Biology subjects

Bejnood, A.

Publications and source records attributed to Bejnood, A..

3 recordsLinked to original sources

bioRxiv2025

Temporal control of sgRNA library activation unlocks large-scale in vivo CRISPR screens

Functional genomics screens have illuminated genetic dependencies in cancer, but conventional in vitro approaches fail to capture vulnerabilities shaped by the tumor microenvironment. Here, we implement CRISPR-StAR (Stochastic Activation by Recombination), a next-generation inducible CRISPR screening platform for large-scale in vivo applications. The system uses a dual lox-based recombination system to enable guide-level normalization and clonal knockout phenotyping. To analyze the rich (barcode-embedded) sequencing output, we developed UMIBB, a superior Bayesian statistical framework for quantifying gene-level dropout and enrichment compared to conventional software packages. Screening a 30,000-sgRNA library in A549 xenografts, followed by clone representation and dropout correlation analyses, showed high fidelity and reproducibility with dropout phenotypes resolvable using as few as 30 tumors for this size library. Validation across multiple tumor models demonstrated that a single tumor can provide reliable, functional annotation for [~]1,000 genes leveraging intra-tumor library controls for normalization. Comparing in vivo and in vitro screens revealed that a substantial subset of tumor suppressor genes exerts strong phenotypic effects only observable in vivo. For example, single-gene knockout and transcriptomic profiling confirmed that KMT2C and KMT2D have contrasting impacts on tumor growth - an insight that would have been overlooked in standard cell culture. Looking ahead, CRISPR-StAR screening, combined with our user-friendly analysis pipeline available on GitHub (R-package), offer an integrated framework for creating in vivo dependency maps that can complement existing vitro datasets like DepMap and Achilles. Critically, our approach reduces animal use by up to 7-fold compared to conventional in vivo dropout screens. This represents a significant ethical and methodological advancement - achieving genome-scale resolution with far fewer animals and greater reproducibility.

genomics↗

bioRxiv2021

Multicellular immune hubs and their organization in MMRd and MMRp colorectal cancer

Immune responses to cancer are highly variable, with mismatch repair-deficient (MMRd) tumors exhibiting more anti-tumor immunity than mismatch repair-proficient (MMRp) tumors. To understand the rules governing these varied responses, we transcriptionally profiled 371,223 cells from colorectal tumors and adjacent normal tissues of 28 MMRp and 34 MMRd patients. Analysis of 88 cell subsets and their 204 associated gene expression programs revealed extensive transcriptional and spatial remodeling across tumors. To discover hubs of interacting malignant and immune cells, we identified expression programs in different cell types that co-varied across patient tumors and used spatial profiling to localize coordinated programs. We discovered a myeloid cell-attracting hub at the tumor-luminal interface associated with tissue damage, and an MMRd-enriched immune hub within the tumor, with activated T cells together with malignant and myeloid cells expressing T-cell-attracting chemokines. By identifying interacting cellular programs, we thus reveal the logic underlying spatially organized immune-malignant cell networks.

cancer biology↗

bioRxiv2019

Clinically-relevant cell type cross-talk identified from a human lung tumor microenvironment interactome

Tumors comprise a complex microenvironment of interacting malignant and stromal cell types. Much of our understanding of the tumor microenvironment comes from in vitro studies isolating the interactions between malignant cells and a single stromal cell type, often along a single pathway. To develop a deeper understanding of the interactions between cells within human lung tumors we performed RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, fibroblasts, and bulk cells from freshly resected human primary non-small-cell lung tumors. We mapped the cell-specific differential expression of prognostically-associated secreted factors and cell surface genes, and computationally reconstructed cross-talk between these cell types to generate a novel resource we call the Lung Tumor Microenvironment Interactome (LTMI). Using this resource, we identified and validated a prognostically unfavorable influence of Gremlin-1 production by fibroblasts on proliferation of malignant lung adenocarcinoma cells. We also found a prognostically favorable association between infiltration of mast cells and less aggressive tumor cell behavior. These results illustrate the utility of the LTMI as a resource for generating hypotheses concerning tumor-microenvironment interactions that may have prognostic and therapeutic relevance.\n\nSummaryRNA-seq profiling of sorted populations from primary lung cancer samples identifies prognostically relevant cross-talk between cell types in the tumor microenvironment.

cancer biology↗