An extensive analytical study conducted at the Terasaki Institute for Biomedical Innovation (TIBI) has revealed an association between favorable survival outcomes for melanoma patients and the presence of higher populations of tissue-resident memory T cells (TRM). Data obtained from this study could be used not only for a TRM-based machine learning model with predictive powers for melanoma prognosis but could also elucidate the role TRM cells can play in the tumor immune microenvironment. This could guide the development of more effective and personalized anti-tumor immunotherapeutic tre ..read more

Prioritization or ranking of different cell types in a single-cell RNA sequencing (scRNA-seq) framework can be performed in a variety of ways, some of these include: i) obtaining an indication of the proportion of cell types between the different conditions under study, ii) counting the number of differentially expressed genes (DEGs) between cell types and conditions in the experiment or, iii) prioritizing cell types based on prior knowledge about the conditions under study (i.e., a specific disease). These methods have drawbacks and limitations thus novel methods for improving cell ranking a ..read more

Natural malaria infections have been genetically analysed at a higher resolution than ever before, giving insights that could help understand and block transmission. For the first time, the developmental stages of the deadliest human malaria parasite have been mapped in high resolution, allowing researchers to understand this ever-adapting adversary in more detail than previously possible. The study, published today (2 May) in Science, details the critical developmental stages of the malaria parasite, Plasmodium falciparum, using single-cell RNA sequencing. This gives detailed information on ..read more

RNA molecules stand as pivotal players, orchestrating a myriad of essential functions such as gene expression regulation, RNA processing, and localization. However, much like a tangled web, the structures of RNA molecules remain enigmatic, holding key insights into their functionality and potential implications for disease diagnosis and treatment. To untangle this web of RNA structures and unlock their secrets, researchers have long sought sophisticated tools capable of analyzing complex in vivo RNA structural data. rnaCrosslinkOO (RNA Crosslink Object-Oriented) is a groundbreaking software p ..read more

Recent developments in single-cell RNA sequencing have opened up a multitude of possibilities to study tissues at the level of cellular populations. However, the heterogeneity in single-cell sequencing data necessitates appropriate procedures to adjust for technological limitations and various sources of noise when integrating datasets from different studies. While many analysis procedures employ various preprocessing steps, they often overlook the importance of selecting and optimizing the employed data transformation methods. Researchers at the University Medical Center Göttingen investigat ..read more

Long non-coding RNAs (lncRNAs) are ubiquitous transcripts with crucial regulatory roles in various biological processes, including chromatin remodeling, post-transcriptional regulation, and epigenetic modifications. While accumulating evidence elucidates mechanisms by which plant lncRNAs modulate growth, root development, and seed dormancy, their accurate identification remains challenging due to a lack of plant-specific methods. Currently, the mainstream methods for plant lncRNA identification are largely developed based on human or animal datasets. Consequently, the accuracy and effectivene ..read more

Immunai’s founders were researchers at MIT when they launched their company to help predict how patients will respond to new treatments. The human immune system is a network made up of trillions of cells that are constantly circulating throughout the body. The cellular network orchestrates interactions with every organ and tissue to carry out an impossibly long list of functions that scientists are still working to understand. All that complexity limits our ability to predict which patients will respond to treatments and which ones might suffer debilitating side effects. The issue often lead ..read more

UT Southwestern-led study produces new tool for cancer-fighting precision medicine UT Southwestern researchers aiming to better understand how cancer cells interact with immune cells used a method called single cell RNA sequencing. (Photo credit: Getty Images) By examining which genes were turned on and off in a mix of cell types from breast cancer biopsies, a team led by UT Southwestern Medical Center researchers developed a tool that can accurately predict which patients with breast cancer will respond to immunotherapies. Their findings, reported in Cell Reports Medicine, co ..read more

Results from evaluation of the company’s SpliceCore® AI/ML platform in Triple Negative Breast Cancer published in Molecular Systems Biology Envisagenics, an AI-driven biotechnology company, today announced the publication in the journal Molecular Systems Biology of study results evaluating the company’s SpliceCore AI/ML platform in Triple Negative Breast Cancer (TNBC). This study demonstrates the efficacy of artificial intelligence and machine learning (AI/ML) for target discovery in triple negative breast cancer (TNBC) and for identifying functional and verifiable splice-swit ..read more

Single-cell RNA sequencing (scRNA-seq) is a powerful tool, unlocking the secrets of cellular heterogeneity, trajectory inference, and the identification of rare cell types. However, the high dimensionality, sparsity, and presence of ”false” zero values in the data can pose challenges to clustering. Furthermore, current unsupervised clustering algorithms have not effectively leveraged prior biological knowledge, making cell clustering even more challenging Researchers at Shenzhen University have developed a novel semi-supervised clustering model dubbed scTPC. Unlike traditional clustering algo ..read more