Biology subjects

Taha, D. M.

Publications and source records attributed to Taha, D. M..

4 recordsLinked to original sources

bioRxiv2026

Topology-aware reconstruction of cellular state landscapes from microscopy using self-supervised learning

Morphology and spatial organisation provide complementary readouts of cellular state. However, reconstructing continuous cellular state landscapes from imaging data remains challenging, particularly in dense biological cultures. Here we present SI-SimCLR, a spatially informed self-supervised learning framework that learns biologically informative representations directly from fluorescence microscopy images without requiring segmentation or manual annotation. Combined with a graph-based partial optimal transport framework, SI-SimCLR enables reconstruction of cellular phenotypic landscapes from static imaging data, revealing how phenotypic substates are organised and connected. To establish and validate this framework, we generated a multimodal dataset of human iPSC-derived astrocytes using high-content imaging and matched bulk transcriptomics. SI-SimCLR resolved distinct interconnected astrocyte substates associated with disease and inflammatory states. ALS astrocytes occupied constrained regions of the morphological landscape. Strikingly, morphology and transcriptomics captured distinct and complementary aspects of astrocyte state variation.Together, our framework establishes a scalable and annotation-free strategy for reconstructing cellular phenotypic landscapes from microscopy data, enabling analysis of cellular heterogeneity, landscape connectivity and phenotypic responses across biological systems.

bioinformatics↗

bioRxiv2020

Automated and unbiased classification of motor neuron phenotypes with single cell resolution in ALS tissue.

Histopathological analysis of tissue sections is an invaluable resource in neurodegeneration research. Importantly, cell-to-cell variation in both the presence and severity of a given phenotype is however a key limitation of this approach, reducing the signal to noise ratio and leaving unresolved the potential of single-cell scoring for a given disease attribute. Here, we developed an image processing pipeline for automated identification and profiling of motor neurons (MNs) in amyotrophic lateral sclerosis (ALS) pathological tissue sections. This approach enabled unbiased analysis of hundreds of cells, from which hundreds of features were readily extracted. Next by testing different machine learning methods, we automated the identification of phenotypically distinct MN subpopulations in VCP- and SOD1-mutant transgenic mice, revealing common aberrant phenotypes in cellular shape. Additionally we established scoring metrics to rank cells and tissue samples for both disease probability and severity. Finally, by adapting this methodology to human post-mortem tissue analysis, we validated our core finding that morphological descriptors strongly discriminate ALS from control healthy tissue at the single cell level. In summary, we show that combining automated image processing with machine learning methods substantially improves the speed and reliability of identifying phenotypically diverse MN populations. Determining disease presence, severity and unbiased phenotypes at single cell resolution might prove transformational in our understanding of ALS and neurodegenerative diseases more broadly.

neuroscience↗

bioRxiv2020

An aberrant cytoplasmic intron retention programme is a blueprint for ALS-related RBP mislocalization

We recently described aberrant cytoplasmic SFPQ intron-retaining transcripts (IRTs) and concurrent SFPQ protein mislocalization as a new hallmark of amyotrophic lateral sclerosis (ALS). However the generalizability and potential roles of cytoplasmic IRTs in health and disease remain unclear. Here, using time-resolved deep-sequencing of nuclear and cytoplasmic fractions of hiPSCs undergoing motor neurogenesis, we reveal that ALS-causing VCP gene mutations lead to compartment-specific aberrant accumulation of IRTs. Specifically, we identify >100 IRTs with increased cytoplasmic (but not nuclear) abundance in ALS samples. Furthermore, these aberrant cytoplasmic IRTs possess sequence-specific attributes and differential predicted binding affinity to RNA binding proteins (RBPs). Remarkably, TDP-43, SFPQ and FUS - RBPs known for nuclear-to-cytoplasmic mislocalization in ALS - avidly and specifically bind to this aberrant cytoplasmic pool of IRTs, as opposed to any individual IRT. Our data are therefore consistent with a novel role for cytoplasmic IRTs in regulating compartment-specific protein abundance. This study provides new molecular insight into potential pathomechanisms underlying ALS and highlights aberrant cytoplasmic IRTs as potential therapeutic targets. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/211557v2_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@153fc1eorg.highwire.dtl.DTLVardef@cd3506org.highwire.dtl.DTLVardef@544868org.highwire.dtl.DTLVardef@15ddb68_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2019

Widespread FUS mislocalization is a molecular hallmark of ALS

Amyotrophic lateral sclerosis (ALS)-causing mutations clearly implicate ubiquitously expressed and predominantly nuclear RNA binding proteins (RBPs), which form pathological cytoplasmic inclusions in this context. However, the possibility that wild-type RBPs mislocalize without necessarily becoming constituents of ALS cytoplasmic inclusions themselves remains unexplored. We hypothesized that nuclear-to-cytoplasmic mislocalization of the RBP Fused in Sarcoma (FUS), in an unaggregated state, may occur more widely in ALS that previously recognized. To address this hypothesis, we analysed motor neurons (MNs) from an human ALS induced-pluripotent stem cells (iPSC) model caused by the VCP mutation. Additionally, we examined mouse transgenic models and post-mortem tissue from human sporadic ALS cases. We report nuclear-to-cytoplasmic mislocalization of FUS in both VCP-mutation related ALS and, crucially, in sporadic ALS spinal cord tissue from multiple cases. Furthermore, we provide evidence that FUS protein binds to an aberrantly retained intron within the SFPQ transcript, which is exported from the nucleus into the cytoplasm. Collectively, these data support a model for ALS pathogenesis whereby aberrant intron-retention in SFPQ transcripts contributes to FUS mislocalization through their direct interaction and nuclear export. In summary, we report widespread mislocalization of the FUS protein in ALS and propose a putative underlying mechanism for this process.

neuroscience↗