Biology subjects

Holcombe, B.

Publications and source records attributed to Holcombe, B..

4 recordsLinked to original sources

bioRxiv2025

Lipid Mediated Formation of Antiparallel Aggregates in Cerebral Amyloid Angiopathy

Cerebral amyloid angiopathy (CAA) is a cerebrovascular disorder marked by amyloid-{beta} (A{beta}) deposition in blood vessel walls, leading to hemorrhage and recurring stroke. Despite significant overlap with Alzheimers disease (AD) through shared A{beta} pathology, the specific structural characteristics of A{beta} aggregates in CAA and their variations between stages of disease severity are yet to be fully understood. Traditional approaches relying on brain-derived fibrils can potentially overlook the polymorphic heterogeneity and chemical associations within vascular amyloids. This study utilizes sub-diffraction, label-free mid-infrared photothermal (MIP) spectroscopic imaging to directly probe the chemical structure and heterogeneity of vascular amyloid aggregates within human brain tissues across different CAA stages. Our results demonstrate a clear increase in {beta}-sheet content within vascular A{beta} deposits corresponding to disease progression. Crucially, we identify a significant presence of antiparallel {beta}-sheet structures, particularly prevalent in moderate/severe CAA. The abundance of antiparallel structures correlates strongly with co-localized lipids, implicating a lipid-mediated aggregation mechanism. We substantiate the ex-vivo observations using nanoscale AFM-IR spectroscopy and demonstrate that A{beta}40 aggregated in vitro with brain-derived lipids adopts antiparallel structural distributions mirroring those found in CAA vascular lesions. This work provides critical insights into the structural distributions of A{beta} aggregates in CAA, highlighting the presence of polymorphs typically associated with transient intermediates, which may lead to alternate mechanisms for neurotoxicity.

neuroscience↗

bioRxiv2025

A Machine Learning-based Approach for Quantification of Protein Secondary Structures from Discrete Frequency Infrared Images

Discrete frequency infrared (IR) imaging is an exciting experimental technique that has shown promise in various applications in biomedical science. This technique often involves acquiring IR absorptive images at specific frequencies of interest that enable pathologically relevant chemical contrast. However, certain applications, such as tracking the spatial variations in protein secondary structure of tissue specimens, necessary for the characterization of neurodegenerative diseases, require deeper analysis of spectral data. In such cases, the conventional analytical approach involves band fitting the hyperspectral data to extract the relative populations of different structures through their fitted areas under the curve (AUCs). While Gaussian spectral fitting for one spectrum is viable, expanding that to an image with millions of pixels, as often applicable for tissue specimens, becomes a computationally expensive process. Alternatives like Principal Component Analysis (PCA) are less structurally interpretable and incompatible with sparsely sampled data. Furthermore, this detracts from the key advantages of discrete frequency imaging by necessitating acquisition of a more finely sampled spectral data that is optimal for curve fitting, resulting in significantly longer data acquisition times, larger datasets and additional computational overhead. In this work we demonstrate that a simple two-step regressive neural network model can be utilized to mitigate these challenges and employ discrete frequency imaging for retrieving the results from band fitting without significant loss of fidelity. Our model reduces the data acquisition time nearly 6-fold by requiring only seven wavenumbers to accurately interpolate spectral information at a higher resolution, and subsequently using the upscaled spectra to accurate predict the component AUCs, which is more than 3000 times faster than spectral fitting. Our approach thus drastically cuts down the data acquisition and analysis time and predicts key differences in protein structure that can be vital towards broadening potential applications of discrete frequency imaging.

biophysics↗

bioRxiv2023

Intermediate antiparallel beta structure in amyloid plaques revealed by infrared spectroscopic imaging

Aggregation of amyloid beta (A{beta}) peptides into extracellular plaques is a hallmark of the molecular pathology of Alzheimers disease (AD). Amyloid aggregates have been extensively studied in-vitro, and it is well known that mature amyloid fibrils contain an ordered parallel {beta} structure. The structural evolution from unaggregated peptide to fibrils can be mediated through intermediate structures that deviate significantly from mature fibrils, such as antiparallel {beta}-sheets. However, it is currently unknown if these intermediate structures exist in plaques, which limits the translation of findings from in-vitro structural characterizations of amyloid aggregates to AD. This arises from the inability to extend common structural biology techniques to ex-vivo tissue measurements. Here we report the use of infrared (IR) imaging, wherein we can spatially localize plaques and probe their protein structural distributions with the molecular sensitivity of IR spectroscopy. Analyzing individual plaques in AD tissues, we demonstrate that fibrillar amyloid plaques exhibit antiparallel {beta}-sheet signatures, thus providing a direct connection between in-vitro structures and amyloid aggregates in AD brain. We further validate results with IR imaging of in-vitro aggregates and show that antiparallel {beta}-sheet structure is a distinct structural facet of amyloid fibrils.

biophysics↗

bioRxiv2022

Structural heterogeneity of amyloid aggregates identified by spatially resolved nanoscale infrared spectroscopy

Amyloid plaques, composed of aggregates of the amyloid beta (A{beta}) protein, are one of the central manifestations of Alzheimers disease pathology. Aggregation of A{beta} from amorphous oligomeric species to mature fibrils has been extensively studied. However, significantly less in known about early-stage aggregates compared to fibrils. In particular, structural heterogeneities in prefibrillar species, and how that affects the structure of later stage aggregates are not yet well understood. Conventional spectroscopies cannot attribute structural facets to specific aggregates due to lack of spatial resolution, and hence aggregates at any stage of aggregation must be viewed as having the same average structure. The integration of infrared spectroscopy with Atomic Force Microscopy (AFM-IR) allows for identifying the signatures of individual nanoscale aggregates by spatially resolving spectra. In this report, we use AFM-IR to demonstrate that amyloid oligomers exhibit significant structural variations as evidenced in their infrared spectra, ranging from ordered beta structure to disordered conformations with predominant random coil and beta turns. This heterogeneity is transmitted to and retained in protofibrils and fibrils. We show for the first time that amyloid fibrils do not always conform to their putative ordered structure and structurally different domains can exist in the same fibril. We further show the implications of these results in amyloid plaques in Alzheimers tissue using infrared imaging, where these structural heterogeneities manifest themselves as lack of expected beta sheet structure.

biophysics↗