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Possible applications in the medical field
The advent of high throughput sequencing techniques revolutionized the medical field allowing gene expression comparisons between healthy and disease conditions. The aim of these studies was mainly to understand the different molecular processes involved in disease development. A significant limitation of the ‘bulk’ approach is that the proportion of the different cell types in the samples are unknown. This can lead, for instance, to a situation where two samples can be formed by different percentage of cancer cells and as results, they display different genes expression signatures.
The single-cell transcriptomic approach overcomes this limitation and can provide a better characterisation of disease states. Single-cell transcriptomic provides a comprehensive and sensitive description of different cell types, of their functions and characteristics. Furthermore, the single-celltranscriptomic data can be reorganized into pseudo-temporal arrangements allowing the reconstruction of the evolution of the cell [129]. Single-cell transcriptomic technologies are massively used in cancer studies to address question regarding origin of tumours, heterogeneity of primary tumour, resistance to drugs, metastasis.
– Origin of tumours: Understanding how are originated represent a focal point in the fight against the tumour. Single-cell RNAseq can provide a better understanding of the process behind this event leading to insights for the creation of new therapies. For instance, Hu et al. showed that basal-like breast cancer might originate from luminal progenitors, and luminal breast cancer might originate from mature luminal cells in BRCA1 mutation carriers after performing single-cell RNA-seq on 82,122 cells isolated from the breast cancer tissues and adjacent or prophylactic normal breast tissues [130].
– Tumour heterogeneity: Tumours are characterised by numerous cellular populations. Moreover, the malignant population can contain genomic subclones and can be also characterised by different physiological states resulting from stress (such as hypoxia, DNA damage, starvation), quiescence, or cell cycle stage. Thanks to scRNA-seq it is possible to describe in a more sensitive way this heterogeneity Numerous detailed descriptions of tumour composition and their micro-environments have been published so far [109, 122, 123].
Single-cell in developmental biology
The field of developmental biology has been a great beneficiary of single-cell transcriptomic techniques. Events of embryogenesis and regeneration are mainly based on individual cell-fate decisions creating a variety of different cellular states that can eventually modulates specification, morphogenesis and/or cell differentiation in a spatial context [129]. The differentiating cells can be described by the gradual variations in their expression profile as they progress toward their differentiated state. Moreover, trajectory inference methods have been developed to maintain and highlight the continuity of cell states in the data [115]. Most of these methods are based on the hypothesis that developmental processes are barely synchronous. Thus, a static snapshot of numerous single-cell transcriptomes will capture every different stage of differentiation. It will open the possibility to detect branching points in cell trajectories and reveal critical information in cell fate decision-making [138]. A representative example of this application can be observed in the study of Ruiz Garcia et al. [29]. Using single-cell transcriptomics and lineage inference, these authors were able to unravel trajectories from basal to luminal cells, providing novel markers for differentiation of specific populations in the upper airway.
Cell atlases
The advent of single-cell transcriptomic technologies began a new era of cell type discovery, encouraging their description and classification using this new cutting-edge approach. An international effort has been organised in order to build an extensive and comprehensive atlas of the many cells forming a living organism. These atlases aim to perform a precise characterisation of the cell diversity and their heterogeneity in complex systems, organs and tissues [131]. Furthermore they can provide a molecular description of rare cell populations and discover and characterise new cell types [139]–[141].
The two first results of cell atlases were the Mouse cell Atlas [142] and Tabula Muris [143]. They were composed of 400k cells and 100k cells, respectively, collected from 51 and 20 organs and tissues from Mus musculus. This first step was important to demonstrate the emerging potential of singlecell transcriptomics technologies and its impact in cell biology.
The natural evolution was the creation in 2016 of the Human Cell Atlas (HCA) Consortium. HCA is an international and collaborative initiative to define all human cell types as thoroughly as possible (www.humancellatlas.org ) [144]. The aim is to integrate all possible definitions of a cell type and describe physiological states, developmental trajectories and physical locations in the human body using all the omics techniques available including epigenomic, transcriptomic and proteomic. Inside the HCA, the Lung Cell Atlas uncovered unique insights about the identities, activities, and lineage relationships of all cells in human lung. High-resolution studies performed on different lung diseases
and conditions allowed to create a comprehensive catalogue of the changes that occur in lung cellular composition and function in health and disease. The aim of the Lung Cell Atlas is to lead to development of novel cell- and disease-specific biomarkers and advancements in therapeutic strategies for lung disease [145].
Quantitative RT-PCR
Extracted RNA (1μg) was reverse transcribed into cDNA using the Multiscribe reverse transcriptase kit (Applied Biosystems). Primers were designed using PrimerBank or adopted from published studies. Gene expression levels were measured using the LightCycler 480 System. Results from the qPCR were normalized using the reference gene RPL32 and relative gene expression was quantified using the ΔΔCt method.
Microarry gene expression analysis
Total RNA integrity was tested with the Agilent BioAnalyser 2100 (Agilent Technologies). After labeling RNA samples with the Cy3 dye using the low RNA input QuickAmp kit (Agilent) following the manufacturer’s instruction, labeled cRNA probes were hybridized on 8×60K high-density SurePrint G3 gene expression human Agilent microarrays.
Dissociation for single-cell RNA-seq
Single-cell analysis was performed at the indicated days of culture. To obtain a single-cell suspension, cells were incubated with 0.1% protease type XIV from S. griseus (Sigma-Aldrich) in supplemented HBSS for 4 h at 4°C. Cells were gently detached from Transwells by pipetting and then transferred to a microtube. Fifty units of DNase I (EN0523 Thermo Fisher Scientific) per 250 μl were directly added and cells were further incubated at room temperature for 10 min. Cells were centrifuged (150 g for 5 min) and resuspended in 500 μl supplemented HBSS containing 10% FCS, centrifuged again (150 g for 5 min) and resuspended in 500 μl HBSS before being mechanically dissociated through a 26 G syringe (four times). Finally, cell suspensions were filtered through a 40 μm porosity Flowmi Cell Strainer (Bel-Art), centrifuged (150 g for 5 min) and resuspended in 500 μl of ice-cold HBSS. Cell concentration measurements were performed with a Scepter 2.0 Cell Counter (Millipore) and Countess automated cell counter (Thermo Fisher Scientific). Cell viability was checked with a Countess automated cell counter (Thermo Fisher Scientific). All steps except the DNAse I incubation were performed on ice.
Table of contents :
I INTRODUCTION
Toxicology: a useful tool for evaluate the risk assessment in the cosmetic industry
1.1 The cosmetic industry
The Respiratory Tract
2.1 Upper airways
2.1.1 Nose and the nasal cavities
2.1.2 Paranasal sinuses
2.1.3 Pharynx
2.1.4 Larynx
2.2 Lower respiratory tract
2.2.1 Trachea
2.2.2 Bronchi and bronchioles
2.2.3 Alveoli
2.3 The airway epithelium cell composition
2.3.1 Basal cells
2.3.2 Suprabasal cells
2.3.3 Secretory cells.
2.3.4 Multiciliated cells
2.3.5 Rare cells
Toxicity of Inhaled toxicants
3.1 Deposition of inhaled toxicants in the respiratory tract
3.2 The respiratory tract protective mechanisms
3.3 Lung cancer
3.3.1 The cellular origins of lung cancer
Chemicals risk assessments
4.1 Standard toxicity tests for risk assessmen
4.2 In vitro models
4.3 Adverse outcome pathway
The Toxicogenomics
5.1 Transcriptomics
5.2 Proteomics
5.3 Metabolomics
5.4 Epigenomics
Single cell RNA sequencing: a powerful tool for the toxicogenomics
6.1 Single cell RNA sequencing
6.2 Single cell study: from bench to bioinformatic analysis
6.3 Tissue dissociation
6.4 Cell isolation
6.5 Unique Molecule Identifier (UMI)
6.6 Single cell sequencing protocols: the droplet based approach
6.7 Statistical data analysis
6.8 Different applications for single-cell transcriptomic approach
6.8.1 Possible applications in the medical field
6.8.2 Single-cell in developmental biology
6.8.3 Cell atlases
6.8.4 Toxicology at single-cell resolution
II MATERIAL AND METHODS
7.1 Cell lines and reagents
7.2 RNA extraction
7.3 Quantitative RT-PCR
7.4 Microarry gene expression analysis
7.5 Dissociation for single-cell RNA-seq
7.6 Single-cell RNA-seq
III RESULTS
Research context and aims
8.1 Non-genotoxic carcinogenic compounds
8.1.1 Cadmium chloride
8.1.2 Hydroquinone
8.1.3 Phorbol 12-myristate 13-acetate
8.2 Aim of the project
Experimental findings
9.1 Setting up the experimental conditions
9.2 Bulk transcriptomic approach: Microarray gene expression analysis
9.3 Setting up the single cell RNA sequencing experiment
9.4 Single-cell RNA sequencing analysis
9.4.1 Presentation of the datasets
9.4.2 BEAS-2B and cell subtype
9.4.3 Hierarchy of cell response
9.4.4 Common signature
9.4.5 Specific signature
IV DISCUSSION
10.3 Towards the elaboration of an in vitro test using transcriptomic approaches
10.3.1 Experimental conditions
10.3.2 The models under the lens of the microarray gene expression analysis
10.3.3 The contribution of the scRNA-seq analysis
10.3.4 What are BEAS-2B cells in relation to cell-types present in the MucilAirTM ALI culture?
10.3.5 A hierarchy in cell response in MucilAirTM ALI culture
10.3.6 A common signature to all cell types
10.3.7 A specific signature to all cell types
V CONCLUSION AND PERSPECTIVES
VI ANNEXES
11.1 A Role for metformin in the treatment of Dupuytren disease?
11.1.1 Dupuytren Disease
11.1.2 Carpal Tunnel Syndrome
11.1.3 Metformin
11.1.4 Scientific article
11.2 Blockade of pro-fibrotic response mediated by the miR-143/145 cluster prevents targeted therapy-induced phenotypic plasticity and resistance in melanoma
11.3 Identification of oncolytic vaccinia restriction factors in canine high-grade mammary tumor cells using single-cell transcriptomics
VI BIBLIOGRAPHY
