Sawyer, M. | Search bioRxiv

bioRxiv2023

Correlative Imaging of 3D Cell Culture on Opaque Bioscaffolds for Tissue Engineering Applications

Three-dimensional (3D) tissue engineering (TE) is a prospective treatment that can be used to restore or replace damaged musculoskeletal tissues such as articular cartilage. However, current challenges in TE include identifying materials that are biocompatible and have properties that closely match the mechanical properties and cellular environment of the target tissue, while allowing for 3D tomography of porous scaffolds as well as their cell growth and proliferation characterization. This is particularly challenging for opaque scaffolds. Here we use graphene foam (GF) as a 3D porous biocompatible substrate which is scalable, reproduceable, and a suitable environment for ATDC5 cell growth and chondrogenic differentiation. ATDC5 cells are cultured, maintained, and stained with a combination of fluorophores and gold nanoparticle to enable correlative microscopic characterization techniques, which elucidate the effect of GF properties on cell behavior in a three-dimensional environment. Most importantly, our staining protocols allows for direct imaging of cell growth and proliferation on opaque GF scaffolds using X-ray MicroCT, including imaging growth of cells within the hollow GF branches which is not possible with standard fluorescence and electron microscopy techniques. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/533202v1_ufig1.gif" ALT="Figure 1"> View larger version (76K): org.highwire.dtl.DTLVardef@1408d72org.highwire.dtl.DTLVardef@1063802org.highwire.dtl.DTLVardef@1472680org.highwire.dtl.DTLVardef@30b3b2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2022

Interpretable deep learning of myelin histopathology in age-related cognitive impairment

Age-related cognitive impairment is multifactorial, with numerous underlying and frequently co-morbid pathological correlates. Amyloid beta (A{beta}) plays a major role in Alzheimers type age-related cognitive impairment, in addition to other etiopathologies such as A{beta}-independent hyperphosphorylated tau, cerebrovascular disease, and myelin damage, which also warrant further investigation. Classical methods, even in the setting of the gold standard of postmortem brain assessment, involve semi-quantitative ordinal staging systems that often correlate poorly with clinical outcomes, due to imperfect cognitive measurements and preconceived notions regarding the neuropathologic features that should be chosen for study. Improved approaches are needed to identify histopathological changes correlated with cognition in an unbiased way. We used a weakly supervised multiple instance learning algorithm on whole slide images of human brain autopsy tissue sections from a group of elderly donors to predict the presence or absence of cognitive impairment (n = 367 with cognitive impairment, n = 349 without). Attention analysis allowed us to pinpoint the underlying subregional architecture and cellular features that the models used for the prediction in both brain regions studied, the medial temporal lobe and frontal cortex. Despite noisy labels of cognition, our trained models were able to predict the presence of cognitive impairment with a modest accuracy that was significantly greater than chance. Attention-based interpretation studies of the features most associated with cognitive impairment in the top performing models suggest that they identified myelin pallor in the white matter. Our results demonstrate a scalable platform with interpretable deep learning to identify unexpected aspects of pathology in cognitive impairment that can be translated to the study of other neurobiological disorders.

pathology↗