Biology subjects

Khamseh, A.

Publications and source records attributed to Khamseh, A..

7 recordsLinked to original sources

bioRxiv2025

Systematic morphological profiling of human and mouse miRNAs in 24M single cells

MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression whose contributions to cell biology remain underexplored. Here, we used Cell Painting to quantify the morphological effect of 2,565 human and 1,900 mouse miRNA mimics on 24 million cells across five cell lines. To do so, we developed a novel single-cell morphological profiling analysis framework, involving stringent batch correction, feature selection, and hit calling. With this, we discovered that 9% of human and 15% of mouse miRNA mimics significantly alter cell morphology in at least one cell line. Eighteen miRNAs caused significant changes in multiple cell lines, including eight orthologous miRNAs that altered morphology in both human and mouse cells. Among the replicating miRNAs were human and mouse miR-155-5p, which affected morphology in at least one replicate of four cell lines. As expected, miRNAs with identical seed sequences induced more similar morphological changes than miRNAs with different seeds. Also, morphological changes were associated with cytotoxicity and annotation confidence. This comprehensive single-cell morphological resource will help elucidate the cellular functions of human and mouse miRNAs.

genomics↗

bioRxiv2025

Epithelial state-transitions permit inflammation-induced tumorigenesis.

Chronic inflammation across tissues is associated with an increased risk of developing cancer1-3. While potentially oncogenic somatic mutations have been demonstrated to persist and expand in healthy organs4-6, what triggers a subset of cells harbouring deleterious mutations to transition into a neoplasm or an aggressive adenoma with poor prognosis7,8 is not well-understood. Unlike normal, healthy cells, benign cells harbouring mutations perceive inflammation in chronic disease differently, potentiating the progression from physiological inflammation to tumorigenesis9. Here, we reveal that a subset of epithelial cells with mutations are poised to transition from pre-neoplastic state to early neoplasm, through rewiring of epithelial IL-1{beta} responses and inflammatory macrophage recruitment. We characterise this process by leveraging a mouse model of biliary tract cancer (cholangiocarcinoma), in which deleterious mutations are introduced to tumour suppressor genes in common cancer pathways (Trp53 and Pten), and by quantifying differences in cell states and corresponding gene expression dependencies in the absence or presence of liver inflammation. Critically, we find that targeting the epithelial-derived signals of tissue-wide inflammation (namely COX2) is insufficient to limit tumorigenesis; rather, targeting the reactivation of oncogene-induced developmental signals, such as NOTCH, prevents this pre-neoplastic to neoplastic transition, demonstrating that oncofoetal switching is a pharmacologically-tractable target in patients with a high risk of developing cancers on the background of inflammation.

cancer biology↗

bioRxiv2025

Systematic identification of context-dependent gene essentiality in Glioblastoma: The GBM-CoDE platform

Glioblastoma (GBM) is a heterogeneous and aggressive brain tumour that is invariably fatal despite maximal treatment. Genetic or transcriptomic biomarkers could be used to stratify patients for treatments, however, pairing biomarkers with appropriate therapeutic targets is challenging. Consequently, therapeutics have not yet been optimised for specific GBM patient subsets. Here we integrate genome-wide CRISPR/Cas9 knockout screening and genetic-annotation data for 60 distinct patient-derived, IDHwildtype, adult GBM cell lines, quantifying the essentiality of 15,145 genes. We describe a novel method using Targeted Learning, to estimate the effect size of GBM-relevant biomarkers on context-dependent gene essentiality (GBM-CoDE). We derive multiple target-biomarker pair hypotheses, which we release in an accessible platform to accelerate translation to biomarker-stratified clinical trials. Two of these (WWTR1 with EGFR mutation/amplification, and VRK1 with VRK2 expression suppression) have been validated in GBM, implying that our additional novel findings may be valid. Our method is readily translatable to other cancers of unmet need.

bioinformatics↗

bioRxiv2023

High order expression dependencies finely resolve cryptic states and subtypes in single cell data

AO_SCPLOWBSTRACTC_SCPLOWSingle cells are typically typed by clustering in reduced dimensional transcriptome space. Here we introduce Stator, a novel method, workflow and app that reveals cell types, subtypes and states without relying on local proximity of cells in gene expression space. Rather, Stator derives higher-order gene expression dependencies from a sparse gene-by-cell expression matrix. From these dependencies the method multiply labels the same single cell according to type, sub-type and state (activation, differentiation or cell cycle sub-phase). By applying the method to data from mouse embryonic brain, and human healthy or diseased liver, we show how Stator first recapitulates other methods cell type labels, and then reveals combinatorial gene expression markers of cell type, state, and disease at higher resolution. By allowing multiple state labels for single cells we reveal cell type fates of embryonic progenitor cells and liver cancer states associated with patient survival.

molecular biology↗

bioRxiv2022

Dispensing with unnecessary assumptions in population genetics analysis

Parametric assumptions in population genetics analysis - including linearity, sources of population stratification and additivity of variance as part of a Gaussian noise - are often made, yet their (approximate) validity depends on variant and traits of interest, as well as genetic ancestry and population dependence structure of the sample cohort. We present a unified statistical workflow, called TarGene, for targeted estimation of effect sizes, as well as two-point and higher-order epistatic interactions of genomic variants on polygenic traits, which dispenses with these unnecessary assumptions. Our approach is founded on Targeted Learning, a framework for estimation that integrates mathematical statistics, machine learning and causal inference. TarGene maximises power whilst simultaneously maximising control over false discoveries by: (i) guaranteeing optimal bias-variance trade-off, (ii) taking into account potential covariate non-linearities, sources of population stratification and dependence structure, and (iii) detecting genetic non-linearities. The necessity of this model-independent approach is demonstrated via extensive simulations. We validate the effectiveness of our method by reproducing previously verified effect sizes on UK Biobank data, whilst simultaneously discovering non-linear effect sizes of additional allelic copies on trait or disease, in a PheWAS study involving 781 traits. Specifically, we demonstrate genetic non-linearity at the FTO locus is significant for 54 traits in this study. We further find three pairs of epistatic loci associated with skin color that have been previously reported to be associated with hair color. Finally, we illustrate how TarGene can be used to investigate higher-order interactions using three variants linked to the vitamin D receptor complex. TarGene provides a platform for comparative analyses across biobanks, or integration of multiple biobanks and heterogeneous populations to simultaneously increase power and control for type I errors, whilst taking into account population stratification and complex dependence structures.

genetics↗

bioRxiv2022

Primary cilia loss promotes reactivation of morphogenesis and cyst-fission through a deregulated TGFβ-ECM-Integrin axis in polycystic liver disease.

Pathological liver cysts are an important comorbidity in multiple diseases and syndromes(1, 2) driven by dysfunction of the primary cilium (PC), a complex sensory organelle that protrudes from the apical surface of biliary epithelial cells (BECs)(3, 4). The essential nature of PC in liver development(5, 6) makes understanding the molecular role of this organelle in the structural maintenance of the adult bile duct challenging. Here, we show that PC loss deletion of Wdr35 in adult mouse BECs is sufficient to cause bile duct expansion, driving cyst formation through the de novo production of a fibronectin-rich pro-cystic microenvironment. This newly formed niche promotes both cell-autonomous changes in cell shape and duct-level mechanical rearrangements that converge to drive cyst-fission, a novel process whereby single, large cysts undergo morphological splitting. This process gives rise to many, smaller polycystic progeny and can be halted by pharmacological inhibition of a specific pro-cystic integrin receptor.

cell biology↗

bioRxiv2019

Pervasive lesion segregation shapes cancer genome evolution

Cancers arise through the acquisition of oncogenic mutations and grow through clonal expansion1, 2. Here we reveal that most mutagenic DNA lesions are not resolved as mutations within a single cell-cycle. Instead, DNA lesions segregate unrepaired into daughter cells for multiple cell generations, resulting in the chromosome-scale phasing of subsequent mutations. We characterise this process in mutagen-induced mouse liver tumours and show that DNA replication across persisting lesions can generate multiple alternative alleles in successive cell divisions, thereby increasing both multi-allelic and combinatorial genetic diversity. The phasing of lesions enables the accurate measurement of strand biased repair processes, the quantification of oncogenic selection, and the fine mapping of sister chromatid exchange events. Finally, we demonstrate that lesion segregation is a unifying property of exogenous mutagens, including UV light and chemotherapy agents in human cells and tumours, which has profound implications for the evolution and adaptation of cancer genomes.

cancer biology↗