Elaboración de informes por sesión

Whith the huge advances in the single cell transciptome analysis and the different techniques that allow the identification of new populations in heterogeneous systems such as the airway epithelium, and the development of techniques for the inferring of cell lineage hierarchies, this technique has become really demanded.

Here I’m going to summarize the main experiments performed in the airway epithelium using scRNAseq. In 2014 the group of Mark A. Krasnow and Stephen R. Quake performed a pioneer single-cell study in distal lung, they analysed a total of 198 single cells. They obtained single cell suspension from micro- dissected portions of distal lung of mice and they isolated the cells using a microfluidic device (C1 system from the company Fluidigm).

In a first analysis they performed scRNAseq over 80 cells and performing Principal component analysis (PCA), the most enriched genes were then analysed by unsupervised hierarchical clustering as well as PCA. Looking at the expression of different known markers they recognized four populations, SCs, MCCs, alveolar type-1 (AT1) and alveolar type-2 (AT2), a fifth population was characterized by the expression of markers of the AT1 and AT2 markers together revealing or a population transitioning from one to the other or a precursor population of both AT1 and AT2.

To reveal the cell trajectories of the differentiation of the cell populations in the distal lung they performed a second single cell analysis, this time performing the analysis over single cell suspensions collected in three different time points during the differentiation, early, intermediate and very late time point. Thanks to this analysis they performed lineage hierarchies of the alveolar cells in the distal lung, they confirmed the existence of a bipotential progenitor that triggered the differentiation of two branches in the lineage to AT1 or AT2 cells.

With their pioneer analysis they decipher these trajectories and also new markers for the different cell populations. Notably studying the different effectors that could have a role in the differentiation, they noticed that the differentiation from the early progenitors to the bipotential progenitors it was not caused by the increased expression of any gene but, in fact, they detected a global downregulation of markers such as the transcription factor Sox1 (Treutlein et al., 2014).

In 2016 the group of Whitsett performed scRNaseq analysis over single cell suspensions from distal lung dissections comparing healthy lung (3 patients, 215 cells) and IFP lungs (6 patients, 325 cells), they did a first purification of the cell suspensions by FACS, sorting all the epithelial cells positive for CD326 marker (EPCAM) marker for epithelial cells, then they isolated the cells using the C1 system from Fluidigm. Analyzing the single cell transcriptome from healthy and IPF patients they detected different cell clusters,

notably in the healthy samples all the cells belonging to the cluster of AT2 cells were almost not present in IPF patients.

The analysis of the expression pattern of different markers associated with chloride transporters such as CLCN2/4/5, SLC6A14, SLC26A4 and CFTR were highly decreased in IPF samples whereas sodium and bicarbonate transporters where increased. They also detected 26 different signaling pathways altered, TGF-b & PI3K/AKT among others. The gene signature of the cell populations founded in the IPF patients showed a clear loss of cell identity, markers normally expressed in AT1 co-expressed with AT2 markers, markers such as SOX2 and SOX9 (usually expressed in very different compartments, conducting and peripheral bud of embryonic lung respectively) were found together in some single cells; all these results together showed that in fact in IPF patients a process of remodeling was taking place. In conclusion, thanks to the technique, they could establish the gene expression diversity of transitional states of IPF cells, providing new information to the biological process of the disease (Xu et al., 2016).

The group of Nora A. Barrett and Alex K. Shalek performed seq-Well massively-parallel scRNAseq, using UMI and barcoded beads in a microarray over 12 samples of cell suspension dissociated from resected sinus tissue, 6 from healthy patients and 6 from patients with polyps with different grades of chronic rhinosinusitis (CRS)(18624 cells in total were analyzed), aiming to reveal the molecular signature of the TH2- type inflammation related to this disease.

They analysed the data performing dimensionality reduction and graph-based algorithms for the cell clustering. They detected clusters of basal and apical cells, MCCs and glandular cells, endothelial cells, fibroblasts, plasma cells, myeloid cells, T cells and mast cells. A closer look into each cell population showed certain heterogeneity. They describe the basal cells as responsible of the secretion of the pro-cytokines IL25, IL-33 and TSLP; they showed that that BCs, SCq, and glandular cells had the most significant links to the disease state.

They performed pseudotime mapping to aligning and reconstructing how BCs differentiate to mature SCs, they found in the inflammatory state that the BCs remained as proliferative by the expression of several transcription factors such as ATF3, AP-1, p63 and KLF5, impairing their differentiation. Analysing the different pathways involved in the disease they showed the activation of the WNT pathway through the high expression of CTNNB1 ( -catenin) which is a key effector of the WNT pathway and expression of CTGF (specific factor of the pathway) (Ordovas-Montanes et al., 2018).Part of their resuts have been intorduced along the introduction of the manuscript.

A very recent published work from the group of Rajagopal, studied the mouse tracheal epithelial cells, previously sorted by FACS, using a combinational method with scRNAseq and genetic cell lineage tracing, they used two different approaches for scRNAseq, first a massively parallel droplet-based γ’ scRNAseq (10X genomics) where they analysed 7193 cells, and second full-length scRNAseq using Smart-Seq2 where they

analysed 301 cells. They found the known clusters of epithelial cells BCs, SC/GCs and MCCs and rare cell types such as tuft, ionocytes and PNECs.

They describe new markers for the different cell populations such Nfia, transcription factor expressed in SCs and regulates Notch signalling required for SCs maintenance, surprisingly in their data the MCCs show also expression of this marker. Or Foxq1 expressed specifically in GCs (Montoro et al., 2018).

Thanks to the scRNAseq technology the detection of rare cell types is possible. In this work Rajagopal and colleagues revealed the different markers defining the rare population of Tuft cells such as Pou2f3, Gfi1b, Spib and Sox9 as transcription factors detected. Moreover, they could detect heterogeneity inside the tuft cell population and they defined three different groups of Tuft cells related with their different maturation points. They characterized also the ionocytes population expressing the V-ATPase subunits Atp6v1c2 and Atp6v0d2, new gene markers for this population are, among others, Ascl3, Foxi1, Foxi2 and Pparg, and they also express Cftr.

After the characterization of the ionocytes population founded through scRNAseq analysis of MTECs, did some functional analysis.

They found that ionocytes express high levels of Cftr. This cell type was detected in the submucosal glands and in nasal and olfactory epithelium. They used a transgenic mouse model KO for Foxi1 and they showed that Foxi1 it was essential for the expression of the ionocytes transcription factor Asl3 and also for the expression of the Majority of the Cftr. They use also this mouse model defective for Foxi1 to test the mucus viscosity, the airway surface liquid (ASL) height and the ciliary beating frequency (CBF) and they found similarities with the CF disease such as the viscosity of the mucus and also the CBF increased. For instance, the height of the epithelium or the pH were not altered (Montoro et al., 2018). More of their results have been introduced along the manuscript.

A second very recent published work by the group of Jaffe perform scRNAseq analysis in MTECs (7662 cells) and in Human bronchial epithelial cells (HBECs) differentiated in vitro in a 3D system of air liquid interface (ALI) (2970 cells), they used a graph based algorithm, SPRING, to analyse the data. They identified the known cell clusters in both models; BCs, SCs, MCCs, tuft, PNECs, and as the results from Montoro et al.(Montoro et al., 2018) they show also heterogeneity in the BC and SC populations. They found the expression of FOXN4 as a marker of immature MCCs, what we called deuterosomal cells as I will discuss in the result part of the manuscript, this transcript is associated with the transcription of ciliated genes in Xenopus(Campbell, Quigley and Kintner, 2016).

They described also a cluster of Ionocytes expressing the transcription factors Foxi1, Aslc3 and Tfcp2l1, that was also highly enriched in Cftr. They showed that the overexpression of FOXI1 in HBECs was sufficient to induce a larger number of cells clustered as ionocytes. In Xenopus the Ionocytes differentiation is modulated by notch signalling, and the authors detected some Notch target genes expressed in the ionocytes cell population, so they tested in HBECs the effect of inhibition of the Notch pathway using DAPT, they

corroborated the effect of DAPT over the MCCs population triggering a decrease in the number of MCCs, an also they detected a decrease of the number of ionocytes, contrary to what was seen in Xenopus.

They analysed the regeneration of MTECs after injury through scRNAseq, one day after injury they detected a basal cycling population (Krt5+) co-expressing other keratins such as Krt14, Krt8 and Krt13/Krt4, that were never co-localizing in homeostasis. At two and three days after injury they detect the direct differentiation of MCCs from BCs, by-passing the SCs population. Seven days after injury they detected total recovery of all the cell populations detected in homeostasis (Plasschaert et al., 2018).

The group of Ido Amit performed scRNAseq in distal lung characterizing immune and non-immune cells. They analyzed 50770 single cells recovered during different stages of lung development in mouse during embryogenesis and after birth. They found rare cell types that appeared in different moments during development such as a novel lung alveolar basophil that exist in the lung at early stage during embryonic development. The analysis of all the ligand-receptor interactions among all the cells revealed the cellular network in the lung during development (Cohen et al., 2018).