Biology subjects

Farris, A. B.

Publications and source records attributed to Farris, A. B..

7 recordsLinked to original sources

bioRxiv2026

Unmasking Early Renal Fibrosis in Polycystic Kidney Disease Using Noninvasive Precision Molecular MRI of Collagen

Fibrotic remodeling of extracellular matrix is a central driver of autosomal dominant polycystic kidney disease (ADPKD) progression since early stages of the disease. Unfortunately, it cannot be assessed with currently available methodologies before irreversible structural and functional decline, creating a diagnostic blind spot that hinders accurate early risk stratification and management. Here, we report the development of Gd-hProCA32.Collagen, a collagen -targeted protein MRI contrast agent that enables precision molecular MRI (pMRI) of early fibrosis by directly imaging collagen type I deposition in vivo before conventional laboratory and imaging methods detect changes in kidneys and liver of Pkhd1PCK/PCK (PCK) rats and Pkd2 mutant mice. Gd-hProCA32.Collagen, used at 10-fold lower dose, outperformed the widely clinically used agent gadobutrol (Gadovist), detecting approximately 2.8-fold greater total renal cyst volume ([~]8,500 vs [~]3,000 mm3, p<0.0001) and 1.5-fold higher total cyst count ([~]245 vs [~]160, p<0.0001), with superior T1W and T2W kidney AUC (p<0.01 and p<0.001) and preferential sensitivity to small and medium cysts. Signal enhancement in kidneys and liver correlated strongly with histological collagen burden quantified by Sirius red staining, whereas Gadovist showed no meaningful correlation. Gd-hProCA32.Collagen also enabled in vivo visualization of previously undetectable changes resembling radial striations at sites of microcyst cluster-adjacent microfibrosis and sustained delayed MRI enhancement due to specific collagen binding. These results reveal a previously inaccessible subclinical fibrotic phase of cystic kidney and liver disease and establish collagen-targeted pMRI as a strategy for early noninvasive detection and spatial mapping of multi-organ extracellular matrix remodeling when conventional biomarkers remain non-discriminating. One-sentence summaryWe report a first-in-class collagen-targeted MRI contrast agent with Precision molecular MRI that noninvasively reveals a previously inaccessible subclinical fibrotic phase of polycystic kidney disease across kidney and liver, overcoming limitations of current diagnostic methodology.

molecular biology↗

bioRxiv2023

EX VIVO GENE EDITING AND CELL THERAPY FOR HEREDITARY TYROSINEMIA TYPE 1

Background & AimsWe previously demonstrated the successful use of in vivo CRISPR gene editing to delete 4-hydroxyphenylpyruvate dioxygenase (HPD) to rescue mice deficient in fumarylacetoacetate hydrolase (FAH), a disorder known as hereditary tyrosinemia type 1 (HT1). The goal of this study was to develop an ex vivo gene editing protocol and apply it as a cell therapy for HT1. MethodsWe isolated hepatocytes from wild-type (C57BL/6) and Fah-/- mice and then used an optimized electroporation protocol to deliver Hpd-targeting CRISPR-Cas9 ribonucleoproteins (RNP) into hepatocytes. Next, hepatocytes were transiently incubated in cytokine recovery media that we formulated to block apoptosis, followed by splenic injection into recipient Fah-/- mice. ResultsWe observed robust engraftment and expansion of transplanted gene-edited hepatocytes from wild-type donors in the liver of recipient mice when transient incubation with our cytokine recovery media was used after electroporation and negligible engraftment without the media (mean 46.8% and 0.83%, respectively, p = 0.0025). Thus, the cytokine recovery media was a critical component of our electroporation protocol. When hepatocytes from Fah-/- mice were used as donors for transplantation, we observed 35% and 28% engraftment for Hpd-Cas9 RNPs and Cas9 mRNA, respectively. Tyrosine, phenylalanine, and biochemical markers of liver injury normalized in both Hpd-targeting Cas9 RNP and mRNA groups independent of drug induced-inhibition of Hpd through nitisinone, indicating correction of disease indicators in Fah-/- mice. ConclusionsThe successful liver cell therapy for HT1 validates our protocol and, despite the known growth advantage of HT1, showcase ex vivo gene editing using electroporation in combination with liver cell therapy to cure a disease model. These advancements showcase the impacts of electroporation combined with transplantation as a cell therapy.

bioengineering↗

bioRxiv2023

CD28-Selective Inhibition Prolongs Non-Human Primate Kidney Transplant Survival

Costimulation blockade using belatacept results in improved renal function after kidney transplant as well as decreased likelihood of death/graft loss and reduced cardiovascular risk; however, higher rates and grades of acute rejection have prevented its widespread clinical adoption. Treatment with belatacept blocks both positive (CD28) and negative (CTLA-4) T cell signaling. CD28-selective therapies may offer improved potency by blocking CD28-mediated costimulation while leaving CTLA-4 mediated coinhibitory signals intact. Here we test a novel domain antibody directed at CD28 (anti-CD28 dAb (BMS-931699)) in a non-human primate kidney transplant model. Sixteen macaques underwent native nephrectomy and received life-sustaining renal allotransplantation from an MHC-mismatched donor. Animals were treated with belatacept alone, anti-CD28 dAb alone, or anti-CD28 dAb plus clinically relevant maintenance (MMF, Steroids) and induction therapy with either anti-IL-2R or T cell depletion. Treatment with anti-CD28 dAb extended survival compared to belatacept monotherapy (MST 187 vs. 29 days, p=0.07). The combination of anti-CD28 dAb and conventional immunosuppression further prolonged survival to MST [~]270 days. Animals maintained protective immunity with no significant infectious issues. These data demonstrate CD28-directed therapy is a safe and effective next-generation costimulatory blockade strategy with a demonstrated survival benefit and presumed advantage over belatacept by maintaining intact CTLA-4 coinhibitory signaling.

immunology↗

bioRxiv2022

Artificial Intelligence based Liver Portal Tract Region Identification and Quantification with Transplant Biopsy Whole-Slide Images

Liver fibrosis staging is clinically important for liver disease progression prediction. As the portal tract fibrotic quantity and size in a liver biopsy correlate with the fibrosis stage, an accurate analysis of portal tract regions is clinically critical. Manual annotations of portal tract regions, however, are time-consuming and subject to large inter- and intra-observer variability. To address such a challenge, we develop a Multiple Up-sampling and Spatial Attention guided UNet model (MUSA-UNet) to segment liver portal tract regions in whole-slide images of liver tissue slides. To enhance the segmentation performance, we propose to use depth-wise separable convolution, the spatial attention mechanism, the residual connection, and multiple up-sampling paths in the developed model. This study includes 53 histopathology whole slide images from patients who received liver transplantation. In total, 6,012 patches derived from 30 images are used for our deep learning model training and validation. The remaining 23 whole slide images are utilized for the model testing. The average liver portal tract segmentation performance of the developed MUSA-UNet is 0.94 (Precision), 0.85 (Recall), 0.89 (F1 Score), 0.89 (Accuracy), 0.80 (Jaccard Index), and 0.91 (Fowlkes-Mallows Index), respectively. The clinical Scheuer fibrosis stage presents a strong correlation with the resulting average portal tract fibrotic area (R=0.681, p<0.001) and portal tract percentage (R=0.335, p=0.02) computed from the MUSA-UNet segmentation results. In conclusion, our developed deep learning model MUSA-UNet can accurately segment portal tract regions from whole-slide images of liver tissue biopsies, presenting its promising potential to assist liver disease diagnosis in a computational manner.

bioinformatics↗

bioRxiv2022

Virtual Liver Needle Biopsy from Reconstructed Three-Dimensional Histopathological Images: Quantification of Sampling Error

IntroductionPrevalently considered as the "gold-standard" for diagnosis of hepatic fibrosis and cirrhosis, the clinical liver needle biopsy is known to be subject to inadequate sampling and a high mis-sampling rate. However, quantifying such sampling bias has been difficult as generating a large number of needle biopsies from the same living patient is practically infeasible. We construct a three-dimension (3D) virtual liver tissue volume by spatially registered high resolution Whole Slide Images (WSIs) of serial liver tissue sections with a novel dynamic registration method. We further develop a Virtual Needle Biopsy Sampling (VNBS) method that mimics the needle biopsy sampling process. We apply the VNBS method to the reconstructed digital liver volume at different tissue locations and angles. Additionally, we quantify Collagen Proportionate Area (CPA) in all resulting virtual needle biopsies in 2D and 3D. ResultsThe staging score of the center 2D longitudinal image plane from each 3D biopsy is used as the biopsy staging score, and the highest staging score of all sampled needle biopsies is the diagnostic staging score. The Mean Absolute Difference (MAD) in reference to the Scheuer and Ishak diagnostic staging scores are 0.22 and 1.00, respectively. The absolute Scheuer staging score difference in 22.22% of sampled biopsies is 1. By the Ishak staging method, 55.56% and 22.22% of sampled biopsies present score difference 1 and 2, respectively. There are 4 (Scheuer) and 6 (Ishak) out of 18 3D virtual needle biopsies with intra-needle variations. Additionally, we find a positive correlation between CPA and fibrosis stages by Scheuer but not Ishak method. Overall, CPA measures suffer large intra- and inter-needle variations. ConclusionsThe developed virtual liver needle biopsy sampling pipeline provides a computational avenue for investigating needle biopsy sampling bias with 3D virtual tissue volumes. This method can be applied to other tissue-based disease diagnoses where the needle biopsy sampling bias substantially affects the diagnostic results.

bioengineering↗

bioRxiv2022

An integrative web-based software tool for multi-dimensional pathology whole-slide image analytics

In the era of precision medicine, human tumor atlas oriented studies have been significantly facilitated by high-resolution, multi-modal tissue based microscopic pathology image analytics. To better support such tissue-based investigations, we develop Digital Pathology Laboratory (DPLab), a publicly available web-based platform, to assist biomedical research groups, non-technical end users, and clinicians for pathology Whole-Slide Image (WSI) visualization, annotation, analysis, and sharing via web browsers. A major advance of this work is the easy-to-follow methods to reconstruct three-dimension (3D) tissue image volumes by registering two-dimension (2D) whole-slide pathology images of serial tissue sections stained by hematoxylin and eosin (H&E), and immunohistochemistry (IHC). The integration of these serial slides stained by different methods provides cellular phenotype and pathophysiologic states in the context of a 3D tissue micro-environment. DPLab is hosted on a publicly accessible server and connected to a backend computational cluster for intensive image analysis computations, with results visualized, downloaded, and shared via a web interface. Equipped with an analysis toolbox of numerous image processing algorithms, DPLab supports continued integration of community-contributed algorithms and presents an effective solution to improve the accessibility and dissemination of image analysis algorithms by research communities. It represents the first step in making next generation tissue investigation tools widely available to the research community, enabling and facilitating discovery of clinically relevant disease mechanisms in a digital 3D tissue space.

bioinformatics↗

bioRxiv2020

Dual blockade of IL-6 and CTLA-4 regresses pancreatic tumors in a CD4+ T cell-dependent manner.

Pancreatic ductal adenocarcinoma (PDAC) is exceptionally resistant to immune checkpoint inhibition (ICI). We previously reported that elevated systemic interleukin-6 (IL-6) and increased numbers of T cells positive for circulating cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) correlate with worse overall survival in patients with PDAC. We postulated that combined blockade of IL-6 and CTLA-4 would significantly enhance anti-tumor immune responses to PDAC. Dual blockade of IL-6 and CTLA-4 in immune competent mice bearing subcutaneously injected pancreatic tumors significantly inhibited tumor growth, accompanied by overwhelming T cell infiltration. Therapeutic efficacy was confirmed in an orthotopic murine model of pancreatic cancer and T cell depletion studies unveiled a unique dependence on CD4+ T cells for anti-tumor activity of dual IL-6 and CTLA-4 blockade. In vitro studies utilizing T cells from a TRP-1 transgenic mouse as an antigen-specific model system demonstrate this combination therapy elicits increased IFN-{gamma} production by activated CD4+ T cells. Additionally, IFN-{gamma} stimulation of pancreatic tumor cells in vitro profoundly increased tumor cell production of CXCR3 specific chemokines (CXCL10 and CXCL9). Further studies blocking CXCR3 in the presence of combined IL-6 and CTLA-4 blockade prevented orthotopic tumor regression, demonstrating a dependence on the CXCR3 axis for anti-tumor efficacy. We also found combination therapy increased intratumoral CD4+ T cells and elicited systemic changes in T-helper subsets. These data represent the first report of IL-6 and CTLA-4 blockade as a means to regress pancreatic tumors with defined operative mechanisms of efficacy. Given these results, this therapeutic combination has potential for immediate clinical translation. One Sentence SummaryBlockade of interleukin-6 in pancreatic cancer enhances CTLA-4 immune checkpoint inhibition to regress tumors in a CD4+ T cell and CXCR3-dependent manner.

cancer biology↗