Biology subjects

Land, M.

Publications and source records attributed to Land, M..

5 recordsLinked to original sources

bioRxiv2026

Random mtDNA mutagenesis maps genotype-phenotype associations across metabolic contexts and lineages

Mitochondrial DNA (mtDNA) mutations cause inherited disease, accumulate with age, and recur in tumors, yet their effects remain unresolved because existing models interrogate one engineered allele at a time. We developed mtDIVE, a random C[->]T mutagenesis platform for pooled mitochondrial forward genetics in human epithelial cells and zebrafish embryos. mtDIVE generates genotypes ranging from single substitutions to >100 mutations per molecule, impairs respiration, and causes lineage-specific developmental delay. Coupling single-cell transcriptomics with mtDNA genotyping revealed that selection on mutant genomes and their transcriptional consequences depend on metabolic state and developmental lineage. Obligate OXPHOS eliminates highly mutated cells by apoptosis, whereas glycolytic conditions promote their competitive displacement. Individual loci exert modest but non-redundant effects that predict cell state beyond aggregate mutation burden, and cancer-recurrent variants are enriched among mutations linked to reduced mitochondrial transcription. Thus, mtDIVE links mtDNA genotypes to cellular phenotypes, separating the consequences of mutation identity from mutational load.

genomics↗

bioRxiv2026

IL-1β/IL-6 signaling circuit in the tumor microenvironment drives prostate cancer development

Despite considerable progress in elucidating mechanisms leading to castration-resistant prostate cancer (CRPC), insight into the early stages of prostate cancer initiation and progression remains limited. Genomic drivers of prostate cancer initiation have been defined through patient tumor sequencing, but the subsequent events responsible for local tissue invasion are poorly understood. Here we leverage a well-studied genetically engineered mouse prostate cancer model (Hi-Myc) that, based on robust and reproducible kinetics for transitioning from pre-invasive prostatic intraepithelial neoplasia (PIN) to invasive prostate adenocarcinoma (PCa), provides an ideal system to systematically address this question using single-cell analysis. Surprisingly, the transcriptomic profiles of early PIN lesions are indistinguishable from those of late-stage, highly invasive tumor cells, suggesting that MYC activation at the PIN stage establishes a transcriptional program that is fully capable of driving invasion but is restrained by the local tumor microenvironment (TME). Indeed, we find that progression to PCa is associated with progressive infiltration of IL-1{beta}+ tumor-infiltrating macrophages at the PIN stage that, based on immunodepletion and cytokine neutralization experiments, are required for the PIN-to-PCa transition. Mechanistically, IL-1{beta} from macrophages acts directly on prostate fibroblasts, leading to the release of IL-6, which drives invasion by activating IL-6R in tumor cells. Collectively, these findings identify a pro-tumorigenic IL-1{beta}/IL-6 signaling circuit mediated through local macrophages and fibroblasts that unleashes the full oncogenic potential of a cancer driver (MYC) activated at the PIN stage. We also find evidence of this circuit in other (non-MYC-driven) prostate cancer models as well as human prostate and lung adenocarcinoma, with implications for TME-specific targeted therapeutics in early-stage disease.

cancer biology↗

bioRxiv2024

A single-cell atlas of spatial and temporal gene expression in the mouse cranial neural plate

The formation of the mammalian brain requires regionalization and morphogenesis of the cranial neural plate, which transforms from an epithelial sheet into a closed tube that provides the structural foundation for neural patterning and circuit formation. Sonic hedgehog (SHH) signaling is important for cranial neural plate patterning and closure, but the transcriptional changes that give rise to the spatially regulated cell fates and behaviors that build the cranial neural tube have not been systematically analyzed. Here we used single-cell RNA sequencing to generate an atlas of gene expression at six consecutive stages of cranial neural tube closure in the mouse embryo. Ordering transcriptional profiles relative to the major axes of gene expression predicted spatially regulated expression of 870 genes along the anterior-posterior and mediolateral axes of the cranial neural plate and reproduced known expression patterns with over 85% accuracy. Single-cell RNA sequencing of embryos with activated SHH signaling revealed distinct SHH-regulated transcriptional programs in the developing forebrain, midbrain, and hindbrain, suggesting a complex interplay between anterior-posterior and mediolateral patterning systems. These results define a spatiotemporally resolved map of gene expression during cranial neural tube closure and provide a resource for investigating the transcriptional events that drive early mammalian brain development.

developmental biology↗

bioRxiv2021

Alignment and Integration of Spatial Transcriptomics Data

Spatial transcriptomics (ST) is a new technology that measures mRNA expression across thousands of spots on a tissue slice, while preserving information about the spatial location of spots. ST is typically applied to several replicates from adjacent slices of a tissue. However, existing methods to analyze ST data do not take full advantage of the similarity in both gene expression and spatial organization across these replicates. We introduce a new method PASTE (Probabilistic Alignment of ST Experiments) to align and integrate ST data across adjacent tissue slices leveraging both transcriptional similarity and spatial distances between spots. First, we formalize and solve the problem of pairwise alignment of ST data from adjacent tissue slices, or layers, using Fused Gromov-Wasserstein Optimal Transport (FGW-OT), which accounts for variability in the composition and spatial location of the spots on each layer. From these pairwise alignments, we construct a 3D representation of the tissue. Next, we introduce the problem of simultaneous alignment and integration of multiple ST layers into a single layer with a low rank gene expression matrix. We derive an algorithm to solve the problem by alternating between solving FGW-OT instances and solving a Non-negative Matrix Factorization (NMF) of a weighted expression matrix. We show on both simulated and real ST datasets that PASTE accurately aligns spots across adjacent layers and accurately estimates a consensus expression matrix from multiple ST layers. PASTE outperforms integration methods that rely solely on either transcriptional similarity or spatial similarity, demonstrating the advantages of combining both types of information. Code availabilitySoftware is available at https://github.com/raphael-group/paste

bioinformatics↗

bioRxiv2020

STARCH: Copy number and clone inference from spatial transcriptomics data

Tumors are highly heterogeneous, consisting of cell populations with both transcriptional and genetic diversity. These diverse cell populations are spatially organized within a tumor, creating a distinct tumor microenvironment. A new technology called spatial transcriptomics can measure spatial patterns of gene expression within a tissue by sequencing RNA transcripts from a grid of spots, each containing a small number of cells. In tumor cells, these gene expression patterns represent the combined contribution of regulatory mechanisms, which alter the rate at which a gene is transcribed, and genetic diversity, particularly copy number aberrations (CNAs) which alter the number of copies of a gene in the genome. CNAs are common in tumors and often promote cancer growth through upregulation of oncogenes or downregulation of tumor-suppressor genes. We introduce a new method STARCH (Spatial Transcriptomics Algorithm Reconstructing Copy-number Heterogeneity) to infer CNAs from spatial transcriptomics data. STARCH overcomes challenges in inferring CNAs from RNA-sequencing data by leveraging the observation that cells located nearby in a tumor are likely to share similar CNAs. We find that STARCH outperforms existing methods for inferring CNAs from RNA-sequencing data without incorporating spatial information.

bioinformatics↗