New single-cell transcriptomic clock reveals intrinsic and systemic T cell aging in COVID-19 and HIV
Researchers Alan Tomusiak from the Buck Institute for Research on Aging and the University of Southern California, and Sierra Lore from the Buck Institute for Research on Aging and the University of Copenhagen, together with corresponding author Eric Verdin from the Buck Institute for Research ...
omnideconv – a unifying framework for using and benchmarking single-cell-informed deconvolution of bulk RNA-seq data
Understanding which cell types are present in a tissue is essential for interpreting gene expression data, especially when samples contain a mixture of many different cells. Bulk transcriptomics measures the average gene expression across all cells in a sample, ...
CASSIA – a multi-agent large language model for automated and interpretable cell annotation
Cell type annotation remains one of the most critical yet time-consuming steps in single-cell RNA sequencing analysis. Researchers from the University of Wisconsin-Madison have developed CASSIA (Collaborative Agent System for Single-cell Interpretable Annotation), a novel multi-agent LLM framework that ...
CONCORD – revealing a coherent cell-state landscape across single-cell datasets
Single-cell experiments have transformed biology by allowing researchers to measure gene activity in thousands to millions of individual cells. Techniques such as RNA sequencing reveal how cells differ from one another, but combining data from different experiments remains difficult. ...
ISOSCELES – predicting glioblastoma drug response using single cell transcriptional states
Glioblastoma is the most common and deadly primary brain cancer in adults, and treatment options have changed very little over the past two decades. One of the biggest challenges in treating this disease is intratumor heterogeneity. Even within a ...
Integrating single-cell multiomics to link genetic variants with cell-type regulatory networks
Understanding how genetic differences influence disease and aging at the cellular level is one of the biggest challenges in modern biology. While large genetic studies called genome-wide association studies, or GWASs, can point to DNA variants linked to conditions ...
CellUntangler – separating distinct biological signals in single-cell data with deep generative models
Single-cell RNA sequencing has transformed how scientists study biology by letting them measure gene activity in individual cells. This level of detail makes it possible to see how cells differ from one another, but it also introduces a challenge. ...
RoCK and ROI – single-cell transcriptomics with multiplexed enrichment of selected transcripts and region-specific sequencing
Single-cell RNA sequencing is a powerful tool that helps scientists see which genes are active inside individual cells, shedding light on everything from how tissues develop to what goes wrong in disease. But traditional approaches often miss important pieces ...
SIDISH – integrating single cell insights with bulk RNA sequencing to identify high risk cells and guide precision therapeutics
Understanding how different cells contribute to disease is one of the biggest challenges in modern biology. Single-cell RNA sequencing gives us a close look at how individual cells behave, but it is still expensive and usually limited to small ...
scPER – a computational method to identify tumor cell subtypes from total RNA sequencing
Understanding what makes one patient respond to cancer treatment while another does not often comes down to the cells inside a tumor. But tumors are complex communities with many different cell types, and figuring out that mix can be ...
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