Cell morphology as a biological fingerprint of chondrocyte phenotype in control and inflammatory conditions

Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning

Cell-based medicinal products (CBMPs) are a growing class of therapeutics that promise new treatments for complex and rare diseases. Given the inherent complexity of the whole human cells comprising CBMPs, there is a need for robust and fast analytical methods for characterization, process monitoring, and quality control (QC) testing during their manufacture. Existing techniques to evaluate and monitor cell quality typically constitute labor-intensive, expensive, and highly specific staining assays. In this work, we combine image-based deep learning with flow imaging microscopy (FIM) to predict cell health metrics using cellular morphology "fingerprints" extracted from images of unstained Jurkat cells (immortalized human T-lymphocyte cells). A supervised (i.e., algorithm trained with human-generated labels for images) fingerprinting algorithm, trained on images of unstained healthy and dead cells, provides a robust stain-free, non-invasive, and non-destructive method for determining cell viability. Results from the stain-free method are in good agreement with traditional stain-based cytometric viability measurements. Additionally, when trained with images of healthy cells, dead cells and cells undergoing chemically induced apoptosis, the supervised fingerprinting algorithm is able to distinguish between the three cell states, and the results are independent of specific treatments or signaling pathways. We then show that an unsupervised variational autoencoder (VAE) algorithm trained on the same images, but without human-generated labels, is able to distinguish between samples of healthy, dead and apoptotic cells along with cellular debris based on learned morphological features and without human input. With this, we demonstrate that VAEs are a powerful exploratory technique that can be used as a process monitoring analytical tool.

Osteoarthriris year in review 2023: genetics, genomics, and epigenetics

OBJECTIVE

To elucidate the scientific advances made in the last 12 months within the realm of osteoarthritis genetics, genomics, and epigenetics. This review paper highlights major research publications that enhance our current understanding of the role of genetics, genomics, and epigenetics in osteoarthritis.

METHODS

A systematic literature search was conducted on pubmed.ncbi.nlm.nih.gov on "March 17, 2023", using the following keywords: "osteoarthritis" in combination with any of these terms: "genetic(s)", "mutation(s)", "genomic(s)", "epigenetic(s)", "DNA methylation", "noncoding RNA", "lncRNA", "circular RNA", "microRNA", "transcriptomic(s)", "RNA sequencing", "single cell RNA sequencing", or "single nucleus RNA sequencing". The selection comprised original research articles published in the English language between the OARSI congresses of 2022 and 2023.

RESULTS

A total of 2178 research articles were identified, which subsequently reduced to 67 unique articles relevant to the field. Current trends in osteoarthritis genetics research involve meta-analyses of various cohorts to explore the impact of gene variants on osteoarthritis-related outcomes, such as pain. Early developmental changes within the joint were also found to influence osteoarthritis through genetic variations. Researchers also prioritize testing the mechanisms and functions of miRNAs, circRNAs, and lncRNAs. Potential drug targets began to emerge; however, independent validation studies are lacking. Single cell RNA sequencing studies revealed unique immune cell populations in the knee; however, no study reported single nucleus RNA sequencing analysis.

CONCLUSIONS

This review focused on recent advances in the above-mentioned themes within the field of osteoarthritis. These advances improve our understanding of the disease's complexity and guide us toward functional assessments of genetic/epigenetic outcomes and toward their translational and clinical applications.

Colloidal Phenol-Amine Coating on Implants for Improved Anti-Inflammation and Osteogenesis

Phenol-amine coatings have attracted significant attention in recent years owing to their adjustable composition and multifaceted biological functionalities. The current preparation of phenol-amine coatings, however, involves a chemical reaction within the solution or interface, resulting in lengthy preparation times and necessitating specific reaction conditions, such as alkaline environments and oxygen presence. The facile, rapid, and eco-friendly preparation of phenol-amine coatings under mild conditions continues to pose a challenge. In this study, we use a macromolecular phenol-amine, Tanfloc, to form a stable colloid under neutral conditions, which was then rapidly adsorbed on the titanium surface by electrostatic action and then spread and fused to form a continuous coating within several minutes. This nonchemical preparation process was rapid, mild, and free of chemical additives. The in vitro and in vivo results showed that the Tanfloc colloid fusion coating inhibited destructive inflammation, promoted osteogenesis, and enhanced osteointegration. These remarkable advantages of the colloidal phenol-amine fusion coating highlight the suitability of its future application in clinical practice.

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.

Articular Cartilage-From Basic Science Structural Imaging to Non-Invasive Clinical Quantitative Molecular Functional Information for AI Classification and Prediction

This review presents the changes that the imaging of articular cartilage has undergone throughout the last decades. It highlights that the expectation is no longer to image the structure and associated functions of articular cartilage but, instead, to devise methods for generating non-invasive, function-depicting images with quantitative information that is useful for detecting the early, pre-clinical stage of diseases such as primary or post-traumatic osteoarthritis (OA/PTOA). In this context, this review summarizes (a) the structure and function of articular cartilage as a molecular imaging target, (b) quantitative MRI for non-invasive assessment of articular cartilage composition, microstructure, and function with the current state of medical diagnostic imaging, (c), non-destructive imaging methods, (c) non-destructive quantitative articular cartilage live-imaging methods, (d) artificial intelligence (AI) classification of degeneration and prediction of OA progression, and (e) our contribution to this field, which is an AI-supported, non-destructive quantitative optical biopsy for early disease detection that operates on a digital tissue architectural fingerprint. Collectively, this review shows that articular cartilage imaging has undergone profound changes in the purpose and expectations for which cartilage imaging is used; the image is becoming an AI-usable biomarker with non-invasive quantitative functional information. This may aid in the development of translational diagnostic applications and preventive or early therapeutic interventions that are yet beyond our reach.

Injectable Thermosensitive Hyaluronic Acid Hydrogels for Chondrocyte Delivery in Cartilage Tissue Engineering

In this study, we synthesize a hyaluronic acid-g-poly(N-isopropylacrylamide) (HPN) copolymer by grafting the amine-terminated poly(N-isopropylacrylamide) (PNIPAM-NH2) to hyaluronic acid (HA). The 5% PNIPAM-NH2 and HPN polymer solution is responsive to temperature changes with sol-to-gel phase transition temperatures around 32 °C. Compared with the PNIPAM-NH2 hydrogel, the HPN hydrogel shows higher water content and mechanical strength, as well as lower volume contraction, making it a better choice as a scaffold for chondrocyte delivery. From an in vitro cell culture, we see that cells can proliferate in an HPN hydrogel with full retention of cell viability and show the phenotypic morphology of chondrocytes. In the HPN hydrogel, chondrocytes demonstrate a differentiated phenotype with the upregulated expression of cartilage-specific genes and the enhanced secretion of extracellular matrix components, when compared with the monolayer culture on tissue culture polystyrene. In vivo studies confirm the ectopic cartilage formation when HPN was used as a cell delivery vehicle after implanting chondrocyte/HPN in nude mice subcutaneously, which is shown from a histological and gene expression analysis. Taken together, the HPN thermosensitive hydrogel will be a promising injectable scaffold with which to deliver chondrocytes in cartilage-tissue-engineering applications.

Polynucleotides Suppress Inflammation and Stimulate Matrix Synthesis in an In Vitro Cell-Based Osteoarthritis Model

Osteoarthritis (OA) is characterized by degeneration of the joint cartilage, inflammation, and a change in the chondrocyte phenotype. Inflammation also promotes cell hypertrophy in human articular chondrocytes (HC-a) by activating the NF-κB pathway. Chondrocyte hypertrophy and inflammation promote extracellular matrix degradation (ECM). Chondrocytes depend on Smad signaling to control and regulate cell hypertrophy as well as to maintain the ECM. The involvement of these two pathways is crucial for preserving the homeostasis of articular cartilage. In recent years, Polynucleotides Highly Purified Technology (PN-HPT) has emerged as a promising area of research for the treatment of OA. PN-HPT involves the use of polynucleotide-based agents with controlled natural origins and high purification levels. In this study, we focused on evaluating the efficacy of a specific polynucleotide sodium agent, known as CONJURAN, which is derived from fish sperm. Polynucleotides (PN), which are physiologically present in the matrix and function as water-soluble nucleic acids with a gel-like property, have been used to treat patients with OA. However, the specific mechanisms underlying the effect remain unclear. Therefore, we investigated the effect of PN in an OA cell model in which HC-a cells were stimulated with interleukin-1β (IL-1β) with or without PN treatment. The CCK-8 assay was used to assess the cytotoxic effects of PN. Furthermore, the enzyme-linked immunosorbent assay was utilized to detect MMP13 levels, and the nitric oxide assay was utilized to determine the effect of PN on inflammation. The anti-inflammatory effects of PN and related mechanisms were investigated using quantitative PCR, Western blot analysis, and immunofluorescence to examine and analyze relative markers. PN inhibited IL-1β induced destruction of genes and proteins by downregulating the expression of MMP3, MMP13, iNOS, and COX-2 while increasing the expression of aggrecan (ACAN) and collagen II (COL2A1). This study demonstrates, for the first time, that PN exerted anti-inflammatory effects by partially inhibiting the NF-κB pathway and increasing the Smad2/3 pathway. Based on our findings, PN can potentially serve as a treatment for OA.

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

Despite lignin's global abundance and its use in biomedical studies, our understanding of how lignin regulates disease through modulation of cell morphology and associated phenotype of human cells is unknown. We combined an automated high-throughput image cell segmentation technique for quantitatively measuring a panel of cell shape descriptors, droplet digital Polymerase Chain Reaction for absolute quantification of gene expression and multivariate data analyses to determine whether lignin could therapeutically modulate the cell morphology and phenotype of inflamed, degenerating diseased human cells (osteoarthritic (OA) chondrocytes) towards a healthier cell morphology and phenotype. Lignin dose-dependently modified all aspects of cell morphology and ameliorated the diseased shape of OA chondrocytes by inducing a less fibroblastic healthier cell shape, which correlated with the downregulation of collagen 1A2 (COL1A2, a major fibrosis-inducing gene), upregulation of collagen 2A1 (COL2A1, a healthy extracellular matrix-inducing gene) and downregulation of interleukin-6 (IL-6, a chronic inflammatory cytokine). This is the first study to show that lignin can therapeutically target cell morphology and change a diseased cells' function towards a healthier cell shape and phenotype. This opens up novel opportunities for exploiting lignin in modulation of disease, tissue degeneration, fibrosis, inflammation and regenerative medical implants for therapeutically targeting cell function and outcome.