Sub-Cluster Identification through Semi-Supervised Optimization of Rare-Cell Silhouettes (SCISSORS) in single-cell RNA-sequencing

Myofibroblastic cancer-associated fibroblast subtype heterogeneity in pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has a fibrotic stroma that has both tumor-promoting and tumor-restraining properties. Different types of cancer-associated fibroblasts (CAFs) have been described. Here, we investigated whether CAFs within the same subtype exhibit heterogeneous functions.

METHODS

We evaluated the gene and protein expression differences in two myofibroblastic CAF (myCAF) lines using single-cell and bulk RNA-sequencing. We utilized proliferation and migration assays to determine the effect of different CAF lines on a tumor cell line.

RESULTS

We found that myCAF lines express an activated stroma subtype gene signature, which is associated with a shorter survival in patients. Although both myCAF lines expressed α-smooth muscle actin (α-SMA), platelet-derived growth factor-α (PDGFR-α), fibroblast-activated protein (FAP), and vimentin, we observed heterogeneity between the two lines. Similarly, despite being consistent with myCAF gene expression overall, heterogeneity within specific genes was observed. We found that these differences extended to the functional level where the two myCAF lines had different effects on the same tumor cell line. The myCAF216 line, which had slightly increased inflammatory CAF-like gene expression and higher protein expression of α-SMA, PDGFR-α, and FAP was found to restrain migration of tumor cells.

CONCLUSIONS

We found that two myCAF lines with globally similar expression characteristics had different effects on the same tumor cell line, one promoting and the other restraining migration. Our study highlights that there may be unappreciated heterogeneity within CAF subtypes. Further investigation and attention to specific genes or proteins that may drive this heterogeneity will be important.

Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference

Single-cell RNA sequencing (scRNA-seq) experiments have become instrumental in developmental and differentiation studies, enabling the profiling of cells at a single or multiple time-points to uncover subtle variations in expression profiles reflecting underlying biological processes. Benchmarking studies have compared many of the computational methods used to reconstruct cellular dynamics; however, researchers still encounter challenges in their analysis due to uncertainty with respect to selecting the most appropriate methods and parameters. Even among universal data processing steps used by trajectory inference methods such as feature selection and dimension reduction, trajectory methods' performances are highly dataset-specific. To address these challenges, we developed Escort, a novel framework for evaluating a dataset's suitability for trajectory inference and quantifying trajectory properties influenced by analysis decisions. Escort evaluates the suitability of trajectory analysis and the combined effects of processing choices using trajectory-specific metrics. Escort navigates single-cell trajectory analysis through these data-driven assessments, reducing uncertainty and much of the decision burden inherent to trajectory inference analyses. Escort is implemented in an accessible R package and R/Shiny application, providing researchers with the necessary tools to make informed decisions during trajectory analysis and enabling new insights into dynamic biological processes at single-cell resolution.

Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference

Single-cell RNA sequencing (scRNA-seq) experiments have become instrumental in developmental and differentiation studies, enabling the profiling of cells at a single or multiple time-points to uncover subtle variations in expression profiles reflecting underlying biological processes. Benchmarking studies have compared many of the computational methods used to reconstruct cellular dynamics, however researchers still encounter challenges in their analysis due to uncertainties in selecting the most appropriate methods and parameters. Even among universal data processing steps used by trajectory inference methods such as feature selection and dimension reduction, trajectory methods' performances are highly dataset-specific. To address these challenges, we developed Escort, a framework for evaluating a dataset's suitability for trajectory inference and quantifying trajectory properties influenced by analysis decisions. Escort navigates single-cell trajectory analysis through data-driven assessments, reducing uncertainty and much of the decision burden associated with trajectory inference. Escort is implemented in an accessible R package and R/Shiny application, providing researchers with the necessary tools to make informed decisions during trajectory analysis and enabling new insights into dynamic biological processes at single-cell resolution.

CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq data

Identifying cell types is crucial for understanding the functional units of an organism. Machine learning has shown promising performance in identifying cell types, but many existing methods lack biological significance due to poor interpretability. However, it is of the utmost importance to understand what makes cells share the same function and form a specific cell type, motivating us to propose a biologically interpretable method. CellTICS prioritizes marker genes with cell-type-specific expression, using a hierarchy of biological pathways for neural network construction, and applying a multi-predictive-layer strategy to predict cell and sub-cell types. CellTICS usually outperforms existing methods in prediction accuracy. Moreover, CellTICS can reveal pathways that define a cell type or a cell type under specific physiological conditions, such as disease or aging. The nonlinear nature of neural networks enables us to identify many novel pathways. Interestingly, some of the pathways identified by CellTICS exhibit differential expression "variability" rather than differential expression across cell types, indicating that expression stochasticity within a pathway could be an important feature characteristic of a cell type. Overall, CellTICS provides a biologically interpretable method for identifying and characterizing cell types, shedding light on the underlying pathways that define cellular heterogeneity and its role in organismal function. CellTICS is available at https://github.com/qyyin0516/CellTICS.

Consistent analysis of differentially expressed genes across 7 cell types in papillary thyroid carcinoma

Single-cell transcriptome sequencing (scRNA-seq) provides a higher resolution of cellular differences than bulk RNA-seq, enabling the dissection of cell-type-specific responses to perturbations in papillary thyroid carcinoma (PTC). However, cellular genomic features are highly heterogeneous and have a large number of genes without any expression signals, which hinders the statistical power to identify differentially expressed genes and may generate many false-positive results. To overcome this challenge, we conducted an integrative analysis on two PTC scRNA-seq datasets and cross-validated consistent differential expression. By combining results from 32 common cell types in the two studies, we identified 31 consistently differentially expressed genes (DEGs) across seven cell types, including B cells, endothelial cells, epithelial cells, monocytes, NK cells, smooth muscle cells, and T cells. Functional enrichment analysis revealed that these genes are important for the adaptive immune response and autoimmune thyroid diseases. The additional disease-free survival analysis also confirmed that these 31 genes significantly affected patient survival time in large scale thyroid cancer cohort. Furthermore, we experimentally validated one of the top consistent DEGs as a potential biomarker gene of PTC epithelial cells, KRT7, which may be a upstream gene for the NF-κB signaling pathway. The result shows that KRT7 may promote thyroid cancer metastasis through the epithelial-mesenchymal transition and NF-κB signaling pathway. In summary, our single-cell transcriptome integration-based approach may provide insights into the important role of NF-κB in the underlying biology of the PTC.

Systems level identification of a matrisome-associated macrophage polarisation state in multi-organ fibrosis

Tissue fibrosis affects multiple organs and involves a master-regulatory role of macrophages which respond to an initial inflammatory insult common in all forms of fibrosis. The recently unravelled multi-organ heterogeneity of macrophages in healthy and fibrotic human disease suggests that macrophages expressing osteopontin (SPP1) associate with lung and liver fibrosis. However, the conservation of this SPP1+ macrophage population across different tissues and its specificity to fibrotic diseases with different etiologies remain unclear. Integrating 15 single-cell RNA-sequencing datasets to profile 235,930 tissue macrophages from healthy and fibrotic heart, lung, liver, kidney, skin, and endometrium, we extended the association of SPP1+ macrophages with fibrosis to all these tissues. We also identified a subpopulation expressing matrisome-associated genes (e.g., matrix metalloproteinases and their tissue inhibitors), functionally enriched for ECM remodelling and cell metabolism, representative of a matrisome-associated macrophage (MAM) polarisation state within SPP1+ macrophages. Importantly, the MAM polarisation state follows a differentiation trajectory from SPP1+ macrophages and is associated with a core set of regulon activity. SPP1+ macrophages without the MAM polarisation state (SPP1+MAM-) show a positive association with ageing lung in mice and humans. These results suggest an advanced and conserved polarisation state of SPP1+ macrophages in fibrotic tissues resulting from prolonged inflammatory cues within each tissue microenvironment.