Researchers at the University of California, Davis have developed advancements in understanding exodermal differentiation in plant roots highlighting the role of two transcription factors in plant adaptation and survival.

Researchers at the University of California, Davis have developed a novel cell segmentation technology for accurate analysis of non-spherical cells and that offers a comprehensive, high-throughput approach for analyzing the transcriptomic and metabolomic data to study complex biological processes at the single-cell level.

The University of California, Riverside has developed a new omics software named, ShowMEPATH, employing a faster and easier approach to compare changes in metabolites within multiple sample groups, along with an automated algorithm to facilitate the process. The software introduces a novel tool to visualize volcano plots, called Parallel Fold Change (PFC) plot. Unlike current software solutions, PFC enables researchers to easily process their large omics data sets to compare various biological networks. The PFC plot is an efficient tool for analyzing and interpreting complex biological comparisons and it helps researchers to efficiently map omics pathways.  Fig 1: This figure illustrates a Parallel Fold Change (PFC) plot and demonstrates the parallel comparison of multiple samples in metabolomics. The tool examines the fold-change patterns of 45 metabolites across 16 scenarios involving 8 genotypes and 3 treatments. Using ShowMEPATH, researchers can identify detailed patterns within biological experiments, with the ability to hover over lines in the PFC plots for seamless access to KEGG modules or pathways, thereby streamlining the exploration of related biological information 

Researchers at the University of California, Davis have developed a technology that offers a sophisticated solution for collecting and measuring gas emissions from surfaces, particularly skin, with high sensitivity and specificity.

An innovative mass spectrometry platform that utilizes sulfoxide-containing MS-cleavable heterobifunctional photoactivated cross-linkers to enhance protein structural elucidation.

Researchers at the University of California, Davis have developed improved variants of a Heparosan synthase supporting efficient synthesis of heparosan, heparin, and heparan sulfate analogs.

High-throughput next-generation sequencing (NGS) systems have allowed for large scale collection of transcriptomic data with single cell resolution. Within this data lies variability allowing researchers to characterize and/or infer certain morphological aspects of interest, such as single cell type, cell state, cell growth trajectories, and inter-cellular gene regulatory networks. All of these qualities are important parts of understanding how cells interact with one another, both for building better cellular models in vitro and for understanding biological processes in vivo. While the size of single cell data has increased massively, NGS techniques for key pieces of analysis have not kept pace, using slow, manual pipelines of domain experts for initial clustering. Attempts to improve NGS classification performance have fallen short as the numbers of cell types (often asymmetric) and cell subtypes have increased while the number of samples per label has become small. The technical variability between NGS experiments can make robust classification between multiple tissue samples difficult. Moreover, the high-dimensional nature of NGS transcriptomic data makes this type of analysis statistically and computationally intractable.

       Our ability to experimentally measure the biomacromolecular structure of proteins and their complexes down to the atomic scale has progressed at a staggering pace in recent years. However, the dynamical conformational changes that affect, to name a few examples, DNA transcription, energy-transfer in photosynthesis and enzyme activity, and the transition from healthy to diseased states, remain difficult to capture. A non-perturbative, label-free approach that is sensitive to individual conformational states is single-protein Raman spectroscopy. However, the time resolution of single-protein Raman spectroscopy is typically limited to milliseconds (10-3 sec), limited by inherent signal strength. Protein conformational dynamics occur over a timescale ranging from tens of seconds down to microseconds (10-6 sec) or even nanoseconds (10-9 sec).       To address these challenges UC Berkeley researchers have developed a novel, high-temporal dynamic range Raman spectrometer capable of measuring sub-microsecond, and even nanosecond, fluctuations in single- and few-molecule spectra. The available dynamic range can be used to study and control of biomolecular dynamics as related to protein-protein interactions, drug discovery, validating computational biophysics capabilities, and many other additional applications. 

Brief description not available

Researchers at UC Berkeley have developed a machine learning model that can aide in the design of more efficient viral vector libraries.Directed evolution of biomolecules to generate large numbers of randomized variants is an important innovation in biochemistry. This methodology can be applied to myriad biomolecules of interest, including viruses. In the case of viral variants, this method may be used to select viral variants or viral vectors with specific properties such as tissue type specificity, increased replication capacity, or enhanced evasion of the immune system. However, testing large numbers of viral variants for specific properties is inherently time consuming and limits potential innovation.The inventors have devised a new method to optimize the functionality of viral libraries with many random variants. Specifically, this methodology comprises a machine learning model that systematically designs more effectively starting libraries by optimizing for a chosen factor. This method works by using a training set of viruses that can be evaluated experimentally for the chosen optimization factor (e.g., packaging efficiency, infectivity of a cell line, etc.). These experiments will then provide a fitness value for each viral variant, and the fitness value matched with viral variant sequences will in turn be used in a supervised machine learning model to select sequences for a larger library that is optimized for the chosen factor.

Researchers at the University of California, Davis have developed a single-use chip for the identification of protein crystals using X-ray based instruments.

Brief description not available

This invention leverages the nuclease activity of CRISPR proteins for the direct, sensitive detection of specific nucleic acid sequences. This all-in-one detection modality includes an internal Nuclease Chain Reaction (NCR), which possesses an amplifying, feed-forward loop to generate an exponential signal upon detection of a target nucleic acid.Cas13 or Cas12 enzymes can be programmed with a guide RNA that recognizes a desired target sequence, activating a non-specific RNase or DNase activity. This can be used to release a detectable label. On its own, this approach is inherently limited in sensitivity and current methods require an amplification of genetic material before CRISPR-base detection. 

Techniques such as genome editing, gene therapy, and CRISPR-based gene expression require robust methods of delivering genetic information. The effectiveness of delivery depends on the amount of DNA or RNA that can be delivered.  In some cases there is a strict upper-limit on the amount of DNA or RNA that can be delivered.  For example, AAV vectors for mammalian gene delivery are limited to genetic cargos of < 5 kb.  In general, and irrespective of the delivery vector, larger DNA constructs are delivered less efficiently and so it is advantageous to use smaller constructs where possible. It is therefore advantageous to compress constructs. Methods of compression that do not require removal of genetic elements (“lossless compression”) are very desirable since size requirements can be met without compromising functionality.     In order to reduce the number of bases (DNA or RNA) required to encode larger constructs, UC Berkeley researchers have developed a method for compressing genetic information.   The method can be applied to two elements which be encoded in the same or different reading and can also be applied to a single genetic elements. 

UCLA researchers in the Department of Molecular and Medical Pharmacology have developed a classifier for the identification and treatment of small cell neuroendocrine cancers and small-round-blue cell tumors not previously identified.

To develop a novel imaging-based single cell RNA-sequencing (scRNA-Seq) platform that allows capturing of spatiotemporal information and cellular behavior of the sequenced cells within tissue.

Researchers led by Yi Xing have developed a novel deep learning algorithm to detect alternative splicing patterns in RNA-seq data

Researchers at the University of California, Davis have developed a device that can capture, analyze, and monitor volatile organic compounds (VOCs) emitted by cell cultures through a bioreactor exhaust line – thus eliminating the need to contact the cell culture directly.

UCLA researchers in the Department of Radiological Sciences have invented a novel interactive tool that can rapidly focus and zoom on a large number of images using eye tracking technology.

UCLA researchers in the Department of Radiological Sciences have developed a novel computation system that uses large imaging datasets to aid in clinical diagnosis and prognosis.

UCLA researchers in the Division of Cardiology at the Geffen School of Medicine have developed a bioinformatics methodology to identify and functionally annotate novel endocrine pathways.

Pharmacophore analysis through examination of Joint Pharmacophore Space of chemical compounds, targets, and chemical/biological properties.

UCLA researchers in the Department of Electrical Engineering have developed a high-throughput, label-free cell classification method based on time-stretch quantitative phase imaging.

Media that contains nutrients and growth factors is necessary to grow all types of cells, a process that is widely used in many fields of research. Such media should be routinely changed either to different media or a fresh batch of the same media. This change currently involves either using a pipette to transfer cells from their current dish of media to a new dish, or aspirating the media out of the dish and replacing it with new media. Both methods have inherent risks to stressing and damaging the cells. Researchers at UCI have developed a unique dish for growing cells that allows for safer aspiration of the old media, which reduces stress and damage to the cells.

This invention consists of software that facilitates modeling and interactive visualization of molecular structures and related data.