Biology subjects

Ibrahim, K. A.

Publications and source records attributed to Ibrahim, K. A..

3 recordsLinked to original sources

bioRxiv2025

A 3-genes interferon signature predicts sustained complete remission in pediatric AML patients

The immunological composition of the microenvironment has shown relevance for diagnosis, prognosis, and therapy in solid tumors but remains underexplored in acute leukemias. We investigated the significance of acute myeloid leukemia (AML) bone marrow microenvironment in predicting chemosensitivity and long-term remission in pediatric patients. We analyzed 32 non-promyelocytic pediatric AML patients at diagnosis using NanoString PanCancer IO 360 assay, RNA sequencing, and deep-phenotype flow cytometry analyses. The findings were validated using the pediatric TARGET AML dataset. A short signature of three interferon (IFN)-related genes (GBP1, PARP12, TRAT1) distinguished patients with chemosensitive disease and reduced minimal residual disease after induction chemotherapy. The signature stratified patients overall, and within the clinically defined "standard-risk" group, patients with high gene expression at diagnosis had significantly longer overall survival. The leukemia microenvironment associated with this signature showed enrichment of non-exhausted CD4+ and CD8+ T cytotoxic lymphocytes and expansion of CD8+ T effector memory cells re-expressing CD45RA (TEMRA) in patients with a favorable prognosis. Our results show the importance of the bone marrow microenvironment in pediatric AML and provide tools for a refined stratification of "standard-risk" patients, lacking adequate risk-oriented therapies. They also offer a promising guide for tackling immune pathways and exploiting immune-targeted therapies. Statement of significanceWe identified a novel three-gene IFN-related signature that distinguished pediatric AML patients by chemosensitivity and remission outcomes. It stratified patients across all risk groups, including the "standard-risk" group, with high expression linked to a T-cell-enriched microenvironment and longer survival. This signature may enhance risk stratification and guide targeted immunotherapy.

cancer biology↗

bioRxiv2023

Label-free Identification of Protein Aggregates Using Deep Learning

Protein misfolding and aggregation play central roles in the pathogenesis of various neurodegenerative diseases (NDDs), including Huntingtons disease, which is caused by a genetic mutation that leads to a polyglutamine repeat length > 35 in exon 1 of the Huntingtin protein (Httex1). Current research on protein aggregation often involves the use of fluorescent labels to visualize and monitor the dynamics of protein expression, which can alter the biophysical properties of proteins and the final ultrastructure, composition, and toxic properties of the formed aggregates. To overcome this limitation, we present a method for label-free identification of NDD-associated aggregates (LINA). Our approach utilizes deep learning to detect unlabeled and unaltered Httex1 aggregates in living cells from transmitted-light images, without the need for fluorescent labeling. We developed pixel-classification and pixel-regression models, which are robust across imaging conditions, and validated them on aggregates formed by different constructs of Httex1. Our results reveal that Httex1 proteins with shorter polyglutamine repeat lengths form aggregates with a higher average dry mass and area, highlighting the differences in their ultrastructure and aggregation mechanisms. LINA enables the dynamic identification of label-free aggregates and measurement of their dry mass and area changes during their growth process. Our highly-robust models offer high speed, specificity, and simplicity to analyze label-free protein aggregation dynamics and obtain high-fidelity information.

bioengineering↗

bioRxiv2020

Homogeneous multifocal excitation for high-throughput super-resolution imaging

Super-resolution microscopies, which allow features below the diffraction limit to be resolved, have become an established tool in biological research. However, imaging throughput remains a major bottleneck in using them for quantitative biology, which requires large datasets to overcome the noise of the imaging itself and to capture the variability inherent to biological processes. Here, we develop a multi-focal flat illumination for field independent imaging (mfFIFI) module, and integrate it into an instant structured illumination microscope (iSIM). Our instrument extends the field of view (FOV) to >100x100 {micro}m2 without compromising image quality, and maintains high-speed (100 Hz), multi-color, volumetric imaging at double the diffraction-limited resolution. We further extend the effective FOV by stitching multiple adjacent images together to perform fast live-cell super-resolution imaging of dozens of cells. Finally, we combine our flat-fielded iSIM setup with ultrastructure expansion microscopy (U-ExM) to collect 3D images of hundreds of centrioles in human cells, as well as of thousands of purified Chlamydomonas reinhardtii centrioles per hour at an effective resolution of [~]35 nm. We apply classification and particle averaging to these large datasets, allowing us to map the 3D organization of post-translational modifications of centriolar microtubules, revealing differences in their coverage and positioning.

biophysics↗