Lifeboat Foundation

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.

Tian et al. developed a bacterial orthogonal DNA replication system by harnessing the temperate phage GIL16 DNA replication machinery, which provides a powerful tool for continuous evolution in prokaryotic cells.

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).

CRISPR genome editing technologies are shaping the future of drug development. We spoke to industry experts to learn more about its potential.

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 (memory cells) 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.

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.”

Scientists consider the role of overweight and obesity in increasing the risk for gastrointestinal cancers to be well established, with colorectal cancers the most common of these malignancies in the United States. According to National Cancer Institute (NCI) data, an estimated 153,020 men and women in the U.S. will be diagnosed with colorectal cancer in 2023, and 52,550 are expected to die of this disease. In two recent studies, researchers addressed the impact of overweight, obesity, and body mass index (BMI) on colorectal and non-colorectal gastrointestinal (GI) cancers at different stages of adult life, as well as the association of BMI and different molecular subtypes in colorectal cancers.

Obesity, BMI, and Cancer Across the Lifespan

In a new retrospective cohort study, researchers found that being overweight or obese in early and middle adulthood increases the risk for developing colorectal and non-colorectal GI cancers throughout a person’s lifetime. Even when an individual starts out as underweight or falls within a normal BMI as a young adult, the risk rises if they later become overweight or obese, according to the research.