Biology subjects

Rosen, Y.

Publications and source records attributed to Rosen, Y..

16 recordsLinked to original sources

bioRxiv2026

Hypothalamic Cell Type Evolution across Vertebrates

The hypothalamus regulates vital functions and is highly conserved among vertebrates occupying diverse niches, but little is known about the evolution of its cell types. Using comparative single-cell transcriptomics across major vertebrate classes, we identified deeply conserved and clade-specific cell types. While homologous cell types express conserved transcription factors and neuropeptides, their regulatory linkages are often rewired across evolution. The paraventricular nucleus largely retains its neuropeptide expression and responds to dehydration with fine cellular resolution from amphibians to mammals, whereas lamina terminalis neurons are specific to terrestrial vertebrates and exhibit gene expression divergence in cell types activated by dehydration and heat. We propose that a conserved set of keystone cell types sustain essential functions across vertebrates, while flexible gene regulatory programs alongside numerous clade-specific cell types offer evolutionary fluidity.

evolutionary biology↗

bioRxiv2026

Servo-Actuated 3D-Printed Disposable Microvalves for Automated, Scalable Organoid Culture in Standard Incubators

Automation of organoid and cell culture processes is essential for achieving scalable and standardized experimentation in regenerative medicine and stem cell research. However, existing microfluidic platforms often rely on complex setups, limiting their integration within standard incubator environments. To address these challenges, we developed a compact, scalable multi-well platform featuring 3D-printed, servo-actuated disposable microvalves for fully automated media and drug exchange. This design eliminates the need for external pressure sources and control channels, providing a simplified and cost-effective solution for organoid culture. The platform integrates an internet-connected microscopy module with a motorized XYZ stage, allowing continuous, real-time imaging of individual wells directly within the incubator. It supports precise and reliable fluid handling under physiological conditions, improving throughput, reproducibility, and accessibility. We validate the platform through bench-top testing and in both mouse and human organoid models. Morphological analysis, immunohistochemistry (IHC), and qPCR demonstrate comparable viability, growth, and gene expression profiles between automated and manual culture conditions. These results establish a robust and scalable framework for fully automated organoid culture, offering a simplified and accessible alternative to conventional microfluidic systems with broad applications in regenerative medicine, drug discovery, and scalable biological screening. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/732526v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@10297f1org.highwire.dtl.DTLVardef@1458d48org.highwire.dtl.DTLVardef@13e7eeeorg.highwire.dtl.DTLVardef@1ff4e5c_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2026

A Modular In-Incubator Microscope for Longitudinal Live Cell Microscopy

Longitudinal live cell imaging is valuable for characterizing dynamic morphological and phenotypic changes in biological systems. However, conventional approaches rely on manual microscope operation, which is labor-intensive, limits imaging frequency, and disrupts the cellular environment. These constraints reduce scalability, increase experimental variability, and restrict both the duration and temporal resolution of continuous imaging. Although automated imaging platforms partially address these limitations, existing solutions are often constrained by the cost, footprint, and inflexibility of in-incubator microscopes or stage-top incubators. Here, we present an automated in-incubator epifluorescence microscope designed for long-term operation. The system features a modular architecture with optional multi-fluorescence imaging, automated plate scanning, configurable light sources, and compatibility with multiple plate formats, including integration with fluidic automation devices. By positioning the light sources and control electronics outside the incubator, the platform improves thermal stability and long-term operational reliability. This approach enables continuous, high-frequency imaging over extended durations, providing a source of rich data for quantifying time-dependent tissue phenotypes, morphological remodeling, and transient biological processes.

bioengineering↗

bioRxiv2026

Cloud-Connected Pluripotent Stem Cell Platform Enhances Scientific Identity in Underrepresented Students

Stem cell research offers unique opportunities for authentic scientific engagement, yet infrastructure requirements have confined participation to elite institutions, perpetuating workforce disparities. We developed an integrated framework combining engineered biology, cloud-connected microscopy, and validated psychometric assessment to make pluripotent stem cell (PSC) experimentation widely accessible. The framework comprises three components: a doxycycline-inducible NGN2 mouse embryonic stem cell line for rapid neuronal specification, low-cost cloud microscopy for remote observation, and the validated Stem Cell Research Identity Scale (SCRIS) for quantifying educational outcomes. Implementation across a Title I high school and urban community college demonstrated significant increases in scientific identity. Students using differentiating PSCs showed broader science identity development than those using neuroblastoma cells, particularly in competence, research readiness, and recognition. High school students showed enhanced research competence gains compared to community college students despite equivalent intervention duration. Demographic analyses revealed enhanced effectiveness for Hispanic and first-generation college students. This framework provides a scalable model for broadening participation in advanced biomedical research.

scientific communication and education↗

bioRxiv2026

Scalable high-fidelity human vascularized cortical assembloids recapitulate neurovascular co-development and cell specialization

Human cortical development involves the coupling of neurogenesis and cerebrovascular growth. However, interactions between neural and vascular cells are largely missing in most brain organoids, which are crucial models for studying neurodevelopment and disease. Here, we establish vascularized cortical assembloids (vCAs) by fusing mesoderm-derived vascular organoids (VOs) with cortical organoids (COs). vCAs self-assemble lumenized networks of endothelial cells, pericytes, and perivascular fibroblasts that acquire blood-brain barrier (BBB) specialization and arteriovenous specification in vitro. Single-cell RNA-sequencing and immunofluorescence imaging revealed that vascularization improves neuroepithelial architecture, reduces hypoxia and apoptosis, expands cortical progenitor pools, and enhances neuronal maturation and connectivity compared with COs. Moreover, atlas-level integration with human neurodevelopmental tissue and cross-protocol benchmarking demonstrate superior transcriptional concordance, especially for vascular cell and glial populations, relative to existing approaches. This scalable and reproducible platform improves fidelity and throughput for modeling human neurovascular co-development and enables systematic studies across brain regions and diseases using engineered or patient-derived iPSCs.

developmental biology↗

bioRxiv2025

PULSAR: a Foundation Model for Multi-scale and Multicellular Biology

Biology emerges from interactions across physical scales, where molecular interactions drive cellular states, which in turn orchestrate multicellular tissue functions that collectively define health and disease. However, current computational models are often constrained to single scales in isolation, failing to integrate the biology that emerges from lower to higher levels [1]. Here we present PULSAR (Patient Understanding Leveraging Single-cell universAl Representation), a multi-scale and multicellular foundation model architecture that explicitly enables information flow from genes to cells to multicellular systems. Applied to the human peripheral immune system, PULSAR extracts a unified donor representation that supports rapid disease classification, biomarker prediction, and forecasting of future clinical events, such as Rheumatoid arthritis onset. As a generative model, PULSAR enables the simulation of cytokine perturbation response across physical resolutions, while its interpretability reveals the key cell types driving disease. Overall, PULSAR opens new avenues for precision medicine by enabling computational reasoning that connects molecular biology to clinical phenotypes.

bioinformatics↗

bioRxiv2025

Incubator-Free Organoid Culture in a Sealed Recirculatory System

Discovery in human biology is pivoting toward high-dimensional computational analysis of 3D in vitro models, but this progress is limited by reliance on conventional cell culture techniques. Realism and data collection are hindered by the environmental instabilities and accessibility constraints of standard incubators. We introduce an automated, sealed recirculatory system that eliminates these barriers, enabling unconstrained instrument integration and infrastructure-independent scalability. By employing gas-tight sealing, a liquid-phase gas buffer and a non-porous plastic gas exchanger, our technology maintains biological stability without the compromises of open-air vessels. This design eliminates the need for CO2 incubators and prevents the evaporative drift that typically plagues conventional open-culture vessels. Operating on the benchtop outside the cell culture suite, we demonstrate that our system supports continuous, multi-week live imaging of vascular organoids while maintaining metabolic viability, structural fidelity and electrophysiological activity in brain organoids comparable to traditional in-incubator cultures. TeaserA sealed benchtop device enables data-rich organoid culture beyond the constraints of the cell culture laboratory.

bioengineering↗

bioRxiv2023

Universal Cell Embeddings: A Foundation Model for Cell Biology

Developing a universal representation space for cells which encompasses the tremendous molecular diversity of cell types within the human body and more generally, across species, would be transformative for cell biology. Recent work using single-cell transcriptomic approaches to create molecular definitions of cell types in the form of cell atlases has provided the necessary data for such an endeavor. Here, we present the Universal Cell Embedding (UCE) foundation model. UCE was trained on a corpus of cell atlas data from human and other species in a completely self-supervised way without any data annotations. UCEs modeling approach is to create a unified biological latent space that can represent cells across diverse tissues and species. This universal cell embedding captures important biological variation despite the presence of experimental noise across diverse datasets. An important aspect of UCEs universality is that new cells can be mapped to this embedding space with no additional data labeling, model training or fine-tuning. We applied UCE to create the Integrated Mega-scale Atlas, embedding 36 million cells, with more than 1,000 uniquely named cell types, from hundreds of experiments, dozens of tissues and eight species. We uncovered new insights about the organization of cell types and tissues within this universal cell embedding space, and leveraged it to infer function of newly discovered cell types. UCEs embedding space exhibits emergent behavior, uncovering biology that it was never explicitly trained for, such as identifying developmental lineages and embedding data from novel species not included in the training set. Overall, by enabling a universal representation for every cell state and type, UCE provides a valuable tool for analysis, annotation and hypothesis generation over single cell data.

cell biology↗

bioRxiv2023

Towards Universal Cell Embeddings: Integrating Single-cell RNA-seq Datasets across Species with SATURN

Analysis of single-cell datasets generated from diverse organisms offers unprecedented opportunities to unravel fundamental evolutionary processes of conservation and diversification of cell types. However, inter-species genomic differences limit the joint analysis of cross-species datasets to homologous genes. Here, we present SATURN, a deep learning method for learning universal cell embeddings that encodes genes biological properties using protein language models. By coupling protein embeddings from language models with RNA expression, SATURN integrates datasets profiled from different species regardless of their genomic similarity. SATURN has a unique ability to detect functionally related genes co-expressed across species, redefining differential expression for cross-species analysis. We apply SATURN to three species whole-organism atlases and frog and zebrafish embryogenesis datasets. We show that cell embeddings learnt in SATURN can be effectively used to transfer annotations across species and identify both homologous and species-specific cell types, even across evolutionarily remote species. Finally, we use SATURN to reannotate the five species Cell Atlas of Human Trabecular Meshwork and Aqueous Outflow Structures and find evidence of potentially divergent functions between glaucoma associated genes in humans and other species.

bioinformatics↗

bioRxiv2021

PhyloVision: Interactive Software for Integrated Analysis of Single-Cell Transcriptomic and PhylogeneticData

Recent advances in CRISPR-Cas9 engineering and single-cell assays have enabled the simultaneous measurement of single-cell transcriptomic and phylogenetic profiles. However, there are few computational tools enabling users to integrate and derive insight from a joint analysis of these two modalities. Here, we describe PhyloVision: an open source software for interactively exploring data from both modalities and for identifying and interpreting heritable gene modules whose concerted expression are associated with phylogenetic relationships. PhyloVision provides a feature-rich, interactive, and shareable web-based report for investigating these modules, while also supporting several other data and meta-data exploration capabilities. We demonstrate the utility of PhyloVision using a published dataset of metastatic lung adenocarcinoma cells, whose phylogeny was resolved using a CRISPR/Cas9-based lineage-tracing system. Together, we anticipate that PhyloVision and the methods it implements will be a useful resource for scalable and intuitive data exploration for any assay that simultaneously measures cell state and lineage.

bioinformatics↗

bioRxiv2021

Development of a Low-Cost System for Simultaneous Longitudinal Biological Imaging

Simultaneous longitudinal imaging across multiple conditions and replicates has been crucial for scientific studies aiming to understand biological processes and disease. Yet, imaging systems capable of accomplishing these tasks are economically unattainable for most academic and teaching laboratories around the world. Here we propose the Picroscope, which is the first low cost system for simultaneous longitudinal biological imaging made primarily using off-the-shelf and 3D-printed materials. The Picroscope is compatible with standard 24-well cell culture plates and captures 3D z-stack image data. The Picroscope can be controlled remotely, allowing for automatic imaging with minimal intervention from the investigator. Here we use this system in a range of applications. We gathered longitudinal whole organism image data for frogs, zebrafish and planaria worms. We also gathered image data inside an incubator to observe 2D monolayers and 3D mammalian tissue culture models. Using this tool, we can measure the behavior of entire organisms or individual cells over long time periods.

bioengineering↗

bioRxiv2019

Cumulus: a cloud-based data analysis framework for large-scale single-cell and single-nucleus RNA-seq

Massively parallel single-cell and single-nucleus RNA-seq (sc/snRNA-seq) have opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so does the need for computational pipelines for scaled analysis. Here, we developed Cumulus, a cloud-based framework for analyzing large scale sc/snRNA-seq datasets. Cumulus combines the power of cloud computing with improvements in algorithm implementations to achieve high scalability, low cost, user-friendliness, and integrated support for a comprehensive set of features. We benchmark Cumulus on the Human Cell Atlas Census of Immune Cells dataset of bone marrow cells and show that it substantially improves efficiency over conventional frameworks, while maintaining or improving the quality of results, enabling large-scale studies.

bioinformatics↗

bioRxiv2019

A single-cell and single-nucleus RNA-seq toolbox for fresh and frozen human tumors

Single cell genomics is essential to chart the complex tumor ecosystem. While single cell RNA-Seq (scRNA-Seq) profiles RNA from cells dissociated from fresh tumor tissues, single nucleus RNA-Seq (snRNA-Seq) is needed to profile frozen or hard-to-dissociate tumors. Each strategy requires modifications to fit the unique characteristics of different tissue and tumor types, posing a barrier to adoption. Here, we developed a systematic toolbox for profiling fresh and frozen clinical tumor samples using scRNA-Seq and snRNA-Seq, respectively. We tested eight tumor types of varying tissue and sample characteristics (resection, biopsy, ascites, and orthotopic patient-derived xenograft): lung cancer, metastatic breast cancer, ovarian cancer, melanoma, neuroblastoma, pediatric sarcoma, glioblastoma, pediatric high-grade glioma, and chronic lymphocytic leukemia. Analyzing 212,498 cells and nuclei from 39 clinical samples, we evaluated protocols by cell quality, recovery rate, and cellular composition. We optimized protocols for fresh tissue dissociation for different tumor types using a decision tree to account for the technical and biological variation between clinical samples. We established methods for nucleus isolation from OCT embedded and fresh-frozen tissues, with an optimization matrix varying mechanical force, buffer, and detergent. scRNA-Seq and snRNA-Seq from matched samples recovered the same cell types and intrinsic expression profiles, but at different proportions. Our work provides direct guidance across a broad range of tumors, including criteria for testing and selecting methods from the toolbox for other tumors, thus paving the way for charting tumor atlases.

genomics↗

bioRxiv2018

An average-case sublinear exact Li and Stephens forward algorithm

Hidden Markov models of haplotype inheritance such as the Li and Stephens model allow for computationally tractable probability calculations using the forward algorithms as long as the representative reference panel used in the model is sufficiently small. Specifically, the monoploid Li and Stephens model and its variants are linear in reference panel size unless heuristic approximations are used. However, sequencing projects numbering in the thousands to hundreds of thousands of individuals are underway, and others numbering in the millions are anticipated.\n\nTo make the Li and Stephens forward algorithm for these datasets computationally tractable, we have created a numerically exact version of the algorithm with observed average case [O](nk0.35) runtime, avoiding any tradeoff between runtime and model complexity. We demonstrate that our approach also provides a succinct data structure for general purpose haplotype data storage. We discuss generalizations of our algorithmic techniques to other hidden Markov models.\n\n2012 ACM Subject ClassificationTheory of computation {longrightarrow} Streaming, sublinear and near linear time algorithms; Applied computing {longrightarrow} Bioinformatics\n\nSupplement Materialhttps://github.com/yoheirosen/sublinear-Li-Stephens.\n\nFundingThis work was supported by the National Human Genome Research Institute of the National Institutes of Health under Award Number 5U54HG007990, the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number 1U01HL137183-01, and grants from the W.M. Keck foundation and the Simons Foundation.\n\nAcknowledgementsWe would like to thank Jordan Eizenga for his helpful discussions throughout the development of this work.

bioinformatics↗

bioRxiv2017

Describing the Local Structure of Sequence Graphs

Analysis of genetic variation using graph structures is an emerging paradigm of genomics. However, defining genetic sites on sequence graphs remains an open problem. Patens invention of the ultra-bubble and snarl, special subgraphs of sequence graphs which can identified with efficient algorithms, represents important first step to segregating graphs into genetic sites. We extend the theory of ultrabubbles to a special subclass where every detail of the ultrabubble can be described in a series and parallel arrangement of genetic sites. We furthermore introduce the concept of bundle structures, which allows us to recognize the graph motifs created by additional combinations of variation in the graph, including but not limited to runs of abutting single nucleotide variants. We demonstrate linear-time identification of bundles in a bidirected graph. These two advances build on initial work on ultrabubbles in bidirected graphs, and define a more granular concept of genetic site.

bioinformatics↗

bioRxiv2017

Modelling haplotypes with respect to reference cohort variation graphs

Current statistical models of haplotypes are limited to panels of haplotypes whose genetic variation can be represented by arrays of values at linearly ordered bi- or multiallelic loci. These methods cannot model structural variants or variants that nest or overlap. A variation graph is a mathematical structure that can encode arbitrarily complex genetic variation. We present the first haplotype model that operates on a variation graph-embedded population reference cohort. We describe an algorithm to calculate the likelihood that a haplotype arose from this cohort through recombinations and demonstrate time complexity linear in haplotype length and sublinear in population size. We furthermore demonstrate a method of rapidly calculating likelihoods for related haplotypes. We describe mathematical extensions to allow modelling of mutations. This work is an essential step forward for clinical genomics and genetic epidemiology since it is the first haplotype model which can represent all sorts of variation in the population.

genomics↗