Periodic Reporting for period 1 - MORPHOMICS-IPF (A multi-layered -omics study to understand the dynamics leading to morphological progression in pulmonary fibrosis)
Reporting period: 2022-08-01 to 2024-07-31
Pulmonary fibrosis (PF) affects more than 500,000 patients in Europe yearly. Rather than a disease itself, it is regarded as a pathobiological mechanism present in multiple rare disease entities, causing excessive scarring in the lung tissue. This leads to architectural distortion, ultimately conferring respiratory failure and death in the vast majority of patients affected. Fascinatingly, some of the processes seen in the fibrotic lung are in se similar to physiological repair mechanisms. Indeed, the lung is a dynamic organ that is capable of lung repair upon injury in physiological conditions. However, in fibrotic interstitial lung diseases, these repair mechanisms appear aberrant and do not resolve. Rather than a linear trajectory of serial events, the pathophysiology of the disease is characterized by bi-directional activation of the main protagonists in multiple, partially overlapping feed forward loops, which we refer to as cell circuits.
To understand the cellular circuits diverting the repair mechanisms to these progressive fibrogenesis processes, we developed an in-depth spatially and (pseudo-)temporally resolved multidimensional omics study set-up. By including multiple samples per fibrotic lung and by staging these samples using microCT, we can compare less affected with more affected lung zones thus modelling evolution through disease progression.
After scanning and staging, a 4-dimensional -omics approach is applied:
1. single nucleus RNA sequencing
2. Spatial transcriptomics: using GeoMx© (Nanostring)
3. Spatially resolved laser-capture microdissection-based proteomics
4. Antibody-based proteomics: Iterative indirect immunofluorescence imaging data (4i)
Immediate aims of the analysis project are to (1) map the evolution of epithelial and mesenchymal cell states, and (2) predict critical cell-cell communication events in the alveolar and terminal airway niches throughout the disease progression.
To understand the cellular circuits diverting the repair mechanisms to these progressive fibrogenesis processes, we developed an in-depth spatially and (pseudo-)temporally resolved multidimensional omics study set-up. By including multiple samples per fibrotic lung and by staging these samples using microCT, we can compare less affected with more affected lung zones thus modelling evolution through disease progression.
After scanning and staging, a 4-dimensional -omics approach is applied:
1. single nucleus RNA sequencing
2. Spatial transcriptomics: using GeoMx© (Nanostring)
3. Spatially resolved laser-capture microdissection-based proteomics
4. Antibody-based proteomics: Iterative indirect immunofluorescence imaging data (4i)
Immediate aims of the analysis project are to (1) map the evolution of epithelial and mesenchymal cell states, and (2) predict critical cell-cell communication events in the alveolar and terminal airway niches throughout the disease progression.
Explant lung biobank and processing: This project made use of a very comprehensive and unique biobank (KU Leuven, Leuven, Belgium) of prospectively collected explant lungs with different forms of PF as well as unused donor lungs (which were not used for transplantation due to a non-pulmonary reason), used as controls. After explantation, these lungs were inflated, frozen in the fumes of liquid nitrogen and processed into lung ‘cores’ (diameter: 1.5 cm, height: 2cm). For this research project, 15 IPF lungs (6 cores/lung) and 15 donor lungs (3 cores/lung, as variability is much lower) were included.
Imaging: All explant lungs were imaged using ex-vivo high-resolution computed tomography (CT) scanning and microCT scanning of the separate cores; This last imaging modality enables visualisation of structural elements, including terminal bronchioli and alveoli. By using such a multi-modal approach, every secondary pulmonary lobule can be visualised and situated with the whole lung, and bronchial structures can be traced from trachea until terminal bronchioles.
After microCT scanning, the cores were divided in 2 parts: the first part was processed into a single nuclei suspension and used for snRNAseq. The other part was formalin fixed and paraffin embedded whereafter slices will be cut and used for ST and SP. Hereafter, we present the workflow for data gathering and quality control (QC).
1. snRNAseq: Individual nuclei were suspended in an oil-in-water suspension whereafter mRNA was sequenced from each nucleus separately (Visium technology, 10x Genomics). QC was performed using the Seurat package in R.
2. Spatial transcriptomics (ST): The GeoMx technology (Nanostring) was used for this goal: per region of interest (ROI, ± 100µmx100µm), all mRNA was sequenced and separately quantified for more than 22000 genes. ST was performed grid-wise (10x10 ROIs, 1 slice per core), to ensure all zones of the cores were captured.
3. Laser capture microdissection Spatial proteomics (LCMD-SP): Whereas ST provide a very accurate and in-depth view of the actual activation of specific molecular pathways, SP unravel the cumulative landscape of all former gene expression. Niches were isolated using an in-house optimized protocol for laser capture microdissection (LCMD) with a PALM MicroBeam (Zeiss). Hereafter, mass spectrometry-based assessment of the proteome was performed, using a recently developed workflow, which allows proteome generation based on only 5000 micro-dissected cells 3. Analysis was performed using Maxquant and Perseus, in which the Schiller lab has built up extensive expertise.
4. Iterative indirect immunofluorescent imaging (4i): Using an iterative immunoflurescent staining protocol, we were able to stain the very same tissue slice with 20+ antibodies, which enabled more in-depth niche-delineation, necessary for the LCMD-SP part of the project.
Validation: Additionally, FFPE material was used for validation using immunohistochemical and immunofluorescent stainings.
Workflow is shown in figure A and B.
Imaging: All explant lungs were imaged using ex-vivo high-resolution computed tomography (CT) scanning and microCT scanning of the separate cores; This last imaging modality enables visualisation of structural elements, including terminal bronchioli and alveoli. By using such a multi-modal approach, every secondary pulmonary lobule can be visualised and situated with the whole lung, and bronchial structures can be traced from trachea until terminal bronchioles.
After microCT scanning, the cores were divided in 2 parts: the first part was processed into a single nuclei suspension and used for snRNAseq. The other part was formalin fixed and paraffin embedded whereafter slices will be cut and used for ST and SP. Hereafter, we present the workflow for data gathering and quality control (QC).
1. snRNAseq: Individual nuclei were suspended in an oil-in-water suspension whereafter mRNA was sequenced from each nucleus separately (Visium technology, 10x Genomics). QC was performed using the Seurat package in R.
2. Spatial transcriptomics (ST): The GeoMx technology (Nanostring) was used for this goal: per region of interest (ROI, ± 100µmx100µm), all mRNA was sequenced and separately quantified for more than 22000 genes. ST was performed grid-wise (10x10 ROIs, 1 slice per core), to ensure all zones of the cores were captured.
3. Laser capture microdissection Spatial proteomics (LCMD-SP): Whereas ST provide a very accurate and in-depth view of the actual activation of specific molecular pathways, SP unravel the cumulative landscape of all former gene expression. Niches were isolated using an in-house optimized protocol for laser capture microdissection (LCMD) with a PALM MicroBeam (Zeiss). Hereafter, mass spectrometry-based assessment of the proteome was performed, using a recently developed workflow, which allows proteome generation based on only 5000 micro-dissected cells 3. Analysis was performed using Maxquant and Perseus, in which the Schiller lab has built up extensive expertise.
4. Iterative indirect immunofluorescent imaging (4i): Using an iterative immunoflurescent staining protocol, we were able to stain the very same tissue slice with 20+ antibodies, which enabled more in-depth niche-delineation, necessary for the LCMD-SP part of the project.
Validation: Additionally, FFPE material was used for validation using immunohistochemical and immunofluorescent stainings.
Workflow is shown in figure A and B.
1. A multimodal dataset
We sequenced >750K nuclei in the snRNAseq dataset after thorough QC. All nuclei were annotated using 2 different approaches. First, a classical manual annotation was persued, using canonical marker genes, retrieved both from own experience and the human lung cell atlas. Seconly, we used Celltypist, an automated annotation tool which use annotations from publicly available datasets to predict celltype identities in the new dataset. Integrating these two approaches, we were able to identify 57 cell type indenties over 4 lineages (epithelial, stromal, endothelial, immune cells), as depicted in figure C.
2. The identification of disease stage-specific cell states
Milopy is a tool to identify regions on the neighborhood graph with specific differential abundances. First, it identifies neighborhoods to which nuclei in its proximity with very high similarity in terms of gene expression, contribute. Secondly, it calculates differential abundance of metadata variables in each neighborhood. Hence, we were able to identify neighborhoods with high abundances of control or IPF nuclei on the one hand, and with IPF nuclei originating from mild disease or severe disease on the other hand.
Interestingly, we found neighborhoods with significant enrichment in both dimensions (ctrl vs IPF, mild vs disease) in all four lineages and in almost all cell types. Hence, in almost all cell types, cell states exist which are enriched in IPF, and more specifically in mild IPF and in more severe IPF. As an example, neighborhoods formed by nuclei with the recently described aberrant basaloid cell identity as well as CTHRC1-positive myofibroblast nuclei showed very high enrichment in IPF vs CTRL but did not show enrichment of a specific disease stage, meaning these identities are found in mild disease already but their presence is not restricted to a specific disease stage.
By plotting median milopy-derived differential abundances of neighborhoods enriched for specific cell types, we identified cell type-specific clusters enriched for nuclei of a specific disease stage within the spectrum. Based on the milopy data, cell states were plotted in a two-dimensional space providing an overview to which extent a cell state is enriched for IPF nuclei (x-axis) and for early disease (y-axis), depicted in figure D.
3. The altering epithelial-mesenchymal dialogue throughout disease progression
In a next step, we evaluated whether the dialogue between epithelial and mesenchymal cell states differ. We assessed disease stage-specific ligand-receptor interactions (LRIs) using Nichenet to clusters occurring within the same disease stage. Whereas some LRIs clearly overlap, some are unique to a specific disease stage.
To validate the ligand receptor interactions and further characterize the niches in which these cell states reside, we have setup a spatially resolved proteomics approach. Using a multiplex IF imaging approach (4i), niches are identified after which these niches are selectively laser captured and further processed for mass spectrometry-based spatial proteomics. As this Marie Curie postdoctoral fellowship was terminated early, this will be pursued by the applicant and his supervisor in a follow-up collaboration.
We sequenced >750K nuclei in the snRNAseq dataset after thorough QC. All nuclei were annotated using 2 different approaches. First, a classical manual annotation was persued, using canonical marker genes, retrieved both from own experience and the human lung cell atlas. Seconly, we used Celltypist, an automated annotation tool which use annotations from publicly available datasets to predict celltype identities in the new dataset. Integrating these two approaches, we were able to identify 57 cell type indenties over 4 lineages (epithelial, stromal, endothelial, immune cells), as depicted in figure C.
2. The identification of disease stage-specific cell states
Milopy is a tool to identify regions on the neighborhood graph with specific differential abundances. First, it identifies neighborhoods to which nuclei in its proximity with very high similarity in terms of gene expression, contribute. Secondly, it calculates differential abundance of metadata variables in each neighborhood. Hence, we were able to identify neighborhoods with high abundances of control or IPF nuclei on the one hand, and with IPF nuclei originating from mild disease or severe disease on the other hand.
Interestingly, we found neighborhoods with significant enrichment in both dimensions (ctrl vs IPF, mild vs disease) in all four lineages and in almost all cell types. Hence, in almost all cell types, cell states exist which are enriched in IPF, and more specifically in mild IPF and in more severe IPF. As an example, neighborhoods formed by nuclei with the recently described aberrant basaloid cell identity as well as CTHRC1-positive myofibroblast nuclei showed very high enrichment in IPF vs CTRL but did not show enrichment of a specific disease stage, meaning these identities are found in mild disease already but their presence is not restricted to a specific disease stage.
By plotting median milopy-derived differential abundances of neighborhoods enriched for specific cell types, we identified cell type-specific clusters enriched for nuclei of a specific disease stage within the spectrum. Based on the milopy data, cell states were plotted in a two-dimensional space providing an overview to which extent a cell state is enriched for IPF nuclei (x-axis) and for early disease (y-axis), depicted in figure D.
3. The altering epithelial-mesenchymal dialogue throughout disease progression
In a next step, we evaluated whether the dialogue between epithelial and mesenchymal cell states differ. We assessed disease stage-specific ligand-receptor interactions (LRIs) using Nichenet to clusters occurring within the same disease stage. Whereas some LRIs clearly overlap, some are unique to a specific disease stage.
To validate the ligand receptor interactions and further characterize the niches in which these cell states reside, we have setup a spatially resolved proteomics approach. Using a multiplex IF imaging approach (4i), niches are identified after which these niches are selectively laser captured and further processed for mass spectrometry-based spatial proteomics. As this Marie Curie postdoctoral fellowship was terminated early, this will be pursued by the applicant and his supervisor in a follow-up collaboration.