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Category: biotech/medical – Page 828

Watch this presentation on LabRoots at: http://www.labroots.com/webcast/keynote-speaker-regulation-a…ippocampus.

In the adult central nervous system (CNS) small populations of neurons are formed in the adult olfactory bulb and dentate gyrus of the hippocampus. In the adult hippocampus, newly born neurons originate from stem cells that exist in the subgranular zone of the dentate gyrus. Progeny of these putative stem cells differentiate into neurons in the granular layer within a month of the cells’ birth, and this late neurogenesis continues throughout the adult life of all mammals. Environmental stimulation can differentially effect the proliferation, migration and differentiation of these cells in vivo. These environmentally induced changes in the structural organization of the hippocampus, result in changes in electrophysiological responses in the hippocampus, as well as in hippocampal related behaviors. We are studying the cellular, molecular, as well as environmental influences that regulate neurogenesis in the adult brain. We have recently identified several molecules that work coordinately to regulate proliferation, survival and differentiation of these adult derived stem cells. In addition, we have demonstrated that specific types of activity can influence the behavior of these newly born cells. Finally, we have developed several methods to monitor the in vivo maturation of neurogenesis in vivo, which has provided insight to the functional importance of neurogenesis to behavior. A consensus model of the function of adult neurogenesis is emerging.

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According to new research, butterflies and moths share “blocks” of DNA that are more than 200 million years old. Scientists from the Universities of Exeter (UK), Lübeck (Germany) and Iwate (Japan) devised a tool to compare the chromosomes (DNA molecules) of different butterflies and moths.

They found blocks of chromosomes that exist in all moth and butterfly species, and also in Trichoptera – aquatic caddisflies that shared a common ancestor with moths and butterflies some 230 million years ago. Moths and butterflies (collectively called Lepidoptera) have widely varying numbers of chromosomes – from 30 to 300 – but the study’s findings show remarkable evidence of shared blocks of homology (similar structure) going back through time.

“DNA is compacted into individual particles or chromosomes that form the basic units of inheritance,” said Professor Richard ffrench-Constant, from the Centre for Ecology and Conservation on Exeter’s Penryn Campus in Cornwall. “If genes are on the same ‘string’, or chromosome, they tend to be inherited together and are therefore ‘linked’.

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A recent work introduces a cellular deconvolution method, MeDuSA, of estimating cell-state abundance along a one-dimensional trajectory from bulk RNA-seq data with fine resolution and high accuracy, enabling the characterization of cell-state transition in various biological processes.

Single-cell transcriptomic techniques continue to revolutionize the resolution of cell analysis, determining discrete cell types and cell states with continuous dynamic transitions that can be related to development and disease progression5. Cells in different states can be computationally ordered according to a pseudo-time series, or cell trajectory6. Both MeDuSA and another method, Cell Population Mapping (CPM)7, were developed to exploit the rich spectrum of single-cell reference profiles to estimate cell-state abundance in bulk RNA-seq data, which enables fine-resolution cellular deconvolution (Fig. 1b). Although CPM effectively tackles the issue of estimating the abundance of cells in different states, MeDuSA further improves the estimation accuracy by employing a LMM (see the equation in Fig. 1c) that takes into account both the cell state of interest (focal state) and the remaining cells of the same cell type (non-focal state) as well as the other cell types.

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To show the capability of the OrganoidChip in enabling higher-resolution imaging, we used confocal microscopy for several organoids immobilized on the chip. Representative images show improved optical segmentation and the ability to resolve single cells within an organoid (Fig. 4 d). The co-localized EthD-1-and Hoechst-stained nuclei are resolvable and can potentially be used to increase the accuracy of viability measurements. Future implementation of 3D-segmentation using AI-assisted algorithms in the analysis pipeline can provide more accurate estimations of cellular viability in larger screens.

Next, we measured the effect of DOX treatment on the beating kinetics of cardiac organoids. To do this, we relied on calcium fluorescence imaging, as it has been shown to be a good approximation of the cardiomyocytes’ action potentials32. Calcium imaging proved beneficial for beating and contraction parameters since smaller beating portions cannot necessarily be detected from brightfield images, particularly when organoids have been compromised as a result of drug treatment.

When assessing drug effects, we observed some degree of variability in the spontaneous contractile behaviour and beating kinetics between cardiac organoids. Such variability often skews any averaged parameter value across organoids and does not reflect the effect of the treatment conditions on organoid health. To address this challenge, we tracked each individual organoid’s beating off-and on-chip. The drug-induced functionality results are therefore reported as averages of fractional changes of each individual organoid’s beating kinetics parameters, measured at 48 h post-treatment, on both the chamber slide and on the chip, relative to its pre-treatment value (Eq. 3).

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Summary: Researchers have innovated a method to produce lab-grown mini brains, known as human brain organoids, free of animal cells, promising a more accurate study and treatment of neurodegenerative conditions.

Previously, brain organoids were grown using a substance derived from mouse sarcomas called Matrigel, leading to inconsistencies due to its undefined composition and variability. The new method uses an engineered extracellular matrix free of animal components, improving the neurogenesis of brain organoids.

This breakthrough allows for more accurate replication of human brain conditions and could open doors for personalized treatment of neurodegenerative diseases such as ALS and Alzheimer’s.

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The immune system is one of the most complex parts of our body. It keeps us healthy by getting rid of parasites, viruses or bacteria, and by destroying damaged or cancer cells. One of its most intriguing abilities is its memory: upon first contact with a foreign component (called antigens) our adaptive immune system takes around two weeks to respond, but responses afterwards are much faster, as if the cells remembered the antigen. But how is this memory attained?

In a recent publication, a team of researchers coordinated by Dr. Ralph Stadhouders, from Erasmus MC, and Dr. Gregoire Stik, Group Leader at the Josep Carreras Leukemia Research Institute, provides new clues on immune memory using state-of-the-art methodologies.

In their research paper, published in the journal Science Immunology, the first-author Anne Onrust-van Schoonhoven and colleagues compared the response of immune cells that had never been in contact with an antigen (called naïve cells) with cells previously exposed to antigen () and sort of knew it. They focused on the differences in the epigenetic control of the cellular machinery and the nuclear architecture of the cells, two mechanisms that could explain the quick activation pattern of memory cells.

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Researchers at New York University (NYU), Columbia University, and the New York Genome Center have developed an artificial intelligence (AI) platform that can predict on-and off-target activity of CRISPR tools that target RNA instead of DNA.

The team paired a deep learning model with CRISPR screens to control the expression of human genes in different ways, akin to either flicking a light switch to shut them off completely or by using a dimmer knob to partially turn down their activity. The resulting neural network, which they called targeted inhibition of gene expression via gRNA design— TIGER—was able to predict efficacy from guide sequence and context. The team suggests the new technology could pave the way to the development of precise gene controls for use in CRISPR-based therapies.

“Our deep learning model can tell us not only how to design a guide RNA that knocks down a transcript completely, but can also ‘tune’ it—for instance, having it produce only 70% of the transcript of a specific gene,” said Andrew Stirn, a PhD student at Columbia Engineering and the New York Genome Center. Stirn is co-first author of the researchers’ published paper in Nature Biotechnology, titled “Prediction of on-target and off-target activity of CRISPR-Cas13D guide RNAs using deep learning.” In their paper, the researchers concluded, “We believe that TIGER predictions will enable ranking and ultimately avoidance of undesired off-target binding sites and nuclease activation, and further spur the development of RNA-targeting therapeutics.”

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