Biology subjects

Anwer, A.

Publications and source records attributed to Anwer, A..

3 recordsLinked to original sources

bioRxiv2026

Programmable Lipid Nanoparticle Targeting via Corona Engineering

Lipid nanoparticles (LNPs) are a versatile platform for in vivo delivery of biomolecules, yet systemically administered LNPs predominantly accumulate in the liver, limiting extrahepatic applications. This tropism arises from LNP adsorption of serum proteins, particularly apolipoprotein E (ApoE), which binds to LDL receptors (LDLR) on hepatocytes. Here, we overcome this tropism with two compatible strategies. First, we engineer dead ApoE mutants (dApoE) with five receptor-binding domain substitutions that selectively disrupt the ApoE-LDLR interaction but retain lipid binding. In cultured cells, pre-coating with these dApoE markedly inhibited LDLR-mediated uptake. Second, we pretreat cells with hyperactive PCSK9 (haPCSK9) to internalize surface LDLR, similarly reducing the LDLR-mediate uptake of LNPs. In vivo, both strategies substantially reduced liver LNP transduction without inducing redistribution to other major organs. To retarget LNP to new cell types we combined antibody conjugation with dApoE or haPCSK9, effectively engineering tropism to T cells, brain and lung tissues in vivo with substantially reduced hepatic background. In pilot studies, this strategy enabled specific delivery of reporter mRNAs to additional tissues, including megakaryocytes, hematopoietic progenitor cells, and cardiac tissue, and in aged T cells, to deliver miRNA cargos that produced a sustained reduction in DNA damage markers following a single systemic dose. dApoE coated CD5-targeted LNPs generated CAR+ T cells that retained cytotoxicity against CD19+ targets, while simultaneously reducing hepatocyte transduction by 90%. These findings establish a modular framework that integrates dApoE and haPCSK9-mediated detargeting with antibody-based retargeting, allowing for improvements in LNP specificity and broadening the therapeutic scope of LNPs.

bioengineering↗

bioRxiv2022

moRphology - dEep Learning Imaging Cells (RELIC) - to Differentiate Between Normal and Pathological Kidney Exfoliated Cells

Chronic kidney disease (CKD) is characterised by progressive loss of kidney function leading to kidney failure. Significant kidney damage can occur before symptoms are detected. Currently, kidney tissue biopsy is the gold standard for evaluation of renal damage and CKD severity. This study explores how to precisely quantify morphology characteristics of kidney cells exfoliated into urine, with a view to establish a future urine-based non-invasive diagnostic for CKD. We report the development of a novel deep learning method, which was able to discover a RELIC (moRphology dEep Learning Imaging Cells) signature that can differentiate between kidney cells exfoliated in human urine of and CKD patients with varying degree of kidney damage and non-CKD controls. Exfoliated proximal tubule cells (PTCs) originating from kidneys were isolated from the urine of patients with different levels of kidney damage using previously published methods. An advanced combination of artificial intelligence techniques, deep learning, swarm intelligence, and discriminative analysis was used to discover a RELIC signature in brightfield microscopy images of exfoliated PTCs. Kidney damage in the study subjects was characterised by assessing kidney tissues obtained through a nephrectomy or kidney biopsy. A special deep learning algorithm was developed and trained to create a predictive tool. The algorithm was then used to analyse data from patients with normal and fibrotic kidneys. Data were then classified according to different groups (healthy or fibrosis) and clustering of the training and validation cells was determined for model validation. We developed a novel deep learning method, to obtain RELIC signatures and identify specific deep morphological features which can be used to differentiate urinary PTC cells shed by people with CKD (confirmed by tissue histology obtained from an invasive kidney biopsy) from those without CKD, with a discriminatory accuracy of 82%. We identified a RELIC signature which can be used on a collection of bright field images of exfoliated urinary PTCs to create a predictive tool and differentiate between normal and pathological kidney cells. This study, for the first time, provides a proof of concept that urinary exfoliated tubule cells in patients with kidney fibrosis and healthy controls differ in appearance (morphology) as observed under a basic brightfield microscope. The results suggest that morphological signatures of exfoliated PTCs have the potential to serve as a non-invasive marker of kidney fibrosis.

bioinformatics↗

bioRxiv2020

Non-invasive real-time imaging of reactive oxygen species (ROS) using multispectral auto-fluorescence imaging technique: a novel tool for redox biology

Detecting reactive oxygen species (ROS) that play a critical role as redox modulators and signalling molecules in biological systems currently requires invasive methods such as ROS - specific indicators for imaging and quantification. We developed a non-invasive, real-time, label-free imaging technique for assessing the level of ROS in live cells and thawed cryopreserved tissues that is compatible with in-vivo imaging. The technique is based on autofluorescence multispectral imaging (AFMI) carried out in an adapted fluorescence microscope with an expanded number of spectral channels spanning specific excitation (365 nm-495 nm) and emission (420 nm-700 nm) wavelength ranges. We established a strong quantitative correlation between the spectral information obtained from AFMI and the level of ROS obtained from CellROX staining. The results were obtained in several cell types (HeLa, PANC1 and mesenchymal stem cells) and in live kidney tissue. Additioanly, two spectral regimes were considered: with and without UV excitation (wavelengths > 400 nm); the latter being suitable for UV-sensitive systems such as the eye. Data were analyzed by linear regression combined with an optimization method of swarm intelligence. This allowed the calibration of AFMI signals to the level of ROS with excellent correlation (R= 0.84, p=0.00) in the entire spectral range and very good correlation (R= 0.78, p=0.00) in the limited, UV-free spectral range. We also developed a strong classifier which allowed us to distinguish moderate and high levels of ROS in these two regimes (AUC= 0.91 in the entire spectral range and AUC = 0.78 for UV-free imaging). These results indicate that ROS in cells and tissues can be imaged non-invasively, which opens the way to future clinical applications in conditions where reactive oxygen species are known to contribute to progressive disease such as in ophthalmology, diabetes, kidney disease, cancer and neurodegenerative diseases.

cell biology↗