Single cell force profiling of human myofibroblasts reveals a biophysical spectrum of cell states

A strategy to quantify myofibroblast activation on a continuous spectrum

Myofibroblasts are a highly secretory and contractile cell phenotype that are predominant in wound healing and fibrotic disease. Traditionally, myofibroblasts are identified by the de novo expression and assembly of alpha-smooth muscle actin stress fibers, leading to a binary classification: "activated" or "quiescent (non-activated)". More recently, however, myofibroblast activation has been considered on a continuous spectrum, but there is no established method to quantify the position of a cell on this spectrum. To this end, we developed a strategy based on microscopy imaging and machine learning methods to quantify myofibroblast activation in vitro on a continuous scale. We first measured morphological features of over 1000 individual cardiac fibroblasts and found that these features provide sufficient information to predict activation state. We next used dimensionality reduction techniques and self-supervised machine learning to create a continuous scale of activation based on features extracted from microscopy images. Lastly, we compared our findings for mechanically activated cardiac fibroblasts to a distribution of cell phenotypes generated from transcriptomic data using single-cell RNA sequencing. Altogether, these results demonstrate a continuous spectrum of myofibroblast activation and provide an imaging-based strategy to quantify the position of a cell on that spectrum.

Automated estimation of cancer cell deformability with machine learning and acoustic trapping

Cell deformability is a useful feature for diagnosing various diseases (e.g., the invasiveness of cancer cells). Existing methods commonly inflict pressure on cells and observe changes in cell areas, diameters, or thickness according to the degree of pressure. Then, the Young’s moduli (i.e., a measure of deformability) of cells are estimated based on the assumption that the degrees of the changes are inversely proportional to Young’s moduli. However, manual measurements of the physical changes in cells are labor-intensive, and the subjectivity of the operators can intervene during this step, thereby causing considerable uncertainty. Further, because the shapes of cells are nonuniform, we cannot ensure the assumption for linear correlations of physical changes in cells with their deformability. Therefore, this study aims at measuring non-linear elastic moduli of live cells (degrees of cell deformability) automatically by employing conventional neural networks (CNN) and multilayer perceptrons (MLP) while preserving (or enhancing) the accuracy of the manual methods. First, we obtain photomicrographs of cells on multiple pressure levels using single-beam acoustic tweezers, and then, we suggest an image preprocessing method for emphasizing changes in cell areas on the photomicrographs. The CNN model is trained to measure the ratios of the cell area change at each pressure level. Then, we apply the multilayer perceptron (MLP) to learn the correlations of the cell area change ratios according to the pressure levels with cell deformability. The accuracy of the CNN was evaluated using two types of breast cancer cells: MDA-MB-231 (invasive) and MCF-7 (noninvasive). The MLP was assessed using five different beads (Young’s moduli from 0.214 to 9.235 kPa), which provides standardized reference data of the non-linear elastic moduli of live cells. Finally, we validated the practicality of the proposed system by examining whether the non-linear elastic moduli estimated by the proposed system can distinguish invasive breast cancer cells from noninvasive ones.

Regulation of SMC traction forces in human aortic thoracic aneurysms

Smooth muscle cells (SMCs) usually express a contractile phenotype in the healthy aorta. However, aortic SMCs have the ability to undergo profound changes in phenotype in response to changes in their extracellular environment, as occurs in ascending thoracic aortic aneurysms (ATAA). Accordingly, there is a pressing need to quantify the mechanobiological effects of these changes at single cell level. To address this need, we applied Traction Force Microscopy (TFM) on 759 cells coming from three primary healthy (AoPrim) human SMC lineages and three primary aneurysmal (AnevPrim) human SMC lineages, from age and gender matched donors. We measured the basal traction forces applied by each of these cells onto compliant hydrogels of different stiffness (4, 8, 12, 25 kPa). Although the range of force generation by SMCs suggested some heterogeneity, we observed that: 1. the traction forces were significantly larger on substrates of larger stiffness; 2. traction forces in AnevPrim were significantly higher than in AoPrim cells. We modelled computationally the dynamic force generation process in SMCs using the motor-clutch model and found that it accounts well for the stiffness-dependent traction forces. The existence of larger traction forces in the AnevPrim SMCs were related to the larger size of cells in these lineages. We conclude that phenotype changes occurring in ATAA, which were previously known to reduce the expression of elongated and contractile SMCs (rendering SMCs less responsive to vasoactive agents), tend also to induce stronger SMCs. Future work aims at understanding the causes of this alteration process in aortic aneurysms.

Quantifying cellular forces: Practical considerations of traction force microscopy for dermal fibroblasts

Traction force microscopy (TFM) is a well-established technique traditionally used by biophysicists to quantify the forces adherent biological cells exert on their microenvironment. As image processing software becomes increasingly user-friendly, TFM is being adopted by broader audiences to quantify contractility of (myo)fibroblasts. While many technical reviews of TFM's computational mechanics are available, this review focuses on practical experimental considerations for dermatology researchers new to cell mechanics and TFM who may wish to implement a higher throughput and less expensive alternative to collagen compaction assays. Here, we describe implementation of experimental methods, analysis using open-source software and troubleshooting of common issues to enable researchers to leverage TFM for their investigations into skin fibroblasts.

Identifying collagen VI as a target of fibrotic diseases regulated by CREBBP/EP300

Fibrotic diseases remain a major cause of morbidity and mortality, yet there are few effective therapies. The underlying pathology of all fibrotic conditions is the activity of myofibroblasts. Using cells from freshly excised disease tissue from patients with Dupuytren's disease (DD), a localized fibrotic disorder of the palm, we sought to identify new therapeutic targets for fibrotic disease. We hypothesized that the persistent activity of myofibroblasts in fibrotic diseases might involve epigenetic modifications. Using a validated genetics-led target prioritization algorithm (Pi) of genome wide association studies (GWAS) data and a broad screen of epigenetic inhibitors, we found that the acetyltransferase CREBBP/EP300 is a major regulator of contractility and extracellular matrix production via control of H3K27 acetylation at the profibrotic genes, ACTA2 and COL1A1 Genomic analysis revealed that EP300 is highly enriched at enhancers associated with genes involved in multiple profibrotic pathways, and broad transcriptomic and proteomic profiling of CREBBP/EP300 inhibition by the chemical probe SGC-CBP30 identified collagen VI (Col VI) as a prominent downstream regulator of myofibroblast activity. Targeted Col VI knockdown results in significant decrease in profibrotic functions, including myofibroblast contractile force, extracellular matrix (ECM) production, chemotaxis, and wound healing. Further evidence for Col VI as a major determinant of fibrosis is its abundant expression within Dupuytren's nodules and also in the fibrotic foci of idiopathic pulmonary fibrosis (IPF). Thus, Col VI may represent a tractable therapeutic target across a range of fibrotic disorders.