Biology subjects

Tabrizi, S.

Publications and source records attributed to Tabrizi, S..

8 recordsLinked to original sources

bioRxiv2026

Molecular determinants of antibody-mediated priming to enhance detection of ctDNA

Liquid biopsies can enable cancer detection and monitoring yet remain limited by low concentrations of ctDNA. To address this limitation, we previously introduced a monoclonal antibody (mAb) "priming agent" that transiently increased the concentration of ctDNA in blood. Here, we investigated the molecular features that drive this effect. In a panel of novel mAbs that bound cfDNA, both those targeting dsDNA and those targeting mononucleosomes increased the concentration of ctDNA. mAbs with high avidity to dsDNA performed best, suggesting dsDNA as the key binding target. One agent preserved short, biologically informative molecules of cfDNA typically depleted at baseline. The Fc domain was dispensable, as F(ab)2 fragments retained priming activity. Leveraging these insights, we engineered single-chain agents using the dsDNA binding domain sso7d, expanding priming strategies beyond immunoglobulins. This study identifies the molecular features and general design principles for mAb-based priming agents to enhance recovery and detection of ctDNA. SignificanceLow concentration of ctDNA limits the sensitivity of liquid biopsies in many applications. This study shows how engineered antibodies and other dsDNA-binding molecules can inhibit clearance of ctDNA from the bloodstream and increase concentration of ctDNA in a blood draw, and identifies the key features that enable this activity. Modulation of cfDNA in the bloodstream using these agents can increase recovery of ctDNA and improve the performance of liquid biopsies for cancer detection.

bioengineering↗

bioRxiv2023

Monkeybread: A Python toolkit for the analysis of cellular niches in single-cell resolution spatial transcriptomics data

Spatial transcriptomics technologies enable the spatially resolved measurement of gene expression within a tissue specimen. With these technologies, researchers can investigate how cells organize into cellular niches which are defined as distinct regions in the tissue comprising a specific composition of cell types or phenotypes. While general-purpose software tools for the exploratory analysis of spatial transcriptomics data exist, there is a need for tools that specialize in the analysis of cellular organization into niches. This can further enhance the downstream application of these data towards drug target discovery, target validation, and biomarker development. We present Monkeybread: A Python toolkit for analyzing cellular organization and intercellular communication in single-cell resolution spatial transcriptomics data. We applied Monkeybread to a human melanoma sample to demonstrate its utility in identifying cellular niches with diverse immunogenic compositions in the tumor microenvironment. We found that these niches were differentially enriched for immunogenic and tolerogenic macrophage populations that could be correlated to T cell abundance. These findings highlight how Monkeybread can be used for revealing underlying biology of the tumor microenvironment, and in the future, for understanding the influence of these niches on response to available treatments and discovery of novel drug targets.

bioinformatics↗

bioRxiv2023

An intravenous DNA-binding priming agent protects cell-free DNA and improves the sensitivity of liquid biopsies

Blood-based, or "liquid," biopsies enable minimally invasive diagnostics but have limits on sensitivity due to scarce cell-free DNA (cfDNA). Improvements to sensitivity have primarily relied on enhancing sequencing technology ex vivo. Here, we sought to augment the level of circulating tumor DNA (ctDNA) detected in a blood draw by attenuating the clearance of cfDNA in vivo. We report a first-in-class intravenous DNA-binding priming agent given 2 hours prior to a blood draw to recover more cfDNA. The DNA-binding antibody minimizes nuclease digestion and organ uptake of cfDNA, decreasing its clearance at 1 hour by over 150-fold. To improve plasma persistence and limit potential immune interactions, we abrogated its Fc-effector function. We found that it protects GC-rich sequences and DNase-hypersensitive sites, which are ordinarily underrepresented in cfDNA. In tumor-bearing mice, priming improved tumor DNA recovery by 19-fold and sensitivity for detecting cancer from 6% to 84%. These results suggest a novel method to enhance the sensitivity of existing DNA-based cancer testing using blood biopsies.

genomics↗

bioRxiv2023

A nanoparticle priming agent reduces cellular uptake of cell-free DNA and enhances the sensitivity of liquid biopsies

Liquid biopsies are enabling minimally invasive monitoring and molecular profiling of diseases across medicine, but their sensitivity remains limited by the scarcity of cell-free DNA (cfDNA) in blood. Here, we report an intravenous priming agent that is given prior to a blood draw to increase the abundance of cfDNA in circulation. Our priming agent consists of nanoparticles that act on the cells responsible for cfDNA clearance to slow down cfDNA uptake. In tumor-bearing mice, this agent increases the recovery of circulating tumor DNA (ctDNA) by up to 60-fold and improves the sensitivity of a ctDNA diagnostic assay from 0% to 75% at low tumor burden. We envision that this priming approach will significantly improve the performance of liquid biopsies across a wide range of clinical applications in oncology and beyond.

genomics↗

bioRxiv2021

CODEC enables 'single duplex' sequencing

Detecting mutations as rare as a single molecule is crucial in many fields such as cancer diagnostics and aging research but remains challenging. Third generation sequencers can read a double-stranded DNA molecule (a single duplex) in whole to identify true mutations on both strands apart from false mutations on either strand but with limited accuracy and throughput. Although next generation sequencing (NGS) can track dissociated strands with Duplex Sequencing, the need to sequence each strand independently severely diminishes its throughput. Here, we developed a hybrid method called Concatenating Original Duplex for Error Correction (CODEC) that combines the massively parallel nature of NGS with the single-molecule capability of third generation sequencing. CODEC physically links both strands to enable NGS to sequence a single duplex with a single read pair. By comparing CODEC and Duplex Sequencing, we showed that CODEC achieved a similar error rate (10-6) with 100 times fewer reads and conferred single duplex resolution to most major NGS workflows.

bioengineering↗

bioRxiv2018

Stability and sensitivity of structural connectomes: effect of thresholding and filtering and demonstration in neurodegeneration

Structural connectomes derived using diffusion tractography are increasingly used to investigate white matter connectivity in neurological diseases. However inherent biases in diffusion tractography algorithms may lead to both false negatives and false positives in connectome construction. A range of graph thresholding approaches and more recently several streamline filtering algorithms have been developed to address these issues. However there is no consensus in the literature regarding the best available approach. Using a cohort of Huntingtons disease patients and healthy controls we compared the effect of several graph thresholding strategies: proportional, absolute, consensus and consistency thresholding, with and without streamline filtering, using Spherical Deconvolution Informed Filtering of tractograms (SIFT2) algorithm. We examined the effect of thresholding strategies on the stability of graph theory metrics and the sensitivity of these measures in neurodegeneration. We show that while a number of graph thresholding procedures result in stable metrics across thresholds, the detection of group differences is highly variable. We also showed that the application of streamline filtering using SIFT2 resultes in better detection of group differences and stronger clinical correlations. We therefore conclude that the application of SIFT2 streamline filtering without graph thresholding may be sufficient for structural connectome construction.

neuroscience↗

bioRxiv2018

Assessment of Functional Connectome Construction Strategies in Neurodegeneration

Connectomics can be used to investigate functional brain networks in neurodegenerative diseases including Huntingtons disease (HD). In this developing field, different connectome construction strategies have emerged in parallel. However, there is a need to understand the influences of different strategies on subsequent analyses when constructing a connectome. This study systematically compares connectome construction strategies based on their biological relevance to functional networks in neurodegeneration.\n\nWe asked which functional connectome construction strategy was best able to discriminate HD gene carriers from healthy controls, and how such a strategy affected modular organization of the network. The major factors compared were principal component-based correction versus wavelet decomposition for physiological noise correction, the type of parcellation atlas (functional, structural and multi-modal), weighted versus binarized networks, and unthresholded versus proportionally thresholded networks. We found that principal component-based correction generated the most discriminatory connectomes, while binarization and proportional thresholding did not increase discrimination between HD gene carriers and healthy controls. When a functional parcellation atlas was used, the highest discrimination rates were obtained. We observed that the group differences in modular organization of the functional connectome were greatly affected by binarization and thresholding, showing no consistent pattern of modularity.\n\nThis study suggests that functional connectome construction strategies using principal component-based correction and weighted unthresholded connectivity matrices may outperform other strategies.

neuroscience↗

bioRxiv2018

Identifying Gene Expression Programs of Cell-type Identity and Cellular Activity with Single-Cell RNA-Seq

Identifying gene expression programs underlying both cell-type identity and cellular activities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cells expression profile may be a mixture of both types of programs, making them difficult to disentangle. Here we illustrate and enhance the use of matrix factorization as a solution to this problem. We show with simulations that a method that we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including the relative contribution of programs in each cell. Applied to published brain organoid and visual cortex scRNA-Seq datasets, cNMF refines the hierarchy of cell types and identifies both expected (e.g. cell cycle and hypoxia) and intriguing novel activity programs. We propose that one of the novel programs may reflect a neurosecretory phenotype and a second may underlie the formation of neuronal synapses. We make cNMF available to the community and illustrate how this approach can provide key insights into gene expression variation within and between cell types.

bioinformatics↗