Biology subjects

Patterson, B.

Publications and source records attributed to Patterson, B..

3 recordsLinked to original sources

bioRxiv2021

Persistence of SARS CoV-2 S1 Protein in CD16+ Monocytes in Post-Acute Sequelae of COVID-19 (PASC) Up to 15 Months Post-Infection

The recent COVID-19 pandemic is a treatment challenge in the acute infection stage but the recognition of chronic COVID-19 symptoms termed post-acute sequelae SARS-CoV-2 infection (PASC) may affect up to 30% of all infected individuals. The underlying mechanism and source of this distinct immunologic condition three months or more after initial infection remains elusive. Here, we investigated the presence of SARS-CoV-2 S1 protein in 46 individuals. We analyzed T-cell, B-cell, and monocytic subsets in both severe COVID-19 patients and in patients with post-acute sequelae of COVID-19 (PASC). The levels of both intermediate (CD14+, CD16+) and non-classical monocyte (CD14Lo, CD16+) were significantly elevated in PASC patients up to 15 months post-acute infection compared to healthy controls (P=0.002 and P=0.01, respectively). A statistically significant number of non-classical monocytes contained SARS-CoV-2 S1 protein in both severe (P=0.004) and PASC patients (P=0.02) out to 15 months post-infection. Non-classical monocytes were sorted from PASC patients using flow cytometric sorting and the SARS-CoV-2 S1 protein was confirmed by mass spectrometry. Cells from 4 out of 11 severe COVID-19 patients and 1 out of 26 PASC patients contained ddPCR+ peripheral blood mononuclear cells, however, only fragmented SARS-CoV-2 RNA was found in PASC patients. No full length sequences were identified, and no sequences that could account for the observed S1 protein were identified in any patient. Non-classical monocytes are capable of causing inflammation throughout the body in response to fractalkine/CX3CL1 and RANTES/CCR5.

immunology↗

bioRxiv2020

Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning

Individuals with systemic symptoms long after COVID-19 has cleared represent approximately ~10% of all COVID-19 infected individuals. Here we present a bioinformatics approach to predict and model the phases of COVID so that effective treatment strategies can be devised and monitored. We investigated 144 individuals including normal individuals and patients spanning the COVID-19 disease continuum. We collected plasma and isolated PBMCs from 29 normal individuals, 26 individuals with mild-moderate COVID-19, 25 individuals with severe COVID-19, and 64 individuals with Chronic COVID-19 symptoms. Immune subset profiling and a 14-plex cytokine panel were run on all patients. Data was analyzed using machine learning methods to predict and distinguish the groups from each other.Using a multi-class deep neural network classifier to better fit our prediction model, we recapitulated a 100% precision, 100% recall and F1 score of 1 on the test set. Moreover, a first score specific for the chronic COVID-19 patients was defined as S1 = (IFN-{gamma} + IL-2)/ CCL4-MIP-1{beta}. Second, a score specific for the severe COVID-19 patients was defined as S2 = (10*IL-10 + IL-6) - (IL-2 + IL-8). Severe cases are characterized by excessive inflammation and dysregulated T cell activation, recruitment, and counteracting activities. While chronic patients are characterized by a profile able to induce the activation of effector T cells with pro-inflammatory properties and the capacity of generating an effective immune response to eliminate the virus but without the proper recruitment signals to attract activated T cells. SummaryImmunologic Modeling of Severity and Chronicity of COVID-19

immunology↗

bioRxiv2020

Scalable production of tissue-like vascularised liver organoids from human PSCs.

A lack of physiological parity between 2D cell culture and in vivo, has paved the way towards more organotypic models. Organoids exist for a number of tissues, including the liver. However, current approaches to generate hepatic organoids suffer drawbacks, including a reliance on extracellular matrices (ECM), the requirement to pattern in 2D culture, costly growth factors and a lack of cellular diversity, structure and organisation. Current hepatic organoid models are generally simplistic, composed of hepatocytes or cholangiocytes, which renders them less physiologically relevant when compared to native tissue. Here we aim to address these drawbacks. To address this, we have developed an approach that does not require 2D patterning, is ECM independent combined with small molecules to mimic embryonic liver development that produces massive quantities of liver like organoids. Using single-cell RNA sequencing and immunofluorescence we demonstrate a liver-like cellular repertoire, a higher order cellular complexity, presenting with vascular luminal structures, innervation and a population of resident macrophage - the Kupffer cells. The organoids exhibit key liver functions including drug metabolism, serum protein production, coagulation factor production, bilirubin uptake and urea synthesis. The organoids can be transplanted and maintained in mice producing human albumin long term. The organoids exhibit a complex cellular repertoire reflective of the organ, have de novo vascularization and innervation, enhanced function and maturity. This is a pre-requisite for a myriad of applications from cellular therapy, tissue engineering, drug toxicity assessment, disease modeling, to basic developmental biology.

cell biology↗