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Biomedical subjects

Nir Yosef

Publications and source records attributed to Nir Yosef.

2 recordsLinked to original sources

Multimodal profiling reveals tissue-directed signatures of human immune cells altered with age.

The immune system comprises multiple cell lineages and subsets maintained in tissues throughout the lifespan, with unknown effects of tissue and age on immune cell function. Here we comprehensively profiled RNA and surface protein expression of over 1.25 million immune cells from blood and lymphoid and mucosal tissues from 24 organ donors aged 20-75 years. We annotated major lineages (T cells, B cells, innate lymphoid cells and myeloid cells) and corresponding subsets using a multimodal classifier and probabilistic modeling for comparison across tissue sites and age. We identified dominant site-specific effects on immune cell composition and function across lineages; age-associated effects were manifested by site and lineage for macrophages in mucosal sites, B cells in lymphoid organs, and circulating T cells and natural killer cells across blood and tissues. Our results reveal tissue-specific signatures of immune homeostasis throughout the body, from which to define immune pathologies across the human lifespan.

Humans↗

Inferring functional pathways from multi-perturbation data.

BACKGROUND: Recently, a conceptually new approach for analyzing gene networks, the Functional Influence Network (FIN) was presented. The FIN approach uses the measured performance of a given cellular function under different multi-perturbations, to identify the main functional pathways and interactions underlying its processing. Here we present and study an iterative, extended version of FIN, the Functional Influence Network Extractor (FINE), which is specifically geared towards the accurate analysis of sparse cellular systems. We employ it to study a conceptually fundamental question of practical importance--how well should we know the system studied (such that we can predict its performance) so that we can understand its workings (i.e., chart its underlying functional network)? RESULTS AND CONCLUSIONS: The performance of FINE is studied in both simulated and biological sparse systems. It successfully obtains an accurate and compact description of the underlying functional network even with limited data, and outperforms FIN. We show that prior estimates of a system's functional complexity are instrumental in determining how much predictive knowledge is required to accurately chart its underlying functional network. AVAILABILITY: The FINE software is available for download at http://www.cns.tau.ac.il/resc.html.

Algorithms↗