22 million human immune cells help decode context-dependent gene circuits
Scientists from Gladstone Institutes, UC San Francisco and Stanford University, in collaboration with Biohub, have unveiled a massive, high-resolution functional map of human immune cells that promises to transform our understanding of how genetics control health and disease.
Scientists from Gladstone Institutes, UC San Francisco, Stanford University, and Biohub have created a massive, high-resolution functional map of human immune cells, revealing critical insights into how genetics control health and disease. Published in Cell, the study represents a significant advancement in immunology and genomics.
By systematically screening 22 million human immune cells, the researchers decoded the dynamic circuits that govern gene function in different health and disease contexts, moving beyond traditional DNA sequencing. This leap offers powerful tools for designing cancer immunotherapies and treating autoimmune conditions.
The study's lead author, Alex Marson, emphasized that this marks the third wave in biology, where the focus is on understanding what happens to cells when targeted changes are made within the genome. The dataset, which is the largest contribution to the Billion Cells Project, will be used to train AI models that can predict cell behavior, accelerate scientific discovery, and uncover new treatments.
The research employed Perturb-seq, a technology that allowed the team to systematically "turn off" nearly 12,800 genes in human T cells, observing how these genes function in their natural state. This approach provided a more accurate representation of immune cell behavior compared to standard experimental cell lines.
Key findings include the intricate ways genes work together to influence immune function, which varies depending on whether cells are resting or fighting an infection. Understanding these context-specific interactions is crucial for developing effective treatments and AI models of biology.
Jonathan Pritchard, a senior author from Stanford University, highlighted that the dataset bridges the gap between a person's DNA and their overall health, helping to interpret genetic associations with complex traits like lymphocyte counts. He emphasized the importance of standardized, large-scale functional genomics datasets for future AI models of biology, which require diverse cell states and health scenarios to make accurate predictions.
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