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Tea, C.

Publications and source records attributed to Tea, C..

2 recordsLinked to original sources

Rapid iPSC inclusionopathy models shed light on formation, consequence and molecular subtype of alpha-synuclein inclusions

Intracellular inclusions accompanying neurodegeneration are histopathologically and ultrastructurally heterogeneous but the significance of this heterogeneity is unclear. iPSC models, while promising for disease modeling, do not form inclusions in a reasonable timeframe and suffer from limited tractability. Here, we developed an iPSC toolbox utilizing piggyBac-based or targeted transgenes to rapidly induce CNS cells with concomitant expression of aggregation-prone proteins. This system is amenable to screening and longitudinal tracking at single-cell and single-inclusion resolution. For proof-of-principle, cortical neuron -synuclein "inclusionopathy" models were engineered to form inclusions through exogenous seeding or -synuclein mutation. These models recapitulated known fibril- and lipid-rich inclusion subtypes, uncovering dynamic interactions between them, and refined the classification of inclusions in postmortem brain. Genetic-modifier and protein-interaction screens pinpointed proteins like RhoA whose sequestration into specific inclusion subtypes is likely to be toxic. This iPSC platform should enhance our understanding of proteinaceous pathologies in neurodegeneration and facilitate therapeutics development.

neuroscience↗

MitoTNT: Mitochondrial Temporal Network Tracking for 4D live-cell fluorescence microscopy data

Mitochondria form a network in the cell that rapidly changes through fission, fusion, and motility. This four-dimensional (4D, x,y,z,time) temporal network has only recently been made accessible through advanced imaging methods such as lattice light-sheet microscopy. Quantitative analysis tools for the resulting datasets however have been lacking. Here we present MitoTNT, the first-in-class software for Mitochondrial Temporal Network Tracking in 4D live-cell fluorescence microscopy data. MitoTNT uses spatial proximity and network topology to compute an optimal tracking. Tracking is >90% accurate in dynamic spatial mitochondria simulations and are in agreement with published motility results in vitro. Using MitoTNT, we reveal correlated mitochondrial movement patterns, local fission and fusion fingerprints, asymmetric fission and fusion dynamics, cross-network transport patterns, and network-level responses to pharmacological manipulations. MitoTNT is implemented in python with a JupyterLab interface. The extendable and user-friendly design aims at making temporal network tracking accessible to the wider mitochondria community.

cell biology↗