Biology subjects

Badylak, S.

Publications and source records attributed to Badylak, S..

4 recordsLinked to original sources

bioRxiv2024

Transcriptomic Landscape and Immune Microenvironment AroundWound Bed Define Regenerative versus Non-regenerative Outcomesin Mouse Digit Amputation

In the mouse distal terminal phalanx (P3), it remains unclear why amputation at less than 33% of the digit results in regeneration, while amputation exceeding 67% leads to non-regeneration. Unraveling the molecular mechanisms underlying this disparity could provide crucial insights for regenerative medicine. In this study, we aim to investigate the tissues within the wound bed to understand the tissue microenvironment associated with regenerative versus non-regenerative outcomes. We employed a P3-specific amputation model in mice, integrated with time-series RNA-seq and a macrophage assay challenged with pro- and anti-inflammatory cytokines, to explore these mechanisms. Our findings revealed that non-regenerative digits exhibit a greater intense early transcriptional response in the wound bed compared to regenerative ones. Furthermore, early macrophage phenotypes differ distinctly between regenerative and non-regenerative outcomes. Regenerative digits also display unique co-expression modules related to Bone Morphogenetic Protein 2 (BMP2). The differentially expressed genes (DEGs) between regenerative and non-regenerative digits are enriched in targets of several transcription factors, such as HOXA11 and HOXD11 from the HOX gene family, showing a time-dependent pattern of enrichment. These transcription factors, known for their roles in bone regeneration, skeletal patterning, osteoblast activity, fracture healing, angiogenesis, and key signaling pathways, may act as master regulators of the regenerative gene signatures. Additionally, we developed a deep learning AI model capable of predicting post-amputation time and level from RNA-seq data, with potential applications in personalized treatment strategies and assessing the impact of interventions on regenerative outcomes.

developmental biology↗

bioRxiv2024

Rapid Assessment of Biomarkers on Single Extracellular Vesicles Using 'Catch and Display' on Ultrathin Nanoporous Silicon Nitride Membranes

Extracellular vesicles (EVs) are particles secreted by all cells that carry bioactive cargo and facilitate intercellular communication with roles in normal physiology and disease pathogenesis. EVs have tremendous diagnostic and therapeutic potential and accordingly, the EV field has grown exponentially in recent years. Bulk assays lack the sensitivity to detect rare EV subsets relevant to disease, and while single EV analysis techniques remedy this, they are undermined by complicated detection schemes often coupled with prohibitive instrumentation. To address these issues, we propose a microfluidic technique for EV characterization called catch and display for liquid biopsy (CAD-LB). CAD-LB rapidly captures fluorescently labeled EVs in the similarly-sized pores of an ultrathin silicon nitride membrane. Minimally processed sample is introduced via pipette injection into a simple microfluidic device which is directly imaged using fluorescence microscopy for a rapid assessment of EV number and biomarker colocalization. In this work, nanoparticles were first used to define the accuracy and dynamic range for counting and colocalization by CAD-LB. Following this, the same assessments were made for purified EVs and for unpurified EVs in plasma. Biomarker detection was validated using CD9 in which Western blot analysis confirmed that CAD-LB faithfully recapitulated differing expression levels among samples. We further verified that CAD-LB captured the known increase in EV-associated ICAM-1 following the cytokine stimulation of endothelial cells. Finally, to demonstrate CAD-LBs clinical potential, we show that EV biomarkers indicative of immunotherapy responsiveness are successfully detected in the plasma of bladder cancer patients undergoing immune checkpoint blockade.

bioengineering↗

bioRxiv2022

Optical Biopsy using a neural network to predict functional state from photos of wounds

BackgroundThe clinical characterization of the functional status of active wounds remains a considerable challenge that at present, requires excision of a tissue biopsy. In this pilot study, we use a convolutional Siamese neural network architecture to predict the functional state of a wound using digital photographs of wounds in a canine model of volumetric muscle loss (VML). Materials and MethodsImages of volumetric muscle loss injuries and tissue biopsies were obtained in a canine model of VML. Gene expression profiles for each image were obtained using RNAseq. These profiles were then converted to functional profiles using a manual review of validated gene ontology databases. A Siamese neural network was trained to regress functional profile expression values as a function of the data contained in an extracted image segment showing the surface of a small tissue biopsy. Network performance was assessed in a test set of images using Mean Absolute Percentage Error (MAPE). ResultsThe network was able to predict the functional expression of a range of functions based with a MAPE ranging from [~]5% to [~]50%, with functions that are most closely associated with the early-state of wound healing to be those best-predicted. ConclusionsThese initial results suggest promise for further research regarding this novel use of ML regression on medical images. The regression of functional profiles, as opposed to specific genes, both addresses the challenge of genetic redundancy and gives a deeper insight into the mechanistic configuration of a region of tissue in wounds. As this preliminary study focuses on the first 14 days of wound healing, future work will focus on extending the training data to include longer time periods which would result in additional functions, such as tissue remodeling, having a larger presence in the training data.

bioinformatics↗

bioRxiv2021

Quantifying the size and healing of volumetric muscle wounds using 3D Slicer on CT scans in a canine model

Volumetric soft tissue and muscle wounds can arise from trauma or necrotizing soft tissue infection. Quantifying the size of these wounds can be challenging, as they often have irregular borders and contours and invariably involve skin loss. 3-dimensional Computed Tomography (3dCT) has been used to characterize the volume of numerous tissue structures, but these use cases invariably involve structures for which clear anatomic borders exist. This is not the case for volumetric soft tissue or muscle wounds, where the volume of the wound being assessed, which is actually a void representing the absence of tissue, does not contain an explicit border at the superficial surface. We present a method that allows quantification of the void size of volumetric muscle wounds using CT scans processed with the software package 3D Slicer. This quantification allows us to chart the progression of healing in such wounds with sequential scans. The development of a means to quantify wound size and healing rate is a necessary capability in order to assess the efficacy of potential therapeutic interventions aimed at enhancing healing of such wounds.

physiology↗