Marco Antonio Mendoza-Parra

SCITRAM predicts master transcription factors from single-cell transcriptomics data by reconstructing gene regulatory networks and modeling transcriptional cascades.

The paper identifies KLF5 and KLF8 as master transcriptional regulators in Alzheimer's disease using human brain organoids and gene regulatory network analysis.

The paper identifies ZC3H12C as a mediator of CAR-T cell dysfunction in solid tumors through an in vivo CRISPR screen and validates its role using single-cell RNA and ATAC-seq.

This study identifies how Oct4 differentially co-opts Bcl11a and Bcl11b to regulate reprogramming to pluripotency by revealing their opposing roles in cell fate conversion.

The paper shows that mitotic arrest in cancer cells enhances uORF/uoORF translation, leading to increased immunopeptide presentation and T-cell mediated killing.

The paper investigates how RAR-specific agonists drive neuron-astroglia cell fate during stem cell differentiation and brain organoid development, revealing distinct transcriptional signatures and spatial organization.

This paper introduces a computational method to enhance spatial omics resolution by inferring pseudo-interstitial pixels from existing data, validated on spatial transcriptomics datasets.

The paper identifies a protective variant CREB3R119G that reduces ALS risk and progression by modulating CREB3 activity in vulnerable neurons.

This study shows that PSEN1 mutations impair microglia differentiation, leading to pro-apoptotic and pro-inflammatory effects in brain organoids.

This study develops a low-cost method for spatial transcriptomics and applies it to create a 3D molecular map of human brain organoids, showing gene expression differences across sections.

This study shows that activating RARβ and RARγ receptors with specific ligands enhances cell specialization during stem cell differentiation into neuronal and glial cell types.

The paper introduces MULTILAYER, a method for clustering spatial transcriptomes into functionally relevant substructures, and evaluates it on public datasets including cancerous prostate biopsies.

The paper identifies patient-specific deregulated regulatory networks in prostate cancer to inform personalized therapeutic strategies.

This paper introduces GRN-loop, a method combining 3D chromatin architecture with gene expression data to identify key transcription factors and DNA elements driving neural lineage commitment.