Biomedical subjects

Jay Shendure

Publications and source records attributed to Jay Shendure.

4 recordsLinked to original sources

PubMed2026

Massively parallel characterization and predictive modelling of neuronal regulatory variation.

Disease-associated variants reside frequently in noncoding cis-regulatory elements (CREs), yet their functional consequences remain poorly understood. We performed a large-scale lentiMPRA in human excitatory neurons, quantifying the impact of >46,000 naturally occurring variants across >27,000 candidate CREs near 524 disease-associated genes. These data improved regulatory variant effect predictions beyond state-of-the-art models. Significant allelic effects occurred at comparable rates across common, rare, and singleton variants, demonstrating that, within MPRA-measurable effects, population frequency carries limited information about per-variant regulatory impact. Variant effect detectability and magnitude were governed primarily by baseline activity of the enclosing regulatory element and local sequence context. Regulatory effects were distributed across numerous transcription factors rather than concentrated in master regulators, consistent with a combinatorial enhancer architecture. We establish a large-scale functional variant catalog and provide a complementary benchmark and resource for developing and evaluating models of noncoding regulatory variation.

Journal Article↗

PubMed2026

Multichannel genomic recording of biological information with ENGRAM.

Molecular recording is an emerging paradigm for measuring biology over time. Enhancer-mediated genomic recording of activity in multiplex (ENGRAM) is a recently described synthetic biology circuit architecture that converts the transient activity of cis-regulatory elements (CREs) into stable genomic records that can be retrospectively recovered via DNA sequencing. Here we provide a step-by-step protocol for conducting ENGRAM experiments and analyzing the resulting data. We also describe key design considerations for ENGRAM recorders, summarize the strengths and limitations of ENGRAM, and highlight applications, including multiplex signal recording and high-throughput CRE screening. In contrast to other systems for DNA-based recording in mammalian systems, ENGRAM relies on prime editing-mediated insertions to record the activity of a given CRE, such that it is inherently multiplexable-for example, four-base-pair insertions can represent the activities of up to 256 distinct CREs. A further contrast lies with ENGRAM's compatibility with DNA Typewriter, which facilitates the capture of signal order. For users with basic skills in molecular biology, mammalian cell culture and DNA sequencing analysis, ENGRAM experiments can typically be completed within 5-6 weeks.

Genomics↗

PubMed2025

Dual-patterned pluripotent stem cells self-organize into a human embryo model with extended anterior-posterior patterning.

Human gastruloids are a powerful class of stem cell-derived models that recapitulate key features of early embryonic development, including symmetry breaking and the emergence of three germ layers1-3. However, they lack anterior embryonic structures and coordinated axial organization4-6. To address this limitation, we pre-patterned human pluripotent stem cells (hPSCs) by exposing them to either anterior (FGF2) or posterior (CHIR99021 [CHIR] & retinoic acid [RA]) cues. Upon mixing, these dual-patterned hPSCs interacted and self-organized into elongated structures with both anterior and posterior features-which we term anterior-posterior (AP) human gastruloids. Anteriorly pre-treated cells robustly intercalated into posteriorly pre-treated cells, collectively giving rise to a continuum of neural tissues-including a brain-like domain, a neural tube-like structure, and neuro-mesodermal progenitors (NMPs)-with segmented somites arrayed bilaterally. Single cell RNA sequencing (scRNA-seq) revealed that human AP gastruloids contain cell types resembling the midbrain-hindbrain boundary (MHB), regionalized hindbrain structures (i .e. rhombomeres 1-8), regionalized neural crest (i.e. cranial, vagal, trunk)7,8 and head mesoderm. Transcriptomic comparisons to primate embryos revealed that human AP gastruloids most closely resemble Carnegie stage 11 (CS11) embryos. While they lack a notochord and full dorsal-ventral polarity, human AP gastruloids recapitulate key spatial and temporal features of early neurulation and somitogenesis. Perturbation of folic acid metabolism or rho-associated kinase (ROCK) signaling induced spinal cord defects, phenocopying aspects of spina bifida and other neural tube defects, highlighting this model's potential for studying congenital disorders9. AP gastruloids may serve as a simple, robust, scalable platform for modeling coordinated human AP body axis development. More broadly, our results suggest that controlled interactions between differentially prepatterned progenitors can initiate self-organization of complex body axis features. The "pattern-and-mix" strategy may serve as a generalizable framework for assembling spatially organized stem cell models of mammalian development.

Journal Article↗

PubMed2024

Meningioma transcriptomic landscape demonstrates novel subtypes with regional associated biology and patient outcome.

Meningiomas, although mostly benign, can be recurrent and fatal. World Health Organization (WHO) grading of the tumor does not always identify high-risk meningioma, and better characterizations of their aggressive biology are needed. To approach this problem, we combined 13 bulk RNA sequencing (RNA-seq) datasets to create a dimension-reduced reference landscape of 1,298 meningiomas. The clinical and genomic metadata effectively correlated with landscape regions, which led to the identification of meningioma subtypes with specific biological signatures. The time to recurrence also correlated with the map location. Further, we developed an algorithm that maps new patients onto this landscape, where the nearest neighbors predict outcome. This study highlights the utility of combining bulk transcriptomic datasets to visualize the complexity of tumor populations. Further, we provide an interactive tool for understanding the disease and predicting patient outcomes. This resource is accessible via the online tool Oncoscape, where the scientific community can explore the meningioma landscape.

Meningioma↗