used. LPS is a major outer surface membrane protein expressed on Gram-negative bacteria. The alveolar epithelium is exposed to LPS-laden aerosols and cell response is monitored mainly by secretion of pro-inflammatory cytokines. Our acinus-on-chip allows quantitative on-line measurements of alveolar barrier function, absorption kinetics and immunologically relevant responses, giving further insight to the role played by type I alveolar cells in lung immunity. Please click Additional Files below to see the full abstract

Sequence features of viral and human Internal Ribosome Entry Sites predictive of their activity

Translation of mRNAs through Internal Ribosome Entry Sites (IRESs) has emerged as a prominent mechanism of cellular and viral initiation. It supports cap-independent translation of select cellular genes under normal conditions, and in conditions when cap-dependent translation is inhibited. IRES structure and sequence are believed to be involved in this process. However due to the small number of IRESs known, there have been no systematic investigations of the determinants of IRES activity. With the recent discovery of thousands of novel IRESs in human and viruses, the next challenge is to decipher the sequence determinants of IRES activity. We present the first in-depth computational analysis of a large body of IRESs, exploring RNA sequence features predictive of IRES activity. We identified predictive k-mer features resembling IRES trans-acting factor (ITAF) binding motifs across human and viral IRESs, and found that their effect on expression depends on their sequence, number and position. Our results also suggest that the architecture of retroviral IRESs differs from that of other viruses, presumably due to their exposure to the nuclear environment. Finally, we measured IRES activity of synthetically designed sequences to confirm our prediction of increasing activity as a function of the number of short IRES elements.Pattern Recognition and Bioinformatic

Performance of trained predictors.

<p>(A) Cross-validation (CV) performance of models trained on all available native IRES sequences shown for different combinations of <i>k</i>-mer lengths, and <i>k</i>-mer count (solid lines) or presence (dashed lines) features (left), with the selected combination marked with a circle. Scatter plot of predicted and true IRES activities for the selected model (middle) coloured according to the local density (blue to red as low to high density). The Receiver Operating Characteristic (ROC) curve and the area under the curve (AUC) for the selected combination. (B) CV performance of models trained for different groups of sequences. Only results for groups with models achieving sufficiently high performance are shown. (C) Training and test performance of the feature and <i>k</i>-mer length combination selected for the group of all native IRESs evaluated using several metrics.</p

Robust and predictive positional features that appear in at least two of the analysed groups.

<p>For each feature, its effect along sequences is shown in a heat map (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005734#pcbi.1005734.g003" target="_blank">Fig 3</a>), and summarised as a consensus effect (located above each of the heat maps) across several groups, chosen as the effect whose directionality and importance are confirmed by at least two groups. Horizontal axes show feature window position relative to the start AUG.</p

Overview of the available data and our analysis approach.

<p>(A) Schematic representation of the bicistronic reporter construct used in [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005734#pcbi.1005734.ref021" target="_blank">21</a>] with eGFP (green) expression used to measure IRES activity of variable sequences (gray), and constitutively expressed mRFP used to control for unique genomic integration. To capture context effects, in our analyses the assayed variable sequences (thick gray) were extended to include flanking regions (solid filling). (B) The available sequences can be divided into 7 groups based on their origin species and location within transcripts. Number of active sequences, i.e. sequences with IRES activity above background levels, and the total number of RNA sequences are shown for each class. (C) Sequences from each of the groups are represented as vectors of sequence <i>k</i>-mer features (UA—orange, AC—green), which are recorded globally and in windows (gray shading). From this large set of features, those unlikely to be predictive are removed based on their weak correlation with IRES activity. Surviving features are used to construct a reduced feature matrix. (D) The reduced feature matrix is used for Random Forest training. Each RF tree consists of decision nodes (coloured according to the variables selected by those nodes during training) and leaf nodes that predict IRES activity (coloured according to their prediction). RF trees are constructed by iteratively selecting for each node a variable and split that yield the highest reduction in weighted variance in the nodes children; normalised variance reduction is shown for every node as a number. (E) Trained RFs are used to make IRES activity predictions for feature vectors <i>x</i> of unseen sequences by following each tree to the leaf node corresponding to <i>x</i> (path and leaves marked in red), and accumulating leaf node predictions to obtain the overall RF prediction <i>f</i>(<i>x</i>). (F) To select features that are most predictive of IRES activity, variance reduction values from (D) are accumulated per tree and averaged across trees to obtain <i>feature importance</i>. Normalised importance is also calculated for use in model interpretation. (G) To understand the effect of a feature (e.g. the AC <i>k</i>-mer), for each of its possible values <i>v</i> the expected prediction is plotted (blue curve). The resulting curve allows for characterising <i>v</i> either as having a positive (increasing curve, blue), or a negative (decreasing curve, red) effect on IRES activity. Expected predictions are approximated as the average of predictions made for training samples with the corresponding feature vector components substituted by value <i>v</i>.</p

Performance of trained predictors.

<p>(A) Cross-validation (CV) performance of models trained on all available native IRES sequences shown for different combinations of <i>k</i>-mer lengths, and <i>k</i>-mer count (solid lines) or presence (dashed lines) features (left), with the selected combination marked with a circle. Scatter plot of predicted and true IRES activities for the selected model (middle) coloured according to the local density (blue to red as low to high density). The Receiver Operating Characteristic (ROC) curve and the area under the curve (AUC) for the selected combination. (B) CV performance of models trained for different groups of sequences. Only results for groups with models achieving sufficiently high performance are shown. (C) Training and test performance of the feature and <i>k</i>-mer length combination selected for the group of all native IRESs evaluated using several metrics.</p

Testing the effect of the number of C/U-rich elements on IRES activity using synthetic oligos.

<p>(A) The TEV IRES element was placed in all possible combinations of 1-8 sites in predefined positions on two background sequences (native and synthetic; coloured lines) to generate synthetic oligos (gray blocks and lines), which were measured using the biscistronic IRES activity reporter assay. (B and C) Oligos were binned into four groups according to the number of placed elements: (left) the fraction of oligos with positive IRES activity from the total designed oligos is shown for each bin; (right) box plots showing the expression levels of oligos with positive IRES activity in each bin. Results are shown for a synthetic background (B) and a native background from the human beta-globin gene (HBB) (C).</p

Summary of the sequence features associated with IRES activity.

<p>(A) Illustration of the sequence features found by our models and their association with IRES activity: (left) k-mer sequence, (middle) the number of sites of a <i>k</i>-mer, and (right) the position of the <i>k</i>-mer relative to the AUG start codon. (B) Illustration of the different life cycles of (left) dsRNA/(+) ssRNA viruses and (right) Retroviruses which may have led to differences in their IRESs sequence features. Retroviruses are integrated into the host genome and RNA-PolII transcribes their mRNA in the nucleus. Thus, their IRES elements are exposed to the nuclear environment including mRNA modifying enzymes (methylation, pseudouridylation etc) and nuclear specific ITAFs that can shuttle with the mRNA to the cytoplasm to facilitate cap-independent recruitment of the ribosome. In contrast, dsRNA and (+) ssRNA viruses that spend their entire replication cycle in the cytoplasm are exposed to cytosolic factors, which in turn can facilitate cap-independent recruitment of the ribosome.</p

3D organic bioelectronics for electrical monitoring of human adult stem cells.

Acknowledgements: A.S. acknowledges funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant, MultiStem (No. 895801). J.S. acknowledges funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant, ICE-METs (No. 842356). JS also acknowledges the Ikerbasque, Basque Foundation for Science, Departamento de Salud del Gobierno Vasco, FUNDACION Vital Fundazioa, Gobierno de España, Ministerio de Ciencia y Educación de España” under grant PID2020-120313 GB-I00/AIE/10.13039/501100011033, and Gobierno Vasco Dpto. Educación for the consolidation of the research groups (IT1633-22). C.B. and R.M.O. acknowledge funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 723951). The authors also wish to acknowledge funding by the Engineering and Physical Sciences Research Council Centre for Doctoral Training in Sensor Technologies and Applications (EP/L015889/1 to CB).Three-dimensional in vitro stem cell models have enabled a fundamental understanding of cues that direct stem cell fate. While sophisticated 3D tissues can be generated, technology that can accurately monitor these complex models in a high-throughput and non-invasive manner is not well adapted. Here we show the development of 3D bioelectronic devices based on the electroactive polymer poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)-(PEDOT:PSS) and their use for non-invasive, electrical monitoring of stem cell growth. We show that the electrical, mechanical and wetting properties as well as the pore size/architecture of 3D PEDOT:PSS scaffolds can be fine-tuned simply by changing the processing crosslinker additive. We present a comprehensive characterization of both 2D PEDOT:PSS thin films of controlled thicknesses, and 3D porous PEDOT:PSS structures made by the freeze-drying technique. By slicing the bulky scaffolds we generate homogeneous, porous 250 μm thick PEDOT:PSS slices, constituting biocompatible 3D constructs able to support stem cell cultures. These multifunctional slices are attached on indium-tin oxide substrates (ITO) with the help of an electrically active adhesion layer, enabling 3D bioelectronic devices with a characteristic and reproducible, frequency dependent impedance response. This response changes drastically when human adipose derived stem cells (hADSCs) grow within the porous PEDOT:PSS network as revealed by fluorescence microscopy. The increase of cell population within the PEDOT:PSS porous network impedes the charge flow at the interface between PEDOT:PSS and ITO, enabling the interface resistance (R1) to be used as a figure of merit to monitor the proliferation of stem cells. The non-invasive monitoring of stem cell growth allows for the subsequent differentiation 3D stem cell cultures into neuron like cells, as verified by immunofluorescence and RT-qPCR measurements. The strategy of controlling important properties of 3D PEDOT:PSS structures simply by altering processing parameters can be applied for development of a number of stem cell in vitro models as well as stem cell differentiation pathways. We believe the results presented here will advance 3D bioelectronic technology for both fundamental understanding of in vitro stem cell cultures as well as the development of personalized therapies