Therapeutic Modulation of Cell Morphology and Phenotype of Diseased Human Cells towards a Healthier Cell State Using Lignin

Comparative analysis of cell morphology in patient-paired primary human osteoblasts from the jaw and the fibula

Previous studies hint at possible differences in osteogenic, osteoimmunologic, and angiogenetic potential among primary human osteoblasts (HOBs) from different origins (iliac and alveolar bone) within the same patient. In this study, HOBs from the jaw and the fibula were investigated for the first time to gain further knowledge about the similarities and differences on the cellular morphological level. Patient-paired HOB cultures from the jaw and fibula of 14 patients (60.3 ± 11.1 years; male: 9; female: 5) were isolated and further processed. Cells were stained with Calcein and Hoechst 33342, and single-cell morphometric shape analysis was performed. For each osteoblast, the shape descriptors area, length, width, aspect ratio, circularity, roundness, and solidity were determined. A site-specific and a gender-specific comparison were conducted. None of the shape descriptors showed any significant differences between HOBs derived from the jaw and the fibula. The same applied to the gender-specific comparison between osteoblasts from female and male patients. Significant correlations between shape descriptors were found. HOBs from both bones possess a comparable cell shape, which might positively influence the ossification between the recipient and the donor bone. Since cell morphology often reflects cell function, both bones might exhibit comparable osteoblast behavior, adding to the favorable outcomes observed with free fibula flaps in reconstructive surgery.

Simple Methodology to Score Micropattern Quality and Effectiveness

Micropatterns (MPs) are widely used as a powerful tool to control cell morphology and phenotype. However, methods for determining the effectiveness of how well cells are controlled by the shape of MPs have been inconsistently used and studies rarely report on this topic, indicating lack of standardization. We introduce an evaluation score that quantitatively assesses the MP fabrication quality and effectiveness, which can be broadly used in conjunction with all currently available MP design types. This score uses four simple and quick steps: (i) scoring MP and (ii) background fabrication quality, (iii) defining the type(s) of MP of interest, and (iv) assigning so-called efficiency descriptors describing cell behavior. These steps are based on visual inspection and quick categorization of various aspects of MP fabrication quality and cell behavior, presented in illustrations and microscopy image examples intended to serve as a reference "atlas." To illustrate the advantage of using this score, we determined differences in cell morphology and F-actin intensity between scored versus nonscored cells. These measurements, which could be different in other studies, were chosen because both are understood as markers of cell phenotype and function. We combined intensity-calibrated immunofluorescence microscopy and image-based single cell protein analysis. Most important, significant differences in cell morphology and cytoskeletal protein content between scored versus nonscored cells were noted: the unconditional inclusion of all experimental read-outs (i.e., all MP data regardless of MP quality and effectiveness) into the final results significantly misjudged the experimental readouts versus only including experimental read-outs of quality-controlled and effective MPs, identified by scoring. Specifically, nonscoring underestimated the F-actin intensity per cell and quantitative cellular morphometric descriptors circularity and solidity and overestimated aspect ratio. Scoring improved the precision of cellular readouts, advocating the use of a MP quality and efficiency score as a quantitative decision-supporting tool in deciding whether or not particular MPs should be used for experiments, saving time and money. This simple scoring methodology can be used for improving MP fabrication, comparing results across studies, benefiting basic science studies and potential future clinical use of MPs by introducing standardization.

Panaxatriol exerts anti-senescence effects and alleviates osteoarthritis and cartilage repair fibrosis by targeting UFL1

INTRODUCTION

Osteoarthritis (OA), the most common degenerative joint disease, can eventually lead to disability. However, no safe or effective intervention is currently available. Therefore, there is an urgent need to develop effective drugs that reduce cartilage damage and treat OA.

OBJECTIVES

This study aimed to ascertain the potential of panaxatriol, a natural small molecule, as a therapeutic drug for alleviating the progression of OA.

METHODS

An in vitro culture of human cartilage explants and C28/I2 human chondrocytes and an in vivo surgically induced OA mouse model were used to evaluate the chondroprotective effect of panaxatriol. The Drug Affinity Responsive Target Stability assay, CRISPR-Cas9 assay, Whole-transcriptome RNA sequencing analysis and agonist or antagonist assays were used to identify the target and potential signaling pathways of panaxatriol. Poly(lactic-co-glycolic acid)-polyethylene glycol (PLGA-PEG) was used to construct the sustained-release system of panaxatriol.

RESULTS

Panaxatriol protected against OA by regulating chondrocyte metabolism. Ubiquitin-fold modifier 1-specific E3 ligase 1 (UFL1) was identified as a novel target of panaxatriol. Whole transcriptome RNA sequencing showed that UFL1 was closely related to cell senescence. Panaxatriol inhibited chondrocyte senescence through UFL1/forkhead box O1 (FOXO1)/P21 and UFL1/NF-κB/SASPs signaling pathways. It also could inhibit fibrocartilage formation during cartilage repair via the UFL1/FOXO1/Collagen 1 signaling pathway. Finally, we constructed a sustained-release system for panaxatriol based on PLGA-PEG, which reduced the number of intra-articular injections, thereby alleviating joint swelling and injury.

CONCLUSIONS

Panaxatriol exerts anti-senescence effects and has the potential to delay OA progression and reduce cartilage repair fibrosis by targeting UFL1.

Prediction of six macrophage phenotypes and their IL-10 content based on single-cell morphology using artificial intelligence

Introduction

The last decade has led to rapid developments and increased usage of computational tools at the single-cell level. However, our knowledge remains limited in how extracellular cues alter quantitative macrophage morphology and how such morphological changes can be used to predict macrophage phenotype as well as cytokine content at the single-cell level.

Methods

Using an artificial intelligence (AI) based approach, this study determined whether (i) accurate macrophage classification and (ii) prediction of intracellular IL-10 at the single-cell level was possible, using only morphological features as predictors for AI. Using a quantitative panel of shape descriptors, our study assessed image-based original and synthetic single-cell data in two different datasets in which CD14+ monocyte-derived macrophages generated from human peripheral blood monocytes were initially primed with GM-CSF or M-CSF followed by polarization with specific stimuli in the presence/absence of continuous GM-CSF or M-CSF. Specifically, M0, M1 (GM-CSF-M1, TNFα/IFNγ-M1, GM-CSF/TNFα/IFNγ-M1) and M2 (M-CSF-M2, IL-4-M2a, M-CSF/IL-4-M2a, IL-10-M2c, M-CSF/IL-10-M2c) macrophages were examined.

Results

Phenotypes were confirmed by ELISA and immunostaining of CD markers. Variations of polarization techniques significantly changed multiple macrophage morphological features, demonstrating that macrophage morphology is a highly sensitive, dynamic marker of phenotype. Using original and synthetic single-cell data, cell morphology alone yielded an accuracy of 93% for the classification of 6 different human macrophage phenotypes (with continuous GM-CSF or M-CSF). A similarly high phenotype classification accuracy of 95% was reached with data generated with different stimuli (discontinuous GM-CSF or M-CSF) and measured at a different time point. These comparably high accuracies clearly validated the here chosen AI-based approach. Quantitative morphology also allowed prediction of intracellular IL-10 with 95% accuracy using only original data.

Discussion

Thus, image-based machine learning using morphology-based features not only (i) classified M0, M1 and M2 macrophages but also (ii) classified M2a and M2c subtypes and (iii) predicted intracellular IL-10 at the single-cell level among six phenotypes. This simple approach can be used as a general strategy not only for macrophage phenotyping but also for prediction of IL-10 content of any IL-10 producing cell, which can help improve our understanding of cytokine biology at the single-cell level.