Biology subjects

Miremadi, A.

Publications and source records attributed to Miremadi, A..

6 recordsLinked to original sources

bioRxiv2026

Spatially resolved transcriptomic and proteomic profiling reveals cell interaction programs that predict Barrett's esophagus progression

Barretts esophagus (BE) is the precursor lesion of esophageal adenocarcinoma (EAC). It affects approximately 5% of adults in the United States and significantly increases the risk of developing EAC. However, current surveillance strategies cannot reliably distinguish patients who will progress from those who will remain stable. Direct studies of progressor BE are extremely limited due to availability of tissue with known progression outcomes, and have largely been restricted to genomic profiling approaches. The premalignant cellular landscape of progressor BE remains poorly understood. Here, we used complementary spatial transcriptomic and proteomic imaging to profile 34 non-dysplastic BE patients under endoscopic surveillance, including those who subsequently progressed to dysplasia or EAC, termed "Progressors" and those who remained stable, termed "Non-progressors". Transcriptomics based Xenium analysis captured 974,604 cells across 70 whole-biopsy regions, while protein based imaging mass cytometry profiled 372,242 cells across 119 selected regions. FUME-TCRseq further quantified T cell clonotypes from matched tissues scrolls. Cellular composition was generally similar between Progressors and Non-progressors. However, Progressors showed increased intestinal Barretts columnar cells, B cells and gastric progenitor-like cells, together with enhanced immune-epithelial interactions, whereas Non-progressors retained coordinated stromal organization. Spatial interaction features strongly outperformed cell composition and density for progression prediction. Combined spatial interaction model achieved an area under the curve (AUC) of 0.97, compared with 0.62 and 0.68 for comparison and density alone. Complementary imaging mass cytometry further resolved the underlying immune programs, identifying cytotoxic and antigen presenting myeloid features enriched in progressors, and CD56 associated memory T cell interactions enriched in non progressors. Together, these findings support a model that BE progression is driven by progressive remodeling of epithelial-immune-stromal architecture rather than emergence of distinct dysplasia-like cell subsets. Increased T cell clonal diversity and recruitment of cytotoxic and antigen-presenting immune niches may also reflect an evolving response to genomic alteration prior to dysplasia. These results establish spatial tissue architecture, rather than specific cell types, captures progression associated microenvironmental states in BE and provides a framework for spatially informed patient stratification and early cancer risk assessment.

systems biology↗

bioRxiv2026

Polyclonal and single clonal patient-derived organoid models of Barrett oesophagus and oesophageal adenocarcinoma establish a platform for the analysis of heterogeneity in disease progression and therapy response

SUMMARY/ABSTRACTOesophageal adenocarcinoma (OAC) is a major cause of morbidity and mortality. OAC and its precursor, Barrett oesophagus (BO), are defined by substantial early heterogeneity, complicating prevention and treatment of OAC and remaining poorly recapitulated by current in vitro and animal model systems. We have generated 116 patient- and healthy donor-derived organoids (PDOs) spanning normal oesophagogastric tissue, BO and OAC. These PDOs capture population diversity and recapitulate phenotypic, genomic and transcriptomic features of their respective disease stages. We develop a single cell-derived clonal organoid approach and show that this enables us to capture the heterogeneity and isolate high-risk, subclonal populations that are difficult to discern and maintain in bulk PDO cultures. Using this platform, we demonstrate functional importance of this biobank across the pre-malignant to invasive disease spectrum, including a role for BO in shaping fibroblast phenotype within assembloids, and diverse responses of OAC to chemotherapy, radiotherapy and targeted CDK4/6 inhibition. HIGHLIGHTSO_LIPatient- and healthy donor-derived organoids (PDOs) provide a functional platform of disease progression and heterogeneity across normal gastric, non-dysplastic and dysplastic Barrett oesophagus (BO) and oesophageal adenocarcinoma (OAC). C_LIO_LIWe provide a quantitative phenotypic and molecular framework to assess the provenance and fidelity of each PDO model given the heterogeneity of this disease. C_LIO_LIPDOs recapitulate key features of non-dysplastic and dysplastic BO, as well as invasive OAC. C_LIO_LISingle cell-derived clonal organoids (sc-organoids) isolate and maintain high-risk subclonal populations. C_LIO_LIOAC PDOs mirror known population level variation in response to systemic anti-cancer therapies. C_LI

cancer biology↗

bioRxiv2023

Dual-modality imaging of immunofluorescence and imaging mass cytometry for whole slide imaging with accurate single-cell segmentation

Imaging mass cytometry (IMC) is a powerful multiplexed tissue imaging technology that allows simultaneous detection of more than 30 makers on a single slide. It has been increasingly used for singlecell-based spatial phenotyping in a wide range of samples. However, it only acquires a small, rectangle field of view (FOV) with a low image resolution that hinders downstream analysis. Here, we reported a highly practical dual-modality imaging method that combines high-resolution immunofluorescence (IF) and high-dimensional IMC on the same tissue slide. Our computational pipeline uses the whole slide image (WSI) of IF as a spatial reference and integrates small FOVs IMC into a WSI of IMC. The high-resolution IF images enable accurate single-cell segmentation to extract robust high-dimensional IMC features for downstream analysis. We applied this method in esophageal adenocarcinoma of different stages, identified the single-cell pathology landscape via reconstruction of WSI IMC images, and demonstrated the advantage of the dual-modality imaging strategy. MotivationHighly multiplexed tissue imaging allows visualization of the spatially resolved expression of multiple proteins at the single-cell level. Although imaging mass cytometry (IMC) using metal isotope-conjugated antibodies has a significant advantage of low background signal and absence of autofluorescence or batch effect, it has a low resolution that hampers accurate cell segmentation and results in inaccurate feature extraction. In addition, IMC only acquires mm2-sized rectangle regions, which limits its application and efficiency when studying larger clinical samples with non-rectangle shapes. To maximize the research output of IMC, we developed the dual-modality imaging method based on a highly practical and technical improvement requiring no extra specialized equipment or agents and proposed a comprehensive computational pipeline that combines IF and IMC. The proposed method greatly improves the accuracy of cell segmentation and downstream analysis and is able to obtain whole slide image IMC to capture the comprehensive cellular landscape of large tissue sections.

bioinformatics↗

bioRxiv2020

Genomic copy number predicts oesophageal cancer years before transformation

Cancer arises through a process of somatic evolution and recent studies have shown that aneuploidies and driver gene mutations precede cancer diagnosis by several years to decades1-4 Here, we address the question whether such genomic signals can be used for early detection and pre-emptive cancer treatment. To this end we study Barretts oesophagus, a genomic copy number driven neoplastic precursor lesion to oesophageal adenocarcinoma5. We use shallow whole genome sequencing of 777 biopsies sampled from 88 patients in surveillance for Barretts oesophagus over a period of up to 15 years. These data show that genomic signals exist that distinguish progressive from stable disease with an AUC of 0.87 and a sensitivity of 50% even ten years prior to histopathological disease transformation. These finding are validated on two independent cohorts of 75 and 248 patients. Compared against current patient management guidelines genomic risk classification enables earlier treatment for high risk patients as well as reduction of unnecessary treatment and monitoring for patients who are unlikely to develop cancer.

genomics↗

bioRxiv2018

Genomic evidence supports a clonal diaspora model for metastases of esophageal adenocarcinoma

Continual evolution of cancer makes it challenging to predict clinical outcomes. Highly varied and unpredictable patient outcomes in esophageal adenocarcinoma (EAC) prompted us to question the pattern and timing of metastatic spread. Whole genome sequencing and phylogenetic analysis of 396 samples across 18 EAC cases demonstrated a stellate pattern on the phylogenetic trees in 90% cases. The age-dependent trinucleotide signature, which can serve as a molecular clock, was absent or reduced in the stellate branches beyond the trunk in most cases (p<0.0001). Clustering of lymph nodes and distant metastases (n=250) demonstrated samples sharing a common clonal origin were widely dispersed anatomically. Metastatic subclones at autopsy were present in tissue and blood samples from earlier time-points. We infer that metastasis occurs rapidly across multiple sites, constituting a model of metastatic spread we term clonal diaspora. This has implications for understanding metastatic progression, clinical staging and patient management.

genomics↗

bioRxiv2018

Low-cost and clinically applicable copy number profiling using repeat DNA

Large-scale cancer genome studies suggest that tumors are driven by somatic copy number alterations (SCNAs) or single-nucleotide variants (SNVs). Due to the low-cost, the clinical use of genomics assays is biased towards targeted gene panels, which identify SNVs. There is a need for a comparably low-cost and simple assay for high-resolution SCNA profiling. Here we present our method, conliga, which infers SCNA profiles from a low-cost and simple assay.

genomics↗