Biology subjects

Al-Eryani, G.

Publications and source records attributed to Al-Eryani, G..

15 recordsLinked to original sources

bioRxiv2026

Scalable single-cell isoform profiling with sequencing-by-expansion

Single-cell RNA sequencing has transformed our understanding of cellular systems, yet the reliance on short-read sequencing restricts analysis to gene-level quantification and obscures the immense biological diversity generated by alternative splicing. While long-read sequencing technologies can capture full-length RNA and resolve transcript isoforms, current platforms remain constrained by throughput and high per-base costs, rendering them impractical for modern million-cell applications. To address this critical limitation, we developed and optimized sequencing-by-expansion (SBX) chemistry for high-throughput single-cell RNA isoform profiling. Integrated within the AXELIOS 1 sequencing platform, SBX employs a unique biochemical conversion process that transforms complementary DNA into expanded surrogate high signal-to-noise polymers called Xpandomers which are sequenced via translocation through a dense nanopore array yielding over 9.5 billion reads in a two-hour run. To leverage this unique data type for long-read single-cell RNA isoform sequencing, we developed the Consensus UMI Deduplication using Longest Length (CUDLL) algorithm, which computationally consolidates variable-length raw SBX reads into single, high-fidelity consensus reads, elevating sequence accuracy to 99.83% and maximizing per transcript read length. We demonstrate that this consensus approach successfully captures the vast isoform diversity of single-cell libraries and enables the accurate measure of differential isoform expression across distinct cell types in peripheral blood mononuclear cells. Furthermore, SBX coupled with CUDLL efficiently resolves T-cell and B-cell receptor clonotypes directly from whole-transcriptome libraries without the need for VDJ-specific target enrichment. Ultimately, this work establishes SBX and the AXELIOS 1 as a transformative platform for high-scale single-cell isoform sequencing.

genetics↗

bioRxiv2026

Accurate strand-specific long-read transcript isoform discovery and quantification at bulk, single-cell, and single-nucleus resolution

Recent advances in long-read transcriptome sequencing enable high-throughput profiling of full-length RNA isoforms in bulk, single-cell, and single-nucleus samples. However, long-read datasets typically contain a mixture of complete and partial transcripts, leading to pervasive ambiguity in read-to-isoform assignment and complicating accurate isoform identification and quantification, particularly in the absence of reliable reference annotations. These challenges are further amplified in single-cell and single-nucleus samples, where coverage is sparse and transcriptional heterogeneity is high. Here, we present the Long Read Alignment Assembler (LRAA), a unified and versatile computational framework for isoform identification and quantification from long-read RNA sequencing data across bulk, single-cell, and single-nucleus transcriptomic samples. LRAA combines splice-graph based structural modeling with expectation maximization based optimization to probabilistically resolve ambiguous read assignments and improve isoform abundance estimation. The framework supports quantification-only, reference-guided, and fully reference-free (de novo) modes of analysis within a single methodological paradigm. We benchmarked LRAA using both simulated and genuine long-read datasets spanning sequencing standards and whole transcriptomes. Central to this evaluation is a novel benchmarking strategy based on Multiplexed Overexpression of Regulatory Factors (MORFs), which provides biologically expressed, barcoded isoforms with unambiguous read-level ground truth. Across all benchmarks, including MORFs, synthetic spike-ins, and whole-transcriptome datasets, LRAA consistently outperformed state-of-the-art methods in isoform identification accuracy, sensitivity, and expression quantification. Finally, we demonstrate the biological utility of LRAA by resolving cell-type-specific isoform usage across peripheral blood immune cell populations and by detecting a pathogenic cryptic isoform of STMN2 with associated transcriptional changes in single-nucleus RNA-seq data from frontal cortex tissue of an individual with frontotemporal dementia (FTD). Together, these results establish LRAA as a robust and general solution for resolving transcript diversity in complex biological systems, from development to disease.

bioinformatics↗

bioRxiv2025

Multi-platform evaluation and optimization of single-cell RNA isoform sequencing

Long-read transcriptomics enables isoform identification and quantification at single-cell resolution, but analysis is complicated by artifacts introduced during library preparation. We characterize the distinct artifact profiles of three popular single-cell platforms and demonstrate their impact on isoform identification. We introduce a new method for the identification of a wide range of artifacts in cDNA libraries and novel biochemical and bioinformatic strategies to significantly reduce their impact on downstream applications. Finally, we provide a comprehensive framework for RNA isoform sequencing analysis and interpretation as the field continues to develop.

genetics↗

bioRxiv2024

Profiling of epithelial functional states and fibroblast phenotypes in hormone therapy-naive localised prostate cancer

Localised prostate cancers (PCa) are heterogeneous and multifocal, with diverse outcomes. Current prognostic methods are epithelium-centric, overlooking the complex cellular landscape within the tumour microenvironment (TME), which remains incompletely characterised. We performed a comprehensive analysis of cancerous and adjacent-benign cores from 24 patients with hormone therapy-naive localised PCa using single-cell RNA-sequencing. By integrating copy number variation and transcriptional signatures, we classified epithelial cells across a malignant spectrum, revealing widespread molecular perturbation. We found an expansion of Club cell phenotypes, suggestive of Luminal dedifferentiation. We also performed a detailed annotation of stromal phenotypes, focusing on fibroblasts, and identified a novel peri-neural fibroblast population. Spatial transcriptomics elucidated the precise anatomical distribution of CAFs within the PCa TME. This study provides a valuable foundation for advancing our understanding of PCa pathobiology and developing a comprehensive cellular model of the disease. Statement of SignificanceOur study leverages single-cell RNA-sequencing and spatial transcriptomics to provide a comprehensive cellular annotation of hormone therapy-naive localised PCa. We reveal widespread molecular perturbations in epithelial cells and map distinct fibroblast populations to specific anatomical niches. Notably, we identify a novel peri-neural phenotype associated with nerves, which merits further functional characterisation and exploration as a potential therapeutic target.

cancer biology↗

bioRxiv2024

Self-reactive B cells traverse a perfect storm of somatic mutagenesis to cause a virus-induced autoimmune disease

The unexplained association between infection and autoimmune disease is strongest for hepatitis C virus-induced cryoglobulinemic vasculitis (HCV-CV). We traced the evolution of the pathogenic rheumatoid factor (RhF) autoantibodies in four HCV-CV patients by deep single cell multi-omic analysis, revealing three sources of B cell somatic mutation converged to drive accumulation of a large disease causing clone. A sensitive method for quantifying low affinity binding revealed three recurring heavy/light chain combinations created by V(D)J recombination bound self IgG but not viral E2 antigen. Whole genome sequencing revealed accumulation of thousands of somatic mutations, at levels comparable to CLL and normal memory B cells, but with 1-2 corresponding to driver mutations found recurrently in B cell leukemia/lymphoma. V(D)J hypermutation created autoantibodies with compromised solubility. In this virus-induced autoimmune disease, infection promotes a perfect storm of somatic mutagenesis in the descendants of a single B cell.

immunology↗

bioRxiv2023

snPATHO-seq: unlocking the pathology archives

Formalin-fixed paraffin-embedded (FFPE) samples are valuable but underutilized in single-cell omics research due to their low DNA and RNA quality. In this study, leveraging recent single-cell genomic technology advances, we introduce a versatile method to derive high-quality single-nucleus transcriptomic data from FFPE samples.

molecular biology↗

bioRxiv2023

An experimental comparison of the Digital Spatial Profiling and Visium spatial transcriptomics technologies for cancer research

BackgroundSpatial transcriptomic technologies are powerful tools for resolving the spatial heterogeneity of gene expression in tissue samples. However, little evidence exists on relative strengths and weaknesses of the various available technologies for profiling human tumour tissue. In this study, we aimed to provide an objective assessment of two common spatial transcriptomics platforms, 10X Genomics Visium and Nanostrings GeoMx DSP. MethodThe abilities of the DSP and Visium platforms to profile transcriptomic features were compared using matching cell line and primary breast cancer tissue samples. A head-to-head comparison was conducted using data generated from matching samples and synthetic tissue references. Platform specific features were also assessed according to manufacturers recommendations to evaluate the optimal usage of the two technologies. ResultsWe identified substantial variations in assay design between the DSP and Visium assays such as transcriptomic coverage and composition of the transcripts detected. When the data was standardised according to manufacturers recommendations, the DSP platform was more sensitive in gene expression detection. However, its specificity was diminished by the presence of non-specific detection. Our results also confirmed the strength and weakness of each platform in characterising spatial transcriptomic features of tissue samples, in particular their application to hypothesis generation versus hypothesis testing. ConclusionIn this study, we share our experience on both DSP and Visium technologies as end users. We hope this can guide future users to choose the most suitable platform for their research. In addition, this dataset can be used as an important resource for the development of new analysis tools.

cancer biology↗

bioRxiv2022

Integration of single-cell RNA and protein data identifies novel clinically-relevant lymphocyte phenotypes in breast cancers

Immune cells are critical determinants of solid tumour aetiology, but the diverse phenotypes of intra-tumoural immune cells remain incompletely characterised. We applied integrated single cell RNA sequencing (scRNA-Seq) and highly multiplexed protein epitope analysis to a cohort of breast cancer samples to resolve cell states within the tumour microenvironment. We reveal novel protein markers for resting and activated tumour infiltrating lymphocytes, and show that high expression of CD103 primarily marks exhausted CD8 rather than tissue resident CD8 T-cells in human breast cancers. We identify two distinct states of activated CD4+ T follicular helper (Tfh) cells. A population resembling conventional Tfh (cTfh) cells were localised primarily to lymphoid aggregates by spatial transcriptomics. In contrast, cancer associated Tfh (caTfh) cells expressing markers of tissue residency and exhaustion co-localized with cancer foci and signalled to macrophages. Importantly, increased caTfh : cTfh ratio associated with improved disease outcome and response to checkpoint immunotherapy.

immunology↗

bioRxiv2022

Reversal of lactate and PD-1-mediated macrophage immunosuppression controls growth of PTEN/p53-deficient prostate cancer

PTEN loss-of-function occurs in approximately 50% of mCRPC patients, and is associated with a poor prognosis, therapeutic outcomes and resistance to immune-checkpoint inhibitors. Recent clinical studies demonstrated that dual PI3K/AKT pathway inhibition and androgen axis blockade led to a modest improvement in progression-free survival of PTEN-deficient mCRPC patients, but the mechanistic basis for this limited efficacy is unknown. To elucidate potential resistance mechanism(s), we performed co-clinical trials in a prostate-specific PTEN/p53-deficient genetically-engineered mouse model, and discovered that the recruitment of PD-1-expressing tumor-associated macrophages (TAM) thwarts the phagocytosis-mediated anti-tumor efficacy of androgen deprivation therapy (ADT)/PI3K inhibitor (PI3Ki) combination. Strikingly, we observed a TAM-dependent [~]3-fold enhancement in the overall response rate with the addition of PD-1 antibody (aPD-1) to ADT/PI3Ki combination therapy. Mechanistically, decreased lactate production from PI3Ki-treated tumor cells suppressed histone lactylation (H3K18lac) within TAM, resulting in their phagocytic activation, which was augmented by concurrent ADT/aPD-1 treatment. Consistent with our murine observations, single cell RNA-sequencing analysis of human metastatic PC samples revealed a direct correlation between high glycolytic activity and phagocytosis suppression. Critically, feedback activation of Wnt/{beta}-catenin signaling observed in non-responder mice following ADT/PI3Ki/aPD-1 combination treatment, restored lactate-mediated H3K18lac and suppressed phagocytosis within macrophages. Altogether, these data suggest that reversal of lactate and PD-1-mediated TAM immunosuppression by PI3Ki and aPD-1, respectively, controls tumor growth in combination with ADT, and warrants further clinical investigation in PTEN/p53-deficient mCRPC patients. One Sentence SummaryInhibition of tumor-cell intrinsic lactate production suppresses PTEN/p53-deficient prostate cancer growth via macrophage activation/phagocytosis

cancer biology↗

bioRxiv2022

STAT3 gain-of-function mutations connect leukemia with autoimmune disease by pathological dysregulation of NKG2Dhi CD8 killer T cells

The association between cancer and autoimmune disease is unexplained, exemplified by T-cell large granular lymphocytic leukemia (T-LGL) where gain-of-function somatic mutations in STAT3 correlate with co-existing autoimmunity. To resolve whether these mutations are the cause or consequence of CD8 clonal expansions and autoimmunity, here we analyse patients with germline STAT3 GOF syndrome and mice with the T-LGL mutation STAT3K658Nor the most common germline mutation, STAT3T716M. STAT3 GOF mutations drove accumulation of effector CD8 T cell clones highly expressing the NKG2D receptor for MHC-I-related molecules expressed on stressed cells, and the genes for inflammatory/cytotoxic granzymes, perforin, interferon-{gamma} and Ccl5/Rantes. CD8 cells were essential to lethal disease in Stat3K658Nmice and their accumulation required NKG2D and the receptor for IL-15 and IL-2, IL2RB. These results demonstrate that STAT3 GOF mutations cause effector CD8 T cell oligoclonal accumulation and that these rogue T cells contribute to autoimmune pathology, supporting the hypothesis that somatic mutations in leukemia/lymphoma driver genes contribute to autoimmune disease. IN BRIEFLeukemia and autoimmune-associated STAT3 gain-of-function mutations dysregulate CD8 T cells to cause autoimmune pathology and oligoclonal expansion of cytotoxic killer CD8 T cells, that depend upon NKG2D and IL2RB receptors for signals displayed on stressed, damaged, infected, or mutated tissues.

immunology↗

bioRxiv2021

DNA barcoding reveals ongoing immunoediting of clonal cancer populations during metastatic progression and in response to immunotherapy

Cancers evade the immune system in order to grow or metastasise through the process of cancer immunoediting. While immune checkpoint inhibitors have been effective for reactivating tumour immunity in some types of cancer, many other solid cancers, including breast cancer, remain largely non-responsive. Understanding the way non-responsive cancers evolve to evade immunity, what resistance pathways are activated and whether this occurs at the clonal level will improve immunotherapeutic design. We tracked cancer cell clones during the immunoediting process and determined clonal transcriptional profiles that allow immune evasion in murine mammary tumour growth in response to immunotherapy with anti-PD1 and anti-CTLA4. Clonal diversity was significantly restricted by immunotherapy treatment in both primary tumours and metastases. These findings demonstrate that immunoediting selects for pre-existing breast cancer cell populations and that immunoediting is not static, it is ongoing during metastasis and immunotherapy treatment. Isolation of immunotherapy resistant clones revealed unique and overlapping transcriptional signatures. The overlapping gene signature was associated with poor survival of basal-like breast cancer patients in two cohorts. At least one of these overlapping genes has an existing small molecule that can potentially be used to improve immunotherapy response.

cancer biology↗

bioRxiv2020

Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships

In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra-and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. We integrate and spatially map tumor-associated types from single cell data to find: segregated epithelial cells, interactions between B and T-cells and myeloid cells, co-localization of macrophage and T-cell subsets. A model is constructed to infer presence of tertiary lymphoid structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define novel interactions between tumor-infiltrating cells in breast cancer and provide tools generalizing across tissues and diseases.

cancer biology↗

bioRxiv2020

Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

BackgroundHigh throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity amongst complex human cancers. scRNA-Seq studies using fresh human surgical tissue is logistically difficult, precludes histopathological triage of samples and limits the ability to perform batch processing. This hinderance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers, and on high throughput scRNA-Seq platforms. ResultsWe show that the viable cryopreservation of human cancers provides high quality single-cell transcriptomes using the Chromium 10X platform. We sequenced a total of [~]120,000 cells from fresh and cryopreserved replicates across three breast cancers, two prostate cancers and a cutaneous melanoma. Importantly, tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We then show that cryopreservation had minimal impacts on results of downstream analyses such as biological pathway enrichment. Further, we demonstrate the advantage of cryopreserving whole-cells for immunophenotyping methods such as CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. ConclusionsOur study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies.

cancer biology↗

bioRxiv2020

Single-cell analysis reveals diverse stromal subsets associated with immune evasion in triple-negative breast cancer

The tumour stroma regulates nearly all stages of carcinogenesis. Stromal heterogeneity in human triple-negative breast cancers (TNBCs) remains poorly understood, limiting the development of stromal-targeted therapies. Single cell RNA-sequencing of five TNBCs revealed two cancer-associated fibroblast (CAF) and two perivascular-like (PVL) subpopulations. CAFs clustered into two states, the first with features of myofibroblasts and the second characterised by high expression of growth factors and immunomodulatory molecules. PVL cells clustered into two states consistent with a differentiated and immature phenotype. We showed that these stromal states have distinct morphologies, spatial relationships and functional properties in regulating the extracellular matrix. Using cell-signalling predictions, we provide evidence that stromal-immune crosstalk acts via a diverse array of immunoregulatory molecules. Importantly, the investigation of gene signatures from inflammatory-CAFs and differentiated-PVL cells in independent TNBC patient cohorts revealed strong associations with cytotoxic T-cell dysfunction and exclusion, respectively. Such insights present promising candidates to further investigate for new therapeutic strategies in the treatment of TNBCs.

cancer biology↗

bioRxiv2018

High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes

High-throughput single-cell RNA-Sequencing is a powerful technique for gene expression profiling of complex and heterogeneous cellular populations such as the immune system. However, these methods only provide short-read sequence from one end of a cDNA template, making them poorly suited to the investigation of gene-regulatory events such as mRNA splicing, adaptive immune responses or somatic genome evolution. To address this challenge, we have developed a method that combines targeted long-read sequencing with short-read based transcriptome profiling of barcoded single cell libraries generated by droplet-based partitioning. We use Repertoire And Gene Expression sequencing (RAGE-seq) to accurately characterize full-length T cell (TCR) and B cell (BCR) receptor sequences and transcriptional profiles of more than 7,138 lymphocytes sampled from the primary tumour and draining lymph node of a breast cancer patient. With this method we show that somatic mutation, alternate splicing and clonal evolution of T and B lymphocytes can be tracked across these tissue compartments. Our results demonstrate that RAGE-Seq is an accessible and cost-effective method for high-throughput deep single cell profiling, applicable to a wide range of biological challenges.

genomics↗