Biology subjects

Yadollahpour, P.

Publications and source records attributed to Yadollahpour, P..

5 recordsLinked to original sources

bioRxiv2026

Unified Transcriptome and Mechanics Map of the Intact Mammalian Preimplantation Embryo In Situ

The development and maintenance of multicellular tissues requires that cell states be closely coupled to their local environment, including geometric and mechanical cues. However, studying this coupling in intact tissues has been challenging, because existing measurement technologies cannot simultaneously assess mechanical properties and molecularly defined cell states. To address this gap, we introduce the Unified Transcriptome and Mechanics Map (UTMM), a method for concurrent measurement of the transcriptome and cytoplasmic stiffness (high-frequency elastic modulus) within intact 3D multicellular structures. UTMM relies on two innovations: (1) a targeted in situ RNA sequencing approach for intact 3D embryos (3DISS), and (2) a strategy that leverages high-frequency intracellular organelle fluctuations to infer cell-level stiffness in vivo. We applied UTMM to mouse embryos from the zygote through the morula stage to characterize how RNA expression and cytoplasmic stiffness become coupled during early lineage specification. Our data reveal that, in the early morula, transcriptional and morphological distinctions emerge between trophectoderm and inner cell mass (ICM) lineages, coinciding with a gradual decrease in cytoplasmic stiffness (softening) across all cells from the 2-cell through morula stages. Furthermore, we observe that early lineage biases align with differential mechanical properties, reflecting distinct emerging developmental programs. When we delayed this softening process via mechanical perturbation, embryonic progression was impeded, highlighting the functional importance of coordinated mechanical and transcriptional changes. Together, these results demonstrate UTMMs ability to bridge molecular and mechanical dynamics in multicellular systems, providing a powerful framework for investigating how biomechanical cues shape cell fate decisions in intact tissues. One Sentence SummaryJoint 3D in situ quantification of spatial transcriptome and cytoplasm mechanics in preimplantation mammalian embryos

developmental biology↗

bioRxiv2023

Evaluation and optimization of sequence-based gene regulatory deep learning models

Neural networks have emerged as immensely powerful tools in predicting functional genomic regions, notably evidenced by recent successes in deciphering gene regulatory logic. However, a systematic evaluation of how model architectures and training strategies impact genomics model performance is lacking. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast, to best capture the relationship between regulatory DNA and gene expression. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. While some benchmarks produced similar results across the top-performing models, others differed substantially. All top-performing models used neural networks, but diverged in architectures and novel training strategies, tailored to genomics sequence data. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide any given model into logically equivalent building blocks. We tested all possible combinations for the top three models and observed performance improvements for each. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets. Overall, we demonstrate that high-quality gold-standard genomics datasets can drive significant progress in model development.

genomics↗

bioRxiv2022

EquiFold: Protein Structure Prediction with a Novel Coarse-Grained Structure Representation

Designing proteins to achieve specific functions often requires in silico modeling of their properties at high throughput scale and can significantly benefit from fast and accurate protein structure prediction. We introduce EquiFold, a new end-to-end differentiable, SE(3)-equivariant, all-atom protein structure prediction model. EquiFold uses a novel coarse-grained representation of protein structures that does not require multiple sequence alignments or protein language model embeddings, inputs that are commonly used in other state-of-the-art structure prediction models. Our method relies on geometrical structure representation and is substantially smaller than prior state-of-the-art models. In preliminary studies, EquiFold achieved comparable accuracy to AlphaFold but was orders of magnitude faster. The combination of high speed and accuracy make EquiFold suitable for a number of downstream tasks, including protein property prediction and design.

bioinformatics↗

bioRxiv2020

Single-nucleus and spatial transcriptomics of archival pancreatic cancer reveals multi-compartment reprogramming after neoadjuvant treatment

Pancreatic ductal adenocarcinoma (PDAC) remains a treatment-refractory disease. Characterizing PDAC by mRNA profiling remains particularly challenging. Previously identified bulk expression subtypes were influenced by contaminating stroma and have not yet informed clinical management, whereas single cell RNA-seq (scRNA-seq) of fresh tumors under-represented key cell types. Here, we developed a robust single-nucleus RNA-seq (snRNA-seq) technique for frozen archival PDAC specimens and used it to study both untreated tumors and those that received neoadjuvant chemotherapy and radiotherapy (CRT). Gene expression programs learned across untreated malignant cell and fibroblast profiles uncovered a clinically relevant molecular taxonomy with improved prognostic stratification compared to prior classifications. Moreover, in the increasingly-adopted neoadjuvant treatment context, there was a depletion of classical-like phenotypes in malignant cells in favor of basal-like phenotypes associated with TNF-NFkB and interferon signaling as well as the presence of novel acinar and neuroendocrine classical-like states, which may be more resilient to cytotoxic treatment. Spatially-resolved transcriptomics revealed an association between malignant cells expressing these basal-like programs and higher immune infiltration with increased lymphocytic content, whereas those exhibiting classical-like programs were linked to sparser macrophage-predominant microniches, perhaps pointing to susceptibility to distinct therapeutic strategies. Our refined molecular taxonomy and spatial resolution can help advance precision oncology in PDAC through informative stratification in clinical trials and insights into differential therapeutic targeting leveraging the immune system.

genomics↗

bioRxiv2020

Molecular Logic of Cellular Diversification in the Mammalian Cerebral Cortex

ABSTRACTThe neocortex has an unparalleled diversity of cell types, which are generated during development through a series of temporally orchestrated events that are under tight evolutionary constraint and are critical for proper cortical assembly and function. However, the molecular logic that governs the establishment and organization of cortical cell types remains elusive, largely due to the large number of cell classes undergoing dynamic cell-state transitions over extended developmental timelines. Here, we have generated a comprehensive single-cell RNA-seq and single-cell ATAC-seq atlas of the developing mouse neocortex, sampled every day throughout embryonic corticogenesis. We computationally reconstruct developmental trajectories across the diversity of cortical cell classes, and infer the gene regulatory programs that accompany their lineage bifurcation decisions and their differentiation trajectories. Finally, we demonstrate how this developmental map pinpoints the origin of lineage-specific developmental abnormalities linked to aberrant corticogenesis in mutant animals. The data provides the first global picture of the regulatory mechanisms governing cellular diversification in the neocortex.Competing Interest StatementP.A. is a SAB member in System 1 Biosciences and Foresite Labs and is a co-founder of FL60. A.R. is a co-founder of and equity holder in Celsius Therapeutics, equity holder in Immunitas, and a SAB member of ThermoFisher Scientific, Syros Pharmaceuticlas, Asimov, and Neogene Therapeutics.View Full Text

developmental biology↗