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Author(s): D. S. Schimel (corresponding author) [1, 21]; J. I. House [1]; K. A. Hibbard [2]; P. Bousquet [3]; P. Ciais [3]; P. Peylin [4]; B. H. Braswell [13]; M. [...]

Microglia are the resident macrophages of the central nervous system (CNS). Their phagocytic activity is central during brain development and homeostasis-and in a plethora of brain pathologies. However, little is known about the composition, dynamics, and function of human microglial phagosomes under homeostatic and pathological conditions. Here, we developed a method for rapid isolation of pure and intact phagosomes from human pluripotent stem cell-derived microglia under various in vitro conditions, and from human brain biopsies, for unbiased multiomic analysis. Phagosome profiling revealed that microglial phagosomes were equipped to sense minute changes in their environment and were highly dynamic. We detected proteins involved in synapse homeostasis, or implicated in brain pathologies, and identified the phagosome as the site where quinolinic acid was stored and metabolized for de novo nicotinamide adenine dinucleotide (NAD + ) generation in the cytoplasm. Our findings highlight the central role of phagosomes in microglial functioning in the healthy and diseased brain., Competing Interests: Declaration of interests R.J. is an advisor/co-founder of Fate Therapeutics and Fulcrum Therapeutics., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Bacterial infections, increasingly resistant to common antibiotics, pose a global health challenge. Traditional diagnostics often depend on slow cell culturing, leading to empirical treatments that accelerate antibiotic resistance. We present a novel large-volume microscopy (LVM) system for rapid, point-of-care bacterial detection. This system, using low magnification (1-2×), visualizes sufficient sample volumes, eliminating the need for culture-based enrichment. Employing deep neural networks, our model demonstrates superior accuracy in detecting uropathogenic Escherichia coli compared to traditional machine learning methods. Future endeavors will focus on enriching our datasets with mixed samples and a broader spectrum of uropathogens, aiming to extend the applicability of our model to clinical samples.