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Open Access Publications from the University of California

Open Access Policy Deposits

This series is automatically populated with publications deposited by UC Irvine Department of Mathematics researchers in accordance with the University of California’s open access policies. For more information see Open Access Policy Deposits and the UC Publication Management System.

Hippocampal ensembles represent sequential relationships among an extended sequence of nonspatial events.


The hippocampus is critical to the temporal organization of our experiences. Although this fundamental capacity is conserved across modalities and species, its underlying neuronal mechanisms remain unclear. Here we recorded hippocampal activity as rats remembered an extended sequence of nonspatial events unfolding over several seconds, as in daily life episodes in humans. We then developed statistical machine learning methods to analyze the ensemble activity and discovered forms of sequential organization and coding important for order memory judgments. Specifically, we found that hippocampal ensembles provide significant temporal coding throughout nonspatial event sequences, differentiate distinct types of task-critical information sequentially within events, and exhibit theta-associated reactivation of the sequential relationships among events. We also demonstrate that nonspatial event representations are sequentially organized within individual theta cycles and precess across successive cycles. These findings suggest a fundamental function of the hippocampal network is to encode, preserve, and predict the sequential order of experiences.

Cover page of Latency reversal plus natural killer cells diminish HIV reservoir in vivo.

Latency reversal plus natural killer cells diminish HIV reservoir in vivo.


HIV is difficult to eradicate due to the persistence of a long-lived reservoir of latently infected cells. Previous studies have shown that natural killer cells are important to inhibiting HIV infection, but it is unclear whether the administration of natural killer cells can reduce rebound viremia when anti-retroviral therapy is discontinued. Here we show the administration of allogeneic human peripheral blood natural killer cells delays viral rebound following interruption of anti-retroviral therapy in humanized mice infected with HIV-1. Utilizing genetically barcoded virus technology, we show these natural killer cells efficiently reduced viral clones rebounding from latency. Moreover, a kick and kill strategy comprised of the protein kinase C modulator and latency reversing agent SUW133 and allogeneic human peripheral blood natural killer cells during anti-retroviral therapy eliminated the viral reservoir in a subset of mice. Therefore, combinations utilizing latency reversal agents with targeted cellular killing agents may be an effective approach to eradicating the viral reservoir.

Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis.


Multiplexed mRNA profiling in the spatial context provides new information enabling basic research and clinical applications. Unfortunately, existing spatial transcriptomics methods are limited due to either low multiplexing or complexity. Here, we introduce a spatialomics technology, termed Multi Omic Single-scan Assay with Integrated Combinatorial Analysis (MOSAICA), that integrates in situ labeling of mRNA and protein markers in cells or tissues with combinatorial fluorescence spectral and lifetime encoded probes, spectral and time-resolved fluorescence imaging, and machine learning-based decoding. We demonstrate MOSAICA's multiplexing scalability in detecting 10-plex targets in fixed colorectal cancer cells using combinatorial labeling of five fluorophores with facile error-detection and removal of autofluorescence. MOSAICA's analysis is strongly correlated with sequencing data (Pearson's r = 0.96) and was further benchmarked using RNAscopeTM and LGC StellarisTM. We further apply MOSAICA for multiplexed analysis of clinical melanoma Formalin-Fixed Paraffin-Embedded (FFPE) tissues. We finally demonstrate simultaneous co-detection of protein and mRNA in cancer cells.

A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV.


To study viral evolutionary processes within patients, mathematical models have been instrumental. Yet, the need for stochastic simulations of minority mutant dynamics can pose computational challenges, especially in heterogeneous systems where very large and very small sub-populations coexist. Here, we describe a hybrid stochastic-deterministic algorithm to simulate mutant evolution in large viral populations, such as acute HIV-1 infection, and further include the multiple infection of cells. We demonstrate that the hybrid method can approximate the fully stochastic dynamics with sufficient accuracy at a fraction of the computational time, and quantify evolutionary end points that cannot be expressed by deterministic models, such as the mutant distribution or the probability of mutant existence at a given infected cell population size. We apply this method to study the role of multiple infection and intracellular interactions among different virus strains (such as complementation and interference) for mutant evolution. Multiple infection is predicted to increase the number of mutants at a given infected cell population size, due to a larger number of infection events. We further find that viral complementation can significantly enhance the spread of disadvantageous mutants, but only in select circumstances: it requires the occurrence of direct cell-to-cell transmission through virological synapses, as well as a substantial fitness disadvantage of the mutant, most likely corresponding to defective virus particles. This, however, likely has strong biological consequences because defective viruses can carry genetic diversity that can be incorporated into functional virus genomes via recombination. Through this mechanism, synaptic transmission in HIV might promote virus evolvability.

Cover page of Deep underground neutrino experiment (DUNE) near detector conceptual design report

Deep underground neutrino experiment (DUNE) near detector conceptual design report


The Deep Underground Neutrino Experiment (DUNE) is an international, world-class experiment aimed at exploring fundamental questions about the universe that are at the forefront of astrophysics and particle physics research. DUNE will study questions pertaining to the preponderance of matter over antimatter in the early universe, the dynamics of supernovae, the subtleties of neutrino interaction physics, and a number of beyond the Standard Model topics accessible in a powerful neutrino beam. A critical component of the DUNE physics program involves the study of changes in a powerful beam of neutrinos, i.e., neutrino oscillations, as the neutrinos propagate a long distance. The experiment consists of a near detector, sited close to the source of the beam, and a far detector, sited along the beam at a large distance. This document, the DUNE Near Detector Conceptual Design Report (CDR), describes the design of the DUNE near detector and the science program that drives the design and technology choices. The goals and requirements underlying the design, along with projected performance are given. It serves as a starting point for a more detailed design that will be described in future documents.

Patient-derived xenograft culture-transplant system for investigation of human breast cancer metastasis.


Metastasis is a fatal disease where research progress has been hindered by a lack of authentic experimental models. Here, we develop a 3D tumor sphere culture-transplant system that facilitates the growth and engineering of patient-derived xenograft (PDX) tumor cells for functional metastasis assays in vivo. Orthotopic transplantation and RNA sequencing (RNA-seq) analyses show that PDX tumor spheres maintain tumorigenic potential, and the molecular marker and global transcriptome signatures of native tumor cells. Tumor spheres display robust capacity for lentiviral engineering and dissemination in spontaneous and experimental metastasis assays in vivo. Inhibition of pathways previously reported to attenuate metastasis also inhibit metastasis after sphere culture, validating our approach for authentic investigations of metastasis. Finally, we demonstrate a new role for the metabolic enzyme NME1 in promoting breast cancer metastasis, providing proof-of-principle that our culture-transplant system can be used for authentic propagation and engineering of patient tumor cells for functional studies of metastasis.

Transcriptome Profiling of Dysregulated GPCRs Reveals Overlapping Patterns across Psychiatric Disorders and Age-Disease Interactions.


G-protein-coupled receptors (GPCRs) play an integral role in the neurobiology of psychiatric disorders. Almost all neurotransmitters involved in psychiatric disorders act through GPCRs, and GPCRs are the most common targets of therapeutic drugs currently used in the treatment of psychiatric disorders. However, the roles of GPCRs in the etiology and pathophysiology of psychiatric disorders are not fully understood. Using publically available datasets, we performed a comprehensive analysis of the transcriptomic signatures of G-protein-linked signaling across the major psychiatric disorders: autism spectrum disorder (ASD), schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorder (MDD). We also used the BrainSpan transcriptomic dataset of the developing human brain to examine whether GPCRs that exhibit chronological age-associated expressions have a higher tendency to be dysregulated in psychiatric disorders than age-independent GPCRs. We found that most GPCR genes were differentially expressed in the four disorders and that the GPCR superfamily as a gene cluster was overrepresented in the four disorders. We also identified a greater amplitude of gene expression changes in GPCRs than other gene families in the four psychiatric disorders. Further, dysregulated GPCRs overlapped across the four psychiatric disorders, with SCZ exhibiting the highest overlap with the three other disorders. Finally, the results revealed a greater tendency of age-associated GPCRs to be dysregulated in ASD than random GPCRs. Our results substantiate the central role of GPCR signaling pathways in the etiology and pathophysiology of psychiatric disorders. Furthermore, our study suggests that common GPCRs' signaling may mediate distinct phenotypic presentations across psychiatric disorders. Consequently, targeting these GPCRs could serve as a common therapeutic strategy to treat specific clinical symptoms across psychiatric disorders.

Deep Learning-Assisted Multiphoton Microscopy to Reduce Light Exposure and Expedite Imaging in Tissues With High and Low Light Sensitivity.



Two-photon excitation fluorescence (2PEF) reveals information about tissue function. Concerns for phototoxicity demand lower light exposure during imaging. Reducing excitation light reduces the quality of the image by limiting fluorescence emission. We applied deep learning (DL) super-resolution techniques to images acquired from low light exposure to yield high-resolution images of retinal and skin tissues.


We analyzed two methods: a method based on U-Net and a patch-based regression method using paired images of skin (550) and retina (1200), each with low- and high-resolution paired images. The retina dataset was acquired at low and high laser powers from retinal organoids, and the skin dataset was obtained from averaging 7 to 15 frames or 70 frames. Mean squared error (MSE) and the structural similarity index measure (SSIM) were outcome measures for DL algorithm performance.


For the skin dataset, the patches method achieved a lower MSE (3.768) compared with U-Net (4.032) and a high SSIM (0.824) compared with U-Net (0.783). For the retinal dataset, the patches method achieved an average MSE of 27,611 compared with 146,855 for the U-Net method and an average SSIM of 0.636 compared with 0.607 for the U-Net method. The patches method was slower (303 seconds) than the U-Net method (<1 second).


DL can reduce excitation light exposure in 2PEF imaging while preserving image quality metrics.

Translational relevance

DL methods will aid in translating 2PEF imaging from benchtop systems to in vivo imaging of light-sensitive tissues such as the retina.

Cover page of Dynamic Changes of Brain Cilia Transcriptomes across the Human Lifespan.

Dynamic Changes of Brain Cilia Transcriptomes across the Human Lifespan.


Almost all brain cells contain primary cilia, antennae-like microtubule sensory organelles, on their surface, which play critical roles in brain functions. During neurodevelopmental stages, cilia are essential for brain formation and maturation. In the adult brain, cilia play vital roles as signaling hubs that receive and transduce various signals and regulate cell-to-cell communications. These distinct roles suggest that cilia functions, and probably structures, change throughout the human lifespan. To further understand the age-dependent changes in cilia roles, we identified and analyzed age-dependent patterns of expression of cilia's structural and functional components across the human lifespan. We acquired cilia transcriptomic data for 16 brain regions from the BrainSpan Atlas and analyzed the age-dependent expression patterns using a linear regression model by calculating the regression coefficient. We found that 67% of cilia transcripts were differentially expressed genes with age (DEGAs) in at least one brain region. The age-dependent expression was region-specific, with the highest and lowest numbers of DEGAs expressed in the ventrolateral prefrontal cortex and hippocampus, respectively. The majority of cilia DEGAs displayed upregulation with age in most of the brain regions. The transcripts encoding cilia basal body components formed the majority of cilia DEGAs, and adjacent cerebral cortices exhibited large overlapping pairs of cilia DEGAs. Most remarkably, specific α/β-tubulin subunits (TUBA1A, TUBB2A, and TUBB2B) and SNAP-25 exhibited the highest rates of downregulation and upregulation, respectively, across age in almost all brain regions. α/β-tubulins and SNAP-25 expressions are known to be dysregulated in age-related neurodevelopmental and neurodegenerative disorders. Our results support a role for the high dynamics of cilia structural and functional components across the lifespan in the normal physiology of brain circuits. Furthermore, they suggest a crucial role for cilia signaling in the pathophysiological mechanisms of age-related psychiatric/neurological disorders.

Cover page of Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics.

Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics.


Advances in single-cell technologies allow scrutinizing of heterogeneous cell states, however, detecting cell-state transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique to identify the underlying stochastic dynamics that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transitions, and distinguishes stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using coarse-grained transition path theory. Downstream analysis identifies distinct genes that mark the transient states or drive the transitions. The method is consistent with the well-established Langevin equation and transition rate theory. Applying MuTrans to datasets collected from five different single-cell experimental platforms, we show its capability and scalability to robustly unravel complex cell fate dynamics induced by transition cells in systems such as tumor EMT, iPSC differentiation and blood cell differentiation. Overall, our method bridges data-driven and model-based approaches on cell-fate transitions at single-cell resolution.